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SSRN 2024年

作者： Ni, Eric Knight, Elizabeth Gerstein, Mark Program in Computational Biology and Bioinformatics Yale University 100 College Street New HavenCT06511 United States Department of Molecular Biophysics & Biochemistry Yale University 266 Whitney Avenue New HavenCT06511 United States Yale School of Medicine 333 Cedar Street New HavenCT06510 United States

Blockchain technology is gaining increased traction in the biomedical sector due to its ability to improve trust and reduce the risk of fraud and errors in the handling of health data. However, the large volume of biomedical datasets has slowed its adoption due to poor scalability. This challenge is especially relevant for applications that rely on blockchain’s strong immutability by storing data directly on-chain. Here, we demonstrate the potential of blockchain to create a secure and trustless environment for managing large on-chain records. Specifically, we detail an efficient, index-based approach for storing data on the Ethereum blockchain. We show that insertion and retrieval speeds remain nearly constant relative to database size, scaling linearly with the amount of data processed. Additionally, we achieve substantial efficiency gains through low-level assembly optimizations on the Ethereum Virtual Machine, underscoring the limitations of the Solidity compiler. Finally, we illustrate this approach through a practical case study, designing a smart contract for storing and querying training certificates on the Ethereum blockchain. Our solution achieves ~2x faster data insertion, ~500x faster retrieval, ~60% lower gas costs, and 50% lower storage usage compared to baseline methods. It won first place for track 1 of the 2022 iDASH secure genome analysis competition. We also demonstrate that this solution readily adapts to other data types, enabling efficient on-chain storage and retrieval of text, RNA-seq, or biomedical image data. © 2024, The Authors. All rights reserved.

关键词： Chains

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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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LLM2Vec Sentence Embeddings Analysis in Sentiment Classification

LLM2Vec Sentence Embeddings Analysis in Sentiment Classifica...

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International Conference on Data and Software Engineering (ICODSE)

作者： Matthew Martianus Henry Nur Adhianti Hervanto Mahmud Isnan Dian Kurnianingrum Chyntia Ika Ratnapuri Bens Pardamean Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia Entrepreneurship Department Bina Nusantara University Jakarta Indonesia Computer Science Department BINUS Graduate Program-Master of Computer Science Bina Nusantara University Jakarta Indonesia

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

Sentiment analysis is crucial method in business intelligence to extract insights, which typically begin with sentiment classification. One of the latest frameworks for generating sentence embeddings for sentiment classification is LLM2Vec, which allows Transformer decoder-based models to generate sentence embeddings for text representation. Its capability is deemed language-agnostic, which, in this study, the framework is leveraged for Tokopedia tweet sentiment analysis to prove the claim. The base decoder models used in the LLM2Vec framework were Llama 3 8B, Llama 2 7B, Sheared Llama 1.3B, and Mistral 7B. Two BERT-based models, which are the Indonesian SBERT model and IndoBERT trained with the SimCSE approach, were employed as a comparison. The generated embeddings were classified using logistic regression, SVM, and MLP Classifier. Classifiers using embedding generated by LLM2Vec with Llama 3 8B and Mistral 7B achieves on-par performance with classifiers that utilize IndoBERT SimCSE embeddings, while classifiers using embeddings generated by LLM2Vec with Llama 2 7B and Sheared Llama 1.3B achieves much lower performance. Classifiers with Indonesian SBERT embeddings achieve the highest F1 score performance. Despite slightly lower performance, this study has proven the language-agnostic capability of LLM2Vec, especially with Llama 3 8B and Mistral 7B in colloquial Bahasa Indonesia sentiment analysis, since none of the base decoders were ever trained using the Bahasa Indonesia corpus.

关键词： Support vector machines Sentiment analysis Logistic regression Social networking (online) Blogs Transformers Encoding Decoding Business intelligence Software engineering

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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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APIR:Aggregating Universal Proteomics Database Search Algorithms for Peptide Identification with FDR Control

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Genomics, Proteomics & bioinformatics 2024年第2期22卷 171-187页

作者： Yiling Elaine Chen Xinzhou Ge Kyla Woyshner MeiLu McDermott Antigoni Manousopoulou Scott B.Ficarro Jarrod A.Marto Kexin Li Leo David Wang Jingyi Jessica Li Department of Statistics and Data Science University of CaliforniaLos AngelesCA 90095USA Department of Immuno-Oncology Beckman Research InstituteCity of Hope National Medical CenterDuarteCA 91010USA Department of Quantitative and Computational Biology University of Southern CaliforniaLos AngelesCA 90089USA Department of Cancer Biology and Blais Proteomics Center Dana-Farber Cancer InstituteDepartment of PathologyBrigham and Women’s Hospital and Harvard Medical SchoolBostonMA 02215USA Department of Pediatrics City of Hope National Medical CenterDuarteCA 91010USA Bioinformatics Interdepartmental Program University of CaliforniaLos AngelesCA 90095USA Department of Human Genetics University of CaliforniaLos AngelesCA 90095USA Department of Computational Medicine University of CaliforniaLos AngelesCA 90095USA Department of Biostatistics University of CaliforniaLos AngelesCA 90095USA

Advances in mass spectrometry(MS)have enabled high-throughput analysis of proteomes in biological *** state-of-the-art MS data analysis relies on database search algorithms to quantify proteins by identifying peptide–spectrum matches(PSMs),which convert mass spectra to peptide *** database search algorithms use distinct search strategies and thus may identify unique ***,no existing approaches can aggregate all user-specified database search algorithms with a guaranteed increase in the number of identified peptides and a control on the false discovery rate(FDR).To fill in this gap,we proposed a statistical framework,Aggregation of Peptide Identification Results(APIR),that is universally compatible with all database search ***,under an FDR threshold,APIR is guaranteed to identify at least as many,if not more,peptides as individual database search algorithms *** of APIR on a complex proteomics standard dataset showed that APIR outpowers individual database search algorithms and empirically controls the *** data studies showed that APIR can identify disease-related proteins and post-translational modifications missed by some individual database search *** APIR framework is easily extendable to aggregating discoveries made by multiple algorithms in other high-throughput biomedical data analysis,e.g.,differential gene expression analysis on RNA sequencing *** APIR R package is available at https://***/yiling0210/APIR.

关键词： Shotgun proteomics Peptide–spectrum match Peptide identification Aggregation of lists FDR control

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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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Statistical testing for uncertainty of P-NET by DeepLIFT

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Discover Applied Sciences 2025年第6期7卷

作者： Goo, Taewan Lee, Chanhee Shin, Sangyeon Kim, Haeyoung Park, Taesung Interdisciplinary Program in Bioinformatics Seoul National University Seoul Korea Republic of Department of Statistics Seoul National University Seoul Korea Republic of

In precision medicine, deep learning models have become essential for identifying biomarkers and predicting diagnosis and prognosis of diseases. Although these models have demonstrated considerable success in producing accurate prognostic and diagnostic predictions, their opaque decision-making processes pose challenges for meaningful biological interpretation. In response, biologically informed neural networks (BINNs), such as P-NET, have been introduced to integrate biological domain knowledge. These models commonly employ measures like SHAP and DeepLIFT to determine the importance of their nodes. However, existing models are limited because they rely on importance estimates that does not consider statistical significance of these estimates. This study proposes a novel statistical testing approach designed to address these limitations. Specifically, it introduces two permutation tests–label and gene permutation test–aimed at identifying significant genes and pathways. Our findings show that this framework can measure the statistical significance of genes and pathways. Additionally, significant pathways are biologically associated with the metastasis mechanism of cancer, thereby enhancing both the interpretability and reliability of BINN-derived insights. This approach confidence in model outputs, thereby advancing the clinical applicability of BINNs in precision medicine. © The Author(s) 2025.

关键词： Diseases

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Ornithine aspartate effects on bacterial composition and metabolic pathways in a rat model of steatotic liver disease

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World Journal of Hepatology 2024年第5期16卷 832-842页

作者： Elisa Carolina Lange Pabulo Henrique Rampelotto Larisse Longo Laura Bainy Rodrigues de Freitas Carolina Uribe-Cruz Mario Reis Alvares-da-Silva Experimental Laboratory of Hepatology and Gastroenterology Hospital de Clínicas de Porto AlegrePorto Alegre 90035-007Brazil Graduate Program in Gastroenterology and Hepatology Universidade Federal do Rio Grande do SulPorto Alegre 90035-007Brazil Bioinformatics and Biostatistics Core Facility Universidade Federal do Rio Grande do SulPorto Alegre 91501-970Brazil Graduate Program in Biological Sciences:Pharmacology and Therapeutics Universidade Federal do Rio Grande do SulPorto Alegre 90035-007Brazil Facultad de Ciencias de la Salud Universidad Católica de las MisionesPosadasMisiones 3300Argentina Division of Gastroenterology Hospital de Clínicas de Porto AlegrePorto Alegre 90035-007RSBrazil Conselho Nacional de Desenvolvimento Científico e Tecnológico(CNPq)researcher Brasília 71605-001Brazil

BACKGROUND Metabolic-dysfunction associated steatotic liver disease(MASLD)is a hepatic manifestation of metabolic *** suggest ornithine aspartate(LOLA)as drug *** To analyze the influence of LOLA intake on gut microbiota using a nutritional model of *** Adult male Sprague Dawley rats were randomized into three groups:Control(10 rats fed with a standard diet),MASLD(10 rats fed with a high-fat and choline-deficient diet),and LOLA(10 rats receiving 200 mg/kg/d LOLA,after the 16th week receiving high-fat and choline-deficient diet).After 28 wk of the experiment,animals were euthanized,and feces present in the intestine were *** fecal DNA extraction,the V4 region of the 16S rRNA gene was amplified followed by sequencing in an Ion S5™*** Alpha and beta diversity metrics were comparable between MASLD and LOLA.3 OTUs were differentially abundant between MASLD and LOLA,which belong to the species Helicobacter rodentium,Parabacteroides goldsteinii,and Parabacteroides *** functional prediction provided two different metabolic profiles between MASLD and *** 9 pathways differentially abundant in MASLD are related to a change in energy source,adenosine/purine nucleotides degradation as well as guanosine and adenosine deoxyribonucleotides *** 14 pathways differentially abundant in LOLA are associated with four major metabolic functions primarily influenced by L-aspartate,including tricarboxylic acid cycle pathways,purine/guanosine nucleotides biosynthesis,pyrimidine ribonucleotides biosynthesis and salvage as well as lipid IVA *** Although LOLA had no influence on alpha and beta diversity in this nutritional model of MASLD,it was associated with changes in specific gut microbes and their related metabolic pathways.

关键词： Animal model Gut microbiota Metabolic-associated steatotic liver disease Metabolic prediction Ornithine aspartate

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Targeting protein-protein interaction interfaces with antiviral N protein inhibitor in SARS-CoV-2

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Biophysical Journal 2024年第4期123卷 478-488页

作者： Hong, Jhen-Yi Lin, Shih-Chao Kehn-Hall, Kylene Zhang, Kai-Min Luo, Shun-Yuan Wu, Hung-Yi Chang, Sui-Yuan Hou, Ming-Hon Institute of Genomics and Bioinformatics and Department of Life Sciences National Chung Hsing University Taichung Taiwan Bachelor Degree Program in Marine Biotechnology College of Life Sciences National Taiwan Ocean University Keelung Taiwan Department of Biomedical Sciences and Pathobiology Virginia-Maryland College of Veterinary Medicine Virginia Polytechnic Institute and State University Blacksburg VA United States Center for Emerging Zoonotic and Arthropod-borne Pathogens Virginia Polytechnic Institute and State University Blacksburg VA United States Department of Chemistry National Chung Hsing University Taichung Taiwan Graduate Institute of Veterinary Pathobiology College of Veterinary Medicine National Chung Hsing University. Taichung Taiwan Department of Clinical Laboratory Sciences and Medical Biotechnology College of Medicine National Taiwan University Taipei Taiwan PhD Program in Medical Biotechnology National Chung Hsing University Taichung Taiwan Biotechnology Center National Chung Hsing University Taichung Taiwan

Coronaviruses not only pose significant global public health threats but also cause extensive damage to livestock-based industries. Previous studies have shown that 5-benzyloxygramine (P3) targets the Middle East respiratory syndrome coronavirus (MERS-CoV) nucleocapsid (N) protein N-terminal domain (N-NTD), inducing non-native protein-protein interactions (PPIs) that impair N protein function. Moreover, P3 exhibits broad-spectrum antiviral activity against CoVs. The sequence similarity of N proteins is relatively low among CoVs, further exhibiting notable variations in the hydrophobic residue responsible for non-native PPIs in the N-NTD. Therefore, to ascertain the mechanism by which P3 demonstrates broad-spectrum anti-CoV activity, we determined the crystal structure of the SARS-CoV-2 N-NTD:P3 complex. We found that P3 was positioned in the dimeric N-NTD via hydrophobic contacts. Compared with the interfaces in MERS-CoV N-NTD, P3 had a reversed orientation in SARS-CoV-2 N-NTD. The Phe residue in the MERS-CoV N-NTD:P3 complex stabilized both P3 moieties. However, in the SARS-CoV-2 N-NTD:P3 complex, the Ile residue formed only one interaction with the P3 benzene ring. Moreover, the pocket in the SARS-CoV-2 N-NTD:P3 complex was more hydrophobic, favoring the insertion of the P3 benzene ring into the complex. Nevertheless, hydrophobic interactions remained the primary stabilizing force in both complexes. These findings suggested that despite the differences in the sequence, P3 can accommodate a hydrophobic pocket in N-NTD to mediate a non-native PPI, enabling its effectiveness against various CoVs. © 2024 Biophysical Society

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Dental Disease Detection based on CNN for Panoramic Dental Radiographs 11

Dental Disease Detection based on CNN for Panoramic Dental R...

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11th IEEE International Conference on Intelligent Computing and Information Systems, ICICIS 2023

作者： El Bagoury, Manar Al-Shetairy, Mirna Ahmed, Omar Mehanny, Samah S Hamdy, Tassneem Omar, Gihan Akram, Marwan Mohamed, Rana Amr, Hebatullah Gad, Walaa Ain Shams University Faculty of Computer and Information Sciences Bioinformatics Program Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Computer Science Department Cairo Egypt Faculty of Dentistry Cairo and Galala Universities Oral Biology Department Cairo Egypt Cairo University Faculty of Dentistry Oral and Maxillofacial Radiology Department Cairo Egypt Future University in Egypt Faculty of Oral and Dental Medicine Oral Radiology Department Cairo Egypt Ain Shams University Faculty of Computer and Information Sciences Information Systems Department Cairo Egypt

ISBN: (纸本)9798350322101

Panoramic dental radiography is one of the examination tools frequently performed in dental clinics and hospitals as it captures a wide range of the dental region and its surroundings, but due to the complex structure of the dental area and lack of time, dentists focus on tiny parts of the images. With the help of Machine Learning, dentists can detect dental defects and anomalies faster and more accurately. In this study, a data set of 733 panoramic radiographs of adult patients was collected from the faculty of Oral and Dental Medicine, Future University, Egypt. Using a combination of pre-trained CNN architecture and a finetuned network, the collected panoramic radiographs were used to determine the optimal architecture. The purpose of this research was to develop a computer-aided detection system based on a finetuned CNN algorithm, and to evaluate the accuracy and usefulness of this system for the detection of proximal and occlusal caries, interdental and inter radicular alveolar bone loss, periapical lesions, and impacted teeth in panoramic radiographs. Different object detection models were tested to detect selected dental diseases that were found in the panoramic radiographs, and after a few experiments, the YOLOv5 architecture was proved to be the most optimal after achieving 0.61 mAP@0.5 and 0.28 mAP@ [0.5-0.95] for the total six classes. This is the best-achieved results to our knowledge. © 2023 IEEE.

关键词： Object detection

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