Search

Tropical tuber crops such as cassava, sweet potatoes and elephant foot yam play vital roles in global food security and economic development, particularly in developing nations. The present study examines the current scenario of tuber crop production and trade dynamics, focusing on India's role both as a producer and an exporter. Using empirical data from various sources including Food and Agriculture Organization of the United Nations and Agricultural and Processed Food Products Export Development Authority, the study analyses production trends, trade patterns and policy frameworks, to elucidate potential pathways for enhancing productivity, sustainability and competitiveness in the tuber crops sector. The analysis reveals significant growth in cassava and sweet potato production globally, with India emerging as a key player. However, climate change, pests and diseases, soil degradation, and trade barriers pose hurdles to the growth of the sector. This study also recommends comprehensive strategies, including climate-resilient agricultural practices, integrated pest management, soil health enhancement and infrastructure development, to address these challenges and promote the sustainable growth of tuber crops. [ABSTRACT FROM AUTHOR]

Pneumonia remains a critical health concern worldwide, necessitating efficient diagnostic tools to enhance patient care. This research proposes a concatenated modified LeNet classifier to classify pneumonia images accurately. The model leverages deep learning techniques to improve the diagnosis of Pneumonia, leading to more effective and timely treatment. Our modified LeNet architecture incorporates a revised Rectified Linear Unit (ReLU) activation function. This enhancement aims to boost the discriminative capacity of the features learned by the model. Furthermore, we integrate batch normalization to stabilize the training process and enhance performance within smaller, less complex, CNN architectures like LeNet. Batch normalization addresses internal covariate shift, a phenomenon where the distribution of activations within a network alter during training. These modifications help to prevent overfitting and decrease computational time. A comprehensive dataset is used to evaluate the model's performance, and the model is benchmarked against relevant deep-learning models. The results demonstrate a high recognition rate, with an accuracy of 96% in pneumonia image recognition. This research suggests that the Concatenated Modified LeNet classifier has the potential to be a highly useful tool for medical professionals in the diagnosis of pneumonia. By offering accurate and efficient image classification, our model could contribute to improved treatment decisions and patient outcomes.

Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable potential in elevating the precision and efficiency of histopathological data analysis. The proposed work introduces a novel approach that harnesses the power of Transfer Learning to capitalize on knowledge gleaned from pre-trained models, adapting it to the nuanced landscape of breast cancer histopathology. Our proposed model, a Transfer Learning-based concatenated model, exhibits substantial performance enhancements compared to traditional methodologies. Leveraging well-established pretrained models such as VGG-16, MobileNetV2, ResNet50, and DenseNet121-each Convolutional Neural Network architecture designed for classification tasks-this study meticulously tunes hyperparameters to optimize model performance. The implementation of a concatenated classification model is systematically benchmarked against individual classifiers on histopathological data. Remarkably, our concatenated model achieves an impressive training accuracy of 98%. The outcomes of our experiments underscore the efficacy of this four-level concatenated model in advancing the accuracy of breast cancer histopathological data analysis. By synergizing the strengths of deep learning and transfer learning, our approach holds the potential to augment the diagnostic capabilities of pathologists, thereby contributing to more informed and personalized treatment planning for individuals diagnosed with breast cancer. This research heralds a promising stride toward leveraging cutting-edge technology to refine the understanding and management of breast cancer, marking a significant advancement in the intersection of artificial intelligence and healthcare.

Convolutional neural networks (CNNs) have been extensively utilized in medical image processing to automatically extract meaningful features and classify various medical conditions, enabling faster and more accurate diagnoses. In this paper, LeNet, a classic CNN architecture, has been successfully applied to breast cancer data analysis. It demonstrates its ability to extract discriminative features and classify malignant and benign tumors with high accuracy, thereby supporting early detection and diagnosis of breast cancer. LeNet with corrected Rectified Linear Unit (ReLU), a modification of the traditional ReLU activation function, has been found to improve the performance of LeNet in breast cancer data analysis tasks via addressing the "dying ReLU" problem and enhancing the discriminative power of the extracted features. This has led to more accurate, reliable breast cancer detection and diagnosis and improved patient outcomes. Batch normalization improves the performance and training stability of small and shallow CNN architecture like LeNet. It helps to mitigate the effects of internal covariate shift, which refers to the change in the distribution of network activations during training. This classifier will lessen the overfitting problem and reduce the running time. The designed classifier is evaluated against the benchmarking deep learning models, proving that this has produced a higher recognition rate. The accuracy of the breast image recognition rate is 89.91%. This model will achieve better performance in segmentation, feature extraction, classification, and breast cancer tumor detection.

Background: Abnormal macrophage function caused by dysfunctional cystic fibrosis transmembrane conductance regulator (CFTR) is a critical contributor to chronic airway infections and inflammation in people with cystic fibrosis (PWCF). Elexacaftor/tezacaftor/ivacaftor (ETI) is a new CFTR modulator therapy for PWCF. Host-pathogen and clinical responses to CFTR modulators are poorly described. We sought to determine how ETI impacts macrophage CFTR function, resulting effector functions and relationships to clinical outcome changes., Methods: Clinical information and/or biospecimens were obtained at ETI initiation and 3, 6, 9 and 12 months post-ETI in 56 PWCF and compared with non-CF controls. Peripheral blood monocyte-derived macrophages (MDMs) were isolated and functional assays performed., Results: ETI treatment was associated with increased CF MDM CFTR expression, function and localisation to the plasma membrane. CF MDM phagocytosis, intracellular killing of CF pathogens and efferocytosis of apoptotic neutrophils were partially restored by ETI, but inflammatory cytokine production remained unchanged. Clinical outcomes including increased forced expiratory volume in 1 s (+10%) and body mass index (+1.0 kg·m -2 ) showed fluctuations over time and were highly individualised. Significant correlations between post-ETI MDM CFTR function and sweat chloride levels were observed. However, MDM CFTR function correlated with clinical outcomes better than sweat chloride., Conclusion: ETI is associated with unique changes in innate immune function and clinical outcomes., Competing Interests: Conflict of interest: The authors declare that no conflicts of interest exists., (Copyright ©The authors 2023. For reproduction rights and permissions contact permissions@ersnet.org.)

Plant precursor miRNAs (pre-miRNA) have conserved evolutionary footprints that correlate with mode of miRNA biogenesis. In plants, base to loop and loop to base modes of biogenesis have been reported. Conserved structural element(s) in pre-miRNA play a major role in turn over and abundance of mature miRNA. Pre-miR396c sequences and secondary structural characteristics across Oryza species are presented. Based on secondary structure, twelve Oryza pre-miR396c sequences are divided into three groups, with the precursor from halophytic Oryza coarctata forming a distinct group. The miRNA-miRNA* duplex region is completely conserved across eleven Oryza species as are other structural elements in the pre-miRNA, suggestive of an evolutionarily conserved base-to-loop mode of miRNA biogenesis. SNPs within O. coarctata mature miR396c sequence and miRNA* region have the potential to alter target specificity and association with the RNA-induced silencing complex. A conserved SNP variation, rs10234287911 (G/A), identified in O. sativa pre-miR396c sequences alters base pairing above the miRNA-miRNA* duplex. The more stable structure conferred by the 'A 10234287911 ' allele may promote better processing vis-à-vis the structure conferred by 'G 10234287911 ' allele. We also examine pri- and pre-miR396c expression in cultivated rice under heat and salinity and their correlation with miR396c expression., (© 2023. The Author(s).)

Chickpea production is vulnerable to drought stress. Identifying the genetic components underlying drought adaptation is crucial for enhancing chickpea productivity. Here, we present the fine mapping and characterization of 'QTL-hotspot', a genomic region controlling chickpea growth with positive consequences on crop production under drought. We report that a non-synonymous substitution in the transcription factor CaTIFY4b regulates seed weight and organ size in chickpea. Ectopic expression of CaTIFY4b in Medicago truncatula enhances root growth under water deficit. Our results suggest that allelic variation in 'QTL-hotspot' improves pre-anthesis water use, transpiration efficiency, root architecture and canopy development, enabling high-yield performance under terminal drought conditions. Gene expression analysis indicated that CaTIFY4b may regulate organ size under water deficit by modulating the expression of GRF-INTERACTING FACTOR1 (GIF1), a transcriptional co-activator of Growth-Regulating Factors. Taken together, our study offers new insights into the role of CaTIFY4b and on diverse physiological and molecular mechanisms underpinning chickpea growth and production under specific drought scenarios., (© 2022 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

Cystic fibrosis (CF) is caused by mutations of the cystic fibrosis transmembrane conductance regulator (CFTR) gene. Chronic inflammation and decline in lung function are major reasons for morbidity in CF. Mutant CFTR expressed in phagocytic cells such as macrophages contributes to persistent infection, inflammation, and lung disease in CF. Macrophages play a central role in innate immunity by eliminating pathogenic microbes by a process called phagocytosis. Phagocytosis is required for tissue homeostasis, balancing inflammation, and crosstalk with the adaptive immune system for antigen presentation. This review focused on (1) current understandings of the signaling underlying phagocytic mechanisms; (2) existing evidence for phagocytic dysregulation in CF; and (3) the emerging role of CFTR modulators in influencing CF phagocytic function. Alterations in CF macrophages from receptor initiation to phagosome formation are linked to disease progression in CF. A deeper understanding of macrophages in the context of CFTR and phagocytosis proteins at each step of phagosome formation might contribute to the new therapeutic development of dysregulated innate immunity in CF. Therefore, the review also indicates future areas of research in the context of CFTR and macrophages.

Fragrance in rice grains is a key quality trait determining its acceptability and marketability. Intensive research on rice aroma identified mutations in betaine aldehyde dehydrogenase (OsBADH2) leading to production of aroma in rice. Gene editing technologies like CRISPR/Cas9 system has opened new avenues for accelerated improvement of rice grain quality through targeted mutagenesis. In this study, we have employed CRISPR/Cas9 tool to create novel alleles of OsBADH2 leading to introduction of aroma into an elite non-aromatic rice variety ASD16. PCR analysis of putative transformants using primers targeting the flanking regions of sgRNA in the 7th exon of OsBADH2 identified 37.5% potential multi-allelic mutations in T0 generation. Sensory evaluation test in the leaves of T0 lines identified thirteen lines belonging to five independent events producing aroma. Sequence analysis of these aromatic T0 lines identified 22 different types of mutations located within -17 bp to +15bp of sgRNA region. The -1/-2 bp deletion in the line # 8-19 and -8/-5 bp deletion in the line # 2-16 produced strong aroma and the phenotype was stably inherited in the T1 generation. Comparative volatile profiling detected novel aromatic compounds viz., pyrrolidine, pyridine, pyrazine, pyradazine and pyrozole in the grains of T1 progenies of line # 8-19. This study has demonstrated the use of CRISPR/Cas9 in creating novel alleles of OsBADH2 to introduce aroma into any non-aromatic rice varieties., Competing Interests: The authors have no competing interests

Naturally evolved saline tolerant rice landraces found along the coastline of India are a valuable genomic resource to explore the complex, polygenic nature of salinity tolerance. In the present study, a set of 28 genome wide SSR markers, 11 salt responsive genic SSR markers and 8 Saltol QTL linked SSR markers were used to estimate genetic relatedness and population structure within a collection of 47 rice landraces (including a tolerant and 2 sensitive checks) originating from geographically divergent coastal regions of India. All three marker types identified substantial genetic variation among the landraces, as evident from their higher PIC values (0.53 for genomic SSRs, 0.43 for Genic SSRs and 0.59 for Saltol SSRs). The markers RM431, RM484 (Genomic SSRs), OsCAX (D), OsCAX (T) (Genic SSRs) and RM562 (Saltol SSR) were identified as good candidates to be used in breeding programs for improving salinity tolerance in rice. STRUCTURE analysis divided the landraces into five distinct populations, with classification correlating with their geographical locations. Principal coordinate and hierarchical cluster analyses (UPGMA and neighbor joining) are in close agreement with STRUCTURE results. AMOVA analysis indicated a higher magnitude of genetic differentiation within individuals of groups (58%), than among groups (42%). We also report the development and validation of a new Cleavage Amplified Polymorphic Sequence (CAPS) marker (OsHKT1;5V395) that targets a codon in the sodium transporter gene OsHKT1;5 (Saltol/SKC1 locus) that is associated with sodium transport rates in the above rice landraces. The CAPS marker was found to be present in all landraces except in IR29, Kamini, Gheus, Matla 1 and Matla 2. Significant molecular genetic diversity established among the analyzed salt tolerant rice landraces will aid in future association mapping; the CAPS marker, OsHKT1;5V395 can be used to map rice landraces for the presence of the SNP (Single Nucleotide Polymorphism) associated with increased sodium transport rates and concomitant salinity tolerance in rice., (Copyright © 2019 Elsevier B.V. All rights reserved.)

Herpes simplex virus hepatitis is a rare but potentially fatal disease without early intervention. Impaired immunity is a major predisposing risk factor but infection in immunocompetent individuals is not unheard of. Diagnosis is complicated by its rarity and nonspecific signs and symptoms on presentation. Identification by liver biopsy is often limited due to concurrent coagulopathy. Early and aggressive treatment is centered on antiviral therapy with acyclovir. We present a case of herpes hepatitis in an immunocompetent woman following abdominal instrumentation.

The availability of genome sequences for several crops and advances in genome editing approaches has opened up possibilities to breed for almost any given desirable trait. Advancements in genome editing technologies such as zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs) has made it possible for molecular biologists to more precisely target any gene of interest. However, these methodologies are expensive and time-consuming as they involve complicated steps that require protein engineering. Unlike first-generation genome editing tools, CRISPR/Cas9 genome editing involves simple designing and cloning methods, with the same Cas9 being potentially available for use with different guide RNAs targeting multiple sites in the genome. After proof-of-concept demonstrations in crop plants involving the primary CRISPR-Cas9 module, several modified Cas9 cassettes have been utilized in crop plants for improving target specificity and reducing off-target cleavage (e.g., Nmcas9, Sacas9, and Stcas9). Further, the availability of Cas9 enzymes from additional bacterial species has made available options to enhance specificity and efficiency of gene editing methodologies. This review summarizes the options available to plant biotechnologists to bring about crop improvement using CRISPR/Cas9 based genome editing tools and also presents studies where CRISPR/Cas9 has been used for enhancing biotic and abiotic stress tolerance. Application of these techniques will result in the development of non-genetically modified (Non-GMO) crops with the desired trait that can contribute to increased yield potential under biotic and abiotic stress conditions.

Background: Terminal drought stress leads to substantial annual yield losses in chickpea (Cicer arietinum L.). Adaptation to water limitation is a matter of matching water supply to water demand by the crop. Therefore, harnessing the genetics of traits contributing to plant water use, i.e. transpiration rate and canopy development dynamics, is important to design crop ideotypes suited to a varying range of water limited environments. With an aim of identifying genomic regions for plant vigour (growth and canopy size) and canopy conductance traits, 232 recombinant inbred lines derived from a cross between ICC 4958 and ICC 1882, were phenotyped at vegetative stage under well-watered conditions using a high throughput phenotyping platform (LeasyScan)., Results: Twenty one major quantitative trait loci (M-QTLs) were identified for plant vigour and canopy conductance traits using an ultra-high density bin map. Plant vigour traits had 13 M-QTLs on CaLG04, with favourable alleles from high vigour parent ICC 4958. Most of them co-mapped with a previously fine mapped major drought tolerance "QTL-hotspot" region on CaLG04. One M-QTL was found for canopy conductance on CaLG03 with the ultra-high density bin map. Comparative analysis of the QTLs found across different density genetic maps revealed that QTL size reduced considerably and % of phenotypic variation increased as marker density increased., Conclusion: Earlier reported drought tolerance hotspot is a vigour locus. The fact that canopy conductance traits, i.e. the other important determinant of plant water use, mapped on CaLG03 provides an opportunity to manipulate these loci to tailor recombinants having low/high transpiration rate and plant vigour, fitted to specific drought stress scenarios in chickpea.

Terminal drought is a major constraint to chickpea productivity. Two component traits responsible for reduction in yield under drought stress include reduction in seeds size and root length/root density. QTL-seq approach, therefore, was used to identify candidate genomic regions for 100-seed weight (100SDW) and total dry root weight to total plant dry weight ratio (RTR) under rainfed conditions. Genomewide SNP profiling of extreme phenotypic bulks from the ICC 4958 × ICC 1882 population identified two significant genomic regions, one on CaLG01 (1.08 Mb) and another on CaLG04 (2.7 Mb) linkage groups for 100SDW. Similarly, one significant genomic region on CaLG04 (1.10 Mb) was identified for RTR. Comprehensive analysis revealed four and five putative candidate genes associated with 100SDW and RTR, respectively. Subsequently, two genes (Ca_04364 and Ca_04607) for 100SDW and one gene (Ca_04586) for RTR were validated using CAPS/dCAPS markers. Identified candidate genomic regions and genes may be useful for molecular breeding for chickpea improvement., (© 2016 The Authors. Plant Biotechnology Journal published by Society for Experimental Biology and The Association of Applied Biologists and John Wiley & Sons Ltd.)

A combination of two approaches, namely QTL analysis and gene enrichment analysis were used to identify candidate genes in the "QTL-hotspot" region for drought tolerance present on the Ca4 pseudomolecule in chickpea. In the first approach, a high-density bin map was developed using 53,223 single nucleotide polymorphisms (SNPs) identified in the recombinant inbred line (RIL) population of ICC 4958 (drought tolerant) and ICC 1882 (drought sensitive) cross. QTL analysis using recombination bins as markers along with the phenotyping data for 17 drought tolerance related traits obtained over 1-5 seasons and 1-5 locations split the "QTL-hotspot" region into two subregions namely "QTL-hotspot_a" (15 genes) and "QTL-hotspot_b" (11 genes). In the second approach, gene enrichment analysis using significant marker trait associations based on SNPs from the Ca4 pseudomolecule with the above mentioned phenotyping data, and the candidate genes from the refined "QTL-hotspot" region showed enrichment for 23 genes. Twelve genes were found common in both approaches. Functional validation using quantitative real-time PCR (qRT-PCR) indicated four promising candidate genes having functional implications on the effect of "QTL-hotspot" for drought tolerance in chickpea.

Physical map of chickpea was developed for the reference chickpea genotype (ICC 4958) using bacterial artificial chromosome (BAC) libraries targeting 71,094 clones (~12× coverage). High information content fingerprinting (HICF) of these clones gave high-quality fingerprinting data for 67,483 clones, and 1,174 contigs comprising 46,112 clones and 3,256 singletons were defined. In brief, 574 Mb genome size was assembled in 1,174 contigs with an average of 0.49 Mb per contig and 3,256 singletons represent 407 Mb genome. The physical map was linked with two genetic maps with the help of 245 BAC-end sequence (BES)-derived simple sequence repeat (SSR) markers. This allowed locating some of the BACs in the vicinity of some important quantitative trait loci (QTLs) for drought tolerance and reistance to Fusarium wilt and Ascochyta blight. In addition, fingerprinted contig (FPC) assembly was also integrated with the draft genome sequence of chickpea. As a result, ~965 BACs including 163 minimum tilling path (MTP) clones could be mapped on eight pseudo-molecules of chickpea forming 491 hypothetical contigs representing 54,013,992 bp (~54 Mb) of the draft genome. Comprehensive analysis of markers in abiotic and biotic stress tolerance QTL regions led to identification of 654, 306 and 23 genes in drought tolerance "QTL-hotspot" region, Ascochyta blight resistance QTL region and Fusarium wilt resistance QTL region, respectively. Integrated physical, genetic and genome map should provide a foundation for cloning and isolation of QTLs/genes for molecular dissection of traits as well as markers for molecular breeding for chickpea improvement.