bluuebunny/crossref_metadata_2025 huggingface dataset:
Quantum Effects in Synaptic Activity: Challenging the Deterministic Model of Neural Processes
Authors: Montgomery Richard
Published: July 2024

In this study, we explore the impact of quantum effects on synaptic activity and their implications for the deterministic model of neural processes. By simulating synaptic events in a large-scale neural network, we demonstrate how small probabilistic quantum events can accumulate and introduce variability in neural behavior. Our model incorporates Dirac delta functions to simulate sudden changes in synaptic probabilities, creating a non-linear, seesaw effect in the cumulative synaptic activity curve. The results reveal that even minimal quantum effects, when scaled to the vast number of synapses in the human brain, can lead to significant dissociations in neural activity. These findings challenge the traditional deterministic view, suggesting that neural processes—and by extension, decision-making and movements—may inherently incorporate elements of randomness. This probabilistic perspective aligns with theories of free will and underscores the need for further empirical research to validate the role of quantum mechanics in neuroscience. Our study provides a theoretical framework for understanding the complex interplay between quantum effects and neural dynamics, opening new avenues for exploring the nature of consciousness and the fundamental principles governing reality.
Xylose Reductase and Xylitol Production from Candida tropicalis ATCC750 Using Hemicellulose Hydrolysate from Cashew Apple Bagasse: Influence of Biotechnological Properties
Authors: Serpa Juliana de França , dos Santos Franciandro Dantas , Alves Soares Carlos Eduardo , Pessela Benevides Costa , Rocha Maria Valderez Ponte
Published: July 2024

Keywords: xylose reductase; cashew apple bagasse; bioprocesses; fluid dynamics; xylitol.
Susceptibility of Foodborne Pathogens to Milk-Origin Lactic Acid Bacteria Supernatants: A Comprehensive Meta-Regression Study
Authors: Fernandes Nathália , Loforte Yara , Cadavez Vasco , Gonzales-Barron Ursula
Published: July 2024

This systematic review and meta-analysis compile the in vitro antimicrobial efficacy of lactic acid bacteria (LAB) supernatant against three common dairy product pathogenic bacteria. After screening and analysis of full papers, identified by searches in PubMed, Scopus, and Web of Science databases, thirty-nine studies were regarded as relevant, and a total of 510 were extracted thereof. The effects of moderators, namely, pathogen’s concentration, susceptibility test method, agar type, incubation time and temperature, LAB supernatant pH and aliquot volume, on inhibition diameters were assessed by adjusting three pathogen-specific meta-regression models. Results showed that, in general terms, strains from the Enterococcus genus (15.90 ± 2.138 mm) displayed the highest inhibition values against L. monocytogenes, whereas Lacticaseibacillus strains were more effective against Staphylococcus aureus (11.89 ± 0.573 mm). Whereas the well diffusion test outperformed spot and disk diffusion tests; the pH of LAB supernatant resulted in high measurements of inhibition diameters (p
An Increase in the Prevalence of Clinically-Relevant Resistance-Associated Substitutions in Four Direct-Acting Antiviral Regimens: A Study Using GenBank HCV Sequences
Authors: Khalil Roaa , Al-Mahzoum Kholoud , Barakat Muna , Sallam Malik
Published: July 2024

Direct-acting antivirals (DAAs) revolutionized therapeutics of chronic hepatitis C. The emergence and transmission of HCV variants with resistance-associated substitutions (RASs) can undermine HCV treatment. This study aimed to assess the prevalence and temporal trends of RASs in HCV with a particular focus on the clinically-relevant RASs (cr-RASs). Near-complete HCV GenBank sequences archived in the Los Alamos HCV Database were analyzed. The study period was divided into two phases; before 2011 and 2011 onwards. Identification of RASs across three DAA classes; NS3, NS5A, and NS5B inhibitors was based on the 2020 EASL guidelines. The AASLD-IDSA recommendations were used to identify cr-RASs for three HCV genotypes/subtypes (1a, 1b, and 3) for four DAA regimens; ledipasvir/sofosbuvir; elbasvir/grazoprevir; sofosbuvir/velpatasvir; and glecaprevir/pibrentasvir. The final HCV dataset comprised 3443 sequences and the prevalences of RASs were 50.4%, 60.2%, and 25.3% in NS3, NS5A, and NS5B, respectively. In subtype 1a, resistance to ledipasvir/sofosbuvir was 32.8%, while resistance to elbasvir/grazoprevir was 33.0%. For genotype 3, resistance to sofosbuvir/velpatasvir and glecaprevir/pibrentasvir was 4.2% and 24.9%, respectively. Significant increase in cr-RASs was observed across the two study phases as follows: for ledipasvir/sofosbuvir in subtype 1a, cr-RASs increased from 30.2% to 35.8% (p=0.019); for elbasvir/grazoprevir in subtype 1a, cr-RASs increased from 30.4% to 36.1% (p=0.018); In subtype 1b, neither ledipasvir/sofosbuvir nor elbasvir/grazoprevir showed any cr-RASs in the first phase, but both were present at a prevalence of 6.5% in the second phase (p<0.001); for sofosbuvir/velpatasvir in genotype 3, cr-RASs increased from 0.9% to 5.2% (p=0.006); and for glecaprevir/pibrentasvir, cr-RASs increased from 12.0% to 29.1% (p<0.001). Rising prevalence of HCV RASs and cr-RASs was discernible. This highlights the necessity for ongoing surveillance and adaptation of novel therapeutics to manage resistance effectively. Updating of the clinical guidelines and treatment regimens are recommended to counteract the evolving resistance to DAAs.
Harnessing Fermentation by Bacillus and Lactic Acid Bacteria for Enhanced Texture, Flavor and Nutritional Value in Plant-Based Matrices
Authors: Fernández-Varela Raquel , Hansen Anders Holmgaard , Svendsen Birgit Albrecht , Moghadam Elahe Ghanei , Bas Arzu , Kračun Stjepan Krešimir , Poulsen Vera Kuzina
Published: July 2024

This article explores the transformative potential of fermentation in elevating the quality of plant-based matrices to match the desirable attributes of traditional dairy and meat products. As the demand for sustainable products without animal welfare issues increases, fermentation emerges as a key process to enhance the organoleptic properties and nutritional content of plant-based analogs. This study explores the effect of fermentation when applied to legume matrices, focusing on the resulting texture, flavor, and nutritional value. A selection of Bacillus subtilis, lactic acid bacteria (LAB) strains, and combinations thereof, showed potential for improving the aforementioned organoleptic and nutritional characteristics of fermented plant bases. In four different legume-derived matrices, fermentation improved texture, degraded undesirable plant carbohydrates, and removed off-flavor compounds, while producing desirable dairy-associated compounds. The degradation of the undesirable beany off-flavor-causing compound hexanal appears to be a universal phenomenon, as every tested strain as well as their combinations exhibited the capability to decrease the hexanal content albeit with varying efficiency. Some LAB strains were found to be capable of producing carotenoids and might hence have the potential for tailoring fermented plant-based matrices for specific applications, such as yellow cheese or red meat analogs.
Comparative Study of Two Decision-Making Measures: The Melbourne Decision-Making Questionnaire Versus the General Decision-Making Style
Authors: Aluja Anton , Balada Ferran , García Luis F. , Garcia Oscar
Published: July 2024

This study compares the Melbourne Decision-Making Questionnaire (MDMQ) and the General Decision-Making Style (GDMS), two of the most widely used decision-making questionnaires in the literature, in a large age- and sex-weighted general population sample of 714 men (45.7%) and 848 women (54.3%) between 18 and 90 years old. The objective was to evaluate the convergent and construct validity between several aspects of decision-making styles questionnaires. The results indicate that the two questionnaires replicate the factorial structure of four and five factors reported in the original studies respectively, through exploratory and confirmatory procedures in our cross-cultural context. The domains of both questionnaires that represent a strong or large correlation are Vigilance with Rational (.50), Hypervigilance, Buck-passing, and Procrastination with Avoidant (.45, .52 and .60). A Structural Equations Model (SEM) between both questionnaires indicates that both latent factors formed by the domains of the MDMQ and the GDMS obtain a correlation of .96. It is concluded that the two questionnaires measure similar aspects of the decision-making construct.
Real-Time Chatbot: Microservices Implementation in Distributed System Architecture
Authors: Uriawan Wisnu , Herdiyanto Reza Fahlevi , Millah Rd Imam Saepul , Irhamnillah Sami , Gunawan Siti Nurhayati
Published: July 2024

The development of a real-time chatbot using microservices architecture has been proposed as a solution to overcome the main challenges faced by single-service chatbots, namely limited scalability and performance. In this research, Agile Methodology is used as a development approach that allows the team to flexibly build and update each microservices service iteratively, according to the changing needs that occur. The results show that the implementation of microservices on a chatbot can result in faster responses to user requests, significantly improving system efficiency. The microservices architecture provides advantages in terms of modularity, where system components such as session management, business logic, and user interface can be organized and developed separately. This makes it possible to upgrade and improve certain parts without disrupting the entire system, as well as providing flexibility in customizing features and scaling services as needs evolve. Future research is expected to further explore the broader integration potential of this microservices system in chatbot applications, as well as evaluate the security and reliability aspects in a broader context of use.
Fruit Variation in Yellow-Fleshed A. chinensis Plants Grown in Southern Italy as a Function of Shoot Type
Authors: Dattola Antonio , Accardo Antonella , Zappia Rocco , Gullo Gregorio
Published: July 2024

One of the goals of modern orcharding is to have a high production of fruits that are uniform in size, organoleptic parameters, and health characteristics. The aim of the work was to study the assortment of different shoot types and their prevailing position along the cane and how shoot type can contribute to on shoot type on fruit quality in Actinidia tree. The experiment was carried out over two years in a commercial orchard of Actinidia chinensis, cv Gold 3. Along the cane, the shoots were classified as Spur shoot (SP), terminate shoot (TE), no terminate shoot (NT), and cut no terminate shoot (CNT). The data were statistically processed using ANOVA and Principal Component Analysis (ACP). Four different fruit categories were obtained from to the four shoot type to compared: a prevailing category (55% of total fruits), which is characterised by its higher soluble solids content, sweetness, and excellent health characteristics, but with a lower hardness of the pulp, which would lead to the hypothesis that harvesting could be brought forward; a second category of fruit (19% of total fruit) with the lowest soluble solids content, but a high antioxidant capacity detached from CNT shoot; another category of fruit (13% of total fruit) produced from NT shoots, with the lowest health value, but good sweetness perception; finally the category with the lowest fruit percentage over total fruit harvested (10%) from SP shoots, characterised by its smaller size. It is to be verified what the performance of each category will be after post-harvest to speculate whether it is possible to differentiate the product at harvest into quality categories or possibly to differentiate the harvest time.
Multifactorial Contributors to the Longevity of Dental Restorations: An Integrated Review of Related Factors
Authors: Santos Maria Jacinta Moraes Coelho , Zare Elham , McDermott Peter , Santos Jr. Gildo Coelho
Published: July 2024

Purpose: This integrated review aims to identify and analyze the multifactorial contributors to the longevity of direct restorations, focusing on tooth-, patient-, and dentist-related factors. Materials and Methods: A literature search was performed using an electronic database, PubMed/Medline, Web of Science, and Scopus, between 1980 and 2024. Titles and abstracts of papers that evaluated aspects categorized into tooth-related, patient-related, and dentist-related factors influencing restoration failure were selected and screened. Full-text assessments were conducted, and the extracted data were compiled, summarized, and synthesized. The reference lists of the collected papers were also screened, and relevant citations were included in this review. Data were gathered from clinical and laboratorial studies, systematic reviews, and meta-analyses to provide a comprehensive understanding of restoration longevity. Results: Among the tooth-related factors, multiple-surface restorations, deep margins, tooth location, and tooth vitality significantly impact restoration survival. Patient-related factors such as medical conditions, risk predictors of caries, age, sex, parafunctional habits, smoking, periodontal health, number of restorations, and socioeconomic status all play crucial roles. Regarding dentist-related factors, the decision-making process, age, experience, and dentist manual dexterity are vital aspects. Furthermore, the technique used, including isolation methods for moisture control, as well as the type of dental practice (large group vs. small practice), notably influenced the restoration survival. Conclusions: The longevity of dental restorations is influenced by a complex interplay of tooth-related, patient-related, and dentist-related factors. Strategies to improve restoration outcomes should consider all these multifactorial contributors. Continuing professional education, diligent patient guidance on the factors that influence restoration survival, careful material selection and restorative technique, and tailored individual treatment are crucial factors to reduce failure rates and improve the lifespan of restorations.
BrainNest: Implementation of TypeScript and MERN Stack to Improve Scalability of Interactive and Personalized E-learning
Authors: Uriawan Wisnu , Putra Rizky Dwi , Siregar Rizqi Ilham , Gunawan Siti Nurhayati , Adriansyah Sumitra , Nurrohman Wiki
Published: July 2024

In the age of digital revolution technology is being used more and more in education around the world to improve student learning. By enabling ongoing skill development and knowledge acquisition through digital platforms e-learning has become a crucial tool. Through E-Learning online learning is becoming increasingly popular and effective in addition to traditional methods of instruction as highlighted by the latest statistics from Badan Pusat Statistik Indonesia (BPS). In order to address the issues of scalability, interactivity and security in e-learning platforms. This study introduces a web-based learning environment that is focused on teaching programming. Leveraging the MERN stack, while also integrating TypeScript for improved code reliability, Next.js for accelerated React.js functionality, Tailwind CSS for responsive design. Backend features include media management with Cloudinary, secure video streaming with VdoCipher, data storage with MongoDB, and caching with Redis. Iterative development will be following agile methodology. In order to create a vibrant and encouraging learning environment the platform includes interactive course pages multimedia content real-time feedback mechanisms and community-building tools. Despite some early difficulties the implementation phase effectively achieved development goals. To further support international online learning initiatives further efforts will be directed toward improving platform security and functionality.
Energy-Efficient Task Offloading in Wireless-Powered MEC: A Dynamic and Cooperative Approach
Authors: He Huaiwen , Zhou Chenghao , Huang Feng , Shen Hong , Li Shuangjuan
Published: July 2024

Mobile Edge Computing (MEC) integrated with Wireless Power Transfer (WPT) is emerging as a promising solution to reduce task delays and extend the battery life of Mobile Devices (MDs). Cooperative user communication, or relay technology, enhances communication efficiency for users located far from the base station by mitigating the double near-far effect caused by distance. This is considered a key technology in Beyond 5G (B5G) and future communication systems. In this paper, we focus on maximizing the long-term energy efficiency (EE) of a user-cooperation WPT-MEC system, while taking into account the uncertain load dynamics at the edge MD and the time-varying state of the wireless channel. The joint optimization of wireless charging time fraction, MD offloading duration, helper node processing time, and transfer power decision presents significant challenges due to the coupling of data offloading among cooperative users and a volatile system environment. To address these challenges, we formulate the problem as a stochastic programming problem and propose an online control algorithm, DOUCA, to solve it. Our approach utilizes Dinkelbach’s method and Lyapunov optimization theory to decouple the sequential decision problem into a deterministic sub-problem for each time slot. For the sub-problem, we use variable substitution to convert the non-convex problem into a convex one, containing only a small number of variables, which can be efficiently solved. Furthermore, we provide a mathematical analysis of our algorithm’s performance. Extensive simulation results demonstrate the effectiveness of our proposed algorithm, as evidenced by an impressive energy efficiency improvement of over 20% compared to benchmark methods. Our algorithm also achieves a trade-off between EE and system stability.
RnP: Management Information System for Camping Equipment using Agile Method
Authors: Uriawan Wisnu , Mulyana Devi , Haekal Firza Pratama Dika , Ghifari Aghiz , Zulfa Salsabila Amanda , Nurul Lathifah Alya , Nurul Yamin Alfin
Published: July 2024

Camping offers invaluable experiences in nature exploration, but the expense of acquiring camping gear can deter potential enthusiasts. To mitigate this, camping gear rental services emerge as a practical solution, promoting accessibility and reducing financial barriers. However, existing rental systems often encounter issues such as limited payment options, inconvenient pickup/return processes, and opaque management. In response, this paper introduces "Rent-n-Play," a web-based camping gear rental platform built on Laravel framework. It enables cashless transactions, offers pickup/delivery services, and employs a real-time admin system for transparent management. User feedback guided platform enhancements, including review systems and responsive support. The "Rent-n-Play" platform enhances user experience, fosters trust, and promotes sustainable camping practices, thus contributing to a more accessible and enjoyable camping culture.
Pneumonia Detection from Chest X-rays Using the CheXNet Deep Learning Algorithm
Authors: Bairwa Himanshu , `Jangid Rinku
Published: July 2024

- Early and accurate detection of pneumonia from chest X-ray images is crucial for timely treatment and patient care. In study presents a robust computational framework leveraging the CheXnet algorithm, a deep learning model, to identify pneumonia from chest X-rays. Developed a neural network architecture trained on a substantial dataset of chest X- ray images labeled as 'Normal' and 'Pneumonia'. The user- friendly interface of the system allows for seamless uploading of images, with the model subsequently providing predictions, a confusion matrix, accuracy metrics, and layer activations visualizations. The algorithm's performance was rigorously evaluated, with a confusion matrix indicating a high number of true positives and true negatives. Specifically, the model detected pneumonia with an accuracy of 92.47%, as shown by the results displayed in the system's interface. The precision, recall, and F1-score for pneumonia detection were 0.90, 0.96, and 0.93 respectively, with the overall system achieving an accuracy of 0.91 across the test set of 624 images. Visualizations of the neural network layers reveal the complex feature mappings the model uses to differentiate between normal and pathological findings. These insights could be vital for enhancing the interpretability of deep learning models in medical diagnostics. The Results indicate that the CheXnet-based neural network is a promising tool for automating pneumonia detection in clinical settings, which can assist radiologists in the diagnostic process and potentially improve patient outcomes through earlier intervention.
A Tumor Homing Peptide-Linked Arsenic Compound Inhibits Pancreatic Cancer Growth and Enhances the Inhibitory Effect of Gemcitabine
Authors: He Hong , Dumesny Chelsea , Carrall Judith A , Dillon Carolyn T , Eutick Mal , Dong Li , Baldwin Graham S. , Nikfarjam Mehrdad
Published: July 2024

Arsenic trioxide (ATO) has been shown to inhibit pancreatic cell growth in vitro and to promote the inhibitory effects of gemcitabine on pancreatic cancer in vivo. However, the high toxicity of ATO associated with the required high doses and indiscriminate targeting has limited its clinical application. This study aimed to determine whether coupling arsenic to a tumor homing peptide would increase the inhibitory potency against pancreatic cancer cells. The effects of this peptide-linked arsenic compound and ATO on pancreatic cancer growth were tested in vitro and in a mouse model. The data demonstrated that the peptide-linked arsenic compound inhibited pancreatic cancer cell growth more potently with IC50 values 10 times lower than ATO. Like ATO, the peptide-linked compound induced cell death and cell cycle arrest. More importantly, the peptide-linked arsenic compound inhibited pancreatic cancer growth in mice and enhanced the inhibitory effect of gemcitabine on pancreatic cancer growth at 2 times lower molar concentration than ATO. These results indicate that the peptide-linked arsenic compound inhibited pancreatic cancer more potently than ATO and suggest a potential clinical use for the combination of gemcitabine with the peptide-linked arsenic compound for the treatment of pancreatic cancer.
Application of Quantum Neural Network for Solar Irradiance Forecasting: A Case Study Using the Folsom Dataset, California
Authors: Oliveira Santos Victor , Marinho Felipe Pinto , Costa Rocha Paulo Alexandre , Thé Jesse Van Griensven , Gharabaghi Bahram
Published: July 2024

Quantum machine learning applications have become viable with the recent advancements in quantum computing. Merging ML with the power of quantum computing holds great potential for data-driven decision-making, as well as the development of more powerful models capable of handling more complex datasets with faster processing time. This area offers potential for improving the accuracy of real-time forecasting renewable energy production. However, the literature on this topic is sparse. Addressing this knowledge gap, this study aims to design, implement, and evaluate the performance of a quantum neural network forecast model for solar irradiance up to 3-hours ahead. The proposed model was compared with Support Vector Regression, Group Method of Data Handling, and Extreme Gradient Boost classical models. Using the best quantum neural network configuration found, the proposed framework could provide competitive results when compared to its competitors, considering forecasting intervals of 5- to 120-minutes ahead, where it was the fourth best-performing paradigm. For 3-hours ahead predictions, the QNN was able to overcome its clas-sical counterparts, but the XGBoost. This fact can be an indication that the quantum model may identify and retrieve relevant spatiotemporal information from the input dataset in such a manner not attainable by the current classical approaches.
Harmonizing Heartbeats: The Mosaic of Cardiac Resynchronization Therapy Responders — A Comprehensive Exploration of Diverse Criteria and Predictors
Authors: Boxhammer Elke , Zauner Sophie , Kraus Johannes , Dinges Christian , Schernthaner Christiana , Danmayr Franz , Kolbitsch Tobias , Granitz Christina , Motloch Lukas J , Hammerer Matthias , Lichtenauer Michael , Hoppe Uta C , Strohmer Bernhard
Published: July 2024

Background: Heart failure (HF) remains a challenging healthcare issue, necessitating innovative therapies like cardiac resynchronization-defibrillation therapy (CRT-D). However, defining CRT-D responders lacks uniformity, impeding effective clinical evaluation. This study explores diverse CRT-D responder definitions, encompassing functional, echocardiographic, and laboratory criteria.

Materials & Methods: A single-center study involving 132 CRT-D patients scrutinized responder criteria, including NYHA stage, LVEF increase, and proBNP decrease. Statistical analyses, such as Kaplan–Meier curves and Cox hazard regression, were employed to evaluate responder characteristics and survival outcomes.

Results: Responder rates varied across criteria, revealing nuanced patient profiles. CRT-D responders, defined by NYHA decrease, LVEF increase, or proBNP decrease, exhibit improved survival rates after 2 and 3 years (p < 0.050). Young age, absence of recent myocardial infarction, and normal right ventricular, echocardiographic parameters emerge as predictors for positive response. In part, drug-based HF therapy correlates with increased responder rates. Cox regression identified LVEF ≥ 5% and proBNP decrease ≥ 25% as independent predictors of extended survival.

Conclusions: CRT-D responder definitions exhibit considerable variability, emphasizing the need for a nuanced, patient-centered approach. Factors like right ventricular function, drug therapy, atrial fibrillation, and renal function influence responses. The study enriches our understanding of CRT-D response, contributing to the foundation for personalized HF management.
A Survey on Neuromorphic Architectures for Running Artificial Intelligence Algorithms
Authors: Al Abdul Wahid Seham , Asad Arghavan , Mohammadi Farah
Published: July 2024

Neuromorphic computing, a brain inspired non-Von Neumann computing system, addresses the challenges posed by the Moore’s law memory wall phenomenon. It has the capability to increasingly enhance performance while maintaining power efficiency. Neuromorphic chip architecture requirements vary depending on the application and optimizing it for large-scale applications remains to be a challenge. Neuromorphic chips are programmed using spiking neural networks which provide them with important properties such as parallelism, asynchronism, and on-device learning. Widely used spiking neuron models include the Hodgkin-Huxley Model, Izhikevich model, integrate-and-fire model and spike response model. Hardware implementation platforms of the chip follow three approaches: analog, digital, or a combination of both. Each platform can be implemented using various memory topologies which interconnects with the learning mechanism. Current neuromorphic computing systems typically use the unsupervised learning spike timing-dependent plasticity algorithms. However, algorithms such as voltage-dependent synaptic plasticity have the potential to enhance performance. This review summarizes the potential neuromorphic chip architecture specifications and highlights which applications they are suitable for.
Revitalizing BCG Immunotherapy for Bladder Cancer: Nanotechnology and Bioengineering Approaches
Authors: Lv Maoxin , Shang Shihao , Liu Kepu , Wang Yuliang , Xu Peng , Song Hao , Zhang Jie , Sun Zelong , Yan Yuhao , Zhu Zheng , Wu Hao , Li Hao
Published: July 2024

Bacillus Calmette-Guérin (BCG) immunotherapy has been a cornerstone treatment for non-muscle invasive bladder cancer for decades and still faces challenges, such as severe immune adverse reactions, which reduce its use as a first-line treatment. This review examines BCG therapy’s history, mechanisms, and current status, highlighting how nanotechnology and bioengineering are revitalizing its application. We discuss novel nanocarrier systems aimed at enhancing BCG's efficacy while mitigating specific side effects. These approaches promise improved tumor targeting, better drug loading, and enhanced stimulation of anti-tumor immune responses. Key strategies involve using functional BCG cell wall components and co-delivering BCG with chemotherapeutics to achieve synergistic effects. While most studies remain experimental, this research direction offers hope for overcoming BCG's limitations and advancing bladder cancer immunotherapy. Further elucidation of BCG's mechanisms and rigorous safety evaluations of new delivery systems will be crucial for translating these innovations into clinical practice.
Mitochondrial Dysfunction in Glycogen Storage Disorders (GSDs)
Authors: Mishra Kumudesh , Kakhlon Or
Published: July 2024

Glycogen storage disorders (GSDs) are a group of inherited metabolic disorders characterized by defects in enzymes involved in glycogen metabolism. Deficiencies in enzymes responsible for glycogen breakdown and synthesis can impair mitochondrial function. For instance, in GSD type II (Pompe disease), acid alpha-glucosidase deficiency leads to lysosomal glycogen accumulation, which secondarily impacts mitochondrial function through dysfunctional mitophagy, which disrupts mitochondrial quality control generating oxidative stress. In GSD type III (Cori disease), the lack of the debranching enzyme causes glycogen accumulation and affects mitochondrial dynamics and biogenesis by disrupting the integrity of muscle fibers. Malfunctional glycogen metabolism can disrupt various cascades thus causing mitochondrial and cell metabolic dysfunction through various mechanisms. These dysfunctions include altered mitochondrial morphology, impaired oxidative phosphorylation, increased production of reactive oxygen species (ROS), and defective mitophagy. The oxidative burden typical of GSDs compromises mitochondrial integrity and exacerbates the metabolic derangements observed in GSDs. The intertwining of mitochondrial dysfunction and GSDs underscores the complexity of these disorders and has significant clinical implications. GSD patients often present with multisystem manifestations, including hepatomegaly, hypoglycemia, and muscle weakness, which can be exacerbated by mitochondrial impairment. Moreover, mitochondrial dysfunction may contribute to the progression of GSD-related complications, such as cardiomyopathy and neurocognitive deficits. Targeting mitochondrial dysfunction thus represents a promising therapeutic avenue in GSDs. Potential strategies include antioxidants to mitigate oxidative stress, compounds that enhance mitochondrial biogenesis, and gene therapy to correct the underlying mitochondrial enzyme deficiencies. Mitochondrial dysfunction plays a critical role in the pathophysiology of GSDs. Recognizing and addressing this aspect can lead to more comprehensive and effective treatments, improving the quality of life of GSD patients.
This review aims to elaborate on the intricate relationship between mitochondrial dysfunction and various types of GSDs. The review presents challenges and treatment options for several GSD
Principal Component Analysis and Cluster Analysis as Tools for Studying the Variability of Abrus precatorius Germplasm
Authors: Sampath Prabakaran , Rajalingam Sowmyapriya , Pari Prabhu , Anandan Krishnamoorthi , Bhardwaj Rakesh , Gupta Veena
Published: July 2024

Abrus precatorius is a medicinal herb originated from Southeast Asia and native to India. The Hindustani centre of origin was found to possess lot of variability especially with respect to seed color. However, no previous researchers have focused on studying the variability by including the entire variability of the region. A total of 99 accessions were analyzed for its biochemical components like moisture content, total phenols, antioxidants, ash content, protein content, total monomeric anthocyanin and flavonols. Principal component analysis (PCA) shown that first three components viz., seed color, phenols and antioxidants are major principal components that contribute to 80.86% of total variability in the germplasm. The dendrogram constructed based on the biochemical data and by using the software Darwin version 6.0. formed three main clusters i.e. cluster 1, cluster 2 and cluster 3 which contain 55 accessions, 40 accessions and 1 accession respectively. Seed color was found to be the main criteria based on which the Abrus accessions were grouped. Accessions from different clusters also differ for their mean phytochemical constituents. Germplasm curators / plant breeders can select such elite genotypes and utilize them in crop improvement programmes.
Self-Efficacy Boosts Online Accounting Learning in Indonesia
Authors: Rokhillah Yuni , Biduri Sarwenda
Published: July 2024

This research investigates the impact of online lectures, learning methods, and learning motivation on accounting students' comprehension during COVID-19, using self-efficacy as a moderating variable. The study employs a quantitative approach with data from 155 accounting students at Universitas Muhammadiyah Sidoarjo, collected via questionnaires and analyzed using SmartPLS. Results show that online lectures, diverse learning methods, and learning motivation significantly enhance understanding of accounting subjects, with self-efficacy moderating these effects. These findings suggest that boosting self-efficacy can improve the effectiveness of online learning and teaching methods.
Highlights:
 

Learning Impact: Online lectures enhance accounting comprehension.
Moderation: Self-efficacy moderates the learning effects.
Implication: Boosting self-efficacy improves online learning outcomes.

 
Keywords: Online lectures, learning methods, learning motivation, self-efficacy, accounting comprehension
CLIS Model Boosts Science Understanding in Indonesian Fifth Graders
Authors: Maghfiroh Faiqotul , Setiyawati Enik
Published: July 2024

This study investigates the impact of the Children Learning In Science (CLIS) model on fifth graders' science concept understanding. Using a quantitative one-group pretest-posttest design, 24 students from SDN Jeruk Purut 1 Gempol were tested with essay questions. The paired sample t-test showed a significant improvement (Sig. 0.00 < 0.05) in science understanding after applying the CLIS model. The N-Gain test indicated a medium-level improvement with a mean score of 0.6513. These results demonstrate that the CLIS model effectively enhances students' science comprehension, suggesting its broader potential for science education.Highlight:
 

Significant impact of CLIS on science concept understanding.
Medium category increase in science concept understanding.
Valid and reliable instruments confirmed by expert judgment.

Keywoard: CLIS, science education, fifth graders, quantitative study, conceptual understanding
Multi-Factor Evaluation of RHL Success in Neniari Village, Indonesia
Authors: Lumapuy Elvira , Matinahoru Jhon. M. , Latumahina Fransina
Published: July 2024

General Background: Forest and land rehabilitation (RHL) is essential for addressing critical land conditions, aiming to transform unproductive lands into areas that can enhance community living standards through increased productivity. Specific Background: In Neniari Village, the RHL program has been implemented with mixed success, utilizing agroforestry systems and involving various species, including both timber and non-timber plants. Knowledge Gap: Existing studies often lack detailed analysis of the factors influencing RHL success at a community and ecological level. Aims: This study seeks to evaluate the overall success of the RHL program in Neniari Village by considering plant survival rates, environmental factors, and community participation. Results: The survival rate of plants averaged 70%, indicating a moderate level of rehabilitation success, which did not meet the targeted benchmarks. Novelty: This research uniquely combines biotic and abiotic factor analyses with community engagement levels to assess RHL outcomes. Implications: The findings highlight the need for enhanced community involvement and improved environmental condition management to boost RHL success rates, suggesting adjustments to future rehabilitation strategies.Highlights:

Community Involvement: Higher levels of community participation are directly linked to the success of rehabilitation efforts, indicating the need for more focused engagement strategies.
Plant Survival: An average survival rate of 70% among various plant species underlines the challenges of RHL in achieving its goals, pointing towards the necessity for improved environmental management.
Agroforestry Benefits: Utilizing agroforestry systems has shown potential benefits in enhancing ecological diversity and economic returns, suggesting its suitability for RHL practices.

Keywords: Forest Rehabilitation, Community Participation, Agroforestry Systems, Plant Survival Rates, Environmental Factors
Strategi Rekrutmen Dosen Berbasis Matrik S.W.O.T. di Institut Agama Islam Al Muhammad Cepu
Authors: Puspita Sari Kharisma , Slamet Mamiek
Published: July 2024

High institution needed the management of employment resources that were suitable, fair, and selective. However, the educational workers from private institution frequently considered as low quality workers because they could not be competitive with others. Al Muhammad Cepu High Institute has a fixed strategies for the recruitment of lecturers from Assistant Professor (AA/Asisten Ahli) up to Full Professor (Guru Besar/GB). In this study, the current researchers aimed to describe strategies in recruiting lecturers. Those strategies was reached based on the S.W.O.T. matrix analysis with the internal and external factors. This was descriptive research by completing the collaborative approach between qualitative and quantitative. Qualitative data were identifications of internal external  factors and synchronization of any norm and applicable rules related to lecturer’s recruitment. Quantitative data were tabulation of IFAS AND EFAS factors.  Next, the writers have found two strategies for recruiting functional-level lecturers that were; 1) open-ended recruitment, and 2) closed-ended recruitment. Moreover, from the current employment data and those applicable recruitment systems, the researchers processed the findings by using the S.W.O.T. approach. This process occurred in some steps like a)identifying internal and external factors, and b)composing IFAS (Internal Factor Analysis Strategy) and EFAS (External Factor Analysis Strategy), c) confirming the statistical rate starts from 0,00 up to 1,00,  d) taking the level from the number of 1,00 up to 4,00, and e) making quadrant matrix and relativity ratio, and 4)explain the challenges, obstacles, and opportunities.
An observational study to determine the optimal physical evaluation site for detecting anemia
Authors: Horibata Ken , Kondo Seigo , Hashimoto Shuji , Takemura Yousuke
Published: July 2024

Background: Clinical pallor has traditionally served as a screening tool for anemia; however, its assessment is inherently subjective and lacks objectivity. Therefore, this study aimed to investigate the correlation between skin and mucosal color, as measured by a spectrophotometer, and venous hemoglobin (Hb) values, to identify the optimal anatomical assessment site for detecting anemia.
Methods: A cross-sectional design. Ninety-two adult Japanese outpatients from an urban small hospital in Japan participated in the study. Using a spectrophotometer, the color of the lower lip and palpebral conjunctiva was measured indirectly, while the color of the nails and palms was measured directly. Skin and mucosal color were quantified using the L*a*b* color scale. The color difference (ΔE*) was employed to evaluate the discriminability of anemia identification across different assessment sites. Additionally, venous Hb values were recorded, with anemia defined as Hb <12.0 g/dL.
Results: The a* values of the lip, palpebral conjunctiva, palm, and inside of the upper arm exhibited a positive correlation with Hb values and were significantly lower in the anemic group. While the ΔE* levels for the palpebral conjunctiva and lip were readily discernible by the human eye when comparing the normal and anemic groups, recognition at this level proved challenging for other sites.
Conclusion: The palpebral conjunctiva and lip demonstrate potential suitability for visual diagnosis of anemia. In the nail, palm, and inside of the upper arm, anemia detection may require highly trained observation. Consequently, reliance on these sites for anemia diagnosis may lead to potential oversight.
Screening of reference genes by qRT-PCR in Chelonus formosanus
Authors: cai qiujiu , jia jingjing , huang weikang , lin zhufeng , feng qing , yao qi , ji xuncong
Published: July 2024

Chelonus formosanus is an important parasitic natural enemy of Lepidopteran insects and has great value in the biological control of insect pests. To screen the reference genes in the real-time fluorescence quantitative PCR (qRT-PCR) assay of Chelonus formosanus, six candidate reference genes were selected based on transcriptome data, including GAPDH, ACT, TU, TF, HSP, and RP. The expression stability of these six internal reference genes was determined by qRT-PCR in different tissues (head, chest, abdomen) and at different temperatures (11℃and 41℃), as well as with two insecticides (dinotefuran and chlorantraniliprole). The BestKeeper, GeNorm, NormFinder, Delta, and RefFinder were used to evaluate the expression stability of each gene after treatments. The results show that ACT is the most stable gene in different tissue assays. TU was the most stable reference gene in different insecticide experiments. The GAPDH was the most unstable gene in tissue assays and insecticide treatments. Furthermore, the expression of RP was the most stable under high and low temperature stresses, while the HSP was significantly affected by temperature treatments. Taken together, this study provides a basis for selecting reference genes under different conditions. It is also conducive to obtaining accurate and reliable data in the gene expression experiments of Chelonus formosanus.
Emergence of potato virus Y outbreaks in tomatoes in Brazil, the disease and spread
Authors: Lucena Vivian dos Santos , Nakasu Erich Yukio Tempel , Pereira José Luiz , Rêgo-Machado Camila de Morais , Rodrigues Cristiano Silva , Ueno Bernardo , Morais Ivair José , Inoue-Nagata Alice Kazuko
Published: July 2024

The emergence of 'Mexican Fire' disease in Brazilian tomato fields, attributed to potato virus Y (PVY), has raised concerns. Characterized by severe necrosis on median leaves, the definitive etiological agent of this disease remained unverified despite PVY detection in symptomatic plants. Our study aimed to elucidate the causal agent, occurrence, spread, and symptomatology of Mexican Fire. Deep sequencing of tomato leaves with typical necrotic symptoms confirmed the association with PVY, reinforcing its role as the causal agent. Serological tests with a PVY-specific polyclonal antibody consistently correlated symptoms with virus presence in a fresh market tomato field, with higher PVY incidence near older tomato and maize plants. Necrotic leaf distribution analysis revealed a predominant occurrence in median leaves, progressing upwards. Deep sequencing of symptomatic field samples exclusively detected PVY, reaffirming its role in symptom induction. Importantly, PVY inoculation under field and greenhouse conditions fulfilled Koch's Postulates, triggering leaf necrosis. Our findings unequivocally establish PVY as the causal agent of Mexican Fire disease, shedding light on its etiology, incidence, spread, and symptom expression, crucial for effective disease management strategies.
Evaluating the impact of Neisserria meningitidis pentavalent vaccine in the African meningitis belt: A modeling study
Authors: Hussaini Nafiu , Habib Abdurrazaq G. , Garba Iliyasu , Colubri Andrés , Musa Salihu S.
Published: July 2024

The African meningitis belt (AMB) countries experience recurring epidemics of \textit{Neisseria meningitidis} (Nm) meningococcal meningitis. This study presents a Susceptible-Carrier-Infected-Recovered (SCIR)-based dynamic model to analyze Nm transmission dynamics in this region, incorporating the impact of pentavalent vaccines. The model serves as a foundational tool for evaluating the effectiveness of these vaccines and aiding decision-making in control and prevention efforts.We examine various aspects of Nm infection epidemiology, focusing on the introduction of pentavalent meningococcal conjugate vaccines targeting serogroups A, C, W-135, Y, and X in AMB countries. Our analysis includes assessing different vaccination strategies for carriers with and without vaccine-covered serogroups, as well as evaluating disease transmissibility and infectivity in high- and low-risk areas.One of the main quantitative findings in our study is identifying the impact of significant decrease in the number of carriers with the implementation of pentavalent vaccines. This reduction underscores the efficacy of these vaccination strategies in reducing Nm transmission and controlling epidemics in these regions.Additionally, important epidemiological parameters such as reproduction numbers are derived to inform effective prevention measures. Climate change, socioeconomic disparities, and population density are identified as potential drivers of increased Nm outbreaks in these countries. However, further research is necessary to better understand disease variability and vulnerability, especially in young children.
DNet :A depression recognition network combining residual network and vision transformer
Authors: Jiang Zhongyi , Gao Xing , Cao Yin , Zhang Yihan , Dong Guanzhong , Chen Yun , Zhu Xuanyan , Zhang Qiaoyang , Bi Ran , Wang Ke
Published: July 2024

Depression is a prevalent and severe global mental disorder, yet its diagnosis and treatment encounter numerous challenges. This study introduces an innovative depression identification network, termed DNet. Our approach utilizes facial images and local facial images as crucial sources of data. Given that facial expressions of individuals with depression at varying severity levels inherently share similar latent facial features, subtle differences exist across multiple facial regions. To achieve higher recognition accuracy, a method is required to fuse advanced semantic features between local and global features.Therefore,we proposeDNet, comprising two key components: the Feature Extraction Module (FEM) and the Vision Transformer (ViT) Block. Specifically, FEM introduces an attention mechanism that considers both channel and positional information of the feature map. Two FEMs are employed to separately process facial and local facial images, extracting critical features to generate highly semantic information-rich feature maps. Subsequently, the feature maps of both images are concatenated along the channel dimension, and the ViT Block is utilized to comprehensively learn advanced semantic features of local and global information related to different facial expression regions. Finally, a 1×1 convolution layer and a fully connected layer are applied to adjust feature channels, yielding more robust predictive results and ultimately outputting depression prediction scores.We experimentally validate the DNet network on the AVEC2014 dataset and our self-constructed CZ2023 dataset, obtaining results of MAE=6.27, RMSE=7.96, and MAE=7.46, RMSE=9.15, respectively. These results affirm the effectiveness of the proposed method.