Separating Overlapping Tissue Layers from Microscopy Images

Manual preparation of tissue slices for microscopy imaging can introduce tissue tears and overlaps. Typically, further digital processing algorithms such as registration and 3D reconstruction from tissue image stacks cannot handle images with tissue tear/overlap artifacts, and so such images are usually discarded. In this paper, we propose an imaging model and an algorithm to digitally separate overlapping tissue data of mouse brain images into two layers. We show the correctness of our model and the algorithm by comparing our results with the ground truth

Abdominopelvic Pain in Patient with Uterus Didelphys and Unilateral Obstructed Hemivagina and Ipsilateral Renal Agenesis (OHVIRA Syndrome)

Introduction: Uterus didelphys with obstructed hemivagina associated with ipsilateral renal agenesis (OHVIRA syndrome) is a rare female urogenital malformation and delay in its diagnosis could lead to several complications. Case presentation: A 21-year-old virgin woman was admitted to the emergency department (ED) with severe abdominal pain, without fever and vaginal discharge. She reported a history of cyclic abdominopelvic pain and dysmenorrhea for 5 years. The primary diagnosis (OHVIRA syndrome) was made using ultrasonography, spiral computed tomography (CT) and magnetic resonance imaging (MRI). In addition, laparoscopy was performed to confirm diagnosis and drain hematosalpinx. Then, hysteroscopy was carried out for septum resection and catheter insertion. At one-month follow-up the ultrasonography showed normal left hemicavity of uterus associated with significant decrease in dysmenorrhea. Conclusion: Being aware of OHVIRA syndrome and clinical suspicion of this rare anomaly are essential for making a timely diagnosis, preventing complications, relieving symptoms, and preserving future fertility

Learning to Rasterize Differentiable

Differentiable rasterization changes the common formulation of primitive rasterization -- which has zero gradients almost everywhere, due to discontinuous edges and occlusion -- to an alternative one, which is not subject to this limitation and has similar optima. These alternative versions in general are ''soft'' versions of the original one. Unfortunately, it is not clear, what exact way of softening will provide the best performance in terms of converging the most reliability to a desired goal. Previous work has analyzed and compared several combinations of softening. In this work, we take it a step further and, instead of making a combinatorical choice of softening operations, parametrize the continuous space of all softening operations. We study meta-learning a parametric S-shape curve as well as an MLP over a set of inverse rendering tasks, so that it generalizes to new and unseen differentiable rendering tasks with optimal softness

Shrinkage Estimation of Expression Fold Change As an Alternative to Testing Hypotheses of Equivalent Expression

Research on analyzing microarray data has focused on the problem of identifying differentially expressed genes to the neglect of the problem of how to integrate evidence that a gene is differentially expressed with information on the extent of its differential expression. Consequently, researchers currently prioritize genes for further study either on the basis of volcano plots or, more commonly, according to simple estimates of the fold change after filtering the genes with an arbitrary statistical significance threshold. While the subjective and informal nature of the former practice precludes quantification of its reliability, the latter practice is equivalent to using a hard-threshold estimator of the expression ratio that is not known to perform well in terms of mean-squared error, the sum of estimator variance and squared estimator bias. On the basis of two distinct simulation studies and data from different microarray studies, we systematically compared the performance of several estimators representing both current practice and shrinkage. We find that the threshold-based estimators usually perform worse than the maximum-likelihood estimator (MLE) and they often perform far worse as quantified by estimated mean-squared risk. By contrast, the shrinkage estimators tend to perform as well as or better than the MLE and never much worse than the MLE, as expected from what is known about shrinkage. However, a Bayesian measure of performance based on the prior information that few genes are differentially expressed indicates that hard-threshold estimators perform about as well as the local false discovery rate (FDR), the best of the shrinkage estimators studied. Based on the ability of the latter to leverage information across genes, we conclude that the use of the local-FDR estimator of the fold change instead of informal or threshold-based combinations of statistical tests and non-shrinkage estimators can be expected to substantially improve the reliability of gene prioritization at very little risk of doing so less reliably

Learning to Rasterize Differentiably

Differentiable rasterization changes the standard formulation of primitive rasterization —by enabling gradient flow from apixel to its underlying triangles— using distribution functions in different stages of rendering, creating a “soft” version ofthe original rasterizer. However, choosing the optimal softening function that ensures the best performance and convergenceto a desired goal requires trial and error. Previous work has analyzed and compared several combinations of softening. Inthis work, we take it a step further and, instead of making a combinatorial choice of softening operations, parameterize thecontinuous space of common softening operations. We study meta-learning tunable softness functions over a set of inverserendering tasks (2D and 3D shape, pose and occlusion) so it generalizes to new and unseen differentiable rendering tasks withoptimal softness

Alterations in Auditory Electrophysiological Responses Associated With Temporary Suppression of Tinnitus Induced by Low-Level Laser Therapy: A Before-After Case Series

Introduction: Tinnitus is the phantom auditory perception of sound in the absence of an external or internal acoustic stimulus. The treatment is difficult due to multiple etiologies and great psychological influence. The purpose of this study was to determine alterations in auditory physiological and electrophysiological responses associated with temporary suppression of tinnitus induced by low-level laser (LLL) irradiation.Methods: This study was conducted on 20 subjects with subjective tinnitus. All subjects signed the informed consent form and satisfied all the study eligibility criteria. Visual analog scale (VAS) for loudness, loudness matching of tinnitus (LMT), pitch matching of tinnitus (PMT), Persian-tinnitus questionnaire (P-TQ) and Persian-tinnitus handicap inventory (P-THI) were conducted pre- and post-low level laser therapy (LLLT) for all the subjects. Electrocochleography (ECochG) and distortion product otoacoustic emissions (DPOAEs) were recorded in 11 subjects. Continuous wave diode lasers, including red (630 nm) and infra-red (808 nm) were applied, and were both designed by the Canadian Optic and Laser (COL) Center. Twelve sessions of laser therapy were performed, 2 sessions per week for each subject. Total dose was 120 Joule/ear/session.Results: LLL irradiation could cause a significant decrease in subjective tests scores consisting of VAS for loudness, PMT, P-TQ, P-THI, but did not result in a significant improvement of objective evaluating parameters except for compound action potential (CAP) amplitude.Conclusion: LLLT might be a subjectively effective treatment for short-term improvement of tinnitus. Defining a new protocol for optimizing LLLT parameters may be an option to improve parameters of objective tests