374 research outputs found
The late Oligocene flora from the RĂo Leona Formation, Argentinian Patagonia
A late Oligocene plant macrofossil assemblage is described from the RĂo Leona Formation, Argentinian Patagonia. This includes a fern, âBlechnum turbioensisâ Frenguelli, one species of conifer, and sixteen angiosperm taxa. Rosaceae, Myrtaceae, Proteaceae, Lauraceae, Anacardiaceae and Typhaceae are represented by one species in each family. Five species are considered to be members of the Fabales. Three leaf taxa together with Carpolithus seeds are placed in the Nothofagaceae. Palynomorphs and permineralized woods complete the floral record of the RĂo Leona Formation, which is considered early late Oligocene based on radiometric dating and palynofloras
Genetic interactions: the missing links for a better understanding of cancer susceptibility, progression and treatment
It is increasingly clear that complex networks of relationships between genes and/or proteins govern neoplastic processes. Our understanding of these networks is expanded by the use of functional genomic and proteomic approaches in addition to computational modeling. Concurrently, whole-genome association scans and mutational screens of cancer genomes identify novel cancer genes. Together, these analyses have vastly increased our knowledge of cancer, in terms of both "part lists" and their functional associations. However, genetic interactions have hitherto only been studied in depth in model organisms and remain largely unknown for human systems. Here, we discuss the importance and potential benefits of identifying genetic interactions at the human genome level for creating a better understanding of cancer susceptibility and progression and developing novel effective anticancer therapies. We examine gene expression profiles in the presence and absence of co-amplification of the 8q24 and 20q13 chromosomal regions in breast tumors to illustrate the molecular consequences and complexity of genetic interactions and their role in tumorigenesis. Finally, we highlight current strategies for targeting tumor dependencies and outline potential matrix screening designs for uncovering molecular vulnerabilities in cancer cells
Gene Expression Integration into Pathway Modules Reveals a Pan-Cancer Metabolic Landscape
BIO2014-57291-R and SAF2017-88908-R from the Spanish Ministry of Economy and Competitivenessgrant PI15/00854 from the FISâPlataforma de Recursos Biomoleculares y BioinformĂĄticosâ PT17/0009/0006 from the ISCIII, cofunded with European Regional Development FundsFP7-PEOPLE-2012-ITN MLPM2012EU H2020-INFRADEV-1-2015-1 ELIXIR-EXCELERAT
A dynamic network approach for the study of human phenotypes
The use of networks to integrate different genetic, proteomic, and metabolic
datasets has been proposed as a viable path toward elucidating the origins of
specific diseases. Here we introduce a new phenotypic database summarizing
correlations obtained from the disease history of more than 30 million patients
in a Phenotypic Disease Network (PDN). We present evidence that the structure
of the PDN is relevant to the understanding of illness progression by showing
that (1) patients develop diseases close in the network to those they already
have; (2) the progression of disease along the links of the network is
different for patients of different genders and ethnicities; (3) patients
diagnosed with diseases which are more highly connected in the PDN tend to die
sooner than those affected by less connected diseases; and (4) diseases that
tend to be preceded by others in the PDN tend to be more connected than
diseases that precede other illnesses, and are associated with higher degrees
of mortality. Our findings show that disease progression can be represented and
studied using network methods, offering the potential to enhance our
understanding of the origin and evolution of human diseases. The dataset
introduced here, released concurrently with this publication, represents the
largest relational phenotypic resource publicly available to the research
community.Comment: 28 pages (double space), 6 figure
Conceptual design and thermal analysis of a modular cryostat for one single coil of a 10 MW offshore superconducting wind turbine
Superconducting generators show the potential to reduce the head mass of large offshore wind turbines. A 10 MW offshore superconducting wind turbine has been investigated in the SUPRAPOWER project. The superconducting coils based on MgB2 tapes are supposed to work at cryogenic temperature of 20 K. In this paper, a novel modular rotating cryostat was presented for one single coil of the superconducting wind turbine. The modular concept and cryogen-free cooling method were proposed to fulfil the requirements of handling, maintenance, reliability of long term and offshore operations. Two stage Gifford-McMahon cryocoolers were used to provide cooling source. Supporting rods made of titanium alloy were selected as support structures of the cryostat in aim of reducing the heat load. The thermal performance in the modular cryostat was carefully investigated. The heat load applied to the cryocooler second stage was 2.17 W@20 K per coil. The corresponding temperature difference along the superconducting coil was only around 1 K.European Commision's FP
Disease networks identify specific conditions and pleiotropy influencing multimorbidity in the general population
Multimorbidity is an emerging topic in public health policy because of its increasing prevalence and socio-economic impact. However, the age- and gender-dependent trends of disease associations at fine resolution, and the underlying genetic factors, remain incompletely understood. Here, by analyzing disease networks from electronic medical records of primary health care, we identify key conditions and shared genetic factors influencing multimorbidity. Three types of diseases are outlined: "central", which include chronic and non-chronic conditions, have higher cumulative risks of disease associations; "community roots" have lower cumulative risks, but inform on continuing clustered disease associations with age; and "seeds of bursts", which most are chronic, reveal outbreaks of disease associations leading to multimorbidity. The diseases with a major impact on multimorbidity are caused by genes that occupy central positions in the network of human disease genes. Alteration of lipid metabolism connects breast cancer, diabetic neuropathy and nutritional anemia. Evaluation of key disease associations by a genome-wide association study identifies shared genetic factors and further supports causal commonalities between nervous system diseases and nutritional anemias. This study also reveals many shared genetic signals with other diseases. Collectively, our results depict novel population-based multimorbidity patterns, identify key diseases within them, and highlight pleiotropy influencing multimorbidity
Increased entropy of signal transduction in the cancer metastasis phenotype
Studies into the statistical properties of biological networks have led to
important biological insights, such as the presence of hubs and hierarchical
modularity. There is also a growing interest in studying the statistical
properties of networks in the context of cancer genomics. However, relatively
little is known as to what network features differ between the cancer and
normal cell physiologies, or between different cancer cell phenotypes. Based on
the observation that frequent genomic alterations underlie a more aggressive
cancer phenotype, we asked if such an effect could be detectable as an increase
in the randomness of local gene expression patterns. Using a breast cancer gene
expression data set and a model network of protein interactions we derive
constrained weighted networks defined by a stochastic information flux matrix
reflecting expression correlations between interacting proteins. Based on this
stochastic matrix we propose and compute an entropy measure that quantifies the
degree of randomness in the local pattern of information flux around single
genes. By comparing the local entropies in the non-metastatic versus metastatic
breast cancer networks, we here show that breast cancers that metastasize are
characterised by a small yet significant increase in the degree of randomness
of local expression patterns. We validate this result in three additional
breast cancer expression data sets and demonstrate that local entropy better
characterises the metastatic phenotype than other non-entropy based measures.
We show that increases in entropy can be used to identify genes and signalling
pathways implicated in breast cancer metastasis. Further exploration of such
integrated cancer expression and protein interaction networks will therefore be
a fruitful endeavour.Comment: 5 figures, 2 Supplementary Figures and Table
On dynamic network entropy in cancer
The cellular phenotype is described by a complex network of molecular
interactions. Elucidating network properties that distinguish disease from the
healthy cellular state is therefore of critical importance for gaining
systems-level insights into disease mechanisms and ultimately for developing
improved therapies. By integrating gene expression data with a protein
interaction network to induce a stochastic dynamics on the network, we here
demonstrate that cancer cells are characterised by an increase in the dynamic
network entropy, compared to cells of normal physiology. Using a fundamental
relation between the macroscopic resilience of a dynamical system and the
uncertainty (entropy) in the underlying microscopic processes, we argue that
cancer cells will be more robust to random gene perturbations. In addition, we
formally demonstrate that gene expression differences between normal and cancer
tissue are anticorrelated with local dynamic entropy changes, thus providing a
systemic link between gene expression changes at the nodes and their local
network dynamics. In particular, we also find that genes which drive
cell-proliferation in cancer cells and which often encode oncogenes are
associated with reductions in the dynamic network entropy. In summary, our
results support the view that the observed increased robustness of cancer cells
to perturbation and therapy may be due to an increase in the dynamic network
entropy that allows cells to adapt to the new cellular stresses. Conversely,
genes that exhibit local flux entropy decreases in cancer may render cancer
cells more susceptible to targeted intervention and may therefore represent
promising drug targets.Comment: 10 pages, 3 figures, 4 tables. Submitte
Next station in microarray data analysis: GEPAS
The Gene Expression Profile Analysis Suite (GEPAS) has been running for more than four years. During this time it has evolved to keep pace with the new interests and trends in the still changing world of microarray data analysis. GEPAS has been designed to provide an intuitive although powerful web-based interface that offers diverse analysis options from the early step of preprocessing (normalization of Affymetrix and two-colour microarray experiments and other preprocessing options), to the final step of the functional annotation of the experiment (using Gene Ontology, pathways, PubMed abstracts etc.), and include different possibilities for clustering, gene selection, class prediction and array-comparative genomic hybridization management. GEPAS is extensively used by researchers of many countries and its records indicate an average usage rate of 400 experiments per day. The web-based pipeline for microarray gene expression data, GEPAS, is available at
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