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Group velocity and impedance matches are prerequisites for high-speed Mach–Zehnder electro-optic (EO) modulators. However, not all platforms can realize matching conditions, restricting high-speed modulation in many practical conditions. Here, we propose and demonstrate a quasi-matching scheme to satisfy the group velocity and characteristic impedance matches by cascading fast-wave and slow-wave traveling wave electrodes. The effective group velocity can be flexibly adjusted by changing the ratio of fast-wave and slow-wave traveling wave electrodes. Moreover, the quasi-matching scheme is experimentally verified by demonstrating a 6 mm long EO modulator on a thin-film lithium-niobate-on-insulator platform with a silica cladding. The radio frequency signal insertion loss at the boundary of the slow-wave and fast-wave electrodes is less than 0.12 dB. The measured small signal EO response of the quasi-matched EO modulator drops less than 2 dB at 67 GHz, while the measured small-signal EO responses of conventional slow and fast traveling wave EO modulators drop 4 dB at 67 GHz. The measured 100 Gb/s on–off key signal eye-diagrams of the quasi-matched EO modulator also exhibit an overwhelming advantage over conventional schemes. Therefore, our results will open many opportunities for high-speed EO modulators in various platforms.

PurposeTo investigate the effects of transcranial direct current stimulation (tDCS) combined with Bosu ball training on the injury potential during drop landing in people with chronic ankle instability (CAI).MethodsA total of 40 participants with CAI were recruited and randomly divided into the tDCS + Bosu and Bosu groups. The people in the tDCS + Bosu group received intervention of tDCS combined with Bosu ball training, and those in the Bosu group received intervention of sham tDCS and Bosu ball training, for 6 weeks with three 20-min sessions per week. Before (week0) and after (week7) the intervention, all participants drop-landed on a trap-door device, with their affected limbs on a moveable platform, which could be flipped 24° inward and 15° forward to mimic an ankle inversion condition. The kinematic data were captured using a twelve-camera motion capture system. Two-way ANOVA with repeated measures was used to analyze data.ResultsSignificant group-by-intervention interactions were detected in the peak ankle inversion angular velocity (p = 0.047, η2p = 0.118), the time to peak ankle inversion (p = 0.030, η2p = 0.139), and the plantarflexion angle at the moment of peak ankle inversion (p = 0.014, η2p = 0.173). Post hoc comparisons showed that compared with week0, the peak ankle inversion angular velocity and the plantarflexion angle at the moment of peak ankle inversion were reduced, the time to peak ankle inversion was advanced in both groups at week7, and the changes were greater in the tDCS + Bosu group compared to the Bosu group. And, a significant intervention main effect was detected in the peak ankle inversion angle in the two groups (p < 0.001, η2p = 0.337).ConclusionCompared with the Bosu ball training, the tDCS combined with Bosu ball training was more effective in reducing the injury potential during drop landing in people with CAI.

Lattice structures have been a hot topic recently owing to their superior mechanical properties, which are significantly influenced by the unit cell structure. By leveraging the power of deep learning, inverse design can be conducted on the unit cell structure based on the mechanical properties of its lattice structure. Assisted by deep learning, this study introduces a novel data-driven approach to design three-dimensional (3D) unit cells for lattice structures with anticipated properties. The approach can be efficiently and accurately applied to various unit cell structures. An auto-encoder is trained to extract the geometric features from unit cell point clouds. The effective mechanical properties of the lattice structures are calculated by combining the homogenization method and the finite element method. Subsequently, a mapping relationship between mechanical properties and geometric features is established through the multi-layer perceptron neural network. The models are ultimately employed to design 3D unit cells given anticipated properties of lattice structures. The results show that the mechanical properties of the generated unit cells satisfy the anticipated values. The applications of proposed method are demonstrated in orthopedic implants, new hybrid unit cells, and functionally gradient structures. Furthermore, the method can be extended to unit cell design across diverse domains.

ObjectiveTo understand the relative bioavailability （RBA） of cadmium in different aquatic products. Based on the consideration of the gender differences and the relative bioavailability of cadmium in different foods， the physiologically based toxicokinetic （PBTK） model of cadmium was further optimized and verified. The correlation between internal and external exposure in quantitative risk assessment of food safety was optimized， and the provisional tolerable daily intake （PTDI） value of cadmium was derived.MethodsThe relative bioavailability of cadmium in different aquatic products was determined in four-week-old Balb/c female mice. The PBTK model of cadmium metabolism was optimized by ABC-MCMC method using combined internal and external exposure data of cadmium in Shanghai residents， and the PTDI was calculated accordingly.ResultsExcept for scallops and squid， the RBA of aquatic samples was less than 1， indicating that the absorption rate of cadmium in aquatic products was lower than that of cadmium chloride. The higher RBA of squid and scallop may be due to the presence of cadmium in the visceral organs， which is conducive to cadmium absorption and its higher concentration of cadmium. Frying at the temperature less than 160 ℃ reduced cadmium absorption but may increase cadmium absorption at the temperature greater than 160 ℃. The optimized model parameters converged well and the model could reasonably estimate urinary cadmium level according to the external exposure of cadmium. The PTDI value was 0.466 4 μg·（kg·day）-1 according to the optimized single-chamber model.ConclusionThe relative bioavailability of cadmium in different foods varies greatly， except for squid and scallops， RBA is less than 1， and cooking processing will affect the RBA of food. The construction of the PBTK model did not only consider the effects of gender differences on cadmium metabolism， but also included the relative bioavailability data to optimize and adjust the correlation coefficient of absorption rate. Compared with the model without RBA adjustment， the adjusted model has enhanced the ability to predict urinary cadmium level， which provides a new， more accurate method for the risk assessment of food safety.

Abstract Existing mobile robots mostly use graph search algorithms for path planning, which suffer from relatively low planning efficiency owing to high redundancy and large computational complexity. Due to the limitations of the neighborhood search strategy, the robots could hardly obtain the most optimal global path. A global path planning algorithm, denoted as EDG*, is proposed by expanding nodes using a well-designed expanding disconnected graph operator (EDG) in this paper. Firstly, all obstacles are marked and their corners are located through the map pre-processing. Then, the EDG operator is designed to find points in non-obstruction areas to complete the rapid expansion of disconnected nodes. Finally, the EDG* heuristic iterative algorithm is proposed. It selects the candidate node through a specific valuation function and realizes the node expansion while avoiding collision with a minimum offset. Path planning experiments were conducted in a typical indoor environment and on the public dataset CSM. The result shows that the proposed EDG* reduced the planning time by more than 90% and total length of paths reduced by more than 4.6%. Compared to A*, Dijkstra and JPS, EDG* does not show an exponential explosion effect in map size. The EDG* showed better performance in terms of path smoothness, and collision avoidance. This shows that the EDG* algorithm proposed in this paper can improve the efficiency of path planning and enhance path quality.

The preparation of Al/Mg/Al laminated metal composites (LMCs) by lamination technique can well combine the good corrosion resistance of Al alloy and the lightweight characteristics of Mg alloy. In this paper, the atmospheric corrosion behavior and underlying mechanism of 6082/AZ31/6082 LMCs with and without side exposure for one year of filed exposure to the tropical marine atmospheric environment were investigated. The results showed that the corrosion rates of AZ31 and 6082 and LMC-C were 55.967, 1.167, and 0.925 g m−2·a−1, respectively. The microstructure of the Al layer on the surface of LMCs underwent a significant transformation, characterized by a noticeable grain refinement and a concomitant increase in dislocation density. Moreover, the potential disparity between the second phase (AlMnFeSi and Mg2Si) and the matrix was diminished. These findings collectively contribute to the enhanced resistance of 6082/AZ31/6082 LMCs to intergranular corrosion and pitting corrosion. However, the accelerated dissolution of the intermediate Mg layer, facilitated by the galvanic corrosion effect during field exposure of the LMCs, resulted in a 4.5-fold increase in corrosion-induced mass loss. Notably, the ultimate tensile strength of the LMCs exhibited insensitivity to field exposure duration, while their plasticity underwent a gradual decline. This comprehensive study elucidates the corrosion behavior and underlying mechanisms of 6082/AZ31/6082 LMCs subjected to a tropical marine atmospheric environment, providing valuable insights for the rational design and development of LMCs with enhanced comprehensive performance.

Abstract Global greenhouse gas emission, major factor driving climate change, has been increasing since nineteenth century. STIRPAT and CEVSA models were performed to estimate the carbon emission peaks and terrestrial ecosystem carbon sinks at the provincial level in China, respectively. Utilizing the growth characteristics and the peak time criteria for the period 1997–2019, the patterns of energy consumption and CO2 emissions from 30 Chinese provinces are categorized into four groups: (i) one-stage increase (5 provinces), (ii) two-stage increase (10 provinces), (iii) maximum around 2013 (13 provinces), and (iv) maximum around 2017 (2 provinces). According to the STIRPAT model, the anticipated time of peak CO2 emissions for Beijing from the third group is ~ 2025 in both business-as-usual and high-speed scenarios. For Xinjiang Uygur autonomous region from the first group and Zhejiang province from the second group, the expected peak time is 2025 to 2030. Shaanxi province from the fourth group is likely to reach carbon emission peak before 2030. The inventory-based estimate of China’s terrestrial carbon sink is ~ 266.2 Tg C/a during the period 1982–2015, offsetting 18.3% of contemporary CO2 emissions. The province-level CO2 emissions, peak emissions and terrestrial carbon sinks estimates presented here are significant for those concerned with carbon neutrality.

ABSTRACT: Objectives: Recently, blaCTX-Ms have become the dominant ESBLs for E. coli strains worldwide. We aim to provide a systematic study on the relationships between sequence types (STs), clinical origins, and the blaCTX-Ms genotypes of E. coli strains. Methods: Totally, 1005 complete sequences of clinical E. coli were collected from NCBI. Multilocus sequence typing (MLST) and antibiotic resistance genes screening were performed. Results: Faeces (26.27%), urine (16.02%), and blood (8.26%) were shown to be the main sources of clinical E. coli isolates. The isolates belong to 153 STs and 26 clonal complexes (CCs). The most prevalent STs were ST2 (11.3%), ST43 (8.6%), and ST8 (5.7%). The positive rate for blaCTX-Ms was 34.7%. Different samples showed significantly different blaCTX-Ms positive rates (P

Deploying user-side energy storage in urban power grids is an effective solution to operational challenges such as insufficient capacity for renewable energy consumption and local overloads. Therefore， an assessment method for energy storage demand on the user side in urban power grids that considers multiple operational scenarios is proposed. The approach involves quantifying the weights between different scenarios in the urban power grid based on their frequency of occurrence and severity. Scenarios including normal operation， insufficient renewable energy consumption， and local overloads are chosen to construct a model to assess energy storage demand on the user side in urban power grids. Case studies are conducted using a 37-node model of an urban power grid in a region of Zhejiang Province. The results indicate that the method is effective in evaluating energy storage demand on the user side in urban power grids and improving the reuse rate of energy storage on the user side across various operational scenarios. Compared to methods for assessing energy storage demand in a single operational scenario， this approach significantly reduces the overall demand for energy storage on the user side in urban power grids and decreases both investment costs and operational costs.

Abstract Background The purpose of this study was to investigate the effects of four different doses of verapamil on the mechanical behaviors of solid and the characteristics of fluid flow in cancellous bone of distal femur of type 2 diabetes rats under dynamic external load. Methods Based on the micro-CT images, the finite element models of cancellous bones and fluids at distal femurs of rats in control group, diabetes group, treatment groups VER 4, VER 12, VER 24, and VER 48 (verapamil doses of 4, 12, 24, and 48 mg/kg/day, respectively) were constructed. A sinusoidal time-varying displacement load with an amplitude of 0.8 μm and a period of 1s was applied to the upper surface of the solid region. Then, fluid-solid coupling numerical simulation method was used to analyze the magnitudes and distributions of von Mises stress, flow velocity, and fluid shear stress of cancellous bone models in each group. Results The results for mean values of von Mises stress, flow velocity and FSS (t = 0.25s) were as follows: their values in control group were lower than those in diabetes group; the three parameters varied with the dose of verapamil; in the four treatment groups, the values of VER 48 group were the lowest, they were the closest to control group, and they were smaller than diabetes group. Among the four treatment groups, VER 48 group had the highest proportion of the nodes with FSS = 1-3 Pa on the surface of cancellous bone, and more areas in VER 48 group were subjected to fluid shear stress of 1-3 Pa for more than half of the time. Conclusion It could be seen that among the four treatment groups, osteoblasts on the cancellous bone surface in the highest dose group (VER 48 group) were more easily activated by mechanical loading, and the treatment effect was the best. This study might help in understanding the mechanism of verapamil’s effect on the bone of type 2 diabetes mellitus, and provide theoretical guidance for the selection of verapamil dose in the clinical treatment of type 2 diabetes mellitus.

This research addresses the limitations of existing autonomous vehicle path planning algorithms, notably their slow processing speeds and suboptimal route efficiency. We introduce an innovative path planning algorithm that synergizes the A* algorithm with the Rapidly-exploring Random Tree (RRT) approach. This hybrid model significantly enhances route timeliness and reliability, particularly in obstacle avoidance scenarios for driverless vehicles. Our methodology employs a 'two-level map' approach, where a lower-resolution grid map is derived from a high-resolution map. Utilizing the A* algorithm on this framework, we ascertain a preliminary 'coarse path' for the navigation target. The RRT algorithm, modified to reduce the traditional redundancy associated with random uniform sampling, is then applied for probabilistic sampling within this defined area. ovel aspect of our approach is the simultaneous generation of two trees, originating from both the start and end points, guided by a target-biased strategy and dual-direction theory. This method probabilistically expands towards the node of the opposite tree, thereby enhancing both the generation speed and trajectory viability. Further refinements are made through a pruning process, optimizing the path, and employing Bezier curves for smoothing, ensuring compliance with the dynamic constraints of Ackerman chassis vehicles. Comparative analysis in complex environments demonstrates the superiority of our proposed algorithm. It outperforms traditional methods with a 400 % increase in planning speed relative to the RRT-Connect algorithm, and a 30 % reduction in average path length. Additionally, the mean curvature of routes generated by our algorithm is 19 % lower than traditional routes, underscoring significant advancements in both the timeliness and viability of the planned routes.

Ocean eddies have a significant impact on marine ecosystems and the climate because they transport essential substances in the ocean. Detection of ocean eddies has become one of the most active topics in physical ocean research. In recent years, research based on deep learning has mainly focused on regional oceans, with small and specific data and relatively general detection results. This study processes the global eddy by pixel-by-pixel classification and generates a global eddy classification map with a resolution of 720 × 1440, which expands the data volume and improves the generality of the data. Moreover, a high-precision attention residual U$^{2}$-Net model, referred to as ARU$^{2}$-Net, is proposed, which is suitable for mining eddy surface features from sea level anomaly (SLA) and sea surface temperature (SST) data in the global ocean. ARU$^{2}$-Net integrates the convolutional block attention module (CBAM). The channel attention of the CBAM module is used to learn the correlation features between the SST and SLA dual channels; the spatial attention mechanism of the CBAM module is used to learn the importance of the spatial location of the eddy, focusing on the locally important regions, which further improves the detection ability of ARU$^{2}$-Net for eddies, and helps ARU$^{2}$-Net to better identify the eddy categories. Finally, we demonstrate the effectiveness of our approach on the global eddy dataset, achieving a test performance of 94.926%, significantly exceeding previous detection in some areas.

Indoor environments are considered challenging due to their perceived degradation. This is attributed to both the homogeneity of indoor scene structures and the scarcity of feature points, particularly in long corridor environments. Additionally, low-resolution 3D laser scanners may not effectively capture the real information of the indoor environment. To address these issues, this paper proposes a tightly coupled SLAM method based on factor graph optimization for laser-inertial fusion. Firstly, a dynamic feature point extraction method is designed to adaptively adjust the feature extraction quantity by detecting indoor degradation, constructing rich and robust feature constraints to enhance pose estimation accuracy. Next, robust and precise SLAM in indoor environments is achieved through factor graph optimization. Finally, validation is conducted on the KITTI dataset and in real scenarios. Experimental results demonstrate that the proposed method exhibits superior accuracy, real-time performance, and robustness compared to ALOAM, LeGO-LOAM, FLOAM, and LIO-SAM in terms of localization, mapping precision, and global consistency. The method effectively reduces cumulative errors and ensures the global consistency of map construction.

We study theoretically and experimentally the acoustic Purcell effect induced by quasi-bound states in the continuum (quasiBICs). A theoretical framework describing the acoustic Purcell effect of a resonant system is developed based on the system’s radiative and dissipative factors, which reveals the critical emission condition for achieving optimum Purcell factors. We show that the quasiBICs contribute to highly confined acoustic field and bring about greatly enhanced acoustic emission, leading to strong Purcell effect. Our concept is demonstrated via two coupled resonators supporting a Friedrich–Wintgen quasiBIC, and the theoretical results are validated by the experiments observing emission enhancement of the sound source by nearly two orders of magnitude. Our work bridges the gap between the acoustic Purcell effect and acoustic BICs essential for enhanced wave-matter interaction and acoustic emission, which may contribute to the research of high-intensity sound sources, high-quality-factor acoustic devices and nonlinear acoustics.

ABSTRACTVibrio alginolyticus, an emergent species of Vibrio genus, exists in aquatic and marine environments. It has undergone genetic diversification, but its detailed genomic diversity is still unclear. Here, we performed a multi-dimensional comparative genomic analysis to explore the population phylogeny, virulence-related genes and potential drug resistance genes of 184 V. alginolyticus isolates. Although genetic diversity is complex, we analysed the population structure using three sub-datasets, including the subdivision for three lineages into sublineages and the distribution of strains in the marine ecological niche. Accessory genes, most of which reclassified V. alginolyticus genomes as different but with relatively close affinities, were nonuniformly distributed among these isolates. We demonstrated that the spread of some post-evolutionary isolates (mainly L3 strains isolated from Chinese territorial seas) was likely to be closely related to human activities, whereas other more ancestral strains (strains in the L1 and L2) tended to be locally endemic and formed clonal complex groups. In terms of pathogenicity, the potential virulence factors were mainly associated with toxin, adherence, motility, chemotaxis, and the type III secretion system (T3SS). We also found five types of antibacterial drug resistance genes. The prevalence of β-lactam resistance genes was 100%, which indicated that there may be a potential risk of natural resistance to β-lactam drugs. Our study reveals insights into genomic characteristics, evolution and potential virulence-associated gene profiles of V. alginolyticus.

Body color is one of the most noticeable phenotypic and economic characteristics in fish. Fish exhibit a diverse array of body color mutations influenced by factors such as artificial selection and natural mutations. Elucidating the genetic mechanisms of these color variants in fish is crucial for developing new breeds with higher ornamental value. In this study, we identified a nonsense mutation in csf1ra (c. 1249 C>T) with whole genome resequencing technology, and which may potentially contribute to the albino phenotype in Channa asiatica. The albino individuals exhibited a substantial decrease in the expression level of csf1ra compared to that of the wild individuals. To unravel the underlying mechanism of csf1ra, we delved into comparative transcriptomic and gene expression analyses. Our findings suggest that csf1ra may impact tyrosine kinase activity. Furthermore, csf1ra may not only regulate the expression levels of genes (sox9, ephb6, and wnt9) involved in melanin-based pigmentation but also influence genes (ampd3, ak1, and bco1) associated with non-melanin-based pigmentation. Our study investigated the genetic basis of the albino mutant of C. asiatica and provided new insights into the function of csf1ra in fish pigmentation.

At present, blockchain technology has been applied in different fields such as finance, Internet of Things, logistics, digital copyright, and public services, but it is less used in the field of paper-cut animation. Therefore, the use of blockchain to design a copyright protection model for paper-cut animation design based on blockchain is crucial to the progress of paper-cut animation design. This work combines various technologies such as blockchain technology, InterPlanetary File System (IPFS) technology, and Node.js technology for integrated design and construction. In the construction of the system model, the relevant characteristics of the blockchain technology show that the blockchain can provide a new and effective method for digital copyright protection; IPFS technology provides distributed and secure storage for massive amounts of oil and gas knowledge and achievement data; and the relevant development framework and technical characteristics of Node.js technology provide a reliable programming foundation for the realization of the system. Through the construction of the digital copyright protection system model based on the blockchain, the blockchain technology is used to realize the protection of the digital copyright of the knowledge achievement data of paper-cut animation. The results show that the model has high security and is more economical and practical than other existing models. In a word, the copyright protection model of paper-cut animation design based on blockchain can fully protect the rights and interests of creative talents of paper-cut animation, thereby promoting the progress of paper-cut animation industry.

ObjectiveTo investigate parental knowledge, attitudes, and practices (KAP) toward adolescent depression.MethodsA cross-sectional survey was conducted between October 2022 and October 2023 at The First Affiliated Hospital of Ningbo University among parents of adolescents diagnosed with depression. A self-administered questionnaire was used to collect the parents’ demographic characteristics and KAP toward adolescent depression.ResultsA total of 522 questionnaires were collected from parents of depressed adolescents. Among the participants, 383 (73.37%) were female. In addition, 426 participants (81.61%) had children aged 14–18. The mean knowledge, attitude, and practice scores were 9.09 ± 2.37 (possible range: 0–12), 37.04 ± 4.11 (possible range: 11–55), and 31.53 ± 3.84 (possible range: 8–40), respectively. There were significant positive correlations between knowledge and attitude (r = 0.225, p

Introduction None of the studies of type 2 diabetes (T2D) subtyping to date have used linked population-level data for incident and prevalent T2D, incorporating a diverse set of variables, explainable methods for cluster characterization, or adhered to an established framework. We aimed to develop and validate machine learning (ML)-informed subtypes for type 2 diabetes mellitus (T2D) using nationally representative data.Research design and methods In population-based electronic health records (2006–2020; Clinical Practice Research Datalink) in individuals ≥18 years with incident T2D (n=420 448), we included factors (n=3787), including demography, history, examination, biomarkers and medications. Using a published framework, we identified subtypes through nine unsupervised ML methods (K-means, K-means++, K-mode, K-prototype, mini-batch, agglomerative hierarchical clustering, Birch, Gaussian mixture models, and consensus clustering). We characterized clusters using intracluster distributions and explainable artificial intelligence (AI) techniques. We evaluated subtypes for (1) internal validity (within dataset; across methods); (2) prognostic validity (prediction for 5-year all-cause mortality, hospitalization and new chronic diseases); and (3) medication burden.Results Development: We identified four T2D subtypes: metabolic, early onset, late onset and cardiometabolic. Internal validity: Subtypes were predicted with high accuracy (F1 score >0.98). Prognostic validity: 5-year all-cause mortality, hospitalization, new chronic disease incidence and medication burden differed across T2D subtypes. Compared with the metabolic subtype, 5-year risks of mortality and hospitalization in incident T2D were highest in late-onset subtype (HR 1.95, 1.85–2.05 and 1.66, 1.58–1.75) and lowest in early-onset subtype (1.18, 1.11–1.27 and 0.85, 0.80–0.90). Incidence of chronic diseases was highest in late-onset subtype and lowest in early-onset subtype. Medications: Compared with the metabolic subtype, after adjusting for age, sex, and pre-T2D medications, late-onset subtype (1.31, 1.28–1.35) and early-onset subtype (0.83, 0.81–0.85) were most and least likely, respectively, to be prescribed medications within 5 years following T2D onset.Conclusions In the largest study using ML to date in incident T2D, we identified four distinct subtypes, with potential future implications for etiology, therapeutics, and risk prediction.

Abstract A road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot.

Global warming has heightened the severity and frequency of extreme high-temperature events, presenting challenges to fish survival and negatively affecting the production of fishery. Gymnocypris eckloni, native to the Tibetan Plateau, is widely cultured in southwest China. Its sensitivity to heat makes it a suitable model for studying the impact of high temperatures on fish. The heart is a vital organ for survival, while is relatively understudied concerning the impact of high temperatures on fish hearts. In this study, G. eckloni was subjected to a seven-day exposure to 27 °C followed by a transfer to 18 °C for a seven-day recovery period. Behavioral tests revealed a significant increase in locomotor speed, distance, and respiratory rate of G. eckloni at 27 °C. Serum indices exhibited significantly elevated stress indicators (cortisol) and kinases associated with heart damage (lactate dehydrogenase, lactate dehydrogenase isoenzyme 1, creatine kinase, creatine kinase isoenzyme, and aspartate transaminase) at 27 °C, and histological analyses have confirmed the occurrence of heart damage subjected to high temperatures. After 7 days of recovery, most of these indicators showed a significant decrease compared to the heat stress group, with no significant differences observed compared to the control group. Transcriptomic analyses have revealed the widespread involvement of ferroptosis, P53 signaling pathway, oxidative phosphorylation, myocardial contraction, and DNA repair pathway in the response of G. eckloni to high temperatures. Our study offers a systematic exploration of the effects of heat stress on G. eckloni, providing valuable insights for the cultivation of high-altitude fish.

Background: For patients with human epidermal growth factor receptor-2 (HER2)-positive advanced or metastatic gastric cancer who have progressed on first-line trastuzumab therapy, the clinical value of the continuous use of trastuzumab beyond progression (TBP) is controversial. Objectives: The present study was conducted to evaluate the efficacy and explore new treatment strategies of TBP for patients with trastuzumab-resistant HER2-positive advanced or metastatic gastric cancer in the era of cancer immunotherapy. Design: Retrospective analysis. Methods: Patients with HER2-positive advanced or metastatic gastric cancer who have failed first-line treatment based on trastuzumab-targeted therapy from June 2019 to December 2020 were retrospectively analyzed. The primary endpoint was progression-free survival (PFS). Secondary endpoints included overall survival (OS), objective response rate (ORR), disease control rate (DCR), and safety. Survival curves of patients were estimated by the Kaplan–Meier method and compared using the log-rank test. Results: In all, 30 patients received TBP with chemotherapy, immunotherapy, or anti-angiogenic therapy, and the other 26 patients received treatment of physician’s choice without trastuzumab. The median PFS in the TBP and non-TBP population was 6.0 [95% confidence interval (CI) = 3.8–8.2] and 3.5 (95% CI = 2.2–4.8) months, respectively ( p = 0.038), and the median OS was 12.3 (95% CI = 10.4–14.2) and 9.0 (95% CI = 6.6–11.4) months ( p = 0.008). The patients who received TBP treatment had more favorable PFS and OS than the non-TBP population. In the TBP group, patients who received trastuzumab plus chemotherapy and immunotherapy had higher ORR (40.0% versus 16.7%), DCR (90.0% versus 50.0%), and showed a significant improvement in PFS (7.0 versus 1.9 m) compared to TBP with chemotherapy alone. Subgroup analysis suggested that patients with male, HER2 positive with immunohistochemistry score 3+ and PFS of first-line treatment less than 6 months had a greater benefit from TBP. The incidence of Grade 3–4 adverse events in the TBP and non-TBP groups was 43.3% and 38.5%. Conclusion: The continuous use of TBP improves PFS and OS in patients with trastuzumab-resistant HER2-positive advanced or metastatic gastric cancer with well-tolerated toxicity. In the era of immunotherapy, TBP combined with chemotherapy and immunotherapy may further enhance the clinical benefit and provide a new treatment strategy. Trial registration: This study is a retrospective study, which does not require clinical registration.