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Abstract Background Anlotinib, an oral multitarget tyrosine kinase inhibitor, has shown the ability to inhibit tumor angiogenesis. This study aimed to assess the effectiveness and safety of anlotinib plus docetaxel, epirubicin, and cyclophosphamide (TEC) as a neoadjuvant chemotherapy regimen for locally advanced TNBC. Methods Locally advanced TNBC patients who had received no prior systemic treatment were eligible for this study. The enrolled patients were scheduled to undergo six cycles of anlotinib (12 mg, d1-14, q3w) plus docetaxel (75 mg/m2, d1, q3w), epirubicin (75 mg/m2, d1, q3w) and cyclophosphamide (600 mg/m2, d1, q3w) prior to surgery, unless there was disease progression or severe toxicity. The primary objective of this study was the safety of this therapeutic regimen, and the secondary objective was the tumor response. The safety of this regimen was assessed using Common Terminology Criteria for Adverse Events (CTCAE) version 4.03 and the efficacy of this treatment was measured using the Response Evaluation Criteria in Solid Tumors version 1.1. Results A total of 18 patients were included in this study. Participants completed an average of 5.56 neoadjuvant treatment cycles. The objective response rate (ORR) was 83.33%, and the disease control rate was 100%, respectively. The pCR was 55.6%. No patients discontinued therapy because of Adverse effects (AEs). Grade 3 or 4 AEs were observed in 5 cases (27.8%), with neutropenia and palmar-plantar erythrodysesthesia syndrome being the most common. Conclusions Anlotinib combined with TEC as neoadjuvant therapy demonstrated manageable toxicity and promising antitumor activity for locally advanced TNBC. Further investigation of this combination regimen is warranted.

Objective: The present study aimed to explore the expression and clinical significance of human papilloma virus-related pathogenic factors (p16, cyclin D1, p53) in patients with head and neck squamous cell carcinoma (HNSCC) and construct a predictive model. Methods: The Cancer Genome Atlas was used to obtain clinical data for 112 patients with HNSCC. Expression of p16, p53, and cyclin D1 was quantified. We used the survival package of the R program to set the cut-off value. Values above the cut-off were considered positive, while values below the cut-off were negative. Kaplan–Meier analysis and univariate and multivariate Cox regression analyses were performed to investigate prognostic clinicopathological indicators and the expression of p16, p53, and cyclin D1. A predictive model was constructed based on the results of multifactor Cox regression analysis, and the accuracy of the predictive model was verified through final calibration analysis. Follow-up of patients with HNSCC at the Affiliated Hospital of Binzhou Medical University was conducted from 2015 to 2017, and reliability of the predictive model was validated based on follow-up data and molecular expression levels. Results: According to the results, expression of p16 and p53 was significantly associated with prognosis (P < .05). The predictive model constructed based on the expression levels of p16 and p53 was useful for evaluating the prognosis of patients with HNSCC. The predictive model was validated using follow-up data obtained from the hospital, and the trend of the follow-up results was consistent with the predictive model. Conclusion: p16 and p53 can be used as key indicators to predict the prognosis of HNSCC patients and as critical immunohistochemical indicators in clinical practice. The survival model constructed based on p16 and p53 expression levels reliably predicts patient prognosis.

This paper introduces the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT), a state-of-the-art deep learning framework designed to enhance multi-variate time series forecasting. The MDSTFT diverges from traditional models by integrating Convolutional Neural Networks (CNNs), Attention-Enhanced Long Short-Term Memory (LSTM) networks, and advanced Transformer models into a cohesive architecture. This framework intricately captures the spatial-temporal dynamics in complex datasets, significantly improving forecasting accuracy. At its core, MDSTFT employs a multi-dimensional spatio-temporal encoder to meticulously unravel spatial correlations among variables, followed by the dynamic processing of temporal patterns through LSTM networks augmented with a sophisticated attention mechanism. This dual-phase processing is intricately designed to capture the essence of time series data, which is then refined through our novel Fusion Transformer Layers. These layers, incorporating advanced self-attention mechanisms, elevate the model’s capacity to discern long-range dependencies and subtle temporal nuances, thus offering a profound understanding of temporal sequences. Evaluated on the “Bank Central Asia Stock Historical Price” dataset, MDSTFT demonstrates unparalleled forecasting accuracy, significantly outperforming both traditional models and contemporary deep learning approaches. This empirical validation not only highlights MDSTFT’s efficacy in financial market forecasting but also its broader applicability across various domains requiring nuanced multi-variate time series analysis. Through integrating multiple deep learning paradigms, MDSTFT not only advances the field of time series forecasting but also sets a new benchmark for predictive analytics, promising enhanced decision-making capabilities in finance, environmental studies, supply chain management, and beyond.

The carbon quota allocation method serves as the foundation for the design of the carbon trading mechanism, which has a significant impact on supply chain production decisions and the operational efficiency of the carbon trading market. To analyze the behavioral decision problem of supply chain members under different carbon quota allocation methods, the low-carbon reference effect is introduced to characterize the effect of consumers’ low-carbon preference on market demand. On this basis, three differential game models are constructed, namely, no emissions penalty, trading under the grandfathering principle, and trading under the benchmarking principle. The results indicate that the implementation of carbon trading policies enhances consumers’ low-carbon reference levels, the carbon emission reduction levels of manufacturers, and the low-carbon publicity levels of retailers. Moreover, the enhancement of the low-carbon reference effect becomes a positive driver of profit growth. Manufacturers are observed to make more efforts in carbon reduction under the benchmarking principle compared to the grandfathering principle. In contrast, the level of low-carbon publicity by retailers remains unchanged. The above findings can provide a scientific basis for the decision-making of emission reduction in low-carbon supply chain enterprises, which has certain theoretical significance.

In the current era, the government consistently emphasizes the pursuit of high-quality development, as evidenced by the ongoing increase in the tertiary industry’s GDP share. As a crucial component of the modern service sector, logistics plays a pivotal role in determining the operational efficiency and overall quality of the industrial economy. This study focuses on constructing a Chongqing logistics express demand prediction index system. It employs an improved BP neural network model to forecast the logistics express demand for Chongqing over the next five years. Given the limited express demand data sequence and the normalized characteristics of the data, the selected training method is the Bayesian regularization approach, with the LeCun Tanh function serving as the hidden layer activation function. Additionally, a genetic algorithm is designed to optimize the initial weights and thresholds of the BP neural network, thereby enhancing prediction accuracy and reducing the number of iterations. The experimental results of the improved GA-BP network are analyzed and compared, demonstrating that the improved BP neural network, utilizing GA optimization, can more reliably and accurately predict regional logistics express demand. According to the findings, the forecast indicates that the logistics express demand for Chongqing in 2026 will be 2,171,642,700 items.

The transition from land to sea affects the wind field in coastal regions. From the perspective of near-coastal offshore wind farms, the coastal transition complicates the task of energy resource assessment by, for example, introducing non-homogeneity into the free wind field. To help elucidate the matter, we quantify the average horizontal wind speed gradients at progressively increasing distances from the German coast using two years of hourly ERA5 reanalysis data, and further describe the dependence of wind speed gradients on the measurement height, atmospheric stability, and season. A vertical wind lidar located on Norderney Island near the German mainland acts as our observational reference for the ERA5 data, where a good agreement (R2=0.93$R^2 =\nobreak 0.93$) is found despite the relatively coarse ERA5 data resolution. Interestingly, the comparison of lidar data with the higher-resolution Weather Research and Forecasting (WRF) mesoscale model yields good but relatively weaker agreement (R2=0.85$R^2 =\nobreak 0.85$). The ERA5 data reveal that, for flow over the North Sea originating from the German mainland from the south, the wind speed at 10 m (110 m) above sea level increases by 30 % (20 %) some 80 km from the coast on average, and by 5 % at larger heights. An increased stratification increases the horizontal wind speed gradient at 10 m above sea level but decreases it at 110 m. Case studies using satellite and flight measurements are first analyzed to help reveal some of the underlying mechanisms governing horizontal wind speed gradients, including cases of decreasing wind speed with increasing distance from the coast, in which stable flow of warm air over the colder sea leads to an overall deceleration of the flow. The accuracy of offshore resource assessment appears to profit from utilising the horizontal wind speed gradient information contained in ERA5 reanalysis data.

Abstract Background The incidence of colorectal cancer and cancer death rate are increasing every year, and the affected population is becoming younger. Traditional Chinese medicine therapy has a unique effect in prolonging survival time and improving the prognosis of patients with colorectal cancer. Oridonin has been reported to have anti-cancer effects in a variety of tumors, but the exact mechanism remains to be investigated. Methods Cell Counting Kit-8 assay (CCK8) and 5-Ethynyl-2'-deoxyuridine (EdU) staining assay, Tranwell, and Wound healing assays were performed to measure cell proliferation, invasion, and migration capacities, respectively. The protein and mRNA expression levels of various molecules were reflected by Western blot and Reverse Transcription quantitative Polymerase Chain Reaction (qRT-PCR). Transcription Factor 4 (TCF4) and its target genes were analyzed by Position Weight Matrices (PWMs) software and the Gene Expression Omnibus (GEO) database. Immunofluorescence (IF) was performed to visualize the expression and position of Endoplasmic Reticulum (ER) stress biomarkers. The morphology of the ER was demonstrated by the ER tracker-red. Reactive Oxygen Species (ROS) levels were measured using a flow cytometer (FCM) or fluorescent staining. Calcium ion (Ca2+) concentration was quantified by Fluo-3 AM staining. Athymic nude mice were modeled with subcutaneous xenografts. Results Oridonin inhibited the proliferation, invasion, and migration of colorectal cancer, and this effect was weakened in a concentration-dependent manner by ER stress inhibitors. In addition, oridonin-induced colorectal tumor cells showed increased expression of ER stress biomarkers, loose morphology of ER, increased vesicles, and irregular shape. TCF4 was identified as a regulator of ER stress by PWMs software and GEO survival analysis. In vitro and in vivo experiments confirmed that TCF4 inhibited ER stress, reduced ROS production, and maintained Ca2+ homeostasis. In addition, oridonin also activated TP53 and inhibited TCF4 transactivation, further exacerbating the elevated ROS levels and calcium ion release in tumor cells and inhibiting tumorigenesis in colorectal cancer cells in vivo. Conclusions Oridonin upregulated TP53, inhibited TCF4 transactivation, and induced ER stress dysregulation in tumor cells, promoting colorectal cancer cell death. Therefore, TCF4 may be one of the important nodes for tumor cells to regulate ER stress and maintain protein synthesis homeostasis. And the inhibition of the TP53/TCF4 axis plays a key role in the anti-cancer effects of oridonin.

Colorectal cancer (CRC) is the most prevalent cancer of the digestive tract. Herba Patriniae (also known as Bai Jiang Cao, HP) have been widely used to manage diarrhea, ulcerative colitis, and several cancers, including CRC. Nonetheless, the molecular mechanisms underlying the pharmacological action of HP on CRC remain unclear. This study investigated the underlying mechanisms of HP against CRC using network pharmacology analysis and in vitro and in vivo experiments. The results revealed nine bioactive compounds of HP. Furthermore, 3460 CRC-related targets of the identified active compounds were predicted from the Gene Expression Omnibus (GEO) database. Furthermore, 65 common targets were identified through the intersection of two related targets. Moreover, ten hub genes, including CDK4, CDK2, CDK1, CCND1, CCNB1, CCNA2, MYC, E2F1, CHEK1, and CDKN1A were identified through the topological analysis. Meanwhile, the GO and KEGG pathway analysis revealed that the core target genes were majorly enriched in the p53 and HIF-1 signaling pathways. Moreover, HP promoted apoptosis and suppressed cell proliferation by activating the p53 signaling pathway in a dose-dependent manner, while a similar effect was observed for Isovitexin (the primary component of HP). Overall, this study provides valuable insights into the underlying mechanisms of HP and its component Isovitexin against CRC, providing a theoretical foundation for additional experimental verification of its clinical application.

In this paper, konjac glucomannan (KGM) and curdlan (CUD) were used to prepare biomass aerogels with different mass ratios (KGM/CUD, K1C0.6, K1C0.9, K1C1.2) based on sol-gel and vacuum freeze-drying methods. The four dominant strains of Aspergillus niger (Fen), Penicillium expansum (L), P. italicum (B3) and Bacillus sp. (WE-3) were used to degrade the composite aerogel, and the degradation morphology, weight change, microscopic morphology, molecular change, thermal stability and strain growth adaptability were investigated. The results showed that the aerogels collapsed and shrinking with abundant thallus on the surface. During the degradation process of the aerogel, the water from the environment was absorbed by the hygroscopicity of KGM, and the degradation process would be accelerated. FTIR showed that the long chains of polysaccharides were degraded into short chains after degradation, and no new groups were produced. SEM pictures expressed that the porous structure of aerogel was destroyed and the three-dimensional network structure collapsed. DSC analysis showed that the thermal stability of aerogel significantly decreased. The fungi Fen, L and B3 were suitable for the growth of KC aerogel environment, while the bacteria WE-3 struggled to adapt. This research was expected to provide theoretical reference for the biodegradation of polysaccharide composites.

At present, the noise of pure electric vehicles is a research hotspot, especially the noise of automatic transmission. In order to reduce the noise problem in the test, this paper proposes a method to optimize the local structure of the gearbox housing. First, the noise is evaluated and analyzed by combining subjective and objective methods, and the subjective score and noise order information are obtained. Then the factors that have great influence on the transmission error are explored, and the gearbox housing is finally determined as the optimization objective. Through finite element analysis, the weak position of the gearbox housing can be located quickly and accurately, and then the static and dynamic stiffness of the housing can be improved by adding and changing stiffeners. The simulation results show that the performance of the optimized housing is significantly improved. After the noise test of the whole vehicle, the noise of the two-speed automatic transmission is significantly reduced, and the subjective evaluation results are good.

Chocolate is a popular food for its unique flavor and taste, rich nutritional value, and the psychological values brought to people. The raw material production of chocolate, product manufacturing, sales and transportation have different degrees of environmental impact. This review explores the environmental hot spots in the life cycle of chocolate and puts forward corresponding suggestions for the improvement. By applying a systematic review method, this paper collected 25 articles on life cycle assessment (LCA) of the environmental impact of the chocolate industry. It is found that the life cycle of chocolate has the highest environmental impact in the raw material production and chocolate manufacturing stages (accounting for 77–97% of total impacts), among which milk powder, sugar and cocoa derivatives are the important contributors to significant environmental burden. Dark chocolate generates the lowest carbon emissions (1.67 kg CO2 eq/kg product) among existing chocolate categories, while the chocolate confectionery products release the highest carbon emissions (6.76 kg CO2 eq/kg product) among chocolate-containing products. Improvement measures are proposed for reducing environmental impacts and for selecting environmentally friendly product formulae. This study can provide benchmarking for the chocolate industry and improves the understanding of life cycle environmental impacts of chocolate products.

Background and objective: Anterior cruciate ligament (ACL) reconstruction calls for artificial ligaments with better bioactivity, however systematic reviews regarding bioactivity enhancement strategies, technologies, and perspectives of artificial ligaments have been rarely found. Methods: Research papers, reviews, and clinical reports related to artificial ligaments were searched and summarized the current status and research trends of artificial ligaments through a systematic analysis. Results: Having experienced ups and downs since the very first record of clinical application, artificial ligaments differing in material, and fabrication methods have been reported with different clinical performances. Various manufacturing technologies have developed and realized scaffold- and cell-based strategies. Despite encouraging in-vivo and in-vitro test results, the clinical results of such new designs need further clinical examinations. Conclusion: As the demand for ACL reconstruction dramatically increases, novel artificial ligaments with better osteoinductivity and mechanical performance are promising. The translational potential of this article: To develop novel artificial ligaments simultaneously possessing excellent osteoinductivity and satisfactory mechanical performance, it is important to grab a glance at recent research advances. This systematic analysis provides researchers and clinicians with comprehensive and comparable information on artificial ligaments, thus being of clinical translational significance.

The presence and bioavailability of potentially toxic elements in the soil–plant system are major limitations to the application of compost to agricultural soils. In this study, the content changes of nutrients and potentially toxic elements in four organic wastes, namely household waste, sewage sludge, chicken manure, and cow/sheep manure, were analyzed during composting, and the quality of compost products was assessed in view of their impact on the environment. The objective of this study was to determine the evolution of 21 metal elements, including macroelements (Na, K, Ca, Mg), microelements (Fe, Mn, Zn, Cu, Mo, Ni), and potentially toxic elements (Ti, Al, Cr, Cd, Pb, Sb, Li, Co, Be, In, and Sn) during composting. Results showed that the contents of these elements varied considerably between different organic wastes. The contents of some elements (Na, K, Ca, Fe, Ti, Cu) increased substantially after composting due to organic matter decomposition and weight loss, whereas other elements (Mg, Co, Mo, Cr, In) decreased due to water leaching. The compost obtained was characterized by heavy metal contents not exceeding the permissible limits. Principal component analysis (PCA) was carried out to further analyze the relationships between the studied elements in different organic waste and compost products. The PCA results revealed that the macroelements (Na, K, Ca, Mg) contents negatively correlated with PC1, whereas microelements and potentially toxic elements positively correlated with PC1. From the results of the PCA, it was possible to classify the different compost products.

Osteoarthritis (OA) is a chronic degenerative osteoarthropathy. Although it has been revealed that a variety of factors can cause or aggravate the symptoms of OA, the pathogenic mechanisms of OA remain unknown. Reliable OA models that accurately reflect human OA disease are crucial for studies on the pathogenic mechanism of OA and therapeutic drug evaluation. This review first demonstrated the importance of OA models by briefly introducing the OA pathological features and the current limitations in the pathogenesis and treatment of OA. Then, it mainly discusses the development of different OA models, including animal and engineered models, highlighting their advantages and disadvantages from the perspective of pathogenesis and pathology analysis. In particular, the state-of-the-art engineered models and their potential were emphasized, as they may represent the future direction in the development of OA models. Finally, the challenges in obtaining reliable OA models are also discussed, and possible future directions are outlined to shed some light on this area.

Specialized gardens, as integral components of botanical gardens, bear multiple functions, encompassing plant collection and conservation, scientific research, and public education, as well as serving aesthetic and recreational purposes. Their quality profoundly reflects the landscape artistry of botanical gardens, directly influencing the quality of visitors’ enjoyment and the overall experience within the botanical garden. This study aims to investigate the spatial vitality of specialized garden plant landscapes, effectively assessing the usage patterns of plant landscape spaces and promoting the optimal utilization of underutilized spaces. Taking Hangzhou Botanical Garden as a case study, considering the warming climate and suitable temperatures in spring, when most plants enter the flowering period and outdoor visitor frequency increases, the primary observational period focuses on spring to measure the spatial vitality of specialized garden plant landscapes. We obtained data through field measurements and on-site observations. Specifically, We measured and recorded information on plant species, quantity, height, crown width, and growth conditions within the plots. Additionally, we employed ground observations and fixed-point photography to document visitor numbers and activity types. We quantified spatial vitality through four indicators: visitor density, space usage intensity, diversity of age group, and richness of activity type. We explored the spatiotemporal distribution patterns of spatial vitality and investigated the relationship between plant landscape characteristics and spatial vitality using variance analysis and correlation analysis. The results indicate that, in spring, the average spatial vitality index of specialized gardens ranks from highest to lowest as follows: Lingfeng Tanmei (1.403), Rosaceae Garden (1.245), Acer and Rhododendron Garden (0.449), and Osmanthus and Crape Myrtle Garden (0.437). Additionally, the spatial vitality of specialized garden plant landscapes in spring is significantly positively correlated with the ornamental period of specialized plants, characteristics of plant viewing, accessible lawn area, spatial accessibility, and spatial enclosure. Therefore, to create vibrant specialized plant landscapes, managers and planners, when engaging in the planning and design of specialized garden plant landscapes, need to fully consider and respect the visual aesthetics and functional needs of visitors. This study will serve as a theoretical reference for subsequent research on the vitality of plant landscape spaces and other small-scale spaces. It will also provide practical guidance for the construction of plant landscapes in specialized gardens within botanical gardens and other urban green spaces.

Drought is a major abiotic stress that reduces crop yields and quality worldwide. Although some genes involved in the response to drought stress have been identified, a more in-depth understanding of the mechanisms underlying wheat tolerance to drought is needed for the control of drought tolerance. Here, we evaluated the drought tolerance of 15 wheat cultivars and measured their physiological–biochemical parameters. Our data showed that the drought tolerance of the resistant wheat cultivars was significantly higher than that of drought-sensitive cultivars, which was associated with a greater antioxidant capacity of the former. Transcriptomic analysis revealed that different mechanisms of drought tolerance exist between the wheat cultivars Ziyou 5 and Liangxing 66. Transcriptomic analysis also revealed a large number of DEGs, including those involved in flavonoid biosynthesis, phytohormone signalling, phenolamides and antioxidants. qRT–PCR was performed, and the results showed that the expression levels of TaPRX-2A were significantly different among the various wheat cultivars under drought stress. Further study revealed that overexpression of TaPRX-2A enhanced tolerance to drought stress through the maintenance of increased antioxidase activities and reductions in ROS contents. Overexpression of TaPRX-2A also increased the expression levels of stress-related genes and ABA-related genes. Taken together, our findings show that flavonoids, phytohormones, phenolamides and antioxidants are involved in the plant response to drought stress and that TaPRX-2A is a positive regulator of this response. Our study provides insights into tolerance mechanisms and highlights the potential of TaPRX-2A overexpression in enhancing drought tolerance in crop improvement programmes.

Supramolecular responsive microcarriers based on chitosan microspheres were prepared and applied for nonenzymatic cell detachment. Briefly, chitosan microspheres (CSMs) were first prepared by an emulsion crosslinking approach, the surface of which was then modified with β-cyclodextrin (β-CD) by chemical grafting. Subsequently, gelatin was attached onto the surface of the CSMs via the host–guest interaction between β-CD groups and aromatic residues in gelatin. The resultant microspheres were denoted CSM-g-CD-Gel. Due to their superior biocompatibility and gelatin niches, CSM-g-CD-Gel microspheres can be used as effective microcarriers for cell attachment and expansion. L-02, a human fetal hepatocyte line, was used to evaluate cell attachment and expansion with these microcarriers. After incubation for 48 h, the cells attached and expanded to cover the entire surface of microcarriers. Moreover, with the addition of adamantane (AD), cells can be detached from the microcarriers together with gelatin because of the competitive binding between β-CD and AD. Overall, these supramolecular responsive microcarriers could effectively support cell expansion and achieve nonenzymatic cell detachment and may be potentially reusable with a new cycle of gelatin attachment and detachment.

Deep learning has gained widespread interest in the task of building semantic segmentation modelling using remote sensing images; however, neural network models require a large number of training samples to achieve better classification performance, and the models are more sensitive to error patches in the training samples. The training samples obtained in semi-supervised classification methods need less reliable weakly labelled samples, but current semi-supervised classification research puts the generated weak samples directly into the model for applications, with less consideration of the impact of the accuracy and quality improvement of the weak samples on the subsequent model classification. Therefore, to address the problem of generating and optimising the quality of weak samples from training data in deep learning, this paper proposes a semi-supervised building classification framework. Firstly, based on the test results of the remote sensing image segmentation model and the unsupervised classification results of LiDAR point cloud data, this paper quickly generates weak image samples of buildings. Secondly, in order to improve the quality of the spots of the weak samples, an iterative optimisation strategy of the weak samples is proposed to compare and analyse the weak samples with the real samples and extract the accurate samples from the weak samples. Finally, the real samples, the weak samples, and the optimised weak samples are input into the semantic segmentation model of buildings for accuracy evaluation and analysis. The effectiveness of this paper’s approach was experimentally verified on two different building datasets, and the optimised weak samples improved by 1.9% and 0.6%, respectively, in the test accuracy mIoU compared to the initial weak samples. The results demonstrate that the semi-supervised classification framework proposed in this paper can be used to alleviate the model’s demand for a large number of real-labelled samples while improving the ability to utilise weak samples, and it can be used as an alternative to fully supervised classification methods in deep learning model applications that require a large number of training samples.

In three-dimensional (3D) airborne light detection and ranging (LiDAR) point-cloud data acquisition, noise point clusters (such as cloud, birds and incomplete scanning ground points) and isolated points are usually generated in the scanning process. Detection and elimination of these noise points directly affect the subsequent processing efficiency of the LiDAR point clouds. In this paper, a noise detection method from airborne LiDAR data based on spatial hierarchical directional relationship and region growing algorithm is proposed. First, the original airborne LiDAR points are divided into regular 3D grids, and the maximum point density unit is searched adaptively to select the initial surface seed points for region growing algorithm. Then, the spherical neighborhood is constructed with the initial seed point as the center, and fourteen main growth directions are generated based on the 3D space topology. Second, candidate seed points in each main direction are determined by the distance threshold. Finally, all LiDAR points are iteratively executed using candidate seed points as new region growing seed points. This paper selects two mountain terrain scenes with different cloud contents as the study area, and the precision, recall rates and F1-score of the proposed method reach 99.8%, 100% and 99.3%, respectively. This method can detect point-cloud clusters and isolated points, thus simplifying the LiDAR point clouds, providing basic support for the subsequent accurate data processing and analysis.

Purpose: This study aims to assess the effects of 8-week virtual reality (VR) training on balance and lower extremity muscle strength in adolescents with intellectual disability (ID).Methods: Thirty adolescents with intellectual disability were randomly divided into the virtual reality group and control group. The participants in the virtual reality group and the control group received the virtual reality training and the physical education (PE) course, respectively, for 8 weeks. The Berg Balance Scale (BBS), Timed Up and Go (TUG) test and lower extremity muscle strength were measured before and after the training.Results: The between-group results showed that the participants in the virtual reality group increased the muscle strength of hip flexors (p < 0.001), hip extensors (p = 0.002), hip abductors (p < 0.001), knee flexors (p < 0.001), knee extensors (p = 0.002) and ankle plantar flexors (p = 0.042) significantly after training, compared to the control group. However, no significant improvement was found in the berg balance scale and timed up and go scores between the virtual reality group and control group after training (p > 0.05). The within-group results showed that the strength of all the muscle groups significantly increased after training in the virtual reality group (p < 0.05) compared to the baseline. However, no significant difference was found in the muscle strength in the control group before and after training. The within-group berg balance scale and timed up and go scores showed no significant improvements in both groups.Conclusion: Virtual reality training intervention might be effective in improving the lower extremity muscle strength, but no significant improvement was found on balance ability in adolescents with intellectual disability.

BackgroundColorectal cancer (CRC) is a common digestive tract malignancy with rising incidence and morbidity worldwide during recent years. Yi-Yi-Fu-Zi-Bai-Jiang-San (YYFZBJS), a traditional Chinese medicine formula, has showed positive effects against cancers. However, the mechanisms underlying its anticancer effects requires investigation.MethodsInformation on bioactive compounds, potential YYFZBJS targets, and CRC-associated genes, was obtained from public databases. The key targets and ingredients as well their corresponding signaling pathways were identified using bioinformatic approaches, including Kyoto encyclopedia of genes and genomes (KEGG) analyses, gene ontology (GO), and protein–protein interaction (PPI). Subsequently, molecular docking was used to verify the main compounds-targets. Potential YYFZBJS therapeutic effects against CRC were validated in vitro and in vivo.ResultsUsing pharmacological network analysis, 40 YYFZBJS active compounds and 21 potential anti-CRC targets were identified. YYFZBJS was an important regulator of CRC through various targets and signaling pathways, particularly the cell cycle and PI3K/AKT pathway. Additionally, YYFZBJS suppressed the proliferation of CRC cells. Flow cytometry showed that YYFZBJS induced apoptosis and cell cycle arrest in the G2/M phase. Western blotting analysis indicated that YYFZBJS reduced the protein levels of CDK1, p-AKT, and p-PI3K, without altering total PI3K and AKT protein levels. In vivo analysis found that YYFZBJS inhibited tumor growth and PI3K/AKT signaling in a mouse model of CRC.ConclusionAs predicted by network pharmacology and validated by the experimental results, YYFZBJS inhibited proliferation, induced apoptosis and arrested cell cycle progression in CRC by modulating the CDK1/PI3K/Akt signaling pathway.

The potential health risks associated with simultaneous presence of residues of heavy metals and antibiotics in the environment and food have been of wide concern. However, the adverse health effects of combined heavy metal and antibiotic exposure at low doses remain unclear. In this study, the effects of combined exposure to florfenicol and copper at low doses during early life on toxicity, gut microbiota, drug resistance genes, and the fecal metabolome were investigated in Sprague–Dawley (SD) rats. The results showed that combined exposure induced inflammatory responses and visceral injury as well as faster weight gain compared with florfenicol or copper exposure alone. Alpha and beta diversity indices indicated that the composition of the gut microbiota and the abundance of bacteria related to energy intake and disease in the combined exposure group were significantly altered. The increase in resistance genes (floR, fexA) induced by florfenicol exposure was suppressed under combined exposure to florfenicol and copper. The fecal metabolome also demonstrated that metabolic pathways related to energy intake and liver injury were significantly affected in the combined exposure group. In conclusion, this study shows that combined exposure to florfenicol and copper during early life can pose a nonnegligible health risk even if the exposure concentration of florfenicol or copper is below the safe limit.

SnRK2.6 (SUCROSE NONFERMENTING 1-RELATED PROTEIN KINASE2.6) has been characterized as a molecular switch for the intracellular abscisic acid (ABA) signal-transduction pathway. Normally, SnRK2.6 is kept in an “off” state, forming a binary complex with protein phosphatase type 2Cs (PP2Cs). Upon stressful conditions, SnRK2.6 turns into an “on” state by its release from PP2Cs and then phosphorylation at Ser175. However, how the ”on” and “off” states for SnRK2.6 are fine-tuned, thereby controlling the initiation and braking processes of ABA signaling, is still largely unclear. SnRK2.6 activity was tightly regulated through protein post-translational modifications (PTM), such as persulfidation and phosphorylation. Taking advantage of molecular dynamics simulations, our results showed that Cys131/137 persulfidation on SnRK2.6 induces destabilized binding and weakened interactions between SnRK2.6 and HAB1 (HYPERSENSITIVE TO ABA1), an important PP2C family protein. This unfavorable effect on the association of the SnRK2.6–HAB1 complex suggests that persulfidation functions are a positive regulator of ABA signaling initiation. In addition, Ser267 phosphorylation in persulfidated SnRK2.6 renders a stable physical association between SnRK2.6 and HAB1, a key characterization for SnRK2.6 inhibition. Rather than Ser175, HAB1 cannot dephosphorylate Ser267 in SnRK2.6, which implies that the retained phosphorylation status of Ser267 could ensure that the activated SnRK2.6 reforms the binary complex to cease ABA signaling. Taken together, our findings expand current knowledge concerning the regulation of persulfidation and phosphorylation on the state transition of SnRK2.6 and provide insights into the fine-tuned mechanism of ABA signaling.

IntroductionThe racial disparities of opportunity to receive the appropriate intervention and lower insurance coverage may result in survival disparities in different races. This study aims to provide a perspective on racial disparities in the survival of breast cancer patients after surgery.MethodsThrough data from the Surveillance, Epidemiology, and End Results (SEER) program, this study estimated the survival of breast cancer patients of different races from 1998 to 2017. Inverse probability weighting (IPW) was utilized to adjust the imbalanced clinicopathological features of patients of different races.ResultsThis study analyzed 214,965 breast cancer patients after surgery. Among them, 130,746 patients received BCS, and the remaining 84,219 breast cancer patients underwent mastectomy. Although Asian or Pacific Islander (API) patients after surgery showed higher survival benefit than that of white patients in the primary data, after adjusting for age at diagnosis, luminal subtype, grade, T stage, and N stage in different races, white individuals had the longest period of survival was higher than that of the minority groups in BCS group [breast cancer-specific survival (BCSS): HRWhitevs.API = 0.402, HRWhitevs.Black = 0.132; P < 0.001; overall survival (OS): HRWhitevs.API = 0.689, HRWhitevs.Black = 0.254; all P < 0.001] and mastectomy group (BCSS: HRWhitevs.API = 0.325, HRWhitevs.Black = 0.128; P < 0.001; OS: HRWhitevs.API = 0.481, HRWhitevs.Black = 0.206; all P < 0.001)ConclusionsWe first identified that the survival benefit of the minority group after surgery was lower than that of white individuals, regardless of tumor chrematistics and surgery types.

Abstract Background Primary thyroid lymphoma (PTL) is a rare thyroid malignancy, there are few large sample studies on PTL and no standardized treatment regimen has been established due to the rarity. The aims of this study were to explore the incidence and prognostic factors of PTL and construct visual prognostic prediction models for post-chemotherapy and postoperative patients. Methods The incidence of PTL in 1975–2017 was extracted from the US Surveillance, Epidemiology, and End Results (SEER) database, then assessed using joinpoint regression software. A total of 1616 eligible PTL patients diagnosed in 1998–2016 were brought into prognostic analysis. Multivariate Cox regression analyses were carried out to reveal independent prognostic elements for overall survival (OS) and cancer-specific survival (CSS). Results PTL incidence showed a relatively steady increase in 1975–1994, which annual percent change (APC) was 4.0%, and steady decreasing in 1994–2017(APC − 2.4%). Age, marital status, lymphoma Ann Arbor stage, histological subtypes, surgery, chemotherapy, and radiation were significantly correlated to OS and CSS. Nomograms were constructed to predict OS and CSS in post-chemotherapy and postoperative PTL patients separately, and were verified to have good reliability. Conclusions The incidence of PTL increased and subsequently decreased. We revealed the prognostic implications and constructed reliable nomograms for post-chemotherapy and postoperative PTL patients.

The carbon, nitrogen and water cycles of terrestrial ecosystems are important biogeochemical cycles. Addressing the relationship of leaf nitrogen (N) and carbon isotope discrimination (Δ) will enhance the understanding of the links between these three cycles in plant leaves because Δ can reflect time-integrated leaf-level water-use efficiency (WUE) over the period when the leaf material is produced. Previous studies have paid considerable attention to the relationship. However, these studies have not effectively eliminated the interference of environmental factors, inter-species, and inter-individual differences in this relationship, so new research is necessary. To minimize these interferences, the present work explored the relationship at the three levels of community, population, and plant individual. Three patterns of positive, negative and no relationship were observed across communities, populations, and individuals, which is dependent on environmental conditions, species, and plant individuals. The results strongly suggested that there is no general pattern for the relationship between leaf N and Δ. Furthermore, the results indicated that there is often no coupling between leaf-level long-term WUE and leaf N in the metabolic process of carbon, N and water in leaves. The main reason for the lack of this relationship is that most plants do not invest large amounts of nitrogen into photosynthesis. In addition, the present study also observed that, for most plant species, leaf N was not related to photosynthetic rate, and that variations in photosynthetic rates are mainly driven by stomatal conductance.

Resolvin D1 (RvD1) was previously reported to relieve inflammation and liver damage in several liver diseases, but its potential role in liver fibrosis remains elusive. The aim of our study was to investigate the effects and underlying mechanisms of RvD1 in hepatic autophagy in liver fibrosis. In vivo, male C57BL/6 mice were intraperitoneally injected with 20% carbon tetrachloride (CCl4, 5 ml/kg) twice weekly for 6 weeks to establish liver fibrosis model. RvD1 (100 ng or 300 ng/mouse) was added daily in the last 2 weeks of the modeling period. In vitro, lipopolysaccharide (LPS)-activated LX-2 cells were co-treated with increasing concentrations (2.5–10 nM) of RvD1. The degree of liver injury was measured by detecting serum AST and ALT contents and H&E staining. Hepatic fibrosis was assessed by masson's trichrome staining and metavir scoring. The qRT-PCR, western blot, immunohistochemistry, and immunofluorescence were applied to liver tissues or LPS-activated LX-2 cells to explore the protective effects of RvD1 in liver fibrosis. Our findings reported that RvD1 significantly attenuated CCl4 induced liver injury and fibrosis by decreasing plasma AST and ALT levels, reducing collagen I and α-SMA accumulation and other pro-fibrotic genes (CTGF, TIMP-1 and Vimentin) expressions in mouse liver, restoring damaged histological architecture and improving hepatic fibrosis scores. In vitro, RvD1 also repressed the LPS induced LX-2 cells activation and proliferation. These significant improvements mainly attributed to the inhibiting effect of RvD1 on autophagy in the process of hepatic stellate cell (HSC) activation, as demonstrated by decreased ratio of LC3-II/I and elevated p62 after RvD1 treatment. In addition, using AZD5363 (an AKT inhibitor that activates autophagy) and AZD8055 (an mTOR inhibitor, another autophagy activator), we further verified that RvD1 suppressed autophagy-mediated HSC activation and alleviated CCl4 induced liver fibrosis partly through AKT/mTOR pathway. Overall, these results demonstrate that RvD1 treatment is expected to become a novel therapeutic strategy against liver fibrosis.

Micro-pitting is a common fatigue failure mode of spur gears. To reduce it, this paper proposes a novel tooth tip relief method. Gear meshing simulation is firstly performed considering the actual contact path, showing that there are four Hertz contact stress peaks in the transition areas of the tooth flank. Equations are then developed for the novel tooth tip relief method: one focuses on the smooth transition between the involute profile and the tip relief region, the other on the smooth transition between the tip relief region and the tooth tip. The results of curvature radius and Hertz contact stress show that the proposed novel method can effectively reduce the Hertz contact stress peaks, which indicates this tip relief method could benefit for reducing the micro-pitting. Finally, both wear simulations and bench tests are conducted, verifying the effectiveness of the proposed method for reducing the micro-pitting of spur gears.

Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment.Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software.Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation.Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Multi-speed transmission is the main development direction of hybrid transmission, which has brought higher shift quality requirements than traditional fuel vehicle transmission. However, there is less research on the shifting control of hybrid transmission, especially for the shifting control of dedicated hybrid transmission (DHT), which uses the wet clutch as a shift element. This paper studies the power-on downshift process of a two-speed series-parallel hybrid transmission, proposes a shifting control strategy based on motor active speed regulation, and deeply analyzes the causes of maximum impact during the shifting process. The results show that the reverse torque produced in the process of eliminating the remaining slip is the root cause of the maximum impact. On this basis, two optimization strategies are proposed to reduce the shift impact and improve the shift quality. The simulation results show that the proposed optimization strategies can effectively suppress the shift impact. In the meanwhile, for any control pressure of the OG (off-going) clutch in the speed phase within the range of (2.44–2.53 bar), a high shift quality in which the maximum impact is controlled lower than 10 m/s3 can be achieved, which has high engineering value and practical significance.

Hybrid electric vehicles (HEVs) have proved a feasible option to reduce fuel consumption and emissions. Furthermore, energy management strategies (EMSs) play a pivotal role in the performance of HEVs. This paper presents a novel real-time EMS, namely fuzzy adaptive-equivalent consumption minimization strategy (Fuzzy A-ECMS), for a parallel HEV. The proposed EMS is formulated by combining the ECMS, which is derived from Pontryagin's minimum principle (PMP), with a fuzzy logic controller adjusting the equivalent factor (EF) based on the deviation between reference state of charge (SOC) and actual SOC for a better SOC trajectory. Improved fuel economy and SOC charge sustainability are the main control objectives. To test and verify the performance of the studied controller, comparative simulations of the Fuzzy A-ECMS and rule-based EMS, conventional SOC-based A-ECMS together with standard ECMS under different standard driving cycles and a real driving cycle are conducted via MATLAB/Simulink and AVL CRUISE. The simulation results show the feasibility and effectiveness of Fuzzy A-ECMS, yielding 0.46% to 5.91% reduction of fuel consumption and more stable SOC charge sustainability compared with the other three EMSs.

The electrochemical detection of hydrogen peroxide (H2O2) has become more and more important in industrial production, daily life, biological process, green energy chemistry, and other fields (especially for the detection of low concentration of H2O2). Metal organic frameworks (MOFs) are promising candidates to replace the established H2O2 sensors based on precious metals or enzymes. This review summarizes recent advances in MOF-based H2O2 electrochemical sensors, including conductive MOFs, MOFs with chemical modifications, MOFs-composites, and MOF derivatives. Finally, the challenges and prospects for the optimization and design of H2O2 electrochemical sensors with ultra-low detection limit and long-life are presented.

We previously reported that L-Cysteine, H2S donor, remarkably attenuated neuroinflammation following hypoxia-ischemia (HI) brain injury in neonatal mice. However, its anti-inflammatory mechanism for HI insult is still unknown. The study focus on the effects of L-Cysteine on immune cell populations, Ca2+ mobilization and phagocytosis after neonatal HI. We found that L-Cysteine treatment skewed CD11b+/CD45low microglia and CD11b+/CD45high brain monocytes/macrophages towards a more anti-inflammatory property 72 h after HI-injured brain. Moreover, L-Cysteine treatment reduced cerebral infiltration of CD4 T cells 7 days following HI insult. Furthermore, CD4 T cell subset analysis revealed that L-Cysteine treatment decreased Th1 and Th2 counts, while increased Th17/Th2 ratio. Moreover, L-Cysteine treatment suppressed LPS-induced cytosolic Ca2+ and LPS-stimulated phagocytosis in primary microglia. The anti-inflammatory effect of L-Cysteine was associated with improving neurobehavioral impairment following HI insult. Our results demonstrate L-Cysteine treatment suppressed the invasion of peripheral immune cells, increasing [Ca2+]i and excessive phagocytosis to improve neurobehavioral deficits following hypoxia-ischemia injury in neonatal mice by H2S release.

Objective. To compare the corneal asphericity and higher-order aberrations (HOAs) of femtosecond laser-assisted in situ keratomileusis (FS-LASIK) with Smart Pulse Technology (SPT) assisted transepithelial photorefractive keratectomy (Trans-PRK) for myopia and myopic astigmatism correction. Methods. This prospective study analyzed 88 eyes of 44 patients treated with FS-LASIK and 64 eyes of 32 patients treated with Trans-PRK. All eyes had low to moderate myopia with or without astigmatism (spherical equivalent (SE) 0.05). After FS-LASIK and Trans-PRK, the Q values in the 6, 7, 8, and 9 mm zones and ISV of the anterior corneal surface significantly increased (P

Hydrogen sulfide (H2S) is an endogenous gaseous molecule that plays an important role in the plant life cycle. The multiple transcription factor ABSCISIC ACID INSENSITIVE 4 (ABI4) was precisely regulated to participate in the abscisic acid (ABA) mediated signaling cascade. However, the molecular mechanisms of how H2S regulates ABI4 protein level to control seed germination and seedling growth have remained elusive. In this study, we demonstrated that ABI4 controls the expression of L-CYSTEINE DESULFHYDRASE1 (DES1), a critical endogenous H2S-producing enzyme, and both ABI4 and DES1-produced H2S have inhibitory effects on seed germination. Furthermore, the ABI4 level decreased during seed germination while H2S triggered the enhancement of the persulfidation level of ABI4 and alleviated its degradation rate, which in turn inhibited seed germination and seedling establishment. Conversely, the mutation of ABI4 at Cys250 decreased ABI4 protein stability and facilitated seed germination. Moreover, ABI4 degradation is also regulated via the 26S proteasome pathway. Taken together, these findings suggest a molecular link between DES1 and ABI4 through the post-translational modifications of persulfidation during early seedling development.

Coupled vibrations are difficult to overcome in automatic transmission with mixed gear trains. In this study, a mathematical model for the bending–torsional–axial–oscillation vibrations of mixed gear trains with parallel-axis gear pairs, a clutch, planetary gear sets and a shaft rotor was developed using the concentrated mass method. The dynamic vibration response and transmission error of the meshing gears in different gears were analysed. The response sensitivity of the vibration parameters was obtained using the design coupling function of the peak–peak dynamic transmission error. The degree of coupling of each part with system vibrations was analysed and it was found that the stiffness and damping of gears influenced the coupled vibrations. Finally, the top seven degrees of coupling were obtained, the first two contributions were taken as the target to improve, the thickness of the P1 gear ring was increased by 5 mm and the bushing was added between the P2 planetary and sun gear to increase the stiffness and damping of gears. It could be validated that the vibration performance improved significantly from 3% to 6%. This article presents an analytical method of theoretical and practical value, which can provide a strong basis for reducing the vibrations and noise in automatic transmission.

The stiffness of mounting system determines the vibration isolation ability of the transmitted path, which is the key factor that affects the vibration and noise of vehicle. In order to improve the vibration isolation ability of the powertrain mounting system, considering the powertrain of front wheel drive car as the research object, the vibration decoupling rate and its corresponding frequency of the powertrain mounting system are analyzed by rigid body dynamics and energy method. The correctness of the calculation program with energy method has been verified by calculated vibration decoupling rate. Based on the genetic algorithm and the fusion robustness analysis, the decoupling rate and modal frequency of the mountings in all directions are considered as the objectives; the stiffness of the three mountings is optimally designed. Through multi-excitation of three methods and vehicle test, the vibration response characteristics and the vibration noise test data of the optimization stiffness are compared and the results shown that the vibration isolation performance has been significantly improved more than 10%. An integrated design method of stiffness optimization design and vibration analysis in vehicle PMS is formed, which has theoretical and practical value, and can reduce vehicle vibration and noise.

The stand density of trees affects stand growth and is useful for estimating other forests structure parameters. We studied tree stand density in Jiufeng National Forest Park in Beijing. The number of spectral local maxima points (NSLMP) calculated within each sample plot was extracted by the spectral maximum filtering method using QuickBird imagery. Regression analysis of NSLMP and the true stand density collected by ground measurements using differential GPS and the total station were used to estimate stand density of the study area. We used NSLMP as an independent variable and the actual stand density as the dependent variable to develop separate statistical models for all stands in the coniferous forest and broadleaf forest. By testing the different combination of Normalized Difference Vegetation Index (NDVI) thresholds and window sizes, the optimal selection was identified. The combination of a 3 × 3 window size and NDVI ≥ 0.3 threshold in coniferous forest produced the best result using near-infrared band (coniferous forest R2 = 0.79, RMSE = 12.60). The best combination for broadleaf forest was a 3 × 3 window size and NDVI ≥ 0.1 with R2 = 0.44, RMSE = 9.02 using near-infrared band. The combination of window size and NDVI threshold for all unclassified forest was 3 × 3 window size and NDVI ≥ 0.3 with R2 = 0.70, RMSE = 11.20 using near-infrared band. A stand density planning map was constructed using the best models applied for different forest types. Different forest types require the use of different combination strategies to best extract the stand density by using the local maximum (LM). The proposed method uses a combination of high spatial resolution imagery and sampling plots strategy to estimate stand density.

The heating load of intermittent heating is not always lower than that of continuous heating for heat storage and release of internal walls. Therefore, the threshold value of daily operation hours exists, and is affected by the configuration of internal walls. A comparative study is performed between continuous and intermittent heating modes to investigate the threshold value of daily operation hours for different internal wall configurations by employing computational fluid dynamic (CFD) models. Meanwhile, field tests on the temperature distribution within a thermal mass was carried out to validate the simulation. The results show that the heating load index of intermittent heating is larger than that of continuous heating with increased amplitude ranging from 31.58% to 152.63%. The threshold value of daily operation hours is, respectively, 18.04 h, 15.80 h, 14.59 h, and 13.46 h for four internal wall configurations. Moreover, with the increase in the insulation level of internal walls, the threshold value of daily operation hours decreases. In addition, the results indicate that it is more economical to use continuous heating when the daily operation hours are more than the threshold values.