Herbal medicines:physician's recommendation and clinical evaluation of St.John's Wort for depression

Why some physicians recommend herbal medicines while others do not is not well understood. We undertook a survey designed to identify factors, which predict recommendation of herbal medicines by physicians in Malaysia. About a third (206 out of 626) of the physicians working at the University of Malaya Medical Centre ' were interviewed face-to-face, using a structured questionnaire. Physicians were asked about their personal use of, recommendation of, perceived interest in and, usefulness and safety of herbal medicines. Using logistic regression modelling we identified personal use, general interest, interest in receiving training, race and higher level of medical training as significant predictors of recommendation. St. John's wort is one of the most widely used herbal remedies. It is also probably the most widely evaluated herbal remedy with no fewer than 57 randomised controlled trials. Evidence from the depression trials suggests that St. John's wort is more effective than placebo while its comparative efficacy to conventional antidepressants is not well established. We updated previous meta-analyses of St. John's wort, described the characteristics of the included trials, applied methods of data imputation and transformation for incomplete trial data and examined sources of heterogeneity in the design and results of those trials. Thirty randomised controlled trials, which were heterogeneous in design, were identified. Our meta-analysis showed that St. John's wort was significantly more effective than placebo [pooled RR 1.90 (1.54-2.35)] and [Pooled WMD 4.09 (2.33 to 5.84)]. However, the remedy was similar to conventional antidepressant in its efficacy [Pooled RR I. 0 I (0.93 -1.10)] and [Pooled WMD 0.18 (- 0.66 to 1.02). Subgroup analyses of the placebo-controlled trials suggested that use of different diagnostic classifications at the inclusion stage led to different estimates of effect. Similarly a significant difference in the estimates of efficacy was observed when trials were categorised according to length of follow-up. Confounding between the variables, diagnostic classification and length of trial was shown by loglinear analysis. Despite extensive study, there is still no consensus on how effective St. lohn's wort is in depression. However, most experts would agree that it has some effect. Our meta-analysis highlights the problems associated with the clinical evaluation of herbal medicines when the active ingredients are poorly defined or unknown. The problem is compounded when the target disease (e.g. depression) is also difficult to define and different instruments are available to diagnose and evaluate it.

Assuming identities:the workings of intertextual metaphors in dating ads

In this article I propose an augmented pragmatic framework for interpreting metaphors of identity, built upon Grice's co-operative principle and incorporating Levinson's concept of uptake and Austin's notion of felicity. The framework is applied to a selection of intertextual identity metaphors drawn from The Guardian's dating ad column, ‘Soulmates’. First I provide a detailed exposition of the textual and discursive workings of a small selection of typical fictional metaphors in these dating ads, to show how co-textual selections steer interpretation and contribute to a metaphor's success, or felicity. Then discussion turns to consideration of how these textual and discursive processes might be mapped onto the proposed pragmatic framework of recognition, uptake and felicity.

Truth values and truth-commitment in interdiscursive dating ads

In this article, I explore issues of commitment to truth in dating ads that use apparently impossible categorizations to project identities for ad writers and their desired others. The article begins with a brief overview of relevant aspects of Text World Theory (especially Gavins's work on dating ads), Sinclair's model of fictional worlds and Routledge and Chapman's account of truth-commitment in discourse, and proposes the need for a framework that allows for a partial suspension of commitment to truth. I then draw on the work of Ivanič and Weldon on identity in writing, in order to develop an account that offers a discourse- and genre-based discussion of how the intertextual metaphors in such ads are interpreted in relation to truth values. I suggest the default stance is that of positive commitment to literal truth and that, when this is not possible, a fall-back mode of negative commitment to metaphorical truth is preferred over an interpretation in which questions of truth are truly suspended. Finally, I consider a related category, of apparently negative dating ad identities, in order to suggest a functional motivation for the inclusion of elements that cannot be interpreted in truth-committed mode. Copyright © 2008 SAGE Publications.

Metaphors of identity in dating ads and newspaper articles

This article investigates metaphors of identity in dating ads and in two types of newspaper writing, 'hard' and 'soft' news articles. It focuses on issues of textualization and processing, and particularly on the role of cotext in decoding metaphors. Taking a pragmatic approach founded in the cooperative principle, it argues that the maxims of quality and relation play related but separable roles in the interpretation of identity metaphors; and that this process is guided and constrained by cotextual selections in the environment of the metaphorical term. The particular kinds of cotextual guidance provided by the writer are seen to vary according to genre-driven issues. These include the purpose and stylistic conventions of the genre in which the metaphor occurs and the circumstances under which the text is composed and read. Differing functional motivations are suggested for the use of identity metaphors in each of the genres considered. © Walter de Gruyter 2007.

The use of the lichen genus Rhizocarpon in lichenometric dating with special reference to Holocene glacial events

Lichenometry is one of the most widely used methods of dating the surface age of substrata including rock surfaces, boulders, walls, and archaeological remains and has been particularly important in dating late Holocene glacial events. Yellow-green species of the crustose genus Rhizocarpon have been the most useful lichens in lichenometry because of their low growth rates and longevity. This review describes: (1) the biology of the genus Rhizocarpon, (2) growth rates and longevity, (3) environmental growth effects, (4) methods of estimating lichen age, (5) the methodology of lichenometry, (6) applications to dating glacial events, and (7) future research. Lichenometry depends on many assumptions, most critically that if the lag time before colonisation of a substratum is known and lichen age can be estimated, then a minimum surface age date can be obtained by measuring the size of the largest Rhizocarpon thallus. Lichen age can be estimated by calibrating thallus size against surfaces of known age (‘indirect lichenometry’), by constructing a growth rate-size curve from direct measurement of growth (‘direct lichenometry’), using radio-carbon (RC) dating, or from lichen ‘growth rings’. Future research should include a more rigorous investigation of the assumptions of lichenometry, especially whether the largest thallus present at a site is a good indicator of substratum age, and further studies on the establishment, development, growth, senescence, and mortality of Rhizocarpon lichens.

We know what you want to buy:a demographic-based system for product recommendation on microblogs

Product recommender systems are often deployed by e-commerce websites to improve user experience and increase sales. However, recommendation is limited by the product information hosted in those e-commerce sites and is only triggered when users are performing e-commerce activities. In this paper, we develop a novel product recommender system called METIS, a MErchanT Intelligence recommender System, which detects users' purchase intents from their microblogs in near real-time and makes product recommendation based on matching the users' demographic information extracted from their public profiles with product demographics learned from microblogs and online reviews. METIS distinguishes itself from traditional product recommender systems in the following aspects: 1) METIS was developed based on a microblogging service platform. As such, it is not limited by the information available in any specific e-commerce website. In addition, METIS is able to track users' purchase intents in near real-time and make recommendations accordingly. 2) In METIS, product recommendation is framed as a learning to rank problem. Users' characteristics extracted from their public profiles in microblogs and products' demographics learned from both online product reviews and microblogs are fed into learning to rank algorithms for product recommendation. We have evaluated our system in a large dataset crawled from Sina Weibo. The experimental results have verified the feasibility and effectiveness of our system. We have also made a demo version of our system publicly available and have implemented a live system which allows registered users to receive recommendations in real time. © 2014 ACM.

Exploring demographic information in social media for product recommendation

In many e-commerce Web sites, product recommendation is essential to improve user experience and boost sales. Most existing product recommender systems rely on historical transaction records or Web-site-browsing history of consumers in order to accurately predict online users’ preferences for product recommendation. As such, they are constrained by limited information available on specific e-commerce Web sites. With the prolific use of social media platforms, it now becomes possible to extract product demographics from online product reviews and social networks built from microblogs. Moreover, users’ public profiles available on social media often reveal their demographic attributes such as age, gender, and education. In this paper, we propose to leverage the demographic information of both products and users extracted from social media for product recommendation. In specific, we frame recommendation as a learning to rank problem which takes as input the features derived from both product and user demographics. An ensemble method based on the gradient-boosting regression trees is extended to make it suitable for our recommendation task. We have conducted extensive experiments to obtain both quantitative and qualitative evaluation results. Moreover, we have also conducted a user study to gauge the performance of our proposed recommender system in a real-world deployment. All the results show that our system is more effective in generating recommendation results better matching users’ preferences than the competitive baselines.

Leveraging product adopter information from online reviews for product recommendation

The availability of the sheer volume of online product reviews makes it possible to derive implicit demographic information of product adopters from review documents. This paper proposes a novel approach to the extraction of product adopter mentions from online reviews. The extracted product adopters are the ncategorise into a number of different demographic user groups. The aggregated demographic information of many product adopters can be used to characterize both products and users, which can be incorporated into a recommendation method using weighted regularised matrix factorisation. Our experimental results on over 15 million reviews crawled from JINGDONG, the largest B2C e-commerce website in China, show the feasibility and effectiveness of our proposed frame work for product recommendation.

Connecting social media to e-commerce:cold-start product recommendation using microblogging information

In recent years, the boundaries between e-commerce and social networking have become increasingly blurred. Many e-commerce websites support the mechanism of social login where users can sign on the websites using their social network identities such as their Facebook or Twitter accounts. Users can also post their newly purchased products on microblogs with links to the e-commerce product web pages. In this paper, we propose a novel solution for cross-site cold-start product recommendation, which aims to recommend products from e-commerce websites to users at social networking sites in 'cold-start' situations, a problem which has rarely been explored before. A major challenge is how to leverage knowledge extracted from social networking sites for cross-site cold-start product recommendation. We propose to use the linked users across social networking sites and e-commerce websites (users who have social networking accounts and have made purchases on e-commerce websites) as a bridge to map users' social networking features to another feature representation for product recommendation. In specific, we propose learning both users' and products' feature representations (called user embeddings and product embeddings, respectively) from data collected from e-commerce websites using recurrent neural networks and then apply a modified gradient boosting trees method to transform users' social networking features into user embeddings. We then develop a feature-based matrix factorization approach which can leverage the learnt user embeddings for cold-start product recommendation. Experimental results on a large dataset constructed from the largest Chinese microblogging service Sina Weibo and the largest Chinese B2C e-commerce website JingDong have shown the effectiveness of our proposed framework.

Mining product adopter information from online reviews for improving product recommendation

We present in this article an automated framework that extracts product adopter information from online reviews and incorporates the extracted information into feature-based matrix factorization formore effective product recommendation. In specific, we propose a bootstrapping approach for the extraction of product adopters from review text and categorize them into a number of different demographic categories. The aggregated demographic information of many product adopters can be used to characterize both products and users in the form of distributions over different demographic categories. We further propose a graphbased method to iteratively update user- and product-related distributions more reliably in a heterogeneous user-product graph and incorporate them as features into the matrix factorization approach for product recommendation. Our experimental results on a large dataset crawled from JINGDONG, the largest B2C e-commerce website in China, show that our proposed framework outperforms a number of competitive baselines for product recommendation.

An angle-based interest model for text recommendation

Building an interest model is the key to realize personalized text recommendation. Previous interest models neglect the fact that a user may have multiple angles of interests. Different angles of interest provide different requests and criteria for text recommendation. This paper proposes an interest model that consists of two kinds of angles: persistence and pattern, which can be combined to form complex angles. The model uses a new method to represent the long-term interest and the short-term interest, and distinguishes the interest on object and the interest on the link structure of objects. Experiments with news-scale text data show that the interest on object and the interest on link structure have real requirements, and it is effective to recommend texts according to the angles.

Lichenometric dating (lichenometry) and the biology of the lichen genus rhizocarpon:challenges and future directions

Lichenometric dating (lichenometry) involves the use of lichen measurements to estimate the age of exposure of various substrata. Because of low radial growth rates and considerable longevity, species of the crustose lichen genus Rhizocarpon have been the most useful in lichenometry. The primary assumption of lichenometry is that colonization, growth and mortality of Rhizocarpon are similar on surfaces of known and unknown age so that the largest thalli present on the respective faces are of comparable age. This review describes the current state of knowledge regarding the biology of Rhizocarpon and considers two main questions: (1) to what extent does existing knowledge support this assumption; and (2) what further biological observations would be useful both to test its validity and to improve the accuracy of lichenometric dates? A review of the Rhizocarpon literature identified gaps in knowledge regarding early development, the growth rate/size curve, mortality, regeneration, competitive effects, colonization, and succession on rock surfaces. The data suggest that these processes may not be comparable on different rock surfaces, especially in regions where growth rates and thallus turnover are high. In addition, several variables could differ between rock surfaces and influence maximum thallus size, including rate and timing of colonization, radial growth rates, environmental differences, thallus fusion, allelopathy, thallus mortality, colonization and competition. Comparative measurements of these variables on surfaces of known and unknown age may help to determine whether the basic assumptions of lichenometry are valid. Ultimately, it may be possible to take these differences into account when interpreting estimated dates.

Collaborative filtering and deep learning based recommendation system for cold start items

Recommender system is a specific type of intelligent systems, which exploits historical user ratings on items and/or auxiliary information to make recommendations on items to the users. It plays a critical role in a wide range of online shopping, e-commercial services and social networking applications. Collaborative filtering (CF) is the most popular approaches used for recommender systems, but it suffers from complete cold start (CCS) problem where no rating record are available and incomplete cold start (ICS) problem where only a small number of rating records are available for some new items or users in the system. In this paper, we propose two recommendation models to solve the CCS and ICS problems for new items, which are based on a framework of tightly coupled CF approach and deep learning neural network. A specific deep neural network SADE is used to extract the content features of the items. The state of the art CF model, timeSVD++, which models and utilizes temporal dynamics of user preferences and item features, is modified to take the content features into prediction of ratings for cold start items. Extensive experiments on a large Netflix rating dataset of movies are performed, which show that our proposed recommendation models largely outperform the baseline models for rating prediction of cold start items. The two proposed recommendation models are also evaluated and compared on ICS items, and a flexible scheme of model retraining and switching is proposed to deal with the transition of items from cold start to non-cold start status. The experiment results on Netflix movie recommendation show the tight coupling of CF approach and deep learning neural network is feasible and very effective for cold start item recommendation. The design is general and can be applied to many other recommender systems for online shopping and social networking applications. The solution of cold start item problem can largely improve user experience and trust of recommender systems, and effectively promote cold start items.

Collaborative filtering and deep learning based hybrid recommendation for cold start problem

Recommender systems (RS) are used by many social networking applications and online e-commercial services. Collaborative filtering (CF) is one of the most popular approaches used for RS. However traditional CF approach suffers from sparsity and cold start problems. In this paper, we propose a hybrid recommendation model to address the cold start problem, which explores the item content features learned from a deep learning neural network and applies them to the timeSVD++ CF model. Extensive experiments are run on a large Netflix rating dataset for movies. Experiment results show that the proposed hybrid recommendation model provides a good prediction for cold start items, and performs better than four existing recommendation models for rating of non-cold start items.