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The COVID-19 pandemic is utterly reshaping the travelling industry. Accordingly, the tourism supply chain is presented with a tremendous challenge for adjusting towards new trends in travel, which stem from changes in tourist behaviours. This paper examines the contemporary millennial behavioural preferences within a tourism value chain perspective, a theme that is largely overlooked in literature. In carrying out the agenda, we employed a multi-attribute decision-making approach in PROMETHEE II, one which is new to the study of tourists’ behaviour. A sample of 316 Indonesian millennials who conducted domestic travel post–COVID-19 outbreak participated in the survey. Research results highlighted imagery user generated content (UGC), direct information from close relatives, accommodation services, self-catered experience and nature-based attractions to mostly resonate with the millennial traveller. In light of the results, we presented managerial implications that shed light for adjustments in the tourism value chain. This study contributes to extend the knowledge of millennials travelling behaviour from an alternative standpoint.

This study explored second language (L2) competence in discontinuous discursive formulaic sequences, namely Chinese correlative connectives (CCCs; e.g. yīnwèi . . . suǒyǐ ‘because . . . so’), in relation to the determinants of formulaic sequence acquisition by scrutinizing L2 Chinese learners’ performance on two controlled tasks. Mixed-effects modeling showed that frequency exerted both positive and negative effects on the task performance of L2 learners on CCCs. Contingency (i.e. the co-occurrence of two constituent connectives) posed a tremendous challenge to the use of obligatory CCCs by L2 learners. In contrast, semantic transparency and first language congruency had a positive effect. The effects of these determinants on L2 performance were qualified by their interactions in different categories of CCCs. The results also indicated that learners were sensitive to frequency and contingency determinants at both construction and constituent word levels. This study enriches our understanding of L2 competence in formulaic language and provides unique insights into L2 learners’ knowledge of CCCs.

AbstractThe substantial cost of new drug research and development has consistently posed a huge burden and tremendous challenge for both pharmaceutical companies and patients. In order to lower the expenditure and development failure rate, repurposing existing and approved drugs and identifying novel interactions between the drug molecules and the target proteins based on computational methods have gained growing attention. Here, we propose the DeepPLA, a novel deep learning-based model that combines ResNet-based 1D CNN and biLSTM, to establish an end-to-end network for protein-ligand binding affinity prediction. We first apply pre-trained embedding methods to encode the raw drug molecular SMILES strings and target protein sequences into dense vector representations. The dense vector representations separately go through ResNet-based 1D CNN modules to derive features. The extracted feature vectors are concatenated and further fed into the biLSTM network after average pooling operation, followed by the MLP module to finally predict binding affinity. We used BindingDB dataset for training and evaluating our DeepPLA model. The result shows that the DeepPLA model reaches a good performance for the protein-ligand binding affinity prediction in terms of R, RMSE, MAE, R2 and MSE with 0.89, 0.68, 0.50, 0.79 and 0.46 on the training set; and scores 0.84, 0.80, 0.60, 0.71 and 0.64 on the independent testing set, respectively. This result suggests the high accuracy of the DeepPLA prediction performance, as well as its high capability in generalization, demonstrating that the DeepPLA can be the potential upgrade to pinpoint new drug-target interactions to find better destinations for proven drugs.

To establish an accurate sea ice model is a tremendous challenge in Arctic acoustic research. Regarding this matter, a multilayered porous sea ice model is proposed based on Biot’s theory in this paper. Assuming that the model is sandwiched between the water and air half-spaces, the reflection coefficient of an incident wave from water into ice is deduced and contrasted with the solution calculated by impedance transfer method (ITM) to demonstrate the verification of the model. Furthermore, the influences of frequency, porosity and layering on reflection coefficients are analyzed. The results reveal that the reflection coefficient is closely associated with layering and porosity. Therefore, it is reasonable and necessary to simultaneously take the layering and porosity of ice into consideration. Different from the existing layered or porous ice model, the presented model synthesizes the layered characteristic and porous structure of ice, which better portrays the real condition of sea ice. It is an improvement of the broadly used stratified or porous sea ice model, which provides ideas for further sea ice modeling.

An increase in the number of Candida species that are resistant to antifungal medication and increases worldwide. Even individuals that are never exposed to antibiotics showing resistance to antifungal drugs. The increase in resistant candida species strains requires a search for novel targets for new antifungal agents. Preventing infection caused by candida species is a tremendous challenge in medicine. Although availability and use of antifungal drugs, disseminated candidiasis leads to a high mortality rate of about 40-60%, poor diagnosis, and improper disease management. Interest in G-quadruplexes as a therapeutic target has been increased in recent years, following the implication of this non-canonical G-quadruplex secondary structure in pathological diseases. However, G-quadruplex has been reported in many pathogens contributing to virulence and pathogenesis, including bacterial pathogens such as Staphylcoccus aureus and Enterococcus spp. Etc, viruses such as SARS-CoV2, HIV, HPV etc., and Fungi such as Candida species, Aspergillus fumigatus, etc. The present aim of the study is to identify and targeting G-Quadruplex forming sequences present in the Candida glabrata. PQSFinder (R Package) identified more than 5000 putative G-quadruplex forming sequences. Out of these, we have used PQS present in the SDH1 gene of Candida glabrata. It may be a key target to ameliorate the C.glabrata infection because it encodes a protein that plays a vital role in energy production in C.glabrata cells shown in the figure. TAn increase in the number of Candida species that are resistant to antifungal medication and increases worldwide. Even individuals that are never exposed to antibiotics showing resistance to antifungal drugs. The increase in resistant candida species strains requires a search for novel targets for new antifungal agents. Preventing infection caused by candida species is a tremendous challenge in medicine. Although availability and use of antifungal drugs, disseminated candidiasis leads to a high mortality rate of about 40-60%, poor diagnosis, and improper disease management. Interest in G-quadruplexes as a therapeutic target has been increased in recent years, following the implication of this non-canonical G-quadruplex secondary structure in pathological diseases. However, G-quadruplex has been reported in many pathogens contributing to virulence and pathogenesis, including bacterial pathogens such as Staphylcoccus aureus and Enterococcus spp. Etc, viruses such as SARS-CoV2, HIV, HPV etc., and Fungi such as Candida species, Aspergillus fumigatus, etc. The present aim of the study is to identify and targeting G-Quadruplex forming sequences present in the Candida glabrata. PQSFinder (R Package) identified more than 5000 putative G-quadruplex forming sequences. Out of these, we have used PQS present in the SDH1 gene of Candida glabrata. It may be a key target to ameliorate the C.glabrata infection because it encodes a protein that plays a vital role in energy production in C.glabrata cells shown in the figure. The structure was built based on the already current structure that is telomeric G4 AGGG (TTAGGG)3 {PDB ID-4G0F} for identifying its stabilization by Gold carbine derivatives. Molecular docking and ADMET analysis show that Compound A has the highest binding affinity and has the best ADMET properties among the two compounds. The present study represents PQS in the SDH1 gene could be a novel antifungal target.The structure was built based on the already current structure that is telomeric G4 AGGG (TTAGGG)3 {PDB ID-4G0F} for identifying its stabilization by Gold carbine derivatives. Molecular docking and ADMET analysis show that Compound A has the highest binding affinity and has the best ADMET properties among the two compounds. The present study represents PQS in the SDH1 gene could be a novel antifungal target.

Abstract Industry 4.0 is a great opportunity and a tremendous challenge for every role of society. Our study combines complex network and qualitative methods to analyze the Industry 4.0 macroeconomic issues and global supply chain, which enriches the qualitative analysis and machine learning in macroscopic and strategic research. Unsupervised complex graph network models are used to explore how industry 4.0 reshapes the world. Based on the in-degree and out-degree of the weighted and unweighted edges of each node, combined with the grouping results based on unsupervised learning, our study shows that the cooperation groups of Industry 4.0 are different from the previous traditional alliances. Macroeconomics issues also are studied. Finally, strong cohesive groups and recommendations for businessmen and policymakers are proposed.

Abstract Amines are prominent in natural products, pharmaceutical agents, and agrochemicals. Moreover, they are synthetically valuable building blocks for the construction of complex organic molecules and functional materials. However, amines, especially aliphatic and aromatic amines with free N-H, are prone to coordinate with transition-metal and deactivating the catalyst, posing a tremendous challenge to the application of Lewis basic amines in the amination of olefins. Herein we present the first case of oxidative amination of simple olefins with various Lewis basic amines. The combination of a palladium catalyst, 2,6-dimethyl-1,4-benzoquinone (2,6-DMBQ), and a phosphorous ligand leads to the efficient synthesis of alkyl and aryl allylamines. A series of allylamines are obtained with good yields and excellent regio- and stereoselectivities. Intramolecular amination to synthesize tetrahydropyrrole and piperidine derivatives was also realized. Mechanistic investigations reveal that the reaction undergoes allylic C(sp3)-H activation and subsequent functionalization.

The rapid growth and expansion of social media platform has filled the gap of information exchange in the day to day life. Apparently, social media is the main arena for disseminating manipulated information in a high range and exponential rate. The fabrication of twisted information is not limited to ones language, society and domain, this is particularly observed in the ongoing COVID-19 pandemic situation. The creation and propagation of fabricated news creates an urgent demand for automatically classification and detecting such distorted news articles. Manually detecting fake news is a laborious and tiresome task and the dearth of annotated fake news dataset to automate fake news detection system is still a tremendous challenge for low-resourced Amharic language (after Arabic, the second largely spoken Semitic language group). In this study, Amharic fake news dataset are crafted from verified news sources and various social media pages and six different machine learning classifiers Naïve bays, SVM, Logistic Regression, SGD, Random Forest and Passive aggressive Classifier model are built. The experimental results show that Naïve bays and Passive Aggressive Classifier surpass the remaining models with accuracy above 96% and F1- score of 99%. The study has a significant contribution to turn down the rate of disinformation in vernacular language.

Abstract Background Extracorporeal life support (ECLS) is an established tool to stabilize severely ill patients with therapy-refractory hemodynamic or respiratory failure. Recently, we established a mobile ECLS retrieval service at our institution. However, data on the outcome of patients receiving ECLS at outside hospitals for transportation into tertiary hospitals is still sparse. Methods We have analyzed all patients receiving ECLS in outside hospitals (Transport group, TG) prior to transportation to our institution and compared the outcome to our in-house ECLS experience (Home Group, HG). Results Between 2012 and 2018, we performed 978 ECLS implantations, 243 of which were performed on-site in tertiary hospitals for ECLS supported transportation. Significantly more veno-venous systems were implanted in TG (n = 129 (53%) vs. n = 327 (45%), p = 0.012). Indication for ECLS support differed between the groups, with more pneumonia; acute respiratory distress syndromes in the TG group and of course, more postcardiotomy patients in HG. Mean age was 47 (± 20) (HG) vs. 48 (± 18) (TG) years, p = 0.477 with no change over time. No differences were seen in ECLS support time (8.03 days ±8.19 days HG vs 7.81 days ±6.71 days TG, p = 0.675). 30-day mortality (n = 379 (52%) (HG) vs. n = 119 (49%) (TG) p = 0.265) and death on ECLS support (n = 322 (44%) (HG) vs. n = 97 (40%) TG, p = 0.162) were comparable between the two groups, despite a more severe SAVE score in the v-a TG (HG: − 1.56 (± 4.73) vs. TG -3.93 (± 4.22) p < 0.001). Mortality rates did not change significantly over the years. Multivariate risk analysis revealed Influenza, Peak Insp. Pressure at implantation, pO2/FiO2 ratio and ECLS Score (SAVE/RESP) as well as ECLS support time to be independent risk factors for mortality. Conclusion Mobile ECLS support is a tremendous challenge. However, it is justified to offer 24 h/7d ECLS standby for secondary and primary hospitals as a tertiary hospital. Increasing indications and total numbers for ECLS support raise the need for further studies to evaluate outcome in these patients.

Abstract Healthcare-acquired infections are a tremendous challenge to the US medical system. Stethoscopes touch many patients, but current guidance from the Centers for Disease Control and Prevention does not support disinfection between each patient. Stethoscopes are rarely disinfected between patients by healthcare providers. When cultured, even after disinfection, stethoscopes have high rates of pathogen contamination, identical to that of unwashed hands. The consequence of these practices may bode poorly in the coronavirus 2019 disease (COVID-19) pandemic. Alternatively, the CDC recommends the use of disposable stethoscopes. However, these instruments have poor acoustic properties, and misdiagnoses have been documented. They may also serve as pathogen vectors among staff sharing them. Disposable aseptic stethoscope diaphragm barriers can provide increased safety without sacrificing stethoscope function. We recommend that the CDC consider the research regarding stethoscope hygiene and effective solutions to contemporize this guidance and elevate stethoscope hygiene to that of the hands, by requiring stethoscope disinfection or change of disposable barrier between every patient encounter.