Arabidopsis \u3ci\u3eGLABROUS1\u3c/i\u3e Gene Requires Downstream Sequences for Function

The Arabidopsis GLABROUSl (GL1) gene is a myb gene homolog required for the initiation of trichome development. In situ hybridiration revealed that the highest levels of GL1 transcripts were present in developing trichomes. In contrast, previous work had shown that putative promoter sequences from the 5‘ noncoding region of the GL1 gene directed the expression of a β-glucuronidase (GUS) reporter gene only in stipules. Deletion analysis of the 3’ noncoding region of GL1 has identified an enhancer that is essential for GL1 function. Sequences fmm the region containing the enhancer, in conjunction with GL1 upstream sequences, direct the expression of a GUS reporter gene in leaf primordia and developing trichomes in addition to stipules, indicating that the downstream enhancer is required for the normal expression pattern of GL1

Effects of menstrual phase on intake of nicotine, caffeine, and alcohol and nonprescribed drugs in women with late luteal phase dysphoric disorder

To investigate the possibility that cigarette smoking and other drug use are affected by menstrual phase in smokers with Late Luteal Phase Dysphoric Disorder (LLPDD), we examined daily diaries rating menstrual symptomatology, smoking, alcohol and nonprescription drug use, and caffeine intake in nine female smokers meeting criteria for LLPDD. Menstrual symptomatology peaked during the premenstrual phase. Smoking, alcohol, and nonprescription drug intake were increased during menses; caffeine intake was unaffected by phase. No systematic intrasubject correlation between symptomatology and smoking was detected. It was concluded that in women with LLPDD, smoking and alcohol and nonprescription drug intake appear to vary as a function of menstrual phase. The lack of intrasubject correlations between symptomatology and intake, and the failure of peak intake to coincide with peak symptomatology, however, indicate that these effects cannot be explained simply as "self-medication" of acute episodes of dysphoric mood.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/31910/1/0000863.pd

A multisite study of a breast density deep learning model for full-field digital mammography and synthetic mammography

PURPOSE: To develop a Breast Imaging Reporting and Data System (BI-RADS) breast density deep learning (DL) model in a multisite setting for synthetic two-dimensional mammographic (SM) images derived from digital breast tomosynthesis examinations by using full-field digital mammographic (FFDM) images and limited SM data. MATERIALS AND METHODS: A DL model was trained to predict BI-RADS breast density by using FFDM images acquired from 2008 to 2017 (site 1: 57 492 patients, 187 627 examinations, 750 752 images) for this retrospective study. The FFDM model was evaluated by using SM datasets from two institutions (site 1: 3842 patients, 3866 examinations, 14 472 images, acquired from 2016 to 2017; site 2: 7557 patients, 16 283 examinations, 63 973 images, 2015 to 2019). Each of the three datasets were then split into training, validation, and test. Adaptation methods were investigated to improve performance on the SM datasets, and the effect of dataset size on each adaptation method was considered. Statistical significance was assessed by using CIs, which were estimated by bootstrapping. RESULTS: Without adaptation, the model demonstrated substantial agreement with the original reporting radiologists for all three datasets (site 1 FFDM: linearly weighted Cohen κ [κ CONCLUSION: A BI-RADS breast density DL model demonstrated strong performance on FFDM and SM images from two institutions without training on SM images and improved by using few SM images

Expression signatures of TP53 mutations in serous ovarian cancers

<p>Abstract</p> <p>Background</p> <p>Mutations in the <it>TP53 </it>gene are extremely common and occur very early in the progression of serous ovarian cancers. Gene expression patterns that relate to mutational status may provide insight into the etiology and biology of the disease.</p> <p>Methods</p> <p>The <it>TP53 </it>coding region was sequenced in 89 frozen serous ovarian cancers, 40 early stage (I/II) and 49 advanced stage (III/IV). Affymetrix U133A expression data was used to define gene expression patterns by mutation, type of mutation, and cancer stage.</p> <p>Results</p> <p>Missense or chain terminating (null) mutations in <it>TP53 </it>were found in 59/89 (66%) ovarian cancers. Early stage cancers had a significantly higher rate of null mutations than late stage disease (38% vs. 8%, p < 0.03). In advanced stage cases, mutations were more prevalent in short term survivors than long term survivors (81% vs. 30%, p = 0.0004). Gene expression patterns had a robust ability to predict <it>TP53 </it>status within training data. By using early versus late stage disease for out of sample predictions, the signature derived from early stage cancers could accurately (86%) predict mutation status of late stage cancers.</p> <p>Conclusions</p> <p>This represents the first attempt to define a genomic signature of <it>TP53 </it>mutation in ovarian cancer. Patterns of gene expression characteristic of <it>TP53 </it>mutation could be discerned and included several genes that are known p53 targets or have been described in the context of expression signatures of <it>TP53 </it>mutation in breast cancer.</p

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy

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

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

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