Relationship of Estrous Cycle to Herpes Simplex Virus Type 2 Susceptibility in Female Mice

In CBA/NJ mice, splenic natural killer (NK) cell activity varies with stages of estrous. Susceptibility of ICR mice to intravaginal inoculation of herpes simplex virus type 2 (HSV-2) decreases with age. Susceptibility of female ICR and CBA/NJ mice to HSV-2 inoculated intravaginally and intraperitoneally was examined during the estrous cycle. In cycling ICR mice, greatest susceptibility to intravaginal inoculation was observed during diestrous and the least during metestrous. CBA/NJ mice were most susceptible to intravaginal inoculation of HSV-2 during diestrous. ICR mice were ovariectomized to mimic diestrous and found to be highly susceptible to intravaginal inoculation at all virus doses. No difference in susceptibility among phases of the estrous cycle was seen following intraperitoneal inoculation

Explainable Model-Agnostic Similarity and Confidence in Face Verification

Recently, face recognition systems have demonstrated remarkable performances and thus gained a vital role in our daily life. They already surpass human face verification accountability in many scenarios. However, they lack explanations for their predictions. Compared to human operators, typical face recognition network system generate only binary decisions without further explanation and insights into those decisions. This work focuses on explanations for face recognition systems, vital for developers and operators. First, we introduce a confidence score for those systems based on facial feature distances between two input images and the distribution of distances across a dataset. Secondly, we establish a novel visualization approach to obtain more meaningful predictions from a face recognition system, which maps the distance deviation based on a systematic occlusion of images. The result is blended with the original images and highlights similar and dissimilar facial regions. Lastly, we calculate confidence scores and explanation maps for several state-of-the-art face verification datasets and release the results on a web platform. We optimize the platform for a user-friendly interaction and hope to further improve the understanding of machine learning decisions. The source code is available on GitHub, and the web platform is publicly available at http://explainable-face-verification.ey.r.appspot.com

Do We Still Need Non-Maximum Suppression? Accurate Confidence Estimates and Implicit Duplication Modeling with IoU-Aware Calibration

Object detectors are at the heart of many semi- and fully autonomous decision systems and are poised to become even more indispensable. They are, however, still lacking in accessibility and can sometimes produce unreliable predictions. Especially concerning in this regard are the -- essentially hand-crafted -- non-maximum suppression algorithms that lead to an obfuscated prediction process and biased confidence estimates. We show that we can eliminate classic NMS-style post-processing by using IoU-aware calibration. IoU-aware calibration is a conditional Beta calibration; this makes it parallelizable with no hyper-parameters. Instead of arbitrary cutoffs or discounts, it implicitly accounts for the likelihood of each detection being a duplicate and adjusts the confidence score accordingly, resulting in empirically based precision estimates for each detection. Our extensive experiments on diverse detection architectures show that the proposed IoU-aware calibration can successfully model duplicate detections and improve calibration. Compared to the standard sequential NMS and calibration approach, our joint modeling can deliver performance gains over the best NMS-based alternative while producing consistently better-calibrated confidence predictions with less complexity. The \hyperlink{https://github.com/Blueblue4/IoU-AwareCalibration}{code} for all our experiments is publicly available

Postmarketing Follow-Up of a Digital Home Exercise Program for Back, Hip, and Knee Pain: Retrospective Observational Study With a Time-Series and Matched-Pair Analysis

Background: Musculoskeletal conditions are the main drivers of global disease burden and cause significant direct and indirect health care costs. Digital health applications improve the availability of and access to adequate care. The German health care system established a pathway for the approval of “Digitale Gesundheitsanwendungen” (DiGAs; Digital Health Applications) as collectively funded medical services through the “Digitale-Versorgung-Gesetz” (Digital Health Care Act) in 2019. Objective: This article presents real-world prescription data collected through the smartphone-based home exercise program “Vivira,” a fully approved DiGA, regarding its effect on self-reported pain intensity and physical inability in patients with unspecific and degenerative pain in the back, hip, and knee. Methods: This study included 3629 patients (71.8% [2607/3629] female; mean age 47 years, SD 14.2 years). The primary outcome was the self-reported pain score, which was assessed with a verbal numerical rating scale. The secondary outcomes were self-reported function scores. To analyze the primary outcome, we used a 2-sided Skillings-Mack test. For function scores, a time analysis was not feasible; therefore, we calculated matched pairs using the Wilcoxon signed-rank test. Results: Our results showed significant reductions in self-reported pain intensity after 2, 4, 8, and 12 weeks in the Skillings-Mack test (T3628=5308; P<.001). The changes were within the range of a clinically relevant improvement. Function scores showed a generally positive yet more variable response across the pain areas (back, hip, and knee). Conclusions: This study presents postmarketing observational data from one of the first DiGAs for unspecific and degenerative musculoskeletal pain. We noted significant improvements in self-reported pain intensity throughout the observation period of 12 weeks, which reached clinical relevance. Additionally, we identified a complex response pattern of the function scores assessed. Lastly, we highlighted the challenges of relevant attrition at follow-up and the potential opportunities for evaluating digital health applications. Although our findings do not have confirmatory power, they illustrate the potential benefits of digital health applications to improve the availability of and access to medical care