Potential gene editing targets for developing haploid inducer stocks in rice and wheat with high haploid induction frequency.

Doubled haploid (DH) breeding is a powerful technique to ensure global food security via accelerated crop improvement. DH can be produced in planta by employing haploid inducer stock (HIS). Widely used HIS in maize is known to be governed by ZmPLA, ZmDMP, ZmPLD3, and ZmPOD65 genes. To develop such HIS in rice and wheat, we have identified putative orthologs of these genes using in silico approaches. The OsPLD1; TaPLD1, and OsPOD6; TaPOD8 were identified as putative orthologs of ZmPLD3 and ZmPOD65 in rice and wheat, respectively. Despite being closely related to ZmPLD3, OsPLD1 and TaPLD1 have shown higher anther-specific expression. Similarly, OsPOD6 and TaPOD8 were found closely related to the ZmPOD65 based on both phylogenetic and expression analysis. However, unlike ZmPLD3 and ZmPOD65, two ZmDMP orthologs have been found for each crop. OsDMP1 and OsDMP2 in rice and TaDMP3 and TaDMP13 in wheat have shown similarity to ZmDMP in terms of both sequence and expression pattern. Furthermore, analogs to maize DMP proteins, these genes possess four transmembrane helices making them best suited to be regarded as ZmDMP orthologs. Modifying these predicted orthologous genes by CRISPR/Cas9-based genome editing can produce a highly efficient HIS in both rice and wheat. Besides revealing the genetic mechanism of haploid induction, the development of HIS would advance the genetic improvement of these crops. Supplementary Information: The online version contains supplementary material available at 10.1007/s13205-023-03857-9.

VGG-COVIDNet: A Novel model for COVID detection from X-Ray and CT Scan images.

In this research work, a new deep learning model named VGG-COVIDNet has been proposed which can classify COVID-19 cases from normal cases over X-Rays and CT scan images of lungs. Medical practitioners use the X-Rays and CT scan images of lungs to identify whether a person is infected from COVID or not. In present times, it is very important to give real time COVID prediction with high reliability of results. Deep learning models equipped with machine learning support have been found very influential in accurate prediction of COVID or Non-COVID cases in real time. However, there are some limitations associated with the performance of these model which are model size, achieving good balance of model size and accuracy, and making a single model fitting well for both X-Ray and CT Scan image datasets. Keeping in mind these performance constraints, this new model (VGG-COVIDNet) has been proposed for real time prediction of COVID cases with good balance of model size and accuracy working well for both type of datasets (CT Scan and X-Ray). In order to control model size, an improved version of VGG-16 architecture has been proposed which contains only 13 convolutional layers and 5 fully connected layers. Multiple dropout layers have been added in the proposed architecture which can drop some percentage of features and applies random transformations to decrease the model over-fitting issue. Keeping in mind the primary goal to increase the model accuracy the proposed model has been trained on different datasets with ReLU activation function which is one of the best non-linear activation functions. Four different capacity datasets with CT scan and X-Ray images have been used to validate the performance of proposed model. The proposed model gives an overall accuracy of more than 90% on both types of input datasets i.e. X-Ray and CT Scan.

Unlocking the hidden variation from wild repository for accelerating genetic gain in legumes.

The fluctuating climates, rising human population, and deteriorating arable lands necessitate sustainable crops to fulfil global food requirements. In the countryside, legumes with intriguing but enigmatic nitrogen-fixing abilities and thriving in harsh climatic conditions promise future food security. However, breaking the yield plateau and achieving higher genetic gain are the unsolved problems of legume improvement. Present study gives emphasis on 15 important legume crops, i.e., chickpea, pigeonpea, soybean, groundnut, lentil, common bean, faba bean, cowpea, lupin, pea, green gram, back gram, horse gram, moth bean, rice bean, and some forage legumes. We have given an overview of the world and India's area, production, and productivity trends for all legume crops from 1961 to 2020. Our review article investigates the importance of gene pools and wild relatives in broadening the genetic base of legumes through pre-breeding and alien gene introgression. We have also discussed the importance of integrating genomics, phenomics, speed breeding, genetic engineering and genome editing tools in legume improvement programmes. Overall, legume breeding may undergo a paradigm shift once genomics and conventional breeding are integrated in the near future.

Lung Disease Classification in CXR Images Using Hybrid Inception-ResNet-v2 Model and Edge Computing.

Chest X-ray (CXR) imaging is one of the most widely used and economical tests to diagnose a wide range of diseases. However, even for expert radiologists, it is a challenge to accurately diagnose diseases from CXR samples. Furthermore, there remains an acute shortage of trained radiologists worldwide. In the present study, a range of machine learning (ML), deep learning (DL), and transfer learning (TL) approaches have been evaluated to classify diseases in an openly available CXR image dataset. A combination of the synthetic minority over-sampling technique (SMOTE) and weighted class balancing is used to alleviate the effects of class imbalance. A hybrid Inception-ResNet-v2 transfer learning model coupled with data augmentation and image enhancement gives the best accuracy. The model is deployed in an edge environment using Amazon IoT Core to automate the task of disease detection in CXR images with three categories, namely pneumonia, COVID-19, and normal. Comparative analysis has been given in various metrics such as precision, recall, accuracy, AUC-ROC score, etc. The proposed technique gives an average accuracy of 98.66%. The accuracies of other TL models, namely SqueezeNet, VGG19, ResNet50, and MobileNetV2 are 97.33%, 91.66%, 90.33%, and 76.00%, respectively. Further, a DL model, trained from scratch, gives an accuracy of 92.43%. Two feature-based ML classification techniques, namely support vector machine with local binary pattern (SVM + LBP) and decision tree with histogram of oriented gradients (DT + HOG) yield an accuracy of 87.98% and 86.87%, respectively.