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The potential of monitoring the movement of typhoons using the precipitable water vapor (PWV) has been confirmed. However, monitoring the movement of typhoon is focused on PWV, making it difficult to describe the movement of a typhoon in detail minutely and resulting in insufficient accuracy. Hence, based on PWV and meteorological data, we propose an improved typhoon monitoring mode. First, the European Centre for Medium-Range Weather Forecasts Reanalysis 5-derived PWV (ERA5-PWV) and the Global Navigation Satellite System-derived PWV (GNSS-PWV) were compared with the reference radiosonde PWV (RS-PWV). Then, using the PWV and atmospheric parameters derived from ERA5, we discussed the anomalous variations of PWV, pressure (P), precipitation, and wind speed during different typhoons. Finally, we compiled a list of critical factors related to typhoon movement, PWV and P. We developed an improved multi-factor typhoon monitoring mode (IMTM) with different models (i.e., IMTM-I and IMTM-II) in different cases with a higher density of GNSS observation or only Numerical Weather Prediction (NWP) data. The IMTM was evaluated through the reference movement speeds of HATO and Mangkhut from the China Meteorological Observatory Typhoon Network (CMOTN). The results show that the root mean square (RMS) of the IMTM-I is 1.26 km/h based on ERA5-P and ERA5-PWV, and the absolute bias values are mostly within 2 km/h. Compared with the models considering the single factor ERA5-P/ERA5-PWV, the RMS of the IMTM-I is improved by 26.3% and 38.5%, respectively. The IMTM-II model manifests a residual of only 0.35 km/h. Compared with the single-factor model based on GNSS-PWV/P, the residual of the IMTM-II model is reduced by 90.8% and 84.1%, respectively. These results propose that the typhoon movement monitoring approach combining PWV and P has evident advantages over the single-factor model and is expected to supplement traditional typhoon monitoring.

Colorectal cancer is a leading type of cancer worldwide and a major contributor to cancer fatalities, and liver metastasis is the most likely distant metastasis in colorectal cancer patients. Classifying and predicting whether liver metastasis occurs in colorectal cancer patients can help doctors timely determine the progress of the disease and form a more reasonable treatment plan, which results in a better prognosis for patients. In this paper, using the Surveillance, Epidemiology, and End Results database, selecting both symmetric and asymmetric features, we extracted the disease-related data of 40,870 patients who were pathologically diagnosed with colorectal cancer from 2010 to 2015 and classified and modeled whether the patients developed liver metastasis to show the symmetry of this study. A total of six deep learning models were utilized, and hyperparameter optimization was performed on the models using the Crested Porcupine Optimizer. The best-performing model was selected and model interpretation was performed to explore the features that affect whether patients develop liver metastasis. Among the six deep learning models selected, the FT-Transformer model, which was hyperparameter optimized by the Crested Porcupine Optimizer, performed the best, with an accuracy of 0.945, with a 95% confidence interval (CI) of [0.942, 0.952], and an AUC of 0.949, with a 95% CI of [0.942, 0.957]. This study can help doctors make medical decisions, detect patients with liver metastases of colorectal cancer earlier, monitor the indicators that have a significant impact on the occurrence of liver metastasis in patients, and use timely surgical treatment, radiotherapy, chemotherapy, and other corresponding therapeutic interventions to improve the survival rate of patients.

Stormwater resource utilization is an important function of coupled grey–green infrastructure (CGGI) that has received little research focus, especially in multi-objective optimization studies. Given the complex water problems in areas with contamination-induced water shortages, it is important to incorporate more objectives into optimization systems. Therefore, this study integrated economic performance, hydrological recovery, water quality protection, and stormwater resource utilization into an optimization framework based on the non-dominant sorting genetic algorithm III (NSGA-III). A sponge city pilot area with contamination-induced water shortages in the Yangtze River Delta was considered, optimizing four objectives under different future multi-dimensional scenarios. The results showed a time series and scenarios composed of shared socioeconomic pathways and representative concentration pathways (SSP-RCP scenarios) which, together, affected future climate change and the benefits of a CGGI. In the near and middle periods, the SSP126 scenario had the greatest influence on stormwater management, whereas, in the far period, the SSP585 scenario had the greatest influence. The far period had the greatest influence under three SSP-RCP scenarios. Under the combined influence of SSP-RCP scenarios and a time series, the SSP585-F scenario had the greatest impact. Specific costs could be used to achieve different and no stormwater-resource utilization effects through different configurations of the CGGI. This provided various construction ideas regarding CGGIs for areas with contamination-induced water shortages.

The cerebellum plays a crucial role in motor learning and memory, and recent studies have proposed various cerebellar models to investigate these functions. This review examines the literature on different levels of cerebellar modeling, including animal models, neuronal and synaptic plasticity models, relevant artificial intelligence (AI) paradigms, and real-time applications. The development of cerebellar models is discussed, from simple to complex and from theory to application. Optimization methods used in AI for optimizing cerebellar neuronal electrophysiology parameters are also highlighted, allowing for the prediction of difficult-to-observe neuronal features. Combining neuroscience and computer science-oriented neural networks, such as the spiking neural network (SNN) and the artificial neural network (ANN), can enable the cerebellar model to adapt to various applications, including robotic control, neurological disease simulation, and drug delivery simulation. This review provides a useful guide for future research on cerebellar modeling.

The low specific speed centrifugal pump plays a crucial role in industrial applications, and ensuring its efficient and stable operation is extremely important for the safety of the whole system. The pump must operate with an extremely high head, an extremely low flow rate, and a very fast speed. The internal flow structure is complex and there is a strong interaction between dynamic and static components; consequently, the hydraulic excitation force produced becomes a significant factor that triggers abnormal vibrations in the pump. Therefore, this study focuses on a low specific speed centrifugal pump and uses a single-stage model pump to conduct PIV and pressure pulsation tests. The findings reveal that the PIV tests successfully captured the typical jet-wake structure at the outlet of the impeller, as well as the flow separation structure at the leading edge of the guide vanes and the suction surface. On the left side of the discharge pipe, large-scale flow separation and reverse flow happen as a result of the flow-through effect, producing a strong vortex zone. The flow field on the left side of the pressure chamber is relatively uniform, and the low-speed region on the suction surface of the guide vanes is reduced due to the reverse flow. The results of the pressure pulsation test showed that the energy of pressure pulsation in the flow passage of the guide vane occurs at the fBPF and its harmonics, and the interaction between the rotor and stator is significant. Under the same operating condition, the RMS value distribution and amplitude at fBPF of each measurement point are asymmetric in the circumferential direction. The amplitude of fBPF near the discharge pipe is lower, while the RMS value is higher. A complex flow structure is shown by the larger amplitude and RMS value of the fBPF on the left side of the pressure chamber. With the flow rate increasing, the energy at fBPF of each measurement point increases first and then decreases, while the RMS value decreases, indicating a more uniform flow field inside the pump.

Understanding changes in ecosystem services (ESs) and quantitatively identifying the drivers that influence these changes are essential for achieving sustainable ecosystem development. In this study, multiple data sources and techniques, including meteorological data, land use/cover data, soil data, the InVEST model, and ArcGIS, were used to analyze the spatiotemporal variation characteristics of carbon storage, habitat quality, soil retention, water yield, and crop product supply in Xinghua City from 2000 to 2015. Additionally, we explored the causes of these changes and the interrelationships among these ESs. The results showed that: (1) During the study period, carbon storage and habitat quality declined, water yield fluctuated and increased, and soil retention had small interannual variations. The supply capacity of crop products first increased rapidly and then stabilized. (2) ESs were influenced by multiple drivers, with altitude having the strongest explanatory power for habitat quality and soil retention, and food production having the strongest explanatory power for crop product supply. (3) Relationships between different ESs were variable and changed over time. This study could enrich the understanding of spatial and temporal changes and drivers of ESs in the plain river network area, which has important implications for future land use planning and sustainable development of ESs.

Many complicated dynamical events may be broken down into simpler pieces and efficiently described by a system that shifts among a variety of conditionally dynamical modes. Building on switching linear dynamical systems, we develop a new model that extends the switching linear dynamical systems for better discovering these dynamical modes. In the proposed model, the linear dynamics of latent variables can be described by a higher-order vector autoregressive process, which makes it feasible to evaluate the higher-order dependency relationships in the dynamics. In addition, the transition of switching states is determined by a stick-breaking logistic regression, overcoming the limitation of a restricted geometric state duration and recovering the symmetric dependency between the switching states and the latent variables from asymmetric relationships. Furthermore, logistic regression evidence potentials can appear as conditionally Gaussian potentials by utilizing the Pólya-gamma augmentation strategy. Filtering and smoothing algorithms and Bayesian inference for parameter learning in the proposed model are presented. The utility and versatility of the proposed model are demonstrated on synthetic data and public functional magnetic resonance imaging data. Our model improves the current methods for learning the switching linear dynamical modes, which will facilitate the identification and assessment of the dynamics of complex systems.

Abstract Background Assessment of health beliefs and risk perception is a critical means to prevent coronary heart disease, but there are few such studies on assessment in the Chinese population. Given the demonstrated value and widespread use of the Attitudes and Beliefs about Cardiovascular Disease Risk Questionnaire (ABCD), this study was designed to translate it into Chinese, and to evaluate its reliability and validity in a Chinese population. Methods The Chinese version of the ABCD was created using the Beaton translation model, which included forward and backward translation. The reliability and construct validity of the Chinese ABCD were examined in a sample of 353 adults who participated in the public welfare projects of the Chinese National Center for Cardiovascular Diseases in Guilin city, Guangxi. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) were performed to examine the factor structure of the Chinse ABCD. The internal consistency of the questionnaire was assessed using Cronbach’s α and corrected item-total correlations. Results We deleted item 7 in the knowledge dimension of the Chinese ABCD and added two items about smoking and sleep knowledge, while retaining 25 of the original items, so that it finally included 27 items. The correlations were .20–.90; the correlations between each item and the total score of the ABCD were .34–.86; and the item-level Content Validity Index (I-CVI) was .86–1.00. The results of the EFA showed that all items were close to .40, and the cumulative variance contribution rate was 63.88%. The model fit was acceptable (χ2 = 698.79, df = 243, χ2/df = 2.87, P

Abstract Cancer cells adopt metabolic reprogramming to promote cell survival under metabolic stress. A key regulator of cell metabolism is AMP-activated protein kinase (AMPK) which promotes catabolism while suppresses anabolism. However, the underlying mechanism of AMPK in handling metabolic stress in cancer remains to be fully understood. In this study, by performing a proteomics screening of AMPK-interacting proteins in non-small-cell lung cancer (NSCLC) cells, we discovered the platelet isoform of phosphofructokinase 1 (PFKP), a rate-limiting enzyme in glycolysis. Moreover, PFKP was found to be highly expressed in NSCLC patients associated with poor survival. We demonstrated that the interaction of PFKP and AMPK was greatly enhanced upon glucose starvation, a process regulated by PFKP-associated metabolites. Notably, the PFKP–AMPK interaction promoted mitochondrial recruitment of AMPK which subsequently phosphorylated acetyl-CoA carboxylase 2 (ACC2) to enhance long-chain fatty acid oxidation, a process helping maintenance of the energy and redox homeostasis and eventually promoting cancer cell survival under glucose starvation. Collectively, we revealed a critical non-glycolysis-related function of PFKP in regulating long-chain fatty acid oxidation via AMPK to alleviate glucose starvation-induced metabolic stress in NSCLC cells.

Different structural composition ratios of sucrose stearates with hydrophilic–hydrophobic balance (HLB) values ranging from 1 to 16 on lipolysis in emulsion were investigated using a simulated gastrointestinal tract (GIT). Results showed a direct correlation between the HLB values of sucrose stearates and the lipolysis rate of emulsions, and a lower HLB value led to diminished lipolysis in the GIT simulation model. Mechanism study indicated that poor emulsifying capacity of sucrose stearates and lipolysis of sucrose stearates with lower HLB value inhibited the digestive behavior of oil. In addition, monoester was mainly hydrolyzed in the gastric phase, whereas sucrose polyesters caused lipolysis in the intestinal phase using an in vitro digestive model and HPLC analysis, further suppressing lipid digestion. Furthermore, a decrease in cell cytotoxicity and proinflammatory effects on Caco-2 and Raw264.7 were observed post-digestion, respectively. This work offers important insights into the effects of the degree of esterification of sucrose stearate on lipid digestion behavior in oil-in-water emulsions.

The flexible job shop scheduling problem (FJSP), one of the core problems in the field of generative manufacturing process planning, has become a hotspot and a challenge in manufacturing production research. In this study, an improved self-learning genetic algorithm is proposed. The single mutation approach of the genetic algorithm was improved, while four mutation operators were designed on the basis of process coding and machine coding; their weights were updated and their selection mutation operators were adjusted according to the performance in the iterative process. Combined with the improved population initialization method and the optimized crossover strategy, the local search capability was enhanced, and the convergence speed was accelerated. The effectiveness and feasibility of the algorithm were verified by testing the benchmark arithmetic examples and numerical experiments.

With the interdisciplinary convergence of biology, medicine and materials science, both research and clinical translation of biomaterials are progressing at a rapid pace. However, there is still a huge gap between applied basic research on biomaterials and their translational products - medical devices, where two significantly different perspectives and mindsets often work independently and non-synergistically, which in turn significantly increases financial costs and research effort. Although this gap is well-known and often criticized in the biopharmaceutical industry, it is gradually widening. In this article, we critically examine the developmental pipeline of biodegradable biomaterials and biomaterial-based medical device products. Then based on clinical needs, market analysis, and relevant regulations, some ideas are proposed to integrate the two different mindsets to guide applied basic research and translation of biomaterial-based products, from the material and technical perspectives. Cartilage repair substitutes are discussed here as an example. Hopefully, this will lay a strong foundation for biomaterial research and clinical translation, while reducing the amount of extra research effort and funding required due to the dissonance between innovative basic research and commercialization pipeline.

The tubular dynamic hydrocyclone (TDH) holds great potential for the pre-deoiling of offshore oil platforms. However, the shear and turbulence in the flow field can cause the oil droplets, the dispersed phase in water, to break up when the swirling flow is produced by the swirler. A design method is proposed for the low-shear rotary swirler (LSRS) of TDH, the aim of which is to reduce the shear force and local turbulence during the fluid forming swirling flow. The blade setting angle of the LSRS is calculated based on the relative velocity vector between the fluid and the swirler. The distribution characteristics of the tangential velocity and turbulence in the TDH with LSRS are simulated by Computational Fluid Dynamics (CFD). The maximum stable droplet diameter is analyzed. The results show that the shear stress and turbulence energy dissipation rates are reduced by 74.6% and 68.5%, respectively, and that the stable droplet diameter is increased by more than 60%, compared to the conventional rotating swirler. In addition, a TDH prototype with LSRS was tested in an offshore oil field by continuous operation for more than 36 h. The average separation efficiency was 83%, and the average underflow oil concentration was 27 mg/L. The research also found that the drastic changes in the tangential velocity along the axial direction were critical to shear. Moreover, the results make up for the deficiency of the spatial variation of the tangential velocity in the dynamic cyclone separator.

Stable integration of hydrogel implants with host tissues is of critical importance to cartilage tissue engineering. Designing and fabricating hydrogels with high adhesive strength, stability and regeneration potential are major challenges to be overcome. This study fabricated injectable adhesive hyaluronic acid (HA) hydrogel modified by aldehyde groups and methacrylate (AHAMA) on the polysaccharide backbone with multiple anchoring mechanisms (amide bond through the dynamic Schiff base reaction, hydrogen bond and physical interpenetration). AHAMA hydrogel exhibited significantly improved durability and stability within a humid environment (at least 7 days), together with higher adhesive strength (43 KPa to skin and 52 KPa to glass), as compared to commercial fibrin glue (nearly 10 KPa) and HAMA hydrogel (nearly 20 KPa). The results showed that AHAMA hydrogel was biocompatible and could be easily and rapidly prepared in situ. In vitro cell culture experiments showed that AHAMA hydrogel could enhance proliferation (1.2-folds after 3 days) and migration (1.5-folds after 12 h) of bone marrow stem cells (BMSCs), as compared to cells cultured in a culture dish. Furthermore, in a rat osteochondral defect model, implanted AHAMA hydrogel significantly promoted integration between neo-cartilage and host tissues, and significantly improved cartilage regeneration (modified O'Driscoll histological scores of 16.0 ± 4.1 and 18.3 ± 4.6 after 4 and 12-weeks of post-implantation in AHAMA groups respectively, 12.0 ± 2.7 and 12.2 ± 2.8 respectively in HAMA groups, 9.8 ± 2.4 and 11.5 ± 2.1 respectively in untreated groups). Hence, AHAMA hydrogel is a promising adhesive biomaterial for clinical cartilage regeneration and other biomedical applications.

Glacial debris flow is a common natural disaster, and its frequency has been increasing in recent years due to the continuous retreat of glaciers caused by global warming. To reduce the damage caused by glacial debris flows to human and physical properties, glacier susceptibility assessment analysis is needed. Most research efforts consider the effect of existing glacier area and ignore the effect of glacier ablation volume change. In this paper, we consider the impact of glacier ablation volume change to investigate the susceptibility of glacial debris flow. The susceptibility to mudslide was evaluated by taking the glacial mudslide-prone ditch of G318 Linzhi section of Sichuan-Tibet Highway as the research object. First, by using a simple band ratio method with manual correction, we produced a glacial mudslide remote sensing image dataset, and second, we proposed a deep-learning-based approach using a weight-optimized glacial mudslide semantic segmentation model for accurately and automatically mapping the boundaries of complex glacial mudslide-covered remote sensing images. Then, we calculated the ablation volume by the change in glacier elevation and ablation area from 2015 to 2020. Finally, glacial debris flow susceptibility was evaluated based on the entropy weight method and Topsis method with glacial melt volume in different watersheds as the main factor. The research results of this paper show that most of the evaluation indices of the model are above 90%, indicating that the model is reasonable for glacier boundary extraction, and remote sensing images and deep learning techniques can effectively assess the glacial debris flow susceptibility and provide support for future glacial debris flow disaster prevention.

Abstract Luneburg lenses and Maxwell fisheye lenses possess distinct properties of focusing, well beyond conventional lenses made of uniform materials. In this paper, a planar broadband bifunctional Luneburg-fisheye lens synthesized by gradient anisotropic metasurface is proposed. The proposed anisotropic metasurface is formed by non-resonant anisotropic cells, so that it can independently realize the equivalent gradient refractive indexes of Luneburg lens and Maxwell fisheye lens along orthogonal directions in a broad band, respectively. To verify the performance of the bifunctional lens, a prototype associated with a feeding log-periodic dipole antenna has been fabricated. Experimental results show that the proposed lens functions well over a wide frequency range with high efficiency and low profile, which coincides well with theoretical predictions and simulated results. It is expected that the proposed design will facilitate the applications of multifunctional metadevices in microwave and optical ranges.

Joint models of longitudinal and time-to-event data have received a lot of attention in epidemiological and clinical research under a linear mixed-effects model with the normal assumption for a single longitudinal outcome and Cox proportional hazards model. However, those model-based analyses may not provide robust inference when longitudinal measurements exhibit skewness and/or heavy tails. In addition, the data collected are often featured by multivariate longitudinal outcomes which are significantly correlated, and ignoring their correlation may lead to biased estimation. Under the umbrella of Bayesian inference, this article introduces multivariate joint (MVJ) models with a skewed distribution for multiple longitudinal exposures in an attempt to cope with correlated multiple longitudinal outcomes, adjust departures from normality, and tailor linkage in specifying a time-to-event process. We develop a Bayesian joint modeling approach to MVJ models that couples a multivariate linear mixed-effects (MLME) model with the skew-normal (SN) distribution and a Cox proportional hazards model. Our proposed models and method are evaluated by simulation studies and are applied to a real example from a diabetes study.

Sufficient variable screening rapidly reduces dimensionality with high probability in ultra-high dimensional modeling. To rapidly screen out the null predictors, a quantile-adaptive sufficient variable screening framework is developed by controlling the false discovery. Without any specification of an actual model, we first introduce a compound testing procedure based on the conditionally imputing marginal rank correlation at different quantile levels of response to select active predictors in high dimensionality. The testing statistic can capture sufficient dependence through two paths: one is to control false discovery adaptively and the other is to control the false discovery rate by giving a prespecified threshold. It is computationally efficient and easy to implement. We establish the theoretical properties under mild conditions. Numerical studies including simulation studies and real data analysis contain supporting evidence that the proposal performs reasonably well in practical settings.

The two-sided and one-sided empirical Bayes test (EBT) rules for the parameter of a generalized exponential distribution with contaminated data (errors in variables) are constructed by a deconvolution kernel method, respectively. Under the type of the supersmooth error distributions and the supersmooth errors with the error level can be controlled situations, the asymptotically optimal uniformly over a class of prior distributions and uniform rates of convergence of the corresponding regret for the proposed EBT rules are obtained with suitable conditions. The example study shows that the assumptions and conditions of the main results of this paper are satisfied easily by calculating.

Cancer therapy is moving beyond traditional chemotherapy to include epigenetic approaches. KDM6 demethylases are dynamic regulation of gene expression by histone demethylation in response to diverse stimuli, and thus their dysregulation has been observed in various cancers. In this review, we first briefly introduce structural features of KDM6 subfamily, and then discuss the regulation of KDM6, which involves the coordinated control between cellular metabolism (intrinsic regulators) and tumor microenvironment (extrinsic stimuli). We further describe the aberrant functions of KDM6 in human cancers, acting as either a tumor suppressor or an oncoprotein in a context-dependent manner. Finally, we propose potential therapy of KDM6 enzymes based on their structural features, epigenetics, and immunomodulatory mechanisms, providing novel insights for prevention and treatment of cancers.

Systemic lupus erythematosus (SLE) is a prototypic autoimmune disease characterized by the loss of immune tolerance. Lupus nephritis (LN) is still a major cause of the morbidity and mortality of SLE. In clinical practice, diagnosis, and therapy of SLE is complicated and challenging due to lack of ideal biomarkers. Exosomes could be detected from numerous kinds of biological fluids and their specific contents are considered as hallmarks of autoimmune diseases. The exosomal miRNA profiles of SLE/LN patients significantly differ from those of the healthy controls making them as attractive biomarkers for renal injury. Exosomes are considered as optimal delivery vehicles owing to their higher stable, minimal toxicity, lower immunogenicity features and specific target effects. Endogenous miRNAs can be functionally transferred by exosomes from donor cells to recipient cells, displaying their immunomodulatory effects. In addition, it has been confirmed that exosomal miRNAs could directly interact with Toll-like receptors (TLRs) signaling pathways to regulate NF-κB activation and the secretion of inflammatory cytokines. The present Review mainly focuses on the immunomodulatory effects of exosomal-miRNAs, the complex interplay between exosomes, miRNAs and TLR signaling pathways, and how the exosomal-miRNAs can become non-invasive diagnostic molecules and potential therapeutic strategies for the management of SLE.

According to the standard, after the anti-corrosion coating is immersed for a certain period of time, if there is no foaming, cracking, falling off, etc., the evaluation result is qualified. However, the adhesion and the corrosion resistance of some of the qualified coatings are greatly reduced. In this paper, eight kinds of anti-corrosion coatings qualified after soaking were selected, and the dry adhesion and wet adhesion after soaking in acidic environment were tested. The test results showed that the adhesion of some coatings after immersion was greatly reduced or lost, the metal matrix has obvious corrosion characteristics; the adhesion of some coatings was reduced; the adhesion of the two kinds of anti-corrosion coatings cured at room temperature was slightly increased. It can be seen that by comparing the dry/wet adhesion of the coating or tracking the change of adhesion during the coating service, the maintenance performance and development trend of the coating performance can be quantitatively grasped.

As most offshore oilfields come to the middle- and late-exploitation period, and with the popularization of tertiary enhanced oil recovery (EOR) technology, the physical properties of produced fluids become more complex, bringing new challenges to oil-water separation. Conventional solutions, such as increasing the input of chemicals, enlarging the volume of the separator equipment, and extending the processing, are usually uneconomical and space-wasting. Electric-field-enhanced oil-water separation equipment was developed in this paper, and a mine field test was carried out. With the average water cut of different sampling ports and the overall dehydration rate taken as indicators, the effects of electric field frequency, inlet flow, chemical type, and addition upon the separation of the device were evaluated. The experimental results showed that for the ABJ mixed liquid, the optimal operating frequency of the electric field is above 3500 Hz. Compared with traditional separation equipment, the advantages of the electric-field-enhanced oil–water separation equipment are more significant in large flow conditions. When the water cut of the platform inlet fluctuates between 78% and 97%, and without the addition of chemicals, the average water cut was reduced to 7% and the average dehydration rate reached 90%, an improvement of about 15%.

With the rapid development of cities, semantic segmentation of urban scenes, as an important and effective imaging method, can accurately obtain the distribution information of typical urban ground features, reflecting the development scale and the level of greenery in the cities. There are some challenging problems in the semantic segmentation of point clouds in urban scenes, including different scales, imbalanced class distribution, and missing data caused by occlusion. Based on the point cloud semantic segmentation network RandLA-Net, we propose the semantic segmentation networks RandLA-Net++ and RandLA-Net3+. The RandLA-Net++ network is a deep fusion of the shallow and deep features of the point clouds, and a series of nested dense skip connections is used between the encoder and decoder. RandLA-Net3+ is based on the multi-scale connection between the encoder and decoder; it also connects internally within the decoder to capture fine-grained details and coarse-grained semantic information at a full scale. We also propose incorporating dilated convolution to increase the receptive field and compare the improvement effect of different loss functions on sample class imbalance. After verification and analysis of our labeled urban scene LiDAR point cloud dataset—called NJSeg-3D—the mIoU of the RandLA-Net++ and RandLA-Net3+ networks is 3.4% and 3.2% higher, respectively, than the benchmark network RandLA-Net.

This paper constructs the double penalized expectile regression for linear mixed effects model, which can estimate coefficient and choose variable for random and fixed effects simultaneously. The method based on the linear mixed effects model by cojoining double penalized expectile regression. For this model, this paper proposes the iterative Lasso expectile regression algorithm to solve the parameter for this mode, and the Schwarz Information Criterion (SIC) and Generalized Approximate Cross-Validation Criterion (GACV) are used to choose the penalty parameters. Additionally, it establishes the asymptotic normality of the expectile regression coefficient estimators that are suggested. Though simulation studies, we examine the effects of coefficient estimation and the variable selection at varying expectile levels under various conditions, including different signal-to-noise ratios, random effects, and the sparsity of the model. In this work, founding that the proposed method is robust to various error distributions at every expectile levels, and is superior to the double penalized quantile regression method in the robustness of excluding inactive variables. The suggested method may still accurately exclude inactive variables and select important variables with a high probability for high-dimensional data. The usefulness of doubly penalized expectile regression in applications is illustrated through a case study using CD4 cell real data.

A bent line expectile regression model can describe the effect of a covariate on the response variable with two different straight lines, which intersect at an unknown change-point. Due to the existence of the change-point, the objective function of the model is not differentiable with respect to the change-point, so it cannot be solved by the method of the traditional linear expectile regression model. For this model, a new estimation method is proposed by a smoothing technique, that is, using Gaussian kernel function to approximate the indicator function in the objective function. It can not only estimate the regression coefficients and change-point location simultaneously, but also have better estimation effect, which compensates for the insufficiency of the previous estimation methods. Under the given regularity conditions, the theoretical proofs of the consistency and asymptotic normality of the proposed estimators are derived. There are two parts of numerical simulations in this paper. Simulation 1 discusses various error distributions at different expectile levels under different conditions, the results show that the mean values of the biases of the estimation method in this paper, and other indicators, are very small, which indicates the robust property of the new method. Simulation 2 considers the symmetric and asymmetric bent lien expectile regression models, the results show that the estimated values of the estimation method in this paper are similar to the true values, which indicates the estimation effect and large sample performance of the proposed method are excellent. In the application research, the method in this paper is applied to the Arctic annual average temperature data and the Nile annual average flow data. The research shows that the standard errors of the estimation method in this paper are very similar to 0, indicating that the parameter estimation accuracy of the new method is very high, and the location of the change-point can be accurately estimated, which further confirms that the new method is effective and feasible.

Pharmacometabolomics has been already successfully used in toxicity prediction for one specific adverse effect. However in clinical practice, two or more different toxicities are always accompanied with each other, which puts forward new challenges for pharmacometabolomics. Gastrointestinal toxicity and myelosuppression are two major adverse effects induced by Irinotecan (CPT-11), and often show large individual differences. In the current study, a pharmacometabolomic study was performed to screen the exclusive biomarkers in predose serums which could predict late-onset diarrhea and myelosuppression of CPT-11 simultaneously. The severity and sensitivity differences in gastrointestinal toxicity and myelosuppression were judged by delayed-onset diarrhea symptoms, histopathology examination, relative cytokines and blood cell counts. Mass spectrometry-based non-targeted and targeted metabolomics were conducted in sequence to dissect metabolite signatures in predose serums. Eventually, two groups of metabolites were screened out as predictors for individual differences in late-onset diarrhea and myelosuppression using binary logistic regression, respectively. This result was compared with existing predictors and validated by another independent external validation set. Our study indicates the prediction of toxicity could be possible upon predose metabolic profile. Pharmacometabolomics can be a potentially useful tool for complicating toxicity prediction. Our findings also provide a new insight into CPT-11 precision medicine. KEY WORDS: Irinotecan, Individual differences, Complicating toxicity, Prediction, Metabolomics, Gastrointestinal toxicity, Biomarkers, Diarrhea

Fundamental to the understanding of the functions of spatial cognition and attention is to clarify the underlying neural mechanisms. It is clear that relatively right-dominant activity in ventral and dorsal parieto-frontal cortex is associated with attentional reorienting, certain forms of mental imagery and spatial working memory for higher loads, while lesions mostly to right ventral areas cause spatial neglect with pathological attentional biases to the right side. In contrast, complementary leftward biases in healthy people, called pseudoneglect, have been associated with varying patterns of cortical activity. Notably, this inconsistency may be explained, at least in part, by the fact that pseudoneglect studies have often employed experimental paradigms that do not control sufficiently for cognitive processes unrelated to pseudoneglect. To address this issue, here we administered a carefully designed continuum of pseudoneglect and control tasks in healthy adults while using functional magnetic resonance imaging (fMRI). Data submitted to partial least square (PLS) imaging analysis yielded a significant latent variable that identified a right-dominant network of brain regions along the intra-occipital and -parietal sulci, frontal eye fields and right ventral cortex in association with perceptual pseudoneglect. Our results shed new light on the interplay of attentional and cognitive systems in pseudoneglect.

As most of the light and easy oil fields have been produced or are nearing their end-life, the emulsion stability is enhanced and water cut is increasing in produced fluid which have brought challenges to oil–water separation in onshore and offshore production trains. The conventional solution to these challenges includes a combination of higher chemical dosages, larger vessels and more separation stages, which often demands increased energy consumption, higher operating costs and larger space for the production facility. It is not always feasible to address the issues by conventional means, especially for the separation process on offshore platforms. Electrostatic coalescence is an effective method to achieve demulsification and accelerate the oil–water separation process. In this paper, a novel compact electrostatic coalescer with helical electrodes was developed and its performance on treatment of water-in-oil emulsions was investigated by experiments. Focused beam reflectance measurement (FBRM) was used to make real-time online measurements of water droplet sizes in the emulsion. The average water droplet diameters and number of droplets within a certain size range are set as indicators for evaluating the effect of coalescence. We investigated the effect of electric field strength, frequency, water content and fluid velocity on the performance of coalescence. The experimental results showed that increasing the electric field strength could obviously contribute to the growth of small water droplets and coalescence. The extreme value of electric field strength achieved in the high-frequency electric field was much higher than that in the power-frequency (50 Hz) electric field, which can better promote the growth of water droplets. The initial average diameters of water droplets increase with higher water content. The rate of increment in the electric field was also increased. Its performance was compared with that of the plate electrodes to further verify the advantages of enhancing electrostatic coalescence and demulsification with helical electrodes. The research results can provide guidance for the optimization and performance improvement of a compact electrocoalescer.

Present knowledge of attention and awareness centres on deficits in patients with right brain damage who show severe forms of inattention to the left, called spatial neglect. Yet the functions that are lost in neglect are poorly understood. In healthy people, they might produce "pseudoneglect"-subtle biases to the left found in various tests that could complement the leftward deficits in neglect. But pseudoneglect measures are poorly correlated. Thus, it is unclear whether they reflect anything but distinct surface features of the tests. To probe for a common mechanism, here we asked whether visual noise, known to increase leftward biases in the grating-scales task, has comparable effects on other measures of pseudoneglect. We measured biases using three perceptual tasks that require judgments about size (landmark task), luminance (greyscales task) and spatial frequency (grating-scales task), as well as two visual search tasks that permitted serial and parallel search or parallel search alone. In each task, we randomly selected pixels of the stimuli and set them to random luminance values, much like a poor TV signal. We found that participants biased their perceptual judgments more to the left with increasing levels of noise, regardless of task. Also, noise amplified the difference between long and short lines in the landmark task. In contrast, biases during visual searches were not influenced by noise. Our data provide crucial evidence that different measures of perceptual pseudoneglect, but not exploratory pseudoneglect, share a common mechanism. It can be speculated that this common mechanism feeds into specific, right-dominant processes of global awareness involved in the integration of visual information across the two hemispheres.

A numerical model of multiphase flow was developed to investigate the heat and mass transfer mechanisms in refueling process. The two-dimensional model takes into account the effects of surface tension, gravity, and viscosity. The heat and mass exchange process of free surface was defined to study isooctane liquid evaporation in refueling process. The reliability of the model was verified by the theoretical solution of the Stephan problem. The evolution of the flow field, temperature field, pressure field, concentration field, and evaporation rate was discussed in detail. It shows that there is a strong turbulence and gas entrainment phenomenon. The volume-averaged temperature of the fuel tank is lower than the ambient temperature, and the maximum difference is about 4°C resulting from evaporation; the curve of evaporation rate versus time is very similar to that of the volume-averaged pressure versus time. Besides, the change in evaporation rate is negligible after 1.75 s.

Lomatogonium rotatum (L.) Fries ex Nym (LR) is used as a traditional Mongolian medicine to treat liver and bile diseases. This study aimed to investigate the hepatoprotective effect of LR on mice with CCl4-induced acute liver injury through conventional assays and metabolomics analysis. This study consisted of male mice (n = 23) in four groups (i.e., control, model, positive control, and LR). The extract of whole plant of LR was used to treat mice in the LR group. Biochemical and histological assays (i.e., serum levels of alanine transaminase (ALT) and aspartate transaminase (AST), and histological changes of liver tissue) were used to evaluate LR efficacy, and metabolomics analysis based on GC-MS and LC-MS was conducted to reveal metabolic changes. The conventional analysis and metabolomic profiles both suggested that LR treatment could protect mice against CCl4-induced acute liver injury. The affected metabolic pathways included linoleic acid metabolism, α-linolenic acid metabolism, arachidonic acid metabolism, CoA biosynthesis, glycerophospholipid metabolism, the TCA cycle, and purine metabolism. This study identified eight metabolites, including phosphopantothenic acid, succinic acid, AMP, choline, glycerol 3-phosphate, linoleic acid, arachidonic acid, and DHA, as potential biomarkers for evaluating hepatoprotective effect of LR. This metabolomics study may shed light on possible mechanisms of hepatoprotective effect of LR.

Four new isocoumarin derivatives, botryospyrones A (1), B (2), C (3), and D (4), and a new natural tryptamine, (3aS, 8aS)-1-acetyl-1, 2, 3, 3a, 8, 8a-hexahydropyrrolo [2,3b] indol-3a-ol (5), were isolated from a marine mangrove endophytic fungus Botryosphaeria ramosa L29, obtained from the leaf of Myoporum bontioides. Their structures were elucidated using spectroscopic analysis. The absolute configurations of compounds 3, 4, and 5 were determined by comparison of their circular dichroism (CD) spectra with the calculated data. The inhibitory activities of compound 1 on Fusarium oxysporum, of compounds 2 and 3 on F. oxysporum and Fusarium graminearum, and of compound 5 on F. oxysporum, Penicillium italicum, and F. graminearum were higher than those of triadimefon, widely used as an agricultural fungicide. Compound 5 was produced after using the strategy we called “using inhibitory stress from components of the host„ (UISCH), wherein (2R, 3R)-3, 5, 7-trihydroxyflavanone 3-acetate, a component of M. bontioides with weak growth inhibitory activity towards B. ramosa L29, was introduced into the culture medium.