A quantitiative LC-MS/MS method for the measurement of arachidonic acid, prostanoids, endocannabinoids, N-acylethanolamines and steroids in human plasma.

Free arachidonic acid is functionally interlinked with different lipid signaling networks including those involving prostanoid pathways, the endocannabinoid system, N-acylethanolamines, as well as steroids. A sensitive and specific LC-MS/MS method for the quantification of arachidonic acid, prostaglandin E2, thromboxane B2, anandamide, 2-arachidonoylglycerol, noladin ether, lineoyl ethanolamide, oleoyl ethanolamide, palmitoyl ethanolamide, steroyl ethanolamide, aldosterone, cortisol, dehydroepiandrosterone, progesterone, and testosterone in human plasma was developed and validated. Analytes were extracted using acetonitrile precipitation followed by solid phase extraction. Separations were performed by UFLC using a C18 column and analyzed on a triple quadrupole MS with electron spray ionization. Analytes were run first in negative mode and, subsequently, in positive mode in two independent LC-MS/MS runs. For each analyte, two MRM transitions were collected in order to confirm identity. All analytes showed good linearity over the investigated concentration range (r>0.98). Validated LLOQs ranged from 0.1 to 190ng/mL and LODs ranged from 0.04 to 12.3ng/mL. Our data show that this LC-MS/MS method is suitable for the quantification of a diverse set of bioactive lipids in plasma from human donors (n=32). The determined plasma levels are in agreement with the literature, thus providing a versatile method to explore pathophysiological processes in which changes of these lipids are implicated.

Tumour border configuration in colorectal cancer: proposal for an alternative scoring system based on the percentage of infiltrating margin.

AIMS Information on tumour border configuration (TBC) in colorectal cancer (CRC) is currently not included in most pathology reports, owing to lack of reproducibility and/or established evaluation systems. The aim of this study was to investigate whether an alternative scoring system based on the percentage of the infiltrating component may represent a reliable method for assessing TBC. METHODS AND RESULTS Two hundred and fifteen CRCs with complete clinicopathological data were evaluated by two independent observers, both 'traditionally' by assigning the tumours into pushing/infiltrating/mixed categories, and alternatively by scoring the percentage of infiltrating margin. With the pushing/infiltrating/mixed pattern method, interobserver agreement (IOA) was moderate (κ = 0.58), whereas with the percentage of infiltrating margins method, IOA was excellent (intraclass correlation coefficient of 0.86). A higher percentage of infiltrating margin correlated with adverse features such as higher grade (P = 0.0025), higher pT (P = 0.0007), pN (P = 0.0001) and pM classification (P = 0.0063), high-grade tumour budding (P < 0.0001), lymphatic invasion (P < 0.0001), vascular invasion (P = 0.0032), and shorter survival (P = 0.0008), and was significantly associated with an increased probability of lymph node metastasis (P < 0.001). CONCLUSIONS Information on TBC gives additional prognostic value to pathology reports on CRC. The novel proposed scoring system, by using the percentage of infiltrating margin, outperforms the 'traditional' way of reporting TBC. Additionally, it is reproducible and simple to apply, and can therefore be easily integrated into daily diagnostic practice.

Ms. Barth. 57 - Expositio moralis in Historiam Scholasticam, Gen. - Lc.

Vorbesitzer: Bartholomaeusstift Frankfurt am Main

Lung Pattern Classification for Interstitial Lung Diseases Using a Deep Convolutional Neural Network

Automated tissue characterization is one of the most crucial components of a computer aided diagnosis (CAD) system for interstitial lung diseases (ILDs). Although much research has been conducted in this field, the problem remains challenging. Deep learning techniques have recently achieved impressive results in a variety of computer vision problems, raising expectations that they might be applied in other domains, such as medical image analysis. In this paper, we propose and evaluate a convolutional neural network (CNN), designed for the classification of ILD patterns. The proposed network consists of 5 convolutional layers with 2×2 kernels and LeakyReLU activations, followed by average pooling with size equal to the size of the final feature maps and three dense layers. The last dense layer has 7 outputs, equivalent to the classes considered: healthy, ground glass opacity (GGO), micronodules, consolidation, reticulation, honeycombing and a combination of GGO/reticulation. To train and evaluate the CNN, we used a dataset of 14696 image patches, derived by 120 CT scans from different scanners and hospitals. To the best of our knowledge, this is the first deep CNN designed for the specific problem. A comparative analysis proved the effectiveness of the proposed CNN against previous methods in a challenging dataset. The classification performance (~85.5%) demonstrated the potential of CNNs in analyzing lung patterns. Future work includes, extending the CNN to three-dimensional data provided by CT volume scans and integrating the proposed method into a CAD system that aims to provide differential diagnosis for ILDs as a supportive tool for radiologists.

Efficiency and Continuity in Public Finance: The Ottoman System of Taxation

Economic historians have recently emphasized the importance of integrating economic and historical approaches in studying institutions. The literature on the Ottoman system of taxation, however, has continued to adopt a primarily historical approach, using ad hoc categories of classification and explaining the system through its continuities with the historical precedent. This paper integrates economic and historical approaches to examine the structure, efficiency, and regional diversity of the tax system. The structure of the system made it possible for the Ottomans to economize on the transaction cost of measuring the tax base. Regional variations resulted from both efficient adaptations and institutional rigidities.

Beilage zur Classification der Rosen : Aufzählung der besten älteren und neueren Sorten aus den betreffenden Gruppen

anonym

Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

anonym

Beilage zur Classification der Rosen : Aufzählung der besten älteren und neueren Sorten aus den betreffenden Gruppen

anonym

Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

anonym

Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

anonym

Beilage zur Classification der Rosen : Aufzählung der besten älteren und neueren Sorten aus den betreffenden Gruppen

anonym

Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

anonym

Classification der Rosen nebst Eintheilung derselben in natürliche Gruppen

anonym

A survey of the implementation status of environmental management systems in U.S. colleges and universities, and, An environmental management system implementation model for U.S. colleges and universities

A census of 925 U.S. colleges and universities offering masters and doctorate degrees was conducted in order to study the number of elements of an environmental management system as defined by ISO 14001 possessed by small, medium and large institutions. A 30% response rate was received with 273 responses included in the final data analysis. Overall, the number of ISO 14001 elements implemented among the 273 institutions ranged from 0 to 16, with a median of 12. There was no significant association between the number of elements implemented among institutions and the size of the institution (p = 0.18; Kruskal-Wallis test) or among USEPA regions (p = 0.12; Kruskal-Wallis test). The proportion of U.S. colleges and universities that reported having implemented a structured, comprehensive environmental management system, defined by answering yes to all 16 elements, was 10% (95% C.I. 6.6%–14.1%); however 38% (95% C.I. 32.0%–43.8%) reported that they had implemented a structured, comprehensive environmental management system, while 30.0% (95% C.I. 24.7%–35.9%) are planning to implement a comprehensive environmental management system within the next five years. Stratified analyses were performed by institution size, Carnegie Classification and job title. ^ The Osnabruck model, and another under development by the South Carolina Sustainable Universities Initiative, are the only two environmental management system models that have been proposed specifically for colleges and universities, although several guides are now available. The Environmental Management System Implementation Model for U.S. Colleges and Universities developed is an adaptation of the ISO 14001 standard and USEPA recommendations and has been tailored to U.S. colleges and universities for use in streamlining the implementation process. In using this implementation model created for the U.S. research and academic setting, it is hoped that these highly specialized institutions will be provided with a clearer and more cost-effective path towards the implementation of an EMS and greater compliance with local, state and federal environmental legislation. ^

SADA and DESYRE DSSs descriptive classification criteria (main issues related to DSS's)

Brownfield rehabilitation is an essential step for sustainable land-use planning and management in the European Union. In brownfield regeneration processes, the legacy contamination plays a significant role, firstly because of the persistent contaminants in soil or groundwater which extends the existing hazards and risks well into the future; and secondly, problems from historical contamination are often more difficult to manage than contamination caused by new activities. Due to the complexity associated with the management of brownfield site rehabilitation, Decision Support Systems (DSSs) have been developed to support problem holders and stakeholders in the decision-making process encompassing all phases of the rehabilitation. This paper presents a comparative study between two DSSs, namely SADA (Spatial Analysis and Decision Assistance) and DESYRE (Decision Support System for the Requalification of Contaminated Sites), with the main objective of showing the benefits of using DSSs to introduce and process data and then to disseminate results to different stakeholders involved in the decision-making process. For this purpose, a former car manufacturing plant located in the Brasov area, Central Romania, contaminated chiefly by heavy metals and total petroleum hydrocarbons, has been selected as a case study to apply the two examined DSSs. Major results presented here concern the analysis of the functionalities of the two DSSs in order to identify similarities, differences and complementarities and, thus, to provide an indication of the most suitable integration options.