Deficiency in β1,3-Galactosyltransferase of a Leishmania major Lipophosphoglycan Mutant Adversely Influences the Leishmania-Sand Fly Interaction

To study the function of side chain oligosaccharides of the cell-surface lipophosphoglycan (LPG), mutagenized Leishmania major defective in side chain biosynthesis were negatively selected by agglutination with the monoclonal antibody WIC79.3, which recognizes the galactose-containing side chains of L. major LPG. One such mutant, called Spock, lacked the ability to bind significantly to midguts of the natural L. major vector, Phlebotomus papatasi, and to maintain infection in the sand fly after excretion of the digested bloodmeal. Biochemical characterization of Spock LPG revealed its structural similarity to the LPG of Leishmania donovani, a species whose inability to bind to and maintain infections in P. papatasi midguts has been strongly correlated with the expression of a surface LPG lacking galactose-terminated oligosaccharide side chains. An in vitro galactosyltransferase assay using wild-type or Spock membranes was used to determine that the defect in Spock LPG biosynthesis is a result of defective beta1,3-galactosyltransferase activity as opposed to a modification of LPG, which would prevent it from serving as a competent substrate for galactose addition. The results of these experiments show that Spock lacks the beta1, 3-galactosyltransferase for side chain addition and that the LPG side chains are required for L. major to bind to and to produce transmissible infection in P. papatasi

Everything is INTERRELATED:Teaching Software Engineering for Sustainability

Sustainability has become an important concern across many disciplines,and software systems play an increasingly central role in addressing it. However, teaching students from software engineering and related disciplines to effectively act in this space requires interdisciplinary courses that combines the concep to of sustainability with software engineering practice and principles. Yet, presently little guidance exist on which subjects and materials to cover in such courses and how, combined with a lack of reusable learning objects. This paper describes a summer school course on Software Engineering for Sustainability (SE4S). We provide a blueprint for this course, in the hope that it can help the community develop a shared approach and methods to teaching SE4S. Practical lessons learned from delivery of this course are also reported here, and could help iterate over the course materials, structure, and guidance for future improvements. The course blueprint, availability of used materials and report of the study results make this course viable for replication and further improvement

Diffusion of e-health innovations in 'post-conflict' settings: a qualitative study on the personal experiences of health workers.

BACKGROUND: Technological innovations have the potential to strengthen human resources for health and improve access and quality of care in challenging 'post-conflict' contexts. However, analyses on the adoption of technology for health (that is, 'e-health') and whether and how e-health can strengthen a health workforce in these settings have been limited so far. This study explores the personal experiences of health workers using e-health innovations in selected post-conflict situations. METHODS: This study had a cross-sectional qualitative design. Telephone interviews were conducted with 12 health workers, from a variety of cadres and stages in their careers, from four post-conflict settings (Liberia, West Bank and Gaza, Sierra Leone and Somaliland) in 2012. Everett Roger's diffusion of innovation-decision model (that is, knowledge, persuasion, decision, implementation, contemplation) guided the thematic analysis. RESULTS: All health workers interviewed held positive perceptions of e-health, related to their beliefs that e-health can help them to access information and communicate with other health workers. However, understanding of the scope of e-health was generally limited, and often based on innovations that health workers have been introduced through by their international partners. Health workers reported a range of engagement with e-health innovations, mostly for communication (for example, email) and educational purposes (for example, online learning platforms). Poor, unreliable and unaffordable Internet was a commonly mentioned barrier to e-health use. Scaling-up existing e-health partnerships and innovations were suggested starting points to increase e-health innovation dissemination. CONCLUSIONS: Results from this study showed ICT based e-health innovations can relieve information and communication needs of health workers in post-conflict settings. However, more efforts and investments, preferably driven by healthcare workers within the post-conflict context, are needed to make e-health more widespread and sustainable. Increased awareness is necessary among health professionals, even among current e-health users, and physical and financial access barriers need to be addressed. Future e-health initiatives are likely to increase their impact if based on perceived health information needs of intended users

Cross validation of bi-modal health-related stress assessment

This study explores the feasibility of objective and ubiquitous stress assessment. 25 post-traumatic stress disorder patients participated in a controlled storytelling (ST) study and an ecologically valid reliving (RL) study. The two studies were meant to represent an early and a late therapy session, and each consisted of a "happy" and a "stress triggering" part. Two instruments were chosen to assess the stress level of the patients at various point in time during therapy: (i) speech, used as an objective and ubiquitous stress indicator and (ii) the subjective unit of distress (SUD), a clinically validated Likert scale. In total, 13 statistical parameters were derived from each of five speech features: amplitude, zero-crossings, power, high-frequency power, and pitch. To model the emotional state of the patients, 28 parameters were selected from this set by means of a linear regression model and, subsequently, compressed into 11 principal components. The SUD and speech model were cross-validated, using 3 machine learning algorithms. Between 90% (2 SUD levels) and 39% (10 SUD levels) correct classification was achieved. The two sessions could be discriminated in 89% (for ST) and 77% (for RL) of the cases. This report fills a gap between laboratory and clinical studies, and its results emphasize the usefulness of Computer Aided Diagnostics (CAD) for mental health care