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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.)