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Background: This study aims to investigate the correlation of VEGF-B and FLT-1 co-expression with the prognosis of gastric cancer (GC). Materials & methods: Primary GC samples and adjacent tissues were obtained from 96 patients. Results: Both VEGF-B and FLT-1 were testified to be upregulated in the human GC compared with adjacent tissues. Spearman’s rank correlation analysis indicated that VEGF-B and FLT-1 expression were correlated (r = 0.321, p = 0.0015). High VEGF-B and FLT-1 co-expression patients showed poor prognosis when compared with low VEGF-B and FLT-1 co-expression patients (p = 0.0169). Conclusion: The high co-expression of VEGF-B and FLT-1 in GC shows a poor prognosis of overall survival, and targeted therapy against the interaction between VEGF-B and FLT-1 is worth further detailed analysis.

Glucocorticoids (GC) are used as the first-line treatment of immune thrombocytopenia (ITP), but 10-20% of patients are insensitive to them. Regulatory T cells (Tregs) can maintain immune tolerance in autoimmune diseases. The present research pooled 55 patients with newly diagnosed ITP and 44 healthy volunteers from seven hospitals. All patients received GC treatment and were divided into GC-sensitive and GC-insensitive groups according to the curative effect after 2 weeks of treatment. The levels of lymphocyte subgroups and Tregs were recorded. As the results indicated, the levels of CD8+ CD25str+ Tregs in the GC-sensitive group were significantly higher than that of the GC-insensitive group (P = 0·005). The optimal critical value of CD8+ CD25str+ Tregs to distinguish GC sensitivity was 0·09%. With GC therapy the level of CD45RO+ /CD8+ CD25str+ Tregs (activated type) decreased after treatment (P = 0·02) and the level of CD45RO- /CD8+ CD25str+ Tregs (initial type) increased slightly (P = 0·11). There were no obvious changes in the level of CD4+ Tregs. These findings support that the level of CD8+ CD25str+ Tregs and its subgroups have a predictive value in judging the sensitivity to GC among patients with ITP. Trial registration: www.chictr.org.cn; ChiCTR-OON-17014165.

Pyrolysis was carried out in an entrained flow drop-tube furnace (DTF) and tube furnace (TF) using Pingzhuang lignite coal with various catalyst concentrations (2 wt%, 4 wt%, and 6 wt%) of KCl and CaCl2 for the syngas component at 800°C–1200°C. Five catalysts (KCl, CaCl2, NiCl2, MnCl2, and ZnCl2) at 6 wt% were chosen for DTF at 800°C–1200°C. An online gas chromatograph analyzer and the Fourier transform infrared spectra were used for the analysis of the syngas and char structure. Results showed that the overall CO2 and CH4 content in DTF was lower than that in TF, mainly due to the CH4 carbon reaction at high temperature. Moreover, the CO% in DTF was higher than in the TF experiment, as char reacts with carbon dioxide to form carbon monoxide. In DTF experiment, the maximum and minimum CO2 content was 15.20% with 6 wt% Mn at 800°C and 0.33% with 6 wt% K at 1100°C, respectively. The maximum CO% was found in raw coal. Concentrations of Mn2+, Zn2+, and K+ can significantly increase H2%, whereas Ca2+ and Ni2+ have a minor effect on H2%; however, the overall presence of catalyst has a positive impact on the H2 content.

Effects of acid washing on coal characteristics are comprehensively studied using a series of experimental approaches, including SEM, XRD, BET, FTIR, TGA and in-situ measurements in a lab-scale pyrolysis furnace. Our study showed that acid-washing has marginal effects on coal crystal structure, the function groups, the temperature at the maximum weight loss rate of pyrolysis and the pyrolysis reactivity, but has significant impacts on coal micro-structure, pyrolysis activation energy, and product-gas compositions. By analyzing the SEM, XRD, BET and FTIR experimental results, it was found that the coal micro-structure becomes scattered and unstable after the acid-washing treatment, and this fundamental structural-variation leads to the reduction of overall reaction activation energy. The pyrolysis experiment concluded that the acid-washing promotes the CH4 production and inhibit CO production, while introducing negligible influence on H2 and CO2 productions.

The catalytic effects of three metal chlorides, including KCl, ZnCl 2 , and MnCl 2 , on the pyrolysis of lignite were investigated by thermogravimetry (TG) coupled with Fourier transform infrared (TG-FTIR) analysis from 105 °C to 1000 °C at a heating rate of 10 °C/min. The procedure of acid washing and the addition of metal chlorides had little effect on the structure of coal. The catalytic effect of inherent mineral became obvious only when the temperature was higher than 660 °C. The addition of metal chlorides promoted the pyrolysis process when the temperature was lower than 400 °C and higher than the critical temperature. DTG curve was fitted into six sub-curves to further analyze the pyrolysis process especially the breakage of chemical bonds. All three metal chlorides had a good catalytic effect on the quantity of CH 4 production but the opposite effect on CO production. MnCl 2 was the most favorable metal chloride for the production of CH 4 . The catalytic mechanism of KCl was different from that of MnCl 2 and ZnCl 2 on coal pyrolysis.

In this study Yimin lignite coal from Inner Mongolia was taken for investigating the influence of hydrothermal dewatering (HTD) on trace elements (Hg, As, Se) under different conditions. First, the coal samples were treated through HTD process at a temperature range of 200–300 °C, then proximate analysis was performed to check changes in the coal. Meanwhile, inductively coupled plasma-mass spectrometry and atomic fluorescence spectrometry were used to detect the concentrations of trace elements. Furthermore, minerals in samples were analyzed by powder X-ray diffraction (XRD). Sequential chemical extraction procedure (SCEP) was also performed to determine the occurrence modes of trace elements. The results showed that HTD treatment is effective in removing moisture and upgrading coal rank, and the operating temperature should be in good control at a certain range due to the loss of volatile mass. Minerals in this type of coal mainly include quartz and kaolinite, and HTD may have an effect on removing minor minerals muscovite and siderite. HTD can remove all three elements mentioned above. The highest removal rate detected is about 15% for As, 45% for Hg and 43% for Se. The experimental result shows that occurrence mode of Hg and As exist mainly in pyritic fraction while Se mainly in pyritic fraction and organic fraction. During HTD, all fractions of Hg and Se decrease obviously. However, for As, the decreasing amount in carbonate and pyritic fractions is much smaller than that in organic and silica bond fractions. It can be inferred that pyrite in coal may not decompose but only lose the bonds with trace elements. Pyrolysis behavior of coal and the strong solubility of sub-critical water should be responsible for the removal of trace elements.

The research methods of protein structure prediction mainly focus on finding effective features of protein sequences and developing suitable machine learning algorithms. But few people consider the importance of weights of features in classification. We propose the GASVM algorithm (classification accuracy of support vector machine is regarded as the fitness value of genetic algorithm) to optimize the coefficients of these 16 features (5 features are proposed first time) in the classification, and further develop a new feature vector. Finally, based on the new feature vector, this paper uses support vector machine and 10-fold cross-validation to classify the protein structure of 3 low similarity datasets (25PDB, 1189, FC699). Experimental results show that the overall classification accuracy of the new method is better than other methods.

The gasification experiments of coal chars with CO2 were carried out isothermally, with K, Ca, Ni, and Zn chloride catalysts, adopting a thermal gravimetric analyzer (TGA) from 800 to 1100 &deg, C. The kinetic characteristic of the samples were described using the volumetric model (VM), the grain model (GM), and the random pore model (PRM). The morphology patterns of the samples were tested applying X-ray diffraction (XRD) and the catalytic mechanisms concerning the phase changes were proposed. The results confirm that the gasification rate and char reactivities are enhanced by K, Ca and Ni chlorides, while ZnCl2 inhibited the process. The catalysis ability shows the following cation order: Ca &gt, K &gt, Ni &gt, Zn. Among the models described above, PRM was proven to give the best fitting value and hence adopted to kinetics parameters calculation. The activation energies in promoting conditions were lower than that of the uncatalyzed cases. In view of the catalytic mechanism, the K metals tend to form intermediate complexes and repeatedly connect with coal char, while the Ca species may follow the oxidation-reduction mechanism and the Ni metals catalyze the gasification process.

The restraint degree of the early-age concrete structure is an important factor for the generation of the thermal stress. When the concrete cannot deform freely as a result of the restrained deformation, the thermal stress, will generate until it increases to the degree that is great enough to affect the service life and safety of the concrete structure. Therefore, a new prediction method based on the time-restraint is proposed in this study. In the field test, a combination of the strain history of the newly cast concrete wall and the linear logarithmic creep model was adopted to calculate the thermal stresses. Compared with the traditional method, the complex structural restraint development can be described by a restraint model. The restraint coefficient of a specified location in the concrete wall changes from complete restraint to non-complete restraint, which can be transformed into a function of restraint coefficient related to the curing age. Compared with previous restraint evaluations, the restraint model reflects the restraint development of early-age concrete structures more authentically. Combined with the new restraint model, the thermal stress can be estimated with the completely restrained stress.

The Wei2 block of Weicheng oilfield is characterized by complicated structure mainly caused by high degree of fault development. Multiple reservoir types are found in this block and the reservoir heterogeneity is severe. The oil and gas reservoirs have already stepped into the stagnant stage of a great water-cut degree together with a rapid production decline rate. Thereby, both stabilizing the oil and gas production and optimizing adjustment for further exploitation make it urgent for geomodelers to build a useful model to predict the inter-well parameters and the distribution of the remaining oil and gas. A three-dimensional geological model established with the help of stochastic modeling technique may provide a perfect window and carrier for fine structure interpretation and reservoir heterogeneity description, compared with a traditional two-dimensional model. Hence, based on stratigraphical layering points, significant surfaces and fault points as well seismic interpretation, an integrated structure model is developed. Using the truncated Gaussian simulation and taking the existing geological maps as references, the sedimentary microfacies model was successfully constructed. Through the use of sequential Gaussian simulation method and the facies-controlled modeling method, the reservoir physical properties are populated. Meanwhile, the comparison between facies-controlled and non-facies-controlled property models indicates that the former is more loyal to previous researching and the representation of heterogeneity is ideal. Finally, the ideas of sample density and reserves fitting are proposed to evaluate the practicability and accuracy of the property models.

The central task of this study was to mine the gene-to-medium relationship. Adequate knowledge of this relationship could potentially improve the accuracy of differentially expressed gene mining. One of the approaches to differentially expressed gene mining uses conventional clustering algorithms to identify the gene-to-medium relationship. Compared to conventional clustering algorithms, self-organization maps (SOMs) identify the nonlinear aspects of the gene-to-medium relationships by mapping the input space into another higher dimensional feature space. However, SOMs are not suitable for huge datasets consisting of millions of samples. Therefore, a new computational model, the Function Clustering Self-Organization Maps (FCSOMs), was developed. FCSOMs take advantage of the theory of granular computing as well as advanced statistical learning methodologies, and are built specifically for each information granule (a function cluster of genes), which are intelligently partitioned by the clustering algorithm provided by the DAVID_6.7 software platform. However, only the gene functions, and not their expression values, are considered in the fuzzy clustering algorithm of DAVID. Compared to the clustering algorithm of DAVID, these experimental results show a marked improvement in the accuracy of classification with the application of FCSOMs. FCSOMs can handle huge datasets and their complex classification problems, as each FCSOM (modeled for each function cluster) can be easily parallelized.

Taking K-successions of the H-Zone of the Pearl River Mouth Basin as a testing example, we used two kinds of approaches to implement the microfacies identification. One is a direct identification, the other is an indirect approach in which we conducted the lithofacies classification first and then identified the microfacies based on previously estimated lithofacies. Both approaches were trained and checked by interpretations of experienced geologists from real subsurface core data. Multinomial logistic regression (MLR) and artificial neural network (ANN) were used in these two approaches as classification algorithms. Cross-validations were implemented. The source data set was randomly divided into training subset and testing subset. Four models, namely, MLR_direct, ANN_direct, MLR_indirect, and ANN_indirect, were trained with the training subset. The result of the testing set shows that the direct approaches (MLR_direct and ANN_direct) perform relatively poor with a total accuracy around 75%. While the indirect approaches (MLR_indirect and ANN_indirect) perform much better with a total accuracy of around 89 and 82%, respectively. This indirect method is simple and reproducible, and it could lead to a robust way of analyzing sedimentary microfacies of horizontal wells with little core data or even are almost never cored while core data are available for nearby vertical wells.

The reversion-inducing cysteine-rich protein with Kazal motifs (RECK) gene was originally identified as a transformation suppressor gene that is widely expressed in normal tissues. In tumor tissues, RECK expression levels are significantly reduced, and the downregulation of RECK has been implicated in tumors that are more aggressive with a poor prognosis. In the present study, RECK expression in peripheral T-cell lymphoma (PTCL; n=82) was examined using immunohistochemistry, and its correlation with clinicopathological factors was analyzed. According to the proportion of positively-stained cells and the staining intensity (SI), the patients were categorized into RECK-negative or RECK-positive groups. RECK expression was observed in 30 of the 82 patients (36.6%). The 3-year survival rate of the patients with RECK-positive tumors (65.5%) was significantly high compared with that of the patients with RECK-negative tumors (20.3%; P=0.046). Reduced RECK expression was found to be significantly correlated with extranodal lymphomatous involvement (P=0.012). The survival analysis showed that RECK-negative expression was an independent and significant factor for predicting a poor prognosis. RECK status is a useful prognostic factor for assessing the biological behavior in PTCL.

To reduce or avoid some of the ambiguities in a heterogeneous reservoir, a fine three-dimensional model would be inevitably established with the application of geostatistical techniques. BD oilfield is a low-permeability fractured reservoir with many fracture types, which composes a multi-azimuthal fracture system, causing strong anisotropy and heterogeneity. Moreover, fracture provides not only accumulation space for oil and gas but also channels for migration of hydrocarbons and water dashing and thus plays a leading role in controlling BD reservoir production. With the continuous exploitation of oil in reservoir for more than 30 years, BD oilfield has currently stepped into high water cut stage, exposing many problems such as low oil recovery, poor exploitation stability, rapid oil production decline, and one-way water intrusion. Therefore, a detailed reservoir characterization, constructed to describe reservoir behavior under strong water drive in a well-developed fractured reservoir, is urgently needed. In this study, combined with three-dimensional geostatistical techniques, an accurate and efficient reservoir parameter model that occurs in a strong heterogeneous fractured reservoir has been constructed through stratigraphic correlation and sedimentary facies analysis. Hence, all the study presented above will be available for well location, remaining oil potential-tapping and oil recovery improvement during the later study work.

Background Computer science and mathematical theories are combined to analyze the complex interactions among genes, which are simplified to a network to establish a theoretical model for the analysis of the structure, module and dynamic properties. In contrast, traditional model of gene regulatory networks often lack an effective method for solving gene expression data because of high durational and spatial complexity. In this paper, we propose a new model for constructing gene regulatory networks using back propagation (BP) neural network based on predictive function and network topology. Results Combined with complex nonlinear mapping and self-learning, the BP neural network was mapped into a complex network. Network characteristics were obtained from the parameters of the average path length, average clustering coefficient, average degree, modularity, and map’s density to simulate the real gene network by an artificial network. Through the statistical analysis and comparison of network parameters of Sea Urchin mRNA microarray data under different temperatures, the value of network parameters was observed. Differentially expressed Sea Urchin genes associated with temperature were determined by calculating the difference in the degree of each gene from different networks. Conclusion The new model we developed is suitable to simulate gene regulatory network and has capability of determining differentially expressed genes.

Structural complexity is obvious in WW fault block of A oilfield, mainly due to high fault development degree. Based on the study of core observation, high resolution sequence stratigraphy and seismic profile, the lower second sub-member of Shahejie Formation of WWfault block was subdivided into 58 layers. Three-dimensional structural model is constructed to display the occurrences of fault systems and horizon surfaces in the area. All wells' layering data, fault data and two-dimensional structural maps are integrated to build a 3-D structural model.

A new temperature stress test machine (TSTM) was developed to improve temperature control accuracy and efficiency. As there is no uniform standard for the threshold value of TSTM, quite different threshold values are used in the test, which results in the influences of the threshold value on the evaluation of thermal stresses and crack risk of concrete not being determined. To illustrate the importance of an appropriate threshold value, different threshold values were used to evaluate the influences on the thermal stresses and cracking resistance of concrete specimens under a complete restraint test with semi-adiabatic temperature development. The results show that the maximum compressive stress of a concrete specimen with a threshold value 3 με was slightly larger than that of 1.5 με when the growth rate of tensile stress of the concrete specimen with the threshold value 3 με was slightly greater than that of concrete specimen with the threshold value 1.5 με. Based on the combination of crack risk coefficient and restraint degree, a new system for evaluating the crack resistance of concrete was proposed, in which different threshold values were used to estimate their influences on the crack risk of concrete. Thus, an appropriate range of threshold values could be determined.

The prediction of protein folding rates is of paramount importance in describing the protein folding mechanism, which has broad applications in fields such as enzyme engineering and protein engineering. Therefore, predicting protein folding rates using the first-order of protein sequence, secondary structure and amino acid properties has become a very active research topic in recent years. This paper presents a new fuzzy cognitive map (FCM) model based on deep learning neural networks which uses data obtained from biological experiments to predict the protein folding rate. FCM extracts the important data features from the protein sequence which then initializes the deep neural networks effectively. It was found that the Levenberg–Marquardt (LM) algorithm for deep neural networks can improve the prediction accuracy of the protein folding rates. The correlation coefficient between the predicted values and those real values obtained from experiments reached 0.94 and 0.9 in two independent numerical tests.

The SVM (Support Vector Machine) is superior to other artificial neural network (such as the BP network) in classification. And its rapid development and the wide application are due to the introduction of the concept of soft margin. However, the traditional soft margin SVM gives the same misclassification costs for the various sample data, thus the processing results of the real data are not satisfactory. In this paper, the traditional SVM soft margin algorithm is improved by paying different costs for different misclassification. So the correct classification rate is further increased. And the BP network, RBF (Radial Basis Function) network, SVM and the improved SVM are applied to classify marine phytoplankton (enteromorpha) and the classification results are analyzed comparatively.