Identification and Evaluation of Epidemic Prediction and Forecasting Reporting Guidelines: A Systematic Review and a Call for Action

INTRODUCTION: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. METHODS: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. RESULTS: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. CONCLUSIONS: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health

Identification and evaluation of epidemic prediction and forecasting reporting guidelines : a systematic review and a call for action

NGR reports funding by NIGMS grant R35GM119582. BMA is supported by Bill and Melinda Gates Foundation through the Global Good Fund. SP and IMB were funded by the Armed Forces Health Surveillance Branch (GEIS: P0116_19_WR_03.11).Introduction: High quality epidemic forecasting and prediction are critical to support response to local, regional and global infectious disease threats. Other fields of biomedical research use consensus reporting guidelines to ensure standardization and quality of research practice among researchers, and to provide a framework for end-users to interpret the validity of study results. The purpose of this study was to determine whether guidelines exist specifically for epidemic forecast and prediction publications. Methods: We undertook a formal systematic review to identify and evaluate any published infectious disease epidemic forecasting and prediction reporting guidelines. This review leveraged a team of 18 investigators from US Government and academic sectors. Results: A literature database search through May 26, 2019, identified 1467 publications (MEDLINE n = 584, EMBASE n = 883), and a grey-literature review identified a further 407 publications, yielding a total 1777 unique publications. A paired-reviewer system screened in 25 potentially eligible publications, of which two were ultimately deemed eligible. A qualitative review of these two published reporting guidelines indicated that neither were specific for epidemic forecasting and prediction, although they described reporting items which may be relevant to epidemic forecasting and prediction studies. Conclusions: This systematic review confirms that no specific guidelines have been published to standardize the reporting of epidemic forecasting and prediction studies. These findings underscore the need to develop such reporting guidelines in order to improve the transparency, quality and implementation of epidemic forecasting and prediction research in operational public health.Publisher PDFPeer reviewe

DreamTel; Diabetes risk evaluation and management tele-monitoring study protocol

<p>Abstract</p> <p>Background</p> <p>The rising prevalence of type 2 diabetes underlines the importance of secondary strategies for the prevention of target organ damage. While access to diabetes education centers and diabetes intensification management has been shown to improve blood glucose control, these services are not available to all that require them, particularly in rural and northern areas. The provision of these services through the Home Care team is an advance that can overcome these barriers. Transfer of blood glucose data electronically from the home to the health care provider may improve diabetes management.</p> <p>Methods and design</p> <p>The study population will consist of patients with type 2 diabetes with uncontrolled A1c levels living on reserve in the Battlefords region of Saskatchewan, Canada. This pilot study will take place over three phases. In the first phase over three months the impact of the introduction of the Bluetooth enabled glucose monitor will be assessed. In the second phase over three months, the development of guidelines based treatment algorithms for diabetes intensification will be completed. In the third phase lasting 18 months, study subjects will have diabetes intensification according to the algorithms developed.</p> <p>Discussion</p> <p>The first phase will determine if the use of the Bluetooth enabled blood glucose devices which can transmit results electronically will lead to changes in A1c levels. It will also determine the feasibility of recruiting subjects to use this technology. The rest of the Diabetes Risk Evaluation and Management Tele-monitoring (DreamTel) study will determine if the delivery of a diabetes intensification management program by the Home Care team supported by the Bluetooth enabled glucose meters leads to improvements in diabetes management.</p> <p>Trial Registration</p> <p>Protocol NCT00325624</p

A Synoptical Classification of the Bivalvia (Mollusca)

The following classification summarizes the suprageneric taxono-my of the Bivalvia for the upcoming revision of the Bivalvia volumes of the Treatise on Invertebrate Paleontology, Part N. The development of this classification began with Carter (1990a), Campbell, Hoeks-tra, and Carter (1995, 1998), Campbell (2000, 2003), and Carter, Campbell, and Campbell (2000, 2006), who, with assistance from the United States National Science Foundation, conducted large-scale morphological phylogenetic analyses of mostly Paleozoic bivalves, as well as molecular phylogenetic analyses of living bivalves. Dur-ing the past several years, their initial phylogenetic framework has been revised and greatly expanded through collaboration with many students of bivalve biology and paleontology, many of whom are coauthors. During this process, all available sources of phylogenetic information, including molecular, anatomical, shell morphological, shell microstructural, bio- and paleobiogeographic as well as strati-graphic, have been integrated into the classification. The more recent sources of phylogenetic information include, but are not limited to, Carter (1990a), Malchus (1990), J. Schneider (1995, 1998a, 1998b, 2002), T. Waller (1998), Hautmann (1999, 2001a, 2001b), Giribet and Wheeler (2002), Giribet and Distel (2003), Dreyer, Steiner, and Harper (2003), Matsumoto (2003), Harper, Dreyer, and Steiner (2006), Kappner and Bieler (2006), Mikkelsen and others (2006), Neulinger and others (2006), Taylor and Glover (2006), Kříž (2007), B. Morton (2007), Taylor, Williams, and Glover (2007), Taylor and others (2007), Giribet (2008), and Kirkendale (2009). This work has also benefited from the nomenclator of bivalve families by Bouchet and Rocroi (2010) and its accompanying classification by Bieler, Carter, and Coan (2010).This classification strives to indicate the most likely phylogenetic position for each taxon. Uncertainty is indicated by a question mark before the name of the taxon. Many of the higher taxa continue to undergo major taxonomic revision. This is especially true for the superfamilies Sphaerioidea and Veneroidea, and the orders Pectinida and Unionida. Because of this state of flux, some parts of the clas-sification represent a compromise between opposing points of view. Placement of the Trigonioidoidea is especially problematic. This Mesozoic superfamily has traditionally been placed in the order Unionida, as a possible derivative of the superfamily Unionoidea (see Cox, 1952; Sha, 1992, 1993; Gu, 1998; Guo, 1998; Bieler, Carter, & Coan, 2010). However, Chen Jin-hua (2009) summarized evi-dence that Trigonioidoidea was derived instead from the superfamily Trigonioidea. Arguments for these alternatives appear equally strong, so we presently list the Trigonioidoidea, with question, under both the Trigoniida and Unionida, with the contents of the superfamily indicated under the Trigoniida.Fil: Carter, Joseph G.. University of North Carolina; Estados UnidosFil: Altaba, Cristian R.. Universidad de las Islas Baleares; EspañaFil: Anderson, Laurie C.. South Dakota School of Mines and Technology; Estados UnidosFil: Araujo, Rafael. Consejo Superior de Investigaciones Cientificas. Museo Nacional de Ciencias Naturales; EspañaFil: Biakov, Alexander S.. Russian Academy of Sciences; RusiaFil: Bogan, Arthur E.. North Carolina State Museum of Natural Sciences; Estados UnidosFil: Campbell, David. Paleontological Research Institution; Estados UnidosFil: Campbell, Matthew. Charleston Southern University; Estados UnidosFil: Chen, Jin Hua. Chinese Academy of Sciences. Nanjing Institute of Geology and Palaeontology; República de ChinaFil: Cope, John C. W.. National Museum of Wales. Department of Geology; Reino UnidoFil: Delvene, Graciela. Instituto Geológico y Minero de España; EspañaFil: Dijkstra, Henk H.. Netherlands Centre for Biodiversity; Países BajosFil: Fang, Zong Jie. Chinese Academy of Sciences; República de ChinaFil: Gardner, Ronald N.. No especifica;Fil: Gavrilova, Vera A.. Russian Geological Research Institute; RusiaFil: Goncharova, Irina A.. Russian Academy of Sciences; RusiaFil: Harries, Peter J.. University of South Florida; Estados UnidosFil: Hartman, Joseph H.. University of North Dakota; Estados UnidosFil: Hautmann, Michael. Paläontologisches Institut und Museum; SuizaFil: Hoeh, Walter R.. Kent State University; Estados UnidosFil: Hylleberg, Jorgen. Institute of Biology; DinamarcaFil: Jiang, Bao Yu. Nanjing University; República de ChinaFil: Johnston, Paul. Mount Royal University; CanadáFil: Kirkendale, Lisa. University Of Wollongong; AustraliaFil: Kleemann, Karl. Universidad de Viena; AustriaFil: Koppka, Jens. Office de la Culture. Section d’Archéologie et Paléontologie; SuizaFil: Kříž, Jiří. Czech Geological Survey. Department of Sedimentary Formations. Lower Palaeozoic Section; República ChecaFil: Machado, Deusana. Universidade Federal do Rio de Janeiro; BrasilFil: Malchus, Nikolaus. Institut Català de Paleontologia; EspañaFil: Márquez Aliaga, Ana. Universidad de Valencia; EspañaFil: Masse, Jean Pierre. Universite de Provence; FranciaFil: McRoberts, Christopher A.. State University of New York at Cortland. Department of Geology; Estados UnidosFil: Middelfart, Peter U.. Australian Museum; AustraliaFil: Mitchell, Simon. The University of the West Indies at Mona; JamaicaFil: Nevesskaja, Lidiya A.. Russian Academy of Sciences; RusiaFil: Özer, Sacit. Dokuz Eylül University; TurquíaFil: Pojeta, John Jr.. National Museum of Natural History; Estados UnidosFil: Polubotko, Inga V.. Russian Geological Research Institute; RusiaFil: Pons, Jose Maria. Universitat Autònoma de Barcelona; EspañaFil: Popov, Sergey. Russian Academy of Sciences; RusiaFil: Sanchez, Teresa Maria. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional de Córdoba; ArgentinaFil: Sartori, André F.. Field Museum of National History; Estados UnidosFil: Scott, Robert W.. Precision Stratigraphy Associates; Estados UnidosFil: Sey, Irina I.. Russian Geological Research Institute; RusiaFil: Signorelli, Javier Hernan. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Centro Nacional Patagónico; ArgentinaFil: Silantiev, Vladimir V.. Kazan Federal University; RusiaFil: Skelton, Peter W.. Open University. Department of Earth and Environmental Sciences; Reino UnidoFil: Steuber, Thomas. The Petroleum Institute; Emiratos Arabes UnidosFil: Waterhouse, J. Bruce. No especifica;Fil: Wingard, G. Lynn. United States Geological Survey; Estados UnidosFil: Yancey, Thomas. Texas A&M University; Estados Unido

Оценка качества образования на основе компетентностного подхода

В работе представлен практический опыт оценки качества образования в новом формате компетентностного подход

Characterization of Coastal Urban Watershed Bacterial Communities Leads to Alternative Community-Based Indicators

BACKGROUND: Microbial communities in aquatic environments are spatially and temporally dynamic due to environmental fluctuations and varied external input sources. A large percentage of the urban watersheds in the United States are affected by fecal pollution, including human pathogens, thus warranting comprehensive monitoring. METHODOLOGY/PRINCIPAL FINDINGS: Using a high-density microarray (PhyloChip), we examined water column bacterial community DNA extracted from two connecting urban watersheds, elucidating variable and stable bacterial subpopulations over a 3-day period and community composition profiles that were distinct to fecal and non-fecal sources. Two approaches were used for indication of fecal influence. The first approach utilized similarity of 503 operational taxonomic units (OTUs) common to all fecal samples analyzed in this study with the watershed samples as an index of fecal pollution. A majority of the 503 OTUs were found in the phyla Firmicutes, Proteobacteria, Bacteroidetes, and Actinobacteria. The second approach incorporated relative richness of 4 bacterial classes (Bacilli, Bacteroidetes, Clostridia and alpha-proteobacteria) found to have the highest variance in fecal and non-fecal samples. The ratio of these 4 classes (BBC:A) from the watershed samples demonstrated a trend where bacterial communities from gut and sewage sources had higher ratios than from sources not impacted by fecal material. This trend was also observed in the 124 bacterial communities from previously published and unpublished sequencing or PhyloChip- analyzed studies. CONCLUSIONS/SIGNIFICANCE: This study provided a detailed characterization of bacterial community variability during dry weather across a 3-day period in two urban watersheds. The comparative analysis of watershed community composition resulted in alternative community-based indicators that could be useful for assessing ecosystem health

Discovery of common and rare genetic risk variants for colorectal cancer.

To further dissect the genetic architecture of colorectal cancer (CRC), we performed whole-genome sequencing of 1,439 cases and 720 controls, imputed discovered sequence variants and Haplotype Reference Consortium panel variants into genome-wide association study data, and tested for association in 34,869 cases and 29,051 controls. Findings were followed up in an additional 23,262 cases and 38,296 controls. We discovered a strongly protective 0.3% frequency variant signal at CHD1. In a combined meta-analysis of 125,478 individuals, we identified 40 new independent signals at P < 5 × 10-8, bringing the number of known independent signals for CRC to ~100. New signals implicate lower-frequency variants, Krüppel-like factors, Hedgehog signaling, Hippo-YAP signaling, long noncoding RNAs and somatic drivers, and support a role for immune function. Heritability analyses suggest that CRC risk is highly polygenic, and larger, more comprehensive studies enabling rare variant analysis will improve understanding of biology underlying this risk and influence personalized screening strategies and drug development.Goncalo R Abecasis has received compensation from 23andMe and Helix. He is currently an employee of Regeneron Pharmaceuticals. Heather Hampel performs collaborative research with Ambry Genetics, InVitae Genetics, and Myriad Genetic Laboratories, Inc., is on the scientific advisory board for InVitae Genetics and Genome Medical, and has stock in Genome Medical. Rachel Pearlman has participated in collaborative funded research with Myriad Genetics Laboratories and Invitae Genetics but has no financial competitive interest

Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Correction to article number 15805 published in June 2017 in Nature Communications, vol 8

Erratum: Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Correction to article number 15805 published in June 2017 in Nature Communications, vol 8

Genetic loci associated with heart rate variability and their effects on cardiac disease risk

Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74 < r(g) < -0.55) and blood pressure (-0.35 < r(g) < -0.20). These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants (GNG11, RGS6) that influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization