A High-Precision Single Shooting Method for Solving Hypersensitive Optimal Control Problems

Solving hypersensitive optimal control problems is a long-standing challenge for decades in optimization engineering, mainly due to the possible nonexistence of the optimal solution to meet the required error tolerance under double-precision arithmetic and the hypersensitivity of the optimal solution with respect to the initial conditions. In this paper, a new high-precision single shooting method is presented to address the above two difficulties. Multiple-precision arithmetic and Taylor series method are introduced to provide the accurate optimal solution with arbitrary higher significant digits and arbitrary higher integral accuracy, respectively. Besides, a new modified bidirectional single shooting method is developed, which fully utilizes the three-segment structure of the hypersensitive optimal control problems and provides appropriate initial guess that is close to the optimal solutions. Numerical demonstrations in a typical hypersensitive optimal control problem are presented to illustrate the effectiveness of this new method, which indicates that the accurate optimal solution of this challenging problem can be easily solved by this simple single shooting method within several iterations

A MapReduce-based nearest neighbor approach for big-data-driven traffic flow prediction

In big-data-driven traffic flow prediction systems, the robustness of prediction performance depends on accuracy and timeliness. This paper presents a new MapReduce-based nearest neighbor (NN) approach for traffic flow prediction using correlation analysis (TFPC) on a Hadoop platform. In particular, we develop a real-time prediction system including two key modules, i.e., offline distributed training (ODT) and online parallel prediction (OPP). Moreover, we build a parallel k-nearest neighbor optimization classifier, which incorporates correlation information among traffic flows into the classification process. Finally, we propose a novel prediction calculation method, combining the current data observed in OPP and the classification results obtained from large-scale historical data in ODT, to generate traffic flow prediction in real time. The empirical study on real-world traffic flow big data using the leave-one-out cross validation method shows that TFPC significantly outperforms four state-of-the-art prediction approaches, i.e., autoregressive integrated moving average, Naïve Bayes, multilayer perceptron neural networks, and NN regression, in terms of accuracy, which can be improved 90.07% in the best case, with an average mean absolute percent error of 5.53%. In addition, it displays excellent speedup, scaleup, and sizeup

An efficient MapReduce-based parallel clustering algorithm for distributed traffic subarea division

Traffic subarea division is vital for traffic system management and traffic network analysis in intelligent transportation systems (ITSs). Since existing methods may not be suitable for big traffic data processing, this paper presents a MapReduce-based Parallel Three-Phase K -Means (Par3PKM) algorithm for solving traffic subarea division problem on a widely adopted Hadoop distributed computing platform. Specifically, we first modify the distance metric and initialization strategy of K -Means and then employ a MapReduce paradigm to redesign the optimized K -Means algorithm for parallel clustering of large-scale taxi trajectories. Moreover, we propose a boundary identifying method to connect the borders of clustering results for each cluster. Finally, we divide traffic subarea of Beijing based on real-world trajectory data sets generated by 12,000 taxis in a period of one month using the proposed approach. Experimental evaluation results indicate that when compared with K -Means, Par2PK-Means, and ParCLARA, Par3PKM achieves higher efficiency, more accuracy, and better scalability and can effectively divide traffic subarea with big taxi trajectory data

Out of the Box Thinking: Improving Customer Lifetime Value Modelling via Expert Routing and Game Whale Detection

Customer lifetime value (LTV) prediction is essential for mobile game publishers trying to optimize the advertising investment for each user acquisition based on the estimated worth. In mobile games, deploying microtransactions is a simple yet effective monetization strategy, which attracts a tiny group of game whales who splurge on in-game purchases. The presence of such game whales may impede the practicality of existing LTV prediction models, since game whales' purchase behaviours always exhibit varied distribution from general users. Consequently, identifying game whales can open up new opportunities to improve the accuracy of LTV prediction models. However, little attention has been paid to applying game whale detection in LTV prediction, and existing works are mainly specialized for the long-term LTV prediction with the assumption that the high-quality user features are available, which is not applicable in the UA stage. In this paper, we propose ExpLTV, a novel multi-task framework to perform LTV prediction and game whale detection in a unified way. In ExpLTV, we first innovatively design a deep neural network-based game whale detector that can not only infer the intrinsic order in accordance with monetary value, but also precisely identify high spenders (i.e., game whales) and low spenders. Then, by treating the game whale detector as a gating network to decide the different mixture patterns of LTV experts assembling, we can thoroughly leverage the shared information and scenario-specific information (i.e., game whales modelling and low spenders modelling). Finally, instead of separately designing a purchase rate estimator for two tasks, we design a shared estimator that can preserve the inner task relationships. The superiority of ExpLTV is further validated via extensive experiments on three industrial datasets

Molecular characterization of immunogenic cell death indicates prognosis and tumor microenvironment infiltration in osteosarcoma

IntroductionOsteosarcoma (OS) is a highly aggressive bone malignancy with a poor prognosis, mainly in children and adolescents. Immunogenic cell death (ICD) is classified as a type of programmed cell death associated with the tumor immune microenvironment, prognosis, and immunotherapy. However, the feature of the ICD molecular subtype and the related tumor microenvironment (TME) and immune cell infiltration has not been carefully investigated in OS.MethodsThe ICD-related genes were extracted from previous studies, and the RNA expression profiles and corresponding data of OS were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus database. The ICD-related molecular subtypes were classed by the "ConsensusclusterPlus" package and the construction of ICD-related signatures through univariate regression analysis. ROC curves, independent analysis, and internal validation were used to evaluate signature performance. Moreover, a series of bioinformatic analyses were used for Immunotherapy efficacy, tumor immune microenvironments, and chemotherapeutic drug sensitivity between the high- and low-risk groups.ResultsHerein, we identified two ICD-related subtypes and found significant heterogeneity in clinical prognosis, TME, and immune response signaling among distinct ICD subtypes. Subsequently, a novel ICD-related prognostic signature was developed to determine its predictive performance in OS. Also, a highly accurate nomogram was then constructed to improve the clinical applicability of the novel ICD-related signature. Furthermore, we observed significant correlations between ICD risk score and TME, immunotherapy response, and chemotherapeutic drug sensitivity. Notably, the in vitro experiments further verified that high GALNT14 expression is closely associated with poor prognosis and malignant progress of OS.DiscussionHence, we identified and validated that the novel ICD-related signature could serve as a promising biomarker for the OS's prognosis, chemotherapy, and immunotherapy response prediction, providing guidance for personalized and accurate immunotherapy strategies for OS

GNG12 as A Novel Molecular Marker for the Diagnosis and Treatment of Glioma

PurposeGNG12 influences a variety of tumors; however, its relationship with glioma remains unclear. The aim of this study was to comprehensively investigate the relationship between GNG12 and the clinical characteristics and prognosis of glioma patients and reveal the mechanisms causing the malignant process of GNG12.Materials and MethodsWe obtained information on clinical samples from multiple databases. The expression level of GNG12 was validated using a RT-qPCR and IHC. KM curves were used to assess the correlation between the GNG12 expression and OS of glioma patients. An ROC curve was drawn to assess the predictive performance of GNG12. Univariate and multivariate Cox analyses were performed to analyze the factors affecting the prognosis of patients with glioma. GSEA and TIMER databases were used to estimate the relationship between GNG12 expression, possible molecular mechanisms, and immune cell infiltration. CMap analysis was used to screen candidate drugs for glioma. Subsequent in vitro experiments were used to validate the proliferation and migration of glioma cells and to explore the potential mechanisms by which GNG12 causes poor prognosis in gliomas.ResultsGNG12 was overexpressed in glioma patients and GNG12 expression level correlated closely with clinical features, including age and histological type, etc. Subsequently, the K-M survival analysis indicated that the expression level of GNG12 was relevant to the prognosis of glioma, and the ROC curve implied that GNG12 can predict glioma stability. Univariate and multivariate analyses showed that GNG12 represents a risk factor for glioma occurrence. GNG12 expression is closely associated with some immune cells. Additionally, several in vitro experiments demonstrated that down-regulation of GNG12 expression can inhibits the proliferation and migration capacity of glioma cells. Ultimately, the results for the GSEA and WB experiments revealed that GNG12 may promote the malignant progression of gliomas by regulating the cell adhesion molecule cell signaling pathway.ConclusionIn this study, we identified GNG12 as a novel oncogene elevated in gliomas. Reducing GNG12 expression inhibits the proliferation and migration of glioma cells. In summary, GNG12 can be used as a novel biomarker for the early diagnosis of human gliomas and as a potential therapeutic target

Impeded Nedd4-1-Mediated Ras Degradation Underlies Ras-Driven Tumorigenesis

RAS genes are among the most frequently mutated proto-oncogenes in cancer. However, how Ras stability is regulated remains largely unknown. Here, we report a regulatory loop involving the E3 ligase Nedd4-1, Ras, and PTEN. We found that Ras signaling stimulates the expression of Nedd4-1, which in turn acts as an E3 ubiquitin ligase that regulates Ras levels. Importantly, Ras activation, either by oncogenic mutations or by epidermal growth factor (EGF) signaling, prevents Nedd4-1-mediated Ras ubiquitination. This leads to Ras-induced Nedd4-1 overexpression, and subsequent degradation of the tumor suppressor PTEN in both human cancer samples and cancer cells. Our study thus unravels the molecular mechanisms underlying the interplay of Ras, Nedd4-1, and PTEN and suggests a basis for the high prevalence of Ras-activating mutations and EGF hypersignaling in cancer. © 2014 The Authors

The Association between Ambient Air Pollution and Allergic Rhinitis: Further Epidemiological Evidence from Changchun, Northeastern China

With the continuous rapid urbanization process over the last three decades, outdoors air pollution has become a progressively more serious public health hazard in China. To investigate the possible associations, lag effects and seasonal differences of urban air quality on respiratory health (allergic rhinitis) in Changchun, a city in Northeastern China, we carried out a time-series analysis of the incidents of allergic rhinitis (AR) from 2013 to 2015. Environmental monitoring showed that PM2.5 and PM10 were the major air pollutants in Changchun, followed by SO2, NO2 and O3. The results also demonstrated that the daily concentrations of air pollutants had obvious seasonal differences. PM10 had higher daily mean concentrations in spring (May, dust storms), autumn (October, straw burning) and winter (November to April, coal burning). The mean daily number of outpatient AR visits in the warm season was higher than in the cold season. The prevalence of allergic rhinitis was significantly associated with PM2.5, PM10, SO2 and NO2, and the increased mobility was 10.2% (95% CI, 5.5%–15.1%), 4.9% (95% CI, 0.8%–9.2%), 8.5% (95% CI, −1.8%–19.8%) and 11.1% (95% CI, 5.8%–16.5%) for exposure to each 1-Standard Deviation (1-SD) increase of pollutant, respectively. Weakly or no significant associations were observed for CO and O3. As for lag effects, the highest Relative Risks (RRs) of AR from SO2, NO2, PM10 and PM2.5 were on the same day, and the highest RR from CO was on day 4 (L4). The results also indicated that the concentration of air pollutants might contribute to the development of AR. To summarize, this study provides further evidence of the significant association between ambient particulate pollutants (PM2.5 and PM10, which are usually present in high concentrations) and the prevalence of respiratory effects (allergic rhinitis) in the city of Changchun, located in Northeastern China. Environmental control and public health strategies should be enforced to address this increasingly challenging problem

T-Bet and Eomes Regulate the Balance between the Effector/Central Memory T Cells versus Memory Stem Like T Cells

Memory T cells are composed of effector, central, and memory stem cells. Previous studies have implicated that both T-bet and Eomes are involved in the generation of effector and central memory CD8 T cells. The exact role of these transcription factors in shaping the memory T cell pool is not well understood, particularly with memory stem T cells. Here, we demonstrate that both T-bet or Eomes are required for elimination of established tumors by adoptively transferred CD8 T cells. We also examined the role of T-bet and Eomes in the generation of tumor-specific memory T cell subsets upon adoptive transfer. We showed that combined T-bet and Eomes deficiency resulted in a severe reduction in the number of effector/central memory T cells but an increase in the percentage of CD62LhighCD44low Sca-1+ T cells which were similar to the phenotype of memory stem T cells. Despite preserving large numbers of phenotypic memory stem T cells, the lack of both of T-bet and Eomes resulted in a profound defect in antitumor memory responses, suggesting T-bet and Eomes are crucial for the antitumor function of these memory T cells. Our study establishes that T-bet and Eomes cooperate to promote the phenotype of effector/central memory CD8 T cell versus that of memory stem like T cells. © 2013 Li et al

A Novel Role for GADD45\u3ci\u3eβ\u3c/i\u3e as a Mediator of \u3ci\u3eMMP-13\u3c/i\u3e Gene Expression during Chondrocyte Terminal Differentiation

The growth arrest and DNA damage-inducible 45β (GADD45β) gene product has been implicated in the stress response, cell cycle arrest, and apoptosis. Here we demonstrated the unexpected expression of GADD45β in the embryonic growth plate and uncovered its novel role as an essential mediator of matrix metalloproteinase-13 (MMP-13) expression during terminal chondrocyte differentiation. We identified GADD45β as a prominent early response gene induced by bone morphogenetic protein-2 (BMP-2) through a Smad1/Runx2-dependent pathway. Because this pathway is involved in skeletal development, we examined mouse embryonic growth plates, and we observed expression of Gadd45β mRNA coincident with Runx2 protein in prehypertrophic chondrocytes, whereas GADD45β protein was localized prominently in the nucleus in late stage hypertrophic chondrocytes where Mmp-13 mRNA was expressed. In Gadd45β−/− mouse embryos, defective mineralization and decreased bone growth accompanied deficient Mmp-13 and Col10a1 gene expression in the hypertrophic zone. Transduction of small interferin