Characterisation of the contribution of the kinase and RNase activities of Ire1α to activation of apoptotic JNK signalling

The unfolded protein response (UPR) is a highly conserved mechanism by which all eukaryotes respond to endoplasmic reticulum (ER) stress. In higher eukaryotes this response is mediated by three ER transmembrane stress sensors: activating transcription factor 6 (ATF6/), PKR-like ER kinase (PERK) and inositol requiring 1 (IRE1/). IRE1 is the most highly conserved of the three ER stress sensors and is also the only sensor to mediated UPR signalling via two different enzymatic domains. It is currently believed that during prolonged ER stress, the RNase domain of IRE1α provides cytoprotection via XBP1 splicing, whilst the kinase domain initiates proapoptotic JNK signalling via interaction with the adaptor protein TRAF2. However, characterising how these domains contribute to cell fate decisions is complicated by the fact that traditional models use ER stress mimetic drugs, which activate all three branches of the UPR and thus make it difficult to attribute downstream events to individual effectors. Therefore, the aim of the research presented in this thesis was to produce a model that allowed isolated activation of IRE1α in order to determine the contribution of its kinase and RNase activities to proapoptotic JNK signalling, without input from other upstream effectors. Using the Fv2E-IRE1α system, the data presented in this thesis provides novel insight into the mechanism by which IRE1α instigates proapoptotic JNK signalling by suggesting that a functional kinase domain is not required for IRE1α to interact with TRAF2 and that endoribonuclease function may be required for downstream JNK activation in humans. Furthermore, evidence is also provided to suggest that, whilst kinase activity is not required for interaction with TRAF2, it is required for downstream JNK activation. This gives rise to the possibility that, contrary to current knowledge, the IRE1α kinase domain has the capacity to phosphorylate proteins other than IRE1α

Investigating differential gene expression in vivo of cardiac birth defects in an avian model of maternal phenylketonuria.

Cardiac malformations (CVMs) are a leading cause of infant morbidity and mortality. CVMs are particularly prevalent when the developing fetus is exposed to high levels of phenylalanine in-utero in mothers with Phenylketonuria. Yet, elucidating the underlying molecular mechanism leading to CVMs has proven difficult. In this study we used RNA-Seq to investigate an avian model of MPKU and establish differential gene expression (DEG) characteristics of the early developmental stages HH10, 12, and 14. In total, we identified 633 significantly differentially expressed genes across stages HH10, 12, and 14. As expected, functional annotation of significant DEGs identified associations seen in clinical phenotypes of MPKU including CVMs, congenital heart defects, craniofacial anomalies, central nervous system defects, and growth anomalies. Additionally, there was an overrepresentation of genes involved in cardiac muscle contraction, adrenergic signaling in cardiomyocytes, migration, proliferation, metabolism, and cell survival. Strikingly, we identified significant changes in expression with multiple genes involved in Retinoic Acid (RA) metabolism and downstream targets. Using qRTPCR, we validated these findings and identified a total of 42 genes within the RA pathway that are differentially expressed. Here, we report the first elucidation of the molecular mechanisms of cardiovascular malformations in MPKU conducted at early developmental timepoints. We provide evidence suggesting a link between PHE exposure and the alteration of RA pathway. These results are promising for potential targeted therapeutic interventions in individuals with MPKU. Additionally, we introduce genes of interest that were cloned for in vivo analysis of mRNA through in situ hybridization

Creating an Inclusive Dance Studio

https://remix.berklee.edu/able-assembly-conference/1011/thumbnail.jp

THE ECONOMICS OF RIPARIAN MANAGEMENT: A LITERATURE REVIEW

Resource /Energy Economics and Policy,

Predicting Performance in an Introductory Programming Course by Logging and Analyzing Student Programming Behavior

The high failure rates of many programming courses means there is a need to identify struggling students as early as possible. Prior research has focused upon using a set of tests to assess the use of a student's demographic, psychological and cognitive traits as predictors of performance. But these traits are static in nature, and therefore fail to encapsulate changes in a student's learning progress over the duration of a course. In this paper we present a new approach for predicting a student's performance in a programming course, based upon analyzing directly logged data, describing various aspects of their ordinary programming behavior. An evaluation using data logged from a sample of 45 programming students at our University, showed that our approach was an excellent early predictor of performance, explaining 42.49% of the variance in coursework marks - double the explanatory power when compared to the closest related technique in the literature

No tests required : comparing traditional and dynamic predictors of programming success.

Research over the past fifty years into predictors of programming performance has yielded little improvement in the identification of at-risk students. This is possibly because research to date is based upon using static tests, which fail to reflect changes in a student's learning progress over time. In this paper, the effectiveness of 38 traditional predictors of programming performance are compared to 12 new data-driven predictors, that are based upon analyzing directly logged data, describing the programming behavior of students. Whilst few strong correlations were found between the traditional predictors and performance, an abundance of strong significant correlations based upon programming behavior were found. A model based upon two of these metrics (Watwin score and percentage of lab time spent resolving errors) could explain 56.3% of the variance in coursework results. The implication of this study is that a student's programming behavior is one of the strongest indicators of their performance, and future work should continue to explore such predictors in different teaching contexts

Footprints and Free Space from a Single Color Image

Understanding the shape of a scene from a single color image is a formidable computer vision task. However, most methods aim to predict the geometry of surfaces that are visible to the camera, which is of limited use when planning paths for robots or augmented reality agents. Such agents can only move when grounded on a traversable surface, which we define as the set of classes which humans can also walk over, such as grass, footpaths and pavement. Models which predict beyond the line of sight often parameterize the scene with voxels or meshes, which can be expensive to use in machine learning frameworks. We introduce a model to predict the geometry of both visible and occluded traversable surfaces, given a single RGB image as input. We learn from stereo video sequences, using camera poses, per-frame depth and semantic segmentation to form training data, which is used to supervise an image-to-image network. We train models from the KITTI driving dataset, the indoor Matterport dataset, and from our own casually captured stereo footage. We find that a surprisingly low bar for spatial coverage of training scenes is required. We validate our algorithm against a range of strong baselines, and include an assessment of our predictions for a path-planning task.Comment: Accepted to CVPR 2020 as an oral presentatio

Self-Supervised Monocular Depth Hints

Monocular depth estimators can be trained with various forms of self-supervision from binocular-stereo data to circumvent the need for high-quality laser scans or other ground-truth data. The disadvantage, however, is that the photometric reprojection losses used with self-supervised learning typically have multiple local minima. These plausible-looking alternatives to ground truth can restrict what a regression network learns, causing it to predict depth maps of limited quality. As one prominent example, depth discontinuities around thin structures are often incorrectly estimated by current state-of-the-art methods. Here, we study the problem of ambiguous reprojections in depth prediction from stereo-based self-supervision, and introduce Depth Hints to alleviate their effects. Depth Hints are complementary depth suggestions obtained from simple off-the-shelf stereo algorithms. These hints enhance an existing photometric loss function, and are used to guide a network to learn better weights. They require no additional data, and are assumed to be right only sometimes. We show that using our Depth Hints gives a substantial boost when training several leading self-supervised-from-stereo models, not just our own. Further, combined with other good practices, we produce state-of-the-art depth predictions on the KITTI benchmark.Comment: Accepted to ICCV 201

Weight and Body Image Perceptions in Nutrition and Dietetics University Students

Stereotypical and prejudicial attitudes towards people considered overweight or obese are documented in professionals ranging from physicians, nurses, fitness and general nutrition professionals, and registered dietitian nutritionists (RDN) and may introduce barriers to equitable care. To identify the prevalence of anti-fat attitudes (AFA); fat phobia (FPS); and body appreciation scores (BA) in nutrition and dietetics’ students (ND) and non-nutrition and dietetics’ students (NND) through a cross-sectional design. During 2018, students (n=297) from two California State Universities completed a survey including three validated instruments. Additionally, height, weight, and waist circumference were collected using NHANES procedures. A series of ANCOVA’s and correlation coefficients were computed. Significant differences existed in BA between ND (M=3.61, SD=0.66) and NND students (M=3.81, SD=0.70); t(288) = 2.49, p=0.013. Scores indicated existing anti-fat attitudes and fat phobia. Significant positive correlations existed between FPS and anthropometrics. Weight related perceptions were identified. A need exists for a fundamental evidence-based training specifically focused on knowledge and awareness related to health metrics and social justice pedagogy to help RDN work unbiasedly with patients of all shapes