456 research outputs found
Mesoscopic Model for Free Energy Landscape Analysis of DNA sequences
A mesoscopic model which allows us to identify and quantify the strength of
binding sites in DNA sequences is proposed. The model is based on the
Peyrard-Bishop-Dauxois model for the DNA chain coupled to a Brownian particle
which explores the sequence interacting more importantly with open base pairs
of the DNA chain. We apply the model to promoter sequences of different
organisms. The free energy landscape obtained for these promoters shows a
complex structure that is strongly connected to their biological behavior. The
analysis method used is able to quantify free energy differences of sites
within genome sequences.Comment: 7 pages, 5 figures, 1 tabl
Towards a Semantic Gas Source Localization under Uncertainty
Towards a Semantic Gas Source Localization under Uncertainty.Communications in Computer and Information Science book series (CCIS, volume 855), doi:10.1007/978-3-319-91479-4_42This work addresses the problem of efficiently and coherently
locating a gas source in a domestic environment with a mobile
robot, meaning efficiently the coverage of the shortest distance as possible
and coherently the consideration of different gas sources explaining
the gas presence. The main contribution is the exploitation, for the
first time, of semantic relationships between the gases detected and the
objects present in the environment to face this challenging issue. Our
proposal also takes into account both the uncertainty inherent in the
gas classification and object recognition processes. These uncertainties
are combined through a probabilistic Bayesian framework to provide a
priority-ordered list of (previously observed) objects to check. Moreover
the proximity of the different candidates to the current robot location
is also considered by a cost function, which output is used for planning
the robot inspection path. We have conducted an initial demonstration
of the suitability of our gas source localization approach by simulating
this task within domestic environments for a variable number of objects,
and comparing it with an greedy approach.Universidad de Málaga. Campus de Excelencia Internacional AndalucÃa Tech
Self-Consistent Pushing and Cranking Corrections to the Meson Fields of the Chiral Quark-Loop Soliton
We study translational and spin-isospin symmetry restoration for the
two-flavor chiral quark-loop soliton. Instead of a static soliton at rest we
consider a boosted and rotating hedgehog soliton. Corrected classical meson
fields are obtained by minimizing a corrected energy functional which has been
derived by semi-classical methods ('variation after projection'). We evaluate
corrected meson fields in the region 300 MeV \le M \le 600 MeV of constituent
quark masses M and compare them with the uncorrected fields. We study the
effect of the corrections on various expectation values of nuclear observables
such as the root-mean square radius, the axial-vector coupling constant,
magnetic moments and the delta-nucleon mass splitting.Comment: 19 pages, LaTeX, 7 postscript figures included using 'psfig.sty', to
appear in Int.J.Mod.Phys.
Invariant Distribution of Promoter Activities in Escherichia coli
Cells need to allocate their limited resources to express a wide range of genes. To understand how Escherichia coli partitions its transcriptional resources between its different promoters, we employ a robotic assay using a comprehensive reporter strain library for E. coli to measure promoter activity on a genomic scale at high-temporal resolution and accuracy. This allows continuous tracking of promoter activity as cells change their growth rate from exponential to stationary phase in different media. We find a heavy-tailed distribution of promoter activities, with promoter activities spanning several orders of magnitude. While the shape of the distribution is almost completely independent of the growth conditions, the identity of the promoters expressed at different levels does depend on them. Translation machinery genes, however, keep the same relative expression levels in the distribution across conditions, and their fractional promoter activity tracks growth rate tightly. We present a simple optimization model for resource allocation which suggests that the observed invariant distributions might maximize growth rate. These invariant features of the distribution of promoter activities may suggest design constraints that shape the allocation of transcriptional resources
Distinct translatome changes in specific neural populations precede electroencephalographic changes in prion-infected mice
Selective vulnerability is an enigmatic feature of neurodegenerative diseases (NDs), whereby a widely expressed protein causes lesions in specific cell types and brain regions. Using the RiboTag method in mice, translational responses of five neural subtypes to acquired prion disease (PrD) were measured. Pre-onset and disease onset timepoints were chosen based on longitudinal electroencephalography (EEG) that revealed a gradual increase in theta power between 10- and 18-weeks after prion injection, resembling a clinical feature of human PrD. At disease onset, marked by significantly increased theta power and histopathological lesions, mice had pronounced translatome changes in all five cell types despite appearing normal. Remarkably, at a pre-onset stage, prior to EEG and neuropathological changes, we found that 1) translatomes of astrocytes indicated reduced synthesis of ribosomal and mitochondrial components, 2) glutamatergic neurons showed increased expression of cytoskeletal genes, and 3) GABAergic neurons revealed reduced expression of circadian rhythm genes. These data demonstrate that early translatome responses to neurodegeneration emerge prior to conventional markers of disease and are cell type-specific. Therapeutic strategies may need to target multiple pathways in specific populations of cells, early in disease
Development of SimCells as a novel chassis for functional biosensors
This work serves as a proof-of-concept for bacterially derived SimCells (Simple Cells), which contain the cell machinery from bacteria and designed DNA (or potentially a simplified genome) to instruct the cell to carry out novel, specific tasks. SimCells represent a reprogrammable chassis without a native chromosome, which can host designed DNA to perform defined functions. In this paper, the use of Escherichia coli MC1000 ∆minD minicells as a non-reproducing chassis for SimCells was explored, as demonstrated by their ability to act as sensitive biosensors for small molecules. Highly purified minicells derived from E. coli strains containing gene circuits for biosensing were able to transduce the input signals from several small molecules (glucarate, acrylate and arabinose) into the production of green fluorescent protein (GFP). A mathematical model was developed to fit the experimental data for induction of gene expression in SimCells. The intracellular ATP level was shown to be important for SimCell function. A purification and storage protocol was developed to prepare SimCells which could retain their functions for an extended period of time. This study demonstrates that SimCells are able to perform as 'smart bioparticles' controlled by designed gene circuits
Multilevel Deconstruction of the In Vivo Behavior of Looped DNA-Protein Complexes
Protein-DNA complexes with loops play a fundamental role in a wide variety of
cellular processes, ranging from the regulation of DNA transcription to
telomere maintenance. As ubiquitous as they are, their precise in vivo
properties and their integration into the cellular function still remain
largely unexplored. Here, we present a multilevel approach that efficiently
connects in both directions molecular properties with cell physiology and use
it to characterize the molecular properties of the looped DNA-lac repressor
complex while functioning in vivo. The properties we uncover include the
presence of two representative conformations of the complex, the stabilization
of one conformation by DNA architectural proteins, and precise values of the
underlying twisting elastic constants and bending free energies. Incorporation
of all this molecular information into gene-regulation models reveals an
unprecedented versatility of looped DNA-protein complexes at shaping the
properties of gene expression.Comment: Open Access article available at
http://www.plosone.org/article/fetchArticle.action?articleURI=info%3Adoi%2F10.1371%2Fjournal.pone.000035
Low-Rank Subspace Override for Unsupervised Domain Adaptation
Current supervised learning models cannot generalize well across domain
boundaries, which is a known problem in many applications, such as robotics or
visual classification. Domain adaptation methods are used to improve these
generalization properties. However, these techniques suffer either from being
restricted to a particular task, such as visual adaptation, require a lot of
computational time and data, which is not always guaranteed, have complex
parameterization, or expensive optimization procedures. In this work, we
present an approach that requires only a well-chosen snapshot of data to find a
single domain invariant subspace. The subspace is calculated in closed form and
overrides domain structures, which makes it fast and stable in
parameterization. By employing low-rank techniques, we emphasize on descriptive
characteristics of data. The presented idea is evaluated on various domain
adaptation tasks such as text and image classification against state of the art
domain adaptation approaches and achieves remarkable performance across all
tasks
Differential (2+1) Jet Event Rates and Determination of alpha_s in Deep Inelastic Scattering at HERA
Events with a (2+1) jet topology in deep-inelastic scattering at HERA are
studied in the kinematic range 200 < Q^2< 10,000 GeV^2. The rate of (2+1) jet
events has been determined with the modified JADE jet algorithm as a function
of the jet resolution parameter and is compared with the predictions of Monte
Carlo models. In addition, the event rate is corrected for both hadronization
and detector effects and is compared with next-to-leading order QCD
calculations. A value of the strong coupling constant of alpha_s(M_Z^2)=
0.118+- 0.002 (stat.)^(+0.007)_(-0.008) (syst.)^(+0.007)_(-0.006) (theory) is
extracted. The systematic error includes uncertainties in the calorimeter
energy calibration, in the description of the data by current Monte Carlo
models, and in the knowledge of the parton densities. The theoretical error is
dominated by the renormalization scale ambiguity.Comment: 25 pages, 6 figures, 3 tables, submitted to Eur. Phys.
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