Genetic diversity of the Eurasian Otter (Lutra lutra) population in Israel.

The Israeli population of Eurasian otter (Lutra lutra) marks the Palearctic southern boundary of the species' distribution in the Levant. During the 20th century, the otter population in Israel experienced a dramatic decline due to anthropogenic habitat alterations. Currently, the otter population in Israel is estimated at about 100 individuals and defined as "Critically Endangered". The aim of this research was to characterize the Israeli otter population in order to determine its genetic diversity and fragmentation state for conservation purposes. Monitoring spraint sites during 2000-2011 along active and historic otter distribution regions indicate both stable and unstable otter subpopulations, mainly along the Jordan River. Four otter subpopulations, representing 57 individuals, were characterized by 12 microsatellites, previously used to characterize the European otter populations. The genetic results indicated three subpopulations correlating with three geographical regions: the Hula Valley, Sea of Galilee, and the Harod Valley. A moderate genetic diversity (F (st) = 0.087-0.123) was found among the subpopulations, suggesting sporadic interactions between individuals from distinct geographical locations along the Jordan Rift Valley. The Israeli otter population was found to be very small, demographically remote and genetically distinct, harboring unique alleles absent from the studied European populations. Therefore, immediate conservation actions are recommended to prevent the deterioration of the isolated, unique, and critically endangered otter population in Israel.

B.1.1.7 Variant Outbreak in an Air Force Military Base-Real-World Data.

OBJECTIVE: To assess the clinical features and infectivity of variant B.1.1.7 among healthy young adults in a military setting. MATERIALS AND METHODS: Positive cases of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in a single military base (March 23, 2020 and February 16, 2021) were included. An epidemiological investigation conducted via phone included questions regarding symptoms, exposure history, smoking status, list of contacts, and recently visited places. Symptoms surveyed included fever, cough, shortness of breath, sore throat, loss of smell or taste, gastrointestinal symptoms (GI), headache, chest pain, and constitutional symptoms. Cases were divided before B.1.1.7 first reported case in Israel (December 23, 2020) (period 1) and after its identification (period 2). Symptom distribution and the risk of a contact to be infected were compared between the periods, using a chi-square test, and a negative binominal regression model, respectively. RESULTS: Of 293 confirmed cases, 89 were reported in the first period and 204 in the second. 56.0% were men with a median age of 19.5 years (interquartile range 18.6-20.5). GI symptoms, loss of taste or smell, headache, fever, and chills were more prevalent in the first period (P < .001, P = .026, P = .034, P = .001, and P < .001, respectively), while fatigue was more common in the second period (P = .008). The risk of a contact to be infected was three times higher in the second period (relative risk = 3.562 [2.414-5.258]). CONCLUSION: An outbreak of SARS-CoV-2 in young healthy adults, during a period with high national-wide B.1.1.7 variant prevalence, is characterized by decreased prevalence of fever, loss of taste or smell and GI symptoms, increased reports of fatigue, and more infected contacts for each index case.

Phenotypic correlates of the working dog microbiome.

Dogs have a key role in law enforcement and military work, and research with the goal of improving working dog performance is ongoing. While there have been intriguing studies from lab animal models showing a potential connection between the gut microbiome and behavior or mental health there is a dearth of studies investigating the microbiome-behavior relationship in working dogs. The overall objective of this study was to characterize the microbiota of working dogs and to determine if the composition of the microbiota is associated with behavioral and performance outcomes. Freshly passed stools from each working canine (Total n = 134) were collected and subject to shotgun metagenomic sequencing using Illumina technology. Behavior, performance, and demographic metadata were collected. Descriptive statistics and prediction models of behavioral/phenotypic outcomes using gradient boosting classification based on Xgboost were used to study associations between the microbiome and outcomes. Regarding machine learning methodology, only microbiome features were used for training and predictors were estimated in cross-validation. Microbiome markers were statistically associated with motivation, aggression, cowardice/hesitation, sociability, obedience to one trainer vs many, and body condition score (BCS). When prediction models were developed based on machine learning, moderate predictive power was observed for motivation, sociability, and gastrointestinal issues. Findings from this study suggest potential gut microbiome markers of performance and could potentially advance care for working canines.