bluuebunny/crossref_metadata_2025_split huggingface dataset:
A Machine Learning-based Predictor to Support University Students with Dyslexia with Personalized Tools and Strategies
Authors: Zingoni Andrea , Taborri Juri , Calabrò Giuseppe 
Published: April 2023

Abstract

Dyslexia is a specific learning disorder that causes issues related to reading, which affects around 10% of the worldwide population. This can compromise comprehension and memorization skills, and result in anxiety and lack of self-esteem, if no support is provided. Moreover, this support should be highly personalized, to be actually helpful. In this paper, a predictor of the most useful methodologies to support students with dyslexia has been created, with a focus on university alumni. The prediction algorithm is based on supervised machine learning techniques; starting from the issues that dyslexic students experience during their career, it is capable of suggesting customized support digital tools and learning strategies for each of them. The algorithm was trained and tested on data acquired through a self-evaluation questionnaire, which was designed and then spread to more than 1200 university students. It allowed 17 useful tools and 22 useful strategies to be detected. The results of the testing showed an average prediction accuracy higher than 92%, which rises to almost 95% by renouncing to guess the less-predictable 5 tools/strategies. In the light of this, it is possible to state that the implemented algorithm can achieve the set goal and, thus, reduce the gap between dyslexic and non-dyslexic students. This achievement paves the way for a new modality of facing the problem of dyslexia by university institutions, which aims at modifying teaching activities toward students’ needs, instead of simply reducing their study load or duties. This complies with the definition and the aims of inclusivity.
GCN-based multi-label node classification in heterogeneous networks
Authors: Bastami Sajad , dowlatshahi mohammad bagher 
Published: April 2023

Abstract
Graph structure data is created in many real applications. Graphs represent data by displaying entities (nodes) and their relationships (edges). Several methods are available for effective searching in these networks when graphs are large and heterogeneous and when the patterns are complex and noisy. Graph embedding provides an effective solution to this problem. In graph embedding, the nodes in the graph are evaluated based on their significance. There will be a wide range of graph-related works presented, including classifications in latent space and neural network representations. A variety of methods can be used to integrate node features and label information. In heterogeneous networks, the learned latent space becomes closer to similar nodes. Due to their rich semantic information and heterogeneity, there are challenges associated with analyzing heterogeneous graphs embedded in latent space. Most of the existing methods consider nodes with a single label. Therefore, these methods cannot manage a network of multilabel nodes that describes complex concepts associated with samples. It improves the embedding of heterogeneous networks by combining labels and modeling heterogeneous networks. This is a challenging task. This article presents a node embedding method based on a graph neural network (GCN) to address these issues. By incorporating label information into heterogeneous network embedding, relationships between labels are improved. In comparison to embedded heterogeneous network algorithms, the presented method performed well with real-world data. Graph embedding is discussed in the first section of this paper. The final portion of this paper discusses the application of multi-label embedding to heterogeneous graphs and future research directions.
Rodent control of chamber brightness leads to the emergence of unique circadian rhythms
Authors: Ogasawara Jun , Takeuchi Yuki , Matsumoto Nobuyoshi , Yasui Masato , Ikegaya Yuji 
Published: April 2023

Abstract
Internal circadian rhythms deviate slightly from 24 h, depending on species and individuals; thus, an environment with a 24-h light/dark cycle may not be ideal. In this study, we enabled mice to control room brightness via nose pokes and investigated subsequent biological rhythms as well as physiological and cognitive functions. The mice immediately learned the relationship between nose pokes and the resulting illuminance change and generated 24.5 ± 0.4 h-cycles in both illuminance and locomotor activity, with the illuminance phase preceding the locomotion phase. This circadian rhythm emerged during the first 3 d and was maintained for at least 30 d. Compared to mice whose light cycle was controlled by other mice (in a separate chamber) or those maintained on a regular 12 h-light/12 h-dark cycle, mice that controlled their own light cycle exhibited fewer mid-sleep awakenings, lower levels of stress-related steroids, and improved cognitive performance. These emergent circadian rhythms provide insight into the evolution of ecological circadian dynamics and may inform the development of efficient lifestyles in humans.
Bacillus Coagulans as potent intervention for irritable bowel syndrome: A systematic review and meta-analysis of randomized control trials
Authors: AbdelQadir Yossef Hassan , Nabhan Ayman Essa , Althawadi Yusuf Jasim , Belal Mohamed Mohamed , Feiter Yasmeen Yasser , Madian Mohamed Salem , Omran Hoda Aly , AbdelAzim Ahmed Alaa , Abdelghafar Yomna Ali , Nashwan Abdulqadir J. 
Published: April 2023

Abstract

Introduction: Irritable bowel syndrome (IBS) is the most common functional GIT disorder. Bacillus coagulans (BC) is a potent spore-forming probiotic that has been proposed as a potent supplement that can improve GIT dysbiosis and relieve IBS symptoms. We aim to assess the effect of BC on different IBS symptoms.
Methods: We searched seven databases for RCTs that compare the use of BC versus placebo in the management of IBS symptoms.
Results: Our search finally retrieved seven RCTs after extensive screening. Our analysis revealed that BC significantly improved the severity of IBS symptoms such as urgency {MD: -1.05, 95% CI: -1.33; -0.77}, bowel habit satisfaction {MD: -1.40, 95% CI: -1.68; -1.13}, straining {MD: -1. 22, 95% CI: -1.61; -0.82}, the passage of gas {MD: -1.25, 95% CI: -1.51; -0.99}, incomplete evacuation {MD: -1.06, 95% CI: -1.55; -0.57}, and the total score of symptom severity {MD: -10.13, 95% CI: -11.61; -8.66, (P < 0.00001)}. We also found a significant improvement in the physician’s global assessment score at 8 weeks {MD: 1.61, 95% CI: 0.61; 2.60, (P = 0.002)}; however, results were not significant at 4 weeks (P = 0.48). Moreover, our results indicate a significant decrease in the discomfort score after 4 and 8 weeks, the bloating score after 2, 4, 8, and 11 weeks and the vomiting score. Regarding abdominal pain, BC significantly lowered pain score after 2, 4, 8, and (11-13) weeks.
Conclusion: BC is a very effective probiotic in reducing the severity of IBS symptoms and abdominal pain with no serious adverse events. Future studies are needed to assess the use of BC as a long-term regimen and compare different subspecies of BC.
Contribution of infection and vaccination to population-level seroprevalence through two COVID waves in Tamil Nadu, India
Authors: TS Selvavinayagam , A Somasundaram , Selvam Jerard Maria , P Sampath , V. Vijayalakshmi , C. Ajith Brabhu Kumar , Subramaniam Sudharshini , K. Pathpan , S. Raju , R. Avudaiselvi , N. Yogananth , Subramanian Gurunathan , A. Roshini , D.N. Dhiliban , Imad Sofia , Tandel Vaidehi , Parasa Rajeswari , Sachdeva Stuti , Ramachandran Sabareesh , Malani Anup , V. Prakash 
Published: April 2023

Abstract
Four rounds of serological surveys were conducted, spanning two COVID waves (October 2020 and April-May 2021), in Tamil Nadu (population 72 million) state in India. Each round included representative populations in each district of the state, totaling ≥20,000 persons per round. State-level seroprevalence was 31.5% in round 1 (October-November 2020), after India’s first COVID wave. Seroprevalence fell to 22.9% in 2 (April 2021), consistent with waning of SARS-Cov-2 antibodies from natural infection. Seroprevalence rose to 67.1% by round 3 (June-July 2021), reflecting infections from the Delta-variant induced second COVID wave. Seroprevalence rose to 93.1% by round 4 (December 2021-January 2022), reflecting higher vaccination rates. Antibodies also appear to wane after vaccination. Seroprevalence in urban areas was higher than in rural areas, but the gap shrunk over time (35.7 v. 25.7% in round 1, 89.8% v. 91.4% in round 4) as the epidemic spread even in low-density rural areas. The study documents substantial waning of SARS-CoV-2 antibodies at the population level and demonstrates how to calculate the extent to which infection and vaccination separately contribute to seroprevalence estimates.
Unsupervised Deep Cross-Modal Hashing Retrieval with Multiple Dense Networks
Authors: Li Bo , Yao Dan , Li Zhixin 
Published: April 2023

Abstract
Cross-modal retrieval is a hot research direction at present, and unsupervised cross-modal hashing has better practical value for its storage, training cost, and query efficiency. However, many current unsupervised cross-modal hashing retrieval methods calculate the similarity by traditional convolution neural networks, which can hardly represent case feature relationships comprehensively. This paper proposes large-scale unsupervised deep cross-modal hashing retrieval with multiple dense networks (MUCH) based on DenseNet and TxtDenseNet. Considering that apply various similarity measures to exploit the structural information of multiple modalities from different perspectives, we design multiple dense feature sampling for cross-modal retrieval. Essentially, MUCH can achieve Feature Augmentation and addresses the inaccurate similarity problem by multiple deep dense networks (MDNs), which are pseudo-graph networks combining the advantages of GCN and CNN. When MUCH transforms data from high-dimensional space into the binary space, MDNs are tasked to abundantly extract feature embeddings for intermodal and inner-modal data, and regulate comprehensive similarity-preserving losses by auxiliary matrices and joint training. It is worth noting that this method applies DenseNet to the whole model for unsupervised cross-modal hash retrieval for the first time. Extensive experiments on three baseline datasets demonstrate that the proposed method significantly outperforms most of the state-of-the-art unsupervised methods.
An integrated understanding of the multifaceted impact of Hospital at Home: a mixed-methods study to articulate and test a HaH programme theory
Authors: Chen Hong , Ignatowicz Agnieszka , Skrybant Magdalena , Lasserson Daniel 
Published: April 2023

Abstract
Background: Hospital at Home (HaH) provides intensive, hospital-level care in private or care homes for acute conditions that would normally require an acute hospital bed, using multidisciplinary healthcare teams. As a programme of complex medical-social interventions, a HaH programme theory has not been fully articulated yet, although implicit in the structures, functions, and activities of the existing HaH services. We aimed to unearth the tacit theory from international evidence and test the soundness of the theory by studying UK HaH services.Methods: We conducted a rapid systematic review of 29 articles adopting a ‘realist review’ approach and examined 11 UK-based services by interviewing up to 3 staff members (e.g. doctors, nurses, service leads, therapists) from each service. The review and interview data were analysed using Framework Analysis and Purposive Text Analysis.Results: Our programme theory has three interlinked components: Organisation, Utilisation and Impact. Central to the programme theory is Impact Theory. It shows multifaceted impacts of HaH on patients and carers and at the system level; and reveals multiple, intertwined pathways of change in which certain outputs of the main HaH functions and activities were the instigating causes and certain clinical, health and wellbeing, and system-level benefits were the effects they eventually produced. Our findings also highlight the main features of HaH that created added value for patients and carers: comprehensiveness of assessment leading to individually tailored, situational appropriate interventions; special and more holistic healing effects of home environment; high quality interactions between professionals and patients and carers; and less complexity and fragmentation of care.Conclusions: We have shown that the multifaceted impacts of HaH add value to patient care, carer support and health system performance, making HaH a better option for some patients who would otherwise need hospital admission. Our Impact Theory helps unpack the “black box” to reveal how these impacts can be brought about, informing how HaH services can organise resources and design processes of care to optimise patient satisfaction and outcomes. It can be used as a basis for formulating and prioritising evaluation questions, designing evaluation research, and interpreting evaluation findings in future HaH service evaluations.
Comparison of patient length of stay in care between home-based care and hospitalized covid-19 patients in northern and West Nile regions, Uganda
Authors: Namusoosa Ritah , Tamale Andrew , Baziira Joel , Ssekyanzi Micheal , Nabadda Susan N. , Kungu Joseph M 
Published: April 2023

Abstract

SARS-CoV-2-caused COVID-19 was first detected in Wuhan, China, in December 2019. Uganda reported her first COVID-19 case on March 21, 2020. The pandemic placed an enormous burden on health systems across the world. This retrospective cross-sectional study compared COVID-19 patient length of stay in care and associated factors for hospitalised patients in Regional Referral Hospitals and those who underwent home-based care in Northern and West Nile regions in Uganda. 400 patients were studied (200 inpatient and 200 home-based).
All patients were confirmed COVID-19 cases with a positive real-time PCR test result. Regardless of signs or symptoms development, all individuals with confirmed SARS-COV2 infection were eligible for admission to the hospital during this period.
It was found that hospitalized and home-based care patients were similar. 61.1% died within 14 days and 59.9% recovered under both types of care. Hospitalized patients stayed 14.8 days and home-based patients 15.0.
The difference in the mean length of stay in care among hospitalized patients and patients under home-based was not statistically significant (t=0.28, p=0.38) and there was no association between type of care and length of stay in care (OR: 0.96: 95% CI 0.64 to 1.43: p=0.837).
Symptom Status of patients and their occupation were found to be one of the important factors influencing recovery. It was observed that symptomatic patients were associated with longer stay in care (OR: 1.96, p=0.01). Likewise, law enforcement officers had a higher likelihood of staying longer compared to people involved in health-related work (OR: 3.28, p=0.03).
Covid-19 patient length of stay in care is not dependent on the approach used in case management. Therefore, the decision of whether a patient should be hospitalised or treated at home requires careful consideration of all relevant factors and an individual evaluation of the patient's circumstances.
Negative impacts of social isolation on behavior and neuronal functions are recovered after short-term social reintroduction in zebrafish.
Authors: Müller Talise Ellwanger , Santos Matheus Mulling dos , Ferreira Sabrina Antunes , Claro Mariana Torri , de Macedo Gabriel Teixeira , Fontana Barbara Dotto , Barbosa Nilda Vargas 
Published: April 2023

Abstract

Recently, social isolation measures were crucial to prevent the spread of the coronavirus pandemic. However, the lack of social interactions affected the population mental health and may have long-term consequences on behavior and brain functions. Here, we evaluated the behavioral, physiological, and molecular effects of a social isolation (SI) in adult zebrafish, and whether the animals recover such changes after their reintroduction to the social environment. Fish were submitted to 12 days of SI, and then reintroduced to social context (SR). Behavioral analyses to evaluate locomotion, anxiety-like and social-related behaviors were performed after SI protocol, and 3 and 6 days after SR. Cortisol and transcript levels from genes involved in neuronal homeostasis (c-fos, egr, bdnf), and serotonergic (5-HT) and dopaminergic (DA) neurotransmission (thp, th) were also measured. SI decreased anxiety-related phenotype, and altered social behaviors in zebrafish. Fish submitted to SI also presented changes in the transcript levels of genes related to neural activity, and 5-HT/DA signaling. Interestingly, most of the behavioral and molecular changes induced by SI were not found again 6 days after SR. Thus, we highlight that SR of zebrafish to their conspecifics played a positive role in social behaviors and in the expression of genes involved in different neuronal signaling pathways that were altered after 12 days of SI. This study brings unprecedented data on the effects of SR in the recovery from SI neurobehavioral alterations, and reinforces the role of zebrafish as a translational model for understanding the neurobiological mechanisms adjacent to SI and resocialization.
Microbially synthesised agriculturally useful urea nanoparticles have no phytotoxicity and show enhanced fertilising effects in Kharif (monsoon) paddy
Authors: Priyam Ayushi , Seth Prerna , Shukla Sadhana , Vineetha U. , Manna Palash Kumar , Singh Pushplata Prasad 
Published: April 2023

Abstract
Urea has been used as a conventional nitrogenous fertiliser since long, however, the nutrient use efficiency of the conventional urea is low due to issues related to ammonia volatilisation. Nanotechnology-based fertilisers are now proposed to reduce the dose of conventional fertilisers and enhance nutrient use efficiency. In the present study, we proposed a novel microbial-assisted synthesis approach for urea nanoparticles (TERI NanoUrea: TNU) using a microbial co-culture approach that included two species from the Bacillaceae family. This approach is an eco-friendly method as opposed to the synthesis of conventional urea. In brief, the synthesis reaction was modelled using Monod’s kinetics for bacterial growth in presence of substrate. Two species of Bacilluswere co-cultured and during the algorithmic growth phase were exposed to the bulk substrate (conventional urea: 46% N). Difference in release patterns of organic acids, carbohydrates, lipids, and proteins during microbial culture exposure to bulk substrate was observed, which resulted in the synthesis of urea nanoparticles (TNU) after 24 hr of incubation. Also, biomolecules released during synthesis of nanoparticles contributed to a biomolecular corona over the nanoparticles. The prepared TNU was thoroughly characterised by using various physicochemical and analytical techniques and it was observed that via microbial synthesis approach, 5-15 nm sized spherical particles possessing inherent characteristics of urea were synthesised. Along with the synthesis and characterisation we also investigated the effects of TNU on the early development of seedlings of a monocot (maize) and two dicot (tomato and okra) species. It was found that the novel biogenic TNU did not show any phytotoxicity to the selected monocot and dicot species and resulted in enhanced seed vigour and plant growth. Further, the TNU was used in Kharif (monsoon) field trials on paddy for nitrogen supplementation to the conventional urea fertilisers. The field trial observations showed the agronomic efficiency of replacing 25% of the conventional urea with TNU (TNU + 75% urea along with 100% treatment with recommended doses of phosphorus and potassium fertilisers). These results suggest that biogenic TNU, can be explored further for its application to various crop systems and can aid in supplementation of conventional urea fertilisers.
Determinants of Neural Tube Defect Among Newborns Admitted to Neonatal Intensive Care Units of Teaching Hospitals in Gedeo Zone and Sidama Region, Southern Ethiopia; A Case Control Study
Authors: Getnet Addisu , Figa Zerihun , Gido Rediet , Aschalew Getanew , Gebeyehu Kasse , Destaw Belete , Abebe Mesfin , Girma Bekahegn , Bimer Kirubel , Kebede Daniel 
Published: April 2023

Abstract
Background: A neural tube defect is a form of congenital malformation that can occur in the early embryonic period and result in child morbidity and mortality. Little emphasis is given to congenital anomalies, especially neural tube defects, which are significant causes of child death. Ethiopia is one of the countries with a high estimated prevalence of neural tube defects. So, identifying factors contributing to it would be significant for planning risk reduction and preventive strategies. Therefore this study aimed to identify determinants of neural tube defects among neonates admitted to neonatal intensive care units.
Methods: A hospital-based, unmatched case control study was conducted on 104 cases and 208 controls selected from neonatal intensive care units of teaching hospitals in Gedeo Zone and Sidama Region, southern Ethiopia. All neural tube defect cases admitted at the time of the data collection period were included, and controls were selected using a simple random sampling method. Data analysis was done using SPSS version 25. Binary logistic regression was used, and variables with a P value less than 0.25 in bivariate analysis were entered into the multivariable logistic regression model. An adjusted odds ratio with a 95% confidence interval was estimated, and finally, variables that show a level of P value less than 0.05 in multivariable analysis were declared statistically significant.
Result; after controlling confounders, factors like unplanned pregnancy 2.20(1.20- 4.041), history of abortions 2.09(1.19-3.67), khat chewing 6.67(2.95-15.06), antipyretic and analgesic medications 2.87(1.47-5.56) and, being a female neonate 2.11(1.21-3.67) were significantly associated with a neural tube defect.
Conclusion: In this study, different determinants were identified and linked with neural tube defects .hence, the behavioral, medical, and obstetrical conditions of mothers need serious evaluation in the pre-pregnancy period. So it is highly recommended to improve preconception counseling and prenatal care practices, which could reduce the risk of neural tube defects.