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Abstract The geological reserves of ordinary heavy oil suitable for thermal injection development in Bohai Oilfield are about 450 million tons. The traditional thermal recovery process uses a two trip injection-production string development method, which frequently starts and stops operations, resulting in additional cold damage and high costs. The efficiency of the jet pump injection-production integrated string system is also low, and production control is difficult. Therefore, based on the results of an integrated field test of electric submersible pump injection and production, this article optimizes its key technologies, removes high-temperature downhole on-off valves, re develops high-temperature deep well exhaust valves, and optimizes the structure of the packer as the installation platform for deep well exhaust valves. Indoor and field tests were conducted, and the results showed that the improved integrated technology of electric submersible pump injection and production met the requirements of 250 ℃ - room temperature and 21 MPa dynamic sealing. It can achieve multiple rounds of non repair injection and production conversion under steam throughput development mode, while reducing platform space requirements and injection heat loss, improving system efficiency and economy. The integrated optimization technology of electric submersible pump injection and production has good stability and applicability, and can be further promoted and applied.

The exploration of biomarkers predicting response to immune checkpoint inhibitors in microsatellite stability colorectal cancer can enable more patients to benefit from immunotherapy. Tumor mutational burden (TMB), POLE/POLD1 mutation, CMS classifications, MGMT methylation, and other indicators own the potential and value of predicting response to immune checkpoint inhibitors in microsatellite stability colorectal cancer. In this paper, we reviewed the related research on predictive biomarkers of immune checkpoint inhibitors in microsatellite stability colorectal cancer, provide a reference for the best treatment strategy for microsatellite stability colorectal cancer.

[Objectives] The sortie generation capacity is an important index of the operational capability of an aircraft carrier and largely determined by the support operation scheduling of the carrier-based aircraft. Therefore a good scheduling of carrier-based aircrafts on the deck can effectively improve the operational capability of aircraft carrier. [Methods] This paper establishes the operation scheduling module by converting the carrier-based aircraft support operation scheduling into job-shop scheduling problem. And through improvement of initial solution, search strategy and tabu list length, an improved tabu search algorithm is proposed to solve the model, with the purpose of minimizing the makespan. [Results] The simulation test results show that the improved tabu search algorithm can solve the carrier-based aircraft support operation scheduling problem effectively, and it is better than the traditional tabu search algorithm in terms of speed calculation and result optimization. [Conclusions] The proposed algorithm provides an effective way to solve the carrier-based aircraft support operation scheduling problem.

In order to detect the early fatigue crack of mechanical components simply, this paper puts forward the ultrasonic testing technology of different side collinear mixing. Firstly, based on the nonlinear ultrasonic theory, the method of calculating the difference frequency and sum frequency nonlinear coefficients of mixing ultrasonic is deduced. Then, the ram-5000 SINAP ultrasonic system is used to detect the aluminum alloy specimens with five different depth fatigue cracks, and the corresponding spectrum diagram is drawn. From the experimental results, we get that the crack depth is positively correlated with the nonlinear coefficients of difference frequency and sum frequency within a certain crack depth. Finally, by analyzing and fitting the experimental data, the prediction models of the difference frequency and sum frequency nonlinear coefficients on the crack depth are established. Through the analysis and combination of the above two prediction models, the prediction model of the mixing relative nonlinear coefficient is established, and the average error of the three prediction models is compared. The results show that the mixing relative nonlinear model has better results. The research work in this paper makes a useful exploration for crack detection and crack depth prediction.

As the Western world’s policy towards China has been changing in recent years, the international public opinion, that China comes into contact with, is also deteriorating, which has a serious negative impact on forming the international image of China and on shaping soft power. In the context of the rapid development of modern information technology, sports events like the Winter Olympics are often accompanied by large-scale media coverage and propaganda, and it is important to deal with the negative coverage of the Olympics involving China in the Western media and to build a good national image through the Winter Olympics. This study focuses on the specific content of Western media reports on China during the 2022 Beijing Winter Olympics cycle, and summarizes and analyzes the characteristics of “distorted facts”, “far-fetched” and “prominent political demands” in the negative reports on China in the Western media during the Winter Olympics. Based on the importance of guiding international public opinion and covering by media of such large sporting events as the Olympic Games, it is urgent and necessary to improve China’s international public opinion crisis response mechanism and enhance national and international media capacity. In this regard, this paper proposes four specific response strategies: strengthening the international communication capacity of mainstream media, enhancing the role of the government in guiding and making decisions, strengthening cooperation with international media; and making full use of our own media platforms.

LiCl was used to improve the surface charge of SiO2 colloidal spheres prepared by stöber method. And vertical deposition method was used to synthesize the photonic crystal with modified SiO2 colloidal spheres. The micromorphology, electrical properties and optical properties were characterized by scanning electron microscope (SEM), zeta potential instrument and UV/VIS/NIR spectrophotometer. The surface charge of SiO2 colloidal spheres can be improved by LiCl solution, and the Zeta potential increases with the increment of LiCl concentration and NH3 concentration. The position of the transmission dip of the photonic crystals is close to the experimental value.

Two Pechini sol-gel processes were used to prepare titania-silica composite particles. The dynamic oxidation behavior of the TiO2-SiO2 powders has been characterized by thermogravimetry-differential scanning calorimetry (TG-DTG-DSC). The crystal phase and microstructure of the composite particles were investigated by X-ray diffraction (XRD) and field emission scanning electron microscope (FE-SEM). The effects of Si:Ti molar ratio and sol-gel process on the TiO2-SiO2 powders were studied. The preparation of the polymeric precursors can influence the morphology of obtained TiO2-SiO2 composite particles. The spherical TiO2-SiO2 composite particles which are 20 nm~400 nm in diameter appear in gel-1 system. However, the TiO2-SiO2 powders obtained by gel-2 system are irregular in shape and 2~15 μm in diameter which show a loose porous structure consisted of very fine granules.

Abstract Background MicroRNAs (miRNAs) play a key role in regulating various biological processes such as participating in the post-transcriptional pathway and affecting the stability and/or the translation of mRNA. Current methods have extracted feature information at different levels, among which the characteristic stem-loop structure makes the greatest contribution to the prediction of putative miRNA precursor (pre-miRNA). We find that none of these features alone is capable of identifying new pre-miRNA accurately. Results In the present work, a pre-miRNA stem-loop secondary structure is translated to a network, which provides a novel perspective for its structural analysis. Network parameters are used to construct prediction model, achieving an area under the receiver operating curves (AUC) value of 0.956. Moreover, by repeating the same method on two independent datasets, accuracies of 0.976 and 0.913 are achieved, respectively. Conclusions Network parameters effectively characterize pre-miRNA secondary structure, which improves our prediction model in both prediction ability and computation efficiency. Additionally, as a complement to feature extraction methods in previous studies, these multifaceted features can reflect natural properties of miRNAs and be used for comprehensive and systematic analysis on miRNA.

Abstract Background The rapid accumulation of data on non-synonymous single nucleotide polymorphisms (nsSNPs, also called SAPs) should allow us to further our understanding of the underlying disease-associated mechanisms. Here, we use complex networks to study the role of an amino acid in both local and global structures and determine the extent to which disease-associated and polymorphic SAPs differ in terms of their interactions to other residues. Results We found that SAPs can be well characterized by network topological features. Mutations are probably disease-associated when they occur at a site with a high centrality value and/or high degree value in a protein structure network. We also discovered that study of the neighboring residues around a mutation site can help to determine whether the mutation is disease-related or not. We compiled a dataset from the Swiss-Prot variant pages and constructed a model to predict disease-associated SAPs based on the random forest algorithm. The values of total accuracy and MCC were 83.0% and 0.64, respectively, as determined by 5-fold cross-validation. With an independent dataset, our model achieved a total accuracy of 80.8% and MCC of 0.59, respectively. Conclusions The satisfactory performance suggests that network topological features can be used as quantification measures to determine the importance of a site on a protein, and this approach can complement existing methods for prediction of disease-associated SAPs. Moreover, the use of this method in SAP studies would help to determine the underlying linkage between SAPs and diseases through extensive investigation of mutual interactions between residues.

Abstract Background Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Given the importance of PPIs, several methods have been developed to detect them. Since the experimental methods are time-consuming and expensive, developing computational methods for effectively identifying PPIs is of great practical significance. Findings Most previous methods were developed for predicting PPIs in only one species, and do not account for probability estimations. In this work, a relatively comprehensive prediction system was developed, based on a support vector machine (SVM), for predicting PPIs in five organisms, specifically humans, yeast, Drosophila, Escherichia coli, and Caenorhabditis elegans. This PPI predictor includes the probability of its prediction in the output, so it can be used to assess the confidence of each SVM prediction by the probability assignment. Using a probability of 0.5 as the threshold for assigning class labels, the method had an average accuracy for detecting protein interactions of 90.67% for humans, 88.99% for yeast, 90.09% for Drosophila, 92.73% for E. coli, and 97.51% for C. elegans. Moreover, among the correctly predicted pairs, more than 80% were predicted with a high probability of ≥0.8, indicating that this tool could predict novel PPIs with high confidence. Conclusions Based on this work, a web-based system, Pred_PPI, was constructed for predicting PPIs from the five organisms. Users can predict novel PPIs and obtain a probability value about the prediction using this tool. Pred_PPI is freely available at http://cic.scu.edu.cn/bioinformatics/predict_ppi/default.html.

Abstract Background MicroRNA (miRNA), which is short non-coding RNA, plays a pivotal role in the regulation of many biological processes and affects the stability and/or translation of mRNA. Recently, machine learning algorithms were developed to predict potential miRNA targets. Most of these methods are robust but are not sensitive to redundant or irrelevant features. Despite their good performance, the relative importance of each feature is still unclear. With increasing experimental data becoming available, research interest has shifted from higher prediction performance to uncovering the mechanism of microRNA-mRNA interactions. Results Systematic analysis of sequence, structural and positional features was carried out for two different data sets. The dominant functional features were distinguished from uninformative features in single and hybrid feature sets. Models were developed using only statistically significant sequence, structural and positional features, resulting in area under the receiver operating curves (AUC) values of 0.919, 0.927 and 0.969 for one data set and of 0.926, 0.874 and 0.954 for another data set, respectively. Hybrid models were developed by combining various features and achieved AUC of 0.978 and 0.970 for two different data sets. Functional miRNA information is well reflected in these features, which are expected to be valuable in understanding the mechanism of microRNA-mRNA interactions and in designing experiments. Conclusions Differing from previous approaches, this study focused on systematic analysis of all types of features. Statistically significant features were identified and used to construct models that yield similar accuracy to previous studies in a shorter computation time.