Miracle-dz/UniFAR-FTUs huggingface dataset:
The spreading of facultative H3K9me3-heterochromatin drives congenital disease

Heterochromatin marked by trimethylated histone 3 at lysine 9 (H3K9me3) plays fundamental roles in reprogramming to direct cell fate determination in higher eukaryotes. However, the upstream factors that guide the establishment and spreading of H3K9me3-heterochromatin, leading to human developmental malformations, remain elusive. In this study, we found that Cdk13, a member of RNA polymerase II (RNAPII) kinase, suppresses congenital heart syndrome by preventing global facultative H3K9me3-heterochromatin spreading. Additionally, Cdk13 directs the phosphorylation of a large set of heterochromatin proteins at specific sites, which are required for the interaction between HP1 and histone H3K9 methyltransferases. Furthermore, we identified a compound, an inhibitor of heterochromatin regulators, that can alleviate syndromic heart defects in Cdk13-mutant mice through the inhibition of H3K9me3-heterochromatin spreading. In summary, this study reveals a novel role and mechanism of Cdk13-triggered facultative H3K9me3-heterochromatin spreading in human genetic disease and paves the way for the treatment of congenital heart syndrome.
Genetic and karyotype divergence between parents affect clonality and sterility in hybrids

Asexual reproduction can be triggered by interspecific hybridization, but its emergence is supposedly rare, relying on exceptional combinations of suitable genomes. To examine how genomic and karyotype divergence between parental lineages affect the incidence of asexual gametogenesis, we experimentally hybridized fishes (Cobitidae) across a broad phylogenetic spectrum, assessed by whole exome data. Gametogenic pathways generally followed a continuum from sexual reproduction in hybrids between closely related evolutionary lineages to sterile or inviable crosses between distant lineages. However, most crosses resulted in a combination of sterile males and asexually-reproducing females. Their gametes usually experienced problems in chromosome pairing, but females also produced a certain proportion of oocytes with premeiotically duplicated genomes, enabling their development into clonal eggs. Interspecific hybridization may thus commonly affect cell cycles in a specific way, allowing the formation of unreduced oocytes. The emergence of asexual gametogenesis appears tightly linked to hybrid sterility and constitutes an inherent part of the extended speciation continuum.
Bayesian Progressive Deep Topic Model with Knowledge Informed Textual Data Coarsening Process

Deep topic models have shown an impressive ability to extract multi-layer document latent representations and discover hierarchical semantically meaningful topics. However, most deep topic models are limited to the single-step generative process, despite the fact that the progressive generative process has achieved impressive performance in modeling image data. To this end, in this paper, we propose a novel progressive deep topic model that consists of a knowledge-informed textural data coarsening process and a corresponding progressive generative model. The former is used to build multi-level observations ranging from concrete to abstract, while the latter is used to generate more concrete observations gradually. Additionally, we incorporate a graph-enhanced de-coder to capture the semantic relationships among words at different levels of observation. Furthermore, we perform a theoretical analysis of the proposed model based on the principle of information theory and show how it can alleviate the well-known “latent variable collapse” problem. Finally, extensive experiments demonstrate that our proposed model effectively improves the ability of deep topic models, resulting in higher-quality latent document representations and topics.
Weighted Sampling without Replacement for Deep Top-k Classification

The top-k classification accuracy is a crucial metric in machine learning and is often used to evaluate the performance of deep neural networks. These networks are typically trained using the cross-entropy loss, which optimizes for top-1 classification and is considered optimal in the case of infinite data. However, in real-world scenarios, data is often noisy and limited, leading to the need for more robust losses. In this paper, we propose using the Weighted Sampling Without Replacement (WSWR) method as a learning objective for top-k loss. While traditional methods for evaluating WSWR-based top-k loss are computationally impractical, we show a novel connection between WSWR and Reinforcement Learning (RL) and apply well-established RL algorithms to estimate gradients. We compared our method with recently proposed top-k losses in various regimes of noise and data size for the prevalent use case of k = 5 . Our experimental results reveal that our method consistently outperforms all other methods on the top-k metric for noisy datasets, has more robustness on extreme testing scenarios, and achieves competitive results on training with limited data.
Implicit Jacobian regularization weighted with impurity of probability output

The success of deep learning is greatly attributed to stochastic gradient descent (SGD), yet it remains unclear how SGD finds well-generalized models. We demonstrate that SGD has an implicit regularization effect on the logit-weight Jacobian norm of neural networks. This regularization effect is weighted with the impurity of the probability output, and thus it is active in a certain phase of training. Moreover, based on these findings, we propose a novel optimization method that explicitly regularizes the Jacobian norm, which leads to similar performance as other state-of-the-art sharpness-aware optimization methods.
SAAL: Sharpness-Aware Active Learning

While deep neural networks play significant roles in many research areas, they are also prone to overfitting problems under limited data instances. To overcome overfitting, this paper introduces the first active learning method to incorporate the sharpness of loss space into the acquisition function. Specifically, our proposed method, Sharpness-Aware Active Learning (SAAL), constructs its acquisition function by selecting unlabeled instances whose perturbed loss becomes maximum. Unlike the Sharpness-Aware learning with fully-labeled datasets, we design a pseudo-labeling mechanism to anticipate the perturbed loss w.r. t. the ground-truth label, which we provide the theoretical bound for the optimization. We conduct experiments on various benchmark datasets for vision-based tasks in image classification, object detection, and domain adaptive semantic segmentation. The experimental re-sults confirm that SAAL outperforms the base-lines by selecting instances that have the potentially maximal perturbation on the loss. The code is available at https://github.com/ YoonyeongKim/SAAL .
Path Selection in a Relational Database Management System

In a high level query and data manipulation language such as SQL, requests are stated non-procedurally, without reference to access paths. This paper describes how System R chooses access paths for both simple (single relation) and complex queries (such as joins), given a user specification of desired data as a boolean expression of predicates. System R is an experimental database management system developed to carry out research on the relational model of data. System R was designed and built by members of the IBM San Jose Research'Laboratory.
ON THE MINIMUM NUMBER OF MINIMAL CODEWORDS

We study the minimum number of minimal codewords in linear codes using techniques from projective geometry. Minimal codewords have been used in decoding algorithms and cryptographic protocols. First, we derive a new lower bound on the number of minimal codewords. Then we give a formula for the minimum number of minimal codewords of linear codes for certain lengths and dimensions. We also determine the exact value of the minimum for a range of values of the length and dimension. As an application, we completed a table of the minimum number of minimal codewords for codes of length up to 15. Finally, we discuss an extension of the geometric approach to minimal subcode supports.
Optimal Convergence Rates for Agnostic Nyström Kernel Learning

Nystr¨om low-rank approximation has shown great potential in processing large-scale kernel matrix and neural networks. However, there lacks a uniﬁed analysis for Nystr¨om approximation, and the asymptotical minimax optimality for Nystr¨om methods usually require a strict condition, assuming that the target regression lies exactly in the hypothesis space. In this paper, to tackle these problems, we provide a reﬁned generalization analysis for Nystr ¨ om approximation in the agnostic setting, where the target regression may be out of the hypothesis space. Speciﬁcally, we show Nystr ¨ om approximation can still achieve the capacity-dependent optimal rates in the agnostic setting. To this end, we ﬁrst prove the capacity-dependent optimal guarantees of Nystr ¨ om approximation with the standard uniform sampling, which covers both loss functions and applies to some agnostic settings. Then, using data-dependent sampling, for example, leverage scores sampling, we derive the capacity-dependent optimal rates that apply to the whole range of the agnostic setting. To our best knowledge, the capacity-dependent optimality for the whole range of the agnostic setting is ﬁrst achieved and novel in Nystr ¨ om approximation.
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption

We propose a theoretical framework to analyze semi-supervised classification under the low density separation assumption in a high-dimensional regime. In particular, we introduce QLDS, a linear classification model, where the low density separation assumption is implemented via quadratic margin maximization. The algorithm has an explicit solution with rich theoretical properties, and we show that particular cases of our algorithm are the least-square support vector machine in the supervised case, the spectral clustering in the fully unsupervised regime, and a class of semi-supervised graph-based approaches. As such, QLDS establishes a smooth bridge between these supervised and unsupervised learning methods. Using recent advances in the random matrix theory, we formally derive a theoretical evaluation of the classification error in the asymptotic regime. As an application, we derive a hyperparameter selection policy that finds the best balance between the supervised and the unsupervised terms of our learning criterion. Finally, we provide extensive illustrations of our framework, as well as an experimental study on several benchmarks to demonstrate that QLDS, while being computationally more efficient, improves over cross-validation for hyperparameter selection, indicating a high promise of the usage of random matrix theory for semi-supervised model selection.
Database resources of the National Center for Biotechnology Information: update

In addition to maintaining the GenBank(R) nucleic acid sequence database, the National Center for Biotechnology Information (NCBI) provides data analysis and retrieval resources for the data in GenBank and other biological data made available through NCBI’s website. NCBI resources include Entrez, PubMed, PubMed Central, LocusLink, the NCBI Taxonomy Browser, BLAST, BLAST Link (BLink), Electronic PCR, OrfFinder, Spidey, RefSeq, UniGene, HomoloGene, ProtEST, dbMHC, dbSNP, Cancer Chromosome Aberration Project (CCAP), Entrez Genomes and related tools, the Map Viewer, Model Maker, Evidence Viewer, Clusters of Orthologous Groups (COGs) database, Retroviral Genotyping Tools, SARS Coronavirus Resource, SAGEmap, Gene Expression Omnibus (GEO), Online Mendelian Inheritance in Man (OMIM), the Molecular Modeling Database (MMDB), the Conserved Domain Database (CDD) and the Conserved Domain Architecture Retrieval Tool (CDART). Augmenting many of the web applications are custom implementations of the BLAST program optimized to search specialized data sets. All of the resources can be accessed through the NCBI home page at: http://www.ncbi. nlm.nih. gov.
Lazy Agents: A New Perspective on Solving Sparse Reward Problem in Multi-agent Reinforcement Learning

Sparse reward remains a valuable and challenging problem in multi-agent reinforcement learning (MARL). This paper addresses this issue from a new perspective, i.e., lazy agents. We empirically illustrate how lazy agents damage learning from both exploration and exploitation. Then, we propose a novel MARL framework called Lazy Agents Avoidance through Influencing External States (LAIES). Firstly, we examine the causes and types of lazy agents in MARL using a causal graph of the interaction between agents and their environment. Then, we mathematically define the concept of fully lazy agents and teams by calculating the causal effect of their actions on external states using the do-calculus process. Based on definitions, we provide two intrinsic rewards to motivate agents, i.e., individual dili-gence intrinsic motivation (IDI) and collaborative diligence intrinsic motivation (CDI). IDI and CDI employ counterfactual reasoning based on the external states transition model (ESTM) we developed. Empirical results demonstrate that our proposed method achieves state-of-the-art performance on various tasks, including the sparse-reward version of StarCraft multi-agent challenge (SMAC) and Google Research Football (GRF). Our code is open-source and available at https: //github.com/liuboyin/LAIES .
The state of world highly migratory, straddling and other high seas fishery resources and associated species

This document describes highly migratory fish stocks, straddling fish stocks, and stocks of other high-seas fishery resources and the fisheries for them, including information on their state of exploitation. Fisheries for highly migratory species are important in all oceans and semi-enclosed seas, except for polar regions. Fisheries for straddling fish stocks are much more localized, primarily occurring in a few regions where continental shelves extend beyond the 200-mile exclusive economic zone (EEZ) while most fisheries for other high-seas fishery resources are deep-water fisheries. Formal assessments are lacking for most of the stocks examined. Nevertheless, the compilation of available assessments and FAO's analyses indicate that about 30 percent of the stocks of highly migratory tuna and tuna-like species, more than 50 percent of the highly migratory oceanic sharks and nearly two-thirds of the straddling stocks and the stocks of other high-seas fishery resources are overexploited or depleted.
Developmental Timeline of Alcohol-Induced Birth Defects

Maternal consumption of alcohol (ethanol) during pregnancy [5] can result in a continuum of embryonic developmental abnormalities that vary depending on the severity, duration, and frequency of exposure of ethanol during gestation [6]. Alcohol is a teratogen, an environmental agent that impacts the normal development of an embryo or fetus [7]. In addition to doserelated concerns, factors such as maternal genetics and metabolism and the timing of alcohol exposure during prenatal development also impact alcohol-related birth defects [8].
Two to Tangle: The Story of Tau and a-Synuclein

Summary Recent research of neurodegenerative diseases has called attention to abnormal protein aggregates found in brain lesions. Alzheimer's disease (AD) and frontotemporal dementia with parkinsonism linked to chromosome 17 (FTDP-17) are part of a collective group of neurodegenerative diseases termed taupathies and are characterized by abnormal filamentous tau inclusions. A_ amyloid plaques and neurofibrillary tangles containing hyperphosphorylated tau are invariant features of AD. The role of tau in AD pathogenesis is controversial. However, multiple tau mutations discovered in FTDP-17 add significance to tau's role in neurodegeneration. To study AD, our lab has identified different sites that become abnormally phosphorylated in adult tau and the potential role for dysfunctional phosphatases. To model FTDP-17, we and other labs have created transgenic mice that either overexpress wild type tau or express mutant tau. These mice develop clear neurodegenerative phenotypes. Analogous to tau aggregates, cytoplasmic inclusions known as Lewy bodies are characteristic of Parkinson's disease (PD). _- synuclein has been linked to both familial and sporadic PD. Following this discovery, our lab has demonstrated that aggregated _- synuclein appears in Lewy bodies of human patients. Transgenic mice and Drosophila models expressing _-synuclein are being used to identify suppressors and enhancers of disease. Investigating the role of tau and _-synuclein in neurodegenerative diseases can aid in the development of effective treatments.
Newcastle University E-prints Citation for Item: Publisher's Copyright Statement: Neuroinformatics Original Research Article

Optimal hierarchical modular topologies for producing limited sustained activation of neural networks. Always use the definitive version when citing. The full-text may be used and/or reproduced and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not for profit purposes provided that: • A full bibliographic reference is made to the original source • A link is made to the metadata record in Newcastle E-prints • The full text is not changed in any way. The full-text must not be sold in any format or medium without the formal permission of the copyright holders. A necessary precondition for attaining a critical point in intricate neural systems, such as the mammalian cerebral cortex, is that initial network activations result in neuronal activation patterns that neither die out too quickly nor spread across the entire network. Without this feature, activation patterns would not be stable, or would lead to a pathological excitation of the whole brain. What are the essential structural and functional parameters that allow complex neural networks to maintain such a dynamic balance of sustained yet limited activity? Most current models of neural network dynamics focus on maintaining the right balance of network activation and rest through functional interactions among populations of inhibitory and excita-tory nodes (Beggs and Plenz, 2003). Alternative balancing mechanisms may be provided by broad external input from neuromodulatory systems or self-sustained neuronal activity (Muresan and Savin, 2007). However, the topology of neural networks may also contribute to critical network dynamics, even in the absence of explicit inhibition. For this reason, we are particularly interested in the relationship of different kinds of neural network topology to the condition of limited sustained activation (LSA). The involvement of inhibitory neuronal populations and other dynamic control mechanisms may then further extend the parameter range for LSA that is provided by principal topological features of the neural network architecture.
Sliding mode control design for parametric uncertain stochastic systems with state delay using functional observer

This study is concerned with the problem of the functional observer-based sliding mode control (SMC) design for parametric uncertain discrete-time delayed stochastic systems includes mismatched parameter uncertainty in the state matrix and in the delayed state matrix. Stability analysis of sliding function is presented in the time delayed stochastic system with a linear matrix inequality approach. Moreover, it is shown that the state trajectories can be driven onto the specified sliding surface despite the presence of state delay, unmatched parameter uncertainty and stochastic noise in the system. The research is motivated by the fact that the system states are not always accessible for the state feedback. Therefore, SMC is estimated using the functional observer technique. To mitigate the side effect of the parameter uncertainty on the estimation error, a sufficient condition of stability is proposed based on Gershgorin disc theorem. The claims made are validated through numerical simulations.
Extreme weather impacts on transport systems

This report summarises the work done in 7 FP project EWENT, its first work package. D1 deliverable introduces a review of extreme weather phenomena and identifies their impacts and consequences on European transport system. All modes of transport are covered. Two main methods are used. First, there is an extensive literature review on extreme weather events and their impacts and consequences. Well over 150 scientific and professional references are studied and listed. This is followed by a review of media reported cases, almost 200 of them. With the help of these two methods and material they provide, critical threshold values for most relevant weather phenomena that affect different transport modes are listed. The phenomena have impacts and consequences which result in deterioration in the service level of transportation system. A dozen different impact mechanisms are charted and annexed to this report. Finally, brief summary of results is drawn, including a first-impressiondiscussion on strategic implications. Precipitation in all its forms (water, snow, hail) seems to dominate the harmful impacts. Road transport system seems to be the most vulnerable of modes.
Indeterminate Renal Mass

CT abdomen without and with IV contrast 9 Either CT or MRI is appropriate. Use thinsection CT. ☢☢☢☢ MRI abdomen without and with IV contrast 8 Either CT or MRI is appropriate. O US kidney retroperitoneal with duplex Doppler 8 O Biopsy renal mass 5 Varies MRI abdomen without IV contrast 3 This procedure can be useful to characterize simple cysts. O Arteriography kidney 1 ☢☢☢ X-ray intravenous urography 1 ☢☢☢ CT abdomen with IV contrast 1 ☢☢☢ CT abdomen without IV contrast 1 ☢☢☢
The International polar year (IPY) circumpolar flaw lead (CFL) system study: Overview and the physical system

Abstract The Circumpolar Flaw Lead (CFL) system study is a Canadian‐led International Polar Year (IPY) initiative with over 350 participants from 27 countries. The study is multidisciplinary in nature, integrating physical sciences, biological sciences and Inuvialuit traditional knowledge. The CFL study is designed to investigate the importance of changing climate processes in the flaw lead system of the northern hemisphere on the physical, biogeochemical and biological components of the Arctic marine system. The circumpolar flaw lead is a perennial characteristic of the Arctic throughout the winter season and forms when the mobile multi‐year (MY) pack ice moves away from coastal fast ice, creating recurrent and interconnected polynyas in the Norwegian, Icelandic, North American and Siberian sectors of the Arctic. The CFL study was 293 days in duration and involved the overwintering of the Canadian research icebreaker CCGS Amundsen in the Cape Bathurst flaw lead throughout the annual sea‐ice cycle of 2007–2008. In this paper we provide an introduction to the CFL project and then use preliminary data from the field season to describe the physical flaw lead system, as observed during the CFL overwintering project. Preliminary data show that ocean circulation is affected by eddy propagation into Amundsen Gulf (AG). Upwelling features arising along the ice edge and along abrupt topography are also detected and identified as important processes that bring nutrient rich waters up to the euphotic zone. Analysis of sea‐ice relative vorticity and sea‐ice area by ice type in the AG during the CFL study illustrates increased variability in ice vorticity in late autumn 2007 and an increase in new and young ice areas in the AG during winter. Analysis of atmospheric data show that a strong northeast–southwest pressure gradient present over the AG in autumn may be a synoptic‐scale atmospheric response to sensible and latent heat fluxes arising from areas of open water persisting into late November 2007. The median atmospheric boundary layer temperature profile over the Cape Bathurst flaw lead during the winter season was stable but much less so when compared to Russian ice island stations.