Lyngvendelbo2382

In fully adjusted models, mortality risk was highest in subjects with lower baseline and time-varying EOC ( less then 100 cells/μL) and was also slightly higher in patients with higher levels (≥550 cells/μL), resulting in a reverse J-shaped relationship. The relationship of ΔEOC with all-cause mortality risk was also a reverse J-shape where both an increase and decrease exhibited a higher mortality risk. CONCLUSIONS Both lower and higher EOCs and changes in EOC over the first 3 months after HD initiation were associated with higher all-cause mortality in incident HD patients. Published by Oxford University Press on behalf of ERA-EDTA 2020. This work is written by US Government employees and is in the public domain in the US.Sulfur (S) is an essential element for plants, and S deficiency causes severe growth retardation. Although the catabolic process of glucosinolates (GSLs), the major S-containing metabolites specific to Brassicales including Arabidopsis, has been recognized as one of the S deficiency (-S) responses in plants, the physiological function of this metabolic process is not clear. Two β-glucosidases (BGLUs), BGLU28 and BGLU30, are assumed to be responsible for this catabolic process as their transcript levels were highly upregulated by -S. To clarify the physiological function of BGLU28 and BGLU30 and their roles in GSL catabolism, we analyzed the accumulation of GSLs and other S-containing compounds in the single and double mutant lines of BGLU28 and BGLU30 and in wild-type plants under different S conditions. GSL levels were highly increased, while the levels of sulfate, cysteine, glutathione and protein were decreased in the double mutant line of BGLU28 and BGLU30 (bglu28/30) under -S. Furthermore, transcript level of Sulfate Transporter1;2, the main contributor of sulfate uptake from the environment, was increased in bglu28/30 mutants under -S. With these metabolic and transcriptional changes, bglu28/30 mutants displayed obvious growth retardation under -S. Overall, our results indicate that BGLU28 and BGLU30 are required for -S-induced GSL catabolism and contribute to sustained plant growth under -S by recycling sulfate to primary S metabolism. © The Author(s) 2020. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. All rights reserved. For permissions, please email journals.permissions@oup.com.MOTIVATION Metagenomics refers to the study of complex samples containing of genetic contents of multiple individual organisms, and thus, has been used to elucidate the microbiome and resistome of a complex sample. The microbiome refers to all microbial organisms in a sample, and the resistome refers to all of the antimicrobial resistance (AMR) genes in pathogenic and non-pathogenic bacteria. Single nucleotide polymorphisms (SNPs) can be effectively used to "fingerprint" specific organisms and genes within the microbiome and resistome, and trace their movement accross various samples. However, in order to effectively use these SNPs for this traceability, a scalable and accurate metagenomics SNP caller is needed. Moreover, such a SNP caller should not be reliant on reference genomes since 95% of microbial species is unculturable, making the determination of a reference genome extremely challenging. In this paper, we address this need. RESULTS We present LueVari, a reference-free SNP caller based on the read colored de Bruijn graph, an extension of the traditional de Bruijn graph that allows repeated regions longer than the k-mer length and shorter than the read length to be identified unambiguously. LueVari is able to identify SNPs in both AMR genes and chromosomal DNA from shotgun metagenomics data with reliable sensitivity (between 91% to 99%) and precision (between 71% to 99%) as the performance of competing methods varies widely. 20-Hydroxyecdysone cost Furthermore, we show that LueVari constructs sequences containing the variation which span up to 97.8% of genes in datasets which can be helpful in detecting distinct AMR genes in large metagenomic datasets. AVAILABILITY Code and datasets are publicly available at https//github.com/baharpan/cosmo/tree/LueVari. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.SUMMARY ShinySOM offers a user-friendly interface for reproducible, high-throughput analysis of high-dimensional flow and mass cytometry data guided by self-organizing maps. The software implements a FlowSOM-style workflow, with improvements in performance, visualizations and data dissection possibilities. The outputs of the analysis include precise statistical information about the dissected samples, and R-compatible metadata useful for batch processing of large sample volumes. AVAILABILITY AND IMPLEMENTATION ShinySOM is free and open-source, available online at gitlab.com/exaexa/ShinySOM. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) 2020. Published by Oxford University Press.MOTIVATION As the density of sampled population increases, especially as studies incorporate aspects of the spatial landscape to study evolutionary processes, efficient simulation of genetic data under the coalescent becomes a primary challenge. Beyond the computational demands, coalescence-based simulation strategies have to be reconsidered because traditional assumptions about the dynamics of coalescing lineages within local populations may be violated (e.g., more than two daughter lineages may coalesce to a parent at low population densities). Specifically, to efficiently assign n lineages to m parents, the order relation between n and m strongly affects the relevant algorithm for the coalescent simulator (e.g., only when $$ n \lt \sqrt2m $$, is it reasonable to assume that two lineages, at most, can be assigned to the same parent). Controlling the details of the simulation model as a function of n and m is then crucial to represent accurately and efficiently the assignment process, but current implementcs are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.MOTIVATION Imputation procedures in biomedical fields have turned into statistical practice, since further analyses can be conducted ignoring the former presence of missing values. In particular, non-parametric imputation schemes like the Random Forest have shown favorable imputation performance compared to the more traditionally used MICE procedure. However, their effect on valid statistical inference has not been analyzed so far. This paper closes this gap by investigating their validity for inferring mean differences in incompletely observed pairs while opposing them to a recent approach that only works with the given observations at hand. RESULTS Our findings indicate that machine learning schemes for (multiply) imputing missing values may inflate type-I-error or result in comparably low power in small to moderate matched pairs, even after modifying the test statistics using Rubin's multiple imputation rule. In addition to an extensive simulation study, an illustrative data example from a breast cancer gene study has been considered. AVAILABILITY The corresponding R-code can be accessed through the authors and the gene expression data can be downloaded at www.gdac.broadinstitute.org. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online. © The Author(s) (2020). Published by Oxford University Press. All rights reserved. For Permissions, please email journals.permissions@oup.com.Importance Biologics revolutionized the treatment of psoriasis. Biologics are given in a fixed dose, but lower doses might be possible. Objective To investigate whether dose reduction (DR) of biologics in patients with stable psoriasis is noninferior to usual care (UC). Design, Setting, and Participants This pragmatic, open-label, prospective, controlled, noninferiority randomized clinical trial was conducted from March 1, 2016, to July 22, 2018, at 6 dermatology departments in the Netherlands. A total of 120 patients with plaque psoriasis and stable low disease activity who were receiving treatment with adalimumab, etanercept, or ustekinumab were studied. Interventions Patients were randomized 11 to DR (n = 60) or UC (n = 60). In the DR group, injection intervals were prolonged stepwise, leading to 67% and 50% of the original dose. Main Outcomes and Measures The primary outcome was between-group difference in disease activity corrected for baseline at 12 months compared with the predefined noninferiority mar0 (IQR, 0.0-2.0) in the UC group (mean difference, 0.8; 95% CI, 0.3-1.3), indicating noninferiority for DR compared with UC. No significant difference was found regarding persistent flares between groups (n = 5 in both groups). Twenty-eight patients (53%; 95% CI, 39%-67%) in the DR group tapered their dose successfully at 12 months. No severe adverse events related to the intervention occurred. Conclusions and Relevance In this trial, noninferiority was not demonstrated for DR of adalimumab, etanercept, and ustekinumab based on the PASI in patients with psoriasis compared with UC with the chosen noninferiority margin. However, the strategy was noninferior based on the DLQI. Dose tapering did not lead to persistent flares or safety issues. Trial Registration ClinicalTrials.gov Identifier NCT02602925.IFN-α can suppress production of T cell polarizing cytokines or induce inhibitory antigen presenting cells that suppress T cell activation. Previous studies showed that IFN-α therapy fails to boost virus-specific T cell immunity in patients with chronic Hepatitis B virus (HBV) infection. Our aim was to determine whether IFN-α exposure alters human antigen presenting cell function in vivo. We investigated the immunomodulatory effects using healthy donor PBMC exposed to IFN-α, and chronic hepatitis B (CHB) patients starting IFN-α therapy. IFN-α increased HLA-DR, CD80, CD86 and PD-L1 expression on healthy donor monocytes. In contrast to the activated phenotype, IFN-α inhibited TLR-induced cytokine production and monocyte-induced T cell proliferation. In CHB patients, peg-IFN treatment induced an interferon-stimulated gene signature in monocytes and increased HLA-DR, CD80, CD86 and PD-L1 expression. As early as 3d after CHB patients started treatment, IFN-α inhibited monocyte cytokine production and T cell stimulation ex vivo. IFN-α-mediated inhibition of IL-12 production, rather than inhibitory receptor expression, was responsible for inhibition of T cell proliferation. Addition of IL-12 restored T cell proliferation to baseline levels. Understanding how professional antigen presenting cells respond to immunomodulation is important for both new innate and adaptive-targeted immunotherapies. © The Author(s) 2020. Published by Oxford University Press for the Infectious Diseases Society of America. All rights reserved. For permissions, e-mail journals.permissions@oup.com.MOTIVATION Imaging mass spectrometry (imaging MS) is a prominent technique for capturing distributions of molecules in tissue sections. Various computational methods for imaging MS rely on quantifying spatial correlations between ion images, referred to as co-localization. However, no comprehensive evaluation of co-localization measures has ever been performed; this leads to arbitrary choices and hinders method development. RESULTS We present ColocML, a machine learning approach addressing this gap. With the help of 42 imaging MS experts from 9 labs, we created a gold standard of 2210 pairs of ion images ranked by their co-localization. We evaluated existing co-localization measures and developed novel measures using tf-idf and deep neural networks. The semi-supervised deep learning Pi model and the cosine score applied after median thresholding performed the best (Spearman 0.797 and 0.794 with expert rankings respectively). We illustrate these measures by inferring co-localization properties of 10273 molecules from 3685 public METASPACE datasets.