A hybrid algorithm for a vehicle routing problem with realistic constraints

Proliferation of multi-national corporations and extremely competitive business environments have led to an unprecedented demand for third-party logistics services. However, recent studies on the vehicle routing problem (VRP) have considered only simple constraints. They also do not scale well to real-world problems that are encountered in the logistics industry. In this paper, we introduce a novel vehicle routing problem with time window and pallet loading constraints; this problem accounts for the actual needs of businesses in the logistics industry such as the delivery of consumer goods and agricultural products. To solve this new VRP, we propose a hybrid approach by combining Tabu search and the artificial bee colony algorithm. A new benchmark data set is generated to verify the performance of the proposed algorithm because the proposed VRP has never been reported in the literature. Experiments are performed for a data set of Solomon's 56 vehicle routing problem with time windows. Our approach is superior to a number of other heuristic algorithms in a comparison on Solomon's VRPTW instances. © 2017 Elsevier Inc

The application of KAZE features to the classification echocardiogram videos

In the computer vision field, both approaches of SIFT and SURF are prevalent in the extraction of scale-invariant points and have demonstrated a number of advantages. However, when they are applied to medical images with relevant low contrast between target structures and surrounding regions, these approaches lack the ability to distinguish salient features. Therefore, this research proposes a different approach by extracting feature points using the emerging method of KAZE. As such, to categorise a collection of video images of echocardiograms, KAZE feature points, coupled with three popular representation methods, are addressed in this paper, which includes the bag of words (BOW), sparse coding, and Fisher vector (FV). In comparison with the SIFT features represented using Sparse coding approach that gives 72% overall performance on the classification of eight viewpoints, KAZE feature integrated with either BOW, sparse coding or FV improves the performance significantly with the accuracy being 81.09%, 78.85% and 80.8% respectively. When it comes to distinguish only three primary view locations, 97.44% accuracy can be achieved when employing the approach of KAZE whereas 90% accuracy is realised while applying SIFT features

Probability distributed time delays: integrating spatial effects into temporal models

Background: In order to provide insights into the complex biochemical processes inside a cell, modelling approaches must find a balance between achieving an adequate representation of the physical phenomena and keeping the associated computational cost within reasonable limits. This issue is particularly stressed when spatial inhomogeneities have a significant effect on system's behaviour. In such cases, a spatially-resolved stochastic method can better portray the biological reality, but the corresponding computer simulations can in turn be prohibitively expensive.Results: We present a method that incorporates spatial information by means of tailored, probability distributed time-delays. These distributions can be directly obtained by single in silico or a suitable set of in vitro experiments and are subsequently fed into a delay stochastic simulation algorithm (DSSA), achieving a good compromise between computational costs and a much more accurate representation of spatial processes such as molecular diffusion and translocation between cell compartments. Additionally, we present a novel alternative approach based on delay differential equations (DDE) that can be used in scenarios of high molecular concentrations and low noise propagation.Conclusions: Our proposed methodologies accurately capture and incorporate certain spatial processes into temporal stochastic and deterministic simulations, increasing their accuracy at low computational costs. This is of particular importance given that time spans of cellular processes are generally larger (possibly by several orders of magnitude) than those achievable by current spatially-resolved stochastic simulators. Hence, our methodology allows users to explore cellular scenarios under the effects of diffusion and stochasticity in time spans that were, until now, simply unfeasible. Our methodologies are supported by theoretical considerations on the different modelling regimes, i.e. spatial vs. delay-temporal, as indicated by the corresponding Master Equations and presented elsewhere

A novel class of microRNA-recognition elements that function only within open reading frames.

MicroRNAs (miRNAs) are well known to target 3' untranslated regions (3' UTRs) in mRNAs, thereby silencing gene expression at the post-transcriptional level. Multiple reports have also indicated the ability of miRNAs to target protein-coding sequences (CDS); however, miRNAs have been generally believed to function through similar mechanisms regardless of the locations of their sites of action. Here, we report a class of miRNA-recognition elements (MREs) that function exclusively in CDS regions. Through functional and mechanistic characterization of these 'unusual' MREs, we demonstrate that CDS-targeted miRNAs require extensive base-pairing at the 3' side rather than the 5' seed; cause gene silencing in an Argonaute-dependent but GW182-independent manner; and repress translation by inducing transient ribosome stalling instead of mRNA destabilization. These findings reveal distinct mechanisms and functional consequences of miRNAs that target CDS versus the 3' UTR and suggest that CDS-targeted miRNAs may use a translational quality-control-related mechanism to regulate translation in mammalian cells

Classification of motor imagery tasks for BCI with multiresolution analysis and multiobjective feature selection

Background: Brain-computer interfacing (BCI) applications based on the classification of electroencephalographic (EEG) signals require solving high-dimensional pattern classification problems with such a relatively small number of training patterns that curse of dimensionality problems usually arise. Multiresolution analysis (MRA) has useful properties for signal analysis in both temporal and spectral analysis, and has been broadly used in the BCI field. However, MRA usually increases the dimensionality of the input data. Therefore, some approaches to feature selection or feature dimensionality reduction should be considered for improving the performance of the MRA based BCI. Methods: This paper investigates feature selection in the MRA-based frameworks for BCI. Several wrapper approaches to evolutionary multiobjective feature selection are proposed with different structures of classifiers. They are evaluated by comparing with baseline methods using sparse representation of features or without feature selection. Results and conclusion: The statistical analysis, by applying the Kolmogorov-Smirnoff and Kruskal-Wallis tests to the means of the Kappa values evaluated by using the test patterns in each approach, has demonstrated some advantages of the proposed approaches. In comparison with the baseline MRA approach used in previous studies, the proposed evolutionary multiobjective feature selection approaches provide similar or even better classification performances, with significant reduction in the number of features that need to be computed

Redox proteomics of the inflammatory secretome identifies a common set of redoxins and other glutathionylated proteins released in inflammation, influenza virus infection and oxidative stress

Protein cysteines can form transient disulfides with glutathione (GSH), resulting in the production of glutathionylated proteins, and this process is regarded as a mechanism by which the redox state of the cell can regulate protein function. Most studies on redox regulation of immunity have focused on intracellular proteins. In this study we have used redox proteomics to identify those proteins released in glutathionylated form by macrophages stimulated with lipopolysaccharide (LPS) after pre-loading the cells with biotinylated GSH. Of the several proteins identified in the redox secretome, we have selected a number for validation. Proteomic analysis indicated that LPS stimulated the release of peroxiredoxin (PRDX) 1, PRDX2, vimentin (VIM), profilin1 (PFN1) and thioredoxin 1 (TXN1). For PRDX1 and TXN1, we were able to confirm that the released protein is glutathionylated. PRDX1, PRDX2 and TXN1 were also released by the human pulmonary epithelial cell line, A549, infected with influenza virus. The release of the proteins identified was inhibited by the anti-inflammatory glucocorticoid, dexamethasone (DEX), which also inhibited tumor necrosis factor (TNF)-α release, and by thiol antioxidants (N-butanoyl GSH derivative, GSH-C4, and N-acetylcysteine (NAC), which did not affect TNF-α production. The proteins identified could be useful as biomarkers of oxidative stress associated with inflammation, and further studies will be required to investigate if the extracellular forms of these proteins has immunoregulatory functions

Assessment of atherosclerotic carotid plaque volume with multidetector computed tomography angiography

Purpose The amount of atherosclerotic plaque and its components (calcifications, fibrous tissue, and lipid core) could be better predictors of acute events than the now currently used degree of stenosis. Therefore, we evaluated a dedicated software tool for volume measurements of atherosclerotic carotid plaque and its components in multidetector computed tomography angiography (MDCTA) images. Materials and Methods Data acquisition was approved by the Institutional Review Board and all patients gave written informed consent. MDCTA images of 56 carotid arteries were analyzed by three observers. Plaque volumes were assessed by manual drawing of the outer vessel contour. The luminal boundary was determined based on a Hounsfield-Unit (HU) threshold. The contribution of different components was measured by the number of voxels within defined ranges of HU-values (calcification >130 HU, fibrous tissue 60–130 HU, lipid core <60 HU). Interobserver variability (IOV) was assessed. Results Plaque volume was 1,259 ± 621 mm3. The calcified, fibrous and lipid volumes were 238 ± 252 mm3, 647 ± 277 mm3 and 376 ± 283 mm3, respectively. IOV was moderate with interclass correlation coefficients (ICC) ranging from 0.76 to 0.99 and coefficients of variation (COV) ranging from 3% to 47%. Conclusion Atherosclerotic carotid plaque volume and plaque component volumes can be assessed with MDCTA with a reasonable observer variability

IKKβ regulates essential functions of the vascular endothelium through kinase-dependent and -independent pathways

Vascular endothelium provides a selective barrier between the blood and tissues, participates in wound healing and angiogenesis, and regulates tissue recruitment of inflammatory cells. Nuclear factor (NF)-κB transcription factors are pivotal regulators of survival and inflammation, and have been suggested as potential therapeutic targets in cancer and inflammatory diseases. Here we show that mice lacking IKKβ, the primary kinase mediating NF-κB activation, are smaller than littermates and born at less than the expected Mendelian frequency in association with hypotrophic and hypovascular placentae. IKKβ-deleted endothelium manifests increased vascular permeability and reduced migration. Surprisingly, we find that these defects result from loss of kinase-independent effects of IKKβ on activation of the serine-threonine kinase, Akt. Together, these data demonstrate essential roles for IKKβ in regulating endothelial permeability and migration, as well as an unanticipated connection between IKKβ and Akt signalling

Genome wide analysis of gene expression changes in skin from patients with type 2 diabetes

Non-healing chronic ulcers are a serious complication of diabetes and are a major healthcare problem. While a host of treatments have been explored to heal or prevent these ulcers from forming, these treatments have not been found to be consistently effective in clinical trials. An understanding of the changes in gene expression in the skin of diabetic patients may provide insight into the processes and mechanisms that precede the formation of non-healing ulcers. In this study, we investigated genome wide changes in gene expression in skin between patients with type 2 diabetes and non-diabetic patients using next generation sequencing. We compared the gene expression in skin samples taken from 27 patients (13 with type 2 diabetes and 14 non-diabetic). This information may be useful in identifying the causal factors and potential therapeutic targets for the prevention and treatment of diabetic related diseases