Making a Pangenome Using the Iterative Mapping Approach

Author(s):  
Cassandria Tay Fernandez
Keyword(s):  
2017 ◽  
Vol 5 (10) ◽  
pp. 273-275 ◽  
Author(s):  
Bura Vijay Kumar ◽  
◽  
◽  
◽  
Srinivas Aluvala ◽  
...  

Author(s):  
Jennifer A Halliday ◽  
Jane Speight ◽  
Sienna Russell-Green ◽  
Eric O ◽  
Virginia Hagger ◽  
...  

Abstract Diabetes distress is a common negative emotional response to the ongoing burden of living with diabetes. Elevated diabetes distress is associated with impaired diabetes self-management and quality of life yet rarely identified and addressed in clinical practice. Health professionals report numerous barriers to the provision of care for diabetes distress, including lack of skills and confidence, but few diabetes distress training opportunities exist. The purpose of this paper is to describe how we utilized Intervention Mapping to plan the development, implementation, and evaluation of a novel diabetes distress e-learning program for diabetes educators, to meet a well-documented need and significant gap in diabetes care. A multidisciplinary team (combining expertise in research, health and clinical psychology, diabetes education, nursing, tertiary education, and website architecture) developed a diabetes distress e-learning program. We followed a six-step process (logic model of the problem, program outcomes and objectives, program design, program production, program implementation plan, and evaluation plan) known as Intervention Mapping. The program is underpinned by educational and psychological theory, including Bloom’s Taxonomy of Educational Objectives and social cognitive theory. We developed a short (estimated 4 h) e-learning program for diabetes educators, which draws on the content of the Diabetes and Emotional Health handbook and toolkit. It integrates a 7As model, which provides a stepwise approach to identifying and addressing diabetes distress. Our diabetes distress e-learning program has been developed systematically, guided by an Intervention Mapping approach. In the next phase of the project, we will trial the e-learning.


2021 ◽  
Vol 13 (3) ◽  
pp. 525
Author(s):  
Yann Forget ◽  
Michal Shimoni ◽  
Marius Gilbert ◽  
Catherine Linard

By 2050, half of the net increase in the world’s population is expected to reside in sub-Saharan Africa (SSA), driving high urbanization rates and drastic land cover changes. However, the data-scarce environment of SSA limits our understanding of the urban dynamics in the region. In this context, Earth Observation (EO) is an opportunity to gather accurate and up-to-date spatial information on urban extents. During the last decade, the adoption of open-access policies by major EO programs (CBERS, Landsat, Sentinel) has allowed the production of several global high resolution (10–30 m) maps of human settlements. However, mapping accuracies in SSA are usually lower, limited by the lack of reference datasets to support the training and the validation of the classification models. Here we propose a mapping approach based on multi-sensor satellite imagery (Landsat, Sentinel-1, Envisat, ERS) and volunteered geographic information (OpenStreetMap) to solve the challenges of urban remote sensing in SSA. The proposed mapping approach is assessed in 17 case studies for an average F1-score of 0.93, and applied in 45 urban areas of SSA to produce a dataset of urban expansion from 1995 to 2015. Across the case studies, built-up areas averaged a compound annual growth rate of 5.5% between 1995 and 2015. The comparison with local population dynamics reveals the heterogeneity of urban dynamics in SSA. Overall, population densities in built-up areas are decreasing. However, the impact of population growth on urban expansion differs depending on the size of the urban area and its income class.


2021 ◽  
Vol 378 ◽  
pp. 113732
Author(s):  
Peter Wriggers ◽  
Blaž Hudobivnik ◽  
Fadi Aldakheel

Sign in / Sign up

Export Citation Format

Share Document