Photobiomodulation Facilitates Rat Cutaneous Wound Healing by Promoting Epidermal Stem Cells and Hair Follicle Stem Cells Proliferation.

BACKGROUND: Cutaneous wound healing represents a common fundamental phenomenon requiring the participation of cells of distinct types and a major concern for the public. Evidence has confirmed that photobiomodulation (PBM) using near-infrared (NIR) can promote wound healing, but the  cells involved and the precise molecular mechanisms remain elusive. METHODS: Full-thickness skin defects with a diameter of 1.0 cm were made on the back of rats and randomly divided into the control group, 10 J, 15 J, and 30 J groups. The wound healing rate at days 4, 8, and 12 postoperatively was measured. HE and Masson staining was conducted to reveal the histological characteristics. Immunofluorescence staining was performed to label the epidermal stem cells (ESCs) and hair follicle stem cells (HFSCs). Western blot was performed to detect the expressions of proteins associated with ESCs and HFSCs. Cutaneous wound tissues were collected for RNA sequencing. Gene ontology and the Kyoto Encyclopedia of Genes and Genomes analysis was performed, and the hub genes were identified using CytoHubba and validated by qRT-PCR. RESULTS: PBM can promote reepithelialization, extracellular matrix deposition, and wound healing, increase the number of KRT14+/PCNA+ ESCs and KRT15+/PCNA+ HFSCs, and upregulate the protein expression of P63, Krt14, and PCNA. Three hundred and sixty-six differentially expressed genes (DEGs) and 7 hub genes including Sox9, Krt5, Epcam, Cdh1, Cdh3, Dsp, and Pkp3 were identified. These DEGs are enriched in skin development, cell junction, and cadherin binding involved in cell-cell adhesion etc., while these hub genes are related to skin derived stem cells and cell adhesion. CONCLUSION: PBM accelerates wound healing by enhancing reepithelialization through promoting ESCs and HFSCs proliferation and elevating the expression of genes associated with stem cells and cell adhesion. This may provide a valuable alternative strategy to promote wound healing and reepithelialization by modulating the proliferation of skin derived stem cells and regulating genes related to cell adhesion.

Robust Multiobjective Optimization for Vehicle Routing Problem With Time Windows.

In this article, we focus on the vehicle routing problem (VRP) with time windows under uncertainty. To capture the uncertainty characteristics in a real-life scenario, we design a new form of disturbance on travel time and construct robust multiobjective VRP with the time window, where the perturbation range of travel time is determined by the maximum disturbance degree. Two conflicting objectives include: 1)the minimization of both the total distance and: 2)the number of vehicles. A robust multiobjective particle swarms optimization approach is developed by incorporating an advanced encoding and decoding scheme, a robustness measurement metric, as well as the local search strategy. First, through particle flying in the decision space, the problem space characteristic under deterministic environment is fully exploited to provide guidance for robust optimization. Then, a designed metric is adopted to measure the robustness of solutions and help to search for the robust optimal solutions during the particle flying process. In addition to the updating process of particle, two local search strategies, problem-based local search and route-based local search, are developed for further improving the performance of solutions. For comparison, we develop several robust optimization problems by adding disturbances on selected benchmark problems. The experimental results validate our proposed algorithm has a distinguished ability to generate enough robust solutions and ensure the optimality of these solutions.

Zero-Shot Learning via Structure-Aligned Generative Adversarial Network.

In this article, we propose a structure-aligned generative adversarial network framework to improve zero-shot learning (ZSL) by mitigating the semantic gap, domain shift, and hubness problem. The proposed framework contains two parts, i.e., a generative adversarial network with a softmax classifier part, and a structure-aligned part. In the first part, the generative adversarial network aims at generating pseudovisual features through the guiding generator and discriminator play the minimax two-player game together. At the same time, the softmax classifier is committed to increasing the interclass distance and reducing intraclass distance. Then, the harmful effect of domain shift and hubness problems can be mitigated. In another part, we introduce a structure-aligned module where the structural consistency between visual space and semantic space is learned. By aligning the structure between visual space and semantic space, the semantic gap between them can be bridged. The performance of classification is improved when the structure-aligned visual-semantic embedding space is transferred to the unseen classes. Our framework reformulates the ZSL as a standard fully supervised classification task using the pseudovisual features of unseen classes. Extensive experiments conducted on five benchmark data sets demonstrate that the proposed framework significantly outperforms state-of-the-art methods in both conventional and generalized settings.

Dose-Response Relationship Between BMI and Hyperuricemia.

OBJECTIVE: The purpose of the current study is to explore the demographic characteristics of hyperuricemia in China. STUDY DESIGN: The cross-sectional study was conducted, and the CHARLS dataset in 2011 was used. METHODS: Logistic regression model was used to assess the association between BMI and hyperuricemia. We also used restricted cubic spline with three knots to assess the dose-response relationship. To investigate the differences in dose-response relationships in various groups, stratification analysis was conducted. RESULTS: Of the 9408 middle and older participants, those who were overweight or obese had higher risk for hyperuricemia (OR=1.26, 95% CI=1.01-1.57; OR=1.90, 95% CI=1.46-2.47). In the subgroup, the effect of obesity on hyperuricemia was robust in varying gender and age. However, among overweight group, those with being male and aged over 60 years had higher risk for hyperuricemia (male: OR=1.42, 95% CI=1.07-1.90; over 60 years: OR=1.28, 95% CI=1.03-1.60). A dose-response relationship was examined between BMI and the risk of hyperuricemia. The result showed that the odds ratio of hyperuricemia and BMI displayed U-shaped nonlinear relationship (P trend<0.001, non-linear P<0.001). CONCLUSION: The association between BMI and hyperuricemia showed positive relationship. Specifically, the group with male and age over 60 years had higher risk of hyperuricemia along with increased weight.

Dose-response relationship between working hours and hypertension: A 22-year follow-up study.

ABSTRACT: Hypertension causes a substantial burden to society. Some studies found that hypertension was associated with the working type and working hours. The purpose of the current study is to assess the dose-response relationship between working hours and hypertension.Data of 12,080 adults aged 18 to 65 years who attended the China Health and Nutrition Survey (CHNS) between 1989 and 2011 were analyzed. Hypertension was determined based on systolic and/or diastolic blood pressure measures, or having doctor-diagnosed hypertension. Multivariable Cox regression and restricted cubic spline to assess the dose-response relationship between working hours and hypertension.A total of 12,080 participants including 5852 females and 6228 males. By the last follow-up (2011), a total of 830 participants were hypertensive, with an incidence of 6.9%. After adjusting socio-demographic, lifestyle factors, as well as occupation type, compared with those who worked 35 to 49 hours per week, participants who worked no more than 34 hours per week (HR: 1.21, 95%CI: 1.03-1.41) and at least 56 hours per week (HR: 1.38, 95%CI: 1.19-1.59) had a higher risk of hypertension. The significant association between long working hours (at least 56 hours per week) and hypertension was observed among females (HR: 1.38, 95%CI: 1.16-1.64) and males (HR: 1.36, 95%CI: 1.04-1.78). Among manual workers, the relationship between long working hours and hypertension was observed (HR: 1.49, 95%CI: 1.10-2.02). The relationship between long working hours (HR: 1.21, 95%CI: 1.01-1.44) and short working hours (HR: 1.37, 95%CI: 1.16-1.61) and hypertension was observed among nonmanual workers. The hazard ratio of hypertension and working time displayed U-shape non-linear relationship (Ptrend < .001, non-linear P < .001). The non-linear response-dose relationship was found in manual worker, nonmanual worker, and male (Ptrend < .001, non-linear P < .001).The association between working time and hypertension showed U-shape relationship. Specifically, overtime work was an important occupational risk factors for adults, and short work time was related to hazard ratio of hypertension in nonmanual workers.

Evolutionary Algorithm for Knee-Based Multiple Criteria Decision Making.

Although numerous effective and efficient multiobjective evolutionary algorithms have been developed in recent years to search for a well-converged and well-diversified Pareto optimal front, most of these designs are computationally expensive and have to maintain a large population of individuals throughout the evolutionary process. Once the Pareto optimal front is found satisfactorily, the cognitive burden is then imposed upon decision makers to handpick one solution for implementation among a massive number of candidates even with powerful multicriteria decision-making tools. With the increase in the number of decision variables and objective functions in the face of real-world applications, these problems have become a daunting challenge. In this article, we propose a recursive evolutionary algorithm, called EvoKneer, to directly search for global knee solutions, but also multiple local knee solutions using the minimum Manhattan distance approach as opposed to an enormous number of Pareto optimal solutions. Compared with the traditional evolutionary approaches, the proposed design herein only preserves nondominated solutions in rank one in each generation. Boundary Individuals Selection is tailored to select only M 2 boundary individuals where M is the number of objectives. Relieving the burden of maintaining a large population size and its diversity throughout a lengthy evolutionary process, this design with a very low computational cost allows the evolutionary algorithm to converge to knee solutions quickly. To facilitate the experimental validations, a simulator with a graphical user interface is developed under the Delphi XE7 platform and made available for public use. In addition, the proposed algorithm is evaluated with the DO2DK, DEB2DK, DEB2DK2, and DEB3DK benchmark functions. The comparison results validate that the proposed EvoKneer algorithm is computationally and efficiently finding all global and local knee solutions.

Spiculation Sign Recognition in a Pulmonary Nodule Based on Spiking Neural P Systems.

The spiculation sign is one of the main signs to distinguish benign and malignant pulmonary nodules. In order to effectively extract the image feature of a pulmonary nodule for the spiculation sign distinguishment, a new spiculation sign recognition model is proposed based on the doctors' diagnosis process of pulmonary nodules. A maximum density projection model is established to fuse the local three-dimensional information into the two-dimensional image. The complete boundary of a pulmonary nodule is extracted by the improved Snake model, which can take full advantage of the parallel calculation of the Spike Neural P Systems to build a new neural network structure. In this paper, our experiments show that the proposed algorithm can accurately extract the boundary of a pulmonary nodule and effectively improve the recognition rate of the spiculation sign.

A Network Framework for Small-Sample Learning.

Small-sample learning involves training a neural network on a small-sample data set. An expansion of the training set is a common way to improve the performance of neural networks in small-sample learning tasks. However, improper constraints in expanding training data will reduce the performance of the neural networks. In this article, we present certain conditions for incorporation of additional training data. According to these conditions, we propose a neural network framework for self-training using self-generated data called small-sample learning network (SSLN). The SSLN consists of two parts: the expression learning network and the sample recall generative network, both of which are constructed based on restricted Boltzmann machine (RBM). We show that this SSLN can converge as well as the RBM. Moreover, the experiment results on MNIST Digit, SVHN, CIFAR10, and STL-10 data sets reveal the superiority of the SSLN over other models.