Suchergebnisse

La terra č tornata ad essere un asset chiave per la crescita economica delle nazioni. A riguardo, il caso cinese č di particolare interesse. La Cina infatti necessita di risorse naturali esterne per mantenere gli attuali tassi di crescita e la cosiddetta diaspora cinese č una strategia per affrontare lo squilibrio demografico di genere. L'Africa č quindi un partner cruciale per sostenere la strategia di espansione globale cinese. Sullo sfondo di queste considerazioni, l'articolo analizza l'acquisizione di terre nel continente africano da parte della Cina. Si delineano innanzitutto i fattori economici e demografici che spingono la Cina verso le terre africane. Viene poi analizzato il caso dei villaggi Baoding, che hanno per lungo tempo costituito un elemento di attrazione e propaganda degli investitori cinesi in Africa. Infine, si prendono in considerazione le strategie e l'ampiezza del land grabbing cinese in Africa, con riferimento alle conseguenze sui regimi di proprietŕ fondiaria e alle relazioni tra capitale e lavoro.

From genome-scale experimental studies to imaging data, behavioral footprints, and longitudinal healthcare records, the convergence of big data in cancer research and the advances in Artificial Intelligence (AI) is paving the way to develop a systems view of cancer. Nevertheless, this biomedical area is largely characterized by the co-existence of big data and small data resources, highlighting the need for a deeper investigation about the crosstalk between different levels of data granularity, including varied sample sizes, labels, data types, and other data descriptors. This review introduces the current challenges, limitations, and solutions of AI in the heterogeneous landscape of data granularity in cancer research. Such a variety of cancer molecular and clinical data calls for advancing the interoperability among AI approaches, with particular emphasis on the synergy between discriminative and generative models that we discuss in this work with several examples of techniques and applications. ; This work was supported by the European Union's Horizon 2020 research and innovation program under grant agreements 826121 ('iPC—individualizedPaediatricCure: cloud‐based virtual‐patient models for precision pediatric oncology'). ; Peer Reviewed ; Postprint (published version)

SUMMARY: Here we introduce omiXcore, a server for calculations of protein binding to large RNAs (> 500 nucleotides). Our webserver allows (i) use of both protein and RNA sequences without size restriction, (ii) pre-compiled library for exploration of human long intergenic RNAs interactions and (iii) prediction of binding sites. RESULTS: omiXcore was trained and tested on enhanced UV Cross-Linking and ImmunoPrecipitation data. The method discriminates interacting and non-interacting protein-RNA pairs and identifies RNA binding sites with Areas under the ROC curve > 0.80, which suggests that the tool is particularly useful to prioritize candidates for further experimental validation. AVAILABILITY AND IMPLEMENTATION: omiXcore is freely accessed on the web at http://service.tartaglialab.com/grant_submission/omixcore. CONTACT: gian.tartaglia@crg.es. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; We acknowledge support of the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' and the CERCA Programme / Generalitat de Catalunya. This work was supported by the European Union Seventh Framework Programme [FP7/2007-13], European Research Council RIBOMYLOME_309545 (Gian Gaetano Tartaglia) and Spanish Ministry of Economy and Competitiveness BFU2014-5505-P (Gian Gaetano Tartaglia)

Access to genome-wide data provides the opportunity to address questions concerning the ability of transcription factors (TFs) to assemble in distinct macromolecular complexes. Here, we introduce the PAnDA (Protein And DNA Associations) approach to characterize DNA associations with human TFs using expression profiles, protein-protein interactions and recognition motifs. Our method predicts TF binding events with >0.80 accuracy revealing cell-specific regulatory patterns that can be exploited for future investigations. Even when the precise DNA-binding motifs of a specific TF are not available, the information derived from protein-protein networks is sufficient to perform high-confidence predictions (area under the ROC curve of 0.89). PAnDA is freely available at http://service.tartaglialab.com/new_submission/panda. ; The research leading to these results has received funding from the European Research Council under the European/nUnion's Seventh Framework Programme (FP7/2007-2013)/ ERC grant agreementRIBOMYLOME 309545; the Fundació La Marató de TV3 (20142731); and the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' (SEV-2012-0208). Funding for Open Access charge: European Union's Seventh Framework Programme (FP7/2007-2013) / ERC grant agreement RIBOMYLOME 309545 and Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013-2017' (SEV-2012-0208).

It has been reported that genes up-regulated in cancer are often down-regulated in neurodegenerative disorders and vice versa. The fact that apparently unrelated diseases share functional pathways suggests a link between their etiopathogenesis and the properties of molecules involved. Are there specific features that explain the exclusive association of proteins with either cancer or neurodegeneration? We performed a large-scale analysis of physico-chemical properties to understand what characteristics differentiate classes of diseases. We found that structural disorder significantly distinguishes proteins up-regulated in neurodegenerative diseases from those linked to cancer. We also observed high correlation between structural disorder and age of onset in Frontotemporal Dementia, Parkinson's and Alzheimer's diseases, which strongly supports the role of protein unfolding in neurodegenerative processes. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545 (Gian Gaetano Tartaglia), and from the Fundació La Marató de TV3 (20142731). We also acknowledge support from AGAUR (2014 SGR00685) and the Spanish Ministry of Economy and Competitiveness (BFU2014-55054-P), 'Centro de Excelencia Severo Ochoa 2013-2017′ (SEV-2012-0208)

BACKGROUND: The large amount of data produced by high-throughput sequencing poses new computational challenges. In the last decade, several tools have been developed for the identification of transcription and splicing factor binding sites. RESULTS: Here, we introduce the SeAMotE (Sequence Analysis of Motifs Enrichment) algorithm for discovery of regulatory regions in nucleic acid sequences. SeAMotE provides (i) a robust analysis of high-throughput sequence sets, (ii) a motif search based on pattern occurrences and (iii) an easy-to-use web-server interface. We applied our method to recently published data including 351 chromatin immunoprecipitation (ChIP) and 13 crosslinking immunoprecipitation (CLIP) experiments and compared our results with those of other well-established motif discovery tools. SeAMotE shows an average accuracy of 80% in finding discriminative motifs and outperforms other methods available in literature. CONCLUSIONS: /nSeAMotE is a fast, accurate and flexible algorithm for the identification of sequence patterns involved in protein-DNA and protein-RNA recognition. The server can be freely accessed at http://s.tartaglialab.com/new_submission/seamote. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007-2013), through the European Research Council, under grant agreement RIBOMYLOME_309545, and from the Spanish Ministry of Economy and Competitiveness/n(SAF2011-26211). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208)

SUMMARY: Here we introduce ccSOL omics, a webserver for large-scale calculations of protein solubility. Our method allows (i) proteome-wide predictions; (ii) identification of soluble fragments within each sequences; (iii) exhaustive single-point mutation analysis. RESULTS: Using coil/disorder, hydrophobicity, hydrophilicity, β-sheet and α-helix propensities, we built a predictor of protein solubility. Our approach shows an accuracy of 79% on the training set (36 990 Target Track entries). Validation on three independent sets indicates that ccSOL omics discriminates soluble and insoluble proteins with an accuracy of 74% on 31 760 proteins sharing <30% sequence similarity. AVAILABILITY AND IMPLEMENTATION: ccSOL omics can be freely accessed on the web at http://s.tartaglialab.com/page/ccsol_group. Documentation and tutorial are available at http://s.tartaglialab.com/static_files/shared/tutorial_ccsol_omics.html. CONTACT: gian.tartaglia@crg.es/nSUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. ; The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7/2007–2013), through the European Research Council,under grant agreement RIBOMYLOME 309545, and from the Spanish Ministry of Economy and Competitiveness (SAF2011-26211). We also acknowledge support from the Spanish Ministry of Economy and Competitiveness, 'Centro de Excelencia Severo Ochoa 2013–2017' (SEV-2012-0208)

Precision Medicine implies a deep understanding of inter-individual differences in health and disease that are due to genetic and environmental factors. To acquire such understanding there is a need for the implementation of different types of technologies based on artificial intelligence (AI) that enable the identification of biomedically relevant patterns, facilitating progress towards individually tailored preventative and therapeutic interventions. Despite the significant scientific advances achieved so far, most of the currently used biomedical AI technologies do not account for bias detection. Furthermore, the design of the majority of algorithms ignore the sex and gender dimension and its contribution to health and disease differences among individuals. Failure in accounting for these differences will generate sub-optimal results and produce mistakes as well as discriminatory outcomes. In this review we examine the current sex and gender gaps in a subset of biomedical technologies used in relation to Precision Medicine. In addition, we provide recommendations to optimize their utilization to improve the global health and disease landscape and decrease inequalities. ; This work is written on behalf of the Women's Brain Project (WBP) (www.womensbrainproject.com/), an international organization advocating for women's brain and mental health through scientific research, debate and public engagement. The authors would like to gratefully acknowledge Maria Teresa Ferretti and Nicoletta Iacobacci (WBP) for the scientific advice and insightful discussions; Roberto Confalonieri (Alpha Health) for reviewing the manuscript; the Bioinfo4Women programme of Barcelona Supercomputing Center (BSC) for the support. This work has been supported by the Spanish Government (SEV 2015–0493) and grant PT17/0009/0001, of the Acción Estratégica en Salud 2013–2016 of the Programa Estatal de Investigación Orientada a los Retos de la Sociedad, funded by the Instituto de Salud Carlos III (ISCIII) and European Regional Development Fund (ERDF). EG has received funding from the Innovative Medicines Initiative 2 (IMI2) Joint Undertaking under grant agreement No 116030 (TransQST), which is supported by the European Union's Horizon 2020 research and innovation programme and the European Federation of Pharmaceutical Industries and Associations (EFPIA). ; Peer Reviewed ; Postprint (published version)

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions.

COVID-19 is an infectious disease caused by the SARS-CoV-2 virus, which has spread all over the world leading to a global pandemic. The fast progression of COVID-19 has been mainly related to the high contagion rate of the virus and the worldwide mobility of humans. In the absence of pharmacological therapies, governments from different countries have introduced several non-pharmaceutical interventions to reduce human mobility and social contact. Several studies based on Anonymized Mobile Phone Data have been published analysing the relationship between human mobility and the spread of coronavirus. However, to our knowledge, none of these data-sets integrates cross-referenced geo-localised data on human mobility and COVID-19 cases into one all-inclusive open resource. Herein we present COVID-19 Flow-Maps, a cross-referenced Geographic Information System that integrates regularly updated time-series accounting for population mobility and daily reports of COVID-19 cases in Spain at different scales of time spatial resolution. This integrated and up-to-date data-set can be used to analyse the human dynamics to guide and support the design of more effective non-pharmaceutical interventions. ; This work was supported by the Generalitat de Catalunya through the project PDAD14/20/00001, and by the H2020 programme under Grant Agreement 825070 (INFORE) and the INB Grant (PT17/0009/0001 - ISCIII-SGEFI/ERDF). ; Peer Reviewed ; Postprint (published version)