Diet and Macronutrient Optimization in Wild Ursids: Grizzly Bears Versus Black Bears

When fed ad libitum, ursids can maximize mass gain by selecting mixed diets wherein protein provides 17 ± 4% of digestible energy.  In the wild, this ability is likely constrained.  By visiting locations of 37 individuals during 274 bear-days, we documented foods consumed by grizzly (Ursus arctos) and black bears (Ursus americanus) in Grand Teton National Park during 2004–2006.  Based on published data, we estimated foods and macronutrients as percentages of daily energy intake.  Using principal components and cluster analyses, we identified 14 daily diet types.  Only 4 diets, accounting for 21% of days, provided optimal protein levels.  Nine diets (75% of days) led to over-consumption of protein, and 1 diet (3% of days) led to under-consumption.  Highest protein levels were associated with animal matter (i.e., insects, vertebrates), which accounted for 46–47% of daily energy for both species.  As predicted: 1) daily diets dominated by vertebrates were positively associated with grizzly bears and protein intake was positively associated with body mass; 2) diets dominated by fruits were positively associated with black bears; and 3) mean protein was highest during spring, when high-energy foods were scarce, however it was also higher than optimal during summer and fall.  Although optimal gain of body mass was constrained, bears opted for the energetically superior trade-off of consuming high-energy, high-protein foods.  Given protein digestion efficiency similar to obligate carnivores, this choice likely supported mass gain, consistent with studies showing monthly increases in percent body fat among bears in this region

Design of Selective Gas Sensors Using Additive-Loaded In2O3 Hollow Spheres Prepared by Combinatorial Hydrothermal Reactions

A combinatorial hydrothermal reaction has been used to prepare pure and additive (Sb, Cu, Nb, Pd, and Ni)-loaded In2O3 hollow spheres for gas sensor applications. The operation of Pd- and Cu-loaded In2O3 sensors at 371 °C leads to selective H2S detection. Selective detection of CO and NH3 was achieved by the Ni-In2O3 sensor at sensing temperatures of 371 and 440 °C, respectively. The gas responses of six different sensors to NH3, H2S, H2, CO and CH4 produced unique gas sensing patterns that can be used for the artificial recognition of these gases

X-Ray Fluorescence Microscopy Reveals Accumulation and Secretion of Discrete Intracellular Zinc Pools in the Lactating Mouse Mammary Gland

The mammary gland is responsible for the transfer of a tremendous amount of zinc ( approximately 1-3 mg zinc/day) from maternal circulation into milk during lactation to support the growth and development of the offspring. When this process is compromised, severe zinc deficiency compromises neuronal development and immune function and increases infant morbidity and/or mortality. It remains unclear as to how the lactating mammary gland dynamically integrates zinc import from maternal circulation with the enormous amount of zinc that is secreted into milk.Herein we utilized X-ray fluorescence microscopy (XFM) which allowed for the visualization and quantification of the process of zinc transfer through the mammary gland of the lactating mouse. Our data illustrate that a large amount of zinc first accumulates in the mammary gland during lactation. Interestingly, this zinc is not cytosolic, but accumulated in large, discrete sub-cellular compartments. These zinc pools were then redistributed to small intracellular vesicles destined for secretion in a prolactin-responsive manner. Confocal microscopy identified mitochondria and the Golgi apparatus as the sub-cellular compartments which accumulate zinc; however, zinc pools in the Golgi apparatus, but not mitochondria are redistributed to vesicles destined for secretion during lactation.Our data directly implicate the Golgi apparatus in providing a large, mobilizable zinc storage pool to assist in providing for the tremendous amount of zinc that is secreted into milk. Interestingly, our study also provides compelling evidence that mitochondrial zinc pools expand in the mammary gland during lactation which we speculate may play a role in regulating mammary gland function

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types