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A concept of an adjustable elliptical journal bearing with variable elliptical ratio was proposed in order to suppress the vibration amplitude of a rotor under synchronous unbalance load at normal speed operating condition. In numerical simulations, dynamic lubrication program is programmed and it could predict the journal center orbits of the bearing. In experiments, adjustable elliptical journal bearing device and testing system are established. With the elliptical ratios adjusting from 0 to 0.7, journal vibrations are simulated and tested. The results obtained from the experiments are in good agreement with that from the numerical simulations. The results indicate that the effects of vibration suppression are satisfied. The proposed adjustable elliptical journal bearing provides an effective way in forced vibration suppression.

Background: It has been shown the impacts of sex on patients' outcomes with preserved ejection fraction (HFpEF), but little is known about the impacts of sex on elderly patients with HFpEF.Methods: A secondary analysis was conducted to evaluate the impacts of sex on outcomes of patients who were ≥70 years of age with HFpEF from the Treatment of Preserved Cardiac Function Heart Failure with an Aldosterone Antagonist Trial (TOPCAT). The primary outcome was composed of cardiovascular (CV) mortality, HF hospitalization. Secondary outcomes included all-cause mortality and all-cause hospitalization. Cox regression models were used to determine sex differences in outcomes.Results: A total of 1619 patients were included: 898 (55.5%) women and 721 (44.5%) men. Their age ranged from 70 to 94 years, similar between women and men. Women had fewer comorbidities than men. The rate of primary outcome was lower in women than in men (18.9% vs. 28.1%, p=0.002), including CV mortality (10.6% vs. 15.4%, p=0.039) and HF hospitalization (13.5% vs. 19.0%, p=0.033). After adjustment for baseline characteristic, the Cox regression analysis showed that woman was a protective factor for CV mortality (hazard ratio [HR] 0.53, 95% confidence interval [CI] 0.40-0.73), HF hospitalization (HR 0.71, 95% CI 0.55-0.93) and all-cause mortality (HR 0.59, 95% CI 0.47-0.75). Although a significant reduction in all-cause mortality associated with spironolactone in women was observed even after adjustment (HR: 0.68; 95% CI: 0.48-0.96; p=0.028), there is not a significant multivariate sex-treatment interaction (p interaction=0.190).Conclusion: Among elderly patients with HFpEF, women had fewer comorbidities and better outcomes. Clinical trial registration: NCT00094302 (TOPCAT). Registered 15 October 2004, https://www.clinicaltrials.gov/ct2/show/NCT00094302

Hepatocellular carcinoma (HCC) is one of the tumors with a higher mortality rate globally, which significantly threatens people's health. Hepatitis C virus (HCV) infection is a major driving factor of HCC. This study aims to determine the key microRNA (miRNA), hub genes, and related pathways, construct potential miRNA–mRNA regulatory networks, and clarify the new molecular mechanism of HCV-related HCC. In this study, 16 differentially expressed miRNAs (DE miRNAs) were identified. The prediction of potential transcription factors and target genes not only found that SP1 and ERG1 may potentially regulate most of the screened DE miRNAs, but it also obtained 2923 and 1782 predicted target genes for the up-regulation and down-regulation of DE miRNAs, respectively. Subsequently, the introduction of differentially expressed genes dataset GSE62232 for target gene verification yielded 98 and 147 potential up-regulation and down-regulation target genes. The gene ontology (GO) and Kyoto encyclopedia of genes and genomes pathway enrichment analysis showed that they were mainly enriched in the cell cycle process, that is, subsequently, 20 hub genes were screened out through the protein–protein interaction network, and related genes were further evaluated using the GEPIA database. Based on the above analysis, the miRNA-hub gene regulatory network was constructed. In short, this research's hub genes and miRNAs closely related to HCV-related HCC were screened and identified through bioinformatics analysis and then built their connection. These results are expected to find potential therapeutic targets for HCV-related HCC.

Chaotic systems are complex dynamical systems that play a very important role in the study of the atmosphere, aerospace engineering, finance, etc. To improve the accuracy of chaotic time series prediction, this study proposes a hybrid model CEEMDAN-LSTM which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and long short-term memory (LSTM). In the model, the original time series is decomposed into several intrinsic mode functions (IMFs) and a residual component. To reduce the difficulty of predicting chaotic time series and provide a high level of predictive accuracy, the LSTM prediction model is built for all each characteristic series from CEEMDAN deposition. Finally, the final prediction results are obtained by combining all the prediction sequences. To test the effectiveness of this model we proposed, we examined the CEEMDAN-LSTM model using the Lorenz-63 system. Further compared to Autoregressive Integrated Moving Average (ARIMA ), Support Vector Regression (SVR), multilayer perceptron (MLP), and the single LSTM model, the results of the experiment show that the proposed model performs better in the prediction of chaotic time series. Besides, the hybrid model proposed in this paper has better results than the LSTM model alone. Therefore, hybrid models based on deep learning methods and signal decomposition methods have great potential in the field of chaotic time series prediction.

Cloud is the most uncertain factor in the climate system and has a huge impact on climate change. Therefore, the study of changes in cloudiness is of great importance for understanding climate and climate change. Cloud detection is also an important research area in satellite remote sensing image pre-processing. But cloud detection is a difficult task due to various noise disturbances in the remote sensing data itself, as well as factors such as ice and snow on the ground. With the rapid development of artificial intelligence technology, deep learning methods have achieved great success in methods such as image processing and classification. In this study, we use the modified U-Net architecture that introduces the attention mechanism for cloud detection. The experimental results show that the method proposed in this paper has a significant accuracy advantage over the traditional cloud detection method, especially in snowy areas and other areas covered by bright non-cloud objects. The effectiveness of this method makes it a great potential for other optical image processing as well.

Atmospheric systems are typically chaotic and their chaotic nature is an important limiting factor for weather forecasting and climate prediction. So far, there have been many studies on the simulation and prediction of chaotic systems using numerical simulation methods. However, there are many intractable problems in predicting chaotic systems using numerical simulation methods, such as initial value sensitivity, error accumulation, and unreasonable parameterization of physical processes, which often lead to forecast failure. With the continuous improvement of observational techniques, data assimilation has gradually become an effective method to improve the numerical simulation prediction. In addition, with the advent of big data and the enhancement of computing resources, machine learning has achieved great success. Studies have shown that deep neural networks are capable of mining and extracting the complex physical relationships behind large amounts of data to build very good forecasting models. Therefore, in this paper, we propose a prediction method for chaotic systems that combines deep neural networks and data assimilation. To test the effectiveness of the method, we use the model to perform forecasting experiments on the Lorenz96 model. The experimental results show that the prediction method that combines neural network and data assimilation is very effective in predicting the amount of state of Lorenz96. However, Lorenz96 is a relatively simple model, and our next step will be to continue the experiments on the complex system model to test the effectiveness of the proposed method in this paper and to further optimize and improve the proposed method.

Genetic variation and genotype of Hepatitis B virus (HBV) are related to the efficiency of interferon alpha (IFN-α)-based antiviral therapy. However, the correlation of variation in interferon-stimulated response element (ISRE) and HBV genotype response to IFN-α therapy remains elusive. Differences of ISRE between genotype B and C HBV were explored using the HBV sequences retrieved from GenBank, and further investigated by ISRE region cloning and sequencing from 60 clinical samples post-IFN-α therapy. Additionally, ISRE mutants were constructed and their relation to responsiveness of IFN-α was evaluated by real-time PCR and Southern blot analysis. ISRE pattern between genotype B and C were found based on both clinical sample sequencing and full-length sequence alignment. The primary difference is the fourth base within the ISRE region, with T and C for genotype B and C, respectively. HBV with genotype C-type ISRE had a higher replicative capability as compared to HBV with genotype B-type ISRE after IFN-α treatment in huh7 cells. Conclusion: Preference of ISRE between genotype B and C HBV are distinct. Single nucleotide difference (C to T) within the HBV ISRE region may link to the efficacy of IFN-α therapy to genotype B and C HBV. Therefore, this study provides a clue for the determination of IFN-α therapy response to HBV treatment.

A new fusion sensing (FS) method was proposed by using the improved fractal box dimension (IFBD) and a developed maximum wavelet coefficient (DMWC) for fault sensing of an online power cable. There are four strategies that were used. Firstly, the traditional fractal box dimension was improved to enlarge the feature distances between the different fault classes. Secondly, the IFBD recognition algorithm was proposed by using the improved fractal dimension feature extracted from the three-phase currents for the first stage of fault recognition. Thirdly, the DMWC recognition algorithm was developed based on the K-transform and wavelet analysis to establish the relationship between the maximum wavelet coefficient and the fault class. Fourthly, the FS method was formed by combining the IFBD algorithm and the DMWC algorithm in order to recognize the 10 types of short circuit faults of online power. The designed test system proved that the FS method increased the fault recognition accuracy obviously. In addition, the parameters of the initial angle, transient resistance, and fault distance had no influence on the FS method.

A simple method combing Mn2+ doping with a hierarchical structure was developed for the improvement of thin-film sensors and efficient detection of the explosives relevant to improvised explosive devices (IEDs). ZnS hierarchical nanospheres (HNs) were prepared via a solution-based route and their sensing performances were manipulated by Mn2+ doping. The responses of the sensors based on ZnS HNs towards 8 explosives generally increase firstly and then decrease with the increase of the doped Mn2+ concentration, reaching the climate at 5% Mn2+. Furthermore, the sensory array based on ZnS HNs with different doping levels achieved the sensitive and discriminative detection of 6 analytes relevant to IEDs and 2 military explosives in less than 5 s at room temperature. Importantly, the superior sensing performances make ZnS HNs material interesting in the field of chemiresistive sensors, and this simple method could be a very promising strategy to put the sensors based on thin-films of one-dimensional (1D) nanostructures into practical IEDs detection.

Over the past years, monoclonal antibodies have been successfully utilized in clinical trials to block or activate key mediators of immune checkpoint pathways, including CTLA-4, PD-1, PD-L1 and OX40 et.al,. Although significant benefits including complete regression and long-term survival were reported among the majority of participants, not all patients respond. In order to optimize the efficacy and minimize toxicities of anticancer immunotherapies, immune checkpoint antibodies are being tested in combination with other conventional therapeutics (radiation, chemotherapy and targeted therapies) and newer immunotherapies (cancer vaccines, oncolytic viruses). However, along the IO drug development process, in vivo efficacy models have always been a rate-limiting step, especially for the combination therapy of two IO antibodies. Therefore, we generated humanized double knock-in mouse to evaluate the in vivo efficacy of combination therapy. For example, humanized double Knock-in B-hPD-1/hOX40 mice pairing with mouse cancer cells, demonstrated a synergistic effect of using hPD-1 and hOX40 antibodies. Additionally, more double knock-in mice targeting immune checkpoint molecules were developed, such as B-hPD-1/hPD-L1, B-hPD-1/hCTLA-4, B-hPD-1/hLAG3, B-hPD-1/hTIM3, B-hPD-1/hTIGIT, B-hPD-1/hBTLA, et al,. Humanized double knock-in mice, represent useful in vivo efficacy models for evaluation of the combination effects of various IO drugs. Citation Format: Tian Gan, James Jin, Chaoshe Guo, Yuelei Shen, Yanan Guo. Humanized double knock-in mouse models for evaluating in vivo efficacy of the combination therapy of immune-oncology drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5688.

Cancer immunotherapy is one of the most promising research areas in the field of cancer therapy. Many pharmaceutical and biotech companies in the world are devoting great effort to develop cancer immunity-related treatment antibodies. However, along the IO drug development process, in vivo efficacy models have always been a rate-limiting step. In most cases, a human monoclonal antibody does not have mouse cross-reactivity. Mouse surrogate antibodies were often used in immune-competent syngeneic mouse models to evaluate in vivo efficacy of IO drugs. However, the efficacy of a surrogate antibody cannot fully represent the human drug in the clinical scenario. Therefore, we generated humanized knock-in mice to evaluate the in vivo efficacy of human IO antibodies. For example, human OX40 knock-in (B-hOX40) mice were generated with a chimeric OX40 receptor, which is recognizable by stimulatory human OX40 antibodies. Additionally, more knock-in mice targeting stimulatory immune checkpoint molecules were developed and validated, such as B-hCD137, B-hGITR, B-hCD27, B-hCD40, B-hCD28, B-hCD3 et al,. All these mouse models response well to the corresponding human IO antibodies, proving that they are powerful tools for in vivo efficacy evaluation of human stimulatory immune checkpoint antibodies. Citation Format: Tongmei Xia, Chaoshe Guo, Yuelei Shen, Yanan Guo. Evaluation of the in vivo efficacy of immune-oncology drugs targeting stimulatory immune checkpoint molecules using humanized knock-in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5694.

Recently, increasingly attention has been paid to the development of immuno-oncology (IO) drugs, due to the promising clinical results acquired by CTLA-4, PD-1 and PD-L1 antibodies. However, along the IO drug development process, in vivo efficacy models have always been a rate-limiting step. Mouse surrogate antibodies were often used in immune-competent mice with murine syngeneic tumor models to evaluate in vivo efficacy of the IO drugs. However, the efficacy of a surrogate antibody cannot fully represent the human drug in the clinical scenario. In most cases, a human monoclonal antibody does not have mouse cross-reactivity. Therefore, we generated humanized knock-in mouse to evaluate the in vivo efficacy of human IO antibodies. For example, human PD-1 knock-in (B-hPD-1) mice were generated with a chimeric PD-1 receptor, which is recognizable by human PD-1 antibodies, and can be used to test human PD-1 antibody in vivo efficacy. Mouse cancer cell lines including colon cancer MC38, melanoma B16F10 and lung cancer LLC1 were genetically modified with over-expression of hPD-L1 and knock-out of mPD-L1, and used to evaluate the in vivo efficacy of human PD-L1 antibodies. Additionally, more knock-in mice targeting inhibitory immune checkpoint molecules were developed, such as B-hPD-L1, B-hCTLA-4, B-hLAG3, B-hTIM3, B-hTIGIT, B-hBTLA, B-hSIRPa et al,. Humanized knock-in mice, pairing with genetically modified mouse cancer cells, represent a promising in vivo efficacy model for the development of IO drugs, and contribute to the clinical translation of animal studies. Citation Format: Baozhu Zhang, Chaoshe Guo, Yuelei Shen, Yanan Guo. Humanized knock-in mouse models for evaluating in vivo efficacy of immune-oncology drugs targeting inhibitory immune checkpoint molecules [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 1149.

A novel slanted grating coupler is proposed and demonstrated for perfectly-vertical coupling between optical fibers and waveguides. It offers a coupling efficiency as high as 88.6% for TE polarizations at the wavelength of $1.55\ \mu\mathrm{m}$ .

The Beijing Spectrometer (BES) is being designed and constructed for use at the Beijing 2 × 2.8 GeV center-of-mass energy electron-positron collider (BEPC). The BES detector will have approximately 20 K channels of electronics, and the beam collision rate will be 1.25 MHz, corresponding to a luminosity of 10 30 −10 31 cm −2 s −1 . A fast programmable track finding processor to be used in the second level trigger is described. This processor, mainly consisting of 3 out of 4 logic and track finding logic, is simple and flexible. Only 96 pieces of 4K × 1 bit random access memory (RAM) chips are used for the track finding logic on 4 layers of the main drift chamber.