Pritchardlangston8774

Chickpea is considered among the most important leguminous crops in the world. However, in recent years drought conditions and/or limited availability of water have significantly reduced the production of chickpea. The current study was aimed to understand the legume stress response at the metabolic level for the determination of chickpea genotypes which can resist yield losses and could be cultivated with limited water availability. Here, we have analyzed two genotypes of chickpea, desi and kabuli under rainfed condition using a GC-MS based untargeted metabolomics approach. Results revealed significant differences in several metabolite features including oxalic acid, threonic acid, inositol, maltose and L-proline between studied groups. Accumulation of plant osmoprotectants such as L-proline, sugars and sugar alcohols was higher in desi genotype than kabuli genotype of chickpea when grown under the rainfed condition. Metabolic pathway analysis suggests that the inositol phosphate metabolism was involved in plant defense mechanisms against the limited water availability.The Black-veined White Aporia crataegi (Linnaeus, 1758), a common and widespread butterfly ranging from northwestern Africa to Europe and Asia, has been extinct in Britain since the 1920s and is on a steady decline in several other parts of its range. In order to investigate genetic diversity within A. crataegi and its correspondence with current subspecies-level taxonomy, we barcoded 173 specimens from across its range including, for the first time, extinct populations from Britain and Korea. Using next generation sequencing we also obtained a sequence for Aporia joubini, a peculiar taxon from China known only by its type specimen collected in the early twentieth century. Our phylogenetic analysis placed A. joubini sister to A. oberthuri, although further taxon sampling may reveal a different scheme. Within A. crataegi, we observed a shallow and weak mitogenomic structure with only a few distinct lineages in North Africa, Sicily, Iran, and Japan. Eurasian populations, including those extinct in Britain and Korea, clustered into a large set of closely allied lineages, consistent with a recent expansion during the Late Pleistocene glacial period. This study highlights the importance of museum collections and the unique opportunities they provide in documenting species diversity and helping conservation efforts.Ischemic heart disease (IHD) is the leading cause of death and chronic disability in the world. IHD affects both the systolic and diastolic function of the heart which progressively leads to heart failure; a structural and functional impairment of filling or ejection of blood from the heart. In this study, the progression of systolic and diastolic dysfunction characterized according to their echocardiographic parameters including left ventricular ejection fraction (EF), grades of diastolic dysfunction and ratio between early mitral inflow velocity and mitral annular early diastolic velocity (E/e'), were correlated with differential regulation of various metals in patients sera samples (n = 62) using inductive coupled plasma-mass spectrometry (ICP-MS). Chromium, nickel and selenium were found significant (p  45%. Selenium level was also decreased significantly (p  less then  0.05) in grade 1A and 2 patients that are considered as higher grades of diastole dysfunction in comparison to grade 0-1. Overall, selenium deficiency was identified in both systolic and diastolic dysfunctions of IHD patients corresponding to the progression of disease that could be related to many metabolic and translational pathways specifically which involve selenoproteins.The identification of suspicious microseismic events is the first crucial step in microseismic data processing. Existing automatic classification methods are based on the training of a large data set, which is challenging to apply in mines without a long-term manual data processing. In this paper, we present a method to automatically classify microseismic records with limited samples in underground mines based on capsule networks (CapsNet). RGFP966 in vivo We divide each microseismic record into 33 frames, then extract 21 commonly used features in time and frequency from each frame. Consequently, a 21 × 33 feature matrix is utilized as the input of CapsNet. On this basis, we use different sizes of training sets to train the classification models separately. The trained model is tested using the same test set containing 3,200 microseismic records and compared to convolutional neural networks (CNN) and traditional machine learning methods. Results show that the accuracy of our proposed method is 99.2% with limited training samples. It is superior to CNN and traditional machine learning methods in terms of Accuracy, Precision, Recall, F1-Measure, and reliability.The reflectance of wheat's canopy exhibits angular sensitivity, which can influence the accuracy of different methods for its leaf area index (LAI) estimation through multi-angular remote sensing. The primary objective of this study was to assess and compare the ability of various methods for LAI estimation from 13 view zenith angles (VZAs). The four methods included (1) common hyper-spectral vegetation indices (VIs), (2) optimal two-band combination VIs (i.e., VIs normalized difference index, simple ratio index, and difference vegetation index), (3) back-propagation neural network (BPNN), and (4) partial least squares regression (PLSR). Our results demonstrated that the red-edge plays a key role in estimating LAI, in that the traditional VIs, optimal two-band VIs, and PLSR including the red-edge band all showed satisfactory performance, with coefficient of determination (R2) > 0.72 in the nadir direction. However, the estimation accuracy of LAI was not positively related with band number, and BPNN gave unsatisfactory results under a larger viewing angle, with R2 ≤ 0.60 for extreme angles. The predictive ability of all four methods declined with an increasing VZA, with reliable LAI estimation near the nadir direction. Importantly, by comparing the four methods, PLSR emerged as superior in both its estimation accuracy and angular insensitivity, with R2 = 0.83 in the nadir direction and ≥ 0.65 for extreme angles. For this reason, we highly recommend it be used with multi-angular remote sensing data, especially in agricultural applications.