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Charge transfer between molecules lies at the heart of many chemical processes. Here, we focus on the ultrafast electron dynamics associated with the formation of charge-transfer-to-solvent (CTTS) states following X-ray absorption in aqueous solutions of Na+, Mg2+, and Al3+ ions. To explore the formation of such states in the aqueous phase, liquid-jet photoemission spectroscopy is employed. Using the core-hole-clock method, based on Auger–Meitner (AM) decay upon 1s excitation or ionization of the respective ions, upper limits are estimated for the metal-atom electron delocalization times to the neighboring water molecules. These delocalization processes represent the first steps in the formation of hydrated electrons, which are determined to take place on a timescale ranging from several hundred attoseconds (as) below the 1s ionization threshold to only 20 as far above the 1s ionization threshold. The decrease in the delocalization times as a function of the photon energy is continuous. This indicates that the excited electrons remain in the vicinity of the studied ions even above the ionization threshold, i.e., metal-ion electronic resonances associated with the CTTS state manifolds are formed. The three studied isoelectronic ions exhibit quantitative differences in their electron energetics and delocalization times, which are linked to the character of the respective excited states. The authors investigate the X-ray-induced electron dynamics, on the sub-femtosecond timescale, of hydrated Na+, Mg2+, and Al3+ ions providing insights into the ultrafast charge-transfer processes in aqueous environments. [ABSTRACT FROM AUTHOR]

Rural development initiatives like India’s Shyama Prasad Mukherji Rurban Mission (SPMRM) require efficient methods to identify villages with high socio-economic growth potential. Traditional planning methods, reliant on surveys and expert opinions, are becoming outdated due to the abundance of informative data available online. This paper proposes a novel framework, eRurban, that utilizes machine learning to automate village ranking and analysis for rural development in India. eRurban leverages data from 250,000 gram panchayats (village clusters) to group villages with similar development trajectories through clustering techniques. A key innovation is the introduction of the ClusterRank algorithm, a novel ranking method that utilizes gradient descent to train ranking coefficients for improved accuracy and efficiency. The effectiveness of ClusterRank is demonstrated by its high Spearman correlation coefficient (0.89) when compared to village rankings generated by SPMRM reports. This cost-effective framework offers valuable insights and guidance for rural development planning in India. By automating village ranking and analysis, eRurban addresses limitations of traditional methods and offers a data-driven solution for optimizing resource allocation and promoting sustainable growth in rural areas. [ABSTRACT FROM AUTHOR]

Computational methods have been established as cornerstones in optical imaging and holography in recent years. Every year, the dependence of optical imaging and holography on computational methods is increasing significantly to the extent that optical methods and components are being completely and efficiently replaced with computational methods at low cost. This roadmap reviews the current scenario in four major areas namely incoherent digital holography, quantitative phase imaging, imaging through scattering layers, and super-resolution imaging. In addition to registering the perspectives of the modern-day architects of the above research areas, the roadmap also reports some of the latest studies on the topic. Computational codes and pseudocodes are presented for computational methods in a plug-and-play fashion for readers to not only read and understand but also practice the latest algorithms with their data. We believe that this roadmap will be a valuable tool for analyzing the current trends in computational methods to predict and prepare the future of computational methods in optical imaging and holography. [ABSTRACT FROM AUTHOR]

Due to the importance of protein-protein interactions in defence mechanism of living body, attempts were made to investigate its attributes, including, but not limited to, binding affinity, and binding region. Contemporary strategies for binding site prediction largely resort to deep learning techniques but turned out to be low precision models. As laboratory experiments for drug discovery tasks utilize this information, increased false positives devalue the computational methods. This emphasize the need to develop enhanced strategies. DeepBindPPI employs deep learning technique to predict the binding regions of proteins, particularly antigen–antibody interaction sites. The results obtained are applied in a docking environment to confirm their correctness. An integration of graph convolutional network with attention mechanism predicts interacting amino acids with improved precision. The model learns the determining factors in interaction from a general pool of proteins and is then fine-tuned using antigen–antibody data. Comparison of the proposed method with existing techniques shows that the developed model has comparable performance. The use of a separate spatial network clearly improved the precision of the proposed method from 0.4 to 0.5. An attempt to utilize the interface information for docking using the HDOCK server gives promising results, with high-quality structures appearing in the top10 ranks. [ABSTRACT FROM AUTHOR]

Breast cancer is the second leading cancer among women in terms of mortality rate. In recent years, its incidence frequency has been continuously rising across the globe. In this context, the new therapeutic strategies to manage the deadly disease attracts tremendous research focus. However, finding new prognostic predictors to refine the selection of therapy for the various stages of breast cancer is an unattempted issue. Aberrant expression of genes at various stages of cancer progression can be studied to identify specific genes that play a critical role in cancer staging. Moreover, while many schemes for subtype prediction in breast cancer have been explored in the literature, stage-wise classification remains a challenge. These observations motivated the proposed two-phased method: stage-specific gene signature selection and stage classification. In the first phase, meta-analysis of gene expression data is conducted to identify stage-wise biomarkers that were then used in the second phase of cancer classification. From the analysis, 118, 12 and 4 genes respectively in stage I, stage II and stage III are determined as potential biomarkers. Pathway enrichment, gene network and literature analysis validate the significance of the identified genes in breast cancer. In this study, machine learning methods were combined with principal component and posterior probability analysis. Such a scheme offers a unique opportunity to build a meaningful model for predicting breast cancer staging. Among the machine learning models compared, Support Vector Machine (SVM) is found to perform the best for the selected datasets with an accuracy of 92.21% during test data evaluation. Perhaps, biomarker identification performed here for stage-specific cancer treatment would be a meaningful step towards predictive medicine. Significantly, the determination of correct cancer stage using the proposed 134 gene signature set can possibly act as potential target for breast cancer therapeutics. [ABSTRACT FROM AUTHOR]

The advancements in the field of high throughput analysis show abnormal expression of long non-coding RNAs (lncRNAs) in many complex diseases. Accurately identifying the disease association of lncRNA is essential in understanding their role in disease mechanism and subsequent therapy. The contemporary methods for predicting lncRNA-disease association use heterogeneous information learned from different biological sources such as lncRNAs, miRNAs, and diseases. However, learning topological features from diverse network structured data is one of the limiting factors of these methods. To address this challenge, we propose a method for lncRNA-disease association prediction based on Deep Belief Network (DBN), referred to as DBNLDA. In this method, three interaction networks such as lncRNA-miRNA similarity (LMS), disease-miRNA similarity (DMS), and lncRNA-disease association (LDA) network are constructed. A new framework based on the node embedding, DBN, and a neural network regression model is used to learn network and local representation of lncRNA-disease pairs. From the node embedding matrices of LMS, DMS, and LDA networks, lncRNA-disease features are learned by DBN layers. These DBN features are used to predict the association score by an ANN regression model. Compared to several state-of-the-art methods, DBNLDA obtained better AUC (0.96) and AUPR (0.967) under five-fold cross-validation. Case studies on breast, lung, and stomach cancer also affirmed the ability of DBNLDA in predicting potential lncRNAs associated with various diseases. [ABSTRACT FROM AUTHOR]

Conventional drug discovery methods rely primarily on in-vitro experiments with a target molecule and an extensive set of small molecules to choose the suitable ligand. The exploration space for the selected ligand being huge; this approach is highly time-consuming and requires high capital for facilitation. Virtual screening, a computational technique used to reduce this search space and identify lead molecules, can speed up the drug discovery process. This paper proposes a ligand-based virtual screening method using an artificial neural network called self-organizing map (SOM). The proposed work uses two SOMs to predict the active and inactive molecules separately. This SOM based technique can uniquely label a small molecule as active, inactive, and undefined as well. This can reduce the number of false positives in the screening process and improve recall; compared to support vector machine and random forest based models. Additionally, by exploiting the parallelism present in the learning and classification phases of a SOM, a graphics processing unit (GPU) based model yields much better execution time. The proposed GPU-based SOM tool can successfully evaluate a large number of molecules in training and screening phases. The source code of the implementation and related files are available at https://github.com/jayarajpbalakrishnan/2_SOM_SCREEN [ABSTRACT FROM AUTHOR]

Each year, about 7-8 million deaths occur due to cancer around the world. More than half of the cancer-related deaths occur in the less-developed parts of the world. Cancer mortality rate can be reduced with early detection and subsequent treatment of the disease. In this paper, we introduce a microfluidic microscopy-based cost-effective and label-free approach for identification of cancerous cells. We outline a diagnostic framework for the same and detail an instrumentation layout. We have employed classical computer vision techniques such as 2D principal component analysis-based cell type representation followed by support vector machine-based classification. Analogous to criminal face recognition systems implemented with help of surveillance cameras, a signature-based approach for cancerous cell identification using microfluidic microscopy surveillance is demonstrated. Such a platform would facilitate affordable mass screening camps in the developing countries and therefore help decrease cancer mortality rate. [ABSTRACT FROM AUTHOR]

Considering the significance of natural antioxidants to preserve meat, the present study was undertaken to evaluate the efficacy of a deflavored and decolorised extract of rosemary (StabilRose™) for the production and preservation of naturally colored fresh meat. Oxidative rancidity of meat and color degradation of paprika oleoresin were exploited as model systems and compared with butylated hydroxyanisole (BHA). The results showed similar efficacy for 3% carnosic acid extract and BHA, with further enhancement in efficacy with respect to the carnosic acid content. A synergetic antioxidant effect of carnosol on carnosic acid content was also noticed to an extent of 1:1 (w/w) ratio, and further increase in carnosol content showed no improvement in the antioxidant efficacy. Finally, stabilized paprika and optimized rosemary extract containing carnosic acid and carnosol in 1:1 (w/w) ratio was successfully applied to produce naturally colored meat suitable for storage at 4 ± 1 °C. [ABSTRACT FROM AUTHOR]

Background: In-silico methods are an integral part of modern drug discovery paradigm. Virtual screening, an in-silico method, is used to refine data models and reduce the chemical space on which wet lab experiments need to be performed. Virtual screening of a ligand data model requires large scale computations, making it a highly time consuming task. This process can be speeded up by implementing parallelized algorithms on a Graphical Processing Unit (GPU). Results: Random Forest is a robust classification algorithm that can be employed in the virtual screening. A ligand based virtual screening tool (GPURFSCREEN) that uses random forests on GPU systems has been proposed and evaluated in this paper. This tool produces optimized results at a lower execution time for large bioassay data sets. The quality of results produced by our tool on GPU is same as that on a regular serial environment. Conclusion: Considering the magnitude of data to be screened, the parallelized virtual screening has a significantly lower running time at high throughput. The proposed parallel tool outperforms its serial counterpart by successfully screening billions of molecules in training and prediction phases. [ABSTRACT FROM AUTHOR]

The relativistic coupled cluster theory is employed to calculate electric quadrupole (E2) transition probabilities among the doublet states of Mg II which are of interest in astrophysical problems. This is the first time a highly correlated fully ab initio method has been used to compute these quantities for this particular ion. The line strengths and transition probabilities of a number of different transitions are reported and compared with those available in the literature. [ABSTRACT FROM AUTHOR]

The article presents a study which investigates on the effects of hydrogen sulphide on two species of shrimps, the Penaeus indicus and Metapenaeus dobsoni. The hydrogen sulphide toxicity bioassays conducted with different size groups of these shrimps in sea water have shown that the 96-hour lethal concentration has declined with increase in size of the shrimps. The sensitivity to the chemical has also varied between species. The toxicity of the chemical is highly influenced by the potential of hydrogen of the medium. It is found that there are comparabilities between the overall lethal toxicity of different size groups with respect to unionized hydrogen sulphide shrimps.