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arXiv 2024年

作者： Johnson, Michael D. Akiyama, Kazunori Baturin, Rebecca Bilyeu, Bryan Blackburn, Lindy Boroson, Don Cárdenas-Avendaño, Alejandro Chael, Andrew Chan, Chi-Kwan Chang, Dominic Cheimets, Peter Chou, Cathy Doeleman, Sheperd S. Farah, Joseph Galison, Peter Gamble, Ronald Gammie, Charles F. Gelles, Zachary Gómez, José L. Gralla, Samuel E. Grimes, Paul Gurvits, Leonid I. Hadar, Shahar Haworth, Kari Hada, Kazuhiro Hecht, Michael H. Honma, Mareki Houston, Janice Hudson, Ben Issaoun, Sara Jia, He Jorstad, Svetlana Kauffmann, Jens Kovalev, Yuri Y. Kurczynski, Peter Lafon, Robert Lupsasca, Alexandru Lehmensiek, Robert Ma, Chung-Pei Marrone, Daniel P. Marscher, Alan P. Melnick, Gary J. Narayan, Ramesh Niinuma, Kotaro Noble, Scott C. Palmer, Eric J. Palumbo, Daniel C.M. Paritsky, Lenny Peretz, Eliad Pesce, Dominic Plavin, Alexander Quataert, Eliot Rana, Hannah Ricarte, Angelo Roelofs, Freek Shtyrkova, Katia Sinclair, Laura C. Small, Jeffrey Kumara, Sridharan Tirupati Srinivasan, Ranjani Strominger, Andrew Tiede, Paul Tong, Edward Wang, Jade Weintroub, Jonathan Wielgus, Maciek Wong, George Zhang, Xinyue Alice Center for Astrophysics | Harvard & Smithsonian 60 Garden Street CambridgeMA02138 United States Black Hole Initiative Harvard University 20 Garden Street CambridgeMA02138 United States Haystack Observatory Massachusetts Institute of Technology WestfordMA01886 United States Mizusawa VLBI Observatory National Astronomical Observatory of Japan Iwate023-0861 Japan MIT Lincoln Laboratory LexingtonMA02421 United States Princeton Gravity Initiative Princeton University PrincetonNJ08544 United States Steward Observatory and Department of Astronomy University of Arizona 933 N. Cherry Ave. TucsonAZ85721 United States Data Science Institute University of Arizona 1230 N. Cherry Ave. TucsonAZ85721 United States Program in Applied Mathematics University of Arizona 617 N. Santa Rita TucsonAZ85721 United States Las Cumbres Observatory 6740 Cortona Drive GoletaCA93117-5575 United States Department of Physics University of California Santa BarbaraCA93106-9530 United States Department of History of Science Harvard University CambridgeMA02138 United States Department of Physics Harvard University CambridgeMA02138 United States NASA Goddard Space Flight Center GreenbeltMD20771 United States Departments of Astronomy and of Physics University of Illinois UrbanaIL61801 United States Instituto de Astrofísica de Andalucía CSIC Glorieta de la Astronomía s/n GranadaE-18008 Spain Department of Physics University of Arizona TucsonAZ85719 United States Joint Institute for VLBI ERIC Dwingeloo7991PD Netherlands Faculty of Aerospace Engineering Delft University of Technology Delft2629 HS Netherlands Department of Mathematics and Physics University of Haifa at Oranim Kiryat Tivon3600600 Israel Haifa Research Center for Theoretical Physics and Astrophysics University of Haifa Haifa3498838 Israel 2-21-1 Osawa Mitaka Tokyo181-8588 Japan Department of Astronomy Graduate School of Science The University of Tokyo 7-3-1 Hongo Bunkyo-ku Tokyo113-

We present the Black Hole Explorer (BHEX), a mission that will produce the sharpest images in the history of astronomy by extending submillimeter Very-Long-Baseline Interferometry (VLBI) to space. BHEX will discover and measure the bright and narrow "photon ring" that is predicted to exist in images of black holes, produced from light that has orbited the black hole before escaping. This discovery will expose universal features of a black hole's spacetime that are distinct from the complex astrophysics of the emitting plasma, allowing the first direct measurements of a supermassive black hole's spin. In addition to studying the properties of the nearby supermassive black holes M87*and Sgr A*, BHEX will measure the properties of dozens of additional supermassive black holes, providing crucial insights into the processes that drive their creation and growth. BHEX will also connect these supermassive black holes to their relativistic jets, elucidating the power source for the brightest and most efficient engines in the universe. BHEX will address fundamental open questions in the physics and astrophysics of black holes that cannot be answered without submillimeter space VLBI. The mission is enabled by recent technological breakthroughs, including the development of ultra-high-speed downlink using laser communications, and it leverages billions of dollars of existing ground infrastructure. We present the motivation for BHEX, its science goals and associated requirements, and the pathway to launch within the next decade. © 2024, CC BY.

关键词： Photons

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Augmentation of wastewater-based epidemiology with machine learning to support global health surveillance

Nature Water

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Nature Water 2025年 1-11页

作者： Aßmann, Eva Greiner, Timo Richard, Hugues Wade, Matthew Agrawal, Shelesh Amman, Fabian Böttcher, Sindy Lackner, Susanne Landthaler, Markus Mangul, Serghei Munteanu, Viorel Psomopoulos, Fotis Smith, Maureen Trofimova, Maria Ullrich, Alexander von Kleist, Max Wyler, Emanuel Hölzer, Martin Irrgang, Christopher Centre for Artificial Intelligence in Public Health Research (ZKI-PH) Robert Koch Institute Berlin Germany Genome Competence Center (MF1) Robert Koch Institute Berlin Germany Department of Infectious Disease Epidemiology Robert Koch Institute Berlin Germany Chief Data Officer Group UK Health Security Agency London United Kingdom Chair of Water and Environmental Biotechnology Institute IWAR Department of Civil and Environmental Engineering Sciences Technical University of Darmstadt Darmstadt Germany CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences Vienna Austria Institute for Hygiene and Applied Immunology Medical University of Vienna Vienna Austria Gastroenteritis and Hepatitis Pathogens and Enteroviruses (FG15) Robert Koch Institute Berlin Germany Berlin Institute for Medical Systems Biology Max Delbrück Center for Molecular Medicine in the Helmholtz Association (MDC) Berlin Germany Institute for Biology Humboldt-Universität zu Berlin Berlin Germany Titus Family Department of Clinical Pharmacy USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences University of Southern California Los Angeles CA United States Department of Biological and Morphofunctional Sciences College of Medicine and Biological Sciences Stefan cel Mare University of Suceava Suceava Romania Department of Computers Informatics and Microelectronics Technical University of Moldova Chisinau Moldova Institute of Applied Biosciences Center for Research and Technology Hellas Thessaloniki Greece Systems Medicine of Infectious Disease (P5) Robert Koch Institute Berlin Germany Department of Mathematics and Computer Science Freie Universität Berlin Berlin Germany

Wastewater-based epidemiology (WBE) has proven to be a valuable tool for monitoring the evolution and spread of global health threats, from pathogens to antimicrobial resistances. Throughout the COVID-19 pandemic, multiple wastewater surveillance programmes have advanced statistical and machine learning methods for detecting pathogens from wastewater sequencing data and correlating measured targets with the represented population to infer meaningful conclusions for public health. Integrating contextual data can account for measurement uncertainties across the WBE workflow that affect the reliability of analyses. However, the broader availability and harmonization of data are major obstacles to method development. Here we review the benefits and limitations of wastewater-related data streams, highlighting the potential of machine learning to leverage these streams for normalization and other WBE applications. We emphasize the relevance of developing global frameworks for integrating WBE with other health surveillance systems and discuss next steps to address current and foreseeable challenges for robust and interpretable machine learning-enhanced WBE. © Springer Nature Limited 2025.

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Ten quick tips for deep learning in biology

arXiv

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arXiv 2021年

作者： Lee, Benjamin D. Gitter, Anthony Greene, Casey S. Raschka, Sebastian Maguire, Finlay Titus, Alexander J. Kessler, Michael D. Lee, Alexandra J. Chevrette, Marc G. Stewart, Paul Allen Britto-Borges, Thiago Cofer, Evan M. Yu, Kun-Hsing Carmona, Juan Jose Fertig, Elana J. Kalinin, Alexandr A. Signal, Beth Lengerich, Benjamin J. Triche, Timothy J. Boca, Simina M. In-Q-Tel Labs School of Engineering and Applied Sciences Harvard University Department of Genetics Harvard Medical School United States Department of Biostatistics and Medical Informatics University of Wisconsin-Madison MadisonWI United States Morgridge Institute for Research MadisonWI United States Department of Systems Pharmacology and Translational Therapeutics Perelman School of Medicine University of Pennsylvania PhiladelphiaPA United States Department of Biochemistry and Molecular Genetics University of Colorado School of Medicine AuroraCO United States Center for Health AI University of Colorado School of Medicine AuroraCO United States Department of Statistics University of Wisconsin Madison United States Faculty of Computer Science Dalhousie University Canada University of New Hampshire Bioeconomy.XYZ United States Department of Oncology Johns Hopkins University United States Institute for Genome Sciences University of Maryland School of Medicine United States Genomics and Computational Biology Graduate Program University of Pennsylvania United States Department of Systems Pharmacology and Translational Therapeutics University of Pennsylvania United States Wisconsin Institute for Discovery Department of Plant Pathology University of Wisconsin-Madison United States Department of Biostatistics and Bioinformatics Moffitt Cancer Center TampaFL United States Section of Bioinformatics and Systems Cardiology Klaus Tschira Institute for Integrative Computational Cardiology University Hospital Heidelberg Germany University Hospital Heidelberg Germany Lewis-Sigler Institute for Integrative Genomics Princeton University PrincetonNJ United States Graduate Program in Quantitative and Computational Biology Princeton University PrincetonNJ United States Department of Biomedical Informatics Harvard Medical School United States Department of Pathology Brigham and Women's Hospital United States Philips Healthcare CambridgeMA United States Philips Research

Machine learning is a modern approach to problem-solving and task automation. In particular, machine learning is concerned with the development and applications of algorithms that can recognize patterns in data and use them for predictive modeling. Artificial neural networks are a particular class of machine learning algorithms and models that evolved into what is now described as deep learning. Given the computational advances made in the last decade, deep learning can now be applied to massive data sets and in innumerable contexts. Therefore, deep learning has become its own subfield of machine learning. In the context of biological research, it has been increasingly used to derive novel insights from high-dimensional biological data. To make the biological applications of deep learning more accessible to scientists who have some experience with machine learning, we solicited input from a community of researchers with varied biological and deep learning interests. These individuals collaboratively contributed to this manuscript's writing using the GitHub version control platform and the Manubot manuscript generation toolset. The goal was to articulate a practical, accessible, and concise set of guidelines and suggestions to follow when using deep learning. In the course of our discussions, several themes became clear: the importance of understanding and applying machine learning fundamentals as a baseline for utilizing deep learning, the necessity for extensive model comparisons with careful evaluation, and the need for critical thought in interpreting results generated by deep learning, among others. © 2021, CC BY.

关键词： Learning algorithms

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Mitochondrial proteome landscape unveils key insights into melanoma severity and treatment strategies

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Cancer 2025年第13期131卷

作者： Yonghyo Kim PhD Viktória Doma MD, PhD Uğur Çakır MD, PhD Magdalena Kuras PhD Lazaro Hiram Betancourt PhD Indira Pla PhD Aniel Sanchez PhD Yutaka Sugihara PhD Roger Appelqvist PhD Henriett Oskolas BSc Boram Lee MSc Jéssica Guedes PhD Gustavo Monnerat PhD Gabriel Reis Alves Carneiro PhD Fábio C. S. Nogueira PhD Gilberto B. Domont PhD Johan Malm MD, PhD Bo Baldetorp MD, PhD Elisabet Wieslander PhD István Balázs Németh MD, PhD A. Marcell Szász MD, PhD Runyu Hong PhD Krzysztof Pawłowski PhD Melinda Rezeli PhD Ho Jeong Kwon PhD Jozsef Timar MD, PhD David Fenyö PhD Sarolta Kárpáti MD, PhD György Marko-Varga PhD Jeovanis Gil PhD Division of Oncology Department of Clinical Sciences Lund Lund University Lund Sweden Therapeutics and Biotechnology Division Data Convergence Drug Research Center Korea Research Institute of Chemical Technology (KRICT) Daejeon Korea Department of Dermatology Venereology and Dermatooncology Semmelweis University Budapest Hungary Department of Pathology Forensic and Insurance Medicine Semmelweis University Budapest Hungary Section for Clinical Chemistry Department of Translational Medicine Lund University Lund Sweden Department of Biomedical Engineering Clinical Protein Science & Imaging Lund University Lund Sweden Departamento de Bioquímica Instituto de Química Universidade Federal do Rio de Janeiro Rio de Janeiro Brazil Department of Dermatology and Allergology University of Szeged Szeged Hungary Department of Bioinformatics Semmelweis University Budapest Hungary Department of Internal Medicine and Oncology Semmelweis University Budapest Hungary Department of Biochemistry and Pharmacology Institute for systems genetics New York University Grossman School of Medicine New York New York USA Department of Molecular Biology University of Texas Southwestern Medical Center Dallas Texas USA Department of Biochemistry and Microbiology Warsaw University of Life Sciences Warszawa Poland Department of Biotechnology College of Life Science and Biotechnology Yonsei University Seoul Korea 1st Department of Surgery Tokyo Medical University Tokyo Japan

Background Melanoma, the deadliest form of skin cancer, exhibits resistance to conventional therapies, particularly in advanced and metastatic stages. Mitochondrial pathways, including oxidative phosphorylation and mitochondrial translation, have emerged as critical drivers of melanoma progression and therapy resistance. This study investigates the mitochondrial proteome in melanoma to uncover novel therapeutic vulnerabilities. Methods Quantitative proteomics was performed on 151 melanoma-related samples from a prospective cohort and postmortem tissues. Differential expression analysis identified mitochondrial proteins linked to disease aggression and treatment resistance. Functional enrichment analyses and in vitro validation using mitochondrial inhibitors were conducted to evaluate therapeutic potential. Results Mitochondrial translation and oxidative phosphorylation (OXPHOS) were significantly upregulated in aggressive melanomas, particularly in BRAF-mutant and metastatic tumors. Inhibition of mitochondrial pathways using antibiotics (doxycycline, tigecycline, and azithromycin) and OXPHOS inhibitors (VLX600, IACS-010759, and BAY 87-2243) demonstrated dose-dependent antiproliferative effects in melanoma cell lines, sparing noncancerous melanocytes. These treatments disrupted mitochondrial function, suppressed key metabolic pathways, and induced apoptosis, highlighting the clinical relevance of targeting these pathways. Conclusions This study reveals mitochondrial pathways as critical drivers of melanoma progression and resistance, providing a rationale for targeting mitochondrial translation and OXPHOS in advanced melanoma. Combining mitochondrial inhibitors with existing therapies could overcome treatment resistance and improve patient outcomes.

关键词： BRAF mutation MCM complex melanoma mitochondrial metabolism mitochondrial proteome mitoribosomes oxidative phosphorylation proteomics

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Social pressure analysis of local events using social media data

Social pressure analysis of local events using social media ...

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IEEE Symposium on systems and Information engineering Design, SIEDS

作者： William Rule Wanyi Duan Nitesh Prakash Ni Zhuang Rafael C. Alvarado Donald E. Brown Data Science Institute University of Virginia Department of Systems and Information Engineering University of Virginia

The lack of access to clear and actionable information and analysis to law enforcement agencies during the “Unite The Right” rally in Charlottesville and the torch-lit march the night before (August 11-12, 2017) was found to be a major handicap in the handling of the situation. To address these issues, this study analyzes online activity associated with events such as this on an ongoing basis and can be provided to local police departments so that they can more effectively monitor information on emerging events and respond appropriately. In this work, we introduce the concept of social pressure to assist human users to identify and track trends that may lead to potentially violent events. We combine existing methods of analyzing social media data for event detection with monitoring of the social pressure that may lead to such events. Our algorithm detects words and phrases appearing on social media that may be of interest due to their pertinence to real-world events or movements. After identifying words and phrases that may correspond to news or events, the social pressure is interpreted from the Latent Dirichlet Allocation topic weights and sentiment scores of the tweets over time. The resulting algorithm is able to consistently detect keywords related to events before they occur, and provide valuable insight into the nature of the events.

关键词： Analytical models Twitter data models Handheld computers Event detection Law enforcement

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Sex-Specific Effects of Prenatal Exposure to Endocrine Disrupting Chemicals on Adverse Birth Outcomes: Results from the Korean Children's Environmental Health Study (Ko-Chens)

SSRN

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SSRN 2025年

作者： Oh, Jongmin Shah, Surabhi Lee, Kyung A. Park, Eunkyo Lee, Dong-Wook Hong, Yun-Chul Song, Sanghwan Kim, Sung Yeon Park, Huibyeol Ha, Eunhee Kim, Suejin Park, Choonghee Song, Sanghwan Lee, Jueun Park, Huibyeol Jo, JungHeum Jung, A-Ra Yu, Seung Do Kim, Hyun Jeong Jung, Soon-Won Hong, Sooyeon Seo, Heung Won Hwang, Namkyoung Kang, Tack Shin Jeong, Dong Jin Lee, Seungjune Kim, Hwan-Cheol Jeong, Kyoung Sook Kim, Byungmi Lee, Seulbi Kim, Hyesook Kook, Hoon Baek, Hee Jo Moon, Jai Dong Park, Won Ju Shin, Myung-Geun Ha, Mina Paik, Ki-Chung Kwon, Ho-Jang Lim, Myung-Ho Yoo, Seung Jin Bae, Sanghyuk Kim, Jin Kyung Bae, Jisuk Hong, Young-Seoub Kim, Yu-Mi Son, Hyunjin Park, Mi-Hye Lim, Dae Hyun Kim, Jeong Hee Hong, Sung-Chul Lee, Keun Hwa Kim, Su-Young Kim, Woo Jin Na, Sunghun Lim, Myoung-Nam Yoo, Young Song, Dae Jin Lee, Jue Seong Yoon, Wonsuck Choe, Seung-Ah Kim, Seunghyun Choung, Ji Tae Kim, Sukyung Kim, Jihyun Ahn, Kangmo Hong, Yun-Chul Choi, Baek-Yong Ryoo, Seung-Woo Son, Seok-Yoon Lee, Ji-Hyeon Lee, Dong-Wook Woo, KuckHyeun Yoon, Seong Yong Hur, Jongil Choi, Kyung-Hwa Park, Myung-Sook Lee, Seung-Hwa Chu, Yeonhee Kim, Jung-Ah Kim, Yangho Lee, Jiho Sim, Chang Sun Kim, A. Ram Oh, Inbo Department of Environmental Medicine School of Medicine Ewha Womans University Seoul Korea Republic of College of Medicine Ewha Womans University Seoul Korea Republic of Department of Human Systems Medicine College of Medicine Seoul National University Seoul Korea Republic of Department of Obstetrics and Gynecology Ewha Womans University College of Medicine Seoul Korea Republic of Department of Occupational and Environmental Medicine Inha University Hospital Inha University Incheon Korea Republic of Institute of Environmental Medicine Medical Research Center Seoul National University Korea Republic of Environmental Health Research Division National Institute of Environmental Research Incheon Korea Republic of Graduate Program in System Health Science and Engineering Ewha Womans University Ewha Medical Research Institute College of Medicine Seoul Korea Republic of Environmental Health Research Division Department of Environmental Health Research National Institute of Environmental Research Ministry of Environment Incheon Korea Republic of Environmental Health Policy Division Office of Environmental Health Ministry of Environment Sejong Korea Republic of Department of Occupational and Environmental Medicine Inha University School of Medicine Incheon Korea Republic of Department of Occupational and Environmental Medicine Wonju Severance Christian Hospital Wonju College of Medicine Yonsei University Wonju Korea Republic of Division of Cancer Prevention National Cancer Control Institute National Cancer Center Goyang Korea Republic of Department of Big Data Strategy National Health Insurance Service Wonju Korea Republic of Department of Food and Nutrition Wonkwang University Iksan Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Pediatrics Chonnam National University Hwasun Hospital Hwasun Korea Republic of Environmental Health Center for Childhood Leukemia and Cancer Department of Occupational and

Objectives Although previous epidemiological studies have reported the effects of fetal exposure to phthalates and phenols on birth outcomes, evidence is limited. The objective of this study was to investigate whether prenatal exposure to endocrine-disrupting chemicals [EDCs;phthalates and bisphenol A (BPA)] is associated with birth outcomes and whether there are sex-specific *** We used data from the Korean CHildren’s ENvironmental health Study (Ko-CHENS) cohort. Exposure to phthalates and BPA was measured by urine tests, and the information on outcomes and confounders was obtained from delivery records, biomarker assessment, and a self-reported questionnaire. We performed multivariate logistic regression to estimate the association between exposure to individual EDCs and each birth outcome. Additionally, to evaluate the effects of EDC mixture exposure, we performed weighted quantile sum regression analysis. Subgroup analysis stratified by maternal age and sex at birth was performed to examine potential differences in *** This study included 2,176 mother-child pairs from the Ko-CHENS. We observed a significant association between the risk of PTB and exposure to BPA during early pregnancy and mono-carboxyoctyl phthalate (MCOP) during late pregnancy, with odds ratios of 1.23 (95% CI: 1.01, 1.49) and 1.29 (95% CI: 1.03, 1.62), respectively. Additionally, there was a positive association between exposure to MCOP during early pregnancy and the risk of LBW (OR: 1.39, 95% CI: 1.03, 1.89). For male infants, exposure to MCOP during early pregnancy was associated with the risk of LBW (OR: 2.44, 95% CI: 1.43, 4.15), and exposure to MCOP during late pregnancy was associated with the risk of PTB (OR 1.45, 95% CI: 1.05, 2.02).Conclusions This study presents quantitative evidence for the association between prenatal exposure to EDCs, especially BPA and MCOP, and PTB/LBW. Moreover, the effect was particularly pronounced for older mothers (≥35 years) an

关键词： Obstetrics

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Glucose-Responsive Charge-Switchable Lipid Nanoparticles for Insulin Delivery

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Angewandte Chemie 2023年第20期135卷

作者： Yun Liu Yanfang Wang Yuejun Yao Juan Zhang Wei Liu Kangfan Ji Xinwei Wei Yuanwu Wang Prof. Xiangsheng Liu Prof. Shiming Zhang Prof. Jinqiang Wang Prof. Zhen Gu Key Laboratory of Advanced Drug Delivery Systems of Zhejiang Province College of Pharmaceutical Sciences Zhejiang University 310058 Hangzhou China Jinhua Institute of Zhejiang University 321299 Jinhua China Contribution: Data curation (lead) Formal analysis (lead) Investigation (lead) Methodology (lead) Software (lead) Validation (lead) Visualization (lead) Writing - original draft (lead) Writing - review & editing (lead) Contribution: Data curation (supporting) Investigation (supporting) Visualization (supporting) Contribution: Data curation (supporting) Formal analysis (supporting) Investigation (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Methodology (supporting) Contribution: Formal analysis (supporting) Investigation (supporting) Contribution: Writing - review & editing (supporting) Contribution: Investigation (supporting) Zhejiang Cancer Hospital Hangzhou Institute of Medicine (HIM) Chinese Academy of Sciences 310022 Hangzhou China Department of Electrical and Electronic Engineering The University of Hong Kong 999077 Hong Kong SAR China Second Affiliated Hospital Zhejiang University School of Medicine 310009 Hangzhou China Contribution: Conceptualization (lead) Data curation (equal) Formal analysis (equal) Funding acquisition (lead) Investigation (lead) Methodology (equal) Project administration (lead) Resources (lead) Software (lead) Supervision (lead) Validation (supporting) Visualization (supporting) Writing - review & editing (lead) Department of General Surgery Sir Run Run Shaw Hospital School of Medicine Zhejiang University 310009 Hangzhou China Zhejiang Laboratory of Systems & Precision Medicine Zhejiang University Medical Center 310009 Hangzhou China MOE Key Laboratory of Macromolecular Synthesis and Functionalization Department of Polymer Science and Engineering Zhejiang University 310009 Hangzhou China Contribution: Funding acquisition (lead) Project administration (lead) Resources (lead) Softwar

Lipid nanoparticle-based drug delivery systems have a profound clinical impact on nucleic acid-based therapy and vaccination. Recombinant human insulin, a negatively-charged biomolecule like mRNA, may also be delivered by rationally-designed positively-charged lipid nanoparticles with glucose-sensing elements and be released in a glucose-responsive manner. Herein, we have designed phenylboronic acid-based quaternary amine-type cationic lipids that can self-assemble into spherical lipid nanoparticles in an aqueous solution. Upon mixing insulin and the lipid nanoparticles, a heterostructured insulin complex is formed immediately arising from the electrostatic attraction. In a hyperglycemia-relevant glucose solution, lipid nanoparticles become less positively charged over time, leading to reduced attraction and subsequent insulin release. Compared with native insulin, this lipid nanoparticle-based glucose-responsive insulin shows prolonged blood glucose regulation ability and blood glucose-triggered insulin release in a type 1 diabetic mouse model.

关键词： Diabetes Drug Delivery Glucose-Responsive Insulin Delivery Lipid Nanoparticles

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An AI-based approach to detect cognitive impairment from digital voice recordings of neuropsychological assessments

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Alzheimer's & Dementia 2022年第S7期18卷

作者： Samad Amini Boran Hao Lifu Zhang Mengting Song Aman Gupta Cody Karjadi Vijaya B. Kolachalama Rhoda Au Ioannis Paschalidis Division of Systems Engineering Boston University Boston MA USA Department of Electrical & Computer Engineering Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston MA USA Hariri Institute for Computing and Computational Science & Engineering Boston University Boston MA USA Boston University School of Medicine Boston MA USA Faculty of Computing & Data Sciences Boston University Boston MA USA The Framingham Heart Study Boston University School of Medicine Boston University School of Public Health Boston MA USA Department of Biomedical Engineering Boston University Boston MA USA

Background Reliable cognitive impairment screening tools that are easy to administer and minimally time consuming are greatly needed. Given the high sensitivity of neuropsychological (NP) exams in detection of cognitive decline, we seek to develop an automated screening tool to detect dementia and mild cognitive impairment (MCI) based on digital voice recordings of NP assessments. This could enable wide-spread screening for dementia and accelerate preventative efforts. Method We used natural language processing methods to create a screening tool that identifies different stages of dementia based on automated transcription of digital voice recordings. The transcribed sentences were classified into 8 main sub-tests including memory assessment, naming and language skill, verbal fluency, general questions, etc. Using the idea of transfer learning, we encoded the participants' sentences into quantitative data. This data and the participants’ demographic variables such as age, sex, Apoe gene, and education were employed to train and test three binary classification tasks, (I) Normal cognition versus Dementia, (II) Normal/MCI versus Dementia, and (III) Normal versus MCI. Result We evaluated the performance of the classification tasks using the digital voice recordings of NP assessments, collected from the Framingham Heart Study, containing 410 cognitively intact subjects, 387 MCI, and 287 subjects with dementia. The average Area Under the Curve (AUC) on the held-out test data reached 92.6%, 88.0%, and 74.4% for differentiating Normal from Dementia, Normal or MCI from Dementia, and Normal from MCI, respectively. Looking at the importance of the sub-tests in differentiating MCI from Normal, we note that general questions can be more useful for assessment of MCI, whereas verbal fluency would not be as useful in this task. Conclusion The proposed approach offers a fully automated identification of MCI and dementia based on a recorded NP test, providing an opportunity to develo

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Quantized frank-wolfe: Faster optimization, lower communication, and projection free

arXiv

引用

arXiv 2019年

作者： Zhang, Mingrui Chen, Lin Mokhtari, Aryan Hassani, Hamed Karbasi, Amin Department of Statistics and Data Science Yale University New HavenCT06511 United States Yale Institute for Network Science Department of Electrical Engineering Yale University New HavenCT06511 United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States Department of Electrical and Systems Engineering University of Pennsylvania PhiladelphiaPA19104 United States Department of Electrical Engineering and Computer Science Yale University New HavenCT06511 United States

How can we efficiently mitigate the overhead of gradient communications in distributed optimization? This problem is at the heart of training scalable machine learning models and has been mainly studied in the unconstrained setting. In this paper, we propose Quantized Frank-Wolfe (QFW), the first projection-free and communication-efficient algorithm for solving constrained optimization problems at scale. We consider both convex and non-convex objective functions, expressed as a finite-sum or more generally a stochastic optimization problem, and provide strong theoretical guarantees on the convergence rate of QFW. This is accomplished by proposing novel quantization schemes that efficiently compress gradients while controlling the noise variance introduced during this process. Finally, we empirically validate the efficiency of QFW in terms of communication and the quality of returned solution against natural baselines. Copyright © 2019, The Authors. All rights reserved.

关键词： Machine learning

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Protoglobin-Catalyzed Formation ofcis-Trifluoromethyl-Substituted Cyclopropanes by Carbene Transfer

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Angewandte Chemie International Edition 2022年第4期62卷

作者： Lucas Schaus Dr. Anuvab Das Dr. Anders M. Knight Dr. Gonzalo Jimenez-Osés Prof. Dr. K. N. Houk Dr. Marc Garcia-Borràs Division of Chemistry and Chemical Engineering California Institute of Technology 1200 E California Blvd. Pasadena CA 91125 USA Contribution: Data curation (lead) Formal analysis (equal) Investigation (lead) Methodology (lead) Validation (equal) Writing - original draft (equal) Contribution: Data curation (equal) Formal analysis (equal) Investigation (supporting) Methodology (supporting) Validation (equal) Writing - review & editing (supporting) Contribution: Investigation (supporting) Methodology (supporting) Center for Cooperative Research in Biosciences (CIC bioGUNE) Basque Research and Technology Alliance (BRTA) Bizkaia Technology Park Building 800 48160 Derio Spain Ikerbasque Basque Foundation for Science 48013 Bilbao Spain Contribution: Data curation (supporting) Formal analysis (supporting) Validation (supporting) Department of Chemistry and Biochemistry University of California Los Angeles CA 90095 USA Contribution: Resources (supporting) Supervision (supporting) Writing - original draft (supporting)

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