Speech Emotion Recognition using Machine Learning Techniques

The objective of this research is to enhance the field of speech emotion recognition by doing research on a wide array of models and methodologies and assessing their effectiveness in different emotion categories. The goal is to create precise and strong models that can accurately detect subtle emotional cues expressed through speech signals, by utilizing both conventional machine learning methods and deep learning techniques. Dataset that has been used is the Ryerson Audio-Visual Database of Emotional Speech and Song (RAVDESS). This study assesses different machine learning models for speech emotion recognition (SER), including Wav2Vec2 based architectures such as MLP, LSTM, and CNN, as well as classic algorithms like SVM, Logistic Regression, AdaBoost, Random Forest, and XGBoosting

ARTICULAR SYNDROME IN THE ELDERLY: COMMON DIFFERENTIALS AND CHALLENGES

Articular syndrome includes a spectrum of inflammatory and non-inflammatory joint involvement ranging from arthralgia to arthritis. Its phenotype, differentials and management differ slightly in the elderly, considering the added effect of physiological changes with ageing, comorbidity and multimorbidity. This review aims to provide an overview of the common differentials of articular syndrome in the elderly, including the inflammatory and degenerative causes. The common inflammatory arthritis in the elderly includes late-onset rheumatoid arthritis, polymyalgia rheumatica, paraneoplastic arthritis, crystal arthropathies, and systemic vasculitis, including ANCA-associated vasculitis, to name a few. The non-inflammatory articular syndromes in this age group predominantly include osteoarthritis and osteoporosis. There are also evident alterations in the gut microbiome associated with inflammatory arthritis and with physiological ageing and osteoarthritis, which have possible mechanistic significance. The management aspect in the geriatric population comes with challenges of addressing multimorbidity, polypharmacy, drug interactions, and not just disease activity. An integrated approach with effective physical therapy, and vocational activities, tailored to each patient is essential for optimal management

Maintenance of Personal Health Record System with Cipher text Policy Attribute-Based Encryption and Quick Decryption

The meteoric rise of information technology coupled with the pervasive use of cloud computing across all industries has prepared the road for the implementation of Personal Health Record (PHR) Systems using cloud computing. The greatest example to utilise is Microsoft Healthvault, which is an online personal health record service offered by the technology giant Microsoft. This service allows users to save, access, and update their personal information, which can then be shared with health care professionals. Some people believe that shifting applications that deal with personal data to cloud computing might result in a loss of control over the data. Therefore, it is essential to have PHR systems that are safe and stored on the cloud. Several frameworks for PHR have been developed, several of which make use of conventional cryptographic methods. However, they are not suitable for the PHR systems since they do not meet the requirements for efficiency, scalability, and appropriateness. Nicely addition, the single owner scenario of standard cryptographic approaches does not fit in with the multi-owner situation of the PHR system. As a result, we propose a PHR framework in which patients have access control and privacy of their personal record using a light weight 64 bit block cypher symmetric encryption algorithm. Additionally, we propose dividing the patient-centric framework into multiple security domains in order to reduce the complexity of key distribution. The plan that has been suggested is adaptable since it enables for break glass judgements to be made in the event of an emergency situation. However, the system does not have integrity, which is something that may be maintained by utilising a digital signature method or an Elliptic Curve Digital Signature Algorithm (ECDSA) scheme to establish integrity of the personal health information

Classification of binary fracture using CNN.

One of the major problems faced by any living organism since infancy are musculoskeletal injuries. To keep it quite simple musculoskeletal injuries are a range of disorders involving muscles, bones, tendons, blood vessels, nerves and other soft tissues. However one of the most common forms of musculoskeletal injuries are fractures. Fractures are one of the most prevalent sores that are faced by any living organism. They are also easily overlooked by the best of physicians. Even with the help of an X-ray, they are one of the hardest symptoms to diagnose. We believe that we can provide a solution to this problem by implementing convolutional neural networks (CNN) image processing algorithms into the field of medicine. We have designed a model using three layers of architecture which has been properly trained to identify the X-ray images that have fractures. To accomplish this we used large datasets that consist of 200 images of human hands, ribs, legs and neck. These large datasets are clearly segregated to identify those images which contain fractures from those images which are perfectly fine. The results gave us accurate predictions using some graphical representations as well as epochs of the various patients

An overview of anti-diabetic plants used in Gabon: Pharmacology and Toxicology

© 2017 Elsevier B.V. All rights reserved.Ethnopharmacological relevance: The management of diabetes mellitus management in African communities, especially in Gabon, is not well established as more than 60% of population rely on traditional treatments as primary healthcare. The aim of this review was to collect and present the scientific evidence for the use of medicinal plants that are in currect by Gabonese traditional healers to manage diabetes or hyperglycaemia based here on the pharmacological and toxicological profiles of plants with anti-diabetic activity. There are presented in order to promote their therapeutic value, ensure a safer use by population and provide some bases for further study on high potential plants reviewed. Materials and methods: Ethnobotanical studies were sourced using databases such as Online Wiley library, Pubmed, Google Scholar, PROTA, books and unpublished data including Ph.D. and Master thesis, African and Asian journals. Keywords including ‘Diabetes’ ‘Gabon’ ‘Toxicity’ ‘Constituents’ ‘hyperglycaemia’ were used. Results: A total of 69 plants currently used in Gabon with potential anti-diabetic activity have been identified in the literature, all of which have been used in in vivo or in vitro studies. Most of the plants have been studied in human or animal models for their ability to reduce blood glucose, stimulate insulin secretion or inhibit carbohydrates enzymes. Active substances have been identified in 12 out of 69 plants outlined in this review, these include Allium cepa and Tabernanthe iboga. Only eight plants have their active substances tested for anti-diabetic activity and are suitables for further investigation. Toxicological data is scarce and is dose-related to the functional parameters of major organs such as kidney and liver. Conclusion: An in-depth understanding on the pharmacology and toxicology of Gabonese anti-diabetic plants is lacking yet there is a great scope for new treatments. With further research, the use of Gabonese anti-diabetic plants is important to ensure the safety of the diabetic patients in Gabon.Peer reviewedFinal Accepted Versio

Ophiuchus: Scalable Modeling of Protein Structures through Hierarchical Coarse-graining SO(3)-Equivariant Autoencoders

Three-dimensional native states of natural proteins display recurring and hierarchical patterns. Yet, traditional graph-based modeling of protein structures is often limited to operate within a single fine-grained resolution, and lacks hourglass neural architectures to learn those high-level building blocks. We narrow this gap by introducing Ophiuchus, an SO(3)-equivariant coarse-graining model that efficiently operates on all-atom protein structures. Our model departs from current approaches that employ graph modeling, instead focusing on local convolutional coarsening to model sequence-motif interactions with efficient time complexity in protein length. We measure the reconstruction capabilities of Ophiuchus across different compression rates, and compare it to existing models. We examine the learned latent space and demonstrate its utility through conformational interpolation. Finally, we leverage denoising diffusion probabilistic models (DDPM) in the latent space to efficiently sample protein structures. Our experiments demonstrate Ophiuchus to be a scalable basis for efficient protein modeling and generation

ATP and adenosine-Two players in the control of seizures and epilepsy development.

Despite continuous advances in understanding the underlying pathogenesis of hyperexcitable networks and lowered seizure thresholds, the treatment of epilepsy remains a clinical challenge. Over one third of patients remain resistant to current pharmacological interventions. Moreover, even when effective in suppressing seizures, current medications are merely symptomatic without significantly altering the course of the disease. Much effort is therefore invested in identifying new treatments with novel mechanisms of action, effective in drug-refractory epilepsy patients, and with the potential to modify disease progression. Compelling evidence has demonstrated that the purines, ATP and adenosine, are key mediators of the epileptogenic process. Extracellular ATP concentrations increase dramatically under pathological conditions, where it functions as a ligand at a host of purinergic receptors. ATP, however, also forms a substrate pool for the production of adenosine, via the action of an array of extracellular ATP degrading enzymes. ATP and adenosine have assumed largely opposite roles in coupling neuronal excitability to energy homeostasis in the brain. This review integrates and critically discusses novel findings regarding how ATP and adenosine control seizures and the development of epilepsy. This includes purine receptor P1 and P2-dependent mechanisms, release and reuptake mechanisms, extracellular and intracellular purine metabolism, and emerging receptor-independent effects of purines. Finally, possible purine-based therapeutic strategies for seizure suppression and disease modification are discussed

Eco-friendly Management of Phytopathogens Through Nanopesticides: A Sustainable Approach

Biopesticides have frequently been the focus of attention on a global scale as a safer alternative to chemical pest control that may provide less damage to both humans and the environment. The usage of biopesticides is rising rapidly worldwide, at 10 percent a year. With the idea of limited application for the most significant impact, nanotechnology has produced novel tools for pest management in agriculture, including nanopesticides and nanosensors. In contrast to conventional chemical pesticides, nanopesticides are formulations of a pesticide’s active component in nanoform that have delayed degradation, targeted distribution, and controlled release of the active ingredient over longer periods. In accordance with lots of studies, incorporating certain biological agents in nanoparticulate systems increases their effectiveness against pests while lowering losses resulting from physical deterioration. The development and evaluation of nanobiopesticides have been the subject of laboratory-only research to date using techniques like the creation of nanocomposites, nanoengineered biopesticides, coating nanoparticles with bio-pesticides, etc. The formulation of appropriate, globally acceptable bio-safety and registration requirements is necessary to enable the effective use of these formulations for pest management at the field level

LAB-Bench: Measuring Capabilities of Language Models for Biology Research

There is widespread optimism that frontier Large Language Models (LLMs) and LLM-augmented systems have the potential to rapidly accelerate scientific discovery across disciplines. Today, many benchmarks exist to measure LLM knowledge and reasoning on textbook-style science questions, but few if any benchmarks are designed to evaluate language model performance on practical tasks required for scientific research, such as literature search, protocol planning, and data analysis. As a step toward building such benchmarks, we introduce the Language Agent Biology Benchmark (LAB-Bench), a broad dataset of over 2,400 multiple choice questions for evaluating AI systems on a range of practical biology research capabilities, including recall and reasoning over literature, interpretation of figures, access and navigation of databases, and comprehension and manipulation of DNA and protein sequences. Importantly, in contrast to previous scientific benchmarks, we expect that an AI system that can achieve consistently high scores on the more difficult LAB-Bench tasks would serve as a useful assistant for researchers in areas such as literature search and molecular cloning. As an initial assessment of the emergent scientific task capabilities of frontier language models, we measure performance of several against our benchmark and report results compared to human expert biology researchers. We will continue to update and expand LAB-Bench over time, and expect it to serve as a useful tool in the development of automated research systems going forward. A public subset of LAB-Bench is available for use at the following URL: https://huggingface.co/datasets/futurehouse/lab-bench40 pages, 5 main figures, 1 main table, 2 supplemental figures, 4 supplemental tables. Submitted to NeurIPS 2024 Datasets and Benchmarks track (in review