The RNN-Transducer (RNN-T) framework for speech recognition has been growing in popularity, particularly for deployed real-time ASR systems, because it combines high accuracy with naturally streaming recognition. One of the drawbacks of RNN-T is that its loss function is relatively slow to compute, and can use a lot of memory. Excessive GPU memory usage can make it impractical to use RNN-T loss in cases where the vocabulary size is large: for example, for Chinese character-based ASR. We introduce a method for faster and more memory-efficient RNN-T loss computation. We first obtain pruning bounds for the RNN-T recursion using a simple joiner network that is linear in the encoder and decoder embeddings; we can evaluate this without using much memory. We then use those pruning bounds to evaluate the full, non-linear joiner network. <<<

This study contrasts results from different correlational methods for examining links between mother and child (N = 72 dyads) reports of early adolescent (M = 11.5 years) behavior problems and relationship negativity and support. Simple (Pearson) correlations revealed a consistent pattern of statistically significant associations, regardless of whether scores came from the same reporter or from different reporters. When correlations between behavior problems and relationship quality differed, within-reporter correlations were always greater in magnitude than between-reporter correlations. Dyadic (common fate) analyses designed for interdependent data decomposed within-reporter correlations into variance shared across reporters (dyadic correlations) and variance unique to specific reporters (individual correlations). Dyadic correlations were responsible for most associations between adolescent behavior problems and relationship negativity; after partitioning variance shared across reporters, no individual correlations emerged as statistically significant. In contrast, adolescent behavior problems were linked to relationship support via both shared variance and variance unique to maternal perceptions. Dyadic analyses provide a parsimonious alternative to multiple contrasts in instances when identical measures have been collected from multiple reporters. Findings from these analyses indicate that same-reporter variance bias should not be assumed in the absence of dyadic statistical analyses. <<<

近年来，随着全球经济的快速发展，计算机科学技术的迅猛进步和发展以及高校智慧管理对于科学技术应用的迫切需求，物联网应用、大数据管理、5G和云计算等前言技术都逐渐在高校智慧管理上得到了体现和普及。而人工智能技术作为当前在高校管理中最受青睐的高科技技术，已在教学研究管理、在线教育和学生校园生活优化等各个方面得到了深度应用并在取得显著成效。本文介绍人工智能现阶段在高校校园管理中的重要应用，以供参考。 <<<

Diffusion models have shown incredible capabilities as generative models; indeed, they power the current state-of-the-art models on text-conditioned image generation such as Imagen and DALL-E 2. In this work we review, demystify, and unify the understanding of diffusion models across both variational and score-based perspectives. We first derive Variational Diffusion Models (VDM) as a special case of a Markovian Hierarchical Variational Autoencoder, where three key assumptions enable tractable computation and scalable optimization of the ELBO. We then prove that optimizing a VDM boils down to learning a neural network to predict one of three potential objectives: the original source input from any arbitrary noisification of it, the original source noise from any arbitrarily noisified input, or the score function of a noisified input at any arbitrary noise level. We then dive deeper into what it means to learn the score function, and connect the variational perspective of a diffusion model explicitly with the Score-based Generative Modeling perspective through Tweedie's Formula. Lastly, we cover how to learn a conditional distribution using diffusion models via guidance. <<<

Evidence regarding whether fathers are “essential” for or contribute substantially to children’s development over and above their financial contributions is still emerging. The question of how exactly fathers matter for children’s development has dominated much of the emerging research on fatherhood and has produced some promising findings. In this chapter, I briefly review the theoretical underpinnings of what fathers do and how it matters for children’s development. I then examine current findings on the association between fathers’ involvement and children’s outcomes over time, and conclude with suggestions for future research. <<<

Deep learning has achieved remarkable success in numerous domains with help from large amounts of big data. However, the quality of data labels is a concern because of the lack of high-quality labels in many real-world scenarios. As noisy labels severely degrade the generalization performance of deep neural networks, learning from noisy labels (robust training) is becoming an important task in modern deep learning applications. In this survey, we first describe the problem of learning with label noise from a supervised learning perspective. Next, we provide a comprehensive review of 62 state-of-the-art robust training methods, all of which are categorized into five groups according to their methodological difference, followed by a systematic comparison of six properties used to evaluate their superiority. Subsequently, we perform an in-depth analysis of noise rate estimation and summarize the typically used evaluation methodology, including public noisy datasets and evaluation metrics. Finally, we present several promising research directions that can serve as a guideline for future studies. <<<

Learning with noisy labels is an important and challenging task for training accurate deep neural networks. Some commonly-used loss functions, such as Cross Entropy (CE), suffer from severe overfitting to noisy labels. Robust loss functions that satisfy the symmetric condition were tailored to remedy this problem, which however encounter the underfitting effect. In this paper, we theoretically prove that any loss can be made robust to noisy labels by restricting the network output to the set of permutations over a fixed vector. When the fixed vector is one-hot, we only need to constrain the output to be one-hot, which however produces zero gradients almost everywhere and thus makes gradient-based optimization difficult. In this work, we introduce the sparse regularization strategy to approximate the one-hot constraint, which is composed of network output sharpening operation that enforces the output distribution of a net-work to be sharp and the ℓ p -norm (p ≤ 1) regularization that promotes the network output to be sparse. This simple approach guarantees the robustness of arbitrary loss functions while not hindering the fitting ability. Experimental results demonstrate that our method can significantly improve the performance of commonly-used loss functions in the presence of noisy labels and class imbalance, and out-perform the state-of-the-art methods. The code is available at https://github.com/hitcszx/lnl_sr. <<<

The N-methyladenosine (mA) is involved in the regulation of cell proliferation and metastasis formation in multiple cancers. However, the biological significance of RNA mA reader IGF2BP1 and the modification of IGF2BP1 itself have not been fully investigated. Here, we analyzed the functions and mechanism of IGF2BP1 in gastric cancer (GC). Results showed that IGF2BP1 upregulated in GC tissue and acted as a predictor of poor prognosis for GC patients. Functionally, IGF2BP1 promoted the migration and aerobic glycolysis of GC cells in vitro. Moreover, IGF2BP1 knockdown repressed the tumor growth in vivo. We also demonstrated that IGF2BP1 directly interacted with c-MYC mRNA via m6A-dependent manner to by stabilize its stability. Overall, these findings demonstrated that mA reader IGF2BP1 facilitated the carcinogenic of GC in mA/c-Myc-dependent manner, which might provide critical therapeutic strategy for GC. <<<

Eduard Ansaldo, Leianna C Slayden, Krystal L Ching, Meghan A Koch, Natalie K Wolf, Damian R Plichta, Eric M Brown, Daniel B Graham, Ramnik J Xavier, James J Moon, Gregory M Barton <<<

Intestinal adaptive immune responses influence host health, yet only a few intestinal bacteria species that induce cognate adaptive immune responses during homeostasis have been identified. Here, we show that , an intestinal bacterium associated with systemic effects on host metabolism and PD-1 checkpoint immunotherapy, induces immunoglobulin G1 (IgG1) antibodies and antigen-specific T cell responses in mice. Unlike previously characterized mucosal responses, T cell responses to are limited to T follicular helper cells in a gnotobiotic setting, without appreciable induction of other T helper fates or migration to the lamina propria. However, -specific responses are context dependent and adopt other fates in conventional mice. These findings suggest that, during homeostasis, contextual signals influence T cell responses to the microbiota and modulate host immune function. <<<

By simultaneously amplifying more than one locus in the same reaction, multiplex PCR is becoming a rapid and convenient screening assay in both the clinical and the research laboratory. While numerous papers and manuals discuss in detail conditions influencing the quality of PCR in general, relatively little has been published about the important experimental factors and the common difficulties frequently encountered with multiplex PCR. We have examined various conditions of the multiplex PCR, using a large number of primer pairs. Especially important for a successful multiplex PCR assay are the relative concentrations of the primers at the various loci, the concentration of the PCR buffer, the cycling temperatures and the balance between the magnesium chloride and deoxynucleotide concentrations. Based on our experience, we propose a protocol for developing a multiplex PCR assay and suggest ways to overcome commonly encountered problems. <<<

ACTIV-3/TICO Study Group, Angela J Rogers, Deborah Wentworth, Andrew Phillips, Katy Shaw-Saliba, Robin L Dewar, Neil R Aggarwal, Abdel G Babiker, Weizhong Chang, Nila J Dharan, Victoria J Davey, Elizabeth S Higgs, Norman Gerry, Adit A Ginde, J W Awori Hayanga, Helene Highbarger, Jeroen L Highbarger, Mamta K Jain, Virginia Kan, Kami Kim, Perrine Lallemand, Bradley G Leshnower, Joseph K Lutaakome, Gail Matthews, Ahmad Mourad, Eleftherios Mylonakis, Ven Natarajan, Maria L Padilla, Lavannya M Pandit, Roger Paredes, Sarah Pett, Srikanth Ramachandruni, M Tauseef Rehman, Brad T Sherman, D Clark Files, Samuel M Brown, Michael A Matthay, B Taylor Thompson, James D Neaton, H Clifford Lane, Jens D Lundgren <<<

BACKGROUND: Levels of plasma SARS-CoV-2 nucleocapsid (N) antigen may be an important biomarker in patients with COVID-19 and enhance our understanding of the pathogenesis of COVID-19.OBJECTIVE: To evaluate whether levels of plasma antigen can predict short-term clinical outcomes and identify clinical and viral factors associated with plasma antigen levels in hospitalized patients with SARS-CoV-2.DESIGN: Cross-sectional study of baseline plasma antigen level from 2540 participants enrolled in the TICO (Therapeutics for Inpatients With COVID-19) platform trial from August 2020 to November 2021, with additional data on day 5 outcome and time to discharge.SETTING: 114 centers in 10 countries.PARTICIPANTS: Adults hospitalized for acute SARS-CoV-2 infection with 12 days or less of symptoms.MEASUREMENTS: Baseline plasma viral N antigen level was measured at a central laboratory. Delta variant status was determined from baseline nasal swabs using reverse transcriptase polymerase chain reaction. Associations between baseline patient characteristics and viral factors and baseline plasma antigen levels were assessed using both unadjusted and multivariable modeling. Association between elevated baseline antigen level of 1000 ng/L or greater and outcomes, including worsening of ordinal pulmonary scale at day 5 and time to hospital discharge, were evaluated using logistic regression and Fine-Gray regression models, respectively.RESULTS: Plasma antigen was below the level of quantification in 5% of participants at enrollment, and 1000 ng/L or greater in 57%. Baseline pulmonary severity of illness was strongly associated with plasma antigen level, with mean plasma antigen level 3.10-fold higher among those requiring noninvasive ventilation or high-flow nasal cannula compared with room air (95% CI, 2.22 to 4.34). Plasma antigen level was higher in those who lacked antispike antibodies (6.42 fold; CI, 5.37 to 7.66) and in those with the Delta variant (1.73 fold; CI, 1.41 to 2.13). Additional factors associated with higher baseline antigen level included male sex, shorter time since hospital admission, decreased days of remdesivir, and renal impairment. In contrast, race, ethnicity, body mass index, and immunocompromising conditions were not associated with plasma antigen levels. Plasma antigen level of 1000 ng/L or greater was associated with a markedly higher odds of worsened pulmonary status at day 5 (odds ratio, 5.06 [CI, 3.41 to 7.50]) and longer time to hospital discharge (median, 7 vs. 4 days; subhazard ratio, 0.51 [CI, 0.45 to 0.57]), with subhazard ratios similar across all levels of baseline pulmonary severity.LIMITATIONS: Plasma samples were drawn at enrollment, not hospital presentation. No point-of-care test to measure plasma antigen is currently available.CONCLUSION: Elevated plasma antigen is highly associated with both severity of pulmonary illness and clinically important patient outcomes. Multiple clinical and viral factors are associated with plasma antigen level at presentation. These data support a potential role of ongoing viral replication in the pathogenesis of SARS-CoV-2 in hospitalized patients.PRIMARY FUNDING SOURCE: U.S. government Operation Warp Speed and National Institute of Allergy and Infectious Diseases. <<<

An orchestrated cellular network, including adaptive lymphocytes and group 3 innate lymphoid cells (ILC3s), maintains intestinal barrier integrity and homeostasis. T cells can monitor environmental insults through constitutive circulation, scanning tissues and forming immunological contacts, a process named immunosurveillance. In contrast, the dynamics of intestinal ILC3s are unknown. Using intravital imaging, we observed that villus ILC3s were largely immotile at steady state but acquired migratory 'patrolling' attributes and enhanced cytokine expression in response to inflammation. We showed that T cells, the chemokine CCL25 and bacterial ligands regulated intestinal ILC3 behavior and that loss of patrolling behavior by interleukin-22 (IL-22)-producing ILC3s altered the intestinal barrier through increased epithelial cell death. Collectively, we identified notable differences between the behavior of ILC3s and T cells, with a prominent adaptation of intestinal ILC3s toward mucosal immunosurveillance after inflammation. <<<

Recent findings indicate that the Japanese IT sector increasingly lags the U.S. IT sector in software innovation and that this underlies Japan's weakening competitive performance vis-à-vis U.S. IT. This article explores alternative explanations for this outcome and analyzes what explains the Japanese software industry's trajectory. The sources are found in the late understanding of the transformational role of software and its value-creating potential as well as in the evolution of the industry's structure. Finally, this article considers what policy makers in other nations might learn from the Japanese experience in building a more vibrant software industry. <<<

Zhaowen Liu, Edmund T Rolls, Zhi Liu, Kai Zhang, Ming Yang, Jingnan Du, Weikang Gong, Wei Cheng, Fei Dai, He Wang, Kamil Ugurbil, Jie Zhang, Jianfeng Feng <<<

MOTIVATION: Advances in neuroimaging and sequencing techniques provide an unprecedented opportunity to map the function of brain regions and identify the roots of psychiatric diseases. However, the results from most neuroimaging studies, i.e. activated clusters/regions or functional connectivities between brain regions, frequently cannot be conveniently and systematically interpreted, rendering the biological meaning unclear.RESULTS: We describe a brain annotation toolbox that generates functional and genetic annotations for neuroimaging results. The voxel-level functional description from the Neurosynth database and gene expression profile from the Allen Human Brain Atlas are used to generate functional/genetic information for region-level neuroimaging results. The validity of the approach is demonstrated by showing that the functional and genetic annotations for specific brain regions are consistent with each other; and further the region by region functional similarity network and genetic similarity network are highly correlated for major brain atlases. One application of brain annotation toolbox is to help provide functional/genetic annotations for newly discovered regions with unknown functions, e.g. the 97 new regions identified in the Human Connectome Project. Importantly, this toolbox can help understand differences between psychiatric patients and controls, and this is demonstrated using schizophrenia and autism data, for which the functional and genetic annotations for the neuroimaging changes in patients are consistent with each other and help interpret the results.AVAILABILITY AND IMPLEMENTATION: BAT is implemented as a free and open-source MATLAB toolbox and is publicly available at http://123.56.224.61:1313/post/bat.SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. <<<

Human object-selective cortex shows a large-scale organization characterized by the high-level properties of both animacy and object size. To what extent are these neural responses explained by primitive perceptual features that distinguish animals from objects and big objects from small objects? To address this question, we used a texture synthesis algorithm to create a class of stimuli-texforms-which preserve some mid-level texture and form information from objects while rendering them unrecognizable. We found that unrecognizable texforms were sufficient to elicit the large-scale organizations of object-selective cortex along the entire ventral pathway. Further, the structure in the neural patterns elicited by texforms was well predicted by curvature features and by intermediate layers of a deep convolutional neural network, supporting the mid-level nature of the representations. These results provide clear evidence that a substantial portion of ventral stream organization can be accounted for by coarse texture and form information without requiring explicit recognition of intact objects. <<<

The human immune system is composed of a distributed network of cells circulating throughout the body, which must dynamically form physical associations and communicate using interactions between their cell-surface proteomes1. Despite their therapeutic potential2, our map of these surface interactions remains incomplete3,4. Here, using a high-throughput surface receptor screening method, we systematically mapped the direct protein interactions across a recombinant library that encompasses most of the surface proteins that are detectable on human leukocytes. We independently validated and determined the biophysical parameters of each novel interaction, resulting in a high-confidence and quantitative view of the receptor wiring that connects human immune cells. By integrating our interactome with expression data, we identified trends in the dynamics of immune interactions and constructed a reductionist mathematical model that predicts cellular connectivity from basic principles. We also developed an interactive multi-tissue single-cell atlas that infers immune interactions throughout the body, revealing potential functional contexts for new interactions and hubs in multicellular networks. Finally, we combined targeted protein stimulation of human leukocytes with multiplex high-content microscopy to link our receptor interactions to functional roles, in terms of both modulating immune responses and maintaining normal patterns of intercellular associations. Together, our work provides a systematic perspective on the intercellular wiring of the human immune system that extends from systems-level principles of immune cell connectivity down to mechanistic characterization of individual receptors, which could offer opportunities for therapeutic intervention. <<<

Yufeng Lin, Harry Cheuk-Hay Lau, Yali Liu, Xing Kang, Yiwei Wang, Nick Lung-Ngai Ting, Thomas Ngai-Yeung Kwong, Jing Han, Weixin Liu, Changan Liu, Junjun She, Sunny Hei Wong, Joseph Jao-Yiu Sung, Jun Yu <<<

BACKGROUND & AIMS: The enteric mycobiota is a major component of the human gut microbiota, but its role in colorectal cancer (CRC) remains largely elusive. We conducted a meta-analysis to uncover the contribution of the fungal mycobiota to CRC.METHODS: We retrieved fecal metagenomic data sets from 7 previous publications and established an additional in-house cohort, totaling 1329 metagenomes (454 with CRC, 350 with adenoma, and 525 healthy individuals). Mycobiota composition and microbial interactions were analyzed. Candidate CRC-enriched fungal species (Aspergillus rambellii) was functionally validated in vitro and in vivo.RESULTS: Multicohort analysis revealed that the enteric mycobiota was altered in CRC. We identified fungi that were associated with patients with CRC or adenoma from multiple cohorts. Signature CRC-associated fungi included 6 enriched (A rambellii, Cordyceps sp. RAO-2017, Erysiphe pulchra, Moniliophthora perniciosa, Sphaerulina musiva, and Phytophthora capsici) and 1 depleted species (A kawachii). Co-occurrent interactions among CRC-enriched fungi became stronger in CRC compared with adenoma and healthy individuals. Moreover, we reported the transkingdom interactions between enteric fungi and bacteria in CRC progression, of which A rambellii was closely associated with CRC-enriched bacteria Fusobacterium nucleatum. A rambellii promoted CRC cell growth in vitro and tumor growth in xenograft mice. We further identified that combined fungal and bacterial biomarkers were more accurate than panels with pure bacterial species to discriminate patients with CRC from healthy individuals (the area under the curve relative change increased by 1.44%-10.60%).CONCLUSIONS: This study reveals enteric mycobiota signatures and pathogenic fungi in stages of colorectal tumorigenesis. Fecal fungi can be used, in addition to bacteria, for noninvasive diagnosis of patients with CRC. <<<

One of the buzzwords in the technological world is “Hyperautomation”. Hyperautomation is the new technological phenomenon in which it can bring intelligent automation processes using Robotic Process Automation (RPA), Artificial Intelligence (AI), Machine Learning (ML) and other technologies. It has a good amount of business applications; that is why many tech giants and start-ups are putting huge investments to reap the fruits of Hyperautomation. The data was collected from different secondary sources from 1-4-2020 to 31-7-2021. The rationale of this conceptual manuscript is to explain the definitions, concepts, technologies and models behind Hyperautomation. This study also emphasis in-depth benefits of hyper-automation specifically in Banking & Finance sector. Moreover, the study also presents some of the industry test cases to explain the Hyperautomation adoption level by different countries across the globe. <<<

As a major event in human civilization, wild plants were successfully domesticated to be crops, largely owing to continuing artificial selection. Here, we summarize new discoveries made during the past decade in crop domestication and breeding. The construction of crop genome maps and the functional characterization of numerous trait genes provide foundational information. Approaches to read, interpret, and write complex genetic information are being leveraged in many plants for highly efficient de novo or re-domestication. Understanding the underlying mechanisms of crop microevolution and applying the knowledge to agricultural productions will give possible solutions for future challenges in food security. <<<

Genomic alterations (GA) in PIK3CA leads to the hyper-activation of the phosphatidylinositol-4, 5-bisphosphate 3-kinase (PI3K) pathway in more than 20% of ovarian cancer (OC) patients. Therefore, PI3K therapies are under clinical evaluation for this subset of patients. Evidently, in clinical trials testing the efficacy of isoform-specific inhibitors of PI3K (PI3Ki), patients having a stable disease eventually relapse, as tumors become resistant to treatment. Hence, there is an urgent clinical need to develop new therapeutic combinations to improve the efficacy of PI3Ki in PIK3CA-driven OC patients. Here we identified the molecular mechanism that limits the efficacy of the beta-sparing PI3Ki, Taselisib (GDC0032), in PIK3CA-mutated OC cell lines (IGROV1 and OAW42) that acquired resistance to GDC0032. By comparing the molecular profile of GDC0032-sensitve and -resistant OC cell lines, we found that AKT/mTOR inhibition is required for GDC0032 efficacy. In resistant cells, the sustained activation of AKT/mTOR was regulated by the upregulation of the insulin growth factor 1 receptor (IGF1R). Knockdown of IGF1R re-sensitized cells to GDC0032 in vitro, and the combination of AEW541, an IGF1R inhibitor, with GDC0032 exhibited potent anti-tumor activity in vitro and in vivo. We further demonstrated that IGF1R regulates tumor cell proliferation in IGROV1 cells, whereas in OAW42, it determines autophagy as well. Overall, our findings suggest that the dual inhibition of PI3K and IGF1R may be considered as a new therapeutic strategy in PIK3CA-driven OC. <<<