Stringent Data Filtering Research Articles

Influence of social media communication on consumer purchase decisions: do luxury hotels value perceived brand authenticity, prestige, and familiarity?

PurposeThis paper aims to examine the influence of social media communications, particularly firm-generated content (FGC) and consumer-generated content (CGC) on predicting consumer purchase decisions (CPD) through the lens of perceived brand authenticity (PBA). This paper also investigates the moderating influence of brand prestige (BP) and brand familiarity in the luxury hotel sector.Design/methodology/approachThis study collected data from 390 consumers who were regularly using social media platforms, traveled frequently and stayed in luxury hotels. Following stringent data filtering, 371 responses were analyzed via structural equation modeling.FindingsThe findings indicate that FGC and CGC significantly strengthened PBA. However, CGC was the effective driver that directly influenced CPD. Likewise, PBA directly and indirectly substantially impacted CPD. Finally, BP’s direct and moderating effects significantly influenced CPD in the luxury hotel sector.Originality/valueThis novel study contributes to signaling theory, social media communications and branding literature in the luxury hotel sector.

Genetic structure and relatedness of brown trout (Salmo trutta) populations in the drainage basin of the Ölfusá river, South-Western Iceland.

Lake Þingvallavatn in Iceland, a part of the river Ölfusá drainage basin, was presumably populated by brown trout soon after it formed at the end of the last Ice Age. The genetic relatedness of the brown trout in Þingvallavatn to other populations in the Ölfusá drainage basin is unknown. After the building of a dam at the outlet of the lake in 1959 brown trout catches declined, though numbers have now increased. The aim of this study was to assess effects of geographic isolation and potential downstream gene flow on the genetic structure and diversity in brown trout sampled in several locations in the western side of the watershed of River Ölfusá. We hypothesized that brown trout in Lake Þingvallavatn constituted several local spawning populations connected by occasional gene flow before the damming of the lake. We also estimated the effective population size (NE) of some of these populations and tested for signs of a recent population bottleneck in Lake Þingvallavatn. We sampled brown trout inhabiting four lakes and 12 rivers within and near the watershed of River Ölfusá by means of electro- and net- fishing. After stringent data filtering, 2,597 polymorphic loci obtained from ddRADseq data from 317 individuals were ascertained as putative neutral markers. Overall, the genetic relatedness of brown trout in the Ölfusá watershed reflected the connectivity and topography of the waterways. Ancestry proportion analyses and a phylogenetic tree revealed seven distinct clusters, some of which corresponded to small populations with reduced genetic diversity. There was no evidence of downstream gene flow from Lake Þingvallavatn, although gene flow was observed from much smaller mountain populations. Most locations showed low NE values (i.e., ~14.6 on average) while the putative anadromous trout from River Sog and the spawning population from River Öxará, that flows into Lake Þingvallavatn, showed notably higher NE values (i.e., 71.2 and 56.5, respectively). No signals of recent population bottlenecks were detected in the brown trout of Lake Þingvallavatn. This is the first time that the genetic structure and diversity of brown trout in the watershed of River Ölfusá have been assessed. Our results point towards the presence of a metapopulation in the watershed of Lake Þingvallavatn, which has been influenced by restoration efforts and is now dominated by a genetic component originated in River Öxará. Many of the locations studied represent different populations. Those that are isolated in headwater streams and lakes are genetically distinct presenting low genetic diversity, yet they can be important in increasing the genetic variation in downstream populations. These populations should be considered for conservation and direct management.

Prediction of progressive collapse resistance of RC frames using deep and cross network model

Compressive membrane/arch action at small deformations and resistance evolution path at large deformations are two key factors in determining whether an RC frame structure can resist progressive collapse. In this study, two deep learning models based on deep and cross network (DCN) are developed to predict the progressive collapse resistance of RC frames. DCN model I is constructed to predict the compressive membrane/arch action resistance, while DCN model II is developed to predict the resistance displacement curve considering the dynamic effect. 464 records regarding structural collapse were collected through extensive literature review and stringent data filtering. They were randomly divided into 80% and 20% for training and testing the two models, to which the column removal scenario, with or without slab, boundary condition, beam net span, beam section, beam reinforcement ratio, and material property were selected as input features. Moreover, Shapley additive explanations (SHAP) method was introduced to interpret the proposed DCN model. The results indicated that the performance of DCN model I in predicting the compressive membrane/arch action resistance was satisfactory with an MAPE value of 10.66% and an R2 value of 0.9799, respectively, while being more accurate than the existing yield line theory and Park’s compressive arch calculation model. The proposed DCN model II can further capture the evolution of the dynamic resistance of RC frames accurately. The two DCN models have been deployed online to public for application.

Short-Term Metformin Treatment Enriches Bacteroides dorei in an Obese Liver Steatosis Zucker Rat Model.

Obesity is the leading cause of health-related diseases in the United States and World. Previously, we reported that obesity can change gut microbiota using the Zucker rat model. Metformin is an oral anti-hyperglycemic agent approved by the FDA to treat type 2 diabetes (T2D) in adults and children older than 10 years of age. The correlation of short-term metformin treatment and specific alterations to the gut microbiota in obese models is less known. Short-term metformin has been shown to reduce liver steatosis. Here we investigate the effects of short-term metformin treatment on population of gut microbiota profile in an obese rat model. Five week old obese (n = 12) female Zucker rats after 1 week of acclimation, received AIN-93 G diet for 8 weeks and then rats were randomly assigned into two groups (6 rats/group): (1) obese without metformin (ObC), or (2) obese with metformin (ObMet). Metformin was mixed with AIN-93G diet at 1,000 mg/kg of diet. Rats were weighed twice per week. All rats were sacrificed at the end of metformin treatment at 10 weeks and fecal samples were collected and kept at −80°C. Total microbial DNA was collected directly from the fecal samples used for shotgun-metagenomics sequencing and subsequently analyzed using MetaPlAn and HUMAnN. After stringent data filtering and quality control we found significant differences (p = 0.0007) in beta diversity (Aitchison distances) between the ObC vs. ObMet groups. Supervised and unsupervised analysis of the log-ratios Bacteroides dorei and B. massiliensis vs. all other Bacteroides spp., revealed that B. dorei and B. massiliensis were enriched in the ObMet group, while the remaining Bacteroides spp. where enriched in the ObC group (p = 0.002). The contributional diversity of pathways is also significantly associated by treatment group (p = 0.008). In summary, in the obese Zucker rat model, short-term metformin treatment changes the gut microbiota profile, particularly altering the composition Bacteroides spp. between ObC and ObMet.

Multi-factor and multi-level predictive models of building natural period

A comprehensive database containing data on approximately 2700 buildings and 6000 full-scale measured period samples was constructed through massive literature searching and stringent data filtering. The newly emerged maximal information coefficient method, which is suitable for large data set statistical analysis, was adopted in conjunction with Kruskal–Wallis analysis of variance to identify factors that significantly affect a building’s fundamental period. It was quantitatively verified that height, predominant structural material, and lateral-force resisting system are the three most important influencing factors. Subsequently, height was used as the dominant regression variable, and material and lateral-force resisting system were used as categorical variables, predictive models in combination with confidence intervals of the fundamental period are provided for multi-factors, including four material types and three structural types. In addition, multi-level empirical formulas of the natural period in other five modes (two translational and three torsional) are provided on the basis of the regression results of the fundamental period. All these predictive models can effectively reflect the tendency of the median and the rational scope of variability of the natural period of buildings.

Comparing fish prey diversity for a critically endangered aquatic mammal in a reserve and the wild using eDNA metabarcoding

Using environmental DNA (eDNA) metabarcoding, we compared fish diversity in two distinct water bodies within the Yangtze River Basin with known populations of the critically endangered Yangtze finless porpoise (Neophocaena asiaeorientalis; YFP): the Tian-e-Zhou Reserve and Poyang Lake. We aimed to create a fish surveying tool for use in the Yangtze River Basin, while also gaining a better understanding of the prey distribution and diversity within two of the remaining strongholds of YFP. 16S rRNA universal primers were developed to amplify fish eDNA. After high-throughput sequencing and stringent data filtering, we identified a total of 75 fish species (6 orders, 9 families, 57 genera) across seasons and regions. Nine of the 75 fish species were among the 28 known YFP prey species, three of which were detected in all water samples. Our eDNA metabarcoding identified many species that had been previously captured using traditional netting practices, but also numerous species not previously collected in these water bodies. Fish diversity was higher in Poyang Lake than in Tian-e-Zhou Reserve, as well as higher in the spring than in summer. These methods provide a broadly applicable tool to quantify fish diversity and distributions throughout the Yangtze River Basin, and to inform conservation strategies of YFP.

Cross-linking Proteomics Indicates Effects of Simvastatin on the TLR2 Interactome and Reveals ACTR1A as a Novel Regulator of the TLR2 Signal Cascade

Toll-like receptor 2 (TLR2) is a pattern recognition receptor that, upon ligation by microbial molecules, interacts with other proteins to initiate pro-inflammatory responses by the cell. Statins (hydroxymethylglutaryl coenzyme A reductase inhibitors), drugs widely prescribed to reduce hypercholesterolemia, are reported to have both pro- and anti-inflammatory effects upon cells. Some of these responses are presumed to be driven by effects on signaling proteins at the plasma membrane, but the underlying mechanisms remain obscure. We reasoned that profiling the effect of statins on the repertoire of TLR2-interacting proteins might provide novel insights into the mechanisms by which statins impact inflammation. In order to study the TLR2 interactome, we designed a coimmunoprecipitation (IP)-based cross-linking proteomics study. A hemagglutinin (HA)-tagged-TLR2 transfected HEK293 cell line was used to precipitate the TLR2 interactome upon cell exposure to the TLR2 agonist Pam3CSK4 and simvastatin, singly and in combination. To stabilize protein interactors, we used two different chemical cross-linkers with different spacer chain lengths. Proteomic analysis revealed important combinatorial effects of simvastatin and Pam3CSK4 on the TLR2 interactome. After stringent data filtering, we identified alpha-centractin (ACTR1A), an actin-related protein and subunit of the dynactin complex, as a potential interactor of TLR2. The interaction was validated using biochemical methods. RNA interference studies revealed an important role for ACTR1A in induction of pro-inflammatory cytokines. Taken together, we report that statins remodel the TLR2 interactome, and we identify ACTR1A, a part of the dynactin complex, as a novel regulator of TLR2-mediated immune signaling pathways.

Abstract T P347: Metabolomic Analysis of PFO-Related Stroke Shows Immediate and Persistent Decrease of Homocysteine Post PFO Closure

Background: PFO, an independent stroke risk factor, enables direct mixing of venous and arterial circulation. As we have found previously, PFO serves not only as a conduit for venous clots, but also enables harmful factors such as serotonin (5HT) to avoid pulmonary filtration to remain within circulation at elevated levels, potentially contributing to a prothrombotic state. We hypothesize that in addition to 5HT, other factors may also be involved. In the context of endovascular PFO closure, a bedside model to understand PFO circulatory signaling, we performed a full metabolomic profile of mediators that may respond to PFO closure. Method: Non-migraine stroke patients were recruited in accordance with IRB approval, plasma was sampled from left and right atria pre and post closure and also from venous blood 3 months post PFO closure. A discovery metabolite screening was performed in 14 patients who underwent PFO closure, and analysis was performed using one-way ANOVA. Result: After stringent data filtering (537 metabolites, one-way ANOVA, p-value <0.01), we identified significant changes in a panel of small molecules after PFO closure - the most prominent change being in homocysteine (HCY). While relative HCY levels (expressed as peak area) in the left atrium (LA) and right atrium (RA) were comparable pre-closure (pre-LA: 5.61 ± 0.09; pre-RA: 5.30 ± 0.48), HCY immediately decreased in LA post PFO closure (post-LA: 4.56 ± 0.04) and HCY level remained low in peripheral venous blood at 3-month follow-up (4.57 ± 0.06; p = 0.0036). Conclusion: We found PFO closure to lower HYC immediately in left atrial (arterial) blood, and this effect persists in peripheral venous circulation at 3 months post procedure. Since high level of HYC is independently associated with stroke and heart disease, our results suggest that mechanical PFO repair may improve circulatory profile of PFO stroke patients. Studies in a larger patient cohort and validation of other important metabolites are ongoing.

Quantifying sequence proportions in a DNA‐based diet study using Ion Torrent amplicon sequencing: which counts count?

A goal of many environmental DNA barcoding studies is to infer quantitative information about relative abundances of different taxa based on sequence read proportions generated by high-throughput sequencing. However, potential biases associated with this approach are only beginning to be examined. We sequenced DNA amplified from faeces (scats) of captive harbour seals (Phoca vitulina) to investigate whether sequence counts could be used to quantify the seals' diet. Seals were fed fish in fixed proportions, a chordate-specific mitochondrial 16S marker was amplified from scat DNA and amplicons sequenced using an Ion Torrent PGM™. For a given set of bioinformatic parameters, there was generally low variability between scat samples in proportions of prey species sequences recovered. However, proportions varied substantially depending on sequencing direction, level of quality filtering (due to differences in sequence quality between species) and minimum read length considered. Short primer tags used to identify individual samples also influenced species proportions. In addition, there were complex interactions between factors; for example, the effect of quality filtering was influenced by the primer tag and sequencing direction. Resequencing of a subset of samples revealed some, but not all, biases were consistent between runs. Less stringent data filtering (based on quality scores or read length) generally produced more consistent proportional data, but overall proportions of sequences were very different than dietary mass proportions, indicating additional technical or biological biases are present. Our findings highlight that quantitative interpretations of sequence proportions generated via high-throughput sequencing will require careful experimental design and thoughtful data analysis.