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 & Gene. The organization has 25084 authors who have published 52975 publications receiving 2388002 citations. The organization is also known as: Universität Basel & Basel University.

The Heidelberg classification of renal cell tumours

Abstract: This paper presents the conclusions of a workshop entitled 'Impact of Molecular Genetics on the Classification of Renal Cell Tumours', which was held in Heidelberg in October 1996. The focus on 'renal cell tumours' excludes any discussion of Wilms' tumour and its variants, or of tumours metastatic to the kidneys. The proposed classification subdivides renal cell tumours into benign and malignant parenchymal neoplasms and, where possible, limits each subcategory to the most commonly documented genetic abnormalities. Benign tumours are subclassified into metanephric adenoma and adenofibroma, papillary renal cell adenoma, and renal oncocytoma. Malignant tumours are subclassified into common or conventional renal cell carcinoma; papillary renal cell carcinoma; chromophobe renal cell carcinoma; collecting duct carcinoma, with medullary carcinoma of the kidney; and renal cell carcinoma, unclassified. This classification is based on current genetic knowledge, correlates with recognizable histological findings, and is applicable to routine diagnostic practice.

Photoremovable Protecting Groups in Chemistry and Biology: Reaction Mechanisms and Efficacy

Abstract: The review covers the knowledge on photoremovable protecting groups and includes all relevant chromophores studied in the time period of 2000–2012; the most relevant earlier works are also discussed.

American College of Rheumatology/European League Against Rheumatism Provisional Definition of Remission in Rheumatoid Arthritis for Clinical Trials

Abstract: Objective Remission in rheumatoid arthritis (RA) is an increasingly attainable goal, but there is no widely used defi nition of remission that is stringent but achievable and could be applied uniformly as an outcome measure in clinical trials. This work was undertaken to develop such a defi nition. Methods A committee consisting of members of the American College of Rheumatology, the European League Against Rheumatism, and the Outcome Measures in Rheumatology Initiative met to guide the process and review prespecifi ed analyses from RA clinical trials. The committee requested a stringent defi nition (little, if any, active disease) and decided to use core set measures including, as a minimum, joint counts and levels of an acute-phase reactant to defi ne remission. Members were surveyed to select the level of each core set measure that would be consistent with remission. Candidate defi nitions of remission were tested, including those that constituted a number of individual measures of remission (Boolean approach) as well as defi nitions using disease activity indexes. To select a defi nition of remission, trial data were analysed to examine the added contribution of patient-reported outcomes and the ability of candidate measures to predict later good radiographic and functional outcomes. Results Survey results for the defi nition of remission suggested indexes at published thresholds and a count of core set measures, with each measure scored as 1 or less (eg, tender and swollen joint counts, C reactive protein (CRP) level, and global assessments on a 0–10 scale). Analyses suggested the need to include a patientreported measure. Examination of 2-year follow-up data suggested that many candidate defi nitions performed comparably in terms of predicting later good radiographic and functional outcomes, although 28-joint Disease Activity Score–based measures of remission did not

Quantum chemistry structures and properties of 134 kilo molecules

Abstract: Computational de novo design of new drugs and materials requires rigorous and unbiased exploration of chemical compound space. However, large uncharted territories persist due to its size scaling combinatorially with molecular size. We report computed geometric, energetic, electronic, and thermodynamic properties for 134k stable small organic molecules made up of CHONF. These molecules correspond to the subset of all 133,885 species with up to nine heavy atoms (CONF) out of the GDB-17 chemical universe of 166 billion organic molecules. We report geometries minimal in energy, corresponding harmonic frequencies, dipole moments, polarizabilities, along with energies, enthalpies, and free energies of atomization. All properties were calculated at the B3LYP/6-31G(2df,p) level of quantum chemistry. Furthermore, for the predominant stoichiometry, C7H10O2, there are 6,095 constitutional isomers among the 134k molecules. We report energies, enthalpies, and free energies of atomization at the more accurate G4MP2 level of theory for all of them. As such, this data set provides quantum chemical properties for a relevant, consistent, and comprehensive chemical space of small organic molecules. This database may serve the benchmarking of existing methods, development of new methods, such as hybrid quantum mechanics/machine learning, and systematic identification of structure-property relationships.

A 3D Face Model for Pose and Illumination Invariant Face Recognition

Abstract: Generative 3D face models are a powerful tool in computer vision. They provide pose and illumination invariance by modeling the space of 3D faces and the imaging process. The power of these models comes at the cost of an expensive and tedious construction process, which has led the community to focus on more easily constructed but less powerful models. With this paper we publish a generative 3D shape and texture model, the Basel Face Model (BFM), and demonstrate its application to several face recognition task. We improve on previous models by offering higher shape and texture accuracy due to a better scanning device and less correspondence artifacts due to an improved registration algorithm. The same 3D face model can be fit to 2D or 3D images acquired under different situations and with different sensors using an analysis by synthesis method. The resulting model parameters separate pose, lighting, imaging and identity parameters, which facilitates invariant face recognition across sensors and data sets by comparing only the identity parameters. We hope that the availability of this registered face model will spur research in generative models. Together with the model we publish a set of detailed recognition and reconstruction results on standard databases to allow complete algorithm comparisons.