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Proteomic Differences in Colonic Epithelial Cells in Ulcerative Colitis Have an Epigenetic Basis

GASTRO HEP ADVANCES 2024年第6期3卷 830-841页

作者： Jelinsky, Scott Lee, Isac Monetti, Mara Breitkopf, Susanne Martz, Flora Kongala, Ramya Culver, Jeffrey Vo, Vanessa Xue, Liang Gieseck III, Richard Dickinson, Caitlyn Kasaian, Marion Lord, James D. Pfizer Dept Inflammat & Immunol 1 Portland St Cambridge MA 02139 USA Pfizer Internal Med Res Unit Cambridge MA USA Benaroya Res Inst Translat Res Program Seattle WA USA Pfizer Machine Learning & Computat Sci Early Clin Dev Cambridge MA USA Mem Sloan Kettering Canc Ctr Prote Core New York NY USA Kymera Therapeut Watertown MA USA Univ Washington Dept Gen Surg Seattle WA USA Novartis Inst Biomed Res Cambridge MA USA

BACKGROUND AND AIMS: The colonic epithelium serves as both a barrier to lumenal contents and a gatekeeper of infl ammatory responses. In ulcerative colitis (UC), epithelial dysfunction is a core feature, but little is known about the cellular changes that may underlie disease pathology. We therefore evaluated how the chromatin epigenetics and proteome of epithelial cells differs between health and UC. METHODS: We sorted live CD326+ epithelial cells from colon biopsies of healthy control (HC) screening colonoscopy recipients and from inflamed or uninflamed colon segments of UC patients on no biologic nor immunomodulator therapy (n = 5-7 subjects per group). Cell lysates were analyzed by proteomic evaluation and nuclei were analyzed for open chromatin with assay for transposase-accessible chromatin using sequencing. RESULTS: Proteins most highly elevated in inflamed UC biopsies relative to HC were those encoded by the HLA-DRA (P = 3.1 x 10-33) and CD74 (P = 1.6 x 10-27), genes associated with antigen presentation, and the antimicrobial dual oxidase 2 (DUOX2) (P = 3.2 x 10-28) and lipocalin-2 (P = 2.2 x 10-26) genes. Conversely, the water channel aquaporin 8 was strikingly less common with inflammation (P = 1.9 x 10-18). Assay for transposase-accessible chromatin using sequencing revealed more open chromatin around the aquaporin 8 gene in HCs (P = 2.0 x 10-2) and more around the DUOX2/DUOXA2 locus in inflamed UC colon (P = 5.7 x 10-4), suggesting an epigenetic basis for differential protein expression by epithelial cells in health and disease. CONCLUSION: Numerous differences exist between the proteome and chromatin of colonic epithelial cells in UC patients and HCs, some of which correlate to suggest specific epigenetic mechanisms regulating the epithelial proteome.

关键词： Proteomics ATAC-Seq DUOX2 AQP8

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Empowering Optimal Control with machine learning: A Perspective from Model Predictive Control 25th

Empowering Optimal Control with Machine Learning: A Perspect...

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25th International Symposium on Mathematical Theory of Networks and Systems (MTNS)

作者： Weinan, E. Han, Jiequn Long, Jihao AI Sci Inst Beijing Peoples R China Peking Univ Ctr Machine Learning Res Beijing Peoples R China Peking Univ Sch Math Sci Beijing Peoples R China Flatiron Inst Ctr Computat Math New York NY 10010 USA Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA

Solving complex optimal control problems have confronted computational challenges for a long time. Recent advances in machine learning have provided us with new opportunities to address these challenges. This paper takes model predictive control, a popular optimal control method, as the primary example to survey recent progress that leverages machine learning techniques to empower optimal control solvers. We also discuss some of the main challenges encountered when applying machine learning to develop more robust optimal control algorithms. Copyright (C) 2022 The Authors.

关键词： machine learning Optimal Control Model Predictive Control

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Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

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BUILDING AND ENVIRONMENT 2022年 219卷

作者： Zaidan, Esmat Abulibdeh, Ammar Alban, Ahmad Jabbar, Rateb Qatar Univ Coll Arts & Sci Dept Int Affairs Policy Planning & Dev Program Doha Qatar Qatar Univ Coll Arts & Sci Dept Humanities Appl Geog & GIS Program POB 2713 Doha Qatar Qatar Univ Coll Engn Machine Learning Doha Qatar

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society to address these uncertainties. More precisely, uncertainties lead to performance gaps between assumed and actual sustainability target outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, and building features (MPSEB). To utilize this model, a thorough face-to-face survey questionnaire was administered to measure these elements. This study explored how these elements affect the patterns of residential energy consumption in a region with numerous expat communities of various ethnic and cultural backgrounds. In particular, the study investigated the patterns of energy behaviors and human-building interactions among the residents of Qatar by collecting empirical evidence and conducting a subsequent survey analysis. machine learning approaches were employed to explore the survey data and determine the interdependencies between features, as well as the significance of the fundamental factors influencing humanbuilding interactions. The XGBoost method was used to conduct a feature importance analysis to determine factors contributing to residential energy consumption. The results revealed the primary behavioral and socioeconomic factors that affect residential energy consumption, and confirmed the influence of human factors in Qatar while considering its diverse population.

关键词： Human -building interactions Preference Motivation Socioeconomic characteristics Building features Energy consumption

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Thermal disorder and phonon softening in the ferroelectric phase transition of lead titanate

arXiv

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arXiv 2024年

作者： Xie, Pinchen Chen, Yixiao Weinan, E. Car, Roberto Program in Applied and Computational Mathematics Princeton University PrincetonNJ08544 United States AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing China Department of Chemistry Department of Physics Program in Applied and Computational Mathematics Princeton Materials Institute Princeton University PrincetonNJ08544 United States

We report an extensive molecular dynamics study of ab-initio quality of the ferroelectric phase transition in crystalline PbTiO3. We model anharmonicity accurately in terms of potential energy and polarization surfaces trained on density functional theory data with modern machine learning techniques. Our simulations demonstrate that the transition has a strong order-disorder character, in agreement with diffraction experiments, and provide fresh insight into the approach to equilibrium upon crossing the transition temperature. We find that the macroscopic polarization appears/disappears due to dipolar switching at the nanometer scale. We also compute the infrared optical absorption spectra in both the ferroelectric and the paraelectric phases, finding good agreement with the experimental Raman frequencies. For a long time, the almost ideal displacive character of the soft mode detected by Raman scattering in the paraelectric phase has been contrasted with the order-disorder character of the transition suggested by diffraction. We settle this issue by showing that the soft mode coexists with a strong Debye relaxation due to the thermal disordering of the dipoles. The Debye relaxation feature is centered at zero frequency and appears near the transition temperature in both the ferroelectric and the paraelectric phases. © 2024, CC BY.

关键词： Ferroelectricity

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A Sensitivity and Performance Analysis of Word2Vec Applied to Emotion State Classification Using a Deep Neural Architecture 16th

A Sensitivity and Performance Analysis of Word2Vec Applied t...

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16th International Symposium on Distributed Computing and Artificial Intelligence (DCAI)

作者： Pasti, Rodrigo Vilasboas, Fabricio G. Roque, Isabela R. de Castro, Leandro N. Univ Prebiteriana Mackenzie Nat Comp & Machine Learning Lab LCoN Grad Program Elect Engn & Comp PPGEEC Sao Paulo Brazil

ISBN: (纸本)9783030238872;9783030238865

Word2Vec has become one of the most relevant neural networks to generate word embeddings for NLP applications. Despite that, little has been investigated in terms of its sensitivity to the word vectors' length (n) and the window size (w). Thus, the present paper performs a sensitivity analysis of Word2Vec when applied to generate word embeddings for a deep neural architecture used to classify emotion states in tweets. Furthermore, we present a computational performance analysis to investigate how the system scales as a function of n and w in different computing environments. The results show that a window size of approximately half the tweet length (8 words) and a value of n = 50 suffices to find good performances. Also, by increasing these values one may unnecessarily increase the computational cost.

关键词： Embeddings

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A Developer Recommendation Method Based on Code Quality

A Developer Recommendation Method Based on Code Quality

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International Joint Conference on Neural Networks (IJCNN) held as part of the IEEE World Congress on Computational Intelligence (IEEE WCCI)

作者： da Silva, Matheus Camilo Janino Cizotto, Andre Armstrong Paraiso, Emerson Cabrera Pontificia Univ Catolica Parana Grad Program Informat Curitiba Parana Brazil Siemens Ltd Machine Learning Comm Curitiba Parana Brazil

ISBN: (纸本)9781728169262

During the development cycle of a project, it is common for software requirements and functionality to change and for code errors to occur. To deal with these unforeseen changes, the artifact known as change request, which is a formal proposal to alter a system, is used. Its assignment is an important step in the development process. Projects can receive a very high number of requests daily, which makes the automation of this process compelling. This work proposes a method for assigning unresolved requests, based on developer's profiles. The proposed method consists of three steps. The first step is to extract code quality metrics, commit data and previously resolved requests, in order to model developers through the mining of repositories. The second step concerns with the selection of the profile of potential developers through the application of natural language processing and information retrieval techniques. And finally, in the third step the appropriate developers are selected based on the quality of their code and the impact of their commits. Results from experimental evaluation show that the method is able to recommend more developers with a positive impact on the repository quality if compared to the iMacPro method.

关键词： Software quality clustering developer recommendation developer profile

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Diagnosis of Schizophrenia in EEG Signals Using dDTF Effective Connectivity and New PreTrained CNN and Transformer Models 10th

Diagnosis of Schizophrenia in EEG Signals Using dDTF Effecti...

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10th International Work-Conference on the Interplay Between Natural and Artificial Computation (IWINAC)

作者： Shoeibi, Afshin Khodatars, Marjane Alinejad-Rorky, Hamid Heras, Jonathan Bagherzadeh, Sara Beheshti, Amin Gorriz, Juan M. Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Macquarie Univ AI Enabled Proc AIP Res Ctr Internship Hlth Data Analyt Program Sydney NSW Australia UNSW Sydney UNSW BioMed Machine Learning Lab Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ La Rioja Dept Math & Comp Sci La Rioja Spain Islamic Azad Univ Dept Biomed Engn Sci & Res Branch Tehran Iran Macquarie Univ Sch Comp Data Sci Lab Sydney NSW 2109 Australia

ISBN: (纸本)9783031611391;9783031611407

Schizophrenia (SZ) is a multifaceted mental disorder that typically emerges in early adulthood, characterized by a spectrum of physiological and cognitive deficits. Electroencephalography (EEG) recordings are pivotal in SZ diagnosis, necessitating the expertise of specialist doctors and psychologists. However, the analysis of EEG signals is labor-intensive and susceptible to human error. This study introduces a deep learning (DL) pipeline for the early detection of SZ using EEG signals. The pipeline includes stages of dataset selection, preprocessing, feature extraction, and classification. For this study, the RepOD dataset, consisting of EEG recordings from 14 subjects with SZ and healthy controls (HC), was utilized. The preprocessing phase involves normalizing and segmenting the EEG data. Subsequently, the EEG signals are divided into various sub-bands via Discrete Wavelet Transform (DWT), and effective connectivity matrices are derived using the directed Directed Transfer Function (dDTF) technique. Following this, state-of-the-art pretrained DL models based on CNNs and transformers are applied to extract features and classify the 2D dDTF images obtained from different EEG sub-bands. Notably, the ConvNext-Tiny architecture demonstrated superior performance, achieving an accuracy of 96% in the beta sub-band. Furthermore, this model surpassed the performance of other DL models in terms of accuracy across additional EEG sub-bands.

关键词： Schizophrenia EEG Detection DWT dDTF Transformers

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Randomized Spline Trees for Functional Data Classification: Theory and Application to Environmental Time Series

arXiv

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arXiv 2024年

作者： Riccio, Donato Maturo, Fabrizio Romano, Elvira Machine Learning Engineer and Student in the Data Science Master’s Degree Program The University of Campania Luigi Vanvitelli Caserta Italy Department of Economics Statistics and Business Faculty of Technological and Innovation Sciences Universitas Mercatorum Rome Italy Department of Mathematics and Physics University of Campania Luigi Vanvitelli Caserta Italy

Functional data analysis (FDA) and ensemble learning can be powerful tools for analyzing complex environmental time series. Recent literature has highlighted the key role of diversity in enhancing accuracy and reducing variance in ensemble *** paper introduces Randomized Spline Trees (RST), a novel algorithm that bridges these two approaches by incorporating randomized functional representations into the Random Forest framework. RST generates diverse functional representations of input data using randomized B-spline parameters, creating an ensemble of decision trees trained on these varied representations. We provide a theoretical analysis of how this functional diversity contributes to reducing generalization error and present empirical evaluations on six environmental time series classification tasks from the UCR Time Series Archive. Results show that RST variants outperform standard Random Forests and Gradient Boosting on most datasets, improving classification accuracy by up to 14%. The success of RST demonstrates the potential of adaptive functional representations in capturing complex temporal patterns in environmental data. This work contributes to the growing field of machine learning techniques focused on functional data and opens new avenues for research in environmental time series *** Codes 62M10, 68T05, 65D07, 68T10 © 2024, CC BY.

关键词： Spatio-temporal data

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MSHCCT: A Multiscale Compact Convolutional Network for High-Resolution Aerial Scene Classification

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IEEE GEOSCIENCE AND REMOTE SENSING LETTERS 2025年 22卷

作者： Jamali, Ali Roy, Swalpa Kumar Lu, Bing Beni, Leila Hashemi Kakhani, Nafiseh Ghamisi, Pedram Simon Fraser Univ Dept Geog Burnaby BC V5A 1S6 Canada Alipurduar Govt Engn & Management Coll Dept Comp Sci & Engn Alipurduar 736206 West Bengal India North Carolina A&T State Univ Dept Built Environm Geomat Program Greensboro NC 27411 USA Univ Tubingen Fac Sci Dept Geosci Soil Sci & Geomorphol GrpCRC ResourceCultures 107 D-72070 Tubingen Germany Univ Tubingen DFG Cluster Excellence Machine Learning D-72070 Tubingen Germany Landbanking Grp GmbH D-80333 Munich Germany Helmholtz Zentrum Dresden Rossendorf HZDR D-09599 Freiberg Germany Univ Lancaster Lancaster LA1 4YR England

The growing popularity of vision transformers (ViTs) in remote sensing image classification is due to their ability to effectively capture long-range dependencies. However, their high computational cost and memory footprint limit their applicability, particularly for small-scale datasets and resource-constrained environments. To address these challenges, we propose the multiscale multihead compact convolutional transformer (MSHCCT), a lightweight yet powerful model that integrates convolutional tokenization with small-scale ViTs to enhance multiscale feature representation while maintaining computational efficiency. Despite a modest increase in parameters and training time, MSHCCT achieves superior classification accuracy and robustness on high-resolution aerial scenes. Importantly, our approach eliminates the need for model pretraining, additional datasets, or multisensor data fusion, ensuring a computationally efficient and practical solution for remote sensing applications. The code will be made publicly available at https://***/aj1365/MSHCCT

关键词： Transformers Computer architecture Remote sensing Feature extraction Computer vision Computational modeling Head Adaptation models Training Scene classification Attention mechanism compact convolutional network image classification vision transformers (ViTs)

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Empowering precision medicine: AI-driven schizophrenia diagnosis via EEG signals: A comprehensive review from 2002-2023

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APPLIED INTELLIGENCE 2023年第24期53卷 31165-31180页

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin Garcia, David Lopez Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. UNSW Sydney Grad Sch Biomed Engn BioMed Machine Learning Lab Sydney NSW 2052 Australia Univ Granada Data Sci & Computat Intelligence Inst Granada Spain Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ Ctr Appl Artificial Intelligence Hlth Data Analyt Program Sydney NSW 2109 Australia Macquarie Univ Sch Comp Data Sci Lab Sydney NSW 2109 Australia Monash Univ Monash Biomed Imaging MBI Melbourne Vic Australia Univ Southern Queensland Sch Math Phys & Comp Springfield Australia Univ Cambridge Dept Psychiat Cambridge England

Schizophrenia (SZ) is a prevalent mental disorder characterized by cognitive, emotional, and behavioral changes. Symptoms of SZ include hallucinations, illusions, delusions, lack of motivation, and difficulties in concentration. While the exact causes of SZ remain unproven, factors such as brain injuries, stress, and psychotropic drugs have been implicated in its development. SZ can be classified into different types, including paranoid, disorganized, catatonic, undifferentiated, and residual. Diagnosing SZ involves employing various tools, including clinical interviews, physical examinations, psychological evaluations, the Diagnostic and Statistical Manual of Mental Disorders (DSM), and neuroimaging techniques. Electroencephalography (EEG) recording is a significant functional neuroimaging modality that provides valuable insights into brain function during SZ. However, EEG signal analysis poses challenges for neurologists and scientists due to the presence of artifacts, long-term recordings, and the utilization of multiple channels. To address these challenges, researchers have introduced artificial intelligence (AI) techniques, encompassing conventional machine learning (ML) and deep learning (DL) methods, to aid in SZ diagnosis. This study reviews papers focused on SZ diagnosis utilizing EEG signals and AI methods. The introduction section provides a comprehensive explanation of SZ diagnosis methods and intervention techniques. Subsequently, review papers in this field are discussed, followed by an introduction to the AI methods employed for SZ diagnosis and a summary of relevant papers presented in tabular form. Additionally, this study reports on the most significant challenges encountered in SZ diagnosis, as identified through a review of papers in this field. Future directions to overcome these challenges are also addressed. The discussion section examines the specific details of each paper, culminating in the presentation of conclusions and findings.

关键词： Schizophrenia Diagnosis EEG Artificial Intelligence machine learning Deep learning

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