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BackgroundDespite successful recanalization after endovascular treatment, many patients with acute ischemic stroke due to large vessel occlusion still show functional dependence, namely futile recanalization.MethodsPubMed and Embase were searched up to April 30, 2021. Studies that reported risk factors for futile recanalization following endovascular treatment of acute ischemic stroke were included. The mean difference (MD) or odds ratio (OR) and 95% confidence interval (95% CI) of each study were pooled for a meta-analysis.ResultsTwelve studies enrolling 2138 patients were included. The pooled analysis showed that age (MD 5.81, 95% CI 4.16 to 7.46), female sex (OR 1.40, 95% CI 1.16 to 1.68), National Institutes of Health Stroke Scale (NIHSS) score (MD 4.22, 95% CI 3.38 to 5.07), Alberta Stroke Program Early CT Score (ASPECTS) (MD −0.71, 95% CI −1.23 to –0.19), hypertension (OR 1.73, 95% CI 1.43 to 2.09), diabetes (OR 1.78, 95% CI 1.41 to 2.24), atrial fibrillation (OR 1.24, 95% CI 1.01 to 1.51), admission systolic blood pressure (MD 4.98, 95% CI 1.87 to 8.09), serum glucose (MD 0.59, 95% CI 0.37 to 0.81), internal carotid artery occlusion (OR 1.85, 95% CI 1.17 to 2.95), pre-treatment intravenous thrombolysis (OR 0.67, 95% CI 0.55 to 0.83), onset-to-puncture time (MD 16.92, 95% CI 6.52 to 27.31), puncture-to-recanalization time (MD 12.37, 95% CI 7.96 to 16.79), and post-treatment symptomatic intracerebral hemorrhage (OR 6.09, 95% CI 3.18 to 11.68) were significantly associated with futile recanalization.ConclusionThis study identified female sex, comorbidities, admission systolic blood pressure, serum glucose, occlusion site, non-bridging therapy, and post-procedural complication as predictors of futile recanalization, and also confirmed previously reported factors. Further large-scale prospective studies are needed.

In the evolutionary multi-objective optimization (EMO) community, an unbounded external archive has been used in some studies for evaluating the performance of EMO algorithms. Those studies show that the unbounded external archive often includes better solutions than the final population. Thus, it is likely that the search ability of an EMO algorithm can be improved by periodically updating the current population using the unbounded external archive (i.e., by periodically choosing good solutions from all the examined solutions as the current population). However, the usefulness of such a global generation update scheme has not been studied in the literature. In this paper, we examine the effect of the periodical global generation update on the performance of well-known and frequently-used EMO algorithms: NSGA-II, MOEA/D and NSGA-III. We use the PBI function with uniformly distributed weight vectors for the periodical global generation update. In our computational experiments, we obtain clearly improved results by the periodical global generation update. We also examine the effect of the frequency of the global generation update (e.g., every 20 generations) on the performance of each EMO algorithm and its run time.

Hypervolume-based multi-objective evolutionary algorithms (HV-MOEAs) are one of the popular algorithm classes in the evolutionary multi-objective optimization (EMO) com-munity. HV-MOEAs, which can directly optimize the HV of a solution set, are useful in various applications. However, the computation time of HV-MOEAs is very long for many-objective problems since the calculation of the hypervolume contribution (HVC) is computationally expensive. Therefore, a number of approximation methods for the HVC calculation were proposed to reduce its time cost. An R2-based hypervolume contribution approximation (R2-HVC) method was proposed for HVC approximation. However, for HV-MOEAs, the point is to find the worst solution, instead of accurately approximating the HVC of each solution. In this paper, a novel method (i.e., two-stage R2-HVC) is proposed for improving the ability of R2-HVC to correctly identify the worst solution (i.e., the solution with the smallest HVC value) in a solution set. In the proposed method, some candidate solutions are selected based on rough HVC approximation in the first stage, and they are carefully evaluated in the second stage. It is shown through computational experiments that the proposed method performs much better than the original R2-HVC method.

The multi-objective evolutionary algorithm based on decomposition (MOEA/D) is one of the most popular algorithms in the field of evolutionary multi-objective optimization (EMO). Even though MOEA/D has been widely used in many studies, it is likely that the performance of MOEA/D is not always optimized since the same MOEA/D implementation is often used on various problems with different characteristics. However, obtaining an appropriate implementation of MOEA/D for a different problem is not always easy, since there exists a wide variety of choices for the components and parameters in MOEA/D. In this paper, we examine the use of a genetic algorithm-based hyper-heuristic procedure to offline tune MOEA/D on a single test problem, a set of similar test problems, and a set of various test problems. A total of 26 benchmark test problems are used in our study. Experimental results show that the MOEA/D tuned for a set of various test problems does not always perform well. It is also shown that the MOEA/D tuned for a single test problem and for a set of similar test problems always has high performance. Our experimental results strongly suggest the necessity of using a tuning procedure to obtain a different MOEA/D implementation for a different type of problems.

The quality of solutions in multiobjective evolutionary algorithms (MOEAs) is usually evaluated by objective functions. However, function evaluations (FEs) are usually time-consuming in real-world problems. A large number of FEs limit the application of MOEAs. In this paper, we propose a fuzzy classifier-based selection strategy to reduce the number of FEs of MOEAs. First, all evaluated solutions in previous generations are used to build a fuzzy classifier. Second, the built fuzzy classifier is used to predict each unevaluated solution's label and its membership degree. The reproduction procedure is repeated to generate enough offspring solutions (classified as positive by the classifier). Next, unevaluated solutions are sorted based on their membership degrees in descending order. The same number of solutions as the population size are selected from the top of the sorted unevaluated solutions. Then, the best half of the chosen solutions are selected and stored in the new population without evaluations. The other half solutions are evaluated. Finally, the evaluated solutions are used together with evaluated current solutions for environmental selection to form another half of the new population. The proposed strategy is integrated into two MOEAs. Our experimental results demonstrate the effectiveness of the proposed strategy on reducing FEs.

Hypervolume subset selection (HSS) aims to select a subset from a candidate solution set so that the hypervolume of the selected subset is maximized. Due to its NP-hardness nature, the greedy algorithms are the most efficient for solving HSS in many-objective optimization. However, when the number of objectives is large, the calculation of the hypervolume contribution in the greedy HSS is time-consuming, which makes the greedy HSS inefficient. To solve this issue, in this paper we propose a greedy approximated HSS algorithm. The main idea is to use an R2-based hypervolume contribution approximation method in the greedy HSS. In the algorithm implementation, a utility tensor structure is introduced to facilitate the calculation of the hypervolume contribution approximation. In addition, the tensor information in the last step is utilized in the current step to accelerate the calculation. We also apply the lazy strategy in the proposed algorithm to further improve its efficiency. We test the greedy approximated HSS algorithm on 3-10 objective candidate solution sets. The experimental results show that the proposed algorithm is much faster than the state-of-the-art greedy HSS algorithm in many-objective optimization while their hypervolume performance is almost the same.

A total of 136 Salmonella isolates from chicken feces and meat samples of the top 12 integrated chicken production companies throughout Korea were collected. Among the 17 ESC-resistant Salmonella, blaCTX-M-15 was the most prevalent gene and two strains carried blaTEM-1/blaCTX-M-15 and blaCMY-2, respectively. The transferable blaCTX-M-15 gene was carried by IncFII plasmid in three isolates and the blaCMY-2 gene carried by IncI1 plasmid in one isolate. blaCMY-2 gene-harboring strain was selected as the donor based on the high frequency of blaCMY-2 gene transfer in vitro and its transfer frequencies were determined at 10−3 transconjugants per recipient. The transfer of blaCMY-2 gene-harboring plasmid derived from chicken isolate into a human pathogen, enteroinvasive Escherichia coli (EIEC), presented in mouse intestine with about 10−1 transfer frequency without selective pressure. From the competition experiment, blaCMY-2 gene-harboring transconjugant showed variable fitness burden depends on the parent strains. Our study demonstrated direct evidence that the blaCMY-2 gene harboring Salmonella from chicken could frequently transfer its ESC-resistant gene to E. coli in a mouse intestine without antimicrobial pressure, resulting in the emergence of multidrug resistance in potentially virulent EIEC isolates of significance to human health, which can increase the risk of therapeutic inadequacy or failures.

The rapid construction of emergency hospitals in areas with a severe COVID-19 outbreak was one of the effective ways to contain and fight the pandemic. However, such rapid construction mega...

Hypervolume is widely used in the evolutionary multi-objective optimization (EMO) field to evaluate the quality of a solution set. For a solution set with $\mu$ solutions on a Pareto front, a larger hypervolume means a better solution set. Investigating the distribution of the solution set with the largest hypervolume is an important topic in EMO, which is the so-called hypervolume optimal $\mu$-distribution. Theoretical results have shown that the $\mu$ solutions are uniformly distributed on a linear Pareto front in two dimensions. However, the $\mu$ solutions are not always uniformly distributed on a single-line Pareto front in three dimensions. They are only uniform when the single-line Pareto front has one constant objective. In this paper, we further investigate the hypervolume optimal $\mu$-distribution in three dimensions. We consider the line- and plane-based Pareto fronts. For the line-based Pareto fronts, we extend the single-line Pareto front to two-line and three-line Pareto fronts, where each line has one constant objective. For the plane-based Pareto fronts, the linear triangular and inverted triangular Pareto fronts are considered. First, we show that the $\mu$ solutions are not always uniformly distributed on the line-based Pareto fronts. The uniformity depends on how the lines are combined. Then, we show that a uniform solution set on the plane-based Pareto front is not always optimal for hypervolume maximization. It is locally optimal with respect to a $(\mu+1)$ selection scheme. Our results can help researchers in the community to better understand and utilize the hypervolume indicator., Comment: This paper has been submitted to a journal for review

Based on dust storm frequency (DSF) data from the China Meteorological Administration, Arctic sea-ice concentration (SIC) data from the Hadley Centre, and atmospheric reanalysis data from the National Centers for Environmental Prediction (NCEP) and National Center for Atmospheric Research (NCAR), temporal variations and regime shifts of East Asian DSF and Arctic SIC during 1961–2015 are revealed, and the possible relationship between them is explored. The results show that East Asian DSF in spring is closely associated with the preceding winter SIC from the northern Greenland Sea to the Barents Sea (20° W–60° E, 74.5° N–78.5° N). In the past half-century, both East Asian DSF and Arctic SIC have shown significant declining trends, with consistent regime shifts in the early 1980s. Further statistical analyses indicate that the abrupt decrease of East Asian DSF in spring may be attributed to the concurrent sharp loss of Arctic SIC in the preceding winter. It is the loss of Arctic SIC that causes the atmospheric circulation anomalies downstream by stimulating a Rossby wave train, resulting in decelerated wind speed, dampened vertical wind shear and restrained synoptic-scale disturbances over the dust source region, eventually leading to the decline in East Asian DSF over decadal timescales.

In this work, a bio-based macromolecule ammonium phytic acid named as PA-AEP was synthesized successfully via neutralization reaction between phytic acid (PA) and N-aminoethylpiperazine (AEP) and served as a monocomponent flame-retardant curing agent to improve the flame retardancy and smoke suppression of epoxy resin (EP). Curing behavior, flame retardancy and combustion behavior of PA-AEP cured epoxy resin were investigated. By comparison with EP/AEP reference sample, EP/PA-AEP samples showed good flame retardancy and smoke suppression effect because of the addition of PA-AEP. The PA-AEP cured EP sample passed UL-94 V-0 rating and achieved to a limiting oxygen index (LOI) value of 28.0% with 20 wt% PA-AEP. Cone calorimeter test results showed that, compared with EP/20% AEP reference sample, both the peak of heat release rate (PHRR), total heat release (THR), the peak of smoke production release (SPR) and total smoke production (TSP) of EP/20 wt% PA-AEP sample were decreased by 44.4%, 55%, 49% and 80.8%, respectively. In addition, by analyzing the char residues and volatile pyrolysis products, it was concluded that PA-AEP mainly played an important role in condensed phase which was associated with the formation of phosphorus-rich intumescent char layer.

Air stagnation modulates the frequency and duration of haze events. Based on meteorological and environmental observation data during 1980 to 2013, the present study analyzed the relationship between the interannual variations of Sea Surface Temperature (SST) over tropical central-eastern Pacific and the number of winter haze days (WHD) over the Southern China (SC) region. The potential preceding signal of Sea Surface Temperature anomalies (SSTAs) in Nino3.4 region associated with ENSO can be used as a predictor of haze occurrences in winter. Results indicate that the detrended WHD in the SC is significantly correlated (r = −0.55) with the contemporary SSTAs in Nino3.4 region. The winters with warm Nino3.4 SSTAs in (El Nino) tend to be accompanied with less haze events in the SC, resulting from more local precipitation and enhanced mid-level winds, which helps to build an unstable condition that is conductive for the decrease of haze occurrence. The precursory signal of WHD variability can be detected in the tropical central-eastern Pacific SST, which is amplified since August to winter season. Based on the August–October mean SSTAs in Nino3.4 and three other identified predictors, we developed a seasonal prediction model of the WHD using RF regression method. The model accounted for 90% of the total variance of the WHD in the SC and ranked the SSTAs in Nino3.4 as the most important predictor. This implies that the SSTAs tropical Pacific play a significant role in the variability of WHD in the SC. Since the predictors can be readily monitored in real time, the model provides a real time forecast tool and could brighten the prospects for seasonal forecast of haze anomalies in vulnerable regions such as the SC.

Meat authentication and meat adulteration have become a growing global concern leading to consumers’ demand for accurate meat identification methods. Since existing methods for meat identification are cumbersome and require specialized equipment and/or training, we developed a specific colorimetric detection method that can rapidly identify horse meat as few as 0.1% contamination in xenogeneic meat sources within 1 h with no false-positive signals observed. Specificity analysis revealed no cross-reactivity with 14 other animal species. Therefore, a rapid on-site survey based on our method can be set up to identify undeclared meat in the food supply chain and contributes to an approach on food analysis by meeting the demands for quick and easy-to-perform analytical methods.

The present study investigated the distribution, antimicrobial susceptibility profiles, serotypes and genotypes of Salmonella isolates from a slaughterhouse (including lairage and slaughter line) and its downstream retail markets to determine the main sources of Salmonella contamination in retail chicken meat. Positive identification rates in the slaughterhouse and retail markets were 9.5% (81/852) and 41.8% (69/165), respectively. Among the Salmonella serovars identified, S. Albany was the most prevalent but other serovars including S. Montevideo and S. Virchow were also present prominently. Antimicrobial susceptibility testing revealed that the highest antimicrobial resistance rate was against nalidixic acid (139/150, 92.7%), that 56.7% (85/150) isolates were multidrug-resistant (MDR), and that the resistance profile of 20.7% (31/150) isolates was ACSSuT (ampicillin, chloramphenicol, streptomycin, sulfonamides and tetracycline). Ten virulence genes were detected in 69 isolates from retail chicken meat; the Salmonella pathogenicity island genes invA, hilA, sipA, sopB, sopD, ssrA, and ssaR were present in most of the isolates (94.2%–100%). Based on analysis of pulsed-field gel electrophoresis (PFGE) results, our findings indicated that MDR isolates could be transmitted from slaughterhouse to its downstream retail markets. In addition, the present study indicates three possible routes of Salmonella contamination in retail chicken meat, including initial presence, cross-contamination from carrier broilers slaughtered on the same day, and contamination from resident flora in slaughterhouse, with the last route being the most common in the present study.

A lipidomics study using liquid chromatography-tandem mass spectrometry and multivariate statistics was conducted in this work to discriminate raw pork meat. A total of 1180 lipid species were identified in the studied pork samples. Four, three and eight lipids were determined as potential discriminatory markers for the five cuts (shoulder, rump, loin, shank and belly) of Tibetan, Jilin and Sanmenxia black pigs, respectively. Distinct lipidomic fingerprints of Tibetan, Jilin and Sanmenxia pork were obtained and they were clearly separated into three clusters by partial least squares discriminant analysis (PLS-DA). The developed PLS-DA model (R2X = 0.603, R2Y = 0.861 and Q2 = 0.752) enables a 91.1% correct classification of pork samples. One-hundred variables, including 61 glycerolipids, 17 glycerophospholipids, 4 sterol lipids, 2 sphingolipids, 3 polyketides, 7 fatty acyls and 6 prenol lipids, were found to have high potential (variable importance in projection value > 1, p-value

Our investigation for the first time explores the possibility of fabrication of a Capsaicin/polyurethane-based bioactive wound dressing. A micro/nanofibrous bandage was synthesized by electrospinning polyurethane supplemented with natural anti-inflammatory agent, Capsaicin. Herein, the best concentration (3%) of Capsaicin in polyurethane to get a consistent solution was standardized in order to acquire the composite micro/nanofibrous dressing. The as-spun bandage has been investigated by scanning electron microscopy and Fourier transform infrared spectroscopy, and biocompatibility of muscle cells on the bioactive bandage was also examined. Free radical scavenging activity of the fabricated micro/nanofiber bandage was estimated using 2,2-diphenyl-1-picrylhydrazyl assay. Inclusion of Capsaicin in polyurethane transformed the morphology as well as dimension of the fibers. Anti-oxidant wound bandage with diameters around 150–500 nm was fabricated by physical unification of polyurethane with natural Capsaicin. Here, polyurethane was used as a foundation polymer, which was blended with Capsaicin to attain desirable characteristics such as better anti-oxidant activity, hydrophilicity and excellent cell attachment. The polyurethane wound bandage possesses enlarged surface, proscribed evaporation, and fluid drainage ability. These results suggest the beneficial influence of antioxidant Capsaicin on wound repairing process. Therefore, a biologically active, natural compounds such as Capsaicin is material of choice for fabrication of future wound dressings.

In this study, fifty-four minerals were simultaneously quantified using inductively coupled plasma mass spectrometry. This study represents the most comprehensive profile of elements ever studied in chicken samples. The effects of age, gender and meat cut on the elements in Taihe black-boned silky fowl muscles were investigated. Principal component analysis of the data indicated that the leg and breast meat samples were able to be clearly separated from each other and that the contents of fourteen elements (23Na, 24Mg, 39K, 31P, 43Ca, 44Ca, 56Fe, 55Mn, 52Cr, 66Zn, 63Cu, 78Se, 88Sr and 193Ir) were significantly different (p

Clustering analysis has been widely used in pattern recognition, image processing, machine learning, and so on. It is a great challenge for most existing clustering algorithms to discover clusters with complex manifolds or great density variation. Most of the existing clustering needs manually set neighborhood parameter K to search the neighbor of each object. In this paper, we use natural neighbor to adaptively get the value of K and natural density of each object. Then, we define two novel concepts, natural core point and the distance between clusters to solve the complex manifold problem. On the basis of above-proposed concept, we propose a novel hybrid clustering algorithm that only needs one parameter M (the number of final clusters) based on minimum spanning tree of natural core points, called NCP. The experimental results on the synthetic dataset and real dataset show that the proposed algorithm is competitive with the state-of-the-art methods when discovering with the complex manifold or great density variation.

Rationale: Ischemic white matter damage frequently results in myelin loss, accompanied with microglial activation. We previously found that directing microglia towards an anti-inflammatory phenotype provided a beneficial microenvironment and helped maintain white matter integrity during chronic cerebral hypoperfusion. However, the molecular mechanisms underlying microglial polarization remain elusive. Methods: Hypoperfusion induced white matter damage mice model and lipopolysaccharide (LPS) induced primary cultured microglia were established. Autophagy activation in microglia was detected both in vivo and in vitro by immunofluorescence, Western blot and electron microscopy. Autophagy inhibitors/agonist were administrated to investigate the role of autophagic process in modulating microglial phenotypes. Quantitative real time-polymerase chain reaction and Western blot were carried out to investigate the possible pathway. Results: We identified rapid accumulation of autophagosomes in primary cultured microglia exposed to LPS and within activated microglia during white matter ischemic damage. Autophagy inhibitors switched microglial function from pro-inflammatory to anti-inflammatory phenotype. Furthermore, we found TLR4, one of the major receptors binding LPS, was most highly expressed on microglia in corpus callosum during white matter ischemic damage, and TLR4 deficiency could mimic the phenomenon in microglial functional transformation, and exhibit a protective activity in chronic cerebral hypoperfusion. Whereas, the anti-inflammatory phenotype of microglia in TLR4 deficiency group was largely abolished by the activation of autophagic process. Finally, our transcriptional analysis confirmed that the up-regulation of STAT1 and down-regulation of STAT6 in microglia exposure to LPS could be reversed by autophagy inhibition. Conclusion: These results indicated that TLR4-dependent autophagy regulates microglial polarization and induces ischemic white matter damage via STAT1/6 pathway.

An accurate and spatially continuous estimation of terrestrial latent heat flux (LE) is fundamental and crucial for the rational utilization of water resources in the Haihe River Basin (HRB). However, the sparsity of flux observation sites hinders the accurate characterization of spatiotemporal LE patterns over the HRB. In this study, we estimated the daily LE across the HRB using the gradient boosting regression tree (GBRT) from global land surface satellite NDVI data, reanalysis data and eddy covariance data. Compared with the random forests (RF) and extra tree regressor (ETR) methods, the GBRT obtains the best results, with R2 = 0.86 and root mean square error (RMSE = 18.1 W/m2. Then, we applied the GBRT algorithm to map the average annual terrestrial LE of the HRB from 2016 to 2018 with a spatial resolution of 0.05°. When compared with the Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS) LE products, the difference between the terrestrial LE estimated by the GBRT algorithm and the GLASS and MODIS products was less than 20 W/m2 in most areas; thus, the GBRT algorithm was reliable and reasonable for estimating the long-term LE estimation over the HRB.

Objective To analyze the clinical characteristics and outcomes of patients diagnosed with acquired neuromyotonia and who were treated with tacrolimus. Methods A single center, retrospective study was performed on patients with acquired meuromyotonia whose treatment included tacrolimus. The clinical information, antibody tests, and electromyography results were reviewed. The Numeric Rating Scale for pain and modified Rankin scale were used to quantify outcomes. Results This study included four patients who presented with fasciculation or myokymia in their limbs. Electromyography suggested peripheral nerve hyperexcitability. Autoantibodies including contactin-associated protein 2 (CASPR2), leucine-rich glioma inactivated protein 1 (LGl1) or IgLON5 antibody were detected in three patients, and another patient had Sjogren's syndrome. Initial treatment included membrane-stabilizing drugs and/or corticosteroids. Tacrolimus was administered at a dose of 3 mg once daily to all patients. All patients showed clinical improvement after the treatment. No recurrence was observed after gradual tapering or discontinuation of therapy during follow-up. Conclusions Tacrolimus may be a therapeutic option for acquired neuromyotonia. Further studies on tacrolimus in larger patient cohort should be performed.

An operational and accurate model for estimating global or regional terrestrial latent heat of evapotranspiration (ET) across different land-cover types from satellite data is crucial. Here, a simplified Priestley–Taylor (SPT) model was developed without surface net radiation (Rn) by combining incident shortwave radiation (Rs), satellite vegetation index, and air relative humidity (RH). Ground-measured ET for 2000–2009 collected by 100 global FLUXNET eddy covariance (EC) sites was used to calibrate and evaluate the SPT model. A series of cross-validations demonstrated the reasonable performance of the SPT model to estimate seasonal and spatial ET variability. The coefficients of determination (R2) of the estimated versus observed daily (monthly) ET ranged from 0.42 (0.58) (p < 0.01) at shrubland (SHR) flux sites to 0.81 (0.86) (p < 0.01) at evergreen broadleaf forest (EBF) flux sites. The SPT model was applied to estimate agricultural ET at high spatial resolution (16 m) from Chinese Gaofen (GF)-1 data and monitor long-term (1982–2018) ET variations in the Three-River Headwaters Region (TRHR) of mainland China using the Global LAnd-Surface Satellite (GLASS) normalized difference vegetation index (NDVI) product. The proposed SPT model without Rn provides an alternative model for estimating regional terrestrial ET across different land-cover types.

Link prediction is a fundamental problem of data science, which usually calls for unfolding the mechanisms that govern the micro-dynamics of networks. In this regard, using features obtained from network embedding for predicting links has drawn widespread attention. Although methods based on edge features or node similarity have been proposed to solve the link prediction problem, many technical challenges still exist due to the unique structural properties of networks, especially when the networks are sparse. From the graph mining perspective, we first give empirical evidence of the inconsistency between heuristic and learned edge features. Then, we propose a novel link prediction framework, AdaSim, by introducing an Adaptive Similarity function using features obtained from network embedding based on random walks. The node feature representations are obtained by optimizing a graph-based objective function. Instead of generating edge features using binary operators, we perform link prediction solely leveraging the node features of the network. We define a flexible similarity function with one tunable parameter, which serves as a penalty of the original similarity measure. The optimal value is learned through supervised learning and thus is adaptive to data distribution. To evaluate the performance of our proposed algorithm, we conduct extensive experiments on eleven disparate networks of the real world. Experimental results show that AdaSim achieves better performance than state-of-the-art algorithms and is robust to different sparsities of the networks.

Simple Summary Investigation of Campylobacter prevalence throughout the entire chicken production process from farms to retail meat is still limited. In this study, we examined the prevalence and antimicrobial susceptibility of Campylobacter in 10 production lines from one of the largest integrated poultry production companies in Korea. The prevalence of Campylobacter in breeder farm, hatchery, broiler farm, slaughterhouse, and retail meat products was 50.0%, 0%, 3.3%, 13.4%, and 68.4%, respectively. Resistance to fluoroquinolones was the most frequently observed, and 16 isolates from breeder farm were resistant to both azithromycin and ciprofloxacin. Diverse pulsed-field gel electrophoresis genotypes were presented with discontinuous patterns along the whole production chain. Thirty percent of Campylobacter-free flocks became positive after slaughtering. An identical genotype was simultaneously detected from both breeder farm and retail meat, even from different production lines. This study reveals that antimicrobial-resistant Campylobacter contamination can occur at all stages of the chicken supply chain. In particular, the breeder farm and slaughterhouse should be the main control points, as they are the potential stages at which antimicrobial-resistant Campylobacter could spread to retail meat products by horizontal transmission. Abstract The aim of this study was to analyze the prevalence, antimicrobial resistance, and genetic diversity of Campylobacter isolates that were obtained from whole chicken production stages in Korea. A total of 1348 samples were collected from 10 production lines. The prevalence of Campylobacter in breeder farm, broiler farm, slaughterhouse, and retail meat products was 50.0%, 3.3%, 13.4%, and 68.4%, respectively, and Campylobacter was not detected at the hatchery stage. Resistance to quinolones/fluoroquinolones was the most prevalent at all stages. Among the multidrug-resistant isolates, 16 isolates (19.8%) from breeder farm were resistant to both azithromycin and ciprofloxacin. A total of 182 isolates were subdivided into 82 pulsed-field gel electrophoresis (PFGE) genotypes with 100% similarity. Diverse genotypes were presented with discontinuous patterns along the whole production chain. Thirty percent of Campylobacter-free flocks became positive after slaughtering. An identical genotype was simultaneously detected from both breeder farm and retail meat, even from different production lines. This study reveals that antimicrobial-resistant Campylobacter contamination can occur at all stages of the chicken supply chain. In particular, the breeder farm and slaughterhouse should be the main control points, as they are the potential stages at which antimicrobial-resistant Campylobacter could spread to retail meat products by horizontal transmission.

The development of building industrialization conforms to the new trend of high efficiency, energy saving and environmental protection. The related government departments have formulated corresponding policies to promote the development of building industrialization. However, numerous building industrialization policies lack uniform standards. As a result, the policy incentive effect of building industrialization is not clear enough, which further affects the investment intention of investors in prefabricated buildings. Therefore, the paper selects 34 recently building industrialization policy documents issued by the country, provinces and cities as a sample, and using the content analysis method to perform quantitative analysis in four-level coding manner. Based on the break-even point theory, the investment strategy is analyzed from quantitative and qualitative policies aspects. Further, nine important policy directions to promote the development of China’s building industrialization will be determined, and a policy subsidy model will be constructed. Finally, combining with a practical project to analyze, the feasibility of the investment model is demonstrated, which is conducive to making scientific decisions.

R2HCA-EMOA is a recently proposed hypervolume-based evolutionary multi-objective optimization (EMO) algorithm. It uses an R2 indicator variant to approximate the hypervolume contribution of each solution. Meanwhile, it uses a utility tensor structure to facilitate the calculation of the R2 indicator variant. This makes it very efficient for solving many-objective optimization problems. Compared with HypE, another hypervolume-based EMO algorithm designed for many-objective problems, R2HCA-EMOA runs faster and at the same time achieves better performance. Thus, R2HCA-EMOA is more attractive for practical use. In this paper, we further improve the efficiency of R2HCA-EMOA without sacrificing its performance. We propose two strategies for the efficiency improvement. One is to simplify the environmental selection, and the other is to change the number of direction vectors depending on the state of evolution. Numerical experiments clearly show that the efficiency of R2HCA-EMOA is significantly improved without deteriorating its performance.

The outbreak of COVID-19 has introduced critical challenges in the architecture, engineering, and construction (AEC) industry; to address these challenges, building information modelling (BIM) can be applied as a project management tool to help enhance collaboration among stakeholders and improve business performance. Amidst the COVID-19 crisis, there is a greater need to explore and implement effective strategies to promote a wider adoption of BIM. However, increasing the willingness of project participants to adopt BIM through event management has not received much attention. Therefore, based on event system theory and innovation diffusion theory, we developed a model to explore the influence of the COVID-19 crisis on the willingness of AEC participants to adopt BIM. Structural equation modelling was performed to test the hypotheses. The results demonstrate that the intention of the AEC project participants to adopt BIM is directly driven by the COVID-19 event criticality and perceived usefulness of BIM. Moreover, the event criticality and BIM technical features (relative advantage, compatibility, and complexity) can indirectly affect this intention, through the perceived usefulness. However, the impact of event disruption and novelty on the BIM adoption intention is not significant. Several recommendations are provided to improve the BIM adoption intention of AEC participants during and after the pandemic.

Recently, a number of many-objective evolutionary algorithms have been proposed in the literature. Those algorithms are often evaluated using the frequently-used DTLZ and WFG test problems. One feature of those test problems is the use of the same distance function in all objectives in each problem. As a result, the distance from each solution to the Pareto front is minimized by optimizing the distance function. This means that the convergence improvement is a single-objective optimization independent of the number of objectives. This feature makes the DTLZ and WFG test problems easy. Recently, some difficult test problems have been proposed by removing this feature. In this paper, we examine the performance of many-objective evolutionary algorithms through computational experiments on a recently-proposed difficult test problem with no distance function. We show that totally different comparison results are obtained for the easy test problems with distance functions (i.e., DTLZ and WFG) and the difficult test problem with no distance function.

Extended-spectrum cephalosporin (ESC) resistance investigated in Salmonella and E. coli from the same chicken was to improve the understanding of the inter-species transmission of ESC resistance determinants in Salmonella and E. coli from a single chicken individual. Fifteen (13.6%) farms and 44 (8.0%) chicken individuals were positive for ESC-resistant E. coli and/or Salmonella, 8 farms (7.3%) and 12 (2.2%) individuals were simultaneously positive for ESC-resistant E. coli and Salmonella. The genetic diversity of ESC resistance determinants in E. coli and Salmonella was observed. Most E. coli isolates (67.6%) produced CTX-M-type of blaCTX-M-55, and 9 isolates (24.3%) produced CMY-type of blaCMY-2. Most Salmonella isolates (94.1%) produced blaCTX-M-15. Two broiler chicken farms were simultaneously positive for blaCMY-2- and blaCTX-M-15-harboring E. coli and Salmonella isolates. Whole-plasmid sequence for the transferable plasmid harboring blaCMY-2 showed genomic diversity of the plasmids from Salmonella and E. coli sourced from the same chicken. The genetic arrangement of blaCMY-2 in Salmonella was IS1294b-ΔISEcp1-blaCMY-2-blc-sugE and ISEcp1-blaCMY-2-blc-sugE in E. coli located on multi-host plasmids of IncI1-pST-2 and IncI1-pST-12. In conclusion, the study illustrates the genetic diversity of ESC resistance determinants in E. coli and Salmonella in a single chicken. Considering the possibility of transmission of antimicrobial resistance to humans through the food chain, a large reservoir of ESC resistance in chicken which could be co-infected with ESC-resistant E. coli and Salmonella poses a serious risk of potential transmission of ESC-resistant E. coli and Salmonella, and their transferable ESC resistant gene, to human simultaneously.

In machine learning field, feature selection can be treated as a bi-objective optimization problem. It is reported that a decomposition-based evolutionary multi-objective optimization algorithm (i.e., MOEA/D-STAT) has good diversity performance when coping with feature selection. However, feature selection is also a time-consuming problem considering a large dataset it involves. The computation time can be easily reduced by introducing the parallelization into MOEA/D-STAT, thanks to the decomposition idea of MOEA/D. To the best of our knowledge, this is the first attempt to implement the parallelization of MOEA/D-STAT for feature selection. In this paper, we consider both master-slave models and island models, which are two different approaches of parallelization. In the master-slave models, different offspring assignment mechanisms are considered. In the island models, different island size specification mechanisms are examined. Our experimental results show that the master-slave models can achieve higher speedup and better performance than the island models.

In general, performance comparison results of optimization algorithms depend on the parameter specifications in each algorithm. For fair comparison, it may be needed to use the best specifications for each algorithm instead of using the same specifications for all algorithms. This is because each algorithm has its best specifications. However, in the evolutionary multi-objective optimization (EMO) field, performance comparison has usually been performed under the same parameter specifications for all algorithms. Especially, the same population size has always been used. In this paper, we discuss this practice from a viewpoint of fair comparison of EMO algorithms. First, we demonstrate that performance comparison results depend on the population size. Next, we explain a new trend of performance comparison where each algorithm is evaluated by selecting a pre-specified number of solutions from the examined solutions (i.e., by selecting a solution subset with a pre-specified size). Then, we discuss the selected subset size specification. Through computational experiments, we show that performance comparison results do not strongly depend on the selected subset size while they depend on the population size.

The importance of employee green behavior (EGB) to an enterprise’s green development goal is increasingly emphasized in many industries. However, to date promoting EGB through interaction, namely between individuals and organizations, has not been a central concern. Therefore, from the perspective of the person-organization fit, this study considers the psychological distance between employees and the organization as a moderating variable, exploring the mechanisms of values fit, needs-supplies fit, and demands-abilities fit on green behaviors as within and outside the scope of employee responsibility. After collecting the results of questionnaires from 412 employees, our hypotheses were tested using the Structural Equation Model (SEM). The results show that (1) person-organization fit can effectively promote EGB in the workplace. However, different types of person-organization fit have different influencing paths and effect-strengths on employees’ task-related green behavior and proactive green behavior. (2) Values fit has the greatest incentive effect on EGB, followed by demands-abilities fit, while needs-supplies fit promotes only eco-helping behavior. (3) Psychological distance has a significant moderating effect on the relationship between the person-organization fit and EGB. The effect of person-organization fit on EGB is enhanced when employees are close with less emotional distance, while the effect is weakened in the case of close expectation distance. Finally, this study provides suggestions for enterprise managers providing ways to motivate EGB through the selection and allocation of human resources.

Most real-world systems evolve over time in which entities and the interactions between entities are added and removed—new entities or relationships appear and old entities or relationships vanish. While most network evolutionary models can provide an iterative process for constructing global properties, they cannot capture the evolutionary mechanisms of real systems. Link prediction is hence proposed to predict future links which also can help us understand the evolution law of real systems. The aim of link prediction is to uncover missing links from known parts of the network or quantify the likelihood of the emergence of future links from current structures of the network. However, almost all existing studies ignored that old nodes tend to disappear and new nodes appear over time in real networks, especially in social networks. It is more challenging for link prediction since the new nodes do not have pre-existing structure information. To solve the temporal link prediction problems with new nodes, here we take into account nodal attribute similarity and the shortest path length, namely, ASSPL, to predict future links with new nodes. The results tested on scholar social network and academic funding networks show that it is highly effective and applicable for ASSPL in funding networks with time-evolving. Meanwhile, we make full use of an efficient parameter to exploit how network structure or nodal attribute has an impact on the performance of temporal link prediction. Finally, we find that nodal attributes and network structure complement each other well for predicting future links with new nodes in funding networks.

Deliberative democracy is not only the Chinese people's summary of China's historical experience, but also the urgent requirement of building China's political civilization. Strengthening democratic construction includes the establishment of inner-party democracy and the construction of people's democracy, and the promotion of people's democracy by inner-party democracy. The democratic politics of the consultative party and the deliberative democracy of the masses are an important part of people's democracy. In order to strengthen the construction of the ruling party, we must strengthen the construction of consultative democracy and promote the further development of people's democracy. This paper mainly analyzes the relationship between the construction of the Communist Party and deliberative democracy.

High-dose steroids, the first-line therapy for acute attacks in neuromyelitis optica spectrum disorder (NMOSD), were ineffective in a proportion of NMOSD attacks. This study aimed to explore possible predictors of high-dose steroid resistance. Demographics and disease characteristics of acute attacks were compared between those who responded to high-dose intravenous methylprednisolone (IVMP) and those resistant to IVMP. In total, 197 attacks in 160 patients were identified in our NMOSD registry. Compared with responders, attacks resistant to high-dose steroids tended to have a higher proportion of previous history of immunosuppressive use (25.5 vs. 15.5%, p = 0.080). Significantly higher levels of proteins in the cerebrospinal fluid (CSF) were found in non-responders than in responders [485.5 (388–656) vs. 387 (291.5–532) mg/L, p = 0.006]. More active lesions were found in the brain stem of non-responders (8 attacks in 55, 14.5%), especially in the pons (7.3%) and medulla (14.5%), as opposed to responders (7 patients in 142, 4.9%). Multivariable logistic regression showed that resistance to high-dose steroid treatment was associated with previous immunosuppressant use [odds ratio (OR), 2.31; 95% confidence interval (CI) 1.002–5.34, p = 0.049], CSF protein level above 450 mg/L (OR 3.42, 95% CI 1.72–6.82, p < 0.001), and active lesions in the brainstem (OR 3.80, 95% CI 1.17–12.32, p = 0.026). In conclusion, NMOSD patients with previous use of immunosuppressants, higher levels of CSF protein, and active lesions in the brainstem are more likely to respond poorly to high-dose IVMP alone during an acute attack.

The methods for accurately fusing medium- and high-spatial-resolution satellite reflectance are vital for monitoring vegetation biomass, agricultural irrigation, ecological processes and climate change. However, the currently existing fusion methods cannot accurately capture the temporal variation in reflectance for heterogeneous landscapes. In this study, we proposed a new method, the spatial and temporal reflectance fusion method based on the unmixing theory and a fuzzy C-clustering model (FCMSTRFM), to generate Landsat-like time-series surface reflectance. Unlike other data fusion models, the FCMSTRFM improved the similarity of pixels grouped together by combining land cover maps and time-series data cluster algorithms to define endmembers. The proposed method was tested over a 2000 km2 study area in Heilongjiang Provence, China, in 2017 and 2018 using ten images. The results show that the accuracy of the FCMSTRFM is better than that of the popular enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM) (correlation coefficient (R): 0.8413 vs. 0.7589; root mean square error (RMSE): 0.0267 vs. 0.0401) and the spatial-temporal data fusion approach (STDFA) (R: 0.8413 vs. 0.7666; RMSE: 0.0267 vs. 0.0307). Importantly, the FCMSTRFM was able to maintain the details of temporal variations in complicated landscapes. The proposed method provides an alternative method to monitor the dynamics of land surface variables over complicated heterogeneous regions.

The hypervolume contribution is an important concept in hypervolume-based evolutionary multi-objective optimization algorithms. It describes the loss of the hypervolume when a solution is removed from the current population. Since its calculation is #P-hard in the number of objectives, its approximation is necessary for many-objective optimization problems. Recently, an R2-based hypervolume contribution approximation method was proposed. This method relies on a set of direction vectors for the approximation. However, the influence of different direction vector generation methods on the approximation quality has not been studied yet. This paper aims to investigate this issue. Five direction vector generation methods are investigated, including Das and Dennis's method (DAS), unit normal vector method (UNV), JAS method, maximally sparse selection method with DAS (MSS-D), and maximally sparse selection method with UNV (MSS-U). Experimental results suggest that the approximation quality strongly depends on the direction vector generation method. The JAS and UNV methods show the best performance whereas the DAS method shows the worst performance. The reasons behind the results are also analyzed.

Since mid-March 2020, global COVID-19 pandemic has experienced an exponential growth in process from sporadic to sudden outbreaks. This paper selects the 8-day surge data of daily cases, death and recovery rates (March 19-26, 2020) from 18 countries with severe pandemic situation to discuss the impact of 9 factors of both socioeconomic and natural on the pathogen outbreak. Moreover, the paper also elaborates analysis and comparison of relatively slow 4-week (February 1-29, 2020) data of China’s surge cases to determine the relationship between social and natural factors and on the spread of pandemic, which provides an effective reference for delaying and controlling the pandemic development.

Salmonella is a type of zoonotic bacteria that represents an economic and public health concern worldwide. Difficulties in sample collection from migratory birds mean little is known about their importance as a reservoir of Salmonella. The present study evaluated the prevalence and potential risk of Salmonella enterica in migratory birds. From 2012–2017, 3661 cloacal swabs from migratory birds were collected in South Korea and tested to isolate S. enterica. Strains were tested for antimicrobial resistance and the presence of virulence genes. Thirty-six S. enterica strains, including S. enterica serovar Typhimurium (n = 19), S. Berta (n = 16), and S. Virchow (n = 1), were isolated from 34 birds. Two migratory birds were simultaneously co-infected with two serotypes. S. enterica was isolated from the Mallard duck, Northern pintail, Eurasian wigeon, Spot-billed duck, Eastern great egret, and Intermediate egret. S. Virchow was resistant to ciprofloxacin, with a point mutation (Ser-83-Phe) in the gyrA gene. Ten virulence genes were detected; sixteen strains were positive for all ten virulence genes. Salmonella was isolated from different migratory bird species and geographic locations with up to 100 % similarity of PFGE type. Eight S. Virchow strains taken from migratory birds, poultry farms, and chicken meat showed the same PFGE type. Salmonella was transmitted across species, space, and time in migratory birds. These birds may play a role in the dispersal of pathogenic and antimicrobial-resistant Salmonella and sporadic Salmonella infections in poultry; therefore, they may represent a direct or indirect public health threat.

Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R2) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m2 and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R2 by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.

Indoleamine 2,3-dioxygenase 1 (IDO1), which degrades the essential amino acid tryptophan, exerts immunosuppressive functions and serves a crucial role in multiple types tumor progression, including non-small-cell lung cancer (NSCLC) and melanoma. Recent studies have reported that T-cell exhaustion is increased during tumor progression, which impairs the antitumor immune response. However, the association between IDO1 and T-cell exhaustion during tumor progression remains unknown. The present study evaluated the effect of IDO1 on T-cell exhaustion in lung cancer mice. The present study demonstrated that IDO1 knockdown by small interfering RNA in the LLC cell line inhibited T-cell exhaustion. Furthermore, the role of IDO1 in T-cell exhaustion during lung cancer progression was determined in an in vivo mouse model using IDO1 short hairpin RNA (shRNA). The results demonstrated that inhibition of IDO1 activity by shRNA administration in vivo significantly delayed the onset and growth of tumors. In addition, the expression levels of the inhibitory receptors programmed death-1 (PD-1) and B and T lymphocyte attenuator (BTLA) were increased in T-cells from the lung tumor-bearing mice, whereas interleukin-2 (IL-2) and tumor necrosis factor-alpha (TNF-α) levels in serum were decreased compared with the control mice. However, no difference in the absolute number of T cells was observed, including CD4+ and CD8+ T cells. In addition, IDO1 knockdown by shRNA inhibited T-cell exhaustion in lung tumor-bearing mice, which was characterized by decreased expression of PD-1 and BTLA on T cells. By contrast, IL-2 and TNF-α levels in serum were increased in IDO1-shRNA-treated mice. By using a shRNA approach, the present study demonstrated that IDO1 activity may be involved in tumor growth, and that IDO1 silencing may inhibit tumor progression by impeding the process of T-cell exhaustion.

The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model (FCMSTRFM) and multisource Vegetation index (VI) data spatial and temporal fusion model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.The rice area is mapped by spectral correlation similarity (SCS) between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class, the extraction results are verified by two methods: ground sample and Google Earth image. high spatial and temporal resolution Leaf area index (LAI) that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI, EVI and LAI. The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield. Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land (SEBAL). Based on the data, the evapotranspiration, LAI and yield of rice, obtained by remote sensing methods, rice water growth function is established by Jensen, Blank, Stewart and Singh model.

The latent heat flux (LE) governs the associated heat flux from the interactions between the land surface and its atmosphere and plays an important role in the surface water and energy balance. The Qilian Mountains is the largest marginal mountain system in the northeast of the Qinghai-Tibet Plateau. An accurate representation of spatio-temporal patterns of LE over Qilian Mountains is essential in many terrestrial biosphere, hydrosphere, and atmosphere applications. Various satellite-derived LE products have been widely used to estimate terrestrial LE, yet each individual LE product exhibits large discrepancies. To reduce uncertainties from individual product and improve terrestrial LE estimation over Qilian Mountains, we produced five satellite-derived LE products using traditional algorithms based on Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI, LAI products and China Meteorological Forcing Dataset (CMFD), and implemented the fusion of these five LE products using Extremely Randomized Trees (Extra-Trees) combining information from ground-based measurements. A validation using ground-based measurements was applied at different plant function types and the validation results demonstrate that the fusion product using Extra-Trees outperformed all the LE products used in the fusion method. Comparing with three other machine learning fusion models such as Gradient Boosting Regression Tree (GBRT), Random Forest (RF) and Gaussian Process Regression (GPR), Extra-Trees exhibits the best performance in terms of both training and validation accuracy. This fusion LE product also outperformed when compared against two state-of-the-art global LE products such as Global Land Surface Satellite (GLASS) and Moderate Resolution Imaging Spectroradiometer (MODIS). The fusion LE product showed improvements in the linear correlation, bias and RMSE at different plant function types. Our results suggest that the fusion method using Extra-Trees could be successfully applied to other region and to enhance the estimation of other hydrometeorological variables.