Synthesis of a Versatile Building Block for the Preparation of 6-N-Derivatized α-Galactosyl Ceramides: Rapid Access to Biologically Active Glycolipids

A concise route to the 6-azido-6-deoxy-α-galactosyl-phytosphingosine derivative 9 is reported. Orthogonal protection of the two amino groups allows elaboration of 9 into a range of 6-N-derivatized α-galactosyl ceramides by late-stage introduction of the acyl chain of the ceramide and the 6-N-group in the sugar head-group. Biologically active glycolipids 6 and 8 have been synthesized to illustrate the applicability of the approach

Machine learning as an online diagnostic tool for proton exchange membrane fuel cells

Proton exchange membrane fuel cells are considered a promising power supply system with high efficiency and zero emissions. They typically work within a relatively narrow range of temperature and humidity to achieve optimal performance; however, this makes the system difficult to control, leading to faults and accelerated degradation. Two main approaches can be used for diagnosis, limited data input which provides an unintrusive, rapid but limited analysis, or advanced characterisation that provides a more accurate diagnosis but often requires invasive or slow measurements. To provide an accurate diagnosis with rapid data acquisition, machine learning methods have shown great potential. However, there is a broad approach to the diagnostic algorithms and signals used in the field. This article provides a critical view of the current approaches and suggests recommendations for future methodologies of machine learning in fuel cell diagnostic applications

Variations in visceral leishmaniasis burden, mortality and the pathway to care within Bihar, India

BACKGROUND: Visceral leishmaniasis (VL) has been targeted by the WHO for elimination as a public health problem (< 1 case/10,000 people/year) in the Indian sub-continent (ISC) by 2020. Bihar State in India, which accounts for the majority of cases in the ISC, remains a major target for this elimination effort. However, there is considerable spatial, temporal and sub-population variation in occurrence of the disease and the pathway to care, which is largely unexplored and a threat to achieving the target. METHODS: Data from 6081 suspected VL patients who reported being clinically diagnosed during 2012-2013 across eight districts in Bihar were analysed. Graphical comparisons and Chi-square tests were used to determine differences in the burden of identified cases by season, district, age and sex. Log-linear regression models were fitted to onset (of symptoms)-to-diagnosis and onset-to-treatment waiting times to estimate their associations with age, sex, district and various socio-economic factors (SEFs). Logistic regression models were used to identify factors associated with mortality. RESULTS: Comparisons of VL caseloads suggested an annual cycle peaking in January-March. A 17-fold variation in the burden of identified cases across districts and under-representation of young children (0-5 years) relative to age-specific populations in Bihar were observed. Women accounted for a significantly lower proportion of the reported cases than men (41 vs 59%, P < 0.0001). Age, district of residence, house wall materials, caste, treatment cost, travelling for diagnosis and the number of treatments for symptoms before diagnosis were identified as correlates of waiting times. Mortality was associated with age, district of residence, onset-to-treatment waiting time, treatment duration, cattle ownership and cost of diagnosis. CONCLUSIONS: The distribution of VL in Bihar is highly heterogeneous, and reported caseloads and associated mortality vary significantly across different districts, posing different challenges to the elimination campaign. Socio-economic factors are important correlates of these differences, suggesting that elimination will require tailoring to population and sub-population circumstances

Research encounters, reflexivity and supervision

Reflexivity in qualitative and ethnographic social science research can provide a rich source of data, especially regarding the affective, performative and relational aspects of interviews with research subjects. This paper explores by means of three case examples different ways of accessing and using such reflexivity. The examples are drawn from an empirical psycho-social study into the identity transitions of first-time mothers in an inner-city multicultural environment. Fieldnotes and supervision were used to engage with researcher subjectivity, to enhance the productive use of reflexivity and to address the emotional work of research. The methodology of the supervision was psychoanalytic, in its use of a boundaried frame and of psychoanalytic forms of noticing oneself, of staying engaged emotionally as well as creating a reflective distance. The examples illustrate how this can enhance the knowledge gained about the research subjects

Characterizing Batteries by In Situ Electrochemical Atomic Force Microscopy: A Critical Review

Although lithium, and other alkali ion, batteries are widely utilized and studied, many of the chemical and mechanical processes that underpin the materials within, and drive their degradation/failure, are not fully understood. Hence, to enhance the understanding of these processes various ex situ, in situ and operando characterization methods are being explored. Recently, electrochemical atomic force microscopy (EC-AFM), and related techniques, have emerged as crucial platforms for the versatile characterization of battery material surfaces. They have revealed insights into the morphological, mechanical, chemical, and physical properties of battery materials when they evolve under electrochemical control. This critical review will appraise the progress made in the understanding batteries using EC-AFM, covering both traditional and new electrode–electrolyte material junctions. This progress will be juxtaposed against the ability, or inability, of the system adopted to embody a truly representative battery environment. By contrasting key EC-AFM literature with conclusions drawn from alternative characterization tools, the unique power of EC-AFM to elucidate processes at battery interfaces is highlighted. Simultaneously opportunities for complementing EC-AFM data with a range of spectroscopic, microscopic, and diffraction techniques to overcome its limitations are described, thus facilitating improved battery performance