University of Basel

About: University of Basel is a education organization based out in Basel, Basel-Stadt, Switzerland. It is known for research contribution in the topics: Population & Transplantation. The organization has 25084 authors who have published 52975 publications receiving 2388002 citations. The organization is also known as: Universität Basel & Basel University.

Ocrelizumab versus Interferon Beta-1a in Relapsing Multiple Sclerosis.

Abstract: BackgroundB cells influence the pathogenesis of multiple sclerosis. Ocrelizumab is a humanized monoclonal antibody that selectively depletes CD20+ B cells. MethodsIn two identical phase 3 trials, we randomly assigned 821 and 835 patients with relapsing multiple sclerosis to receive intravenous ocrelizumab at a dose of 600 mg every 24 weeks or subcutaneous interferon beta-1a at a dose of 44 μg three times weekly for 96 weeks. The primary end point was the annualized relapse rate. ResultsThe annualized relapse rate was lower with ocrelizumab than with interferon beta-1a in trial 1 (0.16 vs. 0.29; 46% lower rate with ocrelizumab; P<0.001) and in trial 2 (0.16 vs. 0.29; 47% lower rate; P<0.001). In prespecified pooled analyses, the percentage of patients with disability progression confirmed at 12 weeks was significantly lower with ocrelizumab than with interferon beta-1a (9.1% vs. 13.6%; hazard ratio, 0.60; 95% confidence interval [CI], 0.45 to 0.81; P<0.001), as was the percentage of patients with disabilit...

Protein structure homology modeling using SWISS-MODEL workspace

Abstract: Homology modeling aims to build three-dimensional protein structure models using experimentally determined structures of related family members as templates. SWISS-MODEL workspace is an integrated Web-based modeling expert system. For a given target protein, a library of experimental protein structures is searched to identify suitable templates. On the basis of a sequence alignment between the target protein and the template structure, a three-dimensional model for the target protein is generated. Model quality assessment tools are used to estimate the reliability of the resulting models. Homology modeling is currently the most accurate computational method to generate reliable structural models and is routinely used in many biological applications. Typically, the computational effort for a modeling project is less than 2 h. However, this does not include the time required for visualization and interpretation of the model, which may vary depending on personal experience working with protein structures.

Coupled quantum dots as quantum gates

Abstract: We consider a quantum-gate mechanism based on electron spins in coupled semiconductor quantum dots. Such gates provide a general source of spin entanglement and can be used for quantum computers. We determine the exchange coupling $J$ in the effective Heisenberg model as a function of magnetic $(B)$ and electric fields, and of the interdot distance $a$ within the Heitler-London approximation of molecular physics. This result is refined by using $\mathrm{sp}$ hybridization, and by the Hund-Mulliken molecular-orbit approach, which leads to an extended Hubbard description for the two-dot system that shows a remarkable dependence on $B$ and $a$ due to the long-range Coulomb interaction. We find that the exchange $J$ changes sign at a finite field (leading to a pronounced jump in the magnetization) and then decays exponentially. The magnetization and the spin susceptibilities of the coupled dots are calculated. We show that the dephasing due to nuclear spins in GaAs can be strongly suppressed by dynamical nuclear-spin polarization and/or by magnetic fields.

Deuterium magnetic resonance: theory and application to lipid membranes.

Abstract: Proton and carbon-13 nmr spectra of unsonicated lipid bilayers and biological membranes are generally dominated by strong proton–proton and proton–carbon dipolar interactions. As a result the spectra contain a large number of overlapping resonances and are rather difficult to analyse. Nevertheless, important information on the structure and dynamic behaviour of lipid systems has been provided by these techniques (Wennerstrom & Lindblom, 1977).

Identifying the Best Machine Learning Algorithms for Brain Tumor Segmentation, Progression Assessment, and Overall Survival Prediction in the BRATS Challenge

Abstract: Gliomas are the most common primary brain malignancies, with different degrees of aggressiveness, variable prognosis and various heterogeneous histologic sub-regions, i.e., peritumoral edematous/invaded tissue, necrotic core, active and non-enhancing core. This intrinsic heterogeneity is also portrayed in their radio-phenotype, as their sub-regions are depicted by varying intensity profiles disseminated across multi-parametric magnetic resonance imaging (mpMRI) scans, reflecting varying biological properties. Their heterogeneous shape, extent, and location are some of the factors that make these tumors difficult to resect, and in some cases inoperable. The amount of resected tumoris a factor also considered in longitudinal scans, when evaluating the apparent tumor for potential diagnosis of progression. Furthermore, there is mounting evidence that accurate segmentation of the various tumor sub-regions can offer the basis for quantitative image analysis towards prediction of patient overall survival. This study assesses thestate-of-the-art machine learning (ML) methods used for brain tumor image analysis in mpMRI scans, during the last seven instances of the International Brain Tumor Segmentation (BraTS) challenge, i.e., 2012-2018. Specifically, we focus on i) evaluating segmentations of the various glioma sub-regions in pre-operative mpMRI scans, ii) assessing potential tumor progression by virtue of longitudinal growth of tumor sub-regions, beyond use of the RECIST/RANO criteria, and iii) predicting the overall survival from pre-operative mpMRI scans of patients that underwent gross tota lresection. Finally, we investigate the challenge of identifying the best ML algorithms for each of these tasks, considering that apart from being diverse on each instance of the challenge, the multi-institutional mpMRI BraTS dataset has also been a continuously evolving/growing dataset.