Significance of AI-assisted techniques for epiphyte plant monitoring and identification from drone images.

Monitoring forest canopies is vital for ecological studies, particularly for assessing epiphytes in rain forest ecosystems. Traditional methods for studying epiphytes, such as climbing trees and building observation structures, are labor, cost intensive and risky. Unmanned Aerial Vehicles (UAVs) have emerged as a valuable tool in this domain, offering botanists a safer and more cost-effective means to collect data.

Leveraging technology-driven strategies to untangle omics big data: circumventing roadblocks in clinical facets of oral cancer.

Oral cancer is one of the 19most rapidly progressing cancers associated with significant mortality, owing to its extreme degree of invasiveness and aggressive inclination. The early occurrences of this cancer can be clinically deceiving leading to a poor overall survival rate. The primary concerns from a clinical perspective include delayed diagnosis, rapid disease progression, resistance to various chemotherapeutic regimens, and aggressive metastasis, which collectively pose a substantial threat to prognosis.

Panoramic view of key cross-talks underpinning the oral squamous cell carcinoma stemness - unearthing the future opportunities.

The clinical management of oral cancer is often frequented with challenges that arise from relapse, recurrence, invasion and resistance towards the cornerstone chemo and radiation therapies. The recent conceptual advancement in oncology has substantiated the role of cancer stem cells (CSC) as a predominant player of these intricacies. CSC are a sub-group of tumor population with inherent adroitness to self-renew with high plasticity.

Stacked ensemble learning based on deep convolutional neural networks for pediatric pneumonia diagnosis using chest X-ray images.

Pneumonia is an acute respiratory infection caused by bacteria, viruses, or fungi and has become very common in children ranging from 1 to 5 years of age. Common symptoms of pneumonia include difficulty breathing due to inflamed or pus and fluid-filled alveoli. The United Nations Children's Fund reports nearly 800,000 deaths in children due to pneumonia.

Pediatric pneumonia diagnosis using stacked ensemble learning on multi-model deep CNN architectures.

Pediatric pneumonia has drawn immense awareness due to the high mortality rates over recent years. The acute respiratory infection caused by bacteria, viruses, or fungi infects the lung region and hinders oxygen transport, making breathing difficult due to inflamed or pus and fluid-filled alveoli. Being non-invasive and painless, chest X-rays are the most common modality for pediatric pneumonia diagnosis.

Explainable artificial intelligence for heart rate variability in ECG signal.

Electrocardiogram (ECG) signal is one of the most reliable methods to analyse the cardiovascular system. In the literature, there are different deep learning architectures proposed to detect various types of tachycardia diseases, such as atrial fibrillation, ventricular fibrillation, and sinus tachycardia. Even though all types of tachycardia diseases have fast beat rhythm as the common characteristic feature, existing deep learning architectures are trained with the corresponding disease-specific features.

Evaluation of Cognition and Cortical Excitability in Huntington's Disease.

Background: Recent advances in neurophysiological techniques have contributed to our understanding of the pathophysiology of Huntington's disease (HD). Studies of the motor cortical excitability and central motor pathways have shown variable results.

Objectives: Our aims were to evaluate the cortical excitability changes in HD using transcranial magnetic stimulation (TMS) and correlate the changes with cognitive impairment.

Biodiesel production from microbial oil derived from wood isolate Trichoderma reesei.

In the present study Trichoderma reesei, a wood isolate can yield high biomass quantities up to 30g/L, yielding 32.4% of lipids of dry cell weight (DCW). Biodiesel production from Trichoderma reesei involved simple unit operations like filtration and ultrasonication, yet giving good lipid yield with desirable bio-diesel properties.

Microbial oil - A plausible alternate resource for food and fuel application.

Microbes have recourse to low-priced substrates like agricultural wastes and industrial efflux. A pragmatic approach towards an emerging field- the exploitation of microbial oils for biodiesel production, pharmaceutical and cosmetic applications, food additives, biopolymer production will be of immense remunerative significance in the near future. Due to high free fatty acid, nutritive content and simpler solvent extraction processes of microbial oils with plant oil, microbial oils can back plant oils in food applications.

Determinants of Onset of Huntington's Disease with Behavioral Symptoms: Insight from 92 Patients.

Background: Huntington's disease (HD) is a genetically mediated neurodegenerative disorder characterized by presence of involuntary movements, behavioral problems and cognitive dysfunctions. Though few patients with HD may have behavioral symptoms at onset of the disease, studies comparing patients with behavioral symptoms at the onset of HD with those having motor symptoms are sparse.

Objective: Objective of this study is to determine the differences in the demographic and genetic characteristics of patients with behavioral symptom at the onset of HD from those with motor symptoms.

Effect of Refractive Status and Axial Length on Peripapillary Retinal Nerve Fibre Layer Thickness: An Analysis Using 3D OCT.

Background: Accurate measurement of retinal nerve fiber layer (RNFL) is now possible with the high resolution optical coherence tomography (OCT). Effect of refractive status of the eye on RNFL thickness may be relevant in the diagnosis of glaucoma and other optic nerve diseases.

Aim: To assess the RNFL thickness and compare its correlation with refractive status and axial length of the eye.

Esthesioneuroblastoma as an unusual cause for dystopia.

Abstract Esthesioneuroblastoma, also known as olfactory neuroblastoma, is an uncommon malignant neoplasm arising from the olfactory epithelium in the roof of the nasal cavity. There are very few case reports published worldwide. The common presenting symptoms of Esthesioneuroblastoma are unilateral nasal obstruction (70%), epistaxis (50%), anosmia, rhinorrhoea, facial pain, headache, excessive lacrimation and rarely proptosis and visual disturbance.