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Abstract Characterizing the temporal evolution of the three‐dimensional structure of the Io plasma torus is essential to understand the dynamics of the Jovian magnetosphere. Optical imaging is a powerful tool to uncover the global torus structure. Currently, two ground‐based optical telescopes with diameters of 0.8 and 1.8 m, respectively, are under construction at the Lenghu Observatory for Planetary Science on the Tibetan Plateau of China, to systematically observe the Io plasma torus at wavelengths between 392 and 1,100 nm. These telescopes will begin to operate in the end of 2023. In order to support the inversion and scientific interpretation of the Io plasma torus images, we perform systematic simulations of the Io plasma torus in this work. First, a three‐dimensional model of electron and ion densities and electron temperature is constructed first. Then, the emissions of O+ 372.7 nm, O+ 372.9 nm, S+ 406.9 nm, S+ 671.8 nm, S+ 673.1 nm, and S++ 953.2 nm are simulated from the perspective of observing from the Earth. The simulated emission intensities and distributions are consistent with previous observations. This work provides a state‐of‐the‐art convenient tool for ground‐based telescope observation of the Io plasma torus at a specific site and time and also benefits future inversion of images to obtain physical parameters of Io plasma torus.

Abstract Background Extensor apparatus rupture is a severe complication after knee arthroplasty, but there have not been many reports on how to perform knee arthroplasty after chronic patellar ligament rupture. We reported a case of total knee arthroplasty (TKA) in a patient with severe osteoarthritis (OA) complicated by chronic patellar ligament rupture. Case presentation In this case, a 67-year-old male patient suffered from patellar ligament rupture due to trauma more than 20 years ago and did not undergo any formal treatment. Physical examination revealed a small amount of fluid and extension lag, and the patella was displaced upward by approximately 5.5 cm. The quadriceps were atrophic and weak. There was significant tenderness on the medial side of the left knee joint. Passive motion of the left knee joint ranged from full extension to 120° of flexion with discomfort during excessive flexion. Active flexion of the knee joint to 120°, and extensor lag was approximately 90°. We reconstructed the extensor apparatus through a quadriceps tendon V-Y quadricepsplasty and Krackow suture technique of the patellar ligament, and osteoarthritis was resolved with TKA. The visual analogue scale (VAS) score decreased from 5 points to 1 point after surgery. Six weeks later, the patient was able to walk normally without a walking stick, and the knee joint could stretch actively to approximately 30°. However, he had obvious extension lag. This problem improved 10 months after surgery. The AKS score increased from 35 to 95 10 months after surgery. The HSS score increased from 43 to 93. Conclusions TKA and ligament reconstruction are options for the treatment of knee OA with chronic patellar ligament rupture. V-Y lengthening of the quadriceps femoris tendon after the Krackow suture technique of the patellar ligament with transpatellar tunnels may be a reasonable choice during TKA.

This paper presents a novel optimization model that explores the optimal investment strategies for DC pension plans with return of premium clauses. We have assumed that the financial market consists of a risk-free asset and a risky asset, where the price of the risky asset follows the CEV model. Under the expected utility criterion, the optimal investment strategies were derived by employing stochastic optimal control theory and the Legendre transformation method. Explicit expressions of the optimal investment strategy were provided when the utility function was specified as exponential, power, or logarithmic. Finally, numerical analysis was conducted to examine the impact of factors such as interest rate, return rate, and volatility of the risky asset on the optimal strategies.

Abstract Objective Mild therapeutic hypothermia (MTH) is an important method for perioperative prevention and treatment of myocardial ischemia–reperfusion injury (MIRI). Modifying mitochondrial proteins after protein translation to regulate mitochondrial function is one of the mechanisms for improving myocardial ischemia–reperfusion injury. This study investigated the relationship between shallow hypothermia treatment improving myocardial ischemia–reperfusion injury and the O-GlcNAcylation level of COX10. Methods We used in vivo Langendorff model and in vitro hypoxia/reoxygenation (H/R) cell model to investigate the effects of MTH on myocardial ischemia–reperfusion injury. Histological changes, myocardial enzymes, oxidative stress, and mitochondrial structure/function were assessed. Mechanistic studies involved various molecular biology methods such as ELISA, immunoprecipitation (IP), WB, and immunofluorescence. Results Our research results indicate that MTH upregulates the O-GlcNACylation level of COX10, improves mitochondrial function, and inhibits the expression of ROS to improve myocardial ischemia–reperfusion injury. In vivo, MTH effectively alleviates ischemia–reperfusion induced cardiac dysfunction, myocardial injury, mitochondrial damage, and redox imbalance. In vitro, the OGT inhibitor ALX inhibits the OGT mediated O-GlcNA acylation signaling pathway, downregulates the O-Glc acylation level of COX10, promotes ROS release, and counteracts the protective effect of MTH. On the contrary, the OGA inhibitor ThG showed opposite effects to ALX, further confirming that MTH activated the OGT mediated O-GlcNAcylation signaling pathway to exert cardioprotective effects. Conclusions In summary, MTH activates OGT mediated O-glycosylation modified COX10 to regulate mitochondrial function and improve myocardial ischemia–reperfusion injury, which provides important theoretical basis for the clinical application of MTH.

Recent advancements in natural language processing, computational linguistics, and Artificial Intelligence (AI) have propelled the use of Large Language Models (LLMs) in Automated Essay Scoring (AES), offering efficient and unbiased writing assessment. This study assesses the reliability of LLMs in AES tasks, focusing on scoring consistency and alignment with human raters. We explore the impact of prompt engineering, temperature settings, and multi-level rating dimensions on the scoring performance of LLMs. Results indicate that prompt engineering significantly affects the reliability of LLMs, with GPT-4 showing marked improvement over GPT-3.5 and Claude 2, achieving 112% and 114% increase in scoring accuracy under the criteria and sample-referenced justification prompt. Temperature settings also influence the output consistency of LLMs, with lower temperatures producing scores more in line with human evaluations, which is essential for maintaining fairness in large-scale assessment. Regarding multi-dimensional writing assessment, results indicate that GPT-4 performs well in dimensions regarding Ideas (QWK=0.551) and Organization (QWK=0.584) under well-crafted prompt engineering. These findings pave the way for a comprehensive exploration of LLMs' broader educational implications, offering insights into their capability to refine and potentially transform writing instruction, assessment, and the delivery of diagnostic and personalized feedback in the AI-powered educational age. While this study attached importance to the reliability and alignment of LLM-powered multi-dimensional AES, future research should broaden its scope to encompass diverse writing genres and a more extensive sample from varied backgrounds.

With the flourishing development of corpus linguistics and technological revolutions in the AI-powered age, automated essay scoring (AES) models have been intensively developed. However, the intricate relationship between linguistic features and different constructs of writing quality has yet to be thoroughly investigated. The present study harnessed computational analytic tools and Principal Component Analysis (PCA) to distill and refine linguistic indicators for model construction. Findings revealed that both micro-features and their combination with aggregated features robustly described writing quality over aggregated features alone. Linear and non-linear models were thus developed to explore the associations between linguistic features and different constructs of writing quality. The non-linear AES model with Random Forest Regression demonstrated superior performance over other benchmark models. Furthermore, SHapley Additive exPlanations (SHAP) was employed to pinpoint the most powerful linguistic features for each rating trait, enhancing the model’s transparency through explainable AI (XAI). These insights hold the potential to substantially facilitate the advancement of multi-dimensional approaches toward writing assessment and instruction.

Kawasaki disease (KD) is an acute autoimmune vascular disease featured with a long stage of febrile. It predominantly afflicts children under 5 years old and causes an increased risk of cardiovascular combinations. The onset and progression of KD are impacted by many aspects, including genetic susceptibility, infection, and immunity. In recent years, many studies revealed that miRNAs, a novel class of small non-coding RNAs, may play an indispensable role in the development of KD via differential expression and participation in the central pathogenesis of KD comprise of the modulation of immunity, inflammatory response and vascular dysregulation. Although specific diagnose criteria remains unclear up to date, accumulating clinical evidence indicated that miRNAs, as small molecules, could serve as potential diagnostic biomarkers and exhibit extraordinary specificity and sensitivity. Besides, miRNAs have gained attention in affecting therapies for Kawasaki disease and providing new insights into personalized treatment. Through consanguineous coordination with classical therapies, miRNAs could overcome the inevitable drug-resistance and poor prognosis problem in a novel point of view. In this review, we systematically reviewed the existing literature and summarized those findings to analyze the latest mechanism to explore the role of miRNAs in the treatment of KD from basic and clinical aspects retrospectively. Our discussion helps to better understand the pathogenesis of KD and may offer profound inspiration on KD diagnosis, treatment, and prognosis.

The use of non-steroidal anti-inflammatory drugs (NSAIDs) in patients with coronavirus disease 2019 (COVID-19) has raised great concerns. The effect of NSAIDs on the clinical status of COVID-19 remains in question. Therefore, we performed a post-hoc analysis from the ORCHID trial. Patients with COVID-19 from the ORCHID trial were categorized into two groups according to NSAID use. The 28-day mortality, hospitalized discharge, and safety outcomes with NSAIDs for patients with COVID-19 were analyzed. A total of 476 hospitalized patients with COVID-19 were included; 412 patients (86.5%) did not receive NSAIDs, while 64 patients (13.5%) took NSAIDs as regular home medication. Patients who took NSAIDs did not have a significant increase in the risk of 28-day mortality (fully adjusted: hazard ratio [HR]: 1.12, 95% CI: 0.52–2.42) in the Cox multivariate analysis. Moreover, NSAIDs did not decrease hospital discharge through 28 days (fully adjusted: HR: 1.02, 95% CI: 0.75–1.37). The results of a meta-analysis including 14 studies involving 48,788 patients with COVID-19 showed that the use of NSAIDs had a survival benefit (summary risk ratio [RR]: 0.70, 95% CI: 0.54–0.91) and decreased the risk of severe COVID-19 (summary: RR: 0.79, 95% CI: 0.71–0.88). In conclusion, the use of NSAIDs is not associated with worse clinical outcomes, including 28-day mortality or hospital discharge in American adult hospitalized patients with COVID-19. Based on current evidence, the use of NSAIDs is safe and should not be cautioned against during the COVID-19 pandemic. Ongoing trials should further assess in-hospital treatment with NSAIDs for patients with COVID-19.

ObjectiveLarge body of studies described individuals with obesity experiencing a worse prognosis in COVID-19. However, the effects of obesity on the prognosis of COVID-19 in patients without comorbidities have not been studied. Therefore, the current study aimed to provide evidence of the relationship between obesity and clinical outcomes in COVID-19 patients without comorbidities.MethodsA total of 116 hospitalized COVID-19 patients without comorbidities from the ORCHID study (Patients with COVID-19 from the Outcomes Related to COVID-19 Treated with Hydroxychloroquine among Inpatients with Symptomatic Disease) were included. Obesity is defined as a BMI of ≥30 kg/m2. A Cox regression analysis was used to estimate the hazard ratio (HR) for discharge and death after 28 days.ResultsThe percentage of obesity in COVID-19 patients without comorbidities was 54.3% (63/116). Discharge at 28 days occurred in 56/63 (84.2%) obese and 51/53 (92.2%) non-obese COVID-19 patients without comorbidities. Four (3.4%) COVID-19 patients without any comorbidities died within 28 days, among whom 2/63 (3.2%) were obese and 2/53 (3.8%) were non-obese. Multivariate Cox regression analyses showed that obesity was independently associated with a decreased rate of 28-day discharge (adjusted HR: 0.55, 95% CI: 0.35–0.83) but was not significantly associated with 28-day death (adjusted HR: 0.94, 95% CI: 0.18–7.06) in COVID-19 patients without any comorbidities.ConclusionsObesity was independently linked to prolonged hospital length of stay in COVID-19 without any comorbidity. Larger prospective trials are required to assess the role of obesity in COVID-19 related deaths.

The task of automated essay scoring (AES) continues to attract interdisciplinary attention due to its commercial and educational importance as well as related research challenges. Traditional AES approaches rely on handcrafted features, which are time-consuming and labor-intensive. Neural network approaches have recently given fantastic results in AES without feature engineering, but they usually require extensive annotated data. Moreover, most of the existing AES models only report a single holistic score without providing diagnostic information about various dimensions of writing quality. Focusing on these issues, we develop a novel approach using multi-task learning (MTL) with fine-tuning Bidirectional Encoder Representations from Transformers (BERT) for multi-dimensional AES tasks. As a state-of-the-art pre-trained language model, a BERT-based approach can improve AES tasks with limited training data. Meanwhile, we deal with long texts by proposing a hierarchical method and using the attention mechanism to automatically determine the contribution of different fractions of the input essay to the final score. For the multi-topic essay scoring tasks on the ASAP dataset, results reveal that our approach outperforms the average quadratic weighted Kappa (QWK) score by 4.5% compared with the strong baseline. We propose a self-collected dataset of Chinese EFL Learners’ Argumentation (CELA) to provide valuable information about writing quality from multiple rating dimensions, including holistic and five analytic scales. For the multi-rating dimensional essay scoring tasks on the CELA dataset, experimental results demonstrate that our model increases the average QWK score by 8.1% compared with the strong baseline.

Background and AimsWe investigated the association between liver fibrosis scores and clinical outcomes in patients with COVID-19.MethodsWe performed a post-hoc analysis among patients with COVID-19 from the trial study Outcomes Related to COVID-19 treated with Hydroxychloroquine among Inpatients with symptomatic Disease (ORCHID) trial. The relationship between aspartate aminotransferase (AST) to platelet ratio index (APRI), non-alcoholic fatty liver disease fibrosis score (NFS), Fibrosis-4 index (FIB-4), and discharge and death during the 28-days of hospitalization was investigated.ResultsDuring the 28 days after randomization, 237 (80.6%) patients were discharged while 31 (10.5%) died among the 294 patients with COVID-19. The prevalence for advanced fibrosis was estimated to be 34, 21.8, and 37.8% for FIB-4 (>2.67), APRI (>1), and NFS (>0.676), respectively. In multivariate analysis, FIB-4 >2.67 [28-days discharge: hazard ratio (HR): 0.62; 95% CI: 0.46–0.84; 28-days mortality: HR: 5.13; 95% CI: 2.18–12.07], APRI >1 (28-days discharge: HR: 0.62; 95% CI: 0.44–0.87; 28-days mortality: HR: 2.85, 95% CI: 1.35–6.03), and NFS >0.676 (28-days discharge: HR: 0.5; 95% CI: 0.35–0.69; 28-days mortality: HR: 4.17; 95% CI: 1.62–10.72) was found to significantly reduce the discharge rate and increase the risk of death. Additionally, FIB-4, APRI, and NFS were found to have good predictive ability and calibration performance for 28-day death (C-index: 0.74 for FIB-4, 0.657 for APRI, and 0.745 for NFS) and discharge (C-index: 0.649 for FIB-4, 0.605 for APRI, and 0.685 for NFS).ConclusionIn hospitalized patients with COVID-19, FIB-4, APRI, and NFS may be good predictors for death and discharge within 28 days. The link between liver fibrosis and the natural history of COVID-19 should be further investigated.

Traditionally, writing quality is measured by human ratings, either holistically or analytically. The present study aimed to investigate the locus of human ratings by analyzing the linguistic features that are predictive of writing quality. One hundred and 44 argumentative writing samples from Chinese learners of English as a foreign language were evaluated by human ratings and quantitative measurement of writing quality indexed by Coh-Metrix. Holistic and analytic human ratings had significant correlations with quantitative measures related to syntactic variety and transformation. Moreover, linear and logistic regressions revealed that syntactic simplicity, words before main verb, syntactic structure similarity in all sentences and across paragraphs, incidence of passive voice and temporal connectives were five valid indices that can consistently differentiate writing quality indexed by human ratings. The present findings have significant pedagogical implications for human ratings on writing quality in the foreign language learning context.

Abstract Background Transmissible gastroenteritis virus (TGEV) infection can cause acute inflammation. Long noncoding RNAs (lncRNAs) play important roles in a number of biological process including inflammation response. However, whether lncRNAs participate in TGEV-induced inflammation in porcine intestinal epithelial cells (IPECs) is largely unknown. Results In this study, the next-generation sequencing (NGS) technology was used to analyze the profiles of lncRNAs in Mock and TGEV-infected porcine intestinal epithelial cell-jejunum 2 (IPEC-J2) cell line. A total of 106 lncRNAs were differentially expressed. Many differentially expressed lncRNAs act as elements to competitively attach microRNAs (miRNAs) which target to messenger RNA (mRNAs) to mediate expression of genes that related to toll-like receptors (TLRs), NOD-like receptors (NLRs), tumor necrosis factor (TNF), and RIG-I-like receptors (RLRs) pathways. Functional analysis of the binding proteins and the up/down-stream genes of the differentially expressed lncRNAs revealed that lncRNAs were principally related to inflammatory response. Meanwhile, we found that the differentially expressed lncRNA TCONS_00058367 might lead to a reduction of phosphorylation of transcription factor p65 (p-p65) in TGEV-infected IPEC-J2 cells by negatively regulating its antisense gene promyelocytic leukemia (PML). Conclusions The data showed that differentially expressed lncRNAs might be involved in inflammatory response induced by TGEV through acting as miRNA sponges, regulating their up/down-stream genes, or directly binding proteins.

Musculoskeletal ultrasound (MSKUS) has been recognized as an important method for the evaluation of diseases of the musculoskeletal system, and contrast-enhanced ultrasound (CEUS) technology is becoming an important branch of it. The development of novel materials and tiny nano-formulations has further expanded ultrasound contrast agents (UCAs) into the field of nanotechnology. Over the years, nanoscale contrast agents have been found to play an unexpected role in the integration of precise imaging for diagnosis and treatment of numerous diseases. It has been demonstrated that nanoscale UCAs (nUCAs) have advantages in imaging over conventional contrast agents, including superior biocompatibility, serum stability, and longer lifetime. The potential value of nUCAs in the musculoskeletal system is that they provide more reliable and clinically valuable guidance for the diagnosis, treatment, and follow-up of related diseases. The frontier of advances in nUCAs, their applications, and insights in MSKUS are reviewed in this paper.

In this paper, we propose a new optimal control model for uncertain systems with jump. In the model, the background-state variables are incorporated, where the background-state variables are governed by an uncertain differential equation. Meanwhile, the state variables are governed by another uncertain differential equation with jump, in which both the background-state variables and the control variables are involved. Under the optimistic value criterion, using uncertain dynamic programming method, we establish the principle and the equation of optimality. As an application, the optimal investment strategy and optimal payment rate for DC pension plans are given, where the corresponding background-state variables represent the salary process. This application in DC pension plans illustrates the effectiveness of the proposed model.

Background Although many studies on reliability and reproducibility of measurement have been performed on coronal Cobb angle, few results about reliability and reproducibility are reported on sagittal alignment measurement including the pelvis. We usually use SurgimapSpine software to measure the Cobb angle in our studies; however, there are no reports till date on its reliability and reproducible measurements. Methods Sixty-eight standard standing posteroanterior whole-spine radiographs were reviewed. Three examiners carried out the measurements independently under the settings of manual measurement on X-ray radiographies and SurgimapSpine software on the computer. Parameters measured included pelvic incidence, sacral slope, pelvic tilt, Lumbar lordosis (LL), thoracic kyphosis, and coronal Cobb angle. SPSS 16.0 software was used for statistical analyses. The means, standard deviations, intraclass and interclass correlation coefficient (ICC), and 95% confidence intervals (CI) were calculated. Results There was no notable difference between the two tools (P = 0.21) for the coronal Cobb angle. In the sagittal plane parameters, the ICC of intraobserver reliability for the manual measures varied from 0.65 (T2–T5 angle) to 0.95 (LL angle). Further, for SurgimapSpine tool, the ICC ranged from 0.75 to 0.98. No significant difference in intraobserver reliability was found between the two measurements (P > 0.05). As for the interobserver reliability, measurements with SurgimapSpine tool had better ICC (0.71 to 0.98 vs 0.59 to 0.96) and Pearson’s coefficient (0.76 to 0.99 vs 0.60 to 0.97). The reliability of SurgimapSpine measures was significantly higher in all parameters except for the coronal Cobb angle where the difference was not significant (P > 0.05). Conclusion Although the differences between the two methods are very small, the results of this study indicate that the SurgimapSpine measurement is an equivalent measuring tool to the traditional manual in coronal Cobb angle, but is advantageous in spino-pelvic measurement in T2-T5, PT, PI, SS, and LL. [ABSTRACT FROM AUTHOR]

PURPOSE: Metastatic renal cell carcinoma (mRCC), as one of the most immunogenic tumors has been the focus of adoptive cellular immunotherapy (ACI), but the effects of ACI on objective response and survival in patients with mRCC are still controversial. Therefore, a systematic review and meta-analysis was performed to address this issue. METHODS: A search was conducted in the PubMed database for randomized clinical trials (RCTs) with ACI in mRCC. All included articles in this study were assessed according to the selection criteria and were divided into two groups: ACI versus no ACI. Outcomes were toxicity, objective response, 1-, 3- and 5-year survival. Risk ratio (RR) and 95% confidence intervals (CI) were calculated using a fixed-effects meta-analysis. Heterogeneity was measured by value of I(2) or P. RESULTS: 4 studies (469 patients) were included. Most of ACI-related adverse reactions were grade 1 or 2 and reversible. ACI provided significant benefit in terms of objective response (RR = 1.65; 95% CI, 1.15 to 2.38; P = 0.007, I(2) = 49%), 1-year survival (RR = 1.30; 95% CI, 1.12 to 1.52; P = 0.0008, I(2) = 0%), 3-year survival (RR = 2.76; 95% CI, 1.85 to 4.14; P