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BackgroundMost of the mortality resulting from COVID-19 has been associated with severe disease. Effective treatment of severe cases remains a challenge due to the lack of early detection of the infection. ObjectiveThis study aimed to develop an effective prediction model for COVID-19 severity by combining radiological outcome with clinical biochemical indexes. MethodsA total of 46 patients with COVID-19 (10 severe, 36 nonsevere) were examined. To build the prediction model, a set of 27 severe and 151 nonsevere clinical laboratory records and computerized tomography (CT) records were collected from these patients. We managed to extract specific features from the patients’ CT images by using a recently published convolutional neural network. We also trained a machine learning model combining these features with clinical laboratory results. ResultsWe present a prediction model combining patients’ radiological outcomes with their clinical biochemical indexes to identify severe COVID-19 cases. The prediction model yielded a cross-validated area under the receiver operating characteristic (AUROC) score of 0.93 and an F1 score of 0.89, which showed a 6% and 15% improvement, respectively, compared to the models based on laboratory test features only. In addition, we developed a statistical model for forecasting COVID-19 severity based on the results of patients’ laboratory tests performed before they were classified as severe cases; this model yielded an AUROC score of 0.81. ConclusionsTo our knowledge, this is the first report predicting the clinical progression of COVID-19, as well as forecasting severity, based on a combined analysis using laboratory tests and CT images.

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Plant root-nodule symbiosis (RNS) with mutualistic nitrogen-fixing bacteria is restricted to a single clade of angiosperms, the Nitrogen-Fixing Nodulation Clade (NFNC), and is best understood in the legume family. Nodulating species share many commonalities, explained either by divergence from a common ancestor over 100 million years ago or by convergence following independent origins over that same time period. Regardless, comparative analyses of diverse nodulation syndromes can provide insights into constraints on nodulation-what must be acquired or cannot be lost for a functional symbiosis-and the latitude for variation in the symbiosis. However, much remains to be learned about nodulation, especially outside of legumes. Here, we employed a large-scale phylogenomic analysis across 88 species, complemented by 151 RNA-seq libraries, to elucidate the evolution of RNS. Our phylogenomic analyses further emphasize the uniqueness of the transcription factor NIN as a master regulator of nodulation and identify key mutations that affect its function across the NFNC. Comparative transcriptomic assessment revealed nodule-specific upregulated genes across diverse nodulating plants, while also identifying nodule-specific and nitrogen-response genes. Approximately 70% of symbiosis-related genes are highly conserved in the four representative species, whereas defense-related and host-range restriction genes tend to be lineage specific. Our study also identified over 900 000 conserved non-coding elements (CNEs), over 300 000 of which are unique to sampled NFNC species. NFNC-specific CNEs are enriched with the active H3K9ac mark and are correlated with accessible chromatin regions, thus representing a pool of candidate regulatory elements for genes involved in RNS. Collectively, our results provide novel insights into the evolution of nodulation and lay a foundation for engineering of RNS traits in agriculturally important crops.

Introduction: The majority of peppers in the US for fresh market and processing are handpicked, and harvesting can account for 20-50% of production costs. Innovation in mechanical harvesting would increase availability; lower the costs of local, healthy vegetable products; and perhaps improve food safety and expand markets. Most processed peppers require removal of pedicels (stem and calyx) from the fruit, but lack of an efficient mechanical process for this operation has hindered adoption of mechanical harvest. In this paper, we present characterization and advancements in breeding green chile peppers for mechanical harvesting. Specifically, we describe inheritance and expression of an easy-destemming trait derived from the landrace UCD-14 that facilitates machine harvest of green chiles. Methods: A torque gauge was used for measuring bending forces similar to those of a harvester and applied to two biparental populations segregating for destemming force and rate. Genotyping by sequencing was used to generate genetic maps for quantitative trait locus (QTL) analyses. Results: A major destemming QTL was found on chromosome 10 across populations and environments. Eight additional population and/or environment-specific QTL were also identified. Chromosome 10 QTL markers were used to help introgress the destemming trait into jalapeño-type peppers. Low destemming force lines combined with improvements in transplant production enabled mechanical harvest of destemmed fruit at a rate of 41% versus 2% with a commercial jalapeńo hybrid. Staining for the presence of lignin at the pedicel/fruit boundary indicated the presence of an abscission zone and homologs of genes known to affect organ abscission were found under several QTL, suggesting that the easy-destemming trait may be due to the presence and activation of a pedicel/fruit abscission zone. Conclusion: Presented here are tools to measure the easy-destemming trait, its physiological basis, possible molecular pathways, and expression of the trait in various genetic backgrounds. Mechanical harvest of destemmed mature green chile fruits was achieved by combining easy-destemming with transplant management.