Hemp Growing

Samples are collected on an annual basis as part of the national disease surveillance programme, and consist of pooled organ homogenate supernatants which are then stored at −80◦C. In these samples, PMCV was first detected from broodstock fish on a marine site in July 2009, with infection in 100% of the pools . It was later detected in December of the same year in 100% of pools of broodstock fish at a second broodstock farm. These are subsequently referred to as the index cases, to be used as the starting point for simulation of the epidemic. The rationale behind setting these two farms as index cases is that they are the earliest detections , and do not seem to be epidemiologically connected. There were 9 parameters in the SIE compartment model . Following the approach used by Widgren et al. , and for model parsimony, the shed rate α was fixed at 1.0 per day, thereby defining the unit of the environmental infectious pressure variable ϕi. In the absence of more detailed data, it was assumed that the threshold for two seawater farms being connected by local spread was an euclidean distance of 10 km. It was further assumed that freshwater farms were not connected via local spread but only via fish movement. In the model, four event types were defined. “Enter” concerns hatchings and international imports. “Internal transfer” occurs on the day that individual fish change their age category, from egg-juvenile to smolt, or from smolt to growth-repro. “External transfer” occurs when fish move from one farm to another. “Exit” is linked with slaughter, cannabis grower supplies euthanasia or international export, and from this point these fish are no longer included in the simulation.

Each of the scheduled events was executed in the model once the simulation, in continuous time, reached the time for any of the events. Individuals were sampled at random from the compartments affected by the event. For example, for an external transfer event of n smolt from farm 1 to farm 2, n smolt were randomly selected from all smolts in farm 1 and placed into the same compartments in farm 2. Fish that entered the model were assumed to be susceptible in their respective age category. Imported fish were assumed to be susceptible, noting the aim of the study to explore spread in Ireland without considering international importation of PMCV. On average, 2,176,111 eggs were imported per year during the study period, according to information from the Irish Marine Institute . Fish remained in the same infection state whilst changing age category, from egg-juvenile to smolt or smolt to growth-repro, or moving between farms. Figure 1 presents the conceptual SIE compartment model for PMCV spread in farmed Atlantic salmon, including indirect transmission via the environment and fish movements between holdings. There is an EU legal requirement for aquaculture production businesses to be registered, and to keep records of all movements of aquaculture animals and products, both into and out of the farm . In Ireland, the storage of these movement data is undertaken by the MI. This database contains several variables, including the date of fish movement, origin and destination sites with geographic coordinates, life stage, species and quantity of fish moved. The present study was based on all fish movement reports to the Fish Health Unit of the MI covering the period from 1 January 2009 to 23 October 2017. This included 648 reports, with information about the identifier of the origin farm, identifier of the destination farm, the number of fish and age group and the date of the movement. Each record was linked to the geographical coordinates of the farms provided by the aquaculture production business records, to allow for incorporation of local spread during the modeling phase of this study.

A farm was considered “active” if any fish were present on the farm, according to movement records. The following data processing steps were used to generate events for the simulation. Enter events, i.e., hatchings or imports , were imputed as needed to ensure that farm-level fish numbers were sufficient to allow fish shipments between farms as recorded in the fish movement database . The date of the imputed hatching events was calculated based on the average residence time of fish prior to shipment in the farm. In total, 90 enter events were imputed, including approximately a third of these during the first year of the simulation . This represented on average 10 imputed enter events per year. Most of this imputed enter events corresponded to eggs or juvenile fish , but some fish were entered as smolts , growth , or broodstock fish . Internal transfer events, i.e., moving from egg juvenile to smolt or from smolt to growth-repro, were imputed when the relevant time during the simulation had been reached. When moving from egg-juvenile to smolt, fish were aged a week prior to shipping to seawater farms, and for aging from smolt to growth-repro, smolts were allowed to remain on the farm for 180 days. Exit events, i.e., mortality, slaughter, or euthanasia, were generated either the day prior to the last shipment of a fish cohort, when it was evident, based on the records, that the fish destination of the whole cohort was another farm , or after a fixed amount of time if it was clear from the records that the farm was the final destination of the fish . The duration of this period was 300 days in freshwater farms and 600 days for seawater farms. Broodstock fish in freshwater farms were assumed to live until 1 week prior to an egg shipment. A total of 55 unique farms were used for the simulation, with the following event types: enter, including reported imports , internal transfer , external transfer , and exit . A time-series was created to explore seasonality in the input data, focusing on the number of events, the number of farms with at least one fish and the number of fish per age category. A further time-series was produced to investigate the proportion of farms connected to at least one other farm, for each month of the year. A smoother was added to each of these time-series, using local polynomial regression fitting in order to describe the temporal trend . The disease spread model was implemented in SimInf , which is an R package for data-driven stochastic disease spread simulations. This package was adapted, in part, from the Unstructured Mesh Reaction–Diffusion Master Equation framework . It interfaces high performance compiled code and OpenMP, which allows work to be divided across multiple processors and computations to be performed in parallel. The disease spread simulations were performed using the SimInf package version 5.1.0 and R version 3.4.2. The simulation was initiated by first supplying the model with an initial state in every farm, together with all events.

At the outset, infection prevalence was assumed to be zero, since the earliest detection of the agent in the country, dry racks for weed on archived broodstock samples, was in July 2009 . The initial environmental infectious pressure, ϕi, was also set to zero, as it was assumed that PMCV was not present in the country at the time of initialization . Based on test results on archived broodstock samples , the introduction of the agent was assumed to have occurred in two separate occasions at two different farms: 1 month prior to the time of sampling of the first positive archived sample in the population of broodstock fish resident in the farm at the time of sampling, and a second time in November 2009, affecting broodstock fish in a broodstock farm where it was later detected in December of that year.With the final model, farm and fish PMCV prevalence were estimated on a daily basis following the simulated introduction of the agent. The spread of the agent was plotted as a time series and the modeled epidemic curves were described. The role of both local spread and fish movement was evaluated by setting either D to zero , or moving all fish shipped to the susceptible compartment at the time of shipment .In addition, we evaluated the effectiveness of an improved bio-security in specific farms. For the purposes of the simulations, our definition of bio-security refers to measures that prevent infected fish from entering a farm, akin to a bio-security strategy that is 100% effective in preventing infected fish from entering or leaving a farm. As described above, this was done by moving all fish shipped to the susceptible compartment at the time of shipment. Six strategies were tested. In the first strategy, all farms were targeted for an increase in bio-security. This would be a very costly approach, but a good ideal for comparison. In the remaining strategies, we targeted the 8 most central farms in terms of a specific farm centrality measure, which were indegree, outdegree, incloseness, outcloseness, and betweenness,using the same methodology described previously by Yatabe et al. . The sample size was chosen arbitrarily, representing ∼25% of farms in Ireland at that time. Briefly, indegree describes the number of different farms from which a farm receives fish, outdegree describes the number of different farms to which a particular farm sends fish, incloseness is an estimate of how close all other farms reach to a respective farm, outcloseness is an estimate of how close a respective farm reaches to other farms, and betweenness is a measure of the degree to which a particular farm falls on the shortest path between all pairs of farms in the network . For estimating these centrality measures, at the beginning of every year the movement records from the preceding 2 years were used for estimation. For example, on 1 January 2011 centrality measures of all farms were estimated based on fish movement data from 1 January 2009 to 31 December 2010, farms were ranked and the top 8 for each centrality measure were selected for an increased bio-security. There were two exceptions to this 2-year window for estimation of centrality measures: the year 2010, where only the data from 2009 was used to estimate centrality measures, and 2009, where no data from previous years were available. Therefore, during this latter year no control measures were applied. The former six strategies were evaluated using two approaches for preventing the spread of a newly introduced agent into the country: firstly, by applying the control measures 1 month after the agent was first detected , from now on the ‘reactive’ approach, and secondly by applying the control measures as a standard practice from before the first detection of the agent , from now on the “proactive” approach.Based on the data available for 2009, a rapid increase was observed that year in the number of active farms and fish . However, this is an artifact as many farms had not yet been involved in fish movement and thereby appeared inactive . As a consequence, our results are reported from the start of 2010. The number of active farms declined slightly during the period 2010–2017 with relatively stable numbers during the 2010-2014 period, and a decrease during the 2015–2017 period . The total farmed Atlantic salmon population in Ireland had an increasing trend from 2010, with a peak of more than 32 million fish in early 2015, to later decrease until the end of the simulation, although not dropping to previous levels, where it reached ∼13 million fish. This increase was related to a large increase in the number of juveniles during 2014 and 2015. The number of fish within each age category varied seasonally, with juvenile fish showing peaks during winter and dips during autumn , the former associated with spawning and the latter with the transition of juvenile fish to smolts prior to stocking in seawater farms in autumn and spring. For the smolts, the converse was true, with peaks during autumn and spring. This age group decreases roughly every 180 days, as this is the amount of time after which they were aged into the growth-repro age group. This in turn determines the peaks of this latter age group. The reduction in the numbers of fish in the growth-repro age group were mainly in spring-summer and autumn-winter, being a mixture of elimination of fish stocked in a farm as smolts after 600 days and elimination of fish stocked at older ages after spending the mean residence time in the farm .

The second peculiarity of the entry process is the existence of a matching stage where upstream entrants and downstream entrants form vertical relationships and decide on API trade. While in principle such a stage can be built into the econometric model, the incremental benefit from doing so is unlikely to compensate for the computational difficulty that it entails. Therefore, I assume that matching in the API market takes the following simplified form: every upstream entrant, including the upstream plants of vertically integrated entrants, is matched with every downstream entrant including units belonging to vertically integrated firms. In other words, every downstream unit is entitled – from a regulatory point of view – to use the API produced by any of the upstream units. Once we ignore patent challenges and allow every upstream entrant to be matched with every downstream entrant, the generic entry process can be characterized as a simple simultaneous-move game with discrete actions. Potential entrants choose their actions and receive payoffs according to the oligopoly game played out in the resulting vertical market structure. I do not explicitly model how firms trade the intermediate good and compete against each other in the post-entry vertical oligopoly; the effects of such interactions among firms are summarized into reduced-form payoff equations that have the actions of rival potential entrants as arguments. For a firm that is a potential entrant in the downstream segment but not in the upstream segment, the action set is {Not Enter, Unintegrated Downstream Entry}. Similarly, a firm that is a potential upstream entrant but not a potential downstream entrant has the action set {Not Enter, Unintegrated Upstream Entry}. Finally, grow vertical the choice set of a firm that is a potential entrant in both segments is {Not Enter, Unintegrated Downstream Entry, Unintegrated Upstream Entry, Vertically Integrated Entry}.

It is assumed that only pure strategy Nash equilibria of the entry game are played. Therefore, market structure outcomes that are not pure strategy Nash equilibria – for example, one unintegrated downstream entry and no other entries, or one independent upstream entrant and no other entrants – are ruled out. Elberfeld shows that vertical entry games are characterized by multiple equilibria even if entry decisions in one vertical segment are made prior to those in the other segment. This implies that the existence of multiple equilibria is an unavoidable aspect of simultaneous-move vertical entry games. Before moving on to the empirical analysis, let us briefly consider the motives for, and effects of, vertical integration in the generics industry. A former executive at Sandoz, one of the largest firms, mentions lower API costs, earlier access to APIs, and stability of supply as the advantages of vertical integration . Others have mentioned the possibility that vertical integration allows better control over the information flow between segments as well as better risk-sharing , which would presumably lead to higher levels of productive investment. These point to the existence of efficiency effects generated through vertical integration. Such effects are likely to benefit final consumers through lower prices, more reliable supply of drugs, or higher product quality. On the other hand, recent antitrust cases suggest that vertical integration can generate anticompetitive foreclosure effects. In FTC v. Mylan et al. , the Federal Trade Commission claimed that an exclusive dealing contract, signed between a finished formulation manufacturer and its upstream supplier, regarding the APIs for lorazepam and clorazepate tablets contributed to price increases of between 1,900 and 3,200 percent for the downstream product.7 Unlike exclusive dealing contracts, vertically integrated entry in the generic drug industry is not subject to antitrust scrutiny.

As a result, such anecdotal evidence on foreclosure effects is not readily available in the case of vertical integration. There is, however, no reason to assume that vertical integration cannot have similar anticompetitive effects.8 Estimation of the model is based on a maximum likelihood framework. At each iteration of the parameter search, I calculate the probability that the market structure observed in each of the sample markets is an equilibrium of the entry game. Loosely speaking, the likelihood contribution of a market is calculated by integrating over the region of the error term vector where the observed market structure is predicted as an equilibrium. Thus, the first tool we need is a mapping from the error term vector to equilibrium market structures. I implement the mapping by programming an equilibrium finding algorithm. The error term vector in my model has fairly high dimensionality and the region of integration is expected to have a complex shape. As a result, we can expect no closed-form solution to exist for the market structure probability. I therefore approximate the integral using a simulation-based method in which draws of the error term vector are taken . For each draw, the equilibrium finding algorithm is run to see if the market structure observed in the data is predicted as an equilibrium . The market structure probability is approximated by calculating the proportion of the draws for which the observed market structure is an equilibrium. In general, vertical entry games are characterized by multiple equilibria . Therefore, the equilibrium finding algorithm often indicates more than one market structure as a possible outcome of the entry game in a given market. When such equilibrium multiplicity occurs, an additional assumption is required in order to assign a unique value to the probability that the observed market structure is generated by the model.18 One possibility is to assume that the equilibrium with the highest joint profits is always realized.. Alternatively, one can specify an equilibrium selection function whose arguments consist of the characteristics of each equilibrium, such as whether or not it is Pareto dominant . Following Bjorn and Vuong , I employ the simpler rule that when there are multiple pure strategy equilibria, each market structure has the same probability of occurring. The entry game for each generic drug market involves a large number of heterogeneous players, which makes the equilibrium finding algorithm time-consuming. Even if one has a fast algorithm, the large number of evaluations that are required during estimation suggests the need to economize on the number of runs of the algorithm. To this end, I employ the method of importance sampling with change-of-variables . The advantage of this method is that the equilibrium finding algorithm needs to be run only once during estimation. In the remainder of this section, I describe the equilibrium finding algorithm, the simulation-based approximation of the market structure probability, and the importance sampling with change-of-variables method.As mentioned at the beginning of Section 2.5, there are 128 generic drug markets that satisfy the following criteria: the market opened up to generic competition during 1993-2005; the downstream product is the first one, among all single-ingredient products using the same API, to become generic; the downstream product is an oral solid, injectable, or topical formulation; and data on market characteristics are available for the product. These are the markets where we are likely to see upstream and downstream entry decisions being made at around the same time. 43 of the 128 markets are subject to a patent challenge by one or more of the generic entrants. These are identified by the Food and Drug Administration’s list of drug markets where one or more Abbreviated New Drug Applications containing a paragraph IV certification have been filed. As discussed in Section 3.3, the existence of a patent challenge changes the market structure formation process from a simultaneous-move entry game to a race to be the first-to- file entrant. Meanwhile, my econometric model is only designed to estimate the parameters of a simultaneous entry game. Therefore, the 43 markets with paragraph IV certification are dropped from the analysis. This leaves a sample of 85 markets that are not subject to patent challenge by any of the entrants. The exclusion of paragraph IV markets raises concerns of sample selection.

If the incidence of paragraph IV certification is correlated with any of the error terms of the model, vertical grow systems the removal of paragraph IV markets from the sample may lead to biased estimates. While acknowledging the importance of such concerns, the estimation conducted in this chapter does not take them into account. The main reason is the difficulty of incorporating selection into the econometric model. As in Chapter 2, sample selection can be modeled by specifying a dichotomous choice process for the determination of paragraph IV status at the market level, and allowing the error term in the paragraph IV equation to be correlated with the remaining error terms of the model.27 In practice,however, I find that the parameter estimates fail to converge when joint estimation of the vertical entry model and the paragraph IV equation is attempted.28 Another factor that may allow us to ignore the sample selection problem is that, according to the results in Chapter 2, the error term of the paragraph IV equation is not likely to be strongly correlated with the error terms of the firm-level payoff equations.29 The definition of downstream and upstream market entry follows that in Chapter 2. Downstream entry into market m is observed when a potential entrant’s ANDA for that market is approved by the FDA. Upstream entry is observed when the potential entrant’s submission of a DMF for that market is publicized by the FDA. Vertical entry occurs when the potential entrant receives ANDA approval and submits a DMF in the same market. The definition of potential entrant status also follows from Chapter 2. A firm is a potential downstream entrant of market m if its previous entry into the downstream segment of another market was less than five years before the market opening date of market m. 30 Similarly, a firm is a potential upstream entrant if its previous upstream entry in another market was not more than seven years before the market opening date. If a firm is both a potential downstream entrant and a potential upstream entrant, it is considered to be a potential vertically integrated entrant. Table 3.1 shows summary statistics for the number of potential entrants and the number of actual entrants in each market. The number of potential upstream entrants is greater than the number of downstream entrants. On average, there are 75.859 potential upstream entrants in a market while the average number of potential downstream entrants is 30.635. 18.929 of those entrants, on average, are potential vertically integrated entrants. The actual number of entrants is much smaller: an average market structure contains 3.365 unintegrated downstream entrants, 3.565 unintegrated upstream entrants, and 0.647 vertically integrated entrants. The estimated coefficients on the two continuous market characteristics, User Population and Per-User Expenditure, are largely within expectations. User Population has a significantly positive coefficient in the unintegrated downstream payoff equation, while in the other equations its coeffi- cient is not significantly different from zero. The Per-User Expenditure variable is not significant in the unintegrated downstream and vertically integrated payoff equations, but it has a significantly positive coefficient in the unintegrated upstream payoff equation. These results support the notion that larger market size and higher willingness-to-pay raise firms’ entry incentives. The dichotomous market characteristic variables have contrasting effects in the three equations. The Gastrointestinal/Endocrine-Metabolic dummy variable has a significantly positive impact on unintegrated downstream payoffs but its effect on vertically integrated payoffs is significantly negative. This conforms to the finding in Chapter 2 that vertical integration probabilities are lower in markets belonging to the gastrointestinal and endocrine-metabolic classes. It may be because the tighter control over upstream manufacturing processes afforded by vertical integration is less important for such drugs than for other drugs. Another finding that agrees with the results in Chapter 2 is that the Injectable dummy variable has a significantly negative coefficient in the unintegrated downstream and upstream equations, while its coefficient in the vertically integrated equation is significantly positive. This confirms the intuition that tighter manufacturing controls through vertical integration are more important for injectables than for other dosage forms. The Post-2000 dummy variable follows the same pattern as the Injectable dummy: it is significantly negative in the unintegrated downstream and upstream equations while being significantly positive in the vertically integrated payoff equation. One possible explanation comes from the finding in Chapter 2 that vertically integrated entry became more common after the year 2000 .

It is unlikely that Mylan and Watson were slower than their rivals at noticing the efficiency effects of vertical integration, given their long histories and large scale of activities. More plausibly, their decisions were made in response to the expectation that generic drug markets were going to become increasingly vertically integrated. The next section discusses how we can test the two leading explanations for the increase in vertical integration within the generic pharmaceutical industry: the existence of bandwagon effects, and the importance of relationship-specific investments to support patent challenges.In the existing theoretical literature on vertical integration, bandwagon behavior is deemed to occur when a firm integrates in response to vertical integration by rivals . In generic drug markets, firms make their entry and vertical integration decisions more or less simultaneously so that we do not observe firms choosing their vertical structures in response to the actions of their rivals. Nevertheless, bandwagon effects can still exist in the sense that firms may become vertically integrated in response to the expected prevalence of vertical integration among rivals. Such a possibility can be examined by seeing if the change in a firm’s payoff from becoming vertically integrated is increasing in the incidence or prevalence of vertical integration among rivals. In other words, we can check whether firms’ payoff functions exhibit strategic complementarity in vertical integration decisions. As Buehler and Schmutzler point out, vertical integration decisions are shown to be strategic substitutes rather than complements in most theoretical models. However, hydroponic rack there are a few important studies such as Ordover et al. , Hart and Tirole , and McLaren that demonstrate the possibility of strategic complementarity.

For instance, one US cement company’s annual report for 1963 mentioned that while it was not inclined to acquire assets in the ready-made concrete industry, the wave of vertical integration among its rivals was forcing the firm to follow suit. I now show, using a simple duopoly model, that when firms’ payoff functions are characterized by strategic complementarity in vertical integration decisions, the following testable prediction arises: a firm’s probability of vertical integration decreases with its rival’s cost of vertical integration. When vertical integration decisions are strategic substitutes, the opposite result holds: the firm’s vertical integration probability increases with the rival’s cost of vertical integration. These results allow us to design a simple econometric test of strategic complementarity. The prediction that vertical integration facilitates early API development during a patent challenge can be tested by seeing if ANDA applicants who make a paragraph IV certification are more likely than other applicants to be vertically integrated. However, my dataset only records whether or not each market is subject to one or more entrants making a paragraph IV certification. I therefore construct a market-level variable that indicates the occurrence of a paragraph IV patent challenge. This indicator variable essentially signifies a switch in the entry process: markets with no paragraph IV patent challenge are characterized by simultaneous entry, while paragraph IV markets are characterized by a race to be first. The empirical strategy is to see whether this switch in the entry process affects firms’ incentives to become vertically integrated. Using the market-level paragraph IV indicator variable as a determinant of firm-level behavior introduces a potential endogeneity problem: markets that are the subject of paragraph IV certification may be attractive to generic entrants in unobservable ways, and those unobserved factors may also influence entry and vertical integration decisions.

This endogeneity can be taken care of by modeling the process of paragraph IV certification, and allowing the error term in the firm-level equations and that in the paragraph IV equation to be correlated. Many authors note that paragraph IV patent challenges have become more common in recent years . Patent challenges may also be more likely in larger markets that offer greater profits to the first-to-file entrant during the exclusivity period. In addition, Grabowski and Hemphill and Sampat note that certain types of secondary patents – particularly those that cover formulations and new uses – tend to be more vulnerable to patent challenge, presumably because it is easier to invalidate or avoid infringing such patents. This suggests the following as possible market-level determinants of paragraph IV certification: market size, the number of originator patents of different types, and year dummy variables. The generic drug markets used for analysis are selected from a database of the US Food and Drug Administration , called the Orange Book, which contains the population of all drug approvals. I begin by selecting a subset of drug markets that opened up to generic competition between January 1, 1993 and December 31, 2005. The set of markets is further narrowed down to those where the relationship between the upstream and downstream segments is relatively straightforward. This is done by first restricting the downstream products to finished formulations containing only one API. When there are multiple single-ingredient formulations containing a given API, I choose only the first of these to open up to generic competition. This is based on the belief that when generic companies make their entry decisions in the first downstream market for a given API, the upstream market structure is not yet formed.

Therefore, it makes sense to view downstream and upstream entry decisions as being made simultaneously. By the time the other downstream markets using the same API open up, the upstream market structure may already be fixed. Because it is not realistic to assume that upstream and downstream actions are decided simultaneously in such markets, they are excluded from the analysis. I also restrict the sample to the following dosage forms which constitute the majority of generic drugs: oral solids, injectables, and topicals. This leaves 177 downstream markets, each defined by a distinct combination of an API and a dosage form. 128 markets remain after removing observations for which market characteristics data could not be obtained. There are 125 corresponding upstream markets, each defined by a distinct API. For three APIs , two different dosage forms went generic on the same day. In these cases, I consider different dosage forms of the same API to constitute independent markets, and combine each of them with data for their respective API markets. Thus, for the three APIs mentioned above, the same upstream market data are used twice. Table A.1 in the Appendix contains a list of the drugs in the sample. A processed version of the FDA data was obtained from a proprietary database called Newport Sourcing, developed and maintained by Thomson Reuters. Table 2.1 and Figure 2.1 presented in Section 2.2 are constructed from the dataset of 128 markets. The econometric model is estimated using observations on 85 of those markets that opened up to generic competition between 1999 and 2005. The reason for restricting the time period in this way is as follows. Between 1992 and 1998, the FDA did not grant 180-day generic exclusivity to the first-to-file paragraph IV applicant. Therefore, during this period generic firms had little incentive to develop their products early in order to engage in patent challenges. Thus, the paragraph IV status of a market is likely to have been irrelevant for the decision to vertically integrate. By limiting the sample to the post-1998 period, we can analyze the role of paragraph IV certification more accurately. To record the two firm-level outcomes – downstream entry and vertical integration – it is first necessary to pinpoint the date when each market opens up to generic competition. Previous authors such as Scott Morton define the market opening date as the approval date of the first ANDA. After comparing ANDA approval dates with the dates when the generic products actually began to be marketed, I find that this definition is not always appropriate. In some cases, vertical growing systems the first generic product is not marketed until several months after its ANDA is approved. During those months,subsequent generic products are not approved by the FDA. I also find a few cases where drugs that appear to be generics are marketed before their ANDAs are approved. The first phenomenon arises when pending patent litigation between the generic entrant and the originator firm, or a settlement between the two, prevent the generic from entering immediately upon ANDA approval. The latter phenomenon is related to a practice called “authorized generics”: the originator gives the generic company a license to sell the product based on the former’s New Drug Application rather than the latter’s ANDA. To accommodate these special cases, I define the market opening date as the first generic approval date or the first generic marketing date, whichever is later. Firm-level entry actions are defined on the basis of market opening dates. Specifically, a potential downstream entrant is considered to have entered the downstream segment if its ANDA is approved by the FDA either before the market opening date or not later than one year after the market opening date.

The relatively narrow window is justified on the grounds that entry timing is an important determinant of profits in generic drug markets; because prices fall rapidly in response to additional entry, most firms enter in the first few months after market opening . As for actions in the upstream segment, a downstream entrant is deemed to have vertically integrated if it submits a Drug Master File to the FDA before the market opening date or no later than one year after the market opening date. I identify a potential downstream entrant in market m as a firm who has entered the downstream segment of any other generic market, including one outside the sample, on a date that is earlier than market m’s opening date but that is no more than five years before that date. Thus, I allow a firm to remain a potential downstream entrant for five years after its last entry. Similarly, a firm is identified as a potential upstream entrant of market m if it has entered the upstream segment of another generic market prior to, but not more than seven years before, market m’s opening date. Therefore, potential entrant status in the upstream segment is allowed to last for seven years after the last entry event. The reason for setting a wider window for potential upstream entrants is that DMF submissions sometimes occur a few years before the market opening date. Firm i is a potential upstream-only entrant in market m if it is a potential upstream entrant but not a potential downstream entrant. To evaluate the potential entrant status of a given firm, it is necessary to accurately identify its previous entries. This requires correct names for the ANDA applicants and DMF holders contained in the FDA data. Similarly, identifying firms’ vertical integration actions, which involves matching the firms found in the downstream ANDA database with those in the upstream DMF database, requires accurate data on firm names. These tasks are complicated by the several mergers and acquisitions that took place in the generics industry during the observation period. As described in Appendix A.2, I use the Newport Sourcing database to attach accurate firm names to the FDA data. Changes in firm ownership are taken into account by assuming that the past entry experience of an acquired firm is fully carried over to the acquiring firm. Table 2.5 presents the distribution of actual entry actions taken by potential downstream entrants in the dataset. The data consist of 92 firms facing 2,539 choice situations spread across 85 markets. 406 of these choice situations result in downstream entry. 76 of the downstream entries lead further to vertical integration. Table 2.6 presents summary statistics for the covariates. The first fourteen variables are market characteristics. “User Population” is a measure of market size, which is expected to have a positive impact on a firm’s probability of downstream entry . However, its impact on a firm’s propensity to vertically integrate is an open question: while Stigler hypothesizes that vertical integration would occur less frequently in larger markets, others note that under certain conditions, the incidence of vertical integration may actually rise with market size. The user population variable is defined as the estimated number of users of each drug in the US during the period immediately before generic entry. It is constructed from results of the National Ambulatory Medical Care Survey and the National Hospital Ambulatory Medical Care Survey .

High runoff rates increase the mobility of contaminants from fields and decrease the HRT within the wetland, thus reducing the opportunity for filtering pathogens. Despite variations in several characteristics among the four flow-through wetlands in the case study described earlier, HRT was a consistently good predictor of E. coli removal efficiency. Mean removal efficiency was 69, 79, 82, and 95 percent for wetlands having mean HRTs of 0.9, 1.6, 2.5, and 11.6 days, respectively . Remarkably, an HRT of less than a day can allow for considerable E. coli retention , which means that a relatively small wetland area can treat runoff from a relatively large agricultural area. The relationship between removal and HRT was not so clear for enterococci . In this case, W-1, with an HRT of 2.5 days, demonstrated a lower removal rate than W-2 or W-3, which had HRTs of 0.9 and 1.6 days, respectively . These differences are clear evidence that different organisms can behave differently in wetlands. As discussed above, there are many parameters that can influence the environmental fate of pathogens in wetlands, including vegetation density, design, age, size, contributing area, and depth. The efficiency with which contaminants can be reduced in agricultural water as it passes through a wetland is largely dependent on the extent to which water is evenly distributed across the wetland area. A wetland’s retention capacity is diminished if its design results in stagnant zones that either reduce the effective treatment area or short-circuit longer flow paths, indoor growing trays decreasing the HRT. Efficient wetlands come in a variety of shapes and sizes.

A wetland should be wide enough to allow sufficient trapping of sediment and other particulate materials and long enough to permit sufficient residence time for nutrient removal. Most researchers agree that the surface area of a wetland should be as large as possible in order to maximize its HRT and storage capacity. The even dispersion of water across the wetland, termed hydraulic efficiency, is largely defined by the wetland’s dimensions and the relative locations of input and output channels. Designs with good hydraulic efficiency have a shape that facilitates complete mixing throughout the wetland without the persistence of stagnant zones, or may incorporate barriers that achieve the same effects . The sediment trap is an important design feature in settings where the input water has a high level of suspended solids . Sediment traps are essentially small swales or ponds positioned between the source of the agricultural water and the main wetland to promote the settling of coarse particles before the water is distributed across the wetland. Sediment traps should be located in easily accessible areas where sediment can conveniently be removed on a regular basis. Incorporation of sediment traps in your design will decrease the amount of sedimentation within the wetland, lengthening the time you can go between dredgings. They also prevent the burial of germinating seedlings in the wetland and help limit channelization and short circuiting of flow paths. The amount of microbial pollutants in wetland soils is significantly higher than in the standing water. Bacteria survive longer in soil than in water . Fecal coliforms can persist in sediments for as long as 6 weeks , so the degree to which sediments are deposited in a wetland has a significant effect on the degree to which bacteria are exported in effluent waters, post-wetland. The survival time for pathogens varies widely in agricultural settings, probably as a result of local differences in environmental conditions .

If conditions are conducive to pathogen survival, any of a number of wetland conditions that cause the re-suspension and entrainment of sediment—e.g., high water flow pulses into wetlands, wave action, or channelization—may lead to the release of waters that contain microbial pollutants. If you manage wetlands to allow for alternating episodes of flooding and drying, you may be able to decrease the survival of microbes in the wetland soil. In addition to desiccation associated with episodes of dry wetland soil, fluctuations in wetted surface area and depth can facilitate a diversity of biological and bio-geochemical conditions that optimize wetland function and minimize the duration of pathogen survival . There are two general options to reduce non-point source pollution from agriculture: on-site farm management practices that control the pollution source or limit the application of excess materials and their subsequent loss from farmlands, and off-site practices that intercept non-point source pollutants before they reach downstream waters. Wetlands can be used within a farm scape as either an on-site farm practice or an off-site tool, where downstream flood plains are converted to wetlands to mitigate pollution at the watershed scale. In settings where the attraction of wildlife is of concern, you may want to consider placing the wetland off-site, but at a place where it will intercept the runoff before it enters a natural water body. This may also require re-routing of the agricultural runoff into an off-site wetland.Since the work of Bain , the formation of market structure and its effect on market outcomes has been a central topic in industrial organization. In many industries, market structure is not only about the number of entrants and their respective market shares; there is also an important vertical aspect.

In particular, the role played by vertical integration – the combination of two or more vertically related functions within the same firm – has been a topic of active theoretical research. Authors have recognized the ability of vertical integration to influence market structure formation by deterring or facilitating entry. Vertical integration may also have a direct impact on market outcomes. For instance, two markets with similar horizontal market structures may have different price levels if the firms in one of them have a higher degree of integration into a vertically related activity. While many empirical studies have examined the relationship between vertical integration and market outcomes, with a few exceptions , vertical integration has not been treated explicitly as part of the market structure formation process. Meanwhile, the empirical entry literature has so far focused on horizontal interactions among firms; vertical interactions, including decisions to integrate, have not been explicitly incorporated into the econometric analysis of entry. The present dissertation fills this gap in the literature by combining the analysis of vertical integration with that of market entry. There are two benefits from doing so. First, the incorporation of vertical integration into the analysis of entry behavior lets us obtain a more accurate understanding of the market structure formation process. Second, utilizing an empirical framework based on market entry behavior allows us to investigate the motives for, and effects of, vertical integration in new and useful ways. The two empirical essays in this dissertation analyze market entry and vertical integration in the US generic pharmaceutical industry. Generic pharmaceuticals are drug products that become available to consumers after the expiration of patents and other market exclusivities that protect the original product. The industry provides a good setting for studying vertical market structure formation because it consists of many markets – one for each original drug – made up of two vertical segments. The upstream segment manufactures active pharmaceutical ingredients and the downstream segment processes APIs into finished formulations to sell to final consumers. In each market, multiple generic firms simultaneously choose their entry and vertical integration actions. Therefore, the industry provides a large number of market observations where vertical market structure formation takes place through the simultaneous and collective actions of individual firms. The first empirical essay, Chapter 2, seeks to explain the increased prevalence of vertically integrated entry in the generics industry since the late 1990s. Using a firm-level dataset covering 85 markets that opened up to generic competition between 1999 and 2005, I investigate the determinants of a generic firm’s decision to vertically integrate. I find that a firm has a higher probability of vertically integrating, mobile vertical grow racks conditional on its decision to enter the downstream segment, if it has greater past entry experience in the upstream API segment. This suggests that a firm’s upstream experience lowers its cost of vertical integration. I also find that a firm is more likely to vertically integrate when the average upstream experience among its rivals is higher. This effect can be divided into two parts. First, higher upstream experience among rivals implies a greater incidence of vertical integration in the equilibrium market structure. Second, the expectation of a more vertically integrated market structure raises the incentive for an individual firm to become vertically integrated. The latter effect suggests that vertical integration is characterized by bandwagon behavior. While bandwagon effects have been widely discussed in the theoretical literature, and anecdotal accounts of bandwagon behavior is not difficult to find, this result represents one of the first pieces of empirical evidence on its existence. The analysis also finds that generic firms are more likely to be vertically integrated in markets where they try to enter by filing a “paragraph IV certification” that challenges the patents held by originator pharmaceutical companies.

Generic entrants have an incentive to engage in such patent challenges, because the first-to-file paragraph IV entrant may be awarded a 180-day exclusivity in the generic market. I argue that in markets characterized by paragraph IV patent challenges, upstream investment into API development tends to be relationship-specific. This is because in such markets, the API has a much higher value if it is used by the first-to-file entrant than when it is used by some other firm. Such relationship specificity does not exist in other generic drug markets. Therefore, the higher relationship specificity of upstream investments in paragraph IV markets is likely to explain the higher incidence of vertical integration in such markets. Chapter 3 is another empirical essay. It specifies the formation of vertical market structure in generic drug markets as the outcome of a simultaneous-move vertical entry game. Firms choose their actions from a set containing up to four elements: unintegrated downstream entry, unintegrated upstream entry, vertically integrated entry, and no entry. The actions of rival firms enter the payoff functions of potential entrants so that vertical rival effects are measured. The estimated rival effects are then used to make inferences about the competitive effects of vertical integration. An econometric model of the vertical entry game is estimated using a dataset consisting of 85 markets that opened up during 1993-2005. Markets that are subject to paragraph IV patent challenges are not included in the analysis, because the entry process in such markets is characterized by a race to be first rather than a simultaneous-move game. The estimates suggest that vertical integration by rival entrants has a significantly positive impact on the payoffs of uninte-grated downstream entrants. This implies that vertical integration has strong efficiency effects that spill over to benefit unintegrated downstream firms. I also find that the profit of an unintegrated upstream entrant falls when, in a market structure consisting of two upstream firms and one downstream firm, the other firms become vertically integrated. This finding is also consistent with the existence of efficiency effects. The usefulness of the vertical entry model lies in its ability to accommodate policy simulations based on estimated parameters. In one such simulation, it is found that a policy that bans vertically integrated entry tends to decrease the number of downstream entrants in equilibrium. Combined with the finding that vertical integration has significant efficiency effects, this result supports the notion that vertically integration plays a procompetitive role in the generic drug industry.While vertical integration is a feature of many businesses, its incidence or prevalence varies across industries, across different markets in the same industry, and among firms operating in the same market. Explaining such variation in vertical integration has long been an active area of industrial organization research. The motives for vertical integration identified in the theoretical literature can be grouped into two major categories: improvement of efficiency for the integrating firm and foreclosure of rival firms from the supply of an input or from access to consumers. Each category is further divided into sub-categories. For instance, efficiency motives include the elimination of double margins, the facilitation of relationship-specific non-contractible investments, and the assurance of an input supply. In addition to these primary motives, a firm’s decision of whether or not to vertically integrate may be influenced by the actions of its rivals. For instance, a downstream firm’s incentive to integrate backward may be greater if a larger proportion of its rivals are vertically integrated. This would be the case if vertical integration has a foreclosure effect that raises the input price faced by the downstream firm. Thus, “bandwagon” behavior, where a firm vertically integrates in response to similar action by rivals, may be profitable under some circumstances.

Three phenomena can be observed from the baseline case AB results at different inlet mass flow rates: the flow above tray 4 always has a lower temperature than that above tray 3 since it is closest to the inlet region; at low inlet mass flow rate , the flow above tray 3 has the highest temperature due to buoyancy effect; and at high inlet mass flow rate , the flow above tray 1 has the highest temperature because it is located closest to the exit. The temperature distribution for case AD is very similar to that of the baseline case due to similar inlet/exit location orientations so that buoyancy is the dominant effect at low inlet mass flow rate . On the other hand, cases BC, BA, and DA have different temperature distributions than the baseline because the inlet and exit are located near the bottom and the top, respectively. Therefore, the temperature of tray 1 is much closer to the inlet temperature and the temperature increases with height except at high mass flow rate where the temperature above tray 4 can be lower than that above tray 3. This is due to strong helical flow mixing inside the room at high inlet mass flow rate, which also explains that the temperature uniformity increases with increasing inlet mass flow rate. Previous studies show that the optimal germination temperature is between 294 K and 297 K. As can be observed from Fig. 11, cases BC, BA, and DA at 0.3, 0.4, and 0.5 kg s−1, respectively, exhibit the desirable temperature range and distribution for lettuce growth. Nevertheless, compared to the baseline at each mass flow rate, case BC at an inlet mass flow rate of 0.3 kg s−1 exhibits the most significant average temperature reduction . Furthermore, indoor grow trays the average temperature of case BC at 0.3 kg s−1 is lower than that of the baseline case at 0.5 kg s−1, which demonstrates the design effectiveness of case BC.

Relative humidity represents the water vapor partial pressure in the IVFS, which has a strong effect on crop growth. It is reported that the ideal RH for lettuce and leafy greens should be between 50–70% . High RH can cause pathogen issues, like mildew and botrytis, and low RH can induce an outer leaf edge burn due to dryness. Therefore, the inlet RH is set to be 85% in this IVFS design. The comparison of RH for each tray between the highest OU and the baseline cases at different inlet mass flow rates is shown in Fig. 12. It can be observed from the baseline case that RH increases with increasing inlet mass flow rate due to the increase of water supply and decreasing room temperature. RH can be calculated from the ratio of the partial pressure of water vapor to the saturation vapor pressure at a given temperature. Therefore, RH can be increased by either increasing water partial pressure or decreasing temperature. In our study, temperature is the dominant parameter because the gas species composition in the system does not vary significantly. Therefore, the trend of RH distribution agrees with that of the temperature distribution in Fig. 11. The only exception exists for the baseline case AB when the inlet mass flow rate is the smallest . Under such conditions, tray 3 has higher temperature and RH than tray 4. To explain this behavior, the results from the CO2 distribution analysis need to be considered. At the lowest inlet flow rate, the flow has lowest circulation and tray 3 is near the end of the fresh inlet flow stream. Therefore, tray 3 has the lowest CO2 and highest H2O concentrations due to photosynthesis as shown in Figs. 10 and 12. Overall, the average RH distributions for cases BC and BA fall within the optimal range.Marijuana use has become increasingly normalized in the US and abroad. Since 1996, California has allowed marijuana for medical use.

An additional 17 states and the District of Columbia have followed suit by either allowing medical marijuana use or legalizing recreation use of marijuana. A trend among young people is smoking marijuana cigars . Marijuana cigars or blunts refer to cannabis rolled with a shell from an inexpensive cigar called a blunt, although any commonly available inexpensive small cigars or cigarillos are likely to be used . Blunt wrappers, which are tobacco leaf rolling papers that come in sealed packages, are also sold for rolling blunts. Due to the tobacco content in the wrapper leaf, smoking marijuana cigars may be considered as concurrent use of marijuana and tobacco. In this paper, we use the term “blunts” to talk about marijuana cigars and the term “blunt cigars” to talk about the inexpensive tobacco cigar that is typically used to make the marijuana cigars. Blunt cigars are cheap, frequently available at urban convenience stores, typically pre-cut with a blunt tip , and sold singly or in small packs of five. The present study examines factors associated with availability of tobacco products commonly used for blunts. Epidemiological surveys indicate that blunts are most commonly used by emerging adults , and that their use is generally increasing across all age groups. In 2005, 3.5% of all American youth aged 12–17 years were estimated to have used blunts in the past month , and a study among young adults aged 18-25 reported that between 2005 and 2008 past month blunts use ranged between 9% and 10.1% . By comparison, in 2011, 4.1% of youth aged 12–17 years, 11% of young adults aged 18-25years, 4.2% of adults aged 26-34 years and 1% of adults aged 35 or older reported using blunts in the past month , 2013. A recent study reported a moderate increase in the annual prevalence of blunt smoking among respondents aged 12-34 years old from 12% in 2004 to 14% in 2010 .

Other studies indicate that blunt smoking appears to be practiced among a growing number of racial/ethnic groups , such as Southeast Asian youth and young adults in California . Previous studies have found that, compared to other intake forms of marijuana, smoking blunts is more associated with male gender, low GPA, poor school attachment, not attending college, not working, and living in low income areas . Also, blunts smokers may have greater odds of being dependent on cannabis and tobacco and are at risk for smoking-related diseases . While tobacco remains the leading cause of preventable and premature death, killing an estimated 443,000 Americans each year , risks associated with marijuana use include impaired respiratory, cardiovascular and cognitive functioning and reduced mental health, as well as impaired driving ability and impaired function in school and at work . Blunts availability is likely to increase blunts use and problems associated with marijuana and tobacco use in local neighborhoods. Previous research suggests that exposure to and availability of drugs increase drug use and abuse . However, very little is known about availability of tobacco products associated with use of blunts. Studying the associations between neighborhood characteristics and availability of tobacco products used for blunts may help to identify areas at risk for blunts use and help policymakers and community advocates make better decisions about allocation of prevention resources. Analyzing 2000-2003 data from the National Survey on Drug Use and Health , Golub and colleagues showed that more than half of past-30-day marijuana users also reported current use of blunts. Among current blunts users, over two-thirds reported no current use of cigars, vertical grow racks for sale indicating blunts smokers may not define this practice as tobacco use. Similarly, a recent study suggested that young people recognize blunts as a form of marijuana use but do not recognize it as cigar use . Qualitative studies have also shown that youths may not consider blunts smoking to be a form of cigar use at all . These studies suggest the importance of studying the relationships between availability of tobacco products associated blunts use and societal-level influences related to normalization of marijuana use. Increased recognition of “recreational drug use” and increased support for legalizing some forms of marijuana use may contribute to normalization of marijuana and therefore to availability of products associated with blunts use. Societal-level influences related to normalization of marijuana use in the community may include rates of adult marijuana use. Recent studies have found that prevalence of adult drinking or smoking in the community are associated with increased underage drinking and youth cigarette smoking . These studies suggested that the level of adult drug use in the community reflect both community drug norms and availability. Medical marijuana policy and availability should also be considered as social influences related to normalization of marijuana. Our previous studies indicated that tobacco and alcohol policies were directly related to community norms . Although blunts smoking and use of other forms of marijuana may be seen as different practices , medical marijuana dispensaries might increase availability and ease of access to marijuana. Also, medical marijuana dispensaries may indirectly affect general acceptability of marijuana in the community.

The present study focuses on the associations between availability of tobacco products for blunts and social factors including neighborhood demographics, community-level marijuana use, medical marijuana policy and access to medical marijuana dispensaries and delivery services.This study used data from access surveys conducted at 1,000 tobacco outlets in 50 California cities with populations between 50,000 and 500,000. The sampling procedures for the 50 cities are described elsewhere in detail . This sample was a purposive geographic sample intended to maximize validity with regard to the geography and ecology of the state. Twenty randomly selected tobacco outlets in each city were surveyed. The sampling procedures for the tobacco outlets and survey procedures are also described in detail . In each city, data for the study were available for between 14 and 20 outlets . The selected tobacco outlets in each city were surveyed by two research assistants. At each outlet, a single research assistant attempted to purchase a pack of cigarettes and conducted a brief observation. After leaving the outlet, the research assistants recorded outlet data on a standardized form including whether blunt cigars, small cigars or cigarillos and blunt wrappers were for sale. Institutional review board approval was obtained prior to study implementation.Adult prevalence of past year marijuana use in each city was ascertained from 8,807 adults over the age of 18 years old who participated in a general population telephone survey conducted in the same 50 cities . Respondents were surveyed through a computer-assisted telephone interview. Listed addresses and telephone numbers obtained from various sources were used to develop a sample for the study. Listed samples of phone numbers is unbiased relative to random digit dialing techniques . Respondents were asked if they ever, even once, used marijuana or hashish. Respondents who had used marijuana or hashish were then asked about the number of days in the past 12 months they used marijuana or hashish. Those who reported never using marijuana or hashish or not doing so in the past 12 months were assigned a value of 0. All the others were assigned the value of 1. Adult prevalence of past year marijuana use was computed as the percent of past 12 month marijuana or hashish users in each city. Because of the skewed distribution, this variable was log10 transformed for analyses.Although California allows medical marijuana use, the state leaves regulations regarding the distribution of medical marijuana to patients up to local jurisdictions. Some localities have banned the distribution of marijuana through storefront dispensaries, have strict regulations on cultivation sites, have density restrictions on dispensaries, or some combination. Between June 2012 and July 2012, local city ordinances and policies around distribution and cultivation of marijuana were reviewed to determine whether the city permitted medical marijuana dispensaries or private cultivation in its jurisdiction. Cities were coded as allowing or not allowing medical marijuana storefront dispensaries and/or private cultivation within city boundaries.The density of medical marijuana dispensaries and delivery services is a measure of physical availability of medical marijuana in each of the 50 cities. Delivery services are an alternative means for users to obtain medical marijuana. These services can be available in any of the 50 cities, but are often more available in cities that do not allow distribution through dispensaries. Locations of storefront dispensaries and delivery service areas were obtained from seven different websites listing the information for these businesses in March – April 2012.

These results suggest that MGL-Tg mice show similar levels of social interaction and social interest as WT mice, regardless of the familiarity or the reciprocity of the social stimulus. Therefore, in contrast to high-fat food, the impaired CPP of MGL-Tg mice to social interactions may represent a problem of consolidation rather than reward processing.It has been postulated that drugs of abuse ‘hijack’ natural reward circuits . We therefore tested MGL-Tg mice in cocaine CPP to see if the drug might recruit 2-AG signaling. In a standard CPP box, we conditioned mice to either saline or cocaine in four 30-min sessions, alternating over a total of 8 days . Both WT and MGL-Tg mice developed a strong post-conditioning preference for the cocaine chamber . This result suggests that the conditioned effects of cocaine do not depend on 2-AG signaling. Furthermore, it indicates that MGL-Tg mice are cognitively able to perform CPP in general.In particular, we have recently shown that social contact increases anandamide mobilization in the NAc, and that this response plays an important role in social reward . If 2-AG is also involved in social-reward processes, as our previous results suggest, social stimulation should increase 2-AG levels. We isolated juvenile C57Bl6J mice for 24 h, and then returned half to their cage-mates while keeping the other half isolated for 6 h . We collected and snap-froze their brains, took micro-punches of tissue, and measured lipid content using liquid chromatography-mass spectrometry . We found that, compared to mice that remained isolated, socially stimulated mice showed an 83% increase in 2-AG levels in the nucleus accumbens and a 40% increase in the ventral hippocampus , cannabis vertical farming but did not show a change in 2-AG levels in the medial prefrontal cortex . This suggests that social interactions cause amobilization of 2-AG signaling.

Levels of the endocannabinoid anandamide, in contrast, were unchanged in the NAc after this 6-h social stimulation . It is important to note, however, that we previously observed an opposite pattern for a 3-h social stimulation, which elicited an increase in levels of anandamide but not 2-AG . It is possible that social contact elevates 2-AG levels due to an increase in general activity. Therefore, we compared 24-h-isolated animals to control animals that were similarly handled but remained social . In this comparison, isolation had no effect on levels of 2-AG in the NAc, but decreased 2-AG by 55% in the vHC and by 50% in the mPFC . This profile of 2-AG change was not the opposite of that elicited by social stimulation – that is, NAc 2-AG increased during stimulation but did not change during isolation, and mPFC did not change during stimulation but decreased during isolation. In contrast, the pattern of 2-AG levels was opposite in the vHC – 2-AG increased during stimulation and decreased during isolation. The magnitudes of these opposing effects in the vHC were relatively comparable . Isolation also did not alter levels of anandamide in the NAc . We interpret these results to mean that social stimulation induces 2-AG signaling in the NAc that is distinct from what is active during baseline social activity, i.e. a ‘social 2-AG tone.’ Other contributions in the mPFC and vHC remain to be determined. Nevertheless, because the NAc is a key region for brain reward, the results suggest that 2-AG likely contributes to the reward of social interactions. These results support the previous finding that MGL-Tg are deficient in social CPP.It has been postulated that the signaling system underlying reward for social interactions overlaps with those involved in the control of other natural rewards .

The available literature emphasizes the important role of the endocannabinoid system in motivated behaviors , raising the theoretical possibility of an endocannabinoid substrate that is common to different natural rewards. Our study provides evidence in support of this possibility through complementary behavioral analysis of a unique model of selective 2-AG reduction with biochemical analysis of 2-AG mobilization in brains regions important for reward. Our main findings are that 2-AG reduction impairs CPP for social interactions, and that social stimulation increases 2-AG levels in the NAc. These results clearly identify 2-AG as a reward signal for social interaction. Additionally, we found that 2-AG reduction impairs CPP to high-fat food. Collectively, our results suggest that 2-AG is involved in regulating the incentive salience of two essential aspects of behavior, feeding and social contact. A limited number of studies have addressed the involvement of endocannabinoid signaling in the control of social behavior. Genetic CB1 deletion and administration of CB1 agonists alter social interactions. The effect of CB1 agonists can be bidirectional, depending on dosage and thus likely the competing circuits involved . Furthermore, increasing anandamide activity via genetic deletion or inhibition of its hydrolytic enzyme, FAAH, increases direct social interactions in mice and social play in rats . Recently, we demonstrated that anandamide-mediated endocannabinoid signaling is important in the control of social reward . Social contact mobilizes anandamide in an oxytocin receptor-dependent manner. Consistently, chemogenetic activation of oxytocin neurons in the hypothalamus increases anandamide mobilization in the NAc. Pharmacological and genetic enhancement of anandamide activity increases social CPP and offsets the effects of oxytocin blockade.

These results suggest that a cooperative oxytocin-driven anandamide signal regulates social reward . Thus, the distinct effects of CB1 agonists and FAAH inhibitors on social behavior present a complex picture with two knowledge gaps: whether 2-AG-mediated activation of CB1 receptors also participates in social behaviors and if so, the type of interaction and associated neural circuits that are regulated. The present study addresses these gaps by exploiting a new transgenic mouse to reduce endogenous 2-AG signaling without overt compensation, using CPP to specifically model social reward rather than interaction in general, and measuring socially mobilized 2-AG levels in brain areas that are part of the reward circuit. At the same time, our study raises an important question for future studies. The temporal profiles of socially stimulated 2-AG versus anandamide are distinct. In the NAc, a 3-h social stimulation increases the levels of anandamide, but not 2-AG , whereas a 6-h stimulation elevates levels of 2-AG, but not anandamide . Distinct temporal profiles of endocannabinoid mobilization have been demonstrated in other situations – for example, in response to drugs of abuse as well as in stress-induced analgesia mediated by the periaqueductal grey – and thus are likely to be functionally meaningful. One plausible explanation is that anandamide is involved in the initial saliency of a social encounter whereas 2-AG is involved in consolidating information from prolonged social contact. This hypothesis is supported by two pieces of evidence. First, oxytocin is thought of as a saliency signal that is more proximal to the initial processing of social sensory information . Oxytocin tightly drives anandamide formation in the NAc, whereas 2-AG production is driven by prolonged social contact but not oxytocin . Secondly, anandamide is more specific to social reward whereas 2-AG is more generalizable to other rewards, as elevating anandamide increases CPP for social interactions but not for high-fat food or cocaine , whereas reducing 2-AG activity reduces CPP to both social interactions and high-fat food . An extensive literature also supports an important role of CB1 signaling in food reward. CB1 receptor antagonism and genetic deletion suppress not only food intake, but also CPP and self-administration . In contrast, information is sparse regarding the individual endocannabinoid transmitters that might be involved. Available studies have found that systemic administration of exogenous anandamide increases food intake , and 2-AG administration into the NAc increases feeding . Exogenous administration, however, does not simulate physiological conditions. The intake phenotype may also equivocate the metabolic and motivational aspects of endocannabinoid signaling. Our study addresses these concerns by specifically manipulating 2-AG signaling and using a model of CPP to represent the rewarding value of high-fat food. A lack of CPP encompasses different impairments in aspects of reward signaling, such as the processing of initial sensory cues, integration and recruitment of limbic regions, and the consolidation of the memory for the reward. In order to specify the component in which 2-AG may play a larger role, cannabis drying rack we also measured the intake of high-fat food and the social interactions of MGL-Tg mice. We found that MGL-Tg mice show less intake of high-fat food but appear normal in direct, reciprocal social interactions as well as social approach. In light of the lack of CPP for both high-fat and social stimuli in MGL-Tg mice, these results may point to a dichotomous role for 2-AG – perhaps in the processing of high-fat reward versus the consolidation of social reward. These speculations are consistent with the aforementioned feeding literature and prolonged action of 2-AG, as compared to anandamide, after social stimulation. In line with this thinking, the phenotype of a lack of CPP across high-fat and social stimuli, but present CPP for cocaine, must be taken to represent a qualitative rather than quantitative difference. That is, reducing 2-AG impairs different processes for the reward signaling of these stimuli, rather than different amounts of the same process. Perhaps because cocaine bypasses 2-AG-regulated signaling and is more dopamine-dependent, CPP develops regardless of 2-AG signaling.

Nevertheless, whether 2-AG influences dopamine in MGL-Tg mice and whether dopamine is the ultimate effector of all these rewards remains to be determined. Indeed, the magnitude of cocaine CPP as a function of dosage does not vary in a graded manner, but develops in an all-or-none fashion, starting at the minimum dose range that we used . Additional investigation into these possibilities is needed. While MGL-Tg mice represent a technological advance to address the aforementioned knowledge gaps, phenotypic results also come with potential weaknesses. First, forebrain 2-AG reduction may alter general cognition so that mice are unable to perform the CPP task. However, our finding that MGL-Tg mice develop normal cocaine CPP argues against this possibility. Second, improper development or socialization may render MGL-Tg abnormal. But this is unlikely because MGL over expression is controlled by the CAMKII promoter, which lacks developmental activity, and MGL-Tg mice do not show overt abnormalities in tests of general health . Normal levels of social interaction and social approach also speak to proper socialization. It remains true that our results do not completely exclude these possibilities. More nuanced forms of cognition or social interaction may not be detected in our tests. In conclusion, our study identifies a common endocannabinoid substrate – 2-AG – in the regulation of two natural rewards, feeding and social contact. This identification provides a basis to motivate further investigation of the circuitry and physiology regulated by 2-AG signaling in natural reward, the dichotomous roles of anandamide and 2-AG in different social contexts, as well as how such signaling may be exploited in addiction.A core feature across autism spectrum disorder is impairment in social functioning. People with ASD restrict themselves to repetitive behaviors and show deficits in social reciprocity and communication1 . The underlying basis for social impairment in ASD is unknown and no pharmacological treatment is available. One theory – the social motivation theory – posits that the psychopathology of ASD is rooted in a decreased desire to be social3. The neural substrates of normal social behavior are only now beginning to emerge . Perhaps the best account so far has come from the study of oxytocin. This neuropeptide is crucial in many aspects of social behavior, including affiliation and reward. Investigations are ongoing into the contributions of the oxytocin system to ASD and oxytocin-based therapies for ASD8 . Recent reports suggest that early treatment with oxytocin may be useful for improving social behavior in animal models as well as in human patients10. Nevertheless, identifying the key neural systems underlying social behavior and understanding how they interact with oxytocin remains an enormous challenge5 . One candidate is the endogenous cannabinoid system, a modulatory neurotransmitter system that may play an important role in social behavior. This signaling complex consists of lipid-derived messengers called ‘endocannabinoids’ whose actions in the brain are mainly, albeit not exclusively, mediated through CB1 cannabinoid receptors. A series of enzymes catalyze endocannabinoid synthesis and degradation to control the activity of these substances. Fatty acid amide hydrolase catalyzes the intracellular hydrolysis of the endocannabinoid anandamide. In an effort aimed at probing anandamide function in social behavior, we found that genetic removal of FAAH in mice increases direct social interactions, while Trezza et al. noted that pharmacological FAAH inhibition promotes social play in juvenilerats. More recently, we identified a signaling mechanism by which oxytocin drives anandamide mediated signaling at CB1 receptors to control the rewarding properties of social interactions.

Similar to Fig. 3, without the analyzer, we can see clearly two horizontally shifted LCP and RCP images, as shown in Fig. 4 I– L. The separation between two images determines how sharp the edge can be resolved, which is also confirmed by the final edgedetection images shown in Fig. 4 M–P. The highest resolution achieved is around 2 μm , close to the diffraction limit of the optical system, by using a meta surface with Λ of 8,000 μm. It is worth to note here that these results are taken under the condition of a planar object at the focal plane. Therefore, for thick 3D object, multiple measurements need to be taken by scanning the focal plane over the objects and combine each focal plane result together. In the case of the samples with circular birefringence inclusions, the LCP and RCP components will gain different phases, so that the combined LP components in the overlapped area may not be completely canceled out after the second linear polarizer. Although this indicates the contrast between the edge and the background will be compromised, additional circular birefringence information can be revealed.Social behavior is a human hallmark. People spend an enormous amount of time engaging in social interactions. They form loose relationships with acquaintances or strong relationships with friends and family members. Together, these connections form social networks that range from schools and companies to ethnic identities and nationalities. The social nature of humans has been recognized since Aristotle . Its crucial role in human life has been studied in a spectrum of fields in the social sciences. Only recently, however, have biological approaches been used to study the underlying physiology of social behavior. These approaches have contributed to understanding the neural signaling that may regulate social behavior as well as the impact of social factors on mental and physical health. These lines of research have given rise to the aggregated field of social neuroscience . Firstly, this field has emphasized the importance of social relationships.

Social contact is essential for the health of the brain and the body . For example, social isolation in critical periods of life wreaks havoc on cognition, vertical racking system emotion and immune responses. Second, social support is a crucial factor for neuropsychiatric and bodily diseases, both in susceptibility and recovery . Examples abound: abnormal social development predicts addictive behaviors and conduct disorder; social support buffers against not only mental illnesses such as depression but also physical ailments such as chronic pain and recovery from myocardial infarction. The multitude of health problems associated with social factors also highlights the complexities and fragilities involved in a growing person navigating the social sphere. The nature of social relationships changes significantly over a lifespan, requiring the development of unique social skills. For instance, a child must transition from a strong maternal attachment to mostly peer contact over the course of adolescence, the young adult must transition from single hood to a state of attachment with a significant other, while the elderly must take on new roles imposed by accrued experience and reduced physical strength. These distinct types of experiences involve a range of cognitive and emotional processes that undoubtedly impact brain development. The impact is likely mutual, as proper development is crucial for ongoing social function. Such mutual interactions must be finely tuned. To date, the neural processes underlying these transitions and the neural systems that regulate social behavior are largely unknown. Perhaps the best biological account so far has been the study of oxytocin. Oxytocin is a peptide, comprised of nine amino acids, that has long been known for its peripheral hormonal effects in parturition and lactation . More recently, research has elucidated its central effects in affiliative behaviors and social reward. Tom Insel, Larry Young, and colleagues performed seminal studies in prairie voles . They compared these rodents, which display monogamous pair bonding, to the genetically similar but polygamous montane voles , and found distinct differences in their oxytocin systems .

One key difference is that compared to montane voles, prairie voles have increased oxytocin receptors in the nucleus accumbens, a brain region important for reward signaling. In a series of pharmacological and genetic studies, these authors elaborated the crucial roles of oxytocin in social memory and pair-bond formation. Since then, work on oxytocin remains an active area of ongoing investigation. Modern neuroscience tools have been used to examine the circuit dynamics of oxytocin signaling. For example: oxytocin neurons of the paraventricular nucleus of the hypothalamus project to the amygdala to modulate evoked fear ; oxytocin neurons coordinate with serotonergic neurons from the dorsal raphe nucleus tocontrol peer social reward ; oxytocin in the hippocampus controls signal-tonoise ratio for information processing ; oxytocin receptors are left-lateralized in the auditory cortex to enable maternal retrieval of separated pups . Human studies have been hampered by the difficulty in delivering oxytocin to the brain, but this obstacle was partly removed by the recent development of an intranasal oxytocin spray. Although the pharmacokinetics of intranasal oxytocin is still unclear, its central effects have been widely documented in numerous high-profile publications. For example, intranasal oxytocin impacts one’s trust of others . Intranasal oxytocin is also being tested in ongoing trials for indications including anxiety, autism spectrum disorder, and drug addiction. Drug development is still at an early stage , but a first a sign of success on sociability in autism-spectrum patients was reported .More recent studies of marijuana use in humans are also in line with the assumption that this drug may affect social behavior. In a survey of students who were experienced marijuana users, more than 70 percent said that intoxication makes them want to interact more with others, because the group takes on ‘a much greater sense of unity, or real social relationship’ . More than 80 percent reported that they felt more insightful and empathetic of others but they are also less able to play social games, which suggests that marijuana makes users more intuitive and social but hinders their social skills .

Worth noting, however, is that, a substantial portion of experienced users also preferred to ingest the drug in isolation, perhaps because they like to feel ‘isolated from things around me’ . This contrasts with more casual users who typically ingest the drug in a social setting. Experimental studies have also found that marijuana alters social behavior. In a study of aggression, two groups were given a shared task with a frustration stimulus. Compared to placebo, the group given marijuana provided self-rated reports of higher cooperativity and decreased hostility . Interestingly, the authors also note that marijuana may have been disinhibitory, such that the users were more willing to express their feelings. Similarly, an experimental study in a small group setting found that active marijuana changed the distribution of social activities by decreasing time spent in verbal exchanges while increasing time spent in co-actation, i.e. engaging in a shared activity, such as playing a game . Together, these studies indicate that active marijuana use can exert powerful effects on social interactions. They also imply that the effects likely depend on the dose, stressful stimuli, and the past experience of the user. Interestingly, the studies note differential effects on the emotional aspects of social experiences versus the skills needed for social interaction . Thus, the neurobiological consequences of marijuana likely involve a range of dissociable effects throughout the engagement and ongoing development of social interactions.The endocannabinoid system, a modulatory neurotransmitter system in the brain, mediates the effects of the psychoactive principle in marijuana, Δ9 -tetrahydrocannabinol. Work on the endocannabinoid system in social behavior remains limited and has emerged more recently. Meanwhile, indoor grow facility the more established roles of this system in anxiety, reward, pain, and cognition have hinted at overlapping functions in social behavior. The endocannabinoid system consists of lipid-derived messengers called ‘endocannabinoids’ whose actions in the brain are mainly, albeit not exclusively, mediated through CB1 cannabinoid receptors. A series of enzymes catalyze the biochemical synthesis and degradation of endocannabinoids to control their signaling activity . These cascades typically start with the release of particular lipid species from phospholipid membranes, involve multiple biochemical pathways, and end with recycling or the directing of products to alternative biochemical signaling cascades. The canonical route for the synthesis ofanandamide is thought to involve a calcium-dependent phospholipase D that releases anandamide by hydrolysis of the phospholipid precursor N-arachidonoylphosphatidylethanolamine . Whether and how other enzymes may be involved is unknown, making the molecular mechanisms and localization of anandamide signaling unclear. Anandamide is mainly degraded via intracellular hydrolysis by the enzyme fatty acid amide hydrolase . On the other hand, the mechanisms of synthesis for the other endocannabinoid 2-arachidonoyl-sn-glycerol are clearer. 2-AG is generated via the hydrolysis of the intracellular second messenger 1,2-diacylglycerol, by diacylglycerol lipase-α . DGL-α can be coupled to different receptors upstream for the activation of 2-AG by different signaling systems. For example, in glutamatergic neurons, DGL-α forms a complex with the type-1 metabotropic glutamate receptor, mGluR5, which is scaffolded by proteins such as Homer-1a .

The efficiency of coupling in this complex represents a molecular mechanism for signaling regulation. 2-AG is mainly degraded by the serine hydrolase monoacylglycerol lipase . Both anandamide and 2-AG are unconventional in the sense that they act in a retrograde manner to suppress presynaptic firing . In this regard, they can be thought of as ‘synaptic circuit breakers’ to curb incoming depolarization . In line with this idea, 2-AG, for example, is generated upon glutamate spillover to the ‘perisynaptic’ region rather than within the post-synaptic density proper – an anatomical feature that allows homeostatic negative feedback. This is not to say, however, that this type of synaptic feedback is the only mode of endocannabinoid signaling. Another unique property of the system is that endocannabinoids can also act as diffuse localmessengers. Anandamide possesses properties of a volume transmitter – diffusing and affecting multiple neighboring cells – whereas 2-AG is thought of more as a point-to-point synaptic transmitter. Accordingly, a third property is that, when the messengers work in concert, their temporal profiles may differ during signaling. For example, when an animal is subjected to acute stress, 2-AG increases within minutes in the periaqueductal grey whereas anandamide rises in the course of an hour to mediate stress-induced analgesia . In other situations, the directionality of changes in 2-AG and anandamide may actually be opposing. For example, non-contingent alcohol exposure increases extracellular 2-AG levels in the nucleus accumbens , but reduces anandamide . In the brain, 2-AG is almost 1000-fold more abundant than anandamide. Although this difference could in part be accounted for by structural, intermediary, or otherwise signaling-incompetent pools of 2-AG, the substantial difference nevertheless suggests dichotomous patterns for signaling action. A fourth unique property is that, as lipid mediators, the endocannabinoids are not stored in traditional vesicles but instead ‘demobilized’ in phospholipid membranes at baseline; they become ‘mobilized’ during signaling activity as they are synthesized ‘on-demand’. This means that molecular mechanisms for their recruitment must involve synthetic enzymes for mobilization. This necessity has implications for understanding interactions between systems, from the circuit down to the molecular level. If the synthetic machinery for anandamide is less well known, for example, it becomes difficult to dissect the molecular interactions needed for its recruitment. This can be seen in the contrast between several well-documented cases of 2-AG recruitment by various G-protein-coupled receptors versus only one for anandamide. G-protein-coupled receptors that recruit 2-AG include type-1metabotropic glutamate receptors , type 1/3 muscarinic acetylcholine receptors , and type-1 orexin receptors . D2 receptors in the dorsal striatum have been shown to recruit anandamide .The brain distribution of components of the endocannabinoid system is consistent with a role in social behavior. CB1 receptors are highly expressed in associational cortical regions of the frontal lobe and subcortical structures that underpin human social-emotional functioning . For example, the behavioral variant of frontotemporal dementia, characterized by indifference to social conflict and eccentric social conduct, involves neurodegeneration of regions in salience networks, including the anterior cingulate cortex, frontoinsular cortex, prefrontal cortex, striatum, and thalamus .

Our survey respondents, because of their large holdings, may be unusually exposed to cannabis growers physically because their larger properties may have more contact with cannabis growers. At the same time, these respondents might be better able to survive economically in a Humboldt County without cannabis. It is unclear if the experiences and perspectives of many Humboldt County smaller landowners would be similar to those of these large landowners. For many in Humboldt County, the impacts of cannabis production on property and the environment are a central concern. Respondents mentioned problems involving shared roads and fences, illegal garbage dumping and contamination, deforestation, fire hazards, feral dogs and impacts on wildlife and domestic livestock. One respondent wrote that “Growers leave a mess, steal water, tear up roads, let guard dogs damage neighbors’ property, including livestock, poison wildlife, increase soil erosion and threaten people.” In many ways, it seems that land ethics are at the center of the concerns that traditional agricultural producers harbor about the new wave of cannabis growers. Though respondents remarked on cannabis growing’s direct impacts on the environment, they also largely agreed that the cannabis industry is causing fewer young people to enter traditional farming careers — and that growers are taking over working lands. It is unknown if the rates at which successive generations stay in the family business are lower in Humboldt County than in rural communities less influenced by cannabis. For families who have managed and lived off these lands for decades — most of them for more than 50 years — these shifting stewardship ethics threaten their immediate environment as well as their very identity.

One of the more controversial questions in drug policy today is whether the trend toward legalizing marijuana for medicinal and adult recreational use could increase illicit marijuana use among young people . Since 1996, 28 U.S. states and the District of Columbia have legalized the production and sale of marijuana for medicinal use , clone rack and eight have legalized marijuana for adult recreational use. Medical marijuana laws could potentially increase the availability of marijuana and reduce perceptions of its harmfulness, leading more young people to try it. State medical marijuana laws include regulations that protect young people from illegally obtaining marijuana . But if these restrictions are not carefully enforced, young people could gain increased access to marijuana through the diversion of medical marijuana into illegal markets, which could also lower its price . Marijuana use by young people is associated with lasting detrimental changes in cognitive functioning of the developing brain, and poor educational and occupational outcomes . Use increases the risk of unintentional injuries and auto fatalities, mood and psychotic disorders, and drug dependence, especially when use is initiated at a young age . Long term marijuana smokers have a disproportionate burden of upper respiratory illnesses, including chronic bronchitis and some cancers, and an increased risk of cardiovascular disease . Medical marijuana producers and retailers are promoting new, more potent products such as oils often used as inhalants with tetrahydrocannabinol concentrations ranging from 40 to 70 percent . They are also developing new products that appeal to youth, such as packaged edibles and candies, that may increase the hazard of overdose due to their relatively slow rates of absorption and perceived intoxication .

These products could increase the risk of overdose in young people, who tend to be less experienced users with low tolerance levels. Studies have reported little evidence that medical marijuana laws increase marijuana use among young people , although well controlled studies consistently report increased consumption in adults . Using the Youth Risk Behavior Survey, researchers have compared high school students’ consumption before and after medical marijuana laws were enacted, finding no evidence of rising consumption on a national basis . A national study of 12–17 year old found that medical marijuana laws had no causal impact on consumption , as did a carefully controlled national study of 13–18 year olds . Wen et al. reported a five percent increase in the likelihood of trying marijuana among 12–20 year olds who dwell in states with medical marijuana laws, but the study was limited by the need to pool such a broad range of ages. Prior studies have ignored or been unable to detect age related variation in the impact of medical marijuana laws by pooling children aged 12–17, or even 12–24, or by studying particular age groups in isolation. Age-related variation is important to capture because young peoples’ access to marijuana and their developmental susceptibility to drug related harms differs by age . Most studies have focused on high school students who are likely to have greater access to marijuana and are more susceptible to social pressures than early adolescents . Meanwhile, young adults different substantially from these younger groups, both in terms of development and access to drugs, being in the peak years of engagement with psychoactive substances during the lifespan .

We performed multi-level, serial cross-sectional analyses on 10 annual waves of the U.S. National Survey on Drug Use and Health , from 2004 to 2013. Unlike many prior studies, ours included the key years of 2010–2013—a period of rapid acceleration in the number of states implementing medical marijuana laws , but before state recreational marijuana laws began implementation. In addition, our analyses compared young people across developmentally distinct age groups to account for important age-related heterogeneity in access to marijuana, in the propensity to experiment with psychoactive substances, and in the potential harms of marijuana use.We examined three dichotomous outcomes at the individual level: self-reports of the accessibility of marijuana, consumption of marijuana within the past month, and initiation or first-time use of marijuana during the past year. The NSDUH framing of the marijuana questions references smoking, edibles, and oils. Individual-level, age-appropriate predictors from the NSDUH dataset were included in the analysis. Across all three age groups, these included sex, race/ethnicity, family income, poor or fair health, and living in an urban area. We included an indicator of poor or fair health status to control for the possibility that participants in medical marijuana states might engage in the legal use of marijuana for health reasons. For early and late adolescents, we also controlled for parental monitoring and participation in group fights, variables that could be indicators of the protective factor of parental involvement and the risk factor of delinquent behavior, respectively. For young adults, additional controls included employment, college attendance, parental status, and marital status. These are strong protective factors mitigating against drug use in this age group . We augmented the NSDUH data with annually updated state-level data on medical marijuana laws and other relevant control variables. For state-level controls, we drew on publicly available sources such as Polidata , including per capita drug courts and whether or not marijuana possession had been decriminalized. We considered a wider range of state-level controls representing demographic, political and religious factors, and aspects of state drug control policies. For the sake of parsimony, we included controls that were most associated with outcome variables. Data collection on state medical marijuana laws included gathering all state statutes and subsequent regulations, 4×8 tray grow and validating information against publicly available data sources and through telephone calls with state officials. Throughout the study, we conducted regular updates to monitor changes in regulations and amendments to state laws . Analyses incorporated a dichotomous measure reflecting whether a state did or did not have a medical marijuana law enacted during any given year of observation. Thus, a law passed or enacted at any point in a calendar year would count that state as a medical marijuana state for that year’s analysis. We also examined a wide range of characteristics of state laws, such as the amounts of marijuana legally allowed for possession and home cultivation, medical conditions covered, and the number of dispensaries in each state. Through a systematic measurement process, we created and validated a scale capturing the capacity of a given medical marijuana law to control marijuana distribution and diversion into illegal markets .We concatenated 10 annual waves of the NSDUH and all state-level indicators into a single data file. We conducted all analyses using Stata version 13 . For descriptive analyses of each survey year, we used weights to adjust for sampling design effects and non-response ; similar weights were not available for multi-year analyses. Following Williams and others , we accounted for shared variance among participants within states by calculating standard errors clustered at the state level in our regression models.

Our analytic approach used logistic regression to predict marijuana consumption and initiation at the individual level separately for early adolescents, late adolescents, and young adults. A key analytic concern is that people in states that pass medical marijuana laws hold more permissive views about the drug . These more positive perceptions about marijuana may drive both the passage of the medical marijuana laws and higher rates of consumption . We incorporated that possibility into our uncontrolled comparisons of young people who dwell in states with medical marijuana laws compared to those who do not . By controlling for state-level fixed effects , we were able to examine whether medical marijuana laws have distinct causal impacts on marijuana consumption and initiation . The coefficients for each state controlled for any state-specific confounding not already captured by other control variables in the models. This technique allowed us to rule out the possibility that unobserved state-level confounders account for any associations found between state medical marijuana laws and young people’s consumption and initiation of use.Using the most recent NSDUH survey, 2013, we compared rates of access to marijuana, past-month marijuana use and past-year initiation across early and late adolescent youth and young adults. Table 1 shows pronounced differences in the populations of young people living in states with medical marijuana laws compared with those who were not. These demographic differences—especially ones associated with drug-related attitudes—underscore the importance of applying individual-level controls in the analysis. For example, in 2013, individuals living in medical marijuana states were disproportionately white and Hispanic. Young adults living in medical marijuana states were comparatively less likely to be married and to have children. Figure 1 shows a positive age gradient in rates of reporting that marijuana is easily accessible and in past-month marijuana use: The highest prevalence occurred among young adults at 19.1%, then 11.9% of late adolescents and 2.2% of early adolescents . In contrast, initiation of marijuana use in the past year was most common among late adolescents , with young adults the next most likely to initiate marijuana use and early adolescents the least likely to have tried marijuana for the first time in the past year .Table 2 shows logistic regression models predicting past month marijuana consumption that include all individual and state-level controls, and annual and state-level fixed effects. Results provided no evidence of a causal relationship between living in a state where medical marijuana was legal and the past month use of marijuana. Across all age groups, the odds ratio associated with medical marijuana state residence was not statistically significant. Table 3 provides similar fully controlled results for logistic regression analyses predicting past-year initiation of marijuana use. Results show that young adults dwelling in states that have legalized medical marijuana are significantly more likely to initiate marijuana use than counterparts in non-medical marijuana states . Such a relationship is not evident for early or late adolescents . We performed additional analyses to rule out several alternate explanations of these findings. Incorporating the amount of time since the passage of the medical marijuana law into our models produced similar results regardless of duration of the law. To rule out the possibility that young adults are more likely to initiate marijuana use due to mental health conditions, which in some states are legally allowed indications for a medical marijuana prescription, we estimated an alternate version of the models that included additional mental health-related variables, specifically, past-year use of mental health treatment and past-year unmet need for mental health treatment. After introduction of these additional controls, the effect of living in medical marijuana state remained statistically significant for young adults . We also considered the possibility that states with less restrictive medical marijuana laws could have more significant impacts on young people.

We examined data from an on-going longitudinal natural experiment , which has followed a cohort of mostly Latinx students starting at the beginning of 9th grade through age 23 currently. Racial/ethnic and socioeconomic disparities exist in youth outcomes and comparative studies suggest that ethnicity may influence perception of school climate. Furthermore, within the Latinx experience there exists a diversity of national and indigenous heritages, degrees of acculturation and integration, and immigration stories. While ethnicity was not the focus of this analysis, we believe study of this predominantly Latinx population makes a valuable contribution to the extant literature. Additionally, RISE-UP examined a range of school climate characteristics, including school safety and order, student-teacher relationships and support, and disciplinary style. We examined a variety of different adolescent behaviors , and academic outcomes , so as to permit a more holistic analysis that incorporates health and behavior with cognitive development.This is a secondary analysis of data from the RISE-UP study, a longitudinal natural experiment designed to assess the effects of high-performing high schools on health behaviors among low-income, minority adolescents in Los Angeles. Five high-performing charter high schools were selected based on: enrollment of predominantly economically disadvantaged students , academic performance in the top tertile of public schools in Los Angeles County based on 2012 Academic Performance Index derived from standardized test scores, and use of an admissions lottery. Eighth grade students who were applying for 9th grade admission into high school were randomly sampled from the admissions lottery list of “winners” and “losers” during two consecutive years in the spring before entry into high school . To be eligible, plants rack students had to speak English or Spanish fluently and reside in Los Angeles County. Of 1509 eligible students, 1270 were enrolled and consented to participate in the study .

Further details of the original study are published elsewhere. 11 The institutional review board of the RAND Corporation and the University of California Los Angeles approved this study. Written parental consent and student assent were obtained from all participants.Participants completed a baseline, face-to-face, computer-assisted survey from March of 8th grade through November of 9th . Similar follow-up surveys were conducted in the spring semester of 10th grade and 11th grade . Interviews were conducted in the patient’s primary language with the aid of bilingual research assistants and in a sufficiently private location of the participant’s choice. A computer-assisted self-interview was used to minimize social desirability bias for potentially sensitive topics related to substance use and sexual and delinquent behaviors. A total of 1159 students completed the survey in 10th grade and 1114 students completed the survey in 11th grade for an 87.8% retention rate through 11th grade.At each survey, students reported their frequency of alcohol and cannabis use in the last 30 days, dichotomized . Students also completed an alcohol misuse scale and a cannabis misuse scale , which assessed high risk substance use behaviors and its negative consequences . Scale items were dichotomized and summed to produce a total score with higher scores representing greater misuse characteristics. Students reported on delinquent behaviors that are associated with negative life outcomes using the delinquent behavior index from the National Longitudinal Study of Adolescent to Adult Health and included: painting graffiti, damaging someone else’s property, shoplifting or stealing, running away from home, driving a car without the owner’s permission, burglary, armed robbery, selling illicit drugs, participation in a gang in the last year, and having ever participated in a gang fight. The score was dichotomized . Students were asked if they carried a weapon such as a real gun or knife in the last 30 days and if they had been in a physical fight in the last 12 months. These questions were combined into one dichotomous variable of any of the two behaviors .

Students responded to several questions about high-risk sex behaviors including not using contraception, ever becoming pregnant, and having multiple sexual partners . Students also answered two questions about bullying at school in the last 12 months, which were dichotomized: 1)whether someone had bullied or picked on them and 2) whether they themselves had bullied or picked on someone . We also collected information on several academic outcomes. For truancy, students reported if they had cut or skipped classes in the last 12 months, dichotomized . Students also responded whether they transferred to another school for any reason in the last academic year, dichotomized . We obtained student grade point average from official school transcript records . We used self-reported GPA when we could not obtain school transcripts . We obtained standardized test scores for each student for 8th grade and 11th grade from the California Department of Education. Math and English proficiency were determined by the California Standardized Testing and Reporting Program and the California Assessment of Student Performance and Progress . We compared those who failed to meet 11th grade standard versus those who were proficient or above. We obtained data on college matriculation into a 4-year college from the National Student Clearinghouse, a nonprofit organization providing enrollment and degree-verification services to colleges and universities. These data were obtained on 10/30/2019 corresponding to about 1.5-2.5 years after the end of 12th grade.In the 10th and 11th grade surveys, students were asked about several aspects of their school environment. These school climate measures are not comprehensive but chosen to represent a diversity of school climate domains. School order refers to the amount of confusion and chaos in the classroom34 and was assessed using a scale based on the Confusion, Hubbub, and Order Scale developed by Matheny and colleagues.

We analyzed the measure as school order, the inverse of school chaos, so that higher scores indicated a more positive schoolclimate . School safety was assessed using the Chicago Consortium on School Research Student Perceptions of Safety Scale, a 4-item measure of self reported safety in and around school . Using a modified questionnaire from the annual survey of Chicago public schools students reported perceptions of teacher-student relationships on a four point scale from strongly disagree to strongly agree. We simply summated the responses to three questions into a single variable representing perceived teacher respect for students . We combined three additional questions into a second variable representing teacher support for college . School disciplinary style was assessed according to student ratings of school support and structure as previously described. These two rating scales were categorized into tertiles, and then combined to create a single perceived school disciplinary style variable with five categories: authoritative , authoritarian , permissive , neglectful , and average .We conducted linear and logistic regression analyses to examine the relationship between each school climate variable and each adolescent health, behavioral, and academic outcome separately. For these analyses, the continuous school climate variables were standardized so that a 1-point change in each scale equaled one standard deviation. All models were adjusted for student gender, Latinx ethnicity, USA birthplace, native English language, parental birthplace, plant growing trays parental employment, parental education and parenting style. In each model, we also controlled for the outcome measures at baseline . For models examining GPA and standardized test scores, we controlled for these outcomes from middle school. All models used generalized estimating equations with a random effect for school to adjust for clustering of outcomes at the school-level. The analyses were restricted to the sample of respondents who completed baseline, 10th grade and 11th grade surveys. Among this analytic sample, values were missing for 2.3% or less of the sample for any single measure. 5.1% of the sample were missing data for the 8th grade standardized test scores, 11.7% were missing 11th grade standardized test scores, 2.3% were missing transcript and self-reported GPA from middle school, and 0.3% were missing transcript and self reported GPA from high school. Missing values were multiply imputed using 100 replicates so as to maximize the use of available data across a large number of variables. Sensitivity analyses using list wise deletion produced similar results. STATA 14.0 was used for all analyses. The original RISE-Up sample was comprised of 1270 students at baseline , 91% of whom completed the 10th grade survey. This study was limited to the 1114 students who completed the baseline through 11th grade surveys. Table 1 summarizes student and parental demographic characteristics. Just under half of the sample were males , 90% were Latinx, 87% were born in the USA, and 40% were native English speakers. One-quarter of students reported having at least one parent born in the USA, 89% had one or more parent working full-time, and 52% had one or more parents graduate from high school. Compared to those in the analytic sample, subjects who were lost to follow up before the 11th grade survey were more likely to be male , white , native English speaker , and have at least 1 parent born in the USA . Those who were lost to follow up were less likely to have at least 1 parent working full-time .

There were no differences between the analytic sample and those lost to follow-up in parental education, birth in the USA, and parenting style.A minority of the sample reported engaging in risky behaviors . At 11th grade, 15% reported using alcohol and 11% reported using cannabis in the last 30 days. One-fifth reported engaging in one or more alcohol misuse behaviors in the past year,such as drinking alcohol at school, getting into trouble because of alcohol, or missing school because of alcohol use . Sixteen percent reported engaging in similar cannabis misuse behaviors . One-fifth of the sample also reported engaging in one or more delinquent behaviors in the last year such as stealing, graffiti, selling drugs or being in a gang. One in eight students reported either carrying a weapon in the last 30 days or being in a physical fight in the last 12 months. Approximately 9% of students reported engaging in high-risk sex. Nearly one in five reported being the victim of bullying and 15% reported bullying others in the last 12 months. Among respondents, 22% of students reported being truant. From the start of 9th grade to the time of 11th grade survey, 23% of students reported changing schools at least once. Mean high school GPA was 2.83 , 35% and 71% of students were proficient in Math and English on 11th grade standardized tests respectively, and 43% matriculated at a 4-year college after high school.In the process of displacing millions of adolescents from school settings across the nation and the world, SARS-CoV-2 has reminded parents and policymakers alike of the irreplaceable role schools have in adolescent growth and health. It has also reinvigorated interest in the importance of the social climate that each school cultivates. These findings add longitudinal evidence that student reported metrics of school climate – including an orderly environment, teacher-student relationships, and disciplinary style – are important upstream predictors of both health and academic outcomes in subsequent years. Departing from the current literature that tends to isolate one or two school climate variables and outcomes, this analysis took a comprehensive approach in analyzing the longitudinal relationship between multiple school climate variables and an array of both health and academic outcomes. This permits a more holistic analysis that better captures the effect of a multifaceted school climate not just on cognitive development but also on health and behavioral development. While perceptions of school order and teacher respect for students were protective for nearly all risky behaviors, perceptions of safety were surprisingly only associated with less bullying. This supports some researchers’ assertion that, except in the case of bullying, school safety is only inconsistently protective for many outcomes. Similarly, perceptions of order and teacher respect for students was beneficial for a number of academic outcomes. Yet surprisingly, teacher support for college was not linked to any of our objective academic outcomes. Other authors have attributed such divergences to variations in measurement, however, as yet unidentified modifiers such as cultural norms cannot be excluded. Aligning well with the literature, neglectful disciplinary style was associated with several serious and concerning behaviors as well as poor academic outcomes . In contrast, permissive disciplinary style was strongly predictive of English proficiency and college matriculation, providing additional evidence for the critical role of teacher support. 

The information given by the total ion chromatogram is limited because of its complexity caused by co-elution and background noise. Instead, single ion chromatograms of different mass to charge ratios were extracted for isolating peaks ofindividual compounds, while avoiding co-elution and background noise. The stacked SIC of different thermal degradation compounds or aerosolized components from vaping aerosols of a) pure VEA, b) the mixture of VEA and extracted THC oil, c) extracted THC oil were shown in Figure 4.2. The black line represents the carbonyl/acids that can be generated from the thermal degradation of both VEA and extracted THC oil, the blue line represents the carbonyls/acids only from the thermal degradation of VEA, while the magenta line represents the thermal degradation carbonyls as well as cannabinoids only from the vaping aerosol of THC oil. Although the SICs were able to separate the co-eluting peaks with different m/z ratios, the isomers having identical m/z with similar structures and polarity are still not able to be separated or clearly identified. For example, multiple peaks are observed for C6H12O in the vaping aerosol of mixture of VEA and THC oil . While hexanal can be formed from terpenes, 97 4-methylpentanal could be formed from the thermal degradation VEA according to the proposed thermal degradation pathway in Scheme 4.1. However, since C6H12O is highly enhanced in the VEA aerosol , we assign the majority of this emission to 4-methylpentanal.Over 30 thermal degradation products from VEA were identified. All of the reported thermal degradation products from VEA are carbonyls/acids in this work, weed drying room consistent with other accounts. Riordan-Short et al. also identified several esters and alkanes with GC-MS .

While around 10 carbonyls and acids are identified both by us and Riordan-Short et al., carbonyls with VEA-specific structures have only been identified in this work. The lack of standard spectra for these VEA-derived compounds in GC-MS libraries may have prevented identification of peaks in the chromatograms of Riordan-Short et al.Moreover, some carbonyls identified by Riordan-Short et al. were not found in this work . The cause for discrepancy is unknown; however, we hypothesize it may be partially due to the difference in vaporization method .Many smaller thermal degradation carbonyls and acids appear to be formed by oxidation and bond cleavage of the aliphatic side-chain of VEA. The bond cleavage pathways for VEA degradation is proposed in Scheme 4.1. A proposed radical reaction mechanism is shown in Scheme 4.2. The thermal degradation reaction is initiated by H-abstraction by radicals such as OH, followed by the rapid reaction with O2 to form peroxy radicals . The peroxy radical can react with other RO2 to form carbonyls or alkoxy radicals. Alkoxy radicals may further react to form carbonyls , alcohols , and possibly alkenes . The primary thermaldegradation products may go through further oxidation steps and form more thermal degradation products . These RO2 –based mechanisms have been well studied and shown to be important in various chemical systems, like the atmosphere, biological redox, or fuel combustion. The relative peak intensity of carbonyls in Figure 4.2a support the proposed radical reaction mechanism in Scheme 4.2, since the most abundant peaks represented the formation of benzylic radical and tertiary radical formed in the first H-abstraction step which can be stabilized by the conjugation effect from benzene ring and positive hyper-conjugation effect from the adjacent C-H bonds. The proposed thermal degradation pathway is also supported by the detection of alkanes, including 2,6-dimethyl-1-heptene and 1-pristene, by Riordan-Short et al. and Mikheev et al., since these alkanes are generated in the proposed mechanism. Thus, our observations suggest that the C-C single bonds on the side-chain of VEA is easily oxidized and cleaved during the vaping process, which will cause the formation of a series of carbonyls that has VEA-specific structure, and also alkenes and alcohols.

These primary products may go through further thermal degradation process to generate secondary thermal degradation products like acids and dicarbonyls. Regarding products like duroquinone, durohydroquinone and ketene that have been identified previously by vaping or heating VEA we could not identified ketene as the it will form the same adduct molecular structure as acetic acid when reacting with 2,4-DNPH. We did not observe duroquinone for unknown reasons, possibly due to the difference in sample collection and methods of detection. Figure 4.2c shows the stacked SIC of vaping aerosol of THC oil. Besides thermal degradation carbonyl compounds, a large variety cannabinoids was also identified by HPLC-HRMS, since the phenolic hydroxyl group in cannabinoid structure is slightly acidic and can also be deprotonated in the negative mode of ESI. The thermal degradations products identified in the vaping aerosol of extracted THC oil may not only generated by THC, but can also from the thermal degradation of other cannabinoids, such as cannabinol , cannabidiol , cannabichromene , cannabigerol and corresponding acid , which have also been identified in the unvaped extracted THC oil. The mechanism of the production of carbonyls identified in the vaping aerosol of extracted THC oil may also involve the oxidation of the aliphatic side-chain followed by bond cleavage, since the main cannabinoids also have the side-chains with 5 carbons.Moreover, CBG may be the source of certain carbonyl products since it has a second side-chain with unsaturated bonds ; the specific mechanism is shown in Scheme 4.4. In contrast to VEA, the oxidation of CBG by OH proceeds through addition to the double bonds in the side chain instead of H-abstraction, consistent with the oxidation of other alkenes. The mechanism for the following steps are similar to the H-abstraction route.

The oxidation may also occur on the six member ring of cannabinoids such as THC can occur through pathways proposed in Scheme 4.3b and Scheme 4.5. For example, OH-initiated H-abstraction on THC can occur at the allylic site and OH-addition can occur at the endocyclic C=C, preferentially forming the tertiary alkyl radical. Then peroxy radical chemistry occurs through similar pathways as VEA, finally generating alcohols and potentially epoxides. Multiple SIC peaks are found at the m/z representing oxidized products of cannabinoids, suggesting a lot of different isomers exist. Our identification results are similar to those of Carbone et al., who utilized NMR for identification. Carbone et al. indicated peroxide products may also be formed during the oxidation process, a mechanism not shown in our schemes but would be consistent with RO2 chemistry. The oxidation products shown in Scheme 4.3b have the same number of carbons as THC; however, some thermal degradation products with different carbon numbers were also identified and are hard to trace back to precursor compounds. It is possible they may already exist in the original unvaped THC oil. Borille et al. found cannabinoid compounds or metabolites and 8 non-cannabinoid constituents in the extracts of cannabis plants by ESI-MS, with carbon number of cannabinoids range from C15 to C55. All molecular formulas of the THC oxidation products shown in Scheme 4.3b were also identified in cannabis extracts, suggesting that these components may already exist in the cannabis plant, and that oxidation from plant metabolism or during extraction couldhave occurred in addition to vaping. Moreover, the C19H28O3 has been identified as Cannabiglendol-C3 ; and there exist many possibilities for C23H34O4 ; C15H16O3/C15H18O3 had been identified as cannabispirenone/ cannabispiran. Some compounds in Table 4.1 still remains unidentified .Besides the oxidation products from vaping THC oil, for which the oxidation mechanism is described in Scheme 4.3, there remains unexplained formation pathway for the generation of some thermal degradation products . Couch et al. found the risk of exposure to VOC including diacetyl and 2,3-pentanedione during the decarboxylation and grinding process of dried cannabis material, but there is no clear mechanism given for their formation. The generation of these compounds may due to the thermal degradation of terpenes and terpenoids. Since there is still over 50% mass in the unvaped THC oil that remains uncharacterized, drying rack for weed it is likely that a portion of that mass are terpenes. Meehan-Atrash et al. identified degradation products from myrcene, limonene and linalool, including methacrolein, hydroxyacetone, methyl vinyl ketone. Tang et al. found 11 thermal degradation products from mixture of terpenoids, 7 of them are carbonyls including formaldehyde, acetaldehyde, acetone, acrolein, methacrolein, valeraldehyde and hexanal. These findings are consistent with the identification results in this work, illustrating that the extracted THC oil is a complex mixture, the complexity of which increases with thermal degradation chemistry. Further research on individual components is still needed for a better understanding on the whole picture of thermal degradation. For the mixture of THC oil and VEA, it is clear from the stacked SIC that the peaks shown in the chromatograph are mainly from aerosolization products of vaped THC oil instead of VEA. It is clear that the total signal from aerosolization products of the mixture is between that of vaping pure VEA and THC oil. Moreover, the oxidation of THC may also be suppressed by adding of VEA. While the signal ratio of cannabinoids in vaping aerosol of the mixture compared to unvaped THC oil is 0.34, the same ratio for oxidated cannabinoids in Scheme 4.3b is 0.22 .

THC was shown tohave a stronger tendency to degrade compared to VEA, since the boiling point for THC is 157 ˚C, while VEA start to decay at 240 ˚C without boiling. Table 4.2 shows the particle mass collected on the glass fiber filter at three temperatures and various e-liquid composition. It is clear that increasing temperature will increase the particle mass on the filter, which is consistent with expectations. However, the particle mass production is non-linearly suppressed with the addition of VEA compared to THC oil at the same temperature. The reason might be the formation of non-ideal solution with significant intermolecular interactions when VEA is added to the THC oil, as Lanzarotta et al.  had found that hydrogen bonding exists between the molecules of VEA and THC. Given the fact that THC has a much higher aerosolization rate compared to VEA , the cartridge may be enriched in VEA since vaping continues until it is 100% VEA. In order to figure out the influence of VEA to the formation of carbonyls, it is informative to normalize the mass of carbonyls by the particle mass collected at the same temperature . While e-cigarette users who used nicotine products will self-titrate nicotine intake in daily use, there is also evidence that people who use higher potency cannabis for recreational purpose can titrate their THC dose. Figure 4.4 shows the normalized mass of 9 thermal degradation carbonyl compounds by particle mass produced from vaping VEA, THC oil and their mixture at 455 ± 10 °F . Within the C4 – C6 carbonyls shown in Figure 4.4, butyraldehyde, valeraldehyde, hexanal are thought to be from the thermal degradation of cannabinoids and terpenes , supported by Tang et al., while isobutyraldehyde, isovaleraldehyde and 4-methylpentanal are from the thermal degradation of VEA , supported by RiordanShort et al. Since some isomers can’t be separated in this work, we discuss the pair of isomers together. From the normalized carbonyl concentration, it is clear that certain carbonyls such as formaldehyde, hexanal/4-methylpentanal, glyoxal, diacetyl/3-oxobutanal are produced in much higher abundance from VEA compared to extracted THC oil. Although some products like formaldehyde can be produced from both VEA and THC, the production of formaldehyde from VEA is more favorable since it involves a tertiary radical intermediate in the first step , which is more stable than the secondary radicals formed from the side-chain of THC. The proposed chemistry is, thus, consistent with higher formaldehyde formation by VEA. The same explanation can also apply to the generation of 4-methylpentanal, which only comes from VEA and thus likely dominates the distribution of the isomer pair over hexanal. The formation of glyoxal, diacetyl and 3-oxobutanal from VEA likewise may be enhanced compared to THC due to higher stability of radical intermediates. Diacetyl is thought to be byproducts of cannabis plants,61 and there is no clear indication of formation of diacetyl from VEA . The formation of its isomer 3-oxobutanal can be expected from VEA, however. The corresponding SIC of diacetyl shows that multiple peaks exists in the vaping aerosol of extracted THC oil, but only one peak shown in the vaping aerosol of pure VEA, suggesting that cannabinoids and terpenes may generate multiple isomers which have the same m/z as diacetyl, but VEA propably generates only 3-oxobutanal.