bluuebunny/crossref_metadata_2025 huggingface dataset:
Human research subjects and current practice of research ethics committees in Czech academic environment: national regulation is urgently needed
Authors: Veselska Renata , Sirucek Jan , Kure Josef
Published: January 2024

Background: The primary objective of this study was to conduct a comprehensive questionnaire survey on the practices of research ethics committees that review academic research projects, specifically related to the Additional Protocol on Biomedical Research CETS No. 195, which was ratified and implemented in 2020. The study aims to provide an unbiased and objective assessment of the current practices employed by the research ethics committees. This research is unique and has never been conducted in Czechia.
Methods: To assess the situation, we developed an online questionnaire containing 18 questions concerning several aspects of the functioning of research ethics committees. The questionnaire was in Czech language and was administered through the Qualtrics platform anonymously. The target group of 61 RECs at research institutions was approached by emails and we received 43 completely filled questionnaires, i.e., response rate of 67%.
Results: We obtained valuable data on the functioning of research ethics committees in Czechia in three main domains: the mandate and composition of the committee; the scope of its agenda; the process of evaluation including the voting procedure. From the results is evident that even more than twenty years after the ratification of the Oviedo Convention ETS No. 164 and three years after the ratification of its Additional Protocol on Biomedical Research CETS No. 195, the responsible assessment of the ethics of research involving human subjects is still not satisfactorily addressed and established for routine practice in Czechia.
Conclusions: The outcomes of our study revealed that the main problem of research ethics in Czechia is the lack of national legislation on research ethics governance. To address this problem, the country requires a legislation framework accompanied by supportive measures aimed at guiding and assisting research ethics committees in the Czech academic environment.
SuBIS: Submodular Subset Selection with Importance Sampling for Data-Efficient Machine Learning
Authors: Trust Paul , Younis Haseeb , Minghim Rosane
Published: January 2024

In machine learning (ML), particularly in fields like natural language processing and computer vision, developing state-of-the-art models faces a significant challenge due to the high computational power needed for training. These models usually require vast datasets and long training periods, resulting in substantial costs and environmental impacts. Even though extremely large-scale models show promising performances without the need for further finetuning through few-shot and zero-shot learning, they still lag behind fine-tuning alternatives by big margins.This research explores training ML models with smaller yet highly representative subsets of datasets, utilizing submodular data selection. We propose a method Submodular Subset Selection with Importance Sampling (SuBIS), a two-stage process that synergizes clustering with importance sampling alongside submodular functions. This approach is designed to enhance dataset diversity while simultaneously reducing computational demands. Our empirical research indicates that training models with as little as \(10%\) carefully selected subsets of the original dataset can achieve performances that are competitively close, within three standard deviations, to those attained using the full training datasets. Moreover, SuBIS demonstrates its efficacy in scaling submodular functions to accommodate extremely large datasets. It substantially reduces the runtime required for these functions on large datasets by nearly a factor of \(10\) without any deterioration in downstream classification performance.
Flood Impacts on Household Livelihoods and Its Mitigation Strategies: Focus of Gelana Woreda, Oromia, Ethiopia
Authors: Deresse Teshome , Baye Simachew
Published: January 2024

This study assessed the impact of floods on household livelihoods and mitigation strategies in Gelana Woreda, Oromia, Ethiopia. The study's goals included determining the contributing cause, calculating the flood's impact, evaluating household mitigation activities in reaction to the threat of flooding, and evaluating how household livelihoods were impacted by the risk of a flood. The results of the study indicate that different flood effect factors cause interpersonal flood variability to increase slightly. Over a ten-year period, seasonal migration (16.6%), disruption of human health (3%), and loss of life (1.5%) were the three most common shocks from flood-induced disasters in the study area; heavy rain/flash flooding (29.7%) and river flooding (49.2%) were the two main causes of these shocks. Based on the GIS-generated flood map, 46.5% of the kebeles chosen for the Gelana district were categorized as having a high risk of flooding; marginally more than 2.3%; and the remaining 22.1%, 19.5%, and 2.3% had extremely high, low, and moderately hazardous levels, respectively, indicating a greater risk of flooding. Whereas Jirme and Bore have areas with extremely low and medium flood threats, the areas surrounding Kersa, Metari, and Shamole Shida are classified as having highly hazardous and moderate hazardous flood danger. However, in the research region, the main obstacles to flood mitigation were informational gaps (15.6%), lack of funding (28.6%), absence of government support (18.6%), and reluctance to join farmer associations (14.1%). Raising farmers' knowledge of agricultural technologies, structural mitigation, environmental restoration, ease of access to credit services, diversification of income streams, building farmers' capacity, promoting multiple strategies as the main flood mitigation strategies, and raising household standards in the study area are therefore important.
Comparison of Deep Learning Algorithms for Site Detection of False Data Injection Attacks in Smart Grids
Authors: Nasir Qassim , Talib Manar Abu , Arshad Muhammad Arbab , Ishak Tracy , Berrim Romaissa , Alsaid Basma , Badway Youssef , Waraga Omnia Abu
Published: January 2024

False Data Injection Attacks (FDIA) pose a significant threat to the stability of smart grids. Traditional Bad Data Detection (BDD) algorithms, deployed to remove low-quality data, can easily be bypassed by these attacks which require minimal knowledge about the parameters of the power bus systems. This makes it essential to develop defence approaches that are generic and scalable to all types of power systems. Deep learning algorithms provide state-of-the-art detection for FDIA while requiring no knowledge about system parameters. However, there are very few works in the literature that evaluate these models for FDIA detection at the level of an individual node in the power system. In this paper, we utilize multi-layer perceptron (MLP), convolutional neural networks (CNN), Long Short-Term Memory (LSTM), attention-based bidirectional LSTM, and hybrid models for the detection of the exact location of the attack node. All the models are evaluated on IEEE-14 and IEEE-118 bus systems in terms of row accuracy (RACC), computational time, and memory space required for training the deep learning model. Each model was further investigated through a manual grid search to determine the optimal architecture of the deep learning model, including the number of layers and neurons in each layer. Finally, the performance of each model was quantified on different variants of the dataset – which varied in their 𝑙2-norm.
Prediction of vaginal birth after previous cesarean delivery using explainable machine learning models
Authors: Thagaard Ida Näslund , Stentoft-Larsen Valdemar , Iglesias Miquel Triana , Demharter Samuel , Krebs Lone
Published: January 2024

Purpose
This study aims to train a novel explainable machine learning method (QLattice) to predict successful vaginal birth after cesarean and compare the performance of these models with other known machine learning- and logistic regression models.
Methods
A Danish cohort study including 11 017 women with a prior cesarean giving birth during year 2004–2016 was used to train and evaluate three machine learning algorithms (LASSO, Random Forest, and QLattice). Grobmans logistic regression model was used as baseline. Two models were developed (antenatal and prelabor).
Results
Overall, 4 897 (44,4%) women had a trial of labor, with 3 441 (70.3%) women delivering vaginally. In the antenatal model predictive variables were epidural (OR = 0.53), breech presentation (OR = 1.70), mothers height (OR = 1.03), pre-pregnancy BMI (OR = 0.95), any vaginal birth (OR = 7.74), and vaginal birth before cesarean (0.24). In the prelabor model induction of labor (OR = 0.59), primary rupture of membranes (OR = 0.52), and infant weight (OR = 0.52) were additional predictors. For the antenatal prediction models, the area under the curve (AUC) for the different methods were QLattice 0.73 (0.70–0.76), LASSO with 62 features 0.75 (0.72–0.78), Random Forest 0.74 (0.71–0.77), and Grobman 0.68 (0.65–0.71). For the Prelabor model, AUC were for QLattice 0.77 (0.74–0.80), LASSO with 67 features 0.77 (0.74–0.80), Random Forest 0.75 (0.72–0.78), and Grobman 0.70 (0.66–0.73).
Conclusions
QLattice performs equal to other machine learning algorithms contributing further by giving explainable insight with fewer variables. Future prospective studies will reveal whether individual decision support tools, based on these models can improve outcomes in women with previous cesarean.
From Efficiency to Excess: Understanding Energy Usage in Green-Certified Homes
Authors: Fan Mingxuan , Agarwal Sumit , Eraral Eduardo , Qin Yu , Zheng Huanhuan
Published: January 2024

Green building certification is critical for reducing carbon emissions in the real estate sector, which accounts for over one-third of the global carbon output. Analysing nationwide account-level water consumption data in Singapore with staggered difference-in-differences regressions, we find an unexpected 3.3% increase in household water consumption for retrofitted residential buildings post-certification. Similar increase is observed for household electricity consumption using block-level data. The increased consumption is unlikely a rebound effect given the lack of household-level efficiency improvements, instead it is attributable to an increase in post-certification house price. The increase in water consumption for household is five-times the reduction for common areas with mandated efficiency improvements. We highlight the importance of enhancing household-level efficiency and implementing stringent certification criteria in offsetting the increase in utility consumption, which is particularly crucial with the rapid proliferation of green buildings around the world.
Construction of an Evaluation Index System for the Emergency Management Capability of Major Infectious Diseases in Urban Communities
Authors: Guan Xiuying , Li Wentao , Cui Nan , Yu Jiacen , An Libin
Published: January 2024

Objective
Taking community as the research perspective, scientifically construct the evaluation index system of emergency management capacity of major infectious diseases in urban communities.
Methods
The research team comprehensively analyzed Introduction to Emergency Management Theory and Practice and the theory of Collaboration, Participation, and Common Interests, policies, and a literature review to form the initial evaluation index system. After that, the final version of the evaluation index system was developed using the modified Delphi and Analytic Hierarchy Process method for the emergency management of the urban community for major infectious diseases.
Results
Twenty-two experts were invited to participate in three rounds of opinion solicitation, and the positive coefficient of experts was 1, the average authority coefficient was 0.88, and the coordination coefficient of the third round was 0.175 (P < 0.001). This system consisted of 4 first-level, 19 second-level, and 54 third-level indexes.
Conclusion
The constructed evaluation index system for the emergency management capacity of major infectious diseases in the urban community setting is novel and scientifically effective, which can provide a reference for future assessment of the cooperation of major infectious diseases emergency management in urban communities.
Pleiotropic effects of PLEC and C1Q on Alzheimer’s disease and cardiovascular traits
Authors: Koskeridis Fotios , Fancy Nurun , Tan Pei Fang , Evangelou Evangelos , Meena Devendra , Elliott Paul , Wang Dennis , Matthews Paul , Dehghan Abbas , Tzoulaki Ioanna
Published: January 2024

Several cardiovascular (CV) traits and diseases co-occur with Alzheimer’s disease (AD). We mapped their shared genetic architecture using multi-trait genome-wide association studies. Subsequent fine-mapping and colocalisation highlighted 19 genetic loci associated with both AD and CV diseases. We prioritised rs11786896, which colocalised with AD, atrial fibrillation (AF) and expression of PLEC in the heart left ventricle, and rs7529220, which colocalised with AD, AF and expression of C1Q family genes. Single-cell RNA-sequencing data, co-expression network and protein-protein interaction analyses provided evidence for different mechanisms of PLEC, which is upregulated in left ventricular endothelium and cardiomyocytes with heart failure (HF) and in brain astrocytes with AD. Similar common mechanisms are implicated for C1Q in heart macrophages with HF and in brain microglia with AD. These findings highlight inflammatory and pleomorphic risk determinants for the co-occurrence of AD and CV diseases and suggest PLEC, C1Q and their interacting proteins as novel therapeutic targets.
Pollen specialist bee species are accurately predicted from visitation, occurrence and phylogenetic data
Authors: Smith Colleen , Bachelder Nick , Russell Avery L. , Morales Vanessa , Mosher Abilene R. , Seltmann Katja C.
Published: January 2024

An animal’s diet breadth is a central aspect of its life history. Yet information about which species have narrow dietary breadths (specialists) and which have comparatively broad dietary breadths (generalists) is missing for many taxa and regions. One possible way to address this gap is to leverage interaction data found on museum specimens and published in the literature. Here, we use bees as our focal taxon to predict dietary specialization and generalization using machine learning models and interaction data, along with a bee phylogeny, and occurrence data for 682 bee species native to the United States. To assess whether our models can transfer to new regions or taxa, we used spatial and phylogenetic blocking in assessing model performance. We found that specialist bees mostly visit their host plants, and that they can be predicted with high accuracy (mean 92% accuracy). Overall model performance was high (mean AUC = 0.84), and our models did a moderate job of predicting generalist bee species, the minority class in our dataset (mean 62% accuracy). Models tested on spatially and phylogenetically blocked data had comparable performance to models tested on randomly blocked data. Our results suggest it is possible to predict specialist bee species in regions and for taxonomic groups where they are unknown but it may be more challenging to predict generalists. Researchers looking to identify pollen specialist and generalist species can generate candidate lists of these species by training models on bees from nearby regions or closely related taxa.
Impact of high body mass index on hepatocellular carcinoma risk in chronic liver disease: population-based prospective cohort study
Authors: Kim Moonho , Jun Baek Gyu , Shin Hwang Sik , Yi Jee-Jeon , Kim Sang Gyune , Yi Sang-Wook
Published: January 2024

We investigated associations between body mass index (BMI) and hepatocellular carcinoma (HCC) in patients with hepatitis B (HBV) and C (HCV) virus infections, alcoholic liver disease (ALD), non-alcoholic fatty liver disease (NAFLD), and liver cirrhosis. We followed 350,608 Korean patients with liver disease from 2003–2006 until December 2018 using national hospital discharge records. Adjusted hazard ratios (HRs) per 5-kg/m2 BMI (≥ 25-kg/m2) increase for HCC risk were calculated using Cox models. HRs (95% confidence intervals) were 1.17 (1.06–1.28), 1.08 (0.87–1.34), 1.34 (1.14–1.58), 1.51 (1.17–1.94), and 1.11 (1.00-1.23) for HBV, HCV, ALD, NAFLD, and liver cirrhosis, respectively. HRs for HBV and liver cirrhosis in women vs men were 1.45 (1.23–1.70) vs 1.06 (0.95–1.19) and 1.27 (1.07–1.50) vs 1.02 (0.90–1.16), respectively. HRs for HBV, HCV, and NAFLD were 1.17 (1.07–1.29), 1.33 (1.03–1.73), and 1.20 (0.87–1.64) in < 65-year-olds and 1.05 (0.70–1.59), 0.74 (0.50–1.10), and 2.40 (1.62–3.54) in ≥ 65-year-olds, respectively. Overweight BMI showed significantly higher HCC risks with HBV, ALD, NAFLD, and liver cirrhosis. Higher BMIs increased HCC risks with HBV, ALD, NAFLD, and liver cirrhosis. Women with HBV and liver cirrhosis had stronger BMI-HCC associations than men. High BMI effect was stronger at older ages with NAFLD and at younger ages with viral hepatitis.
Opinions on Mandatory COVID Vaccinations of United States Healthcare Workers and Educators - In Cross sectional survey Social Media Users are in Favor
Authors: Johnson Calvin , Hector Richard , Pollock Gabriel , Kissen Micheal , Vargas Roberto
Published: January 2024

In the midst of the COVID pandemic, many persons protested to face masks, lock-downs and restrictions on movements. Mandatory vaccinations for healthcare workers also caused protests and some walkouts. As subvariants of COVID-19 will continue to emerge, it is important to assess the opinion of mandatory vaccinations for individuals who come into close contact with the public such as healthcare worker, educators and coaches. Such assessments could be used in policy-making to decrease vaccine hesitancy among these individuals and the general public. Between May 9, 2021 to February 1, 2022 a questionnaire probing the experience with COVID and attitude to COVID getting the COVID vaccine was sent to across 59 social media channels. The data from self-administered questionnaire was analyzed for the response to the question “Should COVID-19 Vaccination be Mandatory?”. The response had three categories coded as (0 = No, 1 = yes – now immediately, 2 = yes – only after full FDA approval) for seven specific subgroups, all healthcare workers, educators and coaches, four age groups as well as pregnant people. We conducted a confirmatory analysis with bivariate tables 1 thru 6 and multinomial logistic regression with predictors sex, age groups, race/ethnicity, marital status, creed, medical practice tables 7 and 8. Associations were assessed at with odds-ratio and 95% confidence intervals. Significant associations excluded 1.0. In multinomial model with 22,198 respondents, women objected more to immediate vaccinations than men, but both were equal on waiting for federal approval. Age category showed more acceptance of immediate vaccinations going from youngest to oldest. Most ethnic minorities were substantially more in favor of vaccinations than Caucasians. The exceptions were Native Americans/Pacific Islander who substantially objected to mandatory vaccinations. All creeds were more in favor of vaccinations than Christians. This was especially so for unbelievers. All healthcare workers regardless of type of practice were more in favor of immediate vaccinations than respondents not in healthcare. This suggests that efforts to promote COVID vaccinations would benefit from addressing the concerns of women, Native Americans and Pacific Islanders to increase participation by persons in these groups.
Future climate projection across Tanzania under CMIP6 with High-Resolution Regional Climate Model
Authors: MAGANG DAWIDO , Ojara Moses , Yunsheng Lou , Philemon Henry
Published: January 2024

Climate change is one of the most pressing challenges faced by developing countries due to lower adaptive capacity, with far- reaching impacts on agriculture. Adaptation is the fundamental option to reduce the impacts. Climate change projection for future with comparison to the past is investigated by focuses on utilization of outputs of high-resolution Regional Climate Models (RCMs) from the Coordinated Regional Climate Downscaling Experiment program (CORDEX-Africa) and Climate Change Knowledge Portal (CCKP) from the world Bank Group. Mid-century (2040–2060) projections of annually, monthly, and seasonal of rainfall, temperature (minimum, maximum) under two emissions scenarios. Mid-century is considered as a critical time for adaptation measures. The Shared Social-economic Pathways (SSPs): SSP2-4.5 and SSP5-8.5 under baseline period of (1991–2020) simulations. The future climate 2040–2060, departure determined with reference period 1995–2014 climate conditions. From the analysis, the result reveal that, across the country historical there’s significantly increase in temperature (minimum, maximum) trend. However, rainfall increased by + 63.2% not significantly. Projected 2040–2060 shows that, throughout the country will experience the increase of the temperature in the anomaly range from 1.4 ºC to 2.8 ºC under SSP5-8.5 emission scenario. Projected rainfall indicates that will be increased in the anomaly range from + 0.6% to + 6.3% and from + 5.2% to + 14.4% under both emissions scenarios.
Impact and Analysis of the “One Visit” System to Optimize the Flow of Medical Services on Patients’ Waiting Time: A Retrospective Study
Authors: Lou Lihua , Yin Shaobo , Xia Mengxuan , Xia Weidong , Wang Ru , Lin Kai , Weng Haoce , Lin Cai , Gao Zhihong
Published: January 2024

Background
Patients’ waiting time exceeded consultation time in outpatient clinics, persisting as a criticized issue. Multiple interventions aimed to reduce patients’ waiting time and enhance satisfaction have been implemented by most Chinese hospitals. However, there is little literature to focus on the full range of services available to patients during their visit.
Methods
We developed and applied an intelligent sensing-driven unattended healthcare system (referred to as “One Visit”). We upgraded hospital facilities and systems to improve the flow of outpatient services, streamline treatment and reduce patients’ waiting time. We compared the waiting time, time of visit, duration of special examination appointment, waiting time for examination of patients, and so on during outpatient clinics in the pilot hospitals before and after the implementation of the “One Visit” system. In addition, we investigated the average length of hospital stay and changes in satisfaction of patients with the outpatient process before and after the implementation of the system.
Results
Post-call waiting time was reduced from 9.23 minutes to 7.83 minutes; time of visit increased from 2.95 minutes to 3.17 minutes; duration of special examination appointment was reduced from an average of 7.94 days to 5.30 days; waiting time for examination was reduced from 48.63 minutes to 45.26 minutes; and the average length of hospital stay was reduced from 9.08 days to 7.27 days. Satisfaction of patients with outpatient services before and after system implementation increased from an average of 78.61% to an average of 80.40%.
Conclusions
With the continuous improvement of the service process, it has been proven that the system effectively guides patients through the entire medical treatment process. The system has increased efficiency in outpatient services and improved satisfaction of patients with the outpatient experience. It is worthy of further promotion.
An Efficient Hybrid Classification Model for Heart Disease Prediction
Authors: Munsif Maaham , Rashid Mehvish , Jabeen Farzana
Published: January 2024

Background: Heart disease prediction is a critical healthcare task for identifying individuals at risk and enabling timely intervention. Accurate prediction models can help reduce morbidity and mortality rates associated with cardiovascular conditions. Various approaches, such as feature selection and hybrid models, have been proposed to improve the effectiveness and accuracy of the prediction of heart disease.
Method: The study employs the Genetic Algorithm-Support Vector
Machine-Convolutional Neural Network (GA-SVM-CNN) approach and evaluates it on three diverse datasets: UCI, Z-Alizadeh Sani, and Cardiovascular Disease Dataset. The genetic algorithm is utilized first to select the most relevant features from the datasets, effectively reducing dimensionality, eliminating irrelevant or redundant features, and choosing the most suitable ones. Subsequently, the hybrid SVM-CNN model is trained using the selected features, harnessing the complementary capabilities of both techniques to enhance prediction accuracy.
Results: The performance of the GA-SVM-CNN approach is assessed using the three benchmark datasets and models. On the UCI dataset, the approach achieves an impressive accuracy of 98%, indicating its effectiveness in accurately predicting heart disease. On the Z-Alizadeh Sani dataset, the approach achieves an accuracy of 97%. On the Cardiovascular Disease Dataset, the approach achieves an accuracy of 86%. These high accuracy rates across different datasets underscore the efficacy of the GA-SVM-CNN approach in heart disease prediction.
Conclusion: The combination of the genetic algorithm’s feature selection and the hybrid SVM-CNN model’s predictive power leads to superior performance in heart disease prediction. By accurately identifying individuals at risk of heart disease, this approach can enable timely interventions and contribute to more effective healthcare interventions.
Adherence to Chemotherapy and Associated Factors among Patients with Cancer in the Amhara Region, Northeast Ethiopia, 2022.
Authors: Ademe Sewunet , Mohammed Tariku , Edmealem Afework , Tegegne Belachew , Bewket Bekalu , Andualem Atsedemariam , Bires Abebaw
Published: January 2024

Background: Chemotherapy compliance is a joint effort and silent understanding between health workers and clients. Inadequate drug compliance is concerning due to the decreased treatment outcome of the drug. This will lead to poor survival, increased relapse, treatment failure, and elevated healthcare costs. A measure of patient treatment adherence is important for attracting the attention of healthcare providers to achieve optimal outcomes such as curative treatment or quality of life improvement.
Objective: To assess the adherence to chemical treatment and associated factors among cancer patients in the Amhara Region, Northeast Ethiopia, in 2022.
Methods and Materials: A retrospective cohort study was conducted with 460 cancer patients in the Amhara region using systematic randomized sampling from May 28, 2022, to June 22, 2022. An interview-based, previously tested questionnaire was used for data collection. The data have been entered into Epi-Data version 4.6 and subsequently exported to Statistical Package for Social Science version 26 for analysis. A bivariable logistic regression was carried out, and variables with a p value of 0.25 were analyzed to select the candidate variable for multivariable logistic regression. A multivariable logistic regression was carried out, and variables with a p value less than 0.05 were considered important factors and are presented as the AOR and 95% CI. The suitability of the model was verified with the Hosmer Lemeshow test, and multicollinearity was verified with a standard error.
Results: Of the total respondents, 94.1% (433) of the cancer patients were involved, and the proportion of patients who adhered to chemotherapy was 42.3%. Having a family history of cancer [AOR = 3.58, 95% CI (2.22, 5.76)], being a woman [AOR = 2.17, 95% CI: (1.31, 3.60)], not having a history of comorbidity [AOR = 2.74, 95% CI (1.56, 4.81)], having no serious side effects from chemotherapy[AOR = 3.50, 95% CI (1.55, 7.90)], or social support [AOR = 1.52, 95% CI (1.21, 1.95)] were associated factors with chemotherapy adherence.
Conclusion and recommendations: Health workers should contribute to the development of social support and mechanisms to reduce the occurrence of side effects of chemotherapy medication among cancer patients who have comorbidities.
Safety and efficacy of anlotinib-based regimen in patients with unresectable or metastatic bone and soft-tissue sarcomas: a retrospective institutional study
Authors: Pang Lina , Zhang Shengli , Wang Liye , Gong Shuai , Zhang Xiaoke , He Wei
Published: January 2024

This retrospective institutional study analyzed the safety and efficacy of anlotinib-chemotherapy combination as a first-line therapy in patients with unresectable or metastatic bone and soft tissue sarcomas (BSTSs). The clinical data of 92 patients with sarcoma treated with chemotherapy combined with or without anlotinib at the First Affiliated Hospital of Zhengzhou University from January 2018 to September 2020 were collected and analyzed. To determine the short-term efficacy, the objective response rate (ORR) and disease control rate (DCR) were analyzed. Long-term survival was assessed using the Kaplan–Meier method, including median progression-free survival (mPFS) and overall survival (mOS). Liposarcoma, synovial sarcoma, and rhabdomyosarcoma were the primary pathological subtypes of the 92 patients. The median patients’ age was 46 (range, 11–75) years. The ORR and DCR of the anlotinib–chemotherapy combination used as first-line therapy were 31.9% and 85.1%, respectively. However, the ORR and DCR were only 6.7% and 57.8% in the chemotherapy alone (the control group), respectively. Compared with the control group, improvements were observed in the mPFS and mOS with anlotinib-based regimen (mPFS, 8.3 vs. 3.0 months; mOS, 59.0 vs. 22.0 months). Anlotinib-associated adverse events were well tolerated and mainly occurred in grades I and II. New anlotinib-related adverse reactions were not noted. Anlotinib-based regimen as a first-line therapy showed a positive effect on the treatment of unresectable or metastatic BSTSs. The anlotinib-associated adverse events were minor and well tolerated.
UBQLN1 functions in telomere maintenance and differentiation of human embryonic stem cells
Authors: Zhao Shuang , Li Jie , Duan Songqi , Liu Chang , Wang Hua , Lu Jiangtao , Zhao Nannan , Sheng Xiaoyan , Wu Yiwei , Li Yanjun , Sun Baofa , Liu Lin
Published: January 2024

Background
Telomeres consist of repetitive DNA sequences at the chromosome ends to protect chromosomal stability, and primarily maintained by telomerase or occasionally by alternative telomere lengthening of telomeres (ALT) through recombination-based mechanisms. Additional mechanisms that may regulate telomere maintenance remain to be explored. Simultaneous measurement of telomere length and transcriptome in the same human embryonic stem cell (hESC) revealed that mRNA expression levels of UBQLN1 exhibit linear relationship with telomere length.
Methods
In this study, we first generated UBQLN1-deficient hESCs and compared with the wild-type (WT) hESCs the telomere length and molecular change at RNA and protein level by RNA-seq and proteomics. Then we identified the potential interacting proteins with UBQLN1 using immunoprecipitation-mass spectrometry (IP-MS). Furthermore, the potential mechanisms underlying the shortened telomeres in UBQLN1-deficient hESCs were analyzed.
Results
We show that Ubiquilin1 (UBQLN1) is critical for telomere maintenance in human embryonic stem cells (hESCs) via promoting mitochondrial function. UBQLN1 deficiency leads to oxidative stress, loss of proteostasis, mitochondria dysfunction, DNA damage, and telomere attrition. Reducing oxidative damage and promoting mitochondria function by culture under hypoxia condition or supplementation with N-acetylcysteine partly attenuate the telomere attrition induced by UBQLN1 deficiency. Moreover, UBQLN1 deficiency/telomere shortening downregulates genes for neuro-ectoderm lineage differentiation.
Conclusions
Altogether, UBQLN1 functions to scavenge ubiquitinated proteins, preventing their overloading mitochondria and elevated mitophagy. UBQLN1 maintains mitochondria and telomeres by regulating proteostasis and plays critical role in neuro-ectoderm differentiation.
Approximate solution for a class of stochastic differential equations driven by stable processes
Authors: Manou-Abi Solym
Published: January 2024

In this paper we consider a general class of stochastic differential equations driven by stable processes with continuous coefficients functions with at most linear growth. An Euler-Maruyama approximate solution is proved as well as the rate of convergence. Our proposed method is new in this context and is based on a level truncation method by separating the large and small jumps of the stable process along the Lévy-Itô decomposition. Along the paper we give some numerical simulations of stochastic models that agree with our results, namely some stable driven Ornstein-Uhlenbeck, Cox-Ingersoll-Ross and Lotka-Volterra type processes.
AMS Subject Classification: 60G17, 60G52, 60H10, 60H20, 65C30, 65C20, 60E07.
Neural alterations of Omicron infection comorbid with insomnia in Chinese: Insight from surface-based morphometry and bibliometric analysis
Authors: Ju Chao , Yang Longtao , Du Yanyao , Liu Suiling , Liu Ping , Chen Yanjing , Huang Yijie , Li Cong , Liu Jun
Published: January 2024

Growing evidence suggests that severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) can affect the central nervous system (CNS), and insomnia-related acute and long-term CNS sequelae may be suffered by infected patients. Nevertheless, as a common sub-typic strain of SARS-CoV-2, it is currently unknown the impacts of Omicron comorbid with insomnia on brain cortex. The purpose of this study was to mine the neural markers involving cortical grey matter volume (GMV), thickness, and surface area within Omicron individuals either with or without insomnia. Additionally, correlations between these morphological metrics and neuropsychiatric assessments were analyzed. 234 participants were recruited from a prospective cross-sectional study cohort during the Omicron pandemic (December 2022-May 2023) at the Second Xiangya Hospital (China). We further divided participants into healthy controls (HCs), Omicron infection with insomnia group (Omicron_insomnia group), and Omicron infection without insomnia group (Omicron_non_insomnia group). Based on T1-weighted MPRAGE image, cortical thickness, surface area, and GMV were compared via surface-based morphometry (SBM) analysis, and significant morphological metrics were then correlated with neuropsychiatric manifestations. Our findings revealed altered cortical morphology in Omicron individuals. Morphological changes in the temporal, frontal, cingulate, transverse collateral, and lingual gyrus were independent from insomnia comorbidity. The decreased cortical thickness in superior temporal gyrus (STG) was a specific feature for Omicron infection comorbid with insomnia. Then, we discovered greater morphological size indicating neuropsychiatric improvement. This study explores the neural mechanisms underlying Omicron infection comorbid with insomnia, which will help to promote improvement in post-Omicron neuropsychiatric prognosis and clinical management.
Personal Characteristics and Explanations in Technology-Enhanced Environments: An Exploratory Study in Computer Science Education
Authors: Félix Esther , Amadieu Franck , Broisin Julien
Published: January 2024

The study of explainable artificial intelligence in education is a major preoccupation of the research community. In this paper, we introduce explanations in a competency-based system, in the context of computer education. Our overall aim is to increase learners' understanding of the levels of mastery of their competencies that are computed automatically, and thus to increase their trust in the system. In particular, we are investigating which of the students' personal characteristics affect their consultation and understanding of the explanations, and what impact these explanations have on their perception and behavior within the system. Our study focuses on 98 first-year computer science students in higher education and combines qualitative and quantitative analyses. The quantitative analyses show significant correlations between certain characteristics studied and the use, perception and understanding of explanations on the one hand, as well as learners' general perception of the system on the other hand. For example, students with a low perception of their ability to succeed are less likely to access and understand explanations, and students with low engagement and performance are less likely to understand explanations. Qualitative analyses, meanwhile, help us to identify certain learner needs in terms of the content, form, and timing of explanations: combining local and counterfactual explanations; combining textual and visual formats; providing explanations in real time.
Information Input System Based-on Object-based Visual Attention
Authors: Lv Yuhong , Hisakata Rumi , Kaneko Hirohiko
Published: January 2024

The objective of this study is to propose a communication assistance system for patients with difficulty moving their extremities and eyes, such as advanced amyotrophic lateral sclerosis (ALS) patients with completely locked-in syndrome (CLIS), based on object-based visual attention utilizing pupil size measurement data. The present research used a hybrid image created by overlapping an image with high spatial frequency (HSF) components and one with low spatial frequency (LSF) components as the stimulus image. Five participants in Experiment 1 and eight in Experiment 2 were instructed to pay attention to the high or low-spatial frequency image. The stimuli in Experiment 1 were constant throughout one trial; however, in Experiment 2, the spatial frequency condition of the given hybrid image was swapped in one trial with different alternation frequencies: 0.16Hz, 0.33Hz, and 0.50Hz. Participants were required to always maintain their attention on the target image. In experiment 1, maximum and mean pupil constriction values were significantly larger when the attentional target was a HSF image than when it was a LSF image. However, when we extrapolated the target based on the difference in pupil measurement data when the subjects focused their attention on images with different spatial frequencies, the accuracy was lower than 70%. In experiment 2, we found that the slope of pupil size change after stimuli swap was significantly larger when the target image was switched from the HSF image to the LSF image than when from LSF to HSF. While estimating the target image based on the above pattern, we found that almost participants show inferred accuracy exceeding 70%, with the highest reaching 95.24%—the lowest alternation frequency (0.16Hz) used in the present experiment results in higher inferred accuracy. This study proposes a low-cost, practice-free, non-invasive visual attention-based communication support system that does not require professional assistance. Since the stimulus image is a hybrid image, patients do not need to divide their attention from eye gaze position; therefore, reducing the complexity of operation.
Adversity Builds Character? Restricted health service utilization and subsequent positive self-care behavior during the early COVID-19 pandemic in China
Authors: WANG ZHICHAO , Zhou Zhongliang , Liu Guanping , Lu Jiao , Lai Sha , Fan Xiaojing , Zhai Xiaohui , Wang Youfa
Published: January 2024

Background
During the COVID-19 pandemic, the reallocation of health resources and the restrictions on daily life imposed by epidemic prevention and control measures to some extent have severely affected the healthcare demand of the general population, but those impacts on adults’ self-care behavior and daily lives remain unclear. This study aimed to investigate the effect of restricted health service utilization arising from the COVID-19 pandemic on self-care behavior among different populations, particularly vulnerable groups, the correlations between restricted health service utilization and self-care behavior were also quantified and compared by subgroup analysis.
Methods
Data on demographics, socioeconomic, health status, and self-care behavior were collected in “The Early China COVID-19 Survey”, it is a cross-sectional anonymous online survey of the general population in China. Self-care behavior was measured by four indicators: weight control(WC), physical activity(PA), prevention behavior(PB) and Online medical consultation(OMC). The multiple linear models and binary logistic regression were used to examine whether restricted health service utilization (RHSU) is associated with self-care behaviors; also, adjusted multivariate logistic regression was used to analyse subgroup heterogeneity.
Results
In total, 8,428 adult participants completed the survey, the mean OMC score was 1.51(SD 1.34), the mean PB score was 18.17 (SD 3.44), and the proportion of participants who engaged in WC and PA was 42.30% and 62.57%, respectively. According to the multiple regression model, the RHSU was significantly positively correlated with all four indicators of self-care(WC: OR = 1.34, p < 0.001, PA: OR = 1.34, p < 0.05, MC: OR = 1.30, p < 0.001, PB:coef = 0.16, p < 0.05). Also we observed some significant differences in the intensity of this relationship by subgroup analysis, precisely, OMC(high vs moderate vs. low infection-risk level: OR = 1.48; 1.41; 1.19, p < 0.1), PA(high vs Moderate and low infection-risk level: OR = 1.51, p < 0.05), PB(Chronic disease groups vs no: coef = 0.46;0.1, p < 0.05).
Conclusions
Restricted health service utilization due to the pandemic had positive correlations with self-care behavior outcomes, and the intensity of partial correlation was significantly different in the subgroups of living area infection risk levels and chronic diseases. These findings have practical value for improving self-care behavior in adult populations during the pandemic.
Lower Humoral and Cellular Immunity following Asymptomatic SARS-CoV-2 Infection in Education (The ACE Cohort)
Authors: Hopkins Georgina , Gomez Nancy , Tucis Davis , Bartlett Laura , Steers Graham , Burns Ellie , Brown Michaela , Harvey-Cowlishaw Tyler , Santos Rute , Lauder Sarah N , Scurr Martin , Capitani Lorenzo , Burnell Stephanie , Rees Tara , Smart Kathryn , Somerville Michelle , Gallimore Awen , Perera Marianne , Potts Martin , Metaxaki Marina , Krishna Benjamin , Jackson Hannah , Tighe Paddy , Onion David , Godkin Andrew , Wills Mark , Fairclough Lucy
Published: January 2024

Purpose
Asymptomatic SARS-CoV-2 infections were widely reported during the COVID-19 pandemic, acting as a hidden source of infection. Many existing studies investigating asymptomatic immunity failed to recruit true asymptomatic individuals. Thus, we conducted a cohort study to evaluate humoral- and cell-mediated responses to infection and vaccination in well-defined asymptomatic young adults (the Asymptomatic COVID-19 in Education [ACE] cohort).
Methods
Asymptomatic testing services located at three UK universities identified asymptomatic young adults who were subsequently recruited with age- and sex-matched symptomatic and uninfected controls. Blood and saliva samples were collected after SARS-CoV-2 Wuhan infection, and again after vaccination. Anti-spike antibody titres, neutralizing antibodies, and spike-specific T-cell responses were measured, against both Wuhan and Omicron variants.
Results
Asymptomatic participants exhibited reduced Wuhan-specific neutralization antibodies pre- and post-vaccination, as well as fewer Omicron-specific neutralization antibodies post-vaccination, compared to symptomatic participants. Lower Wuhan and Omicron-specific IgG titres in asymptomatic individuals were also observed pre- and post-vaccination, compared to symptomatic participants. There were no differences in salivary IgA levels. Conventional flow cytometry analysis and multi-dimensional clustering analysis of indicated unvaccinated asymptomatic participants had significantly fewer Wuhan-specific IL-2 secreting CD4+ CD45RA+ T cells and activated CD8+ T cells than symptomatic participants, though these differences dissipated after vaccination.
Conclusions
Asymptomatic infection results in decreased antibody and T cell responses to further exposure to SARS-CoV-2 variants, compared to symptomatic infection. Post-vaccination, antibody responses are still inferior, but T cell immunity increases to match symptomatic subjects, emphasising the importance of vaccination to help protect asymptomatic individuals against future variants.
Study of the mechanical properties of pervious concrete with a rice husk ash–metakaolin composite admixture
Authors: Yuan Zhang , Cheng Zheng
Published: January 2024

In this paper, the influences of rice husk ash and metakaolin on the mechanical properties of pervious concrete are studied, a balance between compressive strength and the pervious coefficient is found, the reuse of industrial waste residue from rice husk ash is realized, and the mechanical properties of pervious concrete are improved. The response surface test method is used to study the influences of rice husk ash and metakaolin on the compressive strength and perviousness coefficient of the pervious concrete specimen. The response surface experiment method was used to optimize the mix ratio of pervious concrete. The results reveal that the optimal contents of rice husk ash and metakaolin in the mixed pervious concrete test group optimized by the response surface test method is 9.2% rice husk ash and 8.2% metakaolin, with a water-to-binder ratio of 0.41. These parameters increase the compressive strength by 10% compared with that of the reference group under the same conditions, and they meet the requirements of the C20 concrete technical specification for pervious cement concrete pavement. Thus, this material can be applied to both urban and highway pavement. Herein, new concepts and technical references for the development and application of pervious concrete are provided.
Introduction of accelerated BOIN design and facilitation of its application
Authors: Kojima Masahiro , Wende Wu , Zhao Henry
Published: January 2024

During discussions at the Data Science Roundtable meeting in Japan, there were instances where the adoption of the BOIN design was declined, attributed to the extension of study duration and increased sample size in comparison to the 3 + 3 design. We introduce an accelerated BOIN design aimed at completing a clinical phase I trial at a pace comparable to the 3 + 3 design. Additionally, we introduce how we could have applied the BOIN design within our company, which predominantly utilized the 3 + 3 design for most of its clinical oncology dose escalation trials. The accelerated BOIN design is adaptable by using efficiently designated stopping criterion for the existing BOIN framework. Our approach is to terminate the dose escalation study if the number of evaluable patients treated at the current dose reaches 6 and the decision is to stay at the current dose for the next cohort of patients. In addition, for lower dosage levels, considering a cohort size smaller than 3 may be feasible when there are no safety concerns from non-clinical studies. We demonstrate the accelerated BOIN design using a case study and subsequently evaluate the performance of our proposed design through a simulation study. In the simulation study, the average difference in the percentage of correct MTD selection between the accelerated BOIN design and the standard BOIN design was − 2.43%, the average study duration and the average sample size of the accelerated BOIN design was reduced by 14.8 months and 9.22, respectively, compared with the standard BOIN design.