The early stages of heart development: insights from chicken embryos

The heart is the first functioning organ in the developing embryo and the detailed understanding of the molecular and cellular mechanisms involved in its formation provides insights into congenital malformations affecting its function and therefore the survival of the organism. Because many developmental mechanisms are highly conserved, it is possible to extrapolate from observations made in invertebrate and vertebrate model organisms to human. This review will highlight the contributions made through studying heart development in avian embryos, particularly the chicken. The major advantage of chick embryos is their accessibility for surgical manipulations and functional interference approaches, both gain- and loss-of-function. In addition to experiments performed in ovo, the dissection of tissues for ex vivo culture, genomic or biochemical approaches, is straightforward. Furthermore, embryos can be cultured for time-lapse imaging, which enables tracking of fluorescently labeled cells and detailed analyses of tissue morphogenesis. Owing to these features, investigations in chick embryos have led to important discoveries, often complementing genetic studies in mouse and zebrafish. As well as including some historical aspects, we cover here some of the crucial advances made in understanding of early heart development using the chicken model

Enhancing Guaranteed Delays with Network Coding

For networks providing QoS guarantees, this paper determines and evaluates the worst case end-to-end delays for strategies based on network coding and multiplexing. It is shown that the end-to-end delay does not depend on the same parameters with the two strategies. This result can be explained by the fact that network coding can cope with congestions better than classical routing because it processes simultaneously packet from different flows. In counterpart, additional delays like algebraic combinations of packets are adde

Human Automotive Interaction: Affect Recognition for Motor Trend Magazine\u27s Best Driver Car of the Year

Observation analysis of vehicle operators has the potential to address the growing trend of motor vehicle accidents. Methods are needed to automatically detect heavy cognitive load and distraction to warn drivers in poor psychophysiological state. Existing methods to monitor a driver have included prediction from steering behavior, smart phone warning systems, gaze detection, and electroencephalogram. We build upon these approaches by detecting cues that indicate inattention and stress from video. The system is tested and developed on data from Motor Trend Magazine\u27s Best Driver Car of the Year 2014 and 2015. It was found that face detection and facial feature encoding posed the most difficult challenges to automatic facial emotion recognition in practice. The chapter focuses on two important parts of the facial emotion recognition pipeline: (1) face detection and (2) facial appearance features. We propose a face detector that unifies state‐of‐the‐art approaches and provides quality control for face detection results, called reference‐based face detection. We also propose a novel method for facial feature extraction that compactly encodes the spatiotemporal behavior of the face and removes background texture, called local anisotropic‐inhibited binary patterns in three orthogonal planes. Real‐world results show promise for the automatic observation of driver inattention and stress

An Effective Data-Driven Approach for Localizing Deep Learning Faults

Deep Learning (DL) applications are being used to solve problems in critical domains (e.g., autonomous driving or medical diagnosis systems). Thus, developers need to debug their systems to ensure that the expected behavior is delivered. However, it is hard and expensive to debug DNNs. When the failure symptoms or unsatisfied accuracies are reported after training, we lose the traceability as to which part of the DNN program is responsible for the failure. Even worse, sometimes, a deep learning program has different types of bugs. To address the challenges of debugging DNN models, we propose a novel data-driven approach that leverages model features to learn problem patterns. Our approach extracts these features, which represent semantic information of faults during DNN training. Our technique uses these features as a training dataset to learn and infer DNN fault patterns. Also, our methodology automatically links bug symptoms to their root causes, without the need for manually crafted mappings, so that developers can take the necessary steps to fix faults. We evaluate our approach using real-world and mutated models. Our results demonstrate that our technique can effectively detect and diagnose different bug types. Finally, our technique achieved better accuracy, precision, and recall than prior work for mutated models. Also, our approach achieved comparable results for real-world models in terms of accuracy and performance to the state-of-the-art

Revisit Your Welcome Mat: Successes & Challenges in Library Orientation at the Atlanta University Center

A team of four librarians at the Atlanta University Center Robert W. Woodruff Library (RWWL) discuss success and challenges in library orientation for the four institutions they serve – Clark Atlanta University, the Interdenominational Theological Center, Morehouse College and Spelman College. In 2011, a former library colleague described the partnership and coordination details of new student orientation at RWWL. The team will revisit that presentation and offer further best practices for effective, higher-impact orientation. The presentation will share how RWWL met the challenges their unique institution faces and share the successes they achieved since 2011. The presentation will focus on one-shot instruction, orientation collateral (i.e. handouts or giveaways), and the nature of campus collaboration – both precarious and rewarding – in a complicated environment

True Neurogenic Thoracic Outlet Syndrome Following Hyperabduction during Sleep - A Case Report -

True neurogenic thoracic outlet syndrome (TOS) is an uncommon disease and is difficult to diagnose at the early stage and then completely cure. We experienced a case of true neurogenic TOS with typical clinical symptoms and electrophysiologic findings as a result of repetitive habitual sleep posture. A 31-year-old woman who had complained of progressive tingling sensation on the 4th and 5th fingers with shoulder pain was diagnosed of brachial plexopathy at the lower trunk level by electrodiagnostic studies. There was no other cause of brachial plexopathy except her habit of hyperabduction of shoulder during sleep. This case demonstrated that the habitual abnormal posture can be the only major cause of neurogenic TOS. It is of importance to consider TOS with the habitual cause because simple correction of the posture could stabilize or even reverse disease progress

Twists and turns

Computational modelling of the heart tube during development reveals the interplay between tissue asymmetry and growth that helps our hearts take shape