Evaluation of Bt cottons for heliothine complex control and yield potential in Tennessee

Cotton insect pests cost fanners beltwide $1.68 billion in 1995 due to yield losses, and insecticide and insecticide application costs. With increased insect resistance and environmental concerns, new and innovative control methods are needed. Several Bt cotton lines have been developed which carry an insecticidal protein from a soil bacterium inside the plant, which is toxic to many lepidopteran cotton pests. Bt cottons were evaluated for resistance to injury from the bollworm, Helicoverpa zea Boddie, and tobacco budworm, Heliothis virescens F., which are lepidopterans collectively called the Heliothine complex. Data indicate that Bt cotton lines suffered significantly less Heliothine complex injury to squares and bolls throughout the season than conventional cultivars. Bt cotton lines did not require insecticide treatments for the Heliothine complex to prevent yield loss. These studies indicate that Bt cottons could play an important role in future cotton pest management and reduce insect control costs in production agriculture

Extending the M3-Competition: Category and Interval-Specific Time Series Forecasting

The M3-Competition found that simple models outperform more complex ones for time series forecasting. As part of these competitions, several claims were made that statistical models exceeded machine learning (ML) techniques, such as recurrent neural networks (RNN), in prediction performance. These findings may over-generalize the capabilities of statistical models since the analysis measured the total forecasting accuracy across a wide range of industries and fields and with different interval lengths. This investigation aimed to assess how statistical and ML methods compared when individuating series by category and time interval. Utilizing the M3 data and building individual models using Facebook© Prophet and R packages: tswge, forecast, and nnfor, there were significant differences in model performance. The statistical models performed better for monthly – industry, macro, and micro combinations (Wilcoxon signed-rank adjusted p-value \u3c 0.0001) for short-term forecast horizons (h=5). However, the multilayer perceptron (MLP) surpassed the statistical models in quarterly – industry data (p-value \u3c 0.001) for the same forecast length. The statistical models also outperformed ML methods for long-term forecasts in the same category by interval combinations (p-value \u3c 0.01). Thus, identifying which model may have increased performance in specific category, interval and horizon combinations provides direct value for time series analysis

A Theoretical and Experimental Analysis of Radiofrequency Ablation with a Multielectrode, Phased, Duty-Cycled System

Background:   The development of a unique radiofrequency (RF) cardiac ablation system, for the treatment of cardiac arrhythmias, is driven by the clinical need to safely create large uniform lesions while controlling lesion depth. Computational analysis of a finite element model of a three-dimensional, multielectrode, cardiac ablation catheter, powered by a temperature-controlled, multiphase, duty-cycled RF generator, is presented. Methods:   The computational model for each of the five operating modes offered by the generator is compared to independent tissue temperature measurements taken during in vitro ablation experiments performed on bovine myocardium. Results:   The results of the model agree with experimental temperature measurements very closely—the average values for mean error, root mean square difference, and correlation coefficient were 1.9°C, 13.3%, and 0.97, respectively. Lesions are shown to be contiguous and no significant edge effects are observed. Conclusions:   Both the in vitro and computational model results demonstrate that lesion depth decreases consistently as the bipolar-to-unipolar ratio increases—suggesting a clinical application to potentially control lesion depth with higher fidelity than is currently available. The effect of variable design parameters and clinical conditions on RF ablation can now be expeditiously studied with this validated model. (PACE 2010; 33:1089–1100)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/79205/1/j.1540-8159.2010.02801.x.pd

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

Biomarkers for Severity of Spinal Cord Injury in the Cerebrospinal Fluid of Rats

One of the major challenges in management of spinal cord injury (SCI) is that the assessment of injury severity is often imprecise. Identification of reliable, easily quantifiable biomarkers that delineate the severity of the initial injury and that have prognostic value for the degree of functional recovery would significantly aid the clinician in the choice of potential treatments. To find such biomarkers we performed quantitative liquid chromatography-mass spectrometry (LC-MS/MS) analyses of cerebrospinal fluid (CSF) collected from rats 24 h after either a moderate or severe SCI. We identified a panel of 42 putative biomarkers of SCI, 10 of which represent potential biomarkers of SCI severity. Three of the candidate biomarkers, Ywhaz, Itih4, and Gpx3 were also validated by Western blot in a biological replicate of the injury. The putative biomarkers identified in this study may potentially be a valuable tool in the assessment of the extent of spinal cord damage

The Science Performance of JWST as Characterized in Commissioning

This paper characterizes the actual science performance of the James Webb Space Telescope (JWST), as determined from the six month commissioning period. We summarize the performance of the spacecraft, telescope, science instruments, and ground system, with an emphasis on differences from pre-launch expectations. Commissioning has made clear that JWST is fully capable of achieving the discoveries for which it was built. Moreover, almost across the board, the science performance of JWST is better than expected; in most cases, JWST will go deeper faster than expected. The telescope and instrument suite have demonstrated the sensitivity, stability, image quality, and spectral range that are necessary to transform our understanding of the cosmos through observations spanning from near-earth asteroids to the most distant galaxies.Comment: 5th version as accepted to PASP; 31 pages, 18 figures; https://iopscience.iop.org/article/10.1088/1538-3873/acb29

Integrated Genomic Analysis of the Ubiquitin Pathway across Cancer Types

Protein ubiquitination is a dynamic and reversibleprocess of adding single ubiquitin molecules orvarious ubiquitin chains to target proteins. Here,using multidimensional omic data of 9,125 tumorsamples across 33 cancer types from The CancerGenome Atlas, we perform comprehensive molecu-lar characterization of 929 ubiquitin-related genesand 95 deubiquitinase genes. Among them, we sys-tematically identify top somatic driver candidates,including mutatedFBXW7with cancer-type-specificpatterns and amplifiedMDM2showing a mutuallyexclusive pattern withBRAFmutations. Ubiquitinpathway genes tend to be upregulated in cancermediated by diverse mechanisms. By integratingpan-cancer multiomic data, we identify a group oftumor samples that exhibit worse prognosis. Thesesamples are consistently associated with the upre-gulation of cell-cycle and DNA repair pathways, char-acterized by mutatedTP53,MYC/TERTamplifica-tion, andAPC/PTENdeletion. Our analysishighlights the importance of the ubiquitin pathwayin cancer development and lays a foundation fordeveloping relevant therapeutic strategies