Insights and approaches using deep learning to classify wildlife.

The implementation of intelligent software to identify and classify objects and individuals in visual fields is a technology of growing importance to operatives in many fields, including wildlife conservation and management. To non-experts, the methods can be abstruse and the results mystifying. Here, in the context of applying cutting edge methods to classify wildlife species from camera-trap data, we shed light on the methods themselves and types of features these methods extract to make efficient identifications and reliable classifications. The current state of the art is to employ convolutional neural networks (CNN) encoded within deep-learning algorithms. We outline these methods and present results obtained in training a CNN to classify 20 African wildlife species with an overall accuracy of 87.5% from a dataset containing 111,467 images. We demonstrate the application of a gradient-weighted class-activation-mapping (Grad-CAM) procedure to extract the most salient pixels in the final convolution layer. We show that these pixels highlight features in particular images that in some cases are similar to those used to train humans to identify these species. Further, we used mutual information methods to identify the neurons in the final convolution layer that consistently respond most strongly across a set of images of one particular species. We then interpret the features in the image where the strongest responses occur, and present dataset biases that were revealed by these extracted features. We also used hierarchical clustering of feature vectors (i.e., the state of the final fully-connected layer in the CNN) associated with each image to produce a visual similarity dendrogram of identified species. Finally, we evaluated the relative unfamiliarity of images that were not part of the training set when these images were one of the 20 species "known" to our CNN in contrast to images of the species that were "unknown" to our CNN

Finding the association of mRNA and miRNA using Next Generation Sequencing data of Kidney renal cell carcinoma

MicroRNAs (miRNAs) are a class of 22-nucleotide endogenous noncod- ing RNAs, plays important role in regulating target gene expression via repress- ing translation or promoting messenger RNAs (mRNA) degradation. Numerous re- searchers have found that miRNAs have serious effects on cancer. Therefore, study of mRNAs and miRNAs together through the integrated analysis of mRNA and miRNA expression profiling could help us in getting a deeper insight into the can- cer research. In this regards, High-Throughput Sequencing data of Kidney renal cell carcinoma is used here. The proposed method focuses on identifying mRNA- miRNA pair that has a signature in kidney tumor sample. For this analysis, Ran- dom Forests, Particle Swarm Optimization and Support Vector Machine classifier is used to have best sets of mRNAs-miRNA pairs. Additionally, the significance of selected mRNA-miRNA pairs is tested using gene ontology and pathway analysis tools. Moreover, the selected mRNA-miRNA pairs are searched based on changes in expression values of the used mRNA and miRNA dataset

Large-scale discovery of male reproductive tract-specific genes through analysis of RNA-seq datasets

Robertson, M.J., Kent, K., Tharp, N. et al. Large-scale discovery of male reproductive tract-specific genes through analysis of RNA-seq datasets. BMC Biol 18, 103 (2020). https://doi.org/10.1186/s12915-020-00826-

Control and Characterization of Individual Grains and Grain Boundaries in Graphene Grown by Chemical Vapor Deposition

The strong interest in graphene has motivated the scalable production of high quality graphene and graphene devices. Since large-scale graphene films synthesized to date are typically polycrystalline, it is important to characterize and control grain boundaries, generally believed to degrade graphene quality. Here we study single-crystal graphene grains synthesized by ambient CVD on polycrystalline Cu, and show how individual boundaries between coalescing grains affect graphene's electronic properties. The graphene grains show no definite epitaxial relationship with the Cu substrate, and can cross Cu grain boundaries. The edges of these grains are found to be predominantly parallel to zigzag directions. We show that grain boundaries give a significant Raman "D" peak, impede electrical transport, and induce prominent weak localization indicative of intervalley scattering in graphene. Finally, we demonstrate an approach using pre-patterned growth seeds to control graphene nucleation, opening a route towards scalable fabrication of single-crystal graphene devices without grain boundaries.Comment: New version with additional data. Accepted by Nature Material

Circulating microRNAs Reveal Time Course of Organ Injury in a Porcine Model of Acetaminophen-Induced Acute Liver Failure

Acute liver failure is a rare but catastrophic condition which can progress rapidly to multi-organ failure. Studies investigating the onset of individual organ injury such as the liver, kidneys and brain during the evolution of acute liver failure, are lacking. MicroRNAs are short, non-coding strands of RNA that are released into the circulation following tissue injury. In this study, we have characterised the release of both global microRNA and specific microRNA species into the plasma using a porcine model of acetaminophen-induced acute liver failure. Pigs were induced to acute liver failure with oral acetaminophen over 19h±2h and death occurred 13h±3h thereafter. Global microRNA concentrations increased 4h prior to acute liver failure in plasma (P<0.0001) but not in isolated exosomes, and were associated with increasing plasma levels of the damage-associated molecular pattern molecule, genomic DNA (P<0.0001). MiR122 increased around the time of onset of acute liver failure (P<0.0001) and was associated with increasing international normalised ratio (P<0.0001). MiR192 increased 8h after acute liver failure (P<0.0001) and was associated with increasing creatinine (P<0.0001). The increase in miR124-1 occurred concurrent with the pre-terminal increase in intracranial pressure (P<0.0001) and was associated with decreasing cerebral perfusion pressure (P<0.002)

Direct photon production in d+Au collisions at root s(NN)=200 GeV

Direct photons have been measured in root s(NN) = 200 GeV d + Au collisions at midrapidity. A wide p(T) range is covered by measurements of nearly real virtual photons (1 \u3c p(T) \u3c 6 GeV/c) and real photons (5 \u3c p(T) \u3c 16 GeV/c). The invariant yield of the direct photons in d + Au collisions over the scaled p + p cross section is consistent with unity. Theoretical calculations assuming standard cold-nuclear-matter effects describe the data well for the entire p(T) range. This indicates that the large enhancement of direct photons observed in Au + Au collisions for 1.0 \u3c p(T) \u3c 2.5 GeV/c is attributable to a source other than the initial-state nuclear effects

High efficient electrical stimulation of hippocampal slices with vertically aligned carbon nanofiber microbrush array

Long-term neuroprostheses for functional electrical stimulation must efficiently stimulate tissue without electrolyzing water and raising the extracellular pH to toxic levels. Comparison of the stimulation efficiency of tungsten wire electrodes (W wires), platinum microelectrode arrays (PtMEA), as-grown vertically aligned carbon nanofiber microbrush arrays (VACNF MBAs), and polypyrrole coated (PPy-coated) VACNF MBAs in eliciting field potentials in the hippocampus slice indicates that, at low stimulating voltages that preclude the electrolysis of water, only the PPy-coated VACNF MBA is able to stimulate the CA3 to CA1 pathway. Unlike the W wires, PtMEA, as-grown VACNF MBA, and the PPy-coated VACNF MBA elicit only excitatory postsynaptic potentials (EPSPs). Furthermore, the PPy-coated VACNF MBA evokes somatic action potentials in addition to EPSPs. These results highlight the PPy-coated VACNF’s advantages in lower electrode impedance, ability to stimulate tissue through a biocompatible chloride flux, and stable vertical alignment in liquid that enables access to spatially confined regions of neuronal cells

Regioselective Synthesis of Benzimidazolones via Cascade C–N Coupling of Monosubstituted Ureas

A direct method for the regioselective construction of benzimidazolones is reported wherein a single palladium catalyst is employed to couple monosubstituted urea substrates with differentially substituted 1,2-dihaloaromatic systems. In this method, the catalyst is able to promote a cascade of two discrete chemoselective C–N bond-forming processes that allows the highly selective and predictable formation of complex heterocycles from simple, readily available starting materials.National Institutes of Health (U.S.) (Award GM58160)National Institutes of Health (U.S.) (Award GM099817)Lanxess CorporationMassachusetts Institute of Technology. Undergraduate Research Opportunities Progra

Construction and Analysis of an Integrated Regulatory Network Derived from High-Throughput Sequencing Data

We present a network framework for analyzing multi-level regulation in higher eukaryotes based on systematic integration of various high-throughput datasets. The network, namely the integrated regulatory network, consists of three major types of regulation: TF→gene, TF→miRNA and miRNA→gene. We identified the target genes and target miRNAs for a set of TFs based on the ChIP-Seq binding profiles, the predicted targets of miRNAs using annotated 3′UTR sequences and conservation information. Making use of the system-wide RNA-Seq profiles, we classified transcription factors into positive and negative regulators and assigned a sign for each regulatory interaction. Other types of edges such as protein-protein interactions and potential intra-regulations between miRNAs based on the embedding of miRNAs in their host genes were further incorporated. We examined the topological structures of the network, including its hierarchical organization and motif enrichment. We found that transcription factors downstream of the hierarchy distinguish themselves by expressing more uniformly at various tissues, have more interacting partners, and are more likely to be essential. We found an over-representation of notable network motifs, including a FFL in which a miRNA cost-effectively shuts down a transcription factor and its target. We used data of C. elegans from the modENCODE project as a primary model to illustrate our framework, but further verified the results using other two data sets. As more and more genome-wide ChIP-Seq and RNA-Seq data becomes available in the near future, our methods of data integration have various potential applications

Structural and Electronic Decoupling of C_(60) from Epitaxial Graphene on SiC

We have investigated the initial stages of growth and the electronic structure of C_(60) molecules on graphene grown epitaxially on SiC(0001) at the single-molecule level using cryogenic ultrahigh vacuum scanning tunneling microscopy and spectroscopy. We observe that the first layer of C_(60) molecules self-assembles into a well-ordered, close-packed arrangement on graphene upon molecular deposition at room temperature while exhibiting a subtle C_(60) superlattice. We measure a highest occupied molecular orbital–lowest unoccupied molecular orbital gap of ~ 3.5 eV for the C_(60) molecules on graphene in submonolayer regime, indicating a significantly smaller amount of charge transfer from the graphene to C_(60) and substrate-induced screening as compared to C_(60) adsorbed on metallic substrates. Our results have important implications for the use of graphene for future device applications that require electronic decoupling between functional molecular adsorbates and substrates