bluuebunny/crossref_metadata_2025_split huggingface dataset:
Hepatocellular carcinoma and focal nodular hyperplasia showing iso- or hyperintensity in the hepatobiliary phase: differentiation using Gd-EOB-DTPA enhanced MRI radiomics and deep learning features
Authors: Mao Haoyu , Shen Binqing , Zhang Tao , Fan Yanfen , Wang Ximing , Hu Chunhong , Yu Yixing 
Published: May 2024

Abstract

Background To develop and validate radiomics and deep learning models based on Gd-EOB-DTPA enhanced MRI for differentiation between hepatocellular carcinoma (HCC) and focal nodular hyperplasia (FNH) showing iso- or hyperintensity in the hepatobiliary phase (HBP).Methods 112 patients from three hospitals were collected totally. 84 patients from hospital a and b with 54 HCCs and 30 FNHs randomly divided into a training cohort (n = 59: 38 HCC; 21 FNH) and an internal validation cohort (n = 25: 16 HCC; 9 FNH). A total of 28 patients from hospital c (n = 28: 20 HCC; 8 FNH) acted as an external test cohort. 1781 radiomics features were extracted from tumor volumes of interest (VOIs) in the pre-contrast phase (Pre), arterial phase (AP), portal venous phase (PP) and HBP images. 512 deep learning features were extracted from VOIs in the AP, PP and HBP images. Pearson correlation coefficient (PCC) and analysis of variance (ANOVA) were used to select the useful features. Conventional, delta radiomics and deep learning models were established using machine learning algorithms (support vector machine [SVM] and logistic regression [LR]) and their discriminatory efficacy assessed and compared.Results The combined deep learning models demonstrated the highest diagnostic performance in both the internal validation and external test cohorts, with area under the curve (AUC) values of 0.965 (95% confidence interval [CI]: 0.906, 1.000) and 0.851 (95% CI: 0.620, 1.000) respectively. The conventional and delta radiomics models achieved AUCs of 0.944 (95% CI: 0.779–0.979) and 0.938 (95% CI: 0.836–1.000) respectively, which were not significantly different from the deep learning models or each other (P = 0.559, 0.256, and 0.137).Conclusion The combined deep learning models based on Gd-EOB-DTPA enhanced MRI may be useful for discriminating HCC from FNH showing iso-or hyperintensity in the HBP.
Endoscopic diagnosis of eosinophilic esophagitis using a multi-task U-Net: A pilot study
Authors: Park Jooyoung , Kim Ga Hee , Park SeungJu , Hwang Jeongeun , Lim Jisup , Park Kanggil , Ji Sunghwan , Park Kwangbeom , Seo Jun-young , Noh Jin Hee , Ahn Ji Yong , Byeon Jeong-Sik , Kim Do Hoon , Kim Namkug 
Published: May 2024

Abstract

Background and Aims: Endoscopically identifying eosinophilic esophagitis (EoE) is difficult because of its rare incidence and subtle morphology. We aim to develop a robust and accurate convolutional neural network (CNN) model for EoE identification and classification in endoscopic images.
Methods: We collected 548 endoscopic images from 81 patients with EoE and 297 images from 37 normal patients. These datasets were labeled according to the four endoscopic reference score (EREFS) features: edema, ring, exudates and furrow. A multi-task U-Net with auxiliary classifier on various level of skip connections (scaU-Net) was proposed. Then scaU-Net was compared with those of VGG19, ResNet50, EfficientNet-B3, and a typical multi-task U-Net CNNs. The performances of each model were evaluated quantitatively and qualitatively based on accuracy (ACC), area under the receiver operating characteristics (AUROC), and gradient weighted class activation map (Grad-CAM); and also compared to those of 25 human endoscopists.
Results: Our sca4U-Net with 4th level skip connection showed the best performances in ACC (86.9%), AUROC (0.93) and outstanding Grad-CAM results compared to other models, reflecting the importance of utilizing the deepest skip connection. Moreover, the sca4U-Net showed generally better performance when compared with endoscopists with various levels of experiences.
Conclusions: Our method showed robust performance compared to expert endoscopists, and could assist endoscopists of all experience levels in the early detection of EoE- a rare, but clinically important condition.
Evidence of behavioral contagion in captive black-and-white ruffed lemurs (Varecia variegata) and red ruffed lemurs (Varecia rubra)
Authors: Lemes William Padilha , Amici Federica 
Published: May 2024

Abstract

Behavioral contagion is thought to have a significant role in social synchronization and coordination across animal taxa. While there is extensive evidence of behavioral contagion in Haplorrhines (i.e. monkeys and apes), limited research exists in Strepsirrhines (i.e. lemurs). Here, we aimed to investigate the presence of contagious yawning and scratching in two captive groups of black-and-white ruffed lemurs (Varecia variegata) (N = 4) and red ruffed lemurs (Varecia rubra) (N = 4), and further test whether behavioral contagion is modulated by the model’s dominance rank. We conducted all occurrence sampling to examine whether individuals observing a yawning or scratching event (i.e. trigger event) were more likely to yawn or scratch in the following 2 minutes, as compared to individuals who did not observe it. We ran generalized linear mixed models and found that the likelihood of yawning and scratching was higher for individuals observing the trigger event than for individuals who did not observe the event, although the model’s dominance rank had no modulating effect on the probability of showing behavioral contagion. Our findings represent the first evidence of behavioral contagion in this genus and contribute to shed light on the distribution and the possible adaptive function of this phenomenon in primates.
Machine learning visceral obesity-related genes based on MARVELD1 model for predicting the prognosis and immune cell infiltration of ovarian cancer
Authors: Qiao Longwei , Wu Fei , Xu Jiaqi , Zhang Yayun , Liu Meng , Feng Guannan , Liang Yuting , Hou Shunyu , Shen Cong 
Published: May 2024

Abstract

Background:
Visceral obesity elevates high-grade serous carcinoma (HGSOC) risk and prognosis but its effect on biology is underexplored. This study examines obesity-related genes (VORGs) in HGSOC.
Methods
Using consensus clustering, we analyzed TCGA and GSE53963 datasets with VORGs, uncovering unique immune cell patterns and gene functions. Our prognostic model integrates OS, tumor immunity, ICI response, and chemotherapy outcomes. Single-cell data enrich our understanding of gene expression, particularly highlighting MARVELD1 in tumor stem cells. Experimental validation comprised CCK-8, transwell, and colony formation assays.
Results
Based on the expression profiles of VORGs associated with OS, HGSOC patients were stratified into four distinct molecular subtypes. The investigation entailed an exploration of the interplay between each molecular subtype, patient prognosis, and immune infiltration dynamics. Subsequently, a robust prognostic prediction model, encompassing four genes (CXCL9, IGF2, FCGBP, and MARVELD1), was meticulously developed. The model utilized differential genes linked to prognosis through molecular subtyping. It categorized patients into high- and low-risk groups, where the high-risk group exhibited significantly worse outcomes (P < 0.001). Elevated scores were associated with increased stem cell indices, reduced mutation burdens, and heightened immunosuppression. Moreover, these scores showed significant correlations with immune checkpoint genes and chemotherapy sensitivity, emphasizing their crucial role in predicting treatment responses. Turning our attention to finer granularity, analyzing single-cell data revealed diverse gene expression among the four models, especially MARVELD1's prominence in tumor stem cells. MARVELD1 impacted early cellular functions through pathways like reactive oxygen species. Lastly, to validate our findings, in vitro experiments provided compelling evidence that interfering with MARVELD1 effectively curbed the proliferation, invasion, and clone formation potential of ovarian cancer cells.
Conclusion
This study examines VORGs' impact on HGSOC biology, using scRNA-seq and bulk RNA-seq data, presenting a novel risk model correlated with prognosis, immune microenvironment, and drug effectiveness.
Global burden and trends in thyroid cancer among children and adolescents at 5-19 years of age from 1990 to 2019
Authors: Yuan Xuewen , Zhu Tianyi , Shi Mengfan , Zhao Yuhang , Bai Zihao , Wang Yang , Yang Xun , Qi Jirong 
Published: May 2024

Abstract

Background: The global incidence of thyroid cancer (TC) in adolescents varies but increases in an overall trend. A clear illustration of global burden and trends in TC among a specific age population of 5-19 years favors the early prevention and treatment among children and adolescents.
Methods: Global trends in the incidence, prevalence, mortality, and disability-adjusted life years (DALYs) of TC among children and adolescents at 5-19 years of age group stratified by age, sex and socio-demographic index (SDI) from 1990 to 2019 were described through data mining in the Global Burden of Disease Study 2019 (GBD 2019). Joinpoint regression and decomposition analysis were used to assess temporal trends.
Results: The incidence of TC among children and adolescents at 5-19 years of increased from 0.19 (95% UI 0.15, 0.21) per 100,000 individuals in 1990 to 0.26 (95% UI 0.23, 0.28) per 100,000 individuals in 2019. DALYs of TC decreased from 0.83 (95% UI 0.68, 0.94) in 1990 to 0.73 (95% UI 0.64, 0.82) in 2019. The greatest increase in the incidence of TC from 1990 to 2019 was detected in high SDI countries.
Conclusions: Despite the declined incidence and mortality of TC within the past three decades, global burden of TC among children and adolescents at 5-19 years of age vary with sex, age and SDI. Targeted measures and effective collaborations among countries with different SDIs are required to improve health care for children and adolescents with thyroid diseases.
Behavior Modification after a Heart Attack: Insights from Patients and Healthcare Professionals
Authors: Yaqoob Adnan , Barolia Rubina , Ladak Laila , Khan Aamir 
Published: May 2024

Abstract

Behavior modification is important in managing and preventing recurrent cardiac events following a heart attack. By emphasizing lifestyle modifications and addressing psychosocial factors, healthcare providers can empower patients to take control of their cardiovascular health and improve long-term outcomes. Integrating evidence-based behavior modification programs into routine clinical practice and cardiac rehabilitation is essential for achieving optimal secondary prevention following myocardial infarction and enhancing overall patient well-being. This study explores patients’ behavior modification experience and Healthcare professionals’ perspectives on behavior change observed in heart attack survivors.
Methods: We employed a qualitative descriptive study design to answer our research questions. Our participants were a diverse group of 38, including 20 patients who had experienced a heart attack and 18 healthcare professionals who had worked with heart attack survivors. We used a purposive sampling technique to recruit these individuals. Semi-structured in-depth interviews, lasting 40-50 minutes, were conducted with those who agreed to participate. The data were then organized and analyzed with an inductive coding approach, and thematic analysis was used to generate codes, categories, and themes.
Results: The findings of this study show various motivations that drive patients to modify their behaviors after a heart attack. Four themes emerged from participant’s data: (1) Behaviour modification after a heart attack, (2) Perspectives on motivations for behavior change, (3) Unconvinced by behavior change, and (4) Challenges in behavior change.
Conclusion: The findings contribute valuable insights into individuals’ challenges in adopting behavior changes after a heart attack. The identified resistance shows the need for healthcare professionals to employ personalized and comprehensive strategies to motivate individuals toward holistic lifestyle modifications.
De novo designed proteins neutralize lethal snake venom toxins
Authors: Baker David , Torres Susana Vazquez , Valle Melisa Benard , Mackessy Stephen , Menzies Stefanie , Casewell Nicholas , Ahmadi Shirin , Burlet Nick , Muratspahić Edin , Sappington Isaac , Overath Max , Rivera-de-Torre Esperanza , Ledergerber Jann , Laustsen Andreas Hougaard , Boddum Kim , Bera Asim , Kang Alex , Brackenbrough Evans , Cardoso Iara , Crittenden Edouard , Edge Rebecca , Decarreau Justin , Ragotte Robert , Pillai Arvind , Abedi Mohamad , Han Hannah , Gerben Stacey , Murray Analisa , Skotheim Rebecca , Stuart Lynda , Stewart Lance , Fryer Thomas , Jenkins Timothy 
Published: May 2024

Abstract

Snakebite envenoming remains a devastating and neglected tropical disease, claiming over 100,000 lives annually and causing severe complications and long-lasting disabilities for many more1,2. Three-finger toxins (3FTx) are highly toxic components of elapid snake venoms that can cause diverse pathologies, including severe tissue damage3 and inhibition of nicotinic acetylcholine receptors (nAChRs) resulting in life-threatening neurotoxicity4. Currently, the only available treatments for snakebite consist of polyclonal antibodies derived from the plasma of immunized animals, which have high cost and limited efficacy against 3FTxs5,6,7. Here, we use deep learning methods to de novo design proteins to bind short- and long-chain α-neurotoxins and cytotoxins from the 3FTx family. With limited experimental screening, we obtain protein designs with remarkable thermal stability, high binding affinity, and near-atomic level agreement with the computational models. The designed proteins effectively neutralize all three 3FTx sub-families in vitro and protect mice from a lethal neurotoxin challenge. Such potent, stable, and readily manufacturable toxin-neutralizing proteins could provide the basis for safer, cost-effective, and widely accessible next-generation antivenom therapeutics. Beyond snakebite, our computational design methodology should help democratize therapeutic discovery, particularly in resource-limited settings, by substantially reducing costs and resource requirements for development of therapies to neglected tropical diseases
Transcultural adaptation and validation in French of the BBC subjective well-being scale (BBC-SWB) in the general population
Authors: MEGHERBI-MOULAY Omar , JULLIAN Bénédicte , FRANCHITTO Nicolas , IGIER Valérie , SORDES Florence 
Published: May 2024

Abstract

Subjective well-being influences longevity and health maintenance. No specific scale exists in French to measure the physical, psychological, and social dimensions of subjective well-being simultaneously. The BBC Subjective Well-Being Scale (BBC-SWB) is a reliable and valid measure of subjective well-being in the general population. The aim of this study was to translate and cross-culturally adapt the BBC-SWB into a French version and to evaluate its psychometric properties, validity, and reliability in the general adult population in France. After providing their informed consent, a sample of 1419 participants, were asked to complete a sociodemographic questionnaire and respond to a battery of online self-report measures probing subjective well-being, subjective happiness, mental health, quality of life, anxiety, and depression. The process of intercultural adaptation showed a semantic, idiomatic, cultural, and conceptual equivalence between the original version and the French version. Exploratory and confirmatory factor analyses were used to extract and confirm a three-factor structural model in the sample. A 24-item version showed acceptable psychometric properties and measured the three dimensions of well-being. Spearman correlations were performed demonstrating significant concurrent validity. Internal consistency and intraclass and inter-rater correlation coefficients showed excellent reliability. This scale renamed “EBES” for Évaluation du Bien-Être Subjectif, represents a valid, reliable, and recommended instrument for research and clinical intervention purposes. Further study of its practical application to other populations and health conditions is warranted.
Climate Change Trend Using Descriptive Time Series Technique in Machine Learning: A case of Jimma Zone, Southwestern Ethiopia
Authors: Gemmechis Wendafiraw Abdisa 
Published: May 2024

Abstract

Understanding climate variability and monitoring time-series trends of temperature and rainfall is crucial for the sustainable development of our planet. This study utilized historical data from the Global Historical Climatology Network-Monthly (GHCN-M) provided by the National Centers for Environmental Information (NCEI) to analyze the temperature and rainfall data from 2015 to 2022. The analysis was conducted using Python 3.1.1 on Anaconda Jupyter Notebook and the package matplotlib 3.2.1 was used for data visualization. The results revealed a pattern of maximum rainfall between March to May for the years 2020, 2021, and 2022, while for the years 2017, 2018, and 2019, the maximum rainfall was recorded in October, December, and November. Additionally, the annual maximum rainfalls were recorded in the years 2020 and 2022, and the annual maximum temperatures for all study years were recorded in January, February, and March months. On the other hand, the annual minimum temperatures for all study years occurred in June, July, August, and September months. Similarly, annual average temperatures were recorded in January, February, and March months. This study emphasizes the importance of monitoring climate change and its impacts on our planet. By understanding climate variability and time-series trends, we can better prepare for the future and work towards a sustainable world.
Projection of mean and extreme precipitation and air temperature over India: a CMIP6 analysis
Authors: Kushwaha Prabha , Pandey Vivek Kumar , Kumar Prashant , Sardana Divya , Yadav Anshu 
Published: May 2024

ABSTRACT
This study used 15 Coupled Model Intercomparison Project Phase 6 (CMIP6) models to analyze the mean and extreme projection precipitation during the Indian summer monsoon (ISM) season, i.e., June to September (JJAS). A continuous rise in precipitation and its associated unpredictability were predicted by global climate models using the CMIP6. The extreme values are estimated on the basis of generalized extreme value (GEV) distribution. Four socioeconomic paths are used as SSP126, SSP245, SSP370 and SSP585 to understand the impact of low-emission to high-emission scenarios. In all the scenarios, it is seen that all the models predict a significant rise in JJAS mean and extreme precipitation. Moreover, a strong correlation is obtained between the historical (1995–2014) and near-emission future simulation (2021–2040), mid-emission future simulation (2041–2060), high-emission future simulations (2081–2100) of air temperature and precipitation. In extreme emission scenarios, ensemble mean of CMIP6 models shows an increasing amplitude in surface air temperature and precipitation over India in the near future (2021–2040), mid future (2041–2060) and the end of the century (2081–2100). The CMIP6 simulations mainly support the results of all models, but they also demonstrate improved robustness across models.
Representative concentration pathways (RCPs) used as a tool to evaluate climate change impact on maize crop production in the Woybo catchment of Ethiopia
Authors: Shiferaw Yitayish Goshu , Ebstu Edmealem Temesgen , Lohani Tarun Kumar 
Published: May 2024

ABSTRACT
The research was conducted to assess the climate change impact on crop water requirements (CWRs) in the Woybo catchment of southern Ethiopia. The impact of climate change was assessed through climate models under RCP4.5 and RCP8.5 emission scenarios for the 2050s compared to 1971–2005. During the 2050s, the annual mean minimum temperature increased by 1.9–2.3 °C, while the maximum temperature over the catchment increased by 1.4–2.0 °C. The projected rainfall is expected to increase in autumn, summer, and annually by 3.7–16.8 and 8.7–12.2% in the 2050s under RCP4.5 and 8.5, respectively. But it is likely to decrease in winter and spring by 1.5–10.2% and 8.7–2.8%, respectively. The average annual potential evapotranspiration will rise by 0.1–6.8%. Under both RCPs, CWRs may increase from 6.2 to 17.2% during the growing season, while irrigation water requirements (IWRs) may rise from 4.6 to 16.4%. Future period projection results show that potential evapotranspiration and IWRs will increase for maize crops compared to the baseline period. The study will help water managers, users, and agricultural developers in preparing new water-saving strategies and achieving agricultural sustainability.
Connecting digital twins to control collections systems and water resource recovery facilities: From siloed to integrated urban (waste)water management
Authors: Lumley Douglas John , Polesel Fabio , Refstrup Sørensen Henrik , Gustafsson Lars-Göran 
Published: May 2024

ABSTRACT

The use of digital twins is a rapidly emerging field for improved real-time control (RTC) and decision support for the operation of collection systems and water resource recovery facilities (WRRFs). Digital twins for collection systems can help minimize the impacts of flow variation due to extreme weather events, attenuate flows to the WRRF, and reduce sewer overflows and the associated effects. Similarly, digital twins for WRRFs can help improve process, energy, and cost efficiency, fully utilise plant volumes, reduce carbon footprint, and support operator training. The current study provides an overview of two digital twin applications for collection systems (Future City Flow) and WRRFs (TwinPlant) and presents a first example of digital twin integration for proactive collection system-WRRF operation under wet-weather conditions. Current applications of the integrated digital twin are described, including (i) proactive implementation of wet-weather operation mode in WRRF based on inflow forecast and (ii) evaluation of the impacts of RTC in collection systems on WRRF performance. Other potential application examples are described together with the challenges related to the use of this solution. Overall, this new approach has a wide potential to support the cooperation within water utilities towards the adoption of integrated wastewater management.
Evaluation of bio-physical parameters and microbial pollution in water systems
Authors: Ahmed Dhea Sadi , Abd Algabar Fatima Amer , Al-Obaidi Mudhar A. , Mageed Ghanam Hissan 
Published: May 2024

ABSTRACT

This study covers the purification and sterilization stations in different regions of the Iraqi city of Baghdad. To systematically conduct this study, several drinking water samples were collected from various sites in Al-Karkh and Al-Rusafa in Baghdad. These samples were tested bacteriologically, such as Bacterial Total Count, Total Coliform Bacteria, fecal bacteria, and Escherichia coli (E. coli). Furthermore, the physical properties of the total hardness (TH), turbidity, residual chlorine, and pH from June 2021 to July 2022 were measured monthly. The seasonal variation of the research parameters is considered by providing a statistical description. The biological tests indicate that the water is seriously contaminated with fecal coliform in one area of the Al-Karkh region during the months of autumn and summer. Also, the biological tests of the Al-Rusafa region elucidate the dangerous concentrations of plate count bacteria in summer and autumn. Furthermore, the Al-Rusafa region reports cases of E. coli and coliform in the summer, which exceed the Iraqi and WHO guidelines. But, the physical tests ascertain the semi-alkalinity of drinking water with the greatest residual chlorine value of 0.9 ppm in the summer.