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Objective To screen factors influencing the neurological prognosis following lumboperitoneal shunt (LPS) in patients with hydrocephalus. Methods Total 136 patients who underwent LPS in Xuanwu Hospital, Capital Medical University from October 2014 to January 2020. The short-term neurological prognosis post - surgery were evaluated by modified Rankin Scale (mRS) at discharge. Univariate and multivariate stepwise Logistic regression analyses were conducted to screen factors affecting neurological prognosis. Results Of the 136 patients, 65 were categorized into the good prognosis group (mRS score ≤ 3), and 71 into the poor prognosis group (mRS score > 3). Logistic regression analysis indicated that an admission Glasgow Coma Scale (GCS) score of 9-12 (OR = 7.800, 95%CI: 7.205-8.443; P = 0.000) and 3-8 (OR = 6.299, 95%CI: 5.744-6.907; P = 0.006), as well as etiologies including traumatic brain injury (OR = 27.681, 95%CI: 24.270-31.572; P = 0.000), cerebral hemorrhage (OR = 13.017, 95%CI: 11.473-14.769; P = 0.005), subarachnoid hemorrhage (OR = 17.682, 95%CI: 15.683-19.935; P = 0.001), and other causes (OR = 5.851, 95%CI: 5.166-6.628; P = 0.050) were risk factors for poor short-term neurological prognosis in patients with hydrocephalus undergoing LPS. Conclusions The neurological prognosis after LPS in patients with hydrocephalus is influenced by multiple factors. Prognoses vary among patients with different admission GCS scores and different etiologies. Personalized treatment plans should be developed based on specific circumstances.

【Objective】 Soil dielectric constant measures the ability of a soil to transmit electric fields. It is the ratio of the capacitance of a soil sample to the capacitance of air-filled space of the same volume. In this paper, we systematically analyze the factors that influence the dielectric constant, offering an updated perspective on soil dielectric constant modeling. 【Method】 This study systematically identifies the primary factors influencing soil dielectric constant based on soil dielectric theory. Additionally, it categorizes and summarizes existing soil dielectric constant models, facilitating a comparative analysis of their strengths, weaknesses, applications, and future development. 【Result】 The primary influencing factors governing the dielectric constant varied with soil types, with notable dependence on testing frequency. However, the dielectric constant remains a valuable indicator of soil water content. The soil dielectric constant models were broadly categorized into four types: theoretical, semi-empirical, empirical, and boundary models. 【Conclusion】 Presently, there is a dearth of research focusing on the dielectric properties and model development specific to regional soils. Enhancing model accuracy necessitates incorporating the influence of soil-phase composition, mineral composition, and microstructure on the dielectric constant. This refinement will broaden the application studies for assessing and analyzing soil physical and chemical properties based on dielectric constant testing.

In recent years, UAV object tracking has provided technical support across various fields. Most existing work relies on convolutional neural networks (CNNs) or visual transformers. However, CNNs have limited receptive fields, resulting in suboptimal performance, while transformers require substantial computational resources, making training and inference challenging. Mountainous and jungle environments-critical components of the Earth's surface and key scenarios for UAV object tracking-present unique challenges due to steep terrain, dense vegetation, and rapidly changing weather conditions, which complicate UAV tracking. The lack of relevant datasets further reduces tracking accuracy. This article introduces a new tracking framework based on a state-space model called TrackingMamba, which uses a single-stream tracking architecture with Vision Mamba as its backbone. TrackingMamba not only matches transformer-based trackers in global feature extraction and long-range dependence modeling but also maintains computational efficiency with linear growth. Compared to other advanced trackers, TrackingMamba delivers higher accuracy with a simpler model framework, fewer parameters, and reduced FLOPs. Specifically, on the UAV123 benchmark, TrackingMamba outperforms the baseline model OSTtrack-256, improving AUC by 2.59% and Precision by 4.42%, while reducing parameters by 95.52% and FLOPs by 95.02%. The article also evaluates the performance and shortcomings of TrackingMamba and other advanced trackers in the complex and critical context of jungle environments, and it explores potential future research directions in UAV jungle object tracking.

The present study was executed with the purpose of validating ELT Certificate Lesson Observation and Report Task (ELTC-LORT), which was developed by China Language Assessment to certify China's EFL teachers by performance-based testing. The ELT Certificate has high-stakes considering its impacts on candidates' recruitment, ELT in China and quality of education, so it is crucially important for its validation so as to guarantee fairness and justice. The validity of task construct and rating rubric went through a process suited for many-facet Rasch measurement supplemented with qualitative interviews. Participants (N = 40) were provided with a video excerpt from a real EFL lesson, and required to deliver a report on the teacher's performance. Two raters graded the records of the candidates' reports using rating scales developed to measure EFL teacher candidates' oral English proficiency and ability to analyze and evaluate teaching. Many-facet Rasch analysis demonstrated a successful estimation, with a noticeable spread among the participants and their traits, proving the task functioned well in measuring candidates' performance and reflecting the difference of their ability. The raters were found to have good internal self-consistency, but not the same leniency. The rating scales worked well, with the average measures advancing largely in line with Rasch expectations. Semi-structured interviews as well as focus group interviews were executed to provide knowledge regarding the raters' performance levels and the functionalities of the rating scale items. The findings provide implications for further research and practice of the Certificate.

Cisplatin resistance is a major obstacle in the treatment of non-small cell lung cancer (NSCLC). p32 and OPA1 are the key regulators of mitochondrial morphology and function. This study aims to investigate the role of the p32/OPA1 axis in cisplatin resistance in NSCLC and its underlying mechanism. The levels of p32 protein and mitochondrial fusion protein OPA1 are higher in cisplatin-resistant A549/DDP cells than in cisplatin-sensitive A549 cells, which facilitates mitochondrial fusion in A549/DDP cells. In addition, the expression of p32 and OPA1 protein is also upregulated in A549 cells during the development of cisplatin resistance. Moreover, p32 knockdown effectively downregulates the expression of OPA1, stimulates mitochondrial fission, decreases ATP generation and sensitizes A549/DDP cells to cisplatin-induced apoptosis. Furthermore, metformin significantly downregulates the expressions of p32 and OPA1 and induces mitochondrial fission and a decrease in ATP level in A549/DDP cells. The co-administration of metformin and cisplatin shows a significantly greater decrease in A549/DDP cell viability than cisplatin treatment alone. Moreover, D-erythro-Sphingosine, a potent p32 kinase activator, counteracts the metformin-induced downregulation of OPA1 and mitochondrial fission in A549/DDP cells. Taken together, these findings indicate that p32/OPA1 axis-mediated mitochondrial dynamics contributes to the acquired cisplatin resistance in NSCLC and that metformin resensitizes NSCLC to cisplatin, suggesting that targeting p32 and mitochondrial dynamics is an effective strategy for the prevention of cisplatin resistance.