Web Search Terms Research Articles

Turmeric for Treatment of Irritable Bowel Syndrome: A Systematic Review of Population-Based Evidence.

Irritable bowel syndrome (IBS) is a highly prevalent disorder of the gut interaction characterized by abdominal discomfort and pain associated with altered bowel habits in the absence of structural abnormalities. In spite of IBS' high prevalence and disease burden across the globe, no explanations have been given as to its underlying pathophysiology. As for the treatment of IBS, there is no specific medication, and the most beneficial treatment is usually supportive therapy. Recent animal and human studies have demonstrated the therapeutic potential of curcumin or turmeric in the treatment of IBS. We systematically reviewed all available evidence supporting curcumin and turmeric's therapeutic potential in relieving IBS symptoms in the present study. For this purpose, a database search was performed using curcumin, turmeric, and IBS and all their equivalents as of the search terms in Web of Science, Pub-Med, Scopus, Ovid, Embase, and Google Scholar from1990 up to Feb 2021. The investigation was then limited to clinical trials, and then nine articles were collected for data analysis. The findings of the included literature showed that curcumin and turmeric alone or in combination with other medications could improve the severity of IBS as well as the quality of life among people who suffer from IBS symptoms. Overall, medications containing curcumin and turmeric extract due to these compounds' anti-inflammatory effects may improve IBS symptoms, particularly abdominal pain and life quality.

Defining the Protease and Protease Inhibitor (P/PI) Proteomes of Healthy and Diseased Human Skin by Modified Systematic Review.

Proteases and protease inhibitors (P/PIs) are involved in many biological processes in human skin, yet often only specific families or related groups of P/PIs are investigated. Proteomics approaches, such as mass spectrometry, can define proteome signatures (including P/PIs) in tissues; however, they struggle to detect low-abundance proteins. To overcome these issues, we aimed to produce a comprehensive proteome of all P/PIs present in normal and diseased human skin, in vivo, by carrying out a modified systematic review using a list of P/PIs from MEROPS and combining this with key search terms in Web of Science. Resulting articles were manually reviewed against inclusion/exclusion criteria and a dataset constructed. This study identified 111 proteases and 77 protease inhibitors in human skin, comprising the serine, metallo-, cysteine and aspartic acid catalytic families of proteases. P/PIs showing no evidence of catalytic activity or protease inhibition, were designated non-peptidase homologs (NPH), and no reported protease inhibitory activity (NRPIA), respectively. MMP9 and TIMP1 were the most frequently published P/PIs and were reported in normal skin and most skin disease groups. Normal skin and diseased skin showed significant overlap with respect to P/PI profile; however, MMP23 was identified in several skin disease groups, but was absent in normal skin. The catalytic profile of P/PIs in wounds, scars and solar elastosis was distinct from normal skin, suggesting that a different group of P/PIs is responsible for disease progression. In conclusion, this study uses a novel approach to provide a comprehensive inventory of P/PIs in normal and diseased human skin reported in our database. The database may be used to determine either which P/PIs are present in specific diseases or which diseases individual P/PIs may influence.

Short‐term railway passenger demand forecast using improved Wasserstein generative adversarial nets and web search terms

Accurately predicting railway passenger demand is conducive for managers to quickly adjust strategies. It is time-consuming and expensive to collect large-scale traffic data. With the digitization of railway tickets, a large amount of user data has been accumulated. We propose a method to predict railway passenger demand using web search terms data. In order to improve the prediction accuracy, we improved Wasserstein Generative Adversarial Nets (WGAN), which were good at generating and identifying data, by adding a predictor and supervised learning adversarial training to predict railway passenger demand. The improved WGAN could generate virtual data to expand real data, and use parallel data to predict railway passenger demand. We used search times of web search terms on different devices as training data to predict railway passenger demand in Beijing. The results show that the change in demand for railway passenger lags behind the change in the data of web search terms by one month. It is suitable for forecasting in advance. Compared with other forecasting methods, the improved WGAN performance is better, and the mean absolute percentage error is 1.98%. Because it can use mixed data for training and prediction, it has stronger adaptability when data scale decreases.

Durable Queries over Historical Time Series

This paper studies the problem of finding objects with durable quality over time in historical time series databases. For example, a sociologist may be interested in the top 10 web search terms during the period of some historical events; the police may seek for vehicles that move close to a suspect 70 percent of the time during a certain time period and so on. Durable top-k (DTop-k) and nearest neighbor (DkNN) queries can be viewed as natural extensions of the standard snapshot top-k and NN queries to timestamped sequences of values or locations. Although their snapshot counterparts have been studied extensively, to our knowledge, there is little prior work that addresses this new class of durable queries. Existing methods for DTop-k processing either apply trivial solutions, or rely on domain-specific properties. Motivated by this, we propose efficient and scalable algorithms for the DTop-k and DkNN queries, based on novel indexing and query evaluation techniques. Our experiments show that the proposed algorithms outperform previous and baseline solutions by a wide margin.

웹 검색어 선택과정에서의 이용자 불확실성의 유형 : 자연과학연구자들의 정보탐색환경에 대한 고찰

다수의 연구에서 정보추구 과정상 불 확신성(Uncertainty) 의 중요성이 지적되었지만, 실제 정보검색시스템을 이용한 탐색과정에서 이용자들의 불 확신성에 대한 연구는 많지 않았다. 본 연구는 실제로 정보를 추구하는 이용자들의 웹 검색어 선정과정에서의 불 확신성 인식을 조사하여, 정보탐색 과정에서의 다양한 불 확신성 유형을 식별하였다. 불 확신성 유형에 입각하여 발견된 불 확신성의 주요 원인(Origins)은 정보검색시스템 및 서비스 발전을 위한 시사점을 제시하여준다.

An Index of Fame?

This paper discusses the presentation of fame that can be identified through popular search terms. These terms reveal how the rapidly shifting interest in individual identities of ‘fame’ are cast against a continuous sequence of expected and unexpected events including movie releases, annual holidays, murders and terrorist attacks. The central claim of this paper is that fame is continuously reconstituted across a wide spectrum of cultural experiences and actions. Fame is attached to individuals as a personification of mainstream cultural fascination with specific events – whether manufactured or unexpected – and artefacts.