Development of Evidence-Based Rubrics and Instructional Videos for Anesthesia Induction Sequences

Safe and effective induction of anesthesia is a vital component of any anesthesia provider’s skill set. Although both typical sequence and rapid sequence induction are common procedures, much variance in technique exists among providers. Clear, standardized rubrics are an important element in teaching these skills. The purpose of this project was to update the typical and rapid sequence induction rubrics for the Southern Illinois University Edwardsville Nurse Anesthesia Program with the current best practice recommendations. To utilize and reinforce observational learning, instructional videos were also created to accompany the updated rubrics. These materials were presented to the student registered nurse anesthetists (SRNAs) in the course NURS 529 - Orientation to Nurse Anesthesia Practicum prior to beginning clinical rotations. The SRNAs completed a 20-question pretest and viewed the rubrics and instructional videos. One week later, the SRNAs completed an identical posttest and overall evaluation of the educational experience. Results showed that scores improved between the pretest and posttest (78% and 89% respectively) indicating a potential gain in knowledge and skills. Survey results indicated overall buy in and acceptance of the new materials by the SRNAs. These results highlight the importance of multimodal learning in mastering critical, high-level skills such as induction of anesthesia

Universality in the merging dynamics of parametric active contours: a study in MRI-based lung segmentation

Measurement of lung ventilation is one of the most reliable techniques of diagnosing pulmonary diseases. The time consuming and bias prone traditional methods using hyperpolarized H${}^{3}$He and ${}^{1}$H magnetic resonance imageries have recently been improved by an automated technique based on multiple active contour evolution. Mapping results from an equivalent thermodynamic model, here we analyse the fundamental dynamics orchestrating the active contour (AC) method. We show that the numerical method is inherently connected to the universal scaling behavior of a classical nucleation-like dynamics. The favorable comparison of the exponent values with the theoretical model render further credentials to our claim.Comment: 4 pages, 4 figure

Proteome-Wide Effect of 17-β-Estradiol and Lipoxin A4 in an Endometriotic Epithelial Cell Line.

Endometriosis affects approximately 10% of women of reproductive age. This chronic, gynecological inflammatory disease results in a decreased quality of life for patients, with the main symptoms including chronic pelvic pain and infertility. The steroid hormone 17-β Estradiol (E2) plays a key role in the pathology. Our previous studies showed that the anti-inflammatory lipid Lipoxin A4 (LXA4) acts as an estrogen receptor-alpha agonist in endometrial epithelial cells, inhibiting certain E2-mediated effects. LXA4 also prevents the progression of endometriosis in a mouse model via anti-proliferative mechanisms and by impacting mediators downstream of ER signaling. The aim of the present study was therefore to examine global proteomic changes evoked by E2 and LXA4 in endometriotic epithelial cells. E2 impacted a greater number of proteins in endometriotic epithelial cells than LXA4. Interestingly, the combination of E2 and LXA4 resulted in a reduced number of regulated proteins, with LXA4 mediating a suppressive effect on E2-mediated signaling. These proteins are involved in diverse pathways of relevance to endometriosis pathology and metabolism, including mRNA translation, growth, proliferation, proteolysis, and immune responses. In summary, this study sheds light on novel pathways involved in endometriosis pathology and further understanding of signaling pathways activated by estrogenic molecules in endometriotic epithelial cells

Multi-resolution texture classification based on local image orientation

The aim of this paper is to evaluate quantitatively the discriminative power of the image orientation in the texture classification process. In this regard, we have evaluated the performance of two texture classification schemes where the image orientation is extracted using the partial derivatives of the Gaussian function. Since the texture descriptors are dependent on the observation scale, in this study the main emphasis is placed on the implementation of multi-resolution texture analysis schemes. The experimental results were obtained when the analysed texture descriptors were applied to standard texture databases

An edge-based approach for robust foreground detection

Foreground segmentation is an essential task in many image processing applications and a commonly used approach to obtain foreground objects from the background. Many techniques exist, but due to shadows and changes in illumination the segmentation of foreground objects from the background remains challenging. In this paper, we present a powerful framework for detections of moving objects in real-time video processing applications under various lighting changes. The novel approach is based on a combination of edge detection and recursive smoothing techniques.We use edge dependencies as statistical features of foreground and background regions and define the foreground as regions containing moving edges. The background is described by short- and long-term estimates. Experiments prove the robustness of our method in the presence of lighting changes in sequences compared to other widely used background subtraction techniques

IC3: Image Captioning by Committee Consensus

If you ask a human to describe an image, they might do so in a thousand different ways. Traditionally, image captioning models are trained to generate a single "best" (most like a reference) image caption. Unfortunately, doing so encourages captions that are "informationally impoverished," and focus on only a subset of the possible details, while ignoring other potentially useful information in the scene. In this work, we introduce a simple, yet novel, method: "Image Captioning by Committee Consensus" (IC3), designed to generate a single caption that captures high-level details from several annotator viewpoints. Humans rate captions produced by IC3 at least as helpful as baseline SOTA models more than two thirds of the time, and IC3 can improve the performance of SOTA automated recall systems by up to 84%, outperforming single human-generated reference captions, and indicating significant improvements over SOTA approaches for visual description. Code is available at https://davidmchan.github.io/caption-by-committee/Comment: To Appear at EMNLP 202