Expression pattern, immune signature, and prognostic value of RBM10 in human cancers

Background. RNA-binding motif protein 10 (RBM10) regulates the expression of genes involved in immune responses and is associated with a wide spectrum of cancers. Meanwhile, immunotherapy is the most promising cancer treatment of our time; nevertheless, the pan-cancer role of RBM10 remains to be elucidated. Methods. Data from multiple online databases, including ONCOMINE, UALCAN, GEPIA2, Kaplan–Meier Plotter, cBioPortal, STRING, and TIMER were analyzed. The protein expression levels of RBM10 in various tumor types were verified by immunohisto-chemistry (IHC). Results. RBM10 is upregulated in multiple tumors compared with the corresponding normal tissues. In addition, RBM10 is highly mutated in various cancers. We also compared the levels of phosphorylated RBM10 between normal and primary tumor tissues. We found that the expression of RBM10 was positively correlated with Programmed cell death 1 (PD-L1) and Cytotoxic lymphocyte antigen 4 (CTLA4) in most cancers, except Thyroid carcinoma (THCA). Moreover, the expression of RBM10 was significantly related to immune cell infiltration in many cancers, suggesting that it is a promising target for cancer immunotherapy. Conclusions. RBM10 expression is closely related to tumor prognosis and the immune microenvironment. Our findings provide new insights into the role of RBM10 in cancer diagnosis and treatment

Data Calibration Based on Multisensor Using Classification Analysis: A Random Forests Approach

This paper analyzes the problem of meaningless outliers in traffic detective data sets and researches characteristics about the data of monophyletic detector and multisensor detector based on real-time data on highway. Based on analysis of the current random forests algorithm, which is a learning algorithm of high accuracy and fast speed, new optimum random forests about filtrating outlier in the sample are proposed, which employ bagging strategy combined with boosting strategy. Random forests of different number of trees are applied to analyze status classification of meaningless outliers in traffic detective data sets, respectively, based on traffic flow, spot mean speed, and roadway occupancy rate of traffic parameters. The results show that optimum model of random forest is more accurate to filtrate meaningless outliers in traffic detective data collected from road intersections. With filtrated data for processing, transportation information system can decrease the influence of error data to improve highway traffic information services

Long non-coding RNA SNHG1 promotes bladder cancer progression by upregulating EZH2 and repressing KLF2 transcription

Objective: Long Non-Coding RNAs (LncRNAs) act as an indispensable role in cancer development. The study aimed to investigate the role and mechanism of lncRNA Small Nucleolar RNA Host Gene 1 (SNHG1) in Bladder Cancer (BC) progression. Method: The expression, prognostic value, diagnostic value, and correlation of SNHG1, Enhancer of Zeste 2 polycomb repressive complex 2 subunit (EZH2), and Kruppel Like Factor 2 (KLF2) were analyzed through bioinformatics analysis. The expression was also validated in BC tissues and cell lines. Besides, their regulation and binding were tested via qPCR, Western blot, Dual-Luciferase Reporter Assay (DLRA), Argonaute RISC catalytic component 2-RNA Immunoprecipitation (AGO2-RIP), and Chromatin Immunoprecipitation (ChIP). A xenograft model in nude mice was also established. Results: SNHG1 was significantly overexpressed in BC tissues and cells. Importantly, SNHG1 was associated with poor survival, and ROC curves revealed high diagnostic values. Moreover, by CCK8, wound healing, transwell, and Western blot analysis, SNHG1 knockdown significantly inhibited the proliferation, migration, invasion, and epithelial-mesenchymal transition of BC cells. Additionally, in vivo experiments showed that silencing SNHG1 hindered tumorigenesis and tumor growth. Regarding mechanism, the results of AGO2-RIP, ChIP or DLRA showed that SNHG1 played different roles at diverse subcellular sites. In the cytoplasm, SNHG1 acted as a competing endogenous RNA for miR-137-3p to promote EZH2 expression. In the nucleus, SNHG1 could interact with EZH2 to inhibit KLF2 transcription. Conclusion: Our study elucidated that SNHG1 formed a regulatory network and played an oncogenic role in BC, which provided a novel therapeutic target for BC treatment

HowNet Based Chinese Question Classification

PACLIC 20 / Wuhan, China / 1-3 November, 200

Signal Timing Optimization Based on Fuzzy Compromise Programming for Isolated Signalized Intersection

In order to optimize the signal timing for isolated intersection, a new method based on fuzzy programming approach is proposed in this paper. Considering the whole operation efficiency of the intersection comprehensively, traffic capacity, vehicle cycle delay, cycle stops, and exhaust emission are chosen as optimization goals to establish a multiobjective function first. Then fuzzy compromise programming approach is employed to give different weight coefficients to various optimization objectives for different traffic flow ratios states. And then the multiobjective function is converted to a single objective function. By using genetic algorithm, the optimized signal cycle and effective green time can be obtained. Finally, the performance of the traditional method and new method proposed in this paper is compared and analyzed through VISSIM software. It can be concluded that the signal timing optimized in this paper can effectively reduce vehicle delays and stops, which can improve traffic capacity of the intersection as well

Analyzing urban traffic demand distribution and the correlation between traffic flow and the built environment based on detector data and POIs

Purpose This paper aims to determine the urban traffic flow spatiotemporal characteristics and correlation with the built environment using SCATS (Sydney Coordinated Adaptive Traffic System) and POIs (Point of Interests) data of Shenyang, China. Methods A standard analysis framework based on these data is proposed in the paper. The study analyzes the traffic volume spatiotemporal distributions and built environment influence factors determined by the geographical detector. An improved gravity model using simple structural parameters (lanes number and road length) is proposed to estimate the traffic flows of day and peak hour scales for specific flow ranges. Results The results show that the peak hours of different intersections and roads are heterogeneous and reveal trip time flexibility. The correlation between peak hour flows and day flows is significant in the multidimensional analysis. Based on the investigation of lanes, more interesting conclusions are found. In this case, when the numbers of lanes of intersections and roads are more than 14 and 4 respectively, the lane resources are wasted to a great extent. There is also a certain correlation between these factors. Proposed gravity model establishes the connection between structure and function of urban roads. Conclusions Flexible work time and places will be effective methods to reduce traffic congestion. The day flows could be estimated via a traffic survey on peak hour flows, especially in developing cities. The traffic flow mainly concentrates in a relatively small part of city roads. The maximum service traffic volumes exhibit segmentation, we should reconsider the maximum optimal lanes number of intersections and roads under better performance and utilization rate of the network. The effect of lanes number on the service traffic volumes is found to be more significant compared with the other factors. Our conclusions will be helpful for policy-makers and sustainable urban planning. Document type: Articl

Mesosphere data assimilation based on the intelligent optimization of the uncertainty parameters in a theoretical model

The mesosphere, which is located approximately 50–90 km above the Earth's surface, is a crucial part of the Earth's atmosphere. Data assimilation in the mesosphere is essential for accurately simulating and forecasting its state. However, the lack of sufficient observations results in this field being relatively underdeveloped. In this study, we conducted an intelligent optimization particle filtering algorithm to optimize the uncertainty parameters in a physics-based model, which was used to simulate the terrestrial mesosphere. This algorithm was employed to improve the accuracy of mesospheric state simulation via the injection of sparse observations. The mesospheric temperature, density, and pressure profiles, measured by the SABER (Sounding of the Atmosphere using Broadband Emission Radiometry) onboard the TIMED (Thermosphere Ionosphere Mesosphere Energetics and Dynamics) satellite, were injected into the data assimilation model. The comparison results demonstrated that the statistical error in the mesospheric temperature simulation from the data assimilation model is comparable to that from the theoretical model. However, owing to the improved accuracy in simulating individual temperature profile, the assimilation model significantly improved the accuracy of the mesospheric pressure and density estimation. Notably, our model also improved the simulation accuracy of the lower thermosphere, where none of the measurements were injected. Moreover, the results indicated that fine-tuning the uncertainty parameters in the physics-based model can contribute to maintaining the level of forecasting accuracy for the mesosphere over several days' lead time, which is essential for long-term mesospheric prediction capabilities. This study highlights the effectiveness of intelligent optimization of the uncertainty parameters in a theoretical model in improving model accuracy and extending forecast reliability within the mesosphere

Interactions of SARS Coronavirus Nucleocapsid Protein with the host cell proteasome subunit p42

<p>Abstract</p> <p>Background</p> <p>Severe acute respiratory syndrome-associated coronavirus (SARS-CoV) spreads rapidly and has a high case-mortality rate. The nucleocapsid protein (NP) of SARS-CoV may be critical for pathogenicity. This study sought to discover the host proteins that interact with SARS-CoV NP.</p> <p>Results</p> <p>Using surface plasmon resonance biomolecular interaction analysis (SPR/BIA) and matrix-assisted laser desorption/ionization time of flight (MALDI-TOF) mass spectrometry, we found that only the proteasome subunit p42 from human fetal lung diploid fibroblast (2BS) cells bound to SARS-CoV NP. This interaction was confirmed by the glutathione S-transferase (GST) fusion protein pulldown technique. The co-localization signal of SARS-CoV NP and proteasome subunit p42 in 2BS cells was detected using indirect immunofluorescence and confocal microscopy. p42 is a subunit of the 26S proteasome; this large, multi-protein complex is a component of the ubiquitin-proteasome pathway, which is involved in a variety of basic cellular processes and inflammatory responses.</p> <p>Conclusion</p> <p>To our knowledge, this is the first report that SARS-CoV NP interacts with the proteasome subunit p42 within host cells. These data enhance our understanding of the molecular mechanisms of SARS-CoV pathogenicity and the means by which SARS-CoV interacts with host cells.</p

Characterization of cellulase production by carbon sources in two Bacillus species

The induction of cellulase production in two Bacillus spp. was studied by means of measuring cellulase activities under the condition of different carbon sources. The results indicate that cellulase could not be induced by cellulose material as a sole carbon source. Instead, they could be induced by monosaccharide or disaccharide with reducing group. Moreover, the expression of cellulase components was synergistic. When cell wall/envelope enzyme and endoenzyme from two Bacillus spp. acted on these inducers, analysis of reaction products by high performance liquid chromatography (HPLC) revealed that cell wall/envelope enzyme and endoenzyme from two Bacillus spp. were inactive on these inducers. It also indicated that these inducers entered cells directly and served function of induction.Keywords: Bacillus, cellulase, induction, carbon source