Advances in technology have fundamentally changed how information is produced and consumed by all actors involved in tourism. Tourists can now access different sources of information, and they can generate their own content and share their views and experiences. Tourism content shared through social media has become a very influential information source that impacts tourism in terms of both reputation and performance. However, the volume of data on the Internet has reached a level that makes manual processing almost impossible, demanding new analytical approaches. Sentiment analysis is rapidly emerging as an automated process of examining semantic relationships and meaning in reviews. In this article, different sentiment analysis approaches applied in tourism are reviewed and assessed in terms of the datasets used and performances on key evaluation metrics. The article concludes by outlining future research avenues to further advance sentiment analysis in tourism as part of a broader Big Data approach.

Mitochondrial complex II (CII) has been recently identified as a novel target for anti-cancer drugs. Mitochondrially targeted vitamin E succinate (MitoVES) is modified so that it is preferentially localized to mitochondria, greatly enhancing its pro-apoptotic and anti-cancer activity. Using genetically manipulated cells, MitoVES caused apoptosis and generation of reactive oxygen species (ROS) in CII-proficient malignant cells but not their CII-dysfunctional counterparts. MitoVES inhib-ited the succinate dehydrogenase (SDH) activity of CII with IC 50 of 80 M, whereas the electron transfer from CII to CIII was inhibited with IC 50 of 1.5 M. The agent had no effect either on the enzymatic activity of CI or on electron transfer from CI to CIII. Over 24 h, MitoVES caused stabilization of the oxygen-dependent destruction domain of HIF1␣ fused to GFP, indicating promotion of the state of pseudohypoxia. Molecular modeling predicted the succinyl group anchored into the proximal CII ubiquinone (UbQ)-binding site and successively reduced interaction energies for serially shorter phytyl chain homologs of MitoVES correlated with their lower effects on apoptosis induction, ROS generation, and SDH activity. Mutation of the UbQ-binding Ser 68 within the proximal site of the CII SDHC subunit (S68A or S68L) suppressed both ROS generation and apoptosis induction by MitoVES. In vivo studies indicated that MitoVES also acts by causing pseudohypoxia in the context of tumor suppression. We propose that mitochondrial targeting of VES with an 11-carbon chain localizes the agent into an ideal position across the interface of the mitochondrial inner membrane and matrix, optimizing its biological effects as an anti-cancer drug.Mitochondria are emerging as targets for a variety of anti-cancer drugs (1-5) that belong to a group of compounds termed "mitocans" (6, 7). Of these agents, we and others have been studying the group of vitamin E (VE) 2 analogs, epitomized by the "redox-silent" ␣-tocopheryl succinate (␣-TOS) and ␣-tocopheryl acetyl ether (8). Both of these agents proved to be selective inducers of apoptosis in cancer cells and efficient suppressors of tumors in experimental models (9 -16).VE analogs with anti-cancer activity have been classified as mitocans (i.e. small anti-cancer agents that act by selectively destabilizing mitochondria in cancer cells) (6 -8). Of the several groups of mitocans, the anti-cancer VE analogs belong to both the class of BH3 mimetics, which includes compounds interfering with the interactions of the Bcl-2 family proteins (17), as well as to the class of agents that interfere with the mitochondrial electron redox chain. The latter activity is probably the main reason for the strong apoptogenic efficacy of agents like ␣-TOS (18). More specifically, ␣-TOS interferes * This work was supported by grants from the Australian Research Council,

In view of 2020 outbreak of the pandemic COVID-19, the paper examines the relationship between government measures for combating the pandemic and their side effects. Panic buying is identified as one such side effect. Among various models and measures undertaken by government to manage the pandemic, timed-intervention policy is commonly practiced by most countries. This paper examines the timing effect between government measures and panic buying. Three studies were undertaken to understand the timing effect and identify a connection between timed measures and consumer behaviours. Semantic analysis, secondary data search, and big data analytics were deployed to address the research aim. Although claiming a causal relationship is cautioned, the findings reveal a connection between timing of government measures and panic buying. These findings are discussed with the support of real-life evidence. Implications for researchers and practitioners conclude this paper.

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Progression from health to disease is driven by FLTs in the PINE network, which is likely to undergo changes characteristic of system instability. Biomarkers of system instability may effectively predict the critical transition to MDD.

We have built a machine learning method called DDIG-in (FS) based on real human genetic variations from the Human Gene Mutation Database (inherited disease-causing) and the 1000 Genomes Project (GP) (putatively neutral). The method incorporates both sequence and predicted structural features and yields a robust performance by 10-fold cross-validation and independent tests on both FS indels and NS variants. We showed that human-derived NS variants and FS indels derived from animal orthologs can be effectively employed for independent testing of our method trained on human-derived FS indels. DDIG-in (FS) achieves a Matthews correlation coefficient (MCC) of 0.59, a sensitivity of 86%, and a specificity of 72% for FS indels. Application of DDIG-in (FS) to NS variants yields essentially the same performance (MCC of 0.43) as a method that was specifically trained for NS variants. DDIG-in (FS) was shown to make a significant improvement over existing techniques.

With the growth of smartphone usage the number of social media posts has significantly increased and represents potentially valuable information for management, including of natural resources and the environment. Already, evidence of using 'human sensor' in crises management suggests that collective knowledge could be used to complement traditional monitoring. This research uses Twitter data posted from the Great Barrier Reef region, Australia, to assess whether the extent and type of data could be used to Great Barrier Reef organisations as part of their monitoring program. The analysis reveals that large amounts of tweets, covering the geographic area of interest, are available and that the pool of information providers is greatly enhanced by the large number of tourists to this region. A keyword and sentiment analysis demonstrates the usefulness of the Twitter data, but also highlights that the actual number of Reef-related tweets is comparatively small and lacks specificity. Suggestions for further steps towards the development of an integrative data platform that incorporates social media are provided.

In order to facilitate efficient query processing, the information contained in data warehouses is typically stored as a set of materialized views. Deciding which views to materialize represent a challenge in order to minimize view maintenance and query processing costs. Some existing approaches are applicable only for small problems, which are far from reality. In this paper we introduce a new approach for materialized view selection using Parallel Simulated Annealing (PSA) that selects views from an input Multiple View Processing Plan (MVPP). With PSA, we are able to perform view selection on MVPPs having hundreds of queries and thousands of views. Also, in our experimental study we show that our method provides a significant improvement in the quality of the obtained set of materialized views over existing heuristic and sequential simulated annealing algorithms.