A new bifunctional catalyst for tandem Heck-asymmetric dihydroxylation of olefins

A new bifunctional catalyst consisting of active palladium and osmium species anchored on silica gel through a mercaptopropyl spacer and a cinchona alkaloid respectively has been prepared for the first time and used in the heterogeneous tandem Heck-asymmetric dihydroxylation of olefins to afford diols with excellent yields and enantiomeric excesses (ee's) in presence of N-methylmorpholine Noxide or K3Fe(CN)6 as cooxidants

Router-level community structure of the Internet Autonomous Systems

The Internet is composed of routing devices connected between them and organized into independent administrative entities: the Autonomous Systems. The existence of different types of Autonomous Systems (like large connectivity providers, Internet Service Providers or universities) together with geographical and economical constraints, turns the Internet into a complex modular and hierarchical network. This organization is reflected in many properties of the Internet topology, like its high degree of clustering and its robustness. In this work, we study the modular structure of the Internet router-level graph in order to assess to what extent the Autonomous Systems satisfy some of the known notions of community structure. We show that the modular structure of the Internet is much richer than what can be captured by the current community detection methods, which are severely affected by resolution limits and by the heterogeneity of the Autonomous Systems. Here we overcome this issue by using a multiresolution detection algorithm combined with a small sample of nodes. We also discuss recent work on community structure in the light of our results

Emotional intelligence and academic performance among medical students – a correlational study

OBJECTIVE: Emotional intelligence is the ability to monitor one’s emotions and feelings and those of others, to distinguish between them, and to use this information to guide one’s thoughts and actions. A growing body of evidence suggests that highly emotionally intelligent student groups have better academic performance, better emotional awareness, and relationship management. We set forward to determine if any such positive relation exists among medical students. MATERIALS AND METHODS: A descriptive cross-sectional study was conducted on undergraduate medical students of Majmaah University. Convenient sampling was done to enroll the consenting students. A self-administered questionnaire on emotional intelligence was adapted from a model by Paul Mohapel. The questions based on a 5-point Likert scale assessed the four domains of emotional intelligence i.e., emotional awareness, emotional intelligence; demographic details and grade-point averages (GPA) were also collected. The data was tabulated and analyzed using SPSS 22.0 (IBM Corp., Armonk, NY, USA). RESULTS: Hundred and forty medical undergraduates enrolled in the study with a male-to-female ratio of 1:06. The median semester score was 4.47 (range 1.1-5.8) and the median cumulative score was 4.44 (range 2.8-5.0). The emotional management score was highest among those with a CGPA >4.50 (p=0.048). A significantly higher mean emotional awareness score (p<0.001), social-emotional awareness score (p<0.001), and relationship management score (p=0.030), and the mean EQ total was higher among males than for females (p<0.001). A small but significant correlation was observed and also with EQ total score (r= 0.18, p= 0.032). CONCLUSIONS: Emotional management affects the academic performance of medical students. There should be more sessions to improve the emotional intelligence of the students so that it can aid in their academic performance

Archaeological evidence for earlobe stretching in ancient North America: bodily plasticity in the Hopewell

Many natural, complex systems are remarkably stable thanks to an absence of feedback acting on their elements. When described as networks these exhibit few or no cycles, and associated matrices have small leading eigenvalues. It has been suggested that this architecture can confer advantages to the system as a whole, such as "qualitative stability," but this observation does not in itself explain how a loopless structure might arise. We show here that the number of feedback loops in a network, as well as the eigenvalues of associated matrices, is determined by a structural property called trophic coherence, a measure of how neatly nodes fall into distinct levels. Our theory correctly classifies a variety of networks-including those derived from genes, metabolites, species, neurons, words, computers, and trading nations-into two distinct regimes of high and low feedback and provides a null model to gauge the significance of related magnitudes. Because trophic coherence suppresses feedback, whereas an absence of feedback alone does not lead to coherence, our work suggests that the reasons for "looplessness" in nature should be sought in coherence-inducing mechanisms

AngioNet:a convolutional neural network for vessel segmentation in X-ray angiography

Coronary Artery Disease (CAD) is commonly diagnosed using X-ray angiography, in which images are taken as radio-opaque dye is flushed through the coronary vessels to visualize the severity of vessel narrowing, or stenosis. Cardiologists typically use visual estimation to approximate the percent diameter reduction of the stenosis, and this directs therapies like stent placement. A fully automatic method to segment the vessels would eliminate potential subjectivity and provide a quantitative and systematic measurement of diameter reduction. Here, we have designed a convolutional neural network, AngioNet, for vessel segmentation in X-ray angiography images. The main innovation in this network is the introduction of an Angiographic Processing Network (APN) which significantly improves segmentation performance on multiple network backbones, with the best performance using Deeplabv3+ (Dice score 0.864, pixel accuracy 0.983, sensitivity 0.918, specificity 0.987). The purpose of the APN is to create an end-to-end pipeline for image pre-processing and segmentation, learning the best possible pre-processing filters to improve segmentation. We have also demonstrated the interchangeability of our network in measuring vessel diameter with Quantitative Coronary Angiography. Our results indicate that AngioNet is a powerful tool for automatic angiographic vessel segmentation that could facilitate systematic anatomical assessment of coronary stenosis in the clinical workflow