Path sequence selection is important for multimodal transport processes. AND/OR graphs (AOG) have been applied to describe practical multimodal transport route planning problems by using 'AND' and ...'OR' matrices. An AOG-based multimodal transport route planning problem is an NP-hard combinatorial optimization problem. Heuristic evolution methods can be adopted to handle it. While adjacency (AND) relationship issues can be addressed, contradiction (OR) relations are not well addressed by existing multimodal transport route planning methods. Thus, an ineffective result may be obtained in practice. The OR matrix is a conflict matrix that describes the choice of mode of transport in the process of multimodal transport. By using a contradiction matrix together with an adjacency matrix and tabu list, an approach used in existing work, this paper proposes an effective triple-phase generate route method (TPGR) to produce a feasible multimodal transport path sequence based on an AOG. This paper uses energy consumption to evaluate the multimodal transport energy efficiency. The information entropy is applied to describe the risks of the transport process. The energy consumption and the information entropy lead to a novel dual-objective optimization model where route energy consumption and route risk are minimized. An improved ant colony algorithm is developed to effectively generate a set of Pareto solutions for route selection, which are used for the dual-objective multimodal transport route optimization problem. This methodology is applied to practical multimodal transport route selection processes on two maps to verify its effectiveness and feasibility.
Display omitted
•A three-phase flow model applicable to countercurrent mixer-settler was proposed.•Local dead zones and dispersion bands are formed in countercurrent mixer-settler.•The flow pattern ...within the mixing chamber exhibits macroscopic instability.•A fast Fourier transform analysis method was employed to analyze flow data.•The velocity vector distribution at the phase ports reveal the operation mechanism of the countercurrent mixer-settler.
In contrast to the concurrent mixer-settler, the interaction between the mixing and settling chambers have to be taken into account in the simulation of the countercurrent mixer-settler, and no work has been reported for this equipment. In this work, a three-phase flow model based on the Eulerian multiphase model, coupled with a sliding mesh model is proposed for a countercurrent mixer-settler. Based on this, the dispersed phase distribution, flow pattern, and pressure distribution are investigated, which can help to fill the gap in the operation mechanism. In addition, the velocity vector distribution at the phase port shows an intriguing phenomenon that two types of vectors with opposite directions are distributed on the left and right sides of the same plane, which indicates that the material exchange in the mixing and settling chambers is simultaneous. Analysis of this variation at this location by a fast Fourier transform (FFT) method reveals that it is mainly influenced by the mixing chamber and is consistent with the main period of the outlet flow fluctuations. Therefore, by monitoring the fluctuation of the outlet flow and then analyzing it by the FFT method, the state of the whole tank can be determined, which makes it promising for the design of control systems for countercurrent mixer-settlers.
A reasonable braking timing sequence plays an important role in the braking stability of a semi-trailer train, but there is still a lack of objective and comprehensive scientific evaluations of ...braking stability as shown by the braking timing sequence. Aiming at this problem, through the analysis of relevant regulations and standards at home and abroad, an evaluation index hierarchy model of the semi-trailer train braking timing sequence is constructed. The fuzzy analytic hierarchy process is used to determine the weight of the evaluation index of each level of the braking timing sequence, and a comprehensive evaluation is obtained. In order to determine the optimal braking timing sequence of the train during braking, a simulation model was established and simulated. Through the combination of index weight and simulation data, the model can provide a theoretical basis for the subsequent study of the optimal braking timing sequence of semi-trailer trains running under different working conditions.
The function of
is unknown in lung cancer. We evaluated the clinicopathologic significance of
, and its role in non-small-cell lung cancer (NSCLC) progression.
Sixty-three NSCLC patients from Beijing ...Chest Hospital were included. The expression of
was detected by real-time quantitative polymerase chain reaction (RT-qPCR) in tumor tissues and adjacent normal tissues. Then, the clinicopathological significance and prognostic value of
were analyzed by using our cohort and TCGA cohort. Finally, the effect of
on proliferation and motility of NSCLC cell lines were evaluated by cell growth assay, colony formation assay, xenograft tumorigenesis experiment in nude mice and transwell assays respectively.
Compared with adjacent normal tissues,
showed lower expression in NSCLC tumor tissues. As for the relationship between
and clinical characteristics, our results were consistent with those of TCGA data.
was lower in T1 than T2-T4 patients, N1-N3 than N0 patients. Low level
was related to shorter overall survival time (OS) in lung adenocarcinoma (LUAD), and poor first progression time (FP) in LUAD and lung squamous cell carcinoma (LUSC) patients.
was significantly associated with the prognosis of NSCLC patients. Overexpression of
inhibited proliferation and migration of H1299 and HCC827 cells.
expression decreases in tumor tissues with the increase of malignancy grades in NSCLC.
plays an anticancer role by inhibiting cell proliferation, invasion, and metastasis, and has a potential prognostic value in NSCLC.
may serve as a diagnostic marker and therapeutic target for NSCLC.
Reliable and accurate real-time traffic flow state identification is crucial for an intelligent transportation system (ITS). This identification is a prerequisite for alleviating traffic congestion ...and improving highway operation efficiency. In this paper, we propose an improved traffic flow state identification model that is based on selective ensemble learning (SEL). First, we adopted the fuzzy C-means (FCM) clustering method to divide the traffic flow data into three main kinds of traffic flow states and obtained the parameters that correspond to each kind of traffic flow state. Second, we applied the random subspace (RS) algorithm as the ensemble method and support vector machine (SVM) model as base learners to construct the RS-SVM ensemble model for traffic flow identification. Significantly, the discrete binary particle swarm optimization (BPSO) algorithm with global optimization search ability was employed to select the classifiers obtained by the random subspace training in the ensemble system. We experimentally validated the effectiveness of the proposed BPSO-RS-SVM-SEL approach. The research results reveal that compared with other classical traffic flow state identification methods, the proposed model has a higher maximum accuracy of 98.68%. It can be seen that our model improves the classification accuracy of traffic flow state identification and the difference in the ensemble system to a certain extent.
The incidence of gastric cardiac cancer (GCC) has obviously increased recently with poor prognosis. It's necessary to compare GCC prognosis with other gastric sites carcinoma and set up an effective ...prognostic model based on a neural network to predict the survival of GCC patients.
In the population-based cohort study, we first enrolled the clinical features from the Surveillance, Epidemiology and End Results (SEER) data (n = 31,397) as well as the public Chinese data from different hospitals (n = 1049). Then according to the diagnostic time, the SEER data were then divided into two cohorts, the train cohort (patients were diagnosed as GCC in 2010-2014, n = 4414) and the test cohort (diagnosed in 2015, n = 957). Age, sex, pathology, tumor, node, and metastasis (TNM) stage, tumor size, surgery or not, radiotherapy or not, chemotherapy or not and history of malignancy were chosen as the predictive clinical features. The train cohort was utilized to conduct the neural network-based prognostic predictive model which validated by itself and the test cohort. Area under the receiver operating characteristics curve (AUC) was used to evaluate model performance.
The prognosis of GCC patients in SEER database was worse than that of non GCC (NGCC) patients, while it was not worse in the Chinese data. The total of 5371 patients were used to conduct the model, following inclusion and exclusion criteria. Neural network-based prognostic predictive model had a satisfactory performance for GCC overall survival (OS) prediction, which owned 0.7431 AUC in the train cohort (95% confidence intervals, CI, 0.7423-0.7439) and 0.7419 in the test cohort (95% CI, 0.7411-0.7428).
GCC patients indeed have different survival time compared with non GCC patients. And the neural network-based prognostic predictive tool developed in this study is a novel and promising software for the clinical outcome analysis of GCC patients.
Glioblastoma (GBM) is the most common malignant tumor of the central nervous system. GBM causes poor clinical outcome and high mortality rate, mainly due to the lack of effective targeted therapy and ...prognostic biomarkers. Here, we developed a user-friendly Online Survival analysis web server for GlioBlastoMa, abbreviated OSgbm, to assess the prognostic value of candidate genes. Currently, OSgbm contains 684 samples with transcriptome profiles and clinical information from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) and Chinese Glioma Genome Atlas (CGGA). The survival analysis results can be graphically presented by Kaplan-Meier (KM) plot with Hazard ratio (HR) and log-rank
value. As demonstration, the prognostic value of 51 previously reported survival associated biomarkers, such as
(HR = 2.4120,
= 0.0071) and
(HR = 1.5578,
0.001), were confirmed in OSgbm. In summary, OSgbm allows users to evaluate and develop prognostic biomarkers of GBM. The web server of OSgbm is available at http://bioinfo.henu.edu.cn/GBM/GBMList.jsp.
CHIP (C terminus of Hsc-70 interacting protein) is an E3 ligase that links the protein folding machinery with the ubiquitin-proteasome system and has been implicated in disorders characterized by ...protein misfolding and aggregation. Here we investigate the role of CHIP in protecting from ataxin-1-induced neurodegeneration. Ataxin-1 is a polyglutamine protein whose expansion causes spinocerebellar ataxia type-1 (SCA1) and triggers the formation of nuclear inclusions (NIs). We find that CHIP and ataxin-1 proteins directly interact and co-localize in NIs both in cell culture and SCA1 postmortem neurons. CHIP promotes ubiquitination of expanded ataxin-1 both in vitro and in cell culture. The Hsp70 chaperone increases CHIP-mediated ubiquitination of ataxin-1 in vitro, and the tetratricopeptide repeat domain, which mediates CHIP interactions with chaperones, is required for ataxin-1 ubitiquination in cell culture. Interestingly, CHIP also interacts with and ubiquitinates unexpanded ataxin-1. Overexpression of CHIP in a Drosophila model of SCA1 decreases the protein steady-state levels of both expanded and unexpanded ataxin-1 and suppresses their toxicity. Finally we investigate the ability of CHIP to protect against toxicity caused by expanded polyglutamine tracts in different protein contexts. We find that CHIP is not effective in suppressing the toxicity caused by a bare 127Q tract with only a short hemaglutinin tag, but it is very efficient in suppressing toxicity caused by a 128Q tract in the context of an N-terminal huntingtin backbone. These data underscore the importance of the protein framework for modulating the effects of polyglutamine-induced neurodegeneration.
There were new points of interest in performing subsegmentectomy and segmentectomy for patients with early stage non-small cell lung cancer (NSCLC). However, whether patients who underwent ...subsegmentectomy could obtain satisfactory clinical outcomes remains unclear. The present study aimed to compare the clinical outcomes and security of surgical procedures between subsegmentectomy and segmentectomy.
A systematic review and meta-analysis was performed through five online databases to identify the included literatures which presented intact clinical outcome data among different surgical procedures. The included studies were evaluated based on precise and predefined inclusion criteria.
There were 4 published studies identified in this meta-analysis. A total of 325 patients who underwent subsegmentectomy and 904 patients who underwent segmentectomy were involved in this analysis. The duration of drainage MD -0.19; 95%CI (-0.36, -0.02),
= 0.03 and postoperative hospital stay MD -0.30; 95%CI (-0.58, -0.02),
= 0.009 of subsegmentectomy were significantly less than that of segmentectomy. There was no statistically significant difference among recurrence rate OR 0.85; 95%CI (0.21, 3.42),
= 0.82, operation time, blood loss, incidence of complications OR 0.83; 95%CI (0.58, 1.20),
= 0.33 between subsegmentectomy and segmentectomy in patients with stage IA NSCLC.
The meta-analysis was firstly performed to compare perioperative outcomes among surgical procedures. The perioperative outcomes were comparable between subsegmentectomy and segmentectomy. Subsegmentectomy might be an alternative treatment for the deep tumor with size less than 1.5 cm and mainly composed of Ground Glass Opacity (GGO).
Spinocerebellar ataxias (SCAs) are a genetically heterogeneous group of neurodegenerative disorders sharing atrophy of the cerebellum as a common feature. SCA1 and SCA2 are two ataxias caused by ...expansion of polyglutamine tracts in Ataxin-1 (ATXN1) and Ataxin-2 (ATXN2), respectively, two proteins that are otherwise unrelated. Here, we use a Drosophila model of SCA1 to unveil molecular mechanisms linking Ataxin-1 with Ataxin-2 during SCA1 pathogenesis. We show that wild-type Drosophila Ataxin-2 (dAtx2) is a major genetic modifier of human expanded Ataxin-1 (Ataxin-182Q) toxicity. Increased dAtx2 levels enhance, and more importantly, decreased dAtx2 levels suppress Ataxin-182Q-induced neurodegeneration, thereby ruling out a pathogenic mechanism by depletion of dAtx2. Although Ataxin-2 is normally cytoplasmic and Ataxin-1 nuclear, we show that both dAtx2 and hAtaxin-2 physically interact with Ataxin-1. Furthermore, we show that expanded Ataxin-1 induces intranuclear accumulation of dAtx2/hAtaxin-2 in both Drosophila and SCA1 postmortem neurons. These observations suggest that nuclear accumulation of Ataxin-2 contributes to expanded Ataxin-1-induced toxicity. We tested this hypothesis engineering dAtx2 transgenes with nuclear localization signal (NLS) and nuclear export signal (NES). We find that NLS-dAtx2, but not NES-dAtx2, mimics the neurodegenerative phenotypes caused by Ataxin-182Q, including repression of the proneural factor Senseless. Altogether, these findings reveal a previously unknown functional link between neurodegenerative disorders with common clinical features but different etiology.
PDF
