Geomagnetic Virtual Observatories: monitoring geomagnetic secular variation with the Swarm satellites

We present geomagnetic main field and secular variation time series, at 300 equal-area distributed locations and at 490 km altitude, derived from magnetic field measurements collected by the three Swarm satellites. These Geomagnetic Virtual Observatory (GVO) series provide a convenient means to globally monitor and analyze long-term variations of the geomagnetic field from low-Earth orbit. The series are obtained by robust fits of local Cartesian potential field models to along-track and East–West sums and differences of Swarm satellite data collected within a radius of 700 km of the GVO locations during either 1-monthly or 4-monthly time windows. We describe two GVO data products: (1) ‘Observed Field’ GVO time series, where all observed sources contribute to the estimated values, without any data selection or correction, and (2) ‘Core Field’ GVO time series, where additional data selection is carried out, then de-noising schemes and epoch-by-epoch spherical harmonic analysis are applied to reduce contamination by magnetospheric and ionospheric signals. Secular variation series are provided as annual differences of the Core Field GVOs. We present examples of the resulting Swarm GVO series, assessing their quality through comparisons with ground observatories and geomagnetic field models. In benchmark comparisons with six high-quality mid-to-low latitude ground observatories we find the secular variation of the Core Field GVO field intensities, calculated using annual differences, agrees to an rms of 1.8 nT/yr and 1.2 nT/yr for the 1-monthly and 4-monthly versions, respectively. Regular sampling in space and time, and the availability of data error estimates, makes the GVO series well suited for users wishing to perform data assimilation studies of core dynamics, or to study long-period magnetospheric and ionospheric signals and their induced counterparts. The Swarm GVO time series will be regularly updated, approximately every four months, allowing ready access to the latest secular variation data from the Swarm satellites

Dynamic Conduction and Repolarisation Changes in Early Arrhythmogenic Right Ventricular Cardiomyopathy versus Benign Outflow Tract Ectopy Demonstrated by High Density Mapping and Paced Surface ECG Analysis

Aims: The concealed phase of arrhythmogenic right ventricular cardiomyopathy (ARVC) may initially manifest electrophysiologically. No studies have examined dynamic conduction/repolarization kinetics to distinguish benign right ventricular outflow tract ectopy (RVOT ectopy) from ARVC's early phase. We investigated dynamic endocardial electrophysiological changes that differentiate early ARVC disease expression from RVOT ectopy. Methods: 22 ARVC (12 definite based upon family history and mutation carrier status, 10 probable) patients without right ventricular structural anomalies underwent high-density non-contact mapping of the right ventricle. These were compared to data from 14 RVOT ectopy and 12 patients with supraventricular tachycardias and normal hearts. Endocardial & surface ECG conduction and repolarization parameters were assessed during a standard S1-S2 restitution protocol. Results: Definite ARVC without RV structural disease could not be clearly distinguished from RVOT ectopy during sinus rhythm or during steady state pacing. Delay in Activation Times at coupling intervals just above the ventricular effective refractory period (VERP) increased in definite ARVC (43±20 ms) more than RVOT ectopy patients (36±14 ms, p = 0.03) or Normals (25±16 ms, p = 0.008) and a progressive separation of the repolarisation time curves between groups existed. Repolarization time increases in the RVOT were also greatest in ARVC (definite ARVC: 18±20 ms; RVOT ectopy: 5±14, Normal: 1±18, p<0.05). Surface ECG correlates of these intracardiac measurements demonstrated an increase of greater than 48 ms in stimulus to surface ECG J-point pre-ERP versus steady state, with an 88% specificity and 68% sensitivity in distinguishing definite ARVC from the other groups. This technique could not distinguish patients with genetic predisposition to ARVC only (probable ARVC) from controls. Conclusions: Significant changes in dynamic conduction and repolarization are apparent in early ARVC before detectable RV structural abnormalities, and were present to a lesser degree in probable ARVC patients. Investigation of dynamic electrophysiological parameters may be useful to identify concealed ARVC in patients without disease pedigrees by using endocardial electrogram or paced ECG parameters

Colorectal cancer linkage on chromosomes 4q21, 8q13, 12q24, and 15q22

A substantial proportion of familial colorectal cancer (CRC) is not a consequence of known susceptibility loci, such as mismatch repair (MMR) genes, supporting the existence of additional loci. To identify novel CRC loci, we conducted a genome-wide linkage scan in 356 white families with no evidence of defective MMR (i.e., no loss of tumor expression of MMR proteins, no microsatellite instability (MSI)-high tumors, or no evidence of linkage to MMR genes). Families were ascertained via the Colon Cancer Family Registry multi-site NCI-supported consortium (Colon CFR), the City of Hope Comprehensive Cancer Center, and Memorial University of Newfoundland. A total of 1,612 individuals (average 5.0 per family including 2.2 affected) were genotyped using genome-wide single nucleotide polymorphism linkage arrays; parametric and non-parametric linkage analysis used MERLIN in a priori-defined family groups. Five lod scores greater than 3.0 were observed assuming heterogeneity. The greatest were among families with mean age of diagnosis less than 50 years at 4q21.1 (dominant HLOD = 4.51, α = 0.84, 145.40 cM, rs10518142) and among all families at 12q24.32 (dominant HLOD = 3.60, α = 0.48, 285.15 cM, rs952093). Among families with four or more affected individuals and among clinic-based families, a common peak was observed at 15q22.31 (101.40 cM, rs1477798; dominant HLOD = 3.07, α = 0.29; dominant HLOD = 3.03, α = 0.32, respectively). Analysis of families with only two affected individuals yielded a peak at 8q13.2 (recessive HLOD = 3.02, α = 0.51, 132.52 cM, rs1319036). These previously unreported linkage peaks demonstrate the continued utility of family-based data in complex traits and suggest that new CRC risk alleles remain to be elucidated. © 2012 Cicek et al

A Simple HEPA Filtering Facepiece

Shortages of efficient filtering facepiece respirators leave the public vulnerable to transmission of infectious diseases in small particle aerosols. This study demonstrates that a high-filtration-efficiency facepiece capable of filtering out >95% of 0.05μm particles while being worn can be simply produced with available materials

Network Rewiring in Cancer: Applications to Melanoma Cell Lines and the Cancer Genome Atlas Patients

Genes do not work in isolation, but rather as part of networks that have many feedback and redundancy mechanisms. Studying the properties of genetic networks and how individual genes contribute to overall network functions can provide insight into genetically-mediated disease processes. Most analytical techniques assume a network topology based on normal state networks. However, gene perturbations often lead to the rewiring of relevant networks and impact relationships among other genes. We apply a suite of analysis methodologies to assess the degree of transcriptional network rewiring observed in different sets of melanoma cell lines using whole genome gene expression microarray profiles. We assess evidence for network rewiring in melanoma patient tumor samples using RNA-sequence data available from The Cancer Genome Atlas. We make a distinction between “unsupervised” and “supervised” network-based methods and contrast their use in identifying consistent differences in networks between subsets of cell lines and tumor samples. We find that different genes play more central roles within subsets of genes within a broader network and hence are likely to be better drug targets in a disease state. Ultimately, we argue that our results have important implications for understanding the molecular pathology of melanoma as well as the choice of treatments to combat that pathology

Early Adversity and the Prospective Prediction of Depressive and Anxiety Disorders in Adolescents

The current study was a prospective exploration of the specificity of early childhood adversities as predictors of anxiety and depressive disorders in adolescents. Participants were 816 adolescents (414 males, 402 females) with diagnostic information collected at age 15; information on early adversities had been collected from the mothers during pregnancy, at birth, age 6 months, and age 5 years for a related study. Adolescents with "pure" anxiety disorders were compared with adolescents with "pure" depressive disorders (major depressive disorder, dysthymia), and these groups were compared to never-ill controls. Analyses controlled for gender and maternal depression and anxiety disorders. Results indicated that adolescents with anxiety disorders were more likely than depressed youth to have been exposed to various early stressors, such as maternal prenatal stress, multiple maternal partner changes, and more total adversities, whereas few early childhood variables predicted depressive disorders. Even when current family stressors at age 15 were controlled, early adversity variables again significantly predicted anxiety disorders. Results suggest that anxiety disorders may be more strongly related to early strees exposure, while depressive disorders may be related to more proximal stressors or to early stressors not assessed in the current study

PHA4GE quality control contextual data tags:standardized annotations for sharing public health sequence datasets with known quality issues to facilitate testing and training

As public health laboratories expand their genomic sequencing and bioinformatics capacity for the surveillance of different pathogens, labs must carry out robust validation, training, and optimization of wet- and dry-lab procedures. Achieving these goals for algorithms, pipelines and instruments often requires that lower quality datasets be made available for analysis and comparison alongside those of higher quality. This range of data quality in reference sets can complicate the sharing of sub-optimal datasets that are vital for the community and for the reproducibility of assays. Sharing of useful, but sub-optimal datasets requires careful annotation and documentation of known issues to enable appropriate interpretation, avoid being mistaken for better quality information, and for these data (and their derivatives) to be easily identifiable in repositories. Unfortunately, there are currently no standardized attributes or mechanisms for tagging poor-quality datasets, or datasets generated for a specific purpose, to maximize their utility, searchability, accessibility and reuse. The Public Health Alliance for Genomic Epidemiology (PHA4GE) is an international community of scientists from public health, industry and academia focused on improving the reproducibility, interoperability, portability, and openness of public health bioinformatic software, skills, tools and data. To address the challenges of sharing lower quality datasets, PHA4GE has developed a set of standardized contextual data tags, namely fields and terms, that can be included in public repository submissions as a means of flagging pathogen sequence data with known quality issues, increasing their discoverability. The contextual data tags were developed through consultations with the community including input from the International Nucleotide Sequence Data Collaboration (INSDC), and have been standardized using ontologies - community-based resources for defining the tag properties and the relationships between them. The standardized tags are agnostic to the organism and the sequencing technique used and thus can be applied to data generated from any pathogen using an array of sequencing techniques. The tags can also be applied to synthetic (lab created) data. The list of standardized tags is maintained by PHA4GE and can be found at https://github.com/pha4ge/contextual_data_QC_tags. Definitions, ontology IDs, examples of use, as well as a JSON representation, are provided. The PHA4GE QC tags were tested, and are now implemented, by the FDA's GenomeTrakr laboratory network as part of its routine submission process for SARS-CoV-2 wastewater surveillance. We hope that these simple, standardized tags will help improve communication regarding quality control in public repositories, in addition to making datasets of variable quality more easily identifiable. Suggestions for additional tags can be submitted to PHA4GE via the New Term Request Form in the GitHub repository. By providing a mechanism for feedback and suggestions, we also expect that the tags will evolve with the needs of the community.</p

An Efficient Strategy for Evaluating New Non-invasive Screening Tests for Colorectal Cancer: The Guiding Principles

OBJECTIVE: New screening tests for colorectal cancer (CRC) are rapidly emerging. Conducting trials with mortality reduction as the end point supporting their adoption is challenging. We re-examined the principles underlying evaluation of new non-invasive tests in view of technological developments and identification of new biomarkers. DESIGN: A formal consensus approach involving a multidisciplinary expert panel revised eight previously established principles. RESULTS: Twelve newly stated principles emerged. Effectiveness of a new test can be evaluated by comparison with a proven comparator non-invasive test. The faecal immunochemical test is now considered the appropriate comparator, while colonoscopy remains the diagnostic standard. For a new test to be able to meet differing screening goals and regulatory requirements, flexibility to adjust its positivity threshold is desirable. A rigorous and efficient four-phased approach is proposed, commencing with small studies assessing the test\u27s ability to discriminate between CRC and non-cancer states ( CONCLUSION: New non-invasive tests can be efficiently evaluated by a rigorous phased comparative approach, generating data from unbiased populations that inform predictions of their health impact

A randomized trial to assess the impact of an antithrombotic decision aid in patients with nonvalvular atrial fibrillation: the DAAFI trial protocol [ISRCTN14429643]

BACKGROUND: Decision aids are often advocated as a means to assist patient and health care provider decision making when faced with complicated treatment or screening decisions. Despite an exponential growth in the availability of decision aids in recent years, their impact on long-term treatment decisions and patient adherence is uncertain due to a paucity of rigorous studies. The choice of antithrombotic therapy for nonvalvular atrial fibrillation (NVAF) is one condition for which a trade-off exists between the potential risks and benefits of competing therapies, and the need to involve patients in decision making has been clearly identified. This study will evaluate whether an evidence-based patient decision aid for patients with NVAF can improve the appropriateness of antithrombotic therapy use by patients and their family physicians. DESIGN: A multi-center, two-armed cluster randomized trial based in community family practices in which patients with NVAF will be randomized to decision aid or usual care. Patients will receive one of four decision aids depending on their baseline stroke risk. The primary outcome is the provision of "appropriate antithrombotic therapy" at 3 months to study participants (appropriateness defined as per the 2001 American College of Chest Physicians recommendations for NVAF). In addition, the impact of this decision aid on patient knowledge, decisional conflict, well-being, and adherence will be assessed after 3 months, 6 months, and 12 months