Project description:Invasive group A streptococcal (iGAS) disease cases increased in the first half of 2022 in the Netherlands, with a remarkably high proportion of emm4 isolates. Whole-genome sequence analysis of 66 emm4 isolates, 40 isolates from the pre-coronavirus disease 2019 (COVID-19) pandemic period 2009-2019 and 26 contemporary isolates from 2022, identified a novel Streptococcus pyogenes lineage (M4NL22), which accounted for 85 % of emm4 iGAS cases in 2022. Surprisingly, we detected few isolates of the emm4 hypervirulent clone, which has replaced nearly all other emm4 in the USA and the UK. M4NL22 displayed genetic differences compared to other emm4 strains, although these were of unclear biological significance. In publicly available data, we identified a single Norwegian isolate belonging to M4NL22, which was sampled after the isolates from this study, possibly suggesting export of M4NL22 to Norway. In conclusion, our study identified a novel S. pyogenes emm4 lineage underlying an increase of iGAS disease in early 2022 in the Netherlands and the results have been promptly communicated to public health officials.
Project description:Unlabelled: The integrative analysis of multiple high-throughput data sources that are available for a common sample set is an increasingly common goal in biomedical research. Joint and individual variation explained (JIVE) is a tool for exploratory dimension reduction that decomposes a multi-source dataset into three terms: a low-rank approximation capturing joint variation across sources, low-rank approximations for structured variation individual to each source and residual noise. JIVE has been used to explore multi-source data for a variety of application areas but its accessibility was previously limited. We introduce R.JIVE, an intuitive R package to perform JIVE and visualize the results. We discuss several improvements and extensions of the JIVE methodology that are included. We illustrate the package with an application to multi-source breast tumor data from The Cancer Genome Atlas.Availability and implementationR.JIVE is available via the Comprehensive R Archive Network (CRAN) under the GPLv3 license: https://cran.r-project.org/web/packages/r.jive/Contactelock@umn.eduSupplementary informationSupplementary data are available at Bioinformatics online.
Project description:The lifestyle medicine core competencies were developed by a committee of physicians from several medical specialties to provide guidance on the knowledge and skills needed for physicians to provide high quality lifestyle interventions that optimize chronic disease outcomes. These competencies were published in the Journal of the American Medical Association (JAMA) in 2010 and used as the foundation for the first lifestyle medicine course and for the lifestyle medicine board certification examination. In the ensuing years, interest in the field and application has expanded to a variety of health professionals. With evolution of the lifestyle medicine evidence-base, the competencies have been updated. This article sums up the changes in their organization and content. Regular updates are anticipated to align with the ongoing scientific studies and evolution of the field.
Project description:Accuracy and transparency of scientific data are becoming more and more relevant with the increasing concern regarding the evaluation of data reproducibility in many research areas. This concern is also true for quantifying coding and noncoding RNAs, with the remarkable increase in publications reporting RNA profiling and sequencing studies. To address the problem, we propose the following recommendations: (a) accurate documentation of experimental procedures in Materials and methods (and not only in the supplementary information, as many journals have a strict mandate for making Materials and methods as visible as possible in the main text); (b) submission of RT-qPCR raw data for all experiments reported; and (c) adoption of a unified, simple format for submitted RT-qPCR raw data. The Real-time PCR Data Essential Spreadsheet Format (RDES) was created for this purpose.
Project description:Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variables, land cover maps, and terrain data to create the China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 (CARM30). Our product was validated using independent samples and showed average F1 scores of 0.869 and 0.971 for winter and spring rapeseed. The CARM30 has high spatial consistency with existing 10 m and 20 m rapeseed maps. Additionally, the CARM30-derived rapeseed planted area was significantly correlated with agricultural statistics (R2 = 0.65-0.86; p < 0.001). The obtained rapeseed distribution information can serve as a reference for stakeholders such as farmers, scientific communities, and decision-makers.