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Fertile androgenetic mice generated by targeted epigenetic editing of imprinting control regions

Each new mammalian life begins with the fusion of an oocyte and a sperm to produce a fertilized egg containing two sets of genomes, one from the mother and one from the father. Androgenesis, a way for producing offspring solely from male genetic material, is limited in mammals, presumably due to barriers arising from genomic imprinting, an epigenetic mechanism leading to monoallelic gene expression. Here, we report adult mammalian offspring derived from the genetic material of two sperm cells. These mice, which we refer to as androgenetic mice, were produced via targeted DNA methylation editing of seven imprinting control regions (ICRs) through CRISPR-based epigenome engineering. Two sperm cells were injected into an enucleated oocyte to form putatively diploid embryos. Allele-specific epigenetic editing was achieved by injecting guide RNAs with protospacer adjacent motif (PAM) sequences designed to match one allele but not the other. The birth of androgenetic mice that were able to develop to adulthood demonstrates that mammalian androgenesis is achievable by targeted epigenetic remodeling of a few defined ICRs.

Predictors of higher education dropout intention in the post-pandemic era: The mediating role of academic exhaustion

Introduction The phenomenon of dropout in higher education needs the acknowledging of its multi-domain complexity. In the post-pandemic era, exhaustion may be a relevant feature affecting students. This cross-sectional study aimed primarily to test a predictive model of five domains of variables (background, academic, social, psychological, and economic) on dropout intention, in a relation mediated by academic exhaustion. Secondarily, it aimed to assess the structural invariance of this model across working status (working vs. non-working students) and residence status (living away from family’s residence vs. living in family residence). If these groups are differently affected by dropout determinants, specific dropout prevention measures should be implemented. Method A stratified sample of 1402 Portuguese university students aged between 19 and 45 years ( M  = 22.87, SD  = 3.64), selected through a convenience quota method, was assessed for background, academic, social, psychological, and economic variables using self-report instruments. Structural equation modelling was used. Results The predictive model explained 51% of the variance in dropout intention. Academic exhaustion was the stronger predictor (β = 0.523, p < .001), followed by social connecteness to the campus (β = −31, p < .001), vocational difficulties (β = 0.274, p < .001), and course value (β = −0.256, p < .001). Except for the course value, and family educational level, all significant predictors had their effect on dropout intention through academic exhaustion. The model was invariant across working and residence status. Discussion This study shows the relevance of students’ academic exhaustion experiences as a pathway through which different types of factors exert their influence on students´ dropout intentions. The invariance of the predictive model of dropout intention across different groups points the robustness of the model and the relevance of the integrated variables. The results emphasize the importance of student´s individual factors (e.g., academic exhaustion, lack of fit with the course) in dropout decisions, also stressing the role of academic institutions and of the education system in addressing this phenomenon, concerning academic workload, vocational orientation, social environment, and financing.

DNA profile database of Koompassia malaccensis in Malaysia and its application in forensic investigation

Predicting errors in accident hotspots and investigating satiotemporal, weather, and behavioral factors using interpretable machine learning: An analysis of telematics big data

Background Road traffic accidents (RTAs) are a major public health concern with significant health and economic burdens. Identifying high-risk areas and key contributing factors is essential for developing targeted interventions. While machine learning (ML) has been increasingly used to predict RTAs, the lack of interpretability limits its applicability in policymaking. This study aimed to utilize interpretable ML models to predict the occurrence of errors in road accident hotspots using telematics data in Iran and interpret the most influential predictors. Methods We utilized data collected via telematics from 1673 intercity buses throughout the year 2020, spanning cities across all provinces of Iran. Merging this data with a weather-related dataset resulted in a comprehensive dataset containing location, time, weather, and error type variables. After preprocessing, 619,988 records without any missing values were used to train and compare the performance of six machine learning models including logistic regression, K-nearest neighbors, random forest, Extreme Gradient Boosting (XGBoost), Naïve Bayes, and support vector machine. The best model was selected for interpretation using SHAP (SHapley Additive exPlanation). Due to the high imbalance in the outcome, an ensemble approach was applied to train all models. Results XGBoost demonstrated the best performance with an area under the curve (AUC) of 91.70% (95% uncertainty interval: 91.33% − 92.09%). SHAP values highlighted spatial-related variables, particularly the province of error and road type, as the most critical features for predicting errors in accident hotspots in Iran. Fatigue, as a behavioral error, was associated with a higher risk of predicting errors in accident hotspots, and certain weather-related variables including dew points and relative humidity also exhibited importance. However, temporal variables did not contribute significantly to the prediction. Conclusion By integrating spatiotemporal, behavioral, and weather-related variables, our study highlighted the dominance of spatial factors in predicting errors in accident hotspots. These findings underscore the need for targeted road infrastructure improvements and data-driven policymaking to mitigate RTA risks.

The influence of curing time on mechanical properties and microstructure of taphole clay

Fetal hemoglobin enables malaria parasite growth in sickle cells but augments production of transmission stage parasites

Sickle cell trait is the quintessential example of the human evolutionary response to malaria, providing protection against severe disease, but leading to sickle cell disease (SCD) in the homozygous state. Fetal Hemoglobin (HbF) reduces the pathology of SCD and several mutations lead to the prolonged production of HbF into childhood and adult life. HbF has been suggested to contribute to protection against malaria. Two long-term cohorts were genotyped for three quantitative trait loci associated with HbF production and analyzed for HbF titers, malaria clinical episodes and the production of parasite stages infectious to mosquitoes, gametocytes, in asymptomatic infections. Plasmodium falciparum parasites were also grown in vitro in HbSS cells with measured levels of HbF. The genetic determinants of prolonged HbF production were associated with increased HbF titers and that increased HbF afforded protection from malaria disease but increased the production of gametocytes. The presence of HbF in sickled red cells was also shown in in vitro culture to enable parasite persistence in conditions otherwise deleterious for the parasite and enabled complete maturation of gametocytes. The beneficial personal effect of HbF, whether through protection against malaria or alleviating effects of SCD, is seemingly offset by increased parasite transmissibility and potential disease burden for the community. These individuals represent a potentially important reservoir of infection and could be targeted in elimination strategies.

The role of syllabic rhythm in speech perception across languages

The association between routine immunisation and COVID-19 vaccination in small Island developing states

Objectives Understanding the link between routine immunisation (RI) performance and vaccination during an epidemic can provide insights on health systems resilience and investments to strengthen health systems. We examined the relationship between RI performance and COVID-19 vaccination coverage in small island developing states (SIDS). Methods We analysed immunisation and health system performance data in 55 SIDS. Our primary outcome was COVID-19 vaccination coverage at four timepoints (June 2021, December 2021, June 2022 and December 2022). We examined associations with coverage of six childhood immunisations (5-year mean annual coverage for 2015–2019), pandemic-related disruptions to RI, new vaccine introductions, health system performance measures, and economic and demographic characteristics. We calculated Spearman correlation coefficients (r) with p-values (p < 0.05 considered significant) and 95% confidence intervals for continuous variables and mean COVID-19 vaccination coverage by categorical variables. Findings COVID-19 vaccination coverage was higher in countries that sustained pre-pandemic RI coverage during the pandemic, and where HPV, influenza and measles-containing (second dose) vaccines had been introduced. There were weak correlations (|r| < 0.4) between coverage of COVID-19 vaccination and RI, with a few exceptions of moderate correlations with the birth dose of hepatitis B vaccine (June 2022: r = 0.421, p = 0.007; December 2022: r = 0.438, p = 0.005) and first dose of measles vaccine (December 2021: r = 0.420, p = 0.002). COVID-19 vaccination coverage was strongly correlated with the density of physicians (June 2021: 0.897, p < 0.001; December 2021: 0.785, p < 0.001) and moderately correlated with that of nurses and midwives (June 2021: 0.630, p = 0.001; December 2021: 0.605, p = 0.002). COVID-19 vaccination coverage was lower in SIDS with lower country income and development status. Conclusions Countries that achieved high COVID-19 vaccination coverage also sustained RI coverage during the pandemic, demonstrating health system resilience. Our findings highlight the importance of having sufficient skilled health professionals and experience in introducing new vaccines targeting different age groups into national programs, particularly in small island settings.

Cross paradigm fusion of federated and continual learning on multilayer perceptron mixer architecture for incremental thoracic infection diagnosis

Abstract Medical imaging is essential in the study of chest virus infections. Due to data sovereignty issues in healthcare, it is essential to employ federated learning to overcome these obstacles. However, obtaining all relevant data at once is challenging, as it is often acquired incrementally. Therefore, addressing continual learning is imperative. To this end, we combined federated learning with continual learning to construct a transnational infectious disease prediction model. This model was applied to the COVID-XRAY and COVID-CT datasets using 3 and 6 clients, respectively, implementing 4 continual learning algorithms across ten different models. Notably, we integrated MLP-Mixer with Learning without Forgetting (LwF) techniques, achieving an accuracy of 54.34%. This demonstrates the effectiveness of our approach in the early detection, sensing, and timely warning of infectious diseases and ultimately builds a multicentre prediction system for future infectious diseases.

Low and facultative mycorrhization of ferns in a low-montane tropical rainforest in Ecuador

Arbuscular mycorrhizal fungi (AMF) are amongst the most studied obligate plant symbionts and regularly found in terrestrial plants. However, global estimates of AMF abundance amongst all land plants are difficult because i) the mycorrhizal status of many non-commercial, wild plant species is still unknown, ii) numerous plant species engage in facultative symbiosis, meaning that they can, but do not always do, associate with mycorrhiza, and iii) mycorrhizal status can vary within families, genera, and species. To gain deeper insights to the distribution of the plant-AMF symbiosis we investigated the mycorrhizal status in some of the oldest lineages of extant vascular plants, Polypodiophytina (ferns) and lycophytes, in one of the hotspots of natural plant diversification, the tropical rainforest. Providing a new data set of AMF abundance for 82 fern species representing 19 families, we hypothesized that (1) AMF would be found in 60–80% of the studied plants and (2) plant species with AMF symbionts would be more abundant than non-mycorrhizal species. Both hypotheses were rejected while the following observations were made: (1) AMF occurred in 30.5% of studied species, representing 63% of the studied fern families, (2) AMF colonisation was not correlated with species abundance, (3) a small proportion of AMF-hosting ferns was epiphytic (6%) and (4) mycorrhization was inconsistent among different populations of the same species (facultative mycorrhization). While these observations align with previous studies on ferns, they emphasise that mycorrhization is not a taxonomic trait and underscore the challenges in estimating the global abundance of AMF. In addition, the occurrence of AMF in epiphytic plants and no net benefits of AMF for plant abundance indicate that the mycorrhization observed in this study likely comprises the commensalism to parasitism range of the symbiosis spectrum.

Clinicopathological features of type 1 autoimmune pancreatitis without elevated serum IgG4 level

Flipping the curriculum for resident didactics: In-Training and certifying examination scores in an internal medicine residency

Purpose The flipped classroom (FC) model is increasingly used in undergraduate medical education, where it has been associated with improved knowledge acquisition. However, its impact on graduate medical education (GME) remains underexplored. We hypothesized that implementing a flipped didactics curriculum in our internal medicine residency program would improve performance on the Internal Medicine In-Training Examination (IM-ITE) and the American Board of Internal Medicine Certifying Examination (IM-CE). Methods In 2017, we transitioned from a traditional lecture-based (LB) curriculum to a flipped model. Noon conferences were replaced with problem-based learning (PBL) sessions three days per week, with additional active learning formats used on the remaining days. Morning reports were redesigned to deliver foundational content in advance of PBL sessions. The curriculum followed a 13-block annual structure aligned with ABIM content areas. We compared IM-ITE and IM-CE scores of residents who completed the full flipped curriculum to those trained under the prior LB model. Results We analyzed data from 279 residents who completed or were expected to complete training between 2014 and 2024. FC curriculum residents scored significantly higher on all three IM-ITE exams: ITE1 (62.39% vs. 59.53%, p = 0.008), ITE2 (69.78% vs. 66.54%, p = 0.002), and ITE3 (73.42% vs. 71.12%, p = 0.029). IM-CE scores did not significantly differ between groups (FC = 496 vs. LB = 481, p = 0.32). The first-attempt IM-CE pass rate was significantly higher among FC residents (95.5% vs. 84.0%, p = 0.012). Conclusions Residents in the flipped classroom curriculum demonstrated significantly higher IM-ITE scores and first-attempt pass rates on IM-CE compared to those in the lecture-based curriculum. These outcomes were associated with medium effect sizes, suggesting a meaningful educational benefit of the flipped classroom approach in graduate medical education.

Wafer-scale radio frequency ZnO Schottky diodes and arithmetic circuits

Abstract Modern telecommunication technologies, such as the 5G and upcoming 6G networks, rely on devices operating in the radio frequency (RF) spectrum of 0.3–90 GHz and 7–300 GHz, respectively. To meet these demanding frequency requirements, new manufacturing methods and device architectures are gaining increasing attention. However, achieving scalable manufacturing alongside ultra-fast device operation presents formidable techno-economic challenges. Here, we explored a modified version of adhesion lithography (a-Lith) to create coplanar nanogap zinc oxide (ZnO) Schottky diodes for application in diode-logic arithmetic circuits. The planar ZnO diodes offer highly scalable manufacturing and combine high current rectification (> 106) with low reverse currents (≈80 pA) and a remarkable cut-off frequency of over 25 GHz. Engineering the topologies of the planar ZnO diodes enables their facile monolithic integration into multi-bit AND and OR gates over 4-inch glass wafers. By integrating several such logic gates, we demonstrated fully functional monolithic 2-bit Half-Adder circuits, the primary component of an arithmetic logic unit. The work offers an alternative method for developing fast large-area electronics that could lead to a new family of logic circuitry.

Effect of COVID-19 vaccination on the incidence, lethality and mortality of pregnant and postpartum women

Objectives To analyze the incidence of Severe Acute Respiratory Syndrome (SARS), lethality and mortality of Brazilian pregnant and postpartum women infected with SARS-CoV-2, before and after vaccination against Covid-19. Methods This is an ecological study of time series, carried out with secondary data from the Brazilian Ministry of Health, from March 2020 to April 2024. Slopes and trend changes in the time series were identified by Mann-Kendall and generalized fluctuation tests, respectively. The series was modeled using Generalized Additive Models with interaction between time and start of vaccination. A Pearson correlation was used between the number of accumulated doses and the incidence, lethality and mortality rates, in addition to comparing the average rates before and after the vaccination peak of the first dose using ANOVA. Results Significant temporal variations were observed in the incidence rates of SARS, lethality and mortality in pregnant and postpartum women with SARS-CoV-2, before and after vaccination against Covid-19 in Brazil, with a significant decreasing trend after the start of vaccination. It was observed that time alone did not show a significant effect on the reduction of lethality and mortality rates, which occurred only when there was an interaction effect between time and the start of vaccination. The accumulated doses of the vaccine correlated with the decrease in the analyzed rates, which explained 39.18% of variation in the incidence rate, 43.34% in the lethality rate, and 34.81% in the mortality rate. The monthly averages of the incidence, lethality, and mortality rates before and after vaccination reduced significantly. Conclusions The findings of this study indicate that vaccination against Covid-19 in Brazilian pregnant and postpartum women had a positive effect on reducing the incidence of SARS by SARS-CoV-2, lethality and mortality rates in the obstetric population.

Drought conditions, tillage regime and soil phosphorous modulate the incidence of weeds, pests and pathogens in arable crops

Combining acoustic survey and citizen science data yields enhanced species distribution models for tropical rainforest birds

A key goal in ecology is to develop effective ways to understand species’ distributions in order to facilitate both their study and conservation. Many species distribution modeling analyses have been performed using either structured survey data or unstructured citizen science data; these two pools of data have tradeoffs in terms of data density, spatiotemporal coverage, and accuracy. Recent studies have shown that combining structured and unstructured survey data can improve the accuracy of species distribution models for birds, but most of this work has focused on north temperate bird species, using bird atlas data that are less available in the Tropics. Here, we adapted a data pooling approach from the literature on north temperate bird biology to create distribution models for a selection of secretive suboscine bird species that occur in a highly diverse region of the southwestern Amazon. Our approach combined automated acoustic monitoring detections and eBird citizen science data available for the region as well as a high resolution land cover dataset of the region’s key ecological gradients. The pooled models outperformed models constructed solely with eBird data for predicting fine grain species responses to habitat gradients in intact forest, but also retained information from the citizen science dataset about species occurrence patterns in non-vegetated areas away from intact forest, including those subject to human disturbance. We present this hybrid approach as a flexible and repeatable means to produce inferences that would not easily be achievable using a single data source, and provide recommendations for other researchers seeking to replicate these methods in Amazonia as well as in other tropical regions.

Community interventions improve diabetes management and oral health in type 2 diabetes patients with chronic periodontitis

Global, regional and national retinoblastoma burden in children under 10 years of age from 1990 to 2021: Trend analysis based on the Global Burden of Disease Study 2021

Background Retinoblastoma (RB) is the most common malignant eye tumor in children, which poses a great threat to children’s vision and life. Comprehensive global, regional and country-level assessments of retinoblastoma in children under 10 years of age are important to help fine-tune health policies and rationalize the allocation of medical resources. Methods Data on RB-related burden in children under 10 years of age were collected in the 2021 Global Burden of Disease (GBD) study to assess trends in RB burden using mean annual percentage change (AAPC). Absolute and relative health inequalities of RB burden were analyzed using slope index and concentration index. An age-period-cohort model was fitted using package NORDPRED to predict the future RB burden. Results The global number of RB cases in children under 10 years of age in 2021 was 57,333 (95%UI: 34339.65,761.03), the annual standardized prevalence rate (ASPR) was 4.39(95%UI: 2.63, 5.95), and the AAPC (1990−2021) was 0.65(95%CI: 0.44, 0.86). Over the past 30 years, age-standardised mortality (ASMR) and age-standardised DALY(ASDR) have declined globally. At the level of socio-demographic index (SDI) regions, ASIR and ASPR were the highest in the medium-high SDI and high SDI regions, with ASPR being 6.03(95%UI: 3.01–9.21) and 5.44(95%UI: 3.97–7.18), and ASIR being 0.66(95%UI: 0.33–1.01) and 0.59(95%UI: 0.43–0.78). The mortality and DALYs of RB decreased gradually with the increase of SDI. At the country level, China and India are the countries with the highest number of cases, together accounting for about 30% of the global cases, and ASIR is still on the rise in these two countries. The inequality analysis shows that RB burden is heavier in countries with lower SDI. The number of RB cases worldwide is expected to rise slowly, but the global burden will gradually decrease. Conclusion As one of the main causes affecting the life and health of children, with the increase in the number of diseases worldwide, it is necessary for decision-makers to customize relevant intervention policies to provide effective prevention and control measures to help achieve the global Sustainable Development Goals.

Integrated analysis of WGCNA and machine learning identified diagnostic biomarkers in trauma-induced coagulopathy

Assessing self-selection biases in Facebook-recruited online surveys: Evidence from the COVID-19 Health Behavior Survey

During the COVID-19 pandemic, many primary data collection efforts relied on online surveys via social media recruitment. According to the leverage-salience theory, respondents’ varying interest in the survey topic can lead to differential survey responses, potentially introducing biases. In this study, we investigate the potential impact of displaying the survey topic in the survey recruitment materials on survey responses. We use data from the “COVID-19 Health Behavior Survey", a cross-national online survey that we ran between March and August 2020 in eight countries in Europe and North America (N=120,184). Respondents were recruited via targeted advertisements placed on Facebook with varying degrees of reference to the survey topic of COVID-19. The aim of our study is to assess whether stronger (or weaker) topic salience in the ad images is associated with higher (or lower) threat perceptions of COVID-19 and the adoption of preventive behaviors, including face mask use and increased hand-washing. Regression analyses show that in 20 of the 32 models, ad images had no significant effect on the survey outcomes. Factors like the month of survey participation or respondents’ age were more influential. In the remaining models, where unexplained image effects persisted, the impact was minimal. While mask-wearing images were generally associated with lower threat perceptions of COVID-19 to oneself and the family, we found no consistent pattern for the adoption of protective behaviors. Overall, our findings do not provide consistent evidence that higher topic salience in our Facebook-based recruitment materials systematically influenced survey responses. However, in specific countries, certain recruitment images were linked to variations in COVID-19 threat perception and uptake of preventive behaviors. These context-specific effects highlight the importance of careful recruitment design for Facebook-based surveys during health crises.