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Purpose Drawing on solidarity-conflict model, expectancy disconfirmation theory and bottom-up spillover theory, this study aims to explore how intergenerational conflict is linked to parents’ subjective well-being through the serial mediating effects of family intimacy and travel satisfaction, self-efficacy and travel satisfaction. Design/methodology/approach Based on a survey of 411 senior parents who have family travel experience, a structural equation modeling was performed to test the hypotheses. Findings This study demonstrated the negative impacts of intergenerational conflict on parents’ travel satisfaction and subjective well-being and further reveals two serial mediators through individual level and family level (i.e. self-efficacy → travel satisfaction; family intimacy → travel satisfaction). Practical implications The findings of this research generate valuable practical implications for family members and destination organizations. Adult children should consider different generations’ needs to choose tourism products, help parents reduce tension and negative emotions about unusual environments and build confidence. The destination organizations need to design family interaction projects with a sense of rituals to enhance family intimacy. Originality/value This study focusing on the intergenerational conflict of adult children traveling with parents, empirically examines the negative impacts of intergenerational conflict on parents’ subjective well-being, uncovers the detrimental effects of family travel; it breaks the stereotype that family travel is always full of joy, enriching research on intergenerational relationships and family travel.

Purpose Drawing on social learning theory and conservation of resources theory, this study aims to investigate how servant leadership (SL) is linked to employees’ pro-customer deviance (PCD) through the serial mediating effects of perceived organizational support for creativity (POS) and creative self-efficacy (CSE), work autonomy (WA) and CSE. Design/methodology/approach This study used an online questionnaire survey platform to accurately distribute the questionnaire to the target population. Data were collected from 439 frontline employees working in hotels. The data were analyzed with a structural equation modeling approach to identify the complex relationship. Findings Using an online survey, this study demonstrated the significant positive effect of SL on PCD and further revealed the two serial mediating paths (POS → CSE; WA → CSE) of the SL effect. Practical implications The findings of this research generate valuable implications for practitioners and managers. Managers need to be aware of the objectivity and universality of PCD in service delivery scenarios and fully understand how their leadership style influences the internal motivation and external performance of employees engaged in this behavior. Originality/value This study makes a prominent contribution to the hospitality literature by focusing on PCD. This study enriches the research on the antecedents of PCD, constructs a cross-level multipath mechanism model of PCD in the context of SL and reveals the rationalization process and nature of employees’ PCD.

Changes in the amino acid sequence of proteins resulting from nonsynonymous variants in the genome, can have significant effects on protein folding, stability, dynamics, and function, which may ultimately lead to diseases. The analysis of large sets of disease associated variants is a common approach for the study of pathogenic mechanisms.In-silicomutagenesis experiments based on wildtype structures of target proteins are a common approach to this aim, however these do not account for the effect of variants on folding and might not accurately reflect conformational changes. A growing number of experimentally solved protein structures harbouring disease-associated mutations, including single amino acid variants, are deposited in the worldwide Protein Data Bank (PDB). Nevertheless, identifying high-quality structures for specific missense variants of interest remains challenging due to the growing number of deposited protein structures in the PDB, and the lack of a dedicated interface and annotation system to search and retrieve mutant protein structures. As a result, mutant protein structures in the PDB are a powerful source of information which is largely underused. To address these shortcomings, we have developed Mutafy, a publicly available webserver to identify high quality mutant protein structures. Given input human genes, the webserver finds structures of the corresponding coded wildtype proteins and their available solved mutants, selects high quality structures, annotates them with information from biomedical databases to favour their interpretation and selection, and allows for the interactive exploration of the results and 3D visualisation. Mutafy is publicly available without requiring user registration athttps://mutafy.rosalind.kcl.ac.uk.

ObjectiveBased on network pharmacology, the response of Qing-tong-hua-yu Decoction (QTHY) to the regulation of EGFR/MAPK signaling cascade in cerebral ischemia-reperfusion injury was discussed and the possible mechanism of the protective effect of QTHY on the cerebral ischemia-reperfusion injury was studied.MethodsA compound-target disease-function-pathway network was established and analyzed based on the network pharmacology approach used in Chinese medicine. The correlation, which is between effect of the components of QTHY Decoction against CI/RI with EGFR/MAPK signalling cascade response, was observed. And then the degree of neurological deficits in each group was assessed after cerebral ischemia for 2 hours and reperfusion for 3 hours, 24 hours, 3 days and 7 days. Expression levels of EGFR and p44/42MAPK in ischemic brain tissue at different time points in various groups of rats were tested by Western bolt (WB), real-time quantitative PCR (RT-qPCR) and immunohistochemistry (IHC).ResultsNetwork pharmacology analysis revealed that QTHY-mediated treatment involved 439 key targets, in which the effect of QTHY groups against CI/RI was associated with EGFR/MAPK signaling cascade. QTHY treatment reduced neurological deficit scores and improved ischemic changes in rats. In addition, QTHY promoted EGFR and p44/42MAPK expression in the SVZ through the EGFR/MAPK signaling cascade, with varying degrees of improvement at different time points.ConclusionQTHY can better improve cerebral ischemia injury in CI / RI rats and exert the neuroprotective effect of cerebral ischemia-reperfusion injury. This may be related to the potential of QTHY to activate the EGFR / MAPK signaling cascade, which is consistent with the results of network pharmacology analysis.

Based on the Stimulus-Organism-Response framework, this research sought to examine tourists’ environmental concern and destination identification as mediating variables between residents’ environme...

Our previous study had identified ciclopirox (CPX) as a promising lead compound for treatment of ischemic stroke. To find better neuroprotective agents, a series of N-hydroxypyridone derivatives based on CPX were designed, synthesized, and evaluated in this study. Among these derivatives, compound 11 exhibits significant neuroprotection against oxygen glucose deprivation and oxidative stress-induced injuries in neuronal cells. Moreover, compound 11 possesses good blood-brain barrier permeability and superior antioxidant capability. In addition, a complex of compound 11 with olamine-11·Ola possesses good water solubility, negligible hERG inhibition, and superior metabolic stability. The in vivo experiment demonstrates that 11·Ola significantly reduces brain infarction and alleviates neurological deficits in middle cerebral artery occlusion rats. Hence, compound 11·Ola is identified in our research as a prospective prototype in the innovation of stroke treatment.

The point bar is one of the most important reservoirs in a meandering depositional system, and accurately building a 3D architecture model for point bars is crucial to predict hydrocarbon distribution within the reservoir. Unfortunately, we can only obtain a qualitative description about the internal architecture of the point bar due to the limited information or the low resolution of available data (such as reflection seismic data). To build a 3D prototype point bar reservoir model, we analyze the architecture of point bars by integrating high-resolution ground penetrating radar (GPR) data and modern deposition. We found that our GPR data have five main reflection patterns (GPR facies), and GPR facies can be used to relate with architectural elements (the depositional facies and geobodies within depositional facies). The concave-down GPR facies is usually related to the abandoned channel. The continuous, subhorizontal, subparallel GPR facies is commonly related with lateral-accretion sand bodies within the point bar. The multiple stacked small-scale, discontinuous reflections GPR facies is interpreted to be shale drapes within the point bar. We further analyzed the geometry parameters of the identified channels. We found that the nonsymmetric [Formula: see text] of abandoned channel near the channel axis is related to the ratio between the curvature of channel radius [Formula: see text] and channel width [Formula: see text] ([Formula: see text]). Finally, we built two 3D channel reservoir models and our models could provide useful guidance for the architecture analysis of buried meandering fluvial reservoirs.

Graph neural networks (GNNs) constitute a class of deep learning methods for graph data. They have wide applications in chemistry and biology, such as molecular property prediction, reaction prediction, and drug-target interaction prediction. Despite the interest, GNN-based modeling is challenging as it requires graph data preprocessing and modeling in addition to programming and deep learning. Here, we present Deep Graph Library (DGL)-LifeSci, an open-source package for deep learning on graphs in life science. Deep Graph Library (DGL)-LifeSci is a python toolkit based on RDKit, PyTorch, and Deep Graph Library (DGL). DGL-LifeSci allows GNN-based modeling on custom datasets for molecular property prediction, reaction prediction, and molecule generation. With its command-line interfaces, users can perform modeling without any background in programming and deep learning. We test the command-line interfaces using standard benchmarks MoleculeNet, USPTO, and ZINC. Compared with previous implementations, DGL-LifeSci achieves a speed up by up to 6×. For modeling flexibility, DGL-LifeSci provides well-optimized modules for various stages of the modeling pipeline. In addition, DGL-LifeSci provides pretrained models for reproducing the test experiment results and applying models without training. The code is distributed under an Apache-2.0 License and is freely accessible at https://github.com/awslabs/dgl-lifesci.

Alpha-synuclein (α-syn), as a highly soluble presynaptic protein expressed in the brain, plays an important role in recycling synaptic vesicles and regulating the synthesis, storage, and release of neurotransmitters. Accumulation of α-syn in Lewy bodies and Lewy neurites is the pathological hallmark of Parkinson’s disease (PD), so inhibition of α-syn aggregation may provide a novel approach for treating PD. In this study, the 3D structure of α-syn was downloaded from Protein Data Bank (PDB ID: 2N0A). A ligand-based pharmacophore model was conducted on a set of 43 diverse α-syn ligands, and the results suggested that two hydrogen-bond acceptors, one hydrophobic group, and two aromatic rings were significant to the inhibition of α-syn aggregation. A ligand-based 3D-QSAR model was also established with good statistical significance (R2 = 0.920) and excellent predictive ability (Q2 = 0.752). Novel indolinone derivatives were designed and synthesized based on the pharmacophore model. Subsequently, the 3D-QSAR model was used to predict the inhibitory activities towards α-syn aggregation, and the actual inhibitory activities were evaluated by thioflavin-T assay in vitro with the best inhibitory activity reaching 45.08%. The fitting results indicated that the built pharmacophore and 3D-QSAR models provided better reliability and accuracy for compound modification and prediction of the activity thereof. A ligand-based pharmacophore modeling and 3D-QSAR study have been performed on a set of 43 diverse ligands for α-synuclein for the first time. Based on the best pharmacophore modeling, novel indolinone derivatives were designed and synthesized, and the inhibitory activities for α-synuclein aggregation were evaluated by thioflavin-T assay in vitro, which preliminary indicated that five pharmacophore sites (two hydrogen bond acceptors (A), a hydrophobic group (H), and two aromatic rings (R)) in compounds contribute to the inhibitory activities. In the study, the built pharmacophore modeling and 3D-QSAR provided better reliability and accuracy for compound modification and prediction of the activity thereof.

Understanding the hierarchical architectural elements of fluvial sand bodies is important for planning their development strategy and to enhance oil recovery. Red-Green-Blue (RGB) blending of multiple seismic attributes and forwarding seismic modelling are commonly used in the analysis of compound sand bodies. However, RGB blending of multiple seismic attributes can only qualitatively describe the boundaries and thickness of sand bodies. The forward seismic modelling techniques previously documented in the literature are not effective when depicting the geometry of, and stacking relationships between, sand bodies (i.e., reservoir compartmentalisation). Hence, we propose in this work a new workflow that combines fused spectral-decomposition seismic attributes (SDSAs) and forwarding seismic modelling to quantitatively predict sand thickness, and to characterise stacking relationships between sand bodies. First, we employ a Support Vector Machine (SVM) algorithm to fuse high, middle, and low frequency components (attributes) of seismic data so as to quantitatively predict the thickness of sand bodies. Second, we define the seismic waveform response patterns corresponding to the typical conceptual stacked sand bodies. With the constraints of waveform patterns and predicted sand thickness (fused SDSAs), the geometry and stacking relationships of the sand bodies are characterised by forward seismic modelling. To illustrate the effectiveness of our proposed workflow, we apply it to the Neogene Minghuazhen Formation (Nm) of the QHD 32–6 oil field, Bohai Bay Basin, China. We define five architectural elements of a meandering fluvial reservoir by analysing the hierarchy of sand bodies using our workflow. The predicted sand bodies in this workflow were further proven by horizontal drilling and production data.

Carbon capture using an ionic liquid as the solvent has been considered to be an effective way to reduce CO2 emissions. This study aimed to investigate ionic liquid (IL)-based carbon dioxide capture and storage processes compared with the MEA-based process from a power plant’s flue gas. Ionic liquid 1-Butyl-3-methylimidazolium Bis(trifluoromethanesulfonyl) ([bmim][Tf2N]) was used as the solvent. The COSMO-SAC model was selected as the property method for all of the simulations. Based on sensitivity analysis optimization, the optimal process was obtained and the energy saving ratio (ESR) and primary cost saving ratio (PCS) were calculated. The results showed that the ionic liquid-based process performed better than the process using traditional organic solvents monoethanol-amine (MEA) in terms of cost. Due to the non-volatility of ionic liquids, two flash tanks were used instead of the solvent recovery stripper. The ionic liquid process led to 30.01% savings on energy consumption and 29.99% savings on the primary cost. The specific electricity of the CO2 storage was 237 kWh/t (CO2) and 124 kWh/t (CO2) in the two processes. Because the output pressure of CO2 from the IL-based process was lower than that from the MEA-based process, more energy consumption was required.

Our previous study had identified ciclopirox (CPX) as a promising lead compound for treatment of ischemic stroke. To find better neuroprotective agents, a series of

Epoxidation is one of the reactions in drug metabolism. Since epoxide metabolites would bind with proteins or DNA covalently, drugs should avoid epoxidation metabolism in the body. Due to the instability of epoxide, it is difficult to determine epoxidation experimentally. In silico models based on big data and machine learning methods are hence valuable approaches to predict whether a compound would undergo epoxidation. In this study, we collected 884 epoxidation data manually from various sources, and finally got 829 unique sites of epoxidation. Three types of molecular fingerprints with different lengths (1024, 2048 or 4096 bits) were used to describe the reaction sites. Six machine learning methods were used to build the classification models. The training set and test set were randomly divided into 8 : 2, and 54 models were constructed and evaluated. Four best models were selected for feature selection. The features were then chosen and verified by external validation set. The resulted optimal model had the accuracy and AUC (area under the curve) values at 0.873 and 0.944 for the test set, 0.838 and 0.987 for the external validation set, respectively. The models built in this study could accurately predict whether a compound will undergo epoxidation and which part is most susceptible to epoxidation, which is of great significance for drug design.

Short videos have been increasingly in demand recently while also holding promise for tourism marketing. Many destination management organizations have tried to make short videos by shooting the grand landscapes of a destination. However, the effective video characteristics and internal processes of potential tourists while watching such short videos remain unclear. We propose that a short video's narrative aspect can influence destination brand attitude. Accordingly, we developed this study around narrative transportation theory, and extended the antecedent and consequence variables of narrative transportation. We investigated two main questions, as follows: 1) Can narrative short videos have a positive effect in tourism marketing? and 2) What is the mechanism underlying the development of narrative transportation? The effect of narrative transportation, sense of presence, and psychological reactance in short narrative videos on destination brand attitude were investigated. We analyzed data from 408 participants using structural equation modeling analysis. The results indicated that the narrative of short videos promotes brand attitude, and that this effect is achieved by lessening individuals' psychological reactance to official advertisements and providing them with an immersive experience that delivers the sense of presence. These results not only add to the research on the use of narrative in short videos, but also indicate that short videos could be an effective marketing strategy for destinations.

Based on the deficiencies of the existing research on the credit line of supply chain enterprises, this paper taking data sharing as research perspective, established the credit model of supply chain enterprises using data sharing. Through the case study of L Corporation Group, it is found that: 1) the supply chain can integrate information advantages, increase credit line and reduce financing costs; 2) the credit line of supply chain enterprises raises with the increase of data sharing degree; 3) the existing credit models of the banks are conservative, the actual credit line of enterprises is insufficient. The credit model constructed in the paper fully considers the effects of data sharing of related transactions between enterprises on the credit line in the context of supply chain, which can provide ideas for realizing the win-win situation of maximizing the financing scale of supply chain enterprises and minimizing the risk of banks.

Brand attachment is at the core of building strong consumer brand relationships, which has important strategic significance for hotel enterprises and brands in acquiring a sustainable competitive advantage. Currently, neither the mechanisms underlying brand attachment nor the factors influencing hotel brand attachment are clearly understood. Functional, symbolic, and hedonic needs influence brand selection. This paper analyzed the effect of these three factors on consumers’ perceived value and brand attachment. The mediating effects of customer sentimental value were also evaluated. This study expands our understanding of what determines brand attachment by showing that sentimental value mediates the relationships between functional value, symbolic value, hedonic value, and brand attachment. It also has managerial implications for the hotel industry.

FEC-based multiple description coding (MDC) is an effective method for the transmission of scalable video stream over noisy network. However, given the transmission bandwith, it's time-consuming to find the optimal packet number and packet length as well as reasonably allocate bits between source coding and channel coding across the packets. In this paper, we state the optimal unequal error protection(UEP) as a two-level optimization problem and use a rate-optimal solution in initial step of searching. Then, we propose a new scheme, which combines the particle swarm optimization (PSO) with the local search(LS) algorithms, so that optimal solution can be found in more efficient way. Experimental results for the 3D set partitioning in hierarchical trees (SPIHT) coders show that the new algorithm provides close to optimal average peak signal-to-noise ratio performance and that its' running time is obviously lower than that of previously known algorithms.

This paper presents a new scheme for optimal packetization of scalable multimedia data over lossy network. By assigning an unequal amount of forward error correction (FEC) bits to each segment of transmission packets, the proposed algorithm protects a progressive bitstream generated by a three-dimensional set partitioning in hierarchical trees (3-D SPIHT) algorithm. The partical swarm optimization (PSO) algorithm is used to reasonably allocate bits between source coding and channel coding across packets. Experimental results show that the proposed algorithm provides close to optimal average peak signal-to-noise ratio performance and that its' running time is significantly lower than that of previously known solution.