Repositioning of the global epicentre of non-optimal cholesterol

High blood cholesterol is typically considered a feature of wealthy western countries1,2. However, dietary and behavioural determinants of blood cholesterol are changing rapidly throughout the world3 and countries are using lipid-lowering medications at varying rates. These changes can have distinct effects on the levels of high-density lipoprotein (HDL) cholesterol and non-HDL cholesterol, which have different effects on human health4,5. However, the trends of HDL and non-HDL cholesterol levels over time have not been previously reported in a global analysis. Here we pooled 1,127 population-based studies that measured blood lipids in 102.6 million individuals aged 18 years and older to estimate trends from 1980 to 2018 in mean total, non-HDL and HDL cholesterol levels for 200 countries. Globally, there was little change in total or non-HDL cholesterol from 1980 to 2018. This was a net effect of increases in low- and middle-income countries, especially in east and southeast Asia, and decreases in high-income western countries, especially those in northwestern Europe, and in central and eastern Europe. As a result, countries with the highest level of non-HDL cholesterol�which is a marker of cardiovascular risk�changed from those in western Europe such as Belgium, Finland, Greenland, Iceland, Norway, Sweden, Switzerland and Malta in 1980 to those in Asia and the Pacific, such as Tokelau, Malaysia, The Philippines and Thailand. In 2017, high non-HDL cholesterol was responsible for an estimated 3.9 million (95 credible interval 3.7 million�4.2 million) worldwide deaths, half of which occurred in east, southeast and south Asia. The global repositioning of lipid-related risk, with non-optimal cholesterol shifting from a distinct feature of high-income countries in northwestern Europe, north America and Australasia to one that affects countries in east and southeast Asia and Oceania should motivate the use of population-based policies and personal interventions to improve nutrition and enhance access to treatment throughout the world. © 2020, The Author(s), under exclusive licence to Springer Nature Limited

Solvent Entropy-Driven Searching For Protein Modeling Examined And Tested In Simplified Models

Introduction Solvent entropy has an impact on protein folding and protein interactions as well as protein design. However, the enumeration of the different microstates can become difficult. Implementation of the solvent and especially its entropy is not often found in the literature (Shortle et al., 1992; Warwicker, 1997). Exact calculations on entropy are difficult in real systems. Calculating the number of solvent microstates stresses a different perspective of protein folding and protein stability: The global minimum no longer appears as one rare state among a large number of alternative conformations, but as the protein conformation with the highest number of microstate representations of the solvent. A computationally unintensive way is to approximate the impact of the solvent by mean field calculations (Hsiang-Ai and Karplus, 1988; Cramer and Truhlar, 1992; Fraternali and van-Gunsteren, 1996). The mean field calculations neglect the effect of locally ordered structures and ca

A hybrid algorithm for k-medoid clustering of large data sets

In this paper, we propose a novel local search heuristic and then hybridize it with a genetic algorithm for k-medoid clustering of large data sets, which is an NP-hard optimization problem. The local search heuristic selects k-medoids from the data set and tries to efficiently minimize the total dissimilarity within each cluster. In order to deal with the local optimality, the local search heuristic is hybridized with a genetic algorithm and then the Hybrid K-medoid Algorithm (HKA) is proposed. Our experiments show that, compared with previous genetic algorithm based k-medoid clustering approaches - GCA and RAR/sub w/GA, HKA can provide better clustering solutions and do so more efficiently. Experiments use two gene expression data sets, which may involve large noise components

The Randolph Glacier Inventory: a globally complete inventory of glaciers

The Randolph Glacier Inventory (RGI) is a globally complete collection of digital outlines of glaciers, excluding the ice sheets, developed to meet the needs of the Fifth Assessment of the Intergovernmental Panel on Climate Change for estimates of past and future mass balance. The RGI was created with limited resources in a short period. Priority was given to completeness of coverage, but a limited, uniform set of attributes is attached to each of the ∼198 000 glaciers in its latest version, 3.2. Satellite imagery from 1999–2010 provided most of the outlines. Their total extent is estimated as 726 800±34 000 km2. The uncertainty, about ±5%, is derived from careful single-glacier and basin-scale uncertainty estimates and comparisons with inventories that were not sources for the RGI. The main contributors to uncertainty are probably misinterpretation of seasonal snow cover and debris cover. These errors appear not to be normally distributed, and quantifying them reliably is an unsolved problem. Combined with digital elevation models, the RGI glacier outlines yield hypsometries that can be combined with atmospheric data or model outputs for analysis of the impacts of climatic change on glaciers. The RGI has already proved its value in the generation of significantly improved aggregate estimates of glacier mass changes and total volume, and thus actual and potential contributions to sea-level rise

Search for New Particles in Two-Jet Final States in 7 TeV Proton-Proton Collisions with the ATLAS Detector at the LHC

A search for new heavy particles manifested as resonances in two-jet final states is presented. The data were produced in 7 TeV proton-proton collisions by the LHC and correspond to an integrated luminosity of 315 nb(-1) collected by the ATLAS detector. No resonances were observed. Upper limits were set on the product of cross section and signal acceptance for excited-quark (q*) production as a function of q* mass. These exclude at the 95% C. L. the q* mass interval 0: 30< m(q)*< 1:26 TeV, extending the reach of previous experiments