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Abstract Background Hepatocellular carcinoma (HCC) is the sixth most frequently diagnosed cancer worldwide accompanied by a low 5-year survival rate. In our study, we aimed to analyze relevant genetic features that can predict the prognosis of HCC patients by single-cell RNA sequencing (scRNA-seq). Methods Single-cell RNA-seq data of HCC were analyzed from the Gene Expression Omnibus (GEO) database. Using the Seurat package, we performed quality control to remove cells with low quality. After normalization, we detected highly variable genes across the single cells. Then, cell clustering and Cell type annotation were performed using highly variable genes. Then, functional enrichment analyses were performed by GO and KEGG, and cell–cell communication analysis, trajectory analysis were conducted. LASSO-Cox regression analysis was used to perform Survival analysis and ROC evaluation for high and low-risk groups. Validation of the expressions and survival prognosis of the screened genes in HCC. Expression levels of the genes were analyzed by RT-qPCR and western blot in normal liver cell line THLE-3 and HCC cell lines (HuH7, HCCLM9, and HCCLM13). Results A total of 2208 up-regulated and 1447 down-regulated genes were identified in HCC samples. These differentially expressed genes (DEGs) were enriched in several cytokine-related pathways and the MIF-CD74/CXCR4 signaling pathway. By integrating large amounts of RNA sequencing data, we identified 566 prognostic genes associated with HCC cells. Eleven genes were screened using the LASSO-COX risk factor model. Stratifying patients into high- or low-risk groups based on these genes allowed us to effectively predict their survival and ROC curve. Five genes were further found to be associated with poor survival prognosis in HCC and were notably overexpressed in HCC cell lines compared to normal liver cell line. Conclusion This study revealed potential prognostic marker genes in HCC patients, providing insights into predicting patients’ survival rates.

Many rock engineering projects show that the growth of tensile cracks is often an important cause of engineering disasters, and the mechanical behavior of rocks is essentially the transmission, storage, dissipation and release of energy. To investigate the tensile behavior of rock from the perspective of energy, uniaxial tension tests (UTTs) and uniaxial compression tests (UCTs) were carried out on three typical rocks (granite, sandstone and marble). Different unloading points were set before the peak stress to separate elastic energy and dissipated energy. The input energy density ut, elastic energy density ue, and dissipated energy density ud at each unloading point were calculated by integrating stress-strain curves. The results show that there is a strong linear relationship between the three energy parameters and the square of the unloading stress in UCT, but this linear relationship is weaker in UTT. The ue and ud increase linearly with the increase in ut in UCT and UTT. Based on the phenomenon that ue and ud increase linearly with ut, the applicability of Wetp index in UTT was proved and the relative energy storage capacity and absolute energy distribution characteristics of three rocks in UCT and UTT were evaluated. The tensile behavior of marble and sandstone in UTT can be divided into two stages vaguely according to the energy distribution, but granite is not the case. In addition, based on dissipated energy, the damage evolution of three types of rocks in UCT and UTT was discussed. This study provides some new insights for understanding the tensile behavior of rock.

Abstract Dietary intake of live microbes may benefit human health, but less is known about the role in insulin resistance. This study was developed with the goal of evaluating potential relationships between IR and dietary live microbes. The National Health and Nutrition Examination Survey (NHANES) dataset was leveraged to collect data from 6,333 subjects 18 + years of age. The Sanders system for the classification of dietary live microbe intake (containing Low ( 107 CFU/g) levels of live microbes) was then used to separate these patients into three groups (low, medium, or high). Fasting blood glucose and insulin levels were used to approximate IR based on the homeostasis model of insulin resistance (HOMA-IR). Weighted linear regressions were used to assess the relationship between IR and live microbe intake. After fully adjusting for confounding factors, subjects in the groups exhibiting medium and high levels of live microbe intake exhibited HOMA-IR scores that were below those of subjects in the low group. The relationship between live microbe intake and HOMA-IR scores was also potentially impacted by ethnicity. In summary, a negative correlation was detected between dietary live microbe intake and HOMA-IR values.

Scrub root systems play a crucial role in preventing soil erosion and nutrient loss. However, the effects of the root system configuration on soil nutrient dynamics and microbial changes at various evolutionary stages remain poorly understood. In this study, we investigated the relationship between soil physical and chemical properties and the diversity of bacteria and fungi throughout the evolutionary stages of the scrub root systems. This was achieved through a combination of whole-root excavation and root tracing techniques. The results indicated that root diameter was the main factor contributing to the continuous increase in carbon (C): phosphorus (P) and nitrogen (N): P ratios as the scrub sand pile developed. The soil organic carbon (SOC), total soil nitrogen (TN), and total soil phosphorus (TP) contents in the soil in the four evolutionary stages were the highest during the developmental stage, but the change in TP content was not statistically significant (P > 0.05). Partial least squares path modeling and redundancy analysis (RDA) indicated that root system stoichiometric C and N contents were positively correlated with microbial diversity (R2 = 0.85). There was no correlation between the evolutionary stage and soil nutrient TN, whereas soil nutrient TN was negatively correlated with microbial diversity (R2 = −0.92). These findings elucidate the relationship between the evolutionary stage of root chemical measurement characteristics, soil elements and microorganisms, and their subsequent effects on root elemental composition and microbial diversity. This study enhances the current understanding of plant–soil interactions in desert steppe ecosystems.

This study investigated airflow changes and sediment transport in the blowouts of the sandy grassland in Xilingol, Inner Mongolia. The results show that the wind direction inside the blowout is influenced by the direction of the incoming wind. Specifically, when the angle between the incoming wind direction and the long axis of the blowout is less than 15°, both sides of the wall tend to generate a wind flow towards the east exit. Meanwhile, the main airflow along the central axis airflow deflects towards the north side wall. However, when the angle is greater than 15°, the airflow on the north wall first disperses and then converges towards the wellbore. Conversely, the airflow on the south wall blows out in a southeast direction, and the axial airflow deflects towards the south wall. The rate of wind speed acceleration on both sides of the wall increases with height within the blowout but decreases along the central axis section. Higher wind speeds correlate with greater cumulative sand transport heights. Sand transport heights on both sides of the deflation basin are smaller than those along the central axis section, with the opposite observed in the depositional lobe. The rate of sand transport is primarily influenced by wind direction and wind speed, followed by, topography, vegetation coverage, slope, and aspect. These variations in airflow, combined with sediment transport, lead to the blowout taking on a develop deeper, longer, and wider shape.

The agile earth observation satellite scheduling problem (AEOSSP) is a combinatorial optimization problem with time-dependent constraints. Recently, many construction heuristics and meta-heuristics have been proposed; however, existing methods cannot balance the requirements of efficiency and timeliness. In this paper, we propose a graph attention network-based decision neural network (GDNN) to solve the AEOSSP. Specifically, we first represent the task and time-dependent attitude transition constraints by a graph. We then describe the problem as a Markov decision process and perform feature engineering. On this basis, we design a GDNN to guide the construction of the solution sequence and train it with proximal policy optimization (PPO). Experimental results show that the proposed method outperforms construction heuristics at scheduling profit by at least 45%. The proposed method can also calculate the approximate profits of the state-of-the-art method with an error of less than 7% and reduce scheduling time markedly. Finally, we demonstrate the scalability of the proposed method.

The past decade has seen an increase in the number of satellites in orbit and in highly dynamic satellite requests, making the control by ground stations inefficient. The traditional management composed of ground planning with separate onboard execution is seriously lagging in response to dynamically incoming tasks. To meet the demand for the real-time response to emergent events, a multi-autonomous-satellite system with a central-distributed collaborative architecture was formulated by an integer programming model. Based on the structure, evolutionary rules were proposed to solve this problem by the use of sequence solution construction and a constructed heuristic method based on gene expression programming evolution. First, the features of the problem are extracted based on domain knowledge, then, the problem-solving rules are evolved by gene expression programming. The simulation results reflect that the evolutionary rule completely surpasses the three types of heuristic rules with adaptive mechanisms and achieves a solution effect close to meta-heuristic algorithms with a reasonably fast solving speed.

Generic object tracking is a fundamental vision task. Numerous attempts are made to utilize handcrafted features like Hogs, deep convolutional features pretrained independently from other vision tasks, as well as hierarchical features. These methods achieve good balanced accuracy and speed in visual tracking. However, they explore the complementary characteristics of deep and shallow features imperfectly and ignore surrounding background information. In this paper, we exploit multi-cue cascades for building a robust end-to-end visual tracking, which cascades each level response via fully exploring the complementary properties of different levels of learning. Firstly, we crop out image patches and extract the features to construct corresponding levels of learning. Each levels of learning is utilized to cope with different challenges. Secondly, these multi-level learning procedure is embedded into the dynamic siamese networks for an end-to-end training. Additionaly, we take surrounding background information into account in the high level learning procedure. Finally, the outputs of each level are fused and we gain the accuracy and robustness trade-off. Extensive experiments on OTB-2013, OTB-2015 and VOT 2016 demonstrate that the proposed tracker performs favorably in comparison with the state-of-the-art trackers, while being more robust for background clutters.

Dimensionality reduction is commonly used to preprocess high-dimensional data, which is an essential step in machine learning and data mining. An outstanding low-dimensional feature can improve the efficiency of subsequent learning tasks. However, existing methods of dimensionality reduction mostly involve datasets with sufficient labels and fail to achieve effective feature vectors for datasets with insufficient labels. In this paper, an unsupervised multiple layered sparse autoencoder model is studied. Its advantage is that it reduces the reconstruction error as its optimization goal, with the resulting low-dimensional feature being reconstructed to the original dataset as much as possible. Therefore, the reduction of high-dimensional datasets to low-dimensional datasets is effective. First, the relationship among the reconstructed data, the number of iterations, and the number of hidden variables is explored. Second, the dimensionality reduction ability of the sparse autoencoder is proven. Several classical feature representation methods are compared with the sparse autoencoder on publicly available datasets, and the corresponding low-dimensional representations are placed into different supervised classifiers and the classification performances reported. Finally, by adjusting the parameters that might influence the classification performance, the parametric sensitivity of the sparse autoencoder is shown. The extensively low-dimensional feature classification experimental results demonstrated that the sparse autoencoder is more efficient and reliable than the other selected classical dimensional reduction algorithms.

High-sour natural gas usually contains organosulfurs besides H2S, the majority of which exist in the form of mercaptans. These impurities of organosulfurs are required to be removed efficiently and economically for commercial application and the environment. In this work, the adsorption performance of organic sulfur gases [methanethiol (MeSH) and ethanethiol (EtSH)] in 137 953 hypothetical metal-organic frameworks (hMOFs) and 4764 computation-ready experimental MOFs (CoRE-MOFs) were evaluated by a high throughput computational screening technique. The highest adsorption capacities are predicted to be approximately 700 and 980 mg/g for MeSH and EtSH, respectively, which is substantially higher than that in zeolites (∼100 mg/g). Quantitative structure-performance relationships are established between adsorption capacities and MOF textural/energetic properties (including the largest cavity diameter, surface area, void fraction, and isosteric heat). Two machine learning techniques, the back propagation neural network (BPNN) and the partial least-square (PLS) methods, are applied to predict 4764 CoRE-MOFs after training all the data of hMOFs from the large-scale screening. Compared with PLS, BPNN shows better prediction accuracy for MeSH and EtSH, and finds that the isosteric heat among seven MOF features possesses the highest weight for the adsorption of organosulfurs. Finally, the best 8 MOFs are identified for the removal of gaseous organosulfurs from the high-sour natural gas in a variety of industrial situations.

China has been gradually relaxing its ban on the use of low-altitude airspace across the country. To guarantee the high reliability of air traffic management (ATM), conflict detection and conflict resolution (CDR) approaches are indispensable to maintain safe separation between neighbouring small fixed-wing aircraft. In this study, we analyse a temporal and spatial integrated strategy for safety assessment purposes in opening the low-altitude urban airspace of Chinese pilot cities. First, we present a detailed mathematical description of the proposed algorithms based on a spatial grid partitioning system (SGPS). For our system, a conflict detection (CD) algorithm is designed to determine if two trajectories pass through the same grid space within overlapping time windows. A conflict resolution (CR) algorithm integrates a proposed time scheduling-based technique (TST) and vertical change-based technique (VCT), which operate under predetermined basic principles. Then, based on our novel CDR algorithms, a causal model is constructed in graphical modelling and analysis software (GMAS) to generate a state space that can provide a global perspective on scenario dynamics and better understanding of induced conflict occurrences. Finally, simulation results demonstrate that the proposed approach is practical and efficient.

In this paper a solid oxide fuel cell (SOFC) system for cogeneration of heat and power integrated with a stratified heat storage tank is studied. The use of a storage tank with thermal stratification allows one to increase the annual operating hours of CHP: heat can be produced when the request is low (for instance during the night), taking advantage of thermal stratification to increases the heat recovery performance. A model of the SOFC system is presented to estimate the energy required to meet the average electric energy demand of the residence. Two fuels are considered, namely syngas produced by gasification and natural gas. The tank model considers the temperature gradients over the tank height. The results of the numerical simulation are used to size the SOFC system and storage heat tank to provide energy for a small household using two different fuels. In particular it was shown that in the case of syngas, due to larger system heat output, a larger tank volume was required in order to accumulate unused heat over the night. The detailed description of the tank model will be useful to energy system modelers when sizing hot water tanks. Problem formulation is reported also using a Matlab script.

The rising level of CO2 in the atmosphere has attracted attention in recent years. The technique of capturing CO2 from higher CO2 concentrations, such as power plants, has been widely studied, but capturing lower concentrations of CO2 directly from the air remains a challenge. This study uses high-throughput computer (Monte Carlo and molecular dynamics simulation) and machine learning (ML) to study 6013 computation-ready, experimental metal-organic frameworks (CoRE-MOFs) for CO2 adsorption and diffusion properties in the air with very low concentrations of CO2. First, the law influencing CO2 adsorption and diffusion in air is obtained as a structure-performance relationship, and then the law influencing the performance of CO2 adsorption and diffusion in air is further explored by four ML algorithms. Random forest (RF) was considered the optimal algorithm for prediction of CO2 selectivity, with an R value of 0.981, and this algorithm was further applied to analyze the relative importance of each metal-organic framework (MOF) descriptor quantitatively. Finally, 14 MOFs with the best properties were successfully screened out, and it was found that a key to capturing a low concentration CO2 from the air was the diffusion performance of CO2 in MOFs. When the pore-limiting diameter (PLD) of a MOF was closer to the CO2 dynamic diameter, this MOF could possess higher CO2 diffusion separation selectivity. This study could provide valuable guidance for the synthesis of new MOFs in experiments that capture directly low concentration CO2 from the air.

Modeling is a core practice in science and is a meaningful way to learn the subject. This article introduces a modeling-based approach that highlights the idea that modeling is an iterative process and integrates the fundamental parts of scientists' work and key suggestions for teaching through modeling. The lesson "The Structure and Function of Kidneys" from a middle school biology course serves as an example of how to conduct the suggested modeling-based approach. By the end of the lesson, almost all students demonstrated a scientific understanding of the structure of nephrons and their functions. On the basis of the implementation of this lesson, we also provide further suggestions for modeling-based teaching.

The Monte Carlo and molecular dynamics simulations are employed to screen the separation performance of 6013 computation-ready, experimental metal–organic framework membranes (CoRE-MOFMs) for 15 binary gas mixtures. After the univariate analysis, principal component analysis is used to reduce 44 performance metrics of 15 mixtures to a 10-dimension set. Then, four machine learning algorithms (decision tree, random forest, support vector machine, and back propagation neural network) are combined with k times repeated k-fold cross-validation to predict and analyze the relationships between six structural feature descriptors and 10 principal components. Based on the linear correlation value R and the root mean square error predicted by the machine learning algorithm, the random forest algorithm is the most suitable for the prediction of the separation performance of CoRE-MOFMs. One descriptor, pore limiting diameter, possesses the highest weight importance for each principal component index. Finally, the 30 best CoRE-MOFMs for each binary gas mixture are screened out. The high-throughput computational screening and the microanalysis of high-dimensional performance metrics can provide guidance for experimental research through the relationships between the multi-structure variables and multi-performance variables.