An analytical model of the large neutral regions during the late stage of reionization

In this paper we investigate the nature and distribution of large neutral regions during the late epoch of reionization. In the "bubble model" of reionization, the mass distribution of large ionized regions ("bubbles") during the early stage of reionization is obtained by using the excursion set model, where the ionization of a region corresponds to the first up-crossing of a barrier by random trajectories. We generalize this idea, and develop a method to predict the distribution of large scale neutral regions during the late stage of reionization, taking into account the ionizing background after the percolation of HII regions. The large scale neutral regions which we call "neutral islands" are not individual galaxies or minihalos, but larger regions where fewer galaxies formed and hence ionized later, and they are identified in the excursion set model with the first down-crossings of the island barrier. Assuming that the consumption rate of ionizing background photons is proportional to the surface area of the neutral islands, we obtained the size distribution of the neutral islands. We also take the "bubbles-in-island" effect into account by considering the conditional probability of up-crossing a bubble barrier after down-crossing the island barrier. We find that this effect is very important. An additional barrier is set to avoid islands being percolated through. We find that there is a characteristic scale for the neutral islands, while the small islands are rapidly swallowed up by the ionizing background, this characteristic scale does not change much as the reionization proceeds.Comment: 33 pages, 11 figures, accepted by The Astrophysical Journa

Adapting Offline Speech Translation Models for Streaming with Future-Aware Distillation and Inference

A popular approach to streaming speech translation is to employ a single offline model with a \textit{wait-$k$} policy to support different latency requirements, which is simpler than training multiple online models with different latency constraints. However, there is a mismatch problem in using a model trained with complete utterances for streaming inference with partial input. We demonstrate that speech representations extracted at the end of a streaming input are significantly different from those extracted from a complete utterance. To address this issue, we propose a new approach called Future-Aware Streaming Translation (FAST) that adapts an offline ST model for streaming input. FAST includes a Future-Aware Inference (FAI) strategy that incorporates future context through a trainable masked embedding, and a Future-Aware Distillation (FAD) framework that transfers future context from an approximation of full speech to streaming input. Our experiments on the MuST-C EnDe, EnEs, and EnFr benchmarks show that FAST achieves better trade-offs between translation quality and latency than strong baselines. Extensive analyses suggest that our methods effectively alleviate the aforementioned mismatch problem between offline training and online inference.Comment: work in progres

An Embarrassingly Simple Baseline for Imbalanced Semi-Supervised Learning

Semi-supervised learning (SSL) has shown great promise in leveraging unlabeled data to improve model performance. While standard SSL assumes uniform data distribution, we consider a more realistic and challenging setting called imbalanced SSL, where imbalanced class distributions occur in both labeled and unlabeled data. Although there are existing endeavors to tackle this challenge, their performance degenerates when facing severe imbalance since they can not reduce the class imbalance sufficiently and effectively. In this paper, we study a simple yet overlooked baseline -- SimiS -- which tackles data imbalance by simply supplementing labeled data with pseudo-labels, according to the difference in class distribution from the most frequent class. Such a simple baseline turns out to be highly effective in reducing class imbalance. It outperforms existing methods by a significant margin, e.g., 12.8%, 13.6%, and 16.7% over previous SOTA on CIFAR100-LT, FOOD101-LT, and ImageNet127 respectively. The reduced imbalance results in faster convergence and better pseudo-label accuracy of SimiS. The simplicity of our method also makes it possible to be combined with other re-balancing techniques to improve the performance further. Moreover, our method shows great robustness to a wide range of data distributions, which holds enormous potential in practice. Code will be publicly available.Comment: Imbalanced Semi-Supervised Learnin

Duration of untreated psychosis is associated with temporal and occipitotemporal gray matter volume decrease in treatment naive schizophrenia

BACKGROUND: Long duration of untreated psychosis (DUP) is associated with poor treatment outcome. Whether or not DUP is related to brain gray matter volume abnormalities in antipsychotic medication treatment naive schizophrenia remains unclear at this time. METHODS: Patients with treatment-naive schizophrenia and healthy controls went through brain scan using high resolution Magnetic Resonance Imaging. DUP was evaluated using the Nottingham Onset Schedule (NOS), and dichotomized as short DUP ( 26 weeks). Voxel-based methods were used for volumetric measure in the brain. RESULTS: Fifty-seven patients (27 short DUP and 30 long DUP) and 30 healthy controls were included in the analysis. There were significant gray matter volumetric differences among the 3 groups in bilateral parahippocampus gyri, right superior temporal gyrus, left fusiform gyrus, left middle temporal gyrus, and right superior frontal gyrus (p\u27s \u3c 0.01). Compared with healthy controls, the long DUP group had significantly smaller volume in all these regions (p\u27s \u3c 0.05). Compared with the short-DUP group, the long-DUP group had significantly smaller volume in right superior temporal gyrus, left fusiform gyrus, and left middle temporal gyrus (p\u27s \u3c 0.01). CONCLUSION: Our findings suggest that DUP is associated with temporal and occipitotemporal gray matter volume decrease in treatment naive schizophrenia. The brain structural changes in untreated psychosis might contribute to poor treatment response and long-term prognosis in this patient population

The cuproptosis-associated 11 gene signature as a predictor for outcomes and response to Bacillus Calmette-Guerin and immune checkpoint inhibitor therapies in bladder carcinoma

Bladder cancer (BC) or carcinoma (BLCA) is predominantly derived from urothelium and includes non-muscle invasive BC (NMIBC) and muscle invasive BC (MIBC). Bacillus Calmette-Guerin (BCG) has long been applied for NMIBC to effectively reduce disease recurrence or progression, whereas immune checkpoint inhibitors (ICIs) were recently introduced to treat advanced BLCA with good efficacy. For BCG and ICI applications, reliable biomarkers are required to stratify potential responders for better personalized interventions, and ideally, they can replace or reduce invasive examinations such as cystoscopy in monitoring treatment efficacy. Here we developed the cuproptosis-associated 11 gene signature (CuAGS-11) model to accurately predict survival and response to BCG and ICI regimens in BLCA patients. In both discovery and validation cohorts where BLCA patients were divided into high- and low-risk groups based on a median CuAGS-11 score as the cutoff, the high-risk group was associated with significantly shortened overall survival (OS) and progression-free survival (PFS) independently. The survival predictive accuracy was comparable between CuAGS-11 and stage, and their combination-based nomograms showed high consistence between predicted and observed OS/PFS. The analysis of 3 BLCA cohorts treated with BCG unveiled lower response rates and higher frequencies of recurrence or progression coupled with shorter survival in CuAGS-11 high-risk groups. In contrast, almost none of patients underwent progression in low-risk groups. In IMvigor210 cohort of 298 BLCA patients treated with ICI Atezolizumab, complete/partial remissions were 3-fold higher accompanied by significantly longer OS in the CuAGS-11 low- than high-risk groups (P = 7.018E-06). Very similar results were obtained from the validation cohort (P = 8.65E-05). Further analyses of Tumor Immune Dysfunction and Exclusion (TIDE) scores revealed that CuAGS-11 high-risk groups displayed robustly higher T cell exclusion scores in both discovery (P = 1.96E-05) and validation (P = 0.008) cohorts. Collectively, the CuAGS-11 score model is a useful predictor for OS/PFS and BCG/ICI efficacy in BLCA patients. For BCG-treated patients, reduced invasive examinations are suggested for monitoring the CuAGS-11 low-risk patients. The present findings thus provide a framework to improve BLCA patient stratification for personalized interventions and to reduce invasive monitoring inspections

Simultaneous removal of Cr(VI) and 4-chlorophenol through photocatalysis by a novel anatase/titanate nanosheet composite: Synergetic promotion effect and autosynchronous doping

Clean-up of wastewaters with coexisting heavy metals and organic contaminants is a huge issue worldwide. In this study, a novel anatase/titanate nanosheet composite material (labeled as TNS) synthesized through a one-step hydrothermal reaction was demonstrated to achieve the goal of simultaneous removal of Cr(VI) and 4-cholophenol (4-CP) from water. TEM and XRD analyses indicated the TNS was a nano-composite of anatase and titanate, with anatase acting as the primary photocatalysis center and titanate as the main adsorption site. Enhanced photocatalytic removal of co-existent Cr(VI) and 4-CP was observed in binary systems, with apparent rate constants (k(1)) for photocatalytic reactions of Cr(VI) and 4-CP about 3.1 and 2.6 times of that for single systems. In addition, over 99% of Cr(VI) and 4-CP was removed within 120 min through photocatalysis by TNS at pH 7 in the binary system. Mechanisms for enhanced photocatalytic efficiency in the binary system are identified as: (1) a synergetic effect on the photo-reduction of Cr(VI) and photo-oxidation of 4-CP due to efficient separation of electron-hole pairs, and (2) autosynchronous doping because of reduced Cr(III) adsorption onto TNS. Furthermore, TNS could be efficiently reused after a simple acid-base treatment. (C) 2016 Elsevier B.V. All rights reserved.National Natural Science Foundation of China [51508006]; Natural Science Foundation of the Colleges and Universities in Jiangsu Province [15KJB610011]SCI(E)[email protected]; [email protected]

Phytolith-Occluded Carbon Storages in Forest Litter Layers in Southern China: Implications for Evaluation of Long-Term Forest Carbon Budget

Phytolith-occluded carbon (PhytOC) can be preserved in soils or sediments for thousands of years and might be a promising potential mechanism for long-term terrestrial carbon (C) sequestration. As the principal pathway for the return of organic matters to soils, the forest litter layers make a considerable contribution to terrestrial C sequestration. Although previous studies have estimated the phytolith production fluxes in the above-ground vegetations of various terrestrial ecosystems, the storages of phytoliths and PhytOC in litter layers have not been thoroughly investigated, especially in forest ecosystems. Using analytical data of silica, phytoliths, return fluxes and storages of forest litter, this study estimated the phytolith and PhytOC storages in litter layers in different forest types in southern China. The results indicated that the total phytolith storage in forest litter layers in southern China was 24.34 ± 8.72 Tg. Among the different forest types, the phytolith storage in bamboo forest litter layers (15.40 ± 3.40 Tg) was much higher than that in other forests. At the same time, the total PhytOC storage reached up to 2.68 ± 0.96 Tg CO2 in forest litter layers in southern China, of which approximately 60% was contributed by bamboo forest litter layers. Based on the current litter turnover time of different forest types in southern China, a total of 1.01 ± 0.32 Tg of PhytOC per year would be released into soil profiles as a stable C pool during litter decomposition, which would make an important contribution to the global terrestrial long-term biogeochemical C sink. Therefore, the important role of PhytOC storage in forest litter layers should be taken into account in evaluating long-term forest C budgets

1.5um Polarization-Entangled Bell States Generation Based on Birefringence in High Nonlinear Microstructure Fiber

Polarization-entangled photon pair generation based on two scalar scattering processes of the vector four photon scattering has been demonstrated experimentally in high nonlinear microstructure fiber with birefringence. By controlling the pump polarization state, polarization-entangled Bell states can be realized. It is provides a simple way to realize efficient and compact fiber based polarization-entangled photon pair sources.Comment: 12 pages, 3 figures, accepted by optics lette

Global analysis of N6-methyladenosine functions and its disease association using deep learning and network-based methods

<div><p>N6-methyladenosine (m⁶A) is the most abundant methylation, existing in >25% of human mRNAs. Exciting recent discoveries indicate the close involvement of m⁶A in regulating many different aspects of mRNA metabolism and diseases like cancer. However, our current knowledge about how m⁶A levels are controlled and whether and how regulation of m⁶A levels of a specific gene can play a role in cancer and other diseases is mostly elusive. We propose in this paper a computational scheme for predicting m⁶A-regulated genes and m⁶A-associated disease, which includes Deep-m⁶A, the first model for detecting condition-specific m⁶A sites from MeRIP-Seq data with a single base resolution using deep learning and Hot-m⁶A, a new network-based pipeline that prioritizes functional significant m⁶A genes and its associated diseases using the Protein-Protein Interaction (PPI) and gene-disease heterogeneous networks. We applied Deep-m⁶A and this pipeline to 75 MeRIP-seq human samples, which produced a compact set of 709 functionally significant m⁶A-regulated genes and nine functionally enriched subnetworks. The functional enrichment analysis of these genes and networks reveal that m⁶A targets key genes of many critical biological processes including transcription, cell organization and transport, and cell proliferation and cancer-related pathways such as Wnt pathway. The m⁶A-associated disease analysis prioritized five significantly associated diseases including leukemia and renal cell carcinoma. These results demonstrate the power of our proposed computational scheme and provide new leads for understanding m⁶A regulatory functions and its roles in diseases.</p></div

Negative Elongation Factor Controls Energy Homeostasis in Cardiomyocytes

SummaryNegative elongation factor (NELF) is known to enforce promoter-proximal pausing of RNA polymerase II (Pol II), a pervasive phenomenon observed across multicellular genomes. However, the physiological impact of NELF on tissue homeostasis remains unclear. Here, we show that whole-body conditional deletion of the B subunit of NELF (NELF-B) in adult mice results in cardiomyopathy and impaired response to cardiac stress. Tissue-specific knockout of NELF-B confirms its cell-autonomous function in cardiomyocytes. NELF directly supports transcription of those genes encoding rate-limiting enzymes in fatty acid oxidation (FAO) and the tricarboxylic acid (TCA) cycle. NELF also shares extensively transcriptional target genes with peroxisome proliferator-activated receptor α (PPARα), a master regulator of energy metabolism in the myocardium. Mechanistically, NELF helps stabilize the transcription initiation complex at the metabolism-related genes. Our findings strongly indicate that NELF is part of the PPARα-mediated transcription regulatory network that maintains metabolic homeostasis in cardiomyocytes