bluuebunny/crossref_metadata_2025_split huggingface dataset:
Dietary Vitamin A Intake and Associated Factors among Lactating Women in Wondo Genet District, Sidama Region, Southern Ethiopia
Authors: Bolka Amelo , Sikuare Selamawit , Dona Aregahegn , Philipos Assefa 
Published: September 2024

Abstract

Background: Vitamin A deficiency remains a public health problem in Ethiopia, despite the implementation of the national nutrition program. However, evidence on the dietary vitamin A intake adequacy among lactating women is limited. This study was aimed at assessing dietary vitamin A intake and associated factors among lactating women in Wondo Genet district of Sidama Region, Southern Ethiopia.
Methods: A community-based cross-sectional study was conducted among 411 lactating women from January 1 to 28, 2024. A simple random sampling method was followed to select the study respondents. Data were collected using a structured pretested questionnaire. Vitamin A inadequacy was defined as a dietary intake below the recommended daily allowance (RDA). Associated factors of dietary vitamin A inadequacy were identified using a logistic regression model. The outputs were presented using adjusted odds ratios (AOR) with 95% confidence intervals (CI).
Results: The study presented 36.9% (95% CI: 32.2%-41.6%) prevalence of inadequate intake of vitamin A. Large family size (AOR =2.73, 95% CI: 1.70, 4.39), food insecurity (AOR =2.08, 95% CI: 1.29, 3.32), poor knowledge on vitamin A (AOR: 1.81, 95% CI: 1.09, 2.98), not received nutritional counselling (AOR =3.61, 95% CI: 2.17, 6.03) and consumed fewer than three meals per day (AOR =3.56, 95% CI: 2.20, 5.75) were significantly associated with inadequate vitamin A intake.
Conclusion: The study found a high prevalence of inadequate dietary vitamin A intake among lactating women in the area. Independent predictors of inadequate intake included large family size, food insecurity, lack of nutritional counseling, knowledge of vitamin A, and the number of meals consumed per day. We strongly recommend educating lactating women to consume foods rich in vitamin A and participate in income generating activities.
Validation of A Novel Whole Blood Coagulometer Sensitive to the Direct Oral Anticoagulants
Authors: Bakhru Sasha H. , Jiang Xuan , Chen Lirong , Osmani Dardan , Kronen Kelly , Mootoo Daryl , Zappe Stefan , Ansell Jack 
Published: September 2024

Abstract

Readily available and rapid turn-around, bedside assays to measure the effect of the direct oral anticoagulants (DOACs) are not available. This study evaluates a new point-of-care (PoC) coagulometer to assess the anticoagulant effects of the DOACs and low molecular weight heparin. Studies were done in fresh spiked blood from healthy volunteers. PoC coagulometer baseline clotting times were half that of the manual whole blood clotting time (WBCT, legacy method) and exhibited a sensitivity to DOAC anticoagulation approximately twice that of WBCT. All %CV values for both methods were < 10% with most being < 5% indicating acceptable precision of both methods. R2 values for both clotting time and percent rise from baseline were > 0.98 indicating a strong correlation between the two methods. Replicate measurements for all subjects showed a maximum upper %CV value of 5.56% and a maximum upper value of all absolute %Differences of 5.5%, with both criteria meeting predefined acceptance criteria. The dose-response curves for all subjects were linear across the entire tested concentration ranges. The Perosphere Technology PoC coagulometer detects a range of therapeutic levels of the DOACs apixaban, rivaroxaban, edoxaban, as well as the low molecular weight heparin, enoxaparin, with high precision and sensitivity.
Surgical management of small intestinal foreign bodies in children -5 years of experience in a tertiary hospital
Authors: Chen Faling , Lv Zhibao , Liu Jiangbin 
Published: September 2024

Abstract

Objective
To explore the surgical treatment experience of small intestinal foreign bodies (FBs) in children.
Methods
Retrospective analysis of clinical data of 27 patients with surgical treatment of small intestinal FBs from February 2019 to March 2024.
Results
There were 15 males and 12 females with an average age of 4.3 years, and an average duration of 6.9 days. Only 12 patients had a clear history of ingestion of FBs. All patients underwent preoperative X-ray examination, only 17 cases of metal FBS were found, and the remaining 10 cases of non-metallic FBS were accurately diagnosed by CT examination. All patients underwent surgical treatment, including 24 patients who underwent emergency surgery on the day of admission. Intestinal perforation was found in 21 cases (21/27, 77.8%). FBS included magnetic beads or squares, jujube pits, fecaliths, trichobezoar, super absorbent polymer (SAP) beads. FBS were removed from all 26 patients except 1 case who pushed the fecal stone causing obstruction into the colon. The average hospitalization time for patients was 12.4 days, and there were three cases of postoperative complications, including severe intra-abdominal infection, incision infection, and adhesive ileus. All patients had good prognosis during follow-up.
Conclusion
FBs in the small intestine of children often lead to intestinal perforation and can be accompanied by severe systemic infection, with atypical clinical manifestations. CT scanning can detect non-metallic FBs that cannot be visualized by X-ray, and assist in locating the position of the FBs. Surgical intervention is safe and effective, and can achieve good outcome.
Digital Accessibility in Disaster Periods in Turkey: Investigation of Accessibility of Web Pages and Mobile Applications of the Relevant Organisations
Authors: Kalac Mustafa Ozhan , Kilinc Murat , Yilmaz Engin , Yesilada Yeliz 
Published: September 2024

Abstract

Digital accessibility during disasters is critical for people with disabilities to access information in emergencies. The research, which evaluates the accessibility of the relevant web and mobile applications in Turkey in this field, aims to emphasise the importance of digital accessibility in disaster periods and to provide suggestions for improvement. Within the scope of the research, websites and mobile applications provided by relevant governmental and non-governmental organisations were examined. In the evaluation process, factors such as compliance with accessibility standards, user experience, access to information and features for the needs of individuals with disabilities were taken into consideration. The results reveal that there are various accessibility problems in the digital platforms of the relevant organisations in Turkey. In line with the findings, recommendations such as strengthening accessibility policies, complying with standards and increasing awareness raising activities are presented. Facilitating the access of disabled individuals to information during emergency periods is a critical step in supporting inclusiveness. In this context, this research provides an important basis for future studies and policy developments, and aims to raise awareness to public institutions, non-governmental organisations and the private sector that they should make more efforts to make their applications accessible to meet the needs of people with disabilities.
Comparative Analysis of Different Machine Learning Techniques on Traffic Signs Dataset
Authors: MP Ayyoob. , Mehthab P K 
Published: September 2024

Abstract

The rapid advancements in intelligent transportation systems have highlighted the critical importance of accurate and efficient traffic sign recognition for road safety. Traditional methods relying on handcrafted features and classical machine learning techniques have shown limited accuracy and robustness under varying environmental conditions. This study explores the potential of deep learning, specifically Convolutional Neural Networks (CNNs), for traffic sign recognition. We conduct a comparative analysis of various CNN architectures, including custom models and well-established networks like VGG16, VGG19, EfficientNetB0, and ResNet50. Additionally, we investigate hybrid models that combine features from different architectures to enhance accuracy and robustness. Using the GTSRB dataset, we evaluate the performance of these models based on metrics such as test accuracy, test score, and F1-score. Our findings indicate that custom CNN models and certain hybrid combinations, such as Custom CNN x VGG16, achieve superior performance. This research contributes to the academic understanding of machine learning techniques in traffic sign recognition and holds practical significance for improving the safety and efficiency of modern transportation networks.
Hotspot mapping of pest introductions in the EU: A regional analysis of environmental, anthropogenic and spatial effects
Authors: Rosace Maria Chiara , Conesa David V. , López-Quílez Antonio , Marini Lorenzo , Martinez-Beneito Miguel A. , Nardi Davide , Rossi Vittorio , Vicent Antonio , Cendoya Martina 
Published: September 2024

Abstract

Plant pests pose a significant threat to global agriculture, natural ecosystems and biodiversity, causing severe ecological and economic damage. Identifying regions more susceptible to pest introductions is crucial for developing effective prevention, early detection and outbreak response strategies. While historical data on pest introductions in the European Union (EU) exist, they are typically reported at the regional level. This broad aggregation poses a challenge for accurate analysis in plant health research. This study addresses this gap by leveraging existing regional data to identify hotspots for pest introductions within the EU and the UK, through a Bayesian hierarchical spatial model. Specifically, we employed the Besag, York, and Mollié (BYM) model to identify higher risk regions by incorporating covariates and spatial effects to borrow information from neighbouring areas. The results showed a positive effect of annual average temperature, annual average precipitation, and human population density on the risk of pest introduction, highlighting the relevance of the spatial component. Our analysis pinpoints high-risk regions in southern Europe, particularly northern Italy. Additionally, the high human population density and documented pest introductions in the Netherlands contributed to its elevated risk. While limitations exist due to the regional nature of the data, this study represents a methodological advancement, demonstrating the effectiveness of spatial models and offering a robust framework for future studies using regional data. It also provides insights that can inform targeted prevention, early detection and preparedness strategies, ultimately contributing to safeguarding agriculture, natural ecosystems and biodiversity in Europe.
A Review on EEG-based Multimodal Learning for Emotion Recognition
Authors: Pillalamarri Rajasekhar , Shanmugam Udhayakumar 
Published: September 2024

Abstract

Emotion recognition from electroencephalography (EEG) signal is crucial for human-computer interaction, yet poses significant challenges. While various techniques exist for detecting emotions through EEG signals, contemporary studies have explored multimodal approaches as a promising advancement. This paper offers an overview of multimodal techniques in EEG-based emotion identification and discusses recent literature in this area. But these models are computational hungry, which is necessary to address through our research, highlighting the need for further research. A relatively unexplored avenue is combining EEG data with behavioral modalities, considering unpredictable levels of reliability. The suggested review examines the strengths and pitfalls of existing multimodal emotion recognition approaches from 2017 to 2024. Key contributions include a systematic survey on EEG features, exploration of EEG integration with behavioral modalities, and investigation of fusion methods like conventional and deep learning techniques. Finally, key challenges and future research directions in implementing multi-modal emotion identification systems.
Improving data interpretability with new differential sample variance gene set tests
Authors: Rahmatallah Yasir , Glazko Galina 
Published: September 2024

Abstract

Background
Gene set analysis methods have played a major role in generating biological interpretations from omics data such as gene expression datasets. However, most methods focus on detecting homogenous pattern changes in mean expression and methods detecting pattern changes in variance remain poorly explored. While a few studies attempted to use gene-level variance analysis, such approach remains under-utilized. When comparing two phenotypes, gene sets with distinct changes in subgroups under one phenotype are overlooked by available methods although they reflect meaningful biological differences between two phenotypes. Multivariate sample-level variance analysis methods are needed to detect such pattern changes.
Results
We use ranking schemes based on minimum spanning tree to generalize the Cramer-Von Mises and Anderson-Darling univariate statistics into multivariate gene set analysis methods to detect differential sample variance or mean. We characterize these methods in addition to two methods developed earlier using simulation results with different parameters. We apply the developed methods to microarray gene expression dataset of prednisolone-resistant and prednisolone-sensitive children diagnosed with B-lineage acute lymphoblastic leukemia and bulk RNA-sequencing gene expression dataset of benign hyperplastic polyps and potentially malignant sessile serrated adenoma/polyps. One or both of the two compared phenotypes in each of these datasets have distinct molecular subtypes that contribute to heterogeneous differences. Our results show that methods designed to detect differential sample variance are able to detect specific hallmark signaling pathways associated with the two compared phenotypes as documented in available literature.
Conclusions
The results in this study demonstrate the usefulness of methods designed to detect differential sample variance in providing biological interpretations when biologically relevant but heterogeneous changes between two phenotypes are prevalent in specific signaling pathways. Software implementation of the developed methods is available with detailed documentation from Bioconductor package GSAR. The available methods are applicable to gene expression datasets in a normalized matrix form and could be used with other omics datasets in a normalized matrix form with available collection of feature sets.
When to vaccinate for seasonal influenza? Check the peak forecast
Authors: Spencer Julie A , Smith Manhong Z , Osthus Dave , Alexander Prescott C , Valle Sara Y Del 
Published: September 2024

Abstract

Background: Seasonal influenza infects 5-20% of people every year in the United States, resulting in hospitalizations, deaths, and adverse economic impacts. To mitigate these impacts, influenza vaccines are developed and distributed annually; however, growing evidence suggests that vaccine effectiveness (VE) wanes over the course of a flu season. Delaying influenza vaccination for older adults has attracted attention as a potential public health strategy. However, given the uncertainties in seasonal peak, vaccine effectiveness, and waning rates, postponing vaccination could also lead to increased morbidity, motivating an evaluation of a range of potential scenarios.
Methods: We systematically investigated a broad range of vaccination start times for five age groups under six combinations of initial effectiveness and waning rates, based on influenza cases and vaccine uptake data from 10 influenza seasons. We defined the most favorable vaccination schedule as the one that resulted in the greatest reduction in disease burden.
Results: In scenarios with fast waning, all age groups benefit from delaying vaccination regardless of initial VE and peak timing. In scenarios with slower waning, results are mixed. For the 65+ group, high initial VE and slow waning suggests that in early-peaking seasons, early vaccination most effectively reduces disease burden, while in late-peaking seasons delaying vaccination is most effective. For the 65+ group in medium and low initial VE, and slow waning scenarios, delaying vaccination appears to prevent the greatest number of cases, regardless of whether the season peaks early or late.
Conclusion: The most favorable vaccination schedule is sensitive to changes in initial VE, waning rate, and peak timing. Given estimates of these quantities from statistical and immunological models and observations, our methods can inform vaccination recommendations in order to most effectively reduce the annual disease burden caused by seasonal influenza. Specifically, accurate peak timing forecasts for the upcoming season have the potential to guide decisions on when to vaccinate.
Comparative study of the Thermogravimetric Analysis (TGA) and Differential Thermal Analysis (DTA) of Bio-synthesized Magnesium Oxide Nanoparticles Using Rubber (Hevea braziliensis), Awolowo (Chromolaena odorata), and Oil palm (Elaeis guineensis) leaves Extract
Authors: Otabor Godfrey Osatohanmwen , Ikhuoria Esther Uwidia , Onaifo Joshua Osaretin , Ifijen Hilary Ikhazuagbe , Ehigie Aiyevbekpen Clinton 
Published: September 2024

Abstract

Thermogravimetric Analysis (TGA) and Differential Thermal Analysis (DTA) were used to assess the thermal stability of bio-synthesized MgO nanoparticles (MgONPs). TGA revealed that Rubber leaf MgONPs had a multi-step degradation process: initial weight loss at 220°C-325°C, significant loss at 330°C-625°C, with 24.36% residue. Palm Oil leaf (POLE) MgONPs showed initial loss at 300°C-460°C, further decomposition up to 575°C, and 25.5% residue. Awolowo leaf MgONPs had a single degradation phase from 480°C to 790°C, with 28.9% residue, indicating higher stability. DTA analysis showed exothermic peaks at 240°C, 360°C, and 600°C for Rubber leaf MgONPs; 470°C and 560°C for POLE; and 590°C and 660°C for Awolowo leaf MgONPs, highlighting Awolowo's superior thermal stability
Selective Detection of Avian Influenza Virus Subtypes in the Terahertz Region using Array of Gold Cross-shaped Absorber
Authors: Bagheri Mohammad Kazem , Bahadoran Mahdi , Hosseini Mehdi , AhmadNoorden Ahmad Fakhrurrazi 
Published: September 2024

Abstract

Three subtypes of Avian Influenza Viruses (AIVs) - H1N1, H9N2, and H5N2 - were detected in 1.4 THz using metamaterial absorptive biosensor. The proposed sensor was consisted of an array of gold cross-shaped resonator on a silica-gold-silicon wafer. The sensor operates based on the interaction between the virus-infected cells and incident electromagnetic (EM) waves under room temperature condition, resulting in resonance peaks in the absorption spectrum within the terahertz frequency range of 1-2THz. The results were simulated using finite element method (FEM), achieving an ultra-high sensitivity of 53.6 µm/RIU, which corresponds to a figure of merit as high as 28.84. The proposed sensor holds great potential in reducing economic damages in the poultry industry, ensuring public health safety and can also be utilized for the identification of other biological agents or environmental contaminants.
Identification of serum glycan signatures in three major gastrointestinal cancers by high-throughput N-glycome profiling
Authors: Liu Si , Huang Jianmin , Liu Yuanyuan , Lin Jiajing , Zhang Haobo , Cheng Liming , Ye Weimin , Liu Xin 
Published: September 2024

Abstract

Background
Alternative N-glycosylation of serum proteins has been observed in colorectal cancer (CRC), esophageal squamous cell carcinoma (ESCC) and gastric cancer (GC), while comparative study among those three major gastrointestinal cancers has not been reported before. We aimed to identify cancer-specific serum N-glycan signatures and introduce a discriminative model between cancers in the same system.
Methods
The study population was initially screened according to the exclusion criteria process. Serum N-glycan profiling was characterized by a high-throughput assay based on matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF-MS). Diagnostic model was built by random forest, and unsupervised machine learning was performed to illustrate the differentiation between the three major gastrointestinal (GI) cancers.
Results
We have found that three major gastrointestinal cancers strongly associated with significantly decreased mannosylation and mono-galactosylation, as well as increased sialylation of serum glycoproteins. A highly accurate discriminative power (> 0.90) for those gastrointestinal cancers was obtained with serum N-glycome based predictive model. Additionally, serum N-glycome profile was differentially distributed among those three cancer groups, and several altered N-glycans were unique to the specific cancer type.
Conclusions
Serum N-glycome profile was differentially expressed in three major gastrointestinal cancers, providing a new clinical tool for cancer diagnosis and throwing a light upon the cancer-specific molecular signatures.
A Study on the Site Selection of Urban Logistics Centers Utilizing Public Infrastructure
Authors: Chen Jiarong , Lee Jungwook , Lee Hyangsook 
Published: September 2024

Abstract

The COVID-19 pandemic has significantly increased logistics demand, necessitating efficient urban logistics infrastructure. In response, the Korean government plans to utilize vacant spaces within Seoul’s subway vehicle bases and develop large-scale logistics complexes in strategic locations. This study focuses on site selection for urban logistics centers within the Seoul Metropolitan Subway area using Analytical Hierarchy Process (AHP) and Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) methodologies. Key criteria for site selection are categorized into facility, geographical, environmental, and social factors. AHP results indicate geographical factors are most significant, followed by facility, social, and environmental factors. TOPSIS rankings show Sinnae as the most suitable site, followed by Godeok and Cheonwang. Recommendations include strategic infrastructure investment, fostering cooperation between logistics companies and local governments, integrating advanced technologies, implementing green logistics, workforce development, and community engagement. Limitations include reliance on current data and the need for future research incorporating updated data, environmental sustainability, and social equity.