The iWildCam 2019 Challenge Dataset

Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data? In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment

Pattern formation in annular convection

This study of spatio-temporal pattern formation in an annulus is motivated by two physical problems on vastly different scales. The first is atmospheric convection in the equatorial plane between the warm surface of the Earth and the cold tropopause, modeled by the two dimensional Boussinesq equations. The second is annular electroconvection in a thin semetic film, where experiments reveal the birth of convection-like vortices in the plane as the electric field intensity is increased. This is modeled by two dimensional Navier-Stokes equations coupled with a simplified version of Maxwell's equations. The two models share fundamental mathematical properties and satisfy the prerequisites for application of O(2)-equivariant bifurcation theory. We show this can give predictions of interesting dynamics, including stationary and spatio-temporal patterns

Is There a Twelfth Protein-Coding Gene in the Genome of Influenza A? A Selection-Based Approach to the Detection of Overlapping Genes in Closely Related Sequences

Protein-coding genes often contain long overlapping open-reading frames (ORFs), which may or may not be functional. Current methods that utilize the signature of purifying selection to detect functional overlapping genes are limited to the analysis of sequences from divergent species, thus rendering them inapplicable to genes found only in closely related sequences. Here, we present a method for the detection of selection signatures on overlapping reading frames by using closely related sequences, and apply the method to several known overlapping genes, and to an overlapping ORF on the negative strand of segment 8 of influenza A virus (NEG8), for which the suggestion has been made that it is functional. We find no evidence that NEG8 is under selection, suggesting that the intact reading frame might be non-functional, although we cannot fully exclude the possibility that the method is not sensitive enough to detect the signature of selection acting on this gene. We present the limitations of the method using known overlapping genes and suggest several approaches to improve it in future studies. Finally, we examine alternative explanations for the sequence conservation of NEG8 in the absence of selection. We show that overlap type and genomic context affect the conservation of intact overlapping ORFs and should therefore be considered in any attempt of estimating the signature of selection in overlapping gene

Incorporating DNA Sequencing into Current Prenatal Screening Practice for Down's Syndrome

PMCID: PMC3604109This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited

Corn Grades and Feed Value

Corn is traditionally priced on the basis of U.S. No. 2 grade. However, with the release of CCC-owned corn stocks for Emergency Assistance Programs, there is an increase in corn being marketed as No. 4, No. 5 or Sample grade. It is important for producers to understand corn grading standards and the feeding values of these grades

The iWildCam 2019 Challenge Dataset

Camera Traps (or Wild Cams) enable the automatic collection of large quantities of image data. Biologists all over the world use camera traps to monitor biodiversity and population density of animal species. The computer vision community has been making strides towards automating the species classification challenge in camera traps, but as we try to expand the scope of these models from specific regions where we have collected training data to different areas we are faced with an interesting problem: how do you classify a species in a new region that you may not have seen in previous training data? In order to tackle this problem, we have prepared a dataset and challenge where the training data and test data are from different regions, namely The American Southwest and the American Northwest. We use the Caltech Camera Traps dataset, collected from the American Southwest, as training data. We add a new dataset from the American Northwest, curated from data provided by the Idaho Department of Fish and Game (IDFG), as our test dataset. The test data has some class overlap with the training data, some species are found in both datasets, but there are both species seen during training that are not seen during test and vice versa. To help fill the gaps in the training species, we allow competitors to utilize transfer learning from two alternate domains: human-curated images from iNaturalist and synthetic images from Microsoft's TrapCam-AirSim simulation environment