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Broadband carrier capture dynamics mechanism of carbon-related defects in GaN
Fully understanding and modulating the nonlinear absorption in GaN are crucial for designing ultrafast photonic devices. In this work, both the ultra-broadband transient absorption spectra and carrier recombination time in GaN were found to be significantly altered by carbon defects. An energy band model for carbon defect dynamics was established based on transient absorption and photoluminescence spectroscopy. Our model discernibly reveals that CN and tri-carbon in GaN intricately modulate both the absorption spectrum and carrier capture process: The rapid capture of holes by the CN defect significantly reduces the hole recombination time to hundreds of femtoseconds in the near-infrared band. Conversely, the tri-carbon defect exhibited a higher absorption cross section by an order of magnitude than that of free carrier in the visible region with a long carrier recombination time. This work clarifies the modulation mechanisms of complex carbon defects in GaN's nonlinear absorption and provides scientific guidance for designing broadband and integrated ultrafast optical nonlinear devices.
MB based RT-qPCR increase the clinical application of cfEBV DNA for NPC in non-endemic area of China
The synergistic antitumour effect of Carrimycin combined with 5-fluorouracil on colorectal cancer
Experimental verification of superconducting metamaterial quarter-wavelength transmission line resonators with non-uniform mode spacing
Synergistic effect of AP/KP composite oxidizers prepared by electrostatic spraying and promotion of aluminum combustion performance
Development and validation of a risk prediction model for post-traumatic stress disorder among Chinese breast cancer survivors
Benzydamine rescues ethanol-induced teratogenesis in zebrafish FASD model
Abstract Fetal alcohol spectrum disorders (FASD) are a group of physical, behavioral, and cognitive impairments caused by ethanol exposure during pregnancy. Zebrafish have emerged as a useful model for researching FASD and its variants in recent years. Oxidative stress has been identified as the primary damaging pathway, notwithstanding the possibility of other mechanisms at play. In this regard it’s important to put an effort towards antioxidants which can exhibits and bare a potential to counteract the oxidative stress induced by ethanol during embryos development. Previosuly benzydamine has shown to protect macrophages against ethanol-induced condition by stabilizing redox homeostatis. This study aims to repurpose the Non-Steroidal Anti-Inflammatory Drug (NSAID) benzydamine to mitigate ethanol-induced teratogenesis during the early embryonic stage in Zebrafish. Zebrafish embryos were treated with 1% ethanol at 2 h post fertilization (hpf) and co-exposed with benzydamine (5–20 µM) after 2 h of ethanol treatment for 24 h. Reactive oxygen Species (ROS) and biochemical analysis was carried out at 48hpf. 1% ethanol significantly increased the production of ROS along with increased in lipid peroxidation followed by a decrease in glutathione (GSH) level when compoared to the control group (P < 0.001). These conditions were positively encountered by benzydamine (10, 15 µM) and returned to basal level. Involvement of two ethanol metabolizing enzymes cyp2y3 (Cytochrome P450, family 2, subfamily Y, polypeptide 3) and cyp3a65 Cytochrome P450, family 3, subfamily A, polypeptide 65 ) were also studied at 48hpf. 1% ethanol exposure aggregately elevated the expression of these two enzymes which showed a significant decrease in the benzydamine treated groups. Furthermore, the malformations and cellular damage due to 1% ethanol was studied at 96hpf, where 1% ethanol made severe malformation along with muscle fiber alteration, apoptosis in the brain and eye as manifested. These conditions were successfully reverted by benzydamine. In conclusion, ethanol causes oxidative stress, cellular damage along with severe malformation at early embryonic stage, which were partially prevented by the exposure of benzydamine.
Surgical methods and outcomes of inguinal hernia repair in children, adolescents and young adults in a retrospective cohort study
Establishment and validation of a ResNet-based radiomics model for predicting prognosis in cervical spinal cord injury patients
Abstract Cervical spinal cord injury (cSCI) poses a significant challenge due to the unpredictable nature of recovery, which ranges from mild paralysis to severe long-term disability. Accurate prognostic models are crucial for guiding treatment and rehabilitation but are often limited by their reliance on clinical observations alone. Recent advancements in radiomics and deep learning have shown promise in enhancing prognostic accuracy by leveraging detailed imaging data. However, integrating these imaging features with clinical data remains an underexplored area. This study aims to develop a combined model using imaging and clinical signatures to predict the prognosis of cSCI patients six months post-injury, helping clinical decisions and improving rehabilitation plans. We retrospectively analyzed 168 cSCI patients treated at Zhongda Hospital from January 1, 2018, to June 30, 2023. The retrospective cohort was divided into training (134 patients) and testing sets (34 patients) to construct the model. An additional prospective cohort of 43 cSCI patients treated from July 1, 2023, to November 30, 2023, was used as a validation set. Radiomics features were extracted using Pyradiomics and ResNet deep learning from MR images. Clinical factors such as age, smoking history, drinking history, hypertension, diabetes, cardiovascular disease, traumatic brain injury, injury site, and treatment type were analyzed. The LASSO algorithm selected features for model building. Multiple machine learning models, including SVM, LR, NaiveBayes, KNN, RF, ExtraTrees, XGBoost, LightGBM, GradientBoosting, AdaBoosting, and MLP, were used. Receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) assessed the models’ performance. A nomogram was created to visualize the combined model. In Radiomics models, the SVM classifier achieved the highest area under the curve (AUC) of 1.000 in the training set and 0.915 in the testing set. Age, diabetes, and treatment were found clinical risk factors to develop a clinical model. The combined model, integrating radiomics and clinical features, showed strong performance with AUCs of 1.000 in the training set, 0.952 in the testing set and 0.815 in the validation set. And calibration curves and DCA confirmed the model’s accuracy and clinical usefulness. This study shows the potential of a combined radiomics and clinical model to predict the prognosis of cSCI patients.
Bidirectional rotational antagonistic shape memory alloy actuators for high-frequency artificial muscles
Abstract Shape memory alloys (SMA) are commonly utilized in compact actuators due to their high energy density, meaning possible work output in relation to their weight and volume. Their application area is limited by their poor dynamic behavior, caused by the thermal activation characteristics of SMA materials. Typical actuation frequencies of SMA-based actuators range from 1 Hz to 10 Hz. In this work, we introduce an actuator system architecture, termed bidirectional rotational antagonistic (BIRAN) SMA actuator, which uses bundles of thin SMA wires to generate repeated rotational movement at frequencies up to 200 Hz. This marks a new frequency record for electrically activated SMA wire-based actuator systems. The high frequency reported results from the combination of mechanical design, electronics, and control strategy. We describe the fabrication techniques and the power electronics development and demonstrate the system performance through a systematic experimental study. A bio-inspired robotic wing-flapping joint illustrates the expansion of possible SMA-based application areas, pushing the dynamic limitations of this actuator technology.
The coupled effect of climate change and LUCC on meteorological drought in a karst drainage basin, Southwest China
The impact of various calcium ion sources on the curing efficacy of MICP
Identifying the epileptic network by linking interictal functional and structural connectivity
Spatiotemporal evolution patterns of the coupling of carbon productivity and high-quality economic development in China
Abstract This study examines the spatiotemporal evolution of the coordinated development between carbon productivity (CP) and high-quality economic development (HQED) across 30 provinces in China from 2008 to 2021. Using the entropy weight method, coupling coordination degree (CCD), kernel density estimation, spatial autocorrelation analysis, and spatial econometric models, the research identifies several key findings: first, a coupling and coordination relationship characterized by mutual influence and restraint exists between carbon productivity and high-quality economic development. Both carbon productivity and high-quality economic development, along with their coupling coordination degree, have exhibited continuous growth, demonstrating a spatial distribution pattern of “higher in the east than in the west, and higher in the south than in the north,” accompanied by expanding spatial concentration and pronounced regional disparities. Second, the global Moran’s I for the coupling coordination degree is positive, indicating significant spatial effects between carbon productivity and high-quality economic development. The LISA map highlights that high–high clusters are concentrated in the economically advanced eastern coastal areas, while low–low clusters are predominantly located in underdeveloped central and western regions and energy-dependent heavy industrial provinces. Third, the spatial effects of coupling coordination degree are influenced by factors such as economic development level, urbanization, technological progress, environmental regulation, the proportion of the secondary industry, and marketization level. The significance of these factors varies in the decomposition effect. Finally, this study provides policy recommendations. Within the framework of China’s “dual-carbon” goals, promoting the coupling and coordinated development of carbon productivity and high-quality economic development, while fostering balanced regional growth, holds substantial practical importance.
Evolution of indirect reciprocity under emotion expression
Abstract Do emotion expressions impact the evolution of cooperation? Indirect Reciprocity offers a solution to the cooperation dilemma with prior work focusing on the role of social norms in propagating others’ reputations and contributing to evolutionarily stable cooperation. Recent experimental studies, however, show that emotion expressions shape pro-social behaviour, communicate one’s intentions to others, and serve an error-correcting function; yet, the role of emotion signals in the evolution of cooperation remains unexplored. We present the first model of IR based on evolutionary game theory that exposes how emotion expressions positively influence the evolution of cooperation, particularly in scenarios of frequent errors. Our findings provide evolutionary support for the existence of emotion-based social norms, which help foster cooperation among unrelated individuals.
Risk factors associated with medium- to long-term outcome and health-related quality of life of patients with conservatively treated rib fractures
Abstract Patients with rib fractures often suffer from prolonged pain and dyspnea. The purpose of this study was to evaluate the mid- and long-term outcomes and major predisposing risk factors for clinical limitations such as pain or reduced lung capacity in patients with conservatively managed rib fractures to provide a basis for optimizing current therapy. Patients who underwent conservative management of rib fractures between 2014 and 2018 at a level I trauma center were retrospectively reviewed. Inclusion criteria were Injury Severity Score (ISS) ≤ 16 points and a minimum follow-up of 3 years. Outcome parameters were the SF-36 physical and mental component summary score (PCS and MCS, respectively) as well as current pain and respiratory problems. Risk factors evaluated included age, body mass index (BMI), in-hospital days, number of rib fractures, fracture dislocation and serial rib fracture. PCS was comparable to the normal population. The correlation between age and PCS was significant, p = .002. BMI correlated significantly with PCS, p < .001, current pain, p = .034 and respiratory problems, p = .029. No significant correlations were observed for the number of rib fractures and in-hospital days. Fracture dislocation and serial rib fracture showed no effect on PCS, p = .134 and p = .914, respectively, and current pain, p = .916 and p = .357, respectively. In the medium- to long-term, conservative treatment of simple rib fractures or serial rib fractures showed good results, but was negatively affected by a high patient age or BMI.
Monitoring and early warning of ovarian cancer using high-dimensional non-parametric EWMA control chart based on sliding window
Abstract Ovarian tumors are a common ovarian dysfunction that affects women’s daily lives. Although ovarian tumors are generally sensitive to chemotherapy and initially respond well to platinum/taxane-based treatments, the postoperative recurrence rate remains high in advanced cases. Many researchers are dedicated to developing new methods for monitoring and predicting malignant tumors. Traditional approaches use dimensionality reduction techniques, like principal component analysis and deep learning, to select relevant features, followed by univariate or multivariate control charts for monitoring. However, these methods may overlook interactions between features and dimensionality reduction can result in loss of information, potentially affecting the accuracy of the model and leading to delayed alerts and reduced predictive performance. Therefore, this paper develops a new sliding window EWMA control chart based on high-dimensional empirical likelihood ratio tests. This control chart not only monitors data with unknown underlying distributions but is also applicable to high-dimensional data, allowing for monitoring without dimensionality reduction, thus simplifying the process and avoiding information loss. Monte Carlo results show that this method detects changes in indicators and issues alerts more rapidly than the dimensionality-reduced multivariate EWMA control charts. In addition, we further validated the effectiveness of this method through analysis of a tumor resection data example.