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To the best of our knowledge, this is the first work employing BNNs for real-time in vivo neural spike sorting.Many choice problems often involve multiple attributes which are mentally challenging, because only one attribute is neatly sorted while others could be randomly arranged. We hypothesize that perceiving approximately monotonic trends across multiple attributes is key to the overall interpretability of sorted results, because users can easily predict the attribute values of the next items. We extend a ranking principal curve model to tune monotonic trends in attributes and present Imma Sort to sort items by multiple attributes simultaneously by trading-off the monotonicity in the primary sorted attribute to increase the human predictability for other attributes. We characterize how it performs for varying attribute correlations, attribute preferences, list lengths and number of attributes. We further extend Imma Sort with ImmaAnchor and ImmaCenter to improve the learnability and efficiency to search sorted items with conflicting attributes. We demonstrate usage scenarios for two applications and evaluate its learnability, usability, interpretability, and user performance in prediction and search tasks. We find that Imma Sort improves the interpretability and satisfaction of sorting by ≥ 2 attributes. We discuss why, when, where, and how to deploy Imma Sort for real-world applications.Data visualization is powerful in large part because it facilitates visual extraction of values. Yet, existing measures of perceptual precision for data channels (e.g., position, length, orientation, etc.) are based largely on verbal reports of ratio judgments between two values (e.g., [7]). Verbal report conflates multiple sources of error beyond actual visual precision, introducing a ratio computation between these values and a requirement to translate that ratio to a verbal number. Here we observe raw measures of precision by eliminating both ratio computations and verbal reports; we simply ask participants to reproduce marks (a single bar or dot) to match a previously seen one. We manipulated whether the mark was initially presented (and later drawn) alone, paired with a reference (e.g. a second '100%' bar also present at test, or a y-axis for the dot), or integrated with the reference (merging that reference bar into a stacked bar graph, or placing the dot directly on the axis). Reproductions of smaller values were overestimated, and larger values were underestimated, suggesting systematic memory biases. Average reproduction error was around 10% of the actual value, regardless of whether the reproduction was done on a common baseline with the original. In the reference and (especially) the integrated conditions, responses were repulsed from an implicit midpoint of the reference mark, such that values above 50% were overestimated, and values below 50% were underestimated. This reproduction paradigm may serve within a new suite of more fundamental measures of the precision of graphical perception.Multi-parametric prostate MRI (mpMRI) is a powerful tool to diagnose prostate cancer, though difficult to interpret even for experienced radiologists. A common radiological procedure is to compare a magnetic resonance image with similarly diagnosed cases. To assist the radiological image interpretation process, computerized Content-Based Image Retrieval systems (CBIRs) can therefore be employed to improve the reporting workflow and increase its accuracy. In this article, we propose a new, supervised siamese deep learning architecture able to handle multi-modal and multi-view MR images with similar PIRADS score. An experimental comparison with well-established deep learning-based CBIRs (namely standard siamese networks and autoencoders) showed significantly improved performance with respect to both diagnostic (ROC-AUC), and information retrieval metrics (Precision-Recall, Discounted Cumulative Gain and Mean Average Precision). Finally, the new proposed multi-view siamese network is general in design, facilitating a broad use in diagnostic medical imaging retrieval.Retinal fundus images are widely used for the clinical screening and diagnosis of eye diseases. However, fundus images captured by operators with various levels of experience have a large variation in quality. Low-quality fundus images increase uncertainty in clinical observation and lead to the risk of misdiagnosis. However, due to the special optical beam of fundus imaging and structure of the retina, natural image enhancement methods cannot be utilized directly to address this. In this article, we first analyze the ophthalmoscope imaging system and simulate a reliable degradation of major inferior-quality factors, including uneven illumination, image blurring, and artifacts. Then, based on the degradation model, a clinically oriented fundus enhancement network (cofe-Net) is proposed to suppress global degradation factors, while simultaneously preserving anatomical retinal structures and pathological characteristics for clinical observation and analysis. Experiments on both synthetic and real images demonstrate that our algorithm effectively corrects low-quality fundus images without losing retinal details. Moreover, we also show that the fundus correction method can benefit medical image analysis applications, e.g., retinal vessel segmentation and optic disc/cup detection.Moving Object Segmentation (MOS) is a fundamental task in computer vision. Due to undesirable variations in the background scene, MOS becomes very challenging for static and moving camera sequences. Several deep learning methods have been proposed for MOS with impressive performance. However, these methods show performance degradation in the presence of unseen videos; and usually, deep learning models require large amounts of data to avoid overfitting. Recently, graph learning has attracted significant attention in many computer vision applications since they provide tools to exploit the geometrical structure of data. In this work, concepts of graph signal processing are introduced for MOS. First, we propose a new algorithm that is composed of segmentation, background initialization, graph construction, unseen sampling, and a semi-supervised learning method inspired by the theory of recovery of graph signals. Secondly, theoretical developments are introduced, showing one bound for the sample complexity in semi-supervised learning, and two bounds for the condition number of the Sobolev norm. Our algorithm has the advantage of requiring less labeled data than deep learning methods while having competitive results on both static and moving camera videos. Our algorithm is also adapted for Video Object Segmentation (VOS) tasks and is evaluated on six publicly available datasets outperforming several state-of-the-art methods in challenging conditions.

Robotic endoscopes have the potential to dramatically improve endoscopy procedures, however current attempts remain limited due to mobility and sensing challenges and have yet to offer the full capabilities of traditional tools. Endoscopic intervention (e.g., biopsy) for robotic systems remains an understudied problem and must be addressed prior to clinical adoption. This paper presents an autonomous intervention technique onboard a Robotic Endoscope Platform (REP) using endoscopy forceps, an auto-feeding mechanism, and positional feedback.

A workspace model is established for estimating tool position while a Structure from Motion (SfM) approach is used for target-polyp position estimation with the onboard camera and positional sensor. Utilizing this data, a visual system for controlling the REP position and forceps extension is developed and tested within multiple anatomical environments.

The workspace model demonstrates accuracy of 5.5% while the target-polyp estimates are within 5 mm of absolute error. This successful experiment requires only 15 seconds once the polyp has been located, with a success rate of 43% using a 1 cm polyp, 67% for a 2 cm polyp, and 81% for a 3 cm polyp.

Workspace modeling and visual sensing techniques allow for autonomous endoscopic intervention and demonstrate the potential for similar strategies to be used onboard mobile robotic endoscopic devices.

To the authors' knowledge this is the first attempt at automating the task of colonoscopy intervention onboard a mobile robot. While the REP is not sized for actual procedures, these techniques are translatable to devices suitable for in vivo application.

To the authors' knowledge this is the first attempt at automating the task of colonoscopy intervention onboard a mobile robot. While the REP is not sized for actual procedures, these techniques are translatable to devices suitable for in vivo application.

Weight-related social stigma is associated with adverse health outcomes. Health care systems are not exempt of weight stigma, which includes stereotyping, prejudice and discrimination. The objective of this study was to examine the association between body mass index (BMI) class and experiencing discrimination in health care.

We used data from the 2013 Canadian Community Health Survey, which included measurements of discrimination never collected previously on a national scale. Logistic regression analysis was used to assess the risk of self-reported discrimination in health care in adults (≥18 years) across weight categories not obese (BMI < 30 kg/m2), obese class I (BMI = 30-< 35 kg/m2) and obese class II or III (BMI ≥ 35 kg/m2).

One in 15 (6.4%; 95% CI 5.7-7.0%) of the adult population reported discrimination in a health care setting (e.g. guanosine monophosphate disodium salt physician's office, clinic or hospital). Compared with those in the not obese group, the risk of discrimination in health care was somewhat higher among those in the class I obesity category (odds ratio [OR] = 1.20; 95% CI 1.00-1.44) and significantly higher among those in class II/III (OR = 1.52; 95% CI 1.21-1.91), after controlling for sex, age and other socioeconomic characteristics.

Quantified experiences of weight-related discrimination underscore the need to change practitioner attitudes and practices as well as the policies and procedures of the health care system. More research is needed on the social and economic impacts of weight stigma to inform focused investments for reducing discrimination in the health care system as a microcosm of the society it reflects.

Quantified experiences of weight-related discrimination underscore the need to change practitioner attitudes and practices as well as the policies and procedures of the health care system. More research is needed on the social and economic impacts of weight stigma to inform focused investments for reducing discrimination in the health care system as a microcosm of the society it reflects.Vitamin D (VitD) has pleiotropic effects. VitD deficiency is closely involved with obesity and may contribute to the development of lung fibrosis and aggravation of airway hyperresponsiveness (AHR). We evaluated the causal relationship between VitD deficiency and the lung pathologies associated with obesity. In vivo effects of VitD supplementation were analyzed using high-fat diet (HFD)-induced obese mice and TGF-β1 (transforming growth factor-β1) triple transgenic mice. Effects of VitD supplementation were also evaluated in both BEAS-2B and primary lung cells from the transgenic mice. Obese mice had decreased 25-OH VitD and VitD receptor expressions with increases of insulin resistance, renin and angiotensin-2 system (RAS) activity, and leptin. In addition, lung pathologies such as a modest increase in macrophages, enhanced TGF-β1, IL-1β, and IL-6 expression, lung fibrosis, and AHR were found. VitD supplementation to HFD-induced obese mice recovered these findings. TGF-β1-overexpressing transgenic mice enhanced macrophages in BAL fluid, lung expression of RAS, epithelial-mesenchymal transition markers, AHR, and lung fibrosis.