pubassistant.ch: consolidating publication profiles of researchers

Online accounts to keep track of scientific publications, such as Open Researcher and Contributor ID (ORCID) or Google Scholar, can be time consuming to maintain and synchronize. Furthermore, the open access status of publications is often not easily accessible, hindering potential opening of closed publications. To lessen the burden of managing personal profiles, we developed a R shiny app that allows publication lists from multiple platforms to be retrieved and consolidated, as well as interactive exploration and comparison of publication profiles. A live version can be found at pubassistant.ch

Tree root distribution modelling in different environmental conditions

The ability to predict the spatial distribution of tree root system variables (e.g., the Root system Area (RA), the maximum root diameter, the number of roots in diameter classes, the density of fine roots, etc.) under different environmental conditions is relevant to several scientific disciplines and to engineering practice. In this work, three well known analytical models from the literature are assembled into a unique framework called the Root Distribution Model (RDM). RDM models the expected vertical and horizontal distribution of coarse and fine root system variables for mature plants growing in different environmental conditions ranging from moderately humid to arid climates. All soil and moisture dynamic parameters are physically based, which make the model straightforward to calibrate via a single tuning parameter. At this investigative stage, it is shown that the model has the flexibility to represent a broad range of situations where soil moisture may result from precipitation inputs or from water level fluctuations due to either the presence of a water course or of deep aquifers or both. Accordingly, the distribution of the sectional RA may be either positively or negatively skewed, as well as show a peculiar bi-modal structure. The model can be used to study the impact of changing scenarios affecting precipitation, aquifer and channel hydrology

Towards Weyl fermions on the lattice without artefacts

In spite of the breakthrough in non-perturbative chiral gauge theories during the last decade, the present formulation has stubborn artefacts. Independently of the fermion representation one is confronted with unwanted CP violation and infinitely many undetermined weight factors. Renormalization group identifies the culprit. We demonstrate the procedure on Weyl fermions in a real representation

The Role of Scavenger Receptor B1 in Infection with Mycobacterium tuberculosis in a Murine Model

Peer reviewedPublisher PD

Biochemical characterization of the Nocardia lactamdurans ACV synthetase

The L-δ-(α-aminoadipoyl)-L-cysteinyl-D-valine synthetase (ACVS) is a nonribosomal peptide synthetase (NRPS) that fulfills a crucial role in the synthesis of β-lactams. Although some of the enzymological aspects of this enzyme have been elucidated, its large size, at over 400 kDa, has hampered heterologous expression and stable purification attempts. Here we have successfully overexpressed the Nocardia lactamdurans ACVS in E. coli HM0079. The protein was purified to homogeneity and characterized for tripeptide formation with a focus on the substrate specificity of the three modules. The first L-α-aminoadipic acid-activating module is highly specific, whereas the modules for L-cysteine and L-valine are more promiscuous. Engineering of the first module of ACVS confirmed the strict specificity observed towards its substrate, which can be understood in terms of the non-canonical peptide bond position

Probabilistic analysis of COVID-19 patients' individual length of stay in Swiss intensive care units

RATIONALE The COVID-19 pandemic induces considerable strain on intensive care unit resources. OBJECTIVES We aim to provide early predictions of individual patients' intensive care unit length of stay, which might improve resource allocation and patient care during the on-going pandemic. METHODS We developed a new semiparametric distributional index model depending on covariates which are available within 24h after intensive care unit admission. The model was trained on a large cohort of acute respiratory distress syndrome patients out of the Minimal Dataset of the Swiss Society of Intensive Care Medicine. Then, we predict individual length of stay of patients in the RISC-19-ICU registry. MEASUREMENTS The RISC-19-ICU Investigators for Switzerland collected data of 557 critically ill patients with COVID-19. MAIN RESULTS The model gives probabilistically and marginally calibrated predictions which are more informative than the empirical length of stay distribution of the training data. However, marginal calibration was worse after approximately 20 days in the whole cohort and in different subgroups. Long staying COVID-19 patients have shorter length of stay than regular acute respiratory distress syndrome patients. We found differences in LoS with respect to age categories and gender but not in regions of Switzerland with different stress of intensive care unit resources. CONCLUSION A new probabilistic model permits calibrated and informative probabilistic prediction of LoS of individual patients with COVID-19. Long staying patients could be discovered early. The model may be the basis to simulate stochastic models for bed occupation in intensive care units under different casemix scenarios

Dynamical stability and instability of Ricci-flat metrics

In this short article, we improve the dynamical stability and instability results for Ricci-flat metrics under Ricci flow proved by Sesum and Haslhofer, getting rid of the integrability assumption.Comment: 6 page