Weißlicht und Autofluoreszenz-Bronchoskopie zur Früherkennung von Lungentumoren

Am Lungenkarzinom sterben jährlich ca. 37000 Menschen in der Bundesrepublik Deutschland. Die Heilungschancen sind sehr schlecht, wenn das Lungenkarzinom erst spät erkannt wird. Eine Erkennung von Frühstadien der Tumore kann dem abhelfen. Die bisherigen Methoden Computertomographie, Röntgen und Sputumuntersuchungen sind gerade für Frühformen der Tumore wenig sensitiv, so daß in der endoskopischen Diagnostik mit anschließender Biopsieentnahme häufig neue Befunde gefunden werden. Obgleich nur ein Teil der Lunge endoskopisch untersucht werden kann, finden sich hier ca. 60% der gesamten Befunde. Bei der konventionellen Methode der Weisslichtendoskopie werden aber ca. 50% der Frühstadien von Tumoren übersehen. Daher wurde ein endoskopisches System basierend auf einer kontrastreichen Autofluoreszenzdarstellung des Gewebes nach Beleuchtung mit blauem Licht entwickelt. Im Rahmen einer visuellen Darstellung basierend auf den Grundfarben Rot, Grün und Blau wurde das System bezüglich der erhöhten Schichtdicke des Tumorgewebes, und der erhöhten Streuung des Gewebes anhand einer computergestützten Simulation der Photonenverteilung im Modellgewebe optimiert. So erscheint verändertes Gewebe bläulich-rötlich dunkel im Vergleich zum aufgrund seiner oberflächennahen Fluorophoren hellgrün erscheinenden Normalgewebe. In einer zweiarmigen Studie mit einer großen Patientenzahl konnte nachgewiesen werden daß gegenüber einer alleinigen Untersuchung mit Weißlicht in der der zusätzlichen Untersuchung mit der neu entwickelten Autofluoreszenzdarstellung eine Verdopplung der Findungsraten von Frühstadien von Tumoren möglich ist

PASDA: A Partition-based Semantic Differencing Approach with Best Effort Classification of Undecided Cases

Equivalence checking is used to verify whether two programs produce equivalent outputs when given equivalent inputs. Research in this field mainly focused on improving equivalence checking accuracy and runtime performance. However, for program pairs that cannot be proven to be either equivalent or non-equivalent, existing approaches only report a classification result of "unknown", which provides no information regarding the programs' non-/equivalence. In this paper, we introduce PASDA, our partition-based semantic differencing approach with best effort classification of undecided cases. While PASDA aims to formally prove non-/equivalence of analyzed program pairs using a variant of differential symbolic execution, its main novelty lies in its handling of cases for which no formal non-/equivalence proof can be found. For such cases, PASDA provides a best effort equivalence classification based on a set of classification heuristics. We evaluated PASDA with an existing benchmark consisting of 141 non-/equivalent program pairs. PASDA correctly classified 61-74% of these cases at timeouts from 10 seconds to 3600 seconds. Thus, PASDA achieved equivalence checking accuracies that are 3-7% higher than the best results achieved by three existing tools. Furthermore, PASDA's best effort classifications were correct for 70-75% of equivalent and 55-85% of non-equivalent cases across the different timeouts

Why Crude Oil Vapor Pressure Should Be Tested Prior to Rail Transport

Recent crude oil rail car accidents have forced US and Canadian authorities to issue Emergency Testing Orders to ensure safe transportation of crude oils. One crucial parameter in meeting these safety requirements is the testing of the vapor pressure (VP) of crude oil.This paper explains the impact of highly volatile components inside the crude oil on the vapor pressure measurement. It describes typical VP measurement errors and discusses guidelines and technology for proper crude oil classification.It offers measurement data to show the effect of sample outgassing and to describe the impact of temperature changes and the vapor-liquid ratio (V/L) on vapor pressure test results. The second part of the paper discusses methods to measure the True Vapor Pressure (TVP) and Bubble Point Pressure (BPP) of Crude Oils for safety purposes.Key words: Crude oil; Volatility testing; Safety data sheet; Hazardous material regulations (HMR); Emergency testing order; Vapor pressure (VP); ASTM D6377; Reid vapor pressure (RVP); ASTM D323; True vapor pressure (TVP); Vapor-liquid ratio (V/L); Bubble point pressure (BPP); Floating piston cylinder (FPC

Data Augmentation of Wearable Sensor Data for Parkinson's Disease Monitoring using Convolutional Neural Networks

While convolutional neural networks (CNNs) have been successfully applied to many challenging classification applications, they typically require large datasets for training. When the availability of labeled data is limited, data augmentation is a critical preprocessing step for CNNs. However, data augmentation for wearable sensor data has not been deeply investigated yet. In this paper, various data augmentation methods for wearable sensor data are proposed. The proposed methods and CNNs are applied to the classification of the motor state of Parkinson's Disease patients, which is challenging due to small dataset size, noisy labels, and large intra-class variability. Appropriate augmentation improves the classification performance from 77.54\% to 86.88\%.Comment: ICMI2017 (oral session

Aligning Immanuel Kant’s Work and its Translations

his chapter discusses using CLARIN to edit Kant’s work and to con- sider how to align it with its translations, with special attention to Chinese. Kangde 康德 is the two-character phonetic loan that renders Kant’s name in Chinese. We have chosen Kangde 康德 as the name for our vision to express the challenge of setting up the new edition of the Druckschriften and their Chinese translation in the form of aligned corpora, thus opening up the way to further alignments with versions in other languages. From a philosophical-historical and cultural-political perspective, the chapter presents the idea of aligning two parallel corpora of around 1,580,000 German words and the corresponding characters in Chinese. The project is curiosity-driven and lays the foundations for investigating Kant’s philosophy and discussing it in a global context, a long- term effort that relies on the synergies among philosophy, computational lin- guistics, machine learning, translation studies, and China studies. The idea of the alignment is to offer unrivalled material for historical-philosophical investi- gations and serve as a viable infrastructure to be scaled up to other languages. To date, few aligned corpora exist that connect German and Chinese philosoph- ical texts. The tools are not statistically implemented. As suggested by Franco Moretti’s notion of distant reading, experimentation on meaningful patterns in philosophical corpora is a step towards making new machine learning technol- ogies usable for tackling issues in the humanities. Looking forward, we focus on the assumption that philosophers ought to explore new technologies to rethink conventional ways of interpreting texts in the humanities

Stage-based treatment for thymoma in due consideration of thymectomy: a single-center experience and comparison with the literature

BACKGROUND: Thymomas represent an uncommon and heterogeneous group of intrathoracic malignancies which require different treatments corresponding to their individual tumor stage. The objective of this study was to review the efficacy of our applied stage-based treatment for thymoma in due consideration of thymectomy. METHODS: This is a single-center, institutional review board-approved retrospective study of 50 consecutive patients with thymoma treated at our division within 10 years. RESULTS: There were 29 women (58 %) and 21 men (42 %), mean age 58.3 years. Twenty nine (58 %) had clinical symptoms and 14 (28 %) had myasthenia gravis. Forty-five patients (90 %) underwent thymectomy and complete resection was done in 42 cases (93.3 %). Histologic results were 6 subtype A, 5 AB, 8 B1, 12 B2, 12 B3, and 7 C. The Masaoka staging system revealed 20 stage I, 18 stage II, 6 stage III, and 6 stage IV. Two patients had neoadjuvant therapy and 25 received postoperative treatment. Five (11.1 %) had tumor recurrence, treated with re-resection. The 5-year disease-free survival was 91.5 %. Two patients died of tumor progression and three died of other causes (10 %). The 5-year overall survival was 82.3 % and the median survival time was 92.1 months. The 5-year survival rate after thymectomy was 87.2 % and the median survival was 92.1 months. CONCLUSIONS: Complete resection still remains the mainstay in the treatment of non-metastatic thymoma and should be performed whenever feasible. Close multidisciplinary teamwork is mandatory to optimize the neurologic outcome and to prolong postoperative survival