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U.S. Veteran Health Care Utilization Increases after Caregivers' Use of National Caregiver Telephone Support Line (vol 42, pg e111, 2017)

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HEALTH & SOCIAL WORK 2017年第3期42卷 150-150页

作者： Griffin, Joan M. Malcolm, Cari Wright, Pamela Campbell, Emily Hagel Kabat, Margaret Bangerter, Ann K. Sayer, Nina A. Joan M. Griffin PhD is Robert D. and Patricia E. Kern scientific director Kern Center for the Science of Healthcare Delivery and associate professor Division of Health Care Policy and Research Mayo Clinic 200 1st Avenue SW Rochester MN 55905 e-mail: griffin.joan@mayo.edu. Cari Malcolm LCSW is caregiver support program management analyst and Pamela Wright BSE MSW LCSW-R is VA Caregiver Support Line national program manager Care Management and Social Work Services Veterans Health Administration Washington DC. Emily Hagel Campbell MS is a statistician Center for Chronic Disease Outcomes Research Minneapolis VA Health Care Systems. Margaret Kabat LCSW-C CCM is VA Caregiver Support Program national director Care Management and Social Work Services Veterans Health Administration Washington DC. Ann K. Bangerter BS is a computer scientist Center for Chronic Disease Outcomes Research Minneapolis VA Health Care Systems. Nina A. Sayer PhD is deputy director Center for Chronic Disease Outcomes Research Minneapolis VA Health Care Systems and associate professor of medicine and psychiatry University of Minnesota Minneapolis. This project was supported by the Department of Veterans Affairs Veterans Health Administration Office of Research and Development and the Polytrauma/Blast Related Quality Enhancement Research Initiative (PT/BRI QUERI QLP 56-011). The findings and conclusions presented in this article are those of the authors and do not necessarily represent the views of the Department of Veterans Affairs or the Office of Research and Development. Search for other works by this author on: Oxford Academic PubMed Google Scholar Joan M. Griffin PhD is Robert D. and Patricia E. Kern scientific director Kern Center for the Science of Healthcare Delivery and associate professor Division of Health Care Policy and Research Mayo Clinic 200 1st Avenue SW Rochester MN 55905 e-mail: griffin.joan@mayo.edu. Cari Malcolm LCSW is caregiver support program management analyst and Pamela

The U.S. Department of Veterans Affairs (VA) established the national Caregiver Support Line (CSL) in February 2011. The CSL is operated by licensed master’s degree social workers who provide caregivers of veterans with information about caregiver benefits and services, counseling, and referrals to a caregiver support coordinator at the nearest VA medical center. The authors compared differences in veteran health care utilization patterns in the six months before and after a caregiver call to the CSL, hypothesizing that veterans with caregivers using the CSL had improved access to health care services and improved access increased utilization of health care. A pre- and posttest design was used. CSL calls that resulted in referrals to VA health care services or to local VA caregiver support coordinators were included in the sample. Data were extracted from the CSL database and matched to veteran care utilization data using veteran medical record data. Veteran inpatient stays for general medicine, hospice, respite, and long-term care significantly increased after the CSL call, but other inpatient stays (surgery, neurology) did not. Outpatient services for home health, respite, and mental health all significantly increased. Caregivers’ use of the national CSL may help facilitate access for veterans to needed care services.

关键词： access to care caregivers health care utilization telephone support lines veterans

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Legal Design Through AI in Banking Contracts

Legal Design Through AI in Banking Contracts

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Metrology for Extended Reality, Artificial Intelligence and Neural Engineering (MetroXRAINE), IEEE International Conference on

作者： Lucilla Gatt Ilaria Amelia Caggiano Maria Cristina Gaeta Livia Aulino Emiliano Troisi Luigi Izzo Christian Carmine Esposito Alessandra Fabrocini Daniele Marino Research Centre of European Private Law (ReCEPL) Università degli Studi Suor Orsola Benincasa Naples Italy Research Centre of European Private Law (ReCEPL) University Suor Orsola Benincasa Naples IT University of Turin Turin IT Humanities and Technologies: an integrated research path Research Centre of European Private Law (ReCEPL) University Suor Orsola Benincasa Naples IT Computer Science University of Salerno Salerno IT Earth Observation (National PhD program) University La Sapienza - Vanvitelli Rome Caserta IT VISA Barcelona ES

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

Consumer protection in banking is a particularly relevant issue, especially in light of the increasing complexity of banking transactions and the related legal reference cases. For this reason, the adoption of pre-contractual disclosures and contractual texts as complete as possible has been mandated to enable the consumer to be fully aware of the characteristics of the contractual relationship. This has led to the creation of contracts that are particularly detailed and full of content, with graphics designed to reduce the size of the relevant documents but inadequate to allow an understanding of these documents. It is therefore necessary to change the paradigm in the drafting of contractual texts and pre-contractual disclosures through the adoption of legal design methodologies. In particular, added value could be provided by adopting AI and metrology techniques for measuring the level of clarity of contractual texts and subsequently creating a more user-friendly version of these.

关键词： Graphics Extended reality Neural engineering Banking Metrology Particle measurements Consumer protection Complexity theory Artificial intelligence Contracts

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Machine learning for early prediction of circulatory failure in the intensive care unit

arXiv

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arXiv 2019年

作者： Hyland, Stephanie L. Faltys, Martin Hüser, Matthias Lyu, Xinrui Gumbsch, Thomas Esteban, Cristóbal Bock, Christian Horn, Max Moor, Michael Rieck, Bastian Zimmermann, Marc Bodenham, Dean Borgwardt, Karsten Rätsch, Gunnar Merz, Tobias M. Department of Computer Science ETH Zürich Zürich Switzerland Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional PhD Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Medical Informatics Group Zürich University Hospital Zürich Switzerland Department of Intensive Care Medicine University Hospital University of Bern Bern Switzerland Department of Biosystems and Engineering ETH Zürich Basel Switzerland Department of Biology ETH Zürich Zürich Switzerland Swiss Institute for Bioinformatics Lausanne Switzerland Cardiovascular Intensive Care Unit Auckland City Hospital Auckland New Zealand

Intensive care clinicians are presented with large quantities of patient information and measurements from a multitude of monitoring systems. The limited ability of humans to process such complex information hinders physicians to readily recognize and act on early signs of patient deterioration. We used machine learning to develop an early warning system for circulatory failure based on a high-resolution ICU database with 240 patient-years of data. This automatic system predicts 90.0% of circulatory failure events (prevalence 3.1%), with 81.8% identified more than two hours in advance, resulting in an area under the receiver operating characteristic curve of 94.0% and area under the precision recall curve of 63.0%. The model was externally validated in a large independent patient cohort. Copyright © 2019, The Authors. All rights reserved.

关键词： Intensive care units

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Applying fuzzy functions and sequential coordination to optimization of machinery arrangement and pipe routing

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NAVAL ENGINEERS JOURNAL 1998年第6期110卷 43-54页

作者： Wu, BC Young, GS Schmidt, W Choppella, K Dr. Bi-Chu Wu:received a PhD in Mechanical Engineering from the University of Maryland College Park in 1991. She has worked on projects involving naval architecture design optimization solid mechanics and database development. Presently a senwr engineer with Angle Incorporated Dr Wu's research interests are in design optimization and fuzzy logic applications. Dr. Gin-Shu Young: a senior engineer with Angle Incorporated holds a PhD in Mechanical Engineering from the University of Maryland College Park. As a guest researcher with National Institute of Standards and Technologies from 1990 to 1993 he worked on vision-based navigation for autonomous vehicles. His experience also includes applications of optimization fuzzy logic neural network and genetic algorithm methods to engineering system design Mr. William Schmidt:co-founded Angle Incorporated in 1990 and has served as Vice PresidentlChiefScientist during this tame. He holds a B.Sc. in Applied Science from the Naval Acadt?my and an M.Sc. in Physics from the Naval Post Graduate School. He has cner 20 years experience in technical leadership material and personnel management. He has led the application of computer aided design (CAD) and Product Model Information Exchange to the shipbuilding industry. His experience also includes leading the amlication of model based operational analysis to support the Live Fire Test Program for DDG 51 Class Destroyers. Mr. Krishna M. Choppella:is a Sofware Engineer at Eidea Laboratories Incotporated where he works on componentbased distributed enterpvise frameworks. He has been involved in creating data analysis tools for the US Nay by integrating CAD modeis databases and graphical front ends. His work in the Masters degree program in Mechanical Engineering at the University of Texas at Austin was in di0ddase.r spectroscopy of combustion products in porous-matri burners. He received his Bachelors degree in Electrical Engineering in India. He was a Research Associate at the Centre for Laser Technology and Project Engi

Ship design is often multidisciplinary involving several design elements with various types of objectives and constraints (O/C) some easily described as mathematical formulas, others better modeled as descriptive assertions. This paper describes a method based on fuzzy functions and an integrated performance index to model O/C using descriptive assertions to be used with mathematical formulas in optimization. Another issue addressed in this paper concerns the coordination of design elements when sequentially coupled, that is, when one leads the other and the performance of the follower depends greatly on the design of the leader. Based on neuro-fuzzy techniques, the method described here coordinates and optimizes sequentially coupled elements. The two methods are applied to machinery arrangement (MA) and pipe routing (PR). Preliminary models for optimization of MA and PR are described considering convenience, producibility: engine room size, interference and location as factors in the O/C set. Some test results from MA/PR applications are presented and discussed. The methods are generic and can be extended to other elements in ship design. They are mutually independent and may be used separately Two advantages of their use are an improvement in overall performance and a reduction in the need for redesign of elements.

关键词： Optimization

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Correction to: CpG‑creating mutations are costly in many human viruses

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Evolutionary Ecology 2020年第3期34卷 361-362页

作者： Caudill, Victoria R. Qin, Sarina Winstead, Ryan Kaur, Jasmeen Tisthammer, Kaho Pineda, E. Geo Solis, Caroline Cobey, Sarah Bedford, Trevor Carja, Oana Eggo, Rosalind M. Koelle, Katia Lythgoe, Katrina Regoes, Roland Roy, Scott Allen, Nicole Aviles, Milo Baker, Brittany A. Bauer, William Bermudez, Shannel Carlson, Corey Castellanos, Edgar Catalan, Francisca L. Chemel, Angeline Katia Elliot, Jacob Evans, Dwayne Fiutek, Natalie Fryer, Emily Goodfellow, Samuel Melvin Hecht, Mordecai Hopp, Kellen Hopson, E. Deshawn Jaberi, Amirhossein Kim, Jennifer Kinney, Christen Lao, Derek Le, Adrienne Lo, Jacky Lopez, Alejandro G. López, Andrea Lorenzo, Fernando G. Luu, Gordon T. Mahoney, Andrew R. Melton, Rebecca L. Nascimento, Gabriela Do Pradhananga, Anjani Rodrigues, Nicole S. Shieh, Annie Sims, Jasmine Singh, Rima Sulaeman, Hasan Thu, Ricky Tran, Krystal Tran, Livia Winters, Elizabeth J. Wong, Albert Pennings, Pleuni S. Department of Biology San Francisco State University San Francisco USA Department of Biology University of Oregon Eugene USA Quantitative Systems Biology Univeristy of California Merced USA Department of Ecology and Evolution University of Chicago Chicago USA Vaccine and Infectious Disease Division Fred Hutchinson Cancer Research Center Seattle USA Department of Computational Biology School of Computer Science Carnegie Mellon University Pittsburgh USA Department of Infectious Disease Epidemiology London School of Hygiene & Tropical Medicine London UK Department of Biology Emory University Atlanta USA Big Data Institute University of Oxford Oxford UK Department of Environmental Systems Science ETH Zurich Zurich Switzerland Department of Neurological Surgery University of California San Francisco USA Department of Organismic and Evolutionary Biology Harvard University Cambridge USA Department of Plant Biology Carnegie Institution for Science Stanford USA Health Sciences Center University of New Mexico Albuquerque USA UCSD Biomed Sciences PhD Program University of California San Diego USA Biochemistry Molecular Cellular and Developmental Biology Graduate Group University of California Davis USA UCSF Tetrad Graduate Program University of California San Francisco USA

In the original article, the co-author name "Jennifer Kim" has been inadvertently missed during the publication process. The complete author group is given in this correction.

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Brain-like object recognition with high-performing shallow recurrent ANNs

arXiv

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arXiv 2019年

作者： Kubilius, Jonas Schrimpf, Martin Kar, Kohitij Rajalingham, Rishi Hong, Ha Majaj, Najib J. Issa, Elias B. Bashivan, Pouya Prescott-Roy, Jonathan Schmidt, Kailyn Nayebi, Aran Bear, Daniel Yamins, Daniel L.K. DiCarlo, James J. McGovern Institute for Brain Research MIT CambridgeMA02139 United States Brain and Cognition KU Leuven Leuven Belgium Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Center for Brains Minds and Machines MIT CambridgeMA02139 Bay Labs Inc. San FranciscoCA94102 United States Center for Neural Science New York University New YorkNY10003 United States Department of Neuroscience Zuckerman Mind Brain Behavior Institute Columbia University New YorkNY10027 United States Neurosciences PhD Program Stanford University StanfordCA94305 United States Department of Psychology Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States

Deep convolutional artificial neural networks (ANNs) are the leading class of candidate models of the mechanisms of visual processing in the primate ventral stream. While initially inspired by brain anatomy, over the past years, these ANNs have evolved from a simple eight-layer architecture in AlexNet to extremely deep and branching architectures, demonstrating increasingly better object categorization performance, yet bringing into question how brain-like they still are. In particular, typical deep models from the machine learning community are often hard to map onto the brain's anatomy due to their vast number of layers and missing biologically-important connections, such as recurrence. Here we demonstrate that better anatomical alignment to the brain and high performance on machine learning as well as neuroscience measures do not have to be in contradiction. We developed CORnet-S, a shallow ANN with four anatomically mapped areas and recurrent connectivity, guided by Brain-Score, a new large-scale composite of neural and behavioral benchmarks for quantifying the functional fidelity of models of the primate ventral visual stream. Despite being significantly shallower than most models, CORnet-S is the top model on Brain-Score and outperforms similarly compact models on ImageNet. Moreover, our extensive analyses of CORnet-S circuitry variants reveal that recurrence is the main predictive factor of both Brain-Score and ImageNet top-1 performance. Finally, we report that the temporal evolution of the CORnet-S "IT" neural population resembles the actual monkey IT population dynamics. Taken together, these results establish CORnet-S, a compact, recurrent ANN, as the current best model of the primate ventral visual stream. Copyright © 2019, The Authors. All rights reserved.

关键词： Object recognition

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Spatial atlas of the mouse central nervous system at molecular resolution (vol 622, pg 552, 2023)

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NATURE 2024年第7993期625卷 E6-E6页

作者： Shi, Hailing He, Yichun Zhou, Yiming Huang, Jiahao Maher, Kamal Wang, Brandon Tang, Zefang Luo, Shuchen Tan, Peng Wu, Morgan Lin, Zuwan Ren, Jingyi Thapa, Yaman Tang, Xin Chan, Ken Y. Deverman, Benjamin E. Shen, Hao Liu, Albert Liu, Jia Wang, Xiao Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry Massachusetts Institute of Technology Cambridge MA USA John A. Paulson School of Engineering and Applied Sciences Harvard University Boston MA USA Computational and Systems Biology PhD Program Massachusetts Institute of Technology Cambridge MA USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge MA USA Department of Biology Massachusetts Institute of Technology Cambridge MA USA Klarman Cell Observatory Broad Institute of MIT and Harvard Cambridge MA USA Department of Chemistry and Chemical Biology Harvard University Cambridge MA USA Stanley Center for Psychiatric Research Broad Institute of MIT and Harvard Cambridge MA USA

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Task-driven convolutional recurrent models of the visual system

arXiv

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arXiv 2018年

作者： Nayebi, Aran Bear, Daniel Kubilius, Jonas Kar, Kohitij Ganguli, Surya Sussillo, David DiCarlo, James J. Yamins, Daniel L.K. Neurosciences PhD Program Stanford University StanfordCA94305 United States Department of Psychology Stanford University StanfordCA94305 United States Department of Computer Science Stanford University StanfordCA94305 United States Department of Applied Physics Stanford University StanfordCA94305 United States McGovern Institute for Brain Research MIT CambridgeMA02139 United States Department of Brain and Cognitive Sciences MIT CambridgeMA02139 United States Brain and Cognition KU Leuven Leuven Belgium Google Brain Google Inc. Mountain ViewCA94043 United States Wu Tsai Neurosciences Institute StanfordCA94305 United States

Feed-forward convolutional neural networks (CNNs) are currently state-of-the-art for object classification tasks such as ImageNet. Further, they are quantitatively accurate models of temporally-averaged responses of neurons in the primate brain's visual system. However, biological visual systems have two ubiquitous architectural features not shared with typical CNNs: local recurrence within cortical areas, and long-range feedback from downstream areas to upstream areas. Here we explored the role of recurrence in improving classification performance. We found that standard forms of recurrence (vanilla RNNs and LSTMs) do not perform well within deep CNNs on the ImageNet task. In contrast, novel cells that incorporated two structural features, bypassing and gating, were able to boost task accuracy substantially. We extended these design principles in an automated search over thousands of model architectures, which identified novel local recurrent cells and long-range feedback connections useful for object recognition. Moreover, these task-optimized ConvRNNs matched the dynamics of neural activity in the primate visual system better than feedforward networks, suggesting a role for the brain's recurrent connections in performing difficult visual behaviors. Copyright © 2018, The Authors. All rights reserved.

关键词： Neurons

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Task-driven convolutional recurrent models of the visual system 18

Task-driven convolutional recurrent models of the visual sys...

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Proceedings of the 32nd International Conference on Neural Information Processing Systems

作者： Aran Nayebi Daniel Bear Jonas Kubilius Kohitij Kar Surya Ganguli David Sussillo James J. DiCarlo Daniel L. K. Yamins Neurosciences PhD Program Stanford University Stanford CA Department of Psychology Stanford University Stanford CA McGovern Institute for Brain Research MIT Cambridge MA and Brain and Cognition KU Leuven Leuven Belgium McGovern Institute for Brain Research MIT Cambridge MA and Google Brain Google Inc. Mountain View CA Department of Applied Physics Stanford University Stanford CA Google Brain Google Inc. Mountain View CA McGovern Institute for Brain Research MIT Cambridge MA and Department of Brain and Cognitive Sciences MIT Cambridge MA Department of Psychology Stanford University Stanford CA and Department of Computer Science Stanford University Stanford CA and Wu Tsai Neurosciences Institute Stanford CA

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MiR34 contributes to spinal muscular atrophy and AAV9-mediated delivery of MiR34a ameliorates the motor deficits in SMA mice

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Molecular therapy. Nucleic acids 2023年 32卷 144-160页

作者： Tai-Heng Chen Shih-Hsin Chang Yu-Fu Wu Ya-Ping Yen Fang-Yu Hsu Yen-Chung Chen Yang Ming Ho-Chiang Hsu Yi-Ching Su Sheng-Tang Wong Jui-Hung Hung Shih-Hwa Chiou Yuh-Jyh Jong Jun-An Chen PhD Program in Translational Medicine Kaohsiung Medical University Kaohsiung 80708 and Academia Sinica Taipei 11529 Taiwan. Institute of Molecular Biology Academia Sinica Taipei 11529 Taiwan. Department of Pediatrics Kaohsiung Medical University Kaohsiung 80708 Taiwan. School of Post-Baccalaureate Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Neuroscience Program of Academia Sinica Academia Sinica Taipei 11529 Taiwan. Department of Computer Science National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan. Institute of Pharmacology School of Medicine National Yang Ming Chiao Tung University Taipei 11221 Taiwan. Innovative Cellular Therapy Center Department of Medical Research Taipei Veterans General Hospital Taipei 11217 Taiwan. Department of Laboratory Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Translational Research Center of Neuromuscular Diseases Kaohsiung Medical University Hospital Kaohsiung Medical University Kaohsiung 80708 Taiwan. Graduate Institute of Clinical Medicine College of Medicine Kaohsiung Medical University Kaohsiung 80708 Taiwan. Drug Development and Value Creation Research Center Kaohsiung Medical University Kaohsiung 80708 Taiwan. Department of Biological Science and Technology National Yang Ming Chiao Tung University Hsinchu 30093 Taiwan.

Spinal muscular atrophy (SMA) is a neurodegenerative disease characterized by the selective loss of spinal motor neurons (MNs) and concomitant muscle weakness. Mutation of is known to cause SMA, and restoring SMN protein levels via antisense oligonucleotide treatment is effective for ameliorating symptoms. However, this approach is hindered by exorbitant costs, invasive procedures, and poor treatment responses of some patients. Here, we seek to circumvent these hurdles by identifying reliable biomarkers that could predict treatment efficacy. We uncovered that MiR34 exhibits consistent downregulation during SMA progression in both human and rodent contexts. Importantly, family-knockout mice display axon swelling and reduced neuromuscular junction (NMJ) endplates, recapitulating SMA pathology. Introducing MiR34a via scAAV9 improved the motor ability of SMNΔ7 mice, possibly by restoring NMJ endplate size. Finally, we observed a consistent decreasing trend in MiR34 family expression in the cerebrospinal fluid (CSF) of type I SMA patients during the loading phase of nusinersen treatment. Baseline CSF MiR34 levels before nusinersen injection proved predictive of patient motor skills 1 year later. Thus, we propose that MiR34 may serve as a biomarker of SMA since it is associated with the pathology and can help evaluate the therapeutic effects of nusinersen.

关键词： ASO MT: Non-coding RNAs MiR34 SMA biomarker microRNA nusinersen spinal muscular atrophy

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