Stable Isotope Metabolic Labeling with a Novel 15N-Enriched Bacteria Diet for Improved Proteomic Analyses of Mouse Models for Psychopathologies

The identification of differentially regulated proteins in animal models of psychiatric diseases is essential for a comprehensive analysis of associated psychopathological processes. Mass spectrometry is the most relevant method for analyzing differences in protein expression of tissue and body fluid proteomes. However, standardization of sample handling and sample-to-sample variability are problematic. Stable isotope metabolic labeling of a proteome represents the gold standard for quantitative mass spectrometry analysis. The simultaneous processing of a mixture of labeled and unlabeled samples allows a sensitive and accurate comparative analysis between the respective proteomes. Here, we describe a cost-effective feeding protocol based on a newly developed 15N bacteria diet based on Ralstonia eutropha protein, which was applied to a mouse model for trait anxiety. Tissue from 15N-labeled vs. 14N-unlabeled mice was examined by mass spectrometry and differences in the expression of glyoxalase-1 (GLO1) and histidine triad nucleotide binding protein 2 (Hint2) proteins were correlated with the animals' psychopathological behaviors for methodological validation and proof of concept, respectively. Additionally, phenotyping unraveled an antidepressant-like effect of the incorporation of the stable isotope 15N into the proteome of highly anxious mice. This novel phenomenon is of considerable relevance to the metabolic labeling method and could provide an opportunity for the discovery of candidate proteins involved in depression-like behavior. The newly developed 15N bacteria diet provides researchers a novel tool to discover disease-relevant protein expression differences in mouse models using quantitative mass spectrometry

Nitrogen sources on TPOMW valorization through solid state fermentation performed by Yarrowia lipolytica

This manuscript reports the valorization of two-phase olive mill waste (TPOMW) as raw material and carbon source for solid state fermentation using Yarrowia lipolytica as biocatalyst. Due to its chemical characteristics, a combination of different raw materials (TPOMW and wheat bran, WB) was evaluated and two distinct nitrogen sources were applied as supplementation for lipase production. A TPOMW/WB ratio of 1:1 and supplementation with ammonium sulfate was chosen as the best condition. The productivity in 24 h reached 7.8 U/gh and, after four days of process, only decreased about 35%. Process pH ranged from 5.5-5.9, remaining in an acid range. Thus, the successful use of TPOMW, a watery solid by-product with high content of lipids, as raw material for Yarrowia lipolytica growth and lipase production provided an environmental friendly alternative to valorize such waste.The authors kindly acknowledge the financial aid and research scholarships given by CAPES. Maria Alice Zarur Coelho thanks CNPq (Proc. 308890/ 2013-2)

Role of cardiovascular imaging in cancer patients receiving cardiotoxic therapies: a position statement on behalf of the Heart Failure Association (HFA), the European Association of Cardiovascular Imaging (EACVI) and the Cardio‐Oncology Council of the European Society of Cardiology (ESC)

Cardiovascular (CV) imaging is an important tool in baseline risk assessment and detection of CV disease in oncology patients receiving cardiotoxic cancer therapies. This position statement examines the role of echocardiography, cardiac magnetic resonance, nuclear cardiac imaging and computed tomography in the management of cancer patients. The Imaging and Cardio‐Oncology Study Groups of the Heart Failure Association (HFA) of the European Society of Cardiology (ESC) in collaboration with the European Association of Cardiovascular Imaging (EACVI) and the Cardio‐Oncology Council of the ESC have evaluated the current evidence for the value of modern CV imaging in the cardio‐oncology field. The most relevant echocardiographic parameters, including global longitudinal strain and three‐dimensional ejection fraction, are proposed. The protocol for baseline pre‐treatment evaluation and specific surveillance algorithms or pathways for anthracycline chemotherapy, HER2‐targeted therapies such as trastuzumab, vascular endothelial growth factor tyrosine kinase inhibitors, BCr‐Abl tyrosine kinase inhibitors, proteasome inhibitors and immune checkpoint inhibitors are presented. The indications for CV imaging after completion of oncology treatment are considered. The typical consequences of radiation therapy and the possibility of their identification in the long term are also summarized. Special populations are discussed including female survivors planning pregnancy, patients with carcinoid disease, patients with cardiac tumours and patients with right heart failure. Future directions and ongoing CV imaging research in cardio‐oncology are discussed

Automatic architectural style recognition

Procedural modeling has proven to be a very valuable tool in the field of architecture. In the last few years, research has soared to automatically create procedural models from images. However, current algorithms for this process of inverse procedural modeling rely on the assumption that the building style is known. So far, the determination of the building style has remained a manual task. In this paper, we propose an algorithm which automates this process through classification of architectural styles from facade images. Our classifier first identifies the images containing buildings, then separates individual facades within an image and determines the building style. This information could then be used to initialize the building reconstruction process. We have trained our classifier to distinguish between several distinct architectural styles, namely Flemish Renaissance, Haussmannian and Neoclassical. Finally, we demonstrate our approach on various street-side images.Mathias M., Martinovic A., Weissenberg J., Haegler S., Van Gool L., ''Automatic architectural style recognition'', Proceedings 4th ISPRS international workshop 3D-ARCH 2011 : 3D virtual reconstruction and visualization of complex architectures, March 2-4, 2011, Trento, Italy.status: publishe

AUTOMATIC ARCHITECTURAL STYLE RECOGNITION

Procedural modeling has proven to be a very valuable tool in the field of architecture. In the last few years, research has soared to automatically create procedural models from images. However, current algorithms for this process of inverse procedural modeling rely on the assumption that the building style is known. So far, the determination of the building style has remained a manual task. In this paper, we propose an algorithm which automates this process through classification of architectural styles from facade images. Our classifier first identifies the images containing buildings, then separates individual facades within an image and determines the building style. This information could then be used to initialize the building reconstruction process. We have trained our classifier to distinguish between several distinct architectural styles, namely Flemish Renaissance, Haussmannian and Neoclassical. Finally, we demonstrate our approach on various street-side images