Suchergebnisse

Government agencies and other organizations are required to manage and preserve records that they create and use to facilitate future access and reuse. The increasing use of geospatial data and related electronic records presents new challenges for these organizations, which have relied on traditional practices for managing and preserving records in printed form. This article reports on an investigation of current and future needs for managing and preserving geospatial electronic records on the part of localand state-level organizations in the New York City metropolitan region. It introduces the study and describes organizational needs observed, including needs for organizational coordination and interorganizational cooperation throughout the entire data lifecycle.

Vaccination is an essential component of modern public health programs and is among our most cost-effective medical interventions. Yet despite vaccines' clear effectiveness in reducing risks of diseases that previously attacked large proportions of the population, caused many deaths, and left many people with permanent disabilities, current vaccination policies are not without controversy. Vaccines, like all other pharmaceutical products, are not entirely risk-free; while most known side effects are minor and self-limited, some vaccines have been associated with very rare but serious adverse effects. Because such rare effects are often not evident until vaccines come into widespread use, the Federal government maintains ongoing surveillance programs to monitor vaccine safety. The interpretation of data from such programs is complex and is associated with substantial uncertainty. A continual effort to monitor these data effectively and to develop more precise ways of assessing risks of vaccines is necessary to ensure public confidence in immunization programs.

Infection of orthopedic devices by pathogenic bacteria, coupled with the development of bacterial resistance against major antibiotics, have caused severe physical and emotional trauma to patients and posed economic challenges to healthcare institutes and governments worldwide. Antimicrobial peptidomimetics have emerged as promising new candidates to fight the rise of bacterial resistance. Conjugation of these peptidomimetics to bone implant materials has become a viable strategy to combat orthopedic device related infections. In this paper, we report on the synthesis of an anthranilamide-based antibacterial peptidomimetic. The compound, which could possibly be acting through the depolarization of bacterial cell membrane, demonstrated a higher toxicity towards S. aureus compared to E. coli. We further demonstrated the ability of this compound to disrupt pre-formed biofilms. Coating of the compound on hydroxyapatite discs was achieved via physical interactions between the charged hydroxyapatite surface and the peptidomimetic. This was confirmed via X-ray photoelectron spectroscopy. The compound imparted antibacterial property to the discs as determined via antibacterial assays and imaging, while rendering the discs mildly cytotoxic towards human fetal osteoblast cells. This journal is

Background The post-disaster mental health crisis intervention (MHCI) system in China remains immature and unsystematic. We aim to report the perceptions of a large sample of MHCI workers and government administrators and provide recommendations for developing a national mental health disaster response management plan in China. Methods An in-depth qualitative study was conducted, collecting data from 20 focus-group discussions and 25 key stakeholder interviews. These recruited participants who had been involved in different types of disaster rescue across 7 provinces/cities where disasters have recently occurred. We used thematic analysis to analyze the data and relevant findings were extracted for policy recommendation. Results Mental health workers' perspectives were examined in detailed according to four core themes: forms of organization, intervention pathway, intervention strategy and technique, and public health information. Post-disaster MHCI should be approached in teams that are integrated with emergency medicine systems, and be led by unified command management. All levels of local health and family planning commission should prepare post-disaster MHCI work plans and build response teams/emergency centres. Future training for MHCI workers should focus on: building a sense of trust within the team; clarifying each member's role; strengthening the screening, assessment and referrals training for psychological professionals; and providing psychological intervention training for Chinese psychiatrists. It is necessary to set up guiding principles for disaster research ethics, mental health rehabilitation and media interaction. Conclusions Through exploring and analyzing the perceptions of current disaster response mental health workers and government administrators, our findings provide essential recommendations for developing a national to county level post-disaster MHCI emergency management plan and can guide the formulation of relevant laws and regulation in China.

Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used. ; This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST). ; Peer Reviewed ; Postprint (published version)

Vector-space word representations obtained from neural network models have been shown to enable semantic operations based on vector arithmetic. In this paper, we explore the existence of similar information on vector representations of images. For that purpose we define a methodology to obtain large, sparse vector representations of image classes, and generate vectors through the state-of-the-art deep learning architecture GoogLeNet for 20 K images obtained from ImageNet. We first evaluate the resultant vector-space semantics through its correlation with WordNet distances, and find vector distances to be strongly correlated with linguistic semantics. We then explore the location of images within the vector space, finding elements close in WordNet to be clustered together, regardless of significant visual variances (e.g., 118 dog types). More surprisingly, we find that the space unsupervisedly separates complex classes without prior knowledge (e.g., living things). Afterwards, we consider vector arithmetics. Although we are unable to obtain meaningful results on this regard, we discuss the various problem we encountered, and how we consider to solve them. Finally, we discuss the impact of our research for cognitive systems, focusing on the role of the architecture being used. ; This work is partially supported by the Joint Study Agreement no. W156463 under the IBM/BSC Deep Learning Center agreement, by the Spanish Government through Programa Severo Ochoa (SEV-2015-0493), by the Spanish Ministry of Science and Technology through TIN2015-65316-P project and by the Generalitat de Catalunya (contracts 2014-SGR-1051), and by the Core Research for Evolutional Science and Technology (CREST) program of Japan Science and Technology Agency (JST). ; Peer Reviewed ; Postprint (published version)

Chronic obstructive pulmonary disease (COPD) is one of the top three causes of death worldwide, but governments and non-governmental organisations have not given its prevention and treatment the priority it requires. This is particularly true in low- and middle-income countries, where most of the people suffering from this disease live. The United Nations (UN) has targeted a reduction of premature deaths from non-communicable diseases (NCDs) by a third by 2030; however, a coordinated UN/World Health Organization (WHO) strategy to address the burden of COPD (one of the most important NCDs) is still lacking. To explore the extent of the problem and inform the development of policies to improve the situation, the Board of Directors of the Global Initiative for Chronic Obstructive Lung Disease (GOLD) held a 1-day Summit. The key themes that emerged were the need to ensure accurate data on prevalence, raise awareness of the disease among the public, healthcare professionals and governments, including the fact that COPD aetiology goes beyond smoking (and other inhaled pollutants) and includes poor lung development in early life, and ensure that spirometry and both pharmacological and non-pharmacological therapies are available and affordable. Here, we present the actions that must be taken to address the impact of COPD. We believe that the WHO is particularly well-positioned to co-ordinate an attack on COPD, and GOLD will do all it can to help and rally support.

'Vision 50' (Reg. No. CV-1152, PI 679953), a hard red winter (HRW) wheat (Triticum aestivum L.) cultivar, was derived from the cross 'Jagalene'/'Provinciale' using a modified bulk breeding method. Vision 50 was tested as VA09HRW-64 in replicated yield trials in Virginia (2011-2017) and in the USDAARS Uniform Bread Wheat Trials (2012-2017) and released by the Virginia Agricultural Experiment Station in 2016. Vision 50 is a widely adapted, high-yielding, awned, semidwarf (unknown Rht gene) HRW wheat having mid-to late-season spike emergence, strong straw strength, and resistance or moderate resistance to diseases prevalent in the mid-Atlantic region. In the Virginia Bread Wheat Elite Test from 2014 to 2017, Vision 50 produced a mean yield of 5067 kg ha(-1) that was similar to the highest-yielding (5757 kg ha(-1)) cultivar Shirley, a soft red winter wheat check. Vision 50 has acceptable end-use quality on the basis of comparisons with the HRW wheat check cultivar Jagger for wheat protein (11.3 vs. 12.2 g 100 g(-1)), flour yield (72.7 vs. 66.4 g 100 g(-1)), flour water absorption (59.5 vs. 62.3 g 100 g(-1)), dough mixing tolerance (2.7 vs. 3.0), pup-loaf volume (815 vs. 822 cm(3)), and crumb grain scores (4.2 vs. 3.8). ; Virginia Agricultural Experiment Station; Virginia Small Grains Board; Virginia Agricultural Council; Virginia Crop Improvement Association; Mennel Milling Company; Virginia Agricultural Experiment Station (Blacksburg); USDA National Institute of Food and Agriculture, US Department of Agriculture (Washington, DC) ; Vision 50 was developed with financial support from the Virginia Agricultural Experiment Station, the Virginia Small Grains Board, the Virginia Agricultural Council, the Virginia Crop Improvement Association, and the Mennel Milling Company. This work is/was supported by the Virginia Agricultural Experiment Station (Blacksburg) and the USDA National Institute of Food and Agriculture, US Department of Agriculture (Washington, DC). ; Public domain authored by a U.S. government employee

'LCS Compass' (Reg. No. CV-1149, PI 675458), a hard red winter (HRW) wheat (Triticum aestivum L.), was developed and tested as VA10HRW-13 and co-released by the Virginia Agricultural Experiment Station and Limagrain Cereal Seeds, LLC, in 2015. LCS Compass was derived from the cross 'Vision 20' /'Stanof' using a modified bulk breeding method. LCS Compass is a widely adapted, high-yielding, awned, semidwarf (Rht1) HRW wheat with early to medium maturity and resistance or moderate resistance to diseases prevalent in the mid-Atlantic and Great Plains regions of the United States. In the 2013 Uniform Bread Wheat Trial conducted over 18 locations in eastern states, LCS Compass produced an average grain yield of 4609 kg ha(-1) that was similar to 'Vision 30' (4697 kg ha(-1)). In the northern Great Plains, the average grain yield of LCS Compass (4015 kg ha(-1)) over 44 locations in 2013 was similar to 'Jerry' (4013 kg ha(-1)). In the South Dakota crop zone 3 variety test, LCS Compass had a 3-yr (2015-2017) yield average of 5575 kg ha(-1) and was one of highest-yielding cultivars among the 19 cultivars tested over the 3-yr period. LCS Compass has good end-use quality in both the eastern and Great Plains regions of the United States. ; Virginia Agricultural Experiment Station; Virginia Small Grains Board; Virginia Agricultural Council; Virginia Crop Improvement Association; Mennel Milling Company; USDA Cooperative State Research, Education, and Extension ServiceUnited States Department of Agriculture (USDA)National Institute of Food and Agriculture; Virginia Agricultural Experiment Station (Blacks-burg); USDA National Institute of Food and Agriculture, US Department of Agriculture (Washington, DC) ; LCS Compass was developed with financial support from the Virginia Agricultural Experiment Station, the Virginia Small Grains Board, the Virginia Agricultural Council, the Virginia Crop Improvement Association, the Mennel Milling Company, and the USDA Cooperative State Research, Education, and Extension Service. This work was supported by the Virginia Agricultural Experiment Station (Blacks-burg) and the USDA National Institute of Food and Agriculture, US Department of Agriculture (Washington, DC). ; Public domain authored by a U.S. government employee

The objective of this research was to develop widely adapted hard winter wheat (Triticum aestivum L.) varieties to meet the needs of mills, bakeries, and consumers in the eastern and Great Plains regions of the United States. 'LCS Wizard' (Reg. No. CV-1111, PI 669574), a hard red winter (HRW) wheat, was developed and tested as VA08HRW-80 and co-released by the Virginia Agricultural Experiment Station and Limagrain Cereal Seeds, LLC in 2013. LCS Wizard was derived from the three-way cross S.6742/92PAN1#33//92PIN#107 using a modified bulk breeding method. LCS Wizard is a widely adapted, high-yielding, awned, semidwarf (Rht1) HRW wheat with midseason spike emergence and resistance or moderate resistance to diseases prevalent in the mid-Atlantic and Great Plains regions. In the 2014 Uniform Bread Wheat Trial conducted over 17 locations in eastern states, LCS Wizard produced an average grain yield of 4717 kg ha(-1), similar to 'Vision 45' (4650 kg ha(-1)). In the northern Great Plains, the average grain yield over 54 locations in 2012 of LCS Wizard (4419 kg ha(-1)) was slightly lower than that of 'Overland' (4659 kg ha(-1)). In the southern Great Plains, its average grain yield (3844 kg ha(-1)) over 85 locations was slightly higher than that of Fuller (3757 kg ha(-1)). LCS Wizard has acceptable end-use quality in both the eastern and Great Plains regions of the United States. ; Virginia Agricultural Experiment Station; Virginia Small Grains Board; Virginia Agricultural Council; Virginia Crop Improvement Association; Mennel Milling Company; USDA-Cooperative State Research, Education, and Extension ServiceUnited States Department of Agriculture (USDA)National Institute of Food and Agriculture; National Institute of Food and Agriculture, US Department of AgricultureUnited States Department of Agriculture (USDA) [2011-68002-30029] ; LCS Wizard was developed with financial support from the Virginia Agricultural Experiment Station, the Virginia Small Grains Board, the Virginia Agricultural Council, the Virginia Crop Improvement Association, the Mennel Milling Company, and the USDA-Cooperative State Research, Education, and Extension Service. This material is based on work supported by the Hatch Program of the National Institute of Food and Agriculture, US Department of Agriculture under Agreement N. 2011-68002-30029. Any opinions, findings, conclusions, or recommendations expressed in this publication are those of the authors and do not necessarily reflect the view of the US Department of Agriculture. ; Public domain authored by a U.S. government employee