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Plasma proteomics identifies proteins and pathways associated with incident depression in 46,165 adults

science Bulletin 2025年第4期70卷 573-586页

作者： Jujiao Kang Liu Yang Tianye Jia Wei Zhang Lin-Bo Wang Yu-Jie Zhao Jia You Yue-Ting Deng Yi-Jun Ge Wei-Shi Liu Yi Zhang Yi-Lin Chen Xiao-Yu He Barbara J.Sahakian Yucheng TYang Xing-Ming Zhao Jin-Tai Yu Jianfeng Feng Wei Cheng Institute of Science and Technology for Brain-Inspired Intelligence(ISTBI) Fudan UniversityShanghai 200433China Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence Fudan UniversityMinistry of EducationShanghai 200433China Department of Neurology and National Center for Neurological Disorders Huashan HospitalState Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain ScienceShanghai Medical CollegeFudan UniversityShanghai 200433China Department of Psychiatry University of CambridgeCambridge CB20SZUK Department of Computer Science University of WarwickCoventry CV47ALUK School of Data Science Fudan UniversityShanghai 200438China

Proteomic alterations preceding the onset of depression offer valuable insights into its development and potential *** data from 46,165 UK Biobank participants and 2920 plasma proteins profiled at baseline,we conducted a longitudinal analysis with a median follow-up of 14.5 years to explore the relationship between plasma proteins and incident *** regression was then used to assess associations between depression-related proteins and brain structures,genetic factors,and stress-related *** analysis identified 157 proteins associated with incident depression(P<1.71×10^(-5)),including novel associations with proteins such as GAST,PLAUR,LRRN1,BCAN,and ***,higher expression levels of GDF15(P=6.18×10-26)and PLAUR(P=2.88×10^(-14))were linked to an increased risk of depression,whereas higher levels of LRRN1(P=4.28×10^(-11))and ITGA11(P=3.68×10^(-9))were associated with a decreased *** of the 157 proteins is correlated with brain regions implicated in depression,including the hippocampus and middle temporal ***,these protein alterations were strongly correlated with stress-related events,including self-harm events,adult,and childhood *** pathway enrichment analysis highlighted the critical roles of the immune *** and TNF emerged as key proteins in the protein-protein interaction ***3A2,newly linked to incident depression(P=4.35×10^(-10)),was confirmed as a causal factor through Mendelian randomization *** summary,our research identified the proteomic signatures associated with the onset of depression,highlighting its potential for early intervention and tailored therapeutic avenues.

关键词： Plasma proteins Depression Brain structure

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Late and Early Blight Diseases Identification of Potatoes with a Light Weight Hybrid Transfer Learning Model

Late and Early Blight Diseases Identification of Potatoes wi...

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AI IoT Congress (AIIoT), World

作者： Abu Zobayer Bin Siddique Shoibal Das Poonam Tabassum All Moon Tasir Shovon Roy Md. Saifur Rahman M. F. Mridha Ashraful Islam Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh

Potatoes are one of the world’s most important commodities, and leaf maladies such as early and late blight can substantially reduce their yield and quality. Hence, both farmers and researchers must prioritize quick and precise illness diagnosis. In our research, we propose a strategy based on transfer learning for classifying toxic and diseased potato leaf tissue. We specifically used our dataset of potato leaf photos to fine-tune the Mobile-Net model, which was a pre-trained convolutional neural network. To enhance the model’s functionality, we also added a few more layers. Our study found that, in comparison to other state-of-the-art methods, our methodology outperformed them all by achieving a multi-class classification accuracy of 99%. Our method can be used to detect and monitor potato leaf maladies in real-world situations, which could eventually contribute to enhancing potato productivity and food security.

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Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Genre Classification of Bangla Poem Using Machine Learning a...

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AI IoT Congress (AIIoT), World

作者： Syed Tangim Pasha Ashraful Islam Mohammed Masudur Rahman Eshtiak Ahmed Md. Ferdouse Ahmed Foysal Md Zahangir Alam Department of Computing and Information System Daffodil International University Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Department of Computer Science and Engineering Independent University Bangladesh Department of Computer Science and Engineering International Standard University Bangladesh Department of Computer Science and Engineering Daffodil International University Bangladesh

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.

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Influence of early through late fusion on pancreas segmentation from imperfectly registered multimodal magnetic resonance imaging

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Journal of Medical Imaging 2025年第2期12卷

作者： Remedios, Lucas W. Liu, Han Remedios, Samuel W. Zuo, Lianrui Saunders, Adam M. Bao, Shunxing Huo, Yuankai Powers, Alvin C. Virostko, John Landman, Bennett A. Vanderbilt University Department of Computer Science NashvilleTN United States Johns Hopkins University Department of Computer Science BaltimoreMD United States National Institutes of Health Department of Radiology and Imaging Sciences BethesdaMD United States Vanderbilt University Department of Electrical and Computer Engineering NashvilleTN United States Vanderbilt University Medical Center Department of Medicine Division of Diabetes Endocrinology and Metabolism NashvilleTN United States VA Tennessee Valley Healthcare System NashvilleTN United States Vanderbilt University Department of Molecular Physiology and Biophysics NashvilleTN United States University of Texas at Austin Dell Medical School Department of Diagnostic Medicine AustinTX United States University of Texas at Austin Livestrong Cancer Institutes Dell Medical School AustinTX United States University of Texas at Austin Department of Oncology Dell Medical School AustinTX United States University of Texas at Austin Oden Institute for Computational Engineering and Sciences AustinTX United States Vanderbilt University Department of Biomedical Engineering NashvilleTN United States

Purpose: Combining different types of medical imaging data, through multimodal fusion, promises better segmentation of anatomical structures, such as the pancreas. Strategic implementation of multimodal fusion could improve our ability to study diseases such as diabetes. However, where to perform fusion in deep learning models is still an open question. It is unclear if there is a single best location to fuse information when analyzing pairs of imperfectly aligned images or if the optimal fusion location depends on the specific model being used. Two main challenges when using multiple imaging modalities to study the pancreas are (1) the pancreas and surrounding abdominal anatomy have a deformable structure, making it difficult to consistently align the images and (2) breathing by the individual during image collection further complicates the alignment between multimodal images. Even after using state-of-the-art deformable image registration techniques, specifically designed to align abdominal images, multimodal images of the abdomen are often not perfectly aligned. We examine how the choice of different fusion points, ranging from early in the image processing pipeline to later stages, impacts the segmentation of the pancreas on imperfectly registered multimodal magnetic resonance (MR) images. Approach: Our dataset consists of 353 pairs of T2-weighted (T2w) and T1-weighted (T1w) abdominal MR images from 163 subjects with accompanying pancreas segmentation labels drawn mainly based on the T2w images. Because the T2w images were acquired in an interleaved manner across two breath holds and the T1w images on one breath hold, there were three different breath holds impacting the alignment of each pair of images. We used deeds, a state-of-the-art deformable abdominal image registration method to align the image pairs. Then, we trained a collection of basic UNets with different fusion points, spanning from early to late layers in the model, to assess how early through lat

关键词： Image segmentation

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Genomic Language Models: Opportunities and Challenges

arXiv

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arXiv 2024年

作者： Benegas, Gonzalo Ye, Chengzhong Albors, Carlos Li, Jianan Canal Song, Yun S. Computer Science Division University of California Berkeley United States Department of Statistics University of California Berkeley United States Center for Computational Biology University of California Berkeley United States

Large language models (LLMs) are having transformative impacts across a wide range of scientific fields, particularly in the biomedical sciences. Just as the goal of Natural Language Processing is to understand sequences of words, a major objective in biology is to understand biological sequences. Genomic Language Models (gLMs), which are LLMs trained on DNA sequences, have the potential to significantly advance our understanding of genomes and how DNA elements at various scales interact to give rise to complex functions. To showcase this potential, we highlight key applications of gLMs, including functional constraint prediction, sequence design, and transfer learning. Despite notable recent progress, however, developing effective and efficient gLMs presents numerous challenges, especially for species with large, complex genomes. Here, we discuss major considerations for developing and evaluating *** Codes 92-08, 92B20, 68T50, 68T07 © 2024, CC BY-NC-ND.

关键词： Genome

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From Failure to Insight: Analyzing Disk Breakdowns in Large-Scale HPC Environments

From Failure to Insight: Analyzing Disk Breakdowns in Large-...

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2024 Workshops of the International Conference for High Performance Computing, Networking, Storage and Analysis, SC Workshops 2024

作者： George, Anjus Wang, Meng Hanley, Jesse Ransom, Garrett Wilson Bent, John Zimmer, Christopher Oak Ridge National Laboratory National Center for Computational Sciences Oak RidgeTN United States University of Chicago Department of Computer Science ChicagoIL United States Los Alamos National Laboratory HPC Infrastructure Los AlamosNM United States Los Alamos National Laboratory High Performance Computing Los AlamosNM United States

ISBN: (纸本)9798350355543

Disk failure data provides valuable insights for preventing failures, enhancing storage robustness, guiding system design and deployment, and ensuring reliable operations at data centers. This paper introduces two disk failure datasets collected from large-scale HPC production environments over the past five years, comprising over 5,000 failure records from more than 40,000 disks. We analyzed these datasets across multiple dimensions, including temporal, spatial, and relational trends, and performed a comprehensive reliability assessment. Our analysis yielded numerous observations and insights that influence various operational aspects of HPC storage systems. We believe this study offers a holistic understanding of disk failure trends likely to interest the HPC storage community. © 2024 IEEE.

关键词： Cause effect analysis Failure data analysis Hard disk drives HPC storage Reliability Summit Supercomputer

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Time Series Analysis and Forecasting of Air Quality Index of Dhaka City of Bangladesh

Time Series Analysis and Forecasting of Air Quality Index of...

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AI IoT Congress (AIIoT), World

作者： Sheikh Rahmatulla Sakib Kamarun Nahar Sara Md. Tahmid Hossain Rasel Md. Masudul Islam Asif Md. Aynul Hasan Nahid Md. Saifur Rahman M. F. Mridha Ashraful Islam Department of Computer Science and Engineering Bangladesh University of Business and Technology Dhaka Bangladesh Department of Computer Science and Engineering Daffodil International University Dhaka Bangladesh Department of Computer Science American International University-Bangladesh Dhaka Bangladesh Center for Computational and Data Sciences Independent University Bangladesh Dhaka Bangladesh

In Dhaka, the capital city of Bangladesh, various sources including vehicle emissions, industrial activities, brick kilns, building sites, and open rubbish burning contribute to the air pollution problem. To assess the air quality, the Air Quality Index (AQI) is utilized, which categorizes air quality based on pollutant concentration. In this study, we have built ARIMA, Auto-ARIMA, SARIMAX, and VAR models to predict the air quality of Dhaka. Unlike previous studies, we have utilized hourly air pollutants factors such as PM 2.5 , PM 10 , SO 2 , CO, NO 2 , and O 3 to forecast air quality. Our novel approach enables us to predict the monthly and weekly air quality of Dhaka city. Our analysis reveals that the SARIMAX model, which takes into account seasonal patterns, trends, and external factors, is the most accurate in predicting Dhaka city’s air quality. The model’s prediction performance is assessed using statistical indicators such as mean absolute percentage error and root mean square error. The study highlights that the SARIMAX model could aid policymakers in evaluating the efficacy of air pollution control measures.

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Determination of Pure Animal Fat Concentrations and Halal Category from Mixed Animal Fats Raman Spectra 5

Determination of Pure Animal Fat Concentrations and Halal Ca...

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5th Technology Innovation Management and Engineering science International Conference, TIMES-iCON 2024

作者： Wicaksono, Aria Wahyu Amani, Annida Lertvanithphol, Tossaporn Aminah, Nina Siti Alni, Anita Viridi, Sparisoma Bahar, Herman Horprathum, Mati Djamal, Mitra Department of Computational Science Insititut Teknologi Bandung Bandung Indonesia Department of Physics Insititut Teknologi Bandung Bandung Indonesia Optical Thin-Film Laboratory National Electronics and Computer Technology Center Pathum Thani Thailand Instrumentation and Computational Physics Research Group Insititut Teknologi Bandung Bandung Indonesia Organic Chemistry Research Group Insititut Teknologi Bandung Bandung Indonesia Nuclear Physics and Biophysics Research Group Insititut Teknologi Bandung Bandung Indonesia Magnetics Physics and Photonics Research Group Insititut Teknologi Bandung Bandung Indonesia

ISBN: (纸本)9798350362602

—Analysis of chemical and biological compounds generally uses the Raman spectrum because of its non-destructive nature. Raman spectra can be analyzed computationally using chemometric methods. The principal component analysis (PCA) method can be combined with another regression or classification model to determine the concentrations of fat and halal categories from animal fats data. This research aims to determine the best regression methods for finding pure spectrum concentrations in mixed spectra of animal fats based on the lowest root mean square error (RMSE) and mean absolute error (MAE) of the regressor, and to determine the best classifier methods for finding halal categories of animal fats. It is shown that the PCA with decision tree regressor performs better on training data than random forest or K-nearest neighbor to predict pure animal fats concentrations, with mean squared error (MSE) of 0.001 and MAE of 0.002 on training data. The decision tree classifier also performs better than the random forest or K-nearest neighbor classifier in predicting the halal category, with 0.94 recall in the haram class and 0.90 recall in the halal class. However, this model still does not meet the conditions to have 1 recall value in the haram class as expected for real-world applications. ©2024 IEEE.

关键词： Nearest neighbor search

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Machine Learning in Fraud Detection for Financial Sustainability in Credit Card 2nd

Machine Learning in Fraud Detection for Financial Sustainabi...

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2nd International Conference on Sustainable Development Using Machine Learning, Artificial Intelligence, and IoT, ICSD 2024

作者： Kaur, Harleen Chauhan, Ritu Shamsi, Yezdanul Haque Alankar, Bhavya Artificial Intelligence and IoT Lab Center for Computational Biology and Bioinformatics Amity University Noida India Department of Computer Science and Engineering School of Engineering Sciences and Technology Jamia Hamdard New Delhi India

ISBN: (纸本)9783031717284

In today’s financial world, various technologies have been emerged and with the evolution in the technologies it has led to the increase in the fraud cases. The sustainable growth of businesses and the financial markets are severely harmed by financial statement fraud. Consequently, it is crucial and vital to build an efficient methodology to identify financial statement fraud in businesses. Usage of credit card are very frequent these days and due to loopholes in the system fraud cases in credit card is rising which causes huge financial losses. In this paper the focus is on credit card fraud detection in real world with the help of machine learning. In this case Logistic Regression, Decision tree method and SVM (support vector machine) are applied on credit card fraud detection for knowledge discovery among the data. The applied methods can detect the fraudulent transactions with highest accuracy. However, the results are predicted using evaluation metrics such as accuracy, error rate, specificity, sensitivity, and ROC curves are calculated to assess each model's performance. Hence, the predicted results suggested that logistic regression is the most accurate model for prediction of fraud datasets in financial sustainability. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Decentralized finance

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Static network structure cannot stabilize cooperation among Large Language Model agents

arXiv

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arXiv 2024年

作者： Han, Jin Battu, Balaraju Romić, Ivan Rahwan, Talal Holme, Petter Department of Computer Science Aalto University Aalto00076 Finland New York University Abu Dhabi United Arab Emirates Center for Computational Social Science Kobe University Hyogo Kobe657-8501 Japan

Large language models (LLMs) are increasingly used to model human social behavior, with recent research exploring their ability to simulate social dynamics. Here, we test whether LLMs mirror human behavior in social dilemmas, where individual and collective interests conflict. Humans generally cooperate more than expected in laboratory settings, showing less cooperation in well-mixed populations but more in fixed networks. In contrast, LLMs tend to exhibit greater cooperation in well-mixed settings. This raises a key question: Are LLMs about to emulate human behavior in cooperative dilemmas on networks? In this study, we examine networked interactions where agents repeatedly engage in the Prisoner’s Dilemma within both well-mixed and structured network configurations, aiming to identify parallels in cooperative behavior between LLMs and humans. Our findings indicate critical distinctions: while humans tend to cooperate more within structured networks, LLMs display increased cooperation mainly in well-mixed environments, with limited adjustment to networked contexts. Notably, LLM cooperation also varies across model types, illustrating the complexities of replicating human-like social adaptability in artificial agents. These results highlight a crucial gap: LLMs struggle to emulate the nuanced, adaptive social strategies humans deploy in fixed networks. Unlike human participants, LLMs do not alter their cooperative behavior in response to network structures or evolving social contexts, missing the reciprocity norms that humans adaptively employ. This limitation points to a fundamental need in future LLM design—to integrate a deeper comprehension of social norms, enabling more authentic modeling of human-like cooperation and adaptability in networked environments. © 2024, CC BY-SA.

关键词： Social behavior

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