20,664 research outputs found
Hierarchical information clustering by means of topologically embedded graphs
We introduce a graph-theoretic approach to extract clusters and hierarchies
in complex data-sets in an unsupervised and deterministic manner, without the
use of any prior information. This is achieved by building topologically
embedded networks containing the subset of most significant links and analyzing
the network structure. For a planar embedding, this method provides both the
intra-cluster hierarchy, which describes the way clusters are composed, and the
inter-cluster hierarchy which describes how clusters gather together. We
discuss performance, robustness and reliability of this method by first
investigating several artificial data-sets, finding that it can outperform
significantly other established approaches. Then we show that our method can
successfully differentiate meaningful clusters and hierarchies in a variety of
real data-sets. In particular, we find that the application to gene expression
patterns of lymphoma samples uncovers biologically significant groups of genes
which play key-roles in diagnosis, prognosis and treatment of some of the most
relevant human lymphoid malignancies.Comment: 33 Pages, 18 Figures, 5 Table
Structural Prediction of Protein–Protein Interactions by Docking: Application to Biomedical Problems
A huge amount of genetic information is available thanks to the recent advances in sequencing technologies and the larger computational capabilities, but the interpretation of such genetic data at phenotypic level remains elusive. One of the reasons is that proteins are not acting alone, but are specifically interacting with other proteins and biomolecules, forming intricate interaction networks that are essential for the majority of cell processes and pathological conditions. Thus, characterizing such interaction networks is an important step in understanding how information flows from gene to phenotype. Indeed, structural characterization of protein–protein interactions at atomic resolution has many applications in biomedicine, from diagnosis and vaccine design, to drug discovery. However, despite the advances of experimental structural determination, the number of interactions for which there is available structural data is still very small. In this context, a complementary approach is computational modeling of protein interactions by docking, which is usually composed of two major phases: (i) sampling of the possible binding modes between the interacting molecules and (ii) scoring for the identification of the correct orientations. In addition, prediction of interface and hot-spot residues is very useful in order to guide and interpret mutagenesis experiments, as well as to understand functional and mechanistic aspects of the interaction. Computational docking is already being applied to specific biomedical problems within the context of personalized medicine, for instance, helping to interpret pathological mutations involved in protein–protein interactions, or providing modeled structural data for drug discovery targeting protein–protein interactions.Spanish Ministry of Economy grant number BIO2016-79960-R; D.B.B. is supported by a
predoctoral fellowship from CONACyT; M.R. is supported by an FPI fellowship from the
Severo Ochoa program. We are grateful to the Joint BSC-CRG-IRB Programme in
Computational Biology.Peer ReviewedPostprint (author's final draft
Nanoroughness, Surface Chemistry and Drug Delivery Control by Atmospheric Plasma Jet on Implantable Devices
Implantable devices need specific tailored surface morphologies and chemistries to interact with the living systems or to actively induce a biological response also by the release of drugs or proteins. These customised requirements foster technologies that can be implemented in additive manufacturing systems. Here we present a novel approach based on spraying processes that allows to control separately topographic features in the submicron range ( 3d 60 nm - 2 \ub5m), ammine or carboxylic chemistry and fluorophore release even on temperature sensitive biodegradable polymers such as polycaprolactone (PCL). We developed a two-steps process with a first deposition of 220 nm silica and poly(lactic-co-glycolide) (PLGA) fluorescent nanoparticles by aerosol followed by the deposition of a fixing layer by atmospheric pressure plasma jet (APPJ). The nanoparticles can be used to create the nano-roughness and to include active molecule release, while the capping layer ensures stability and the chemical functionalities. The process is enabled by a novel APPJ which allows deposition rates of 10 - 20 nm\ub7s-1 at temperatures lower than 50 \ub0C using argon as process gas. This approach was assessed on titanium alloys for dental implants and on PCL films. The surfaces were characterized by FT-IR, AFM and SEM. Titanium alloys were tested with pre-osteoblasts murine cells line, while PCL film with fibroblasts. Cell behaviour was evaluated by viability and adhesion assays, protein adsorption, cell proliferation, focal adhesion formation and SEM. The release of a fluorophore molecule was assessed in the cell growing media, simulating a drug release. Osteoblast adhesion on the plasma treated materials increased by 20% with respect to commercial titanium alloys implants. Fibroblast adhesion increased by a 100% compared to smooth PCL substrate. The release of the fluorophore by the dissolution of the PLGA nanoparticles was verified and the integrity of the encapsulated drug model confirmed
Correlation functions quantify super-resolution images and estimate apparent clustering due to over-counting
We present an analytical method to quantify clustering in super-resolution
localization images of static surfaces in two dimensions. The method also
describes how over-counting of labeled molecules contributes to apparent
self-clustering and how the effective lateral resolution of an image can be
determined. This treatment applies to clustering of proteins and lipids in
membranes, where there is significant interest in using super-resolution
localization techniques to probe membrane heterogeneity. When images are
quantified using pair correlation functions, the magnitude of apparent
clustering due to over-counting will vary inversely with the surface density of
labeled molecules and does not depend on the number of times an average
molecule is counted. Over-counting does not yield apparent co-clustering in
double label experiments when pair cross-correlation functions are measured. We
apply our analytical method to quantify the distribution of the IgE receptor
(Fc{\epsilon}RI) on the plasma membranes of chemically fixed RBL-2H3 mast cells
from images acquired using stochastic optical reconstruction microscopy (STORM)
and scanning electron microscopy (SEM). We find that apparent clustering of
labeled IgE bound to Fc{\epsilon}RI detected with both methods arises from
over-counting of individual complexes. Thus our results indicate that these
receptors are randomly distributed within the resolution and sensitivity limits
of these experiments.Comment: 22 pages, 5 figure
Exploring the relationship between the Engineering and Physical Sciences and the Health and Life Sciences by advanced bibliometric methods
We investigate the extent to which advances in the health and life sciences
(HLS) are dependent on research in the engineering and physical sciences (EPS),
particularly physics, chemistry, mathematics, and engineering. The analysis
combines two different bibliometric approaches. The first approach to analyze
the 'EPS-HLS interface' is based on term map visualizations of HLS research
fields. We consider 16 clinical fields and five life science fields. On the
basis of expert judgment, EPS research in these fields is studied by
identifying EPS-related terms in the term maps. In the second approach, a
large-scale citation-based network analysis is applied to publications from all
fields of science. We work with about 22,000 clusters of publications, each
representing a topic in the scientific literature. Citation relations are used
to identify topics at the EPS-HLS interface. The two approaches complement each
other. The advantages of working with textual data compensate for the
limitations of working with citation relations and the other way around. An
important advantage of working with textual data is in the in-depth qualitative
insights it provides. Working with citation relations, on the other hand,
yields many relevant quantitative statistics. We find that EPS research
contributes to HLS developments mainly in the following five ways: new
materials and their properties; chemical methods for analysis and molecular
synthesis; imaging of parts of the body as well as of biomaterial surfaces;
medical engineering mainly related to imaging, radiation therapy, signal
processing technology, and other medical instrumentation; mathematical and
statistical methods for data analysis. In our analysis, about 10% of all EPS
and HLS publications are classified as being at the EPS-HLS interface. This
percentage has remained more or less constant during the past decade
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