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Highly Efficient Organosulfur and Lithium-Metal Hosts Enabled by C@Fe3N Sponge

Angewandte Chemie 2022年第3期135卷

作者： Dr. Jiarui He Dr. Amruth Bhargav Hyunki Sul Prof. Arumugam Manthiram Materials Science and Engineering Program & Texas Materials Institute The University of Texas at Austin Austin TX 78712 USA Contribution: Conceptualization (equal) Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (lead) Validation (lead) Writing - original draft (lead) Contribution: Data curation (equal) Formal analysis (equal) Investigation (equal) Methodology (supporting) Validation (equal) Writing - review & editing (equal) Contribution: Data curation (supporting) Formal analysis (supporting) Methodology (supporting) Validation (supporting) Writing - review & editing (supporting) Contribution: Conceptualization (supporting) Funding acquisition (lead) Methodology (supporting) Project administration (lead) Resources (lead) Supervision (lead) Validation (equal) Writing - review & editing (equal)

Lithium-organosulfur (Li-OS) batteries, despite possessing high theoretical specific capacity, encounter a few practical challenges, including unsatisfactory lifespan and low active material utilization under realistic conditions. Here, diisoropyl xanthogen polysulfide (DIXPS) has been selected as a model organosulfur compound to investigate the practical feasibility of Li-OS batteries under realistic conditions. A well-designed freestanding carbon sponge decorated with Fe 3 N nanoparticles (C@Fe 3 N) is introduced into the Li-OS cells as a scaffold for both Li and DIXPS. The lithiophilic property of the C@Fe 3 N host guides uniform lithium deposition at the anode, and the catalysis of the DIXPS conversion reaction promotes the kinetics at the cathode. Impressively, the synergistic effect of C@Fe 3 N leads to an extremely stable cycling performance over 1 000 cycles in a Li-OS full cell under realistic conditions.

关键词： Electrochemistry Lithiophilic Host Lithium-Organosulfur Batteries Pouch Cells Shuttling-Free Cathode

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Artificial Intelligence in Obsessive-Compulsive Disorder: A Systematic Review

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Current treatment options in psychiatry 2025年第1期12卷 23页

作者： Jiyeong Kim Juan Pablo Gonzalez Pacheco Ashleigh Golden Elias Aboujaoude Peter van Roessel Aayushi Gandhi Pavithra Mukunda Tatevik Avanesyan Haopeng Xue Ehsan Adeli Jane Paik Kim Manish Saggar Shannon Wiltsey Stirman Eric Kuhn Kaustubh Supekar Kilian M Pohl Carolyn I Rodriguez Stanford Center for Digital Health Stanford School of Medicine Stanford CA USA. Department of Psychiatry and Behavioral Sciences Stanford School of Medicine Stanford CA USA. Program in Internet Health and Society Cedars-Sinai Medical Center Los Angeles CA USA. Artificial Intelligence for Mental Health Initiative (AI4MH) Department of Psychiatry and Behavioral Sciences Stanford University Stanford CA USA. Department of Computer Science Stanford University Stanford CA USA. Department of Biomedical Data Science Stanford School of Medicine Stanford CA USA. Dissemination and Training Division National Center for PTSD VA Palo Alto Health Care System Palo Alto CA USA. Veterans Affairs Palo Alto Health Care System Palo Alto CA USA. Department of Electrical Engineering Stanford University Stanford CA USA.

Purpose of Review:Obsessive-compulsive disorder (OCD) is a chronic and disabling condition, often leading to significant functional impairments. Despite its early onset, there is an average delay of 17 years from symptom onset to diagnosis and treatment, resulting in poorer outcomes. This systematic review aims to synthesize current findings on the application of AI in OCD, highlighting opportunities for early symptom detection, scalable therapy training, clinical decision support, novel therapeutics, computer vision-based approaches, and multimodal biomarker discovery. Recent Findings:While previous reviews focused on biomarker-based OCD detection and treatment using machine learning (ML), the findings of the current review add information about novel applications of deep learning technology, specifically generative artificial intelligence (GenAI) and natural language processing (NLP). Among the included 13 articles, most studies (84.6%) utilized secondary data analyses, primarily through GenAI/NLP. Nearly 77% of these studies were published in the past two years, with high quality of evidence. The primary focus areas were enhancing treatment and management, and timely OCD detection (both 38.5%); followed by AI tool development for broader mental health applications. Summary:AI technologies offer transformative potential for improvements related to OCD if diagnosis occurs earlier after onset; thereby lessening the consequential economic burden. Prioritizing investment in ethically sound AI research could significantly improve OCD outcomes in mental health care. Supplementary Information:The online version contains supplementary material available at 10.1007/s40501-025-00359-8.

关键词： AI Diagnosis Mental health OCD Obsessive-compulsive disorder Treatment

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Uncertainty quantification in multivariate mixed models for mass cytometry data

arXiv

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arXiv 2019年

作者： Seiler, Christof Kronstad, Lisa M. Simpson, Laura J. Le Gars, Mathieu Vendrame, Elena Blish, Catherine A. Holmes, Susan Department of Statistics Stanford University Department of Data Science and Knowledge Engineering Maastricht University Netherlands Immunology Program Department of Medicine Stanford University School of Medicine Immunology Program Department of Medicine Stanford University School of Medicine Chan Zuckerberg Biohub Department of Statistics Stanford University

Mass cytometry technology enables the simultaneous measurement of over 40 proteins on single cells. This has helped immunologists to increase their understanding of heterogeneity, complexity, and lineage relationships of white blood cells. Current statistical methods often collapse the rich single-cell data into summary statistics before proceeding with downstream analysis, discarding the information in these multivariate datasets. In this article, our aim is to exhibit the use of statistical analyses on the raw, uncompressed data thus improving replicability, and exposing multivariate patterns and their associated uncertainty profiles. We show that multivariate generative models are a valid alternative to univariate hypothesis testing. We propose two models: a multivariate Poisson log-normal mixed model and a logistic linear mixed model. We show that these models are complementary and that either model can account for different confounders. We use Hamiltonian Monte Carlo to provide Bayesian uncertainty quantification. Our models applied to a recent pregnancy study successfully reproduce key findings while quantifying increased overall protein-to-protein correlations between first and third trimester. Copyright © 2019, The Authors. All rights reserved.

关键词： Proteins

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Novel strategy for disease risk prediction incorporating predicted gene expression and DNA methylation data:a multi-phased study of prostate cancer

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Cancer Communications 2021年第12期41卷 1387-1397页

作者： Chong Wu Jingjing Zhu Austin King Xiaoran Tong Qing Lu Jong Y.Park Liang Wang Guimin Gao Hong-Wen Deng Yaohua Yang Karen E.Knudsen Timothy R.Rebbeck Jirong Long Wei Zheng Wei Pan David V.Conti Christopher A Haiman Lang Wu Department of Statistics Florida State UniversityTallahasseeFL 32304USA Cancer Epidemiology Division Population Sciences in the Pacific ProgramUniversity of Hawaii Cancer CenterUniversity of Hawaii at ManoaHonoluluHI 96813USA Department of Epidemiology and Biostatistics Michigan State UniversityEast LansingMI 48824USA Department of Biostatistics University of FloridaGainesvilleFL 32603USA Department of Cancer Epidemiology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Tumor Biology H.Lee Moffitt Cancer Center and Research InstituteTampaFL 33612USA Department of Public Health Sciences University of ChicagoChicagoIL 60637USA Center of Bioinformatics and Genomics Department of Global Biostatistics and Data ScienceTulane UniversityNew OrleansLA 70112USA Division of Epidemiology Department of MedicineVanderbilt Epidemiology CenterVanderbilt-Ingram Cancer CenterVanderbilt University Medical CenterNashvilleTN 37203USA Department of Cancer Biology Sidney Kimmel Cancer CenterThomas Jefferson UniversityPhiladelphiaPA 19107USA Department of Medical Oncology Dana-Farber Cancer InstituteBostonMA 02215USA Department of Epidemiology Harvard TH Chan School of Public HealthBostonMA 02115USA Division of Biostatistics University of MinnesotaMinneapolisMN 55455USA Department of Preventive Medicine Keck School of MedicineUniversity of Southern California/Norris Comprehensive Cancer CenterLos AngelesCA 90033USA

Background:DNA methylation and gene expression are known to play important roles in the etiology of human diseases such as prostate cancer(PCa).However,it has not yet been possible to incorporate information of DNA methylation and gene expression into polygenic risk scores(PRSs).Here,we aimed to develop and validate an improved PRS for PCa risk by incorporating genetically predicted gene expression and DNA methylation,and other genomic information using an integrative ***:Using data from the PRACTICAL consortium,we derived multiple sets of genetic scores,including those based on available single-nucleotide polymorphisms through widely used methods of pruning and thresholding,LDpred,LDpred-funt,AnnoPred,and EBPRS,as well as PRS constructed using the genetically predicted gene expression and DNA methylation through a revised pruning and thresholding *** the tuning step,using the UK Biobank data(1458 prevalent cases and 1467 controls),we selected PRSs with the best *** an independent set of data from the UK Biobank,we developed an integrative PRS combining information from individual ***,in the testing step,we tested the performance of the integrative PRS in another independent set of UK Biobank data of incident cases and ***:Our constructed PRS had improved performance(C statistics:76.1%)over PRSs constructed by individual benchmark methods(from 69.6%to 74.7%).Furthermore,our new PRS had much higher risk assessment power than family *** overall net reclassification improvement was 69.0%by adding PRS to the baseline model compared with 12.5%by adding family ***:We developed and validated a new PRS which may improve the utility in predicting the risk of developing *** innovative method can also be applied to other human diseases to improve risk prediction across multiple outcomes.

关键词： risk prediction polygenic risk scores predicted gene expression predicted DNA methylation integrative models prostate cancer

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Transfer Learning from Chest X-Ray Pre-trained Convolutional Neural Network for Learning Mammogram data

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Procedia Computer science 2018年 135卷 400-407页

作者： Bens Pardamean Tjeng Wawan Cenggoro Reza Rahutomo Arif Budiarto Ettikan Kandasamy Karuppiah Computer Science Department BINUS Graduate Program - Master of Computer Science Program Bina Nusantara University Jakarta Indonesia 11480 Bioinformatics and Data Science Research Center Bina Nusantara University Jakarta Indonesia 11480 Computer Science Department School of Computer Science Bina Nusantara University Jakarta Indonesia 11480 NVIDIA Corporation

Breast cancer is one of the deadliest cancer for female nowadays. Despite of the rapid advancement in medical image analysis with the rise of deep learning, development of breast cancer detection system is limited due to relatively small size of the publicly available mammogram dataset. In this paper, we discover an effective configuration for transfer learning from Chest X-Ray pre-trained Convolutional Neural Network to overcome the small-size mammogram dataset problem. We found that the best configuration achieve 90.38% validation accuracy for modified.

关键词： Transfer learning Convolutional neural network Medical image analysis Chest X-ray Mammogram

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Prevalence of hearing loss, tinnitus, vertigo and sudden deafness among patients with polymyositis and dermatomyositis

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Scientific Reports 2024年第1期14卷 1-7页

作者： Hung, Shih-Han Xirasagar, Sudha Dang, Luong Huu Viet-Nhi, Nguyen-Kieu Cheng, Yen-Fu Chen, Chin-Shyan Lin, Herng-Ching Department of Otolaryngology School of Medicine Taipei Medical University Taipei 110 Taiwan Department of Otolaryngology Wan Fang Hospital Taipei 110 Taiwan International Ph.D. Program in Medicine College of Medicine Taipei Medical University Taipei 110 Taiwan Department of Health Services Policy and Management Arnold School of Public Health University of South Carolina Columbia SC United States Department of Otolaryngology Faculty of Medicine University of Medicine and Pharmacy at Ho Chi Minh City Ho Chi Minh City Viet Nam Department of Medical Research Taipei Veterans General Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery Taipei Veterans General Hospital Taipei Taiwan Department of Otolaryngology-Head and Neck Surgery School of Medicine National Yang Ming Chiao Tung University Taipei Taiwan Research Center of Data Science on Healthcare Industry College of Management Taipei Medical University Taipei Taiwan Department of Economics National Taipei University New Taipei City Taiwan School of Health Care Administration College of Management Taipei Medical University Taipei Taiwan Research Center of Sleep Medicine Taipei Medical University Hospital Taipei Taiwan

Little is known about a possible association of autoimmune inner ear disease among patients diagnosed with polymyositis (PM)/dermatomyositis (DM). This study aimed to explore differences in the prevalence of inner ear symptoms among patients with and without PM/DM using a nationwide population-based dataset. data for this study were retrieved from the Taiwan National Health Insurance Research database. The study sample included 1622 patients diagnosed with PM/DM and 8109 propensity-score matched comparison patients without PM/DM. We performed multivariate logistic regressions to calculate odds ratios (ORs) and 95% confidence interval (CI) for tinnitus, hearing loss, sudden deafness, and vertigo among patients with PM/DM versus comparison patients. Chi-square tests showed statistically significant differences between patients with PM/DM and comparison patients in the prevalence of tinnitus (16.1% vs. 12.7%, p < 0.001), non-conductive hearing loss (9.2% vs. 6.8%, p < 0.001), and vertigo (14.4% vs. 11.1%, p < 0.001). The adjusted ORs for tinnitus, non-conductive hearing loss, and vertigo, respectively, were 1.332 (95% CI = 1.147–1.547), 1.399 (95% CI = 1.154–1.696), and 1.374 (95% CI = 1.173–1.611) for patients with PM/DM when compared to comparison patients. Our study finds that patients with PM/DM have higher prevalence rates of tinnitus, non-conductive hearing loss, and vertigo than comparison patients. © The Author(s) 2024.

关键词： Dermatomyositis Epidemiology Inner ear disorders Polymyositis Tinnitus

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Navigating the challenges in creating complex data systems: a development philosophy

arXiv

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arXiv 2022年

作者： Dittmer, Sören Roberts, Michael Gilbey, Julian Biguri, Ander Preller, Jacobus Rudd, James H.F. Aston, John A.D. Schönlieb, Carola-Bibiane Selby, Ian Breger, Anna Thorpe, Matthew Weir-McCall, Jonathan R. Gkrania-Klotsas, Effrossyni Korhonen, Anna Jefferson, Emily Langs, Georg Yang, Guang Prosch, Helmut Stanczuk, Jan Tang, Jing Babar, Judith Sánchez, Lorena Escudero Teare, Philip Patel, Mishal Wassin, Marcel Holzer, Markus Walton, Nicholas Lió, Pietro Shadbahr, Tolou Sala, Evis Department of Applied Mathematics and Theoretical Physics University of Cambridge Cambridge United Kingdom Addenbrooke’s Hospital Cambridge University Hospitals NHS Trust Cambridge United Kingdom Department of Medicine University of Cambridge Cambridge United Kingdom Department of Pure Mathematics and Mathematical Statistics University of Cambridge Cambridge United Kingdom ZeTeM University of Bremen Bremen Germany Department of Radiology University of Cambridge Cambridge United Kingdom Faculty of Mathematics University of Vienna Austria Department of Mathematics University of Manchester Manchester United Kingdom Royal Papworth Hospital Cambridge Royal Papworth Hospital NHS Foundation Trust Cambridge United Kingdom Language Technology Laboratory University of Cambridge Cambridge United Kingdom Population Health and Genomics School of Medicine University of Dundee Dundee United Kingdom Department of Biomedical Imaging and Image-guided Therapy Computational Imaging Research Lab Medical University of Vienna Vienna Austria National Heart and Lung Institute Imperial College London London United Kingdom Research Program in Systems Oncology Faculty of Medicine University of Helsinki Helsinki Finland Data Science & Artificial Intelligence AstraZeneca Cambridge United Kingdom Clinical Pharmacology & Safety Sciences AstraZeneca Cambridge United Kingdom Contextflow GmbH Vienna Austria Institute of Astronomy University of Cambridge Cambridge United Kingdom Department of Computer Science and Technology University of Cambridge Cambridge United Kingdom

In this perspective, we argue that despite the democratization of powerful tools for data science and machine learning over the last decade, developing the code for a trustworthy and effective data science system (DSS) is getting harder. Perverse incentives and a lack of widespread software engineering (SE) skills are among many root causes we identify that naturally give rise to the current systemic crisis in reproducibility of DSSs. We analyze why SE and building large complex systems is, in general, hard. Based on these insights, we identify how SE addresses those difficulties and how we can apply and generalize SE methods to construct DSSs that are fit for purpose. We advocate two key development philosophies, namely that one should incrementally grow – not biphasically plan and build – DSSs, and one should always employ two types of feedback loops during development: one which tests the code’s correctness and another that evaluates the code’s efficacy. Copyright © 2022, The Authors. All rights reserved.

关键词： Software engineering

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Keratin-associated epidermolysis bullosa simplex: phenotypes and challenges in clinical trials - a narrative review and systematic update

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Orphanet journal of rare diseases 2025年第1期20卷 313页

作者： Verena Wally Tobias Welponer Hans-Peter Wiesinger Anja Diem Konstantin Thiel Martin Geroldinger Georg Zimmermann Julia I Hummel Sonja Dorfer Josefina Piñón Hofbauer Johann W Bauer Martin Laimer Research Program for Molecular Therapy of Genodermatoses Department of Dermatology and Allergology EB House Austria University Hospital of the Paracelsus Medical University Salzburg 5020 Austria. v.wally@salk.at. Department of Dermatology and Allergology University Hospital of the Paracelsus Medical University Salzburg Salzburg 5020 Austria. v.wally@salk.at. Master Programme Public Health Center for Public Health and Healthcare Research Paracelsus Medical University Salzburg 5020 Austria. v.wally@salk.at. Department of Dermatology and Allergology University Hospital of the Paracelsus Medical University Salzburg Salzburg 5020 Austria. Institute of Nursing Science and Practice Center for Public Health and Healthcare Research Paracelsus Medical University Salzburg Austria. Institute of General Practice Family Medicine and Preventive Medicine Paracelsus Medical University Salzburg Austria. Research Program for Molecular Therapy of Genodermatoses Department of Dermatology and Allergology EB House Austria University Hospital of the Paracelsus Medical University Salzburg 5020 Austria. Research Programme Biomedical Data Science Paracelsus Medical University Salzburg Austria. IDA Lab Salzburg Team Biostatistics and Big Medical Data Paracelsus Medical University Salzburg Austria. Department of Artificial Intelligence and Human Interfaces Faculty of Digital and Analytical Sciences Paris Lodron University Salzburg Austria. Department of Biosciences and Medical Biology University of Salzburg Salzburg 5020 Austria.

INTRODUCTION:Clinical research on innovative therapies for the rare genodermatosis epidermolysis bullosa (EB) faces significant challenges, including small sample sizes, disease heterogeneity with intra- and inter-individual variability, limited understanding of pathogenic mechanisms and natural disease course, as well as the lack of patient-centred core outcomes. Moreover, existing tools and techniques to assess disease activity and dynamics are heterogeneous, inconsistent, and may fail to consider or inaccurately emphasize particularities of individual patients and distinct EB subtypes. METHODS:In order to exemplify the differences between keratin-associated subtypes of EB simplex (k-EBS), we summarized respective clinical characteristics in a narrative way. In addition, we performed a systematic review of the literature published over the last 5 years, with the aim to give an overview on outcomes and their assessments used in these patient populations. RESULTS:This review summarises the methodological scope, strengths and limitations of outcome assessments in clinical trials for the k-EBS, a group of inherited skin fragility diseases characterised by their distinct phenotype of epidermal blistering. CONCLUSIONS:By presenting an overview of the clinical spectrum of k-EBS, we identified key gaps in current assessment methodologies and propose alternative approaches to optimise the evaluation of skin blistering, with the aim of enhancing the accuracy, reliability, and patient-relevance of clinical outcomes.

关键词： Blisters Case reports Clinical trials Epidermolysis bullosa simplex

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Environmental risk factors associated with community diarrheal disease in Ethiopia

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BMC Public Health 2025年第1期25卷 1959页

作者： LaPolt, Devin Smith, Sage Gazu, Lina Alonso, Silvia Teshale, Amete Mihret Azmeraye, Binyam Moges Ayana, Galana Mamo Angaw, Dessie Abebaw Degefaw, Desalegne Garsow, Ariel V. Beczkiewicz, Aaron Yimer, Getnet Krakowski, Michala J. Scharff, Robert Seyoum, Eyasu T. Kowalcyk, Barbara Department of Food Science and Technology The Ohio State University Columbus OH United States College of Public Health The Ohio State University Columbus OH United States Health Program International Livestock Research Institute Addis Ababa Ethiopia National Clinical Bacteriology and Mycology Reference Laboratory Ethiopian Public Health Institute Addis Ababa Ethiopia The Ohio State Global One Health Initiative Eastern Africa Regional Office Addis Ababa Ethiopia Department of Epidemiology and Biostatistics School of Public Health College of Health and Medical Science Haramaya University Harar Ethiopia Department of Epidemiology and Biostatistics College of Medicine and Health Sciences School of Public Health University of Gondar Gondar Ethiopia Department of Food Science and Technology Center for Foodborne Illness Research and Prevention The Ohio State University Columbus OH United States Department of Genetics Penn Center for Global Genomics & Health Equity Perelman School of Medicine University of Pennsylvania Philadelphia PA United States Department of Human Sciences The Ohio State University Columbus OH United States Translational Data Analytics Institute The Ohio State University Columbus OH United States Milken Institute School of Public Health George Washington University Washington DC United States

Background: Diarrhea is a major contributor to mortality in sub-Saharan Africa, where access to improved sanitation and clean water is limited. Identifying factors associated with diarrhea across geographical regions and age groups can inform resource allocation toward water infrastructure, healthcare, and disease mitigation. The objective of this study was to identify environmental risk factors associated with diarrhea in the general population across multiple communities in Ethiopia. Methods: A prospective cross-sectional study was conducted in three regions in Ethiopia from October 2021-October 2022. REDCAP mobile app was used to collect data during face-to-face interviews using a structured, pretested questionnaire. Descriptive statistics characterized household environmental exposures. Univariate and multivariable logistic regression were used to identify factors associated with diarrhea. Results: A total of 2,436 households participated in the study. Of these, 10.3% of households reported at least one case of diarrhea during the previous four weeks. Household animal ownership varied by site, with Addis Ababa reporting the lowest animal ownership and Harar reporting the highest ownership. Across all sites, pit latrines without covers were the most common sanitation facility. Water piped to yard was the most frequent source of drinking water and most households did not use treated water (boiled/filtered) when handling food. No environmental factors were associated with diarrhea in Addis Ababa. In Gondar, drinking water from unprotected wells was associated with increased odds of diarrhea [COR:4.81(95%CI:2.03,11.43)]. Dry season was associated with decreased odds of diarrhea compared to short [COR:0.42(95%CI:0.24,0.75)] and long rains seasons [COR:0.55(95%CI: 0.34,0.88)]. In Harar, drinking water from communal taps was associated with increased odds of diarrhea [COR:2.02(95%CI:1.32,3.09)]. Conclusion: Multiple environmental factors for diarrhea were identified. G

关键词： Diarrheal diseases Environmental health Ethiopia WASH

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Modeling temporal networks with bursty activity patterns of nodes and links

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Physical Review Research 2020年第2期2卷 023073-023073页

作者： Takayuki Hiraoka Naoki Masuda Aming Li Hang-Hyun Jo Department of Computer Science Aalto University Espoo 00076 Finland Asia Pacific Center for Theoretical Physics Pohang 37673 Republic of Korea Department of Mathematics University at Buffalo State University of New York Buffalo New York 14260-2900 USA Computational and Data-Enabled Science and Engineering Program University at Buffalo State University of New York Buffalo New York 14260-5030 USA Department of Zoology University of Oxford Oxford OX1 3PS United Kingdom Department of Biochemistry University of Oxford Oxford OX1 3QU United Kingdom Department of Physics The Catholic University of Korea Bucheon 14662 Republic of Korea

The concept of temporal networks provides a framework to understand how the interaction between system components changes over time. In empirical communication data, we often detect non-Poissonian, so-called bursty behavior in the activity of nodes as well as in the interaction between nodes. However, such reconciliation between node burstiness and link burstiness cannot be explained if the interaction processes on different links are independent of each other. This is because the activity of a node is the superposition of the interaction processes on the links incident to the node, and the superposition of independent bursty point processes is not bursty in general. Here we introduce a temporal network model based on bursty node activation, and we show that it leads to heavy-tailed interevent time distributions for both node dynamics and link dynamics. Our analysis indicates that activation processes intrinsic to nodes give rise to dynamical correlations across links. Our framework offers a way to model competition and correlation between links, which is key to understanding dynamical processes in various systems.

关键词： Collective behavior in networks Dynamics of networks Scaling laws of complex systems Social systems Communication networks Multiple time scale dynamics Networks & random structures Social networks Evolving network models Network Models Non-Markovian processes Time series analysis

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