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Correction: Diversity of sulfur cycling halophiles within the Salton Sea, California's largest lake

BMC microbiology 2025年第1期25卷 225页

作者： Linton Freund Caroline Hung Talyssa M Topacio Charles Diamond Alyson Fresquez Timothy W Lyons Emma L Aronson Genetics Genomics and Bioinformatics Program University of California Riverside 900 University Ave Riverside CA 92521 USA. lintonfreund@***. Department of Microbiology and Plant Pathology University of California Riverside 900 University Ave Riverside CA 92521 USA. Department of Earth and Planetary Sciences University of California Riverside 900 University Ave Riverside CA 92521 USA.

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Juntas Contra el Virus del Papiloma Humano: protocol for a pilot randomized controlled trial of an HPV self-sampling intervention for underscreened Latinas

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Pilot and feasibility studies 2025年第1期11卷 65页

作者： Carolyn Y Fang Marisol Cora-Cruz Pratistha Koirala Sophia Perez Minzi Li Brian L Egleston Yuku Chen Gina Mantia-Smaldone Omar Martinez Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Ave. Philadelphia PA 19111 USA. carolyn.fang@fccc.edu. Cancer Prevention and Control Program Fox Chase Cancer Center 333 Cottman Ave. Philadelphia PA 19111 USA. Department of Gynecologic Oncology Fox Chase Cancer Center Philadelphia PA USA. Biostatistics and Bioinformatics Facility Fox Chase Cancer Center Philadelphia PA USA. College of Medicine University of Central Florida Orlando FL USA.

BACKGROUND:Rates of cervical cancer incidence and mortality are persistently higher among Latina women in the continental United States (US) and women in Puerto Rico (a US territory) compared with non-Hispanic White (NHW) women. Multiple factors contribute to low participation in cancer screening, including structural barriers (e.g., low access to healthcare services, racism/discrimination, lack of culturally and linguistically adequate information), cultural concerns, and low perceived risk and awareness of cervical cancer. Although community-based education and navigation support can be effective in overcoming some barriers to screening, structural barriers and limited access remain formidable challenges to overcome. Emerging technologies supporting self-sampling for high-risk human papillomavirus (HPV) testing may offer a valuable evidence-based strategy for empowering Latina women to engage in cervical cancer screening. Thus, the objective of this study is to assess the feasibility and acceptability of a novel HPV self-sampling intervention for underscreened Latina women. METHODS:The study will be a randomized controlled feasibility trial involving 100 Latina women who have not received cervical cancer screening within the recommended guidelines. Participants will be randomly assigned to the intervention condition, which includes a synchronous three-session group cervical cancer educational program delivered virtually along with a mailed HPV self-sampling kit (to obtain self-collected cervical samples for HPV testing), or to a comparison condition that involves receipt of the mailed HPV self-sampling kit with written information about cervical cancer screening and nearby clinics. Study assessments will be obtained at baseline (i.e., study entry) and 1-month post-program. The primary outcome of feasibility will be measured through study enrollment and intervention completion. In addition, acceptability of study materials and the self-sampling procedures will be a

关键词： Cervical cancer Human papillomavirus (HPV) Latinas Screening Self-sampling

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Genome-wide identification, characterization, and expression analysis of the MADS-box gene family in grass pea (Lathyrus sativus) under salt stress conditions

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BMC Genomics 2025年第1期26卷 1-15页

作者： Abdelsattar, Mohamed Nassar, Ahmed E. Mousa, Khaled H. Hussein, Ahmed El-Baghdady, Manal M. S. Radwan, Khaled H. Ibrahim, Manar S. El Allali, Achraf Hamwieh, Aladdin Alsamman, Alsamman M. Kehel, Zakaria Agricultural Genetic Engineering Research Institute (AGERI) Agricultural Research Center (ARC) Giza Egypt International Center for Agricultural Research in the Dry Areas (ICARDA) Giza Egypt National Biotechnology Network of Expertise ASRT Cairo Egypt Bioinformatics Laboratory College of Computing Mohammed VI Polytechnic University Ben Guerir Morocco Biodiversity and Crop Improvement Program International Center for Agricultural Research in the Dry Areas Rabat Morocco

The MADS-box gene family possesses significant potential to improve crop production under harsh conditions by regulating growth, development, and the expression of floral organs. The grass pea (Lathyrus sativus), a crop grown predominantly in arid and semi-arid regions, could benefit greatly from the functions of MADS-box genes, which are not yet well characterized in this promising plant. In this study, a comprehensive analysis of all MADS-box genes in grass pea was performed at both the genomic and transcriptomic levels. A total of 46 genes were identified and classified based on their MADS-box domains. A comparative phylogenetic analysis with apple, Arabidopsis, and rice categorized the grass pea genes into 31 type I genes (M $$\alpha$$ , M $$\beta$$ , M $$\gamma$$ ) and 15 type II genes (MIKCc, MIKC*). Annotation analysis revealed variations in the intron-exon structures of the genes, with most type I genes being intronless. Ten distinct conserved motifs were identified across the genes. Structural analysis revealed the presence of MEF2-like and SRF-TF domains in the grass pea proteins. Protein-protein interaction analysis revealed extensive interactions among type II MADS-box genes, while enrichment analysis showed their involvement in various aspects of plant life, particularly floral organ development. Examination of the cis-elements in the promoter regions of the genes revealed up to 76 potential cis-elements, which were categorized into four groups based on their putative role in transcriptional regulation. RNA-seq was used to profile gene expression under different conditions to gain insights into their potential functional significance. Quantitative PCR (qPCR) analysis validated the expression levels of eight selected genes (LSMADS_D1, LSMADS_R5, LSMADS_R7, LSMADS_R9, LSMADS_D11, LSMADS_D13, LSMADS_R13, and LSMADS_D29) under salt stress conditions and confirmed their involvement in stress responses. This study represents the first genome-wide exploration

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consexpressionR: an R package for consensus differential gene expression analysis

arXiv

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arXiv 2025年

作者： Costa-Silva, Juliana Menotti, David Lopes, Fabricio M. Department of Informatics Federal University of Paraná Rua Coronel Francisco Heráclito dos Santos 100 Paraná81531-990 Brazil Department of Computer Science Bioinformatics Graduate Program Federal University of Technology - Paraná Av. Alberto Carazzai 1640 - Cornélio Procópio ParanáPostal code: 86300-000 Brazil

Motivation: Bulk RNA-Seq is a widely used method for studying gene expression across a variety of contexts. The significance of RNA-Seq studies has grown with the advent of high-throughput sequencing technologies. Computational methods have been developed for each stage of the identification of differentially expressed genes. Nevertheless, there are few studies exploring the association between different types of methods. In this study, we evaluated the impact of the association of methodologies in the results of differential expression analysis. By adopting two data sets with qPCR data (to gold-standard reference), seven methods were implemented and assessed in R packages (EBSeq, edgeR, DESeq2, limma, SAMseq, NOISeq, and Knowseq), which was performed and assessed separately and in association. The results were evaluated considering the adopted qPCR data. Results: Here, we introduce consexpressionR, an R package that automates differential expression analysis using consensus of at least seven methodologies, producing more assertive results with a significant reduction in false positives. Availability: consexpressionR is an R package available via Source code and support are available at GitHub (https://***/costasilvati/consexpressionR). © 2025, CC BY-NC-ND.

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CD19 CAR-T With Axicabtagene Ciloleucel in R/R Large B-Cell Lymphoma With/Without Prior Autologous Stem Cell Transplant

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Clinical Lymphoma, Myeloma and Leukemia 2025年第6期25卷 432-439页

作者： Chen, David T. Goloubeva, Olga Rapoport, Aaron P. Dahiya, Saurabh Atanackovic, Djordje Hardy, Nancy Kocoglu, Mehmet Lutfi, Forat Alkhaldi, Hanan Claiborne, John Preston Lee, Seung Tae Kline, Kathryn Law, Jennie Y. Yared, Jean A. Division of Hematology/Oncology Department of Medicine Greenebaum Comprehensive Cancer Center University of Maryland Medical Center University of Maryland School of Medicine Baltimore MD United States Department of Epidemiology and Public Health Division of Biostatistics and Bioinformatics University of Maryland Marlene and Stewart Greenebaum Comprehensive Cancer Center University of Maryland School of Medicine Baltimore MD United States Transplantation & Cellular Therapy Program Division of Hematology/Oncology Department of Internal Medicine Greenebaum Comprehensive Cancer Center University of Maryland Medical Center University of Maryland School of Medicine Baltimore MD United States Sidney Kimmel Comprehensive Cancer Center Johns Hopkins University School of Medicine Baltimore MD United States

Background: Anti-CD19 CAR-T therapy has been a breakthrough in treatment of primary refractory or relapsed large B-cell lymphoma (r/r LBCL) and is poised to supplant previous second line of high dose chemotherapy and autologous stem cell transplantation (HDT/ASCT). However, in clinical practice, high risk patients with chemoimmunotherapy sensitive disease continue to receive salvage chemoimmunotherapy or cannot access CAR-T in a timely manner and thus may still proceed to HDT/ASCT. Little is known about clinical outcomes of CAR-T in patients who receive HDT/ASCT compared to those who are transplant-naïve. Design: We conducted a retrospective study of patients with r/r LBCL who previously underwent HDT/ASCT or were transplant-naïve (n = 97) and received axicabtagene ciloleucel after at least 2 prior therapy lines between 1/1/2018 to 12/31/2021. Primary endpoint was progression-free survival (PFS). Secondary endpoints were overall survival (OS), nonrelapse mortality (NRM), and cumulative incidence of relapse/progression. Results: 82 (84.5%) patients were transplant-naïve and 15 (15.5%) previously received HDT/ASCT. No differences were found in the incidence of high-grade cytokine release syndrome or immune effector cell-associated neurotoxicity syndrome, length of hospital admission, or incidence of cytopenia at day 30. 90-day response, PFS, OS, cumulative incidence of relapse/progression, and NRM were not different. Factors that adversely affected outcomes were prior bridging therapy, elevated LDH or thrombocytopenia at time of lymphodepleting chemotherapy, and worse ECOG performance status. Conclusion: Prior treatment with HDT/ASCT does not compromise the safety and efficacy of anti-CD19 CAR-T therapy, suggesting a continued role for HDT/ASCT in treatment of select patients with r/r DLBCL. © 2024

关键词： Autologous HCT Axi-cel CAR-T DLBCL HSCT

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Integrating computational and experimental insights into osmolyte-driven activation of Geobacillus kaustophilus L-asparaginase for acrylamide mitigation

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Journal of Molecular Liquids 2025年 423卷

作者： Özdemir, F.İnci Servili, Burak Demirtaş, Özge Şükür, Gözde Tülek, Ahmet Yildirim, Deniz Gebze Technical University Department of Molecular Biology and Genetics Kocaeli Gebze41400 Turkey Kadir Has University of Science and Engineering Bioinformatics and Genetics Program İstanbul Fatih35430 Turkey Iğdır University Postgraduate Education Institute Department of Bioengineering and Sciences Iğdır Merkez76000 Turkey Cukurova University Faculty of Ceyhan Engineering Department of Chemical Engineering Adana Ceyhan01950 Turkey

Osmolytes play a critical role in enhancing the stability and activity of enzymes for industrial applications. This study systematically investigated the effects of various osmolytes on the activity, optimal pH, temperature, stability, metal ion effects, storage, and acrylamide mitigation performance of L-asparaginase from the thermophilic Geobacillus kaustophilus (GkASNase). The experimental findings were further supported by computationally integrated tools such as homology modeling, docking, and molecular dynamics (MD) simulations. Among the selected osmolytes (maltose, sorbitol, trehalose, glycine, and sucrose), GkASNase showed the highest stability during 30 days of storage in the presence of maltose and arginine. Maltose increased GkASNase activity approximately 2-fold at 37 °C and 55 °C. In the presence of osmolytes, the Km values of GkASNase decreased and the Vmax values increased compared to controls at 37 °C and 55 °C. In the presence of osmolytes, the acrylamide mitigation performance of GkASNase increased by 1.7-fold in a 15 min reaction. The computational analysis indicates that L-asparagine as substrate enhances protein compactness and stability, while arginine as osmolyte increases flexibility and optimizes water distribution around the enzyme. These findings provide novel insights into enzyme stabilization that have implications for therapeutic and biotechnological applications. © 2025 Elsevier B.V.

关键词： Maltose

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Mapping the Scientific Landscape of Integrated Scheduling Towards Industry 5.0 with A Bibliometric Analysis

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Procedia Computer Science 2025年 253卷 84-93页

作者： Anak Agung Ayu Putri Ardyanti Zhiqiang Xie Andrea Stevens Karnyoto Bens Pardamean Ni Made Estiyanti School of Computer Science and Technology Harbin University of Science and Technology Harbin 150000 China Faculty of Information Technology and Design Primakara University Bali 80226 Indonesia Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department BINUS Graduate Program - Master of Computer Science Bina Nusantara University Jakarta Indonesia 11480 Doctoral School of Management and Business University of Debrecen Debrecen Hungary

The industry is rapidly transitioning from the 4.0 era to the 5.0 era, prompting renewed interest among scholars in scheduling problems. They allow operations to process and assemble various components simultaneously. This bibliometric analysis provides a comprehensive understanding of the diverse research perspectives on the potential challenges of integrated scheduling within the framework. We identified 357 Scopus articles and analysed co-occurrence keyword (CNK) by VOSViewer tools and Global Citation Score (GCS) by Scopus tools. The research data obtained from 1993 to 2023 mainly in the subject areas of Engineering, Computer Science, Maths, and others. CNK identified six cluster keywords, and nine most cited articles based on normalised GCS. These results contribute to our understanding of the development of scheduling algorithms, shifting from an emphasis on manufacturing transfers to contemporary demand-centric research directions. Furthermore, this research provides a valuable perspective for policymakers, plant personnel, and manufacturing managers to make informed decisions.

关键词： integrated scheduling assembly job shop scheduling heuristic algorithm genetic algorithm bibliometric analysis

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Gumbel-Softmax Flow Matching with Straight-Through Guidance for Controllable Biological Sequence Generation

arXiv

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arXiv 2025年

作者： Tang, Sophia Zhang, Yinuo Tong, Alexander Chatterjee, Pranam Department of Biomedical Engineering Duke University United States Management and Technology Program University of Pennsylvania United States Center of Computational Biology Duke-NUS Medical School Singapore Mila Quebec AI Institute Canada Université de Montréal Canada Department of Computer Science Duke University United States Department of Biostatistics and Bioinformatics Duke University United States

Flow matching in the continuous simplex has emerged as a promising strategy for DNA sequence design, but struggles to scale to higher simplex dimensions required for peptide and protein generation. We introduce Gumbel-Softmax Flow and Score Matching, a generative framework on the simplex based on a novel Gumbel-Softmax interpolant with a time-dependent temperature. Using this interpolant, we introduce Gumbel-Softmax Flow Matching by deriving a parameterized velocity field that transports from smooth categorical distributions to distributions concentrated at a single vertex of the simplex. We alternatively present Gumbel-Softmax Score Matching which learns to regress the gradient of the probability density. Our framework enables high-quality, diverse generation and scales efficiently to higher-dimensional simplices. To enable training-free guidance, we propose Straight-Through Guided Flows (STGFlow), a classifier-based guidance method that leverages straight-through estimators to steer the unconditional velocity field toward optimal vertices of the simplex. STGFlow enables efficient inference-time guidance using classifiers pre-trained on clean sequences, and can be used with any discrete flow method. Together, these components form a robust framework for controllable de novo sequence generation. We demonstrate state-of-the-art performance in conditional DNA promoter design, sequence-only protein generation, and target-binding peptide design for rare disease treatment. © 2025, CC BY-NC-ND.

关键词： Velocity distribution

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Learning Multi-cellular Representations of Single-Cell Transcriptomics Data Enables Characterization of Patient-Level Disease States

Learning Multi-cellular Representations of Single-Cell Tran...

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Liu, Tianyu De Brouwer, Edward Kuo, Tony Diamant, Nathaniel Missarova, Alsu Wang, Hanchen Hao, Minsheng Bravo, Hector Corrada Scalia, Gabriele Regev, Aviv Heimberg, Graham Research and Early Development Genentech South San FranciscoCA94080 United States Interdepartmental Program in Computational Biology and Bioinformatics Yale University New HavenCT06511 United States Roche Informatics F. Hoffmann-La Roche Ltd. Mississauga Canada Department of Computer Science Stanford University Palo AltoCA94035 United States

ISBN: (纸本)9783031902512

Single-cell RNA-seq (scRNA-seq) has become a prominent tool for studying human biology and disease. The availability of massive scRNA-seq datasets and advanced machine learning techniques has recently driven the development of single-cell foundation models that provide informative and versatile cell representations based on expression profiles. However, to understand disease states, we need to consider entire tissue ecosystems, simultaneously considering many different interacting cells. Here, we tackle this challenge by generating patient-level representations derived from multi-cellular expression context measured with scRNA-seq of tissues. We develop PaSCient, a novel model that employs a multi-level representation learning paradigm and provides importance scores at the individual cell and gene levels for fine-grained analysis across multiple cell types and gene programs characteristic of a given disease. We apply PaSCient to learn a disease model across a large-scale scRNA-seq atlas of 12.5 million cells from over 2,700 patients. Comprehensive and rigorous benchmarking demonstrates the superiority of PaSCient in disease classification and its multiple downstream applications, including dimensionality reduction, gene/cell type prioritization, and patient subgroup discovery. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Biomolecules

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Quantum variational autoencoder utilizing regularized mixed-state latent representations

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Physical Review A 2025年第4期111卷 042416-042416页

作者： Gaoyuan Wang Jonathan Warrell Prashant S. Emani Mark Gerstein Program in Computational Biology and Bioinformatics Yale University New Haven Connecticut 06520 USA Department of Molecular Biophysics and Biochemistry Yale University New Haven Connecticut 06520 USA NEC Laboratories America Princeton New Jersey 08540 USA Department of Computer Science Yale University New Haven Connecticut 06520 USA Department of Statistics & Data Science Yale University New Haven Connecticut 06520 USA Department of Biomedical Informatics & Data Science Yale University New Haven Connecticut 06520 USA

A major challenge in near-term quantum computing is its application to large real-world datasets due to scarce quantum hardware resources. One approach to enabling tractable quantum models for such datasets involves finding low-dimensional representations that preserve essential information for downstream analysis. In classical machine learning, variational autoencoders (VAEs) facilitate efficient data compression, representation learning for subsequent tasks, and novel data generation. However, no quantum model has been proposed that exactly captures all of these features for direct application to quantum data on quantum computers. Some existing quantum models for data compression lack regularization of latent representations, thus preventing direct use for generation and control of generalization. Others are hybrid models with only some internal quantum components, impeding direct training on quantum data. To address this, we present a fully quantum framework, ζ-QVAE, which encompasses all the capabilities of classical VAEs and can be directly applied to map both classical and quantum data to a lower-dimensional space, while effectively reconstructing much of the original state from it. Our model utilizes regularized mixed states to attain optimal latent representations. It accommodates various divergences for reconstruction and regularization. Furthermore, by accommodating mixed states at every stage, it can utilize the full data density matrix and allow for a training objective defined on probabilistic mixtures of input data. Doing so, in turn, makes efficient optimization possible and has potential implications for private and federated learning. In addition to exploring the theoretical properties of ζ-QVAE, we demonstrate its performance on representative genomics and synthetic data. Our results indicate that ζ-QVAE consistently learns representations that better utilize the capacity of the latent space and exhibits similar or better performance compared with

关键词： Quantum computers

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