Automatic detection of tuberculosis using VGG19 with seagull-algorithm.

Due to various reasons, the incidence rate of communicable diseases in humans is steadily rising, and timely detection and handling will reduce the disease distribution speed. Tuberculosis (TB) is a severe communicable illness caused by the bacterium Mycobacterium-Tuberculosis (M. tuberculosis), which predominantly affects the lungs and causes severe respiratory problems. Due to its significance, several clinical level detections of TB are suggested, including lung diagnosis with chest X-ray images. The proposed work aims to develop an automatic TB detection system to assist the pulmonologist in confirming the severity of the disease, decision-making, and treatment execution. The proposed system employs a pre-trained VGG19 with the following phases: (i) image pre-processing, (ii) mining of deep features, (iii) enhancing the X-ray images with chosen procedures and mining of the handcrafted features, (iv) feature optimization using Seagull-Algorithm and serial concatenation, and (v) binary classification and validation. The classification is executed with 10-fold cross-validation in this work, and the proposed work is investigated using MATLAB® software. The proposed research work was executed using the concatenated deep and handcrafted features, which provided a classification accuracy of 98.6190% with the SVM-Medium Gaussian (SVM-MG) classifier

Colon histology slide classification with deep-learning framework using individual and fused features

Cancer occurrence rates are gradually rising in the population, which reasons a heavy diagnostic burden globally. The rate of colorectal (bowel) cancer (CC) is gradually rising, and is currently listed as the third most common cancer globally. Therefore, early screening and treatments with a recommended clinical protocol are necessary to trat cancer. The proposed research aim of this paper to develop a Deep-Learning Framework (DLF) to classify the colon histology slides into normal/cancer classes using deep-learning-based features. The stages of the framework include the following: (ⅰ) Image collection, resizing, and pre-processing; (ⅱ) Deep-Features (DF) extraction with a chosen scheme; (ⅲ) Binary classification with a 5-fold cross-validation; and (ⅳ) Verification of the clinical significance. This work classifies the considered image database using the follwing: (ⅰ) Individual DF, (ⅱ) Fused DF, and (ⅲ) Ensemble DF. The achieved results are separately verified using binary classifiers. The proposed work considered 4000 (2000 normal and 2000 cancer) histology slides for the examination. The result of this research confirms that the fused DF helps to achieve a detection accuracy of 99% with the K-Nearest Neighbor (KNN) classifier. In contrast, the individual and ensemble DF provide classification accuracies of 93.25 and 97.25%, respectively

Molecular genetics and phenotypic assessment of foxtail millet (Setaria italica (L.) P. Beauv.) landraces revealed remarkable variability of morpho-physiological, yield, and yield‐related traits

Foxtail millet (Setaria italica (L.) P. Beauv.) is highly valued for nutritional traits, stress tolerance and sustainability in resource-poor dryland agriculture. However, the low productivity of this crop in semi-arid regions of Southern India, is further threatened by climate stress. Landraces are valuable genetic resources, regionally adapted in form of novel alleles that are responsible for cope up the adverse conditions used by local farmers. In recent years, there is an erosion of genetic diversity. We have hypothesized that plant genetic resources collected from the semi-arid climatic zone would serve as a source of novel alleles for the development of climate resilience foxtail millet lines with enhanced yield. Keeping in view, there is an urgent need for conservation of genetic resources. To explore the genetic diversity, to identify superior genotypes and novel alleles, we collected a heterogeneous mixture of foxtail millet landraces from farmer fields. In an extensive multi-year study, we developed twenty genetically fixed foxtail millet landraces by single seed descent method. These landraces characterized along with four released cultivars with agro-morphological, physiological, yield and yield-related traits assessed genetic diversity and population structure. The landraces showed significant diversity in all the studied traits. We identified landraces S3G5, Red, Black and S1C1 that showed outstanding grain yield with earlier flowering, and maturity as compared to released cultivars. Diversity analysis using 67 simple sequence repeat microsatellite and other markers detected 127 alleles including 11 rare alleles, averaging 1.89 alleles per locus, expected heterozygosity of 0.26 and an average polymorphism information content of 0.23, collectively indicating a moderate genetic diversity in the landrace populations. Euclidean Ward’s clustering, based on the molecular markers, principal coordinate analysis and structure analysis concordantly distinguished the genotypes into two to three sub-populations. A significant phenotypic and genotypic diversity observed in the landraces indicates a diverse gene pool that can be utilized for sustainable foxtail millet crop improvement

NPR3 and NPR4 are receptors for the immune signal salicylic acid in plants

Salicylic acid (SA) is a plant immune signal produced upon pathogen challenge to induce systemic acquired resistance (SAR). It is the only major plant hormone for which the receptor has not been firmly identified. SAR in Arabidopsis requires the transcription cofactor NPR1 (nonexpresser of PR genes 1), whose degradation serves as a molecular switch for SAR. Here we show that NPR1 paralogues, NPR3 and NPR4, are SA receptors that bind SA with different affinities and function as adaptors of the Cullin 3 ubiquitin E3 ligase to mediate NPR1 degradation in an SA-regulated manner. Accordingly, the npr3 npr4 mutant accumulates higher levels of NPR1 and is insensitive to SAR induction. Moreover, this mutant is defective in pathogen effector-triggered programmed cell death and immunity. Our study reveals the mechanism of SA perception in determining cell death and survival in response to pathogen challenge

The glycine decarboxylase complex multienzyme family in Populus

In plants, the glycine decarboxylase complex (GDC) cooperates with serine hydroxymethyltransferase (SHMT) to mediate photorespiratory glycine-serine interconversion. GDC is also postulated to be an integral component of one-carbon (C1) metabolism in heterotrophic tissues, although molecular evidence in plants is scarce. An initial report of a xylem-specific isoform of GDC component H-protein, PtgdcH1, in aspen (Populus tremuloides Michx.) provided molecular evidence consistent with an important role for GDC in plant C1 metabolism. PtgdcH1 is phylogenetically distinct from the leaf-abundant photorespiratory PtgdcH3, but both isoforms restored GDC activity in a yeast H-protein knockout mutant, suggesting their functional equivalence. The Populus genome contains eight transcriptionally active GDC genes, encoding four H-proteins, two T-proteins, and single P- and L-proteins. The two Populus T-protein isoforms, PtgdcT1 and PtgdcT2, exhibited differential expression in leaves and xylem, similar to PtgdcH3 and PtgdcH1. In silico identification of AC elements in the promoters of xylem-abundant PtgdcH1 and PtgdcT2, as well as many lignin biosynthetic genes of Populus is consistent with a prominent role for GDC in methyl-intensive lignification during wood formation. The AC element is absent from Arabidopsis GDC promoters, and GDC expression has not been linked to secondary growth in this herbaceous annual. Taken together, the results suggest that the association of distinct H-protein and T-protein isoforms with photorespiration and C1 metabolism is a distinguishing feature of Populus, and may signify molecular adaptation of GDC to cope with the C1 demands of lignification in woody perennials. © 2007 The Author(s)

Plants promote mating and dispersal of the human pathogenic fungus <i>Cryptococcus</i>

<div><p>Infections due to <i>Cryptococcus</i> are a leading cause of fungal infections worldwide and are acquired as a result of environmental exposure to desiccated yeast or spores. The ability of <i>Cryptococcus</i> to grow, mate, and produce infectious propagules in association with plants is important for the maintenance of the genetic diversity and virulence factors important for infection of animals and humans. In the Western United States and Canada, <i>Cryptococcus</i> has been associated with conifers and tree species other than <i>Eucalyptus</i>; however, to date <i>Cryptococcus</i> has only been studied on live <i>Arabidopsis thaliana</i>, <i>Eucalyptus sp</i>., and <i>Terminalia catappa</i> (almond) seedlings. Previous research has demonstrated the ability of <i>Cryptococcus</i> to colonize live plants, leaves, and vasculature. We investigated the ability of <i>Cryptococcus</i> to grow on live seedlings of the angiosperms, <i>A</i>. <i>thaliana</i>, <i>Eucalyptus camaldulensis</i>, <i>Colophospermum mopane</i>, and the gymnosperms, <i>Pseudotsuga menziesii</i> (Douglas fir), and <i>Tsuga heterophylla</i> (Western hemlock). We observed a broad-range ability of <i>Cryptococcus</i> to colonize both traditional infection models as well as newly tested conifer species. Furthermore, <i>C</i>. <i>neoformans</i>, C. <i>deneoformans</i>, <i>C</i>. <i>gattii</i> (VGI), <i>C</i>. <i>deuterogattii</i> (VGII) and <i>C</i>. <i>bacillisporus</i> (VGIII) were able to colonize live plant leaves and needles but also undergo filamentation and mating on agar seeded with plant materials or in saprobic association with dead plant materials. The ability of <i>Cryptococcus</i> to grow and undergo filamentation and reproduction in saprobic association with both angiosperms and gymnosperms highlights an important role of plant debris in the sexual cycle and exposure to infectious propagules. This study highlights the broad importance of plants (and plant debris) as the ecological niche and reservoirs of infectious propagules of <i>Cryptococcus</i> in the environment.</p></div

<i>Cryptococcus</i> can colonize live Douglas fir and Western hemlock trees.

<p>Scanning electron micrographs are shown of mixed mating strains of (A) <i>Cryptococcus deneoformans</i> (VNIV) producing filaments on Douglas fir trees, and (B) colonizing Eastern hemlock trees. Colonization of <i>Cryptococcus neoformans</i> (VNIV) on Douglas fir (C) or Western hemlock (D), and of <i>C</i>. <i>deuterogattii</i> (VGII) <i>x C</i>. <i>gattii</i> (VGI) on Douglas fir (E) or Hemlock (F) are shown. Scale bar = 5 μm.</p

Conifer trees are susceptible to colonization by <i>Cryptococcus</i>.

<p>Colony forming units (CFUs + SEM) at three weeks post-inoculation are shown. (A) <i>Cryptococcus</i> cells were recovered from Douglas fir and Eastern hemlock seedlings. <i>C</i>. <i>neoformans</i> was recovered from both Douglas fir and Eastern hemlock; however, recovery of <i>C</i>. <i>deneoformans</i>, <i>C</i>. <i>gattii</i>, <i>C</i>. <i>bacillisporus</i>, <i>C</i>. <i>deuterogattii</i> strains was inconsistent. In a plant infection trial, Douglas fir, <i>Eucalyptus</i>, and Mopane infection models were compared utilizing engineered strains containing various drug-resistant cassettes. <i>Cryptococcus</i> cells were recovered from <i>Eucalyptus</i>, Mopane, and Douglas fir infected with individual and mated strains. Error bars represent +SEM.</p

<i>Cryptococcus neoformans</i> (VNIV) can colonize mature soil grown <i>Arabidopsis thaliana</i>.

<p>(A) Mating mixtures can induce chlorosis, (B) Colony forming units (CFUs + SEM) indicate that both individual and mating strains of <i>Cryptococcus</i> can colonize <i>A</i>. <i>thaliana</i> plants. Chlorosis was only associated with mated mixtures of <i>C</i>. <i>neoformans</i> (VNI).</p