Defect formation on surfaces bombarded by energetic multiply charged proteins: Implications for the conformation of gas-phase electrosprayed ions

Indirect information on the conformation of highly charged molecular ions may be obtained by monitoring their collisional cross sections and the course of simple gas-phase reactions such as hydrogen-deuterium exchange. In this work, another indirect but more visually oriented approach is explored: electrosprayed protein ions are accelerated toward a highly oriented pyrolytic graphite surface and the resulting single-ion defects are imaged by scanning force and tunneling microscopy. All protein impacts generated shallow hillocks: the shapes depended on the identity and charge state of the incident protein. Lysozyme and myoglobin, both compact, globular proteins in the native state, produced compact, almost circular hillocks. However, hillocks generated by myoglobin that had been denatured in the solution phase were elongated, and the elongation was positively correlated with the charge state of the ion. It appears that structural information about gas-phase multiply charged proteins can be derived from imprints generated by energetic protein impacts on surfaces

Raman spectroscopy on carbon nanotubes at high pressure

Raman spectroscopy has been the most extensively employed method to study carbon nanotubes at high pressures. This review covers reversible pressure-induced changes of the lattice dynamics and structure of single- and multi-wall carbon nanotubes as well as irreversible transformations induced by high pressures. The interplay of covalent and van-der-Waals bonding in single-wall nanotube bundles and a structural distortion near 2 GPa are discussed in detail. Attempts of transforming carbon nanotubes into diamond and other "superhard" phases are reviewed critically.Comment: 33 pages, 20 figures, review article, to appear in J. Raman Spectroscop

A new high-background-rejection dark matter Ge cryogenic detector

A new design of a cryogenic germanium detector for dark matter search is presented, taking advantage of the coplanar grid technique of event localisation for improved background discrimination. Experiments performed with prototype devices in the EDELWEISS II setup at the Modane underground facility demonstrate the remarkably high efficiency of these devices for the rejection of low-energy $\beta$, approaching 10$^5$ . This opens the road to investigate the range beyond 10$^{-8}$ pb in the WIMP-nucleon collision cross-sections, as proposed in the EURECA project of a one-ton cryogenic detector mass.Comment: submitted to Physical Review Letter

Erfarenheter av att donera en njure : en litteraturstudie

Det vanligaste organet att transplantera är njuren. Cirka 30 % av alla njurtransplantationer sker från en levande donator och vanligtvis är det familjemedlemmar som donerar en av sina njurar. Under senare år har även nära vänner accepterats som njurdonatorer. Det finns mycket forskning på den som får ett organ men inte så mycket på den som donerar. Syftet med studien var att beskriva levande njurdonatorers erfarenheter av att donera en njure. Som metod att undersöka detta valdes en litteraturstudie där både kvalitativa och kvantitativa artiklar granskades. Tre huvudkategorier av erfarenheter hos den levande njurdonatorn framkom: motiv till att donera en njure, hur njurdonationen påverkar den egna hälsan samt vardag efter donationen. Resultatet visar att de vanligaste motiven till levande njurdonation är kärlek och altruism. Njurdonatorns hälsa påverkas inte negativt på lång sikt. Efter donationen erfars ökad självkänsla, förbättrade relationer samt en förenklad vardag

Individual-specific networks for prediction modelling – A scoping review of methods

International audienceBackground: Recent advances in biotechnology enable the acquisition of high-dimensional data on individuals, posing challenges for prediction models which traditionally use covariates such as clinical patient characteristics. Alternative forms of covariate representations for the features derived from these modern data modalities should be considered that can utilize their intrinsic interconnection. The connectivity information between these features can be represented as an individual-specific network defined by a set of nodes and edges, the strength of which can vary from individual to individual. Global or local graph-theoretical features describing the network may constitute potential prognostic biomarkers instead of or in addition to traditional covariates and may replace the often unsuccessful search for individual biomarkers in a high-dimensional predictor space. Methods: We conducted a scoping review to identify, collate and critically appraise the state-of-art in the use of individual-specific networks for prediction modelling in medicine and applied health research, published during 2000-2020 in the electronic databases PubMed, Scopus and Embase. Results: Our scoping review revealed the main application areas namely neurology and pathopsychology, followed by cancer research, cardiology and pathology (N = 148). Network construction was mainly based on Pearson correlation coefficients of repeated measurements, but also alternative approaches (e.g. partial correlation, visibility graphs) were found. For covariates measured only once per individual, network construction was mostly based on quantifying an individual's contribution to the overall group-level structure. Despite the multitude of identified methodological approaches for individual-specific network inference, the number of studies that were intended to enable the prediction of clinical outcomes for future individuals was quite limited, and most of the models served as proof of concept that network characteristics can in principle be useful for prediction. Conclusion: The current body of research clearly demonstrates the value of individual-specific network analysis for prediction modelling, but it has not yet been considered as a general tool outside the current areas of application. More methodological research is still needed on well-founded strategies for network inference, especially on adequate network sparsification and outcome-guided graph-theoretical feature extraction and selection, and on how networks can be exploited efficiently for prediction modelling