Audio signal analysis using a modified forward-forward algorithm with enhanced segmentation for soil pest detection.

The presence of pests in soil costs the agriculture industry billions of dollars every year since it reduces crop yields and raises preventive costs. The pest detection in soil is vital for maintaining healthy crops, optimizing pest management, and ensuring economic and ecological sustainability. There are several invasive and non-invasive methods available for pest detection, where invasive methods are costly as well as time-consuming compared to the non-invasive methods.

Exploring the potential of lightweight large language models for AI-based mental health counselling task: a novel comparative study.

In recent years, Transformer-based large language models (LLMs) have significantly improved upon their text generation capability. Mental health is a serious concern that can be addressed using LLM-based automated mental health counselors. These systems can provide empathetic responses to individuals in need while considering the negative beliefs, stigma, and taboos associated with mental health issues.

Skin cancer detection through attention guided dual autoencoder approach with extreme learning machine.

Skin cancer is a lethal disease, and its early detection plays a pivotal role in preventing its spread to other body organs and tissues. Artificial Intelligence (AI)-based automated methods can play a significant role in its early detection. This study presents an AI-based novel approach, termed 'DualAutoELM' for the effective identification of various types of skin cancers.

Physiological roles of endocytosis and presynaptic scaffold in vesicle replenishment at fast and slow central synapses.

After exocytosis, release sites are cleared of vesicular residues to replenish with transmitter-filled vesicles. Endocytic and scaffold proteins are thought to underlie this site-clearance mechanism. However, the physiological significance of this mechanism at diverse mammalian central synapses remains unknown.

Computational models for prediction of protein-protein interaction in rice and .

Introduction: Plant-microbe interactions play a vital role in the development of strategies to manage pathogen-induced destructive diseases that cause enormous crop losses every year. Rice blast is one of the severe diseases to rice () due to () fungus. Protein-protein interaction (PPI) between rice and fungus plays a key role in causing rice blast disease.

Gradient Boosting Machine and Efficient Combination of Features for Speech-Based Detection of COVID-19.

In recent times, speech-based automatic disease detection systems have shown several promising results in biomedical and life science applications, especially in the case of respiratory diseases. It provides a quick, cost-effective, reliable, and non-invasive potential alternative detection option for COVID-19 in the ongoing pandemic scenario since the subject's voice can be remotely recorded and sent for further analysis. The existing COVID-19 detection methods including RT-PCR, and chest X-ray tests are not only costlier but also require the involvement of a trained technician.

Microtubule assembly by tau impairs endocytosis and neurotransmission via dynamin sequestration in Alzheimer's disease synapse model.

Elevation of soluble wild-type (WT) tau occurs in synaptic compartments in Alzheimer's disease. We addressed whether tau elevation affects synaptic transmission at the calyx of Held in slices from mice brainstem. Whole-cell loading of WT human tau (h-tau) in presynaptic terminals at 10-20 µM caused microtubule (MT) assembly and activity-dependent rundown of excitatory neurotransmission.

ANOVA-particle swarm optimization-based feature selection and gradient boosting machine classifier for improved protein-protein interaction prediction.

Feature fusion and selection strategies have been applied to improve accuracy in the prediction of protein-protein interaction (PPI). In this paper, an embedded feature selection framework is developed by integrating a cost function based on analysis of variance (ANOVA) with the particle swarm optimization (PSO), termed AVPSO. Initially, the features of the protein sequences extracted using pseudo-amino acid composition (PseAAC), conjoint triad composition, and local descriptor are fused.

Improved prediction of protein-protein interaction using a hybrid of functional-link Siamese neural network and gradient boosting machines.

In this paper, for accurate prediction of protein-protein interaction (PPI), a novel hybrid classifier is developed by combining the functional-link Siamese neural network (FSNN) with the light gradient boosting machine (LGBM) classifier. The hybrid classifier (FSNN-LGBM) uses the fusion of features derived using pseudo amino acid composition and conjoint triad descriptors. The FSNN extracts the high-level abstraction features from the raw features and LGBM performs the PPI prediction task using these abstraction features.

Deep Neural Network and Extreme Gradient Boosting Based Hybrid Classifier for Improved Prediction of Protein-Protein Interaction.

Understanding the behavioral process of life and disease-causing mechanism, knowledge regarding protein-protein interactions (PPI) is essential. In this paper, a novel hybrid approach combining deep neural network (DNN) and extreme gradient boosting classifier (XGB) is employed for predicting PPI. The hybrid classifier (DNN-XGB) uses a fusion of three sequence-based features, amino acid composition (AAC), conjoint triad composition (CT), and local descriptor (LD) as inputs.

Dynamin-1 deletion enhances post-tetanic potentiation and quantal size after tetanic stimulation at the calyx of Held.

Key Points: Post-tetanic potentiation (PTP) is attributed mainly to an increase in release probability (P ) and/or readily-releasable pool (RRP) in many synapses, but the role of endocytosis in PTP is unknown. Using the calyx of Held synapse from tissue-specific dynamin-1 knockout (cKO) mice (P16-20), we report that cKO synapses show enhanced PTP compared to control. We found significant increases in both spontaneous excitatory postsynaptic current (spEPSC) amplitude and RRP size (estimated by a train of 30 APs at 100 Hz) in cKO over control during PTP.

Equal sensitivity of Cav1.2 and Cav1.3 channels to the opposing modulations of PKA and PKG in mouse chromaffin cells.

Mouse chromaffin cells (MCCs) express high densities of L-type Ca2+ channels (LTCCs), which control pacemaking activity and catecholamine secretion proportionally to their density of expression. In vivo phosphorylation of LTCCs by cAMP-PKA and cGMP–PKG, regulate LTCC gating in two opposing ways: the cAMP-PKA pathway potentiates while the cGMP–PKG cascade inhibits LTCCs. Despite this, no attempts have been made to answer three key questions related to the two Cav1 isoforms expressed in MCCs (Cav1.

Are Ca(v)1.3 pacemaker channels in chromaffin cells? Possible bias from resting cell conditions and DHP blockers usage.

Mouse and rat chromaffin cells (MCCs, RCCs) fire spontaneously at rest and their activity is mainly supported by the two L-type Ca(2+) channels expressed in these cells (Ca(v)1.2 and Ca(v)1.3).

CaV1.3 as pacemaker channels in adrenal chromaffin cells: specific role on exo- and endocytosis?

Voltage-gated L-type calcium channels (LTCCs) are expressed in adrenal chromaffin cells. Besides shaping the action potential (AP), LTCCs are involved in the excitation-secretion coupling controlling catecholamine release and in Ca (2+) -dependent vesicle retrieval. Of the two LTCCs expressed in chromaffin cells (CaV1.

Loss of Cav1.3 channels reveals the critical role of L-type and BK channel coupling in pacemaking mouse adrenal chromaffin cells.

We studied wild-type (WT) and Cav1.3(-/-) mouse chromaffin cells (MCCs) with the aim to determine the isoform of L-type Ca(2+) channel (LTCC) and BK channels that underlie the pacemaker current controlling spontaneous firing. Most WT-MCCs (80%) were spontaneously active (1.