FASTtrack applied pharmaceutical practice

This revision guide takes the student pharmacist or pharmacy technician through the main stages involved in pharmaceutical dispensing. It gives bullet points of basic information on applied pharmacy practice followed by questions and answers. This reference text accompanies the compulsory dispensing courses found in all undergraduate MPharm programmes and equivalent technical training courses. Changes for the new edition include: * Information on revisions to the community pharmacy contract. * Additional content on new advanced community pharmacy services. * Revised worked examples and student questions. * Updated prescription labelling information, including the use of new cautionary and warning labels. * Updated references and bibliography.

Vulnerability of exporting nations to the development of a carbon label in the United Kingdom

Carbon labels inform consumers about the amount of greenhouse gases (GHGs) released during the production and consumption of goods, including food. In the future consumer and legislative responses to carbon labels may favour goods with lower emissions, and thereby change established supply chains. This may have unintended consequences. We present the carbon footprint of three horticultural goods of different origins supplied to the United Kingdom market: lettuce, broccoli and green beans. Analysis of these footprints enables the characterisation of three different classes of vulnerability which are related to: transport, national economy and supply chain specifics. There is no simple relationship between the characteristics of an exporting country and its vulnerability to the introduction of a carbon label. Geographically distant developing countries with a high level of substitutable exports to the UK are most vulnerable. However, many developing countries have low vulnerability as their main exports are tropical crops which would be hard to substitute with local produce. In the short term it is unlikely that consumers will respond to carbon labels in such a way that will have major impacts in the horticultural sector. Labels which require contractual reductions in GHG emissions may have greater impacts in the short term.

Visualizing uncertainty in multi-spectral remotely sensed imagery

Error and uncertainty in remotely sensed data come from several sources, and can be increased or mitigated by the processing to which that data is subjected (e.g. resampling, atmospheric correction). Historically the effects of such uncertainty have only been considered overall and evaluated in a confusion matrix which becomes high-level meta-data, and so is commonly ignored. However, some of the sources of uncertainty can be explicity identified and modelled, and their effects (which often vary across space and time) visualized. Others can be considered overall, but their spatial effects can still be visualized. This process of visualization is of particular value for users who need to assess the importance of data uncertainty for their own practical applications. This paper describes a Java-based toolkit, which uses interactive and linked views to enable visualization of data uncertainty by a variety of means. This allows users to consider error and uncertainty as integral elements of image data, to be viewed and explored, rather than as labels or indices attached to the data. © 2002 Elsevier Science Ltd. All rights reserved.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

A Linear Time Algorithm for Computing Longest Paths in Cactus Graphs

ACM Computing Classification System (1998): G.2.2.

Applying Associative Classifier PGN for Digitised Cultural Heritage Resource Discovery

Resource discovery is one of the key services in digitised cultural heritage collections. It requires intelligent mining in heterogeneous digital content as well as capabilities in large scale performance; this explains the recent advances in classification methods. Associative classifiers are convenient data mining tools used in the field of cultural heritage, by applying their possibilities to taking into account the specific combinations of the attribute values. Usually, the associative classifiers prioritize the support over the confidence. The proposed classifier PGN questions this common approach and focuses on confidence first by retaining only 100% confidence rules. The classification tasks in the field of cultural heritage usually deal with data sets with many class labels. This variety is caused by the richness of accumulated culture during the centuries. Comparisons of classifier PGN with other classifiers, such as OneR, JRip and J48, show the competitiveness of PGN in recognizing multi-class datasets on collections of masterpieces from different West and East European Fine Art authors and movements.

Unfolding kernel embeddings of graphs:enhancing class separation through manifold learning

In this paper, we investigate the use of manifold learning techniques to enhance the separation properties of standard graph kernels. The idea stems from the observation that when we perform multidimensional scaling on the distance matrices extracted from the kernels, the resulting data tends to be clustered along a curve that wraps around the embedding space, a behavior that suggests that long range distances are not estimated accurately, resulting in an increased curvature of the embedding space. Hence, we propose to use a number of manifold learning techniques to compute a low-dimensional embedding of the graphs in an attempt to unfold the embedding manifold, and increase the class separation. We perform an extensive experimental evaluation on a number of standard graph datasets using the shortest-path (Borgwardt and Kriegel, 2005), graphlet (Shervashidze et al., 2009), random walk (Kashima et al., 2003) and Weisfeiler-Lehman (Shervashidze et al., 2011) kernels. We observe the most significant improvement in the case of the graphlet kernel, which fits with the observation that neglecting the locational information of the substructures leads to a stronger curvature of the embedding manifold. On the other hand, the Weisfeiler-Lehman kernel partially mitigates the locality problem by using the node labels information, and thus does not clearly benefit from the manifold learning. Interestingly, our experiments also show that the unfolding of the space seems to reduce the performance gap between the examined kernels.

Attributed graph kernels using the Jensen-Tsallis q-differences

We propose a family of attributed graph kernels based on mutual information measures, i.e., the Jensen-Tsallis (JT) q-differences (for q  ∈ [1,2]) between probability distributions over the graphs. To this end, we first assign a probability to each vertex of the graph through a continuous-time quantum walk (CTQW). We then adopt the tree-index approach [1] to strengthen the original vertex labels, and we show how the CTQW can induce a probability distribution over these strengthened labels. We show that our JT kernel (for q  = 1) overcomes the shortcoming of discarding non-isomorphic substructures arising in the R-convolution kernels. Moreover, we prove that the proposed JT kernels generalize the Jensen-Shannon graph kernel [2] (for q = 1) and the classical subtree kernel [3] (for q = 2), respectively. Experimental evaluations demonstrate the effectiveness and efficiency of the JT kernels.

Trans-language, trans-culture, trans-translation?

The growth of the discipline of translation studies has been accompanied by are newed reflection on the object of research and our metalanguage. These developments have also been necessitated by the diversification of professions within the language industry. The very label translation is often avoided in favour of alternative terms, such as localisation (of software), trans creation (of advertising), trans editing (of information from press agencies). The competences framework developed for the European Master’s in Translation network speaks of experts in multilingual and multimedia communication to account for the complexity of translation competence. This paper addresses the following related questions: (i) How can translation competence in such awide sense be developed in training programmes? (ii) Do some competences required in the industry go beyond translation competence? and (iii) What challenges do labels such as trans creation pose?

A new unsupervised convolutional neural network model for Chinese scene text detection

As one of the most popular deep learning models, convolution neural network (CNN) has achieved huge success in image information extraction. Traditionally CNN is trained by supervised learning method with labeled data and used as a classifier by adding a classification layer in the end. Its capability of extracting image features is largely limited due to the difficulty of setting up a large training dataset. In this paper, we propose a new unsupervised learning CNN model, which uses a so-called convolutional sparse auto-encoder (CSAE) algorithm pre-Train the CNN. Instead of using labeled natural images for CNN training, the CSAE algorithm can be used to train the CNN with unlabeled artificial images, which enables easy expansion of training data and unsupervised learning. The CSAE algorithm is especially designed for extracting complex features from specific objects such as Chinese characters. After the features of articficial images are extracted by the CSAE algorithm, the learned parameters are used to initialize the first CNN convolutional layer, and then the CNN model is fine-Trained by scene image patches with a linear classifier. The new CNN model is applied to Chinese scene text detection and is evaluated with a multilingual image dataset, which labels Chinese, English and numerals texts separately. More than 10% detection precision gain is observed over two CNN models.

The Routledge handbook of forensic linguistics

The Routledge Handbook of Forensic Linguistics provides a unique work of reference to the leading ideas, debates, topics, approaches and methodologies in Forensic Linguistics. Forensic Linguistics is the study of language and the law, covering topics from legal language and courtroom discourse to plagiarism. It also concerns the applied (forensic) linguist who is involved in providing evidence, as an expert, for the defence and prosecution, in areas as diverse as blackmail, trademarks and warning labels. The Routledge Handbook of Forensic Linguistics includes a comprehensive introduction to the field written by the editors and a collection of thirty-seven original chapters written by the world’s leading academics and professionals, both established and up-and-coming, designed to equip a new generation of students and researchers to carry out forensic linguistic research and analysis. The Routledge Handbook of Forensic Linguistics is the ideal resource for undergraduates or postgraduates new to the area.

Semi-supervised Gaussian process latent variable model with pairwise constraints

In machine learning, Gaussian process latent variable model (GP-LVM) has been extensively applied in the field of unsupervised dimensionality reduction. When some supervised information, e.g., pairwise constraints or labels of the data, is available, the traditional GP-LVM cannot directly utilize such supervised information to improve the performance of dimensionality reduction. In this case, it is necessary to modify the traditional GP-LVM to make it capable of handing the supervised or semi-supervised learning tasks. For this purpose, we propose a new semi-supervised GP-LVM framework under the pairwise constraints. Through transferring the pairwise constraints in the observed space to the latent space, the constrained priori information on the latent variables can be obtained. Under this constrained priori, the latent variables are optimized by the maximum a posteriori (MAP) algorithm. The effectiveness of the proposed algorithm is demonstrated with experiments on a variety of data sets. © 2010 Elsevier B.V.

An empirical study of international private label branding in the food sector in Hungary

Private label branding strategies differ to that of the manufacturer. The study aims to identify optimal private label branding strategies for (a) utilitarian products and (b) hedonistic products, considering the special factors reflected in consumer behavior related to private labels in Hungary. The issue of House of Brands and Branded House strategies are discussed and evaluated in the light of retail business models. Focus group interviews and factor analysis of the survey found differences in branding strategies preferred by consumers for the two product categories. The study also outlines a strong trend in possible private label development based on consumer’s changing attitude in favor of national products.

Alternative ways for private label manufacturing

Private labels are a growing phenomenon globaly. retatlers become stronger and stronger by offering their own quality private label product for customers in all segments. Certainly they do not open factories to produce these items but rather search for dedicated private label producers or pressure branded goods manufacturers to produce it for them. The article deals with the strategic choiches manufacturers can have and suggest the necessary factors that need to be evaluated to decide on the winning business model - in considering wether or not to enter in private label production - through literature and a case study on the ice cream market in Hungary

Strategic options of firms considering private label production

Private labels are a growing phenomenon globaly. Retailers become stronger and stronger by offering their own quality private label product for customers in all segments. Certainly they do not open factories to produce these items but rather search for dedicated private label producers or pressure branded goods manufacturers to produce it for them. The article deals with the strategic choices manufacturers can have and suggest the necessary factors that need to be evaluated to decide on the winning business model- in considering wether or not to enter in private label production- through literature and a case study on the ice cream market in Hungary.