47 research outputs found
Multidimensional Conservation Laws: Overview, Problems, and Perspective
Some of recent important developments are overviewed, several longstanding
open problems are discussed, and a perspective is presented for the
mathematical theory of multidimensional conservation laws. Some basic features
and phenomena of multidimensional hyperbolic conservation laws are revealed,
and some samples of multidimensional systems/models and related important
problems are presented and analyzed with emphasis on the prototypes that have
been solved or may be expected to be solved rigorously at least for some cases.
In particular, multidimensional steady supersonic problems and transonic
problems, shock reflection-diffraction problems, and related effective
nonlinear approaches are analyzed. A theory of divergence-measure vector fields
and related analytical frameworks for the analysis of entropy solutions are
discussed.Comment: 43 pages, 3 figure
Free backbone carbonyls mediate rhodopsin activation
Conserved prolines in the transmembrane helices of G-protein-coupled receptors (GPCRs) are often considered to function as hinges that divide the helix into two segments capable of independent motion. Depending on their potential to hydrogen-bond, the free C=O groups associated with these prolines can facilitate conformational flexibility, conformational switching or stabilization of the receptor structure. To address the role of conserved prolines in family A GPCRs through solid-state NMR spectroscopy, we focus on bovine rhodopsin, a GPCR in the visual receptor subfamily. The free backbone C=O groups on helices H5 and H7 stabilize the inactive rhodopsin structure through hydrogen-bonds to residues on adjacent helices. In response to light-induced isomerization of the retinal chromophore, hydrogen-bonding interactions involving these C=O groups are released, thus facilitating repacking of H5 and H7 onto the transmembrane core of the receptor. These results provide insights into the multiple structural and functional roles of prolines in membrane proteins
A comprehensive assessment of N-terminal signal peptides prediction methods
Background: Amino-terminal signal peptides (SPs) are short regions that guide the targeting of secretory proteins to the correct subcellular compartments in the cell. They are cleaved off upon the passenger protein reaching its destination. The explosive growth in sequencing technologies has led to the deposition of vast numbers of protein sequences necessitating rapid functional annotation techniques, with subcellular localization being a key feature. Of the myriad software prediction tools developed to automate the task of assigning the SP cleavage site of these new sequences, we review here, the performance and reliability of commonly used SP prediction tools. Results: The available signal peptide data has been manually curated and organized into three datasets representing eukaryotes, Gram-positive and Gram-negative bacteria. These datasets are used to evaluate thirteen prediction tools that are publicly available. SignalP (both the HMM and ANN versions) maintains consistency and achieves the best overall accuracy in all three benchmarking experiments, ranging from 0.872 to 0.914 although other prediction tools are narrowing the performance gap. Conclusion: The majority of the tools evaluated in this study encounter no difficulty in discriminating between secretory and non-secretory proteins. The challenge clearly remains with pinpointing the correct SP cleavage site. The composite scoring schemes employed by SignalP may help to explain its accuracy. Prediction task is divided into a number of separate steps, thus allowing each score to tackle a particular aspect of the prediction.12 page(s
Invasive Group B Streptococcus Disease With Recurrence and in Multiples: Towards a Better Understanding of GBS Late-Onset Sepsis.
Group B Streptococcus (GBS) is a common intestinal colonizer during the neonatal period, but also may cause late-onset sepsis or meningitis in up to 0.5% of otherwise healthy colonized infants after day 3 of life. Transmission routes and risk factors of this late-onset form of invasive GBS disease (iGBS) are not fully understood. Cases of iGBS with recurrence (n=25) and those occurring in parallel in twins/triplets (n=32) from the UK and Ireland (national surveillance study 2014/15) and from Germany and Switzerland (retrospective case collection) were analyzed to unravel shared (in affected multiples) or fixed (in recurrent disease) risk factors for GBS disease. The risk of iGBS among infants from multiple births was high (17%), if one infant had already developed GBS disease. The interval of onset of iGBS between siblings was 4.5 days and in recurrent cases 12.5 days. Disturbances of the individual microbiome, including persistence of infectious foci are suggested e.g. by high usage of perinatal antibiotics in mothers of affected multiples, and by the association of an increased risk of recurrence with a short term of antibiotics [aOR 4.2 (1.3-14.2), P=0.02]. Identical GBS serotypes in both recurrent infections and concurrently infected multiples might indicate a failed microbiome integration of GBS strains that are generally regarded as commensals in healthy infants. The dynamics of recurrent GBS infections or concurrent infections in multiples suggest individual patterns of exposure and fluctuations in host immunity, causing failure of natural niche occupation
Functional impairment of systemic scleroderma patients with digital ulcerations: Results from the DUO registry
Demographic, clinical and antibody characteristics of patients with digital ulcers in systemic sclerosis: data from the DUO Registry
OBJECTIVES: The Digital Ulcers Outcome (DUO) Registry was designed to describe the clinical and antibody characteristics, disease course and outcomes of patients with digital ulcers associated with systemic sclerosis (SSc).
METHODS: The DUO Registry is a European, prospective, multicentre, observational, registry of SSc patients with ongoing digital ulcer disease, irrespective of treatment regimen. Data collected included demographics, SSc duration, SSc subset, internal organ manifestations, autoantibodies, previous and ongoing interventions and complications related to digital ulcers.
RESULTS: Up to 19 November 2010 a total of 2439 patients had enrolled into the registry. Most were classified as either limited cutaneous SSc (lcSSc; 52.2%) or diffuse cutaneous SSc (dcSSc; 36.9%). Digital ulcers developed earlier in patients with dcSSc compared with lcSSc. Almost all patients (95.7%) tested positive for antinuclear antibodies, 45.2% for anti-scleroderma-70 and 43.6% for anticentromere antibodies (ACA). The first digital ulcer in the anti-scleroderma-70-positive patient cohort occurred approximately 5 years earlier than the ACA-positive patient group.
CONCLUSIONS: This study provides data from a large cohort of SSc patients with a history of digital ulcers. The early occurrence and high frequency of digital ulcer complications are especially seen in patients with dcSSc and/or anti-scleroderma-70 antibodies
Cyclophosphamid ist geeignet zur Remissionsinduktion bei Aortitis im Rahmen einer Riesenzellarteriitis
AI-based optimisation of total machining performance: a review
Advanced modelling and optimisation techniques have been widely used in recent years to enable intelligent manufacturing and digitalisation of manufacturing processes. In this context, the integration of artificial intelligence in machining provides a great opportunity to enhance the efficiency of operations and the quality of produced components. Machine learning methods have already been applied to optimise various individual objectives concerning process characteristics, tool wear, or product quality in machining. However, the overall improvement of the machining process requires multi-objective optimisation approaches, which are rarely considered and implemented. The state-of-the-art in application of various optimisation and artificial intelligence methods for process optimisation in machining operations, including milling, turning, drilling, and grinding, is presented in this paper. The Milling process and deep learning are found to be the most widely researched operation and implemented machine learning technique, respectively. The surface roughness turns out to be the most critical quality measure considered. The different optimisation targets in artificial intelligence applications are elaborated and analysed to highlight the need for a holistic approach that covers all critical aspects of the machining operations. As a result, the key factors for a successful total machining performance improvement are identified and discussed in this paper. The AI methods were investigated and analysed in the frame of the IMPACT project initiated by the CIRP