Hierarchical structuring of Cultural Heritage objects within large aggregations

Huge amounts of cultural content have been digitised and are available through digital libraries and aggregators like Europeana.eu. However, it is not easy for a user to have an overall picture of what is available nor to find related objects. We propose a method for hier- archically structuring cultural objects at different similarity levels. We describe a fast, scalable clustering algorithm with an automated field selection method for finding semantic clusters. We report a qualitative evaluation on the cluster categories based on records from the UK and a quantitative one on the results from the complete Europeana dataset.Comment: The paper has been published in the proceedings of the TPDL conference, see http://tpdl2013.info. For the final version see http://link.springer.com/chapter/10.1007%2F978-3-642-40501-3_2

Finding co-solvers on Twitter, with a little help from Linked Data

In this paper we propose a method for suggesting potential collaborators for solving innovation challenges online, based on their competence, similarity of interests and social proximity with the user. We rely on Linked Data to derive a measure of semantic relatedness that we use to enrich both user profiles and innovation problems with additional relevant topics, thereby improving the performance of co-solver recommendation. We evaluate this approach against state of the art methods for query enrichment based on the distribution of topics in user profiles, and demonstrate its usefulness in recommending collaborators that are both complementary in competence and compatible with the user. Our experiments are grounded using data from the social networking service Twitter.com

Glioblastoma multiforme: a multidisciplinary approach to overcome chemoresistance and find new therapeutic strategies

Objectives: Glioblastoma multiforme is the most frequent malignant brain tumor. Patients die within 15 months after diagnosis. The failure of current therapies is ascribed to a subpopulation of cells with stem-like properties, called glioma stem cells (GSCs). The aim of this study is to develop new effective therapies. Moreover, we want to better characterize the orthotopic xenograft model established by GSCs injection into NOD/SCID mice. Materials and methods: We tested Temolomide and Valproic acid treatments, alone and in combination, on seven GSC lines by MTT assay and we sequenced p53. Moreover, we characterized our xenograft model investigating the expression of stemness and differentiation markers by immunohistochemistry on FFPE tissues and by immunofluorescence on the correspondent cell line. Finally, we performed aCGH on the DNA extracted from the cell line and from FFPE tissues. Results: GSCs were resistant to Temozolomide and slightly sensitive to Valproic acid. The two drugs exerted a synergistic effect when combined performing a pre-conditioning with Valproic acid. Furthermore, several cell lines carry p53 mutations. IF and IHC showed a perfect correspondence for stemness markers expression, but discordant data for the others. aCGH analysis evidenced numerous alterations specific for the ex vivo sample, suggesting the presence of an in vivo clonal selection. Discussion: This work shows the importance of murine microenvironment in GSCs phenotype in vivo and suggests the possibility to use our combined treatment for therapeutic purposes. Conclusions: Orthotopic models from GSCs and in vitro grown cell lines represent good models for the development of GSC-targeted therapies

Distributed agents for online spatial searches

As the availability and utilisation of online data blossoms, automated online searches—whether to answer a simple question, seek specific sensor readings, or investigate research in a particular domain—have raised a number of issues. Simple search tools do not access the deep web of services and online forms, and cannot handle knowledge domain-specific search problems, but specialist search tools can have a narrow domain and applicability. Some online tools circumvent these problems by putting more filter controls into the hands of users, but this leads to more complex interfaces which can raise usability barriers. A distributed approach, where specialised search agents act autonomously to find contextualised information, can provide a useful compromise between a simple, general search interface and specialist searches. This paper outlines work in progress on design and use of specialist search agents, with a case study to find public transportation bus stops within a spatial region. The approach is demonstrated with a proof of concept web interface, developed to interpret a text query to find and show bus stop locations within a named boundary by coordinating multiple online search agents. Search agents were designed to follow a common model to allow for future development of agent types, including specialist agents used in the case study to search standard open web services and extract spatial features

Satellites Form Fast and Late: a Population Synthesis for the Galilean Moons

Stars and planetary system

mspecLINE: bridging knowledge of human disease with the proteome

<p>Abstract</p> <p>Background</p> <p>Public proteomics databases such as PeptideAtlas contain peptides and proteins identified in mass spectrometry experiments. However, these databases lack information about human disease for researchers studying disease-related proteins. We have developed mspecLINE, a tool that combines knowledge about human disease in MEDLINE with empirical data about the detectable human proteome in PeptideAtlas. mspecLINE associates diseases with proteins by calculating the semantic distance between annotated terms from a controlled biomedical vocabulary. We used an established semantic distance measure that is based on the co-occurrence of disease and protein terms in the MEDLINE bibliographic database.</p> <p>Results</p> <p>The mspecLINE web application allows researchers to explore relationships between human diseases and parts of the proteome that are detectable using a mass spectrometer. Given a disease, the tool will display proteins and peptides from PeptideAtlas that may be associated with the disease. It will also display relevant literature from MEDLINE. Furthermore, mspecLINE allows researchers to select proteotypic peptides for specific protein targets in a mass spectrometry assay.</p> <p>Conclusions</p> <p>Although mspecLINE applies an information retrieval technique to the MEDLINE database, it is distinct from previous MEDLINE query tools in that it combines the knowledge expressed in scientific literature with empirical proteomics data. The tool provides valuable information about candidate protein targets to researchers studying human disease and is freely available on a public web server.</p

Causality - Complexity - Consistency: Can Space-Time Be Based on Logic and Computation?

The difficulty of explaining non-local correlations in a fixed causal structure sheds new light on the old debate on whether space and time are to be seen as fundamental. Refraining from assuming space-time as given a priori has a number of consequences. First, the usual definitions of randomness depend on a causal structure and turn meaningless. So motivated, we propose an intrinsic, physically motivated measure for the randomness of a string of bits: its length minus its normalized work value, a quantity we closely relate to its Kolmogorov complexity (the length of the shortest program making a universal Turing machine output this string). We test this alternative concept of randomness for the example of non-local correlations, and we end up with a reasoning that leads to similar conclusions as in, but is conceptually more direct than, the probabilistic view since only the outcomes of measurements that can actually all be carried out together are put into relation to each other. In the same context-free spirit, we connect the logical reversibility of an evolution to the second law of thermodynamics and the arrow of time. Refining this, we end up with a speculation on the emergence of a space-time structure on bit strings in terms of data-compressibility relations. Finally, we show that logical consistency, by which we replace the abandoned causality, it strictly weaker a constraint than the latter in the multi-party case.Comment: 17 pages, 16 figures, small correction

Artificial Sequences and Complexity Measures

In this paper we exploit concepts of information theory to address the fundamental problem of identifying and defining the most suitable tools to extract, in a automatic and agnostic way, information from a generic string of characters. We introduce in particular a class of methods which use in a crucial way data compression techniques in order to define a measure of remoteness and distance between pairs of sequences of characters (e.g. texts) based on their relative information content. We also discuss in detail how specific features of data compression techniques could be used to introduce the notion of dictionary of a given sequence and of Artificial Text and we show how these new tools can be used for information extraction purposes. We point out the versatility and generality of our method that applies to any kind of corpora of character strings independently of the type of coding behind them. We consider as a case study linguistic motivated problems and we present results for automatic language recognition, authorship attribution and self consistent-classification.Comment: Revised version, with major changes, of previous "Data Compression approach to Information Extraction and Classification" by A. Baronchelli and V. Loreto. 15 pages; 5 figure

Propranolol reduces IFN-γ driven PD-L1 immunosuppression and improves anti-tumour immunity in ovarian cancer

The immune system plays an important role in controlling epithelial ovarian cancer (EOC). EOC is considered to be a "cold tumour," a tumour that has not triggered a strong response by the immune system. However, tumour infiltrating lymphocytes (TILs) and the expression of programmed cell death ligand (PD-L1) are used as prognostic indicators in EOC. Immunotherapy such as PD-(L)1 inhibitors have shown limited benefit in EOC. Since the immune system is affected by behavioural stress and the beta-adrenergic signalling pathway, this study aimed to explore the impact of propranolol (PRO), a beta-blocker, on anti-tumour immunity in both in vitro and in vivo EOC models. Noradrenaline (NA), an adrenergic agonist, did not directly regulate PD-L1 expression but PD-L1 was significantly upregulated by IFN-γ in EOC cell lines. IFN-γ also increased PD-L1 on extracellular vesicles (EVs) released by ID8 cells. PRO significantly decreased IFN-γ levels in primary immune cells activated ex vivo and showed increased viability of the CD8+ cell population in an EV-immune cell co-incubation. In addition, PRO reverted PD-L1 upregulation and significantly decreased IL-10 levels in an immune-cancer cell co-culture. Chronic behavioural stress increased metastasis in mice while PRO monotherapy and the combo of PRO and PD-(L)1 inhibitor significantly decreased stress-induced metastasis. The combined therapy also reduced tumour weight compared to the cancer control group and induced anti-tumour T-cell responses with significant CD8 expression in tumour tissues. In conclusion, PRO showed a modulation of the cancer immune response by decreasing IFN-γ production and, in turn, IFN-γ-mediated PD-L1 overexpression. The combined therapy of PRO and PD-(L)1 inhibitor decreased metastasis and improved anti-tumour immunity offering a promising new therapy