Variety Of Sources Research Articles

FACILITATORS OF AND BARRIERS TO ADULT DAY SERVICES PROVISION: PROVIDERS’ PERSPECTIVES

Abstract Adult day services (ADS) can help people living with various chronic conditions remain in their community, and may improve well-being among participants and their family caregivers. Despite the potential benefits, knowledge of the ADS landscape is underdeveloped, especially as it pertains to the experiences of ADS providers since the COVID-19 pandemic. This study aimed to understand the facilitators and barriers experienced by ADS providers. We conducted two focus groups with seventeen ADS providers in Ohio. Thematic analysis was used to identify themes in the data. Several facilitators for providing ADS were identified, including dedicated, consistent, and committed staff; partnerships with local organizations; sufficient reimbursement; and diverse funding sources. Barriers included: staffing issues (e.g., recruitment and retention); referral and recruitment of ADS participants; limited knowledge, awareness, and misconceptions about ADS among families and case managers; transportation services (e.g., inadequate and safe escort of participants to and from ADS centers); and insufficient or delayed reimbursement. Providers identified the following solutions with the potential to mitigate perceived barriers: educational initiatives to foster understanding of ADS among key stakeholders (e.g., regulators, case managers, community members) and additional funding and media publicity support from local, state, and federal authorities. As one of the only studies that seeks to understand the landscape of ADS in Ohio, this research is uniquely situated to provide crucial insights for tailoring interventions that consider ADS providers’ unique challenges. It also informs future research focusing on improving the quality of ADS.

Molecular identification and antimicrobial activity of Streptomyces spp. isolated from soil in Garmian Region, Iraq

Multidrug-resistant pathogens present a significant obstacle in the prevention and treatment of infectious diseases, making the discovery of new antimicrobial agents from diverse sources essential. Actinobacteria, especially Streptomyces genus, are well-known for their prolific production of secondary metabolites with antimicrobial capabilities. The geographical niches of Iraq are one of the least explored ones for Actinobacteria strains. Hence, this study aimed to isolate Actinobacteria species from the soil in the Garmian region of Iraq and evaluate their potential antimicrobial activity A total of forty soil samples were collected and screened using various standard culture media to facilitate isolation, morphological identification, and evaluation of antimicrobial activity. Molecular identification and species confirmation were performed through amplification of the 16S rRNA gene via PCR, followed by sequence comparison with the NCBI database. The obtained two distinct strains of Streptomyces were identified, with one demonstrating significant antimicrobial activity against several pathogens, namely, Escherichia coli ATCC 25922, Staphylococcus aureus ATCC 25923, and Candida albicans ATCC 10231. Both isolated strains were registered in NCBI under accession numbers OR564031 and OR564032, corresponding to isolates 26 and 29, respectively. Their sequences exhibited high similarity to Streptomyces rochei strain SCOS (Accession No. KX575853.1) with 99.04% identity, and Streptomyces sp. WAC06273 strain (Accession No. CP042278.1) with 98.81% respectively. These Streptomyces strains from Iraqi soils show promise as potential candidates for novel antibiotic discovery. Further exploration of Streptomyces in soils from Garmian region, along with identification and purification of the active metabolites identified in this study, is strongly recommended.

Evaluating Ensemble Learning Techniques for Class Imbalance in Machine Learning: A Comparative Analysis of Balanced Random Forest, SMOTE-RF, SMOTEBoost, and RUSBoost

Purpose: This research aims to identify the optimal ensemble learning method for mitigating class imbalance in datasets utilizing various advanced techniques which include balanced random forest (BRF), SMOTE-random forest (SMOTE-RF), RUSBoost, and SMOTEBoost. The methods were systematically evaluated against conventional algorithms, including random forest and AdaBoost, across heterogeneous datasets with varying class imbalance ratios. Methods: This study utilized 13 secondary datasets from diverse sources, each with binary class outputs. The datasets exhibited varying degrees of class imbalance, offering scenarios to assess the effectiveness of ensemble learning techniques and traditional machine learning approaches in managing class imbalance issues. Study data were split into training (80%) and testing (20%), with stratified sampling applied to maintain consistent class proportions across both sets. Each method underwent hyperparameter optimization with distinct settings with repetition over 10 iterations. The optimal method was evaluated based on balanced accuracy, recall, and computation time. Result: Based on the evaluation, the BRF method exhibited the highest performance in balanced accuracy and recall when compared to SMOTE-RF, RUSBoost, SMOTEBoost, random forest, and AdaBoost. Conversely, the classical random forest method outperformed other techniques in terms of computational efficiency. Novelty: This study presents an innovative analysis of advanced ensemble learning techniques, including BRF, SMOTE-random forest, SMOTEBoost, and RUSBoost, which demonstrate significant effectiveness in addressing class imbalance across various datasets. By systematically optimizing hyperparameters and applying stratified sampling, this research produces findings that redefine the benchmarks of balanced accuracy, recall and computational efficiency in machine learning.

Deep learning in cybersecurity: Enhancing threat detection and response

Deep learning (DL) has changed the cybersecurity domain by providing sophisticated tools for detecting and mitigating an evolving landscape of cyber threats. This study explores the application of deep learning techniques, including convolutional neural networks (CNNs) and recurrent neural networks (RNNs), in real-time threat detection and response. These models excel in identifying patterns and anomalies within vast and complex datasets, enabling accurate detection of malware, phishing attempts, and insider threats. Their ability to autonomously learn from diverse sources, such as network traffic, user behaviour, and system logs, enhances the efficacy of cybersecurity systems. Despite these advancements, the field faces significant challenges, including adversarial attacks designed to exploit vulnerabilities in deep learning algorithms. These attacks manipulate input data to deceive models, potentially bypassing security mechanisms and compromising critical systems. Addressing this issue requires a multi-faceted approach, integrating robust training methods, data augmentation, and defensive mechanisms such as adversarial training and gradient masking. Furthermore, explainability and interpretability of deep learning models remain crucial for building trust and improving decision-making in security operations. The paper also emphasizes the importance of a proactive, layered defense strategy to counteract sophisticated cyber threats. This includes combining deep learning with traditional cybersecurity measures and incorporating threat intelligence to enhance system resilience. By bridging the gap between state-of-the-art DL methodologies and practical applications in cybersecurity, this research provides a roadmap for improving threat detection and response capabilities, ultimately contributing to the development of secure, adaptive, and resilient cyber infrastructures.

Driving Safer: A Look at How Men and Women Use Advanced Driver Assistance Systems

Objective — This study investigates the nuanced landscape of gender-specific patterns in utilization of Advanced Driver Assistance Systems (ADAS), aiming to contribute insights to enhance road safety. Methods — Leveraging data from diverse sources, including Scopus, Web of Science, and Google Scholar, the method employed involves a meticulous examination of studies exploring gender differences in driving behaviors and ADAS adoption. Key factors influencing these differences, were analyzed including age, experience, and attitudes toward technology. Cultural and societal influences on gender-specific driving behaviors are explored, shedding light on the intricate interplay of perceptions and expectations. Results — The review identifies challenges in ADAS adoption, including limited awareness, misconceptions, and resistance to change. Opportunities for improvement are then outlined, encompassing comprehensive user training, clear communication of ADAS features, customizable settings, and incentives for ADAS-equipped vehicles. This study showed that men tend towards aggressive driving behaviors, while women prioritize caution and adherence to traffic rules. Women propensity for safety-oriented features such as lane-keeping assistance, while men display a preference for convenience-focused features such as adaptive cruise control and parking assistance. Conclusion — By addressing challenges and embracing opportunities, the study advocates for a future where ADAS technologies contribute significantly to safer and more equitable roadways for all drivers.

Income Inequality: A Journey of 200 Years of Economic Thought

The phrase "the rich get richer, and the poor get poorer" encapsulates a phenomenon that has long plagued societies worldwide. Most recently, Riddell et al. (2024) showed that, since 2020, the five richest men in the world have doubled their fortunes. At the same time, almost five billion people have seen their wealth fall. It further argued that corporations and its monopoly power are responsible for driving this unrelenting income inequality. This trend is confirmed by Chen et al. (2019) through an investigation into nearly 1 million firms covering 27 countries. Between 2000 - 2015, there has been a notable increase in corporate market power, especially in advanced economies. However, there is no easy solution because inequality is deeply rooted in numerous historical, political, and socioeconomic factors. Therefore, it is imperative to look beyond numbers to understand inequality better. In "Visions of Inequality: From the French Revolution to the End of the Cold War," veteran economist Branko Milanovic takes readers on a journey through the 200-year evolution of economic thought on income inequality. He achieved it by drawing from a diverse source, including original writings, correspondence, and biographical accounts of luminaries in the economics discipline.

Using machine learning to predict disease outbreaks and enhance public health surveillance

Disease outbreaks pose significant challenges to public health systems, often requiring rapid response strategies to mitigate widespread health and economic impacts. Traditional methods of outbreak prediction and surveillance, while effective, often lack the capacity to process and analyse the vast quantities of heterogeneous data generated in modern healthcare ecosystems. Machine learning (ML) offers transformative potential in this domain, leveraging its ability to process large datasets, identify complex patterns, and provide real-time insights. By integrating diverse data sources such as electronic health records (EHRs), social media feeds, climate data, and genomic sequences, ML algorithms can predict disease outbreaks with unprecedented accuracy. Supervised learning models, for instance, have been successfully applied to forecast influenza trends, while unsupervised clustering techniques have been employed to detect anomalies indicative of emerging infectious diseases. Moreover, ML facilitates advanced public health surveillance by automating data processing pipelines, enhancing real-time monitoring capabilities, and enabling resource optimization for outbreak responses. Despite these advances, the adoption of ML in public health surveillance is not without challenges. Issues related to data privacy, ethical considerations, algorithm interpretability, and integration with existing public health infrastructures remain significant hurdles. Addressing these challenges requires a multidisciplinary approach, incorporating robust data governance frameworks, improved algorithm transparency, and collaborations between technology developers and public health stakeholders. This paper highlights the critical role of ML in transforming public health surveillance, focusing on its application in disease outbreak prediction. It underscores the importance of continued innovation, regulatory support, and ethical considerations in advancing ML-driven solutions for global health security.

Data-driven approaches to mitigate academic stress and improve student mental health

This paper critically examines the efficacy and transformative potential of data-driven methodologies to assess, monitor, and mitigate academic stress, thereby enhancing student mental health. We aim to uncover latent stress patterns and trigger points within academic environments by utilizing a robust framework of advanced analytics, machine learning, and predictive modeling. Applying these technologies allows for the strategic customization of interventions tailored to individual and group needs in real time. By synthesizing data across multiple educational settings—including K-12 schools and higher education institutions—this study provides comprehensive insights into how varied data sources and modeling techniques can be harmonized to effectively detect and address student stress. The outcomes highlighted in this paper demonstrate the significant impact of data-driven methodologies not only in improving student well-being but also in fostering an educational atmosphere that prioritizes mental health. Our findings underscore the critical role that technological integration in educational strategies plays in revolutionizing student support systems and setting a new standard for mental health care within academic institutions.

Challenges and Prospects of Creating Abai Poetic Corpus

This scientific article explores the complexities and prospects of creating a poetic corpus of Abai Kunanbayev. The primary goal of the project is to digitize and annotate Abai's poetry, addressing challenges such as the accessibility of diverse sources, linguistic barriers, technical difficulties, and cultural interpretations. It presents the results obtained from the creation of the corpus, highlighting its significance in preserving Kazakh cultural heritage and advancing scholarly research. The discussion explores the implications of the corpus for linguistic studies, educational initiatives, and international collaborations in digital humanities. The conclusion reflects on the project's achievements, outlines future directions for expanding the corpus, and emphasizes its role in promoting and preserving Abai Kunanbayev literary legacy.

Antimicrobial resistance, virulence gene profiling, and genetic diversity of multidrug-resistant Pseudomonas aeruginosa isolates in Mazandaran, Iran

BackgroundPseudomonas aeruginosa is a major cause of healthcare-associated infections (HAIs), particularly in immunocompromised patients, leading to high morbidity and mortality rates. This study aimed to investigate the antimicrobial resistance patterns, virulence gene profiles, and genetic diversity among P. aeruginosa isolates from hospitalized patients in Mazandaran, Iran.MethodsFrom September 2021 to April 2022, 82 non-duplicate P. aeruginosa isolates were collected from diverse clinical sources. Identification was confirmed using API 20 NE (bioMérieux, Marcy l’Etoile, France). Antimicrobial susceptibility testing was conducted using the Kirby-Bauer disk diffusion method according to CLSI guidelines to assess resistance to a range of antibiotics. The virulence profile (exoT, exoY, exoU, toxA, plcH, plcN, algD, aprA, lasB and exoS) of each P. aeruginosa isolate was determined by PCR. The genetic diversity among the strains was evaluated using the random amplification of polymorphic DNA (RAPD) technique. Clustering was based on a Dice similarity coefficient of ≥ 85%.ResultsOf the 82 total strains, P. aeruginosa exhibited the highest and lowest resistance toward ticarcillin-clavulanate (98.78%) and colistin (0%), respectively. Moreover, 100% of the P. aeruginosa isolates were MDR. The following prevalence of virulence factor genes was observed: aprA, lasB, algD, toxA, plcH, exoY, and exoT in 100% of isolates. The plcN, exoS, and exoU were identified 98.78%, 67.07%, and 45.12%, respectively. The RAPD patterns obtained with primers 272 and 208 had respectively 2–19 and 6–17 bands. According to the Dice similarity coefficient of higher than 85%, 56 and 39 clusters were recognized.ConclusionThe high rate of multidrug resistance combined with the widespread presence of virulence genes in P. aeruginosa isolates highlights the potential for increased infection severity, morbidity, and mortality in hospitalized patients. The substantial genetic diversity observed among isolates suggests that P. aeruginosa in this region may rapidly evolve, necessitating ongoing surveillance and more targeted antimicrobial strategies.Clinical trial numberNot applicable.

Household livelihood diversification and its determinant factors in Robe town and its surrounding hinterlands, Bale zone, southeast Ethiopia

Livelihood diversification is a set of activities that households engage in to generate revenue, and sustain life, or maximize financial benefit. This study examined the impact of rural-urban connectivity on household income diversification into farm and non-farm businesses, as well as the underlying driving factors in Robe Town’s hinterland, Bale Zone, Southeastern Ethiopia. Structured survey questions, key informant interviews, focus group discussions, and field observations were used to collect quantitative and qualitative data. A total of 260 samples were taken, representing 5% of households in hinterland villages within a 20-kilometre radius. Both multiple linear regression and descriptive statistics were utilized to analyze the data. According to the descriptive statistics, 40% of the 260 households generated income from non-farm sources. The data also showed that most households (84.3%) earned their income from agricultural activities. It was further revealed that income generated from non-farm businesses accounted for 16.3% of the total household income share. The average Simpson Diversity Index (SDI) for household livelihood income source diversification was 0.5087, indicating a modest level of livelihood diversity. In the hinterland, 21.9% of households have less diverse income sources, while 58.5% and 19.6% have moderately and highly diverse income sources, respectively. Landholding size, education level, irrigation use, harvesting of forest products, non-farm activities, Tropical Livestock Unit (TLU), farm commercialization, and rural-urban linkages are statistically significant drivers of farm households’ livelihood diversification (P < 0.05). The findings have policy implications; therefore, they call for expanding rural services, rural non-farm businesses, and improved linkages between rural and urban areas to facilitate the development of a portfolio of livelihood activities that sustain life, foster wealth creation, initiate vibrant inclusive socioeconomic development in the region, and ultimately reduce poverty.

Safety Considerations of Trace Metals in Locally Produced Nutritive Food-Drinks Consumed in Yenagoa Metropolis, Nigeria.

This study assessed the safety of trace metal concentrations in locally produced nutritive food-drinks consumed in Yenagoa metropolis, Bayelsa State, Nigeria. Three different drink types (viz, tiger nut juice, a mixture of tiger nutand soya bean juiceand soya bean juice) were purchased from various locations in Yenagoa metropolis, Bayelsa State, Nigeria, between January and February 2024. Thirty samples were analyzed for trace metals using atomic absorption spectrophotometry. Detected metal concentrations ranged from 0.077 to 0.458 mg/L for iron, 0.159 to 1.251 mg/L for copper, 0.000 to 0.070 mg/L for zinc, 0.000 to 0.080 mg/L for cadmium, and 0.000 to 0.068 mg/L for manganese. Three (zinc, manganese, and copper) of the five detected metals had concentrations below the World Health Organization (WHO) and/or Standard Organization of Nigeria (SON) permissible limits using drinking water guidelines. Pearson correlation analysis indicated diverse metal sources, while principal component analysis (PCA) revealed significant variance driven by essential and trace metals. At the same time, crucial metals like zinc and iron dominate, and the presence of cadmium raises health concerns. The results suggest that while iron, zinc, and manganese generally pose minimal health risks, elevated copper and cadmium levels present potential non-carcinogenic risks in some samples. Hazard Index (HI) values (0.673-5.349) highlight the need for regulatory attention due to the cumulative effects of multiple trace metals. Although the calculated cancer risk (CR) values suggest no significant carcinogenic threat, continuous monitoring and mitigation are crucial, particularly for vulnerable populations who may experience prolonged exposure.

MicroMetaSense: Coupling Plasmonic Metasurfaces with Fluorescence for Enhanced Detection of Microplastics in Real Samples.

Diverse analytical techniques are employed to scrutinize microplastics (MPs)─pervasive at hazardous concentrations across diverse sources ranging from water reservoirs to consumable substances. The limitations inherent in existing methods, such as their diminished detection capacities, render them inadequate for analyzing MPs of diminutive dimensions (microplastics: 1-5 μm; nanoplastics: < 1 μm). Consequently, there is an imperative need to devise methodologies that afford improved sensitivity and lower detection limits for analyzing these pollutants. In this study, we introduce a holistic strategy, i.e., MicroMetaSense, reliant on a metal-enhanced fluorescence (MEF) phenomenon in detecting a myriad size and types of MPs (i.e., poly(methyl methacrylate) (PMMA) and poly(ethylene terephthalate) (PET)) down to 183-205 fg, as well as validated the system with real samples (tap and lake) and artificial ocean samples as a real-world scenario. To obtain precise size distribution in nanometer scale, MPs are initially processed with an ultrafiltration on-a-chip method, and subsequently, the MPs stained with Nile Red dye are subjected to meticulous analysis under a fluorescence microscope, utilizing both a conventional method (glass substrate) and the MicroMetaSense platform. Our approach employs a metasurface to augment fluorescence signals, leveraging the MEF phenomenon, and it demonstrates an enhancement rate of 36.56-fold in detecting MPs compared to the standardized protocols. This low-cost ($2), time-saving (under 30 min), and highly sensitive (183-205 femtogram) strategy presents a promising method for precise size distribution and notable improvements in detection efficacy not only for laboratory samples but also in real environmental samples; hence, signifying a pivotal advancement in conventional methodologies in MP detection.

The Use of Advanced Technology (Artificial Intelligence &amp; Machine Learning) To Prevent the Funding of Terrorism in Indonesia

The financing of terrorism in Indonesia, has become increasingly complex due to the involvement of various domestic terrorist organizations. These groups obtain funds through diverse sources such as direct donations, membership fees, self-funding, and the misuse of non-profit organizations (NPOs). This study aims to explore and analyze the potential and challenges of utilizing advanced technologies, particularly Artificial Intelligence (AI) and Machine Learning (ML), in preventing terrorist financing in Indonesia. The research employs a qualitative descriptive approach, utilizing secondary data from FATF reports and related literature. The findings indicate that AI and ML can significantly enhance the detection and investigation of suspicious financial activities, provide real-time transaction monitoring, and facilitate inter-agency collaboration. However, challenges such as data limitations, regulatory complexity, high implementation costs, and data security and privacy issues must be addressed to fully leverage these technologies. This study provides recommendations for developing a supportive regulatory framework, enhancing inter-agency cooperation, and investing in better data infrastructure to effectively utilize AI and ML in combating terrorist financing.

Children as agents of cultural adaptation.

The human capacity for culture is a key determinant of our success as a species. While much work has examined adults' abilities to create and transmit cultural knowledge, relatively less work has focused on the role of children (approx. 3-17 years) in this important process. In the cases where children are acknowledged, they are largely portrayed as acquirers of cultural knowledge from adults, rather than cultural producers in their own right. In this paper, we bring attention to the important role that children play in cultural adaptation by highlighting the structure, function, and ubiquity of the large body of knowledge produced and transmitted by children, known as peer culture. Supported by evidence from diverse disciplines, we argue that children are independent producers and maintainers of these autonomous cultures, which exist with regularity across diverse societies, and persist despite compounding threats. Critically, we argue peer cultures are a source of community knowledge diversity, encompassing both material and immaterial knowledge related to geography, ecology, subsistence, norms, and language. Through a number of case studies, we further argue that peer culture products and associated practices - including exploration, learning, and the retention of abandoned adult cultural traits - may help populations adapt to changing ecological and social conditions, contribute to community resilience, and even produce new cultural communities. We end by highlighting the pressing need for research to more carefully investigate children's roles as active agents in cultural adaptation.

Big Data Analytics for Real-Time Marketing: Opportunities and Challenges

Big Data Analytics has revolutionized real-time marketing by enabling organizations to process vast amounts of data to deliver personalized and timely customer experiences. This research explores the opportunities provided by Big Data Analytics in transforming marketing strategies, such as enhanced customer segmentation, predictive analytics for targeted campaigns, and dynamic content delivery. It highlights how real-time data processing facilitates agile decision-making, allowing businesses to adapt to rapidly changing market conditions. Simultaneously, the study addresses the challenges of Big Data Analytics, including data privacy concerns, the integration of diverse data sources, and the complexities of algorithmic biases. By analyzing case studies and emerging trends, the research underscores the critical role of Big Data in fostering innovation while proposing actionable solutions to overcome its limitations. The findings demonstrate that effective utilization of Big Data Analytics can empower businesses to stay competitive in the fast-paced digital landscape.

Genetic Insights and Predictive Models in Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive neurodegenerative disorder characterized by a significant genetic component, making the identification of genetic risk factors critical for predicting disease onset and progression. This review explores the genetic characteristics associated with AD, focusing on key genes such as APOE, APP, PSEN1, and PSEN2, and their roles in amyloid deposition, tau tangles, and overall disease pathology. By utilizing genetic databases like the Alzheimer’s Disease Neuroimaging Initiative (ADNI), AlzGene, and the National Alzheimer’s Coordinating Center (NACC), researchers can integrate genetic findings with neuroimaging techniques such as magnetic resonance imaging (MRI) and positron emission tomography (PET) to better understand the interplay between genetic, environmental, and lifestyle factors in AD development. In addition, advanced machine learning models, including support vector machines (SVM), random forest (RF), convolutional neural networks (CNN), autoencoders (AE), and deep learning algorithms, combined with Polygenic Risk Scores (PRS), are increasingly used to integrate genetic, imaging, and clinical data for more precise AD risk prediction. While these advancements offer promising pathways for early diagnosis and intervention, significant challenges remain. These include difficulties in data integration across diverse sources, the development of robust and personalized predictive models, and addressing ethical concerns related to the use of genetic data. Future research will need to focus on overcoming these challenges to create more effective personalized prediction models that incorporate genetic, environmental, and lifestyle factors. Such developments hold the potential to revolutionize early detection, diagnosis, and treatment strategies for AD.

Microbiological Safety of Drinking Water in Dhaka City: A Study on Supply and Treated Water in Dhaka, Bangladesh

Access to safe and clean drinking water is a fundamental human right and a critical public health issue worldwide. The present study attempted to determine the microbiological quality and detection of pathogenic bacteria in different municipal water supplied in the different places of Dhaka city. This study involved the microbiological analysis of sixteen drinking water samples collected from diverse sources, including four from supply points, six from consumer points, and six treated water samples, obtained from Siddeswari, Sonargaon, Mirpur, and Badda. All the samples were contaminated with different microorganisms, and the total viable bacterial count ranged from 103 to 10⁷ CFU/mL. Pathogenic microbes like Pseudomonas spp. was present in all the samples, while Klebsiella spp., Salmonella spp., and Staphylococcus spp. were predominantly present in seven, six, and eleven samples, respectively. We also aimed to determine whether Shigella spp. was present in all the samples, but it was completely absent. Escherichia coli was detected in samples from both supply and consumer points within the range of 102 to 103 cfu/mL. The results indicate that the treatment procedure is not sufficiently effective in eliminating all pathogenic bacteria, and unhygienic environments contribute to post-treatment contamination. The study revealed that government should take necessary action to ensure public health safety, particularly with regard to access to safe drinking water. Stam. J. Microbiol. 2024; 14(1):36-40

Assessing the Plant Growth-Promoting Potential of Diverse Feather Protein Hydrolysates Produced by Keratinolytic Bacteria

Poultry slaughterhouses generate substantial quantities of feather waste, posing potential environmental pollution. These feathers can be degraded by microorganisms, yielding amino acid-rich protein hydrolysates. This research aims to assess and compare the microbial degradation rates of feathers from chickens (CK), guinea fowls (GF), and pigeons (PG) for generating amino acid-rich hydrolysates for cowpea growth promotion. Feather-degrading bacteria were isolated from diverse sources, and their capacity to degrade feathers was evaluated by examining keratinase yields, degradation rate, and amino acid composition. Physico-chemical conditions were optimised using a one-factor-at-a-time approach to enhance keratinase yield, while the growth-promoting potential of the different hydrolysates on cowpea was assessed in a pot experiment in a screen house. The results revealed that Lysinibacillus sp., from the soil of a slaughterhouse dumpsite, completely degraded 1g/L of GF within 7 days, followed by CK and PG within 8 days. Following optimisation, the keratinase yield improved to 190 U/ml after the degradation of 2g/L of feathers under optimal conditions of pH 8 and temperature of 40°C. The hydrolysate derived from CK exhibited higher amino acid content compared to that from GF and PG, with the exception of alanine, which was more abundant in the GF hydrolysate. All the hydrolysates contained eight essential amino acids, which significantly enhanced various growth parameters of cowpeas, including root length, shoot length, root nodules, leaf area, chlorophyll and leaf numbers after four weeks of planting. The study suggests that Lysinibacillus sp. is a promising isolate for efficiently degrading feathers to produce protein hydrolysates, potentially aiding the growth of cowpeas.

Parkinson's Disease Prediction: An Attention-Based Multimodal Fusion Framework Using Handwriting and Clinical Data.

Neurodegenerative diseases (NGD) encompass a range of progressive neurological conditions, such as Alzheimer's disease (AD) and Parkinson's disease (PD), characterised by the gradual deterioration of neuronal structure and function. This degeneration manifests as cognitive decline, movement impairment, and dementia. Our focus in this investigation is on PD, a neurodegenerative disorder characterized by the loss of dopamine-producing neurons in the brain, leading to motor disturbances. Early detection of PD is paramount for enhancing quality of life through timely intervention and tailored treatment. However, the subtle nature of initial symptoms, like slow movements, tremors, muscle rigidity, and psychological changes, often reduce daily task performance and complicate early diagnosis. To assist medical professionals in timely diagnosis of PD, we introduce a cutting-edge Multimodal Diagnosis framework (PMMD). Based on deep learning techniques, the PMMD framework integrates imaging, handwriting, drawing, and clinical data to accurately detect PD. Notably, it incorporates cross-modal attention, a methodology previously unexplored within the area, which facilitates the modelling of interactions between different data modalities. The proposed method exhibited an accuracy of 96% on the independent tests set. Comparative analysis against state-of-the-art models, along with an in-depth exploration of attention mechanisms, highlights the efficacy of PMMD in PD classification. The obtained results highlight exciting new prospects for the use of handwriting as a biomarker, along with other information, for optimal model performance. PMMD's success in integrating diverse data sources through cross-modal attention underscores its potential as a robust diagnostic decision support tool for accurately diagnosing PD.