Numerical investigation of the wake bi-stability behind a notchback Ahmed body

Large-eddy simulations are used to investigate the origin of the wake asymmetry and symmetry behind notchback Ahmed bodies. Two different effective backlight angles, beta(1) = 17.8 degrees and beta(2) = 21.0 degrees, are simulated resulting in wake asymmetry and symmetry in flows without external perturbations, in agreement with previous experimental observations. In particular, the asymmetric case presents a bi-stable nature showing, in a random fashion, two stable mirrored states characterized by a left or right asymmetry for long periods. A random switch and several attempts to switch between the bi-stability are observed. The asymmetry of the flow is ascribed to the asymmetric separations and reattachments in the wake. The deflection of the near-wall flow structures behind the slant counteracting the asymmetry drives the wake to be temporarily symmetric, triggering the switching process of the bi-stable wake. The consequence of deflection that forces the flow structure to form on the opposite side of the slant is the decisive factor for a successful switch. Modal analysis applying proper orthogonal decomposition is used for the exploration of the wake dynamics of the bi-stable nature observed

Accuracy improvement of fuel cell prognostics based on voltage prediction

Proton exchange membrane fuel cell (PEMFC) is a promising hydrogen technique with various application prospects. However, all the PEMFCs are subject to degradation resulting from mechanical and chemical aging. To tackle this challenge, accurately predicting fuel cell degradation is essential for its durability optimization. In this study, an enhanced data-driven prognostic framework is developed to accurately predict short-term and medium-term degradation using only fuel cell voltage as the input feature. Firstly, a local outlier factor (LOF) algorithm is adopted for automatic detection of outliers in raw data collected from actual sensing environments. Then, an advanced deep learning model, residual–CNN–LSTM-random attention, is proposed to optimize voltage prediction to better indicate future PEMFC degradation trend. The proposed work is validated by the IEEE PHM 2014 Data Challenge. Compared to state-of-the-art methods, the proposed framework provides superior prediction accuracies with high stability. For instance, the framework improves short-term prediction, achieving a root mean square error (RMSE) of 0.0021 and a mean absolute percentage error (MAPE) of 0.0323 at steady state when training stops at 600 h. For medium-term prediction, our method also attains better results with an RMSE of 0.0085 and a MAPE of 0.4237 under same working conditions. Additionally, the comparative analyses demonstrate a lower computational burden and higher suitability of proposed work for practical applications

Online systemic energy management strategy of fuel cell system with efficiency enhancement

Temperature plays a crucial role in efficiency improvement and lifespan extension of the fuel cell system which encourages energy management strategy (EMS) taking thermal into consideration. However, sluggish thermal response prevents the fuel cell performance from tracking the optimal states during scenarios with significant power variations, which was disregarded in the previous works. To solve this issue, an online hydrogen consumption minimization guarantee strategy (HCMG) including thermal management is proposed which is divided into two parts: 1) primary power distribution strategy, where a model predictive control (MPC) based EMS is employed herein to distribute power between fuel cell and battery with the objectives of minimizing hydrogen consumption as well as maintaining the state of charge (SOC), and 2) HCMG, where a modified MPC based method is exploited herein to track the reference power and optimal temperature with minimum hydrogen consumption by adjusting both the duty cycle of fan and fuel cell current. The presented approach ascertains hydrogen consumption reduction for 3.448% even under relatively extensive power changes, during which the temperature cannot reach the optimal value in a brief time. The real-time simulation results show the effectiveness of the proposed technique compared with previous EMS methods under various driving cycles

Determinants of depression, problem behavior, and cognitive level of adolescents in China: Findings from a national, population-based cross-sectional study

IntroductionWe aimed to assess the associated factors for adolescent depression, problem behavior and cognitive level in China.MethodsA total of 2,584 adolescents aged from 10 to 15 years old in 2018 were included for analyses. Information on a comprehensive set of potential determinants was collected by the questionnaire, including demographic, health-, school- and family-related factors. Differences in average scores of depression, problem behavior, and cognitive level across subgroups were assessed by two independent sample t-tests and one-way analysis of variance (ANOVA). The clinical relevance among subgroups was assessed by the effect size. Multivariate linear regression models were applied to identify the statistically significant determinants.ResultsSchool-related factors and parental depressive status were strongly associated with depression. Low maternal education, poor/bad health of adolescents, high academic pressure, and parental depression were significantly associated with behavior problems. The socioeconomic factors, poor academic performance and father’s depression were significantly associated with adolescent cognitive level.DiscussionMultiple associated factors were identified for depression, problem behavior, and cognition of Chinese adolescents, which will provide insights into developing more targeted public health policies and interventions to improve their mental health

Fibroblast Growth Factor Receptor 1 Drives the Metastatic Progression of Prostate Cancer

BACKGROUND: No curative therapy is currently available for metastatic prostate cancer (PCa). The diverse mechanisms of progression include fibroblast growth factor (FGF) axis activation. OBJECTIVE: To investigate the molecular and clinical implications of fibroblast growth factor receptor 1 (FGFR1) and its isoforms (α/β) in the pathogenesis of PCa bone metastases. DESIGN, SETTING, AND PARTICIPANTS: In silico, in vitro, and in vivo preclinical approaches were used. RNA-sequencing and immunohistochemical (IHC) studies in human samples were conducted. OUTCOME MEASUREMENTS AND STATISTICAL ANALYSIS: In mice, bone metastases (chi-square/Fisher's test) and survival (Mantel-Cox) were assessed. In human samples, FGFR1 and ladinin 1 (LAD1) analysis associated with PCa progression were evaluated (IHC studies, Fisher's test). RESULTS AND LIMITATIONS: FGFR1 isoform expression varied among PCa subtypes. Intracardiac injection of mice with FGFR1-expressing PC3 cells reduced mouse survival (α, p < 0.0001; β, p = 0.032) and increased the incidence of bone metastases (α, p < 0.0001; β, p = 0.02). Accordingly, IHC studies of human castration-resistant PCa (CRPC) bone metastases revealed significant enrichment of FGFR1 expression compared with treatment-naïve, nonmetastatic primary tumors (p = 0.0007). Expression of anchoring filament protein LAD1 increased in FGFR1-expressing PC3 cells and was enriched in human CRPC bone metastases (p = 0.005). CONCLUSIONS: FGFR1 expression induces bone metastases experimentally and is significantly enriched in human CRPC bone metastases, supporting its prometastatic effect in PCa. LAD1 expression, found in the prometastatic PCa cells expressing FGFR1, was also enriched in CRPC bone metastases. Our studies support and provide a roadmap for the development of FGFR blockade for advanced PCa. PATIENT SUMMARY: We studied the role of fibroblast growth factor receptor 1 (FGFR1) in prostate cancer (PCa) progression. We found that PCa cells with high FGFR1 expression increase metastases and that FGFR1 expression is increased in human PCa bone metastases, and identified genes that could participate in the metastases induced by FGFR1. These studies will help pinpoint PCa patients who use fibroblast growth factor to progress and will benefit by the inhibition of this pathway.Fil: Labanca, Estefania. University of Texas; Estados UnidosFil: Yang, Jun. University of Texas; Estados UnidosFil: Shepherd, Peter D. A.. University of Texas; Estados UnidosFil: Wan, Xinhai. University of Texas; Estados UnidosFil: Starbuck, Michael W.. University of Texas; Estados UnidosFil: Guerra, Leah D.. University of Texas; Estados UnidosFil: Anselmino, Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Bizzotto, Juan Antonio. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Dong, Jiabin. University of Texas; Estados UnidosFil: Chinnaiyan, Arul M.. University Of Michigan Medical School; Estados UnidosFil: Ravoori, Murali K.. University of Texas; Estados UnidosFil: Kundra, Vikas. University of Texas; Estados UnidosFil: Broom, Bradley M.. University of Texas; Estados UnidosFil: Corn, Paul G.. University of Texas; Estados UnidosFil: Troncoso, Patricia. University of Texas; Estados UnidosFil: Gueron, Geraldine. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Instituto de Química Biológica de la Facultad de Ciencias Exactas y Naturales; ArgentinaFil: Logothethis, Christopher J.. University of Texas; Estados UnidosFil: Navone, Nora. University of Texas; Estados Unido

A functional genomic approach to actionable gene fusions for precision oncology

Fusion genes represent a class of attractive therapeutic targets. Thousands of fusion genes have been identified in patients with cancer, but the functional consequences and therapeutic implications of most of these remain largely unknown. Here, we develop a functional genomic approach that consists of efficient fusion reconstruction and sensitive cell viability and drug response assays. Applying this approach, we characterize similar to 100 fusion genes detected in patient samples of The Cancer Genome Atlas, revealing a notable fraction of low-frequency fusions with activating effects on tumor growth. Focusing on those in the RTK-RAS pathway, we identify a number of activating fusions that can markedly affect sensitivity to relevant drugs. Last, we propose an integrated, level-of-evidence classification system to prioritize gene fusions systematically. Our study reiterates the urgent clinical need to incorporate similar functional genomic approaches to characterize gene fusions, thereby maximizing the utility of gene fusions for precision oncology