Collections of Gymnopus sect. Levipedes from the Republic of Korea have been studied. Two new species, Gymnopus dryophiloides and G. brunneodiscus, are described based on their macro- and micromorphological and phylogenetic characteristics. Three other species, referred to as Gymnopus spp. 1, 2, and 3, are distinguished as separate taxa without formal descriptions. Taxonomic and phylogenetic positions of all taxa have been inferred and confirmed by analyses of ITS and LSU sequence data. Their detailed descriptions, illustrations and an identification key are provided.During a survey of putative fungal pathogens infecting oak trees in the Gangwon Province of the Republic of Korea, a fungus resembling a Ceratocystis sp. was repeatedly isolated from natural wounds on Quercus variabilis. Morphological comparisons and DNA sequence comparisons based on partial β-tubulin and TEF-1α gene regions showed that the fungus resided in a distinct lineage. This novel Ceratocystis species is described here as C. quercicola sp. nov. CT-707 ic50 This is the first novel species of Ceratocystis to be reported from Korea. A pathogenicity test showed that it can cause lesions on inoculated trees but that it had a very low level of aggressiveness. The discovery of this fungus suggests that additional taxa residing in Ceratocystis are likely to be discovered in Korea in the future.Many existing procedures for detecting multiple change-points in data sequences fail in frequent-change-point scenarios. This article proposes a new change-point detection methodology designed to work well in both infrequent and frequent change-point settings. It is made up of two ingredients one is "Wild Binary Segmentation 2" (WBS2), a recursive algorithm for producing what we call a 'complete' solution path to the change-point detection problem, i.e. a sequence of estimated nested models containing 0 , … , T - 1 change-points, where T is the data length. The other ingredient is a new model selection procedure, referred to as "Steepest Drop to Low Levels" (SDLL). The SDLL criterion acts on the WBS2 solution path, and, unlike many existing model selection procedures for change-point problems, it is not penalty-based, and only uses thresholding as a certain discrete secondary check. The resulting WBS2.SDLL procedure, combining both ingredients, is shown to be consistent, and to significantly outperform the competition in the frequent change-point scenarios tested. WBS2.SDLL is fast, easy to code and does not require the choice of a window or span parameter. Novel coronavirus 2019 (COVID-19) has been the focus of the medical world since being declared a pandemic in March 2020. While the pathogenesis and heterogeneity of COVID-19 manifestations is still not fully understood, viral evasion of cellular immune responses and inflammatory dysregulation are believed to play essential roles in disease progression and severity. We present the first case of a patient with COVID-19 with massive pulmonary embolism treated successfully with systemic thrombolysis, VA-ECLS, and bail out catheter directed thrombolysis. He was discharged from the hospital after an eventful hospital course on therapeutic anticoagulation with warfarin. We present the first case of a patient with COVID-19 with massive pulmonary embolism (PE) treated successfully with systemic thrombolysis, VA-ECLS and bail out catheter directed thrombolysis. In our experience catheter directed thrombolysis comes with an acceptable bleeding risk despite use of mechanical circulatory support, particularly with meticulous attention to vascular access and dose response monitoring.We present the first case of a patient with COVID-19 with massive pulmonary embolism (PE) treated successfully with systemic thrombolysis, VA-ECLS and bail out catheter directed thrombolysis. In our experience catheter directed thrombolysis comes with an acceptable bleeding risk despite use of mechanical circulatory support, particularly with meticulous attention to vascular access and dose response monitoring.Real-world evidence (RWE) provides a potential rich source of additional information to the body of data available from randomized clinical trials (RCTs), but there is a need to understand the strengths and limitations of RWE before it can be applied to clinical practice. To gain insight into current thinking in clinical decision making and utility of different data sources, a representative sampling of US cardiologists selected from the current, active Fellows of the American College of Cardiology (ACC) were surveyed to evaluate their perceptions of findings from RCTs and RWE studies and their application in clinical practice. The survey was conducted online via the ACC web portal between 12 July and 11 August 2017. Of the 548 active ACC Fellows invited as panel members, 173 completed the survey (32% response), most of whom were board certified in general cardiology (n = 119, 69%) or interventional cardiology (n = 40, 23%). The survey results indicated a wide range of familiarity with and utilization of RWE amongst cardiologists. Most cardiologists were familiar with RWE and considered RWE in clinical practice at least some of the time. However, a significant minority of survey respondents had rarely or never applied RWE learnings in their clinical practice, and many did not feel confident in the results of RWE other than registry data. These survey findings suggest that additional education on how to assess and interpret RWE could help physicians to integrate data and learnings from RCTs and RWE to best guide clinical decision making.Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs.