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Accurate nuclei segmentation and classification in histology images are critical for cancer detection but remain challenging due to color inconsistency, blurry boundaries, and overlapping nuclei. Manual segmentation is time-consuming and labor-intensive, highlighting the need for efficient and scalable automated solutions. This study proposes a deep learning framework that combines segmentation and classification to enhance nuclei evaluation in histopathology images. The framework follows a two-stage approach: first, a SegNet model segments the nuclei regions, and then a DenseNet121 model classifies the segmented instances. Hyperparameter optimization using the Hyperband method enhances the performance of both models. To protect data privacy, the framework employs a FedAvg-based federated learning scheme, enabling decentralized training without exposing sensitive data. For efficient deployment on edge devices, full integer quantization is applied to reduce computational overhead while maintaining accuracy. Experimental results show that the SegNet model achieves 91.4% Mean Pixel Accuracy (MPA), 63% Mean Intersection over Union (MIoU), and 90.6% Frequency-Weighted IoU (FWIoU). The DenseNet121 classifier achieves 83% accuracy and a 67% Matthews Correlation Coefficient (MCC), surpassing state-of-the-art models. Post-quantization, both models exhibit performance gains of 1.3% and 1.0%, respectively. The proposed framework demonstrates high accuracy and efficiency, highlighting its potential for real-world clinical deployment in cancer diagnosis.

User authentication for an accurate biometric system is the demand of the hour in today's world. When somebody attempts to take on the appearance of another person by introducing a phony face or video before the face detection camera and gets illegitimate access, a face presentation attack usually happens. To effectively protect the privacy of a person, it is very critical to build a face authentication and anti-spoofing system. This paper introduces a novel and appealing face spoof detection technique, which is primarily based on the study of contrast and dynamic texture features of both seized and spoofed photos. Valid identification of photo spoofing is anticipated here. A modified version of the DoG filtering method, and local binary pattern variance (LBPV) based technique, which is invariant to rotation, are designated to be used in this paper. Support vector machine (SVM) is used when feature vectors are extracted for further analysis. The publicly available NUAA photo-imposter database is adapted to test the system, which includes facial images with different illumination and area. The accuracy of the method can be assessed using the false acceptance rate (FAR) and false rejection rate (FRR). The results express that our method performs better on key indices compared to other state-of-the-art techniques following the provided evaluation protocols tested on a similar dataset.

Ankylosing spondylitis(AS), a highly heritable complex inflammatory arthritis. Although, a handful of non-HLA risk loci have been identified, capturing the unexplained genetic contribution to AS pathogenesis remains a challenge attributed to additive, pleiotropic and epistatic-interactions at the molecular level. Here, we developed multiple integrated genomic approaches to quantify molecular convergence of non-HLA loci with global immune mediated diseases. We show that non-HLA genes are significantly sensitive to deleterious mutation accumulation in the general population compared with tolerant genes. Human developmental proteomics (prenatal to adult) analysis revealed that proteins encoded by non-HLA AS risk loci are 2-fold more expressed in adult hematopoietic cells.Enrichment analysis revealed AS risk genes overlap with a significant number of immune related pathways ( p   <  0.0001 to 9.8 × 10 -12 ). Protein-protein interaction analysis revealed non-shared AS risk genes are highly clustered seeds that significantly converge (empirical; p   <  0.01 to 1.6 × 10 -4 ) into networks of global immune mediated disease risk loci. We have also provided initial evidence for the involvement of STAT2 / 3 in AS pathogenesis. Collectively, these findings highlight molecular insight on non-HLA AS risk loci that are not exclusively connected with overlapping immune mediated diseases; rather a component of common pathophysiological pathways with other immune mediated diseases. This information will be pivotal to fully explain AS pathogenesis and identify new therapeutic targets.

Tyrosinase plays a central role in melanin biosynthesis, and its dysregulation has been implicated in the pathogenesis of various pigmentation disorders. The precise identification of tyrosinase inhibitory peptides (TIPs) is critical, as these bioactive molecules hold significant potential for therapeutic and cosmetic applications, including the treatment of hyperpigmentation and the development of skin-whitening agents. To date, computational methods have received significant attention as a complement to experimental methods for the in silico identification of TIPs, reducing the need for extensive material resources and labor-intensive processes. In this study, we propose an innovative computational approach, BLSAM-TIP, which combines a bidirectional long short-term memory (BiLSTM) network and a self-attention mechanism (SAM) for accurate and large-scale identification of TIPs. In BLSAM-TIP, we first employed various multi-source feature embeddings, including conventional feature encodings, natural language processing-based encodings, and protein language model-based encodings, to encode comprehensive information about TIPs. Secondly, we integrated these feature embeddings to enhance feature representation, while a feature selection method was applied to optimize the hybrid features. Thirdly, the BiLSTM-SAM architecture was specially developed to highlight the crucial features. Finally, the features from BiLSTM-SAM was fed to deep neural networks (DNN) in order to identify TIPs. Experimental results on an independent test dataset demonstrate that BLSAM-TIP attains superior predictive performance compared to existing methods, with a balanced accuracy of 0.936, MCC of 0.922, and AUC of 0.988. These results indicate that this new method is an accurate and efficient tool for identifying TIPs. Our proposed method is available at https://github.com/saeed344/BLSAM-TIP for TIP identification and reproducibility purposes.

DNA-binding proteins (DBPs) play a significant role in all phases of genetic processes, including DNA recombination, repair, and modification. They are often utilized in drug discovery as fundamental elements of steroids, antibiotics, and anticancer drugs. Predicting them poses the most challenging task in proteomics research. Conventional experimental methods for DBP identification are costly and sometimes biased toward prediction. Therefore, developing powerful computational methods that can accurately and rapidly identify DBPs from sequence information is an urgent need. In this study, we propose a novel deep learning-based method called Deep-WET to accurately identify DBPs from primary sequence information. In Deep-WET, we employed three powerful feature encoding schemes containing Global Vectors, Word2Vec, and fastText to encode the protein sequence. Subsequently, these three features were sequentially combined and weighted using the weights obtained from the elements learned through the differential evolution (DE) algorithm. To enhance the predictive performance of Deep-WET, we applied the SHapley Additive exPlanations approach to remove irrelevant features. Finally, the optimal feature subset was input into convolutional neural networks to construct the Deep-WET predictor. Both cross-validation and independent tests indicated that Deep-WET achieved superior predictive performance compared to conventional machine learning classifiers. In addition, in extensive independent test, Deep-WET was effective and outperformed than several state-of-the-art methods for DBP prediction, with accuracy of 78.08%, MCC of 0.559, and AUC of 0.805. This superior performance shows that Deep-WET has a tremendous predictive capacity to predict DBPs. The web server of Deep-WET and curated datasets in this study are available at https://deepwet-dna.monarcatechnical.com/ . The proposed Deep-WET is anticipated to serve the community-wide effort for large-scale identification of potential DBPs.

Antiviral peptides (AVPs), produced by all living organisms, play a vital role as the first line of immune defense against viral infections. AVPs present a promising path for developing novel antiviral therapies that target diverse viruses, including those resistant to existing drugs. However, identifying AVPs using wet lab methods is often costly and requires significant effort, and existing computational methods still have certain limitations. In this study, a novel attention-based Gated Recurrent Unit framework, named GRUATT-AVP, is proposed for accurate and fast AVPs identification. In GRUATT-AVP, several Natural Language Processing (NLP) based encoding mechanisms, including One-Hot Encoding, Word2Vec, GloVe, FastText, and ProtBert, are adopted to encode the peptide sequences. Sequentially, different embedding dimensions based on the k-mer with fixed lengths (1–6) and pooling were explored, aiming to capture the local context within the sequences. After that, we conducted another experiment to determine the best feature selection technique and integrated the SHAP technique to eliminate noise and less important encoded features, thereby improving the model’s generalization performance. Finally, the most informative subset was fed into our developed GRUATT-AVP model to construct the GRUATT-AVP for classification. To understand the contribution of each component in the GRUATT-AVP model, an ablation study was performed, and the outcomes showed that our proposed model outperforms its other variants, establishing the model’s stability and efficacy. In terms of AVP prediction results, GRUATT-AVP demonstrated better performance compared to several state-of-the-art classifiers, with an accuracy of 94.8% and an AUC of 0.986, suggesting promising therapeutic potential against viral infections. To ensure wide accessibility and practical usage, the GRUATT-AVP web server is available at https://gruatt-avp.vercel.app/ .

The Coronavirus Disease 2019 (COVID-19) still tends to propagate and increase the occurrence of COVID-19 across the globe. The clinical and epidemiological analyses indicate the link between COVID-19 and Neurological Diseases (NDs) that drive the progression and severity of NDs. Elucidating why some patients with COVID-19 influence the progression of NDs and patients with NDs who are diagnosed with COVID-19 are becoming increasingly sick, although others are not is unclear. In this research, we investigated how COVID-19 and ND interact and the impact of COVID-19 on the severity of NDs by performing transcriptomic analyses of COVID-19 and NDs samples by developing the pipeline of bioinformatics and network-based approaches. The transcriptomic study identified the contributing genes which are then filtered with cell signaling pathway, gene ontology, protein-protein interactions, transcription factor, and microRNA analysis. Identifying hub-proteins using protein-protein interactions leads to the identification of a therapeutic strategy. Additionally, the incorporation of comorbidity interactions score enhances the identification beyond simply detecting novel biological mechanisms involved in the pathophysiology of COVID-19 and its NDs comorbidities. By computing the semantic similarity between COVID-19 and each of the ND, we have found gene-based maximum semantic score between COVID-19 and Parkinson's disease, the minimum semantic score between COVID-19 and Multiple sclerosis. Similarly, we have found gene ontology-based maximum semantic score between COVID-19 and Huntington disease, minimum semantic score between COVID-19 and Epilepsy disease. Finally, we validated our findings using gold-standard databases and literature searches to determine which genes and pathways had previously been associated with COVID-19 and NDs. •Developing an integrated pipeline to explore why COVID-19 and Neurological diseases interact.•Identifying contributory genes, signaling pathways, gene ontology to predict their relationship.•Performing protein-protein interaction to identify hub protein for therapeutic targets.•To assess the effect of COVID-19 on NDs, we have applied semantic similarity measures.•Verifying our findings with gold benchmark databases and literature searches.

Accurate identification of DNA-binding proteins (DBPs) is critical for both understanding protein function and drug design. DBPs also play essential roles in different kinds of biological activities such as DNA replication, repair, transcription, and splicing. As experimental identification of DBPs is time-consuming and sometimes biased toward prediction, constructing an effective DBP model represents an urgent need, and computational methods that can accurately predict potential DBPs based on sequence information are highly desirable. In this paper, a novel predictor called DeepDNAbP has been developed to accurately predict DBPs from sequences using a convolutional neural network (CNN) model. First, we perform three feature extraction methods, namely position-specific scoring matrix (PSSM), pseudo-amino acid composition (PseAAC) and tripeptide composition (TPC), to represent protein sequence patterns. Secondly, SHapley Additive exPlanations (SHAP) are employed to remove the redundant and irrelevant features for predicting DBPs. Finally, the best features are provided to the CNN classifier to construct the DeepDNAbP model for identifying DBPs. The final DeepDNAbP predictor achieves superior prediction performance in K-fold cross-validation tests and outperforms other existing predictors of DNA–protein binding methods. DeepDNAbP is poised to be a powerful computational resource for the prediction of DBPs. The web application and curated datasets in this study are freely available at: http://deepdbp.sblog360.blog/. [Display omitted]

Osteosarcoma is the most common type of bone cancer that tends to occur in teenagers and young adults. Due to crowded context, inter-class similarity, inter-class variation, and noise in H&E-stained (hematoxylin and eosin stain) histology tissue, pathologists frequently face difficulty in osteosarcoma tumor classification. In this paper, we introduced a hybrid framework for improving the efficiency of three types of osteosarcoma tumor (nontumor, necrosis, and viable tumor) classification by merging different types of CNN-based architectures with a multilayer perceptron (MLP) algorithm on the WSI (whole slide images) dataset. We performed various kinds of preprocessing on the WSI images. Then, five pre-trained CNN models were trained with multiple parameter settings to extract insightful features via transfer learning, where convolution combined with pooling was utilized as a feature extractor. For feature selection, a decision tree-based RFE was designed to recursively eliminate less significant features to improve the model generalization performance for accurate prediction. Here, a decision tree was used as an estimator to select the different features. Finally, a modified MLP classifier was employed to classify binary and multiclass types of osteosarcoma under the five-fold CV to assess the robustness of our proposed hybrid model. Moreover, the feature selection criteria were analyzed to select the optimal one based on their execution time and accuracy. The proposed model achieved an accuracy of 95.2% for multiclass classification and 99.4% for binary classification. Experimental findings indicate that our proposed model significantly outperforms existing methods; therefore, this model could be applicable to support doctors in osteosarcoma diagnosis in clinics. In addition, our proposed model is integrated into a web application using the FastAPI web framework to provide a real-time prediction.

The decision-making process and the information flow from physicians to patients regarding deliveries through cesarean section (C-section) has not been adequately explored in Bangladeshi context. Here, we aimed to explore the extent of information received by mothers and their family members and their involvement in the decision-making process. We conducted a qualitative exploratory study in four urban slums of Dhaka city among purposively selected mothers (n = 7), who had a cesarean birth within one-year preceding data collection, and their family members (n = 12). In most cases, physicians were the primary decision-makers for C-sections. At the household level, pregnant women were excluded from some crucial steps of the decision-making process and information asymmetry was prevalent. All interviewed pregnant women attended at least one antenatal care visit; however, they neither received detailed information regarding C-sections nor attended any counseling session regarding decisions around delivery type. In some cases, pregnant women and their family members did not ask health care providers for detailed information about C-sections. Most seemed to perceive C-sections as risk-free procedures. Future research could explore the best ways to provide C-section-related information to pregnant women during the antenatal period and develop interventions to promote shared decision-making for C-sections in urban Bangladeshi slums.