Multiobjective Quantum Evolutionary Algorithm for the Vehicle Routing Problem with Customer Satisfaction

The multiobjective vehicle routing problem considering customer satisfaction (MVRPCS) involves the distribution of orders from several depots to a set of customers over a time window. This paper presents a self-adaptive grid multi-objective quantum evolutionary algorithm (MOQEA) for the MVRPCS, which takes into account customer satisfaction as well as travel costs. The degree of customer satisfaction is represented by proposing an improved fuzzy due-time window, and the optimization problem is modeled as a mixed integer linear program. In the MOQEA, nondominated solution set is constructed by the Challenge Cup rules. Moreover, an adaptive grid is designed to achieve the diversity of solution sets; that is, the number of grids in each generation is not fixed but is automatically adjusted based on the distribution of the current generation of nondominated solution set. In the study, the MOQEA is evaluated by applying it to classical benchmark problems. Results of numerical simulation and comparison show that the established model is valid and the MOQEA is effective for MVRPCS

Modeling of Biological Intelligence for SCM System Optimization

This article summarizes some methods from biological intelligence for modeling and optimization of supply chain management (SCM) systems, including genetic algorithms, evolutionary programming, differential evolution, swarm intelligence, artificial immune, and other biological intelligence related methods. An SCM system is adaptive, dynamic, open self-organizing, which is maintained by flows of information, materials, goods, funds, and energy. Traditional methods for modeling and optimizing complex SCM systems require huge amounts of computing resources, and biological intelligence-based solutions can often provide valuable alternatives for efficiently solving problems. The paper summarizes the recent related methods for the design and optimization of SCM systems, which covers the most widely used genetic algorithms and other evolutionary algorithms

Web-based tool for dynamic functional outcome after acute ischemic stroke and comparison with existing models

BackgroundAcute ischemic stroke (AIS) is one of the leading causes of death and adult disability worldwide. In the present study, we aimed to develop a web-based risk model for predicting dynamic functional status at discharge, 3-month, 6-month, and 1-year after acute ischemic stroke (Dynamic Functional Status after Acute Ischemic Stroke, DFS-AIS).MethodsThe DFS-AIS was developed based on the China National Stroke Registry (CNSR), in which eligible patients were randomly divided into derivation (60%) and validation (40%) cohorts. Good functional outcome was defined as modified Rankin Scale (mRS) score ≤ 2 at discharge, 3-month, 6-month, and 1-year after AIS, respectively. Independent predictors of each outcome measure were obtained using multivariable logistic regression. The area under the receiver operating characteristic curve (AUROC) and plot of observed and predicted risk were used to assess model discrimination and calibration.ResultsA total of 12,026 patients were included and the median age was 67 (interquartile range: 57–75). The proportion of patients with good functional outcome at discharge, 3-month, 6-month, and 1-year after AIS was 67.9%, 66.5%, 66.9% and 66.9%, respectively. Age, gender, medical history of diabetes mellitus, stroke or transient ischemic attack, current smoking and atrial fibrillation, pre-stroke dependence, pre-stroke statins using, admission National Institutes of Health Stroke Scale score, admission blood glucose were identified as independent predictors of functional outcome at different time points after AIS. The DFS-AIS was developed from sets of predictors of mRS ≤ 2 at different time points following AIS. The DFS-AIS demonstrated good discrimination in the derivation and validation cohorts (AUROC range: 0.837-0.845). Plots of observed versus predicted likelihood showed excellent calibration in the derivation and validation cohorts (all r = 0.99, P < 0.001). When compared to 8 existing models, the DFS-AIS showed significantly better discrimination for good functional outcome and mortality at discharge, 3-month, 6-month, and 1-year after AIS (all P < 0.0001).ConclusionThe DFS-AIS is a valid risk model to predict functional outcome at discharge, 3-month, 6-month, and 1-year after AIS.Electronic supplementary materialThe online version of this article (doi:10.1186/s12883-014-0214-z) contains supplementary material, which is available to authorized users

MicroRNA-29b-3p promotes 5-fluorouracil resistance <em>via</em> suppressing TRAF5-mediated necroptosis in human colorectal cancer

Drug resistance in colorectal cancer is a great challenge in clinic. Elucidating the deep mechanism underlying drug resistance will bring much benefit to diagnosis, therapy and prognosis in patients with colorectal cancer. In this study, miR-29b-3p was shown to be involved in resistance to 5-fluorouracil (5-FU)-induced necroptosis of colorectal cancer. Further, miR-29b-3p was shown to target a regulatory subunit of necroptosis TRAF5. Rescue of TRAF5 could reverse the effect of miR-29b-3p on 5-FU-induced necroptosis, which was consistent with the role ofnecrostatin-1 (a specific necroptosis inhibitor). Then it was demonstrated that miR-29b-3p was positively correlated with chemo-resistance in colorectal cancer while TRAF5 negatively. In conclusion, it is deduced that miR-29b-3p/TRAF5 signaling axis plays critical role in drug resistance in chemotherapy for colorectal cancer patients by regulating necroptosis. The findings in this study provide us a new target for interfere therapy in colorectal cancer

Polydopamine nanoparticles for treatment of acute inflammation-induced injury

Nanotechnology-mediated anti-inflammatory therapy is emerging as a novel strategy for treatment of inflammation-induced injury. However, one of the main hurdles for these anti-inflammatory nano-drugs is their potential toxic side effects in vivo. Herein, we uncovered that polydopamine (PDA) nanoparticles with structure and chemical properties similar to melanin, a natural bio-polymer, displayed significant anti-inflammation therapeutic effect on acute inflammation-induced injury. PDA with enriched phenol groups functioned as a radical scavenger to eliminate reactive oxygen species (ROS) generated during inflammatory responses. As revealed by in vivo photoacoustic imaging with a H2O2-specific nanoprobe, PDA nanoparticles remarkably reduced intracellular ROS levels in murine macrophages challenged with either H2O2 or lipopolysaccharide (LPS). The anti-inflammatory capacity of PDA nanoparticles was further demonstrated in murine models of both acute peritonitis and acute lung injury (ALI), where diminished ROS generation, reduced proinflammatory cytokines, attenuated neutrophil infiltration, and alleviated lung tissue damage were observed in PDA-treated mice after a single dose of PDA treatment. Our work therefore presents the great promise of PDA nanoparticles as a biocompatible nano-drug for anti-inflammation therapy to treat acute inflammation-induced injury

Genetic Basis of Virulence Attenuation Revealed by Comparative Genomic Analysis of Mycobacterium tuberculosis Strain H37Ra versus H37Rv

Tuberculosis, caused by Mycobacterium tuberculosis, remains a leading infectious disease despite the availability of chemotherapy and BCG vaccine. The commonly used avirulent M. tuberculosis strain H37Ra was derived from virulent strain H37 in 1935 but the basis of virulence attenuation has remained obscure despite numerous studies. We determined the complete genomic sequence of H37Ra ATCC25177 and compared that with its virulent counterpart H37Rv and a clinical isolate CDC1551. The H37Ra genome is highly similar to that of H37Rv with respect to gene content and order but is 8,445 bp larger as a result of 53 insertions and 21 deletions in H37Ra relative to H37Rv. Variations in repetitive sequences such as IS6110 and PE/PPE/PE-PGRS family genes are responsible for most of the gross genetic changes. A total of 198 single nucleotide variations (SNVs) that are different between H37Ra and H37Rv were identified, yet 119 of them are identical between H37Ra and CDC1551 and 3 are due to H37Rv strain variation, leaving only 76 H37Ra-specific SNVs that affect only 32 genes. The biological impact of missense mutations in protein coding sequences was analyzed in silico while nucleotide variations in potential promoter regions of several important genes were verified by quantitative RT-PCR. Mutations affecting transcription factors and/or global metabolic regulations related to in vitro survival under aging stress, and mutations affecting cell envelope, primary metabolism, in vivo growth as well as variations in the PE/PPE/PE-PGRS family genes, may underlie the basis of virulence attenuation. These findings have implications not only for improved understanding of pathogenesis of M. tuberculosis but also for development of new vaccines and new therapeutic agents

A co-occurrence region based Bayesian network stepwise remote sensing image retrieval algorithm

Although scholars have conducted numerous researches on content-based image retrieval and obtained great achievements, they make little progress in studying remote sensing image retrieval. Both theoretical and application systems are immature. Since remote sensing images are characterized by large data volume, broad coverage, vague themes and rich semantics, the research results on natural images and medical images cannot be directly used in remote sensing image retrieval. Even perfect content-based remote sensing image retrieval systems have many difficulties with data organization, storage and management, feature description and extraction, similarity measurement, relevance feedback, network service mode, and system structure design and implementation. This paper proposes a remote sensing image retrieval algorithm that combines co-occurrence region based Bayesian network image retrieval with average high-frequency signal strength. By Bayesian networks, it establishes correspondence relationships between images and semantics, thereby realizing semantic-based retrieval of remote sensing images. In the meantime, integrated region matching is introduced for iterative retrieval, which effectively improves the precision of semantic retrieval