Nucleic acid binding proteins (NABP) include DNA binding proteins (DBP) and RNA binding proteins (RBP), which play an essential role in gene regulation, transcriptional control, RNA processing and different disease pathways. In order to figure out NABP, experiments take more time and money, and it just cannot keep up with a huge amount of protein sequences. Many existing models face issues in understanding complex relationships among protein sequences. They often depend on high-dimensional and redundant features and lack interpretability. To solve these problems, this study introduces Explainable AI to classify multi-class NABP. Initially, data are collected from the UniProtKB dataset. Then, in the pre-processing step, the duplicate and undesirable information is removed from the protein sequence. Next, in the feature extraction stage, the Python ProtLearn library is used to collect features from protein sequences. Moreover, the feature selection stage is utilized to remove redundant and unnecessary characteristics from the protein prediction method. Feature selection is performed by two methods, namely the Improved Orangutan optimization algorithm (IOOA) and Autoencoder (AE), in order to select the best features. Finally, the protein prediction process is carried out with the aid of the proposed optimized Residual Dense assisted Multi-Scale Attention Transformer (Opt-DMA-Trans). In order to enhance the interpretability of models, the Shapley Additive explanations (SHAP) technique is employed for the decision-making process. The investigational results attain an accuracy of 98.95%, precision 98.95%, and F1-score 98.75%, which shows that the proposed model is effective in predicting multiclass NABP.

Predicting an amino acid’s biological activity from its structure, sequence, or other pertinent information is known as protein function prediction. Because it sheds light on the biological processes involving proteins, this discipline is essential for drug development, molecular biology, and bioinformatics. A specific kind of deep learning model called Temporal Convolutional Networks (TCNs) is made for sequence modeling applications where the data’s order is important. These networks address the limitations of recurrent neural networks and extend traditional convolutional neural networks to handle sequential or time-series data effectively. TCNs have gained prominence in sequence-based tasks like protein function prediction due to their ability to process long-range dependencies, a key characteristic of many protein sequences. They achieve this by utilizing dilated and causal convolutions, making them well-suited for such applications. In this study, we introduce a hybrid Temporal Convolutional Network (TCN)-BiGRU model for predicting protein functions. The model obtained Fmax values of 0.436 (BP), 0.579 (CC), and 0.678 (MF) for the yeast dataset, and 0.634 (CC), 0.707 (MF), and 0.520 (BP) for the human dataset.

Purpose Generally, the scientific comparison has been done with the help of the overall impact of scholars. Although it is very easy to compare scholars, but how can we assess the scientific impact of scholars who have different research careers? It is very obvious, the scholars may gain a high impact if they have more research experience or have spent more time (in terms of research career in a year). Then we cannot compare two scholars who have different research careers. Many bibliometrics indicators address the time-span of scholars. In this series, the h-index sequence and EM/EM'-index sequence have been introduced for assessment and comparison of the scientific impact of scholars. The h-index sequence, EM-index sequence, and EM'-index sequence consider the yearly impact of scholars, and comparison is done by the index value along with their component value. The time-series indicators fail to give a comparative analysis between senior and junior scholars if there is a huge difference in both scholars' research careers.Design/methodology/approach We have proposed the cumulative index calculation method to appraise the scientific impact of scholars till that age and tested it with 89 scholars data.Findings The proposed mechanism is implemented and tested on 89 scholars' publication data, providing a clear difference between the scientific impact of two scholars. This also helps in predicting future prominent scholars based on their research impact.Research limitations This study adopts a simplistic approach by assigning equal credit to all authors, regardless of their individual contributions. Further, the potential impact of career breaks on research productivity is not taken into account. These assumptions may limit the generalizability of our findingsPractical implications The proposed method can be used by respected institutions to compare their scholars impact. Funding agencies can also use it for similar purposes.Originality/value This research adds to the existing literature by introducing a novel methodology for comparing the scientific impact of scholars. The outcomes of this research have notable implications for the development of more precise and unbiased research assessment frameworks, enabling a more equitable evaluation of scholarly contributions.

The high death rate of colorectal cancer has a long-term impact on human life around the world. Early detection of colorectal cancer leads to an increase in the survival rate of the patients. Overfitting issues occurred due to the presence of imbalanced datasets. To resolve the challenges, a novel deep learning-based approach based on an effective optimization strategy with a bidirectional vision transformer model for colorectal cancer detection. Disease classification is performed using two datasets: colorectal histology images and the NCT-CRC-HE-100 K dataset. Initially, the histopathological images from the dataset are trained and pre-processed using a Trimmed Pixel density-based median filter (TPDMF), which removes noise from the input images and enhances the quality of the images. The features of the pre-processed image are extracted using the Capsule Assisted Res2Net (CAR2N) model, and an optimized Bidirectional Vision Transformer (OBVT) model is presented to identify colorectal cancer. Here, the Bi- Long short-term memory (Bi-LSTM) model is used in the Multi-layer perception module of the vision transformer to reduce the complexity of the detection process. The proposed methodology uses a chaotic sequence-based Snake optimization algorithm (Ch-SOA) to tune the hyperparameters in the proposed model. The proposed model can be analyzed using different performance metrics like accuracy, precision, sensitivity, specificity, False negative rate (FNR), and False positive rate (FPR). The proposed model can obtain an accuracy of 97.11458 % for the colorectal histology images dataset and 97.01235 % for the NCT-CRC-HE-100 K dataset. From this analysis, the proposed model can obtain better results than other existing models.

An essential step in the drug development process is the accurate detection of drug-target interactions (DTI). The importance of binding affinity values in understanding protein-ligand interactions was previously disregarded, and DTI prediction was only seen as a binary classification problem. In this regard, we introduced the DFDTA-MultiAtt model for predicting the drug target binding affinity in two stages using the structural and sequential information. The first step of the first stage involves retrieving features from sequence data using a bi-directional long short term memory (Bi-LSTM) architecture together with a multi-attention module and dilated convolutional neural network (dilated-CNN) architecture, and the second step features are learnt from structure representation once again using a dilated-CNN. To predict the binding affinity, the second stage uses an ensemble learning model. The proposed model also produces findings with a greater overall accuracy when compared to contemporary state-of-the-art methods. The model generates an enormous +0.006 concordance index (CI) score on the Davis dataset and reduces the mean square error (MSE) by 0.174 on the KIBA dataset.

Text summarization has become recognized as a prominent task within the realm of natural language processing (NLP) in the recent years. It condenses enormous amounts of information into comprehensible summaries, which promotes effective information intake and understanding. Recent advancements in Hindi and English languages have leverage a wide range of statistical and linguistic information. This paper offers an extensive examination of ten statistical and linguistic features with sum up of these ten features of each sentences in the document for text summarization at $n$ % compression rate. It covers Hindi and English datasets from the indian language summarization (ILSUM)-2022 competition. The performance of proposed approach is better as comparisons to the other state-of-the-art methods in term of Rouge-1, Rouge-2 and Rouge-L based on recall metrics for English and Hindi languages.

Deep learning approaches, such as convolution neural networks (CNNs) and deep recurrent neural networks (RNNs), have been the backbone for predicting protein function, with promising state-of-the-art (SOTA) results. RNNs with an in-built ability (i) focus on past information, (ii) collect both short-and-long range dependency information, and (iii) bi-directional processing offers a strong sequential processing mechanism. CNNs, however, are confined to focusing on short-term information from both the past and the future, although they offer parallelism. Therefore, a novel bi-directional CNN that strictly complies with the sequential processing mechanism of RNNs is introduced and is used for developing a protein function prediction framework, Bi-SeqCNN. This is a sub-sequence-based framework. Further, Bi-SeqCNN $^+$ is an ensemble approach to better the prediction results. To our knowledge, this is the first time bi-directional CNNs are employed for general temporal data analysis and not just for protein sequences. The proposed architecture produces improvements up to +5.5% over contemporary SOTA methods on three benchmark protein sequence datasets. Moreover, it is substantially lighter and attain these results with (0.50-0.70 times) fewer parameters than the SOTA methods.

Depression, a prevalent adult symptom, can arise from various sources, including mental health conditions and social interactions. With the rise of social media, adults often share their daily experiences, potentially revealing their emotional state on social platforms, like X (formerly Twitter) and Facebook. In this study, we present Ensemble (E) of Convolutional Neural Network (C), Attention-based Long Short-Term Memory (L) Network, and Support Vector Machine (S) (E-CLS), utilizing Term Frequency-Inverse Document Frequency (TF-IDF) vectors, Global Vectors for Word Representation (GloVe) and Bidirectional Encoder Representations from Transformers (BERT) word embeddings. This model effectively identifies depressive posts. Validated with a Twitter-derived depressive dataset, E-CLS achieves an impressive F_1 -score of 0.91, surpassing existing machine-learning and deep-learning models by 2

Understanding a protein's function based solely on its amino acid sequence is a crucial but intricate task in bioinformatics. Traditionally, this challenge has proven difficult. However, recent years have witnessed the rise of deep learning as a powerful tool, achieving significant success in protein function prediction. Their strength lies in their ability to automatically learn informative features from protein sequences, which can then be used to predict the protein's function. This study builds upon these advancements by proposing a novel model: CNN-CBAM+BiGRU. It incorporates a Convolutional Block Attention Module (CBAM) alongside BiGRUs. CBAM acts as a spotlight, guiding the CNN to focus on the most informative parts of the protein data, leading to more accurate feature extraction. BiGRUs, a type of Recurrent Neural Network (RNN), excel at capturing long-range dependencies within the protein sequence, which are essential for accurate function prediction. The proposed model integrates the strengths of both CNN-CBAM and BiGRU. This study's findings, validated through experimentation, showcase the effectiveness of this combined approach. For the human dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +1.0 % for cellular components, +1.1 % for molecular functions, and +0.5 % for biological processes. For the yeast dataset, the suggested method outperforms the CNN-BIGRU+ATT model by +2.4 % for the cellular component, +1.2 % for molecular functions, and +0.6 % for biological processes.

The need for automatic text summarization is natural: there is a huge volume of information available online, which prompts for a widespread interest in extracting relevant information in a concise and understandable manner. Here, automated text summarization has been treated as an extractive single -document summarization problem in the proposed system. To solve this problem, a particle swarm optimisation (PSO) algorithmbased approach is suggested, with the goal of producing good summaries in terms of content coverage, informativeness, and readability. This paper introduces XSumm-PSO: a new approach based on PSO optimization technique in a supervised manner for extractive summarization. Further, this paper also contributes a new feature "incorrect word " that captures misspelled words in the candidate sentences. This feature is combined with nine existing features used by proposed model to generate error free summaries. As a result, the proposed XSumm-PSO framework produces superior performance achieving improvements of +2.7%, +0.8%, and +0.8% for ROUGE -1, ROUGE -2, and ROUGE -L scores, respectively, on DUC 2002 dataset, over state-of-the-art techniques. The corresponding improvements on the CNN/DailyMail dataset are +0.97%, +0.25%, and +0.49%. We also performed sample t -test, showing the proposed approach is statistically consistent across various runs.

COVID-19 has changed the world. The pandemic was a public health crisis with profound implications for society. Accordingly, the quantum of waste generated also went up especially fast. The global nature of the pandemic demanding global communication and coordination, also required locally adopted responses for specific reasons like lack of opportunity to network and create bonding and bridging ties as lockdown conditions prohibited human interactions. This situation that needs the substitution of human interactions and communication with technology however did not encourage strong ties and only allowed for the sustenance of weak ties along with pressures of financial sustainability on the social enterprise in solid waste management (SWM). This research paper attempts to explore whether the SWM social enterprises could remain true to their mission in these uncertain times or would need to replace their true beneficiaries with technology. This would then test their aspects of ‘mission agility’ versus ‘mission drift’ as discussed in the article. The findings also lead us to conclude that a fine balance between objectives of value capture and value creation in determining the switch over to technology by SWM enterprises underscored the importance of social mission to them. The data for this research was collected through qualitative and semi-structured interviews of Project Managers / Program Coordinators of various organisations working in the field of SWM.

Proteins are represented in various ways, each contributing differently to protein-related tasks. Here, information from each representation (protein sequence, 3D structure, and interaction data) is combined for an efficient protein function prediction task. Recently, uni-modal has produced promising results with state-of-the-art attention mechanisms that learn the relative importance of features, whereas multi-modal approaches have produced promising results by simply concatenating obtained features using a computational approach from different representations which leads to an increase in the overall trainable parameters. In this paper, we propose a novel, light-weight cross-modal multi-attention (CrMoMulAtt) mechanism that captures the relative contribution of each modality with a lower number of trainable parameters. The proposed mechanism shows a higher contribution from PPI and a lower contribution from structure data. The results obtained from the proposed CrossPredGO mechanism demonstrate an increment in $F_{\max}$Fmax in the range of +(3.29 to 7.20)% with at most 31% lower trainable parameters compared with DeepGO and MultiPredGO.

Silver Nanoparticles (Ag-NPs) are increasingly used in various fields including medical, food, healthcare consumer and industrial purposes due to their unique physical and chemical properties. The conventional physical and chemical methods of synthesis of Ag-NPs seem to be very expensive and hazardous. Interestingly, biologically prepared Ag-NPs show high yield, solubility and high stability. The biologically Ag-NPs was characterized by UV-visible spectroscopy and SEM with EDAX. The appearance of specific absorbance peak at 440 nm indicates the presence of Ag-NPs. The development of Ag-NPs as anti-arthritic molecules is one of the most interesting approaches for arthritic treatment as it can overcome poor delivery and the problem of drug resistance. The anti-arthritic activity of developed MO-AgNPs was determined by albumin denaturation assay. MO-AgNPs (Moringa Olifera leaves extract loaded silver nanoparticles) exhibit 25% higher anti-arthritic activity when compared with standard Aspirin drug.

This paper advances the self-attention mechanism in the standard transformer network specific to the modeling of the protein sequences. We introduce a novel context-window based scaled self-attention mechanism for processing protein sequences that is based on the notion of (i) local context and (ii) large contextual pattern. Both notions are essential to building a good representation for protein sequences. The proposed context-window based scaled self-attention mechanism is further used to build the multi context-window based scaled (MCWS) transformer network for the protein function prediction task at the protein sub-sequence level. Overall, the proposed MCWS transformer network produced improved predictive performances, outperforming existing state-of-the-art approaches by substantial margins. With respect to the standard transformer network, the proposed network produced improvements in F1-score of +2.30% and +2.08% on the biological process (BP) and molecular function (MF) datasets, respectively. The corresponding improvements over the state-of-the-art ProtVecGen-Plus+ProtVecGen-Ensemble approach are +3.38% (BP) and +2.86% (MF). Equally important, robust performances were obtained across protein sequences of different lengths.

The short-and-long range interactions amongst amino-acids in a protein sequence are primarily responsible for the function performed by the protein. Recently convolutional neural network (CNN)s have produced promising results on sequential data including those of NLP tasks and protein sequences. However, CNN's strength primarily lies at capturing short range interactions and are not so good at long range interactions. On the other hand, dilated CNNs are good at capturing both short-and-long range interactions because of varied - short-and-long - receptive fields. Further, CNNs are quite light-weight in terms of trainable parameters, whereas most existing deep learning solutions for protein function prediction (PFP) are based on multi-modality and are rather complex and heavily parametrized. In this paper, we propose a (sub-sequence + dilated-CNNs)-based simple, light-weight and sequence-only PFP framework Lite-SeqCNN. By varying dilation-rates, Lite-SeqCNN efficiently captures both short-and-long range interactions and has (0.50-0.75 times) fewer trainable parameters than its contemporary deep learning models. Further, Lite-SeqCNN + is an ensemble of three Lite-SeqCNNs developed with different segment-sizes that produces even better results compared to the individual models. The proposed architecture produced improvements upto 5% over state-of-the-art approaches Global-ProtEnc Plus, DeepGOPlus, and GOLabeler on three different prominent datasets curated from the UniProt database.

Agadatantra, the branch of toxicology in Ayurveda, refers to various methods of eliminating toxins from the body that arises from animate, inanimate, and artificial poisons. Scorpion stings are a serious health concern in rural areas. In Agadatantra, the symptoms of scorpion sting are explained under Vrischika visha (~scorpion venom poisoning). The main characteristics of scorpion venom poisoning are Vahnivat dahanam (~burning sensation like fire), Kshipram urdhvarohanam (~quick spreading in an upward direction), Danshe ruja tishthati (~pain remains at bite site), Sadyah ati ruk (~instant severe pain), Danshashyavata (~brownish discoloration of bite site), and Dansha toda (~ pricking type of pain at bite site). This is a case report of a 24-year-old male, who had an episode of Vrischika damsha (~scorpion bite) and presented to the outpatient department with complaints of severe edema, pain and redness, and itching. The first line of management for scorpion stings is typically symptomatic including strong analgesics, which can be systemic (opiates or paracetamol) or localized (such as a cold compress). Antivenom is the specific treatment for scorpion envenomation that is used in conjunction with supportive measures. In Ayurveda classics, Hinguadi gulika is one of the formulations mentioned in Vrischikavisha prakarana, which has been referred to as the most potent Lepana (~anointment) for scorpion venom poisoning. In comparison to existing conventional treatment modalities, Hinguadi gulika lepana is a very simple and very cost-effective procedure. Hinguadi gulika was given in combination with Kulakadi kashaya which is widely practiced in all Visha (~poison) conditions. In the present case, Hinguadi gulika and Kulakadi kashayam have shown assuring results in the management of acute scorpion envenomation.

Measurements of the shape or geometry of any natural form, including those of plants, animals, and relief features, are referred to as morphometry. Morphometry is the accurate measurement of landforms. One of the crucial requirements for effective management and planning of water resources within the watershed is an understanding of the behavior of the surface drainage network. In order to comprehend and interpret the dynamics of the watersheds drainage system and to understand the drainage network features, morphometric study of a watershed is a vital stage in watershed development and management. Watershed characterization includes morphometry as a key component. Aspects that are dimensional, aerial, and relief are typically present for the conservation of surface and ground water, watershed development and management strategies are crucial. Applying mathematical equations and statical measures to data from topographic maps and satellite images, morphometric attributes reflect a measurement of the earths surface and are processed in accordance with the principles of quantitative analysis. A study of drainage morphometry and its impact on the hydrology of the Mathugad watershed, Grahwal Himalaya, Uttarakhand (India) has been made. Total basin covers 77.12 sq km area. Shuttle Radar Topographic Mission (SRTM) data and on 1:50000 scale and Survey of India Toposheet as reference were utilized to create slope maps, aspect grids, and digital elevation models (DEMs) for the thorough investigation. Geographic information systems (GIS) were employed to assess the linear, areal, and relief aspects of the morphometric parameters. Numerous morphometric features of the Mathugad watershed have been intended by applying GIS techniques and using SRTM data. The research shows that the rainfall has a moderate to large impact on how stream segments develop in the basin area thrusting and faultings controlling influence is primarily responsible for the basins elongated structure. Relief ratio indicates that the discharge capability of these watersheds is very high and the groundwater potential is meager. Dendritic drainage pattern in the area shows that the area consists of homogeneous rock material which is structurally undisturbed. Form factor and circulatory ratio statics indicates basin shape is elongated the results clearly indicate relations among various morphometric attributes of the basin and help to understand their role in sculpturing the surface of the region. These studies are very useful for planning rainwater harvesting and watershed management.