In the last decade, there has been a marked increase in opioid-related human deaths in the U.S. However, the effects of the growth in opioid use on vulnerable populations, such as pet dogs, are largely unknown. The objective of this study was to investigate potential risk factors at the dog, county, and state-levels that contributed to accidental dog opioid poisonings. Dog demographic information was collected during calls to the Animal Poison Control Center (APCC), operated by the American Society for the Prevention of Cruelty to Animals, about pet dog exposures to poisons from 2006-2014. Data concerning state-level opioidrelated human death rates and county-level human opioid prescription rates were collected from databases accessed from the Centers for Disease Control and Prevention. A multilevel logistic regression model with random intercepts for county and state was fitted to explore associations between the odds of a call to the APCC being related to dog opioid poisonings with the following independent variables: sex, weight, age, reproductive status, breed class, year, source of calls, county-level human opioid prescription rate, and state-level opioid human death rate. There was a significant non-linear positive association between accidental opioid dog poisoning calls and county-level human opioid prescription rates. Similarly, the odds of a call being related to an opioid poisoning significantly declined over the study period. Depending on the breed class, the odds of a call being related to an opioid poisoning event were generally lower for older and heavier dogs. The odds of a call being related to an opioid poisoning were significantly higher for intact compared to neutered dogs, and if the call was made by a veterinarian compared to a member of the public. Veterinarians responding to poisonings may benefit from knowledge of trends in the use and abuse of both legal and illegal drugs in human populations.

Detailed phylogenetic and comparative genomic analyses are reported on 140 genome sequenced cyanobacteria with the main focus on the heterocyst-differentiating cyanobacteria. In a phylogenetic tree for cyanobacteria based upon concatenated sequences for 32 conserved proteins, the available cyanobacteria formed 8-9 strongly supported clades at the highest level, which may correspond to the higher taxonomic clades of this phylum. One of these clades contained all heterocystous cyanobacteria; within this clade, the members exhibiting either true (Nostocales) or false (Stigonematales) branching of filaments were intermixed indicating that the division of the heterocysts-forming cyanobacteria into these two groups is not supported by phylogenetic considerations. However, in both the protein tree as well as in the 16S rRNA gene tree, the akinete-forming heterocystous cyanobacteria formed a distinct clade. Within this clade, the members which differentiate into hormogonia or those which lack this ability were also separated into distinct groups. A novel molecular signature identified in this work that is uniquely shared by the akinete-forming heterocystous cyanobacteria provides further evidence that the members of this group are specifically related and they shared a common ancestor exclusive of the other cyanobacteria. Detailed comparative analyses on protein sequences from the genomes of heterocystous cyanobacteria reported here have also identified eight conserved signature indels (CSIs) in proteins involved in a broad range of functions, and three conserved signature proteins, that are either uniquely or mainly found in all heterocysts-forming cyanobacteria, but generally not found in other cyanobacteria. These molecular markers provide novel means for the identification of heterocystous cyanobacteria, and they provide evidence of their monophyletic origin. Additionally, this work has also identified seven CSIs in other proteins which in addition to the heterocystous cyanobacteria are uniquely shared by two smaller clades of cyanobacteria, which form the successive outgroups of the clade comprising of the heterocystous cyanobacteria in the protein trees. Based upon their close relationship to the heterocystous cyanobacteria, the members of these clades are indicated to be the closest relatives of the heterocysts-forming cyanobacteria.

With current trends in cannabis legalization, large efforts are being made to understand the effects of less restricted legislation on human consumption, health, and abuse of these products. Little is known about the effects of cannabis legalization and increased cannabis use on vulnerable populations, such as dogs. The objective of this study was to examine the effects of different state-level cannabis legislation, county-level socioeconomic factors, and dog-level characteristics on dog cannabis poisoning reports to an animal poison control center (APCC). Data were obtained concerning reports of dog poisoning events, county characteristics, and state cannabis legislation from the American Society for the Prevention of Cruelty to Animals’ (ASPCA) APCC, the US Census Bureau, and various public policy-oriented and government websites, respectively. A multilevel logistic regression model with random intercepts for county and state was fitted to investigate the associations between the odds of a call to the APCC being related to a dog being poisoned by a cannabis product and the following types of variables: dog characteristics, county-level socioeconomic characteristics, and the type of state-level cannabis legislation. There were significantly higher odds of a call being related to cannabis in states with lower penalties for cannabis use and possession. The odds of these calls were higher in counties with higher income variability, higher percentage of urban population, and among smaller, male, and intact dogs. These calls increased throughout the study period (2009–2014). Reporting of cannabis poisonings were more likely to come from veterinarians than dog owners. Reported dog poisonings due to cannabis appear to be influenced by dog-level and community-level factors. This study may increase awareness to the public, public health, and veterinary communities of the effects of recreational drug use on dog populations. This study highlights the need to educate dog owners about safeguarding cannabis products from vulnerable populations.

Evolutionary relationships amongst Chlorobia and Ignavibacteria species/strains were examined using phylogenomic and comparative analyses of genome sequences. In a phylogenomic tree based on 282 conserved proteins, the named Chlorobia species formed a monophyletic clade containing two distinct subclades. One clade, encompassing the genera Chlorobaculum, Chlorobium, Pelodictyon, and Prosthecochloris, corresponds to the family Chlorobiaceae, whereas another clade, harboring Chloroherpeton thalassium, Candidatus Thermochlorobacter aerophilum, Candidatus Thermochlorobacteriaceae bacterium GBChlB, and Chlorobium sp. 445, is now proposed as a new family (Chloroherpetonaceae fam. nov). In parallel, our comparative genomic analyses have identified 47 conserved signature indels (CSIs) in diverse proteins that are exclusively present in members of the class Chlorobia or its two families, providing reliable means for identification. Two known Ignavibacteria species in our phylogenomic tree are found to group within a larger clade containing several Candidatus species and uncultured Chlorobi strains. A CSI in the SecY protein is uniquely shared by the species/strains from this “larger Ignavibacteria clade”. Two additional CSIs, which are commonly shared by Chlorobia species and the “larger Ignavibacteria clade”, support a specific relationship between these two groups. The newly identified molecular markers provide novel tools for genetic and biochemical studies and identification of these organisms.

Researchers have begun studying the impact of human opioid and cannabinoid use on dog populations. These studies have used data from an animal poison control center (APCC) and there are concerns that due to the illicit nature and social stigma concerning the use of these drugs, owners may not always be forthcoming with veterinarians or APCC staff regarding pet exposures to these toxicants. As a result, models derived from APCC data that examine the predictability of opioid and cannabinoid dog poisonings using pet demographic and health disorder information may help veterinarians or APCC staff more reliably identify these toxicants when examining or responding to a call concerning a dog poisoned by an unknown toxicant. The fitting of epidemiologically informed statistical models has been useful for identifying factors associated with various health conditions and as predictive tools. However, machine learning, including lasso regression, has many useful features as predictive tools, including the ability to incorporate large numbers of independent variables. Consequently, the objectives of our study were: 1) identify pet demographic and health disorders associated with opioid and cannabinoid dog poisonings using ordinary and mixed logistic regression models; and 2) compare the predictive performance of these models to analogous lasso logistic regression models. Data were obtained from reports of dog poisoning events collected by the American Society for the Prevention of Cruelty to Animals’ (ASPCA) Animal Poisoning Control Center, from 2005–2014. We used ordinary and mixed logistic regression models as well as lasso logistic regression models with and without controlling for autocorrelation at the state level to train our models on half the dataset and test their predictive performance on the remainder. Although epidemiologically informed logistic regression models may require substantial knowledge of the disease systems being investigated, they had the same predictive abilities as lasso logistic regression models. All models had relatively high predictive parameters except for positive predictive values, due to the rare nature of calls concerning opioid and cannabinoid poisonings. Ordinary and mixed logistic regression models were also substantially more parsimonious than their lasso equivalents while still allowing for the epidemiological interpretation of model coefficients. Controlling for autocorrelation had little effect on the predictive performance of all models, but it did reduce the number of variables included in lasso models. Several disorder variables were associated with opioid and cannabinoid calls that were consistent with the acute effects of these toxicants. These models may help build diagnostic evidence concerning dog exposure to opioids and cannabinoids, saving time and resources when investigating these cases.

While a substantial amount of research has focused on the abuse of opioids and cannabinoids in human populations, few studies have investigated accidental poisoning events in pet populations. The objective of this study was to identify whether poisoning events involving opioids and cannabinoids clustered in space, time, and space-time, and compare the locations of clusters between the two toxicants. Data were obtained concerning reports of dog poisoning events from the American Society for the Prevention of Cruelty to Animals’ (ASPCA) Animal Poisoning Control Center (APCC), from 2005–2014. The spatial scan statistic was used to identify clusters with a high proportion of these poisoning events. Our analyses show that opioid and cannabinoid poisoning events clustered in space, time, and space-time. The cluster patterns identified for each toxicant were distinct, but both shared some similarities with human use data. This study may help increase awareness to the public, public health, and veterinary communities about where and when dogs were most affected by opioid and cannabinoid poisonings. This study highlights the need to educate dog owners about safeguarding opioid and cannabinoid products from vulnerable populations.