Laser annealing of silicon on sapphire

Silicon-implanted silicon-on-sapphire wafers have been annealed by 50-ns pulses from a Q-switched Nd : YAG laser. The samples have been analyzed by channeling and by omega-scan x-ray double diffraction. After irradiation with pulses of a fluence of about 5 J cm^–2 the crystalline quality of the silicon layer is found to be better than in the as-grown state

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

Lattice regularization and symmetries

Finding the relation between the symmetry transformations in the continuum and on the lattice might be a nontrivial task as illustrated by the history of chiral symmetry. Lattice actions induced by a renormalization group procedure inherit all symmetries of the continuum theory. We give a general procedure which gives the corresponding symmetry transformations on the lattice. © SISSA 2006

Strategien zur Reduktion der CT-Strahlendosis

Zusammenfassung: Die rasante technische Weiterentwicklung der CT hat in den letzten Jahren zu einer deutlichen Zunahme der diagnostischen Möglichkeiten geführt, mit dem Resultat, dass die CT-Untersuchungszahlen weltweit angestiegen sind. Dies hat ebenfalls Auswirkung auf die Strahlenexposition der Bevölkerung. Bis heute sind zahlreiche Publikationen erschienen, die gezeigt haben, dass eine Dosisreduktion erreicht werden kann, ohne dadurch die Bildqualität und Sensitivität der CT zu beeinträchtigen. Die Mehrzahl der Strategien zur Dosisoptimierung sind einfach anzuwenden und unabhängig von der Detektorkonfiguration des CT-Scanners. Im vorliegenden Übersichtsartikel werden die wichtigsten Methoden vorgestellt: indikationsabhängige Methoden (z.B. rechtfertigende Indikation, Reduktion der Röhrenspannung für die CT-Angiographie, Wahl von Kollimation und Pitchfaktor, Minimierung der Untersuchungsphasen, Senkung der Röhrenspannung und des -stroms für die Nativphase), herstellerabhängige Methoden (z.B. automatische Röhrenstrommodulation, adaptive Filter zur Reduktion des Bildrauschens, iterative Bildrekonstruktion) und allgemeine Methoden (z.B. Patientenzentrierung im Isozentrum der CT-Gantry, Reduktion der Scanlänge, Anwendung von Röntgenschutzmitteln, Reduktion der Röhrenspannung und/oder des -stroms für den CT-Planungsscan

OntoGene in BioCreative II

BACKGROUND: Research scientists and companies working in the domains of biomedicine and genomics are increasingly faced with the problem of efficiently locating, within the vast body of published scientific findings, the critical pieces of information that are needed to direct current and future research investment. RESULTS: In this report we describe approaches taken within the scope of the second BioCreative competition in order to solve two aspects of this problem: detection of novel protein interactions reported in scientific articles, and detection of the experimental method that was used to confirm the interaction. Our approach to the former problem is based on a high-recall protein annotation step, followed by two strict disambiguation steps. The remaining proteins are then combined according to a number of lexico-syntactic filters, which deliver high-precision results while maintaining reasonable recall. The detection of the experimental methods is tackled by a pattern matching approach, which has delivered the best results in the official BioCreative evaluation. CONCLUSION: Although the results of BioCreative clearly show that no tool is sufficiently reliable for fully automated annotations, a few of the proposed approaches (including our own) already perform at a competitive level. This makes them interesting either as standalone tools for preliminary document inspection, or as modules within an environment aimed at supporting the process of curation of biomedical literature

A comparison of machine learning classifiers for pediatric epilepsy using resting-state functional MRI latency data

Epilepsy affects 1 in 150 children under the age of 10 and is the most common chronic pediatric neurological condition; poor seizure control can irreversibly disrupt normal brain development. The present study compared the ability of different machine learning algorithms trained with resting-state functional MRI (rfMRI) latency data to detect epilepsy. Preoperative rfMRI and anatomical MRI scans were obtained for 63 patients with epilepsy and 259 healthy controls. The normal distribution of latency z-scores from the epilepsy and healthy control cohorts were analyzed for overlap in 36 seed regions. In these seed regions, overlap between the study cohorts ranged from 0.44-0.58. Machine learning features were extracted from latency z-score maps using principal component analysis. Extreme Gradient Boosting (XGBoost), Support Vector Machines (SVM), and Random Forest algorithms were trained with these features. Area under the receiver operating characteristics curve (AUC), accuracy, sensitivity, specificity and F1-scores were used to evaluate model performance. The XGBoost model outperformed all other models with a test AUC of 0.79, accuracy of 74%, specificity of 73%, and a sensitivity of 77%. The Random Forest model performed comparably to XGBoost across multiple metrics, but it had a test sensitivity of 31%. The SVM model did not perform \u3e70% in any of the test metrics. The XGBoost model had the highest sensitivity and accuracy for the detection of epilepsy. Development of machine learning algorithms trained with rfMRI latency data could provide an adjunctive method for the diagnosis and evaluation of epilepsy with the goal of enabling timely and appropriate care for patients