Identification of Hypoxia-Regulated Proteins Using MALDI-Mass Spectrometry Imaging Combined with Quantitative Proteomics

Hypoxia is present in most solid tumors and is clinically correlated with increased metastasis and poor patient survival. While studies have demonstrated the role of hypoxia and hypoxia-regulated proteins in cancer progression, no attempts have been made to identify hypoxia-regulated proteins using quantitative proteomics combined with MALDI-mass spectrometry imaging (MALDI-MSI). Here we present a comprehensive hypoxic proteome study and are the first to investigate changes in situ using tumor samples. In vitro quantitative mass spectrometry analysis of the hypoxic proteome was performed on breast cancer cells using stable isotope labeling with amino acids in cell culture (SILAC). MS analyses were performed on laser-capture microdissected samples isolated from normoxic and hypoxic regions from tumors derived from the same cells used in vitro. MALDI-MSI was used in combination to investigate hypoxia-regulated protein localization within tumor sections. Here we identified more than 100 proteins, both novel and previously reported, that were associated with hypoxia. Several proteins were localized in hypoxic regions, as identified by MALDI-MSI. Visualization and data extrapolation methods for the in vitro SILAC data were also developed, and computational mapping of MALDI-MSI data to IHC results was applied for data validation. The results and limitations of the methodologies described are discussed. 2014 American Chemical Societ

Bayesian model averaging for ligand discovery

High-throughput screening (HTS) is now a standard approach used in the pharmaceutical industry to identify potential drug-like lead molecules. The analysis linking biological data with molecular properties is a major goal in both academic and pharmaceutical research. This paper presents a Bayesian analysis of high-dimensional descriptor data using Markov chain Monte Carlo (MCMC) simulations for learning classification trees as a novel method for pharmacophore and ligand discovery. We use experimentally determined binding affinity data with the protein pyruvate kinase to train and assess our model averaging algorithm and then apply it to a large database of over 3.7 million molecules. We compare the results of a number of variations on the central Bayesian theme to that of two Neural Network (NN) architectures and that of Support Vector Machines (SVM). The main Bayesian algorithm, in addition to achieving high specificity and sensitivity, also lends itself naturally to classifying test sets with missing data and providing a ranking for the classified compounds. The approach has been used to select and rank potential biologically active compounds and could provide a powerful tool in compound testing

4D Reconstruction of Tangible Cultural Heritage Objects from Web-Retrieved Images

The number of digital images that are available online today has reached unprecedented levels. Recent statistics showed that by the end of 2013 there were over 250 billion photographs stored in just one of the major social media sites, with a daily average upload of 300 million photos. These photos, apart from documenting personal lives, often relate to experiences in well-known places of cultural interest, throughout several periods of time. Thus from the viewpoint of Cultural Heritage professionals, they constitute valuable and freely available digital cultural content. Advances in the fields of Photogrammetry and Computer Vision have led to significant breakthroughs such as the Structure from Motion algorithm which creates 3D models of objects using their 2D photographs. The existence of powerful and affordable computational machinery enables the reconstruction not only of single structures such as artefacts, but also of entire cities. This paper presents an overview of our methodology for producing cost-effective 4D – i.e. in space and time – models of Cultural Heritage structures such as monuments and artefacts from 2D data (pictures, video) and semantic information, freely available ‘in the wild’, i.e. in Internet repositories and social media. State-of-the-art methods from Computer Vision, Photogrammetry, 3D Reconstruction and Semantic representation are incorporated in an innovative workflow with the main goal to enable historians, architects, archaeologists, urban planners and other cultural heritage professionals to reconstruct cost-effective views of historical structures out of the billions of free images floating around the web and subsequently interact with those reconstructions

Longitudinal evaluation of clinically early relapsing-remitting multiple sclerosis with diffusion tensor imaging.

Diffusion tensor imaging (DTI) parameters such as mean diffusivity (MD) and fractional anisotropy (FA) assess aspects of structural integrity within tissue. In relapsing-remitting (RR) multiple sclerosis (MS), abnormalities in normal appearing brain tissue (NABT) have been shown cross-sectionally. The evolution of these abnormalities over time is unclear. We present a longitudinal study investigating early RR MS subjects. The aims were to determine DTI changes over two years and assess the potential of DTI as a longitudinal quantitative marker at this stage of MS. Fifteen controls and 28 patients with RR MS (median disease duration 1.9 years; median EDSS 1.5) had DTI yearly for two years. NABT and whole brain tissue (NABT plus lesions) FA and MD histograms analysed. At baseline, differences in FA were noted between patients and controls (mean [p = 0.042] and peak height [p = 0.008]), while at two years differences in MD were observed (mean [p = 0.008] and peak location [p = 0.024]). However there were no significant DTI differences in longitudinal rates of change between patients and cohorts. In conclusion, although subtle NABT abnormalities were detected in early RR MS, the absence of longitudinal change suggests a limited role for global DTI assessment of NABT in following the early disease course

Cloud-based 3D Reconstruction of Cultural Heritage Monuments using Open Access Image Repositories

A large number of photographs of cultural heritage items and monuments is publicly available in various Open Access Image Repositories (OAIR) and social media sites. Metadata inserted by camera, user and host site may help to determine the photograph content, geo-location and date of capture, thus allowing us, with relative success, to localise photos in space and time. Additionally, developments in Photogrammetry and Computer Vision, such as Structure from Motion (SfM), provide a simple and cost-effective method of generating relatively accurate camera orientations and sparse and dense 3D point clouds from 2D images. Our main goal is to provide a software tool able to run on desktop or cluster computers or as a back end of a cloud-based service, enabling historians, architects, archaeologists and the general public to search, download and reconstruct 3D point clouds of historical monuments from hundreds of images from the web in a cost-effective manner. The end products can be further enriched with metadata and published. This paper describes a workflow for searching and retrieving photographs of historical monuments from OAIR, such as Flickr and Picasa, and using them to build dense point clouds using SfM and dense image matching techniques. Computational efficiency is improved by a technique which reduces image matching time by using an image connectivity prior derived from low-resolution versions of the original images. Benchmarks for two large datasets showing the respective efficiency gains are presented

4D reconstruction of the past: The image retrieval and 3D model construction pipeline

One of the main characteristics of the Internet era we are living in, is the free and online availability of a huge amount of data. This data is of varied reliability and accuracy and exists in various forms and formats. Often, it is cross-referenced and linked to other data, forming a nexus of text, images, animation and audio enabled by hypertext and, recently, by the Web3.0 standard. Our main goal is to enable historians, architects, archaeolo- gists, urban planners and affiliated professionals to reconstruct views of historical monuments from thousands of images floating around the web. This paper aims to provide an update of our progress in designing and imple- menting a pipeline for searching, filtering and retrieving photographs from Open Access Image Repositories and social media sites and using these images to build accurate 3D models of archaeological monuments as well as enriching multimedia of cultural / archaeological interest with metadata and harvesting the end products to EU- ROPEANA. We provide details of how our implemented software searches and retrieves images of archaeological sites from Flickr and Picasa repositories as well as strategies on how to filter the results, on two levels; a) based on their built-in metadata including geo-location information and b) based on image processing and clustering techniques. We also describe our implementation of a Structure from Motion pipeline designed for producing 3D models using the large collection of 2D input images (>1000) retrieved from Internet Repositories.Euro-agriwot,European Cooperation in Science and Technology (COST),Geosystems Hellas,Intergraph,Li-Co