'DIRTY POLITICS' AND THE FAILURE OF DEMOCRATIC PROMISE: CITIZENS' ALIENATION FROM POLITICS IN SERBIA

When a society is under going profound social, political, and cultural change, its collective identities are being redefined, the political community reconceived, new rules of the political game established and routinized. In such periods the status ofthe political sphere, professional politicians and political institutions is of utmost importance. This includes not just the objective role and place of politics in social life but also the prestige and reputation of political actors: how do rank-and-filecitizens view them, how much are they esteemed? It is the job of politicians to govern the country and make decisions affecting everybody. These decisions areoften difficult and painful, and not all citizens approve of them at all times. Forthese reasons, professional politicians must maintain a two fold relationship towards their social base: to represent it as best they can, but also to be able to resist the passions of majority when the common interest and political wisdom so require. If governmental decisions, including unpopular ones, are to be acknowledged and implemented, politicians must enjoy some genuine authority in the eyes of citizenry.This holds regardless of the fact that active democratic citizenship and civil society always presume healthy criticism of those in power

Acronyms as an integral part of multi–word term recognition - A token of appreciation

Term conflation is the process of linking together different variants of the same term. In automatic term recognition approaches, all term variants should be aggregated into a single normalized term representative, which is associated with a single domain–specific concept as a latent variable. In a previous study, we described FlexiTerm, an unsupervised method for recognition of multi–word terms from a domain–specific corpus. It uses a range of methods to normalize three types of term variation – orthographic, morphological and syntactic variation. Acronyms, which represent a highly productive type of term variation, were not supported. In this study, we describe how the functionality of FlexiTerm has been extended to recognize acronyms and incorporate them into the term conflation process. The main contribution of this study is not acronym recognition per se, but rather its integration with other types of term variation into the term conflation process. We evaluated the effects of term conflation in the context of information retrieval as one of its most prominent applications. On average, relative recall increased by 32 percent points, whereas index compression factor increased by 7 percent points. Therefore, evidence suggests that integration of acronyms provides non–trivial improvement of term conflation

Targeting primary cilia-mediated mechanotransduction to promote whole bone formation

Osteoporosis is a devastating condition characterized by decreased bone mass, and affects over 50% of the population over 50 years old. Progression of osteoporosis results in significantly heightened risk of fracture leading to loss of mobility, prolonged rehabilitation, and even mortality due to extended hospitalization. Current therapeutic options exist to combat low bone mass, but these treatments are being met with increasing concern as reports emerge of atypical fractures and necrosis. Thus, new therapeutic strategies are required. Bone is highly dynamic, and it has long been known that physical load is a potent stimulus of bone formation. Despite this, none of the current treatments for bone disease leverage the inherent mechanosensitivity of bone – the ability of bone cells to sense and respond to mechanical forces such as exercise. One potential therapeutic target is the primary cilium. Primary cilia are solitary antenna-like organelles, and over the last 20 years have been identified as a critical cellular mechanosensor. Primary cilia and cell mechanotransduction are critical to the function of numerous cells and tissues. Thus, understanding primary cilia-mediated mechanotransduction has potential applications in treating kidney and liver disease, atherosclerosis, osteoarthritis, and even certain cancers. Previous work from our group has demonstrated that disruption of the cilium impairs bone cell mechanosensitivity, resulting in abrogated whole bone adaptation in response to physical load. In this thesis we examine the potential of targeting the primary cilium to enhance bone cell mechanosensitivity and promote whole bone formation. First, we demonstrate the pharmacologically increasing primary cilia length significantly enhances cell mechanotransduction. Next, we expand our list of candidate compounds to manipulate ciliogenesis through the use of high-throughput drug screening. We developed an automated platform for culturing, staining, imaging, and analyzing nearly 7000 small molecules with known biologic activity, and classify them based on mechanism of action. One of these compounds is then used in a co-culture model to study the effects of manipulating osteocyte primary cilia-mediated mechanosensing on pro-osteogenic paracrine signaling to promote the activity of bone-forming osteoblasts and osteogenic differentiation of mesenchymal stem cells. Finally, we translate our in vitro findings into an in vivo model of load-induced bone formation using the same compound to enhance cell mechanotransduction. We demonstrate that we can sensitize bones to mechanical stimulation to enhance load-induced bone formation in healthy and osteoporotic animals, with minimal adverse effects. Together, this work demonstrates the therapeutic potential and viability of targeting primary cilia-mediated mechanotransduction for treating bone diseases

Clinical text data in machine learning: Systematic review

Background: Clinical narratives represent the main form of communication within healthcare providing a personalized account of patient history and assessments, offering rich information for clinical decision making. Natural language processing (NLP) has repeatedly demonstrated its feasibility to unlock evidence buried in clinical narratives. Machine learning can facilitate rapid development of NLP tools by leveraging large amounts of text data. Objective: The main aim of this study is to provide systematic evidence on the properties of text data used to train machine learning approaches to clinical NLP. We also investigate the types of NLP tasks that have been supported by machine learning and how they can be applied in clinical practice. Methods: Our methodology was based on the guidelines for performing systematic reviews. In August 2018, we used PubMed, a multi-faceted interface, to perform a literature search against MEDLINE. We identified a total of 110 relevant studies and extracted information about the text data used to support machine learning, the NLP tasks supported and their clinical applications. The data properties considered included their size, provenance, collection methods, annotation and any relevant statistics. Results: The vast majority of datasets used to train machine learning models included only hundreds or thousands of documents. Only 10 studies used tens of thousands of documents with a handful of studies utilizing more. Relatively small datasets were utilized for training even when much larger datasets were available. The main reason for such poor data utilization is the annotation bottleneck faced by supervised machine learning algorithms. Active learning was explored to iteratively sample a subset of data for manual annotation as a strategy for minimizing the annotation effort while maximizing predictive performance of the model. Supervised learning was successfully used where clinical codes integrated with free text notes into electronic health records were utilized as class labels. Similarly, distant supervision was used to utilize an existing knowledge base to automatically annotate raw text. Where manual annotation was unavoidable, crowdsourcing was explored, but it remains unsuitable due to sensitive nature of data considered. Beside the small volume, training data were typically sourced from a small number of institutions, thus offering no hard evidence about the transferability of machine learning models. The vast majority of studies focused on the task of text classification. Most commonly, the classification results were used to support phenotyping, prognosis, care improvement, resource management and surveillance. Conclusions: We identified the data annotation bottleneck as one of the key obstacles to machine learning approaches in clinical NLP. Active learning and distant supervision were explored as a way of saving the annotation efforts. Future research in this field would benefit from alternatives such as data augmentation and transfer learning, or unsupervised learning, which does not require data annotation

AI-assisted patent prior art searching - feasibility study

This study seeks to understand the feasibility, technical complexities and effectiveness of using artificial intelligence (AI) solutions to improve operational processes of registering IP rights. The Intellectual Property Office commissioned Cardiff University to undertake this research. The research was funded through the BEIS Regulators’ Pioneer Fund (RPF). The RPF fund was set up to help address barriers to innovation in the UK economy

Patient triage by topic modelling of referral letters: Feasibility study

Background: Musculoskeletal conditions are managed within primary care but patients can be referred to secondary care if a specialist opinion is required. The ever increasing demand of healthcare resources emphasizes the need to streamline care pathways with the ultimate aim of ensuring that patients receive timely and optimal care. Information contained in referral letters underpins the referral decision-making process but is yet to be explored systematically for the purposes of treatment prioritization for musculoskeletal conditions. Objective: This study aims to explore the feasibility of using natural language processing and machine learning to automate triage of patients with musculoskeletal conditions by analyzing information from referral letters. Specifically, we aim to determine whether referral letters can be automatically assorted into latent topics that are clinically relevant, i.e. considered relevant when prescribing treatments. Here, clinical relevance is assessed by posing two research questions. Can latent topics be used to automatically predict the treatment? Can clinicians interpret latent topics as cohorts of patients who share common characteristics or experience such as medical history, demographics and possible treatments? Methods: We used latent Dirichlet allocation to model each referral letter as a finite mixture over an underlying set of topics and model each topic as an infinite mixture over an underlying set of topic probabilities. The topic model was evaluated in the context of automating patient triage. Given a set of treatment outcomes, a binary classifier was trained for each outcome using previously extracted topics as the input features of the machine learning algorithm. In addition, qualitative evaluation was performed to assess human interpretability of topics. Results: The prediction accuracy of binary classifiers outperformed the stratified random classifier by a large margin giving an indication that topic modelling could be used to predict the treatment thus effectively supporting patient triage. Qualitative evaluation confirmed high clinical interpretability of the topic model. Conclusions: The results established the feasibility of using natural language processing and machine learning to automate triage of patients with knee and/or hip pain by analyzing information from their referral letters

Idiom–based features in sentiment analysis: cutting the Gordian knot

In this paper we describe an automated approach to enriching sentiment analysis with idiom–based features. Specifically, we automated the development of the supporting lexico–semantic resources, which include (1) a set of rules used to identify idioms in text and (2) their sentiment polarity classifications. Our method demonstrates how idiom dictionaries, which are readily available general pedagogical resources, can be adapted into purpose–specific computational resources automatically. These resources were then used to replace the manually engineered counterparts in an existing system, which originally outperformed the baseline sentiment analysis approaches by 17 percentage points on average, taking the F–measure from 40s into 60s. The new fully automated approach outperformed the baselines by 8 percentage points on average taking the F–measure from 40s into 50s. Although the latter improvement is not as high as the one achieved with the manually engineered features, it has got the advantage of being more general in a sense that it can readily utilize an arbitrary list of idioms without the knowledge acquisition overhead previously associated with this task, thereby fully automating the original approach

Head to head: Semantic similarity of multi-word terms

Terms are linguistic signifiers of domain–specific concepts. Semantic similarity between terms refers to the corresponding distance in the conceptual space. In this study, we use lexico–syntactic information to define a vector space representation in which cosine similarity closely approximates semantic similarity between the corresponding terms. Given a multi–word term, each word is weighed in terms of its defining properties. In this context, the head noun is given the highest weight. Other words are weighed depending on their relations to the head noun. We formalized the problem as that of determining a topological ordering of a direct acyclic graph, which is based on constituency and dependency relations within a noun phrase. To counteract the errors associated with automatically inferred constituency and dependency relations, we implemented a heuristic approach to approximating the topological ordering. Different weights are assigned to different words based on their positions. Clustering experiments performed on such a vector space representation showed considerable improvement over the conventional bag–of–word representation. Specifically, it more consistently reflected semantic similarity between the terms. This was established by analyzing the differences between automatically generated dendrograms and manually constructed taxonomies. In conclusion, our method can be used to semi–automate taxonomy construction

Acceptability of a digital healthintervention alongside physiotherapy to support patients following anterior cruciateligament reconstruction

Background: Physiotherapy rehabilitation following surgical reconstruction to the Anterior Cruciate Ligament (ACL) can take up to 12 months to complete. Given the lengthy rehabilitation process, a blended intervention can be used to compliment face-to-face physiotherapy with a digital exercise intervention. In this study, we used TRAK, a web–based tool that has been developed to support knee rehabilitation, which provides individually tailored exercise programs with videos, instructions and progress logs for each exercise, relevant health information and a contact option that allows a patient to email a physiotherapist for additional support. The aim of this study was to evaluate the acceptability of TRAK–based blended intervention in post ACL reconstruction rehabilitation. Methods: A qualitative research design using semi-structured interviews was used on a convenience sample of participants following an ACL reconstruction, and their treating physiotherapists, in a London NHS hospital. Participants were asked to use TRAK alongside face-to-face physiotherapy for 16 weeks. Interviews were carried out, audio recorded, transcribed verbatim and coded by two researchers independently. Data were analyzed using thematic analysis. Results: Of the 25 individuals that were approached to be part of the study, 24 consented, comprising 8 females and 16 males, mean age 30 years. 17 individuals used TRAK for 16 weeks and were available for interview. Four physiotherapists were also interviewed. The six main themes identified from patients were: the experience of TRAK rehabilitation, personal characteristics for engagement, strengths and weaknesses of the intervention, TRAK in the future and attitudes to digital healthcare. The main themes from the physiotherapist interviews were: potential benefits, availability of resources and service organization to support use of TRAK. Conclusions: TRAK was found to be an acceptable method of delivering ACL rehabilitation alongside face-to-face physiotherapy. Patients reported that TRAK, specifically the videos, increased their confidence and motivation with their rehabilitation. They identified ways in which TRAK could be developed in the future to meet technological expectations and further support rehabilitation. For Physiotherapists time and availability of computers affected acceptability. Organization of care to support integration of digital exercise interventions such as TRAK into a blended approach to rehabilitation is required