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Decoding senescence of aging single cells at the nexus of biomaterials, microfluidics, and spatial omics

NPJ AGING 2024年第1期10卷 57页

作者： Venkataraman, Abhijeet Kordic, Ivan Li, Jiaxun Zhang, Nicholas Bharadwaj, Nivik Sanjay Fang, Zhou Das, Sandip Coskun, Ahmet F. Emory Univ Georgia Inst Technol Wallace H Coulter Dept Biomed Engn Atlanta GA 30322 USA Georgia Inst Technol Parker H Petit Inst Bioengn & Biosci 315 Ferst Dr NW Atlanta GA 30332 USA Georgia Inst Technol Woodruff Sch Mech Engn Atlanta GA USA Georgia Inst Technol Interdisciplinary Bioengn Grad Program Atlanta GA 30332 USA Georgia Inst Technol Machine Learning Grad Program Atlanta GA USA

Aging has profound effects on the body, most notably an increase in the prevalence of several diseases. An important aging hallmark is the presence of senescent cells that no longer multiply nor die off properly. Another characteristic is an altered immune system that fails to properly self-surveil. In this multi-player aging process, cellular senescence induces a change in the secretory phenotype, known as senescence-associated secretory phenotype (SASP), of many cells with the intention of recruiting immune cells to accelerate the clearance of these damaged senescent cells. However, the SASP phenotype results in inducing secondary senescence of nearby cells, resulting in those cells becoming senescent, and improper immune activation resulting in a state of chronic inflammation, called inflammaging, in many diseases. Senescence in immune cells, termed immunosenescence, results in further dysregulation of the immune system. An interdisciplinary approach is needed to physiologically assess aging changes of the immune system at the cellular and tissue level. Thus, the intersection of biomaterials, microfluidics, and spatial omics has great potential to collectively model aging and immunosenescence. Each of these approaches mimics unique aspects of the body undergoes as a part of aging. This perspective highlights the key aspects of how biomaterials provide non-cellular cues to cell aging, microfluidics recapitulate flow-induced and multi-cellular dynamics, and spatial omics analyses dissect the coordination of several biomarkers of senescence as a function of cell interactions in distinct tissue environments. An overview of how senescence and immune dysregulation play a role in organ aging, cancer, wound healing, Alzheimer's, and osteoporosis is included. To illuminate the societal impact of aging, an increasing trend in anti-senescence and anti-aging interventions, including pharmacological interventions, medical procedures, and lifestyle changes is discussed, includ

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

arXiv

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arXiv 2022年

作者： Weinan, E. Han, Jiequn Long, Jihao AI for Science Institute Beijing China Center for Machine Learning Research School of Mathematical Sciences Peking University Beijing China Center for Computational Mathematics Flatiron Institute New York10010 United States Program of Applied and Computational Mathematics Princeton University Princeton08544 United States

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 © 2022, The Authors. All rights reserved.

关键词： machine learning

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Quantum Markov blankets for meta-learned classical inferential paradoxes with suboptimal free energy

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BEHAVIORAL AND BRAIN SCIENCES 2024年 47卷 e150页

作者： Clark, Kevin B. Cures Within Reach Chicago IL 60602 USA Felidae Conservat Fund Mill Valley CA 94941 USA Natl Sci Fdn Adv Cyberinfrastruct Coordinat Ecosys Campus & Domain Champ Program Multitier Assistance Training & Computat Help MATC Alexandria VA 22314 USA Univ Penn Penn Ctr Innovat Expert Network Philadelphia PA 19104 USA NASA Ames Res Ctr NASA Astrobiol Program Network Life Detect NfoLD Mountain View CA 94035 USA NASA Ames Res Ctr Multi & Syst Biol NASA GeneLab Mountain View CA 94035 USA NASA Ames Res Ctr Artificial Intelligence & Machine Learning Anal Wo NASA GeneLab Mountain View CA 94035 USA NASA Ames Res Ctr Frontier Dev Lab Mountain View CA 94035 USA SETI Inst Mountain View CA 94043 USA Peace Innovat Inst NL-2511 The Hague Netherlands Stanford Univ Palo Alto CA 94305 USA Max Planck Alumni Assoc Shared Interest Grp Nat & Artificial Intelligence Berlin Germany Inst Elect & Elect Engineers IEEE Biometr & Nanotechnol Councils New York NY 10016 USA

Quantum active Bayesian inference and quantum Markov blankets enable robust modeling and simulation of difficult-to-render natural agent-based classical inferential paradoxes interfaced with task-specific environments. Within a non-realist cognitive completeness regime, quantum Markov blankets ensure meta-learned irrational decision making is fitted to explainable manifolds at optimal free energy, where acceptable incompatible observations or temporal Bell-inequality violations represent important verifiable real-world outcomes.

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Ab Initio Generalized Langevin Equation

arXiv

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arXiv 2022年

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

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multi-scale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field-driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse-grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise-driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of collective variables by adopting a local approximation for the memory kernel and retaining only short-range noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude, and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far-infrared absorption frequency. © 2022, CC BY.

关键词： Molecular dynamics

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Ab initio generalized Langevin equation

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PROCEEDINGS OF THE NATIONAL ACADEMY OF SCIENCES OF THE UNITED STATES OF AMERICA 2024年第14期121卷 e2308668121-e2308668121页

作者： Xie, Pinchen Car, Roberto Weinan, E. Princeton Univ Program Appl & Computat Math Princeton NJ 08544 USA Princeton Univ Dept Chem Princeton NJ 08544 USA Princeton Univ Princeton Mat Inst Princeton NJ 08544 USA Princeton Univ Dept Phys Princeton NJ 08544 USA AI Sci Inst Beijing 100080 Peoples R China Peking Univ Ctr Machine Learning Res Beijing 100084 Peoples R China Peking Univ Sch Math Sci Beijing 100084 Peoples R China

We introduce a machine learning-based approach called ab initio generalized Langevin equation (AIGLE) to model the dynamics of slow collective variables (CVs) in materials and molecules. In this scheme, the parameters are learned from atomistic simulations based on ab initio quantum mechanical models. Force field, memory kernel, and noise generator are constructed in the context of the Mori-Zwanzig formalism, under the constraint of the fluctuation-dissipation theorem. Combined with deep potential molecular dynamics and electronic density functional theory, this approach opens the way to multiscale modeling in a variety of situations. Here, we demonstrate this capability with a study of two mesoscale processes in crystalline lead titanate, namely the field -driven dynamics of a planar ferroelectric domain wall, and the dynamics of an extensive lattice of coarse -grained electric dipoles. In the first case, AIGLE extends the reach of ab initio simulations to a regime of noise -driven motions not accessible to molecular dynamics. In the second case, AIGLE deals with an extensive set of CVs by adopting a local approximation for the memory kernel and retaining only shortrange noise correlations. The scheme is computationally more efficient than molecular dynamics by several orders of magnitude and mimics the microscopic dynamics at low frequencies where it reproduces accurately the dominant far -infrared absorption frequency.

关键词： multiscale modeling generalized Langevin equation machine learning

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Is more evidence needed for thrombectomy in basilar artery occlusion? The BASICS and BEST meta- analytical approaches

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Stroke & Vascular Neurology 2021年第4期6卷 671-672,I0094-I0096页

作者： Davi J Fontoura Solla Felipe Coelho Argolo Karol P Budohoski Angelos G Kolias JoséGuilherme M P Caldas Jamary Oliveira-Filho Division of Neurosurgery Department of NeurologyUniversity of Sao PauloSao PauloBrazil NIHR Global Health Research Group on Neurotrauma Cambridge UniversityCambridgeUK Machine Learning for Mental Health Lab King's College LondonLondonUK Division of Neurosurgery Department of Clinical NeurosciencesAddenbrooke's HospitalCambridge UniversityCambridgeUK Radiology University of Sao PauloSao PauloBrazil Postgraduate Program in Health Sciences Federal University of BahiaSalvadorBahiaBrazil

INTRODUCTION The benefit of mechanical thrombectomy for stroke associated with anterior circulation large vessel occlusion(LVO)is well established and among the most effective treatments in medicine.1 Stroke associated with basilar artery occlusion(BAO)was not included in the seminal thrombectomy randomised clin-ical trials(RCTs).BAO accounts for 1%of the ischaemic strokes and 5%-10%of the LVO,but 70%-80%of BAO victims evolve to death or full dependence.23 The terminal nature of the brainstem and thalamic circulations may predispose to such dismal *** two RCT designed to assess the efficacy of early(<6-8 hours since symptoms onset)thrombectomy for BAO treatment,the BEST and BASICS trials.24 Given the prior evidence for thrombectomy benefit for anterior circu-lation stroke is well settled;BAO stroke shares a similar physiopathology with very poor prog-nosis.

关键词： throm occlusion basilar

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The low abundance of CpG in the SARS-CoV-2 genome is not an evolutionarily signature of ZAP

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SCIENTIFIC REPORTS 2022年第1期12卷 1-8页

作者： Afrasiabi, Ali Alinejad-Rokny, Hamid Khosh, Azad Rahnama, Mostafa Lovell, Nigel Xu, Zhenming Ebrahimi, Diako UNSW Sydney BioMed Machine Learning Lab Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ New South Wales UNSW Data Sci Hub Sydney NSW 2052 Australia Macquarie Univ AI Enabled Proc AIP Res Ctr Hlth Data Analyt Program Sydney NSW 2109 Australia Yazd Univ Dept Biol Yazd *** Iran Univ Kentucky Dept Plant Pathol Lexington KY 40546 USA UNSW Sydney Grad Sch Biomed Engn Sydney NSW 2052 Australia Univ Texas Hlth Sci Ctr San Antonio Dept Microbiol Immunol & Mol Genet San Antonio TX 78229 USA Texas Biomed Res Inst San Antonio TX 78227 USA

The zinc finger antiviral protein (ZAP) is known to restrict viral replication by binding to the CpG rich regions of viral RNA, and subsequently inducing viral RNA degradation. This enzyme has recently been shown to be capable of restricting SARS-CoV-2. These data have led to the hypothesis that the low abundance of CpG in the SARS-CoV-2 genome is due to an evolutionary pressure exerted by the host ZAP. To investigate this hypothesis, we performed a detailed analysis of many coronavirus sequences and ZAP RNA binding preference data. Our analyses showed neither evidence for an evolutionary pressure acting specifically on CpG dinucleotides, nor a link between the activity of ZAP and the low CpG abundance of the SARS-CoV-2 genome.

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Canonical Response Parameterization: Quantifying the structure of responses to single-pulse intracranial electrical brain stimulation

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PLOS COMPUTATIONAL BIOLOGY 2023年第5期19卷 e1011105页

作者： Miller, Kai J. Mueller, Klaus-Robert J. Valencia, Gabriela Ojeda Huang, Harvey J. Gregg, Nicholas M. Worrell, Gregory A. J. Hermes, Dora Mayo Clin Dept Neurol Surg Rochester MN 55905 USA Mayo Clin Dept Biomed Engn & Physiol Rochester MN 55905 USA Google Res Brain Team Berlin Germany Berlin Inst Technol Dept Comp Sci Machine Learning Grp Berlin Germany Korea Univ Dept Artificial Intelligence Seoul South Korea Max Planck Inst Informat Saarbrucken Germany Mayo Clin Med Scientist Training Program Rochester MN USA Mayo Clin Dept Neurol Rochester MN USA

Single-pulse electrical stimulation in the nervous system, often called cortico-cortical evoked potential (CCEP) measurement, is an important technique to understand how brain regions interact with one another. Voltages are measured from implanted electrodes in one brain area while stimulating another with brief current impulses separated by several seconds. Historically, researchers have tried to understand the significance of evoked voltage polyphasic deflections by visual inspection, but no general-purpose tool has emerged to understand their shapes or describe them mathematically. We describe and illustrate a new technique to parameterize brain stimulation data, where voltage response traces are projected into one another using a semi-normalized dot product. The length of timepoints from stimulation included in the dot product is varied to obtain a temporal profile of structural significance, and the peak of the profile uniquely identifies the duration of the response. Using linear kernel PCA, a canonical response shape is obtained over this duration, and then single-trial traces are parameterized as a projection of this canonical shape with a residual term. Such parameterization allows for dissimilar trace shapes from different brain areas to be directly compared by quantifying cross-projection magnitudes, response duration, canonical shape projection amplitudes, signal-to-noise ratios, explained variance, and statistical significance. Artifactual trials are automatically identified by outliers in sub-distributions of cross-projection magnitude, and rejected. This technique, which we call "Canonical Response Parameterization" (CRP) dramatically simplifies the study of CCEP shapes, and may also be applied in a wide range of other settings involving event-triggered data. Author summaryWe introduce a new machine learning technique for quantifying the structure of responses to single-pulse intracranial electrical brain stimulation. This approach allows voltage resp

关键词： Functional electrical stimulation Evoked potentials Signal to noise ratio Event-related potentials Electroencephalography Electrode potentials Principal component analysis Vision

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DeepSigns: A predictive model based on Deep learning for the early detection of patient health deterioration

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EXPERT SYSTEMS WITH APPLICATIONS 2021年 165卷 113905-113905页

作者： da Silva, Denise Bandeira Schmidt, Diogo da Costa, Cristiano Andre Righi, Rodrigo da Rosa Eskofier, Bjoern Univ Vale Rio Sinos Unisinos Appl Comp Grad Program SOFTWARELAB Software Innovat Lab Sao Leopoldo Brazil Friedrich Alexander Univ Erlangen Nurnberg FAU Dept Comp Sci Machine Learning & Data Analyt Lab D-91058 Erlangen Germany

Early diagnosis of critically ill patients depends on the attention and observation of medical staff about different variables, as vital signs, results of laboratory tests, among other. Seriously ill patients usually have changes in their vital signs before worsening. Monitoring these changes is important to anticipate the diagnosis in order to initiate patients' care. Prognostic indexes play a fundamental role in this context since they allow to estimate the patients' health status. Besides, the adoption of electronic health records improved the availability of data, which can be processed by machine learning techniques for information extraction to support clinical decisions. In this context, this work aims to create a computational model able to predict the deterioration of patients' health status in such a way that it is possible to start the appropriate treatment as soon as possible. The model was developed based on Deep learning technique, a Recurrent Neural Networks, the Long Short-Term Memory, for the prediction of patient's vital signs and subsequent evaluation of the patient's health status severity through Prognostic Indexes commonly used in the health area. Experiments showed that it is possible to predict vital signs with good precision (accuracy > 80%) and, consequently, predict the Prognostic Indexes in advance to treat the patients before deterioration. Predicting the patient's vital signs for the future and use them for the Prognostic Index' calculation allows clinical times to predict future severe diagnoses that would not be possible applying the current patient's vital signs (50%-60% of cases would not be identified).

关键词： Predictive scores machine learning Deep learning LSTM Health informatics

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Empowering Precision Medicine: AI-Driven Schizophrenia Diagnosis via EEG Signals: A Comprehensive Review from 2002-2023

arXiv

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arXiv 2023年

作者： Jafari, Mahboobeh Sadeghi, Delaram Shoeibi, Afshin Alinejad-Rokny, Hamid Beheshti, Amin García, David López Chen, Zhaolin Acharya, U. Rajendra Gorriz, Juan M. Internship in BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia Data Science and Computational Intelligence Institute University of Granada Spain BioMedical Machine Learning Lab The Graduate School of Biomedical Engineering UNSW Sydney SydneyNSW2052 Australia UNSW Data Science Hub The University of New South Wales SydneyNSW2052 Australia Health Data Analytics Program Centre for Applied Artificial Intelligence Macquarie University Sydney2109 Australia Data Science Lab School of Computing Macquarie University SydneyNSW2109 Australia Monash University Melbourne Australia School of Mathematics Physics and Computing University of Southern Queensland Springfield Australia Department of Psychiatry University of Cambridge United Kingdom

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. © 2023,

关键词： Electroencephalography

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