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Abstract Due to their potential impact on population growth, many studies have investigated factors affecting infant survival in mammal populations under human care. Here we used more than 30 years of Association of Zoos and Aquariums (AZA) studbook data and contraception data from the AZA Reproductive Management Center, along with logistic regression models, to investigate which factors affect infant survival in fourEulemurspecies managed as Species Survival Plans® in AZA. Across species, infant survival to 1 month ranged from 65% to 78%. Previous experience producing surviving offspring was positively correlated to infant survival in collared (Eulemur collaris), crowned (Eulemur coronatus), and mongoose (Eulemur mongoz) lemurs. Both dam age and previous use of contraception were negatively correlated to infant survival for collared lemurs, though our results suggest the latter may be confounded with other factors. Blue‐eyed black lemurs (Eulemur flavifrons) were affected by birth location, suggesting differences in husbandry that may affect infant survival. These results can be used to assist in reproductive planning or to anticipate the likelihood of breeding success. Population managers may also be able to focus their reproductive planning on younger dams or those with previous experience to predict successful births. Future studies should seek to determine what aspects of previous dam success are most important to infant survival, investigate sire‐related factors, and examine factors related to cause of death in infants that may lead to differential survival. Our hope is to present a framework that may be useful for investigating infant survival in other mammal species' breeding programs. 

Sex differences in gene expression tend to increase with age across a variety of species, often coincident with the development of sexual dimorphism and maturational changes in hormone levels. However, because most transcriptome-wide characterizations of sexual divergence are framed as comparisons of sex-biased gene expression across ages, it can be difficult to determine the extent to which age-biased gene expression within each sex contributes to the emergence of sex-biased gene expression. Using RNAseq in the liver of the sexually dimorphic brown anole lizard ( Anolis sagrei ), we found that a pronounced increase in sex-biased gene expression with age was associated with a much greater degree of age-biased gene expression in males than in females. This pattern suggests that developmental changes in males, such as maturational increases in circulating testosterone, contribute disproportionately to the ontogenetic emergence of sex-biased gene expression. To test this hypothesis, we used four different experimental contrasts to independently characterize sets of genes whose expression differed as a function of castration and/or treatment with exogenous testosterone. We found that genes that were significantly male-biased in expression or upregulated as males matured tended to be upregulated by testosterone, whereas genes that were female-biased or downregulated as males matured tended to be downregulated by testosterone. Moreover, the first two principal components describing multivariate gene expression indicated that exogenous testosterone reversed many of the feminizing effects of castration on the liver transcriptome of maturing males. Collectively, our results suggest that developmental changes that occur in males contribute disproportionately to the emergence of sex-biased gene expression in the Anolis liver, and that many of these changes are orchestrated by androgens such as testosterone. 

The ability to determine whether a robot's grasp has a high chance of failing, before it actually does, can save significant time and avoid failures by planning for re-grasping or changing the strategy for that special case. Machine Learning (ML) offers one way to learn to predict grasp failure from historic data consisting of a robot's attempted grasps alongside labels of the success or failure. Unfortunately, most powerful ML models are black-box models that do not explain the reasons behind their predictions. In this paper, we investigate how ML can be used to predict robot grasp failure and study the tradeoff between accuracy and interpretability by comparing interpretable (white box) ML models that are inherently explainable with more accurate black box ML models that are inherently opaque. Our results show that one does not necessarily have to compromise accuracy for interpretability if we use an explanation generation method, such as Shapley Additive explanations (SHAP), to add explainability to the accurate predictions made by black box models. An explanation of a predicted fault can lead to an efficient choice of corrective action in the robot's design that can be taken to avoid future failures. 

Rivers and streams contribute to global carbon cycling by decomposing immense quantities of terrestrial plant matter. However, decomposition rates are highly variable and large-scale patterns and drivers of this process remain poorly understood. Using a cellulose-based assay to reflect the primary constituent of plant detritus, we generated a predictive model (81% variance explained) for cellulose decomposition rates across 514 globally distributed streams. A large number of variables were important for predicting decomposition, highlighting the complexity of this process at the global scale. Predicted cellulose decomposition rates, when combined with genus-level litter quality attributes, explain published leaf litter decomposition rates with high accuracy (70% variance explained). Our global map provides estimates of rates across vast understudied areas of Earth and reveals rapid decomposition across continental-scale areas dominated by human activities. 

River ecosystems receive and process vast quantities of terrestrial organic carbon, the fate of which depends strongly on microbial activity. Variation in and controls of processing rates, however, are poorly characterized at the global scale. In response, we used a peer-sourced research network and a highly standardized carbon processing assay to conduct a global-scale field experiment in greater than 1000 river and riparian sites. We found that Earth’s biomes have distinct carbon processing signatures. Slow processing is evident across latitudes, whereas rapid rates are restricted to lower latitudes. Both the mean rate and variability decline with latitude, suggesting temperature constraints toward the poles and greater roles for other environmental drivers (e.g., nutrient loading) toward the equator. These results and data set the stage for unprecedented “next-generation biomonitoring” by establishing baselines to help quantify environmental impacts to the functioning of ecosystems at a global scale.