OBJECTIVE: Plant-based diets are associated with a lower risk of chronic diseases. Large-scale proteomics can identify objective biomarkers of plant-based diets, and improve our understanding of the pathways that link plant-based diets to health outcomes. This study investigated the plasma proteome of four different plant-based diets [overall plant-based diet (PDI), provegetarian diet, healthful plant-based diet (hPDI), and unhealthful plant-based diet (uPDI)] in the Atherosclerosis Risk in Communities (ARIC) Study and replicated the findings in the Framingham Heart Study (FHS) Offspring cohort.
METHODS: ARIC Study participants at visit 3 (1993-1995) with completed food frequency questionnaire (FFQ) data and proteomics data were divided into internal discovery (n = 7690) and replication (n = 2543) data sets. Multivariable linear regression was used to examine associations between plant-based diet indices (PDIs) and 4955 individual proteins in the discovery sample. Then, proteins that were internally replicated in the ARIC Study were tested for external replication in FHS (n = 1358). Pathway overrepresentation analysis was conducted for diet-related proteins. C-statistics were used to predict if the proteins improved prediction of plant-based diet indices beyond participant characteristics.
RESULTS: In ARIC discovery, a total of 837 diet-protein associations (PDI = 233; provegetarian = 182; hPDI = 406; uPDI = 16) were observed at false discovery rate (FDR) < 0.05. Of these, 453 diet-protein associations (PDI = 132; provegetarian = 104; hPDI = 208; uPDI = 9) were internally replicated. In FHS, 167/453 diet-protein associations were available for external replication, of which 8 proteins (PDI = 1; provegetarian = 0; hPDI = 8; uPDI = 0) replicated. Complement and coagulation cascades, cell adhesion molecules, and retinol metabolism were over-represented. C-C motif chemokine 25 for PDI and 8 proteins for hPDI modestly but significantly improved the prediction of these indices individually and collectively (P value for difference in C-statistics<0.05 for all tests).
CONCLUSIONS: Using large-scale proteomics, we identified potential candidate biomarkers of plant-based diets, and pathways that may partially explain the associations between plant-based diets and chronic conditions.

The major human pathogen Streptococcus pneumoniae has been the subject of intensive clinical and basic scientific study for over 140 years. In multiple instances, these efforts have resulted in major breakthroughs in our understanding of basic biological principles as well as fundamental tenets of bacterial pathogenesis, immunology, vaccinology, and genetics. Discoveries made with S. pneumoniae have led to multiple major public health victories that have saved the lives of millions. Studies on S. pneumoniae continue today, where this bacterium is being used to dissect the impact of the host on disease processes, as a powerful cell biology model, and to better understand the consequence of human actions on commensal bacteria at the population level. Herein we review the major findings, i.e., puzzle pieces, made with S. pneumoniae and how, over the years, they have come together to shape our understanding of this bacterium\'s biology and the practice of medicine and modern molecular biology.

The Internet of Things (IoT) includes billions of sensors and actuators (which we refer to as IoT devices) that harvest data from the physical world and send it via the Internet to IoT applications to provide smart IoT services and products. Deploying, managing, and maintaining IoT devices for the exclusive use of an individual IoT application is inefficient and involves significant costs and effort that often outweigh the benefits. On the other hand, enabling large numbers of IoT applications to share available third-party IoT devices, which are deployed and maintained independently by a variety of IoT device providers, reduces IoT application development costs, time, and effort. To achieve a positive cost/benefit ratio, there is a need to support the sharing of third-party IoT devices globally by providing effective IoT device discovery, use, and pay between IoT applications and third-party IoT devices. A solution for global IoT device sharing must be the following: (1) scalable to support a vast number of third-party IoT devices, (2) interoperable to deal with the heterogeneity of IoT devices and their data, and (3) IoT-owned, i.e., not owned by a specific individual or organization. This paper surveys existing techniques that support discovering, using, and paying for third-party IoT devices. To ensure that this survey is comprehensive, this paper presents our methodology, which is inspired by Systematic Literature Network Analysis (SLNA), combining the Systematic Literature Review (SLR) methodology with Citation Network Analysis (CNA). Finally, this paper outlines the research gaps and directions for novel research to realize global IoT device sharing.

BACKGROUND: Decision-making in healthcare is increasingly complex; notably in hospital environments where the information density is high, e.g., emergency departments, oncology departments, and psychiatry departments. This study aims to discover decisions from logged data to improve the decision-making process.
METHODS: The Design Science Research Methodology (DSRM) was chosen to design an artifact (algorithm) for the discovery and visualization of decisions. The DSRM\'s different activities are explained, from the definition of the problem to the evaluation of the artifact. During the design and development activities, the algorithm itself is created. During the demonstration and evaluation activities, the algorithm was tested with an authentic synthetic dataset.
RESULTS: The results show the design and simulation of an algorithm for the discovery and visualization of decisions. A fuzzy classifier algorithm was adapted for (1) discovering decisions from a decision log and (2) visualizing the decisions using the Decision Model and Notation standard.
CONCLUSIONS: In this paper, we show that decisions can be discovered from a decision log and visualized for the improvement of the decision-making process of healthcare professionals or to support the periodic evaluation of protocols and guidelines.

BACKGROUND: A copy of Scleromyceti Sueciae, a work on which the nomenclature of many fungi is based was known to occur in Scotland\'s Glasgow University Botany Department but the buildings were devastated by fire in 2001 and the whereabouts of this important work, if it existed, was lost. Its re-finding is reported herein.
RESULTS: The Glasgow copy of Scleromyceti Sueciae, an uncut first edition of Fries\' work, was located in the Glasgow Museums in its original cabinet being transferred there years before the fire and its specimens being now databased. It is one of the few existing uncut copies of this important scientific work and one of the best-preserved copies of the first edition.
CONCLUSIONS: The discovery of this first edition of Scleromyceti Sueciae emphasizes the significance to reserve special conservation for important collections by early mycologists. It also allows interested mycologists world-wide to know of the existence in Glasgow of an uncut, first edition copy.

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Tandem repeat DNA sequences constitute a significant proportion of the human genome. While previously considered to be functionally inert, these sequences are now broadly accepted as important contributors to genetic diversity. However, the polymorphic nature of these sequences can lead to expansion beyond a gene-specific threshold, causing disease. More than 50 pathogenic repeat expansions have been identified to date, many of which have been discovered in the last decade as a result of advances in sequencing technologies and associated bioinformatic tools. Commonly utilised diagnostic platforms including Sanger sequencing, capillary array electrophoresis, and Southern blot are generally low throughput and are often unable to accurately determine repeat size, composition, and epigenetic signature, which are important when characterising repeat expansions. The rapid advances in bioinformatic tools designed specifically to interrogate short-read sequencing and the development of long-read single molecule sequencing is enabling a new generation of high throughput testing for repeat expansion disorders. In this review, we discuss some of the challenges surrounding the identification and characterisation of disease-causing repeat expansions and the technological advances that are poised to translate the promise of genomic medicine to individuals and families affected by these disorders.

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The story of the discovery of hepatitis E originated in the late 1970s with my extreme belief that there was a hidden saga in the relationship between jaundice and pregnancy in developing countries and the opportunity for a massive epidemic of viral hepatitis, which hit the Gulmarg Kashmir region in November 1978. Based on data collected from a door-to-door survey, the existence of a new disease, epidemic non-A, non-B hepatitis, caused by a hitherto unknown hepatitis virus, was announced. This news was received by the world community with hype and skepticism. In the early 1980s, the world watched in awe as an extreme example of human self-experimentation led to the identification of VLP. In 1990, a cDNA clone from the virus responsible for epidemic non-A, non-B hepatitis was isolated. Over the years, we traversed three eras of ambiguity, hope, and hype of hepatitis E research and conducted several seminal studies to understand the biology of HEV and manifestations of hepatitis E. Many milestones have been reached on the long and winding road of hepatitis E research to understand the structure, biology, and diversity of the agent, changing the behavior of the pathogen in developed countries, and the discovery of a highly effective vaccine.