28,287 research outputs found
The impact of cellular characteristics on the evolution of shape homeostasis
The importance of individual cells in a developing multicellular organism is
well known but precisely how the individual cellular characteristics of those
cells collectively drive the emergence of robust, homeostatic structures is
less well understood. For example cell communication via a diffusible factor
allows for information to travel across large distances within the population,
and cell polarisation makes it possible to form structures with a particular
orientation, but how do these processes interact to produce a more robust and
regulated structure? In this study we investigate the ability of cells with
different cellular characteristics to grow and maintain homeostatic structures.
We do this in the context of an individual-based model where cell behaviour is
driven by an intra-cellular network that determines the cell phenotype. More
precisely, we investigated evolution with 96 different permutations of our
model, where cell motility, cell death, long-range growth factor (LGF),
short-range growth factor (SGF) and cell polarisation were either present or
absent. The results show that LGF has the largest positive impact on the
fitness of the evolved solutions. SGF and polarisation also contribute, but all
other capabilities essentially increase the search space, effectively making it
more difficult to achieve a solution. By perturbing the evolved solutions, we
found that they are highly robust to both mutations and wounding. In addition,
we observed that by evolving solutions in more unstable environments they
produce structures that were more robust and adaptive. In conclusion, our
results suggest that robust collective behaviour is most likely to evolve when
cells are endowed with long range communication, cell polarisation, and
selection pressure from an unstable environment
Solving the G-problems in less than 500 iterations: Improved efficient constrained optimization by surrogate modeling and adaptive parameter control
Constrained optimization of high-dimensional numerical problems plays an
important role in many scientific and industrial applications. Function
evaluations in many industrial applications are severely limited and no
analytical information about objective function and constraint functions is
available. For such expensive black-box optimization tasks, the constraint
optimization algorithm COBRA was proposed, making use of RBF surrogate modeling
for both the objective and the constraint functions. COBRA has shown remarkable
success in solving reliably complex benchmark problems in less than 500
function evaluations. Unfortunately, COBRA requires careful adjustment of
parameters in order to do so.
In this work we present a new self-adjusting algorithm SACOBRA, which is
based on COBRA and capable to achieve high-quality results with very few
function evaluations and no parameter tuning. It is shown with the help of
performance profiles on a set of benchmark problems (G-problems, MOPTA08) that
SACOBRA consistently outperforms any COBRA algorithm with fixed parameter
setting. We analyze the importance of the several new elements in SACOBRA and
find that each element of SACOBRA plays a role to boost up the overall
optimization performance. We discuss the reasons behind and get in this way a
better understanding of high-quality RBF surrogate modeling
Hierarchical k-nearest neighbours classification and binary differential evolution for fault diagnostics of automotive bearings operating under variable conditions
International audienc
Transcriptional landscape of neuronal and cancer stem cells
Tumor mass is composed by heterogeneous cell population including a subset of “cancer stem cells” (CSC).
Oncogenic signals foster CSC by transforming tissue stem cells or by reprogramming progenitor/differentiated
cells towards stemness. Thus, CSC share features with cancer and stem cells (e.g. self-renewal, hierarchical
developmental program leading to differentiated cells, epithelial/mesenchimal transition) and these latter are
maintained by the constitutive activation of stemness-promoting signals. CSC could trigger tumor formation,
drive to resistance to conventional therapeutics and underlie patients’ relapse. Indeed, stem cell signatures
have been associated with poor prognosis in various.
This background makes the identification of CSC molecular features mandatory to highlight the survival inner
working and to design novel CSC specific therapeutic strategies.
Medulloblastoma (MB) is the most common childhood malignant brain tumor and a leading cause of cancerrelated
morbidity and mortality. Current multimodal therapies are effective in about 50% of patients but often
cause long-term side effects, i.e. developmental, neurological, neuroendocrine and psychosocial deficits
(Northcott PA Nature Rev cancer 2012). For many years, MB treated as a single tumor entity despite the
divergent tumor histology, patients’ outcome and drug sensitivity, and also by the diversity of the stem cell of
origin. Very recently the scenario of human MB has dramatically changed since its heterogeneous biology has
been addressed by high-throughput gene expression analysis (oligonucleotide microarrays) or by the powerful
genomic next-generation sequencing. These led to the identification of four tumor subgroups (WNT, SHH,
Group 3 and Group 4) uncovering the existence of a highly diverse mutational spectra and gene expression.
However a quantitative approach has not yet been applied to the transcriptional landscape of Medulloblastoma
stem cells (MbSC) through RNA Next Generation Sequencing (RNA-Seq) technology. This is a relevant issue,
since RNA-Seq is able to interrogate the genome wide global transcriptome including new transcripts,
alternative spliced isoforms and non-coding RNAs.
Lower rhombic lip progenitors of the dorsal brainstem are considered the trigger cells in WNT tumors; in SHH
subgroup initiation cells are Prominin1+ CD15+ stem cells from the subventricular zone requiring the
commitment to Math1+ granule cell progenitors [GCP] of the external granule cell layer [EGL]; while Math1+ or
Math1- EGL-GCP or Prominin1+/lineage-negative stem cells sustain the MYC driven Group 3.
MbSC derived from SHH tumors and postnatal normal cerebellar stem cells (NcSC) have been reported to
share several features. A key signal for both of them is Hedgehog. Furthermore, both NcSC and MbSC display
up-regulation of stemness genes (e.g Sox2, Nestin, Nanog, Prom1). Finally, constitutive activation of the Shh
pathway by conditional deletion of Ptch1 inhibitory receptor in NcSC, promote medulloblastoma in vivo,
producing a mouse model of the human SHH tumor. Acquisition of stemness features may therefore represent
the first step of oncogenic conversion. Cooperation with additional oncogenic signals is however needed to
enhance MbSC tumorigenicity.
In order to understand the MbSCs transcriptional programs, we analyze by RNA-Seq, MbSC derived from
Ptch1+/- tumors (Ptch1+/- MbSC). This choice, of a genetically determined model of MB, has allowed us to
work with Ptch1+/- MbSC together with appropriate NcSC counterpart, and to analyze biological replicates
doing statistical analysis.
We identify a number of transcripts, annotated ones, novel isoforms, and long non-coding RNAs,
characterizing MbSC and/or NcSC. Some of these genes control stemness or are cancer related and
conserved in human medulloblastomas. Interestingly a subset of them, belonging to cell stress response, are
of prognostic relevance being significantly related to clinical outcome. Correlation of genes expression
characterizing MbSC with survival information from our human medulloblastomas database further
demonstrates the significance of these findings. Our data suggest that the modulation of normal and cancer
stem cell functions observed in vitro is effective in dissecting the transcriptional programs underlying the in
vivo behavior of human medulloblastomas
Fast indoor scene classification using 3D point clouds
A representation of space that includes both geometric and semantic information enables a robot to perform high-level tasks in complex environments. Identifying and categorizing environments based on onboard sensors are essential in these scenarios. The Kinect™, a 3D low cost sensor is appealing in these scenarios as it can provide rich information. The downside is the presence of large amount of information, which could lead to higher computational complexity. In this paper, we propose a methodology to efficiently classify indoor environments into semantic categories using Kinect™ data. With a fast feature extraction method along with an efficient feature selection algorithm (DEFS) and, support vector machines (SVM) classifier, we could realize a fast scene classification algorithm. Experimental results in an indoor scenario are presented including comparisons with its counterpart of commonly available 2D laser range finder data
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