Pavan Kumar

Biomolecular condensates with multiphasic architectures organize specific biomolecular processes in different compartments and dynamically reconfigure their structure to regulate their biological functions. Here, we employ multiphase coacervates as model condensates to illustrate pH-responsive dynamic reconf

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Liquid-liquid phase separation and lipid membranes both contribute to cellular compartmentalization. Here, the authors investigate interactions between coacervates and lipid membranes of varying order using giant unilamellar vesicles, and show that selective wetting of distinct lipid domains depends on coacervate composition.

Emmer wheat (Triticum turgidum subsp. dicoccum), an allotetraploid species of wheat, is a rich source of nutritional as well as biotic and abiotic stress tolerance traits. It is an important genetic resource for improving grain protein content in durum and bread wheat, yet its genetic basis for protein accumulation and yield-related traits are less studied. In this study, a diverse panel of 150 emmer wheat germplasm accessions conserved in national genebank of India was evaluated for grain protein content (GPC) and thousand grain weight (TGW) across four environments (New Delhi, Pune, Karnal, and Ludhiana) during winter season 2021–2022 using an alpha lattice design. Genotypes were highly significant for GPC and TGW and substantial phenotypic variation (12.16–17.76% for GPC and 25.98 to 38.64 g for TGW) was observed based on pooled data. GPC and TGW were negatively correlated, with principal component analysis revealing opposing contributions of these traits to total phenotypic variation. We identified superior donors for GPC and TGW based on multi-environmental evaluation. Accessions EC578064, EC299074, EC577402, IC534016, and IC32513, exhibited consistently high GPC with moderate TGW across environments, while IC138455 and IC32513 were stable genotypes for GPC. Genome-wide association analysis using 6800 polymorphic SNP markers based on the 35 K Axiom Wheat Breeders’ array identified 21 significant marker–trait associations (MTAs) across environments, out of these eight markers were located on chromosome 5B. Nine stable and novel MTAs were identified, which consistently expressed across locations, highlighting genomic regions with less genotype × environment interaction. These stable MTAs were linked to genes involved in nitrate transport, stress tolerance, grain development, and yield regulation, providing biologically meaningful targets for protein improvement in emmer wheat. Overall, this study resulted in the identification of promising donor genotypes for GPC and TGW, and linked markers and candidate genes for possible deployment into breeding program for the development of high yielding and protein rich wheat cultivars.

Deep learning has demonstrated high efficiency in histopathological image analysis, particularly in lung cancer classification. However, the stability of these models with image corruption and cross-dataset validation remains an important practical concern. In this study, we explored the potential of adding spectral information derived from the discrete wavelet transform (DWT) and spatial convolutional representations to enhance the robustness of multi-class lung cancer classification between Normal, Adenocarcinoma and Squamous cell carcinoma. The lightweight ResNet18 backbone was used to obtain spatial features, and spectral descriptors were obtained through wavelet sub-bands and integrated through early feature-level fusion. The models were trained and evaluated using the LC25000 dataset. Subsequently, it was tested under controlled perturbations, such as Gaussian noise and Gaussian blur. Three random seeds were used to assess performance variability, and paired t-tests were conducted as an indicative statistical measure of the results. Under clean conditions, the spatial and hybrid models were nearly saturated, and there was no significant difference between them (spatial: 99.85 ± 0.26; hybrid: 99.72 ± 0.22; p = 0.1217). The hybrid model exhibited higher robustness when Gaussian noise (σ = 0.05) was added, which resulted in 84.89% ± 4.52% accuracy versus 74.99% ± 7.20% of the spatial baseline (p = 0.0443) with an observed effect size (Cohen’s d = 2.64), noting that these estimates are based on a limited number of runs and should be interpreted with caution. The same behavior was observed in Gaussian blur perturbations, where the hybrid representation was slightly more stable. We also investigated a simplified adaptive gating mechanism process and found that the learned gate parameter also tends to converge towards spatial feature dominance with a model trained with clean data. Finally, cross-dataset validation with LungHist700 showed a slight increase in the balanced accuracy of the hybrid model (0.5158) over the spatial baseline (0.4722). These results indicate that spectral and spatial features can be used to enhance robustness to image corruption and still yield high classification accuracy, indicating that spectral–spatial representations can improve robustness under controlled perturbations, whereas their impact on cross-dataset generalization remains limited. The results further indicate that robustness improvements are strongly influenced by training strategies, such as noise augmentation, whereas the contribution of fusion is comparatively moderate.

Lung nodule detectionis necessary for lung cancer treatment, which is crucial for treating the patients. However, the current dataset comprises only a limited amount of lung Computed Tomography (CT) images, with a significant imbalance between non-nodule and nodule samples. This disparity reduces the performance of neural networks and makes training more difficult. Early detection of lung tumor is crucial for identifying patients with ahigh chance of effective treatment, and this relies heavily on the preciserecognition of malignant lung nodules in CT scans. Recently, Deep Neural Network (DNN)techniques have been successfully employed to address various computer vision challenges, demonstrating their potential in this domain. Yet, low training values present significant challenges in the diagnosis of malignant nodules. The size and shape of a lump are crucial factors in determining malignancy in lung cancer. Therefore, it is important to address limitations of conventional techniques through the application of deep learning strategies. The developed lung nodule classification framework contains three main stages: image collection, segmentation, and classification. Initially, a substantial number of CT images used for validation are gathered from publicly available sources. These images are processed through a segmentation module, where effective segmentation is performed using a 3D Trans-DenseUnet++ (3D-TDUnet++) model. This segmentation process segregates the lung nodules from nearby tissues and also ignores inappropriate background structures, which helps the suggested approachfocus only on the region of interest. Also, it enhances the feature extraction process and reduces noise from images, which strengthens the classification accuracy and overall reliability of the developed method. After segmentation, the attained segmented images is further given into the classification phase by using an Adaptive DenseNet combined with a Long Short-Term Memory (LSTM) layer (ADNet-LSTM).This synergy of the classification-based deep learning model empowers an automated differentiation between malignant and benign lung nodules, which yields an accurate and efficient clinical decision-making process. It efficiently reduces the manual effort, improves the early detection process and also enhances the detection consistency. Additionally, the parameters of an ADNet-LSTM are optimized with the help of Intensified Fitness-based Red-Tailed Hawk Algorithm (IF-RTHA). Furthermore, some experimental validations are performed over the developed models with several performance metrics to ensure model’s reliability. The accuracy of the IF-RTH-ADNet-LSTM model is 94.98% higher than the existing works, such as RAN, Densenet, LSTM, and ADNet-DenseNet, as 90.21%, 92.38%, 91.68%, and 92.87% using the lung nodule dataset. Thus, it is revealed that the offered lung nodule classification framework performed well for evaluating the malignancy risk of lung nodules found on CT images and also it has the efficiency to provide better decisions for clinicians.

Al2195 is a third-generation Al-Li alloy that exhibits an enhanced strength/weight ratio, making it an attractive material for weight-critical applications. The objective of the present investigation is to study the mechanical, wear, and corrosion behavior of magnesium oxide (MgO) nanoparticle-reinforced Al2195 alloy composites. MgO-filled metal matrix composites (MMCs) are manufactured via stir casting process with different compositions of filler. The experiments were conducted based on Taguchi’s L9 orthogonal array design. The process parameters such as the reinforcement content (0.5, 1.5, and 2.5 wt.% of MgO), stirring temperature (620, 700, and 830 °C), stirring time (5, 10, and 20 min), and stirring speed (220 RPM, 420 RPM, and 650 RPM) were considered for optimization. Taguchi design and grey relational analysis (GRA) are employed for multi-response optimization to simultaneously optimize conflicting goals such as maximizing mechanical properties and minimizing wear rate and corrosion. A confirmation test is performed using optimum parameters obtained from the GRA in order to verify the improvement in properties. Finally, the SEM analysis is carried out to study the wear mechanism. The results show that the MgO content has significant influence on the mechanical, wear, and corrosion behavior of composite materials. The improved mechanical, wear, and corrosion properties are achieved for the composite materials reinforced with 1.5% MgO filler. The maximum tensile strength of ~ 224 MPa, impact energy of 94 J, and hardness of ~ 94 Hv were found in single-objective optimization using Taguchi method. The multi-objective performance is successfully converted into a single-output GRG by using GRA method, and the maximum value of GRG is observed at an optimum parameter setting of 1.5 wt.% MgO content, stirring speed of 420 RPM, stirring time of 20 min, and stirring temperature of 620 ℃.

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Breast cancer is a condition in which abnormal breast cells grow and divide uncontrollably, resulting in the development of cancerous tissue.