Search

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.)

Circadian clock rhythms are shown to be intertwined with crop adaptation. To realize the adaptive value of changes in these rhythms under crop domestication and improvement, there is a need to compare the genetics of clock and yield traits. We compared circadian clock rhythmicity based on Chl leaf fluorescence and transcriptomics among wild ancestors, landraces, and breeding lines of barley under optimal and high temperatures. We conducted a genome scan to identify pleiotropic loci regulating the clock and field phenotypes. We also compared the allelic diversity in wild and cultivated barley to test for selective sweeps. We found significant loss of thermal plasticity in circadian rhythms under domestication. However, transcriptome analysis indicated that this loss was only for output genes and that temperature compensation in the core clock machinery was maintained. Drivers of the circadian clock (DOC) loci were identified via genome-wide association study. Notably, these loci also modified growth and reproductive outputs in the field. Diversity analysis indicated selective sweep in these pleiotropic DOC loci. These results indicate a selection against thermal clock plasticity under barley domestication and improvement and highlight the importance of identifying genes underlying for understanding the biochemical basis of crop adaptation to changing environments., (© 2021 The Authors New Phytologist © 2021 New Phytologist Foundation.)

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., (© 2021 The Authors. Published by Elsevier B.V.)

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., 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., (© 2020 Elsevier B.V.)

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., (© 2019 John Wiley & Sons Ltd.)

Key message Development of MAALs and disomic introgression lines derived from the cross between O. sativa and O. rhizomatis to exploit and utilize the valuable traits for rice improvement. The CC genome wild species, Oryza rhizomatis, possesses valuable traits for rice improvement. Unlike other CC genome wild rice, O. rhizomatis is less studied and none of the research has focused on the utilization of this resource in rice breeding. The transfer of novel genes governing the valuable traits from O. rhizomatis is difficult due to high genome incompatibility with O. sativa. Here we report the development of backcross progenies and complete sets of monosomic alien addition lines (MAALs) for the first time from O. rhizomatis in O. sativa line IR31917-45-3-2. Autotetraploid IR31917-45-3-2 (4x = AAAA) was used to generate allotriploid F 1 , and the F 1 plant was backcrossed to IR31917-45-3-2 (2x). Forty-seven BC 1 F 1 and 73 BC 2 F 1 plants were produced with chromosome numbers ranging from 24 to 33 (2x + 9) and 24 to 27 (2x + 3), respectively. A complete set of MAALs were identified by morphological, cytological and marker-based analysis. A total of 116 CC genome-specific InDel markers across the 12 chromosome of rice were used to detect O. rhizomatis chromosome segments in F 1 , BC 1 F 1 , BC 2 F 2 , MAALs and disomic introgression lines (DILs). Expressions of major phenotypic traits inherited from O. rhizomatis were observed in MAAL-derived DILs. Small chromosomal segments of O. rhizomatis for chromosomes 1, 2, 4, 5, 6, 7, 10 and 12 were detected in DILs, and some of the introgression lines showed insect resistance against brown planthopper and green leafhopper. These newly developed MAALs and DILs will be useful for gene mining and more precise faster transfer of favorable genes to improve rice cultivars.

Cultivated rice ( Oryza sativa L.) is very sensitive to salt stress. So far a few rice landraces have been identified as a source of salt tolerance and utilized in rice improvement. These tolerant lines primarily use Na + exclusion mechanism in root which removes Na + from the xylem stream by membrane Na + and K + transporters, and resulted in low Na + accumulation in shoot. Identification of a new donor source conferring high salt tolerance is imperative. Wild relatives of rice having wide genetic diversity are regarded as a potential source for crop improvement. However, they have been less exploited against salt stress. Here, we simultaneously evaluated all 22 wild Oryza species along with the cultivated tolerant lines including Pokkali, Nona Bokra, and FL478, and sensitive check varieties under high salinity (240 mM NaCl). Based on the visual salt injury score, three species ( O . alta, O . latifolia , and O . coarctata ) and four species ( O . rhizomatis, O . eichingeri, O . minuta , and O . grandiglumis ) showed higher and similar level of tolerance compared to the tolerant checks, respectively. All three CCDD genome species exhibited salt tolerance, suggesting that the CCDD genome might possess the common genetic factors for salt tolerance. Physiological and biochemical experiments were conducted using the newly isolated tolerant species together with checks under 180 mM NaCl. Interestingly, all wild species showed high Na + concentration in shoot and low concentration in root unlike the tolerant checks. In addition, the wild-tolerant accessions showed a tendency of a high tissue tolerance in leaf, low malondialdehyde level in shoot, and high retention of chlorophyll in the young leaves. These results suggest that the wild species employ tissue tolerance mechanism to manage salt stress. Gene expression analyses of the key salt tolerance-related genes suggested that high Na + in leaf of wild species might be affected by OsHKT1;4 -mediated Na + exclusion in leaf and the following Na + sequestration in leaf might be occurring independent of tonoplast-localized OsNHX1. The newly isolated wild rice accessions will be valuable materials for both rice improvement to salinity stress and the study of salt tolerance mechanism in plants.

Background & Objectives: Knowledge on prevalence of malaria vector species of a certain area provides important information for implementation of appropriate control strategies. The present study describes a rapid method for screening of major Anopheline vector species and at the same time detection of Plasmodium falciparum sporozoite infection and blood meal preferences/trophic preferences., Methods: The study was carried from February 2012 to March 2013 in three seasons, i.e. rainy, winter and summer in Jhumpura PHC of Keonjhar district, Odisha, India. Processing of mosquitoes was carried out in two different methods, viz. mosquito pool (P1) and mosquito DNA pool (P2). Pool size for both the methods was standardized for DNA isolation and multiplex PCR assay. This PCR based assay was employed to screen the major vector com- position in three different seasons of four different ecotypes of Keonjhar district. Pearson's correlation coefficient was determined for a comparative analysis of the morphological identification with the pool prevalence assay for each ecotype., Results: A pool size of 10 was standardized for DNA isolation as well as PCR. PCR assay revealed that the average pool prevalence for all ecotypes was highest for An. annularis in winter and summer whereas for An. culicifacies it was rainy season. Foothill and plain ecotypes contributed to highest and lowest vectorial abundance respectively. The results of the prevalence of vector species in pool from PCR based assay were found to be highly correlated with that of the results of morphological identification., Interpretation & Conclusion: Screening by pool based PCR assay is relatively rapid as compared to conventional identification and can be employed as an important tool in malaria control programmes.

Background: Anopheles fluviatilis exists as a complex of sibling species S, T, U and V exhibiting distinct variations. Sibling species S is considered as the main vector and anthropogenic whereas T, U and V are zoophagic non-vectors. This study was performed in a forested village of Keonjhar district, Odisha to identify the status of An. fluviatilis sibling species., Methods: Mosquito collections were made from cattle sheds (CS), human dwellings (HD) and mixed dwellings (MD) from June 2012 to May 2013. The proportion of An. fluviatilis collected from different habitats was compared with An. culicifacies. PCR assays were conducted to reveal their sibling species composition, host preference and sporozoite rate., Results: Anopheles fluviatilis was the dominant species followed by An. culicifacies. The relative proportion of collection was high in MD and HD for An. fluviatilis and An. culicifacies respectively. PCR assay confirmed 9.4% S and 75.5% T. Mean collection of sibling species T and S were significantly high in MD and HD. Human blood index (HBI) of 0.88 and 0.61 was confirmed for sibling species S and T respectively with 13% sporozoite rate for S., Conclusions: High density of the sibling T was found in the study site with a shift in resting habitat and blood feeding preference. GenBank submissions: KJ451071.1, KJ451072.1, KJ451073.1, KJ451074.1, KJ451432.1, KJ451433.1, KJ451434.1, KJ451435.1, KJ451428.1, KJ451429.1, KJ451430.1, KJ451431.1., (© The Author 2015. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)