D Y Patil International University

Metatranscriptomic analysis of Wyeomyia confusa mosquitoes collected in the Atlantic Forest (Pindamonhangaba, São Paulo, Brazil) led to the identification of a previously uncharacterized virus, designated Wyeomyia confusa Lispivirus (WcLispV-SP), classified within the family Lispiviridae, genus Canmovirus. The viral genome consists of a negative-sense single-stranded RNA (ssRNA−) of 12,698 nucleotides, encoding six open reading frames (ORFs): nucleoprotein (N), two hypothetical proteins (HP/1 and HP/2), glycoprotein (G), ORFan protein, and RNA-dependent RNA polymerase (RdRp-L). Phylogenetic analysis supports the classification of WcLispV-SP as a distinct species within the genus Canmovirus. Structural analysis of the RdRp revealed conserved domains and catalytic motifs characteristic of members of the order Mononegavirales, supporting its functional integrity. These findings expand the known diversity of the Lispiviridae family and highlight the utility of metagenomic approaches for the discovery and characterization of RNA viruses associated with Neotropical sylvatic mosquitoes.

Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mechanism, and optimized hyperparameters to classify brain tumors into different categories as glioma, meningioma, pituitary tumor, and no tumor. The approach leverages state-of-the-art preprocessing techniques, transfer learning, and Gradient-weighted Class Activation Mapping (Grad-CAM) visualization on a dataset of 7023 MRI images to enhance both performance and interpretability. The proposed model achieves 99% test accuracy and impressive precision and recall figures and outperforms traditional approaches like Support Vector Machines (SVM) with Histogram of Oriented Gradients (HOG), Local Binary Pattern (LBP) and Principal Component Analysis (PCA) by a significant margin. Moreover, the model eliminates the need for manual labelling—a common challenge in this domain—by employing end-to-end learning, which allows the proposed model to derive meaningful features hence reducing human input. The integration of attention mechanisms further promote feature selection, in turn improving classification accuracy, while Grad-CAM visualizations show which regions of the image had the greatest impact on classification decisions, leading to increased transparency in clinical settings. Overall, the synergy of superior prediction, automatic feature extraction, and improved predictability confirms the model as an important application to neural networks approaches for brain tumor classification with valuable potential for enhancing medical imaging (MI) and clinical decision-making.

In this article, we investigate the cosmological viability of a modified symmetric teleparallel gravity model within the f(Q) framework. We derive observational constraints on the model parameters by performing a Markov Chain Monte Carlo analysis using a combined dataset consisting of cosmic chronometers, PantheonPlus SH0ES, and DESI BAO DR2. Our analysis yields the best-fit values for the model parameters m=-0.386 ± 0.090 and n=-1.055 ± 0.047, along with the cosmological parameters at present: H_0 = 73.19 ± 0.25, q_0 = -0.51 ± 0.6, and ω_0 = -0.73 ± 0.3, at 68% CL. Furthermore, we examine the physical behavior of the model, focusing on the effective equation of state and deceleration parameter. Our findings indicate that the model experiences a transition from the early deceleration phase to the late-time cosmic acceleration, and the transition occurs at a redshift z_tr = 0.573. We also analyse the om(z) diagnostic, which reflects a positive slope, supporting the behavior of the equation of state parameter in the quintessence region.

Agriculture is essential to global food security and sustainable development. The use of machine learning (ML) and Internet of Things (IoT) has the potential to enhance crop production and improve resource management through enhanced precision agriculture. This article describes a smart crop recommender system developed to support both ML-based decision making and real-time environmental sensing. At the core of the system is a capacitive soil moisture sensor and a temperature-humidity sensor interfaced using an Arduino Nano. Using a nRF24L01 transceiver employing the 2.4 GHz Enhanced Shock Burst (ESB) protocol, the sensor information can be wirelessly transmitted. Data collected from the sensors are sent to an ESP32 module that posts the information to the Blynk web application through Wi-Fi, allowing real-time remote access. As the data source for the crop recommender model, the Blynk application provides the data needed to perform data analysis on the various ML algorithms (Random Forest, Bagging, Decision Tree, Gradient Boosting, and Enhanced Gaussian Naive Bayes [EGNB]) to determine the most suitable type of crop based on the current soil and environmental conditions. Based on the analysis, the EGNB model showed superiority with an accuracy of 99.55

This study emphasizes upon the synthesis of three different types of gold nanoparticles-Carbon quantum dots (AuNP-CQD) nanocolloids, which were named as AuNP-CQD (EA), AuNP-CQD (G) and AuNP-CQD (S). Primarily, before conjugating AuNP with different types of CQD, CQD were synthesized via hydrothermal method with varying precursors as the carbon source. Ethanolamine, Glycine, and Serine were used as the carbon sources for AuNP-CQD (EA), AuNP-CQD (G), and AuNP-CQD (S), respectively, and citric acid was used as the reducing agent. The aforementioned AuNP-CQD were further functionalized with diclofenac specific aptamer (DCLApt) for the colorimetric detection of diclofenac sodium. For optical characterisation, UV-Visible spectroscopy was used. For further characterization of the colorimetric system, FTIR and DLS techniques were used. Advanced analytical techniques such as XRD and XPS were utilized to outline the extensive details of structural and surface characteristics of the prepared nanocolloids. Microscopic evaluation techniques such as SEM and TEM were utilized to visualize the morphologies of AuNP, CQDs and AuNP-CQDs. AuNP are bound to CQD through electrostatic interactions. The prepared nanocolloids were utilized for the colorimetric detection of diclofenac in diclofenac sodium formulations. The LOD for AuNP-CQD (EA), AuNP-CQD (G) and AuNP-CQD (S) were recorded at 53.08 nM, 27.12 nM and 18.85 nM, respectively. The prepared nanocolloids were also utilized for the quantification of diclofenac sodium in human serum samples with recovery rates ranging from 94.27 - 98.79 % for AuNP-CQD (EA), 97.84 - 105.25 % for AuNP-CQD (G) and 95.17 - 99.23 % for AuNP-CQD (S) with RSD values ranging from 6.87 - 3.46 %, 2.1 - 2.04 % and 5.86 - 1.22 % for AuNP-CQD (EA), AuNP-CQD (G) and AuNP-CQD (S) respectively. This performance of AuNP-CQD surpasses the biosensing capabilities of pristine AuNP and other nanocomposites which establish the dominance of CQD and AuNP-CQD in colorimetric biosensing and providing a basis for future studies and multifarious applications.