The continuous pollution routing problem

In this paper, we presented an ε-accurate approach to conduct a continuous optimization on the pollution routing problem (PRP). First, we developed an ε-accurate inner polyhedral approximation method for the nonlinear relation between the travel time and travel speed. The approximation error was controlled within the limit of a given parameter ε, which could be as low as 0.01% in our experiments. Second, we developed two ε-accurate methods for the nonlinear fuel consumption rate (FCR) function of a fossil fuel-powered vehicle while ensuring the approximation error to be within the same parameter ε. Based on these linearization methods, we proposed an ε-accurate mathematical linear programming model for the continuous PRP (ε-CPRP for short), in which decision variables such as driving speeds, travel times, arrival/departure/waiting times, vehicle loads, and FCRs were all optimized concurrently on their continuous domains. A theoretical analysis is provided to confirm that the solutions of ε-CPRP are feasible and controlled within the predefined limit. The proposed ε-CPRP model is rigorously tested on well-known benchmark PRP instances in the literature, and has solved PRP instances optimally with up to 25 customers within reasonable CPU times. New optimal solutions of many PRP instances were reported for the first time in the experiments

New exploration of creativity: Cross-validation analysis of the factors influencing multiteam digital creativity in the transition phase

Multiteam digital creativity (MTDC) is a new domain of creativity study that fits the new developments of the digital era, thus scholars have called for exploring MTDC in the fine-graining phase. This paper responds to this call, and adopts two studies and cross-validation analysis to explore the theoretical framework of the impact factors of MTDC in the transition phase. Study 1 adopts the qualitative analysis method of rooted theory to explore a more comprehensive impact factor and to maximize the new theory’s saturation. Study 2 adopts the CL-WG DEMATEL method, one analysis method of group decision-making and optimized concept lattice, which could cross-validation analyze the results of Study 1 and further determine the importance of the factors. The results of the studies indicate that the influencing factors of MTDC are multilevel, and the factors such as the organizational digital climate, team psychological empowerment, individual digital cognition and emotion, and leadership competence have greater impacts on MTDC. This indicates that the transition phase has a unique internal mechanism. This paper constructs a theoretical framework of factors influencing MTDC in the transition phase and provides new theoretical and practical references for how organizations could fully stimulate MTDC in the digital era. In addition, the cross-validated analytical method further enriches the study tools in the domain of organizational behavior

Augmenting Large Language Model Translators via Translation Memories

Using translation memories (TMs) as prompts is a promising approach to in-context learning of machine translation models. In this work, we take a step towards prompting large language models (LLMs) with TMs and making them better translators. We find that the ability of LLMs to ``understand'' prompts is indeed helpful for making better use of TMs. Experiments show that the results of a pre-trained LLM translator can be greatly improved by using high-quality TM-based prompts. These results are even comparable to those of the state-of-the-art NMT systems which have access to large-scale in-domain bilingual data and are well tuned on the downstream tasks.Comment: Accepted to Findings of ACL 202

Attention Performance Measured by Attention Network Test Is Correlated with Global and Regional Efficiency of Structural Brain Networks

Functional neuroimaging studies have indicated the involvement of separate brain areas in three distinct attention systems: alerting, orienting and executive control (EC). However, the structural correlates underlying attention remains unexplored. Here, we utilized graph theory to examine the neuroanatomical substrates of the three attention systems measured by attention network test (ANT) in 65 healthy subjects. White matter connectivity, assessed with DTI deterministic tractography was modeled as a structural network comprising 90 nodes defined by the Automated Anatomical Labeling (AAL) template. Linear regression analyses were conducted to explore the relationship between topological parameters and the three attentional effects. We found a significant positive correlation between EC function and global efficiency of the whole brain network. At the regional level, node-specific correlations were discovered between regional efficiency and all three ANT components, including dorsolateral superior frontal gyrus, thalamus and parahippocampal gyrus for EC, thalamus and inferior parietal gyrus for alerting, and paracentral lobule and inferior occipital gyrus for orienting. Our findings highlight the fundamental architecture of interregional structural connectivity involved in attention and could provide new insights into the anatomical basis underlying human behavior

The aperiodic facility layout problem with time-varying demands and an optimal master-slave solution approach

In many seasonal industries, customer demands are constantly changing over time, and accordingly the facility layout should be re-optimized in a timely manner to adapt to changing material handling patterns among manufacturing departments. This paper investigates the aperiodic facility layout problem (AFLP) that involves arranging facilities layout and re-layout aperiodically in a dynamic manufacturing environment during a given planning horizon. The AFLP is decomposed into a master problem and a combination set of static facility layout problems (FLPs, the slave problems) without loss of optimality, and all problems are formulated as mixed-integer linear programming (MILP) models that can be solved by MIP solvers for small-sized problems. An exact backward dynamic programming (BDP) algorithm with a computational complexity of O(n 2) is developed for the master problem, and an improved linear programming based problem evolution algorithm (PEA-LP) is developed for the traditional static FLP. Computational experiments are conducted on two new problems and twelve well-known benchmark problems from the literature, and the experimental results show that the proposed solution approach is promising for solving the AFLP with practical sizes of problem instances. In addition, the improved PEA-LP found new best solutions for five benchmark problems

Optimal mathematical programming and variable neighborhood search for k-modes categorical data clustering

The conventional k-modes algorithm and its variants have been extensively used for categorical data clustering. However, these algorithms have some drawbacks, e.g., they can be trapped into local optima and sensitive to initial clusters/modes. Our numerical experiments even showed that the k-modes algorithm could not identify the optimal clustering results for some special datasets regardless the selection of the initial centers. In this paper, we developed an integer linear programming (ILP) approach for the k-modes clustering, which is independent to the initial solution and can obtain directly the optimal results for small-sized datasets. We also developed a heuristic algorithm that implements iterative partial optimization in the ILP approach based on a framework of variable neighborhood search, known as IPO-ILP-VNS, to search for near-optimal results of medium and large sized datasets with controlled computing time. Experiments on 38 datasets, including 27 synthesized small datasets and 11 known benchmark datasets from the UCI site were carried out to test the proposed ILP approach and the IPO-ILP-VNS algorithm. The experimental results outperformed the conventional and other existing enhanced k-modes algorithms in literature, updated 9 of the UCI benchmark datasets with new and improved results

An H5N1 M2e-based multiple antigenic peptide vaccine confers heterosubtypic protection from lethal infection with pandemic 2009 H1N1 virus

Background. A 2009 global influenza pandemic caused by a novel swine-origin H1N1 influenza A virus has posted an increasing threat of a potential pandemic by the highly pathogenic avian influenza (HPAI) H5N1 virus, driving us to develop an influenza vaccine which confers cross-protection against both H5N1 and H1N1 viruses. Previously, we have shown that a tetra-branched multiple antigenic peptide (MAP) vaccine based on the extracellular domain of M2 protein (M2e) from H5N1 virus (H5N1-M2e-MAP) induced strong immune responses and cross-protection against different clades of HPAI H5N1 viruses. In this report, we investigated whether such M2e-MAP presenting the H5N1-M2e consensus sequence can afford heterosubtypic protection from lethal challenge with the pandemic 2009 H1N1 virus. Results. Our results demonstrated that H5N1-M2e-MAP plus Freund's or aluminum adjuvant induced strong cross-reactive IgG antibody responses against M2e of the pandemic H1N1 virus which contains one amino acid variation with M2e of H5N1 at position 13. These cross-reactive antibodies may maintain for 6 months and bounced back quickly to the previous high level after the 2nd boost administered 2 weeks before virus challenge. H5N1-M2e-MAP could afford heterosubtypic protection against lethal challenge with pandemic H1N1 virus, showing significant decrease of viral replications and obvious alleviation of histopathological damages in the challenged mouse lungs. 100% and 80% of the H5N1-M2e-MAP-vaccinated mice with Freund's and aluminum adjuvant, respectively, survived the lethal challenge with pandemic H1N1 virus. Conclusions. Our results suggest that H5N1-M2e-MAP has a great potential to prevent the threat from re-emergence of pandemic H1N1 influenza and possible novel influenza pandemic due to the reassortment of HPAI H5N1 virus with the 2009 swine-origin H1N1 influenza virus. © 2010 Zhao et al; licensee BioMed Central Ltd.published_or_final_versio

Latilactobacillus sakei Furu2019 and stachyose as probiotics, prebiotics, and synbiotics alleviate constipation in mice

IntroductionSlow transit constipation (STC) is a common disorder in the digestive system. This study aimed to evaluate the effects of stachyose (ST) and Latilactobacillus sakei Furu 2019 (L. sakei) alone or combined on diphenoxylate-induced constipation and explore the underlying mechanisms using a mouse model.MethodsICR mice were randomly divided into five groups. The normal and constipation model groups were intragastrically administrated with PBS. The ST, L. sakei, and synbiotic groups were intragastrically administrated with ST (1.5 g/kg body weight), alive L. sakei (3 × 109 CFU/mouse), or ST + L. sakei (1.5 g/kg plus 3 × 109 CFU/mouse), respectively. After 21 days of intervention, all mice except the normal mice were intragastrically administrated with diphenoxylate (10 mg/kg body weight). Defecation indexes, constipation-related intestinal factors, serum neurotransmitters, hormone levels, short-chain fatty acids (SCFAs), and intestinal microbiota were measured.ResultsOur results showed that three interventions with ST, L. sakei, and synbiotic combination (ST + L. sakei) all alleviated constipation, and synbiotic intervention was superior to ST or L. sakei alone in some defecation indicators. The RT-PCR and immunohistochemical experiment showed that all three interventions relieved constipation by affecting aquaporins (AQP4 and AQP8), interstitial cells of Cajal (SCF and c-Kit), glial cell-derived neurotrophic factor (GDNF), and Nitric Oxide Synthase (NOS). The three interventions exhibited a different ability to increase the serum excitatory neurotransmitters and hormones (5-hydroxytryptamine, substance P, motilin), and reduce the serum inhibitory neurotransmitters (vasoactive intestinal peptide, endothelin). The result of 16S rDNA sequencing of feces showed that synbiotic intervention significantly increased the relative abundance of beneficial bacteria such as Akkermansia, and regulated the gut microbes of STC mice. In conclusion, oral administration of ST or L. sakei alone or combined are all effective to relieve constipation and the symbiotic use may have a promising preventive effect on STC

Exploring the Genetic Correlation Between Growth and Immunity Based on Summary Statistics of Genome-Wide Association Studies

The relationship between growth and immune phenotypes has been presented in the context of physiology and energy allocation theory, but has rarely been explained genetically in humans. As more summary statistics of genome-wide association studies (GWAS) become available, it is increasingly possible to explore the genetic relationship between traits at the level of genome-wide summary statistics. In this study, publicly available summary statistics of growth and immune related traits were used to evaluate the genetic correlation coefficients between immune and growth traits, as well as the cause and effect relationship between them. In addition, pleiotropic variants and KEGG pathways were identified. As a result, we found negative correlations between birthweight and immune cell count phenotypes, a positive correlation between childhood head circumference and eosinophil counts (EO), and positive or negative correlations between childhood body mass index and immune phenotypes. Statistically significant negative effects of immune cell count phenotypes on human height, and a slight but significant negative influence of human height on allergic disease were also observed. A total of 98 genomic regions were identified as containing variants potentially related to both immunity and growth. Some variants, such as rs3184504 located in SH2B3, rs13107325 in SLC39A8, and rs1260326 located in GCKR, which have been identified to be pleiotropic SNPs among other traits, were found to also be related to growth and immune traits in this study. Meanwhile, the most frequent overlapping KEGG pathways between growth and immune phenotypes were autoimmune related pathways. Pleiotropic pathways such as the adipocytokine signaling pathway and JAK-STAT signaling pathway were also identified to be significant. The results of this study indicate the complex genetic relationship between growth and immune phenotypes, and reveal the genetic background of their correlation in the context of pleiotropy

Two Strains of Lactobacilli Effectively Decrease the Colonization of VRE in a Mouse Model

Vancomycin-resistant Enterococcus (VRE) infection is a serious challenge for clinical management and there is no effective treatment at present. Fecal microbiota transplantation (FMT) and probiotic intervention have been shown to be promising approaches for reducing the colonization of certain pathogenic bacteria in the gastrointestinal tract, however, no such studies have been done on VRE. In this study, we evaluated the effect of FMT and two Lactobacillus strains (Y74 and HT121) on the colonization of VRE in a VRE-infection mouse model. We found that both Lactobacilli strains reduced VRE colonization rapidly. Fecal microbiota and colon mRNA expression analyses further showed that mice in FMT and the two Lactobacilli treatment groups restored their intestinal microbiota diversity faster than those in the phosphate buffer saline (PBS) treated group. Administration of Lactobacilli restored Firmicutes more quickly to the normal level, compared to FMT or PBS treatment, but restored Bacteroides to their normal level less quickly than FMT did. Furthermore, these treatments also had an impact on the relative abundance of intestinal microbiota composition from phylum to species level. RNA-seq showed that FMT treatment induced the expression of more genes in the colon, compared to the Lactobacilli treatment. Defense-related genes such as defensin α, Apoa1, and RegIII were down-regulated in both FMT and the two Lactobacilli treatment groups. Taken together, our findings indicate that both FMT and Lactobacilli treatments were effective in decreasing the colonization of VRE in the gut