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Prostate cancer is a hormone-dependent malignancy, whose onset and progression are closely related to the activity of the androgen receptor (AR) signaling pathway. Due to this critical role of AR signaling in driving prostate cancer, therapy targeting the AR pathway has been the mainstay strategy for metastatic prostate cancer treatment. The utility of these agents has expanded with the emergence of second-generation AR antagonists, which began with the approval of enzalutamide in 2012 by the United States Food and Drug Administration (FDA). Together with apalutamide and darolutamide, which were approved in 2018 and 2019, respectively, these agents have improved the survival of patients with prostate cancer, with applications for both androgen-dependent and castration-resistant disease. While patients receiving these drugs receive a benefit in the form of prolonged survival, they are not cured and ultimately progress to lethal neuroendocrine prostate cancer (NEPC). Here we summarize the current state of AR antagonist development and highlight the emerging challenges of their clinical application and the potential resistance mechanisms, which might be addressed by combination therapies or the development of novel AR-targeted therapies.

Traffic flow forecasting is a critical input to intelligent transportation systems. Accurate traffic flow forecasting can provide an effective reference for implementing traffic management strategies, developing travel route planning, and public transportation risk assessment. Recent deep learning approaches of spatiotemporal neural networks to predict traffic flow show promise, but could be difficult to separately model the spatiotemporal aggregation in traffic data and intrinsic correlation or redundancy of spatiotemporal features extracted by the filter of the convolutional network. This can introduce biases in the predictions that interfere with subsequent planning decisions in transportation. To solve the mentioned problem, the filter attention-based spatiotemporal neural network (FASTNN) was proposed in this paper. First, the model used 3-dimensional convolutional neural networks to extract universal spatiotemporal dependencies from three types of historical traffic flow, the residual units were employed to prevent network degradation. Then, the filter spatial attention module was constructed to quantify the spatiotemporal aggregation of the features, thus enabling dynamic adjustment of the spatial weights. To model the intrinsic correlation and redundancy of features, this paper also constructed a lightweight module, named matrix factorization based resample module, which automatically learned the intrinsic correlation of the same features to enhance the concentration of the model on information-rich features, and used matrix factorization to reduce the redundant information between different features. The FASTNN has experimented on two large-scale real datasets (TaxiBJ and BikeNYC), and the experimental results show that the FASTNN has better prediction performance than various baselines and variant models.

As China becomes increasingly urbanised, flooding has become a regular occurrence in its major cities. Assessing the effects of future climate change on urban flood volumes is crucial to informing better management of such disasters given the severity of the devastating impacts of flooding (e.g. the 2016 flooding events across China). Although recent studies have investigated the impacts of future climate change on urban flooding, the effects of both climate change mitigation and adaptation have rarely been accounted for together in a consistent framework. In this study, we assess the benefits of mitigating climate change by reducing greenhouse gas (GHG) emissions and locally adapting to climate change by modifying drainage systems to reduce urban flooding under various climate change scenarios through a case study conducted in northern China. The urban drainage model – Storm Water Management Model – was used to simulate urban flood volumes using current and two adapted drainage systems (i.e. pipe enlargement and low-impact development, LID), driven by bias-corrected meteorological forcing from five general circulation models in the Coupled Model Intercomparison Project Phase 5 archive. Results indicate that urban flood volume is projected to increase by 52 % over 2020–2040 compared to the volume in 1971–2000 under the business-as-usual scenario (i.e. Representative Concentration Pathway (RCP) 8.5). The magnitudes of urban flood volumes are found to increase nonlinearly with changes in precipitation intensity. On average, the projected flood volume under RCP 2.6 is 13 % less than that under RCP 8.5, demonstrating the benefits of global-scale climate change mitigation efforts in reducing local urban flood volumes. Comparison of reduced flood volumes between climate change mitigation and local adaptation (by improving drainage systems) scenarios suggests that local adaptation is more effective than climate change mitigation in reducing future flood volumes. This has broad implications for the research community relative to drainage system design and modelling in a changing environment. This study highlights the importance of accounting for local adaptation when coping with future urban floods.

Understanding historical changes in flood damage and the underlying mechanisms is critical for predicting future changes for better adaptations. In this study, a detailed assessment of flood damage for 1950–1999 is conducted at the state level in the conterminous United States (CONUS). Geospatial datasets on possible influencing factors are then developed by synthesizing natural hazards, population, wealth, cropland and urban area to explore the relations with flood damage. A considerable increase in flood damage in CONUS is recorded for the study period which is well correlated with hazards. Comparably, runoff indexed hazards simulated by the Variable Infiltration Capacity (VIC) model can explain a larger portion of flood damage variations than precipitation in 84% of the states. Cropland is identified as an important factor contributing to increased flood damage in central US while urbanland exhibits positive and negative relations with total flood damage and damage per unit wealth in 20 and 16 states, respectively. Overall, flood damage in 34 out of 48 investigated states can be predicted at the 90% confidence level. In extreme cases, ~76% of flood damage variations can be explained in some states, highlighting the potential of future flood damage prediction based on climate change and socioeconomic scenarios.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

Background In the context of public health emergencies, the presence of medical and health talents (MHT) is critically important for support in any country or region. This study aims to analyze the spatial and temporal distributions and evolution of MHT in China and propose strategies and recommendations for promoting a balanced distribution. Methods This research used data from 31 provinces in China to construct a multidimensional index system for measuring the agglomeration level of MHT. The indices include talent agglomeration density (TAD), talent agglomeration scale (TAS), talent agglomeration intensity (TAI), and talent agglomeration equilibrium (TAE). Using provincial data from the years 1982, 1990, 2000, 2010, and 2020, a spatiotemporal analysis of the MHT agglomeration levels was conducted. Furthermore, the regional dynamic distribution of MHT was analyzed using kernel density estimation diagrams. The spatial autocorrelation of MHT was assessed through global and local Moran’s I, and the spatial gap and decomposition of MHT were analyzed using the Dagum Gini coefficient. Results From the temporal level, the TAD and TAI of MHT showed an increasing trend over the studied period, whereas TAS decreased and TAE first increased and then decreased from 1982 to 2020. At the spatial level, the TAD, TAS, TAI, and TAE of MHT exhibited varied patterns among the eastern, central, and western regions of China, showing significant geographical disparities, generally demarcated by the Hu Huanyong Line. The regional dynamic distribution level of MHT in the country and the three regions were expanding. Spatial autocorrelation analysis using global and local Moran’s I for TAD, TAS, TAI, and TAE demonstrated significant regional differences. The Dagum Gini coefficient of TAD, TAS, TAI, and TAE revealed divergent trends in regional disparities, with overall declines in disparities for TAD and TAI, a slight increase for TAS, and fluctuating patterns for TAE. Conclusions From a temporal perspective, the overall number of MHT in China has been increasing annually at the national and provincial levels. From the spatial perspective, TAD, TAS, TAI, and TAE exhibit significant differences among the three regions. Kernel analysis reveals that the distribution differences are gradually expanding in national level and varying in regional level. Moreover, the global and local Moran’s I indices reveal varying spatial autocorrelation for TAD, TAS, TAI, and TAE. The Dagum Gini coefficients of TAD, TAS, TAI, and TAE show different patterns of decomposition.

To investigate the impact of freshwater discharge variations from the Nanliu River on the phytoplankton community structure in Lianzhou Bay, we conducted surveys of total chlorophyll- a (Tchl- a ) concentrations and specific pigment groups during wet and dry seasons of 2016. During wet season, freshwater discharge introduced substantial freshwater, nutrients, and suspended particulate matter into Lianzhou Bay. Due to reduced light availability, Tchl- a concentrations at most stations were lower compared to dry season. The result indicated that light availability often exerts a greater influence than nutrient availability in estuarine environments. Furthermore, the spatial distribution of Tchl- a concentrations in Lianzhou Bay is correlated with the feeding activities of bivalve aquaculture and water movement within the bay. During the high-water period, the phytoplankton community was primarily composed of diatoms (35.0%), cyanobacteria (21.0%), prasinophytes (13.0%), chlorophytes (19.0%), and cryptophytes (11.0%). In contrast, the low-water period saw a phytoplankton community dominated by diatoms (62.0%), prasinophytes (18.0%), cyanobacteria (12.0%), and cryptophytes (7.0%). The absence of osmotic regulation in chlorophytes restricts their distribution primarily to estuarine environments characterized by high water availability. In contrast, diatoms exhibit a greater affinity for well-illuminated conditions, thereby contributing significantly to primary productivity in neritic zones and during periods of low water flow.

The purpose of this research was to explore the fresh keeping effect of modified atmosphere packaging (MAP) with different gas ratios combined with gelatin active coatings containing eugenol on Chinese sea bass stored at −0.9 °C for 36 days. The results showed that MAP3 (60% CO2/10% O2/30% N2), together with gelatin active coatings containing eugenol, could prevent water loss, which maintained high field NMR, MRI, and organoleptic evaluation results. This hurdle technology could also effectively delay the bacterial reproduction, protein degradation, and alkaline accumulation, so it showed the lowest K value, total volatile basic nitrogen, free amino acids, total viable count, Pseudomonas spp., and H2S-producing bacteria, which better maintain the quality of sea bass.

The objective of this research was to explore the antimicrobial activity and mechanism of carvacrol against Vibrio Parahemolyticus, Shewanella putrefaciens, Staphylococcus aureus and Pseudomonas fluorescens and evaluate the effect of the addition of carvacrol/β-cyclodextrin emulsions to flaxseed gum (FSG)-sodium alginate (SA) edible films on the preservation of Chinese sea bass (Lateolabrax maculatus) fillets during refrigerated storage. The minimum inhibitory concentration (MIC) of carvacrol against V. parahemolyticus, S. putrefaciens, S. aureus and P. fluorescens were 0.5, 0.5, 0.125, and 0.5 mg/mL, respectively. Alkaline phosphatase activity assay, nucleotide and protein leakage, and scanning electron microscope demonstrated that carvacrol damaged the external structure of the tested bacterial cells causing leakage of cytoplasmic components. At the same time, when FSG-SA films containing carvacrol used as coating agents for Chinese sea bass fillets cold storage, FSG-SA films containing 1.0 or 2.0 mg/mL carvacrol could significantly reduce TVB-N content, K-value, the degree of microbial deterioration and maintain quality of sea bass fillets according to organoleptic evaluation results.

The development of high-fidelity digital twins for scintillation spectrometer detectors demands precise experimental characterization of timing parameters. This work presents a comprehensive solution comprising a digitizer-based time-correlated single-photon counting (TCSPC) system and an extended convolution model for decay time analysis. We introduce a physics-driven calibration principle, validating the system response against an independent physical benchmark to ensure fidelity. The proposed convolution model advances beyond the conventional model by incorporating additional parameters to account for scintillator-induced timing broadening and delay, thereby decoupling this effect from instrumental response. The model’s descriptive power was statistically validated through its application to fast scintillators, while its physical accuracy was robustly confirmed through the precise extraction of typical decay times from slow scintillators. This methodology establishes a reliable workflow from measurement to parameterization, directly supplying the decoupled inputs required for the digital twins of scintillation detectors.