9,118 research outputs found

    Adaptations in physiological and neuronal function during diet-induced obesity

    Get PDF
    Obesity significantly increases the risk of developing chronic conditions including type II diabetes, cardiovascular disease, and some cancers. The rate of obesity has tripled globally since 1975, which is in part due to the sudden prevalence and overconsumption of palatable high-fat diets (HFDs). Obesity profoundly perturbs the neural control of energy balance, affecting diverse cell types within the hypothalamus. However, an incomplete understanding of how HFD impacts the regulation of energy balance hinders our ability to more effectively treat obesity. In this thesis, I describe the physiological and neuronal response to HFD feeding in rodents. We identified that HFD exposure elevates the body weight set point, which is initially driven by a transient hyperphagia. This hyperphagia coincides with increased excitatory transmission to lateral hypothalamic orexin (ORX) neurons, which regulate acute food intake. This suggests that ORX neurons may be involved in the initial hyperphagia, implicating them in the development of obesity. As HFD prolongs, body weight gain slows and reaches a new steady state regardless of age at the start, duration of feeding, or palatability of the diet. This sustained weight coincides with increased synaptic contacts to melanin-concentrating hormone (MCH) neurons, which promote weight gain and food intake, likely contributing to the maintenance of obesity. The molecular mechanism underlying the establishment of a new set point remains elusive. During HFD feeding, the presence of a chronic low-grade hypothalamic inflammation exacerbates weight gain, therefore we reasoned that inflammatory factors could modulate appetite-promoting neurons to maintain a new set point. We found that the inflammatory mediator prostaglandin E2 (PGE2) activate MCH neurons via its EP2 receptor (EP2R). Suppressing PGE2-EP2R on MCH neurons partially protects against excess weight gain and fat accumulation in the liver during HFD feeding. This mechanism could contribute to the maintenance of an elevated body weight set point in during diet-induced obesity. Without long-term treatment options in face of the increasing rates of obesity, we are in desperate need of novel interventions. In the future, we hope that targeting EP2R on MCH neurons can lower body weight set point and aid in combatting obesity

    Parámetros genéticos de los caracteres morfológicos lineales de la raza caprina murciano-granadina y sus relaciones con otros caracteres funcionales

    Get PDF
    Linear appraisal systems (LAS) are effective strategies for systematically collecting zoometric information from animal populations. Traditionally applied LAS in goats was developed considering the variability and scales found in highly selected breeds. Implementing LAS may reduce time, personnel, and resource needs when performing zoometric large-scale collection. Moreover, selection for zoometrics defines individuals’ productive longevity, endurance, enhanced productive abilities, and consequently, long-term profitability. As a result, traditional LAS may no longer cover the different contexts of goat breeds widespread throughout the world, and departures from normality may be indicative of the different stages of selection at which a certain population can be found. In the first study, an evaluation of the distribution and symmetry properties of twenty-eight zoometric traits was developed. After symmetry analysis was performed, the scale readjustment proposal suggested specific strategies should be implemented such as scale reduction of lower or upper levels, determination of a setup moment to evaluate and collect information from young (up to 2 years) and adult bucks (over 2 years), the addition of upper categories in males due to upper values in the scale being incorrectly clustered together. Thus, the particular analysis of each variable permits determining specific strategies for each trait and serve as a model for other breeds, either selected or in terms of selection. The aim of the second study was to propose a method to optimize and validate LAS in opposition to traditional measuring protocols routinely implemented in Murciano-Granadina goats. The data sample consisted of 41323 LAS and traditional measuring records, belonging to 22727 herdbook registered primipara does, 17111 multipara does, and 1485 bucks. Each record comprised information on 17 linear traits for primipara and multipara does, and 10 traits for bucks. All zoometric parameters were scored on a 9-points scale. Cronbach’s alpha values suggested a high internal consistency of the optimized variable panel. Model fit, variability explanation power, and predictive power (MSE, AIC/AICc, and BIC, respectively) suggested a model comprising zoometric LAS scores performed better than traditional zoometry. Optimization procedures result in reduced models able to capture variability for dairy-related zoometric traits without noticeable detrimental effects on model validity properties. The third study aimed to perform a particular analysis of each variable that permits determining specific strategies for each trait and serves as a model for other breeds. Among the strategies proposed are the reduction/readjustment of the levels in the scale as it happens for limb-related traits, the extension of the scale as it occurs in the stature of males, or the subdivision of the scale used in males into two categories, bucks younger than two years and bucks of two years old and older. Murciano- Granadina goat breed has drifted towards better dairy-linked conformation traits but without losing the grounds of the zoometric basis which confers it with enhanced adaptability to the environment. Hence, such strategies can help to achieve a better understanding of the momentum of selection for dairy-linked zoometric traits in Murciano-Granadina population and their future evolution to enhance the profitability and efficiency of breeding plans. The objective of the fourth study was to evaluate the progress of heritabilities of the traits comprising the linear appraisal system in the Murciano-Granadina breed during the complete decade from December 2011 to December 2021. The estimated values for heritability were obtained from multivariate analyzes using the BLUP methodology and MTDFREML software. For 2021 heritabilities, a simple animal model was applied to records collected from 22727 primiparous goats and 17111 multiparous goats belonging to 85 herds. The model included the linear and quadratic and linear components of the covariates age and days in milk, respectively. The fixed effects considered in the model were herd, reproductive status, calving month, and herd/year interaction. The animal was considered as a random effect. The variables studied included five characteristics related to structure and capacity, two traits related to dairy structure, six related to the mammary system, and three related to legs and feet. The heritabilities for structure and capacity characters progressed from 0.22 to 0.28 including non-convergent variables in June 2012 to values between 0.10 and 0.41 with all variables converging in June 2021. Heritabilities for dairy structure progressed from 0.18 with nonconvergent variables in 2011 to 0.17 to 0.25 in 2021. Heritabilities for mammary system traits progressed from 0.12 to 0, 27 with non-convergent variables in 2012 to between 0.10 and 0.41 in 2021. For legs and feet, heritabilities progressed from 0.16 to 0.17 with non-convergent variables to 0.09 a 0.22. Genetic progress is not only evident in heritability values, but there has been a notable reduction in the standard error of heritabilities from 0.1000 (0.080-0.120) to 0.000 (0.000-0.001) from 2011 to 2021. These results provide evidence of the enhancement in the effectiveness and precision of the linear qualification system applied during the past decade and its successful integration into the breeding program of the Murciano- Granadina breed. The fifth study estimates genetic and phenotypic parameters for zoometric/LAS traits in Murciano-Granadina goats, estimate genetic and phenotypic correlations among all traits, and to determine whether major area selection would be appropriate or if adaptability strategies may need to be followed. Heritability estimates for the zoometric/LAS traits were low to high, ranging from 0.09 to 0.43 and the accuracy of estimation has improved after decades rendering standard errors negligible. Scale inversion of specific traits may need to be performed before major areas selection strategies are implemented. Genetic and phenotypic correlations suggest that negative selection against thicker bones and higher rear insertion heights, indirectly results in the optimization of selection practices in the rest of the traits, especially of those in the structure and capacity and mammary system major areas. The integration and implementation of the strategies proposed within Murciano-Granadina breeding program maximize selection opportunities and the sustainable international competitiveness of the Murciano- Granadina goat in the dairy goat breed panorama. The objective of the sixth study was to develop a discriminant canonical analysis (DCA) tool that permits outlining the role of the individual haplotypes of each component of the casein complex (αS1, β, αS2, and κ-casein) on zoometrics/linear appraisal breeding values. The relationship of the predicted breeding value for 17 zoometric/Linear appraisal traits and αS1, β, αS2, and κ-casein genes haplotypic sequences was assessed. Results suggest that, although a lack of significant differences (P>0.05) was reported across the predictive breeding values of zoometric/linear appraisal traits for αS1, αS2 and κ casein, significant differences were found for β Casein (P0,05) en los valores de cría predichos de los rasgos de zoometría/calificación lineal para la αS1, αS2 y κ-caseína, se encontraron diferencias significativas para la β-caseína (P<0,05), respectivamente. La presencia de secuencias haplotípicas de β-caseína GAGACCCC, GGAACCCC, GGAACCTC, GGAATCTC, GGGACCCC, GGGATCTC y GGGGCCCC, vinculadas a combinaciones diferenciales de mayores cantidades de leche de mayor calidad en términos de su composición, también puede estar relacionada con una mayor valoración zoométrica/lineal de la predicción de los valores de cría. La selección debe realizarse con cuidado, dado que la consideración de animales aparentemente deseables que presentan la secuencia haplotípica GGGATCCC en el gen de la β- caseína, debido a sus valores genéticos predichos positivos para ciertos rasgos de zoometría/calificación lineal, como la altura de la inserción trasera, la calidad ósea , la inserción anterior, la profundidad de ubre, la vista lateral de patas traseras y la vista trasera de patas traseras pueden conducir a una selección indirecta frente al resto de rasgos de zoometría/calificación lineal y a su vez conducir a una selección ineficiente hacia un tipo morfotipo lechero óptimo en cabras Murciano-Granadina. Por el contrario, la consideración de animales que presentan la secuencia haplotípica GGAACCCC implica también considerar animales que aumentan el potencial genético para todos los rasgos de zoometría/calificación lineal, haciéndolos así recomendables como reproductores. La información derivada de los presentes análisis mejorará la selección de individuos reproductores que busquen un tipo lechero bastante deseable, a través de la determinación de las secuencias haplotípicas que presentan en el locus β-caseína. Todos estos estudios persiguen la obtención de un conocimiento más profundo de los caracteres morfológicos lineales de la raza caprina Murciano-Granadina y sus relaciones con otras características funcionales. Esto sienta las bases para estrategias de normalización y mejora de la capacidad productiva y el morfotipo lechero de la cabra Murciano-Granadina y ayudará a alcanzar su consolidación competitiva en el panorama caprino lechero internacional

    Spatial epidemiology of a highly transmissible disease in urban neighbourhoods: Using COVID-19 outbreaks in Toronto as a case study

    Get PDF
    The emergence of infectious diseases in an urban area involves a complex interaction between the socioecological processes in the neighbourhood and urbanization. As a result, such an urban environment can be the incubator of new epidemics and spread diseases more rapidly in densely populated areas than elsewhere. Most recently, the Coronavirus-19 (COVID-19) pandemic has brought unprecedented challenges around the world. Toronto, the capital city of Ontario, Canada, has been severely impacted by COVID-19. Understanding the spatiotemporal patterns and the key drivers of such patterns is imperative for designing and implementing an effective public health program to control the spread of the pandemic. This dissertation was designed to contribute to the global research effort on the COVID-19 pandemic by conducting spatial epidemiological studies to enhance our understanding of the disease's epidemiology in a spatial context to guide enhancing the public health strategies in controlling the disease. Comprised of three original research manuscripts, this dissertation focuses on the spatial epidemiology of COVID-19 at a neighbourhood scale in Toronto. Each manuscript makes scientific contributions and enhances our knowledge of how interactions between different socioecological processes in the neighbourhood and urbanization can influence spatial spread and patterns of COVID-19 in Toronto with the application of novel and advanced methodological approaches. The findings of the outcomes of the analyses are intended to contribute to the public health policy that informs neighbourhood-based disease intervention initiatives by the public health authorities, local government, and policymakers. The first manuscript analyzes the globally and locally variable socioeconomic drivers of COVID-19 incidence and examines how these relationships vary across different neighbourhoods. In the global model, lower levels of education and the percentage of immigrants were found to have a positive association with increased risk for COVID-19. This study provides the methodological framework for identifying the local variations in the association between risk for COVID-19 and socioeconomic factors in an urban environment by applying a local multiscale geographically weighted regression (MGWR) modelling approach. The MGWR model is an improvement over the methods used in earlier studies of COVID-19 in identifying local variations of COVID-19 by incorporating a correction factor for the multiple testing problem in the geographically weighted regression models. The second manuscript quantifies the associations between COVID-19 cases and urban socioeconomic and land surface temperature (LST) at the neighbourhood scale in Toronto. Four spatiotemporal Bayesian hierarchical models with spatial, temporal, and varying space-time interaction terms are compared. The results of this study identified the seasonal trends of COVID-19 risk, where the spatiotemporal trends show increasing, decreasing, or stable patterns, and identified area-specific spatial risk for targeted interventions. Educational level and high land surface temperature are shown to have a positive association with the risk for COVID-19. In this study, high spatial and temporal resolution satellite images were used to extract LST, and atmospheric corrections methods were applied to these images by adopting a land surface emissivity (LSE) model, which provided a high estimation accuracy. The methodological approach of this work will help researchers understand how to acquire long time-series data of LST at a spatial scale from satellite images, develop methodological approaches for atmospheric correction and create the environmental data with a high estimation accuracy to fit into modelling disease. Applying to policy, the findings of this study can inform the design and implementation of urban planning strategies and programs to control disease risks. The third manuscript developed a novel approach for visualization of the spread of infectious disease outbreaks by incorporating neighbourhood networks and the time-series data of the disease at the neighbourhood level. The findings of the model provide an understanding of the direction and magnitude of spatial risk for the outbreak and guide for the importance of early intervention in order to stop the spread of the outbreak. The manuscript also identified hotspots using incidence rate and disease persistence, the findings of which may inform public health planners to develop priority-based intervention plans in a resource constraint situation

    Modular lifelong machine learning

    Get PDF
    Deep learning has drastically improved the state-of-the-art in many important fields, including computer vision and natural language processing (LeCun et al., 2015). However, it is expensive to train a deep neural network on a machine learning problem. The overall training cost further increases when one wants to solve additional problems. Lifelong machine learning (LML) develops algorithms that aim to efficiently learn to solve a sequence of problems, which become available one at a time. New problems are solved with less resources by transferring previously learned knowledge. At the same time, an LML algorithm needs to retain good performance on all encountered problems, thus avoiding catastrophic forgetting. Current approaches do not possess all the desired properties of an LML algorithm. First, they primarily focus on preventing catastrophic forgetting (Diaz-Rodriguez et al., 2018; Delange et al., 2021). As a result, they neglect some knowledge transfer properties. Furthermore, they assume that all problems in a sequence share the same input space. Finally, scaling these methods to a large sequence of problems remains a challenge. Modular approaches to deep learning decompose a deep neural network into sub-networks, referred to as modules. Each module can then be trained to perform an atomic transformation, specialised in processing a distinct subset of inputs. This modular approach to storing knowledge makes it easy to only reuse the subset of modules which are useful for the task at hand. This thesis introduces a line of research which demonstrates the merits of a modular approach to lifelong machine learning, and its ability to address the aforementioned shortcomings of other methods. Compared to previous work, we show that a modular approach can be used to achieve more LML properties than previously demonstrated. Furthermore, we develop tools which allow modular LML algorithms to scale in order to retain said properties on longer sequences of problems. First, we introduce HOUDINI, a neurosymbolic framework for modular LML. HOUDINI represents modular deep neural networks as functional programs and accumulates a library of pre-trained modules over a sequence of problems. Given a new problem, we use program synthesis to select a suitable neural architecture, as well as a high-performing combination of pre-trained and new modules. We show that our approach has most of the properties desired from an LML algorithm. Notably, it can perform forward transfer, avoid negative transfer and prevent catastrophic forgetting, even across problems with disparate input domains and problems which require different neural architectures. Second, we produce a modular LML algorithm which retains the properties of HOUDINI but can also scale to longer sequences of problems. To this end, we fix the choice of a neural architecture and introduce a probabilistic search framework, PICLE, for searching through different module combinations. To apply PICLE, we introduce two probabilistic models over neural modules which allows us to efficiently identify promising module combinations. Third, we phrase the search over module combinations in modular LML as black-box optimisation, which allows one to make use of methods from the setting of hyperparameter optimisation (HPO). We then develop a new HPO method which marries a multi-fidelity approach with model-based optimisation. We demonstrate that this leads to improvement in anytime performance in the HPO setting and discuss how this can in turn be used to augment modular LML methods. Overall, this thesis identifies a number of important LML properties, which have not all been attained in past methods, and presents an LML algorithm which can achieve all of them, apart from backward transfer

    Deep learning for unsupervised domain adaptation in medical imaging: Recent advancements and future perspectives

    Full text link
    Deep learning has demonstrated remarkable performance across various tasks in medical imaging. However, these approaches primarily focus on supervised learning, assuming that the training and testing data are drawn from the same distribution. Unfortunately, this assumption may not always hold true in practice. To address these issues, unsupervised domain adaptation (UDA) techniques have been developed to transfer knowledge from a labeled domain to a related but unlabeled domain. In recent years, significant advancements have been made in UDA, resulting in a wide range of methodologies, including feature alignment, image translation, self-supervision, and disentangled representation methods, among others. In this paper, we provide a comprehensive literature review of recent deep UDA approaches in medical imaging from a technical perspective. Specifically, we categorize current UDA research in medical imaging into six groups and further divide them into finer subcategories based on the different tasks they perform. We also discuss the respective datasets used in the studies to assess the divergence between the different domains. Finally, we discuss emerging areas and provide insights and discussions on future research directions to conclude this survey.Comment: Under Revie

    Exploring cognitive mechanisms involved in self-face recognition

    Get PDF
    Due to the own face being a significant stimulus that is critical to one’s identity, the own face is suggested to be processed in a quantitatively different (i.e., faster and better recognition) and qualitatively different (i.e., processed in a more featural manner) manner compared to other faces. This thesis further explored the cognitive mechanisms (perceptual and attentional systems) involved in the processing of the own face. Chapter 2 explored the role of holistic and featural processing involved in the processing of self-face (and other faces) with eye-tracking measures in a passive-viewing paradigm and a face identification task. In the passive-viewing paradigm, the own face was sampled in a more featural manner compared to other faces whereas when asked to identify faces, all faces were sampled in a more holistic manner. Chapter 3 further explored the role of holistic and featural processing in the identification of the own face using the three standard measures of holistic face processing: The face inversion task, the composite face task, and the part-whole task. Compared to other faces, individuals showed a smaller “holistic interference” by a task irrelevant bottom half for the own face in the composite face task and a stronger feature advantage for the own face, but inversion impaired the identification of all faces. These findings suggest that self-face is processed in a more featural manner, but the findings do not deny the role of holistic processing. The final experimental chapter, Chapter 4, explored the modulation effects of cultural differences in one’s self-concept (i.e., independent vs. interdependent self-concept) and a negative self-concept (i.e., depressive traits) on the attentional prioritization for the own face with a visual search paradigm. Findings showed that the attentional prioritization for the own face over an unfamiliar face is not modulated by cultural differences of one’s self-concept nor one’s level of depressive traits, and individuals showed no difference in the attentional prioritization for both the own face and friend’s face, demonstrating no processing advantage for the own face over a personally familiar face. These findings suggests that the attentional prioritization for the own face is better explained by a familiar face advantage. Altogether, the findings of this thesis suggest that the own face is processed qualitatively different compared to both personally familiar and unfamiliar face, with the own face being processed in a more featural manner. However, in terms of quantitative differences, the self-face is processed differently compared to an unfamiliar face, but not to a familiar face. Although the specific face processing strategies for the own face may be due to the distinct visual experience that one has with their face, the attentional prioritization of the own face is however, better explained by a familiar face advantage rather than a self-specificity effect

    Resilience and food security in a food systems context

    Get PDF
    This open access book compiles a series of chapters written by internationally recognized experts known for their in-depth but critical views on questions of resilience and food security. The book assesses rigorously and critically the contribution of the concept of resilience in advancing our understanding and ability to design and implement development interventions in relation to food security and humanitarian crises. For this, the book departs from the narrow beaten tracks of agriculture and trade, which have influenced the mainstream debate on food security for nearly 60 years, and adopts instead a wider, more holistic perspective, framed around food systems. The foundation for this new approach is the recognition that in the current post-globalization era, the food and nutritional security of the world’s population no longer depends just on the performance of agriculture and policies on trade, but rather on the capacity of the entire (food) system to produce, process, transport and distribute safe, affordable and nutritious food for all, in ways that remain environmentally sustainable. In that context, adopting a food system perspective provides a more appropriate frame as it incites to broaden the conventional thinking and to acknowledge the systemic nature of the different processes and actors involved. This book is written for a large audience, from academics to policymakers, students to practitioners

    Art and design learning journey: interactions between learners and materials

    Get PDF
    This thesis is an empirical explorative and new materialist qualitative research journey representing a secondary school art and design teacher’s awakening to the importance and vitality of art education to young learners with regards to their own intrinsic learning journey and their subsequent wider outlook on life. Secondary education and specifically art education is vulnerable and prone to political whims, lack of interest and shifts of policy since 1768 and the founding of the Royal Academy. The historical and political lineage of art and design education is outlined along with the lasting impact of language used within more recent statutory documentation. Little research currently exists that specifically looks at what is generated within the processes of making and doing that are intrinsic to creative activity and are lived out in every art and design classroom environment. Within this thesis I will explore the rich potential for haptic and tacit knowledge to be generated within the creative process, driven by heuristic experiences. I will also highlight the generation of powerful emotional relationships generated between human and non-human actants which occur as students engage with making and doing within the art classroom. Through working directly with different creative processes and materials, including research, poetry, design, and ceramics, two classes of year 9 students explored both collaboratively and individually the value of making and responding to both their own learning experience and that of working with others. The physicist and academic Karen Barad offers a novel platform of diffractive analysis with which to interpret the research project data in order to challenge the accepted positionality of merely working through a creative process in a procedural way. Diffractive analysis is also central in the analysis of the intra-actions between human and nonhuman actants opening up further discussions challenging established hierarchy and status quo presently found in secondary education. The genesis of the creative process is explored through the material discursive phenomena created through the intra-actions between human and nonhuman matter
    corecore