Institution
New York State Department of Health
Government•Albany, New York, United States•
About: New York State Department of Health is a government organization based out in Albany, New York, United States. It is known for research contribution in the topics: Population & Gene. The organization has 7060 authors who have published 10232 publications receiving 464819 citations. The organization is also known as: NYSDOH & NYS DOH.
Topics: Population, Gene, Public health, Virus, RNA
Papers published on a yearly basis
Papers
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TL;DR: With adequate recognition and effective engagement of all issues, BCI systems could eventually provide an important new communication and control option for those with motor disabilities and might also give those without disabilities a supplementary control channel or a control channel useful in special circumstances.
6,803 citations
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TL;DR: This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system.
Abstract: Many laboratories have begun to develop brain-computer interface (BCI) systems that provide communication and control capabilities to people with severe motor disabilities. Further progress and realization of practical applications depends on systematic evaluations and comparisons of different brain signals, recording methods, processing algorithms, output formats, and operating protocols. However, the typical BCI system is designed specifically for one particular BCI method and is, therefore, not suited to the systematic studies that are essential for continued progress. In response to this problem, we have developed a documented general-purpose BCI research and development platform called BCI2000. BCI2000 can incorporate alone or in combination any brain signals, signal processing methods, output devices, and operating protocols. This report is intended to describe to investigators, biomedical engineers, and computer scientists the concepts that the BCI2000 system is based upon and gives examples of successful BCI implementations using this system. To date, we have used BCI2000 to create BCI systems for a variety of brain signals, processing methods, and applications. The data show that these systems function well in online operation and that BCI2000 satisfies the stringent real-time requirements of BCI systems. By substantially reducing labor and cost, BCI2000 facilitates the implementation of different BCI systems and other psychophysiological experiments. It is available with full documentation and free of charge for research or educational purposes and is currently being used in a variety of studies by many research groups.
2,560 citations
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01 Jun 2000
TL;DR: The first international meeting devoted to brain-computer interface research and development is summarized, which focuses on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users.
Abstract: Over the past decade, many laboratories have begun to explore brain-computer interface (BCI) technology as a radically new communication option for those with neuromuscular impairments that prevent them from using conventional augmentative communication methods. BCI's provide these users with communication channels that do not depend on peripheral nerves and muscles. This article summarizes the first international meeting devoted to BCI research and development. Current BCI's use electroencephalographic (EEG) activity recorded at the scalp or single-unit activity recorded from within cortex to control cursor movement, select letters or icons, or operate a neuroprosthesis. The central element in each BCI is a translation algorithm that converts electrophysiological input from the user into output that controls external devices. BCI operation depends on effective interaction between two adaptive controllers, the user who encodes his or her commands in the electrophysiological input provided to the BCI, and the BCI which recognizes the commands contained in the input and expresses them in device control. Current BCI's have maximum information transfer rates of 5-25 b/min. Achievement of greater speed and accuracy depends on improvements in signal processing, translation algorithms, and user training. These improvements depend on increased interdisciplinary cooperation between neuroscientists, engineers, computer programmers, psychologists, and rehabilitation specialists, and on adoption and widespread application of objective methods for evaluating alternative methods. The practical use of BCI technology depends on the development of appropriate applications, identification of appropriate user groups, and careful attention to the needs and desires of individual users. BCI research and development will also benefit from greater emphasis on peer-reviewed publications, and from adoption of standard venues for presentations and discussion.
2,121 citations
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TL;DR: Novel features are a suite of operations relating to the determination, modeling, and correction of the contrast transfer function and the availability of the entire documentation in hypertext format.
2,117 citations
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Centers for Disease Control and Prevention1, United States Department of Health and Human Services2, Colorado Department of Public Health and Environment3, Yale University4, Veterans Health Administration5, Emory University6, Alabama Department of Public Health7, Maryland Department of Health8, New Mexico Department of Health9, New York State Department of Health10, University of Rochester11, Ohio Department of Health12, Vanderbilt University13, Lake County14
TL;DR: It is suggested that older adults have elevated rates of COVID-19-associated hospitalization and the majority of persons hospitalized with CO VID-19 have underlying medical conditions, which underscore the importance of preventive measures to protect older adults and persons with underlyingmedical conditions, as well as the general public.
Abstract: Since SARS-CoV-2, the novel coronavirus that causes coronavirus disease 2019 (COVID-19), was first detected in December 2019 (1), approximately 1.3 million cases have been reported worldwide (2), including approximately 330,000 in the United States (3). To conduct population-based surveillance for laboratory-confirmed COVID-19-associated hospitalizations in the United States, the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was created using the existing infrastructure of the Influenza Hospitalization Surveillance Network (FluSurv-NET) (4) and the Respiratory Syncytial Virus Hospitalization Surveillance Network (RSV-NET). This report presents age-stratified COVID-19-associated hospitalization rates for patients admitted during March 1-28, 2020, and clinical data on patients admitted during March 1-30, 2020, the first month of U.S. surveillance. Among 1,482 patients hospitalized with COVID-19, 74.5% were aged ≥50 years, and 54.4% were male. The hospitalization rate among patients identified through COVID-NET during this 4-week period was 4.6 per 100,000 population. Rates were highest (13.8) among adults aged ≥65 years. Among 178 (12%) adult patients with data on underlying conditions as of March 30, 2020, 89.3% had one or more underlying conditions; the most common were hypertension (49.7%), obesity (48.3%), chronic lung disease (34.6%), diabetes mellitus (28.3%), and cardiovascular disease (27.8%). These findings suggest that older adults have elevated rates of COVID-19-associated hospitalization and the majority of persons hospitalized with COVID-19 have underlying medical conditions. These findings underscore the importance of preventive measures (e.g., social distancing, respiratory hygiene, and wearing face coverings in public settings where social distancing measures are difficult to maintain)† to protect older adults and persons with underlying medical conditions, as well as the general public. In addition, older adults and persons with serious underlying medical conditions should avoid contact with persons who are ill and immediately contact their health care provider(s) if they have symptoms consistent with COVID-19 (https://www.cdc.gov/coronavirus/2019-ncov/symptoms-testing/symptoms.html) (5). Ongoing monitoring of hospitalization rates, clinical characteristics, and outcomes of hospitalized patients will be important to better understand the evolving epidemiology of COVID-19 in the United States and the clinical spectrum of disease, and to help guide planning and prioritization of health care system resources.
2,016 citations
Authors
Showing all 7073 results
Name | H-index | Papers | Citations |
---|---|---|---|
Solomon H. Snyder | 232 | 1222 | 200444 |
Rakesh K. Jain | 200 | 1467 | 177727 |
Kurunthachalam Kannan | 126 | 820 | 59886 |
John O. Holloszy | 124 | 377 | 45823 |
Thomas J. Ryan | 116 | 675 | 67462 |
Ettore Appella | 116 | 509 | 57926 |
John P. Giesy | 114 | 1162 | 62790 |
Iain B. McInnes | 111 | 655 | 59238 |
Joachim Frank | 103 | 473 | 39475 |
Don C. Des Jarlais | 101 | 657 | 110906 |
Shinsuke Tanabe | 98 | 722 | 37445 |
Haig H. Kazazian | 98 | 315 | 35197 |
Leonore A. Herzenberg | 97 | 356 | 37747 |
F. Peter Guengerich | 91 | 623 | 36151 |
Leslie A. Leinwand | 91 | 377 | 26587 |