bluuebunny/crossref_metadata_2025_split huggingface dataset:
Comparative Analyses of Relationship among Age, Anthropometric Parameters and Gait Characteristics of Ambulant with Normal and Forward Head Postures
Authors: Onigbinde Ayodele Teslim , Griffin Olusegun Gbenga , Borode Adebusola Olabisi , Ayodeji Adedayo Oluwabunmi , Ugwu Chinonso Amanda , Ibitoye Adebukola Grace , Kekere Tosin Faith , Ajao Daniel Durotoluwa 
Published: October 2024

Abstract

Background
Forward Head Posture (FHP) is an epidemic that has become more prevalent in recent times, with paucity of data on its effects on gait. This study aims to assess, compare and determine the relationship among age, anthropometric parameters and gait characteristics of ambulant with Normal and Forward Head Postures.
Methods
84 apparently healthy participants consented to participate in this pre-experimental study. Age, sex, weight and height of the participants were obtained. The participants were trained and instructed to walk a 8 meter walkway, with the floor sprinkled with white powder to obtain foot mark while maintaining two head postures (69° and 120° for normal and Forward Head Postures respectively). A rest interval of 30 minutes was observed before changing postures. Foot prints obtained from the powdered gait walkway were used to assess foot angle, cadence, stride length, step length and walking velocity. Data obtained were analyzed using descriptive and inferential statistics.
Results
The mean age of the participants was 21.92 ± 2.55years. Ambulation with normal head posture and forward head posture had mean values for foot angle (7.74 ± 2.02°, 7.58 ± 1.95°) cadence (107.83 ± 11.69cm, 111.02 ± 11.26cm) step length (65.45 ± 7.97cm, 68.68 ± 7.30cm) stride length (131.15 ± 17.11cm, 135.96 ± 15.12cm) and walking velocity (1.19 ± 0.32m/s, 1.13 ± 0.20m/s) respectively. There was significant difference in only the gait step length during ambulation with FHP (t = -2.74, p = 0.007). Cadence have significant relationships with age, height and step length (r = -0.28, p = 0.01; r = − .0.28, p = 0.01 and r = 0.43, 0.01). Step length have significant relationships only with weight and height (r = 0.34, p = 0.01 and r = 0.43, 0.01) while stride length had significant relationship only with height (r = 0.32, p = 0.01) in abnormal head posture. Similar trends were observed during ambulation with normal head posture.
Conclusions
Forward Head Posture (FHP) did not significantly alter most gait characteristics when compared to normal head posture. Cadence and step length have significant relationships with age, weight and height, irrespective of normal or abnormal posture. In FHP ambulation, Stride length had significant relationship only with height.
Healthcare workers' experiences of participating in life-sustaining treatment decision-making in a Chinese intensive care unit：A qualitative study
Authors: Zhang Chao , Wu Yajing , He Qianqian , Xu Cuiping 
Published: October 2024

Abstract

Background: With the aging of the population and advancements in medical technology, making decisions about life-sustaining treatment (LST) poses emotional dilemmas for physicians and nurses.
Methods: A phenomenological qualitative research method was used. Ten nurses and ten doctors were selected from the intensive care unit of a tertiary hospital in China using convenience sampling for face-to-face semi-structured interviews, after which the interviews were recorded verbatim, and themes were identified and coded using Colaizzi analysis.
Findings: The decision-making experience of healthcare professionals withdrawing ICU patients from LST can be categorized into three themes and six subthemes, including healthcare professionals' perceptions of LST (professional and humanitarian perspectives), factors influencing healthcare professionals' decisions to withdraw LST (personal factors of healthcare professionals, decision-making conflicts, lack of preparedness, and ethical dilemmas), and healthcare professionals' behavioral preferences for performing LST (nurses' behavioral preferences, physicians' behavioral preferences).
Conclusion: Decision-making about whether a patient needs to withdraw LST can result in decision-making dilemmas due to differences in healthcare personnel's personal attributes and decision-making conflicts; secondly, healthcare personnel have complex multiple emotional experiences during the engagement process, and nurses do not have a high level of voice in the decision-making team due to the complex clinical environment and professional constraints; and lastly, trust between healthcare personnel and patients needs to be strengthened. In the future, it is hoped that clinical staff in the healthcare team can emphasize interdisciplinary cooperation and enhance the trust between healthcare professionals and patients from the patient's perspective.
Microalgae impact on inactivation of indicator virus in a large-scale wastewater treatment system using microalgae
Authors: Jahan Solène , Pruvost Jérémy , Cogne Guillaume , Titica Mariana , Fallowfield Howard 
Published: October 2024

Abstract

Wastewater treatment systems using microalgae called High Rate Algal Ponds (HRAP) have been increasingly considered as alternative solutions to classical activated sludge systems. In these paddlewheel-mixed shallow raceways (30 cm depth), oxygenation of the HRAP by microalgae replaces artificial aeration for aerobic degradation of organic matter. In HRAP, pathogen removal mainly relies on ultraviolet (UV) radiation from the sun. UV radiation induces photochemical modifications of DNA and RNA, leading to pathogen inactivation. However, high turbidity due to microalgae and detritus from the wastewater reduces UV penetration in HRAP. Paddlewheel mixing has then a profound impact on the treated water quality by exposing microbial pathogens to higher UV irradiation at the pond surface. Microalgae are expected to contribute significantly to turbidity in HRAP, however, they are also responsible for high oxygen concentration, high pH and, in the presence of UVA, production of Reactive Oxygen Species (ROS) favoring disinfection, questioning the relative impact of microalgae on pathogen inactivation. The purpose of this study was to investigate, in a laboratory UVA cabinet, the impact of microalgae on indicator viruses’ inactivation, in terms of UVA attenuation (inhibition of inactivation) and production of ROS (enhancement of inactivation). This study highlighted a significant negative impact of microalgae due to UVA attenuation over 30 cm depth together with a strong inherent capacity to produce ROS for virus inactivation, confirming the relevance of vertical mixing for disinfection in Peterborough HRAP.
Microbial approach towards anode biofilm engineering enhances extracellular electron transfer for bioenergy production
Authors: Anwad Anwar , Said Alia , Mubarak Mohammad S. 
Published: October 2024

Abstract

The application of microbial electrolysis cells (MEC) is a biological approach to enhance growing high amounts of electroactive biofilm for extracellular electron transfer. The electroactive biofilm degrades the organics by oxidizing them at the anode and producing electric energy. The addition of waste-activated sludge (WAS) with fat grease oil (FOG) produces an optimal reactor environment for microbial growth to enhance the exchange of electrons between cells via microbial electrolysis. The novel study investigates the microbial approach to increase the EET in microbial electrolysis cells. Results revealed that metabolites in an EAM grow viable cells that initiate high EET at anode sites. At optimum WAS with FOG addition, the production of volatile fatty acid and current generation yield were 2.94 ± 0.19 g/L and 17.91 ± 7.23 mA, respectively. Analysis of the bio-electrochemical changes showed that the anodic biofilm enhances intercellular electron transfer, increases NADH-NAD ratio 28.6, and increases metabolites yield-fluxes which would be responsible for bio-electricity production. Taken together, results indicated that the electrolysis highlights MEC performance in terms of power generation of 788 mV with 200 mL of anode volume of active viable cells by utilizing WAS with 11% FOG. The engineered strains exhibited excellent workability for power generation and EET activity. This study shed light on the anode biofilm engineering how growth cell volume, intercellular electron transfer, increases NADH-NAD ratio is a evidence to increase the EET of EAB for efficient current production.
Research on Drug-Drug Interaction Prediction Using Capsule Neural Network Based on Self-Attention Mechanism
Authors: Chen Xing-xin , Miao Zhen , Nie Bin 
Published: October 2024

Abstract

Multi-drug combinations are an effective strategy for the teatment of complex diseases. Due to the numerous unknown interactions between drugs, accurate prediction of drug-drug interactions (DDIs) is essential to avoid adverse drug reactions that can cause significant harm to patients. Therefore, DDI prediction is crucial in pharmacology.Methods: In this paper, we propose a multi-source feature fusion DDI prediction method based on the self-attention mechanism of a capsule neural network (ACaps-DDI). This method effectively integrates the chemical information of a drug's internal substructure, as well as the bioinformation of the drug's external targets and enzymes, to predict drug-drug interactions.Results: Comparison experiments on two benchmark datasets show that the six classification metrics of the ACaps-DDI model outperform those of the other seven comparison models, demonstrating the superior performance and generalization ability of the ACaps-DDI model. Ablation studies further validate the effectiveness of certain ACaps-DDI modules. Finally, case validation with three drugs—cannabidiol, torasemide, and dexamethasone—demonstrates the model's effectiveness in predicting unknown drug interactions.
Conclusion: The ACaps-DDI model has demonstrated a good predictive effect on known drugs and some predictive ability on unseen drugs, which is of great practical significance for clinical drug interaction studies.
Effects of different fertilizer combinations on yield and nitrogen use efficiency of summer maize in Jiangsu province
Authors: Wang Jue , Guo Jian , Li Guanghao , Lu Weiping , Lu Dalei 
Published: October 2024

Abstract

Rational application of nitrogen fertilizer plays a crucial role in promoting crop growth and increasing yields. The application of slow-release fertilizer has emerged as an important agronomic measure to increase yield and production efficiency further. In this study, a field trial with maize hybrid Jiangyu877 as material was conducted to investigate the effects of six fertilizer combinations [no fertilizer (P1), common nitrogen fertilizer application (P2), slow-release nitrogen fertilizer application (P3), common nitrogen fertilizer and slow-release nitrogen fertilizer 1:1 application (P4), 1:2 application (P5) and 2:1 application (P6)] on yield and nitrogen use efficiency. Fertilization significantly increased maize yield, with the best increase in the treatment combining common and slow-release nitrogen fertilizers. Among all treatments receiving equal amounts of nitrogen, the P5 treatment exhibited superior yield and higher nitrogen use efficiency than other treatments. Compared with the P3 treatment, the dry matter accumulation of P5 increased by 6.6 ~ 13.7%, nitrogen accumulation increased by 13.0 ~ 16.3%, and nitrogen use efficiency increased by 8.1 ~ 11.5%. Therefore, compared with the P3 treatment, the 1000-grain weight and the number of grains per panicle were significantly increased, the test increased yield by 2.6 ~ 4.5%, and the net income increased by 17.1%~80.4% in three years. In conclusion, in the actual production of summer maize in Jiangsu Province, under the planting density of 75,000 plants ha− 1, the recommended fertilization method was common nitrogen and slow-release nitrogen (1:2) (common nitrogen 75 kg ha− 1: slow-release nitrogen 150 kg ha− 1) one-time base application, which synergistic improvement of yield and efficiency could be realized.
Perceptions and Treatment Seeking for Abnormal Uterine Bleeding among Women: A Qualitative Study
Authors: Gupta Ishan , Gupta Hardik , B AUB Qualitative Research Consortium MBBS Batch , Kansal Subham , Puri Surabhi , Salve Harshal R , Misra Puneet , Kumar Rakesh 
Published: October 2024

Abstract

Background
Barriers in seeking professional help for abnormal uterine bleeding (AUB) contribute to poor social, mental, emotional well-being of women in the Indian community. This study was undertaken to assess the perception and treatment seeking behaviour for AUB among women of reproductive age (WRA) group in rural Haryana.
Methods
A qualitative study was conducted using in-depth interviews (IDIs) and focused group discussions (FGDs) with various stakeholders, including WRA, accredited social health activists (ASHA), multipurpose health workers (MPW), and gynaecologists. Thematic analysis was done to analyse both IDIs and FGDs.
Results
Four key domains were identified: perceptions about AUB, treatment-seeking behaviour, perceived impact, and barriers to seeking treatment. According to WRA, AUB was characterized by changes in the frequency, duration, amount, and quality of menstrual bleeding. They believed that AUB is associated with factors such as anaemia, weakness, Copper-T insertion, consumption of specific food items as well as inadequate nutrition. MPWs and ASHAs further added that AUB can lead to headaches, anaemia, uterine infections, stroke, and fainting. Barriers hindering seeking treatment included lack of accessible healthcare facilities, long queues in hospitals, high travel and medication costs, limited availability of female doctors, lack of support from family, illiteracy and a fear of potential side effects of drugs.
Conclusions
Gaps were identified in the understanding of causes as well as complications of AUB. Limited availability of female doctors, healthcare facilities, and insufficient family support pose major barriers to seeking treatment while the stigma surrounding menstrual health prevents open discussions within families.
Identification of atopic dermatitis-associated diseases based on the National Health and Nutrition Examination Survey (NHANES) 2013-2018
Authors: Liu Yuan , Chen Xi , Su Ziyue , Wang Yiting , Xue Yintong , Li Yan , Gao Xiang , Wang Lijun , Hao Jie , Wang Yuedan , Chu Ming 
Published: October 2024

Abstract

Background
Atopic dermatitis (AD) is the most common chronic inflammatory skin disease. Massive cohort studies revealed that AD was associated with allergic diseases, inflammatory diseases, autoimmune diseases, cardiovascular diseases, and mental disorders.
Objective
We comprehensively and systematically analyzed the correlation between AD and diseases to identify AD-associated diseases (ADADs).
Methods
We involved 17924 individuals from the National Health and Nutrition Examination Survey (NHANES) (2013–2018) dataset, and analyzed the correlation between AD and 422 diseases classified by International Classification of Diseases-10 (ICD-10) using four logistic regression models.
Results
We found that AD is significantly associated with 33 diseases: (1) allergic diseases, including urticaria, allergic rhinitis, allergy, asthma, other seasonal allergic rhinitis; (2) inflammatory diseases, including noninfective gastroenteritis and colitis, acute atopic conjunctivitis, osteoarthritis, and unspecified chronic bronchitis; (3) mental disorders with impairment (MDI), including comorbid mental disorders, schizophrenia and sleep disorder; (4) malignant tumors, including malignant neoplasm of prostate, malignant (primary) neoplasm and malignant neoplasm of breast; (5) other symptoms and diseases, other symptoms and diseases, such as wheezing, pruritus and gout. Notably, non-infective gastroenteritis and colitis showed the strongest correlation (OR: 38.39, 95% CI: 3.08-478.01) among the 33 ADADs.
Conclusion
We identified 33 ADADs based on the NHANES (2013–2018) dataset, which provide new insights into understanding the development of these ADADs associated with AD.
Polyphenol-Driven Structural Alterations and Antibacterial Potency of the IgY-Catechin Complex
Authors: Liu Lili , Wang Yanli , Zhang Mengjun , Cheng Weiwei , Ding Yue , He Jialiang 
Published: October 2024

Abstract

The binding of proteins with polyphenols can alter the properties of the proteins, thereby expanding their application scenarios. Yolk immunoglobulin (IgY), an easily obtainable protein from eggs, undergoes property changes upon binding with catechin, which is significant for broadening the application of IgY. This study investigates the binding modes, structural changes, stability, and antibacterial properties of the IgY-catechin complex using computational chemistry, spectroscopy, and antibacterial assays. Molecular dynamics simulations analysis revealed that catechin binds to residues PHE503, THR501, THR505, GLU511 of IgY respectively. Fourier transform infrared spectroscopy(FT-IR) and circular dichroism(CD) displayed an increase in hydrogen bond content within the complex, a 5.48% increase in α-helix content, and a decrease in random coil content. Scanning electron microscopy (SEM) showed that the complex had a smoother and more regular surface. The IgY-catechin complex exhibited improved acid-base stability but slightly reduced thermal stability compared to IgY. Fluorescence inverted microscopy and nucleic acid-protein leakage assays indicated that the complex disrupted the cell membranes of both Escherichia coli (E. coli) and Staphylococcus aureus (S. aureus), leading to the leakage of intracellular genetic material. These findings confirmed that the interaction between catechin and IgY altered the protein structure of IgY, enhanced its bacteriostatic ability. This research provides a new approach to extending the application range of IgY and improving its comprehensive utilization.
Improved Profuse Clustering and Support Vector Machine Based Detection Model ForDiabetic Retinopathy
Authors: JUIT Praveen Modi , NMIMS Yugal Kumar 
Published: October 2024

Abstract

Diabetic retinopathy (DR) is a condition that can affect the eyes due to elevated glucose levels in the body and is a leading cause of blindness in diabetic patients. Early detection of DR poses significant challenges for ophthalmologists due to factors such as irrelevant illuminations, blurred and darkened regions, and non-uniform light distribution. Although several algorithms have been developed to address the issues related to the DR, still issues like non-uniform reflectance, overfitting, oversaturation, and biased solutions remain unresolved. This work aims to tackle these issues by presenting an automated model for the effective and accurate diagnosis of diabetic retinopathy. The proposed model addresses image saturation problems using a weighted contrast-limited adaptive histogram equalization (WCLAHE) technique, and removes irregular boundaries with the bilinear interpolation method. Additionally, an enhanced clustering method is employed to identify appropriate segments in fundus images by integrating a kernel-based K-medoids algorithm. Overfitting and biased solution issues are mitigated through an improved support vector machine. The efficiency of the proposed model was evaluated on a fundus image dataset consisting of 3200 images, including both diabetic retinopathy and non-diabetic retinopathy cases. The results are measured in terms of accuracy, sensitivity, specificity, F1-Score, and AUC, and were compared with KNN, SVM, ANN, VGG16, and VGG19 techniques. The proposed model achieved an accuracy of 90.06%, sensitivity of 92.13%, specificity of 94.49%, and an F1-Score of 93.29% using a 10-fold cross-validation method.
Knowledge, attitude and practices of people living in Awka Metropolis, on malaria treatment, prevention and control
Authors: Ezejiegu Chinelo , Ozohili Somtochukwu , Anyigor Benjamin , Onwuzuligbo Chukwunonso , Ogwaluonye Uchenna 
Published: October 2024

Abstract

Background: The World Health Organization (WHO), malaria report has consistently shown the poor performances of malaria-endemic countries such as Nigeria in controlling the malaria parasite.
Methods: The study is a cross-sectional mixed quantitative and qualitative analysis. Regarding existing literature on the Knowledge, Attitude and Practices (KAP) on malaria treatment, prevention and control within the study area, a structured questionnaire suiting for the study was drafted with reliability analysis done following the review of the study instrument by three field experts.
Results: The study involved a total of 350 respondents who filled out the questionnaires independently. Sixty percent (60.3%) had good knowledge of malaria prevention methods. Using a Likert scale mean of 3.97, the sample population was shown to have a positive attitude towards the prevention of malaria, also a mean of 3.7 indicated that the population showed a positive attitude to treatment. Insecticide Treated Nets (ITNs) were poorly used while other methods of prevention were preferred. Above 90% of the participants agreed that malaria is preventable and cited several preventive methods.
Conclusion: In conclusion, the study respondents had good knowledge of malaria infection, treatment and control, although the majority knowing this, do not follow the accurate prevention practices. The study exposed the need to train and retrain the communities on the benefits of adequate prevention and treatment practices as a tool for effective malaria control and eradication.
Real-world Adaptation for enhancedphoto-realistic and semantic Style Transfer in indoor Panoramas
Authors: Tukur Muhammad , Shah Uzair , Jashari Sara , Boraey Yehia , Pintore Giovanni , Gobbetti Enrico , Fetais Noora , Schneider Jens , Agus Marco 
Published: October 2024

Abstract

We present a novel geometry-aware, shading-independent, photo-realistic, andsemantic style transfer method for indoor panoramic scenes—for practical, real-world use. Unlike previous methods requiring separate inputs, our approach employs a multitask dense prediction architecture to infer multiple pixel-wise sig-nals from a single panoramic image. This comprehensive approach automaticallyderives essential signals (depth, semantic layers, shading, and reflectance) from a single 360-degree panoramic indoor photo, significantly enhancing usability for real-world scenarios. Our method extends the capabilities of semantic-aware gen-erative adversarial architectures by introducing two innovative strategies that address the geometric characteristics of indoor scenes and improve overall performance. We incorporate robust geometry losses that utilize layout and depthinference during training to ensure shape consistency between the generatedscenes and the ground truth. Secondly, we employ an hybrid end-to-end edge-driven scheme based on convolutional neural network for performing Intrinsic Image Decomposition (IID) in a way to extract the albedo and normalized shading signals in form of obscurance and highlights from the original scenes. We perform the style transfer on the albedo rather than on full RGB images, effectively preventing shading-related bleeding issues. Additionally, we apply super-resolution to the resulting scenes to enhance image quality and capturefine details. We tested this extended model on both real-world and syntheticdata. Experimental results demonstrate that our proposed enhanced architecture outperforms state-of-the-art style transfer models in terms of perceptualand accuracy metrics, achieving an 18.91% lower ArtFID (Art-Fr´echet InceptionDistance), a 13.99% higher PSNR (Peak Signal-to-Noise Ratio), and an 8.99%higher SSIM (Structural Similarity). The visual results show that our method iseffective in producing realistic and visually pleasing indoor scenes for a varietyof applications in the Architecture, Engineering and Construction field.