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Direct exploitation, which includes the trade of wild animals for their parts, is a major driver of extinction. Digital communication tools, particularly the internet, have facilitated the trade in endangered species. Here, we automatically collected data to analyze online sales of threatened animals across 148 English-text online mar- ketplaces. We created a tool that searched for online sales of 13,267 animal species at risk of global extinction, as classified by the International Union for Conservation of Nature (IUCN), as well as 706 animal species on Ap- pendix I of the Convention for International Trade in Endangered Species (CITES), for which international commercial trade is prohibited. Examining a period of 15 weeks in 2018, we identified 10,699 unique listings selling body parts or eggs of threatened species, of which 4131 contained a full species name (common or sci- entific). These 4131 results were then filtered by keywords and, finally, manually vetted, which yielded 546 sale listings for 83 species. Of these 546 listings, 61 % advertised shark trophies (mainly jaws), 73 % of which were taken from species listed as endangered or critically endangered. Just four websites hosted >95 % of listings. We identified 18 species for sale that are included on CITES Appendix I. We also identified 13 species for which the IUCN had not identified intentional use as a threat. This work expands current understanding about the dealing of endangered and potentially illegal species online, specifies taxa threatened by online trade, and highlights emerging opportunities and persistent challenges to preventing the trafficking of threatened species. 

Estimation of evolutionary relationships among lineages that rapidly diversified can be challenging, and, in such instances, inaccurate or unresolved phylogenetic estimates can lead to erroneous conclusions regarding historical geographical ranges of lineages. One example underscoring this issue has been the historical challenge posed by untangling the biogeographic origin of elapoid snakes, which includes numerous dangerously venomous species as well as species not known to be dangerous to humans. The worldwide distribution of this lineage makes it an ideal group for testing hypotheses related to historical faunal exchanges among the many continents and other landmasses occupied by contemporary elapoid species. We developed a novel suite of genomic resources, included worldwide sampling, and inferred a robust estimate of evolutionary relationships, which we leveraged to quantitatively estimate geographical range evolution through the deep-time history of this remarkable radiation. Our phylogenetic and biogeographical estimates of historical ranges definitively reject a lingering former ‘Out of Africa’ hypothesis and support an ‘Out of Asia’ scenario involving multiple faunal exchanges between Asia, Africa, Australasia, the Americas and Europe. 

Aerosol particles with rare specific properties act as nuclei for ice formation. The presence of ice nucleating particles in the atmosphere leads to heterogeneous freezing at warm temperatures and thus these particles play an important role in modulating microphysical properties of clouds. This work presents an ice nucleation cold stage instrument for measuring the concentration of ice nucleating particles in liquids. The cost is ∼ $10 k including an external chiller. Using a lower cost heat sink reduces the cost to ∼ $6 k. The instrument is suitable for studying ambient ice nucleating particle concentrations and laboratory-based process-level studies of ice nucleation. The design plans allow individuals to self-manufacture the cold-stage using 3D printing, off-the-shelf parts, and a handful of standard tools. Software to operate the instrument and analyze the data is also provided. The design is intended to be simple enough that a graduate student can build it as part of a course or thesis project. Costs are kept to a minimum to facilitate use in classroom demonstrations and laboratory classes. 

Julia package to communicate with the TE Technology TC-36-25-RS232 temperature controller. 

Container build that bundles csDAQ and DropfreezingDetection 

Design files for the cold stage instrument 

Purpose Prior studies show convolutional neural networks predicting self-reported race using x-rays of chest, hand and spine, chest computed tomography, and mammogram. We seek an understanding of the mechanism that reveals race within x-ray images, investigating the possibility that race is not predicted using the physical structure in x-ray images but is embedded in the grayscale pixel intensities. Approach Retrospective full year 2021, 298,827 AP/PA chest x-ray images from 3 academic health centers across the United States and MIMIC-CXR, labeled by self-reported race, were used in this study. The image structure is removed by summing the number of each grayscale value and scaling to percent per image (PPI). The resulting data are tested using multivariate analysis of variance (MANOVA) with Bonferroni multiple-comparison adjustment and class-balanced MANOVA. Machine learning (ML) feed-forward networks (FFN) and decision trees were built to predict race (binary Black or White and binary Black or other) using only grayscale value counts. Stratified analysis by body mass index, age, sex, gender, patient type, make/model of scanner, exposure, and kilovoltage peak setting was run to study the impact of these factors on race prediction following the same methodology. Results MANOVA rejects the null hypothesis that classes are the same with 95% confidence (F 7.38, P < 0.0001) and balanced MANOVA (F 2.02, P < 0.0001). The best FFN performance is limited [area under the receiver operating characteristic (AUROC) of 69.18%]. Gradient boosted trees predict self-reported race using grayscale PPI (AUROC 77.24%). Conclusions Within chest x-rays, pixel intensity value counts alone are statistically significant indicators and enough for ML classification tasks of patient self-reported race.