Browse Articles

Copper Chelate Targeting Externalized Phosphatidylserine Inhibits PD-L1 Expression and Enhances Cancer Immunotherapy

Efficacy and safety of esketamine for emergency endotracheal intubation in ICU patients: a double-blind, randomized controlled clinical trial

Modelling the effects of elevated methylglyoxal levels on vascular and metabolic complications

Multi-omics analysis reveals the panoramic picture of TOP2A in pan-cancer

Residual useful life prediction of lithium-ion battery based on accuracy SoH estimation

Self-reactive impedance surfaces for enhanced quasi-line wave propagation in the terahertz spectrum

Collision Timing and Provenance Shifts from the Late Oligocene to Middle Miocene in the Southeasternmost Zagros Orogen

Thermal hydrolysis of poultry byproducts for the production of microbial media

Interactive effects of irrigation and fertilization on the growth and physiological characteristics of greenhouse tomatoes, Solanum lycopersicum L.

COVID-19 affected elite track-and-field athletes’ Olympic preparation before Tokyo 2020 compared to Rio 2016

Abstract The COVID-19 pandemic has had a significant impact on elite sport by postponing the Olympic Games Tokyo 2020 four months before the original start. This impacted athletes’ macro-cycle periodization, psychological stressors and resources. We analyse whether track-and-field athletes were able to maintain their performance levels successfully across the last two Olympic cycles, controlling for age, gender and doping prevalence. For this, worldwide competition results (excluding multi-events & relays) of at least national level since London 2012 and up to Tokyo 2020 were retrieved. Individual performance curves were analysed using hierarchical multilevel modelling. Individual baselines (random intercept) and developments (random slope) were analysed. 2,383 athletes (52% male) recorded 15,766 outcomes since London 2012. The final conditional growth model (ICC = 48%) shows that performances increased in the wake of Olympic games, dropped significantly in 2020 and recovered beyond previous form in 2021. There was no significant difference between men’s and women’s developments. Age was a significant predictor (b = 0.17, SE = 0.02), but doping violations was not (b = 0.01, SE = 0.03). These results showcase performance trends in international athletics and their variability, present an overall successful periodization to achieve peak performance at Tokyo 2020, and discuss predictions for track and field at Paris 2024.

An integrated CRITIC and EDAS model using linguistic T spherical fuzzy Hamacher aggregation operators and its application to group decision making

Author Correction: OPEFB pretreatment using the low-cost N,N,N-dimethylbutylammonium hydrogen sulfate ionic liquid under varying conditions

A novel nonlinear model for estimating ideal packing density of mixtures

Risk factors for rotator cuff tear in Syrian adults: a cross-sectional study

An evaluation of speech therapy care in the surrounding area of an interdisciplinary cleft lip and palate tertiary care center

Abstract The anatomical deformation in cleft patients requires speech therapy to support cleft patients as best as possible. The aim of this study was to evaluate the standard of knowledge of therapists concerning clefts. Furthermore, the study aimed to determine whether there was a difference between therapists with and without treatment experience in cleft patients as well as among therapists with more or less years of general professional experience. We developed a questionnaire about different areas of speech therapy: “General,” “Speech therapy,” “Development opportunities and influences,” and “Interdisciplinary collaboration.” For a total of 50 questions, we used single-, multiple-choice questions and the visual analog scale (VAS). Speech therapists with experience in treating cleft patients (n = 43) felt more confident regarding their knowledge and abilities than therapists without experience (n = 61), especially concerning nonspecialist disciplines and cleft specifications. No difference was found in therapy duration, indications, influences, and potential for development. Professional experience (years) and cleft experience correlate; with more knowledge in the group with more than 8 years of experience. Cleft centers remain first choice for patients’ care thanks to the higher number of patients, daily treatment routine, the direct contact among examiners, and a common treatment concept.

Exploring the low-carbon development path of resource-based cities based on scenario simulation

Abstract Resource-based cities (RBCs) have historically been constrained by their inherent characteristics, impeding rapid shifts in energy consumption patterns and exerting substantial pressure on regional decarbonization efforts. Herein, 18 RBCs in southwestern China were taken as the research object. Firstly, a resilience index system was constructed for the resource ecosystem and socio-economic system of RBCs, and the optimization mutation level algorithm was used to measure the resilience level of each city. Secondly, an interval prediction model was established for carbon emissions in RBCs based on the GA-DBN-KDE algorithm. Finally, by setting 16 scenarios, the carbon emission range and “carbon peak” time range of RBCs in Southwest China from 2023 to 2040 were predicted, and the scientific path of low-carbon development of RBCs was explored under differentiated scenarios. The research results indicated that: (1) The carbon emissions and urban resilience levels of RBCs in southwestern China were both on the rise; (2) The interval prediction model based on GA-DBN-KDE demonstrated excellent prediction performance; (3) The simulation results of 16 scenarios revealed varying specific paths for 18 cities to achieve carbon peak, underscoring the necessity for city-specific policy formulation. Overall, this paper provides a new analytical method for the low-carbon transformation and development of RBCs, further forging a basis for decision-makers to formulate carbon reduction measures.

Joint association of TyG index and LDL-C with all-cause and cardiovascular mortality among patients with cardio-renal-metabolic disease

Integrating D–S evidence theory and multiple deep learning frameworks for time series prediction of air quality

Abstract Accurate prediction of air quality time series data is helpful to identify and warn air pollution events in advance. Although the current air quality prediction models have made some progress in improving the accuracy of prediction, due to the impact of specific pollutants or complex meteorological conditions, these models still have the problems of low prediction accuracy, robustness and generalization ability in univariate prediction. In order to solve these problems, this study proposes a framework that integrates D–S evidence theory and a variety of deep learning models. The air quality data of three representative cities with climate characteristics in China are obtained and five indicators on air pollutants are collected. The preprocessed data are divided by time length to form short-term, medium-term and long-term input data, and MLP, RNN, CNN, LSTM, BI-LSTM and GRU models are established respectively. By comparing the performance indicators of the six models, three most suitable models are selected to predict the short, medium and long-term data respectively. Taking the prediction results and reliability as the three evidence bodies of the theory, a fusion model based on D–S evidence theory is established. For the three performance indicators MAE, RMSE and MAPE of the model, the best result of the fusion model increases the performance by 7.42%, 4.25% and 12.82% compared with the sub optimal architecture. This shows that integrating D–S evidence theory and a variety of deep learning algorithms provides an effective method to accurately predict the long-term air quality level in most urban areas.

Jointly exploring client drift and catastrophic forgetting in dynamic learning

Abstract Federated and Continual Learning have emerged as promising paradigms for the privacy-aware use of Deep Learning in dynamic environments by addressing spatial and temporal constraints on data availability. However, Client Drift and Catastrophic Forgetting are fundamental obstacles to ensuring robust performance. Existing work only addresses these problems separately, neglecting the fact that the root cause behind them, namely an unexpected shift in the data distribution, is connected. We propose a unified analysis framework for building a controlled test environment where we can jointly model spatial and temporal shifts, more closely emulating real dynamic settings. By generating a 3D landscape of the combined performance impact, we show that a moderate combination of both shifts can even improve the performance of the resulting model (“Generalization Bump”). We apply a simple and commonly used method from continual learning in the federated setting and observe this reoccurring phenomenon.

A computational framework to study the etiology of grandiose narcissism

Abstract Grandiose narcissism is characterized by ambivalent interaction behavior (i.e., grandiose self-presentation and rivalrous devaluation of others) and strong oscillations in self-esteem over time. In the light of emotional and social problems associated with these self-esteem regulation patterns and the increasing prevalence of narcissistic tendencies, causal and formalized models for prevention and intervention are needed. Here, we present a computational model of narcissistic self-esteem regulation implementing established, verbal theories of narcissism to identify key etiological and disorder-maintaining mechanisms. Across four studies, we show that parental praise and overvaluation lead to typical grandiose-narcissistic behavioral patterns (i.e., entitled self-presentation and rivalry) and strong self-esteem oscillations. Underlying these phenomena, we identify two maintaining mechanisms that offer targets for intervention and empirical falsification: tolerance development, characterized by an ever-increasing desire for social recognition, and a vicious cycle of rivalry, characterized by the frequent use of other-devaluing behavior and massive drops in self-esteem.