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Abstract Ubiquitination is a post-translational modification (PTM), which is critical to maintain cell homeostasis. Ubiquitin-specific protease 24 (USP24) plays roles in various diseases, the mechanisms by which USP24 regulates hepatocellular carcinoma (HCC) remain poorly understood. In this study, USP24 is found to be significantly downregulated in HCC. Knocking down USP24 promotes HCC proliferation and migration, whereas USP24 overexpression inhibits HCC in vitro and in vivo. The endogenous interaction between USP24 and Beclin1 is confirmed. Mechanically, USP24 delays Beclin1 degradation by reducing its K48-linked ubiquitination, the effects of overexpressing USP24 on HCC proliferation can be partially reversed by silencing Beclin1. We find that increased autophagy is accompanied by ferroptosis in USP24 overexpressed HCC cells and USP24 increases the susceptibility of HCC to sorafenib. Collectively, this study highlights the critical role of USP24 in regulating autophagy-dependent ferroptosis by decreasing Beclin1 ubiquitination, suggesting that targeting USP24 may be a strategy for treating HCC.

This paper proposes an improvement in the test method for determining the flexural dynamic modulus of elasticity of strawboard with two triangular prisms as supports, for quality control and classification. Free-plate modal and free-plate transient excitation methods were used to test the elastic modulus of 1/4-plate and whole-plate strawboards. The dynamic test results were verified with the four-point bending method and tensile method. The results show that the elastic moduli of strawboards is approximately 2 GPa. The dynamic test method proposed is efficient, simple, repeatable, and accurate. This method is more suitable for factory applications than existing dynamic testing methods. The framed cases produced by the strawboard all meet the performance requirements in GB/T 7284 (2016).

Abstract. Background. Controlling Nutritional Status (CONUT) score was used for screening the preoperative nutritional status. The correlation between the CONUT score and the prognosis of patients with prostate cancer (PCa) has yet to be elucidated. Herein, we analyzed the prognostic value of CONUT scores in patients with PCa who underwent laparoscopic radical prostatectomy. Materials and methods. Data of 244 patients were retrospectively evaluated. Perioperative variables and follow-up data were analyzed. The patients were categorized into 2 groups according to their preoperative CONUT scores. Postoperative complication and incontinence rates were also compared. The Kaplan-Meier method was used to estimate the median biochemical recurrence-free survival (BCRFS) between the 2 groups. Univariate and multivariate Cox regression analyses were performed to identify the potential prognostic factors for BCRFS. Results. Patients were categorized into the low-CONUT group (CONUT score

Electronic monitoring systems (EMSs) have been widely used in global fishing vessels as an effective tool to obtain reliable information about catches and fishing operations in order to verify compliance with national and international regulations. Though EMS implementation in tuna vessels has not yet been made a mandatory requirement by tuna regional fisheries management organizations, many Chinese longliners have the system on board for traceability and safety purposes. Based on interviews with managers and skippers on Chinese tuna longliners, this paper firstly identifies the challenges in electronic monitoring management that have hindered fishermen’s confidence to implement EMS at a larger scale. Then this paper proposes a work plan to facilitate EMS implementation from the perspective of Chinese tuna longliners, in which the adoption of EM standards, specifications, and procedures, and identification of EM data fields are the top priorities. To fully address concerns raised by tuna longliners, a cloud computing platform for EMS data storage, transmission, and review could help to protect data safety and confidentiality. Artificial intelligence technology is recommended to increase cost-efficiency in data review procedures, in addition to compliance incentives and financial incentives from policy makers.

Aiming at the problems of large statistical error and the poor real-time performance of catch weight in the ocean fishing tuna industry, an algorithm based on improved YOLOv8-Pose for albacore tuna (Thunnus alalunga) fork length extraction and weight estimation is proposed, with reference to the human body’s pose estimation algorithm. Firstly, a lightweight module constructed using a heavy parameterization technique is used to replace the backbone network, and secondly, a weighted bidirectional feature pyramid network BIFPN is utilized. Finally, the upper and lower jaw and tail feature points of the albacore tuna (Thunnus alalunga) were extracted using the key point detection algorithm, and the weight of the albacore tuna (Thunnus alalunga) was estimated based on the fitted relationship between fork length and weight. The experimental results show that the improved YOLOv8-Pose algorithm reduces the number of model parameters by 13.63% and the number of floating-point operations by 14.03% compared with the baseline model without decreasing the accuracy of the target detection and key point detection and improves the model inference speed by 374%. At the same time, it reduces the drift of the key point detection, and the error of the comparison with the actual albacore tuna (Thunnus alalunga) body weight is not more than 10%. The improved key point detection algorithm has high detection accuracy and inference speed, which provides accurate yield data for pelagic fishing and is expected to solve the existing statistical problems and improve the accuracy and real-time performance of data in the fishing industry.

We introduce a novel method that combines satellite data, advanced clustering techniques, machine learning feature extraction, and statistical models to enhance fishery forecasting accuracy. Focusing on jumbo flying squid in the southeast Pacific Ocean near Peru, we utilize MODIS-Aqua and MODIS-Terra satellite data on sea surface temperature (SST) to construct a deep convolutional embedded clustering (DCEC) model and extract the monthly SST features (FM) based on an optimized number of clusters determined by the Davies–Bouldi index (DBI). We use the extracted FM to construct a series of Generalized Additive Models (GAM) to forecast the catch per unit effort (CPUE) of jumbo flying squid within a spatial resolution of 0.5° × 0.5°. Our results demonstrate the following findings: (1) The SST feature clusters obtained through the DCEC model could capture the SST monthly variations; (2) The GAM models with FM outperform the models with the traditional monthly average SST in terms of predictive accuracy; (3) Using both FM and average SST together can further improve model performance. This study demonstrates the effectiveness of the DCEC combined with DBI in extracting marine environmental features and highlights the ocean environment feature extraction method to enhance the precision and reliability of fishery forecasting models.

Visualising the fishing behaviour of vessels and quantifying the spatial distribution of fishing effort is the scientific basis for assessing and managing fisheries resources. The information on the dynamics of fishing vessel voyages provided by the automatic identification system (AIS) of vessels serves as high-precision fishery data and provides a means of quantifying fishing effort with high spatial and temporal resolution in the tuna longline fishery. Based on the AIS data of five tuna longliners operating in the waters near the Marshall Islands from 2020 to 2021, this study used three methods, namely the threshold screening method, the construction of a BP neural network and the support vector machine (SVM) to identify the fishing and non-fishing status of the tuna longliners, respectively. This study investigates the status identification and fishing effort estimation of the tuna longliner (VESSEL A) in 2021 based on the constructed optimal model, and spatial correlation analyses are performed between the fishing effort estimated in hours based on AIS data and in hooks based on fishing logbook data, by month. The results showed (1) the recognition accuracy of the threshold screening method is 89.9%, the recognition accuracy of the BP neural network classification model is 95.11%, the kappa coefficient is 0.51, the recognition accuracy of the SVM classification model is 95.74% and the kappa coefficient is 0.52; (2) in comparison, the SVM classification model performs better than the other two status identification methods for tuna longliners; and (3) the correlation coefficients between the two types of effort of VESSEL A were greater than 0.79 on all fishing months, indicating that there was no significant difference in the spatial and temporal distribution between the two types of effort. This study suggests that the SVM model can be used to identify the status and estimate the fishing effort of longliners.