Sensory Integration and Negative Behaviors

This PowerPoint is a presentation on data that has been collected on students with severe behavioral and multiple disabilities. Data was collected during escalated moments in the students school day while deep pressure was given to the students instead of other physical interventions. The study examines if sensory integration can lead to the students behavior changing from negative to a more positive, safe behavior

Learning to estimate a surrogate respiratory signal from cardiac motion by signal-to-signal translation

In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique developed for EMT surrogate signals. In our method, we test two families of neural networks to translate noisy internal motion to external surrogate: 1) fully connected networks and 2) convolutional neural networks. Our dataset consists of cardiac perfusion SPECT acquisitions for which cardiac motion was estimated (input: center-of-count-mass - COM signals) in conjunction with a respiratory surrogate motion signal acquired using a commercial Vicon Motion Tracking System (GT: EMT signals). We obtained an average R-score of 0.76 between the predicted surrogate and the EMT signal. Our goal is to lay a foundation to guide the optimization of neural networks for respiratory motion correction from SPECT without the need for an EMT.Comment: Medical Imaging Meets NeurIP

Comprehensive Assessment of Fine-Grained Wound Images Using a Patch-Based CNN With Context-Preserving Attention

Goal: Chronic wounds affect 6.5 million Americans. Wound assessment via algorithmic analysis of smartphone images has emerged as a viable option for remote assessment. Methods: We comprehensively score wounds based on the clinically-validated Photographic Wound Assessment Tool (PWAT), which comprehensively assesses clinically important ranges of eight wound attributes: Size, Depth, Necrotic Tissue Type, Necrotic Tissue Amount, Granulation Tissue type, Granulation Tissue Amount, Edges, Periulcer Skin Viability. We proposed a DenseNet Convolutional Neural Network (CNN) framework with patch-based context-preserving attention to assess the 8 PWAT attributes of four wound types: diabetic ulcers, pressure ulcers, vascular ulcers and surgical wounds. Results: In an evaluation on our dataset of 1639 wound images, our model estimated all 8 PWAT sub-scores with classification accuracies and F1 scores of over 80%. Conclusions: Our work is the first intelligent system that autonomously grades wounds comprehensively based on criteria in the PWAT rubric, alleviating the significant burden that manual wound grading imposes on wound care nurses

Induction of apoptosis with tobacco smoke and related products in A549 lung epithelial cells in vitro

BackgroundThis study has investigated the ability of tobacco smoke, and ingredients of tobacco smoke, to induce apoptosis in the airway epithelial cell line A549.MethodA549 cells were treated with 80 μg/ml Tobacco smoke condensate (TSC), 10 mM Nicotine, 10 μM paraldehyde, 10 μM hydrogen peroxide, 1 μM Taxol® (Paclitaxel), 100%, 50% and 25% cigarette smoke extract (CSE). Following 4–48 h incubation apoptosis was measured morphologically following staining of cells with DAPI. TUNEL staining was also used to assess DNA damage after 24 and 48 h incubation. In addition, loss of mitochondrial cytochrome C and activation of Bax-α, early events in the apoptotic process, were measured after 4 h of incubation.ResultsIncubation of A549 cells with vehicle, Taxol, TSC, nicotine, paraldehyde, hydrogen peroxide and CSE caused a time-dependent detachment of the cells from the flask between 6 and 48 h. DAPI staining revealed that the cells remaining adhered to the flask appeared healthy whereas some of those that had detached appeared to be either apoptotic or indeterminate. Treatment with Taxol, TSC, nicotine, paraldehyde, hydrogen peroxide and CSE caused a significant increase in the number of apoptotic cells. Similarly, treatment with Taxol, TSC, nicotine, hydrogen peroxide and CSE caused a significant increase in the number of apoptotic cells among the cells that had detached from the culture plate. After 4 h of incubation, Taxol, TSC, hydrogen peroxide and CSE caused a significant reduction in mitochondrial cytochrome C and an increase in cytosolic cytochrome C. At the same time point, hydrogen peroxide and CSE significantly increased the concentration of Bax-α in the mitochondria.ConclusionTobacco smoke initiates apoptosis in A549 airway epithelial cells as a result of mitochondrial damage and that this results in a cell detachment and full apoptosis. This effect appears to result from factors in tobacco smoke other than nicotine and may result from free radical activity. However, additional stable factors may also be involved since the free radical content of TSC is likely to be lo

Do Novice and Expert Users of Clinical Decision Support Tools Need Different Explanations?

A key requirement for the successful adoption of clinical decision support systems (CDSS) is their ability to provide users with reliable explanations for any given recommendation which can be challenging for some tasks such as wound management decisions. Despite the abundance of decision guidelines, wound non-expert (novice hereafter) clinicians who usually provide most of the treatments still have decision uncertainties. Our goal is to evaluate the use of a Wound CDSS smartphone App that provides explanations for recommendations it produces. The App utilizes wound images taken by the novice clinician using smartphone camera. This study experiments with two proposed variations of rule-tracing explanations called verbose-based and gist-based. Deriving upon theories of decision making, and unlike prior literature that says rule-tracing explanations are only preferred by novices, we hypothesize that, rule-tracing explanations are preferred by both clinicians but in different forms: novices prefer verbose-based rule-tracing and experts prefer gist-based rule tracing

Semantic Segmentation of Smartphone Wound Images: Comparative Analysis of AHRF and CNN-Based Approaches

Smartphone wound image analysis has recently emerged as a viable way to assess healing progress and provide actionable feedback to patients and caregivers between hospital appointments. Segmentation is a key image analysis step, after which attributes of the wound segment (e.g. wound area and tissue composition) can be analyzed. The Associated Hierarchical Random Field (AHRF) formulates the image segmentation problem as a graph optimization problem. Handcrafted features are extracted, which are then classified using machine learning classifiers. More recently deep learning approaches have emerged and demonstrated superior performance for a wide range of image analysis tasks. FCN, U-Net and DeepLabV3 are Convolutional Neural Networks used for semantic segmentation. While in separate experiments each of these methods have shown promising results, no prior work has comprehensively and systematically compared the approaches on the same large wound image dataset, or more generally compared deep learning vs non-deep learning wound image segmentation approaches. In this paper, we compare the segmentation performance of AHRF and CNN approaches (FCN, U-Net, DeepLabV3) using various metrics including segmentation accuracy (dice score), inference time, amount of training data required and performance on diverse wound sizes and tissue types. Improvements possible using various image pre- and post-processing techniques are also explored. As access to adequate medical images/data is a common constraint, we explore the sensitivity of the approaches to the size of the wound dataset. We found that for small datasets ( \u3c 300 images), AHRF is more accurate than U-Net but not as accurate as FCN and DeepLabV3. AHRF is also over 1000x slower. For larger datasets ( \u3e 300 images), AHRF saturates quickly, and all CNN approaches (FCN, U-Net and DeepLabV3) are significantly more accurate than AHRF

Hidden Gibbs random fields model selection using Block Likelihood Information Criterion

Performing model selection between Gibbs random fields is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot be computed using standard analytical or numerical methods. Furthermore, such unobserved fields cannot be integrated out and the likelihood evaluztion is a doubly intractable problem. This forms a central issue to pick the model that best fits an observed data. We introduce a new approximate version of the Bayesian Information Criterion. We partition the lattice into continuous rectangular blocks and we approximate the probability measure of the hidden Gibbs field by the product of some Gibbs distributions over the blocks. On that basis, we estimate the likelihood and derive the Block Likelihood Information Criterion (BLIC) that answers model choice questions such as the selection of the dependency structure or the number of latent states. We study the performances of BLIC for those questions. In addition, we present a comparison with ABC algorithms to point out that the novel criterion offers a better trade-off between time efficiency and reliable results

Detection of New Delhi Metallo-β-Lactamase (Encoded by \u3ci\u3ebla\u3c/i\u3e\u3csub\u3eNDM-1\u3c/sub\u3e) in \u3ci\u3eAcinetobacter schindleri\u3c/i\u3e during Routine Surveillance

A carbapenem-resistant Alcaligenes faecalis strain was isolated from a surveillance swab of a service member injured in Afghanistan. The isolate was positive for blaNDM by real-time PCR. Species identification was reevaluated on three identification systems but was inconclusive. Genome sequencing indicated that the closest relative was Acinetobacter schindleri and that blaNDM-1 was carried on a plasmid that shared \u3e99% identity with one identified in an Acinetobacter lwoffii isolate. The isolate also carried a novel chromosomally encoded class D oxacillinase

A disordered region controls cBAF activity via condensation and partner recruitment

Intrinsically disordered regions (IDRs) represent a large percentage of overall nuclear protein content. The prevailing dogma is that IDRs engage in non-specific interactions because they are poorly constrained by evolutionary selection. Here, we demonstrate that condensate formation and heterotypic interactions are distinct and separable features of an IDR within the ARID1A/B subunits of the mSWI/SNF chromatin remodeler, cBAF, and establish distinct sequence grammars underlying each contribution. Condensation is driven by uniformly distributed tyrosine residues, and partner interactions are mediated by non-random blocks rich in alanine, glycine, and glutamine residues. These features concentrate a specific cBAF protein-protein interaction network and are essential for chromatin localization and activity. Importantly, human disease-associated perturbations in ARID1B IDR sequence grammars disrupt cBAF function in cells. Together, these data identify IDR contributions to chromatin remodeling and explain how phase separation provides a mechanism through which both genomic localization and functional partner recruitment are achieved