Whole genome resequencing unveils low-temperature stress tolerance specific genomic variations in jute (Corchorus sp.).

Jute (Corchorus sp.), a commercially important and eco-friendly crop, is widely cultivated in Bangladesh, India, and China. Some varieties of this tropical plant such as the Corchorus olitorius. Variety accession no. 2015 (acc. 2015) has been found to be low-temperature tolerant. The current study was designed to explore the genome-wide variations present in the tolerant plant acc. 2015 in comparison to the sensitive farmer popular variety Corchorus olitorius var. O9897 using the whole genome resequencing technique. Among different variations, intergenic Single Nucleotide Polymorphism (SNPs) and Insertion-Deletion (InDels) were found in the highest percentage whereas approximately 3% SNPs and 2% InDels were found in exonic regions in both plants. Gene enrichment analysis indicated the presence of acc. 2015 specific SNPs in the genes encoding peroxidase, ER lumen protein retaining receptor, and hexosyltransferase involved in stress response (GO:0006950) which were not present in sensitive variety O9897. Besides, distinctive copy number variation regions (CNVRs) comprising 120 gene loci were found in acc. 2015 with a gain of function from multiple copy numbers but absent in O9897. Gene ontology analysis revealed these gene loci to possess different receptors like kinases, helicases, phosphatases, transcription factors especially Myb transcription factors, regulatory proteins containing different binding domains, annexin, laccase, acyl carrier protein, potassium transporter, and vesicular transporter proteins that are responsible for low temperature induced adaptation pathways in plants. This work of identifying genomic variations linked to cold stress tolerance traits will help to develop successful markers that will pave the way to develop genetically modified cold-resistant jute lines for year-round cultivation to meet the demand for a sustainable fiber crop economy.

ECG waveform generation from radar signals: A deep learning perspective.

Cardiovascular diagnostics relies heavily on the ECG (ECG), which reveals significant information about heart rhythm and function. Despite their significance, traditional ECG measures employing electrodes have limitations. As a result of extended electrode attachments, patients may experience skin irritation or pain, and motion artifacts may interfere with signal accuracy. Additionally, ECG monitoring usually requires highly trained professionals and specialized equipment, which increases the treatment's complexity and cost. In critical care scenarios, such as continuous monitoring of hospitalized patients, wearable sensors for collecting ECG data may be difficult to use. Although there are issues with ECG, it remains a valuable tool for diagnosing and monitoring cardiac disorders due to its non-invasive nature and the detailed information it provides about the heart. The goal of this study is to present an innovative method for generating continuous ECG waveforms from non-contact radar data by using Deep Learning. The method can eliminate the need for invasive or wearable biosensors and expensive equipment to collect ECGs. In this paper, we propose the MultiResLinkNet, a one-dimensional convolutional neural network (1D CNN) model for generating ECG signals from radar waveforms. With the help of a publicly accessible radar benchmark dataset, an end-to-end DL architecture is trained and assessed. There are six ports of raw radar data in this dataset, along with ground truth physiological signals collected from 30 participants in five distinct scenarios: Resting, Valsalva, Apnea, Tilt-up, and Tilt-down. By using strong temporal and spectral measurements, we assessed our proposed framework's ability to convert ECG data from Radar signals in three distinct scenarios, namely Resting, Valsalva, and Apnea (RVA). ECG segmentation performed better by MultiResLinkNet than by state-of-the-art networks in both combined and individual cases. As a result of the simulations, the resting, valsalva, and RVA scenarios showed the highest average temporal values, respectively: 66.09523 ± 19.33, 60.13625 ± 21.92, and 61.86265 ± 21.37. In addition, it exhibited the highest spectral correlation values (82.4388 ± 18.42 (Resting), 77.05186 ± 23.26 (Valsalva), 74.65785 ± 23.17 (Apnea), and 79.96201 ± 20.82 (RVA)), along with minimal temporal and spectral errors in almost every case. The qualitative evaluation revealed strong similarities between generated and actual ECG waveforms. As a result of our method of forecasting ECG patterns from remote radar data, we can monitor high-risk patients, especially those undergoing surgery.

Accuracy of Al Algorithms in Diagnosing Periodontal Disease Using Intraoral Images.

Background: Periodontal disease, characterized by inflammation and damage to tooth-supporting structures, poses a prevalent oral health concern. Early detection is crucial for effective management. Materials and Methods: This study comprised of 60 patients with varying degrees of periodontal disease. Intraoral images were captured using digital cameras, and AI algorithms were trained to analyze these images for signs of periodontal disease. Clinical diagnoses, conducted by experienced periodontal specialists, were used as the reference standard. Results: The AI algorithms achieved an overall accuracy of 87% in diagnosing periodontal disease. Sensitivity was 90%, indicating the AI's ability to correctly identify 90% of true cases, while specificity stood at 84%, demonstrating its capability to accurately classify 84% of non-diseased cases. In comparison, clinical diagnosis yielded an overall accuracy of 86%. Statistical analysis showed no significant difference between AI-based diagnosis and clinical examination (P > 0.05). Conclusion: This study underscores the promising potential of AI algorithms in diagnosing periodontal disease through intraoral image analysis.