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Biocatalytic enantioselective formation and ring-opening of oxetanes

Correction: LGBTQ+ identities in the Indian audiovisual advertisements: A content analysis

Patterns of physical activity in hunter-gatherer children compared with US and UK children

Abstract Contemporary hunter-gatherers are highly active, but little is known about physical activity levels in hunter-gatherer children. We analysed 150 days of accelerometer data from 51 BaYaka hunter-gatherer children (aged 3–18) in the Republic of Congo, comparing it with British and American children using samples from Millennium Cohort Study (MCS) and National Health and Nutrition Examination Survey (NHANES). BaYaka children were highly active, engaging in over 3 h of moderate-to-vigorous physical activity (MVPA) daily, surpassing British adolescents by over 70 min. Unlike US children, whose activity diminished with age, BaYaka children’s activity levels increased, irrespective of gender. This trend suggests that formal education may suppress activity among American children, a pattern not seen in the BaYaka community. Reflecting their foraging lifestyle, activity patterns varied within and between days in BaYaka children, a contrast to the more uniform daily activity observed in American children. Furthermore, our data challenges the concept of ‘teenage chronotypes’ prevalent in post-industrial societies, with adolescent BaYaka maintaining shorter sleep phases and later bedtimes, synchronized with sunrise. These findings highlight the impact of a foraging upbringing on children’s activity levels, providing a benchmark for understanding childhood physical activity and wellbeing.

Metagenomic global survey and in-depth genomic analyses of Ruminococcus gnavus reveal differences across host lifestyle and health status

Prevalence and associated factors of diabetes among adult populations of Hawassa town, southern Ethiopia: A community based cross-sectional study

Introduction Knowing the magnitude and preventable risk factors of diabetes has a significant contribution in targeted prevention intervention which ultimately ensures the existence of healthier and productive individuals in a country. Diabetes has untoward impact on health, social and economic consequences. Exploring preventable risk factors are extremely important because of their potential association and interaction with diabetes. Therefore, the aim of this study was to investigate the magnitude and modifiable risk factors of diabetes among adult populations in Hawassa town, southern Ethiopia. Methods A community based cross-sectional study was conducted from September, 2023 to November, 2023 among adult populations. A multi-stage sampling technique was employed to select 1,113 study participants between the ages of 20–69 years. An interviewer-administered questionnaire was used to collect data. Additionally, participants were also instructed to fast overnight, after which the standard fasting blood glucose test was conducted. A binary logistic regression model was fitted to identify independent predictors of diabetes. Results The overall prevalence of diabetes was 14.4% (95% Confidence Interval (CI): 12.4%, 16.4%). Being male (Adjusted Odds Ratio (AOR):2.10; 95% CI: 1.34, 3.29), being unable to read and write (AOR: 3.38; 95% CI: 1.09, 10.47), read and write (AOR: 3.38; 95% CI: 1.09, 10.47) and medium cycle (AOR 2.79; 95% CI: 1.02, 7.63) compared to college and above, consume less than 5 servings of fruits on daily base (AOR: 2.80; 95% CI: 1.18, 6.62), having ever chewed khat (AOR 6.50; 95% CI: 4.07, 10.39) and being overweight and obese (AOR: 2.43; 95% CI: 1.54, 3.83) were independently associated with diabetes mellitus (DM). Conclusion This study identified a high prevalence of diabetes among adults in Hawassa, driven by various risk factors. This presents an opportunity to mitigate diabetes risk through public health measures, including avoiding khat chewing, promoting healthy diets, managing overweight and obesity, implementing community-based screening, enhancing health literacy, and integrating health information into daily life.

Dynamic characteristics and safe operation speed threshold of metro train passing through curved bridge considering resilient wheel

Global water gaps under future warming levels

Abstract Understanding the impacts of climate change on water resources is crucial for developing effective adaptation strategies. We quantify “water gaps”, or unsustainable water use – the shortfall where water demand exceeds supply, resulting in scarcity. We quantify baseline and future water gaps using a multi-model analysis that incorporates two plausible future warming scenarios. The baseline global water gap stands at 457.9 km3/yr, with projections indicating an increase of 26.5 km3/yr (+5.8%) and 67.4 km3/yr (+14.7%) under 1.5 °C and 3 °C warming scenarios, respectively. These projections highlight the uneven impact of warming levels on water gaps, emphasizing the need for continued climate change mitigation to alleviate stress on water resources. Our results also underscore the unequal adaptation needs across countries and basins, influenced by varying warming scenarios, with important regional differences and model variability complicating future projections. Robust water management strategies are needed to tackle the escalating water scarcity caused by global warming.

Entity-enhanced BERT for medical specialty prediction based on clinical questionnaire data

A medical specialty prediction system for remote diagnosis can reduce the unexpected costs incurred by first-visit patients who visit the wrong hospital department for their symptoms. To develop medical specialty prediction systems, several researchers have explored clinical predictive models using real medical text data. Medical text data include large amounts of information regarding patients, which increases the sequence length. Hence, a few studies have attempted to extract entities from the text as concise features and provide domain-specific knowledge for clinical text classification. However, it is still insufficient to inject them into the model effectively. Thus, we propose Entity-enhanced BERT (E-BERT), which utilizes the structural attributes of BERT for medical specialty prediction. E-BERT has an entity embedding layer and entity-aware attention to inject domain-specific knowledge and focus on relationships between medical-related entities within the sequences. Experimental results on clinical questionnaire data demonstrate the superiority of E-BERT over the other benchmark models, regardless of the input sequence length. Moreover, the visualization results for the effects of entity-aware attention prove that E-BERT effectively incorporate domain-specific knowledge and other information, enabling the capture of contextual information in the text. Finally, the robustness and applicability of the proposed method is explored by applying it to other Pre-trained Language Models. These effective medical specialty predictive model can provide practical information to first-visit patients, resulting in streamlining the diagnostic process and improving the quality of medical consultations.

Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study

Abstract Multiple myeloma (MM) is an incurable blood cancer with unclear aetiology. Proteomics is a valuable tool in exploring mechanisms of disease. We investigated the causal relationship between circulating proteins and MM risk, using two of the largest cohorts with proteomics data to-date. We performed bidirectional two-sample Mendelian randomization (MR; forward MR = causal effect estimation of proteins and MM risk; reverse MR = causal effect estimation of MM risk and proteins). Summary statistics for plasma proteins were obtained from genome-wide association studies performed using SomaLogic (N = 35,559; deCODE) and Olink (N = 34,557; UK Biobank; UKB) proteomic platforms and for MM risk from a meta-analysis of UKB and FinnGen (case = 1649; control = 727,247) or FinnGen only (case = 1085; control = 271,463). Cis- SNPs associated with protein levels were used to instrument circulating proteins. We evaluated proteins for the consistency of directions of effect across MR analyses (with 95% confidence intervals not overlapping the null) and corroborating evidence from genetic colocalization. In the forward MR, 994 (SomaLogic) and 1570 (Olink) proteins were instrumentable. 440 proteins were analysed in both deCODE and UKB; 302 (69%) of these showed consistent directions of effect in the forward MR. Seven proteins had 95% confidence intervals (CIs) that did not overlap the null in both forward MR analyses and did not have evidence for an effect in the reverse direction: higher levels of dermatopontin (DPT), beta-crystallin B1 (CRYBB1), interleukin-18-binding protein (IL18BP) and vascular endothelial growth factor receptor 2 (KDR) and lower levels of odorant-binding protein 2b (OBP2B), glutamate-cysteine ligase regulatory subunit (GCLM) and gamma-crystallin D (CRYGD) were implicated in increasing MM risk. Evidence from genetic colocalization did not meet our threshold for a shared causal signal between any of these proteins and MM risk (h4 < 0.8). Our results highlight seven circulating proteins which may be involved in MM risk. Although evidence from genetic colocalization suggests these associations may not be robust to the effects of horizontal pleiotropy, these proteins may be useful markers of MM risk. Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions.

Tunable control of Cas12 activity promotes universal and fast one-pot nucleic acid detection

Hierarchical image classification using transfer learning to improve deep learning model performance for amazon parrots

Abstract Numerous studies have proven the potential of deep learning models for classifying wildlife. Such models can reduce the workload of experts by automating species classification to monitor wild populations and global trade. Although deep learning models typically perform better with more input data, the available wildlife data are ordinarily limited, specifically for rare or endangered species. Recently, citizen science programs have helped accumulate valuable wildlife data, but such data is still not enough to achieve the best performance of deep learning models compared to benchmark datasets. Recent studies have applied the hierarchical classification of a given wildlife dataset to improve model performance and classification accuracy. This study applied hierarchical classification by transfer learning for classifying Amazon parrot species. Specifically, a hierarchy was built based on diagnostic morphological features. Upon evaluating model performance, the hierarchical model outperformed the non-hierarchical model in detecting and classifying Amazon parrots. Notably, the hierarchical model achieved the mean Average Precision (mAP) of 0.944, surpassing the mAP of 0.908 achieved by the non-hierarchical model. Moreover, the hierarchical model improved classification accuracy between morphologically similar species. The outcomes of this study may facilitate the monitoring of wild populations and the global trade of Amazon parrots for conservation purposes.

Asymmetric synthesis of stereogenic-at-iridium(III) complexes through Pd-catalyzed kinetic resolution

Abstract Metal-centered chirality has been recognized for over one century, and stereogenic-at-metal complexes where chirality is exclusively attributed to the metal center due to the specific coordination pattern of achiral ligands around the metal ion, has been broadly utilized in diverse areas of natural science. However, synthesis of these molecules remains constrained. Notably, while asymmetric catalysis has played a crucial role in the production of optically active organic molecules, its application to stereogenic-at-metal complexes is less straightforward. In this study, we introduce a kinetic resolution strategy employing a Pd-catalyzed asymmetric Suzuki-Miyaura cross-coupling reaction that efficiently produces optically active stereogenic-at-iridium complexes from racemic mixtures with high selectivity (achieving an s-factor of up to 133). This method enables further synthesis of complexes relevant to chiral metallodrugs and photosensitizers, underscoring the practical utility of our approach. Mechanistic studies suggest that reductive elimination is likely the turnover-limiting step over the Suzuki-Miyaura cross-coupling.

Significance of supervision sampling in control of communicable respiratory disease simulated by a new model during different stages of the disease

Abstract The coronavirus disease 2019 (COVID-19) interventions in interrupting transmission have paid heavy losses politically and economically. The Chinese government has replaced scaling up testing with monitoring focus groups and randomly supervising sampling, encouraging scientific research on the COVID-19 transmission curve to be confirmed by constructing epidemiological models, which include statistical models, computer simulations, mathematical illustrations of the pathogen and its effects, and several other methodologies. Although predicting and forecasting the propagation of COVID-19 are valuable, they nevertheless present an enormous challenge. This paper emphasis on pandemic simulation models by introduced respiratory-specific transmission to extend and complement the classical Susceptible-Exposed-(Asymptomatic)-Infected-Recovered SE(A)IR model to assess the significance of the COVID-19 transmission control features to provide an explanation of the rationale for the government policy. A novel epidemiological model is developed using mean-field theory. Utilizing the SE(A)IR extended framework, which is a suitable method for describing the progression of epidemics over actual or genuine landscapes, we have developed a novel model named SEIAPUFR. This model effectively detects the connections between various stages of infection. Subsequently, we formulated eight ordinary differential equations that precisely depict the population’s temporal development inside each segment. Furthermore, we calibrated the transmission and clearance rates by considering the impact of various control strategies on the epidemiological dynamics, which we used to project the future course of COVID-19. Based on these parameter values, our emphasis was on determining the criteria for stabilizing the disease-free equilibrium (DEF). We also developed model parameters that are appropriate for COVID-19 outbreaks, taking into account varied population sizes. Ultimately, we conducted simulations and predictions for other prominent cities in China, such as Wuhan, Shanghai, Guangzhou, and Shenzhen, that have recently been affected by the COVID-19 outbreak. By integrating different control measures, respiratory-specific modeling, and disease supervision sampling into an expanded SEI (A) R epidemic model, we found that supervision sampling can improve early warning of viral activity levels and superspreading events, and explained the significance of containments in controlling COVID-19 transmission and the rationality of policy by the influence of different containment measures on the transmission rate. These results indicate that the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission, and the proportion of supervision sampling should be proportional to the transmission rate, especially only aimed at preventing a resurgence of SARS-CoV-2 transmission in low-prevalence areas. Furthermore, The incidence hazard of Males and Females was 1.39(1.23–1.58), and 1.43(1.26–1.63), respectively. Our investigation found that the ratio of peak sampling is directly related to the transmission rate, and both decrease when control measures are implemented. Consequently, the control measures during the pandemic interrupted the transmission chain mainly by inhibiting respiratory transmission. Reasonable and effective interventions during the early stage can flatten the transmission curve, which will slow the momentum of the outbreak to reduce medical pressure.

Stress landscape of folding brain serves as a map for axonal pathfinding

Abstract Understanding the mechanics linking cortical folding and brain connectivity is crucial for both healthy and abnormal brain development. Despite the importance of this relationship, existing models fail to explain how growing axon bundles navigate the stress field within a folding brain or how this bidirectional and dynamic interaction shapes the resulting surface morphologies and connectivity patterns. Here, we propose the concept of “axon reorientation” and formulate a mechanical model to uncover the dynamic multiscale mechanics of the linkages between cortical folding and connectivity development. Simulations incorporating axon bundle reorientation and stress-induced growth reveal potential mechanical mechanisms that lead to higher axon bundle density in gyri (ridges) compared to sulci (valleys). In particular, the connectivity patterning resulting from cortical folding exhibits a strong dependence on the growth rate and mechanical properties of the navigating axon bundles. Model predictions are supported by in vivo diffusion tensor imaging of the human brain.

Applying YOLOv6 as an ensemble federated learning framework to classify breast cancer pathology images

Abstract The most common carcinoma-related cause of death among women is breast cancer. Early detection is crucial, and the manual screening method may lead to a delayed diagnosis, which would delay treatment and put lives at risk. Mammography imaging is advised for routine screening to diagnose breast cancer at an early stage. To improve generalizability, this study examines the implementation of Federated Learning (FedL) to detect breast cancer. Its performance is compared to a centralized training technique that diagnoses breast cancer. Although FedL has been famous as a safeguarding privacy algorithm, its similarities to ensemble learning methods, such as federated averaging (FEDAvrg), still need to be thoroughly investigated. This study examines explicitly how a YOLOv6 model trained with FedL performs across several clients. A new homomorphic encryption and decryption algorithm is also proposed to retain data privacy. A novel pruned YOLOv6 model with FedL is introduced in this study to differentiate benign and malignant tissues. The model is trained on the breast cancer pathological dataset BreakHis and BUSI. The proposed model achieved a validation accuracy of 98% on BreakHis dataset and 97% on BUSI dataset. The results are compared with the VGG-19, ResNet-50, and InceptionV3 algorithms, showing that the proposed model achieved better results. The tests reveal that federated learning is feasible, as FedAvrg trains models of outstanding quality with only a few communication rounds, as shown by the results on a range of model topologies such as ResNet50, VGG-19, InceptionV3, and the proposed Ensembled FedL YOLOv6.

Asteroid Bennu contains salts from ancient brine

Stroma-derived Dickkopf-1 contributes to the suppression of NK cell cytotoxicity in breast cancer

Abstract Mechanisms related to tumor evasion from NK cell-mediated immune surveillance remain enigmatic. Dickkopf-1 (DKK1) is a Wnt/β-catenin inhibitor, whose levels correlate with breast cancer progression. We find DKK1 to be expressed by tumor cells and cancer-associated fibroblasts (CAFs) in patient samples and orthotopic breast tumors, and in bone. By using genetic approaches, we find that bone-derived DKK1 contributes to the systemic DKK1 elevation in tumor-bearing female mice, while CAFs contribute to DKK1 at primary tumor site. Systemic and bone-specific DKK1 targeting reduce tumor growth. Intriguingly, deletion of CAF-derived DKK1 also limits breast cancer progression, without affecting its levels in circulation, and regardless of DKK1 expression in the tumor cells. While not directly supporting tumor proliferation, stromal-DKK1 suppresses NK cell activation and cytotoxicity by downregulating AKT/ERK/S6 phosphorylation. Importantly, increased DKK1 levels and reduced cytotoxic NK cells are detected in women with progressive breast cancer. Our findings indicate that DKK1 represents a barrier to anti-tumor immunity through suppression of NK cells.

Investigation and evaluation of palygorskite microstructure following acid pretreatment and its potential use as an adsorbent for copper

Rising infrastructure inequalities accompany urbanization and economic development

Abstract Impending global urban population growth is expected to occur with considerable infrastructure expansion. However, our understanding of attendant infrastructure inequalities is limited, highlighting a critical knowledge gap in the sustainable development implications of urbanization. Using satellite data from 2000 to 2019, we examine country-level population-adjusted biases in infrastructure distribution within and between regions of varying urbanization levels and derive four key findings. First, we find long-run positive associations between infrastructure inequalities and both urbanization and economic development. Second, our estimates highlight increasing infrastructure inequalities across most of the countries examined. Third, we find greater future infrastructure inequality increases in the global south, where inequalities will rise more in countries with substantial urban primacy. Fourth, we find that infrastructure inequality may evolve differently than economic inequalities. Overall, advancing sustainable development vis-à-vis urbanization and economic development will require intentional infrastructure planning for spatial equity.

A comparative analysis of collagen based dressings and polysaccharide microspheres for hemostasis management in hepatic stab wounds