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Obtaining robust estimates of population abundance is a central challenge hindering the conservation and management of many threatened and exploited species. Close-kin mark-recapture (CKMR) is a genetics-based approach that has strong potential to improve the monitoring of data-limited species by enabling estimates of abundance, survival, and other parameters for populations that are challenging to assess. However, CKMR models have received limited sensitivity testing under realistic population dynamics and sampling scenarios, impeding the application of the method in population monitoring programs and stock assessments. Here, we use individual-based simulation to examine how unmodeled population dynamics and aging uncertainty affect the accuracy and precision of CKMR parameter estimates under different sampling strategies. We then present adapted models that correct the biases that arise from model misspecification. Our results demonstrate that a simple base-case CKMR model produces robust estimates of population abundance with stable populations that breed annually; however, if a population trend or non-annual breeding dynamics are present, or if year-specific estimates of abundance are desired, a more complex CKMR model must be constructed. In addition, we show that CKMR can generate reliable abundance estimates for adults from a variety of sampling strategies, including juvenile-focused sampling where adults are never directly observed (and aging error is minimal). Finally, we apply a CKMR model that has been adapted for population growth and intermittent breeding to two decades of genetic data from juvenile lemon sharks (Negaprion brevirostris) in Bimini, Bahamas, to demonstrate how application of CKMR to samples drawn solely from juveniles can contribute to monitoring efforts for highly mobile populations. Overall, this study expands our understanding of the biological factors and sampling decisions that cause bias in CKMR models, identifies key areas for future inquiry, and provides recommendations that can aid biologists in planning and implementing an effective CKMR study, particularly for long-lived data-limited species.

Most genetic studies consider autism spectrum disorder (ASD) and developmental disorder (DD) separately despite overwhelming comorbidity and shared genetic etiology. Here, we analyzed de novo variants (DNVs) from 15,560 ASD (6,557 from SPARK) and 31,052 DD trios independently and also combined as broader neurodevelopmental disorders (NDDs) using three models. We identify 615 NDD candidate genes (false discovery rate [FDR] < 0.05) supported by ≥1 models, including 138 reaching Bonferroni exome-wide significance (P < 3.64e-7) in all models. The genes group into five functional networks associating with different brain developmental lineages based on single-cell nuclei transcriptomic data. We find no evidence for ASD-specific genes in contrast to 18 genes significantly enriched for DD. There are 53 genes that show mutational bias, including enrichments for missense (n = 41) or truncating (n = 12) DNVs. We also find 10 genes with evidence of male- or female-bias enrichment, including 4 X chromosome genes with significant female burden (DDX3X, MECP2, WDR45, and HDAC8). This large-scale integrative analysis identifies candidates and functional subsets of NDD genes.

Age-based stock assessments are sometimes rejected by review panels due to large retrospective patterns. When this occurs, data-limited approaches are often used to set catch advice, under the assumption that these simpler methods will not be impacted by the problems causing retrospective patterns in the age-based assessment. This assumption has never been formally evaluated. Closed-loop simulations were conducted where a known source of error caused a retrospective pattern in an agebased assessment. Twelve data-limited methods, an ensemble of a subset of these methods, and a statistical catch-at-age model with retrospective adjustment were all evaluated to examine their ability to prevent overfishing and rebuild overfished stocks. Overall, none of the methods evaluated performed best across the scenarios. A number of methods performed consistently poorly, resulting in frequent and intense overfishing and low stock sizes. The retrospective adjusted statistical catch-at-age assessment performed better than a number of the alternatives explored. Thus, using a data-limited approach to set catch advice will not necessarily result in better performance than relying on the age-based assessment with a retrospective adjustment. Key words: closed-loop simulation, data-limited methods, management advice, retrospective analysis, Introduction In the US, age-based, integrated, fisheries stock assessment models are frequently used to estimate annual stock abundance (biomass), fishing mortality rates, and management reference points (Maunder and Punt 2013). [...]

An evaluation of forensic hair analysisbetween experts, novices and the recently developed machine learning platform, HairNet, was conducted to assess accuracy and reliability. Our hypothesis stated experts and the machine learning platform will outperform novices in classifications of hair as human or non-human and suitability for nDNA analysis based on specialist knowledge and from training of the model. Statistically significant differences between novices and experts were found and attributed to training and experience for more complex classifications. For more simplistic classifications, no statistically significant difference between the novice and the experts was found. HairNet proved responses similar to expert responses in all classifications. Encouraging feedback was received regarding the use of technology and machine learning. The utilization of technology undoubtedly holds great promise to become part of the forensic tool kit for improving the efficiency and reliability of forensic hair analysis and in research, education and competency testing. [ABSTRACT FROM AUTHOR]

Dairy workers experience a high degree of bioaerosol exposure, composed of an array of biological and chemical constituents, which have been tied to adverse health effects. A better understanding of the variation in the magnitude and composition of exposures by task is needed to inform worker protection strategies. To characterize the levels and types of exposures, 115 dairy workers grouped into three task categories on nine farms in the high plains Western United States underwent personal monitoring for inhalable dust, endotoxin, 3-hydroxy fatty acids (3-OHFA), muramic acid, ergosterol, and ammonia through one work shift. Eighty-nine percent of dairy workers were exposed to endotoxin at concentrations exceeding the recommended exposure guidelines (adjusted for a long work shift). The proportion of workers with exposures exceeding recommended guidelines was lower for inhalable dust (12%), and ammonia (1%). Ergosterol exposures were only measurable on 28% of samples, primarily among medical workers and feed handlers. Milking parlor workers were exposed to significantly higher inhalable dust, endotoxin, 3-OHFA, ammonia, and muramic acid concentrations compared to workers performing other tasks. Development of large modern dairies has successfully made progress in reducing worker exposures and lung disease prevalence. However, exposure to endotoxin, dust, and ammonia continues to present a significant risk to worker health on North American dairies, especially for workers in milking parlors. This study was among the first to concurrently evaluate occupational exposure to assayable endotoxin (lipid A), 3-hydroxy fatty acids or 3-OHFA (a chemical measure of cell bound and noncell-bound endotoxins), muramic acid, ergosterol, and ammonia among workers on Western U.S. dairies. There remains a need for cost-effective, culturally acceptable intervention strategies integrated in OHS Risk Management and production systems to further optimize worker health and farm productivity.

Abstract--The National Marine Fisheries Service conducts fishery stock assessments to provide the best scientific information available for the U.S. regional fishery management councils. The assessment models applied in the United [...]

A deep learning model called HairNet was developed to conduct forensic hair analysis, including the classification of hair as human and suitability for nuclear DNA (nDNA) analysis. The training and testing data used were microscopic images of hair features including the medulla and the hair root. The final model iterations obtained 100% accuracy on the medulla dataset to classify hair as human or non-human and between 96% and 100% accuracy on the hair root dataset to classify human hair as suitable for nDNA analysis depending on the grouping of root types. The greatest impact on accuracy was the quantity and quality of the training and testing data and therefore the critical step in model development. The application of ML to forensic methodology is a novel and innovative approach and a means to improve objectivity; however, the creation of training and testing data initially requires expert human judgement and therefore collaboration is essential in the development of benchmark datasets. This research demonstrates how deep learning can be successfully applied to forensic methodology and the possibilities for other forensic disciplines. [ABSTRACT FROM AUTHOR]

Like most teacher educators, the authors are aware that prospective teachers enter programs with many experiences in schools, and social studies classrooms in particular, that influence their beliefs about schooling, what it means to teach, their subject, and students. These experiences and beliefs inform how they then experience their program and, possibly, how they will teach. Those in social studies have documented very little about exactly where their students are in their thinking when they begin their social studies education methods courses. However, more research on the inner workings of social studies teacher education is needed. In the past, many have seen the apprenticeship of observation as a barrier to transformation in teacher education. Rather than viewing their prospective teachers' experiences as barriers, or as deficits to be overcome, the authors choose to embrace Segall's (2002) understanding that "it is not "whether" or not teacher education changes prospective views about teaching and learning, but rather, how and in what ways it does so" (p. 168). The larger study from which this piece arose was designed to look at these subtle changes teachers make in their program. In this article, the authors report on their examination of their memories and visions of social studies education as a way to see what they bring from their apprenticeships of observations. (Contains 2 tables and 2 notes.)

During stock assessment, fishery-dependent observations are often used to develop indices of abundance or biomass from catch per unit of effort (CPUE) and contribute catch at size or age information. However, fisher behavior, rather than scientific sampling protocols, determines the spatial and temporal locations of fishery-dependent observations. As a result, trends from fishery-dependent data may be a function of fishing activity rather than fish population changes. This study evaluates whether data collected from commercial fishing fleets in the Gulf of Mexico are representative of trends in fish population size. A coupled bioeconomic agent-based model was developed to generate simulated fishery data, which were used to populate an age-structured stock assessment. Comparison of stock assessment results with simulated fish population dynamics showed that management advice from assessment models based on fishery-dependent data could be biased. Assessment of fish with small home ranges harvested by fishing fleets that frequent the same fishing grounds could cause overestimation of fishing mortality. Not accounting for the spatial structure of the fishers or fish can cause biased estimates of population status. Dans le cadre d'evaluations de stocks, des observations dependantes de la peche sont souvent utilisees pour definir des indices d'abondance ou de biomasse a partir des captures par unite d'effort (CPUE) et fournissent de l'information sur les prises selon la taille ou l'age. Ce sont toutefois les comportements des pecheurs, plutot que des protocoles d'echantillonnage scientifiques, qui determinent l'emplacement dans l'espace et le temps d'observations dependantes de la peche. Les tendances revelees par les donnees dependantes de la peche peuvent donc etre une fonction de l'activite de peche plutot que de changements dans les populations de poissons. L'etude tente d'etablir si les donnees tirees de flottes de peche commerciale dans le golfe du Mexique sont representatives des tendances de la taille des populations de poissons. Un modele qui jumelle des approches bio-economique et multiagents a ete mis au point pour generer des donnees de peche simulees qui ont ete utilisees comme intrants pour une evaluation des stocks structuree par age. Une comparaison des resultats de l'evaluation des stocks a la dynamique simulee des populations de poissons montre que les avis de gestion tires de modeles d'evaluation reposant sur des donnees dependantes de la peche pourraient etre biaises. L'evaluation de poissons caracterises par de petits domaines vitaux peches par des flottes de peche qui frequentent les memes lieux de peche pourrait entrainer une surestimation de la mortalite par peche. La non-prise en compte de la structure spatiale des pecheurs ou des poissons peut se traduire par des estimations biaisees de l'etat des populations. [Traduit par la Redaction], Introduction The importance of understanding human behavior in the context of fisheries management has been well acknowledged (Branch et al. 2006; Fulton et al. 2011). Fisher behavior has often been [...]