180 research outputs found
Matrix Pencils and Entanglement Classification
In this paper, we study pure state entanglement in systems of dimension
. Two states are considered equivalent if they can be
reversibly converted from one to the other with a nonzero probability using
only local quantum resources and classical communication (SLOCC). We introduce
a connection between entanglement manipulations in these systems and the
well-studied theory of matrix pencils. All previous attempts to study general
SLOCC equivalence in such systems have relied on somewhat contrived techniques
which fail to reveal the elegant structure of the problem that can be seen from
the matrix pencil approach. Based on this method, we report the first
polynomial-time algorithm for deciding when two states
are SLOCC equivalent. Besides recovering the previously known 26 distinct SLOCC
equivalence classes in systems, we also determine the
hierarchy between these classes
Target prediction and a statistical sampling algorithm for RNA-RNA interaction
It has been proven that the accessibility of the target sites has a critical
influence for miRNA and siRNA. In this paper, we present a program, rip2.0, not
only the energetically most favorable targets site based on the
hybrid-probability, but also a statistical sampling structure to illustrate the
statistical characterization and representation of the Boltzmann ensemble of
RNA-RNA interaction structures. The outputs are retrieved via backtracing an
improved dynamic programming solution for the partition function based on the
approach of Huang et al. (Bioinformatics). The time and space
algorithm is implemented in C (available from
\url{http://www.combinatorics.cn/cbpc/rip2.html})Comment: 7 pages, 10 figure
The effects of death and post-mortem cold ischemia on human tissue transcriptomes
Post-mortem tissues samples are a key resource for investigating patterns of gene expression. However, the processes triggered by death and the post-mortem interval (PMI) can significantly alter physiologically normal RNA levels. We investigate the impact of PMI on gene expression using data from multiple tissues of post-mortem donors obtained from the GTEx project. We find that many genes change expression over relatively short PMIs in a tissue-specific manner, but this potentially confounding effect in a biological analysis can be minimized by taking into account appropriate covariates. By comparing ante- and post-mortem blood samples, we identify the cascade of transcriptional events triggered by death of the organism. These events do not appear to simply reflect stochastic variation resulting from mRNA degradation, but active and ongoing regulation of transcription. Finally, we develop a model to predict the time since death from the analysis of the transcriptome of a few readily accessible tissues.Peer ReviewedPostprint (published version
Transcriptome analysis reveals high tumor heterogeneity with respect to re-activation of stemness and proliferation programs
Significant alterations in signaling pathways and transcriptional regulatory programs together represent major hallmarks of many cancers. These, among all, include the reactivation of stemness, which is registered by the expression of pathways that are active in the embryonic stem cells (ESCs). Here, we assembled gene sets that reflect the stemness and proliferation signatures and used them to analyze a large panel of RNA-seq data from The Cancer Genome Atlas (TCGA) Consortium in order to specifically assess the expression of stemness-related and proliferation-related genes across a collection of different tumor types. We introduced a metric that captures the collective similarity of the expression profile of a tumor to that of ESCs, which showed that stemness and proliferation signatures vary greatly between different tumor types. We also observed a high degree of intertumoral heterogeneity in the expression of stemness- and proliferation-related genes, which was associated with increased hazard ratios in a fraction of tumors and mirrored by high intratumoral heterogeneity and a remarkable stemness capacity in metastatic lesions across cancer cells in single cell RNA-seq datasets. Taken together, these results indicate that the expression of stemness signatures is highly heterogeneous and cannot be used as a universal determinant of cancer. This calls into question the universal validity of diagnostic tests that are based on stem cell markers
Predictive biometrics: A review and analysis of predicting personal characteristics from biometric data
Interest in the exploitation of soft biometrics information has continued to develop over the last decade or so. In comparison with traditional biometrics, which focuses principally on person identification, the idea of soft biometrics processing is to study the utilisation of more general information regarding a system user, which is not necessarily unique. There are increasing indications that this type of data will have great value in providing complementary information for user authentication. However, the authors have also seen a growing interest in broadening the predictive capabilities of biometric data, encompassing both easily definable characteristics such as subject age and, most recently, `higher level' characteristics such as emotional or mental states. This study will present a selective review of the predictive capabilities, in the widest sense, of biometric data processing, providing an analysis of the key issues still adequately to be addressed if this concept of predictive biometrics is to be fully exploited in the future
A partition function algorithm for interacting nucleic acid strands
Recent interests, such as RNA interference and antisense RNA regulation, strongly motivate the problem of predicting whether two nucleic acid strands interact
IntaRNA: efficient prediction of bacterial sRNA targets incorporating target site accessibility and seed regions
Motivation: During the last few years, several new small regulatory RNAs (sRNAs) have been discovered in bacteria. Most of them act as post-transcriptional regulators by base pairing to a target mRNA, causing translational repression or activation, or mRNA degradation. Numerous sRNAs have already been identified, but the number of experimentally verified targets is considerably lower. Consequently, computational target prediction is in great demand. Many existing target prediction programs neglect the accessibility of target sites and the existence of a seed, while other approaches are either specialized to certain types of RNAs or too slow for genome-wide searches
Approaches to link RNA secondary structures with splicing regulation
In higher eukaryotes, alternative splicing is usually regulated by protein
factors, which bind to the pre-mRNA and affect the recognition of splicing
signals. There is recent evidence that the secondary structure of the pre-mRNA
may also play an important role in this process, either by facilitating or by
hindering the interaction with factors and small nuclear ribonucleoproteins
(snRNPs) that regulate splicing. Moreover, the secondary structure could play a
fundamental role in the splicing of yeast species, which lack many of the
regulatory splicing factors present in metazoans. This review describes the
steps in the analysis of the secondary structure of the pre-mRNA and its
possible relation to splicing. As a working example, we use the case of yeast
and the problem of the recognition of the 3-prime splice site.Comment: 21 pages, 7 figure
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