Evaluation of the NAS-ILAB Matrix for Monitoring International Labor Standards: Project Report

The Bureau of International Labor Affairs (ILAB) engaged the National Research Council of the National Academy of Sciences (NAS) to recommend a method to monitor and evaluate labor conditions in a given country. The method focuses on 5 labor standards: freedom of association and collective bargaining, forced or compulsory labor, child labor, discrimination, and acceptable conditions of work

Changes in lignin and cellulose content during leaf litter decomposition in a Mediterranean ecosystem

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health

Enabling Global Clinical Collaborations on Identifiable Patient Data: The Minerva Initiative

The clinical utility of computational phenotyping for both genetic and rare diseases is increasingly appreciated; however, its true potential is yet to be fully realized. Alongside the growing clinical and research availability of sequencing technologies, precise deep and scalable phenotyping is required to serve unmet need in genetic and rare diseases. To improve the lives of individuals affected with rare diseases through deep phenotyping, global big data interrogation is necessary to aid our understanding of disease biology, assist diagnosis, and develop targeted treatment strategies. This includes the application of cutting-edge machine learning methods to image data. As with most digital tools employed in health care, there are ethical and data governance challenges associated with using identifiable personal image data. There are also risks with failing to deliver on the patient benefits of these new technologies, the biggest of which is posed by data siloing. The Minerva Initiative has been designed to enable the public good of deep phenotyping while mitigating these ethical risks. Its open structure, enabling collaboration and data sharing between individuals, clinicians, researchers and private enterprise, is key for delivering precision public health.status: publishe

EMERGEncy ID NET: Review of a 20-Year Multisite Emergency Department Emerging Infections Research Network

As providers of frontline clinical care for patients with acute and potentially life-threatening infections, emergency departments (EDs) have the priorities of saving lives and providing care quickly and efficiently. Although these facilities see a diversity of patients 24 hours per day and can collect prospective data in real time, their ability to conduct timely research on infectious syndromes is not well recognized. EMERGEncy ID NET is a national network that demonstrates that EDs can also collect data and conduct research in real time. This network collaborates with the Centers for Disease Control and Prevention (CDC) and other partners to study and address a wide range of infectious diseases and clinical syndromes. In this paper, we review selected highlights of EMERGEncy ID NET's history from 1995 to 2017. We focus on the establishment of this multisite research network and the network's collaborative research on a wide range of ED clinical topics

EMERGEncy ID NET: Review of a 20-Year Multisite Emergency Department Emerging Infections Research Network

As providers of frontline clinical care for patients with acute and potentially life-threatening infections, emergency departments (EDs) have the priorities of saving lives and providing care quickly and efficiently. Although these facilities see a diversity of patients 24 hours per day and can collect prospective data in real time, their ability to conduct timely research on infectious syndromes is not well recognized. EMERGEncy ID NET is a national network that demonstrates that EDs can also collect data and conduct research in real time. This network collaborates with the Centers for Disease Control and Prevention (CDC) and other partners to study and address a wide range of infectious diseases and clinical syndromes. In this paper, we review selected highlights of EMERGEncy ID NET's history from 1995 to 2017. We focus on the establishment of this multisite research network and the network's collaborative research on a wide range of ED clinical topics

Siponimod versus placebo in secondary progressive multiple sclerosis (EXPAND): a double-blind, randomised, phase 3 study

International audienc