Hemp Growing

Possibly due to the non-uniformity in diagnosis, there also have been few consistent patterns of having a higher number of males or females being diagnosed with a specific mental illness. However, Dr. Osei reported that women in Ghana are most commonly diagnosed with depression and bipolar disorder while the majority of mentally ill men are diagnosed with bipolar disorder or substance abuse, specifically cannabis. Many Ghanaian women also suffer from anxiety while men suffer from alcohol abuse, but both sexes are unlikely to visit a psychiatric hospital for these problems. The most common reason for admission into a psychiatric hospital in Ghana is schizophrenia. There has been no attempt to get an accurate number of the amount of Ghanaians currently suffering from a mental illness thus far but the next census is supposed to include questions addressing the amount of mentally ill inhabitants in a household. Because medical professionals are preoccupied with long hours of clinical work, there is a severe lack of information and hard data focused on mental health and neuroscience in Ghana. There has been no attempt to obtain an accurate number of the amount of Ghanaians currently suffering from a mental illness thus far, but the next census is supposed to include questions addressing the amount of mentally ill inhabitants in a household. Schizophrenia, depression, epilepsy, and substance abuse of alcohol and cannabis are the most common diagnoses at the Accra Psychiatric Hospital and the Pantang Hospital. The records department at the Accra Psychiatric Hospital imparted the most common diagnoses for new cases in 2010, seen in Table 4, while Table 5 shows the most common psychiatric diagnoses amongst outpatients at the Pantang Hospital. Between 2009 and 2010, the Pantang Hospital saw a drastic increase in the number of outpatients diagnosed with mental disorders due to alcohol abuse, cocaine use , and other psychoactive substance use .

Outpatient diagnoses of behavioral syndromes ,growing rack adult personality and behavioral disorders, and epilepsy also displayed a significant increase in 2010. Schizophrenia and depression remained the leading diagnoses in 2009 and 2010. The most common cases for admission at the Pantang Hospital can be seen in Table 6.The Pantang hospital receives its funding from the government, internally generated funds, and donations. In 2010, their aid totalled to 1,742,185.25 GH cedis, 1,245,730.85 of which was given by the government. Donations amount to .88% of funding and internally generated funds from drugs and services represent 27.6% of annual income. Unfortunately, the hospital has been increasingly in debt over the past six years, and in 2010, the indebtedness totalled to 766,994.09 GH cedis. The government spends about four million Ghana cedis a year on the Accra Psychiatric Hospital, as the hospital is in debt and usually expends seven million a year, but receiving ten million would help things run more smoothly. Overshadowed by stigma, the psychiatric hospital receives little in donations, which amounts to about one percent of the total funding. Because of funding, the Accra Psychiatric hospital has 60–80% of the medicine they need, and most of the medicine they are given is older generation which cause negative side effects such as twisting of the neck, numb tongues, and metabolic problems. At Pantang Hospital, the in-patient sector of the hospital consists of nine wards with 50 beds, and any extra patients sleeping on thin mattresses. The VIP wards, which cost money to stay in, do not necessarily receive better treatment, but the rooms are less crowded , the food is better quality, and there is air conditioning. At the Accra Psychiatric Hospital there are 3 infirmaries and 23 wards total; 16 are male, six are female, and there is one children’s ward. One of the female and one of the male wards are reserved for geriatric cases. The largest special ward is reserved for forensic court cases and more aggressive males, 234 of them in total, though the official occupancy is only 60 for that particular ward. The largest female equivalent ward has 110 patients but the average number of patients in a ward is close to 50.

Because there are only 500 beds and currently1,000 inpatients at the Accra Psychiatric Hospital, the congestion leaves 500 patients to sleep on the concrete or on thin mats either inside or outside. There are no fans or air conditioning either. The ones forced outside without insecticides or mosquito nets are subject to the rigors of the weather during the day and the disease carrying mosquitoes at night. These unlucky ones also share their space with ants, cockroaches, and rats. Though there is tap water available, drinking it is not encouraged, so patients have to pay a small fee for filtered drinking water. Patients eat three low quality meals a day that usually consist of rice, adding up to 3.60 cedis a day , a recent increase from the 1.20 cedis spent before 2011. Uniforms are not provided so the patients are free to wear their own or donated clothes, however it is a common and disturbing sight to see people running around stark naked or half naked with tattered clothes hanging loosely off their body. The congestion of patients and the conditions of their living situation are human rights violations in and of themselves. To add to the situation, Dr. Osei admitted that behind the scenes, patients are sometimes physically or medically punished by nurses who are trying to control more patients than is feasibly possible. An undercover journalist also witnessed this injustice as well as pervasive drug trafficking between patients and employees. Although these acts are strictly discouraged, it is hard to prevent these human violations from occurring due to a lack of staff and security. Records are kept analog, in a room full of bulging, tattered folders; though they are trying to digitalize the system, it is difficult with only 15 of the necessary 100 computers. There is also an intercom that works 80% of the time. The building, initially built as a prison and not as a hospital, is 100 years old, which makes it gruelling to clean and maintain. There is asbestos in the roofing, sewage system pipes have broken, and the buildings look like a rundown dog pound instead of a pristine, sanitary hospital. In fact, people in the West would be appalled by the conditions even if it actually were a place reserved for rogue dogs. If the Mental Health Bill passes, then remodelling of the building might start in seven years’ time.

Dr. Osei proposes that if the buildings and wards ameliorate into sane conditions, then the morale of both the workers and patients will improve, and people will not want to leave the second they arrived. Pantang Hospital’s accretion of debt from insufficient funding over the past six years has led to unfinished structures, outdated equipment, shortage of prescribers, inadequate treatment programs , poor food quality, deficient road networks, old vehicles, under-supplied water and electricity, and encroachment of land and security. During my interview with Dr. Dzadey,drain trays for plants the electricity went out in true Ghanaian fashion, and was followed by many scolding and worried phone calls about the number of the samples the laboratory was losing every minute the generator refused to work. Water enters the pipes only twice a month so there is not enough water or disinfectants to properly clean the estate. In addition to that, the hospital is constantly buying water to fill tanks and filtered water to give to patients. The regular wards feed each patient on a mere 60 pesewas a day, but in 2008 it was rightfully increased to 2.5 cedis. Though the walls are covered in perma-dirt, and dust and a smell of sanitation chemicals lingers in the air, the facilities are much nicer and newer than at the Accra Psychiatric Hospital. The outpatient psych department is located in a three-story building, with a television in the lobby, and there is air conditioning in the consultant rooms. Possibly due to the workload and training, Dr. Dzadey also commented on how the nurses do not have the proper understanding on how to take care of patients. They complete their tasks, such as administering medicine, but there seems to be a lack of compassion in regards to keeping the patients’ best interests at heart. Ghana has only 11 psychiatrists, four of them at the higher, board certified consultant level, and 6 retired psychiatrists, four of whom continue to work at private psychiatric hospitals. In order to have an effective mental health care system in Ghana, Dr. Osei believes that there should be at least 80 working psychiatrists with half of them at the consultant level. To become a consultant psychiatrist in Ghana, one will have to complete six or seven of medical school, then five years of post-doctoral work in psychiatry. “Brain drain is a phrase all too common to the mental health care system in Ghana. Shockingly, there are currently twenty Ghanaian psychiatrists practicing in the U.K. when there are only seventeen psychiatrists in all of Ghana. While here is one retired occupational therapist in Ghana, Dr. Osei conservatively requests for twenty. In actuality, every mental health unit should have an occupational therapist, so the ideal number would be around 200. Hence, in Table 7 I averaged 20 and 200 to get a more accurate estimate of the amount needed. Furthermore, there are only 600 Psychiatric Nurses presently working when there should be at least 3,000 in order to care for most of Ghana’s mentally ill.

Psychiatric Nurses train at either Pantang or Ankaful, and complete one year of general nursing and two years of specialized psychiatric nursing. Clinical psychologists are regrettably not even recognized by Ghana’s Ministry of Health, and any clinical psychologists working at a Psychiatric Hospital have to be listed under another title on the payroll. A concise summary of the lack of mental health personnel is presented in Table 7. There is a severe lack of human resources at the Psychiatric Hospitals. Seven psychiatrists are working at the Accra Psychiatric Hospital when there should not be less than 30 psychiatrists. Dr. Osei referred to this number as his “dream figure. Table 8 presents other current and proposed numbers of staff. Although there are no trained psychiatric social workers, there are two generic social workers employed by the hospital. The hospital also has two volunteers who help feed and bathe the children in the children’s ward. There should be two security workers in every ward and some more patrolling the hospital, which led to the suggested fifty. Because of the lack of security, many patients escape by jumping over a wall, exiting through a ceiling, or simply walking out of the front entrance. Also, there is typically one incident involving a worker being injured or killed by a patient per year. While some nurses declared that a patient killed another nurse early in 2011, Dr. Osei said that the most recent incident was someone who was blinded in one eye after being hit by a patient. Many of the staff is forced to work at the hospital through either a nursing program or the national service requirement. The staff is terribly limited due to a combination of factors revolving around money and stigma. There are poor working conditions and the little pay reduces any incentive. For a 600-person workforce there are only 28 accommodation units, so most employees are dissuaded because they have to find their own housing closer to the hospital, or pay for transportation into the workplace from their home. As a result, many nurses have confirmed interest in moving to a different country in order to work in a more amicable and rewarding environment, which would further diminish the number of psychiatric nurses the Health Ministry has managed to train. In 2010, the staff strength of Pantang Hospital numbered 524, with two psychiatrists, one clinical psychologist, three medical assistants, three pharmacists, 260 psychiatric nurses, two welfare officers, 34 ward assistants, one bio-statistician, one biomedical scientist, eight occupational therapists, and zero occupational therapists. Dr. Dzadey suggests that the minimum number of psychiatrists the hospital should have is five, around one psychiatrist per two wards and one in OPD, but ideally, the number should be ten so that each ward has its own psychiatrist. In order to gradually reach that ten, the hospital can aim for five permanent psychiatrists and five training or rotating psychiatrists.

In EC-Trademarks and Geographical Indications, the United States argued that Article 12 of the European Communities measure 2081/92 was in violation of, inter alia, its obligations under Article 3.1 of the TRIPS Agreement.More specifically, the United States claimed that geographical indications of origin for agricultural products and foodstuff of other WTO member states outside of the European Union “can only be registered under the Regulation if that Member satisfies the conditions in Article 12, which require it to adopt a system for GI protection that is equivalent to that in the European Communities and provide reciprocal protection to products from the European Communities.”Thus, this measure gave nationals of other WTO member states treatment less favorable than that of EC nationals. According to the Panel, two elements are required to show that a Member acted inconsistently with its obligations under 3.1: “ the measure at issue must apply with regard to the protection of intellectual property; and the nationals of other Members must be accorded ‘less favourable’ treatment than the Member’s own nationals.”The Panel first defined the terms “protection”and “intellectual property”under the first element. After determining that element one was satisfied, the Panel turned to a more involved discussion of the second element.The Panel noted the important distinction that Article 3.1 applies to “nationals not products”, unlike its predecessor, GATT Article III:4,before moving to its discussion of less favorable treatment.The Panel turned to the GATT III:4 interpretation of the phrase “treatment no less favourable” to determine its meaning under Article 3.1.Using that interpretation, the Panel determined that the appropri-ate analysis was “whether the difference in treatment affects the ‘effective equality of opportunities’ between the nationals of other Members and the European Communities’ own nationals with regard to the ‘protection’ of intellectual property rights, to the detriment of nationals of other Members.”

After analyzing the measure at hand, the Panel determined that because of the equivalence and reciprocity requirements,4×4 grow tray “the Regulation accords treatment to the nationals of other Members less favourable than that it accords to the European Communities’ own nationals, inconsistently with Article 3.1 of the TRIPS Agreement.”Much like Article 2.1 of the TBT Agreement and Article 3.1 of the TRIPS Agreement, Article III:4 of the GATT seeks to prevent discrimination between WTO member states.The GATT was the predecessor to the TBT and the TRIPS Agreements, and it introduced the important “National Treatment” principle that is essential to a successful international trading system.The GATT was originally signed after the Second World War in 1947 by only 23 countries in order to create some uniformity in international trade.In 1994, “[t]he WTO replaced GATT as an international organization, but the General Agreement still exists as the WTO’s umbrella treaty for trade in goods . . . ”Because the GATT is the founding agreement of the WTO, there have been many disputes outlining the obligations of member states. One dispute, Korea–Measures Affecting Imports of Fresh, Chilled and Frozen Beef , is a useful example of how Article III:4 works. In Korea-Beef, the United States requested consultations with Korea relating to, inter alia, violations under Article III:4 of the GATT.Korea was regulating the retail sale of beef via a “dual-retail system” through the “Management Guidelines for Imported Beef.”This system specified that “imported beef may only be sold in specialized imported-beef shops.”88 The United States argued that this practice violated Korea’s national treatment obligations under Article III:4.The United States contended that because “imported beef does not enjoy the same competitive opportunity to be sold in the same manner and in the same stores in which Korean beef is sold, it is treated less favourably than domestic beef.”Korea disagreed, stating that its dual-retail system had “perfect regulatory symmetry” and amounted to neither de jure nor de facto discrimination.

Considering both arguments, the Appellate Body presented a three-element test for finding a violation of Article III:4.According to the panel, an Article III:4 violation is established when it is shown that the “imported and domestic products at issue are ‘like products’; that the measure at issue is a ‘law, regulation, or requirement affecting their internal sale, offering for sale, purchase, transportation, distribution, or use’; and that the imported products are accorded ‘less favourable’ treatment than that accorded to like domestic products.”Of the three elements, the United States and Korea were only disputing the final “less favorable treatment” element.The Panel found that a measure based “exclusively on criteria relating to the nationality or origin of products is incompatible with Article III:4.”However, the Appellate Body noted that providing different treatment to imported products does not necessarily violate Article III:4, provided the treatment is no less favorable.The Appellate Body found that differences between treatment of imported and domestic products are “neither necessary, nor sufficient to show a violation of Article III:4.”Further, “[w]hether or not imported products are treated ‘less favourably’ than like domestic products should be assessed instead by examining whether a measure modifies the conditions of competition in the relevant market to the detriment of imported products.”While examining the Panel Report, the Appellate Body found that the number of imported beef shops was drastically lower than the number of domestic beef shops.The Appellate Body disagreed with Korea’s argument, stating that the restrictive choice of only selling imported or domestic beef resulted in a “reduction of access to normal retail channels.”Therefore, the Appellate Body held that Korea’s treatment of imported beef was less favorable than the treatment of domestic beef, resulting in a violation of Article III:4.As previously noted, Article 2.1 of the TBT Agreement seeks to prevent member states from enacting measures that accord imported products less favorable treatment than like domestic products.Indonesia claims that Australia’s TPPA is in violation of Article 2.1.This, however, cannot be true, because the TPPA applies to all tobacco products sold in Australia.

This test is comprised of three elements, all of which must be satisfied to show a violation of Article 2.1.First, the Panel will ask whether the TPPA is a technical regulation. According to the definition set out in Annex 1.1 of the TBT Agreement, the TPPA falls squarely into this category.The first element of the test will likely be satisfied. The second element of the test requires the products being examined to be “like.”Based on the Appellate Body’s finding in Clove Cigarettes that the analysis must be “based on the competitive relationship between and among the products”, it is fair to say the imported and domestic products are like.Unlike Clove Cigarettes, Australia-Plain Packaging does not require an analysis of different types of tobacco products, as the TPPA applies to all tobacco products.Therefore, the second element of the test will likely be satisfied. In all likelihood, the only element of this test in contention is the third and final element. This element begs the question: are tobacco products imported to Australia accorded less favorable treatment than domestic tobacco products?The Appellate Body in Clove Cigarettes noted that Article 2.1 prohibits both de jure and de facto discrimination.Because the TPPA encompasses all tobacco products, it does not discriminate against imports, and therefore can only be de facto discrimination. Based on the Appellate Body’s analysis,greenhouse racking for a violation to occur, the TPPA must modify the conditions of competition in Australia to Indonesia’s detriment vis-à- vis the group of like domestic products.However, the Appellate Body stated that this analysis is not dispositive when the regulation is not de jure discriminatory.Because the TPPA applies to all tobacco products, it is not discriminatory on its face, and can only be de facto discrimination.Therefore, according to the Appellate Body, a further analysis is necessary.To determine whether this is truly a discriminatory measure, the Panel will analyze whether the impact on Indonesia “stems exclusively from a legitimate regulatory distinction.”Specifically, the Panel will look to “the design, architecture, revealing structure, operation, and application of the technical regulation at issue, and, in particular, whether that technical regulation is even-handed . . . ”The TPPA is even-handed in that it applies to all tobacco products. Its design and operation show this to be true, as domestic cigarettes are subject to the same packaging regulations as imported cigarettes.Further, Australia-Plain Packaging differs from Clove Cigarettes because of the TPPA’s blanket ban. Recall from the earlier discussion on Clove Cigarettes that the United States’ measure did stem from a legitimate regulatory distinction because menthol cigarettes were exempt while all other flavored cigarettes were banned.The TPPA simply cannot be placed into the same line of reasoning. It applies to all tobacco products, domestic and imported, unlike Clove Cigarettes where one like domestic product was not banned.The very reason the United States’ measure in Clove Cigarettes did not comply with Article 2.1 can be attributed to the fact that it did not apply to all flavored tobacco products.For the foregoing reasons, the Panel likely found that the TPPA satisfies the third element of the test and therefore is in compliance with Article 2.1 of the TBT Agreement.

The second factor requires an analysis of the trade restrictiveness of the TPPA.This factor is Indonesia’s best argument for an Article 2.2 violation. The TPPA is quite restrictive, and it is likely that Indonesia has made that fact known to the Panel. However, these factors are not dispositive, and a trade-restrictive measure is not an automatic violation of Article 2.2.Further, because the TPPA has a legitimate objective, as explicitly mentioned in the text of Article 2.2, the Panel is more likely to give this factor less weight.The third factor favors Australia. This factor looks at the “nature of the risks at issue and the gravity of consequences that would arise from non-fulfilment of the objective.”As with the first factor, this Paper will implicate the dangers of tobacco use.It is no secret that tobacco use is dangerous, and the Panel likely determined that the risks at issue are serious. If Australia was forced to repeal the TPPA and its progress was halted, tobacco use could gradually increase until it reached its prior average. The consequence the Panel will look to is returning to pre-plain packaging Australia, which does not seem as dire as other consequences to non-fulfillment of legitimate objectives. However, when considering a study completed by the Australian Government Department of Health that found a decrease of .55 percent in Australian smokers from 2012 to 2015, the consequences the Panel must consider are counted in lives.The latest census indicated that 24.8 million people live in Australia.Assuming the rate of decrease remains stagnant, 136,400 people are not using tobacco that would have before. This is a rough estimate based on presumptions, but it shows how great the consequences could be. For the foregoing reasons, the Panel likely found that the TPPA is not more trade restrictive than necessary to fulfill a legitimate objective.Because of the lack of WTO jurisprudence on Article 3.1, it is likely that the Panel turned to its analysis in EC-Trademarks and Geographical Indications for guidance. As discussed earlier, the two-element test for a violation of Article 3.1 is “ the measure at issue must apply with regard to the protection of intellectual property; and the nationals of other Members must be accorded ‘less favourable’ treatment than the Member’s own nationals.”Based on the Panel’s definitions of “protection”and “intellectual property”,it is unlikely that the first element will be in dispute. However, based on the language in its request for consultations, Indonesia plans to argue the second element.In EC-Trademarks and Geographical Indications, the Panel determined that the correct analysis of the second element involves a determination of whether the measure “affects the ‘effective equality of opportunities’ between the nationals of other Members and the European Communities’ own nationals.”Based on the Panel’s analysis, it is unlikely that the Panel found merit in Indonesia’s argument. Unlike the EC’s measure in EC-Trademarks and Geographical Indications, Australia’s TPPA does not alter the “effective equality of opportunities.” The language of the TPPA refers to the regulation of retail packaging for all tobacco products in Australia.It does not matter whether the tobacco products are made in Australia or imported from another country. The regulations apply to any tobacco product sold.Although the TPPA will treat Indonesia less favorably than before its enactment, the TPPA does not treat Indonesia less favorably than any other country, including Australia.

Exclusionary criteria at each follow-up time-point consisted of endorsement of an emergent Axis I disorder as measured by a structured diagnostic interview . Participants were asked to refrain from alcohol and substance use for at least 24 hours prior to all baseline or follow-up assessments, verified via breath alcohol concentration and urine drug screen. The University of California San Diego Human Research Protections Program approved the study protocol and procedures . Data for the current project was selected among the first 15 years of annual follow-up assessments. Participants were included in the present analysis if they: transitioned to frequent binge drinking, averaging ≥ one binge episode per week for at least a one-year period, at any point during the 15-year follow-up period, and provided usable neuroimaging data within 3 years of their 18th birthday. fMRI Go/No-go data was selected from available scan time points to be closest to their 18th birthday and prior to their transition to frequent binge drinking . This time point was selected because all participants had begun moderate drinking by this time and it represented a proximal time point to the average age of transition , thus reducing the potential for influence from extraneous developmental factors. Time to transition to frequent binge drinking was calculated as the difference in time between age of transition onset and age at scan. The neuroimaging data of 8 participants in this sample are also included in the report by Wetherill et al., 2013. Youths were administered comprehensive interviews at baseline, including the assessment of demographics, living situations, and alcohol and drug use. The Hollingshead Index of Social Position score , an index of socioeconomic status , was calculated for each subject using parental socioeconomic background information to characterize the youth’s rearing environment. Higher values indicate lower SES .

Corroborative information from an informant was used to support youth report on demographic background and family history topics. The annual follow-up assessments were similarly structured. The Customary Drinking and Drug Use Record structured interview was used to assess history of alcohol consumption and alcohol-related problems, as well as additional substance use information. For the purposes of this study, a binge drinking episode was defined as the consumption of ≥5 alcoholic drinks for males, or ≥4 drinks for females, in a single occasion. Consistent with previous analysis on the parent data set ,vertical farming units imaging data were collected using a 3.0 Tesla General Electric short bore Excite-2 system with an eight channel phase-array head coil. A high-resolution T1-weighted sequence including a sagittally acquired spoiled gradient recalled sequence was acquired. BOLD signal was measured with T2*-weighted axially acquired echo-planar images . Field maps with two different echo times were used to measure signal dropout and field inhomogeneities. Stimuli for the task were back-projected from a laptop to a screen at the foot of the scanner bed and were visible via an angled mirror attached to the head coil. Task performance and behavior was recorded using a fiber-optic response box compatible with MRI . Response inhibition was assessed during scanning via an event-related Go/No-Go paradigm . The task consisted of a serial presentation of blue shapes, which included 64 large circles, 16 small circles, 43 large squares, and 57 small squares. The duration of each stimulus was 200ms and the intertrial interval was 1,500ms. Participants were asked to press a button each time a large circle, small circle, or large square shape was presented but to withhold their response when a small square was presented . Baseline constituted ~114 seconds scattered throughout the task. Primary analyses contrasted BOLD response during no-go correct rejection trials relative to no-go false alarm trials . The no-go correct rejection versus go contrast was also evaluated. Correct rejections were determined by the absence of a motor response during no-go trials.

False alarms were defined as a button press following a no-go stimulus. Processing of imaging data was conducted using the bug-corrected version of the Analysis of Functional NeuroImages software . Abnormal signals and artifacts were removed from the data, and the time series data were aligned temporally and coregistered to a maximally stable base volume using an iterated least squares algorithm . AFNI’s 3dSkullStrip was used to skull strip each participant’s high-resolution T1-weighted image. Participants’ anatomical and functional data sets were co-registered and warped to Talairach space . Functional data were resampled to 3mm2 voxels, and activations maps were spatially smoothed using a 5mm full-width half maximum Gaussian filter. Motion was estimated for each participant and used as a control in task analyses . 3dDespike was used to detect outliers in the motion parameters. Significant outliers in the time-series data were censored or despiked. Analysis of time series data utilized multiple regression controlling for linear drift, baseline signal, and motion from 6 motion parameters calculated above. Regressors of interest and no interest convolved with a modified gamma variate function that modeled anticipated hemodynamic response. Beta weights were converted to percent signal change which were used for further analysis.Performance measures evaluated in the Go/No-Go task included percent correct on inhibitory trials, β , and d′ . Similar to methods previously used by the authors , activation was masked by an average skull-stripped anatomical image from all participants. Whole brain, voxel-level analysis on the masked data was conducted using a paired t-test to contrast no-go correct rejection vs. no-go false alarm trials. To control for variability in age at scan acquisition, time to transition to frequent binge drinking was chosen for the correlation analysis and entered as the covariate of interest in the model. The Clustsim nonparametric randomization/permutation option of 3dttest++ was used with a conservative voxel-wise alpha of 0.001 and cluster-wise alpha of 0.05, resulting in an estimated a cluster size threshold of 18 contiguous voxels. This method of Type I error control has been shown to produce false positive rates compatible with the nominal 95% confidence interval .

A second model not containing the covariate of interest was run to validate the task by showing task-relevant activation in this sample using the same Type 1 error correction as above. As expected, successful inhibition in this sample of frequent binge drinkers was associated with activation in a number of regions previously implicated in the literature, including the fronto-striatal system , validating the use of this paradigm in our high risk sample. In the correlation analysis, a single cluster of activation, including portions of the left insula, inferior frontal gyrus , and precentral gyrus, elicited during successful inhibitory control, was found to predict time to transition to high-risk frequent binge drinking in adolescents who were already engaged in moderate alcohol use. Specifically, greater BOLD response in this cluster predicted longer time to transition, implying that lower magnitude of activation during successful inhibition could serve as a temporal warning of future high-risk impulsive behavior. The IFG, precentral gyrus,weed drying room and insula have been consistently implicated as critical regions involved in response inhibition . Although the right IFG/insula are most commonly implicated , a number of studies have also implicated key roles for the left IFG/insula in this process . This study provides further support for the involvement of the left IFG/insula in inhibitory control by demonstrating the predictive utility of activation in these top-down executive control regions for the onset of impulsive binge drinking behavior. The correlation with time to transition to frequent binge drinking also supports the notion that this pattern of alcohol consumption is likely driven, at least in part, by deficiencies in inhibitory control and suggests opportunities for intervention prior to the onset of this very high-risk behavior. Inhibitory control interventions, particularly those utilizing Go/No-go paradigms, have demonstrated effectiveness for short-term health behavior change , which may be all that is needed to delay onset of this high-risk drinking pattern beyond the critical neurodevelopmental stage of adolescence. The results of this study should be interpreted within the context of its strengths and limitations. The prospective correlational design is a strength of the study, as it avoids issues related to the selection of comparable controls and addresses the question of whether the magnitude of the BOLD signal during inhibitory control contains clinically relevant predictive information. Another strength is the well-characterized sample of frequent binge drinkers who were already engaged in moderate alcohol use at scan acquisition. Few attempts have been made to identify unique risk factors for adolescents that are already engaged in moderate alcohol use, despite the exceptionally high prevalence of adolescent alcohol users. Limitations of the study include the relatively small sample size for fMRI studies and small number of no-go false alarm trials included in the analysis. The use of conservative statistical thresholding and the consistence of implicated regions with the extent literature on inhibitory control provides support for the validity of the results; however, additional studies using more difficult tasks within larger samples are needed to confirm these within-subjects effects. Furthermore, the sample is comprised predominately of White adolescents with high educational attainment, potentially limiting the generalizability of the results. Thus, replication within a more diverse sample of adolescents is warranted. In conclusion, this study suggests that BOLD response in portions of the IFG, insula, and precentral gyrus during successful inhibitory control could prove valuable as a temporally specific risk marker for future frequent binge drinking behavior.

Early identification of adolescents at-risk for this pattern of alcohol use is of great importance given the potential for neural consequences associated with alcohol use during neurodevelopment . The increased study of risk factors for youth already engaged in moderate alcohol use could provide additional insights into meaningful pathways for intervention that were previously overlooked.Alcohol use disorder is a highly prevalent, chronic relapsing disorder with a high disease burden in the United States. Despite current and lifetime prevalence rates of 13.9% and 29.1%, respectively, it remains largely untreated as only 7.7% of those with 12-month and 19.8% of those with lifetime diagnoses sought treatment in 2012– 2013. In spite of low treatment rates, pharmacotherapy offers a promising treatment method for AUD. The Federal Drug Administration has approved of four medications for AUD: disulfiram , oral naltrexone , extended-release injectable naltrexone , and acamprosate . However, these currently approved pharmacotherapies are only modestly effective, so there is still a great need to develop more effective interventions. Medications development is a very costly, cumbersome, and inefficient process that can take nearly 20 years from discovery to market. In particular, the development of treatments for alcoholism has been difficult with over 20 medications having been tested in humans yet only three were able to receive FDA approval, the last of which was granted over a decade ago. Therefore, there is a pressing need to develop valid and efficient methods to decrease the cost and length of medications development to better shepherd novel compounds from the lab to dissemination. The development of novel medications for AUD is a high priority research area, but the drug development process is long and challenging, with many compounds stuck in the transition from preclinical to clinical testing, also known as the “valley of death”. Beyond the “valley of death,” there is an overall need to develop effective methodologies for efficiently running clinical trials, particularly in screening novel compounds in early phase 2 trials. Early phase 2 trials, also known as “proof-of concept” studies, help determine if a novel medication is safe, tolerable, and efficacious using clinically relevant phenotypes such as cue-induced craving or subjective response to alcohol. These trials largely incorporate human laboratory paradigms to assess medication efficacy, providing valuable information on whether or not the medication warrants a larger clinical trial. However, human laboratory paradigms have not always demonstrated translational validity and often lack the ecological validity of clinical trials where medication efficacy is established through clinically meaningful endpoints. Therefore, there are major opportunities to refine this process of screening novel medications by combining the internal validity of human laboratory models and the external validity of clinical trials. To that end, the current study aims to develop and validate a novel early efficacy paradigm to screen medications for AUD. This early efficacy paradigm is the practice quit attempt model adapted from the smoking cessation medication development literature. In the original practice quit attempt model, individuals who report intrinsic motivation to quit smoking undergo a 7-day practice quit attempt while taking study medication. Individuals with high intrinsic motivation to quit smoking fared better on active medication, compared to placebo, on increased abstinence, while individuals with low intrinsic motivation showed no effect of active medication.

Presently, Montana and Michigan are the only MML states in the continental United States that do not share a border with another MML state.28 In addition, Figure E.1 shows that Montana and its bordering states are relatively unimportant trafficking routes for marijuana; cannabis smuggled through these states is primarily sourced from Washington and Oregon and is destined for larger, more urban centers in the midwest. Therefore, growth in the legal medical marijuana market in Montana is unlikely to have significant supply spillovers to its neighboring states. Table E.1 compares estimates of the effects of growth in the legal market on past month use restricting the sample to only include Montana and its bordering states in Panel A, to the estimates using the entire sample of states in Panel B. If there are substantial supply spillovers from MML states with large markets to other states, then we would expect the effect sizes in Panel A to exceed those in Panel B. From Table E.1, for youths aged 12-25, the effects of legal market size on past-month use are larger in Panel A than in Panel B, but for adults aged 26 and older they are quite similar. This is consistent with growth in the legal medical marijuana market having supply spillovers across states in the black market, where adolescents and young adults have substantially greater access than older adults. Table E.2 replicates the analysis of Table E.1 using prevalence of past-year initiation as the outcome variable. The results are similar. Thus, there appear to be supply spillovers from medical marijuana markets to recreational marijuana markets used by youths in other states. The differences between the estimates from Montana’s case study to those using the entire sample suggests that the effects of medical marijuana market growth on adolescent and young adult use may be twice as large as shown in the primary results if cross-state supply spillovers are accounted for.

If the decision to report marijuana use is more closely related to beliefs about legal penalties or social disapproval compared to availability,cannabis drying racks then the results from Table 2.8 suggest that the effects of legal market growth on adolescent marijuana use are a true measure of consumption changes and not of reporting behavior. Tables F.1-F.2 provide additional supporting evidence that the primary results of this paper are not driven by reporting bias. Table F.1 reports estimates for the effects of registration rates on the prevalence of past-month marijuana use by adolescents separately for the time period before the Ogden Memo and after the Cole Memo. If changes in reporting behavior are more likely to be driven by law passage than by legal market size, then registration rates should have no effect on reported past-month except due to the federal government’s memos. As evidenced in Table F.1, the coefficient estimates for adolescent past-month use are not significantly different if examined before the federal policy reduced enforcement with the Ogden Memo, or after the federal government increased enforcement with the Cole Memo. However, adolescent reporting behavior may be more sensitive to changes in risks from social or community disapproval than to changes in perceived disapproval from law enforcement. If this were the case, then changes in state marijuana policy or changes in federal enforcement policy may have less effect on adolescent reporting behavior than changes in perceived social stigma associated with cannabis consumption, which is likely highly correlated with the number of legal users and suppliers visible in the community. To address this potential concern, estimates of the effects of legal medical marijuana market size on juvenile arrests for marijuana possession are shown in Table F.2. Since adolescents for the most part do not qualify as medical marijuana patients, it is unlikely that there were significant state enforcement changes regarding juvenile arrests for marijuana-related crimes, and thus effects of legal market size on adolescent marijuana arrests are likely highly correlated with effects of legal market size on adolescent cannabis use.

Annual data on juvenile arrests from 1994-2012 were obtained from the Uniform Crime Reports County-Level Detailed Arrest Files compiled by the Inter-University Consortium for Political and Social Research. County data were aggregated up to the state level. Table F.2 reports coefficient estimates for the effect of registration rates on the juvenile marijuana possession arrest rates. In Columns -, a log-linear ordinary least-squares specification is employed, with the dependent variable constructed as the natural log of the number of juvenile arrests for marijuana possession per 100,000 of the relevant-aged population for Columns -, or the natural log of the number of juvenile marijuana possession arrests in Columns -. Columns – employ a negative binomial specification. For all model specifications, growth in the legal market size has a positive effect on juvenile arrests for marijuana possession of similar effect size to that found for the effects on adolescent past-month use. This suggests that the observed effects on self-reported use are not driven solely by changes in reporting behavior. Alcohol abuse is a global problem, constituting the seventh leading risk factor for death and disability . Worldwide, over 100 million people had an alcohol use disorder in 2016. Statistics from the National Survey on Drug Use and Health show that >85% of adults in the United States report ever having consumed alcohol, with >25% reporting binge drinking in the past month . The proportion of adults in the United States with an AUD is estimated to be 6.2% . Alcohol use behaviors are complex, and how and why people drink is partially influenced by genetic factors. However, identifying the genetic factors that increase the risk for harmful drinking has been challenging, partially because patterns of alcohol use are dynamic across the lifespan. The terms used to describe alcohol use and abuse are as diverse as the behaviors themselves. Hazardous drinking describes heavy drinking that places an individual at risk for future harm. Harmful drinking and alcohol abuse are defined as drinking that causes mental or physical damage to the individual.

These descriptive terms were devised to identify individuals who would benefit from brief interventions and are assessed using screening questionnaires such as the Alcohol Use Disorders Identification Test . Alcohol dependence was, until recently, defined according to the DSM-IV and required the presence of 3 or more of 7 criteria in a 12-month period. The DSM-IV made a distinction between alcohol abuse and dependence that was removed under DSM-V and replaced with ‘mild’ to ‘severe’ definitions of AUD. Genetic studies encompass the wide range of alcohol use phenotypes; in this review we mirror the language used in the original studies. AUD can be viewed as the end point of a series of transitions , which begin with the initiation of use, continue with the escalation to hazardous drinking and culminate in compulsive harmful use that persists despite negative consequences. Genome-wide association studies have been instrumental in discovering novel genetic loci associated with multiple psychiatric conditions. In the field of AUD genetics, studies have mostly focused on either levels of consumption or AUD diagnosis. Recent GWAS have now begun to identify hundreds of genome-wide significant variants, and provide evidence that the components of alcohol use behavior have a distinct genetic architecture. In this review, we provide an overview of recent molecular genetic findings of alcohol use behaviors from the largest GWAS performed to date. Other reviews have elegantly summarized findings from twin and family studies of heritability, linkage, candidate gene and GWAS [e.g. ], and we extend on recent reviews of the molecular genetics of AUD by including additional GWAS of alcohol use behaviors that identify genome-wide significant hits . In addition, we discuss the application of polygenic methods, which provide mounting evidence that alcohol use and misuse are partially distinct. Finally, we delineate future directions to investigate the different etiologic sources that underlie the life course of alcohol use behaviors.For decades,pots for cannabis plants candidate gene studies were used to determine the contribution of specific genes that increase risk for AUD. Candidate gene studies tended to focus on genes that influenced pharmacokinetic and pharmacodynamic factors. One exception to this are the genes encoding ethanol metabolizing enzymes, particularly alcohol dehydrogenase and aldehyde dehydrogenase , which have repeatedly been shown to have the largest impact on alcohol consumption and risk for AUD . As study designs have evolved to incorporate GWAS, researchers have been able to scan the whole genome without any hypotheses about the underlying biology of alcohol use behaviors. Initial efforts focused on collecting clinically-defined cases of AUD, but these ascertainment strategies could not amass the large sample sizes required for GWAS . Accordingly, multi-ethnic and clinically-defined samples have been combined through the Psychiatric Genomic Consortium of Substance Use Disorders working group.

The efforts of the PGC-SUD have led to a trans-ancestral meta-analysis consisting of almost 15,000 AD cases and almost 38,000 controls from 28 independent cohorts , identifying a single locus , which was robustly associated with AD. More recently, using information from electronic health records to infer AUD status, a GWAS of 274,424 multi-ethnic individuals from the Million Veterans Program cohort identified 10 loci associated with AUD . Kranzler et al showed that alcohol consumption and AUD were genetically correlated but distinct, thus allowing them to adjust for consumption in the AUD GWAS and for AUD in the GWAS of consumption. In parallel with these efforts, which have focused on clinical diagnoses, other GWAS have incorporated continuous measures of alcohol use. These include self-reported weekly alcohol intake or the scores from screening questionnaires such as the AUDIT . The AUDIT can be decomposed to provide a measure of alcohol use from the first 3 questions and misuse from questions 4-10 . These quantitative measures are available in large population-based cohorts such as the UK Biobank , MVP and 23andMe. The GWAS meta-analysis of AUDIT identified 10 associated risk loci . Large consortia were also formed to collate quantitative measures of alcohol use, including AlcGen and the GWAS & Sequencing Consortium of Alcohol and Nicotine Use . GSCAN have recently identified nearly 100 loci associated with alcohol consumption . The MVP study also examined alcohol consumption, allowing for an explicit comparison between AUD and consumption in a single population; of the 18 loci detected in that study, 5 were common to both AUD diagnosis and alcohol consumption. As the prior two paragraphs make clear, population based cohorts have provided larger sample sizes, which are critical for obtaining adequate power for GWAS. Their use can come at the cost of missing more severe alcohol use phenotypes. For example, the frequency of AUD in the UKB is lower than the population average [7% ], indicating that certain population studies may be underpowered to detect genetic effects specific to dependence . The frequency of AUD in the MVP, on the contrary, was much higher [20%, ]. Despite these limitations, population based cohorts provide a cost-effective strategy for obtaining very large samples, compared to traditional study designs that require obtaining a diagnosis from clinically trained staff. Beyond the alcohol metabolizing genes, the region containing the genes beta-klotho and the Fibroblast growth factor 21 has been robustly associated with alcohol consumption. The AlcGen consortium was the first to show that the A allele of rs11940694 , located in the intron of KLB, was associated with reduced alcohol consumption . This finding has since been replicated – the same SNP was associated with alcohol consumption and alcohol misuse . Beta-klotho is a transmembrane protein that acts as a cofactor for the circulating hormone fibroblast growth factor 21 by facilitating its binding to FGF receptors . Interestingly the FGF21 gene, which is located on chromosome 19, was also associated with AUDIT scores at the gene-level in humans . Beta-klotho is primarily expressed in the liver, adipose tissue and pancreas , and recent studies have shown that it regulates brain specific functions related to alcohol consumption in mice. For example, mice lacking brain expressed Klb showed increased ethanol preference . Furthermore, FGF21 was found to suppress ethanol consumption in wild-type mice but had no effect on mice lacking Klb in the brain.

By reducing the perceived risk of federal prosecution for legal producers in compliance with state law, the Ogden Memo should have increased benefits to patients by increasing medical marijuana availability. The Cole Memo should have had the opposite effect. If supply-side factors are an important determinant of the relative value of medical marijuana participation, then the federal memos may have influenced patient take-up through their effects on medical marijuana access. The magnitude of these effects will depend on the regulatory framework for legal production established by state MML policy. To measure medical marijuana participation, I collected data on the number of registered medical marijuana patients for all states with mandatory registration programs as of 2014. The full listing of data sources for each state — which include direct contact with state officials, state department reports and websites , academic papers, and local news articles — is provided in Appendix A-. This paper uses monthly data from 1999-2014, and Table 1.2 presents count tabulations of data availability by year and state. The measure of interest is the registration rate, calculated as the percent of the resident adult population registered as medical marijuana patients.As shown in Table 1.2, data availability on registered patient counts varies across states. Some states provide monthly statistics, while others collect data quarterly or annually. For states with smaller registration programs , administrative records were not made available and had to be collected from older news articles and archived web pages. For states with more developed registration systems, statistics could be found starting from the program’s inception, but the frequency of data collection increased substantially following the Ogden Memo in 2009. For months with missing data,cannabis equipment registered patient counts were linearly interpolated using the two closest months of data.

This new dataset presents the most comprehensive state panel of medical marijuana participation made available as yet. The solid line in Figure 1.2 plots the total number of individuals registered as medical marijuana patients from 1997-2014 in states that required patient registration. As the data show, registered patient counts were relatively flat during the period of federal intervention from 1997-2008, but the Ogden Memo led to a rapid increase in medical marijuana patient participation. The spike in patient take-up coincided with significant growth in the number of legal medical marijuana producers. According to estimates by Sevigny et al. , from 2008-2010 the number of medical marijuana dispensaries increased from around 1,400 to 3,800, and the number of legal producers grew from less than 20,000 to almost 90,000.As shown in Figure 1.2, medical marijuana participation stalled following the Cole Memo. Patient registration rates resumed growth in mid-2013 when Deputy Attorney General James Cole released a second memorandum re-clarifying that federal enforcement resources should focus on large-scale marijuana operations only if they are suspected of engaging in certain criminal activities such as trafficking across states lines, distributing to minors, and supporting cartels . While the aggregate data suggest that these federal memos significantly affected trends in medical marijuana participation, the magnitude of these changes varied widely across states. To illustrate this variation, Figure 1.3 graphs trends in adult per capita patient registration rates for states with effective registry dates prior to 2010. Some states saw exponential growth in registration rates following the Ogden Memo and declines in registered patient counts at the time of the first Cole Memo. Other states show an up-tick in patient registration with the Ogden Memo but appear to have been relatively unaffected by the Cole Memos. Finally, a few states have seen relatively flat trends in medical marijuana participation since program enactment. Summary statistics for the variables used in this paper’s regression analysis are presented in Table 1.3.

Columns and show the mean and standard deviation in monthly medical marijuana registration rate data and for the other included control variables in the models. Column presents the standard deviation across state averages, such that comparing columns and indicates how much of the data variation comes from differences across versus within states. Several policy variables are included in P oljt. Supply-related regulations include a dummy variable for whether state-licensed dispensaries were legalized, and a variable for the laxness of caregiver restrictions equal to 1 if caregivers could grow for an unlimited number of patients, equal to 0.5 if they could grow for multiple patients but faced production limits, and equal to 0 if they could grow for zero or only one patient. A dummy variable for whether the state had decriminalized marijuana is included to reflect the relative risk of arrest for marijuana possession should a patient not register. To capture the relative cost of obtaining a doctor’s recommendation, I include a variable equal to one if chronic pain was allowed as a qualifying condition, equal to0.5 if chronic pain was allowed as a qualifying condition with certain restrictions, and equal to 0 if chronic pain was not allowed as a qualifying condition. Finally, continuous variables for medical marijuana possession limits and registration fees are also included. Table 1.5 examines the effects of the Ogden and Cole Memos, controlling for specific state MML regulations. Since Table 1.3 shows many of these policies are time-invariant within states, specifications control for year but not state fixed effects. From columns and , the significant effects of the Ogden and Cole Memos hold even when controlling for changes in state-specific regulations. Consistent with the predictions of section 1.3, laxer restrictions on suppliers, lower costs for qualifying, and higher possession limits are associated with significantly higher medical marijuana participation. Having a decriminalization law and higher registration fees are associated with significantly lower take-up. Columns and of Table 1.5 allow the effects of specific state MML regulations to vary with time since the Ogden and Cole Memo announcements.

The effects of the Ogden and Cole Memos alone lose significance. Allowing pain as a qualifying condition also significantly increases medical marijuana participation, but it has no differential effect following the Ogden and Cole Memos. This suggests that benefits associated with increased legal availability are an important determinant of medical marijuana patient participation, and that legal production increased in response to the Ogden Memo most in states that did not impose strict capacity constraints on legal suppliers.These findings suggest that the effects of the federal memos on medical marijuana suppliers was an important driver of patient registration. For Colorado,vertical grow shelf there is sufficient data to disaggregate registered patient counts by those patients with and without a designated caregiver. For Colorado, Figure 1.4 shows that, indeed, the most substantial growth in registered patient counts was seen by patients reporting a primary caregiver as their source of marijuana; similarly, the Cole Memo led a larger reduction in registered patient counts among patients with caregivers compared to patients without caregivers. Figure 1.5 provides further evidence that interest in medical marijuana flows from producers to patients. The graph shows quarterly data for Google search interest in the phrases “how to become a patient” and “how to become a caregiver.” Data was collected from Google Trends, which measures relative search interest over time for these phrases from a sample of total searches. The spike in search interest for becoming a caregiver occurs at the time of the Ogden Memo, and it clearly precedes that of search interest in becoming a patient. This suggests that producers responded more rapidly to the announcement effects of the Ogden Memo than users, and is consistent with evidence that incentives to obtain information about a program are influenced by the expected net benefit of participating . To assess the relative role of supply and demand in driving medical marijuana patient registration, ideally one would have detailed state-level time series data on potency-adjusted marijuana prices. Unfortunately, since marijuana remains illegal at the federal level, accurate price data is highly limited. The most widely used data on marijuana prices comes from two data sources. High Times is an online magazine where users can submit the price they paid for their last marijuana purchase. The magazine reports individual price submissions by city and strain of cannabis. Priceofweed.com is a website that collects user-submitted data in real-time on the price of marijuana purchases and classifies them into “high”, “medium”, or “low” quality.

For completeness, I present evidence based on this crowd-sourced data, but they are intended only as suggestive evidence and should be interpreted with caution. Table 1.6 presents estimates for the effects of registration rates on the natural log of price per ounce of high-potency marijuana. For the regressions, data on high quality marijuana prices was aggregated at the state-quarter level and converted to price-per-ounce. Outlying price values were dropped.The results from Table 1.6 show that increases in registration rates significantly predict lower prices. This suggests that, even if higher medical marijuana participation rates to some extent reflect increased demand, they reflect even larger effects on supply. A number of studies have exploited state-time variation in the enactment of MMLs to estimate their effects on marijuana use in the general population. Findings have varied substantially, with estimates ranging from significantly negative, to statistically insignificant, to significantly positive for an excellent review. However, the standard difference-in-differences approach employed in these studies implicitly assumes that the “treatment effect” of MML enactment is dichotomous, i.e. the policy change occurs at a specified date, and it is implemented completely and equally across states. Whether this assumption holds will depend on the mechanisms by which MMLs induce changes in behavior. According to deterrence theory, by reducing the perceived severity of legal or informal sanctions associated with marijuana consumption, MML enactment should ceteris paribus increase demand. Since the passage of MMLs provided similar legal protections and represented a shift in either governmental or social acceptance of marijuana, ex-ante these effects should occur simultaneously with law enactment and be similar across states. This prediction relies on three conditions: that the statutory policy change is actually implemented, that no offsetting changes in enforcement occur simultaneously, and that the public is aware of the change in policy . Since MMLs provide protection from state-level but not federal prosecution, citizens may be even less likely to update their expectations about potential prosecution until it is observed or known that the federal government will not intervene. Since awareness about laws and enforcement policies will be diffused through social networks, personal experience, and the mass media, the federal memos and their coverage by the media and marijuana advocacy groups may have had far greater effects on public perception than MML enactment alone. To provide suggestive evidence that knowledge about MMLs was limited prior to the Ogden Memo, Table 1.7 presents state-representative data on MML awareness from the National Survey of Drug Use and Health , which starting in 2002, asked respondents the following: “In your state, has marijuana been approved for medical use?” Table 1.7 reports cross-sectional variation in the percent of youths and adults who responded “yes” to this question, comparing the 2008-2009 and 2010-2011 for each state with an MML prior to 2009.18Although these are not causal effects, they provide some useful insights. The first striking feature of Table 1.7 is the wide range of awareness across MML states. Oregon was the only state in 2008-2009 where over half of adult respondents correctly reported that the state had an MML. In contrast, less than 18% of adults in Nevada correctly responded that their state had an MML. On average, youths aged 12-17 are less aware of MML existence, but there is similar variation across states. The share of adolescents correctly reporting their state had an MML in 2008-2009 ranged from 25% in Vermont to 47% in Oregon. This variation in awareness is not explained by differences in how long the MML has been in effect. Also of note is the substantial increase in awareness of MML status following the Ogden Memo for Colorado, Montana, and Michigan. In two years, the share of adults who correctly responded that their state law allowed for the use of medical marijuana nearly doubled. These states also show the largest increase in awareness among youths. From Tables 1.1 and 1.5, these were also states with MMLs allowing caregivers to serve multiple patients and experiencing the greatest growth in medical marijuana patient participation following the Ogden Memo.

Nevertheless, the demonstrations of cognitive recovery in abstinent PSU, and potential effects of smoking status on such recovery, are consistent with our observations of corresponding recovery in abstinent AUD . The 19 PSU not studied at follow-up differed significantly from abstinent PSU at baseline on several important variables: they had more years of cocaine use over lifetime, and performed worse on cognitive efficiency, processing speed, and visuospatial learning. As such, these differences should be tested as potential predictors of relapse in future larger studies. Several factors limit the generalizability of our findings. Our cross-sectional sample size was modest and therefore our longitudinal sample of abstinent PSU was small; as not uncommon in clinical samples, about half of our PSU cohort relapsed between baseline and follow-up, a rate comparable to what has been reported elsewhere . This made us focus our longitudinal results reporting on the main effects of time and to de-emphasize the reporting of time-by-smoking status interactions. Larger studies are needed to examine the potential effects of smoking status and gender on neurocognitive recovery during abstinence from substances. The study sample was drawn from treatment centers of the Veterans Affairs system in the San Francisco Bay Area and a communitybased healthcare provider, and the ethnic breakdown of the study groups was different . Therefore, our sample may not be entirely representative of community-based substance use populations in general. Although preliminary, the within subject statistics are meaningful as they are more informative for assessing change over time than larger cross-sectional studies at various durations of abstinence. In addition, premorbid biological factors and other behavioral factors not assessed in this study may have influenced cross-sectional and longitudinal outcome measures. Nonetheless,flood tray our study is important and of clinical relevance in that it describes deficits in neurocognition and inhibitory control of detoxified PSU that are different from those in AUD, and that appear to recover during abstinence from substances, potentially as a function of smoking status.

Our cross-sectional and longitudinal findings are valuable for improving current substance use rehabilitation programs. The higher impulsivity and reduced cognitive abilities of PSU compared to AUD, likely the result of long-term comorbid substance use, and the lack of improvements in learning and memory during abstinence indicate a potentially reduced ability of PSU to acquire new cognitive skills necessary for remediating maladaptive behavioral patterns that impede successful recovery. As such, PSU may require a post-detox treatment approach that accounts for these specific deficits relative to AUD. Our results show that PSU able to maintain abstinence for 4 months had less total lifetime years of cocaine use and performed better on cognitive efficiency, processing speed and visuospatial learning than those PSU not restudied ; these variables may therefore be valuable for predicting future abstinence or relapse in PSU. Additionally, and if confirmed in larger studies, our preliminary results on differential neurocognitive change in smoking and nonsmoking PSU may inform a treatment design that addresses the specific needs of these subgroups within this largely understudied population of substance users. Potentially, concurrent treatment of cigarette smoking in treatment-seeking PSU may also help improve long-term substance use outcomes, just as recently proposed for treatment seeking individuals with AUD . Finally, our findings on neurocognitive improvement in PSU imply that cognitive deficits are to some extent a consequence of long-term substance use , which have the potential for remediation with abstinence. This information is of clinical relevance and of psychoeducational value for treatment providers and treatment-seeking PSU alike. Rates of adult marijuana use have grown substantially in the US over the past decade, particularly among those aged 26 years or older and those that are daily cigarette smokers . This is of concern, as evidence indicates that marijuana use can lead to addiction and use of other substances, motor vehicle accidents, impaired brain development, psychiatric conditions, and respiratory problems . Concurrent use of tobacco and marijuana is common, although not uniform across tobacco products .

Estimates indicate that between 25% and 52% of tobacco smokers use cannabis and, among past month marijuana users, 68% also reported tobacco use . Further, concurrent use of marijuana and tobacco increased from 2003 to 2012 . Use of marijuana and cigarettes or cigars is commonly reported among tobacco users , though less is known about the relationship between marijuana use and multiple tobacco products. About 40% of current tobacco users in the US report use of multiple products, and cigarettes + e-cigarettes are the most common combination . Among multiple product users, there are also differences in types of products used and prevalence of use by age, gender, and race/ethnic group .The opportunities for co-administration provided by modification of tobacco products may represent one source of interest in tobacco products among marijuana concurrent users . For example, electronic devices engineered to aerosolize tobacco, marijuana, and other substances may be marketed to promote cooccurring use . Tobacco products that can be readily modified for marijuana use may not only increase tobacco use among marijuana users, but also expand the types of tobacco products used by an individual. Frequent use of marijuana has been linked with persistent tobacco use and greater tobacco dependence among youth and adults and higher expectancies that each substance promotes the use of the other . Among concurrent users, motivation to reduce marijuana and the relative perception of harm from use of marijuana have been lower than for tobacco. Concurrent use of marijuana and tobacco products may promote persistent tobacco use and decreased motivation to alter use of marijuana , reduce users’ interest in quitting tobacco , and reduce success in cessation . By impairing cessation efforts, concurrent use may serve to exacerbate the negative consequences from both tobacco and marijuana.

The health effects of persistent tobacco use and use of marijuana show clear associations with acute and chronic respiratory symptoms . When combusted, both inhaled tobacco and marijuana smoke deposit tar and other constituents in the lung . Cellular abnormalities associated with respiratory disorders including bronchitis and chronic obstructive pulmonary disease have been observed in both tobacco and marijuana users . A US population examination of respiratory symptoms among marijuana smokers suggested higher rates of bronchitis, coughing, phlegm production, and wheezing after statistically adjusting for cigarette smoking . The effects of marijuana use on respiratory health among other and multiple tobacco product user groups, including users of aerosolized products in the US population, have not been examined. The rapidly changing landscape of tobacco and marijuana products and consumption devices, particularly in the context of marijuana legalization and increasing use, indicate the importance of characterizing patterns of tobacco and marijuana use. Our primary aim is to describe the relationship between current marijuana use and pattern of current tobacco product use using a comprehensive assessment of tobacco products. We hypothesized that users of tobacco would have higher rates of current marijuana use than those not currently using tobacco products and that effects would be strongest for current users of tobacco products with inhaled routes of administration that accommodate co-administration relative to current non-inhaled tobacco product users. We also hypothesized that among current tobacco users, concurrent use of any tobacco product and marijuana would be associated with increased tobacco dependence and a decreased motivation to quit tobacco use. Finally,grow table we hypothesized that concurrent users of inhaled tobacco products and marijuana would have higher rates of respiratory conditions relative to those not currently using tobacco products.We used logistic regression to estimate the relationship between current marijuana use among users and non-users of tobacco products, with non-tobacco-users as the reference group . Planned covariates for all models included age, gender, and racial/ ethnic group. With a focus on current tobacco users, Hypothesis 2 evaluated reports of marijuana use for each user group using cigarette only users as the reference . We then added a term reflecting levels of tobacco dependence and re-evaluated the independent association between tobacco use group and current marijuana use . Logistic regression models were also used to explore relationships between current marijuana use, past quit attempts, and current intentions to quit among current users of tobacco products. Lastly, for Hypothesis 3, logistic regression models were used to estimate the odds of a respiratory condition for current tobacco users relative to those not currently using tobacco.

A dummy coded term for current marijuana use was used in interaction terms to assess potential moderating effects of marijuana use on relationships between tobacco product use and respiratory conditions.Among current tobacco users, we first estimated any increase in odds of marijuana use of each user group in reference to cigarette only users . E-cigarette only, hookah only, and cigarette + e-cigarette users did not differ significantly from cigarette only users in the odds of reporting current marijuana use. Cigar only and multiple product users had significantly greater odds of reporting current marijuana use than cigarette only users. Smokeless only users had significantly reduced odds of marijuana use compared to cigarette only users. We examined the hypothesis that tobacco dependence may account for significant differences in current marijuana use across tobacco user groups by adding the TD scale to the demographically adjusted model . This hypothesis was not supported, as each significant association noted in Model B remained significant in Model C, and each effect size remained largely unchanged. Follow-up exploratory analysis did not suggest any moderated relationship between tobacco user groups and TD scores, as the set of interaction terms when added after all other lower-order terms was not significant = 0.53, p = 0.78).The present study examined the differential prevalence of co-use of marijuana among distinct types of tobacco product user groups, impacts on tobacco dependence, efforts to stop tobacco, and current respiratory problems. Cigar only and multiple product users consistently had the highest rates of marijuana use. These product-specific patterns were maintained after adjusting for differences in demographics and after accounting for the strong relationship between tobacco dependence and current marijuana use. Tobacco product characteristics that may afford opportunity for delivery of marijuana may increase their appeal to concurrent users of marijuana. Alternatively, concurrent use may serve to promote expanded use of tobacco products . Rates of concurrent use decreased steadily across age groups for all tobacco product users except smokeless only users, whose rates remained similar to non-users across age groups. While we adjust statistically for the influence of age on the relationship between product use and increased current marijuana use, the increase in concurrent use of these products among youth, where cigar and multiple product use is most common, is of particular concern. However, the extent to which the currently observed greater rates of concurrent use among youth differ from historical patterns is unknown. Differences in product user groups also reflect, in part, differences in tobacco dependence . Associations between concurrent use and dependence are well documented and have plausible mechanisms via enhanced reinforcement, conditioned pairings to strengthen cues for concurrent use, and amelioration of cognitive deficits of marijuana use alone . When examining the relative impact of levels of dependence, no observed relationship between product user group and concurrent use of marijuana was affected. This suggests an additive effect of dependence rather than a sole common causal relationship. Previous studies have suggested that marijuana use is associated with persistent tobacco use and decreased efforts to quit tobacco . Present findings indicate that although concurrent marijuana use did not correspond with reduced intentions to quit tobacco, it was associated with lower likelihood of reporting a past year quit attempt among some user groups. Concurrent users may be more dependent on marijuana , may have an increased difficulty with marijuana cessation when they continue to use tobacco , may have more psychosocial impairments , and may be less motivated to reduce tobacco use . Current marijuana use may be a barrier to tobacco cessation, not because it interferes directly with intentions to quit, but because it is associated with reduced chances that a concurrent user will be attempting to quit.Additive effects of concurrent use of marijuana and tobacco on respiratory symptoms heighten public health concerns over potential exacerbation of health effects of marijuana on lung disorders and increased odds of respiratory conditions among both users and non-users of tobacco products .

Within the DBTL cycle, synthetic biology tool kits have had the greatest impact on the “build” phase. Rapid and precise diversity generation, including the construction and integration of expression assemblies into a platform, is a vital prerequisite to screening. Libraries of well characterized genetic parts provide metabolic engineers with a set of elements that can precisely control the expression of a pathway gene. To this end, vector sets, promoter sets, terminator sets, and signal peptide sets are the most common control elements used. A vector is a circular fragment of DNA that harbors pathway genes, a selection marker, and an origin of replication which dictates copy number and plasmid stability. Integration of synthetic biology constructs directly into the genome may obfuscate the use vectors, however shuttle vectors for cloning of constructs are generally still employed. Promoters are regulatory elements directly upstream of a gene of interest, which recruit transcriptional elements for gene expression. Promoters may be constitutive or inducible . The promoter “strength” correlates to the copy number of mRNA upon induction; promoters are often referred to as tight or leaky . Terminators are the regulatory elements downstream of the protein coding sequence, signaling transcriptional termination, and impact the half-life of mRNA. Signal peptides may be employed to direct expression to an organelle for localization or secretion. Prior to use, these genetic parts must be assembled into a single contiguous DNA fragment. Sequence independent cloning techniques such as Gibson assembly and yeast homologous recombination have replaced traditional methods such as digestion-ligation.Furthermore, gene fragments can now be affordably synthesized, circumventing strain procurement and DNA isolation.A once tedious and unpredictable process,rolling grow table heterologous gene expression has been streamlined using reliably functional elements; gene expression is now definitively “engineerable”.

As we gain a more comprehensive understanding of sophisticated cellular programs, we will be able to assemble even more robust and dynamic synthetic biology circuits. Once such systems are constructed, integration into the heterologous host is the final hurdle in the “build” phase. The recent discovery of CRISPR/Cas9 has ameliorated this challenge. Cas9, an RNA-guided DNA endonuclease, enables genomic modifications with unprecedented precision, greatly accelerating strain construction.Following the “build” phase, a screening approach is required in order to “test” the performance of synthetic constructs. Direct measurement of product titer using chromatography, mass spectrometry, and spectrophotometry and comparison to an authentic standard is the most accurate quantification method. Advancements in instrumentation have increased the throughput and accuracy while decreasing costs, however these methods are still considered low-to-medium throughput, requiring 1 minute – 1 hour per sample. Meanwhile, indirect measurements of product titer employing biological readouts have enabled high-throughput testing of strains. So called “biosensors” transduce chemical inputs into physiological outputs in order to establish a correlation between a titer and a selectable phenotype. Biosensors enable screening of constructs on the order of seconds or less per sample. In rare circumstances, a natural product is produced in sufficient quantities and has a unique enough absorbance spectrum to function directly as the selectable chromophore. More typically, a genetically-encoded biosensor must be engineered that robustly actuates a signal that can be correlated to the metabolite’s concentration. Biosensors consist of a sensor-actuator pair and are either RNA-based or protein-based. The sensor-input consists of binding of the biosensor to the secondary metabolite. Then, an actuator-output is generated resulting in modulation of transcription or translation of a selectable protein.

The genetic circuit may also encode Boolean logic in order to improve biosensor properties such as dynamic range or sensitivity.Selection is then performed either in situ or ex situ . For example, a cell viability screen can be established by tying a biosensor output to expression of an antibiotic resistance gene or complementation of an auxotroph. On the other hand, biosensor-dependent expression of a fluorescent protein enables high-throughput fluorescence-activated cell sorting for rapid analysis of entire populations of cells. Microbial opioid production has benefited greatly from the use of biosensors, as both RNA and protein based metabolite sensors have been reported for benzylisoquinoline alkaloid pathway intermediates.Adaptive laboratory evolution has also emerged as an efficient method to circumvent traditional DBTL strain construction. ALE employs natural selection and in vivo diversity generation for population-wide engineering, and has been primarily applied to primary metabolic products.Although several generalizable biosensor development platforms have been proposed, research towards rapid expansion of the variety of sensed metabolites is ongoing. Compared to organic synthesis and biochemical engineering, synthetic biology is a relatively nascent applied science. Despite this, immense progress has been made in the last 20 years, and a number of recent success stories illustrate the field’s potential. Research groups now routinely refactor pathways with more than 10 steps in A. nidulans and N. benthamiana, and pathways with more than 20 steps have been reconstituted using both S. cerevisiae and synthetic biochemistry. The ongoing challenge for these platforms is to improve titers and reduce costs sufficiently to compete with traditional production methods. General strategies range from improving flux through pathway bottlenecks to ameliorating growth defects from metabolic burden or toxicity, however a more nuanced engineering approach is often required. In depth discussions of the engineering strategies enabling benchmark production of the psychoactive natural products described in this review accompany the bio-synthetic pathway descriptions. Of all the psychoactive compounds that are either isolated as natural products or produced synthetically, hallucinogens may impart the most dramatic shifts in one’s psyche.

This broad class of substances can induce potent alterations to consciousness, mood, and perception resulting in vivid visual hallucinations, synesthesia, and a warped sense of time and space. The precise mixture of perceptual and somatic effects of hallucinogens is highly compound specific and thus has led to many debates on accurate nomenclature. There is yet to be a consensus with terms such as “psychedelic” and “entheogen” often used interchangeably with “hallucinogen” in different contexts. Natural sources of hallucinogens famously include “magic mushrooms” of the Psilocybe genus and other fungi such as ergot and fly agaric. Other well-known sources of hallucinogens are from the spineless cactus, peyote, the psychoactive brew, ayahuasca, and with a recent resurgence, nutmeg.Most natural hallucinogens are alkaloids derived from amino acids such a L-tryptophan, L-tyrosine , and L-glutamic acid, with one notable exception being the terpenoid salvinorin A. Numerous extensive reviews exist on the history, pharmacology, and potential as therapeutics of hallucinogens which we recommend.The serotonin or 5-hydroxytryptamine receptors, named for their native ligand, serotonin, have been implicated in the modulation of sensory perception, mood, cognition, memory,indoor plant table and more through the peripheral and central nervous systems There are many subtypes, and with the exception of 5-HT3 which is a ligand-gated ion channel, the rest are G-protein-coupled receptors, each with unique spatial distribution and localization in the brain.Phylogenetic analysis and low sequence identity demonstrates early divergence, implicating 5-HT receptors as one of the oldest receptor systems.The relationship between 5-HT receptors was first determined through testing of LSD. While hallucinogenic compounds like 3 have been shown to target multiple 5-HT receptors, the 5-HT2A receptor is most commonly associated with the majority of psychotropic effects.Previously, structure-activity relationship studies between 5-HT2A and numerous psychoactive compound scaffolds have demonstrated that hallucinogenic potency is not necessarily a function of affinity, likely due to more nuanced mechanisms of functional selectivity.However, a recent crystal structure of 3 complexed with 5-HT2B was reported and combined with molecular dynamic simulations, identified a molecular basis for the particular potency of 3. The authors demonstrate that the diethylamide side chain of 3 adopts a restrictive conformation when bound to 5-HT2B that increases residence time and improves β-arrestin translocation to the cell membrane. This enhanced β-arrestin translocation results in desensitization of the cell to stimuli by uncoupling G-proteins from receptors and could explain the long duration of action of 3.N,N-dimethyltryptamine is likely the most pervasive psychoactive compound across species and is found in dozens of plant and animal species, including humans.Root, bark, and leaf preparations from plants such as Psychotria viridis, containing DMT and its structural analogs have been used in shamanic ritual practices for at least 1000 years.Interestingly, in addition to plants, structural analogs 5- methoxy-N,N-dimethyltryptamine and bufotenin, are also found in the toxin of the Colorado River toad Incilius alvarius, formerly known as Bufo alvarius, whose remains have been found as a part of Olmec ritual ceremonies dating back to pre-Columbian Mesoamerica .Referred to colloquially as the “Psychedelic Toad of the Sonoran Desert,” exudates from the amphibian’s specialized glands may contain up to fifteen percentage dry weight , representing the most notable example of a psychoactive natural product of animal origin.

DMT was first isolated from the shrub Mimosa tenuiflora in 1946 by Oswaldo Gonçalves de Lima,but its hallucinogenic effects were not discovered for another decade., like all L-tryptophan derived hallucinogens, is a serotonin receptor agonist. While the functional selectivity of towards the 5HT2A receptor is believed to be necessary for its effects, can bind to many serotonin receptors that may also contribute to its psychoactivity.While the precise role of endogenous in humans has yet to be ascertained,one study speculates it may have a role in protecting from hypoxia.Further, has shown promise as a therapeutic anti-depressive agent and is known to promote neural plasticity.Interestingly, brominated forms of DMT such as, 5-bromo-N,N-dimethyltryptamine , have been isolated from the marine sponges and show particular promise as antidepressives.Finally, has limited neurotoxicity and only exhibits cardiovascular effects when taken intravenously in large doses, furthering its therapeutic potential.Psilocybin 1, one of the major natural products from hallucinogenic Psilocybe sp. , was first isolated from Psilocybe mexicana by Albert Hofmann in 1958 .The description of “magic mushrooms” in scientific literature and the subsequent isolation and characterization of their psychoactive metabolites was the culmination of decades of effort to identify the sacred mushroom that the South American Aztecs referred to as teonanacatl, meaning “god’s flesh.”Psilocybin 1 itself is not psychoactive, but rather exists as a prodrug. After ingestion, psilocybin 1 is metabolized through dephosphorylation and becomes psilocin, a potent psychotropic 5HT2A receptor agonist.In addition to its psychoactivity, has shown some promise as a therapeutic for treating depression, anxiety and tobacco addiction.A bio-synthetic pathway for psilocybin was proposed based on isotope feeding studies as early as 1968.Agurell et al. hypothesized that following decarboxylation, L-tryptophan, now tryptamine , would be methylated iteratively to form the psychoactive dimethyltryptamine . This was a reasonable hypothesis because indolethylamine-N-methyltransferases were a popular enzyme for study at the time following their discovery rat, rabbit, and human tissues.Recently, a psilocybin bio-synthetic cluster from Psilocybe cubensis and Psilocybe cyanescens was identified and characterized by Fricke et al. .The authors first sequenced the genomes of both Psilocybe sp. Then, using a combination of a methyltransferase, a hydroxylase, and a kinase as queries, a putative bio-synthetic cluster present in both species was identified and characterized. Fricke et al. determined that the iterative N-methylation was the terminal step of psilocybin biosynthesis by enzymatic action of PsiM whose sequence is unrelated to the well-characterized mammalian indolethylamineN-methyltransferases, and thus revised the hypothesis that DMT is an intermediate in psilocybin biosynthesis. Starting from L-tryptophan, PsiD catalyzes a decarboxylation reaction to yield. The amino acid sequence for PsiD diverges from the more common PLP-dependent aromatic amino acid decarboxylases and instead shares similarity with the PLP-independent phosphatidylserine decarboxylases. PsiH, a P450 monooxygenase, then hydroxylates the indole C4 to yield 4-hydroxytryptamine . Next PsiK, a predicted kinase, catalyzes the phosphorylation of 4-hydroxytryptamine into norbaeocystin using ATP as the phosphate donor. Phosphoryltransferase are relatively uncommon in natural product biosynthesis. Recent examples include the biosynthesis of calyculin protoxins and the lasso peptide paeninodin, in which phosphorylation plays a role in self-immunity which could highlight the importance of dedicated kinases.Lastly, PsiM methylates the terminal amine in in an iterative fashion using SAM as a methyl donor to give 1. PsiM only methylates phosphorylated tryptamine , indicating that psilocybin biosynthesis is nearly linear. In water, 1 undergoes spontaneous hydrolysis of the phosphate group to form , but PsiK accepts psilocin as a substrate and readily phosphorylates to reform psilocybin 1. As previously mentioned, this hydrolysis results in the psychoactive form, upon ingestion by vertebrates.

We hypothesized that higher levels of current marijuana use and greater cumulative marijuana exposure would be associated with worsening cognitive processing speed and flexibility and the magnitude of effects will be greater in HIV+ compared to HIV- men. The MACS is an ongoing prospective cohort study of the natural and treated history of HIV infection among Men who have Sex with Men in the United States. 6,972 men were enrolled during the history of the study in three waves: 4,954 men in 1984–1985, 668 in 1987–1991, and 1350 in 2001–2003 and at 4 centers located in Baltimore/Washington DC, Chicago, Los Angeles, and Pittsburgh. The study design of the MACS has been described previously and only the design relevant to the current analyses are described here. Participants return every 6 months for physical examinations, HIV testing, laboratory testing, structured clinical interviews, collection of data on cigarette smoking, alcohol use, illicit drug use and cognitive function assessments. The institutional review boards at the respective study centers approved the MACS study protocols and all participants provided informed consent. Participants were eligible for the current study if they had two or more cognitive function assessments over the study period . Furthermore, HIV+ individuals were eligible if they initiated highly active antiretroviral therapy and reported continuous use for one year. Exclusion criteria for all participants included history of: a learning disorder , stroke, seizures, peripheral neuropathy, multiple sclerosis,flood table and head injury with loss of consciousness greater than 1 hour. Socio-demographic characteristics included participant’s selfreported age, race/ethnicity status and educational attainment. Study participants were classified according to the MACS study center and MACS cohort status .

Depressive symptoms were evaluated at every study visit with the Center for Epidemiologic Studies Depression scale . Current alcohol use was self-reported at every study visit and categorized. We computed cumulative exposure to alcohol in drink-years with 1 drink-year equivalent to consuming a standard drink of alcohol every day for a year. We calculated the average number of drinks consumed per week for each participant by multiplying the average number of drinking-days per week by the average number of drinks consumed per drinking-day. Alcohol drink-years was computed by adding the total average number of drinks consumed during all follow-up visits . Cigarette use was self-reported at every study visit. We categorized current smoking status at every visit into three groups: never former and current smoker. Cumulative exposure to cigarettes was computed and defined in pack-years, with one-packyear of exposure equivalent to 7300 cigarettes . Stimulant/recreational drug use was selfreported at every study visit. Participants were considered to be users of stimulant drugs if they reported the use of: crack cocaine, other forms of cocaine and methamphetamines and ecstasy. Participants self-reported their frequency of use of poppers  using similar response options as for marijuana use, but categorized as any use in the past six months. We used similar approach for marijuana use-years to compute cumulative exposure to stimulants and poppers in use-years. Participants were categorized as having a history of injection drug use , if they self-reported ever injecting any substance. Hypertension was assessed at every visit and was defined as systolic blood pressure greater than 140 mmHg, or diastolic blood pressure greater than 90 mmHg or diagnosed with hypertension and use of medications. Diabetes status was classified using a combination of HgA1C values ≥6.5 and diagnosed with diabetes and use of medication.

Participants were classified at each MACS study visit as HCV positive if they were found to be in the process of seroconversion, acute infection, chronic infection, clearing , or previously HCV positive, but now clear of HCV RNA. HIV-serostatus was assessed using enzyme-linked immunosorbent assay with confirmatory Western blot tests on all participants at each participant’s initial study visit and at every semiannual visit thereafter for participants who were initially HIV– to confirm their serostatus. Plasma HIV RNA concentrations were measured using the COBAS Ultrasensitive Amplicor HIV-1 monitor assay for HIV RNA , with a sensitivity of 50 copies of HIV per RNA/mm3 . Standardized flow cytometry was used by each MACS center to quantify CD4+ T-lymphocyte subset levels . Antiretroviral therapy and ART adherence was self-reported at every study visit. ART was classified as none, nucleoside reverse transcriptase inhibitors , protease inhibitors , and non-NRTIs . Adherence to ART was assessed in the MACS beginning from October 1998 using a scale measuring four levels of adherence, which has been described previously . ART use prior to October 1998 was considered 100% adherent. We computed cumulative years of each class of ART at each study visit, weighted for self-reported adherence. The weights for the four levels of adherence were 1, 0.975, 0.85, 0.375, 0 for adherence levels of 100%, 95–99%, 75–94% and less than 75%. History of clinical AIDS was determined according to the 1993 CDC definition of AIDS . Data analyses was conducted from April 1, 1996, to September 30, 2013. April 1, 1996 was chosen as the baseline because that was when most men in the MACS initiated HAART . We used linear mixed effects models to test associations between current and cumulative exposure to marijuana and changes in cognitive function measures using SAS PROC MIXED, to account for correlations between repeated cognitive function measures over time, from the same participants. We specified an unstructured covariance matrix for the repeated outcomemeasures as this achieved the best model fit compared to other covariance structures . We used robust standard errors from the robust empirical covariance estimator. Time since baseline was used as the longitudinal metric for time.

Models included both linear and nonlinear time trends. We fit linear mixed effects models over the 17-year follow-up period, using maximum likelihood estimation and allowing for random intercepts and random slopes to account for individual differences in baseline cognitive function and to allow for subject-specific rates of cognitive change. We performed stratified analysis by HIV-serostatus. We modeled each cognitive function outcome separately on current marijuana use, as well as on cumulative exposure to marijuana. The primary coefficients of interest were interactions between current and cumulative marijuana-use-years with time. The model for the HIV- men adjusted for time-stable covariates and time-varying covariates . The models for the HIV + men additionally adjusted for time-stable and time-varying HIV-specific parameters . The models assessing cumulative exposure to marijuana use and cognitive function included time-varying cumulative exposure variables including pack-years of smoking, alcohol drink-years,indoor plant table stimulant and popper use-years. We log transformed test scores from the TMTA and TMTB to approximate a normal distribution. To facilitate interpretation, we transformed the regression coefficients of the TMTA and TMTA models using the formula, 100 where β is the regression coefficient . Because the longitudinal metric for time was measured in years, the coefficients can be interpreted as annual percent change in test scores across time. We used inverse probabilities of attrition weights  to adjust for selective attrition . Cohen’s f 2 effect sizes was calculated to understand the magnitude of the associations between current and cumulative exposure to marijuana use in all models. Cohen’s f 2 was calculated using SAS PROC MIXED procedures introduced by Selya et al , with f 2 ≥ 0.02, f 2 ≥ 0.15, and f 2 ≥ 0.35 representing small, medium, and large effect sizes, respectively . All statistical analyses were conducted in SAS version 9.4 . Statistical tests for significance was defined as p <0.05.Among HIV+ men only, there were no statistically significant association between cumulative marijuana use-years and changes in any cognitive function domain . Conversely, in the HIV- men only, each additional 5 marijuana use-years was significantly associated with a decline in TMTA scores by 0.18 percent annually . Similarly to the findings for current marijuana use, all effect sizes were very small, falling below Cohen’s f 2 criteria for a small effect size and were similar in the HIV+ and HIV- men. In exploratory analysis, we tested for interactions between history of AIDS, detectable viral load and CD4 counts by current and cumulative marijuana use but the results were not significant . In all models, the most consistent set of covariates that were associated with increased rate of decline across cognitive function tests was advancing age, non-white race and lower education. Supplemental tables 1–4 show the full model estimates stratified by HIV-serostatus. In addition, we conducted a series of sensitivity analyses to assess the impact of multiple imputation on our findings. Specifically, we re-ran all our models without imputing missing marijuana values and compared it to our extant results; and the results for current and cumulative marijuana-use-years remained relatively consistent .

In this analysis of HIV+ MSM in the MACS followed for 17-years, we found current monthly and daily marijuana use to be significantly associated with slowed cognitive processing speed, but not cognitive flexibility. Additionally, we found no significant associations between cumulative exposure to marijuana and changes in cognitive processing speed and flexibility. Among the HIV- MSM, we found no statistically significant association between current marijuana use across all cognitive function measures, although, each additional 5 marijuana-use-years was associated with significant decline in one measure of cognitive processing speed. Our findings of significant associations between current monthly and daily marijuana use with slowed cognitive processing speed differ from other studies of HIV+ individuals that found no significant associations . For instance, Thames et al. in a small crosssectional study of 89 HIV+ and HIV- subjects found that HIV+ subjects with moderate-toheavy marijuana use demonstrated no significant associations with slowed processing speed than none-users . In a more recent study, Thames et al. found no significant difference on tasks of processing speed among HIV+ subjects when levels of marijuana use increased over 1.4grams/week . These studies were cross-sectional with modest sample sizes compared to our study which used cognitive function assessments at multiple time-point from a large sample. However, our study found no significant associations between current marijuana use and decline in cognitive flexibility, which is consistent with other studies of HIV+ individuals that assessed this cognitive domain . Our findings of no significant association with current marijuana use and processing speed and flexibility in HIV- men mirror findings from other longitudinal studies of adults conducted in the general population . For example, one longitudinal study of 2,404 adults, 22 years of age at baseline followed for 8-years in an Australian cohort found no significant differences in performance on tasks of processing speed in some marijuana using groups versu none-using groups . Similarly, another longitudinal study of 1,897 adults, mean age at baseline of 42 years followed for nearly 8 years found no significant differences in performance on tasks of cognitive processing speed between weekly or more and less than weekly marijuana use in the past year compared to nonuse . In addition, we note that all of the coefficients from our models, were of very small magnitude, falling below Cohen’s f 2 criteria for a small effect size suggesting that our findings, likely do not represent clinically meaningful declines in cognitive function. Our study is among the first to longitudinally assess the impact of cumulative exposure to marijuana and changes in cognitive function performance for a 17-year follow-up period. Among the HIV- men, our study found cumulative exposure to marijuana was associated with statistically significant decline in one measure of cognitive processing speed and not in the other . This is inconsistent with studies in the general population that have found no significant associations with cumulative or chronic marijuana use and decline in cognitive processing speed . For example, in a recent cohort study of 3,385 men and women in the Coronary Artery Risk Development in Young Adults study, cumulative marijuana use for 25 years was not statistically significantly associated with worse cognitive processing speed . Although that study assessed cognitive function at a single time-point when participants mean age was ~50 years compared to our study, which had cognitive function assessments at multiple time-points beyond 50 years – when cognitive decline may be more apparent. However, in one study that comprised 1,037 individuals in a New Zealand birth cohort study followed-up for 20 years found that diagnosis with cannabis dependence at 3 or more study waves was associated with widespread declines in cognitive domains including memory, executive function and cognitive processing speed in adulthood .

Due to the cross-sectional nature, it was not possible to ascertain the vaccination status of calves in the study with certainty, instead we relied on the in-person survey reported vaccination protocols, although respondents were asked to answer the questions with respect to the current cohort of calves on their premises during the visit. If calves were older than the age of vaccination stated in the questionnaire they were considered vaccinated. However, the true vaccination status and time since vaccination may have been different for an unknown number of calves. No associations between reported vaccination status and BRD were found when calves at least 7 d older than age of vaccination were considered vaccinated to account for time to initiate an immune response. Our findings correlated with a randomized clinical trial by Windeyer et al. of 2,874 heifer calves on 19 commercials dairy farms in Minnesota and Ontario, in which they found no difference in risk of BRD between calves vaccinated at 2 or 5 wk of age or both with a commercial multi-valent modified live vaccine against the common respiratory viruses compared with a placebo control group. Those authors cited interference by maternal antibodies, unresponsiveness of the neonatal immune system, timing of immunity relative to pathogen exposure, disease caused by pathogens other than the viruses in the vaccine, or herd immunity as possible explanations for their findings. The vaccination of dams may only be effective in preventing calfhood BRD in combination with adequate transfer of passive immunity. Many factors can affect the efficiency of transfer of passive immunity, from the amount, quality,cannabis grow facility layout and timing of colostrum fed to the storage time and temperature . It is possible that any positive effect of dam vaccination may have been diluted by other variables affecting transfer of passive immunity.

In a longitudinal study following over 11,300 preweaned calves on 5 dairies in California and associating management factors to BRD in the calves, vaccinating dams with either a killed or a modified live vaccine reduced the risk of disease in calves. . The longitudinal study design combined with close follow-up of management practices by verifying changes every 1 to 4 mo in a few dairies may have been the reason that study found these associations, whereas in the present study they were not apparent. The complexity of factors involving vaccination of neonatal calves combined with the present study design make it difficult to extend recommendations based on our findings.Although the benefits of colostrum on calf health have been previously reported , we found no association between volume, source, quality, or storage conditions of colostrum and BRD in the study herds. Windeyer et al. reported serum total protein levels ≥5.7 g/dL during the first week of life as well as supplementation with an antibody product at birth to be negatively associated with BRD, but the predictive value positive was low when using serum total protein levels at the cut point of <5.7 g/dL to predict BRD. In a study by Virtala et al. , low post colostral total serum IgG concentrations ≤1,200 mg/dL was associated with increased risk of BRD in a prospective cohort study of 410 preweaned heifer calves. Diagnostic accuracy of prediction in that study was 54%. Both groups of authors stated that additional factors could play a role in the development of BRD. The current study did not include data on failure of transfer of passive immunity for the study calves. The data on volume of colostrum fed as well as whether colostrum from first-calf heifers was used and if the quality of colostrum was assessed served as proxies for adequate passive transfer. The fact that all colostrum-management variables had to be assessed on a herd-level basis may have masked the effect of colostrum quality fed to individual calves in the study, as it was unknown which calves received colostrum from first-calf heifers or colostrum stored at a certain temperature or for a certain amount of time if there was variability within a dairy.The cross-sectional nature of the study prohibits drawing of causal inferences. Although a longitudinal study design would have allowed us to draw causal inferences, the large number and wide geographic distribution of dairies enrolled did not allow such a study.

However, findings reported here provide the basis for hypothesizing potentially causal factors and promote the design of further studies with longitudinal design to directly examine housing and management factors found to be associated with BRD. Participating dairies were not chosen randomly from all dairies in California, and it could be argued that those willing to invite researchers onto their dairy might differ from those who do not. However, considering the wide geographic distribution of dairies, the inclusion of both organic and conventional operations, and a wide range of herd sizes, as well as representation of Holstein and Jersey breeds, in the sample further ensures that the spectrum of California dairy operations was captured in the current study population.Among PLWH, the average self-reported marijuana use in the past six months ranges between 25% and 56% . The prevalence of daily or nearly daily marijuana use among PLWH is on the rise . The increasing prevalence of marijuana use among PLWH corresponds with recent trends in passage of state laws governing recreational and medical marijuana across the United States. Currently, 33 states and the District of Columbia have passed legislation allowing marijuana for medical or recreational use . PLWH report using marijuana as a self-medicating strategy to manage HIV related symptoms such as nausea, pain, mood problems, and poor appetite . However, data on the potential benefits or adverse health effects of marijuana use in this population are limited . The endocannabinoid system is comprised of two endogenous receptors: cannabinoid receptors 1 and cannabinoid receptors 2 . CBR1 are located primarily in the central nervous system including the brain , while CBR2 are on cells and tissues of the immune system . The primary component in marijuana, delta-9- tetrahydrocannabinol partially binds to and activates CBR1 . Stimulation of CBR1 by THC increases appetite and promotes caloric consumption , suggesting that regular marijuana use may promote weight gain and concomitant higher body mass index , an established risk factor for type 2 diabetes . By contrast, studies have shown that marijuana use is either not associated with BMI/waist circumference or significantly associated with lower BMI and smaller waist circumference . Additionally, prior studies have found lower odds of metabolic syndrome , hyperglycemia , insulin resistance , and mean fasting glucose among current marijuana users compared with nonusers.

One recently published meta-analysis including eight cross sectional studies reported that marijuana use was associated with reduced odds of type 2 diabetes . In addition to cross-sectional findings, at least two longitudinal studies have examined the association of marijuana use with incident type 2 diabetes. Bancks et al. prospectively followed up 2,758 men and women in the Study of Coronary Artery Risk Development in Young Adults , who contributed more than 50,000 person-years of follow-up and found no statistically significant association between marijuana use and incident type 2 diabetes, but found a higher risk of prediabetes in participants with greater lifetime marijuana use compared to never users . Similarly, in their analysis of a population-based cohort of men and women in Sweden, Danielsson et al. found no statistical relationship between marijuana use and incident type 2 diabetes. Majority of prior studies of the association between marijuana use and type 2 diabetes have been cross-sectional,indoor grow shelves and existing longitudinal studies have employed few measurement occasions of cannabis use or long follow-up measurement intervals and few follow-ups of participants. As such, prospective analysis of data with multiple, short-term spacing of marijuana measurement over a long-term period of participant follow-up, better documents changes in marijuana use in relation to incident type 2 diabetes. In addition, a prospective analysis of this type can better explore potential reverse associations between marijuana use and incident type 2 diabetes, i.e., pre–type 2 diabetes symptoms resulting in an individual potentially ceasing or reducing marijuana use during the follow-up period. In addition, prior studies have not adequately addressed the role of other confounders on the relationship between marijuana use and type 2 diabetes. For instance, marijuana use is positively associated with tobacco smoking and illicit drug use such as cocaine, heroin, methamphetamine, and other stimulants . Tobacco smoking and stimulant use have been associated with suppression of appetite and lower body weight . Further, to our knowledge, no longitudinal analysis has addressed this question despite the high prevalence of marijuana use and two-fold higher prevalence of type 2 diabetes among PLWH compared with the prevalence in the general population of adults . The objective of this analysis is to determine whether self-reported frequency of marijuana use is associated with incident type 2 diabetes in women and men living with and at risk for HIV.

We aimed to address this question by examining the potential role of reverse causality on the relationship between self-reported marijuana use and type 2 diabetes using prospectively collected data from two large, long-term cohort studies of women and men living with and at risk for HIV with long-term follow-up. Given what is known about the mechanisms relating marijuana use to stimulation of appetite and increased caloric intake, we hypothesized that marijuana use will be associated with increased risk of type 2 diabetes. The Multicenter AIDS Cohort study  and Women’s Interagency HIV Study  are wellestablished, ongoing, prospective multicenter cohorts of men who have sex with men and women living with or at risk for HIV in the United States, respectively. Eligibility criteria and follow-up procedures for the MACS and WIHS have been previously described. Participants in the MACS were recruited at four centers: Baltimore, Maryland/Washington, DC; Chicago, Illinois; Los Angeles, California; and Pittsburgh, Pennsylvania. Participants in the WIHS were recruited from ten sites in Brooklyn, New York; the Bronx/Manhattan, New York; Washington, DC; Chicago, Illinois; San Francisco, California; Los Angeles, California; Chapel Hill, North Carolina; Atlanta, Georgia; Birmingham, Alabama/Jackson, Mississippi; and Miami, Florida. The MACS enrolled men who have sex with men across three waves: 4,954 in 1984–1985, 668 in 1987– 1991, and 1350 in 2001–2003 . The WIHS enrolled women across 4 waves: 2623 in 1994–1995, 1,143 in 2000–2001, 371 in 2011–2012, and 845 in 2013–2015 . Data in both cohorts are collected using structured interviews and standardized physical and laboratory assessments, with study visits typically occurring every six months. HIV status was assessed by enzyme-linked immunosorbent assay with confirmatory testing at baseline for HIV-positive participants. The study questionnaires used in the MACS are available at www.aidscohortstudy.org and in the WIHS at https:// statepi.jhsph.edu/wihs/wordpress/. The institutional review boards at the respective study centers approved the MACS and WIHS study protocols, and all participants provided written informed consent. For both cohorts, the index visit was defined as the first visit at which fasting blood glucose data were available and we included HIV-positive participants with confirmatory testing at baseline and HIV-negative participants. For the WIHS, we included participants who were active beginning from October 2000 to September 2017. Fasting glucose was measured at each follow-up visit beginning from October 2000 through March 2003 and then annually thereafter. Hemoglobin A1C was measured beginning from October 2000, annually through March 2006, suspended from April 2006 through October 2010 and then measured annually thereafter. Of the 4,099 active WIHS participants in October 2000, we excluded those with prevalent type 2 diabetes at the index visit , those with less than 3 follow-up visits and those who seroconverted during the follow-up period , leaving a final analysis sample of 3,578. For the MACS, we included participants who were active beginning from April 1999 to September 2017. Fasting glucose and Hemoglobin A1C was measured in the MACS biannually during the study follow-up period. Of the 3,570 active MACS participants in April 1999, we excluded those with prevalent type 2 diabetes at the index visit , those with less than 3 follow-up visits and those who seroconverted during the follow-up period , leaving a final analysis sample of 2,682. 90 mm Hg, or diagnosed with hypertension and use of medications. Family history of diabetes, alcohol use, smoking status, and illicit drug use were self-reported at every visit.

Self-reports by medical users in California and Canada indicate that a substantial proportion substitute marijuana for alcohol and other drugs. In 1996, California voters passed an initiative Proposition 215, which legalized marijuana use for medical purposes. Because the Proposition was implemented in an abrupt and uniform manner, legalization presented a “natural experiment.” To estimate the causal impact of legalization on suicide, annual time series of total, gun, and non-gun suicides were analyzed by comparing California with an estimated counterfactual state in a Synthetic Control Group design. The synthetic control time series for California were constructed as a weighted combination of 41 states that did not legalize medical marijuana during the time frame. Post-intervention differences between California and its constructed control time series were interpreted as the causal effect of the medical marijuana law on suicide.Findings reveal that rates of total suicide and gun suicide dropped significantly in the aftermath of Proposition 215. Findings also reveal, however, that legalization’s impact on non-gun suicides is considerably smaller, and arguably no different than what would be expected to occur by chance. Confidence in these findings is underscored by the methodological approach undertaken in the study. A strength of the Synthetic Control Group Design is that it allows us to examine the net effect of medical marijuana legalization on suicide. Despite the strengths of this design,planting racks important limitations remain, many of which present opportunities for future directions in research.

Because we examine suicide trends over eight post-intervention years, we are fairly confident that the effects are permanent. Because our time series end in 2005, on the other hand, it is difficult to generalize our theoretical result to subsequent years. We are limited by the fact that medical marijuana laws began to proliferate across the U.S. after 2005, threatening to contaminate the “donor pool” of untreated states. In virtually all the states that legalized medical marijuana after 2005, moreover, reforms were not implemented abruptly or uniformly, making confident causal interpretations more difficult. Another limitation that presents a future direction relates to the mechanisms that may account for the findings of the study. What are the mechanisms responsible for the sharp decline in total, but especially gun, suicides following medical marijuana legalization in California? We proposed mechanisms related to the substitution of marijuana for alcohol and other related substances; marijuana use itself, which may reduce actual motivation for suicide; the inability of medical marijuana patients to purchase firearms; and changes in the culture of recreational substance use, leading to fewer unsupervised opportunities to commit suicide in the home. Each of these pathways should be tested, although many will require additional data collection. For example, one likely fruitful research direction would be to collect annual data on alcohol consumption in California and assess whether it is a plausible mechanism by which medical marijuana legalization could cause a reduction in gun suicides. Beyond adjudicating these various pathways, testing mechanisms could yield insight into why we do not find the expected reduction in non-gun suicides following legalization. Unfortunately, we do not have the data to test these mechanisms, yet it will be essential for future researchers to do so.

Correlation coefficients between environmental and biomarker measurements are widely used in environmental health assessments and epidemiology to explain the exposure associations between environmental media and human body burdens. As a result considerable attention and effort have been given to interpretation of these coefficients. However, there is limited information available on how the variance in environmental measurements, the relative contribution of exposure sources, and the elimination half-life affect the reliability of the resulting correlation coefficients. To address this information gap, we conducted a simulation study for various exposure scenarios of home-based exposure to explore the impacts of pathway-specific scales of exposure variability on the resulting correlation coefficients between environmental and biomarker measurements. Biomonitoring data, including those from blood, urine, hair, etc., have been used extensively to identify and quantify human exposures to environmental and occupational contaminants. However, because the measured levels in biologic samples result from multiple sources, exposure routes, and environmental media, the levels mostly fail to reveal how the exposures are linked to the source or route of exposure. Thus, comparison of biologic samples with measurements from a single environmental medium results in weak correlations and lacks statistically significance. In addition, cross-sectional biological sample sets that track a single marker have large population variability and do not capture longitudinal variability, especially for compounds with relatively short biologic half-lives, which can be on the order of days such as pesticides and phthalates. Therefore, in the case where the day-to-day variability of biological sample measurements is large, the use of biomarker samples with a low number of biological measurements in epidemiologic studies as a dependent variable can result in a misclassification of exposure as well as questions of reliability.

For chemicals frequently found at higher levels in indoor residential environments than in outdoor environments, it is common to assume that major contributions to cumulative intake are home-based exposure and/or food ingestion. This simplification can be further justified because people generally spend more than 70 percent of their time indoors. Compounds with significant indoor sources and long half-lives in the human body– on the order of years for chemicals such as polybrominateddiphenyl ethers –have been found to have positive associations between indoor dust or air concentrations and serum concentrations in U.S. populations. On the other hand, extant research has not reported significant associations between indoor samples and biomarkers for chemicals primarily associated with food-based exposures, for example, bisphenol-A [18] and perfluorinated compounds. For chemicals with both homeand food-based exposure pathways and short body half-lives , as is the case for many pesticides, a significant association between indoor samples and biomarkers is found less frequently or relatively weak compared to PBDEs . To better interpret these types of findings, we provide here a simulation study for various exposure scenarios to explore the role of the chemical properties and exposure conditions that are likely to give rise to a significant contribution from indoor exposures. We then assess for these situations the magnitude and variance of the associated correlation coefficients between biomarker and indoor levels. The objectives of this study are to generate simulated correlation coefficients between environmental measurements and biomarkers with different contributions of home-based exposure to total exposure and different day-to-day and population variability of intake from both residential environments and food, to interpret the contribution of home-based exposure to human body burden for two hypothetical compounds whose half-lives are on the order of days and years,trimming tray and to determine how the pattern of variability in exposure attributes impacts the resulting correlation coefficients linking biomarker levels to exposure media concentrations.

In this study, our first step is to synthetically generate daily environmental concentrations and food exposure concentrations based on variations of day-to-day intake from residential environments and food as well as different relative contributions of home-based and food-based exposure. As different chemicals are likely to have different relative contributions from the homebased and food-based exposure pathways, we conducted our simulations across the full range of relative contributions between the two pathways to address all plausible scenarios for various compounds. We combine the simulated home-based exposures associated with indoor environmental concentrations and food concentrations, assuming that the total intake results only from home-based exposure and food ingestion. From these inputs we estimate time-dependent biomarker concentrations using a onecompartment pharmacokinetic model. We then computed correlation coefficients between simulated environmental and biomarker concentrations. In order to facilitate numerous simulations, several simplifications are made regarding a representative environmental medium for home exposure, a distribution of environmental and food intake, and sources of exposure. First, we select chemical concentrations from indoor wipe samples as a way to represent home-based exposures that result from all potential exposure routes, including inhalation, nondietary dust ingestion, and dermal uptake. From these wipe concentrations, resulting home-based exposure can be assumed to be linearly related to Cwipe and Ehome and Cwipe are assumed for simplicity to be equal. In addition, we assume that a contaminated food intake rate represents food exposures . Second, we select Cwipe and Efood from log-normal distributions of variability across both population and time. Lastly, we assume that the total intake accounting for biomonitoring data results from Ehome and Efood, excluding any other exposure pathways.Because some indoor contaminants are considered potential threats to human health, many studies have applied significant resources to examine the relationship between exposure to indoor pollutants and adverse health effects. However, these studies are potentially limited by the use of a single or a few environmental and biological samples. The significant implications of this situation are reflected in our results. Multi-day, multi-person sample analyses are costly and labor-intensive.

In addition, the resulting R2 values from these studies are not interpreted or poorly interpreted in terms of variability and contribution of exposure sources and the biological half-life of a compound. In this regard, the simulation study in this paper provides an important step towards interpreting the relative contribution of home-based exposure to human body burden for two compounds whose biological half-lives are significantly different . Although these two compounds do not cover the full range of chemical substances, bracketing half lives allows us to quantify thesignificance of source, measurement, and exposure pattern variability for disaggregating body burden. In particular, it shows that exposure variability and different contributions of exposure sources are more interconnected than commonly considered in many experimental studies. The work also brings to attention the need to understand the impact of a chemical half-life on the relationship between environmental exposures and biomonitoring data. The sensitivity of day-to-day variability of wipe concentrations and food exposures on the resulting R2 values also points to the importance of understanding variability and contribution of exposure sources. Finally, future work includes computing the relative number of samples needed for various levels of confidence to disaggregate body burden for various types of compounds , environments, and exposure pathways. Despite the lack of experimental data, the simulated results provide key insights on the role of the variability and contribution of exposure sources and biological half-lives in quantifying a relationship between indoor exposure and human body burden. This approach will be useful for designing future exposure and epidemiologic studies that includes indoor environmental samples and biomonitoring data.Bovine respiratory disease complex is one of the most common causes of death in dairy calves and poses a significant welfare and economic burden on the industry . Reported morbidities for calves in the 1991 NAHMS study evaluating the health of preweaning heifers on dairies was only 8.9% in calves up to 8 weeks of age . In 2010, respiratory disease in dairy heifers was reported as the cause in 22.5% of deaths before and 46.5% of deaths after weaning . In addition, 18.1% of preweaning heifers on dairy heifer-raising operations were reportedly affected by pneumonia, making it the second most common calf illness after diarrhea . Hence, over the last few decades, no improvement has been reported in morbidity from BRD in dairy calves. Given the lack of improvement in BRD incidence in US dairy cattle, despite the availability of numerous vaccines and antimicrobial drugs labeled for BRD, novel approaches that target prevention in addition to control should be evaluated. The complexity of etiologic agents and predisposing factors for BRD combined with the difficulty of accurate diagnosis pose challenges in the prevention and control of this disease on the farm that may be addressed using a risk-assessment approach in combination with a disease-scoring system. A multitude of tests can be used to identify pneumonia calves in a herd; however, scoring systems require minimal training, are low cost, and can be reasonably accurate, making them a viable tool to estimate the disease burden in the herd . Repeated use of a risk-assessment tool to target preventive management practices combined with a scoring system to benchmark the burden of BRD in a calf herd over time may offer a low-cost, rapid, and comprehensive control program for BRD. In contrast to a chronic disease, such as Johne’s disease, where changes implemented to control the disease may not result in a reduction in incidence for many years, BRD primarily presents as an acute disease.