Branching ratios of Bc Meson Decaying to Pseudoscalar and Axial-Vector Mesons

We study Cabibbo-Kobayashi-Maskawa (CKM) favored weak decays of Bc mesons in the Isgur-Scora-Grinstein-Wise (ISGW) quark model. We present a detailed analysis of the Bc meson decaying to a pseudoscalar meson (P) and an axial-vector meson (A). We also give the form factors involving transition in the ISGW II framework and consequently, predict the branching ratios of decays.Comment: 19 pages,7 table

NLO QCD Corrections to BcB_c-to-Charmonium Form Factors

The $B_c(^1S_0)$ meson to S-wave Charmonia transition form factors are calculated in next-to-leading order(NLO) accuracy of Quantum Chromodynamics(QCD). Our results indicate that the higher order corrections to these form factors are remarkable, and hence are important to the phenomenological study of the corresponding processes. For the convenience of comparison and use, the relevant expressions in asymptotic form at the limit of $m_c\rightarrow0$ for the radiative corrections are presented

Functional consequences of seven novel mutations in the CYP11B1 Gene: four mutations associated with nonclassic and three mutations causing classic 11 -Hydroxylase Deficiency

Context: Steroid 11β-hydroxylase (CYP11B1) deficiency (11OHD) is the second most common form of congenital adrenal hyperplasia (CAH). Cases of nonclassic 11OHD are rare compared with the incidence of nonclassic 21-hydroxylase deficiency. Objective: The aim of the study was to analyze the functional consequences of seven novel CYP11B1 mutations (p.M88I, p.W116G, p.P159L, p.A165D, p.K254_A259del, p.R366C, p.T401A) found in three patients with classic 11OHD, two patients with nonclassic 11OHD, and three heterozygous carriers for CYP11B1 mutations. Methods: We conducted functional studies employing a COS7 cell in vitro expression system comparing wild-type (WT) and mutant CYP11B1 activity. Mutants were examined in a computational three-dimensional model of the CYP11B1 protein. Results: All mutations (p.W116G, p.A165D, p.K254_A259del) found in patients with classic 11OHD have absent or very little 11β-hydroxylase activity relative to WT. The mutations detected in patients with nonclassic 11OHD showed partial functional impairment, with one patient being homozygous (p.P159L; 25% of WT) and the other patient compound heterozygous for a novel mild p.M88I (40% of WT) and the known severe p.R383Q mutation. The two mutations detected in heterozygous carriers (p.R366C, p.T401A) also reduced CYP11B1 activity by 23 to 37%, respectively. Conclusion: Functional analysis results allow for the classification of novel CYP11B1 mutations as causative for classic and nonclassic 11OHD, respectively. Four partially inactivating mutations are predicted to result in nonclassic 11OHD. These findings double the number of mild CYP11B1 mutations previously described as associated with mild 11OHD. Our data are important to predict phenotypic expression and provide important information for clinical and genetic counseling i

Homogeneous Bubble Nucleation driven by local hot spots: a Molecular Dynamics Study

We report a Molecular Dynamics study of homogenous bubble nucleation in a Lennard-Jones fluid. The rate of bubble nucleation is estimated using forward-flux sampling (FFS). We find that cavitation starts with compact bubbles rather than with ramified structures as had been suggested by Shen and Debenedetti (J. Chem. Phys. 111:3581, 1999). Our estimate of the bubble-nucleation rate is higher than predicted on the basis of Classical Nucleation Theory (CNT). Our simulations show that local temperature fluctuations correlate strongly with subsequent bubble formation - this mechanism is not taken into account in CNT

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

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 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

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