University of Bremen

About: University of Bremen is a education organization based out in Bremen, Germany. It is known for research contribution in the topics: Population & Glacial period. The organization has 14563 authors who have published 37279 publications receiving 970381 citations. The organization is also known as: Universität Bremen.

Advances and future directions of biochar characterization methods and applications

Abstract: Biochar is a carbon-rich by-product of the thermal conversion of organic feedstocks and is primarily used as a soil amendment. Identification and quantification of biochar properties are important to ensure optimal outcomes for agricultural or environmental applications. Advanced spectroscopic techniques have recently been adopted in biochar characterization. However, biochar characterization approaches rely entirely on the user's choice and accessibility to the new technology. The selection of proper methods is vital to accurately and consistently assess biochar properties. This review critically evaluates current biochar characterization methods of proximate, ultimate, physicochemical, surface and structural analyses, and important biochar properties for various applications.

Chemical kinetics, speleothem growth and climate

Abstract: The morphology and stratigraphy of speleothems are controlled by parameters that depend on climate. These are the water supply rates feeding the speleothem, e.g. a stalagmite, the growth rates dependent on the chemical kinetics of calcite precipitation and the supersaturation of the solution from which calcite is precipitated. To elucidate the basic principles of speleothem growth, a physical…chemical model of calcite precipitation is used to estimate growth rates under various geologically relevant conditions. Furthermore, we present a model that allows the computation of the growth history of stalagmites, i.e. their morphology and stratigraphy under varying climatic conditions. This enables us to see how climatic signals are inscribed into stalagmites. Owing to the counter-balancing effects of some parameters, it is not possible to read climatic conditions backwards from the morphology and stratigraphy of a speleothem in a simple way, but a basic understanding of the growth of speleothems can be a helpful supporting tool in the interpretation of palaeoclimatic records.

HMGA2 overexpression in non-small cell lung cancer.

Abstract: Lung cancer is still the leading cause of death from cancer worldwide primarily because of the fact that most lung cancers are diagnosed at advanced stages. Overexpression of the high mobility group protein HMGA2 has been observed in a variety of malignant tumors and often correlates with poor prognosis. Herein, HMGA2 expression levels were analyzed in matching cancerous and non-cancerous lung samples of 17 patients with adenocarcinoma (AC) and 17 patients with squamous cell carcinoma (SCC) with real-time quantitative RT-PCR (qRT-PCR). Transcript levels were compared to results obtained by immunohistochemistry (IHC). HMGA2 expression was detectable by qRT-PCR in all samples tested and varied from 5422 to 16 991 545 copies per 250 ng total RNA in the carcinoma samples and from 289 to 525 947 copies in the non-cancerous tissue samples. In 33/34 non-small cell lung cancer (NSCLC) samples tested, an overexpression of HMGA2 was revealed with statistically highly significant differences between non-neoplastic and tumor samples for both AC (P < 0.0001) as well as for SCC (P < 0.0001). Expression varies strongly and is increased up to 911-fold for AC and up to 2504-fold for SCC, respectively, with statistically significant higher increase in SCC (P < 0.05). The results presented herein indicate that HMGA2 overexpression is a common event in NSCLC and could serve as molecular marker for lung cancer. © 2007 Wiley-Liss, Inc.

Revisiting the evaluation of defect prediction models

Abstract: Defect Prediction Models aim at identifying error-prone parts of a software system as early as possible. Many such models have been proposed, their evaluation, however, is still an open question, as recent publications show.An important aspect often ignored during evaluation is the effort reduction gained by using such models. Models are usually evaluated per module by performance measures used in information retrieval, such as recall, precision, or the area under the ROC curve (AUC). These measures assume that the costs associated with additional quality assurance activities are the same for each module, which is not reasonable in practice. For example, costs for unit testing and code reviews are roughly proportional to the size of a module.In this paper, we investigate this discrepancy using optimal and trivial models. We describe a trivial model that takes only the module size measured in lines of code into account, and compare it to five classification methods. The trivial model performs surprisingly well when evaluated using AUC. However, when an effort-sensitive performance measure is used, it becomes apparent that the trivial model is in fact the worst.