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BACKGROUND Neonatal early-onset sepsis (EOS) is one of the main causes of global neonatal mortality and morbidity, and initiation of early antibiotic treatment is key. However, antibiotics may be harmful. METHODS We performed a secondary analysis of results from the Neonatal Procalcitonin Intervention Study, a prospective, multicenter, randomized, controlled intervention study. The primary outcome was the diagnostic accuracy of serial measurements of C-reactive protein (CRP), procalcitonin (PCT), and white blood count (WBC) within different time windows to rule out culture-positive EOS (proven sepsis). RESULTS We analyzed 1678 neonates with 10 899 biomarker measurements (4654 CRP, 2047 PCT, and 4198 WBC) obtained within the first 48 hours after the start of antibiotic therapy due to suspected EOS. The areas under the curve (AUC) comparing no sepsis vs proven sepsis for maximum values of CRP, PCT, and WBC within 36 hours were 0.986, 0.921, and 0.360, respectively. The AUCs for CRP and PCT increased with extended time frames up to 36 hours, but there was no further difference between start to 36 hours vs start to 48 hours. Cutoff values at 16 mg/L for CRP and 2.8 ng/L for PCT provided a sensitivity of 100% for discriminating no sepsis vs proven sepsis. CONCLUSIONS Normal serial CRP and PCT measurements within 36 hours after the start of empiric antibiotic therapy can exclude the presence of neonatal EOS with a high probability. The negative predictive values of CRP and PCT do not increase after 36 hours. + ID der Publikation: unilu_51625 + Sprache: Englisch + Letzte Aktualisierung: 2021-04-26 13:29:30

This project publishes the code used in the following publication: Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer’s disease, NeuroImage: Clinical, 2021 Link: https://doi.org/10.1016/j.nicl.2021.102712, arxiv.org/2012.08769 Starting point: Overview.ipynb {"references": ["Bron et al., Cross-Cohort Generalizability of Deep and Conventional Machine Learning for MRI-based diagnosis and prediction of Alzheimer's disease, NeuroImage: Clinical, 2021"]} https://gitlab.com/radiology/neuro/bron-cross-cohort/-/tree/v1.0

AbstractGenome sizes of eukaryotic organisms vary substantially, with whole genome duplications (WGD) and transposable element expansion acting as main drivers for rapid genome size increase. The two North American mudminnows, Umbra limi and U. pygmaea, feature genomes about twice the size of their sister lineage Esocidae (e.g., pikes and pickerels). However, it is unknown whether all Umbra species share this genome expansion and which causal mechanisms drive this expansion. Using flow cytometry, we find that the genome of the European mudminnow is expanded similarly to both North American species, ranging between 4.5-5.4 pg per diploid nucleus. Observed blocks of interstitially located telomeric repeats in Umbra limi suggest frequent Robertsonian rearrangements in its history. Comparative analyses of transcriptome and genome assemblies show that the genome expansion in Umbra is driven by extensive DNA transposon expansion without WGD. Furthermore, we find a substantial ongoing expansion of repeat sequences in the Alaska blackfish Dallia pectoralis, the closest relative to the family Umbridae, which might mark the beginning of a similar genome expansion. Our study suggests that the genome expansion in mudminnows, driven mainly by transposon expansion, but not WGD, occurred before the separation into the American and European lineage.Significance StatementNorth American mudminnows feature genomes about twice the size of their sister lineage Esocidae (e.g., pikes and pickerels). However, neither the mechanism underlaying this genome expansion, nor whether this feature is shared amongst all mudminnows is currently known. Using cytogenetic analyses, we find that the genome of the European mudminnow also expanded and that extensive chromosome fusion events have occurred in some Umbra species. Furthermore, comparative genomics based on de-novo assembled transcriptomes and genome assemblies, which have recently become available, indicates that DNA transposon activity is responsible for this expansion.

High‐throughput sequencing (HTS) is increasingly being used for the characterization and monitoring of biodiversity. If applied in a structured way, across broad geographical scales, it offers the potential for a much deeper understanding of global biodiversity through the integration of massive quantities of molecular inventory data generated independently at local, regional and global scales. The universality, reliability and efficiency of HTS data can potentially facilitate the seamless linking of data among species assemblages from different sites, at different hierarchical levels of diversity, for any taxonomic group and regardless of prior taxonomic knowledge. However, collective international efforts are required to optimally exploit the potential of site‐based HTS data for global integration and synthesis, efforts that at present are limited to the microbial domain. To contribute to the development of an analogous strategy for the nonmicrobial terrestrial domain, an international symposium entitled “Next Generation Biodiversity Monitoring” was held in November 2019 in Nicosia (Cyprus). The symposium brought together evolutionary geneticists, ecologists and biodiversity scientists involved in diverse regional and global initiatives using HTS as a core tool for biodiversity assessment. In this review, we summarize the consensus that emerged from the 3‐day symposium. We converged on the opinion that an effective terrestrial Genomic Observatories network for global biodiversity integration and synthesis should be spatially led and strategically united under the umbrella of the metabarcoding approach. Subsequently, we outline an HTS‐based strategy to collectively build an integrative framework for site‐based biodiversity data generation. The international symposium “Next Generation Biodiversity Monitoring” held in November 2019 in Nicosia (Cyprus) was organized by the iBioGen project, which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No. 810729. G.F.F. is funded by the European Research Council under the European Union’s Horizon 2020 programme, grant agreement No. 772284 (IceCommunities). Peer reviewed

Next Generation Sequencing data of leukodystrophy gene panel analysis and segregation study data Sudy supported by Italian Ministry of Health. Grant Numbers: GR2016_02363337, RF2016_02361285

1. Description of methods used for collection/generation of data: This dataset comprises of parasitoids caught in 2016 at 19 Arctic and Sub-Arctic localities during two consecutive six-day-long trapping periods aimed to take place during the flowering peak of the mountain avens (Dryas spp.). Each location had three to four trapping sites (A-D) in Dryas heath type habitats, each with ten 5cm by 4.5cm white sticky traps cut out from sticky board (Barrettine Environmental, UK [product no longer available]). Sticky traps were embedded in growths of Dryas spp. The parasitoids were subsequently picked of off the sticky traps, their whole DNA was extracted and half of their Cytochrome Oxidase I barcode region was amplified using Primers B-F and HCO. The processing of samples was done in three parts (Data1, Data2, Data2) with slightly different methodology. See the supplementary information of the recommended publication for more details. Datasets were sequenced at the Helsinki Functional Genomics Unit (FuGU) in two separate MiSeq v3 2x300bp runs (Data1 and Data2). Additionally, a set of samples from a specific site (Zackenberg) were sequenced as part of larger set at the FIMM Technology Centre in a HiSeq2500 2x250bp run (Data3). Additionally, Dryas flower count, flowering phenology and flower damage by insect herbivores was recorded at the start, after a week (day 6) and in the end (day 12). These counts were done in 3 to 5 1/4 square meter monitoring plots per trapping site. Microclimate was recorded at one trapping site per locality using Lascar EL-USB-2 tempeerature and air humidity loggers under a small white plastic dome at ~ 10 cm height. 2. Methods for processing the data: Initially, paired-end reads were merged and trimmed for quality using 32-bit usearch version 11 (Edgar 2010) with the command ‘fastq_mergepairs’. Primers were removed using software cutadapt version 1.14 (Martin 2011) with 15% mismatch rate. The reads were then collapsed into unique sequences (singletons removed) with command ‘fastx_uniques’. The subsequent clustering steps differed slightly for different data sets, due to the origin of the data (MiSeq vs. HiSeq2500), as follows. For Data1 and Data2, the newly-collapsed unique sequences were cleaned of chimeras using command ‘uchime_denovo’ and clustered into 96% OTUs (OTU = Operational Taxonomical Unit) using command ‘cluster_size’ using USEARCH. The choice of 96% clustering threshold was based on empirical optimization, considering both the rapid genetic divergence in CO1, as well as potential sequencing errors. For Data3, the unique sequences were denoised (i.e., chimeras were removed) and reads were clustered into ZOTUs (= ‘zero-radius OTU’) with command ‘unoise3’ using USEARCH version 11. These ZOTUs do not practically differ from traditional clustering of OTUs (which are based on pre-set percentage threshold), but according to Edgar and Flyvbjerg (2015), the UNOISE algorithm performs better for certain heterogenous data sets in (i) removing chimeras, (ii) PhiX sequences and (iii) Illumina artefacts. Then OTUs and ZOTUs were mapped back to the original trimmed reads with command ‘usearch_global’ (‘search_exact’ for Data3) to establish the total number of reads in each sample using 64-bit software vsearch (Rognes et al. 2016). Overall, we were able to map 25,673,920 reads (Data1: 4,261,291; Data2: 12,095,141; Data3: 9,317,488) to our original samples. These reads were subject to further filtering: from each sample, each OTU/ZOTU with less reads than 2% of the total reads in that sample were discarded, which also cleared most of the extraction and PCR negative controls. Finally, samples producing less than 37 reads (a threshold chosen by analysing the data as a whole) were removed from the subsequent analysis. The taxonomic assignations were initially done independently for each dataset (using identical criteria), but the final assignations were carried out using the whole, combined (Data1+Data2+Data3) dataset. The OTUs/ZOTUs were initially identified into genus-level using the RDP classifier with a very recently constructed COI-RDP database v3.2 (with 60% probability threshold for genus-level assignation) following Porter and Hajibabaei (2018). In cases where the database was clearly insufficient to reach a genus-level assignation, we used local BLAST against all the retrieved COI sequences in BOLD (Altschul et al. 1990; Ratnasingham and Hebert 2007) and chose the most probable match. Taxonomic information for remaining hits was retrieved manually from BOLD using BIN code (from earlier steps) or the actual OTU/ZOTU sequence. Finally, identifications were checked against our preliminary identification notes taken at the beginning of DNA extraction, and potentially false assignations (due to for example contamination in certain steps, or clear errors in the database) were either removed or assigned to the likely correct out/ZOTU. As the end result of all the bioinformatic steps, we arrived at a list of 460 parasitoid taxa (listed in OTU_info.csv Those dataset specific OTUs which were collapsed to one in the merging of the datasets are also listed in this file). Climatic impacts are especially pronounced in the Arctic, which as a region is warming twice as fast as the rest of the globe. Here, we investigate how mean climatic conditions and rates of climatic change impact parasitoid insect communities in 16 localities across the Arctic. We focus on parasitoids in a wide-spread habitat, Dryas heathlands, and describe parasitoid community composition in terms of larval host use (i.e. parasitoid use of herbivorous Lepidoptera versus pollinating Diptera) and functional groups (i.e. parasitoids adhering to an idiobiont versus koinobiont lifestyle). Of the latter, we expect idiobionts to be generally associated with poorer tolerance to cold temperatures. To further test our findings, we assess whether similar climatic variables are associated with host abundances in a 22-year time series from Northeast Greenland. We find that sites which have experienced a temperature rise in summer while retaining cold winters to be dominated by parasitoids of Lepidoptera, with the pattern reversed among the parasitoids of Diptera. The rate of summer temperature rise is further associated with higher levels of herbivory, suggesting higher availability of lepidopteran hosts and changes in ecosystem functioning. We also detect a matching signal over time, as higher summer temperatures, coupled with cold early winter soils, are related to high herbivory by lepidopteran larvae, and to declines in the abundance of dipteran pollinators. Collectively, our results suggest that in parts of the warming Arctic, Dryas is being simultaneously exposed to increased herbivory and reduced pollination. Our findings point to potential drastic and rapid consequences of climate change on multitrophic-level community structure and on ecosystem functioning and highlight the value of collaborative systematic sampling effort. The included README_PanArcticParasitods.txt file contains detailed metadata on all included variables. There are some missing data e.g.: In the in situ microclimate measurements misising values are denoted with, NA In the parasitoid data, samples which failed to to produce parasitoid reads are marked with NA. In the Dryas data, two localities have their data data missing, and are not included in this table, where as one site Kobbejford had no Dryas and has counts of Loiseleuria procumbens as a phenological indicator in stead. This is marked in the datapoint notes. Concerning OTUs: Our OTUs are based on only a half of the CO1 barcode region and we used a quite wide clustering treshold of 96%. Also the clustering was done globally on a dataset containing closely related species from different continents. While this works well for the intended purpose of looking at functional group dominance at site level, the taxonomic resolution is inadequate for applications requiring true species level information. In such occasiton we encourage contacting the corresponding author. The DNA extracts are stored at the Department of Agricultural Sciences of the University of Helsinki, Finland.