Neutrino Phenomenology from Democratic Approach

In this article, we derive the tiny neutrino masses and mixings from the democratic and diagonal texture approach, which consistent with the recent experimental oscillation data. The unitary rotation matrices, which diagonalize the neutrino mass matrices are obtained by a specific parametrization of the Pontecorvo-Maki-Nakagawa-Sakata (PMNS) mixing matrix. From which, we tried to calculate all three mixing angles as well as the Dirac CP-violating phase interms of model mixing parameters. In particular, the deviation from the tribimaximal mixing is explained in this model. Along with the Jarlskog parameter in terms of model parameter and neutrino-less double beta decay (NDBD) has been discussed briefly.Comment: Minor corrections in model; title and abstract changed accordingl

Type III seesaw under A4A_4 modular symmetry with leptogenesis and muon g−2g-2

We make an attempt to study neutrino phenomenology in the framework of type-III seesaw by considering $A_4$ modular symmetry in the super-symmetric context. In addition, we have included local $U(1)_{B-L}$ symmetry which eventually helps us to avoid certain unwanted terms in the superpotential. Hitherto, the seesaw being type-III, it involves the fermion triplet superfields $\Sigma$, along with which, we have included a singlet weighton field $(\rho)$. In here, modular symmetry plays a crucial role by avoiding the usage of excess flavon (weighton) fields. Also, the Yukawa couplings acquire modular forms which are expressed in terms of Dedekind eta function $\eta(\tau)$. However, for numerical analysis we use $q$ expansion expressions of these couplings. Therefore, the model discussed here is triumphant enough to accommodate the observed neutrino oscillation data. Additionally, it also successfully explains leptogenesis and sheds some light on the current results of muon ($g-2$).Comment: 23 pages, 15 figure

Neutrino Phenomenology and Dark matter in an A4A_4 flavour extended B-L model

We present an $A_4$ flavor extended $\rm B-L$ model for realization of eV scale sterile neutrinos, motivated by the recent experimental hints from both particle physics and cosmology. The framework considered here is a gauged $\rm B-L$ extension of standard model without the introduction of right-handed neutrinos, where the gauge triangle anomalies are canceled with the inclusion of three exotic neutral fermions $N_{i}$ ($i=1,2,3$) with $\rm B-L$ charges $-4,-4$ and $5$. The usual Dirac Yukawa couplings between the SM neutrinos and the exotic fermions are absent and thus, the model allows natural realization of eV scale sterile-like neutrino and its mixing with standard model neutrinos by invoking $\rm A_4$ flavor symmetry. We demonstrate how the exact tri-bimaximal mixing pattern is perturbed due to active-sterile mixing by analyzing $1+3$ case in detail. We also show the implication of eV scale sterile-like neutrino on various observables in neutrino oscillation experiments and the effective mass in neutrinoless double beta decay. Another interesting feature of the model is that one of three exotic fermions is required to explain eV scale phenomena, while other two fermions form stable dark matter candidates and their total relic density satisfy the observed $3\sigma$ limit of Planck data. We constrain the gauge parameters associated with $U(1)$ gauge extension, using relic density and collider bounds.Comment: 29 pages, 13 figures, version to appear in EPJ

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