Hemp Growing

One caveat for the locomotor studies is that the arena was not as large compared with the size of a lobster as the similar ratio for typical open field studies conducted in rodents. Similarly, the water depth was limited to that necessary to cover the lobster to facilitate the video tracking for this initial investigation. Nevertheless, the animals were able to express movement, turn around, change direction, etc., and traveled about 20 m after the vehicle exposure condition. It would be of interest in future studies to assess locomotor behavior in a larger arena or to assess behavior in a deeper aquatic environment. In the nociception experiment, lobsters were observed to respond to warm water immersion of claw, tail or antenna in a temperature-dependent manner . This provides evidence of thermal nociception in the lobster for the first time , and is consistent with prior work which has shown thermal nociception in crayfish, using a warm metal stimulus on the claw and antenna . No response to immersion in maintenance temperature salt water was observed in this study, using the 15 s cutoff. . At temperatures from 40 to 48 °C, however, the lobsters made distinct motor responses upon immersion of the tail, the claws or the antenna. Tail immersion resulted in a clear response of legs and claws and/or a strong flick of the tail which was in many cases repetitious . These latter behaviors can be considered within escape responses of lobsters and crayfish, dry racks for weed with evidence from the latter more plentiful. In the crayfish, sudden onset stimuli evoke tail movements associated with lateral and/or medial giant neuronal activity whereas more gradual stimuli evoke tail movements which do not involve the lateral or medial giant .

This study observed a range of apparent behavioral responses including gradual movements, strong flips and repeated flipping of the tail suggesting a diversity of sensory experiences from the sudden to the gradual. Additional experimentation would be required to further dissociate the thermoceptive responses under various conditions but the critical factor for this study was the detection of the hot water stimulus. Immersion of the claws or antenna also resulted in a distinct movement to remove the appendage from the water, consistent with thermoception. Temperature dependent differences in response latency were observed for the warm water challenges, with 40 °C less noxious than 44 or 48 °C. This graded response is what is observed with a similar nociceptive assay in rats and further enhances confidence in the specificity of the response to the noxious stimulus. The pronounced difference in sensitivity between the crusher and cutter claws provides another important validation of the model. Prior reports have focused on claw morphology and muscle fiber type and this extends this by demonstrating a clear behavioral insensitivity of the crusher compared with the cutter claw. Finally, the effect of THC vapor exposure on thermal nociception was minimal under the tested conditions. Surprisingly, despite the locomotor effect of 30 min of THC vapor exposure, there was no impact relative to vehicle vapor exposure on the latency of the response to warm water immersion . It required 60 min of exposure to THC to produce any significant effect , which was very small in magnitude. Although THC has limited anti-nociceptive impact in rodents relative to opioids and has a limited dose-effect range due to this low ceiling, it is typically more robust in rodents than what was observed here.

The present results and the work on lobster neuromuscular junctions suggest that THC has specific neuropharmacological effect, however, there does not appear to be a vertebrate endocannabinoid receptor expressed in the lobster or crayfish ; this is similar to behavioral effects in Drosophila melanogaster which likewise lack CB1/2 analogs. This turns attention to the possibility that THC acts in the lobster via temperature activating transient receptor potential channels. THC appears to function as a ligand at TRPV2, TRPV3, TRPV4, TRPA1, and TRPV8, as reviewed , and the Caribbean spiny lobster expresses 17 TRP channels including TRPA and TRPV homologs . A further clue is provided by the fact that another cannabis constituent which does not have substantial activity at endocannabinoid receptors , cannabidiol , also had inhibitory effects in Turkanis and Karler , and CBD modulates several THC sensitive TRPs . One apparent concern for this interpretation is that mammal TRPA1 is activated by noxious low, but not high, temperature. Nevertheless, thermosensitive TRPs may be a particularly good candidates for any anti-nociceptive effects in decapod crustaceans given that TRPA1 from invertebrates are indeed activated by high temperature. In some work, TRPV1 activates at about 48 °C , in other work over 43 °C as reviewed . There may be species differences in the activation temperature and as a comprehensive review observes that while TRPA1 activation temperatures vary across species/ortholog, activation is always above the preferred temperature of the species . Overall, the likely activation temperature for TRP homologs in the lobster are likely to accord with the temperature range established here for thermal nociception, i.e., observed at 40 °C and above, but not at 22 °C . A prior finding, however, that capsaicin did not influence the apparent thermal nociceptors in crayfish may suggest that whatever small effect was produced on nociception here was not mediated by a capsaicin sensitive TRP channel, such as TRPA1 or TRPV1. Alternately, the fact that TRPV1 can desensitize to capsaicin may have resulted in an apparent divergent result for noxious heat and capsaicin stimuli, presumably as a consequence of the specific methods used and potentially the species/ortholog in question. Finally, it is possible that there may be an as yet undiscovered ortholog of mammalian cannabinoid receptors in the lobster.

For example, the invertebrate C. elegans expresses a cannabinoid-like receptor that appears to mediate effects of the endogenous cannabinoid agonists 2-arachidonoylglycerol and anandamide . There are a few limitations to this study that may be useful to address in any future work. In the lobster behavior experiments, housing for the range of 4–21 days prior to a given assessment was never explicitly tested for potential effects. No major changes were noticed, but it is not impossible this would contribute. Likewise, we selected a fixed housing temperature, within the range described for this species in their natural habitat, so any effect of housing at one or the other end of their temperature range was not determined. As mentioned above, the locomotor assessment was conducted in an arena too small to divide into zones, e.g. Center vs Periphery, that in the context of a rodent test would permit assessment of anxiety-like avoidance of the center; a larger arena might facilitate such investigations. The nociception assay provides strong evidence for detection of a warm water stimulus, hydroponic rack system however it is possible that additional analysis of the response of the tail would provide further insight. Because the main goal was to determine if any response would be made, and there is no available information on how lobsters would respond to a thermally noxious stimulus, it was decided to operationalize the first clearly detectable response as the target latency. Future studies which determine the consistency of the strong flip, repeated flipping or the slower withdrawal response, both between and within individuals, would further define this response. In conclusion, these data confirm a method for studying the effects of aerosol THC exposure in a lobster model. Duration-dependent levels of THC were observed in the species’ tissues and a reduction in locomotor behavior was produced. The animals also responded in a temperature-dependent manner to the immersion of tail, claw or antenna in a hot water bath, indicating thermal nociception. This latter conclusion was further enhanced by the observation of differential sensitivity in the cutter and crusher claws. Further experimentation will be required to fully investigate other behavioral outcomes, including anxiety-like measures. Despite emerging scientific evidence on the adverse health risks of marijuana smoke, many people think that marijuana smoke is less toxic than tobacco smoke. Marijuana smoke contains chemicals known to cause cancer and reproductive toxicity, many of which are also in tobacco smoke. Indeed, except for the psychoactive ingredient — THC versus nicotine — marijuana smoke is similar to tobacco smoke. This similarity makes it likely that marijuana use will have comparable health effects as tobacco, a prediction supported by recent findings that marijuana and tobacco secondhand smoke exposure both have adverse cardiovascular effects. For example, combustible marijuana use and secondhand marijuana smoke exposure significantly impair blood vessel function, similar to tobacco, in ways that would increase the risk of atherosclerosis , heart attack, and stroke. Marijuana smokers are also at an increased risk of respiratory problems including chronic bronchitis, as marijuana smoke is associated with inflammation of the large airways, increased airway resistance, and lung hyperinflation.

Marijuana smokers also report increased rates of respiratory infections and pneumonia compared to nonsmokers. Increased marijuana use may produce other adverse effects such as long-lasting detrimental changes in brain function in adolescents, increased risk for addiction , and elevated risks of mental health disorders . Increased marijuana use also may result in increased traffic accidents from driving while impaired by marijuana. Aerosolizers are used for both nicotine and marijuana, and do not involve combustion, so produce fewer toxic chemicals than combusted products. While not fully understood, e-cigarettes do, however, expose users and bystanders to nicotine, ultrafine particles, and other toxins. Research on the health effects of marijuana leaf aerosolizers, THC concentrate aerosolizers, and liquid THC-filled e-cigarettes is hampered by the same factors that hamper research on marijuana in general. However, there is evidence on the adverse health risks of flavorants used in both e-cigarettes and marijuana products , which contain the chemical diacetyl. Marijuana remains a Schedule I substance under the Federal Controlled Substances Act, in which the use, sale, and possession of cannabis is a criminal offense under federal law, and which has resulted in a huge deficit in knowledge on marijuana use and secondhand exposure, whereas tobacco is now one of the most comprehensively researched substances. In addition, because, at least at the present time, adults who use marijuana often also use tobacco, it is difficult to separate the effects of these two products. These factors also make it difficult to study the possible medical benefits from certain forms and chemical components of marijuana. It is important to emphasize, however, that the current situation in which there is relatively little evidence on the health effects of marijuana, is not the same as evidence of little or no adverse effect. In California tobacco is legal, but its use is increasingly denormalized, while marijuana is illegal but becoming more socially accepted. This reality is reflected by the fact that, in California, more youth are now using marijuana than tobacco. Between 2011-2013, 24% of 11th graders and 15% of 9th graders reported past 30 day marijuana use, compared to 12% of 11th graders and 7% of 9th graders for past 30 day cigarette use. If current tobacco and marijuana use trends in California continue, with tobacco use continuing to fall and marijuana use continuing to increase, and as retail marijuana becomes legal in more places and time passes, we are likely to develop a more detailed and precise understanding of the associated health risks. Arguments for marijuana policy reform generally are centered on social justice, public safety, and the economic impact of marijuana criminalization. Some marijuana policy reformists argue that legalizing retail marijuana for recreational use will eliminate the incarceration of responsible users and nonviolent dealers and shrink or eliminate existing illicit markets without significantly increasing the health harms and costs of marijuana use. Others advocate for policy change somewhere between incarceration and legalization, often advocating for decriminalizing possession and lesser penalties for production and distribution. Full legalization advocates generally envision a commercial marijuana regulatory framework modeled on state alcohol regulations. They also argue that the revenues from new marijuana taxes will cover the costs not only of overseeing and regulating legal sales but also will cover programs to prevent youth initiation and control abusive use associated with increased access to marijuana, with revenue to spare for the state government general fund. Whether these predictions materialize will depend on how the production, distribution, marketing, and sale of the newly-legalized marijuana market are structured and regulated, and what the new legal marijuana industry looks like and how it operates.

Because I used a voluntary identification card database as a proxy for all medical marijuana users in California, many approximations may be under estimated. While there was some evidence of a substitution effect between alcohol and medical marijuana from the arrest data, the true effect could be much larger. This would justify why in previous studies, there has been stronger evidence of substitution effects. It was particularly interesting that there was no evidence of a substitution effect between marijuana and narcotics. Because the proxy for narcotics use was arrest rates, it is possible that the actual substitution effect could not be observed. Many users who substitute narcotics for medical marijuana are originally prescribed a legal amount of prescription-drugs, thus having no reason for an arrest to take place. It is also possible that many medical marijuana cardholders do not use for medical purposes and the actual medical users did not identify themselves in this database. While this proxy is not definitive of all effects that marijuana has on society, it offers insight on the true impact that marijuana use can have on crime and substance use. An improvement of this study would be to use data from states that have passed recreational marijuana laws and comparing it to those who have not. However, at the moment we can use this analysis to better understand how the current marijuana laws affect California. Significant changes have taken place in the policy landscape surrounding cannabis legalization, production, pipp racking system and use around the globe and across the United States. Over the last couple of decades, 35 states and the District of Columbia have legalized cannabis for medical conditions; of these, 15 states and the District of Columbia have also legalized adult use of cannabis.

These landmark changes in policy have impacted cannabis use patterns and the perceived levels of risk. However, despite this changing landscape, evidence regarding the short- and long-term health effects of cannabis use remains inconclusive. Several research studies have examined cannabis use in many forms, however, often these research conclusions are not appropriately translated and/or communicated to policy makers, health-care providers, state health officials, and other stakeholders who have been charged with influencing and enacting policies, procedures, and laws related to cannabis use . Other relevant challenges include the availability of cannabinoid-based study medications, federal regulations, and other constraints associated with clinical trials. Oncologists frequently discuss the clinical use of cannabis with their patients although most feel they lack an adequate knowledge base to advise effectively . The National Academies of Sciences, Engineering and Medicine’s report on the Health Effects of Cannabis and Cannabinoids found strong evidence in support of the use of cannabinoids for chemotherapy-induced nausea and vomiting as well as pain . Despite those findings, many oncologists prefer to recommend approved pharmaceuticals with larger bodies of supporting evidence. Increasingly patients are hearing of people healing their malignancies with highly concentrated cannabis oils . Although there is a significant body of preclinical evidence suggesting anticancer effects of cannabinoids, translation to clinical benefit has not yet occurred. Hence, oncologists and cancer researchers are likely to be particularly interested in seeing cannabis research advance. This review highlights challenges and barriers to cannabis and cannabinoid research from the perspectives of administrators from the National Institute on Drug Abuse, National Institutes of Health ; the US Food and Drug Administration ; and clinical researchers. Barriers specifically to studying cannabis, cannabinoids, and cancer are emphasized.Federal restrictions on clinical cannabis research result from its legal status as defined by the Controlled Substances Act [CSA ] and international treaties. However, federal and state laws conflict, with diverse state regulations allowing personal possession and recreational and medical use.

Laws and regulations on federal cannabinoid research have been changing recently, and more changes are expected . Cannabis remains a federal schedule I controlled substance. Schedule I substances include those determined to have high potential for abuse, no currently accepted medical use, and a lack of accepted safety for use under medical supervision. Additionally, the 1961 Single Convention on Narcotic Drugs makes it illegal to grow, possess, or distribute cannabis except under strict conditions. One of those restrictions is that nations may designate a single source of research marijuana. NIDA has served as the single source in the United States since 1968. However, because of a recent re-interpretation of the Single Convention requirements, the DEA recently published a new rule that will potentially allow the approval of additional growers and producers of cannabis for research . Currently, an estimated 41 applications are pending. Conflicting federal and state cannabis regulations hinder research in several ways, including the inability of researchers to access products that are legal in their state, a lack of standardization and quality control of cannabis and cannabis-derived products within and across states, and no national oversight of this standardization and quality control or the industry.The administrative challenges for cannabinoid research include the single domestic source requirement for cannabis, complex and lengthy registration processes, and schedule I classification of nonintoxicating cannabis components such as CBD. Scientific challenges include the complexity of cannabis plants ; difficulty in designing blinded, controlled studies ; and the inability to study products available from dispensaries in states where they exist. Researchers who order cannabis from NIDA for human research in the United States must obtain FDA Investigational New Drug authorization, DEA schedule I registration, and institutional review board approval.

Despite misconceptions, NIDA has no role in determining qualifications. If researchers receive FDA, DEA, and IRB approval, NIDA fulfills orders for cigarettes and bulk cannabis in various THC and CBD concentrations, plus placebos. NIDA’s research cannabis is consistent, reproducible, pesticide free, and herbicide free. Although the cannabis provided by NIDA tracks the average THC potency of the cannabis generally available, NIDA does recognize the need for greater varieties of products, including improved placebos and more formulations , a larger range of potencies, and variable terpene content .There is broad public interest in expanding the availability of cannabis-based products for both medical and non-medical use. In responding to this demand, the mission of the FDA is focused on advancing public health by overseeing the investigation, approval, and production of safe, effective, and high-quality medical products, including those that are synthesized chemically or derived from the cannabis plant. As described above, the Agricultural Improvement Act of 2018 had an important impact on the FDA’s actions in this area. Importantly, the Farm Bill stipulates that the FDA’s authorities under the Federal Food, Drug, and Cosmetic Act are unchanged, so that hemp-based drug products will be subject to the same authorities and requirements as any other drug product. To date, the consequences of FDA regulation of cannabis and cannabinoids, including hemp, pipp vertical racks include the approval of 4 drug products. Three of these products are synthetic THC or similar to THC and are approved to treat nausea from cancerchemotherapy. The fourth product, Epidiolex, is made from highly purified CBD from cannabis. It is approved for certain rare seizure disorders and, more recently, for tuberous sclerosis complex. The marketing approval of Epidiolex, particularly in light of the DEA’s placement of FDA-approved CBD-containing products in schedule V according to the CSA , shows that the clinical development of cannabis-derived medicines derives from the concerted evolution of biomedical knowledge and regulatory flexibility. Beyond the 4 FDA approvals mentioned above, the agency has more generally performed a scientific assessment of cannabis-derived CBD and concluded that, although CBD is psychoactive, it does not have the same abuse potential as THC . In addition to reviewing marketing applications for drugs and their indications, the FDA also regulates clinical studies with cannabis and cannabinoids. For these avenues of research, the investigator submits an Investigational New Drug application to the FDA for review. The application includes a detailed description of the study protocol and information about the investigational drug, including a summary of previous human experience with the investigational drug; animal pharmacology and toxicology; chemistry, manufacturing, and controls information on the investigational drug; and evidence that it was manufactured according to current good manufacturing practices . Given that state legalization may have facilitated the municipal sale and use of many new cannabis-derived products around the United States, it is important for investigators to be aware of FDA regulations before engaging in clinical studies with cannabis and cannabinoids. As the FDA works to regulate cannabis-derived products appropriately, there is a great deal that is unknown about CBD, and even less is known about the dozens of cannabinoids and other compounds present in cannabis extracts.

For example, little is known about the effects of long-term human use of CBD and the impact of CBD in susceptible populations: children, pregnant women, and the elderly. Based on data from the drug development program for Epidiolex and from the published literature, there are known toxicities of concern related to CBD use . For example, there are signals of potential liver injury, potential male reproductive toxicity, and clinically important drug-drug interactions. We do not know exactly how serious these signals are, and it is important that we continue efforts to assess them. Going forward, we also need to identify ways of answering the many remaining questions about the safety of CBD, as well as the many other compounds found in cannabis. With so much yet to be learned, the FDA is committed to supporting scientific cannabinoid research and development. To support human drug development of cannabis and cannabis-derived compounds, the FDA has created several resources to aide investigators as they develop their clinical studies and use real-world data to fill the scientific gaps of knowledge. Some examples of these resources include information about the conduct of clinical studies and how to request formal meetings , considerations for using botanicals , a frequently asked questions website , recently released draft guidance about manufacturing cannabis-derived drugs , and the newly published FDA Voices Blog . The FDA’s research agenda is aimed at supporting studies to develop the data that is needed to understand how cannabinoids can be used safely in drug products and other consumer goods, such as dietary supplements, cosmetics, and pet foods. The FDA also is exploring policy options to enable broader availability of safe, effective, and high-quality cannabinoid products. There is substantial interest in US Congress legislation, and the FDA is actively offering assistance to state and nongovernmental partners in understanding the evolving cannabinoid landscape. The FDA also continues to take enforcement actions whenever violative marketing of cannabinoid products is identified. For example, during the COVID-19 pandemic, the FDA had to take action in multiple instances where makers of CBD products made antiviral, curative claims despite the lack of any supporting evidence on their safety or efficacy. In summary, the FDA has a well-defined and multifaceted role in the cannabinoid space. Its role has been strengthened and clarified in some respects through recent activities on the legislative level. Most importantly, the FDA continues to support the scientific assessment of cannabis-derived compounds. Because of broad interest in expanding the availability of these products, the FDA is considering many different regulatory options for responding to this interest, always informed by our commitment to protect patients and advance our national public health interests.The clinical researcher striving to respond to public health priorities related to the surge in cannabis and cannabinoid use is met with a number of regulatory hurdles. With rapid expansion of new products, novel methods of use, and growing populations using these products for medical indications or for non-medical use, these restrictions are a major contributing factor to the limited data published addressing the most urgent issues. Apart from questions on the potential effectiveness of products on the market for certain indications, a more immediate concern relates to the safety of these products. There are increasingly popular product categories and modes of delivery that are available for purchase in state-regulated dispensaries that have yet to be tested under controlled conditions. Some of these products are hypothesized to have potentially significant negative effects, such as high-potency extracts geared toward delivering efficient intoxicating effects, as well as products such as specific minor cannabinoids and terpenes that, based on preclinical literature, may be safe but have yet to be assessed in humans. In an effort to elucidate both the safety and the potential therapeutic uses of these products for a range of indications for which they are already approved in an overwhelming majority of the United States, researchers must work tirelessly through institutional, regulatory, funding, and drug supply hurdles, all of which significantly influence the scientific impact, public health relevancy, and efficiency of investigations.

Using the USDA Quick Stats tool to find census data on Milk – Operations with Sales for California returns the years 1997, 2002, 2007, 2012 and 2017 and comes with the county name and the number of operations.All available years of the county crop reports were downloaded from the USDA NASS site as .CSV files and compiled using R Studio for processing and analysis. A total number of 442 crop types were reported. After merging the 41 years into a single data frame, I filtered the dataset to include only milk related commodities, of which there were three possible types: Milk Market Fluid, which refers to Grade A beverage milk ; Milk Manufacturing, which refers to milk that is used to make butter, cheeses or milk powder; and Milk Cow’s Unspecified, which simply means that the county did not distinguish between fluid and manufacturing milks. While they are reported differently depending on the county, these three types of milk are the same in terms of form and units . Milk is measured in hundredweights, notated as Cwt, which is equal to 100 pounds or11.63 gallons of fluid milk. For the purpose of my analysis, I summed the different production values for the three categories of milk to allow for comparison across all counties. Lastly, I pivoted the table to make the data compatible with a shape file for mapping, so there was one row per county with a column for each year of production, 1980-2020. This pivot removes any associated data such as price per unit or value, which can be addressed in a separate data frame or re-joined if desired but was not included in this analysis. At this point, any holes in the data were identified where the original county reports were missing production quantities for certain counties and years. This may be due to errors in the compiled report, or that the county only reported the total monetary value of the commodity, rolling benches without reporting the hundredweights of milk produced. Table 2 summarizes the counties and years that were missing data, and what steps I took to fill in the data. I first searched directly in the County crop reports online and filled in blank data in an excel sheet.

If the crop reports did not report milk production quantities that year, I used a simple average formula to interpolate the data from the previous and following year so as to not leave any blank cells, which would appear the same as zeros on the maps. For a few counties, the reports from 2018 onward were missing, so I extrapolated numbers from the most recent year. In the end, 41 out of 58 of California counties remained in the dataset, meaning 41 counties reported milk production at least once between 1980 and 2020. Of these 41 counties, 22 of them had milk production reported for the full time-period , while 18 counties stopped reporting milk production at some point between 1981-2014. California has 58 counties; the smallest is San Francisco County and the largest is San Bernardino. These counties are grouped into eight agricultural districts by the CA County Agriculture Commissioner’s Data Listing. The initial analysis is presented at the county level for all the Census years and decades, after which the data is aggregated into regions, as shown in Figure 3, to show the broader patterns. Based on the history of California’s dairy industry, and the current milk production rates, I distinguish Marin and Sonoma from the Central Coast counties as its own agricultural region in the context of dairy production, creating nine regions.This research involves two sets of spatial-temporal data from 58 counties spanning 41 years. When visualizing changes over space and time, a static map or time-based chart will inevitably sacrifice nuances, obscuring changes through the years or spatial relationships between counties. I developed a methodology to map changes over time using a sequence of maps designed to be viewed in succession, either flipping through full pages or as an animation in a GIF file. The result is a unique visual of the data that captures the spatial relationship while maintaining the temporal resolution. To provide further detail, the maps are supplemented with data tables summarized by decade and with basic calculations like percent share and percent change to quantify the effect that the maps give visually.

To visualize the milk production and dairy operations data I had acquired, I created bubble maps, or maps using proportional symbols, to address the problem of using county-level data in symbolizing quantities. A map with proportional symbols uses point sizes to represent the number of farms per county, or volume of milk produced. This is an alternative to maps where a color gradient fills the shape of the county, known as a choropleth map. While there are benefits and drawbacks to both types of symbology, I decided to use proportional symbols because the size of the counties in California is irregular, and the size of the counties would affect the visual weight of the color. I executed the same steps to create the maps of milk production and number of operations for all years, as follows. There are at least two methods to create the maps of proportional symbols, and I used both – the first in R for creating a series of 41 maps automatically and the second in ArcGIS Pro for more detailed cartographic design. In R Studio, I created centroids of each county based on a shape file of California County Boundaries , and then joined the processed and interpolated data to the shape file based on county name. I used the “tmap” package with the “tm_bubbles” function to create circles based on the quantity of milk production or operations within each county. I set the “size.max” as the maximum production quantity, or number of operations, for the full dataset to standardize symbology across all the years. I set the scale to 5, the style to “fixed,” and set breaks to 2,500,000 for production. I used a function to iterate over each column of the dataset to create maps of production for all 41 years automatically. In ArcGIS Pro, the visualization was almost identical but the steps to create the maps were different. I symbolized the data from each year using proportional symbols and set the maximum point size for each year to a fraction of that year’s maximum value to standardize the sizes of the symbols across all years. For the production maps, this fraction was one over one million – for example in 1980 the maximum production value was 24,711,000 Cwt, and I set the maximum symbol size for the 1980 map to 24.7 points.

The minimum symbol size for production was 1pt throughout all years because the minimum production values were always less than half a million. For the operations maps, the fraction was one fifth – so in 1997 the maximum number of operations in a county was 325, and I set the maximum symbol size to 65 points. The minimum symbol size was calculated the same way, but the minimum number was always less than 6 so the minimum point size was always one. Taking the same fraction of each year’s minimum and maximum created a uniform scale of point sizes across the multiple years. If I were to leave the minimum and maximum the same for multiple years, rolling grow table the values would be stretched or compressed within that range, but the sizes would not correspond to the same values across different years. This did mean that each of the years had to be symbolized individually, which I did for five decades for the production maps and five years for the operations maps. Since the maps in ArcGIS were created to be static maps published in this thesis submission, or a journal submission, where a GIF animation will not work, I decided to average the 41 years of production data by decade, so as to not be forced to cherry-pick 5 singular years, thus losing 37 years of data, when creating a series of five maps. While averaging the decades does reduce the temporal resolution, the averages still represent the general annual production rate of each county. I calculated the average annual production in Excel for 1980-1989, 1990-1999, 2000- 2009, and 2010-2019. I left 2020 on its own and symbolized its own map, since the data seemed dissimilar to the production rates even a few years before, and is the start of a new decade. There are a few limitations to the data involving the quality and time-period and scale of the data that must be acknowledged. The data had 45 counties in the production dataset and 55 counties in the operations dataset. This points to potential flaws and differences in both the county crop reports and the Census of Agriculture data. The annual county crop reports are compiled from independent reports, they do not claim to cover all of agriculture or report everything. In the reports, counties aggregate the revenues of many commodities and may exclude small quantities. This may result in underestimates of production. Missing data will also increase errors in the percent share calculation of other counties. As described in the data processing subsection, missing data was addressed using interpolation . The Census of Agriculture has the opposite problem where any operation with milk sales is included in the dataset regardless of size or actual commercial dairy status, possibly resulting in overestimates of the number of active dairy operations. The data is also limited to the time-period of available data and that two datasets do not cover the same length of time.

The county crop reports were available annually from 1980, while the Census of Agriculture was only available every 5 years beginning in 1997. This limits the capacity for comparison of production and number of operations by year, as the beginning dates are 17 years apart and the resolution of the data is so different. This also keeps our analysis in the contemporary period, whereas we know from our literature review that milk has been produced commercially in California since the late 1800s, about 150 years. Finally there is the problem of resolution and geolocation. The finest scale data readily available is at the county level. The county sizes vary greatly and many of them overlap with mountain ranges or deserts that are not agriculturally productive, therefor diluting the actual area. When I use proportional symbols, they are plotted in the center of the county, or the centroid. For large counties like San Bernardino and Tulare where more than half of the state is desert or mountains respectively, this can create a misleading effect in the maps. This problem persists, and possibly made worse, when I use graduated colors or dot density maps as well, as the full area is filled with color or dots, while actual milk production may occur only in a small corner of the county. This problem would be ameliorated with more information on the locations of the dairies, but without it, the maps should be interpreted with the understanding that the dots are not accurately located where milk production occurs in the county, and instead should be interpreted as a symbolic marker of the county’s production rates. The following two sections contain maps visualizing the data collected on milk production and operations at the county-level, each followed by tables of the quantities and percent shares of production and operations that the maps were based on for more detail. The California Agricultural Commissioner’s Annual Crop Reports provide data on individual county milk production rates from 1980-2020. Figures 4-8 are maps of the average milk production by decade. Table 3 shows the average annual volume of milk produced for each county by decade, and Table 4 shows each county’s percent share of the total average annual production. Table 5 shows the actual change and percent change between the 1980s and 2010s of average annual production for each county. The Census of Agriculture provides data on the number of operations with milk sales for each county for the years 1997, 2002, 2007, 2012 and 2017. Figures 9-13 are maps showing the decreasing number of operations by county through all 5 of the Census years. Table 6 shows the quantities and percent share of operations for each county for all 5 years. Table 7 shows the actual change and percent change in number of operations between 1997 and 2017.

Future studies of peer coaching might consider using validated measures more sensitive to a patient coaching intervention such as the Patient Activation Measure. In sum, veteran peer-delivered motivational coaching failed to achieve enhanced MH treatment engagement in rural veterans compared to control, yet veterans in both arms responded to MH assessment, feedback, and personalized referrals for MH treatment with higher rates of engagement in care than previously observed in this population. Among rural veterans with MH problems, peer coaching resulted in modest improved MH symptoms, quality of life indicators, and engagement in self-care activities, which may have mitigated perceived need for clinician-directed MH treatment. Further research is needed to explore this potential expanded therapeutic role for peer coaches, including risks, benefits, and cost effectiveness.When considering all those impacted by the illegal production of cannabis and cocaine, one must take into account the wide expanse of stakeholders. A partial list of these stakeholders is as follows: animal species, plant species, environmentalists, anti-drug advocates, lobbyists, landowners, citizens, law enforcement, drug agencies, governments, and international organizations. Essentially, any person or animate object negatively affected by drug production, trafficking, selling, or use, is suffering because of the plantation, cultivation, hydroponics flood tray and manufacturing processes. These effects occur in both direct, and indirect forms.

Direct effects caused by the production of cocaine and cannabis include: the clear-cutting of forests, the intentional poisoning of plant and animal species, the toxification and depletion of watersheds, and the emission of greenhouse gasses and air pollutants. Indirect effects of marijuana and cocaine production include: biodiversity loss, ecosystem degradation, drug trafficking, theft, violent crime, drug addiction, drug enforcement and treatment costs, and government destabilization. If the initial, and environmentally destructive, production stages of cannabis and cocaine ceased to transpire, there would exist no physical and useable form of these drugs. Without a consumable form, none of the aforementioned indirect issues would occur as a result of the direct effects of cocaine and cannabis cultivation. The first step to revitalizing our approach towards stopping environmental damage, resulting from drug production, is to create intrastate and international networks that combine the abilities of the two types of agencies with the most at stake, law enforcement and environmental. By combining the resources and talents of various organizations, we can increase the funding and efficiency of efforts focused on preventing ecosystem degradation and illicit drug production. Through raising public awareness of the environmental impacts of illicit drug production, we can stem the consumer base for these drugs while concurrently raising legislative support from concerned constituents. If afflicted parties, regardless of their national or organizational identifications, wish to sincerely resolve this enduring issue, cooperation will be required. In the United States, a majority of states have legalized the use of cannabis for some purposes, and California has been a forerunner in cannabis legalization.

California was the first state to legalize medical cannabis use in 1996. To date, 35 states have legalized medical cannabis; 15 of these 35 have also legalized adult recreational cannabis, and another 13 states permit the use of products with lowtetrahydrocannabinol for medical purposes. Modes of cannabis use include smoking, vaping, and as an edible. Cannabis when smoked may be wrapped in paper or placed within a hollowed-out cigar . In 2018, modes of recent cannabis use among young adults in California were reported as 81% smoking ; 47% vaping; 43% blunt use; and 35% eating/drinking; 78% reported more than one method. Potential mental health harms of cannabis use include increased risk of developing schizophrenia and other psychoses, with heavier cannabis use associated with greater risk. Depression, anxiety, and suicidal thoughts also have been linked to cannabis use. Whether the associations are causal is unknown. Smoking cannabis can affect lung health, with regular use associated with chronic bronchitis. Smoking cannabis during pregnancy is associated with low birthweight; studies of adverse effects of prenatal cannabis use on offspring behavior and cognitive development have been equivocal. Studies of brain structural measures and cannabis use in youth and young adults also have produced mixed results. To characterize the evidence on the health benefits of cannabis use, the National Academies of Sciences, Engineering, and Medicine published a comprehensive indepth review of 10,000 studies. The report found strong evidence from randomized control trials to support the conclusions that cannabis or its constituents are effective for treating chronic pain; as antiemetics in the treatment of chemotherapy induced nausea and vomiting; and for improving patient-reported multiple sclerosis spasticity symptoms. With regards to mental health, other research has found an anxiolytic-like effect of cannabidiol in patients with social anxiety disorder .

There also is moderate evidence for cannabinoids, mainly nabiximols, in improving short-term sleep outcomes in those with chronic medical conditions. Few studies have examined cannabis’s effects on well-being, a construct related to quality of life, and findings have been mixed. Since 1990, there has been an increasing trend in favor of legalizing cannabis in the United States. The Pew Research Center reported that 59% of Americans favor legalizing cannabis for medical and recreational use, while another 32% support cannabis use for medical purposes only; only 8% opposed legalizing cannabis. Since 2002, adult use of cannabis has been increasing. In 2019, 31.6 million Americans reported cannabis use in the past 30 days, with prevalence of 23% among adults aged 18- to-25 and 10.2% among adults 26 years and older. Data from the 2018 California Health Interview Survey showed that among adults 18 years and older, 33% reported cannabis use within the past month. In California, on November 8, 2016, voters passed Prop 64, the Adult Use of Marijuana Act, supporting the legalization of recreational cannabis use for adults 21 years or older. Prop 64 proposed to create a system to regulate the cannabis market and impose taxes on the retail sale and cultivation of cannabis; and allowed for use in a private home or at a business licensed for onsite consumption, and prohibited use while driving and in public areas including federal areas such as parks, as it is illegal under federal law. On January 1, 2018, California was authorized to begin issuing licenses to operate recreational cannabis businesses, legalizing sales from licensed retail outlets and the purchasing of cannabis for recreational use. States that legalized recreational cannabis use had a higher prevalence of cannabis use and greater use of products such as cannabis edibles, drinks, and high potency concentrate than in those that had not. Among the first four states that legalized cannabis for recreational purposes , there were increases in frequent cannabis use and cannabis use disorder among adults aged 26 and older following recreational cannabis legalization. A recent California study found no increase in cannabis use after legalization of recreational cannabis; however, the study sample was restricted to young adults aged 18–24 who used tobacco, so the findings may not generalize to the broader population. Beliefs on the health benefits of cannabis was found to be higher in states that had legalized cannabis for recreational use. With expanding legalization and increases in cannabis use, examining patterns of cannabis use and the factors that might drive cannabis use trends over time is needed. With California’s legalization of recreational cannabis use, we sought to characterize, from pre- to post-policy implementation, adults’ use patterns, exposure to others’ cannabis use, and perceptions of the benefits or harms of cannabis use to physical and mental health and well being. We hypothesized an increase in adult cannabis use and exposure to others’ use as well as more positive health perceptions of cannabis use over time.In this prospective observational study on cannabis policy changes in California, legalized recreational cannabis use was associated with greater self-reported past 30-day use one-month post-legalization, and in the univariate model, remained significantly higher at 6-months post legalization.

Compared to California state data, our study sample had a lower frequency of past 30-day cannabis use . Likely related, the sample had more harmful perceptions of cannabis use: 43% of respondents at baseline and 42% at 1-month post-legalization perceived cannabis use to be harmful, whereas United States data from the Substance Abuse and Mental Health Services Administration estimated 12% of young adults 18 to 25 years old and 29% of adults 26 years or older perceived great risk from smoking cannabis monthly. Surprisingly, hydro flood table exposure to others’ use of cannabis did not change from pre- to post-legalization of recreational cannabis use in our study time frame; however, it is possible that exposure to cannabis smoke may have increased earlier among our study population. A California Department of Public Health survey found that the rate of cannabis exposure in 2016 among adults aged 18 to 64 years was 21.5%, and by 2018 had doubled to 40%, which is similar to our findings on cannabis exposure. In late-2016, Prop 64 was voted on and approved by voters of California and could explain the increase of secondhand cannabis exposure from 2016 to 2018. Significant correlates of cannabis use at all time points included depression diagnosis, while having an other mental illness diagnosis was significantly associated with cannabis use only 6-months post-legalization. A recent study found that from 2005 to 2017, the prevalence of cannabis use among people in the United States with depression was increasing and that those with depression experienced a rapid decrease in perception of risk of cannabis use . While not tested in our study, it is possible those with depression or other mental illness experienced decreasing perceptions of risk of cannabis use. Despite the growing evidence of cannabis use as an effective treatment in chronic pain and its use in mitigating side effects of cancer therapies, our study showed that those with pain and those with a history of cancer were not significantly more likely to use cannabis. Among our California study sample of adults between the ages of 23 to 86 with a mean age of 56 years, we found younger age associated with cannabis use preand post-legalization. These findings are consistent with previous national studies on adults in the United States where cannabis use decreased with increasing age. By 6-months post-legalization, perceived health benefits of cannabis use significantly increased, and in the multivariate model, health perceptions were associated with cannabis use over time. Notably, perceptions of health benefits of cannabis use for mental health showed the largest increase. The literature suggests potential anxiolytic effects of CBD, but also points to the association of mental health harms with high-potency cannabis use. Though our sample had an overall positive perception of cannabis use in benefiting physical health and well being, there was no significant association between cannabis use with pain or cancer, common conditions for which cannabis has been used to treat. Mass marketing and health promotions from cannabis dispensaries also may have contributed to the increase in perceived health benefits of cannabis. Since the legalization of recreational cannabis in California, which includes the selling of cannabis, dispensary ads and mass marketing campaigns promoting uses of cannabis have proliferated. Endorsements from social media influencers and celebrities, may also be adding to overall positive perception of cannabis use. As a response to the mass marketing, Los Angeles and San Diego counties have proposed restrictions on where cannabis ads and billboards can be placed. Much is to be learned on whether these restrictions will impact perceptions and use. To ensure health harms are not ignored, public health interventions such as educational programs and health communications are needed to increase awareness. Study limitations include that the data were self reported by a relatively small convenience sample, and thus may not be generalizable to other populations. The sample was more non-Hispanic White than the general population in California; however, a similar percentage of Californians voted in favor for Proposition 64. Rather than remove respondents who did not complete all the surveys and conduct a complete case analysis that could lead to less power and biased results, we used GEE analyses, which is useful in dealing with missing data and does not require imputation . Missingness was not associated with cannabis use, and therefore consistent with the assumption that outcome data were missing completely at random. To account for missing data, we did adjust for employment, which was associated with attrition, in all models. If perceptions of the health benefits of cannabis use increase over time and become more widespread , cannabis use may increase further.

The mean latency of our experiments is 0.605s, with a maximum value of 0.834s. This delay has two main components: the operating system polling frequency and the time it takes for transceivers to complete the fault recovery process, before ports come up and data transfer can start or resume. PicOS polls the transceivers every 250ms; furthermore it only supports duplex auto negotiation mode for SFP. On the other hand, the SFP vendor informed that the initialization delay varies across different brands, but it should be consistent with the SFF8431 guidelines for SFP+ DWDM. After a fault is detected in the link, in less than the maximum value of t start up plus t reset , the optical transmitter reset the laser circuits and disable the tx fault flag.Moreover, the Polatis optical switch takes 25ms at most to complete the optical reconfiguration, as listed in the technical documentation. With our MBB approach we first updated the flows in the EPS tables sending instructions from the controller, then we provisioned the new link with the optical switch, and finally we updated the flow again in the EPS to force the traffic to pass through the new path. In a single stream of data, RTT grows up to hundreds of millisecond in the OST reconfiguration, but does not grow larger than 2.5 ms with the MBB mode. On average, the packet loss decreases from 2.8% in OST to 0.93% in MBB. Link unavailability due to RTO events went from 598 ms to 121 ms, 80% less. With MBB, throughput drops 0.6 Gbps, grow rack compared to the drop by 6 Gbps in OST. Bandwidth steering was also improved with our MBB approach. This scenario was recreated with two data streams at 20 Gbps in total, forcing a bottleneck of 10 Gbps in the testbed.

Comparing performance during reconfiguration, packet loss went from 1.28% with OST to 0.44% MBB. Link unavailability decreased from 727 ms to 185 ms, 74% less. RTT grows up to hundreds of millisecond in OST, but is not larger than 2.5 ms like in the single data stream scenario. We did not achieve hitless reconfiguration, that is, 0% packet loss, because there still was a link unavailability that generated packet drops. However, we decreased packet loss with our MBB approach, getting closer to the ideal goal. The first step to reach zero packet loss would be to decrease the transceiver locking and switch polling latency, so we do not trigger the TCP RTO timer. Other options would be to play with different RTO values, or to replace TCP with other protocols. In the future, our testbed will be used for deploying optical networks with technologies other than MEMS, and running experiments in different areas such as machine learning and heterogeneous computing. The modular design allows modifying blocks of software or hardware described in chapters of architecture and infrastructure, without affecting other elements of the system. Agriculture in the United States has undergone massive consolidation over the past 50 years and the same is true in California. Several economic and market factors have contributed to farm consolidation, but new regulations on agriculture have also played a role . Compliance costs associated with increased regulatory burdens can decrease producer profits and limit market entry . Small producers may be particularly harmed by the need to achieve compliance, as economies of scale provide larger producers an advantage . Small firms may lack sufficient capital to change production methods to comply with regulations, or even to manage the burdens associated with reporting.

The cannabis industry has historically resisted widespread farm consolidation, perhaps due to its status as an unregulated, and illicit or semi-licit, activity. While the amount of cannabis produced in California is substantial , evidence from 2016 suggests that most outdoor cannabis was then produced on farms smaller than one acre . When Proposition 64 legalized non-medicinal cannabis in 2016, its size provisions explicitly acknowledged the state’s desire to see cannabis farms remain small . Initial regulations limited each permit to an area no greater than one acre and limited each entity to only one permit. Federal laws against cannabis have also encouraged small farms: Farmers with more than 99 plants potentially face federal minimum sentences of five years in prison . Local permitting may also favor smaller producers. Each jurisdiction in California can create its own permitting system, and possessing a local permit is a condition for obtaining a state permit. Most local jurisdictions place limitations on field sizes, and these limitations can encourage small-scale farming. While local permits may provide an avenue for local governments to protect small farmers , they also add another layer of regulation, potentially increasing entry costs. Beginning with California’s first attempt to implement a comprehensive regulatory system for the cultivation and distribution of legal cannabis, through the 2015 passage of the Medical Marijuana Regulation and Safety Act, stakeholders have expressed concerns that the permitting process privileges large farms over small. MacEwan et al. calculate that, due to the nature of regulatory costs, the type of small cannabis farmer prevalent in Northern California is the “least likely to participate in the regulated market.” Yet to date, empirical evidence on cannabis producers’ engagement with the formal market under the new regulatory framework has been lacking. In particular, there is a large evidence gap about the types of farms that participate in the regulated market and those that do not.

We remedy that gap by combining information about farmers who have started the permit application process with a unique dataset of cannabis farms in Humboldt County in 2012 and 2016. Humboldt County is one of the largest cannabis producing regions in California and perhaps the world. Cannabis farming began there in the early 1960s, with rapid expansion following in the 1970s, and cannabis has been among the most valuable crops in the county at least since a proposition legalizing medical cannabis was approved by voters in 1996 . Recent studies suggest that at least 5,000 cannabis farms operate in Humboldt County . In the lead-up to the enactment of regulated cultivation of cannabis — which began for the medicinal market in 2016 and for the adult-use market in 2018 — the region experienced a cannabis boom, with the number of plants under cultivation increasing by 150% between 2012 and 2016 . This time of massive cannabis expansion is often referred to locally as the “green rush.” To track both permitted and unpermitted cannabis growers, we used data created by Butsic et al. . In their study, Butsic et al. hand-digitized cannabis farms using very high resolution satellite imagery. Cannabis production was measured in both 2012 and in 2016. Outdoor plants were counted and the number of plants inside greenhouses was estimated based on greenhouse size. Of the 1,724 farms in the dataset, 942 started producing cannabis between 2012 and 2016 and 782 produced at least some positive amount in both 2012 and 2016 . For permit data, we used publicly available data from the Humboldt County Planning Department, compiled from applications for commercial cannabis cultivation permits . We were able to combine the farm location data with the permit data based on the unique parcel identification that existed in both datasets. In total, hydroponic rack applications were received for cultivation on 1,945 unique parcels. Of these, 533 were located within our study area . We also include data describing farm/parcel characteristics. Locational variables such as distance to public roads and cities are used to proxy for transportation cost, while distances to endangered and threatened fish species habitat proxy for the environmental sensitivity of a site. Distance to ocean provides a summary measure of the coastal environment of the farm. Biophysical characteristics such as slope and presence of prime agricultural soils are used to describe the growing conditions of a site, while zoning designations are used to identify areas where growing cannabis is allowed . We also determined if a timber harvest plan had been associated with a parcel at any point since 1997. The overall aim of our empirical analysis is to describe the type of cannabis farms likely to apply for a permit.

To do this we use a twofold approach. First, we compare farms that applied for a permit and farms that did not in terms of the means of their farm and parcel characteristics. We use a simple two-tailed test to determine if the univariate mean differences between these groups are statistically significant. We focus on differences in farm size , farm-size expansion during the “green rush” period and tenure of the farm. In a second step we estimate models of application decisions using multivariate regressions, which allow us to isolate the impact of each characteristic while controlling for variation in others . We implement two such models. Our main specification is a probit model in which the binary dependent variable is equal to 1 if a permit application was submitted for parcel i. The size of the farm is included with a quadratic specification and the other parcel and farm characteristics enter the model linearly as independent variables. We use the probit model to estimate the marginal contribution of each of these variables to the likelihood that a parcel applies for a permit.The average farm size in 2016 was 432 plants, with a median of 263 plants, a minimum of 14 and a maximum of 12,901 . Over 90% of farms produced fewer than 1,000 plants and fewer than 2% produced more than 2,000. Examining permit application rates by farm size reveals a distinct size gradient , as application rates increase substantially over farm-size categories. This pattern holds for both existing and new farms, but the rise is much sharper for the latter. Approximately 10% of small new farms apply for a permit, but rates jump to 61% and 50%, respectively, for the largest farm size groupings. We found a significant difference in size between farms that applied for a cannabis permit in 2016 relative to those that did not apply . The trend according to which larger farms applied for permits at higher rates held true regardless of production type . The size differences are proportionally similar for both greenhouse and outdoor plants, so we do not find evidence that the relationship between farm size and permit application is solely driven by production method. Our regression models confirm that this result is robust to controlling for other covariates. In all our regression specifications, the coefficient on the total number of plants in 2016 is positive and statistically significant at the 1% level. The effect size of the number of plants indicates that, controlling for parcel characteristics, an increase of 100 plants increases the probability of applying for a permit by 2.4% , with the slope of the relationship declining for extremely large farms . The overall marginal effect is similar for existing and new farms, , though the declining marginal effect for very large farms is driven by new farms , and is robust to the inclusion of watershed fixed effects . The pattern also holds for size in 2012. Restricting the sample to existing farms, an increase of 100 plants in 2012 increases the probability of application by 3.1%.We first categorize growth of existing farms according to the proportionate change in plants produced between 2012 and 2016. The “declining production” group consists of farms that shrank by more than 5% ; “minimal change” farms experienced between −5% and 5% growth ; “moderate growth” farms grew between 5% and 50% and “high growth” farms grew by more than 50% . Within the sample of existing farms, there is a clear gradient of application rates with respect to growth between 2012 and 2016 . The farms least likely to apply are those that declined in size, followed by those with minimal growth. Application rates for existing farms that grew moderately jump to over 40%, with high growth farms the most likely to apply. Note that across all expansion rates for existing farms, application rates are significantly higher than the average rate for new farms.

Though in North Carolina, U.S. government officials only used Google Earth to verify code violation complaints, in places like India, New York, Argentina and Greece, Google Earth was used in the active reconnaissance of committed crimes. Marine researchers have also used analyses of Google Earth to evaluate the veracity of fish-catch reports made to the UN. Spain’s Green Party has reported illegal bottom trawling of beaches for fish using Google Earth images, as well. Google Earth has also been used to detect illegal dumping. For example, in Florida, a sheriff’s deputy used Google Earth to apprehend an individual who dumped a large boat; in Mississippi, a landowner identified a stolen and illegally dumped truck on his property using Google Earth; while in Bangalore, Google Earth was used to identify unauthorized and illegal waste dumping site. Illegal logging is also actively identified using Google Earth by such groups as local police departments in the Philippines, the Finnish Association for Nature Conservation and their associated NGOs in Russia, the Amazon Conservation Team and associated indigenous groups. Amateur Google Earth users have reported potential body-dumping based on the imagery available, as well. Some of the issues associated with Google Earth arise from the fact that its images are made available by a privately-owned corporation and are technology driven. Thus, as Sheppard and Cizek note, the visualizations of the Earth made available by this interface are more geared towards “efficiency, convenience…entertainment value, popular demand, flood tray and profit” than they are towards “truth, deeper understanding, improved civil discourse, safer and more informed decisions, and other ethical considerations”. As these and other authors point out, realism in landscape visualization is not the same as accuracy or validity.

Virtual globes, like Google Earth, may suffer from low data resolution, interfering with image clarity and accuracy, missing data or inaccurately displayed data. Further, it is often impossible to know the exact date of the imagery available on Google Earth and whether all images in a scene are from the same date . Thus, a potential crime sighted on Google Earth may be months or even years old or may be exaggerated by differing image dates. Finally, these data may be manipulated by the producers of these virtual globes for various privacy reasons; some areas are intentionally blurred or objects are not displayed. More significant than spatial and temporal accuracy is the consumption and use of these images by untrained or informal interpreters. These informal interpreters may not understand the temporal or spatial inaccuracies inherent in these data. Goodchild points out that users of Google Earth may be misled to think it is more accurate than it is in reality. Despite the fact that Google Earth images’ absolute positional accuracy is sufficient for assessing remote sensing products of moderate resolution, errors and positional inaccuracies are still a problem. Trained remote sensing analysts understand these limitations and may be able to account for them, whereas casual users may not. For example, in the case mentioned above, where amateur Google Earth users reported a dumped body, their interpretation of the image was flawed. In this case, the “dumped body” turned out to be a swimming dog. The dog’s watery trail on the cedar wood dock and the dog lying on that dock appeared to be a bloodied body rather than a picture of a sunny day at a lake. Un-validated identifications of “crimes” using Google Earth images by amateur analysts unfamiliar with the inaccuracies of these images or the nuances of image interpretation may be problematic for several reasons. First, they may cause law enforcement officers to seek places or things that are not where they are purported to be, are no longer present or never existed in the first place. This may result in a waste of funds, resources and personnel hours. Second, the misidentification of a site as a place where a crime is or has occurred opens that place and its residents up to potentially needless intrusion, intimidation, surveillance or violence.

Despite the increasing ease with which satellite images and other spatially explicit data flow to us, ethical and scientific rigor should not be laid aside. Finally, as Purdy and Leung note, Earth Observation data like those used in products like Google Earth may have their evidential weight in a court of law seriously reduced if un-validated, because the medium by which it was taken, the data management systems used or even the date the image was taken may be unknown. Given the potential for amateur misinterpretation or overconfidence in Google Earth images, it is obvious that crimes detected in this manner must be validated to ensure appropriate, timely and safe responses by government of law enforcement officers. While there have been a few cases where crimes detected using Google Earth were validated, either by fly-overs or personal ground validation missions , in the majority of cases, there is no discussion of accuracy assessment or validation. This dangerous trend toward trained and untrained analysts taking Google Earth images as “truth” with no validation may have broad reaching potential impacts on law enforcement efforts and personal security. Despite the fact that cutting-edge technologies are being used to remotely detect crime, the accuracy assessments of those analyses lag well behind current remote sensing standards. Indeed, as we have shown above, some studies that attempt to remotely sense crime do not perform accuracy assessments at all, depend on the opinions of “experts” or “surrogate ground truth data”, all of which are deemed to be substandard by today’s remote sensing community. Many of the studies noted above performed no accuracy assessment at all; they did not even use Google Earth or Digital Globes to validate their data. Particularly, in situations that may have life-and-death implications or serious environmental effects , law enforcement officers must strive to be as accurate as possible in their targeting of crimes and criminals. Although drones or unmanned aerial vehicles/systems may present excellent options for accuracy assessment, offering up quiet, real-time, high resolution imagery of remote or distant areas without threat to human life, they are not ideal solutions in every situation. The equipment, licensing, training and maintenance required to acquire and safely maintain a UAV may be well beyond the means of many local police departments or underfunded government agencies. In the United States, the Federal Aviation Administration has seriously restricted the use of unmanned aircraft in national airspace . Further, there are serious questions about the constitutionality of using UAVs for law enforcement. Critics of UAV use by law enforcement argue that these vehicles impede an individual’s reasonable expectation of privacy as protected by the fourth amendment Despite these concerns, law enforcement is increasingly using UAVs to detect crimes and facilitate law enforcement . In the following section, we propose some alternate or additional means of validating remotely sensed crime. We hope that this initial thought experiment may help spark a conversation about the methods and ethics of remote sensing in law enforcement. We define “first order” accuracy assessments as those described in the accepted remote sensing protocol , which include ground-based validation or the use of imagery of higher resolutions than the imagery to be validated.

Since these first order assessments can be limited by security, funding and terrain issues and drone use presents funding and legal issues, we propose a “second order” level of accuracy assessment. This second order accuracy assessment analyzes the larger geographical and social context in which remotely sensed crimes are detected by remote sensors. Such assessments could utilize crowd sourcing, big data mining, landscape-scale ecological data and anonymous surveys to determine whether and how crimes are occurring and where remote sensing analysts think they are. Second order accuracy assessments may allow remote sensors and law enforcement officers to confirm that crimes are taking place where analysts say they are without facing rugged terrain, 4×8 grow tray insecure conditions or using costly overflight methods. Further, second order validation may enable analysts to gain better contextual understandings of those crimes, allowing for more ethical and proportionate responses by law enforcement. While these second order validation techniques may not be as reliable as first order techniques, they are better than no validation at all. Alternatively, these second order techniques could be incorporated into interdisciplinary crime detection techniques that may increase detection accuracy. Urban areas are well suited to second order accuracy assessments because of the amount of available social data produced and available at any given moment. For example, Oakland’s Domain Awareness Center plans to link public and private cameras and sensors within the city limits into a single hub for law enforcement use . While highly controversial, these centers present numerous opportunities to validate remotely sensed crimes with closed-circuit television , as well as readily available on-the-ground policing. Rural or more remote areas present more of a challenge, however. These places typically lack surveillance cameras and mounted sensors. It is also in these places that large-scale drug production, human and drug smuggling frequently occur. Thus, here, we use illicit cannabis production as a case study to think through three potential second order accuracy assessment techniques in non-urban zones. Though we acknowledge that each of these methods would require further development and thought and that methods may exist beyond those we propose here, it is our hope that this will be the first effort in a larger conversation as to second order validation techniques in the remote sensing of crime. Social media: Location-based social network analysis may be helpful in validating crimes remotely sensed in other ways through geolocated self-reporting or observations by others. LBSN has been shown to provide reliable spatio-temporal information about incidents occurring in a broad landscape. For example, researchers from the Institute of Environment and Sustainability in Italy used a Twitter application programming interface to retrieve tweets and related metadata for a specific topic, the 2009 Marseille forest fire. These tweets were then organized into meaningful summary statistics using data mining and web crawling scripts. These researchers found that the LBSN data collected were temporally synchronized with actual events and provided some geographically accurate reporting. They note that Twitter “could offer promising seeds for crawlers to collect event-related data where time and location matter”. Some products already exist to facilitate such second order validation of crimes. Products like SensePlace2, Twitter-based event detection and analysis system, DataSift, Gnip, SABESS, and others, enable those interested in crime or emergency detection to gather and aggregate publicly-available, geo-located, time-stamped information in real time about where and when an incident may have occurred, who was involved and how serious it was. Because these data are publicly available, issues that other forms of remote sensing bring up in terms of the invasion of privacy are avoided. Further, because reports are on the ground and produced by humans, they may offer information on the context of crimes and their perpetrators and an interpretation of the events that took place rather than leaving this work up to far-removed remote sensing analysts. While connectivity in rural areas is more limited than in urban spaces, the Pew Research Group has found that as of January 2014, 88% of rural Americans have a cellphone and 43% of rural Americans have smartphones, making such data gathering feasible in these areas. Landscape-scale ecological data: Remote sensing of large-scale cannabis production can be validated using landscape-scale ecological data, as well. Down-stream water quality is one way remote sensing of these grow sites can be validated, for example. Large-scale outdoor cannabis production can threaten water quality through water diversion, erosion and sediment deposition due to grading, terracing, road construction, deforestation and clearing; as well as the inputs of harmful chemicals or other pollutants, such as rodenticides, fungicides, herbicides, fertilizers, trash, human waste, gasoline, oil and insecticides, into local water sources. Using stream water quality analysis that picks up the chemical signatures of such pollutants may be one way to affirm that remote sensing analysts were correct in their characterization of given drug production sites. Though no studies using this approach to detect upstream drug growth exist to date, similar methods have been used in the early detection of sudden oak death. Stream monitoring efforts are able to detect Phytophthora ramorum even before signs of infection are even visible from over-flights.

Census tracts are convenient units of analysis because they have similar population sizes, their boundaries align with the physical environment, and they are intended to be homogenous with respect to population characteristics and living conditions . Thus they roughly approximate city neighborhoods. From a routine activities perspective, I argue that it is reasonable to assume in the case of densely populated cities like San Francisco that likely offenders, in choosing whether, where, and when to commit a crime—that is, in weighing the target suitability and guardianship of potential victims—are going to consider targets within an area roughly the size of a census tract. Maps presented in the forthcoming analysis should illustrate the geographic implications of this assumption. Another advantage to using census tracts as the spatial unit of analysis is that there is an abundance of demographic information available at the census tract level via the U.S. Census Bureau and ACS. This provides for an excellent range of control variables. But this approach is not without its disadvantages.The present model does not account for criminal activity in other tracts and therefore misses the “spillover effects” that a land use such as MCDs may have on crime in neighboring tracts. This presents a significant limitation for the present model—although one that could theoretically be corrected for, to some extent, through more sophisticated spatial analyses. Considering the lack of empirical evidence currently available with respect to this issue—and its significant implications for policy making and future research—I argue that, as a preliminary analysis, this study has tremendous value despite this and other limitations. It may not account for inter-tract crime, but it does provide new knowledge about the nature of intra-tract crime.

City residents probably are concerned about businesses in adjacent neighborhoods; but when it comes to crime they are concerned, first and foremost, grow tray with the people next door.In this paper I examine the relationship between MCDs and crime in San Francisco for the year 2010. San Francisco provides an excellent case study for analyzing the social impact of MCDs because, unlike most jurisdictions in which MCDs have emerged in recent years, the local government in San Francisco has been regulating MCDs effectively for years. In some other cities, local governments and MCD operators have undergone heated legal battles with one another. This has resulted in a “regulatory vacuum” with respect to MCDs in most jurisdictions in which they exist . Perhaps the most notable example of this is Los Angeles, where MCDs have been in legal limbo for years. In early 2012 the Los Angeles Times editorial board, responding to a recent motion to ban all MCDs in the city, described the L.A. city government’s approach to dispensary regulation as a “roller-coaster ride”. At one point there were an estimated 1,000 MCDs operating throughout the city, leading to the observation that medical cannabis was more popular in L.A. than Starbucks. By contrast, in San Francisco, MCDs are governed by a comprehensive municipal ordinance that has survived judicial scrutiny to date. The San Francisco “Medical Cannabis Act” sets up a permitting system for MCDs and places certain restrictions on their location and operation. The Act requires that all MCDs comply with California state law as well as guidelines written in 2008 by then-Attorney General Jerry Brown entitled “Guidelines For The Security And Non-Diversion Of Marijuana Grown For Medical Use”. Brown’s guidelines state that “a properly organized and operated collective or cooperative that dispenses medical marijuana through a storefront may be lawful under California law” if they meet certain requirements, including: operating on a not-for-profit basis, obtaining all of the relevant permits and licenses from state and local government, taking steps to verify that their members are qualified patients under state law, acquiring and distributing only marijuana that has been cultivated legally, prohibiting sales to non-members, and providing adequate security .

Regarding security, Brown states that MCDs must “provide adequate security to ensure that patients are safe and that the surrounding homes or businesses are not negatively impacted by nuisance activity such as loitering or crime.” Expanding on this theme, the San Francisco Medical Cannabis Act prohibits “any breach of peace… or any disturbance of public order or decorum by any tumultuous, riotous or disorderly conduct” within permitted MCDs. MCDs are required to submit security plans as part of their permit application. This study examines whether such security protocols amount to capable guardianship, which is an effective deterrent against crime according to routine activities theory . San Francisco is not the only municipality that has regulated MCDs. Other California cities have enacted similar ordinances, including two prominent examples that can be found directly across the water from San Francisco in the cities of Berkeley and Oakland. But as a case study San Francisco has several advantages over these and other alternatives. First and foremost, it is a major city with a large sample of MCDs in the year for which data are collected. By comparison, Berkeley and Oakland have smaller populations and “hard caps” on the number of dispensaries allowed. So although they present interesting pieces of the legal, social, and political puzzles presented by California’s medical cannabis law, their small sample size limits the extent to which they are useful cases for empirical study. In cities that do not regulate MCDs, it is difficult to determine when and where dispensaries operate, and for how long. It is also much harder, in the case of unregulated dispensaries, to infer whether MCDs implement security protocols amounting to capable guardianship against crime, from a routine activities perspective . Simply put, San Francisco is the largest California to have enacted meaningful legislation with respect to MCDs. It has done so in a way that reasonably controls for crime, at least in theoretical terms. Thus it provides an excellent case study for analyzing the spatial relationship between crime and locally regulated MCDs.

Crime data were collected from the San Francisco Police Department in late 2011. The Crime Analysis Unit provided lists of serious crimes reported in 2010 along with the date and approximate location of each crime. Here, “serious crimes” refer to those classified as Part I offenses by the Federal Bureau of Investigation in its Uniform Crime Reports. The crime variables used in this analysis include measures of “violent crime” as well as “property crime” ; both as total counts and as rates per 1,000 residents. The lists of crimes and addresses were geocoded and aggregated into census tracts using ArcGIS software. Geocoding refers to the process by which tabular data are attributed spatial components by a geodatabase. Geocoding resulted in a successful match for more than 98% of all crimes, which were the aggregated into census tracts using a “spatial join” analysis. The remaining 1-2% of crimes were discarded from analysis. In addition, some reported crimes were removed from analysis because their geocoding confidence ratings were below 95%. In the end 43,688 reported crimes were analyzed out of the original 44,422 for which the San Francisco Police Department provided 2010 data. For the purpose of analysis, these crime measures were aggregated together by census tract and transformed by natural logarithm to correct for a right-skewed distribution. Descriptive statistics for these various categories of crime are presented in their original form in Table 4.1. The primary independent variable under review is the density of MCDs. Lists of MCD names and addresses were compiled using information provided by the San Francisco Department of Public Health. These MCDs were located across 16 census tracts primarily in the downtown area, as illustrated by Maps 4.1 and 4.2. The MCD addresses were geocoded to 100%. As with crime frequency, data for MCDs are presented in two forms. Descriptive statistics presented in Table 4.1 include MCD density as the number of dispensaries per square mile in a given census tract. For the regression analyses presented by Table 4.2, this variable is transformed by natural logarithm to address a right-skewed distribution. Crime rates by census tract are presented in Map 4.1 and Map 4.2 on the following pages. MCD locations are marked by green crosses. Property crimes include arson, burglary, larceny-theft, vandalism, and vehicle theft. Violent crimes include assault and robbery. Census tracts are assigned to one of five classes based on their crime rates. In addition to the primary variables already discussed, hydroponic trays data were also collected for several neighborhood characteristics that could potentially confound the relationship between MCDs and crime. These neighborhood characteristics are drawn from social disorganization theory, which associates higher rates of crime with socioeconomic disadvantage, family disruption, residential instability, and population heterogeneity . From these, the present study examines the criminogenic effect of poverty, unemployment, percent of single-parent households, percent of housing units that are vacant, and percent of the population between the ages of 18 and 24. Demographic data are collected from the American Community Survey database of the United States Census Bureau via the American FactFinder website, as well as the Demographic Research Unit of the California Department of Finance. With regard to the census data, variables are constructed from the ACS 5-year estimates for the year 2010.

Criminological research has found that indicators of socioeconomic disadvantage—including poverty and unemployment—have been associated with higher crime rates . In the present study economic data are collected from the ACS. The U.S. Census Bureau calculates the poverty status of individuals based on whether their total income in the past 12 months falls below the applicable poverty threshold, which is determined by age, family size, and family composition . The 2010 poverty thresholds range from $11,139 for a single individual living alone to $42,156 for a family of eight or more people living in the same household. For the present analysis, “poverty” means the number of individuals with incomes under their applicable poverty threshold in the past twelve months, divided by the total number of people for whom poverty status is calculated within a given tract.According to the United States Census Bureau, an individual is considered unemployed if he or she did not have a job and has been actively looking for work during the last four weeks and was available to start a job at the time of the survey . “Unemployment”, in the present analysis, means the unemployment rate in each tract as estimated by the ACS. Research examining crime rates in the United States during the 1990’s suggests that the job market can provide powerful explanations for criminal behavior . Poverty and unemployment are important measures in the model currently being tested, as they control for varying levels of socioeconomic disadvantage across city neighborhoods, which according to social disorganization theory affect crime rates in significant ways .In this study I use “family stability” as an inverse measure of family disruption. I calculate family stability by taking the number of individuals living in married couple family housing and dividing it by the number of people living in single-parent family households. Scholars of both routine activities theory and social disorganization theory predict that higher concentrations of married-couple families are associated with lower crime rates in urban areas, because more parents can provide more supervision and therefore more social control. From a routine activities perspective, both “family stability” and “residential stability” correspond with the notion of capable guardianship. According to social disorganization theory, residential instability weakens a community’s social cohesion and therefore its ability to deter and prevent crime within its territory. In this study I use vacancy rates as measure of residential instability. I calculate “vacancy” by dividing the number of vacant housing units within a census tract by the total number of units within that tract. Data for this measure comes from the Census 2010 Redistricting Plan. Another indicator of residential turnover discussed in the criminological literature is the percent of the sample population that is young . The idea is that neighborhoods with a high concentration of young adults will have correspondingly fewer older adults and children, which results in a lack of social cohesion and crime-preventive capacity much in the same way as the other precursors of “social disorganization” already discussed. The variable “percent young” was constructed using ACS population estimates by dividing the total number of individuals between the ages of 18 and 29 by the total tract population.

Both in vitro studies and animal models show that CB2R mediated anti-inflammatory activity may account for the neuroprotective action of the ECS by decreasing glial reactivity. Both natural and synthetic cannabinoids are neuroprotective after various types of CNS insults, such as stroke. Preclinical models show activation of CBRs can trigger removal of activated immune cells, down regulate pro-inflammatory cytokine and chemokine production, and inhibit HIV-associated synapse loss and neural injury. In vitro, THC treatment suppresses several pro-inflammatory factors, including TNF-α, IL-6, and IL-8, and decreases monocyte-derived interleukin IL-1ß production and astrocyte secretion of MCP-1 and IL-6 from a human coculture system. We recently reported that more recent cannabis use was associated with significantly lower IL-16 levels in cerebrospinal fluid and lower soluble tumor necrosis factor receptor type-II and IP-10 levels in plasma. An additional benefit of cannabis, likely linked to its anti-inflammatory effects, is stabilization of the blood–brain barrier , which we demonstrated in a separate report, showing that more frequent use of cannabis was associated with better markers of BBB integrity in PWH. Recently, studies have determined that cannabis is associated with reduced markers of immune activation and inflammation in CSF. In sum, there is substantial evidence that cannabinoids display beneficial anti-inflammatory effects that are relevant to HIV infection. Anti-inflammatory effects of cannabinoids in the brain may translate to clinical bene- fits, particularly with respect to neurocognition. HIV causes T-cell and monocyte migration to the brain and subsequent interactions with astrocytes and microglia lead to the secretion of neurotoxic cytokines and chemokines. These pro-inflammatory factors are linked to worse neurocognitive performance in PWH , and lowering them might benefit neurocognitive function.

We showed that lower levels of the monocyte activation marker, vertical growing system monocyte chemoattractant protein type 1 related to better performance in tests of learning ability, and that lower IP-10 also related to better learning as well as delayed recall and motor skills. These cognitive domains frequently show deficits in virally suppressed PWH. However, not all studies support neuroprotective effects of cannabis. For example, a brain diffusion tensor imaging study suggested axonal loss in the uncinate fasciculus, which is involved in verbal memory and emotion, in cannabis users. This study also showed greater than normal age-dependent fractional anisotropy declines in white matter tracts and globus pallidus of cannabis users, suggesting reduced neuronal integrity in these regions. For example, a review of 107 case reports over a total of 55 years described strokes associated with intake of both raw and synthetic cannabis. Affected individuals were most frequently young males with chronic tobacco smoking and unusually high levels of cannabis and alcohol consumption just before their stroke. Ischemic strokes and much more common than hemorrhagic strokes with cannabis use. Proposed underlying mechanisms explaining a possible link between stroke and cannabis use, reactive oxygen species generation inducing oxidative stress, cerebral artery luminal stenosis, cerebral auto-dysregulation, cardioembolism, reversible cerebral vasoconstriction syndrome and angiopathy. None of these reports focused on HIV infection, where effects of cannabis on chronic inflammation may counterbalance adverse vascular effects. An additional limitation of the historical literature is the many confounds associated with cannabis consumption that themselves are risk factors for stroke. These include concomitant tobacco smoking and intake of alcohol and synthetic cannabinoids, as well as a variety of other comorbidities. In the case of ROS, similar to tobacco smoke, these could be generated as a byproduct of marijuana combustion rather than a specific effect of cannabinoids.

Smoking involves inhalation of products of combustion that may be the source of adverse vascular effects. Thus, no strong link between cannabis and stroke has been yet established. These effects may be eliminated when using cannabis in oral or vaporized form. Ongoing trials may be found at clinicaltrials.gov. Finally, there is considerable evidence that, rather than being a risk factor for stroke, cannabis may be vasculo- and neuro-protective. Thus, cannabinoids may have significant therapeutic value in stroke, as suggested in a recent systemic review and meta-analysis by England et al. showing that all sub-classes of cannabinoids, cannabis-derived, synthetic, specific CB1R, and CB2R agonists significantly reduced infarct volume in transient and permanent ischemia and improved both early and late functional outcome in experimental stroke when given after stroke onset. In large mammals, cerebral vessels perfused with cannabinoids demonstrated relaxation rather than constriction. Cannabis disturbs cognition acutely, but its longer-term effects on brain function in HIV are not well understood. While there is limited evidence of increased cognitive impairment in some cannabis-using PWH, chronic exposure may also reduce inflammation, possibly resulting in improved CNS outcomes among PWH. Still, thedegree and pattern of cannabis exposure that may be therapeutic, neutral, or harmful is not understood. We hypothesize that an “optimal” level of cannabis exposure will improve some HIV-related outcomes. Studies of human and mouse cannabinoid systems in the context of neuroinflammatory exposures show that CB2Rs are highly upregulated during inflammatory insult and their selective activation reduces vascular inflammation, pathological microglial activation and BBB dysfunction, thus indirectly decreasing oxidative stress and subsequent cell death, and HIV-associated synapse loss. Taken together, this literature cumulatively suggests there may be some therapeutic potential of compounds that target the cannabinoid system through modulation of neurotoxic and inflammatory processes in HIV disease and other neuroinflammatory diseases.

To evaluate the effects of cannabis use in PWH and people without HIV and BBB permeability and soluble urokinase plasminogen activator receptor , a receptor for uPA, a matrix-degrading proteolytic enzyme that disrupts the basal lamina around cerebral capillaries. We found a statistically significant interaction between HIV serostatus and frequency of cannabis use such that more frequent use was associated with lower concentrations of uPAR in CSF in PWH, but not in PWoH. Within PWH, higher CSF uPAR levels correlated with higher CSAR values and more inflammation . These findings suggest that cannabis may have a beneficial impact on HIV-associated BBB injury and neuroinflammation, and since BBB disruption may permit increased entry of toxins with consequent CNS injury, these results support the potential therapeutic role of cannabis among PWH and may have important treatment implications for ART effectiveness and toxicity. In a recent report, we hypothesized that more recent cannabis use would be associated with reduced biomarkers of immune activation and inflammation in CSF. This hypothesis was based on previous research demonstrating that selective stimulation of CB2R suppressed neuroinflammation and microglial activation. We measured a panel of pro-inflammatory cytokines -16, C-reactive protein , IL- 6, CXCL-10, sCD14 and soluble tumor necrosis factor receptor type II in CSF and blood plasma in PWH and PWoH who did or did not use cannabis at various levels from none too heavy. Participants were 35 PWH and 21 PwoH cannabis ever users, 15 never users. We calculated factor scores of biomarkers using exploratory factor analysis separately for CSF and plasma. We used multiple linear regression to evaluate the association of factor scores with the effects of cannabis use, HIV status and their interaction. Of the three CSF biomarker factors identified, one that loaded on CRP, IL-16 and sTNFRII was associated with more recent cannabis use in both HIV status groups. In plasma, more recent cannabis use was associated with lower values on a biomarker factor loading on sTNFRII and IP-10. Thus, we found recent cannabis use to be associated with lower levels of inflammatory biomarkers, both in CSF and blood plasma, but in different patterns, consistent with compartmentalization of immune effects. Cannabinoids are highly lipid soluble and sequestered in brain tissue, and thus our findings are consistent with specific anti-neuroinflammatory effects that may benefit PWH with or at risk for neurocognitive impairment. However, other studies of cohorts differing in age and HIV disease characteristics have reported conflicting findings. Cannabis as a clinical intervention in HIV disease would be a significant contribution to the field. When ingested, inhaled, or absorbed, THC and CBD, along with other exoge-nous cannabis components, are anti-inflammatory and counter oxidative stress. Patients report that cannabis has less harmful effects than other drugs. However, how to dry cannabis cannabis used improperly can have adverse side effects. People who use cannabis frequently with heavy doses have a higher risk of developing psychiatric symptoms. Long-term users have a reduction in hippocampal volume, affecting memory and verbal learning. In areas with high cannabis use, hospital emergency rooms report increased prevalence of visits due to nausea/vomiting, cardiovascular, psychiatric complaints. Caution is recommended for adolescents using cannabis due to the effect of cannabis on the developing brain. Adolescents who used cannabis regularly developed deficits in executive function involved in planning and decision making, as well as memory loss. Cerebral white matter organization is altered, affecting neural communication, potentially leading to higher impulsivity in adolescents. Further investigations are needed to refine the effects of dose, timing, and cannabis compound on this relationship, which could inform guidelines for safe cannabis use among populations vulnerable to NCI, cognitive decline, and inflammation. Clinical trials are needed to support recommendations to balance the trade-offs between therapeutic benefits and harm.Sixteen states have legalized the medical use of cannabis, and several others may do so by the end of the 2012 election cycle. 

Medical cannabis dispensaries —storefronts that dispense the drug to qualified patients—have proliferated in several of these jurisdictions. California was the first state to legalize medical cannabis, and there are now hundreds of dispensaries serving an estimated 300,000 patients throughout the Golden State . In the legal grey area between California law and federal prohibition, a handful of cities have taken it upon themselves to regulate MCDs within their jurisdiction. Unfortunately, the debate surrounding medical cannabis tends to be intensely polarized, heavy on political ideology, and light on empirical evidence. In this paper I aim to contribute knowledge that can be used objectively by researchers and policymakers interested in the effects of locally regulated MCDs on crime. Using spatial data and linear regression models, I explore the relationship between MCDs and crime across 189 census tracts in San Francisco. In framing a land use question, this study strives to analyze dispensaries in the same way that local government officials and staff might analyze bars, retail stores, or any other business in their community. Some constituents may want them, and others may not. From a policy standpoint, I argue that the interesting question is not whether to allow for the distribution of medical cannabis, but how. Every other legal substance—from alcohol to azithromycin—is dispensed by some type of storefront retailer. In California, cannabis is a legal substance within certain medical contexts. In this study I investigate whether, and to what extent, storefronts that provide medical cannabis are associated with higher crime rates.Law enforcement groups have mounted organized resistance against the proliferation of MCDs, focusing their attacks first and foremost on California—the state that opened Pandora’s box, as it were. A 2009 report entitled Marijuana Dispensaries and the Federal Government: Recommendations to the Obama Administration stated that: “In, California, dispensaries have had 13 years to flourish, and it is in California that their abuses have become evident” . In 2009 the California Police Chiefs Association also published its White Paper on Marijuana Dispensaries. It alleges that dispensaries “have been tied to organized criminal gangs” and that murders and armed robberies occur commonly as “ancillary byproducts of their operation” . It goes on to relate a series of sensational anecdotes involving “hooded home invaders”, victims bleeding to death, and even poisonings . In neither report do the authors include statistical analyses indicating that MCDs attract these crimes at rates higher than any other business. Proponents of medical cannabis dispensaries contend that regulated dispensaries actually reduce crime and restrict access to cannabis by minors, and they point to several successful jurisdictions in making the case that regulation is preferable to prohibition. In a report released in early 2011, Americans for Safe Access, a non-profit advocacy organization representing medical cannabis patients and MCDs, cites agreement from local government and law enforcement officials representing municipalities as diverse as Kern County, Oakland, and Sebastopol. In Oakland, a notoriously high-crime jurisdiction, city administrator Barbara Killey was quoted as saying that since enacting its dispensary ordinance, “the areas around the dispensaries may be some of the safest areas of Oakland” . In this paper I aim to test these competing claims empirically using data collected for the year 2010 across 189 census tracts in San Francisco.

This system yielded 24 g/L of isobutanol, close to titers reported in microbes. However, the limiting factor of the system was identified as enzyme instability in high isobutanol, and the authors were able to determine precisely which enzymes were the least stable. Since, the lab has stabilized many of the enzymes in the pathway by either engineering the enzyme or using a more thermostable variant. Current titers in a 15 mL bioreactor have reached nearly 300 g/L isobutanol orders of magnitude higher than microbial production . While the monoterpene and isobutanol pathways required sophisticated methods for cofactor regeneration, that may not always be the case. You et al utilized an 5 step enzymatic system to convert starch into myo-inositol , which is co-factor independent. The enzymes were expressed in E. coli and purified using a simple heating step. The resulting biosynthesis of starch to myo-inositol yielded 95 g/L in 48 hours on an 18,000 L scale. This was a remarkable study because of the scale and final titers achieved. It demonstrates that it is possible to translate the enzymatic systems to an industrial scale. Synthetic biochemistry is still in its infancy, but it has the potential to be a powerful tool for the production of natural products. There are a few obstacles that need to be addressed before more complex systems are industrially relevant. First, since synthetic biochemistry does not use cell lysates or cells, so cofactors like ATP, coenzyme A and NAD+ need to be added to start the system. While recycling these cofactors and using them for several iterations would reduce the associated cost, it is important to find an inexpensive source for these molecules. Additionally, dry racks for weed the enzyme catalysts can also be an expensive component of these systems, however this cost can be overcome by recycling the enzymes and using them for several iterations.

The use of thermostable enzymes is a key factor, demonstrated by You et al. The thermostable enzymes are able to be purified by a simple heating step, reducing the cost associated with protein purification. Additionally, they are able to recycle the enzymes and use them in subsequent bioreactor runs. While the obstacles might take time to solve, they are definitely solvable problems, making synthetic biochemistry an alternative approach for the biosynthesis of natural products. Described above are several bio-based approaches for the production of natural products. When seeking a bio-based approach for natural product production, it is important to recognize that different systems will work better for different molecules. In the case of paclitaxel it is clear that plant cell culture is currently the best method, however for monoterpenes a synthetic biochemistry approach may be a better option based on titer. The focus of this thesis is to evaluate a synthetic biochemistry approach for the production of cannabinoids and other prenylated aromatic polyketides. Due to the low cannabinoid titers seen with metabolic engineering it is possible that an alternative approach may be more successful. The low titers are most likely due to competition for the precursor acetyl-CoA. It requires 9 acetylCoA molecules to produce 1 molecule of THC or CBD. In addition to the 9 molecules of acetylCoA needed for cannabinoid biosynthesis, there are other essential competing pathways like fatty acid biosynthesis. Additionally the 9 acetyl-CoA molecules are split between the isoprenoid pathway and the polyketide pathway , and engineering the yeast to express each enzyme at the level needed to balance the acetyl-CoA flux is extremely difficult. A synthetic biochemistry approach for this pathway may be the better option due to better control over reaction components, flux, and a sufficient supply of the precursor acetyl-CoA. Due to these advantages, synthetic biochemistry could be a useful tool for the sustainable production of cannabinoids.

Prenylated natural products are a large class of bioactive molecules with demonstrated medicinal properties.1 Examples include prenyl-flavanoids, prenyl-stilbenoids and cannabinoids . Cannabinoids in particular show immense therapeutic potential with over 100 ongoing clinical trials as antiemetics, anticonvulsants, antidepressants and analgesics2–6 . Nevertheless, despite the therapeutic potential of prenyl-natural products, their study and use is limited by the lack of cost-effective production methods. Plant-derived prenyl-compounds are difficult to isolate due to the structural similarity of contaminating molecules, and the variable composition between crops. These challenges are further exacerbated when attempting to isolate low abundance compounds. Many chemical syntheses have been developed to address the challenges associated with making prenylated natural products, but they are generally impractical for drug manufacturing due to the degree of complexity and low yields. Microbial production is a useful alternative to natural extraction for prenylated natural products, but comes with many challenges such as the need to divert carbon flux from central metabolism and product toxicity to name a few. For example, prenyl natural products like prenylnaringenin, prenyl-resveratrol and cannabidiol are derived from a combination of the metabolic pathways for fatty acid, isoprenoid, and polyketide biosynthesis. So high-level production requires efficient re-routing of long, essential and highly regulated pathways. Despite the challenges, many groups have engineered microbes to produce unprenylated polyketides, like naringenin, resveratrol and olivetolate, but at relatively low levels . Obtaining prenylated products is even more challenging because GPP is an essential metabolite that is toxic to cells at moderate concentrations, creating a significant barrier for high level microbial production. So, in spite of intense interest, to our knowledge there are no published reports of the complete biosynthesis of prenyl-flavonoids, prenyl-stilbenoids or cannabinoids in recombinant microbes.

Much recent effort has focused on alternative methods for cannabinoid production. Two groups have produced the polyketide cannabinoid intermediate, olivetolic acid at low levels in yeast or E. coli , but did not prenylate OA or produce a cannabinoid from the bio-synthesized OA . In other work, tetrahydrocannabinolic acid THCA was produced in cell extracts from either exogenously added geranyl-pyrophosphate and OA in a two enzyme pathway or from cannabigerolic acid using a single enzyme 17. However, it is unclear how GPP or CBGA could be obtained at sufficient levels for economical production due to the high cost of these molecules. Here we propose an alternative biological approach to prenylated natural product biosynthesis using a cell-free enzymatic platform we call synthetic biochemistry, which has shown great promise for the production of bio-based molecules. The synthetic biochemistry approach frees us from worrying about the toxicity of products and intermediates, affords rapid design-build-test cycles, precise control of all system components, and complete flexibility in pathway design. Nevertheless, building highly complex systems involving dozens of enzymes, associated cofactors and myriad metabolites on a large scale outside the context of the cell is an enormous challenge. One of the keys to making commercially viable cell-free systems is reducing enzyme costs by employing stable enzymes that can last for long periods of time. Recently Zhang and co-workers converted maltodextrin into inositol at a 20,000 L scale in a 5 enzyme system using thermophilic enzymes purified by simple heating step, vertical farming pros and cons demonstrating that at least simple cell-free systems can reach industrial scale. Another key requirement is designing systems that effectively generate and recycle high energy cofactors H so that they can be used many times. We have previously reported a flexible enzymatic purge valve and rheostats for the regulating the supply of reducing equivalents and ATP, allowing us to build systems that run for many days and produce high titers of isobutanol and terpenes. Here we employ these concepts to develop cell-free production of a variety of prenylated compounds. We use glucose as a feedstock to produce GPP and optimize the system for the high-titer production of the cannabinoid compounds CBGA and cannabigerovarinic acid .Our synthetic biochemistry approach is outlined in Figure 2-1 and Figure 2-5 and expands on a system we developed previously for terpene production. First, glucose is broken down via a modified glycolysis pathway to produce high energy cofactors ATP and NADPH in addition to the carbon building block, acetyl-CoA using an alternative pyruvate oxidation pathway. The acetyl-CoA is then assembled into the prenyl-donor compound, GPP, via the mevalonate pathway using the ATP and NADPH produced from glycolysis. Importantly, a purge valve introduced into the glycolysis pathway balances NADPH production and consumption while maintaining carbon flux. The prenylation module then uses the GPP to prenylate exogenously added substrate to yield the desired prenylated product. To expand the capabilities of our synthetic biochemistry platform we developed a prenylating system that employs a non-specific prenylating enzyme such as NphB, AtaPT, or NovQ to produce an array of prenyl-compounds derived from glucose.

We then further engineered NphB using Rosetta to specifically prenylate OA. As a first test of the system, we built the full cell-free system to generate GPP from glucose and employed wild-type NphB to prenylate its preferred substrate 1,6 dihydroxynapthalene . 1,6 DHN was added at the beginning of the reaction along with glucose. Up to ~400 mg/L of prenylated product was obtained from 2.5 mM 1,6 DHN. However, increasing the 1,6 DHN concentration from 2.5 to 5 mM, decreased final titers 2-fold suggesting that 1,6 DHN inhibited one or more enzymes . Enzyme assays revealed that pyruvate dehydrogenase was inhibited by 1,6 DHN, as well as olivetol, resveratrol, and olivetolate . Therefore, to engineer a general prenylation system, we sought to eliminate PDH. To remove the need for PDH, we implemented al PDH bypass . In the PDH bypass, pyruvate is converted to acetyl-CoA using a pyruvate oxidase to produce acetylphosphate followed by the action of acetyl-phosphate transferase . The PDH bypass had two advantages. First, PDH is a large enzyme complex that is difficult to work with, so bypassing PDH streamlines enzyme production. More importantly, initial experiments revealed that the bypass is not subject to the inhibition seen at higher concentrations of 1,6 DHN. Once we confirmed the PDH bypass improved 1,6 DHN titers, we began to optimize the system as a general prenylation system. We varied co-factor concentrations, protein levels, and environmental conditions such as temperature and pH to identify the ideal set of conditions. Throughout this process we found that ATP, NADP+ , phosphate and NphB concentrations had the greatest impact on the final titer. As shown in Figure 2-2A, when we employed the PDH bypass, we found a 4- fold increase in titers of 5-prenyl-1,6 DHN when starting with 5 mM 1,6 DHN . When utilizing the PDH bypass system, approximately 50% of 1,6 DHN was converted in 24 hours, reaching a final titer of 705 ± 12 mg/L . We then tested the ability of the PDH bypass cell-free system to prenylate a variety of aromatic substrates . Thus, it is possible to produce a variety of prenylated natural products using a cell-free enzymatic system to generate the expensive co-substrates GPP and DMAPP. Further, the ease with which an exogenous substrate can be added to a synthetic biochemistry system is a great advantage because it is often not possible to add co-substrates exogenously to microbes since they cannot enter the cell.16 To test whether we could use synthetic biochemistry to produce high levels of therapeutically relevant prenylated products, we focused optimization efforts on cannabinoids due to the growing interest in new ways to make these medically important compounds. As shown in Figure 2-2D the initial system produced the cannabinoid precursor CBGA at a constant rate of 2.1 mg L-1 hr-1 over 72 hours and reached a final titer of only 132 mg L-1 . Although the system produced CBGA, there were two problems. First, the turnover rate of the prenyl transferase NphB for CBGA production is extremely poor . Second, prenylation of OA by NphB is highly non-specific, generating a major side-product, 2-O-geranyl olivetolate16. We therefore sought to improve CBGA production by enhancing the activity and specificity of NphB by design. Redesign of NphB to improve CBGA synthesis Briefly, OA was docked into the active site of the NphB crystal structure 32, then Rosetta was used to predict mutations that would improve OA binding. We narrowed the Rosetta results to a 22 construct library , and screened for CBGA production . We made several key observations during the initial screen, Figure 2-12: Y288A and Y288N by themselves dramatically enhanced activity, as predicted by computation; The presence of Y288N in any construct decreased the enzyme yield suggesting Y288N may be a destabilizing mutation ; The addition of G286S in the Y288N background appeared to improve activity further over Y288N , suggesting that G286S could be another favorable mutation; We noted an activity improvement of Y288A/F213N/A232S over Y288A/F213N suggesting that A232S may also be a favorable mutation.

Feeding different fatty acids to our engineered strains yielded cannabinoid analogues with modifications in the part of the molecule known to alter receptor binding affinity and potency. We also demonstrated that our biological system can be complemented by simple synthetic chemistry to further expand the accessible chemical space. Our work presents a platform for the production of natural and unnatural cannabinoids, which will allow for more rigorous study of cannabinoids and ultimately the development of new treatments for a variety of human ailments. We initiated construction of the cannabinoid-producing yeast by focusing first on production of OA , an initial intermediate in the cannabinoid biosynthetic pathway. Two Cannabis enzymes, a tetraketide synthase and an olivetolic acid cyclase ,6 have been reported to produce OA from hexanoyl-CoA and malonyl-CoA. To produce OA in yeast, we introduced a CsTKS and CsOAC expression cassette into S. cerevisiae to generate strain yCAN01 . The strain produced 0.2 mg/L OA from galactose , consistent with the fact that S. cerevisiae maintains low intracellular levels of hexanoyl-CoA. To increase the supply of hexanoyl-CoA, we fed 1 mM hexanoic acid, which can be converted to hexanoyl-CoA by an endogenous acyl activating enzyme , and observed six-fold higher OA production compared to no exogenous hexanoic acid feeding. A known byproduct of TKS, hexanoyl triacetic acid lactone ,6 was also detected . To optimize the conversion of hexanoic acid to hexanoyl-CoA, we introduced into yCAN01 an AAE from Cannabis , which is thought to catalyze this step in planta. The resulting strain showed a two-fold increase in OA titer when fed 1 mM hexanoic acid . To produce hexanoyl-CoA from galactose and complete the OA pathway, we introduced into yCAN01 a previously reported hexanoyl-CoA pathway.

The resulting strain produced 1.6 mg/L OA . CBGA, the precursor to THCA, CBDA, and numerous other cannabinoids, how to dry cannabis is produced from the mevalonate pathway intermediate GPP and OA by GOT. GOT activity was detected in Cannabis extracts over two decades ago, and a Cannabis GOT was patented ten years later. To enable in vivo testing of CsPT1, we constructed a GPP-over producing strain with an upregulated mevalonate pathway and a mutant gene of the endogenous farnesyl pyrophosphate synthase ERG, which preferentially produces GPP over FPP.However, we were unable to observe any GOT activity when we expressed CsPT1 or truncations thereof in yCAN10. To identify an enzyme with GOT activity that would function in yeast, we searched for candidate prenyltransferase enzymes from Cannabis and other organisms. These included NphB, a soluble PT from Streptomyces sp. with GOT activity in vitro, as well as HlPT1L and HlPT2, two PTs involved in bitter acid biosynthesis in Humulus lupulus, a close relative of Cannabis. In addition, we mined published Cannabis transcriptomes for GOT candidates.We set out to establish a biosynthetic approach for the production of this class of cannabinoid analogues from different fatty acids, hypothesizing that the observed promiscuity of our pathway towards butanoylCoA would translate to other precursors . To probe the analogue production capability of our engineered strains, we fed yCAN31 an array of 19 different fatty acids with various chain lengths, branching and degrees of saturation . LC-MS analysis revealed the production of OA and CBGA analogues from pentanoic acid , heptanoic acid , 4-methylhexanoic acid , 5-hexenoic acid and 6-heptynoic acid . Subsequent supplementation of yCAN40 with this subset of fatty acids yielded the respective THCA analogues . Furthermore, the functionalization of the pharmacophore with an alkene or alkyne terminal group enabled simple post-fermentation modification and thus the construction of side chains intractable to direct incorporation. As proof of concept, we performed copper-catalyzed azide-alkyne cycloaddition on the respective 6- heptynoic acid CBGA as well as THCA analogues with an azide-PEG3-biotin conjugate.

The corresponding products were detected by LC-MS demonstrating that the accessible chemical space of our process can be further expanded. Our results illustrate a novel avenue towards the production of cannabinoid analogues with tailored C3 sidechains. In summary, we engineered yeast strains capable of producing the major cannabinoids found in Cannabis from galactose. Pending the identification of novel cannabinoid synthases, we expect to be able to produce a large fraction of this class of natural molecules. Additionally, we further expanded the chemical space of cannabinoids by establishing and harnessing the intrinsic promiscuity of the cannabinoid pathway to produce unnatural cannabinoids including molecules with side groups amenable to further chemical derivatization. This work lays the foundation for the large-scale fermentation of cannabinoids, independent of Cannabis cultivation, which will enable the pharmacological study of these highly promising compounds and ultimately new and better medicines.Concerns about global climate change, energy security, and unstable fuel prices have caused many decision makers and policy experts worldwide to closely examine the need for more sustainable transportation strategies. Sustainable strategies include clean fuels, vehicle technologies, transportation demand management, and integrated land use and transportation strategies . Bikesharing—the shared use of a bicycle fleet—is one mobility strategy that could help address many of these concerns. In recent years, interest in this evolving concept has spread across the globe. At present, there are an estimated 100 programs in approximately 125 cities around the world with over 139,300 bicycles on four continents and another 45 planned in 22 nations in 2010. Despite rapid global motorization, worldwide bicycle use has generally increased over the past 30 years. Indeed, bicycling in Dutch, German, and Danish cities increased between 20 to 43% between 1975 and 1995 . While cycling growth and trends vary worldwide, bike sharing offers a transportation alternative to increase bicycle use by integrating cycling into the transportation system and making it more convenient and attractive to users. The principle of bike sharing is simple.

Individuals use bicycles on an “as-needed” basis without the costs and responsibilities of bike ownership. Bike sharing is short-term bicycle access, which provides its users with an environmentally friendly form of public transportation. This flexible short-term usage scheme targets daily mobility and allows users to access public bicycles at unattended bike stations. Bicycle reservations, pick-up, and drop-off are self-service. Commonly concentrated in urban settings, bike sharing programs also provide multiple bike station locations that enable users to pick up and return bicycles to different stations. Bike sharing programs typically cover bicycle purchase and maintenance costs, as well as storage and parking responsibilities . Besides individual user perks, bike sharing also offers environmental, social, and transportation-related benefits. For instance, bike sharing provides a low-carbon solution to the “last mile” problem. The “last mile” refers to the short distance between home and public transit and/or transit stations and the workplace, which may be too far to walk. Thus, bike sharing has the potential to play an important role in bridging the gap in existing transportation networks, as well as encouraging individuals to use multiple transportation modes. Potential bike sharing benefits include: 1) increased mobility options; 2) cost savings from modal shifts; 3) lower implementation and operational costs ; 4) reduced traffic congestion; 5) reduced fuel use; 6) increased use of public transit and alternative modes ; 7) increased health benefits; and 8) greater environmental awareness. The ultimate goal of bike sharing is to expand and integrate cycling into transportation systems, so that it can more readily become a daily transportation mode. In recent years, bike sharing also has expanded to college and work campuses throughout North America. Indeed, there are over 65 college/university bike sharing programs operating throughout North America and another 10 programs planned in 2010. Examples of college/university programs worldwide include “CibiUAM” at the Universidad Autonoma de Madrid in Spain and “Velocampus Leeds” at the University of Leeds in the United Kingdom . The focus of this paper, however, is on citywide systems that are open to residents and visitors, as opposed to closed systems that are only accessible to students and employees of a university or major employer. Furthermore, the authors do not address bike rental programs, best way to dry cannabis which also have expanded worldwide. Unlike bike sharing, bike rental traditionally targets users interested in leisure-oriented mobility and are most prevalent in areas with a high tourist concentration. Bike rental systems generally consist of a single or limited number of bike stations that are operated by a service attendant. A majority of bike rental programs also require users to return rented bicycles to the original bike station and are generally operated on an hourly pricing basis. Over the last 43 years, bike sharing’s evolution has been categorized into three key phases . These include the first generation, called “White Bikes” ; the second generation: “Coin-Deposit Systems;” and the third generation or “Information Technology -Based Systems” . In this paper, the authors propose a fourth generation, called: “Demand-Responsive, Multi-Modal Systems,” which builds upon the third. This paper is organized into seven sections. First, the authors present a history of bike sharing in Europe, the Americas, and Asia, focused upon the first two generations. Next, current bike sharing activities are discussed in Europe, the Americas, and Asia. Third, bike sharing business models and vendors are described. Next, the authors summarize the current understanding of the social and environmental benefits associated with bike sharing. Fifth, lessons learned are presented. Next, a fourth bike sharing generation is proposed with an eye toward future developments and innovation.

Finally, the authors conclude with a summary and recommendations for future bike sharing research.Despite earlier experiences, the bike sharing concept caught on and led to the first generation of bike sharing known as “White Bikes” . In a free bike sharing system, the bicycle is the main program component. Other distinguishing characteristics of first generation bike sharing include that bicycles were usually painted one bright color, unlocked, and placed haphazardly throughout an area for free use. Other cities that implemented a free bike system were La Rochelle, France in 1974 and Cambridge in the UK in 1993, called “Green Bike Schemes.” Soon after Green Bike Scheme’s launch, the almost 300 shared bicycles in Cambridge were stolen, resulting in program failure . However, the La Rochelle initiative, called “Vélos Jaunes” or “Yellow Bikes,” proved to be successful and continues to operate today. La Rochelle’s Mayor, Michel Crépeau, created Vélos Jaunes. Similar to Amsterdam’s White Bike Plan, Vélos Jaunes was launched as an environmentally progressive measure. Through the strong support of La Rochelle’s Urban Community, Vélos Jaunes became the first successful bike sharing program in France. Problems with Free Bike Systems led the city government and the City Bike Foundation of Copenhagen, Denmark to launch a bike sharing service that was different from any previous system. In January 1995, “Bycyken” was launched as the first large-scale urban bike sharing program in Europe. This initiative included 1,100 specially designed bicycles that were locked and placed throughout downtown Copenhagen at designated city bike racks . Bicycles were unlocked with a 20 DKK coin deposit that was refunded upon bicycle return. Bycyken of Copenhagen is famous not only because it continues to operate with more than 2,000 bicycles and 110 city bike racks today but also because it led to the second generation of bike sharing, known as “Coin-Deposit Systems.” The main components of this generation are: 1) distinguishable bicycles ; 2) designated docking stations in which bikes can be locked, borrowed, and returned; and 3) small deposits to unlock the bikes. Soon after the implementation of coin-deposit systems, the Copenhagen model led to a series of European bike sharing programs including: “Bycykler” in Sandnes, Norway ; “City Bikes” in Helsinki, Finland ; and “Bycykel” in Arhus, Denmark . The experience of these coin-deposit systems demonstrated that second-generation systems were more expensive to operate than early systems. In many cases, local governments also provided bike sharing organizations with funding. The incorporation of designated bicycle stations and the use of coin-deposit locks in second-generation systems created a much more reliable bike sharing system that was both dependable and more theft resistant. While amounts vary by country, coin deposit fees are generally low . Also, these systems do not issue a time limit for bicycle use, which means that bikes are often used for long time periods or not returned at all. The major problem with coin-deposit systems is bicycle theft, which can be attributed to customer anonymity.