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Abstract Weeds are attractive models for basic and applied research due to their impacts on agricultural systems and capacity to swiftly adapt in response to anthropogenic selection pressures. Currently, a lack of genomic information precludes research to elucidate the genetic basis of rapid adaptation for important traits like herbicide resistance and stress tolerance and the effect of evolutionary mechanisms on wild populations. The International Weed Genomics Consortium is a collaborative group of scientists focused on developing genomic resources to impact research into sustainable, effective weed control methods and to provide insights about stress tolerance and adaptation to assist crop breeding.

Rumor data classification in social media seems essential research due to its dependency on digital communications, and this rumor data makes social media unstable. In Natural Language Processing, word embedding, feature extraction, and classification techniques are challenging in rumor detection research. In this research, two phases of feature extraction techniques are involved for performing word embedding and deep feature extraction. In the first phase, Bidirectional Encoder Representations from Transformers with the Optimized Convolutional Neural Network (BERT-OPCNN) techniques are integrated, and in the second phase, the fastText with information gain of an ant colony optimization technique (FIAC) is proposed. Finally, the vectors formed using BERT-OPCNN and the FIAC embedding model are classified using a customized Bi-LSTM. The experiment is computed and compared with existing techniques for balanced and unbalanced datasets. The results evaluation shows that our proposed FIAC embedding with BERT-OPCNN outperforms all other existing techniques using the customized Bi-LSTM classifier.

Coronary Artery Disease (CAD) is a key factor in several serious heart conditions, such as ischemic heart disease, myocardial infarction, and heart failure. Detecting and treating CAD early is crucial for preventing further progression of the disease. Computer-aided techniques are needed to automate the characterization of CAD conditions. In this paper, a novel approach for the automated identification of CAD utilizing improved energy estimation of electrocardiogram (ECG) signals is developed. Due to the multi-component nature of ECG and the nonlinear characteristics of energy operators, estimating their instantaneous energy may lead to cross-component interactions complicating the interpretation. To overcome this issue, variational mode decomposition with a data-driven and adaptively initialized number of modes is suggested to estimate the accurate energy of ECG beat. In this process, the instantaneous energy of each decomposed component is computed for enhanced energy estimation of each ECG beat. Multiple statistical features calculated from the localized region of ECG beat could effectively classify CAD-affected ECG beats with more than 99.80 % accuracy with an optimized ensemble classifier and a 10-fold cross validation scheme. Moreover, experimental findings have revealed the effectiveness of the proposed methodology when compared to contemporary approaches employing identical datasets.

Yellow leaf disease (YLD) is one of the important viral diseases in sugarcane. It is caused by Sugarcane yellow leaf virus (ScYLV), a positive sense single stranded RNA virus primarily transmits through infected setts and secondarily transmits through aphid Melanaphis sacchari. Earlier, it was identified as a minor disease in India, but in recent years it attained the epidemic status with the disease incidences from 30 to 50 % in plant crop and more than 70% in ratoon crops which significantly affected the crop productivity. At present, it is being effectively managed through tissue culture derived virus free planting materials. However, after transplantation of TC plants in the field, it succumbed to the virus and made difficulties to sustain the high yield and high sugar varieties in field for long time. Hence, the present study was taken up to identify the yellow leaf (YL) resistant lines from the large pool of parental and commercial hybrids. During the year 2015-21, YLD incidence, ScYLV resistant (apparent) and susceptible lines were identified based on natural screening in parental population maintained at National Hybridization Garden, ICAR-SBI, Coimbatore. The highest YLD incidence of 24.20% was observed with least 75.63% resistance during the year 2021 followed by 23.70% incidence with 76.33% resistance in 2019. In contrast, the least YLD incidence of 7.90% with high resistance of 92.09% was observed during the year 2017. During last seven years, YLD incidence was observed in the ranges of 7.90% to 24.20% in the parental population. Based on seven years of field phenotyping, 105 apparently resistant lines were identified in the study which could be used for YL resistant breeding programme in the future.

AbstractPlant height (PHT) in maize (Zea mayse.g.

Global climate change has increased the number of severe flooding events that affect agriculture, including rice production in the U.S. and internationally. Heavy rainfall can cause rice plants to be completely submerged, which can significantly affect grain yield or completely destroy the plants. Recently, a major effect submergence tolerance QTL during the vegetative stage, qSub8.1, which originated from Ciherang-Sub1, was identified in a mapping population derived from a cross between Ciherang-Sub1 and IR10F365. Ciherang-Sub1 was, in turn, derived from a cross between Ciherang and IR64-Sub1. Here, we characterize the qSub8.1 region by analyzing the sequence information of Ciherang-Sub1 and its two parents (Ciherang and IR64-Sub1) and compare the whole genome profile of these varieties with the Nipponbare and Minghui 63 (MH63) reference genomes. The three rice varieties were sequenced with 150 bp pair-end whole-genome shotgun sequencing (Illumina HiSeq4000), followed by performing the Trimmomatic-SOAPdenovo2-MUMmer3 pipeline for genome assembly, resulting in approximate genome sizes of 354.4, 343.7, and 344.7 Mb, with N50 values of 25.1, 25.4, and 26.1 kb, respectively. The results showed that the Ciherang-Sub1 genome is composed of 59–63% Ciherang, 22–24% of IR64-Sub1, and 15–17% of unknown sources. The genome profile revealed a more detailed genomic composition than previous marker-assisted breeding and showed that the qSub8.1 region is mostly from Ciherang, with some introgressed segments from IR64-Sub1 and currently unknown source(s).

The presence of heavy metal ions in water bodies constitutes a significant environmental hazard. The development of sustainable and cost-effective adsorbent materials for their removal is an urgent priority. In alignment with this critical objective, the present study explores the potential of a novel composite material for water remediation. This composite, fabricated from biochar and magnesium ferrite nanoparticles, targets the removal of hexavalent chromium and divalent nickel. While prior research has explored the application of rice husk as an adsorbent, no investigation, to our knowledge, has examined the potential of magnesium ferrite-rice husk composites for this purpose. Initial screening identified the biochar-magnesium ferrite composite (pre-calcination) as the most effective adsorbent. This composite displayed a superior surface area (151 m²/g) compared to calcined magnesium ferrite (91 m²/g) and achieved exceptional removal efficiencies for both chromium (50 mg/g) and nickel (54 mg/g). Optimal chromium removal occurred at pH 1 with a 110-minute contact time, while nickel favored a pH of 6 and the same contact time. The adsorption process was characterized as physisorption and endothermic. Notably, the composite exhibited efficient regeneration (82% for nickel and 90% for chromium) using simple acid/base solutions. The BJH analysis of pore characteristics indicated an average pore diameter of 1.5365 nm and a total pore volume of 0.17 cm³/g. The research findings demonstrate the composite's effectiveness as a sustainable adsorbent for capturing heavy metal ions from water. [ABSTRACT FROM AUTHOR]

Brain tumors (BT) are abnormal cell growth from the brain or the surrounding cells. It is categorized into 2 major types such as malignant (cancerous) and benign (non-cancerous). Classifying and detecting BTs is critical for knowledge of their mechanisms. Magnetic Resonance Imaging (MRI) is a helpful but time-consuming system, that needs knowledge for manual examination. A new development in Computer-assisted Diagnosis (CAD) and deep learning (DL) allows more reliable BT detection. Typical machine learning (ML) depends on handcrafted features, but DL achieves correct outcomes without such manual extraction. DL methods, particularly convolutional neural networks (CNNs) and recurrent neural networks (RNNs) can exposed to optimum outcomes in the domain of medical image analysis, comprising the classification and recognition of BTs in MRI and CT scans. Thus, the study designs an automated BT Detection and Classification using the Osprey Optimization Algorithm with Deep Learning (BTDC-OOADL) method on MRI Images. The BTDC-OOADL technique deeply investigates the MRI for the identification of BT. In the proposed BTDC-OOADL algorithm, the Wiener filtering (WF) model is applied for the elimination of noise. Besides, the BTDC-OOADL algorithm exploits the MobileNetV2 technique for the procedure of feature extractor. In the meantime, the OOA is utilized for the optimum hyperparameter choice of the MobileNetv2 model. Finally, the graph convolutional network (GCN) model can be deployed for the classification and recognition of BT. The experimental outcome of the BTDC-OOADL methodology can be tested under benchmark dataset. The simulation values infer the betterment of the BTDC-OOADL system with recent approaches. [ABSTRACT FROM AUTHOR]