Cutaneous infection with Mycobacterium fortuitum after subcutaneous injection of human chorionic gonadotropin

Background: Weight loss clinics are common in the United States. Unfortunately, some offer dubious weight loss methods such as self-administered human chorionic gonadotropin (HCG) injections. HCG products are unregulated, yet, widely available. Infection is among the risks potentially associated with this treatment. We report a case of skin infection caused by Mycobacterium fortuitum after HCGinjection.Case Presentation: A 51-year-old woman with a history of hypogammaglobulinemia presented with an eight week history of a tender abdominal lesion. The lesion was at the site of a previous HCG injection.Acid-fast bacilli (AFB) culture grew Mycobacterium fortuitum. Based on the organism’s susceptibility profile, sulfamethoxazole-trimethoprim 800-160mg and ciprofloxacin 500 mg were both prescribed. The case was referred to the Missouri Department of Health. Based on clinical progress treatment was continued for a total of six months.Conclusions: This case illustrates the potential for cutaneous infection by Mycobacterium fortuitum and other rapidly growing mycobacteria after HCGinjections. Clinicians should maintain a high index for suspicion for rapidly growing mycobacteria when evaluating persistent skin lesions at sites of trauma or skin puncture

A User-Centered Active Learning Approach for Appliance Recognition

Smart homes offer new possibilities for energy management. One key enabler of these systems is the ability to monitor energy consumption at the appliance level. Existing approaches rely mainly on data from aggregated smart meter readings, but lack sufficient accuracy to recognize several appliances. Conversely, smart outlets are a suitable alternative since they can provide accurate electrical readings on individual appliances. Previous approaches for appliance recognition based on smart outlets use passive machine learning, which are deficient in the flexibility and scalability to work with highly heterogeneous appliances in smart homes. In this paper, we propose a stream-based active learning approach, called K-Active-Neighbors (KAN), to address the problem of appliance recognition in smart homes. KAN is an interactive framework in which the user is asked to label signatures of recently used appliances. Differently from previous work, we consider the realistic case in which the user is not always available to participate in the labeling process. Therefore, the system simultaneously learns the signatures and also the user willingness to interact with the system, in order to optimize the learning process. We develop an Arduino-based smart outlet to test our approach. Results show that, compared to previous solutions, KAN achieves higher accuracy in up to 41% less time

Supplementary Material: Rapid Subsurface Analysis of Frequency Domain Thermoreflectance Images with K-Means Clustering

Supplementary Material pertaining to the manuscript: Rapid Subsurface Analysis of Frequency Domain Thermoreflectance Images with K-Means Clustering. Results are presented in the order they are referenced in the manuscript: the full FDTR hyperspectral images for frequencies from 1 kHz to 60 MHz; FDTR performed on SiO2 and doped-Si calibration samples; FDTR sensitivity plots for the pixel-selective thermal analysis; FDTR sensitivity bounding the upper value of G2 that can be determined with the current analysis methods; K-means silhouette plots justifying the use of K = 3 in the current work; Zoomed in colorbar thermophysical property maps; and FDTR buried interface sensitivity for various conditions

Massive phytoplankton blooms under Arctic sea ice

Phytoplankton blooms over Arctic Ocean continental shelves are thought to be restricted to waters free of sea ice. Here, we document a massive phytoplankton bloom beneath fully consolidated pack ice far from the ice edge in the Chukchi Sea, where light transmission has increased in recent decades because of thinning ice cover and proliferation of melt ponds. The bloom was characterized by high diatom biomass and rates of growth and primary production. Evidence suggests that under-ice phytoplankton blooms may be more widespread over nutrient-rich Arctic continental shelves and that satellite-based estimates of annual primary production in these waters may be underestimated by up to 10-fold