bluuebunny/crossref_metadata_2025_split huggingface dataset:
DeepSecure Net: An Ensemble DPATMFNet approach with Enhanced Feature Selection for Advanced SDN Network Intrusion Detection System
Authors: Saikam Jalaiah , Ch Koteswararao 
Published: August 2024

Abstract

By enabling the control and administration of the entire network from a single location, a Software-Defined Network (SDN) was created to streamline network administration. SDN controllers find intruders appealing because they make good targets. Attackers can take control of an SDN controller and use it to route traffic according to their requirements, which can have disastrous effects on the network. Although integrating SDN with deep learning strategies opens up novel avenues for IDS deployment defense, the detection models' efficacy depends on the quality of the training data. While deep learning for non-identifiable detection systems (NIDSs) has yielded promising results recently for several problems, most studies overlooked the impact of imbalanced and redundant datasets. Therefore, to improve the detection of network intrusions via binary and multiclass categorization, we proposed a novel enhanced ensemble DL-based Dual Parallel Attention Transformer (DPAT) with a Modular Deep Fully Convolutional Network (MDFCN), termed DPATMFNet approach. An Enhanced AlexNet method extracts the features from the input data. The Boosted Binary Meerkat Optimization Algorithm (BBMOA) is applied to choose the key features. The proposed system categorizes attacks, separates malicious from benign traffic, and identifies outstanding performance sub-attack types. Three of the most current realistic datasets were used for training and evaluation to demonstrate the effectiveness of the suggested system. We examined and contrasted its performance with that of other IDSs. The experimental findings indicate that the proposed system performs better than others at identifying various attacks. The suggested datasets achieve accuracy, detection rate, and precision above 99% compared to existing approaches. The results show how effective the proposed model is at obtaining high accuracy while requiring a shorter training period.
Does heart rate variability predict patient prognosis and influence known prognostic factors in women with ovarian cancer?
Authors: Toledano-Hacohen Mirit , Gidron Yori , Dahli Heba , Younes Grace , Lavie Ofer , Sagi-Dain Lena , Segev Yakir 
Published: August 2024

Abstract

Objective
Vagal nerve activity, indexed by heart rate variability (HRV), may play a protective role in many cancers. For example, high HRV was found to predict better overall survival in patients with liver, lung, pancreatic and breast cancers. This study aimed to evaluate the prognostic role of HRV in ovarian cancer.
Methods
This was a retrospective comparative cross-sectional study. All patients with histologically proven ovarian cancer treated at one tertiary center between 2014 and 2021 were included. HRV was derived and analyzed from patients’ electrocardiograms at the time of diagnosis. The primary outcome was overall survival.
Results
The final cohort included 104 women aged 64.7±12.3 years. Most of the patients (83.8%) had advanced disease stages (stages III and IV). Using multivariate logistic regression, controlling for age, cancer stage, surgical outcomes, and treatment type, log-HRV significantly predicted survival in patients younger than 60 years (OR = 0.01; 95% CI: 0.00–0.93, p < 0.05). Finally, examination of the effects of HRV on the influence of known prognostic factors revealed that tumor stage tended to predict survival only for patients with low HRV, whereas surgical outcomes and treatment type significantly predicted survival only in patients with high HRV.
Conclusions
Our study confirmed that vagal nerve activity, indexed by HRV, might predict survival in patients with ovarian cancer, especially in women younger than 60 years. In addition, HRV may determine the effects of known prognostic factors on survival. The results of our study suggest that the HRV should be considered when estimating patient prognosis and treatment success in patients with ovarian cancer.
Network public opinion evolution simulation modeling based on generative adversarial network and SEIR model
Authors: Wang Jintao , Yin Yulong , Wei Lina 
Published: August 2024

Abstract

It is often accompanied by the spread of network public opinion events when an emergency occurs, which is easy to cause obvious emotional fluctuations in society. Therefore, how to build a more realistic evolution model of public opinion, so as to grasp and predict the development trend of public opinion in time, is an urgent issue. Based on the Generative Adversarial Network (GAN) and SEIR model, this paper constructs the GAN-SEIR model for the simulation of the evolution of public opinion in social networks. Firstly, an evolution model of network public opinion is constructed by referring to the SEIR epidemic model. Secondly, based on the generative adversarial network, the relationship between the interaction of each element in the system is determined, so that the information propagation in the GAN-SEIR model is more consistent with the complexity of actual propagation. Then, the system dynamics module of Anglogic platform is used to simulate the evolution trend of public opinion. By simulating the logical structure of each related element in the network public opinion system, and the future development trend of the network public opinion is obtained from the internal motivation of the public opinion event system. Finally, the actual data of China Eastern Airlines 3.21 accident was used as a real case support for verification. The results show that the error between the predicted data and the actual data is within 3%, which proves that the model can effectively predict and track the development of network public opinion.
Capparis erythrocarpos (Isert) leaf and its principal constituent betulinic acid, elicit anti- malarial activity via immunomodulatory action and alleviation of thrombocytopenia
Authors: Kumatia Emmanuel Kofi , Tung Nguyen Huu , Asase Alex 
Published: August 2024

Abstract

Malaria is a mosquito-borne disease which kills more than half a million people annually. This has made the search for novel anti-malarial agents to complement the current ones an issue of global importance. Capparis erythrocarpos is used to treat various diseases in traditional medicine. However, its anti-malarial activity and individual constituents of its leaf are not known. This study described anti-malarial activity of the 70% ethanol extract of C. erythrocarpos leaf (CEL) and its major constituent, betulinic acid (BA). BA was isolated from CEL and characterized using NMR and LC-MS spectroscopies. Anti-malarial activity was evaluated using the curative test against Plasmodium berghei in mice. CEL (30–150 mg/kg p.o.) and BA (10–25 mg/kg p.o.) significantly (P < 0.05) reduced Percentage parasitemia with anti-malarial activity of 42.26–72.86%. Lonart (10 mg/kg p.o), the reference drug, produced anti-malarial activity of 47.66%. CEL and BA also, increased (P < 0.05) WBCs, platelets, body weight loss and MST whereas, reducing RBCs and hemoglobin concentration. In conclusion, CEL and BA possessed remarkable anti-malarial activity by activation of the immune system and reversing thrombocytopenia without protecting against weight lost and anemia. CEL and BA could therefore be developed as new anti-malarial agents.
ϑ-Spiral Discovery Reveals a New Class of Biological Building Block
Authors: Garton Michael 
Published: August 2024

Abstract

Our entire living world is constructed from just seven major protein secondary structures: α-helices, β-sheets, 310-helices, π-helices, turns, bends, and polyPro-helices. All other polypeptide is classified as unstructured coil. Because only a tiny fraction of theoretically possible protein sequences have ever been sampled by evolutionary processes, it is conceivable that other secondary structures remain undiscovered. Using physical and mathematical modelling, we identified an unprecedented structure with spiral geometry. Upon using this to search uncharted polypeptide sequence space in silico, we discovered sequences that appeared to support the spiral structure. Theta-spirals (ϑ-spirals) were confirmed in vitro using NMR spectroscopy, becoming the eighth protein secondary structure, and revealing a new class of biological building block: noncanonical protein secondary structures.
Knowledge, Attitudes, and Practices of Voluntary Blood Donation Among University Students in Nigeria: Ambrose Alli University as a Case Study
Authors: Saka Sulymon A. , Marvis Eromosele E. , Mezu Nneoma M. , Isiaka Ayotunde A. , Asogwa Collins O. , Odafen Perpetua I. , Akhigbe Marvelous E. , David Nzomiwu C. , Emekolom Obiajulum N. 
Published: August 2024

Abstract

Background
Adequate and safe blood supply has remained a challenge in developing countries, including Nigeria. Young students can play a fundamental role in blood donation, saving the lives of many people; hence, studying their knowledge, attitudes, and practices related to blood donation is essential.
Objective
The objectives of this study were to assess the knowledge, attitudes, and practices of voluntary blood donation among Ambrose Alli University students and the factors associated with the prevalence of blood donation.
Methodology:
This was a cross-sectional descriptive study carried out at Ambrose Alli University, Ekpoma. A total of 250 students were recruited. Pretested questionnaires were used to assess their knowledge, attitudes, and practices related to voluntary blood donation.
Statistical analysis
The responses were collated and analysed with the Statistical Package for Social Sciences (SPSS) v21. The associations between blood donation practices and the gender of the respondents and the age, year of study, department, and employment status of the parents were tested via chi-square tests where appropriate; p < 0.05 was considered statistically significant.
Result
The study revealed that the mean age of the respondents was 22.3 ± 9.049) years, with females accounting for 51.6% of the sample. A total of 34% (85) have good knowledge, and the respondents have a positive attitude (mean 3.23) toward donation; however, only 13.6% have donated blood with a male-to-female ratio of 2:1. The study revealed significant associations between knowledge of blood donation and several sociodemographic features, including age (p = 0.003), year of study (p = 0.025), faculty of the respondents (p < 0.001) and employment status of the father or male guardian (p = 0.035). Furthermore, blood donation practices were significantly associated with the year of study (p = 0.028) and faculty (p < 0.001). Logistic regression revealed that senior students were less likely to donate blood than first-year students were. Fourth-year students had an odds ratio (OR) of 0.20 (95% CI: 0.05–0.87, P = 0.032), and fifth-year students had an OR of 0.23 (95% CI: 0.06–0.93, P = 0.039).
Conclusion
Despite having a fair level of knowledge regarding blood donation as well as a positive attitude toward it, the respondents portray a poor practice culture with respect to blood donation.
Evaluating Generative AI to Extract Qualitative Data from Peer-Reviewed Documents
Authors: Spillias Scott , Ollerhead Katherine , Andreotta Matthew , Annand-Jones Ruby , Boschetti Fabio , Duggan Joseph , Karcher Denis , Paris Cecile , Shellock Rebecca , Trebilco Rowan 
Published: August 2024

Abstract

Uptake of AI tools in knowledge production processes is rapidly growing. Here, we explore the ability of generative AI tools to reliably extract qualitative data from peer-reviewed documents. Specifically, we evaluate the capacity of multiple AI tools to analyse literature and extract relevant information for a systematic literature review, comparing the results to those of human reviewers. We address how well AI tools can discern the presence of relevant contextual data, whether the outputs of AI tools are comparable to human extractions, and whether the difficulty of question influences the performance of the extraction. While the AI tools we tested (GPT4-Turbo and Elicit) were not reliable in discerning the presence or absence of contextual data, at least one of the AI tools consistently returned responses that were on par with human reviewers. These results highlight the utility of AI tools in the extraction phase of evidence synthesis for supporting human-led reviews and underscore the ongoing need for human oversight.
Evidence of spatial clustering pattern of malaria distribution in Epe, Lagos, Nigeria
Authors: Bello Abdulrahman Babatunde , Hassan Adesola Adebolade 
Published: August 2024

Abstract

Investigation of the spatial distribution patterns of malaria with the goal of detecting intense transmission areas and clustering is prerequisite to planning an effective targeted malaria intervention. Parasitological sampling was done in 24 settlements: 12 from 6 wards in Epe community and 12 from 3 wards in Orimedu constituency, Lagos, Nigeria. Blood samples were collected from 360 participants with georeferenced residences, selected through multistage systematic sampling technique. Each study area was delineated into 3 zones, namely; very high (red), high (yellow) and low (green) malaria risk zones according distances of 500m, 1000m and 1500m respectively away from perennial water bodies using Global Moran’s Index Spatial Autocorrelation analysis. Clustering and Outlier analysis was carried out to generate predictive malaria risk maps. Overall malaria prevalence was 32.2%., being significantly higher in Orimedu (52.6%) than Epe (47.4%), p < 005 There was statistically significant variation in the spatial distribution of malaria in Epe and Orimedu wards, p < 005 and settlements, p < 005. Prevalence was 28%, 32% and 50% in the red, yellow and green zones respectively in Epe. Yellow zone accounted for the highest prevalence (67%), in Orimedu, followed by the red zone (39%) while the green zone was uninhabited. Malaria clustering was detected in Uraka, Iberikodo and Sagidan settlements in Epe (Moran’s Index = 062, p < 005) while Orimedu has a random distribution pattern (Moran’s Index = 008, p > 005). The multiple distinct malaria risk zones coupled with the characteristic steep topographic may be responsible for forcing malaria to localize and cluster in Epe compared to Orimedu.
Machine Learning Approaches for Predicting Company Bankruptcy: A Comparative Study
Authors: Ali Umair , Fahad Shah , Ali Ammar 
Published: August 2024

Abstract

The present study explores the utilization of machine learning classifiers for the purpose of forecasting firm bankruptcy. The dataset consisted of financial metrics and was used to evaluate six different classifiers which included; Support Vector Classifier, Logistic Regression, K-Nearest Neighbors, Naive Bayes, Decision Tree, and Random Forest. In terms of accuracy in the original data (96.77%) and scaled data (96.70%), Random Forest Classifier emerged as the best performing classifier. This research indicates that careful choice of a model is crucial and also implies that machine learning has a great potential in improving risk management and financial decision making. The implications of these result for various domains in finance suggest that hybrid models should be researched and explained in better detail by future work to further improve accuracy and transparency. Furthermore, the use of machine learning can raise predictive accuracy among financial institutions, which will lower risks thereby increasing overall performance that contributes to financial stability.
Biochemical and Mechanical Impact of Storage Techniques on Ovine Temporomandibular Joint Discs
Authors: Trindade Daniela , Calado Cecília R. C. , Silva João C. , Maurício Ana C. , Alves Nuno , Moura Carla 
Published: August 2024

Abstract

The temporomandibular joint disc plays a fundamental role in daily activities, and when it is compromised, severely disturbs oral health and quality of life. Decellularization is gaining interest in tissue engineering (TE) applications, but requires maintaining the native structure and composition to mimic human disc properties. This study characterizes the native ovine disc and compares conservation protocols to preserve its morphology, biochemical content (sulfated glycosaminoglycans, total and soluble collagen), and mechanical and thermal behavior. Three storage protocols were tested: (i) freezing at -20°C in phosphate-buffered saline (PBS) and thawing at 4°C (PBS + 4°C); (ii) freezing at -20°C in PBS and thawing at room temperature (RT) (PBS + RT); and (iii) wrapping the discs in PBS-embedded gauze, freezing at -20°C, and thawing at RT (Gauze + RT). Protocols were evaluated at 1, 7, and 14 days, and compared with a native disc, and a collagenase-treated discs. All conservation protocols caused changes, though less pronounced than degradation. The PBS + 4°C and PBS + RT protocols maintained original morphology, yet highlighted, a contrasting biochemical and mechanical outcome based on the thawing method. Thermal analysis revealed collagen structure changes within the first 7 days of freezing. The Gauze + RT protocol showed no significant biochemical changes over time, but the disc became dehydrated and with a higher compression modulus. For TE approaches involving decellularization, it is crucial to consider these alterations. For powdered tissue applications, the Gauze + RT method for 14 days is recommended due to minimal structural impact.
Taxation and Foreign Direct Investments: a Revisionist study
Authors: silva emmanuel marques , Martinez Antonio Lopo , Zanoteli Eduardo Jose , Neto Alfredo Sarlo 
Published: August 2024

Abstract

This study revisits research on Taxation and Foreign Direct Investments (FDI), highlighting their critical impact on economic growth and technological advancement in transitioning economies. Utilizing bibliometric analysis through the Proknow-C method, it delves into how taxation shapes FDI. The focus is on key areas such as determinants, countries, institutions, and incentives. It specifically acknowledges the scholarly contributions of Devereux M. P. among others. The study exposes the intricate dynamics between taxation and FDI, aiming to broaden the understanding and inform future research, with profound implications for developing effective policy-making in emerging economies.
Effects of Nicotinamide Mononucleotide Supplementation on Muscle and Liver Functions Among the Middle-Aged and Elderly: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Authors: Wang Jian-peng , Wang Li , Wang Ting , Zhang Yi-dan , Zhou Ao-jia , Wang Zi-ping , Xiong Zhang-e 
Published: August 2024

Introduction:
Nicotinamide Mononucleotide (NMN) has gained attention as a precursor
to Nicotinamide Adenine Dinucleotide (NAD+) in recent years, commonly utilized in
anti-aging therapies. The anti-aging effects of NMN on muscle and liver functions in middleaged
and elderly people are still unclear.

Objective:
Based on available randomized controlled trials, we conducted a meta-analysis to
evaluate the impact of NMN on muscle and liver functions in middle-aged and elderly individuals.

Methods:
We conducted searches on three electronic databases (PubMed, Embase, Web of Science)
for randomized controlled trials involving NMN interventions in middle-aged and elderly
populations. Through the Cochrane Handbook, we assessed the specific methodological quality.
All statistical analyses were obtained by Stata15, and statistical significance was set as P<0.05.

Results:
There were 412 participants from 9 studies in this meta-analysis. Based on changes in
gait speed (SMD: 0.34 m/s, 95%CI [0.03, 0.66] p = 0.033), NMN had significant effects on muscle
mass. Moreover, NMN had a better effect on ALT (SMD: -0.29 IU/L, 95%CI [-0.55, -0.03] p
= 0.028). Subgroup analysis indicated that administering a small dose of NMN exerted the most
prominent impact on Homeostasis Model Assessment-Insulin Resistance (HOMA-IR).

Conclusion:
NMN has positive efficacy in enhancing muscle function, reducing insulin resistance
and lowering aminotransferase levels in middle-aged and elderly individuals. NMN is
an encouraging and considerable drug for anti-aging treatment.
Genomic Face-off: An In Silico Comparison of the Probiotic Potential of Lactobacillus spp. and Akkermansia muciniphila
Authors: Fakruddin Md. , Karim Md. Abu Bakar , Ul Islam Bakhtiar , Safa Ashrafus , Sultana Jinath , Bulbul Nayeema , Shishir Md. Asaduzzaman 
Published: August 2024

Introduction:
The gut microbiota plays a crucial role in maintaining human health, and
probiotics have gained significant attention for their potential benefits. Among the diverse array of
gut bacteria, Akkermansia muciniphila, and Lactobacillus spp. have emerged as promising candidates
for their putative probiotic properties.

Methods:
In this study, we conducted a comprehensive comparative in silico analysis of the
genomes of A. muciniphila and Lactobacillus to decipher their probiotic potential. Utilizing a
range of bioinformatics tools, we evaluated various genomic attributes, including functional gene
content, metabolic pathways, antimicrobial peptide production, adhesion factors, and stress response
elements. These findings revealed distinctive genomic signatures between the two genera.
A. muciniphila genomes exhibited a high prevalence of mucin-degrading enzymes, suggesting a
specialized adaptation for mucin utilization in the gut environment.

Results:
Additionally, the presence of specific pathways for short-chain fatty acid production highlighted
its potential impact on host health. Lactobacillus genomes, on the other hand, demonstrated
a diverse repertoire of functional genes associated with probiotic attributes, including the production
of antimicrobial peptides and adhesion factors, indicating potential for host-microbe interactions
and immune modulation. Furthermore, this analysis unveiled the genetic basis of stress tolerance
in both genera, revealing conserved mechanisms for surviving the dynamic conditions of
the gut ecosystem.

Conclusion:
This study also shed light on the distribution of antibiotic-resistance genes, allowing
us to assess safety concerns associated with their potential use as probiotics. Overall, this comparative
in silico exploration provides valuable insights into the genomic foundation of A. muciniphila
and Lactobacillus probiotic potential. These findings contribute to the understanding of their respective
roles within the gut microbiota and offer a foundation for further experimental investigations.
As probiotic applications continue to expand, this study advances our knowledge of the genetic
underpinnings that govern their functionality and highlights promising avenues for future
therapeutic interventions and personalized health strategies.
Endometriosis: A Comprehensive and Concise Review of Potential Biomarkers and Treatment Therapies
Authors: Aziz Namra , Wal Pranay , Pyne Chandana , Singh Shweta , Borkar Sudarshan N. , Pandey Swarnima , Sharma Mukesh Chandra 
Published: August 2024

Abstract:
Females of reproductive age can develop endometriosis, an inflammatory gynecological
illness that is hormone-dependent. The variable lesions that make up the functional endometrium
outside of the uterus cavity are what set this disorder apart in terms of its clinical and pathological
aspects. Around 10% of women in their reproductive years have endometriosis worldwide,
which significantly lowers their quality of life overall. Infertility and chronic pelvic discomfort are
the two main symptoms that significantly affect women's reproductive health and general well-being.
The pathophysiological underpinnings of endometriosis remain unclear despite years of intensive
research. Understanding the fundamental mechanisms that produce endometriosis is crucial,
as demonstrated by the substantial public and private health consequences. This article offers an
overview of recent advancements in biomarkers regarding endometriosis treatment options, based
on a thorough literature study.