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Abstract High-quality ocean in situ profile observations are fundamental for ocean and climate research and operational oceanographic applications. Here we describe a new global ocean subsurface temperature profile database named the Chinese Academy of Science (CAS) Oceanography Data Center version 1 (CODC-v1). This database contains over 17 million temperature profiles between 1940–2023 from all available instruments. The major data source is the World Ocean Database (WOD), but CODC-v1 also includes some data from some Chinese institutes which are not available in WOD. The data are quality-controlled (QC-ed) by a new QC system that considers the skewness of local temperature distributions, topographic barriers, and the shift of temperature distributions due to climate change. Biases in Mechanical Bathythermographs (MBTs), eXpendable Bathythermographs (XBTs), and Bottle data (OSD) are all corrected using recently proposed correction schemes, which makes CODC-v1 a bias-corrected dataset. These aspects ensure the data quality of the CODC-v1 database, making it suitable for a wide spectrum of ocean and climate research and applications.

Abstract Background Colorectal signet-ring cell carcinoma (SRCC) is a rare cancer with a bleak prognosis. The relationship between its clinicopathological features and survival remains incompletely elucidated. Tumor deposits (TD) have been utilized to guide the N staging in the 8th edition of American Joint Committee on Cancer (AJCC) staging manual, but their prognostic significance remains to be established in colorectal SRCC. Patients and methods The subjects of this study were patients with stage III/IV colorectal SRCC who underwent surgical treatment. The research comprised two cohorts: a training cohort and a validation cohort. The training cohort consisted of 631 qualified patients from the SEER database, while the validation cohort included 135 eligible patients from four independent hospitals in China. The study assessed the impact of TD on Cancer-Specific Survival (CSS) and Overall Survival (OS) using Kaplan-Meier survival curves and Cox regression models. Additionally, a prognostic nomogram model was constructed for further evaluation. Results In both cohorts, TD-positive patients were typically in the stage IV and exhibited the presence of perineural invasion (PNI) (P

Abstract Data augmentation diversifies the information in the dataset. For class imbalance, the copy‐paste augmentation generates new class information to alleviate the impact of this problem. However, these methods rely excessively on human intuition. Over‐fitting or under‐fitting can occur while adding the class information, which is inappropriate. The authors propose a self‐adaptive data augmentation: the copy‐paste with self‐adaptation (CPA) algorithm, which improves the phenomenon of over‐fitting and under‐fitting. For the CPA, the evaluation results of a model are taken as an important adjustment basis. The evaluation results are combined with the information of class imbalance to generate a set of class weights. Different number of class information will be replenished according to class weights. Finally, the generated images will be inserted into the training dataset and the model will start formal training. The experimental results show that CPA can alleviate class imbalance. For TT100 K dataset, YOLOv3 is trained with the optimised dataset and its AP is increased by 2% for VOC2007 dataset, the mAP of RetinaNet on optimised dataset is 78.46, which is 1.2% higher than original dataset. For COCO2017 dataset, SSD300 is trained with the optimised dataset and its AP is increased by 1.3%.

For the application of high-frequency current detection in power systems, such as very fast transient current, lightning current, partial discharge pulse current, etc., current sensors with a quick response are indispensable. Here, we propose a high-frequency magnetoelectric current sensor, which consists of a PZT piezoelectric ceramic and Metglas amorphous alloy. The proposed sensor is designed to work under d15 thickness-shear mode, with the resonant frequency around 1.029 MHz. Furthermore, the proposed sensor is fabricated as a high-frequency magnetoelectric current sensor. A comparative experiment is carried out between the tunnel magnetoresistance sensor and the magnetoelectric sensor, in the aspect of high-frequency current detection up to 3 MHz. Our experimental results demonstrate that the d15 thickness-shear mode magnetoelectric sensor has great potential for high-frequency current detection in smart grids.

Abstract Submerged macrophytes play a key role in the restoration of shallow eutrophic lakes. However, in some subtropical lakes, benthivorous fishes dominate the fish assemblages and influence the growth of submerged plants. A comprehensive understanding of the direct and indirect effects of benthivorous fishes on submerged plants is important. We conducted mesocosm experiments to examine the effects of three densities of benthivorous fish, Misgurnus anguillicaudatus, on the water properties, the growth, asexual reproduction, and the germination of turions of Potamogeton crispus L. Our results showed that fish disturbance increased TN, TP, PO4–P, NH4–N, and NO3–N of the water, raising the extinction coefficient K, Chl a, and the periphyton biomass. Benthivorous fish disturbance reduced the total biomass, root length, relative growth rate (RGR), and branching number while increasing the plant height of P. crispus. The P stoichiometric homeostasis coefficient (HP) (except turions) and HN was lower in plant tissues due to fish disturbance. Benthivorous fish disturbances promoted turions formation (e.g., increased turions total numbers and biomass) of P. crispus. Moreover, P. crispus exhibited transgenerational plasticity for benthivorous fish affecting turion emergence. The maximum final germination rate occurred only when fish density in the mother plant grow experiment matched that in the turion germination experiment. Turions generated by P. crispus disturbed by low‐density fish exhibited increased germination rates. Our findings suggest that controlling benthivorous fish reduces its indirect and direct effects on submerged vegetation, facilitating the successful restoration of these plants.

Abstract The loss function is a criterion to evaluate the learning quality of a deep convolutional neural network, which represents the gap between prediction and ground truth. However, as the most commonly used loss function in image classification tasks, Cross‐Entropy loss does not encourage the model to distinguish the similarity between features. In this work, the authors investigate inter‐class separability of similar features learnt by convolutional networks and propose a loss function called Error Refactor Loss (ER‐Loss). ER‐Loss is based on the error caused by convolutional networks; it can improve the inter‐class separability and is simple to implement and can easily replace the Cross‐Entropy loss. Compared with softmax loss, ER‐Loss adds a dynamic penalty item which can help ER‐Loss monitor the actual situation of model training and adjust the value of the penalty item according to model training. The ER‐Loss on CIFAR100 and part of ImageNet ILSVRC 2012 is evaluated and the experimental result showed that the ER‐Loss can improve the accuracy of the model.

With the continuous development of various types of fixed marine observation equipment, satellite remote sensing technology and computer simulation technology, modern marine scientific research has entered the era of big data. Interactive ocean visualization has become ubiquitous owing to the use of ocean data in studies of marine disasters, global climate change and fisheries. However, the primary challenge in analyzing large amounts of ocean data originates from the complexity of the data themselves. Therefore, an interactive multi-scale, multivariate visualization system with dynamic expansion potential is needed for analyzing larger volumes of ocean data. In this study, a unified visual data service was constructed, and a component-based interactive visualization structure for multi-dimensional, spatiotemporal ocean data is presented in this paper. Based on this structure, users can easily customize the system to visualize other types of scientific data.

The ocean is a critical part of the global ecosystem. The marine ecosystem balance is crucial for human survival and sustainable development. However, due to the impacts of global climate change and human activities, the ocean is rapidly changing, which poses an enormous threat to human health and the economy. “Conserve and sustainably use the oceans, seas and marine resources” is one of the 17 Sustainable Development Goals (SDGs). Therefore, it is urgent to construct a transformative marine scientific solution to promote sustainable development. Marine data is the basis of ocean cognition and governance. Marine science has ushered in the era of big data with continuous advances in modern marine data acquisition. While big data provides a large amount of data for SDG research, it simultaneously brings unprecedented challenges. This study introduces an overall framework of a system for solving the current problems faced by marine data serving SDGs from the perspective of marine data management and application. Also, it articulates how the system helps the SDGs through two application cases of managing fragmented marine data and developing global climate change data products.

In this study, sugarcane bagasse (SCB) was treated with sodium hydroxide and bleached to separate the non-cellulose components to obtain cellulose (CE) fibres. Cross-linked cellulose–poly(sodium acrylic acid) hydrogel (CE–PAANa) was successfully synthesised via simple free-radical graft-polymerisation to remove heavy metal ions. The structure and morphology of the hydrogel display an open interconnected porous structure on the surface of the hydrogel. Various factors influencing batch adsorption capacity, including pH, contact time, and solution concentration, were investigated. The results showed that the adsorption kinetics were in good agreement with the pseudo-second-order kinetic model and that the adsorption isotherms followed the Langmuir model. The maximum adsorption capacities calculated by the Langmuir model are 106.3, 333.3, and 163.9 mg/g for Cu(II), Pb(II), and Cd(II), respectively. Furthermore, X-ray photoelectron spectroscopy (XPS) and energy-dispersive X-ray spectrometry (EDS) results demonstrated that cationic exchange and electrostatic interaction were the main heavy metal ions adsorption mechanisms. These results demonstrate that CE–PAANa graft copolymer sorbents from cellulose-rich SCB can potentially be used for the removal of heavy metal ions.

Background: Diabetes mellitus (DM) is a comorbidity commonly presenting with metabolic-dysfunction-associated fatty liver disease (MAFLD); however, few tests for interaction have been reported. Our target was to evaluate the prognostic implications of DM in patients with different forms of MAFLD. Methods: Using data from the Third National Health and Nutrition Examination Survey (NHANES III) in the United States, we screened 14,797 participants aged 20–74 who received ultrasound examinations from 1988–1994. Among them, 4599 patients met the diagnosis of MAFLD, and we defined mortality as the outcome event. Survival analysis of competitive risk events was performed using Cox regression and sub-distributed risk ratio (SHR). Results: During 21.1 years of follow-up, cardiovascular diseases seemed to be the most common cause of death among MAFLD patients. Of them, DM was present in 25.48% and was independently associated with increased risk of all-cause mortality (HRs: 1.427, 95% CIs: 1.256–1.621, p < 0.001) and cause-specific mortality (cardiovascular-related mortality (HRs: 1.458, 95% CIs: 1.117–1.902, p = 0.005), non-cardiovascular-related mortality (HRs: 1.423, 95% CIs: 1.229–1.647, p < 0.001), and non-cancer-related mortality (HRs: 1.584, 95% CIs: 1.368–1.835, p < 0.001), respectively). Surprisingly, this association was more significant for young patients (p-value for interaction

The measurement and decoupling of currents in multi-core power cables is a significant concern for power operators and holds immense potential for optimizing the monitoring and control of urban distribution networks. This paper aims to provide a widely applicable method for reconstructing current measurements. A YJLV22-3∗300 power cable is taken as an example, specifically focusing on the effect of steel armor on the measurement of the magnetic field generated by the current. Sample tests and field experiments are conducted to verify the spatial distribution of the magnetic flux density. Then the inverse problem of calculating current from the magnetic field is discussed. The defects of the existing methods are shown, and a new method for the inverse problem with the measured waveform of the tangential component of the magnetic flux density is proposed. The feasibility of the new method has been verified. The least-squares method is introduced to obtain the generalized inverse of the position coefficient matrix by maximum rank decomposition to extrapolate the conductor current matrix. A query method is proven to efficiently generate this matrix. Finally, the inverse problem is modeled as a stochastic search problem to compare the efficiency and stability of different algorithms, and CAM-ES performs best. The future research direction is toward developing and testing hardware measurement systems. [ABSTRACT FROM AUTHOR]

BackgroundStudies on the relationship between the atherogenic index of plasma (AIP) and bone mineral density (BMD) among adult women in the United States are limited. The purpose of this study was to explore this association using a sizable, nationally representative sample.MethodsData from the 2011 to 2018 National Health and Nutrition Examination Survey (NHANES) were used in this observational study. The AIP was computed as log10 (triglycerides/high-density lipoprotein cholesterol). Total BMD was measured via dual-energy X-ray densitometry. We constructed multiple linear regression models to evaluate the correlation between the AIP and BMD. The non-linear relationship was characterized by smooth curve fitting and generalized additive models. We also conducted subgroup and interaction analyses.ResultsIn this study, we included 2,362 adult women with a mean age of 38.13 ± 12.42 years. The results of multiple linear regression analysis, the AIP and total BMD showed a negative association (β = −0.021, 95%CI: −0.037, −0.006). The curve fitting analysis and threshold effect analysis showed a non-linear relationship between the two variables, and the inflection point of the AIP was found to be -0.61. The total BMD decreased significantly when the AIP reached this value (β = −0.03, 95%CI: −0.04, −0.01). The results of the subgroup analysis showed that AIP and total BMD had a strong negative relationship in participants who were below 45 years old (β = -0.023; 95% CI: -0.041, -0.004), overweight (BMI ≥ 25 kg/m2) (β = -0.022; 95% CI: -0.041, -0.002), had a higher education level (β = -0.025; 95% CI: -0.044, -0.006), and had no partners (β = -0.014; 95% CI: -0.06, -0.009).ConclusionsWe found a negative correlation between the AIP and total BMD. Clinicians should pay attention to patients with high AIP, which might indicate a low BMD and has reference significance in preventing osteoporosis.

Aiming at the problems of the poor trajectory-tracking performance and low control accuracy of unmanned vehicles under complex working conditions, we first estimate the lateral force of tires using the square root cubature Kalman filter (SRCKF) in order to correct the lateral stiffness of the tires online, which reduces the model bias caused by constant lateral stiffness, and then adopt a Gaussian function-based adaptive time-domain model predictive control method to improve the trajectory-tracking control accuracy of unmanned vehicles under complex working conditions. Finally, the proposed control algorithm is validated via Carsim and MATLAB/Simulink joint simulation. The results show that compared with the classical model predictive control (MPC) algorithm, the proposed control algorithm reduces the average lateral tracking error by 73.07% and the peak beta and the peak yaw rate by 50.89% and 47.51%, respectively, so that the unmanned vehicle is able to maintain good tracking performance and control accuracy.

Prostate abscess, a rare condition often associated with prostate bacterial infections, often occurs in immunosuppressive individuals and manifests as fever and lower urinary tract symptoms. Clinical practice lacks standardized diagnostic and treatment protocols for prostate abscesses, resulting in predominantly empirical approaches with uncertain outcomes. This study presents a case of a giant prostate abscess, diagnosed in a patient exhibiting fever, lower urinary tract symptoms (including dysuria, urinary frequency, urgency, and weakness), and anal pain. The diagnosis was confirmed through prostate magnetic resonance imaging and transrectal color ultrasound examinations. Treatment included targeted anti-infective therapy (based on the urine culture results), urine flow diversion (suprapubic bladder puncture stomy), ultrasound-guided perineal puncture drainage of the prostatic abscess, intermittent abscess cavity irrigation, and urethral electroprostatectomy. The patient experienced a complete recovery and significantly improved quality of life. This successful case underscores several key points: (1) the importance of targeted anti-infective therapy based on etiological findings in prostate abscess treatment; (2) early urine flow diversion, precise puncture drainage, and intermittent abscess cavity irrigation may be one of crucial elements in abscess management; (3) the potential significance of transurethral prostate resection following abscess resolution in preventing recurrence. It is hoped that this case report offers new valuable insights for diagnosing and treating prostate abscesses. Slightly different from previous treatment experience, we extra used early urine diversion, intermittent abscess cavity irrigation, and etiological electroprostatectomy, which might also hold promise as potential therapies.

BackgroundSmall cell carcinoma of the bladder is rare and has a poor prognosis. This study aimed to investigate whether radiotherapy after bladder-sparing surgery could improve the survival benefits of patients.MethodsThis population-based retrospective cohort study used data from the Surveillance, Epidemiology, and End Results cohort in the United States to investigate small cell carcinoma of the bladder. Univariate and multivariate Cox regression analyses were used to identify significant risk factors influencing the clinical prognosis. A propensity score matching (PSM) algorithm was used to reduce the interference of confounding factors in each study group. The matched groups underwent Kaplan–Meier survival analysis to assess the potential survival benefits.ResultsUnivariate regression analysis demonstrated that age (P