bluuebunny/crossref_metadata_2025_split huggingface dataset:
NCOurd: modelling length distributions of NCO events and gene conversion tracts
Authors: Hardarson Marteinn T , Palsson Gunnar , Halldorsson Bjarni V 
Published: August 2023

AbstractMotivationMeiotic recombination is the main driving force of human genetic diversity, along with mutations. Recombinations split into crossovers, separating large chromosomal regions originating from different homologous chromosomes, and non-crossovers (NCOs), where a small segment from one chromosome is embedded in a region originating from the homologous chromosome. NCOs are much less studied than mutations and crossovers as NCOs are short and can only be detected at markers heterozygous in the transmitting parent, leaving most of them undetectable.ResultsThe detectable NCOs, known as gene conversions, hide information about NCOs, including their number and length, waiting to be unveiled. We introduce NCOurd, software, and algorithm, based on an expectation–maximization algorithm, to estimate the number of NCOs and their length distribution from gene conversion data.Availability and implementationhttps://github.com/DecodeGenetics/NCOurd
ProtoCell4P: an explainable prototype-based neural network for patient classification using single-cell RNA-seq
Authors: Xiong Guangzhi , Bekiranov Stefan , Zhang Aidong 
Published: August 2023

Abstract

Motivation
The rapid advance in single-cell RNA sequencing (scRNA-seq) technology over the past decade has provided a rich resource of gene expression profiles of single cells measured on patients, facilitating the study of many biological questions at the single-cell level. One intriguing research is to study the single cells which play critical roles in the phenotypes of patients, which has the potential to identify those cells and genes driving the disease phenotypes. To this end, deep learning models are expected to well encode the single-cell information and achieve precise prediction of patients’ phenotypes using scRNA-seq data. However, we are facing critical challenges in designing deep learning models for classifying patient samples due to (i) the samples collected in the same dataset contain a variable number of cells—some samples might only have hundreds of cells sequenced while others could have thousands of cells, and (ii) the number of samples available is typically small and the expression profile of each cell is noisy and extremely high-dimensional. Moreover, the black-box nature of existing deep learning models makes it difficult for the researchers to interpret the models and extract useful knowledge from them.

Results
We propose a prototype-based and cell-informed model for patient phenotype classification, termed ProtoCell4P, that can alleviate problems of the sample scarcity and the diverse number of cells by leveraging the cell knowledge with representatives of cells (called prototypes), and precisely classify the patients by adaptively incorporating information from different cells. Moreover, this classification process can be explicitly interpreted by identifying the key cells for decision making and by further summarizing the knowledge of cell types to unravel the biological nature of the classification. Our approach is explainable at the single-cell resolution which can identify the key cells in each patient’s classification. The experimental results demonstrate that our proposed method can effectively deal with patient classifications using single-cell data and outperforms the existing approaches. Furthermore, our approach is able to uncover the association between cell types and biological classes of interest from a data-driven perspective.

Availability and implementation
https://github.com/Teddy-XiongGZ/ProtoCell4P.
Enhancing cryo-EM maps with 3D deep generative networks for assisting protein structure modeling
Authors: Maddhuri Venkata Subramaniya Sai Raghavendra , Terashi Genki , Kihara Daisuke 
Published: August 2023

Abstract

Motivation
The tertiary structures of an increasing number of biological macromolecules have been determined using cryo-electron microscopy (cryo-EM). However, there are still many cases where the resolution is not high enough to model the molecular structures with standard computational tools. If the resolution obtained is near the empirical borderline (3–4.5 Å), improvement in the map quality facilitates structure modeling.

Results
We report EM-GAN, a novel approach that modifies an input cryo-EM map to assist protein structure modeling. The method uses a 3D generative adversarial network (GAN) that has been trained on high- and low-resolution density maps to learn the density patterns, and modifies the input map to enhance its suitability for modeling. The method was tested extensively on a dataset of 65 EM maps in the resolution range of 3–6 Å and showed substantial improvements in structure modeling using popular protein structure modeling tools.

Availability and implementation
https://github.com/kiharalab/EM-GAN, Google Colab: https://tinyurl.com/3ccxpttx.
Few-shot biomedical named entity recognition via knowledge-guided instance generation and prompt contrastive learning
Authors: Chen Peng , Wang Jian , Lin Hongfei , Zhao Di , Yang Zhihao 
Published: August 2023

Abstract

Motivation
Few-shot learning that can effectively perform named entity recognition in low-resource scenarios has raised growing attention, but it has not been widely studied yet in the biomedical field. In contrast to high-resource domains, biomedical named entity recognition (BioNER) often encounters limited human-labeled data in real-world scenarios, leading to poor generalization performance when training only a few labeled instances. Recent approaches either leverage cross-domain high-resource data or fine-tune the pre-trained masked language model using limited labeled samples to generate new synthetic data, which is easily stuck in domain shift problems or yields low-quality synthetic data. Therefore, in this article, we study a more realistic scenario, i.e. few-shot learning for BioNER.

Results
Leveraging the domain knowledge graph, we propose knowledge-guided instance generation for few-shot BioNER, which generates diverse and novel entities based on similar semantic relations of neighbor nodes. In addition, by introducing question prompt, we cast BioNER as question-answering task and propose prompt contrastive learning to improve the robustness of the model by measuring the mutual information between query–answer pairs. Extensive experiments conducted on various few-shot settings show that the proposed framework achieves superior performance. Particularly, in a low-resource scenario with only 20 samples, our approach substantially outperforms recent state-of-the-art models on four benchmark datasets, achieving an average improvement of up to 7.1% F1.

Availability and implementation
Our source code and data are available at https://github.com/cpmss521/KGPC.
CDNM: Clustering-Based Data Normalization Method For Automated Vulnerability Detection
Authors: Wu Tongshuai , Chen Liwei , Du Gewangzi , Zhu Chenguang , Cui Ningning , Shi Gang 
Published: August 2023

Abstract
The key to deep learning vulnerability detection framework is pre-processing source code and learning vulnerability features. Traditional source code representation techniques take a complete normalization to user-defined symbols but ignore the semantic information associated with vulnerabilities. The current mainstream vulnerability feature learning model is Recurrent Neural Network (RNN), whose time-series structure determines its insufficient remote information acquisition capability. This paper proposes a new vulnerability detection framework to solve the above problems. We propose a new data normalization method in the source code pre-processing phase. The user-defined symbols are clustered using the unsupervised clustering algorithm K-means. The normalized classification is performed according to the clustering results, which preserves the primary semantic information in the source code and ensures the smoothness of the sample data. In the feature extraction stage, we input the source code after performing text representation into Bidirectional Encoder Representations for Transformers (BERT) for feature automation learning, which enhances semantic information extraction and remote information acquisition. Experimental results show that the vulnerability detection precision of this method is 18.3% higher than that of the current mainstream vulnerability detection framework in the real-world data collected by ourselves. Further, our method improves the precision of the state-of-the-art method by 4.2%.
Causal Roles of Lifestyle, Psychosocial Characteristics, and Sleep Status in Sarcopenia: A Mendelian Randomization Study
Authors: Liu Mingchong , Yu Daqian , Pan Yutao , Ji Shengchao , Han Ning , Yang Chensong , Sun Guixin 
Published: August 2023

Abstract

Background
Many studies reported that lifestyle, psychosocial characteristics, and sleep status related to sarcopenia, although few studies provided evidence of causal relationships between them.

Methods
The data used in our study were from UK Biobank, FinnGen Release 8, and large genome-wide association study meta-analyses. Two-sample Mendelian randomization was conducted to identify the causal associations of 21 traits of lifestyle, psychosocial characteristics, and sleep status with 6 traits of sarcopenia. Benjamini–Hochberg correction was performed to reduce the bias caused by multiple tests. Risk factor analyses were performed to explore the potential mechanism behind the exposures.

Results
Mendelian randomization analyses after adjustment proved the causal roles of coffee intake, education years, smoking, leisure screen time, and moderate-to-vigorous intensity physical activity during leisure time in sarcopenia was proven although providing no significant evidence for causal roles for carbohydrates intake, protein intake, alcohol, and sleep status in sarcopenia.

Conclusions
Our results strongly support that coffee intake, education years, smoking, leisure screen time, and moderate-to-vigorous intensity physical activity during leisure time played significantly causal roles in sarcopenia, which may provide new intervention strategies for preventing the development of sarcopenia.
Commercial coverage of specialty drugs, 2017–2021
Authors: Rucker Julia A , Beinfeld Molly T , Jenkins Nola B , Enright Daniel E , Henderson Rochelle R , Chambers James D 
Published: August 2023

Abstract
Health plans guide their enrollees’ access to specialty drugs through coverage policies. We examined a set of health plan policies to determine if they have become more or less stringent over time. We did so by comparing the consistency of policies with Food and Drug Administration (FDA) label indications. We considered coverage policies for the same 187 specialty drugs issued by 17 large US commercial health plans from 2017 through 2021. Overall, the proportion of policies that were consistent with the FDA label declined from 57.1% in 2017 to 45.1% in 2021; the proportion of policies that were more restrictive than the FDA label increased from 39.5% to 51.7%. The proportion of policies excluding drug coverage remained approximately constant (3.4% in 2017; 3.2% in 2021). Trends in coverage restrictiveness varied across plans. For 13 plans, the proportion of policies with restrictions increased over time, while for 4 plans it declined.
CmoNAC1 in pumpkin rootstocks improves salt tolerance of grafted cucumbers by binding to the promoters of CmoRBOHD1, CmoNCED6, CmoAKT1;2 and CmoHKT1;1 to regulate H2O2, ABA signaling and K+/Na+ homeostasis
Authors: Peng Yuquan , Cao Haishun , Cui Lvjun , Wang Ying , Wei Lanxing , Geng Shouyu , Yang Li , Huang Yuan , Bie Zhilong 
Published: August 2023

Abstract
The NAC transcription factor is a type of plant-specific transcription factor that can regulate plant salt tolerance, but the underlying mechanism is unclear in grafted vegetables. H2O2 and ABA in pumpkin rootstocks can be transported to cucumber scion leaves, promoting stomatal closure to improve salt tolerance of grafted cucumbers. Despite these observations, the regulatory mechanism is unknown. Here, our research revealed that CmoNAC1 is a key transcription factor that regulates H2O2 and ABA signaling in pumpkin roots under salt stress. The function of CmoNAC1 was analyzed using root transformation and RNA-seq, and we found that pumpkin CmoNAC1 promoted the production of H2O2 and ABA via CmoRBOHD1 and CmoNCED6, respectively, and regulated K+/Na+ homeostasis via CmoAKT1;2, CmoHKT1;1, and CmoSOS1 to improve salt tolerance of grafted cucumbers. Root knockout of CmoNAC1 resulted in a significant decrease in H2O2 (52.9% and 32.1%) and ABA (21.8% and 42.7%) content and K+/Na+ ratio (81.5% and 56.3%) in leaf and roots of grafted cucumber, respectively, while overexpression showed the opposite effect. The root transformation experiment showed that CmoNCED6 could improve salt tolerance of grafted cucumbers by regulating ABA production and K+/Na+ homeostasis under salt stress. Finally, we found that CmoNAC1 bound to the promoters of CmoRBOHD1, CmoNCED6, CmoAKT1;2, and CmoHKT1;1 using yeast one-hybrid, luciferase, and electrophoretic mobility shift assays. In conclusion, pumpkin CmoNAC1 not only binds to the promoters of CmoRBOHD1 and CmoNCED6 to regulate the production of H2O2 and ABA signals in roots, but also binds to the promoters of CmoAKT1;2 and CmoHKT1;1 to increase the K+/Na+ ratio, thus improving salt tolerance of grafted cucumbers.
Rising to the Challenge: Mounting an Acute Phase Immune Response Has No Long-Term Negative Effects on Captive Sparrow Migratory Body Composition or Migratory Restlessness
Authors: Kelly T R , Butnari A C , MacDougall-Shackleton E A , MacDougall-Shackleton S A 
Published: August 2023

Synopsis
Migratory animals may trade-off between investing energy in immune defense versus investing in energy reserves needed for seasonal migration. However, these trade-offs are often masked by other sources of variation and may not be detected through observational field studies of free-living animals. Moreover, observational studies can rarely distinguish the costs of pathogenic infection from those of mounting an immune response. To disentangle such effects, we conducted an immune challenge experiment. We captured song sparrows (Melospiza melodia) and white-throated sparrows (Zonotrichia albicollis) in autumn migratory condition, challenged the sparrows with non-infectious antigens that induce an acute-phase immune response, then monitored body composition and migratory restlessness behavior. For both species, body mass was higher the day after exposure to keyhole limpet hemocyanin (KLH) compared to controls. White-throated sparrows, but not song sparrows, increased lean mass 1 week after exposure to lipopolysaccharide (LPS), suggesting that effects of immune upregulation on body composition may be long-lasting and specific to certain combinations of hosts and antigens. White-throated sparrows exposed to KLH increased nocturnal migratory restlessness (Zugunruhe) for the week following exposure. These findings suggest that short-term activation of the acute immune response does not constrain migratory physiology in these songbirds.
Stable Isomorphisms of Operator Algebras
Authors: Kakariadis Evgenios T A , Katsoulis Elias G , Li Xin 
Published: August 2023

Abstract
Let ${{\mathcal{A}}}$ and ${{\mathcal{B}}}$ be operator algebras with $c_{0}$-isomorphic diagonals and let ${{\mathcal{K}}}$ denote the compact operators. We show that if ${{\mathcal{A}}}\otimes{{\mathcal{K}}}$ and ${{\mathcal{B}}}\otimes{{\mathcal{K}}}$ are isometrically isomorphic, then ${{\mathcal{A}}}$ and ${{\mathcal{B}}}$ are isometrically isomorphic. If the algebras ${{\mathcal{A}}}$ and ${{\mathcal{B}}}$ satisfy an extra analyticity condition a similar result holds with ${{\mathcal{K}}}$ being replaced by any operator algebra containing the compact operators. For nonselfadjoint graph algebras this implies that the graph is a complete invariant for various types of isomorphisms, including stable isomorphisms, thus strengthening a recent result of Dor-On, Eilers, and Geffen. Similar results are proven for algebras whose diagonals satisfy cancellation and have $K_{0}$-groups isomorphic to ${{\mathbb{Z}}}$. This has implications in the study of stable isomorphisms between various semicrossed products.
The Stanford Medicine data science ecosystem for clinical and translational research
Authors: Callahan Alison , Ashley Euan , Datta Somalee , Desai Priyamvada , Ferris Todd A , Fries Jason A , Halaas Michael , Langlotz Curtis P , Mackey Sean , Posada José D , Pfeffer Michael A , Shah Nigam H 
Published: August 2023

Abstract

Objective
To describe the infrastructure, tools, and services developed at Stanford Medicine to maintain its data science ecosystem and research patient data repository for clinical and translational research.

Materials and Methods
The data science ecosystem, dubbed the Stanford Data Science Resources (SDSR), includes infrastructure and tools to create, search, retrieve, and analyze patient data, as well as services for data deidentification, linkage, and processing to extract high-value information from healthcare IT systems. Data are made available via self-service and concierge access, on HIPAA compliant secure computing infrastructure supported by in-depth user training.

Results
The Stanford Medicine Research Data Repository (STARR) functions as the SDSR data integration point, and includes electronic medical records, clinical images, text, bedside monitoring data and HL7 messages. SDSR tools include tools for electronic phenotyping, cohort building, and a search engine for patient timelines. The SDSR supports patient data collection, reproducible research, and teaching using healthcare data, and facilitates industry collaborations and large-scale observational studies.

Discussion
Research patient data repositories and their underlying data science infrastructure are essential to realizing a learning health system and advancing the mission of academic medical centers. Challenges to maintaining the SDSR include ensuring sufficient financial support while providing researchers and clinicians with maximal access to data and digital infrastructure, balancing tool development with user training, and supporting the diverse needs of users.

Conclusion
Our experience maintaining the SDSR offers a case study for academic medical centers developing data science and research informatics infrastructure.
Functional characterization of Bombyx mori (Lepidoptera: Bombycidae) C-type lectin 5
Authors: Sun Yan-Xia , Zhang Bang-Xian , Liu Fang-Fang , Rao Xiang-Jun 
Published: August 2023

Abstract
C-type lectins (CTLs) are an important family of pattern recognition receptors (PRRs) that regulate immune responses. The CTL5 gene of the silkworm Bombyx mori L. (Lepidoptera: Bombycidae) encodes a protein comprised of 223 amino acids, containing a signal peptide and a carbohydrate recognition domain (CRD). Our previous study showed that CTL5 can facilitate the clearance of bacteria from larval hemocoel but the underlying mechanisms are unclear. In this study, we found that CTL5 was mainly expressed in fourth-instar larvae, adult moths, and the larval epidermis. CTL5 expression showed differential responses to both pathogenic stimuli and the molting hormone 20-hydroxyecdysone. The full-length (FL) and truncated (ΔN/ΔC/ΔNC) CTL5 recombinant proteins can bind to hemocytes, polysaccharides, bacteria, and spores of the entomopathogenic fungus Beauveria bassiana. Yeast 2-hybrid assays showed that the recombinant proteins can interact with integrin β2–β5 subunits. Recombinant proteins increased the phagocytic rate of hemocytes. Injection of recombinant CTL5 stimulated the expression of many immune genes in hemocytes, mainly antimicrobial peptides and immune signaling molecules. Additionally, transcriptomic sequencing of CTL5-stimulated hemocytes revealed 265 upregulated and 580 downregulated genes. Functional enrichment and the gene set enrichment analyses showed that differentially expressed genes were mainly enriched in innate immune responses and signaling. Our study suggests that CTL5 may act as an opsonin to enhance the clearance of pathogens by regulating both humoral and cellular responses.
Rights-Based Climate Litigation in Brazil: An Assessment of Constitutional Cases Before the Brazilian Supreme Court
Authors: de Andrade Moreira Danielle , Nina Ana Lucia B , de Figueiredo Garrido Carolina , Neves Maria Eduarda Segovia Barbosa 
Published: August 2023

AbstractThis article presents a systematic analysis of climate litigation in the Brazilian Supreme Court. It argues that climate litigation in Brazil is centred on the protection of human rights and the court is ready (and eager) to draw a closer connection between climate and human rights. The climate litigation movement in Brazil follows in the wake of more than 40 years of a rich environmental legal framework and jurisprudence. This long trajectory includes adopting a chapter in the 1988 Brazilian Constitution dedicated to the right to an ecologically balanced environment. The Constitution determines this is a fundamental human right, encompassing the present and future generations. Building on this context, recent climate litigation cases question how climate stability fits within this constitutional framework. As the country’s constitutional tribunal, the Supreme Court recently received important climate cases addressing Brazil’s climate policy implementation. This article proceeds in three parts. First, the article contextualizes the climate litigation movement in Brazil’s broader environmental legal framework. Second, the article describes the Supreme Court climate docket of concentrated control cases. It focuses on the human right to an ecologically balanced environment and its connections with other constitutionally protected human rights. Third, the article examines the cases’ judicial reasoning, considering how the Supreme Court addressed climate change in the decisions (interlocutory or on the merits) available thus far. The ultimate goal of this article is to deepen the understanding of how the Supreme Court approaches climate as a human right through the methodical examination of the court’s pronouncements.