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This article explores the roles of agrarian working class women in agrarian struggles in postcolonial Bihar, India. The article argues that the working class women played critical roles in mobilizing rural masses and providing a gender dimension of the struggles. They actively participated in the struggles, applying innovative ideas and techniques to resist their socio-economic exploitations committed by upper and middle caste landlords and rich peasants in rural Bihar. They took their decisions collectively while resisting the exploitation. The collective leadership and participation of the women in the struggles was a major noticeable feature in terms of women's roles; nonetheless, in the 1980s, women's grassroots leadership emerged at local levels; thus, the article also explores some women leaders who were locally famous and played a critical role in mobilizing women and in confronting exploitative systems. Additionally, the article studies various women's organizations and justice delivery mechanisms that were active in the rural areas of the state and resisted women's exploitation, gender-based violence, and police repression. In the 1990s, Dalit women began arming themselves to fight against the violence perpetrated by the caste-based private militias and oppressors. Finally, the article deals with the factors responsible for the emerging militancy of Dalit women.

A comprehensive comparative study was conducted on synthesized (CS) and commercially procured (CP) cerium oxide (CeO₂) samples, and evaluating their computational, structural, microstructural, biocompatibility, and electrical properties. First-principles computational studies revealed that CS exhibited greater volume optimization than CP, although both samples demonstrated a band gap of 2.4–2.5 eV, consistent with the semiconducting nature of CeO₂. The density of states analysis indicated a strong hybridization between Ce-4f and O-2p orbitals, with CS, displaying enhanced electronic density near the Fermi level. X-ray diffraction studies followed by Rietveld refinement confirmed the fluorite structure. Microstructural analysis showed dense, agglomerated morphologies in both samples. However, CS exhibited a higher oxygen content than CP, implying variation in defect concentrations. FTIR confirmed phase purity with characteristic Ce–O vibrations at 435 and 1631 cm¹, while Raman spectroscopy supported this by revealing the F₂g mode (~ 465 cm¹) typical of fluorite-structured CeO₂. Electrical impedance spectroscopy revealed higher ionic conductivity in CS, with a lower grain boundary blocking factor (αgb = 0.42) compared to CP (αgb = 0.62), likely due to differences in defect density and microstructure. Biocompatibility tests showed that CeO₂-300 (CS) had the highest inhibitory efficacy (IC₅₀ ≈ 65.94 µg/ml), followed by CeO₂-800 (≈ 74.1 µg/ml) and CeO₂-Pure (CP) (≈ 86.88 µg/ml), indicating the influence of synthesis on biological response. These results highlight the critical impact of synthesis methods on the biocompatibility and electrical performance of CeO₂ materials useful as solid electrolyte in IT-SOFCs application.

Abiotic stresses have become a major challenge in recent years due to their pervasive nature and shocking impacts on plant growth, development, and quality. MicroRNAs (miRNAs) play a significant role in plant response to different abiotic stresses. Thus, identification of specific abiotic stress–responsive miRNAs holds immense importance in crop breeding programmes to develop cultivars resistant to abiotic stresses. In this study, we developed a machine learning–based computational model for prediction of miRNAs associated with four specific abiotic stresses such as cold, drought, heat and salt. The pseudo K-tuple nucleotide compositional features of Kmer size 1 to 5 were used to represent miRNAs in numeric form. Feature selection strategy was employed to select important features. With the selected feature sets, support vector machine (SVM) achieved the highest cross-validation accuracy in all four abiotic stress conditions. The highest cross-validated prediction accuracies in terms of area under precision-recall curve were found to be 90.15, 90.09, 87.71, and 89.25% for cold, drought, heat and salt respectively. Overall prediction accuracies for the independent dataset were respectively observed 84.57, 80.62, 80.38 and 82.78%, for the abiotic stresses. The SVM was also seen to outperform different deep learning models for prediction of abiotic stress–responsive miRNAs. To implement our method with ease, an online prediction server “ASmiR” has been established at https://iasri-sg.icar.gov.in/asmir/ . The proposed computational model and the developed prediction tool are believed to supplement the existing effort for identification of specific abiotic stress–responsive miRNAs in plants.

The microbe-mediated conversion of nitrate (NO3−) to ammonium (NH4+) in the nitrogen cycle has strong implications for soil health and crop productivity. The role of prokaryotes, eukaryotes and their phylogeny, physiology, and genetic regulations are essential for understanding the ecological significance of this empirical process. Several prokaryotes (bacteria and archaea), and a few eukaryotes (fungi and algae) are reported as NO3− reducers under certain conditions. This process involves enzymatic reactions which has been catalysed by nitrate reductases, nitrite reductases, and NH4+-assimilating enzymes. Earlier reports emphasised that single-cell prokaryotic or eukaryotic organisms are responsible for this process, which portrayed a prominent gap. Therefore, this study revisits the similarities and uniqueness of mechanism behind NO3− -reduction to NH4+ in both prokaryotes and eukaryotes. Moreover, phylogenetic, physiological, and genetic regulation also shed light on the evolutionary connections between two systems which could help us to better explain the NO3−-reduction mechanisms over time. Reports also revealed that certain transcription factors like NtrC/NtrB and Nit2 have shown a major role in coordinating the expression of NO3− assimilation genes in response to NO3− availability. Overall, this review provides a comprehensive information about the complex fermentative and respiratory dissimilatory nitrate reduction to ammonium (DNRA) processes. Uncovering the complexity of this process across various organisms may further give insight into sustainable nitrogen management practices and might contribute to addressing global environmental challenges.HighlightsDNRA is one of the potent beneficial pathways to maintain nitrogen pool in the system.Nitrate reduction is governed by both prokaryotes and eukaryotes.Evolutionary linkage between these organisms is also established for NO3- reduction to ammonium.F-DNRA needs energy through substrate-level phosphorylation, whereas R-DNRA through oxidative phosphorylation.Post-translational and epigenetic modifications are the major factors influencing NO3- reduction.

In the present study sodium carbonate modified Bambusa tulda was utilised for the removal of crystal violet dye from aqueous solution. The functional group characterization and the surface morphology was done by Fourier Transform Infrared Spectroscopy (FTIR) and Scanning Electron Microscope (SEM). It confirms the hydroxyl groups and carboxyl group present on the surface of modified Bambusa tulda. The optimum condition for the removal of crystal violet was taken place at pH 7, 200 rpm, dose at 10gm/l, initial concentration 50 mg/l, at equilibrium time 60 minutes and 298 K temperature with maximum adsorption capacity of 20.84 mg/gm. The adsorption of crystal violet by modified Bambusa tulda best fits in Langmuir isotherm model with R 2 value 0.924 and Pseudo 2 nd order rate equation model with R 2 value of 0.999. Other parameters like isosteric heat analysis, thermodynamics profile and activation energy were investigated. Thus, modified Bambusa tulda can be an efficient and economically used as an alternative for activated carbon for the removal of crystal violet from waste water.

Smartphone-based gait recognition has been considered a unique and promising technique for biometric-based identification. It is integrated with multiple sensors to collect inertial data while a person walks. However, captured data may be affected by several covariate factors due to variations of gait sequences such as holding loads, wearing types, shoe types, etc. Recent gait recognition approaches either work on global or local features, causing failure to handle these covariate-based features. To address these issues, a novel weighted multi-scale CNN (WMsCNN) architecture is designed to extract local to global features for boosting recognition accuracy. Specifically, a weight update sub-network (Ws) is proposed to increase or reduce the weights of features concerning their contribution to the final classification task. Thus, the sensitivity of these features toward the covariate factors decreases using the weight updated technique. Later, these features are fed to a fusion module used to produce global features for the overall classification. Extensive experiments have been conducted on four different benchmark datasets, and the demonstrated results of the proposed model are superior to other state-of-the-art deep learning approaches.

Over years of intensive cultivation and imbalanced fertilizer use, the soils of the Indian subcontinent have become deficient in several nutrients and are impoverished in organic matter. Recently, this region has started emphasizing a shift from inorganic to organic farming to manage soil health. However, owing to the steadily increasing demands for food by the overgrowing populations of this region, a complete shift to an organic farming system is not possible. The rice–wheat cropping system (RWCS) is in crisis because of falling or static yields. The nations of this region have already recognized this problem and have modified farming systems toward integrated nutrient management (INM) practices. The INM concept aims to design farming systems to ensure sustainability by improving soil health, while securing food for the population by improving crop productivity. Therefore, this paper was synthesized to quantify the impact and role of INM in improving crop productivity and sustainability of the RWCS in the context of the Indian subcontinent through meta-analysis using 338 paired data during the period of 1989–2016. The meta-analysis of the whole data for rice and wheat showed a positive increase in the grain yield of both crops with the use of INM over inorganic fertilizers only (IORA), organic fertilizers only (ORA), and control (no fertilizers; CO) treatments. The increase in grain yield was significant at p < 0.05 for rice in INM over ORA and CO treatments. For wheat, the increase in grain yield was significant at p < 0.05 in INM over IORA, ORA, and CO treatments. The yield differences in the INM treatment over IORA were 0.05 and 0.13 Mg ha−1, respectively, in rice and wheat crops. The percent yield increases in INM treatment over IORA, ORA, and CO treatments were 2.52, 29.2, and 90.9, respectively, in loamy soil and 0.60, 24.9, and 93.7, respectively, in clayey soil. The net returns increased by 121% (INM vs. CO) in rice, and 9.34% (INM vs. IORA) and 127% (INM vs. CO) in wheat crop. Use of integrated nutrient management had a positive effect on soil properties as compared to other nutrient management options. Overall, the yield gain and maintenance of soil health due to INM practices over other nutrient management practices in RWCS can be a viable nutrient management option in the Indian subcontinent.

Background Yellow or stripe rust, caused by the fungus Puccinia striiformis  f. sp. tritici  ( Pst ) is an important disease of wheat that threatens wheat production. Since developing resistant cultivars offers a viable solution for disease management, it is essential to understand the genetic basis of stripe rust resistance. In recent years, meta-QTL analysis of identified QTLs has gained popularity as a way to dissect the genetic architecture underpinning quantitative traits, including disease resistance. Results Systematic meta-QTL analysis involving 505 QTLs from 101 linkage-based interval mapping studies was conducted for stripe rust resistance in wheat. For this purpose, publicly available high-quality genetic maps were used to create a consensus linkage map involving 138,574 markers. This map was used to project the QTLs and conduct meta-QTL analysis. A total of 67 important meta-QTLs (MQTLs) were identified which were refined to 29 high-confidence MQTLs. The confidence interval (CI) of MQTLs ranged from 0 to 11.68 cM with a mean of 1.97 cM. The mean physical CI of MQTLs was 24.01 Mb, ranging from 0.0749 to 216.23 Mb per MQTL. As many as 44 MQTLs colocalized with marker–trait associations or SNP peaks associated with stripe rust resistance in wheat. Some MQTLs also included the following major genes- Yr5 , Yr7 , Yr16 , Yr26 , Yr30 , Yr43 , Yr44 , Yr64 , YrCH52 , and YrH52 . Candidate gene mining in high-confidence MQTLs identified 1,562 gene models. Examining these gene models for differential expressions yielded 123 differentially expressed genes, including the 59 most promising CGs. We also studied how these genes were expressed in wheat tissues at different phases of development. Conclusion The most promising MQTLs identified in this study may facilitate marker-assisted breeding for stripe rust resistance in wheat. Information on markers flanking the MQTLs can be utilized in genomic selection models to increase the prediction accuracy for stripe rust resistance. The candidate genes identified can also be utilized for enhancing the wheat resistance against stripe rust after in vivo confirmation/validation using one or more of the following methods: gene cloning, reverse genetic methods, and omics approaches.

There is a long history of coastal erosion caused by frequent storm surges in the coastal regions of Australia, which imposes great threats to communities and infrastructures alongside the beach. Old Bar Beach, New South Wales, Australia, is one such hotspot famous for its extreme coastal erosion. To apply remedial measures such as beach nourishment effectively and economically, estimating/reconstructing the subsurface hydrogeology over the coastal areas is essential. A geophysical tool such as a ground-penetrating radar (GPR) which works on the principle of reflecting electromagnetic (EM) waves, can be conveniently deployed to delineate the soil and rock profiling, water-table depth, bedrock depth, and the subsurface structural features. Here, DeepLabv3+ architecture based newly developed deep convolutional neural networks (DCNNs) were used to establish an inherent non-linear relationship between the GPR data and the EM wave velocity. The presented DCNNs have a lesser number of layers, a lesser number of trainable (learnable) parameters, a high convergence rate and, at the same time, achieve prediction accuracy comparable to that of well-established DeepLabv3+ networks, having high trainable parameters and a relatively low convergence rate. Here, firstly the DCNNs were trained and validated on small 1D datasets. Each dataset contains a 1D GPR trace and a corresponding EM velocity model. The DCNNs turned out to be quite promising in the 1D case, with training, validation, and testing accuracy of approximately 95%, 94%, and 95%, respectively. Secondly, 1D trained weights were applied to 2D synthetic GPR data for EM velocity prediction, and the accuracy of prediction achieved was approximately 95%. Seeing the excellent performance of the DCNNs in the 2D prediction case using 1D trained weights, a large amount of 1D synthetic datasets (approximately 1.2 million) were generated and gaussian noise was added to it to replicate the real field scenario. Thirdly, topographically corrected GPR data acquired over the Old Bar Beach were inverted using the DCNNs trained on 1.2 million 1D synthetic datasets to obtain the subsurface high-resolution, high-precision EM velocity, and εr distribution information to understand the hydrogeology over the beach. The findings presented in this paper agree well with the previous hydrogeological studies carried out using GPR. Our findings show that DCNNs, along with GPR, can be successfully used in coastal environments for the quick and accurate hydrogeological investigation required for the implementation of coastal erosion mitigation methods such as beach nourishment.

Formation of mature miRNAs and their expression is a highly controlled process. It is very much dependent upon the post-transcriptional regulatory events. Recent findings suggest that several RNA binding proteins beyond Drosha/Dicer are involved in the processing of miRNAs. Deciphering of conditional networks for these RBP-miRNA interactions may help to reason the spatio-temporal nature of miRNAs which can also be used to predict miRNA profiles. In this direction, >25TB of data from different platforms were studied (CLIP-seq/RNA-seq/miRNA-seq) to develop Bayesian causal networks capable of reasoning miRNA biogenesis. The networks ably explained the miRNA formation when tested across a large number of conditions and experimentally validated data. The networks were modeled into an XGBoost machine learning system where expression information of the network components was found capable to quantitatively explain the miRNAs formation levels and their profiles. The models were developed for 1,204 human miRNAs whose accurate expression level could be detected directly from the RNA-seq data alone without any need of doing separate miRNA profiling experiments like miRNA-seq or arrays. A first of its kind, miRbiom performed consistently well with high average accuracy (91%) when tested across a large number of experimentally established data from several conditions. It has been implemented as an interactive open access web-server where besides finding the profiles of miRNAs, their downstream functional analysis can also be done. miRbiom will help to get an accurate prediction of human miRNAs profiles in the absence of profiling experiments and will be an asset for regulatory research areas. The study also shows the importance of having RBP interaction information in better understanding the miRNAs and their functional projectiles where it also lays the foundation of such studies and software in future.