Recent advancements in cloud computing, artificial intelligence, and the internet of things (IoT) create new opportunities for autonomous industrial environments monitoring. Nevertheless, detecting anomalies in harsh industrial settings remains challenging. This paper proposes an edge-fog-cloud architecture with mobile IoT edge nodes carried on autonomous robots for thermal anomalies detection in aluminum factories. We use companion drones as fog nodes to deliver first response services and a cloud back-end for thermal anomalies analysis. We also propose a self-driving deep learning architecture and a thermal anomalies detection and visualization algorithm. Our results show our robot surveyors are low-cost, deliver reduced response time, and more accurately detect anomalies compared to human surveyors or fixed IoT nodes monitoring the same industrial area. Our self-driving architecture has a root mean square error of 0.19 comparable to VGG-19 with a significantly reduced complexity and three times the frame rate at 60 frames per second. Our thermal to visual registration algorithm maximizes mutual information in the image-gradient domain while adapting to different resolutions and camera frame rates.Saprochaete clavata and Saprochaete capitata are emerging fungal pathogens that are responsible for life threatening infections in immunocompromised patients, particularly in the setting of profound neutropenia. They have been associated with multiple hospital outbreaks mainly in Europe. In this article, we present a comprehensive review of the epidemiology, clinical presentation, diagnosis, antifungal susceptibility and treatment of these organisms. The diagnosis of invasive Saprochaete disease is challenging and relies primarily on the isolation of the fungi from blood or tissue samples. Both species are frequently misidentified as they are identical macroscopically and microscopically. Internal transcribed spacer sequencing and matrix-assisted laser desorption ionization-time of flight mass spectrometry are useful tools for the differentiation of these fungi to a species level. Saprochaete spp. are intrinsically resistant to echinocandins and highly resistant to fluconazole. Current literature suggests the use of an amphotericin B formulation with or without flucytosine for the initial treatment of these infections. https://www.selleckchem.com/products/thiostrepton.html Treatment with extended spectrum azoles might be promising based on in vitro minimum inhibitory concentration values and results from case reports and case series. Source control and recovery of the immune system are crucial for successful therapy.In clinical practice, patients with anaplastic lymphoma kinase (ALK)-rearrangement-positive non-small-cell lung cancer commonly receive sequential treatment with ALK tyrosine kinase inhibitors. The third-generation agent lorlatinib has been shown to inhibit a wide range of ALK resistance mutations and thus offers potential benefit in later lines, although real-world data are lacking. This multicenter study retrospectively investigated later-line, real-world use of lorlatinib in patients with advanced ALK- or ROS1-positive lung cancer. Fifty-one patients registered in a compassionate use program in Austria, who received second- or later-line lorlatinib between January 2016 and May 2020, were included in this retrospective real-world data analysis. Median follow-up was 25.3 months. Median time of lorlatinib treatment was 4.4 months for ALK-positive and 12.2 months for ROS-positive patients. ALK-positive patients showed a response rate of 43.2%, while 85.7% percent of the ROS1-positive patients were considered responders. Median overall survival from lorlatinib initiation was 10.2 and 20.0 months for the ALK- and ROS1-positive groups, respectively. In the ALK-positive group, lorlatinib proved efficacy after both brigatinib and alectinib. Lorlatinib treatment was well tolerated. Later-line lorlatinib treatment can induce sustained responses in patients with advanced ALK- and ROS1-positive lung cancer.The multidisciplinary nature of the work required for research in the COVID-19 pandemic has created new challenges for health professionals in the battle against the virus. They need to be equipped with novel tools, applications, and resources-that have emerged during the pandemic-to gain access to breakthrough findings; know the latest developments; and to address their specific needs for rapid data acquisition, analysis, evaluation, and reporting. Because of the complex nature of the virus, healthcare systems worldwide are severely impacted as the treatment and the vaccine for COVID-19 disease are not yet discovered. This leads to frequent changes in regulations and policies by governments and international organizations. Our analysis suggests that given the abundance of information sources, finding the most suitable application for analysis, evaluation, or reporting, is one of such challenges. However, health professionals and policy-makers need access to the most relevant, reliable, trusted, and latest ine of these applications will not only increase productivity but will also allow us to explore new dimensions for using existing applications with more control, better management, and greater outcome of their research. In addition, the features used in our framework can be applied for future evaluations of similar applications and health professionals can adapt them for evaluation of other applications not covered in this analysis.Pedestrian dead reckoning (PDR) plays an important role in modern life, including localisation and navigation if a Global Positioning System (GPS) is not available. Most previous PDR methods adopted foot-mounted sensors. However, humans have evolved to keep the head steady in space when the body is moving in order to stabilise the visual field. This indicates that sensors that are placed on the head might provide a more suitable alternative for real-world tracking. Emerging wearable technologies that are connected to the head also makes this a growing field of interest. Head-mounted equipment, such as glasses, are already ubiquitous in everyday life. Whilst other wearable gear, such as helmets, masks, or mouthguards, are becoming increasingly more common. Thus, an accurate PDR method that is specifically designed for head-mounted sensors is needed. It could have various applications in sports, emergency rescue, smart home, etc. In this paper, a new PDR method is introduced for head mounted sensors and compared to two established methods. The data were collected by sensors that were placed on glasses and embedded into a mouthguard. The results show that the newly proposed method outperforms the other two techniques in terms of accuracy, with the new method producing an average end-to-end error of 0.88 m and total distance error of 2.10%.Digital-enabled manufacturing systems require a high level of automation for fast and low-cost production but should also present flexibility and adaptiveness to varying and dynamic conditions in their environment, including the presence of human beings; however, this presence of workers in the shared workspace with robots decreases the productivity, as the robot is not aware about the human position and intention, which leads to concerns about human safety. This issue is addressed in this work by designing a reliable safety monitoring system for collaborative robots (cobots). The main idea here is to significantly enhance safety using a combination of recognition of human actions using visual perception and at the same time interpreting physical human-robot contact by tactile perception. Two datasets containing contact and vision data are collected by using different volunteers. The action recognition system classifies human actions using the skeleton representation of the latter when entering the shared workspace and the contact detection system distinguishes between intentional and incidental interactions if physical contact between human and cobot takes place. Two different deep learning networks are used for human action recognition and contact detection, which in combination, are expected to lead to the enhancement of human safety and an increase in the level of cobot perception about human intentions. The results show a promising path for future AI-driven solutions in safe and productive human-robot collaboration (HRC) in industrial automation.Trichogramma spp. is an important egg parasitoid wasp for biocontrol of agriculture and forestry insect pests. Trehalose serves as an energy source or stress protectant for insects. To study the potential role of trehalose in cold resistance on an egg parasitoid, cDNA for trehalose-6-phosphate synthase (TPS) and soluble trehalase (TRE) from Trichogramma dendrolimi were cloned and characterized. Gene expressions and enzyme activities of TdTPS and TdTRE were determined in larvae, prepupae, pupae, and adults at sustained low temperatures, 13 °C and 16 °C. TdTPS and TdTRE expressions had similar patterns with higher levels in prepupae at 13 °C and 16 °C. TdTPS enzyme activities increased with a decrease of temperature, and TdTRE activity in prepupae decreased sharply at these two low temperatures. In vitro reared T. dendrolimi could complete entire development above 13 °C, and the development period was prolonged without cold injury. Results indicated trehalose might regulate growth and the metabolic process of cold tolerance. Moreover, 13 °C is the cold tolerance threshold temperature and the prepupal stage is a critical developmental period for in vitro reared T. dendrolimi. These findings identify a low cost, prolonged development rearing method, and the cold tolerance for T. dendrolimi, which will facilitate improved mass rearing methods for biocontrol.There is a lack of prediction markers for early diabetic nephropathy (DN) in patients with type 2 diabetes mellitus (T2DM). The aim of this systematic review and meta-analysis was to evaluate the performance of two promising biomarkers, urinary kidney injury molecule 1 (uKIM-1) and Chitinase-3-like protein 1 (YKL-40) in the diagnosis of early diabetic nephropathy in type 2 diabetic patients. A comprehensive search was performed on PubMed by two reviewers until May 2020. For each study, a 2 × 2 contingency table was formulated. Sensitivity, specificity, and other estimates of accuracy were calculated using the bivariate random effects model. The hierarchical summary receiver operating characteristic curve hsROC) was used to pool data and evaluate the area under curve (AUC). The sources of heterogeneity were explored by sensitivity analysis. Publication bias was assessed using Deek's test. The meta-analysis enrolled 14 studies involving 598 healthy individuals, 765 T2DM patients with normoalbuminuria, 549 T2DM patients with microalbuminuria, and 551 T2DM patients with macroalbuminuria, in total for both biomarkers. The AUC of uKIM-1 and YKL-40 for T2DM patients with normoalbuminuria, was 0.85 (95%CI; 0.82-0.88) and 0.91 (95%CI; 0.88-0.93), respectively. The results of this meta-analysis suggest that both uKIM-1 and YKL-40 can be considered as valuable biomarkers for the early detection of DN in T2DM patients with the latter showing slightly better performance than the former.