60,283 research outputs found
Small Candidate Set for Translational Pattern Search
In this paper, we study the following pattern search problem: Given a pair of point sets A and B in fixed dimensional space R^d, with |B| = n, |A| = m and n >= m, the pattern search problem is to find the translations T\u27s of A such that each of the identified translations induces a matching between T(A) and a subset B\u27 of B with cost no more than some given threshold, where the cost is defined as the minimum bipartite matching cost of T(A) and B\u27. We present a novel algorithm to produce a small set of candidate translations for the pattern search problem. For any B\u27 subseteq B with |B\u27| = |A|, there exists at least one translation T in the candidate set such that the minimum bipartite matching cost between T(A) and B\u27 is no larger than (1+epsilon) times the minimum bipartite matching cost between A and B\u27 under any translation (i.e., the optimal translational matching cost). We also show that there exists an alternative solution to this problem, which constructs a candidate set of size O(n log^2 n) in O(n log^2 n) time with high probability of success. As a by-product of our construction, we obtain a weak epsilon-net for hypercube ranges, which significantly improves the construction time and the size of the candidate set. Our technique can be applied to a number of applications, including the translational pattern matching problem
The mRNA-bound proteome of the human malaria parasite Plasmodium falciparum.
BackgroundGene expression is controlled at multiple levels, including transcription, stability, translation, and degradation. Over the years, it has become apparent that Plasmodium falciparum exerts limited transcriptional control of gene expression, while at least part of Plasmodium's genome is controlled by post-transcriptional mechanisms. To generate insights into the mechanisms that regulate gene expression at the post-transcriptional level, we undertook complementary computational, comparative genomics, and experimental approaches to identify and characterize mRNA-binding proteins (mRBPs) in P. falciparum.ResultsClose to 1000 RNA-binding proteins are identified by hidden Markov model searches, of which mRBPs encompass a relatively large proportion of the parasite proteome as compared to other eukaryotes. Several abundant mRNA-binding domains are enriched in apicomplexan parasites, while strong depletion of mRNA-binding domains involved in RNA degradation is observed. Next, we experimentally capture 199 proteins that interact with mRNA during the blood stages, 64 of which with high confidence. These captured mRBPs show a significant overlap with the in silico identified candidate RBPs (p < 0.0001). Among the experimentally validated mRBPs are many known translational regulators active in other stages of the parasite's life cycle, such as DOZI, CITH, PfCELF2, Musashi, and PfAlba1-4. Finally, we also detect several proteins with an RNA-binding domain abundant in Apicomplexans (RAP domain) that is almost exclusively found in apicomplexan parasites.ConclusionsCollectively, our results provide the most complete comparative genomics and experimental analysis of mRBPs in P. falciparum. A better understanding of these regulatory proteins will not only give insight into the intricate parasite life cycle but may also provide targets for novel therapeutic strategies
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Novel translational approaches to the search for precision therapies for acute respiratory distress syndrome.
In the 50 years since acute respiratory distress syndrome (ARDS) was first described, substantial progress has been made in identifying the risk factors for and the pathogenic contributors to the syndrome and in characterising the protein expression patterns in plasma and bronchoalveolar lavage fluid from patients with ARDS. Despite this effort, however, pharmacological options for ARDS remain scarce. Frequently cited reasons for this absence of specific drug therapies include the heterogeneity of patients with ARDS, the potential for a differential response to drugs, and the possibility that the wrong targets have been studied. Advances in applied biomolecular technology and bioinformatics have enabled breakthroughs for other complex traits, such as cardiovascular disease or asthma, particularly when a precision medicine paradigm, wherein a biomarker or gene expression pattern indicates a patient's likelihood of responding to a treatment, has been pursued. In this Review, we consider the biological and analytical techniques that could facilitate a precision medicine approach for ARDS
An improved cosmic crystallography method to detect holonomies in flat spaces
A new, improved version of a cosmic crystallography method for constraining
cosmic topology is introduced. Like the circles-in-the-sky method using CMB
data, we work in a thin, shell-like region containing plenty of objects. Two
pairs of objects (quadruplet) linked by a holonomy show a specific distribution
pattern, and three filters of \emph{separation, vectorial condition}, and
\emph{lifetime of objects} extract these quadruplets. Each object is
assigned an integer , which is the number of candidate quadruplets
including as their members. Then an additional device of -histogram
is used to extract topological ghosts, which tend to have high values of .
In this paper we consider flat spaces with Euclidean geometry, and the filters
are designed to constrain their holonomies. As the second filter, we prepared
five types that are specialized for constraining specific holonomies: one for
translation, one for half-turn corkscrew motion and glide reflection, and three
for -th turn corkscrew motion for and 6. {Every multiconnected
space has holonomies that are detected by at least one of these five filters.}
Our method is applied to the catalogs of toy quasars in flat -CDM
universes whose typical sizes correspond to . With these simulations
our method is found to work quite well. {These are the situations in which
type-II pair crystallography methods are insensitive because of the tiny number
of ghosts. Moreover, in the flat cases, our method should be more sensitive
than the type-I pair (or, in general, -tuplet) methods because of its
multifilter construction and its independence from .}Comment: 12 pages, 8 figures, accepted for publication in A&A (2011
Cellular decision-making bias: the missing ingredient in cell functional diversity
Cell functional diversity is a significant determinant on how biological
processes unfold. Most accounts of diversity involve a search for sequence or
expression differences. Perhaps there are more subtle mechanisms at work. Using
the metaphor of information processing and decision-making might provide a
clearer view of these subtleties. Understanding adaptive and transformative
processes (such as cellular reprogramming) as a series of simple decisions
allows us to use a technique called cellular signal detection theory (cellular
SDT) to detect potential bias in mechanisms that favor one outcome over
another. We can apply method of detecting cellular reprogramming bias to
cellular reprogramming and other complex molecular processes. To demonstrate
scope of this method, we will critically examine differences between cell
phenotypes reprogrammed to muscle fiber and neuron phenotypes. In cases where
the signature of phenotypic bias is cryptic, signatures of genomic bias
(pre-existing and induced) may provide an alternative. The examination of these
alternates will be explored using data from a series of fibroblast cell lines
before cellular reprogramming (pre-existing) and differences between fractions
of cellular RNA for individual genes after drug treatment (induced). In
conclusion, the usefulness and limitations of this method and associated
analogies will be discussed.Comment: 18 pages; 6 figures, 2 tables, 4 supplemental figure
Inter and intra-hemispheric structural imaging markers predict depression relapse after electroconvulsive therapy: a multisite study.
Relapse of depression following treatment is high. Biomarkers predictive of an individual's relapse risk could provide earlier opportunities for prevention. Since electroconvulsive therapy (ECT) elicits robust and rapidly acting antidepressant effects, but has a >50% relapse rate, ECT presents a valuable model for determining predictors of relapse-risk. Although previous studies have associated ECT-induced changes in brain morphometry with clinical response, longer-term outcomes have not been addressed. Using structural imaging data from 42 ECT-responsive patients obtained prior to and directly following an ECT treatment index series at two independent sites (UCLA: n = 17, age = 45.41±12.34 years; UNM: n = 25; age = 65.00±8.44), here we test relapse prediction within 6-months post-ECT. Random forests were used to predict subsequent relapse using singular and ratios of intra and inter-hemispheric structural imaging measures and clinical variables from pre-, post-, and pre-to-post ECT. Relapse risk was determined as a function of feature variation. Relapse was well-predicted both within site and when cohorts were pooled where top-performing models yielded balanced accuracies of 71-78%. Top predictors included cingulate isthmus asymmetry, pallidal asymmetry, the ratio of the paracentral to precentral cortical thickness and the ratio of lateral occipital to pericalcarine cortical thickness. Pooling cohorts and predicting relapse from post-treatment measures provided the best classification performances. However, classifiers trained on each age-disparate cohort were less informative for prediction in the held-out cohort. Post-treatment structural neuroimaging measures and the ratios of connected regions commonly implicated in depression pathophysiology are informative of relapse risk. Structural imaging measures may have utility for devising more personalized preventative medicine approaches
Positional estimation techniques for an autonomous mobile robot
Techniques for positional estimation of a mobile robot navigation in an indoor environment are described. A comprehensive review of the various positional estimation techniques studied in the literature is first presented. The techniques are divided into four different types and each of them is discussed briefly. Two different kinds of environments are considered for positional estimation; mountainous natural terrain and an urban, man-made environment with polyhedral buildings. In both cases, the robot is assumed to be equipped with single visual camera that can be panned and tilted and also a 3-D description (world model) of the environment is given. Such a description could be obtained from a stereo pair of aerial images or from the architectural plans of the buildings. Techniques for positional estimation using the camera input and the world model are presented
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