bluuebunny/crossref_metadata_2025_split huggingface dataset:
Effect of Computer-based Cognitive Remediation Therapy on Neurocognitive Functioning of Patient with Schizophrenia
Authors: Singh Geetesh Kumar , Gupta Mahima , Mishra Jyoti , Bajaj Manoj 
Published: December 2024

Abstract

Introduction: Schizophrenia patients often suffer from cognitive impairments that affect their social and occupational functioning. Computer-based cognitive remediation therapy (CCRT) has shown potential in improving neurocognitive function. This study evaluated the effectiveness of CCRT in improving neurocognitive function in schizophrenia patients.
Methods: In this study, ten individuals diagnosed with schizophrenia and aged between 30 and 50, hospitalized for minimum six months and had up to eighth-grade education, were included. Neurocognitive assessments using the NIMHANS Neurocognitive Battery were conducted at baseline and after the intervention. CCRT sessions conducted twice weekly for six months alongside standard treatment.
Results: Neurocognitive function improved significantly: mental speed by 36%, focused attention by 17.93%, sustained attention by 59.24%, response inhibition by 82.76%, comprehension by 42.99%, verbal learning by 193.62%, immediate recall of logical memory by 66.83%, and delayed recall by 50.23%. Effect sizes suggested clinically significant changes across domains, with a positive correlation between CCRT session quantity and cognitive functioning.
Conclusion: These findings indicate CCRT as a promising intervention for enhancing cognitive function in schizophrenia patients. The significant improvements in multiple cognitive domains highlight its potential. Further research with larger samples and longer follow-up periods is required to validate these results and optimize CCRT protocols.
Impacts of Climate Change on Lepidoptera Biodiversity in Iran: Insights from Long-Term Climate Data and GBIF Records
Authors: Shojaaddini Mohammad 
Published: December 2024

Abstract

This study examined the impacts of climate change on Lepidoptera biodiversity across Iran’s ecoregions using Global Biodiversity Information Facility (GBIF), observational records, and long-term climate data from 1993 to 2022. Biodiversity metrics were analyzed, and Generalized Linear Model (GLM) analysis were used to identify key climatic and ecological drivers of family richness. Spatial analysis revealed a mean family richness of 5.89 (SD=7.13) and a mean Shannon-Wiener Index of 0.83 (SD=0.93) across ecoregions. Biodiversity hotspots, such as the Caspian Hyrcanian mixed forests, Zagros Mountains forest steppe, and Elburz Range forest steppe, exhibited consistently high family richness (19, 17, and 16 families, respectively) and balanced family compositions. In contrast, arid ecoregions, including the Registan-North Pakistan sandy desert and Baluchistan xeric woodlands suffered from insufficient sampling, limiting biodiversity assessments. Temporal analyses revealed forested regions had relatively complete accumulation curves while arid and semi-arid regions displayed uneven and incomplete sampling. Comparative analyses demonstrated that GBIF documented 27 unique families out of 70 families in observational records. Negative Binomial GLM showed that temperature positively influenced family richness, while higher precipitation negatively impacted family-level distribution. Comparative analysis between GBIF data and observational records revealed complementary strengths. GBIF provided broad-scale trends, while localized surveys uncovered specialist families often overlooked in global datasets. These findings emphasize the need to prioritize conservation efforts in biodiversity-rich ecoregions and addressing sampling gaps in underrepresented areas to mitigate climate change impacts on Lepidoptera in Iran.
The distribution of sport performance gene variations through COVID-19 disease severity
Authors: Yenmis Guven , Kallenci Ilayda , Dokur Mehmet , Koc Suna , Yalinkilic Sila Basak , Atak Evren , Demirbilek Mahmut , Arkan Hulya 
Published: December 2024

Abstract

Background
Since its emergence in 2020, researchers worldwide have been collaborating to better understand the SARS-CoV-2 disease’s pathophysiology. Disease severity can vary based on several factors, including comorbidities and genetic variations. Notably, recent studies have highlighted the role of genes associated with athletic performance, such as ACE, ACTN3, and PPARGC1A, in influencing muscle function, cardiovascular health, and the body's metabolic response. Given that these genes also impact oxidative metabolism, inflammation, and respiratory efficiency, we hypothesized that they might play a critical role in the host’s response to SARS-CoV-2 infection.
Aims
This study aimed to investigate the association between disease severity and genetic polymorphisms in these sports performance-related genes, specifically ACE rs4646994, ACTN3 rs1815739, and PPARGC1A rs8192678.
Methods
A total of 422 COVID-19-positive patients were included in the study. The participants were divided into three groups: a severe group (77 patients) requiring intensive care unit (ICU) admission, a mild group (300 patients) exhibiting at least one symptom, and an asymptomatic control group. Genotyping was performed using restriction fragment length polymorphism PCR.
Results
The D allele and DD genotype of ACE and the T allele and TT genotype of ACTN3 were found to confer protective effects against severe SARS-CoV-2 infection. Conversely, the PPARGC1A TC genotype and the ACE-PPARGC1A ins/ins + TC combined genotype were associated with increased disease severity (p < 0.05).
Conclusions
Although vaccination has reduced the severity of SARS-CoV-2, the virus continues to impact human health. Interindividual differences due to these genetic variations will broaden the horizon of knowledge on the pathophysiology of the disease.
Leveraging key-points Motion History Maps for Human Activity Recognition
Authors: Minutillo Luca , Altilio Rosa , Chirico Francesco , Foglia Goffredo 
Published: December 2024

Abstract

There has been a great interest in investigating machine learning techniques for rec-ognizing human activities from video sequences or images. The task is particularlychallenging due to potential problems that could appear related to clutter, partial oc-clusion, changes in scale, viewpoint, lighting, and appearance. Additional, many fieldsof applications could require an activity recognition tool including video surveillancesystems, human-computer interaction, e-health applications, and robotics. In this pa-per, we present a novel two-phase approach for extracting and learning knowledgefrom a subset of possible human actions, working in a multi person scenario. Ourapproach first employs a deep neural network to perform the pose estimation and theskeleton key-points extraction of a subject. Successively, the key points are used tofirstly track each subject in the scene and secondly build the motion history images.These ones represent the inputs of a convolutional neural network able to recognizeand correctly classify seven different possible human actions. Despite every sub-blockof our machine learning pipeline being an off-the-shelf solution by itself, the wholesystem represents a pretty impactful innovation. In fact, it enables fast and efficientreal-time multi-target activity recognition, which is something seldomly investigatedin literature, but of great importance for real world applications. Experimental evalu-ations demonstrate that the solution is competitive with existing approaches, reachingan accuracy near to 90% on a real world-dataset.
Clinical Insights of Honeybee - Propolis Extract: Identification of Biochemical Compound using GC-MS and FTIR Analysis
Authors: Selvaraj Jothi 
Published: December 2024

Abstract

Propolis, a natural resinous substance collected by honeybees from various plant sources, has garnered significant interest for its diverse pharmacological properties. This study provides a comprehensive analysis of propolis, focusing on its extraction, biochemical characterization, and detailed identification of its bioactive compounds using Gas Chromatography-Mass Spectrometry (GC-MS) and Fourier Transform Infrared Spectroscopy (FTIR). The optimized extraction techniques to identify propolis compounds were followed by a thorough biochemical analysis to identify key constituents. GC-MS analysis revealed a complex mixture of phenolic acids, flavonoids, and terpenes, which are known for their antimicrobial, anti-inflammatory, and antioxidant activities. FTIR spectroscopy complemented these findings by providing detailed insights into the functional groups present in the propolis extract. The study underscores the potential of propolis in clinical applications, highlighting its rich biochemical profile that can be harnessed for therapeutic purposes. The integration of GC-MS and FTIR analyses offers a robust framework for the precise identification and characterization of propolis compounds, paving the way for future research and development in medical and pharmaceutical fields.
The role of Agglomeration on export survival in the context of weak institutions: Evidence from Malawi using a Survival Analysis
Authors: Banda Tadala , Chigaru Farai 
Published: December 2024

Abstract

Increased exports have the potential to spur economic growth and augment the trajectory of poverty in sub-Saharan Africa. A Plethora of literature has explored trade creation or the introduction of new export products as the sources of increased export performance. While this is so, many developing countries still face the challenge of having short-lived export spells. The identification of the drivers of these short-lived export spells has however been ignored. Increasing the lifespan of the existing exports is a cost-effective and viable way to maintain export contribution to economic growth, especially for countries with weak institutions. We investigate the determinants of export survival, with a specific focus on the role of agglomeration. We hypothesize that strategically identifying trade partners based on their level of agglomeration has the potential to boost export survival for the exporter countries. Using Malawi as a case study, we analyze export data from 117 trading partners over 20 years (2003–2022). The Kaplan-Meier analysis reveals a first-year export survival rate of 38%, with a median duration of just one year. To further explore the role of agglomeration economies on export survival, we employ a multivariate parametric inverse Gaussian frailty survival model using a proportional hazard Weibull distribution. Our findings indicate that agglomeration economies significantly enhance the survival of Malawi’s exports. These findings are timely and important as they provide evidence for policy formulation for countries with export-oriented growth, especially with institutions where hurdles to export growth exist.
JEL Codes: C41, C14, F14, R12
Prediction of groundwater levels using a long short-term memory (LSTM) technique
Authors: Thakur Abhinav , Chandel Abhishish , Shankar Vijay 
Published: December 2024

ABSTRACT

The precise prediction of groundwater levels is a challenging task due to the complex relationships between hydrological parameters and the lack of in situ climate data. The present research proposed an integrated machine learning model for groundwater level prediction based on long short-term memory (LSTM) along with principal component analysis (PCA) and discrete wavelength transform (DWT), i.e. PCA–DWT–LSTM model. The proposed model was developed using 23 years (2000–2022) of seasonal groundwater level data and climatic variables for nine wells in the district of Kangra in Himachal Pradesh, India. The proposed model attains higher ranges of R2 (0.8253–0.8828) and lower ranges of root mean square error (RMSE) (0.1011–2.0025) than the alternative model (PCA–LSTM), having R2 and RMSE values in the range of 0.7019–0.8005 and 0.2662–2.9565, respectively. Moreover, when compared to the hybrid models, the accuracy of the DWT-based models is much higher. The developed model (PCA–DWT–LSTM) improves the accuracy and interpretability of groundwater level prediction and has the potential to estimate the accurate groundwater level, particularly in the regions where obtaining the hydrogeological data is difficult.
Acetyl-L-Carnitine Aids in Preservation of Cholinergic Neurons and Memory in the Drosophila melanogaster Model of Alzheimer's Disease
Authors: Bolshakova Olga I. , Slobodina Alexandra D. , Slepneva Elizaveta E. , Sarantseva Svetlana V. 
Published: December 2024

Background:
The lack of effective therapy for the treatment of Alzheimer's disease demands
both the search for new drugs and the reconsideration of already known substances currently
used in other areas of medicine. Drosophila melanogaster offers the potential to model features
of Alzheimer's disease, study disease mechanisms, and conduct drug screening.

Objectives:
The purpose of this work was to analyze the neuroprotective properties of the drug
“carnicetine”, which is an acetylated form of the natural low molecular weight compound L-carnitine.
The drug is able to cross the blood-brain barrier and is currently used as a means of improving
cellular metabolism.

Methods:
Using tissue-specific drivers, direct expression of amyloid beta peptide (42 amino
acids) was exhibited in certain groups of neurons in the Drosophila melanogaster brain, namely in
dopaminergic and cholinergic neurons. The effect of acetyl-L-carnitine (carnicetine) on the death
of these neurons and the memory of flies was analyzed.

Results:
The expression of amyloid beta peptide in dopaminergic or cholinergic neurons resulted
in neurodegeneration of cholinergic neurons in the Drosophila brain and memory impairment.
The use of carnicetine added to animal food made it possible to treat these disorders. At the same
time, no effect on dopaminergic neurons was noted.

Conclusion:
The data obtained confirmed the neuroprotective properties of the drug under study,
demonstrating its participation in the restoration of the cholinergic system and the feasibility of using
carnicetine for the treatment of Alzheimer's disease.
Recent Updates on Alzheimer’s Disease: Pathogenesis, Pathophysiology, Molecular Approaches and Natural Bioactive Compounds Used in Contemporary Time to Alleviate Disease
Authors: Mishra Arun Kumar , Srivastava Alankar , Raj Varsha , Saini Vipin , Khan Gyas , Singh Harpreet , Mishra Amrita , Paliwal Sarvesh 
Published: December 2024

:
Alzheimer's disease (AD), characterised by gradual memory loss and neurodegeneration,
is an important risk to global health. Despite the recent advances in the field of neuroscience,
the complex biological mechanisms underlying the aetiology and pathology of AD have not
been elucidated yet. The development of amyloid-beta plaques, hyperphosphorylation of tau protein,
oxidative stress, and neuroinflammation have been identified as important components. The
genesis of AD has been illuminated by advances in molecular techniques, which have shown the
contributions of environmental, genetic, and epigenetic variables.

:
Ongoing research is focused on the potential of bioactive compounds as therapeutic agents. Quercetin,
epigallocatechin gallate, huperzine A, ginsenosides, omega-3 fatty acids, vitamin E, zinc,
bacosides from brahmi, and withanolide A from ashwagandha are among the compounds that
have demonstrated encouraging effects in modifying disease pathways. These bioactive substances
demonstrate their potential for symptomatic relief by providing neuroprotective, antioxidant,
anti-inflammatory, and cognitive-enhancing properties. The present review presents the recent
findings on AD pathogenesis, molecular mechanisms, and the impact of natural compounds, offering
a comprehensive perspective on current and emerging strategies for managing this debilitating
condition.
A Multivariate Intuitionistic Fuzzy Grey Model for Forecasting Electricity Consumption
Authors: Yörür Bahadır , Erginel Nihal , Şentürk Sevil 
Published: December 2024

Background:
Whether in the short, medium or long term, forecasting electricity consumption has always been an essential study area. In the literature, many methods are used for future forecasting and are being improved daily to achieve better results.

Objective:
The main objective of this study is to make the most accurate long-term electricity consumption forecast, which is the basis for optimal future planning in the energy sector. Electric consumption forecasting is performed regionally since planning at the regional level is essential for more precise planning.

Methods:
There may be different variables that affect electricity consumption. This study ex-tends the multivariate grey model for electricity consumption prediction to intuitionistic trian-gular fuzzy numbers for nine regions. In the grey model, population, export, and gross domes-tic product variables were used as independent variables, and future predictions for these vari-ables were obtained through the univariate intuitionistic triangular fuzzy grey model.

Results:
The results of the proposed method are compared with those of the classical univari-ate grey model, univariate intuitionistic triangular fuzzy grey model, and classical multivariate grey model. The results show that the error values of the proposed method are lower.

Conclusion:
The study contributes to the development of the grey model. More accurate pre-diction results are obtained with the proposed method compared to similar methods
Evaluation of Functional Properties and the Lipase Inhibitory Activity of Proteins from Amaranthus cruentus Seeds
Authors: Nair Arya , Suvarna Vasanti , Mallya Rashmi 
Published: December 2024

Aims/Background:
Obesity is characterized by excessive body fat and is a chronic and complex disease. The medications used to date for the treatment of obesity have exhibited various side effects. Thus, new substances must be sought from alternative sources as anti-obesity drugs. Recently, there has been increased interest in the study of bioactive peptides generated from plant proteins for functional food development. Plant proteins have exhibited pancreatic lipase inhibitory activity and can be potentially studied as sources of anti-obesity drugs for long-term use. Amaranthus cruentus seeds are very nutritious, with high protein con-tent and medicinal properties. Hence, this study focuses on pancreatic lipase inhibitory activity and functional properties of Amaranthus cruentus seed protein isolates.

Materials and Methods:
Proteins were isolated using conventional and thermal isoelectric pre-cipitation methods. The protein's ability to inhibit lipases was assessed using both synthetic and natural substrates. The functional properties, such as nitrogen solubility, foaming test, emulsi-fication test, and water and oil holding abilities, were evaluated. The protein isolates were char-acterized by SDS-PAGE and DSC. Cell line studies were performed on 3T3-L1 cell lines to determine the cytotoxicity and anti-adipogenic activity of protein isolates.

Results:
Proteins 1, 2, 3, and 4 were found to have high protein yield and protein content. SDS-PAGE characterization showed protein bands at different molecular weights (kDa). Protein iso-lates demonstrated good functional properties and antilipase activity as compared to flour. In the cell line experiments, protein 1 exhibited a dose-dependent reduction in the lipid content.

Conclusion:
A. cruentus seed protein could pave the way for the development of nutraceutical formulation in the treatment of obesity.
Game-Based Promotion of Assertiveness to Mitigate the Effects of Bullying in High School Students: Development and Evaluation Study
Authors: Lepe-Salazar Francisco , Mejía-Romero Fernando , Benicio-Rodríguez Dámaso , Hernández-Reyes Aylin , Nakajima Tatsuo , Salgado-Torres Sarita 
Published: December 2024

Background
For years, Mexico has reported the highest global incidence of school bullying, with approximately 19% of students going through some form of hostile peer interactions. Despite numerous interventions, these harmful conducts remain deeply entrenched in educational environments.

Objective
To address this issue, we developed Bernstein, a serious game that promotes assertiveness—an essential protective factor that reduces the negative effects of bullying.

Methods
Bernstein was designed using multiple composite scenarios, a methodology grounded in cognitive behavioral therapy. To evaluate the game’s effectiveness, we conducted an exploratory trial using the Rathus Assertiveness Schedule to assess assertiveness levels before and after the intervention. Participants were high school students who met the inclusion criteria of being open to playing a serious game (with no prior gaming experience required) and having access to a computer with internet connectivity for remote participation. A total of 100 students (65 boys and 35 girls) enrolled in the intervention; however, only 46 participants in the treatment group and 46 in the control group completed the study, resulting in a dropout rate of 8% (8/100). A paired 1-tailed t test was used to compare pre- and postintervention scores within each group, and a one-way ANOVA was conducted to compare the average score improvements between the 2 groups.

Results
The treatment group showed a pretest mean Rathus Assertiveness Schedule score of –2.78 (SD 25.93) and a posttest mean of 1.69 (SD 29.48), with a paired 1-tailed t test yielding a P value of .01. The control group had a pretest mean of 2.07 (SD 25.69) and a posttest mean of –2.39 (SD 32.98), with a paired 1-tailed t test yielding a P value of .04. The one-way ANOVA (between groups) yielded a P value of .006, indicating a statistically significant difference, favoring Bernstein over the alternative protocol. Participant feedback highlighted the game’s engaging narrative and character design, although usability issues, such as navigation challenges, were noted as areas for improvement.

Conclusions
The results suggest that Bernstein is a promising tool for promoting assertiveness in high school students, providing a potential strategy for addressing bullying-related issues. The study underscores the value of integrating Bernstein into educational programs, offering students a safe and interactive environment to develop resilience. As an exploratory trial, this study faced limitations affecting the generalizability of findings, including the remote format’s impact on facilitator guidance and a relatively small sample size. Further trials with larger, more diverse groups are recommended to validate these early results and enhance Bernstein’s scalability as part of a comprehensive antibullying strategy.