Spectral and Polarization Properties of Photospheric Emission From Stratified Jets

We explore the spectral and polarization properties of photospheric emissions from stratified jets in which multiple components, separated by a sharp velocity shear regions, are distributed in lateral direction. Propagation of thermal photons injected at high optical depth region are calculated until they escape from the photosphere. It is found that presence of the lateral structure within the jet leads to non-thermal feature of the spectra and significant polarization signal in the resulting emission. The deviation from thermal spectra as well as the polarization degree tends to be enhanced as the velocity gradient in the shear region increases. In particular, we show that emissions from multi-component jet can reproduce the typical observed spectra of gamma-ray bursts (GRBs) irrespective to the position of the observer when a velocity shear region is closely spaced in various lateral ($\theta$) positions. The degree of polarization associated in the emission is significant (> few%) at wide range of observer angles and can be higher than 30%.Comment: 21 pages, 12 figures, accepted for publication in Ap

Membrane-associated collagens with interrupted triple-helices (MACITs):evolution from a bilaterian common ancestor and functional conservation <i>in C. elegans</i>

Protein sequence alignment of human collagens XIII, XXIII, XXV and six alternative spliced variants of COL-99. The protein sequence of the newly identified COL-99f was compared with the other COL-99 variants and human collagens XIII, XXIII and XXV. Putative furin cleavage residues in these proteins and the peptides for producing the COL-99 antibodies AB5625.11 and AB693 are highlighted in the sequence. (PDF 22 kb

Automatic Diagnosis of Late-Life Depression by 3D Convolutional Neural Networks and Cross-Sample Entropy Analysis From Resting-State fMRI

Resting-state fMRI has been widely used in investigating the pathophysiology of late-life depression (LLD). Unlike the conventional linear approach, cross-sample entropy (CSE) analysis shows the nonlinear property in fMRI signals between brain regions. Moreover, recent advances in deep learning, such as convolutional neural networks (CNNs), provide a timely application for understanding LLD. Accurate and prompt diagnosis is essential in LLD; hence, this study aimed to combine CNN and CSE analysis to discriminate LLD patients and non-depressed comparison older adults based on brain resting-state fMRI signals. Seventy-seven older adults, including 49 patients and 28 comparison older adults, were included for fMRI scans. Three-dimensional CSEs with volumes corresponding to 90 seed regions of interest of each participant were developed and fed into models for disease classification and depression severity prediction. We obtained a diagnostic accuracy \u3e 85% in the superior frontal gyrus (left dorsolateral and right orbital parts), left insula, and right middle occipital gyrus. With a mean root-mean-square error (RMSE) of 2.41, three separate models were required to predict depressive symptoms in the severe, moderate, and mild depression groups. The CSE volumes in the left inferior parietal lobule, left parahippocampal gyrus, and left postcentral gyrus performed best in each respective model. Combined complexity analysis and deep learning algorithms can classify patients with LLD from comparison older adults and predict symptom severity based on fMRI data. Such application can be utilized in precision medicine for disease detection and symptom monitoring in LLD

Predicting Suicidality in Late-Life Depression by 3D Convolutional Neural Network and Cross-Sample Entropy Analysis of Resting-State fMRI

BACKGROUND: Predicting suicide is a pressing issue among older adults; however, predicting its risk is difficult. Capitalizing on the recent development of machine learning, considerable progress has been made in predicting complex behavior such as suicide. As depression remained the strongest risk for suicide, we aimed to apply deep learning algorithms to identify suicidality in a group with late-life depression (LLD). METHODS: We enrolled 83 patients with LLD, 35 of which were non-suicidal and 48 were suicidal, including 26 with only suicidal ideation and 22 with past suicide attempts, for resting-state functional magnetic resonance imaging (MRI). Cross-sample entropy (CSE) analysis was conducted to examine the complexity of MRI signals among brain regions. Three-dimensional (3D) convolutional neural networks (CNNs) were used, and the classification accuracy in each brain region was averaged to predict suicidality after sixfold cross-validation. RESULTS: We found brain regions with a mean accuracy above 75% to predict suicidality located mostly in default mode, fronto-parietal, and cingulo-opercular resting-state networks. The models with right amygdala and left caudate provided the most reliable accuracy in all cross-validation folds, indicating their neurobiological importance in late-life suicide. CONCLUSION: Combining CSE analysis and the 3D CNN, several brain regions were found to be associated with suicidality

The Multimodal Information based Speech Processing (MISP) 2022 Challenge: Audio-Visual Diarization and Recognition

The Multi-modal Information based Speech Processing (MISP) challenge aims to extend the application of signal processing technology in specific scenarios by promoting the research into wake-up words, speaker diarization, speech recognition, and other technologies. The MISP2022 challenge has two tracks: 1) audio-visual speaker diarization (AVSD), aiming to solve ``who spoken when'' using both audio and visual data; 2) a novel audio-visual diarization and recognition (AVDR) task that focuses on addressing ``who spoken what when'' with audio-visual speaker diarization results. Both tracks focus on the Chinese language, and use far-field audio and video in real home-tv scenarios: 2-6 people communicating each other with TV noise in the background. This paper introduces the dataset, track settings, and baselines of the MISP2022 challenge. Our analyses of experiments and examples indicate the good performance of AVDR baseline system, and the potential difficulties in this challenge due to, e.g., the far-field video quality, the presence of TV noise in the background, and the indistinguishable speakers.Comment: 5 pages, 4 figures, to be published in ICASSP202

A feedback regulatory loop between methyltransferase PRMT1 and orphan receptor TR3

PRMT1, an arginine methyltransferase, plays an important role in numerous cellular processes. In this study, we demonstrate a feedback regulatory loop between PRMT1 and the orphan receptor TR3. Unlike another orphan receptor HNF4, TR3 is not methylated by PRMT1 although they physically interact with each other. By delaying the TR3 protein degradation, PRMT1 binding leads to the elevation of TR3 cellular protein level, thereby enhances the DNA binding and transactivation activity of TR3 in a non-methyltransferase manner. Another coactivator SRC-2 acts synergistically with PRMT1 to regulate TR3 functions. In turn, TR3 binding to the catalytic domain of PRMT1 causes an inhibition of the PRMT1 methyltransferase activity. This repression results in the functional changes in some of PRMT1 substrates, including STAT3 and Sam68. The negative regulation of PRMT1 by TR3 was further confirmed in both TR3-knockdown cells and TR3-knockout mice with the use of an agonist for TR3. Taken together, our study not only identifies a regulatory role of PRMT1, independent on methyltransferase activity, in TR3 transactivation, but also characterizes a novel function of TR3 in the repression of PRMT1 methyltransferase activity

Biology and Clinical Implications of the 19q13 Aggressive Prostate Cancer Susceptibility Locus

Genome-wide association studies (GWAS) have identified rs11672691 at 19q13 associated with aggressive prostate cancer (PCa). Here, we independently confirmed the finding in a cohort of 2,738 PCa patients and discovered the biological mechanism underlying this association. We found an association of the aggressive PCa-associated allele G of rs11672691 with elevated transcript levels of two biologically plausible candidate genes, PCAT19 and CEACAM21, implicated in PCa cell growth and tumor progression. Mechanistically, rs11672691 resides in an enhancer element and alters the binding site of HOXA2, a novel oncogenic transcription factor with prognostic potential in PCa. Remarkably, CRISPR/Cas9-mediated single-nucleotide editing showed the direct effect of rs11672691 on PCAT19 and CEACAM21 expression and PCa cellular aggressive phenotype. Clinical data demonstrated synergistic effects of rs11672691 genotype and PCAT19/CEACAM21 gene expression on PCa prognosis. These results provide a plausible mechanism for rs11672691 associated with aggressive PCa and thus lay the ground work for translating this finding to the clinic

Meta-analysis Followed by Replication Identifies Loci in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as Associated with Systemic Lupus Erythematosus in Asians

Systemic lupus erythematosus (SLE) is a prototype autoimmune disease with a strong genetic involvement and ethnic differences. Susceptibility genes identified so far only explain a small portion of the genetic heritability of SLE, suggesting that many more loci are yet to be uncovered for this disease. In this study, we performed a meta-analysis of genome-wide association studies on SLE in Chinese Han populations and followed up the findings by replication in four additional Asian cohorts with a total of 5,365 cases and 10,054 corresponding controls. We identified genetic variants in or near CDKN1B, TET3, CD80, DRAM1, and ARID5B as associated with the disease. These findings point to potential roles of cell-cycle regulation, autophagy, and DNA demethylation in SLE pathogenesis. For the region involving TET3 and that involving CDKN1B, multiple independent SNPs were identified, highlighting a phenomenon that might partially explain the missing heritability of complex diseases