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Ensuring Access to Safe and Nutritious Food for All Through the Transformation of Food Systems
Bayesian networks for disease diagnosis: What are they, who has used them and how?
A Bayesian network (BN) is a probabilistic graph based on Bayes' theorem,
used to show dependencies or cause-and-effect relationships between variables.
They are widely applied in diagnostic processes since they allow the
incorporation of medical knowledge to the model while expressing uncertainty in
terms of probability. This systematic review presents the state of the art in
the applications of BNs in medicine in general and in the diagnosis and
prognosis of diseases in particular. Indexed articles from the last 40 years
were included. The studies generally used the typical measures of diagnostic
and prognostic accuracy: sensitivity, specificity, accuracy, precision, and the
area under the ROC curve. Overall, we found that disease diagnosis and
prognosis based on BNs can be successfully used to model complex medical
problems that require reasoning under conditions of uncertainty.Comment: 22 pages, 5 figures, 1 table, Student PhD first pape
Examples of works to practice staccato technique in clarinet instrument
Klarnetin staccato tekniğini güçlendirme aşamaları eser çalışmalarıyla uygulanmıştır. Staccato
geçişlerini hızlandıracak ritim ve nüans çalışmalarına yer verilmiştir. Çalışmanın en önemli amacı
sadece staccato çalışması değil parmak-dilin eş zamanlı uyumunun hassasiyeti üzerinde de
durulmasıdır. Staccato çalışmalarını daha verimli hale getirmek için eser çalışmasının içinde etüt
çalışmasına da yer verilmiştir. Çalışmaların üzerinde titizlikle durulması staccato çalışmasının ilham
verici etkisi ile müzikal kimliğe yeni bir boyut kazandırmıştır. Sekiz özgün eser çalışmasının her
aşaması anlatılmıştır. Her aşamanın bir sonraki performans ve tekniği güçlendirmesi esas alınmıştır.
Bu çalışmada staccato tekniğinin hangi alanlarda kullanıldığı, nasıl sonuçlar elde edildiği bilgisine
yer verilmiştir. Notaların parmak ve dil uyumu ile nasıl şekilleneceği ve nasıl bir çalışma disiplini
içinde gerçekleşeceği planlanmıştır. Kamış-nota-diyafram-parmak-dil-nüans ve disiplin
kavramlarının staccato tekniğinde ayrılmaz bir bütün olduğu saptanmıştır. Araştırmada literatür
taraması yapılarak staccato ile ilgili çalışmalar taranmıştır. Tarama sonucunda klarnet tekniğin de
kullanılan staccato eser çalışmasının az olduğu tespit edilmiştir. Metot taramasında da etüt
çalışmasının daha çok olduğu saptanmıştır. Böylelikle klarnetin staccato tekniğini hızlandırma ve
güçlendirme çalışmaları sunulmuştur. Staccato etüt çalışmaları yapılırken, araya eser çalışmasının
girmesi beyni rahatlattığı ve istekliliği daha arttırdığı gözlemlenmiştir. Staccato çalışmasını yaparken
doğru bir kamış seçimi üzerinde de durulmuştur. Staccato tekniğini doğru çalışmak için doğru bir
kamışın dil hızını arttırdığı saptanmıştır. Doğru bir kamış seçimi kamıştan rahat ses çıkmasına
bağlıdır. Kamış, dil atma gücünü vermiyorsa daha doğru bir kamış seçiminin yapılması gerekliliği
vurgulanmıştır. Staccato çalışmalarında baştan sona bir eseri yorumlamak zor olabilir. Bu açıdan
çalışma, verilen müzikal nüanslara uymanın, dil atış performansını rahatlattığını ortaya koymuştur.
Gelecek nesillere edinilen bilgi ve birikimlerin aktarılması ve geliştirici olması teşvik edilmiştir.
Çıkacak eserlerin nasıl çözüleceği, staccato tekniğinin nasıl üstesinden gelinebileceği anlatılmıştır.
Staccato tekniğinin daha kısa sürede çözüme kavuşturulması amaç edinilmiştir. Parmakların
yerlerini öğrettiğimiz kadar belleğimize de çalışmaların kaydedilmesi önemlidir. Gösterilen azmin ve
sabrın sonucu olarak ortaya çıkan yapıt başarıyı daha da yukarı seviyelere çıkaracaktır
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European Heart Rhythm Association (EHRA)/Heart Rhythm Society (HRS)/Asia Pacific Heart Rhythm Society (APHRS)/Latin American Heart Rhythm Society (LAHRS) Expert Consensus Statement on the state of genetic testing for cardiac diseases.
GENDERED EMBODIMENT, STABILITY AND CHANGE: WOMEN’S WEIGHTLIFTING AS A TOOL FOR RECOVERY FROM EATING DISORDERS
This thesis explores the everyday embodied experiences of women who use amateur weightlifting as a vehicle for recovery from eating disorders. Within online spaces and on social media, women frequently share their experiences of using weightlifting to overcome issues relating to disordered eating, body image, and mental health. In particular, women with a history of eating disorders credit weightlifting to be integral to their recovery journey. However, there is a dearth of research on women’s experiences with exercise during eating disorder recovery and no research that identifies weightlifting as beneficial to this process. To the contrary, discursive links are drawn between the practices of self-surveillance exercised by both eating disorder sufferers and weightlifters alike. In this regard, engagement with weightlifting during eating disorder recovery may signal the transferal of pathology from one set of behaviours to another. That is, from disordered eating to rigid and self-regulatory exercise routines. This thesis examines how women subjectively navigate and make sense of this pathologisation.
The data for this research comes from longitudinal semi-structured interviews and photo elicitation with 19 women, living in the United Kingdom, who engaged in weightlifting during their eating disorder recovery. In addition, to build up a holistic picture and to explore how this phenomenon also ‘takes place’ online, I conducted a netnography of the overlapping subcultures of female weightlifting and eating disorder recovery on Instagram. Women’s standpoint theory and interpretative phenomenological analysis are combined to form the underpinning theoretical and analytical tools used to engage with these three rich data sets. Moreover, throughout I draw on an eclectic range of disciplinary perspectives, in order to bring together multiple fields of research and develop novel theoretical frameworks.
In the findings, I argue that women’s experiences using weightlifting as a tool for recovery from eating disorders manifests in an embodied sense of multiplicity. In this sense, understandings of the body that are often viewed as ontologically distinct (muscularity/thinness/fatness) hang-together at once in the lived experience of a single individual.
I argue that women, particularly those who have previously struggled with an eating disorder, are too readily positioned as vulnerable to media and representation. To theoretically combat these ideas regarding women’s assumed passivity, I develop the concept of ‘digital pruning’ to account for women’s agency in relation to new media.
I contend that weightlifting offers women in recovery from eating disorders a new framework for approaching eating and exercise. Specifically, weightlifting’s norms and values legitimate occupying a larger body, which gives women in recovery permission to eat and gain-weight in a way that is both culturally sanctioned and health-promoting.
Finally, I explore identity transformation as a specific tenet of recovery from eating disorders. I argue that, on social media, recovery identities are characterised by personal empowerment, resilience, and independence. While offline, quieter and less culturally glorified aspects of recovery (such as relationships of care) are central to women’s accounts of developing a new sense of self as they transition away from an eating disorder identity.
In summary, this thesis is an examination of the ways in which women strategically navigate pathology in relation to their bodies, social media, food/exercise practices, and identity. I argue that women develop a set of ‘DIY’ recovery practices that allow them to consciously channel and draw on their negative experiences with eating disorders, to develop new ways of living that serve their overall wellbeing. Weightlifting is integral to this process, as it provides women transitioning out of this difficult phase in their lives with new ways of relating to their bodies and of being in the world. I situate this phenomenon within a neoliberal socio-political climate in which individuals are required to take personal responsibility for their mental health and wellbeing, despite living within conditions which are not conducive to recovery
Attitudes towards Animals and Meat Consumption: The Role of Ideology and Individual Differences
Humans' relationships with non-human animals are complicated and complex. This thesis aims to address questions on how people think about animals' moral standing, how information about food animals' sentience is (mis)remembered, and how people evaluate laboratory-grown meat relative to traditional meat. The first empirical chapter of the thesis, Chapter 3, explores the question of whether higher human supremacy beliefs are associated with a greater perceived moral divide between animals of high and low status. Across two studies (N = 196 and N = 256), the findings suggest that people holding stronger human supremacy beliefs also perceive a greater moral divide between animals, which may serve as a legitimising strategy to preserve not only the existing human-animal hierarchy, but also greater hierarchical divides between other animals. The second set of studies, presented in Chapter 4 (N = 253 and N = 255), focuses on food animals specifically, investigating the ideologically motivated memory processes involved in the processing of objective information about these animals' sentience. Indeed, dominance-based ideologies were significant predictors for targeted memory errors for information on food animals' sentience, but not for information on their uses (e.g., in medical science), suggesting that differences in ideological attitudes interfere with the correct recall of sentience information for food animals. The final set of studies, presented in Chapter 5 (total N = 1,169), turns its focus to the psychological barriers to acceptance of laboratory-grown meat, which is structurally identical to traditionally farmed meat and presents solutions to the ethical, environmental, and public health issues associated with traditional animal agriculture. The three experiments consistently demonstrated that omnivores who were wearier about new food technologies evaluated clean meat more negatively than traditional meat. Experiment 3 further demonstrated that safety concerns, but not naturalness concerns, partly explained why those wearier of novel food technologies evaluated clean meat less positively. Taken together, the findings highlight the role of general concerns about the use of new food technology as a psychological barrier to clean meat acceptance. This thesis thus adds to the growing body of literature on human-animal intergroup relations, providing further evidence for the ways in which individual differences and ideology affect peoples' thinking about animals of different socio-cultural status, as well as attitudes towards meat substitutes
Hunting Wildlife in the Tropics and Subtropics
The hunting of wild animals for their meat has been a crucial activity in the evolution of humans. It continues to be an essential source of food and a generator of income for millions of Indigenous and rural communities worldwide. Conservationists rightly fear that excessive hunting of many animal species will cause their demise, as has already happened throughout the Anthropocene. Many species of large mammals and birds have been decimated or annihilated due to overhunting by humans. If such pressures continue, many other species will meet the same fate. Equally, if the use of wildlife resources is to continue by those who depend on it, sustainable practices must be implemented. These communities need to remain or become custodians of the wildlife resources within their lands, for their own well-being as well as for biodiversity in general. This title is also available via Open Access on Cambridge Core
Developing automated meta-research approaches in the preclinical Alzheimer's disease literature
Alzheimer’s disease is a devastating neurodegenerative disorder for which there is no cure. A crucial part of the drug development pipeline involves testing therapeutic interventions in animal disease models. However, promising findings in preclinical experiments have not translated into clinical trial success. Reproducibility has often been cited as a major issue affecting biomedical research, where experimental results in one laboratory cannot be replicated in another. By using meta-research (research on research) approaches such as systematic reviews, researchers aim to identify and summarise all available evidence relating to a specific research question. By conducting a meta-analysis, researchers can also combine the results from different experiments statistically to understand the overall effect of an intervention and to explore reasons for variations seen across different publications. Systematic reviews of the preclinical Alzheimer’s disease literature could inform decision making, encourage research improvement, and identify gaps in the literature to guide future research. However, due to the vast amount of potentially useful evidence from animal models of Alzheimer’s disease, it remains difficult to make sense of and utilise this data effectively. Systematic reviews are common practice within evidence based medicine, yet their application to preclinical research is often limited by the time and resources required. In this thesis, I develop, build-upon, and implement automated meta-research approaches to collect, curate, and evaluate the preclinical Alzheimer’s literature. I searched several biomedical databases to obtain all research relevant to Alzheimer’s disease. I developed a novel deduplication tool to automatically identify and remove duplicate publications identified across different databases with minimal human effort. I trained a crowd of reviewers to annotate a subset of the publications identified and used this data to train a machine learning algorithm to screen through the remaining publications for relevance. I developed text-mining tools to extract model, intervention, and treatment information from publications and I improved existing automated tools to extract reported measures to reduce the risk of bias. Using these tools, I created a categorised database of research in transgenic Alzheimer’s disease animal models and created a visual summary of this dataset on an interactive, openly accessible online platform. Using the techniques described, I also identified relevant publications within the categorised dataset to perform systematic reviews of two key outcomes of interest in transgenic Alzheimer’s disease models: (1) synaptic plasticity and transmission in hippocampal slices and (2) motor activity in the open field test.
Over 400,000 publications were identified across biomedical research databases, with 230,203 unique publications. In a performance evaluation across different preclinical datasets, the automated deduplication tool I developed could identify over 97% of duplicate citations and a had an error rate similar to that of human performance. When evaluated on a test set of publications, the machine learning classifier trained to identify relevant research in transgenic models performed was highly sensitive (captured 96.5% of relevant publications) and excluded 87.8% of irrelevant publications. Tools to identify the model(s) and outcome measure(s) within the full-text of publications may reduce the burden on reviewers and were found to be more sensitive than searching only the title and abstract of citations. Automated tools to assess risk of bias reporting were highly sensitive and could have the potential to monitor research improvement over time. The final dataset of categorised Alzheimer’s disease research contained 22,375 publications which were then visualised in the interactive web application. Within the application, users can see how many publications report measures to reduce the risk of bias and how many have been classified as using each transgenic model, testing each intervention, and measuring each outcome. Users can also filter to obtain curated lists of relevant research, allowing them to perform systematic reviews at an accelerated pace with reduced effort required to search across databases, and a reduced number of publications to screen for relevance. Both systematic reviews and meta-analyses highlighted failures to report key methodological information within publications. Poor transparency of reporting limited the statistical power I had to understand the sources of between-study variation. However, some variables were found to explain a significant proportion of the heterogeneity. Transgenic animal model had a significant impact on results in both reviews. For certain open field test outcomes, wall colour of the open field arena and the reporting of measures to reduce the risk of bias were found to impact results. For in vitro electrophysiology experiments measuring synaptic plasticity, several electrophysiology parameters, including magnesium concentration of the recording solution, were found to explain a significant proportion of the heterogeneity. Automated meta-research approaches and curated web platforms summarising preclinical research could have the potential to accelerate the conduct of systematic reviews and maximise the potential of existing evidence to inform translation
The Neural Mechanisms of Value Construction
Research in decision neuroscience has characterized how the brain makes decisions by assessing the expected utility of each option in an abstract value space that affords the ability to compare dissimilar options. Experiments at multiple levels of analysis in multiple species have localized the ventromedial prefrontal cortex (vmPFC) and nearby orbitofrontal cortex (OFC) as the main nexus where this abstract value space is represented. However, much less is known about how this value code is constructed by the brain in the first place. By using a combination of behavioral modeling and cutting edge tools to analyze functional magnetic resonance imaging (fMRI) data, the work of this thesis proposes that the brain decomposes stimuli into their constituent attributes and integrates across them to construct value. These stimulus features embody appetitive or aversive properties that are either learned from experience or evaluated online by comparing them to previously experienced stimuli with similar features. Stimulus features are processed by cortical areas specialized for the perception of a particular stimulus type and then integrated into a value signal in vmPFC/OFC.
The project presented in Chapter 2 examines how food items are evaluated by their constituent attributes, namely their nutrient makeup. A linear attribute integration model succinctly captures how subjective values can be computed from a weighted combination of the constituent nutritive attributes of the food. Multivariate analysis methods revealed that these nutrient attributes are represented in the lateral OFC, while food value is encoded both in medial and lateral OFC. Connectivity between lateral and medial OFC allows this nutrient attribute information to be integrated into a value representation in medial OFC.
In Chapter 3, I show that this value construction process can operate over higher-level abstractions when the context requires bundles of items to be valued, rather than isolated items. When valuing bundles of items, the constituent items themselves become the features, and their values are integrated with a subadditive function to construct the value of the bundle. Multiple subregions of PFC including but not limited to vmPFC compute the value of a bundle with the same value code used to evaluate individual items, suggesting that these general value regions contextually adapt within this hierarchy. When valuing bundles and single items in interleaved trials, the value code rapidly switches between levels in this hierarchy by normalizing to the distribution of values in the current context rather than representing all options on an absolute scale.
Although the attribute integration model of value construction characterizes human behavior on simple decision-making tasks, it is unclear how it can scale up to environments of real-world complexity. Taking inspiration from modern advances in artificial intelligence, and deep reinforcement learning in particular, in Chapter 4 I outline how connectionist models generalize the attribute integration model to naturalistic tasks by decomposing sensory input into a high dimensional set of nonlinear features that are encoded with hierarchical and distributed processing. Participants freely played Atari video games during fMRI scanning, and a deep reinforcement learning algorithm trained on the games was used as an end-to-end model for how humans evaluate actions in these high-dimensional tasks. The features represented in the intermediate layers of the artificial neural network were found to also be encoded in a distributed fashion throughout the cortex, specifically in the dorsal visual stream and posterior parietal cortex. These features emerge from nonlinear transformations of the sensory input that connect perception to action and reward. In contrast to the stimulus attributes used to evaluate the stimuli presented in the preceding chapters, these features become highly complex and inscrutable as they are driven by the statistical properties of high-dimensional data. However, they do not solely reflect a set of features that can be identified by applying common dimensionality reduction techniques to the input, as task-irrelevant sensory features are stripped away and task-relevant high-level features are magnified.</p
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