Scalar invariant transform based deep learning framework for detecting heart failures using ECG signals.

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.

Cytonuclear interactions modulate the plasticity of photosynthetic rhythmicity and growth in wild barley.

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.

Application of CRISPR/Cas9-mediated gene editing for abiotic stress management in crop plants.

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.

Detection of COVID19 from X-ray images using multiscale Deep Convolutional Neural Network.

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.

A novel data augmentation approach for mask detection using deep transfer learning.

At the onset of 2020, the world saw the rise and spread of a global pandemic named COVID-19 which caused numerous deaths and affected millions of people around the world. Due to its highly contagious nature, this disease spread across the world within a short span of time. It forced almost all the nations to implement strict social distancing rules along with use of face masks to reduce the risk of getting infected. While the virus is still on loose, markets and business firms have reopened to keep the economy alive. This calls for modification of existing technological models to cater for the safety of individuals and stop the spread of virus in public places. One such stringent implementation to achieve this safety would be deployment of a mask detection model. The proposed mask detection models can serve as a vital utility in the coming years for ensuring proper enforcement of safety protocols. This research paper explores the use of state of the art YOLOv3 model, a deep transfer learning object detection technique, to develop a mask detection model. Along with the implementation of a standard approach of any object detection algorithm, this paper has proposed the use of a data augmentation approach for mask detection. The proposed model focuses on generating an augmented dataset from the standard dataset with the help of data augmentation done by using image filtering techniques such as grayscale and Gaussian blur. This augmented dataset is used for training the object detection model for mask detection. The mean average precision for the Data augmentation based mask detection model is observed to be 99.8% while training. Finally, a comparison on the model performance is evaluated for the standard and proposed augmented data approach. The experiment conducted showed that the average confidence level for Standard mask detection model was 0.94, 0.93, 0.91 for images of individuals (type A), images with groups of people (type B) and video with the group of people (type C) respectively. The average confidence levels for the Data augmentation based mask detection model for types A, B and C are 0.97, 0.96 0.93 respectively. This paper therefore concludes that the proposed Data augmentation based mask detection model performs better than the Standard mask detection model.

Outlier-SMOTE: A refined oversampling technique for improved detection of COVID-19.

Almost every dataset these days continually faces the predicament of class imbalance. It is difficult to train classifiers on these types of data as they become biased towards a set of classes, hence leading to reduction in classifier performance. This setback is often tackled by the use of various over-sampling or under-sampling algorithms. But, the method which stood out of all the numerous algorithms was the Synthetic Minority Oversampling Technique (SMOTE). SMOTE generates synthetic samples of the minority class by oversampling each data-point by considering linear combinations of existing minority class neighbors. Each minority data sample generates an equal number of synthetic data. As the world is suffering from the plight of COVID-19 pandemic, the authors applied the idea to help boost the classifying performance whilst detecting this deadly virus. This paper presents a modified version of SMOTE known as Outlier-SMOTE wherein each data-point is oversampled with respect to its distance from other data-points. The data-point which is farther than the other data-points is given greater importance and is oversampled more than its counterparts. Outlier-SMOTE reduces the chances of overlapping of minority data samples which often occurs in the traditional SMOTE algorithm. This method is tested on five benchmark datasets and is eventually tested on a COVID-19 dataset. F-measure, Recall and Precision are used as principle metrics to evaluate the performance of the classifier as is the case for any class imbalanced data set. The proposed algorithm performs considerably better than the traditional SMOTE algorithm for the considered datasets.

Thermal plasticity of the circadian clock is under nuclear and cytoplasmic control in wild barley.

Temperature compensation, expressed as the ability to maintain clock characteristics (mainly period) in face of temperature changes, that is, robustness, is considered a key feature of circadian clock systems. In this study, we explore the genetic basis for lack of robustness, that is, plasticity, of circadian clock as reflected by photosynthesis rhythmicity. The clock rhythmicity of a new wild barley reciprocal doubled haploid population was analysed with a high temporal resolution of pulsed amplitude modulation of chlorophyll fluorescence under optimal (22°C) and high (32°C) temperature. This comparison between two environments pointed to the prevalence of clock acceleration under heat. Genotyping by sequencing of doubled haploid lines indicated a rich recombination landscape with minor fixation (less than 8%) for one of the parental alleles. Quantitative genetic analysis included genotype by environment interactions and binary-threshold models. Variation in the circadian rhythm plasticity phenotypes, expressed as change (delta) of period and amplitude under two temperatures, was associated with maternal organelle genome (the plasmotype), as well as with several nuclear loci. This first reported rhythmicity driven by nuclear loci and plasmotype with few identified variants, paves the way for studying impact of cytonuclear variations on clock robustness and on plant adaptation to changing environments.