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Nucleotide-binding leucine-rich repeat (NLR) disease resistance genes typically confer resistance against races of a single pathogen. Here, we report that Yr87/Lr85, an NLR gene from Aegilops sharonensis and Aegilops longissima, confers resistance against both P. striiformis tritici (Pst) and Puccinia triticina (Pt) that cause stripe and leaf rust, respectively. Yr87/Lr85 confers resistance against Pst and Pt in wheat introgression as well as transgenic lines. Comparative analysis of Yr87/Lr85 and the cloned Triticeae NLR disease resistance genes shows that Yr87/Lr85 contains two distinct LRR domains and that the gene is only found in Ae. sharonensis and Ae. longissima. Allele mining and phylogenetic analysis indicate multiple events of Yr87/Lr85 gene flow between the two species and presence/absence variation explaining the majority of resistance to wheat leaf rust in both species. The confinement of Yr87/Lr85 to Ae. sharonensis and Ae. longissima and the resistance in wheat against Pst and Pt highlight the potential of these species as valuable sources of disease resistance genes for wheat improvement., Competing Interests: Competing interests The authors declare the following competing interests: D.S., R.A., R.K, E.M., A.M-D., and A.S. are inventors on the US patent application 63/250,413 filed by Ramot (Tel Aviv University) and relating to the use of Yr87/Lr85 for leaf and stripe rust resistance in transgenic wheat. All other authors claim no competing interests., (© 2024. The Author(s).)

The prevalence of colorectal cancer, primarily emerging from polyps, underscores the importance of their early detection in colonoscopy images. Due to the inherent complexity and variability of polyp appearances, the task stands difficult despite recent advances in medical technology. To tackle these challenges, a deep learning model featuring a customized U-Net architecture, AdaptUNet is proposed. Attention mechanisms and skip connections facilitate the effective combination of low-level details and high-level contextual information for accurate polyp segmentation. Further, wavelet transformations are used to extract useful features overlooked in conventional image processing. The model achieves benchmark results with a Dice coefficient of 0.9104, an Intersection over Union (IoU) coefficient of 0.8368, and a Balanced Accuracy of 0.9880 on the CVC-300 dataset. Additionally, it shows exceptional performance on other datasets, including Kvasir-SEG and Etis-LaribDB. Training was performed using the Hyper Kvasir segmented images dataset, further evidencing the model's ability to handle diverse data inputs. The proposed method offers a comprehensive and efficient implementation for polyp detection without compromising performance, thus promising an improved precision and reduction in manual labour for colorectal polyp detection., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Authors.)

Heart diseases are leading to death across the globe. Exact detection and treatment for heart disease in its early stages could potentially save lives. Electrocardiogram (ECG) is one of the tests that take measures of heartbeat fluctuations. The deviation in the signals from the normal sinus rhythm and different variations can help detect various heart conditions. This paper presents a novel approach to cardiac disease detection using an automated Convolutional Neural Network (CNN) system. Leveraging the Scale-Invariant Feature Transform (SIFT) for unique ECG signal image feature extraction, our model classifies signals into three categories: Arrhythmia (ARR), Congestive Heart Failure (CHF), and Normal Sinus Rhythm (NSR). The proposed model has been evaluated using 96 Arrhythmia, 30 CHF, and 36 NSR ECG signals, resulting in a total of 162 images for classification. Our proposed model achieved 99.78% accuracy and an F1 score of 99.78%, which is among one of the highest in the models which were recorded to date with this dataset. Along with the SIFT, we also used HOG and SURF techniques individually and applied the CNN model which achieved 99.45% and 78% accuracy respectively which proved that the SIFT-CNN model is a well-trained and performed model. Notably, our approach introduces significant novelty by combining SIFT with a custom CNN model, enhancing classification accuracy and offering a fresh perspective on cardiac arrhythmia detection. This SIFT-CNN model performed exceptionally well and better than all existing models which are used to classify heart diseases., (© 2024. The Author(s).)

In plants, the contribution of the plasmotype (mitochondria and chloroplast) in controlling the circadian clock plasticity and possible consequences on cytonuclear genetic makeup have yet to be fully elucidated. A genome-wide association study in the wild barley (Hordeum vulgare ssp. spontaneum) B1K collection identified overlap with our previously mapped DRIVERS OF CLOCKS (DOCs) loci in wild-cultivated interspecific population. Moreover, we identified non-random segregation and epistatic interactions between nuclear DOCs loci and the chloroplastic RpoC1 gene, indicating an adaptive value for specific cytonuclear gene combinations. Furthermore, we show that DOC1.1, which harbours the candidate SIGMA FACTOR-B (SIG-B) gene, is linked with the differential expression of SIG-B and CCA1 genes and contributes to the circadian gating response to heat. High-resolution temporal growth and photosynthesis measurements of B1K also link the DOCs loci to differential growth, Chl content and quantum yield. To validate the involvement of the Plastid encoded polymerase (PEP) complex, we over-expressed the two barley chloroplastic RpoC1 alleles in Arabidopsis and identified significant differential plasticity under elevated temperatures. Finally, enhanced clock plasticity of de novo ENU (N-Ethyl-N-nitrosourea) -induced barley rpoB1 mutant further implicates the PEP complex as a key player in regulating the circadian clock output. Overall, this study highlights the contribution of specific cytonuclear interaction between rpoC1 (PEP gene) and SIG-B with distinct circadian timing regulation under heat, and their pleiotropic effects on growth implicate an adaptive value., (© 2024 The Authors. Physiologia Plantarum published by John Wiley & Sons Ltd on behalf of Scandinavian Plant Physiology Society.)

Abiotic stresses, including drought, salinity, cold, heat, and heavy metals, extensively reducing global agricultural production. Traditional breeding approaches and transgenic technology have been widely used to mitigate the risks of these environmental stresses. The discovery of engineered nucleases as genetic scissors to carry out precise manipulation in crop stress-responsive genes and associated molecular network has paved the way for sustainable management of abiotic stress conditions. In this context, the clustered regularly interspaced short palindromic repeat-Cas (CRISPR/Cas)-based gene-editing tool has revolutionized due to its simplicity, accessibility, adaptability, flexibility, and wide applicability. This system has great potential to build up crop varieties with enhanced tolerance against abiotic stresses. In this review, we summarize the latest findings on understanding the mechanism of abiotic stress response in plants and the application of CRISPR/Cas-mediated gene-editing system towards enhanced tolerance to a multitude of stresses including drought, salinity, cold, heat, and heavy metals. We provide mechanistic insights on the CRISPR/Cas9-based genome editing technology. We also discuss applications of evolving genome editing techniques such as prime editing and base editing, mutant library production, transgene free and multiplexing to rapidly deliver modern crop cultivars adapted to abiotic stress conditions., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Kumar, Prusty, Pandey, Singh, Bohra, Guo and Varshney.)

The Coronavirus disease 2019 (COVID19) pandemic has led to a dramatic loss of human life worldwide and caused a tremendous challenge to public health. Immediate detection and diagnosis of COVID19 have lifesaving importance for both patients and doctors. The availability of COVID19 tests increased significantly in many countries, thereby provisioning a limited availability of laboratory test kits Additionally, the Reverse Transcription-Polymerase Chain Reaction (RT-PCR) test for the diagnosis of COVID 19 is costly and time-consuming. X-ray imaging is widely used for the diagnosis of COVID19. The detection of COVID19 based on the manual investigation of X-ray images is a tedious process. Therefore, computer-aided diagnosis (CAD) systems are needed for the automated detection of COVID19 disease. This paper proposes a novel approach for the automated detection of COVID19 using chest X-ray images. The Fixed Boundary-based Two-Dimensional Empirical Wavelet Transform (FB2DEWT) is used to extract modes from the X-ray images. In our study, a single X-ray image is decomposed into seven modes. The evaluated modes are used as input to the multiscale deep Convolutional Neural Network (CNN) to classify X-ray images into no-finding, pneumonia, and COVID19 classes. The proposed deep learning model is evaluated using the X-ray images from two different publicly available databases, where database A consists of 1225 images and database B consists of 9000 images. The results show that the proposed approach has obtained a maximum accuracy of 96% and 100% for the multiclass and binary classification schemes using X-ray images from dataset A with 5-fold cross-validation (CV) strategy. For dataset B, the accuracy values of 97.17% and 96.06% are achieved using multiscale deep CNN for multiclass and binary classification schemes with 5-fold CV. The proposed multiscale deep learning model has demonstrated a higher classification performance than the existing approaches for detecting COVID19 using X-ray images., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2022 Elsevier B.V. All rights reserved.)