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How well do crop modeling groups predict wheat phenology, given calibration data from the target population?
No full textPredicting phenology is essential for adapting varieties to different environmental conditions and for crop management. Therefore, it is important to evaluate how well different crop modeling groups can predict phenology. Multiple evaluation studies have been previously published, but it is still difficult to generalize the findings from such studies since they often test some specific aspect of extrapolation to new conditions, or do not test on data that is truly independent of the data used for calibration. In this study, we analyzed the prediction of wheat phenology in Northern France under observed weather and current management, which is a problem of practical importance for wheat management. The results of 27 modeling groups are evaluated, where modeling group encompasses model structure, i.e. the model equations, the calibration method and the values of those parameters not affected by calibration. The data for calibration and evaluation are sampled from the same target population, thus extrapolation is limited. The calibration and evaluation data have neither year nor site in common, to guarantee rigorous evaluation of prediction for new weather and sites. The best modeling groups, and also the mean and median of the simulations, have a mean absolute error (MAE) of about 3 days, which is comparable to the measurement error. Almost all models do better than using average number of days or average sum of degree days to predict phenology. On the other hand, there are important differences between modeling groups, due to model structural differences and to differences between groups using the same model structure, which emphasizes that model structure alone does not completely determine prediction accuracy. In addition to providing information for our specific environments and varieties, these results are a useful contribution to a knowledge base of how well modeling groups can predict phenology, when provided with calibration data from the target population
Critical functions of N<sup>6</sup>-adenosine methylation of mRNAs in T cells.
No full textThe existence of N6-adenosine methylation (m6A) of mRNA has been known for a long time, but only recently its regulatory potential was uncovered. Current research deciphers the molecular determinants leading to the deposition of this modification and consequences for modified mRNAs. It also evaluates the importance of such modifications for specific cell types and programs. In this review, we summarize the current knowledge on m6A modification of mRNAs in conventional and regulatory T cells and T-cell-driven immune responses and pathology. We discuss the impact of m6A modification on T cell activation including cytokine and antigen receptor signaling or sensing of double-stranded RNAs (dsRNA)
Quality-aware cine cardiac MRI reconstruction and analysis from undersampled K-space data.
No full textCine cardiac MRI is routinely acquired for the assessment of cardiac health, but the imaging process is slow and typically requires several breath-holds to acquire sufficient k-space profiles to ensure good image quality. Several undersampling-based reconstruction techniques have been proposed during the last decades to speed up cine cardiac MRI acquisition. However, the undersampling factor is commonly fixed to conservative values before acquisition to ensure diagnostic image quality, potentially leading to unnecessarily long scan times. In this paper, we propose an end-to-end quality-aware cine short-axis cardiac MRI framework that combines image acquisition and reconstruction with downstream tasks such as segmentation, volume curve analysis and estimation of cardiac functional parameters. The goal is to reduce scan time by acquiring only a fraction of k-space data to enable the reconstruction of images that can pass quality control checks and produce reliable estimates of cardiac functional parameters. The framework consists of a deep learning model for the reconstruction of 2D+t cardiac cine MRI images from undersampled data, an image quality-control step to detect good quality reconstructions, followed by a deep learning model for bi-ventricular segmentation, a quality-control step to detect good quality segmentations and automated calculation of cardiac functional parameters. To demonstrate the feasibility of the proposed approach, we perform simulations using a cohort of selected participants from the UK Biobank (n = 270), 200 healthy subjects and 70 patients with cardiomyopathies. Our results show that we can produce quality-controlled images in a scan time reduced from 12 to 4 s per slice, enabling reliable estimates of cardiac functional parameters such as ejection fraction within 5% mean absolute error
Colocalization analysis of pancreas eQTLs with risk loci from alcoholic and novel non-alcoholic chronic pancreatitis GWAS suggests potential disease causing mechanisms.
No full textBackground: Previous genome-wide association studies (GWAS) identified genome-wide significant risk loci in chronic pancreatitis and investigated underlying disease causing mechanisms by simple overlaps with expression quantitative trait loci (eQTLs), a procedure which may often result in false positive conclusions. Methods: We conducted a GWAS in 584 non-alcoholic chronic pancreatitis (NACP) patients and 6040 healthy controls. Next, we applied Bayesian colocalization analysis of identified genome-wide significant risk loci from both, our recently published alcoholic chronic pancreatitis (ACP) and the novel NACP dataset, with pancreas eQTLs from the GTEx V8 European cohort to prioritize candidate causal genes and extracted credible sets of shared causal variants. Results: Variants at the CTRC (p = 1.22 × 10−21) and SPINK1 (p = 6.59 × 10−47) risk loci reached genome-wide significance in NACP. CTRC risk variants colocalized with CTRC eQTLs in ACP (PP4 = 0.99, PP4/PP3 = 95.51) and NACP (PP4 = 0.99, PP4/PP3 = 95.46). For both diseases, the 95% credible set of shared causal variants consisted of rs497078 and rs545634. CLDN2-MORC4 risk variants colocalized with CLDN2 eQTLs in ACP (PP4 = 0.98, PP4/PP3 = 42.20) and NACP (PP4 = 0.67, PP4/PP3 = 7.18), probably driven by the shared causal variant rs12688220. Conclusions: A shared causal CTRC risk variant might unfold its pathogenic effect in ACP and NACP by reducing CTRC expression, while the CLDN2-MORC4 shared causal variant rs12688220 may modify ACP and NACP risk by increasing CLDN2 expression
Deutsche Epidemiologie publiziert im Vergleich zu anderen medizinischen Disziplinen bemerkenswert erfolgreich.
No full textDeepClassPathway: Molecular pathway aware classification using explainable deep learning.
No full textOBJECTIVE: HPV-associated head and neck cancer is correlated with favorable prognosis; however, its underlying biology is not fully understood. We propose an explainable convolutional neural network (CNN) classifier, DeepClassPathway, that predicts HPV-status and allows patient-specific identification of molecular pathways driving classifier decisions. METHODS: The CNN was trained to classify HPV-status on transcriptome data from 264 (13% HPV-positive) and tested on 85 (25% HPV-positive) head and neck squamous carcinoma patients after transformation into 2D-treemaps representing molecular pathways. Grad-CAM saliency was used to quantify pathways contribution to individual CNN decisions. Model stability was assessed by shuffling pathways within 2D-images. RESULTS: The classification performance of the CNN-ensembles achieved ROC-AUC/PR-AUC of 0.96/0.90 for all treemap variants. Quantification of the averaged pathway saliency heatmaps consistently identified KRAS, spermatogenesis, bile acid metabolism, and inflammation signaling pathways as the four most informative for classifying HPV-positive patients and MYC targets, epithelial-mesenchymal transition, and protein secretion pathways for HPV-negative patients. CONCLUSION: We have developed and applied an explainable CNN classification approach to transcriptome data from an oncology cohort with typical sample size that allows classification while accounting for the importance of molecular pathways in individual-level decisions
Detection of polycyclic aromatic hydrocarbons in high organic carbon ultrafine particle extracts by electrospray ionization ultrahigh-resolution mass spectrometry.
No full textThe detection of polycyclic aromatic hydrocarbons (PAHs) by electrospray ionization (ESI) without additional reagents or targeted setup changes to the ionization source was observed in ultrafine particle (UFP) extracts, with high organic carbon (OC) concentrations, generated by a combustion aerosol standard (CAST) soot generator. Particulate matter (PM) was collected on filters, extracted with methanol, and analyzed by ESI Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS). Next to oxygen-containing species, pure hydrocarbons were found to be one of the most abundant compound classes, detected as [M + Na]+ or [M + H]+ in ESI+ and mostly as [M - H]- in ESI-. The assigned hydrocarbon elemental compositions are identified as PAHs due to their high aromaticity index (AI > 0.67) and were additionally confirmed by MS/MS experiments as well as laser desorption ionization (LDI). Thus, despite the relatively low polarity, PAHs have to be considered in the molecular attribution of these model aerosols and/or fresh emissions with low salt content investigated by ESI
Identifying causal serum protein-cardiometabolic trait relationships using whole genome sequencing.
No full textCardiometabolic diseases, such as type 2 diabetes and cardiovascular disease, have a high public health burden. Understanding the genetically-determined regulation of proteins that are dysregulated in disease can help to dissect the complex biology underpinning them. Here, we perform a protein quantitative trait locus (pQTL) analysis of 255 serum proteins relevant to cardiometabolic processes in 2893 individuals. Meta-analysing whole-genome sequencing (WGS) data from two Greek cohorts, MANOLIS (n = 1356; 22.5x WGS) and Pomak (n = 1537; 18.4x WGS), we detect 302 independently-associated pQTL variants for 171 proteins, including 12 rare variants (minor allele frequency [MAF] < 1%). We additionally find 15 pQTL variants that are rare in non-Finnish European populations, but have drifted up in frequency in the discovery cohorts here. We identify proteins causally associated with cardiometabolic traits, including MEP1B for high-density lipoprotein levels; and describe a knock-out Mep1b mouse model. Our findings furnish insights into the genetic architecture of the serum proteome, identify new protein-disease relationships, and demonstrate the importance of isolated populations in pQTL analysis
VERSE: A Vertebrae labelling and segmentation benchmark for multi-detector CT images.
No full textVertebral labelling and segmentation are two fundamental tasks in an automated spine processing pipeline. Reliable and accurate processing of spine images is expected to benefit clinical decision support systems for diagnosis, surgery planning, and population-based analysis of spine and bone health. However, designing automated algorithms for spine processing is challenging predominantly due to considerable variations in anatomy and acquisition protocols and due to a severe shortage of publicly available data. Addressing these limitations, the Large Scale Vertebrae Segmentation Challenge (VERSE) was organised in conjunction with the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) in 2019 and 2020, with a call for algorithms tackling the labelling and segmentation of vertebrae. Two datasets containing a total of 374 multi-detector CT scans from 355 patients were prepared and 4505 vertebrae have individually been annotated at voxel level by a human-machine hybrid algorithm (https://osf.io/nqjyw/, https://osf.io/t98fz/). A total of 25 algorithms were benchmarked on these datasets. In this work, we present the results of this evaluation and further investigate the performance variation at the vertebra level, scan level, and different fields of view. We also evaluate the generalisability of the approaches to an implicit domain shift in data by evaluating the top-performing algorithms of one challenge iteration on data from the other iteration. The principal takeaway from VERSE: the performance of an algorithm in labelling and segmenting a spine scan hinges on its ability to correctly identify vertebrae in cases of rare anatomical variations. The VERSE content and code can be accessed at: https://github.com/anjany/verse
Low emission zones reduced PM<sub>10</sub> but not NO<sub>2</sub> concentrations in Berlin and Munich, Germany.
No full textLow emission zones (LEZs) aiming at improving the air quality in urban areas have been implemented in many European cities. However, studies are limited in evaluating the effects of LEZ, and most of which used simple methods. In this study, a general additive mixed model was utilized to account for confounders in the atmosphere and validate the effects of LEZ on PM10 and NO2 concentrations in two German cities. In addition, the effects of LEZ on elemental carbon (EC) and total carbon (TC) in Berlin were also evaluated. The LEZ effects were estimated after taking into account air pollutant concentrations at a reference site located in the regional background, and adjusting for hour of the week, public holidays, season, and wind direction. The LEZ in Berlin, and the LEZ in combination with the heavy-duty vehicle (HDV) transit ban in Munich significantly reduced the PM10 concentrations, at both traffic sites (TS) and urban background sites (UB). The effects were greater in LEZ stage 3 than in LEZ stages 2 and 1. Moreover, compared with PM10, LEZ was more efficient in reducing EC, a component that is considered more toxic than PM10 mass. In contrast, the LEZ had no consistent effect on NO2 levels: no effects were observed in Berlin; in Munich, the combination of the LEZ and the HDV transit ban reduced NO2 at UB site in LEZ stage 1, but without further reductions in subsequent stages of the LEZ. Overall, our study indicated that LEZs, which target the major primary air pollution source in the highly populated city center could be an effective way to improve urban air quality such as PM mass concentration and EC level