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Flood risk assessment and mapping are considered essential tools for the improvement of flood management. This research aims to construct a more comprehensive flood assessment framework by emphasizing factors related to human resilience and integrating them with meteorological and geographical factors. Moreover, two ensemble learning models, namely voting and stacking, which utilize heterogeneous learners, were employed in this study, and their prediction performance was compared with that of traditional machine learning models, including support vector machine, random forest, multilayer perceptron, and gradient boosting decision tree. The six models were trained and tested using a sample database constructed from historical flood events in Hefei, China. The results demonstrated the following findings: (1) the RF model exhibited the highest accuracy, while the SVR model underestimated the extent of extremely high-risk areas. The stacking model underestimated the extent of very-high-risk areas. It should be noted that the prediction results of ensemble learning methods may not be superior to those of the base models upon which they are built. (2) The predicted high-risk and very-high-risk areas within the study area are predominantly clustered in low-lying regions along the rivers, aligning with the distribution of hazardous areas observed in historical inundation events. (3) It is worth noting that the factor of distance to pumping stations has the second most significant driving influence after the DEM (Digital Elevation Model). This underscores the importance of considering human resilience factors. This study expands the empirical evidence for the ability of machine learning methods to be employed in flood risk assessment and deepens our understanding of the potential mechanisms of human resilience in influencing urban flood risk.

Malignant peripheral nerve sheath tumors (MPNSTs) are highly aggressive Schwann cell-derived sarcomas that are sporadic or associated with Neurofibromatosis 1 (NF1) gene mutations. Traditional therapies are usually ineffective for treating MPNSTs, so new targets need to be identified for the treatment of MPNSTs. In the present study, the role of the mitochondrial translocator protein (TSPO) in the regulation of cell proliferation and the cell cycle in MPNSTs was investigated. TSPO expression was lower in MPNSTs than in NFs. Loss-of-function experiments revealed that TSPO deficiency promoted MPNST cell growth, migration, and invasion and influenced the cell cycle in vitro and in vivo. In addition, TSPO depletion suppressed cell apoptosis by downregulating the expression of caspase-3, caspase-8, HSP60, p27, p53, and BCL-2 and suppressed the cell cycle by upregulating CDK1, CDK2, CCNB1 and CCNA2. Furthermore, CDK1 was determined to be an upstream target of TSPO-mediated regulation via RNA-seq, qPCR, and Western blotting. Specifically, depletion of CDK1 weakened the effect of TSPO deficiency on cell proliferation and migration. More importantly, CDK1 knockdown induced significant cell cycle arrest in the G2/M phase. In summary, TSPO deficiency regulates the cell cycle in MPNSTs by targeting CDK1, which may be an effective molecular target for prognosis evaluation and treatment.

The parameters of hydrological models should be determined before applying those models to estimate or predict hydrological processes. The Xin’anjiang (XAJ) hydrological model is widely used throughout China. Since the prediction in ungauged basins (PUB) era, the regionalization of the XAJ model parameters has been a subject of intense focus; nevertheless, while many efforts have targeted parameters related to runoff yield using in-site data sets, classic regression has predominantly been applied. In this paper, we employed remotely sensed underlying surface data and a machine learning approach to establish models for estimating the runoff routing parameter, namely, CS, of the XAJ model. The study was conducted on 114 catchments from the Catchment Attributes and MEteorology for Large-sample Studies (CAMELS) data set, and the relationships between CS and various underlying surface characteristics were explored by a gradient-boosted regression tree (GBRT). The results showed that the drainage density, stream source density and area of the catchment were the three major factors with the most significant impact on CS. The best correlation coefficient (r), root mean square error (RMSE) and mean absolute error (MAE) between the GBRT-estimated and calibrated CS were 0.96, 0.06 and 0.04, respectively, verifying the good performance of GBRT in estimating CS. Although bias was noted between the GBRT-estimated and calibrated CS, runoff simulations using the GBRT-estimated CS could still achieve results comparable to those using the calibrated CS. Further validations based on two catchments in China confirmed the overall robustness and accuracy of simulating runoff processes using the GBRT-estimated CS. Our results confirm the following hypotheses: (1) with the help of large sample of catchments and associated remote sensing data, the ML-based approach can capture the nonstationary and nonlinear relationships between CS and the underlying surface characteristics and (2) CS estimated by ML from large samples has a robustness that can guarantee the overall performance of the XAJ mode. This study advances the methodology for quantitatively estimating the XAJ model parameters and can be extended to other parameters or other models.

Malignant peripheral nerve sheath tumor (MPNST) is a highly aggressive and recurrent soft tissue sarcoma. It most commonly occurs secondary to neurofibromatosis type I, and it has a 5-year survival rate of only 8–13%. To better study the tumor heterogeneity of MPNST and to develop diverse treatment options, more tumor-derived cell lines are needed to obtain richer biological information. Here, we established a primary cell line of relapsed MPNST RsNF cells derived from a patient diagnosed with NF1 and detected the presence of NF1 mutations and SUZ12 somatic mutations through whole-exome sequencing(WES). Through tumor molecular marker targeted sequencing and single-cell transcriptome sequencing, it was found that chromosome 7 copy number variation (CNV) was gained in this cell line, and ZNF804B , EGFR , etc., were overexpressed on chromosome 7. Therefore, RsNF cells can be used as a useful tool in NF1 -associated MPNST genomic amplification studies and to develop new therapeutic strategies.

Precipitation is a key component of the water cycle. Different precipitation data sources have strengths and weaknesses. To combine these strengths and achieve accurate precipitation data, this study introduces a dual-layer neural network (D-ConvLSTM) based on a convolutional long short-term memory neural network (ConvLSTM) that integrates ground station data (1 h interval) and grid precipitation data generated by the China Meteorological Administration Multi-source merged Precipitation Analysis System (CMPAS, 1 h interval, 0.05° × 0.05°) through a two-layer network for precipitation identification and correction. To evaluate the performance of the proposed model, D-ConvLSTM, optimal interpolation (OI), and a single-layer ConvLSTM model are evaluated in the Dadu River Basin, China. The results show that D-ConvLSTM outperforms the CMPAS in all the metrics compared with the OI and ConvLSTM, with improvements of 18.9% and 19.8% in the critical success index (CSI) and Kling–Gupta efficiency (KGE), respectively. D-ConvLSTM enhances gridded precipitation under various conditions, including areas without station data, different intensities, and regions. Furthermore, this study analyzes the impact of training data distribution on the performance of the D-ConvLSTM model and enhances model performance by adjusting the training data distribution. The analysis reveals that the ratio of dry to wet data in the training set affects the model’s identification performance. The ratio of overestimation to underestimation of gridded data compared with station observations influences value correction. This study offers a new model for merging station and gridded precipitation data and provides insights for enhancing the accuracy of neural network merging.

Swarm robotics is a specific research field of multirobotics where a large number of mobile robots are controlled in a coordinated way. Formation control is one of the most challenging goals for the coordination control of swarm robots. In this paper, a behavior-based control design approach is proposed for two kinds of important formation control problems: efficient initial formation and formation control while avoiding obstacles. In this approach, a classification-based searching method for generating large-scale robot formation is presented to reduce the computational complexity and speed up the initial formation process for any desired formation. The behavior-based method is applied for the formation control of swarm robot systems while navigating in an unknown environment with obstacles. Several groups of experimental results demonstrate the success of the proposed approach. These methods have potential applications for various swarm robot systems in both the simulation and the practical environments.

Malignant peripheral nerve sheath tumor (MPNST) is an aggressive soft tissue sarcoma characterized by high recurrence and poor prognosis, necessitating the search for novel therapeutic targets and strategies. This study investigated the expression of mitochondrial translocator protein (TSPO) in MPNST and its role in regulating ferroptosis. TSPO expression was analyzed in adjacent non-tumor tissues, benign neurofibromas, and malignant tissues using real-time PCR, western blotting, immunohistochemistry staining. Expression levels of classic ferroptosis markers, including AKR1C1 and FTH1 were assessed. Ferroptosis was evaluated by measuring cell viability, ferroptosis marker levels, and intracellular Fe and reactive oxygen species (ROS) levels. Oxidized phospholipid profiles of wild-types and knockdown MPNST cells were determined using liquid chromatography-mass spectrometry. The potential role of GPX4 in mediating TSPO's effect on ferroptosis was investigated . Compared with adjacent non-tumor tissues and benign neurofibromas, TSPO expression was significantly lower in MPNST specimens. Notably, TSPO expression positively correlated with the classic ferroptosis markers AKR1C1 and FTH1. TSPO-knockdown MPNST cells exhibited significant resistance to ferroptotic cell death. Additionally, biochemical characterization indicated that TSPO deficiency decreased intracellular Fe and ROS. Furthermore, oxidized phospholipids were remarkably reduced in TSPO-knockdown cells. TSPO enriches cellular oxidized phospholipids by downregulating GPX4-GSH antioxidant pathway. Furthermore, GPX4 is elevated in malignant tumors compared to benign specimens and negatively correlated with TSPO expression in clinical tumor specimens. Our findings revealed that TSPO deficiency inhibited ferroptosis in MPNST cells by upregulating GPX4 antioxidant pathway, suggesting that mitochondrial TSPO-GPX4-ferroptosis axis may be a promising therapeutic target for improving the outcomes of patients with MPNST.

There are a number of widely used machine components, such as rolling element bearings, gears and cams, which operate in the lubrication regime known as Elastohydrodynamics (EHD), where lubricant film thickness is governed by hydrodynamic action of convergent geometry, elastic deformation between non-conformal contacting surfaces, and the increase of lubricant viscosity with pressure. Variable loading conditions occur not only in all the machine components mentioned above, but also in natural joints such as hip or knee joints of humans or many vertebrates. Experimental studies of the behaviour of EHD films under variable loading are scarce and to authors’ knowledge systematic studies of the evolution of lubricant film thickness in EHD contacts subjected to forced harmonic variation of load are even less common. The aim of the present study is to explore the effect of load amplitude on the EHD film behaviour. This is done in alternating cycles with the load varying about a fixed, preset value at various amplitudes. Experimental results are compared with a simple theoretical analysis based on the speed of change of contact’s dimensions, a semi-analytical solution which includes both speed variation and squeeze effect, and finally with a full numerical solution.

Statistical post-processing for multi-model grand ensemble (GE) hydrologic predictions is necessary, in order to achieve more accurate and reliable probabilistic forecasts. This paper presents a case study which applies Bayesian model averaging (BMA) to statistically post-process raw GE runoff forecasts in the Fu River basin in China, at lead times ranging from 6 to 120 h. The raw forecasts were generated by running the Xinanjiang hydrologic model with ensemble forecasts (164 forecast members), using seven different “THORPEX Interactive Grand Global Ensemble” (TIGGE) weather centres as forcing inputs. Some measures, such as data transformation and high-dimensional optimization, were included in the experiment after considering the practical water regime and data conditions. The results indicate that the BMA post-processing method is capable of improving the performance of raw GE runoff forecasts, yielding more calibrated and sharp predictive probability density functions (PDFs), over a range of lead times from 24 to 120 h. The analysis of percentile forecasts in two different flood events illustrates the great potential and prospects of BMA GE probabilistic river discharge forecasts, for taking precautions against severe flooding events.

Mountain flood disasters in China’s southeastern coastal watershed are not predictable and are sudden. With rapid urbanization and development in the middle and lower reaches of the region, the accumulation of wealth and population has magnified the flood risk. Exploring flood numerical simulation technology suitable for the rapid economic development of mountainous basins, effective flood models are the key tools for controlling and mitigating flood disasters. In this paper, we established a 1D/2D real-time dynamic coupling hydraulic model, aimed at exploring the applicability of the model in flood simulation of mountainous river basins with rapid economic development. The Luojiang River Basin (Huazhou Section) in Guangdong Province was used as the case study. The model’s ability was validated against the 22 July 2010 and 14 August 2013 inundation events that occurred there. The simulation results show that the output of the flood model is highly similar to the observation and survey results of historical flood events. The research results prove that the 1D/2D coupling model is not only an applicable tool for exploring flood spread characteristics such as flood range, velocity, depth, arrival time, and duration, but also can feed back the impact of water conservancy projects such as dikes on flood spread in the basin. It is of great significance to effectively guide the comprehensive design and management of subsequent wading projects in mountain river basins, and to improve flood prevention and disaster reduction capabilities in mountain areas.