Predicting Rules for Cancer Subtype Classification using Grammar-Based Genetic Programming on various Genomic Data Types

With the advent of high-throughput methods more genomic data then ever has been generated during the past decade. As these technologies remain cost intensive and not worthwhile for every research group, databases, such as the TCGA and Firebrowse, emerged. While these database enable the fast and free access to massive amounts of genomic data, they also embody new challenges to the research community. This study investigates methods to obtain, normalize and process genomic data for computer aided decision making in the field of cancer subtype discovery. A new software, termed FirebrowseR is introduced, allowing the direct download of genomic data sets into the R programming environment. To pre-process the obtained data, a set of methods is introduced, enabling data type specific normalization. As a proof of principle, the Web-TCGA software is created, enabling fast data analysis. To explore cancer subtypes a statistical model, the EDL, is introduced. The newly developed method is designed to provide highly precise, yet interpretable models. The EDL is tested on well established data sets, while its performance is compared to state of the art machine learning algorithms. As a proof of principle, the EDL was run on a cohort of 1,000 breast cancer patients, where it reliably re-identified the known subtypes and automatically selected the corresponding maker genes, by which the subtypes are defined. In addition, novel patterns of alterations in well known maker genes could be identified to distinguish primary and mCRPC samples. The findings suggest that mCRPC is characterized through a unique amplification of the Androgen Receptor, while a significant fraction of primary samples is described by a loss of heterozygosity TP53 and NCOR1

Interactive design exploration for constrained meshes

In architectural design, surface shapes are commonly subject to geometric constraints imposed by material, fabrication or assembly. Rationalization algorithms can convert a freeform design into a form feasible for production, but often require design modifications that might not comply with the design intent. In addition, they only offer limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm. We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimization subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a unified way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved efficiently and accurately. Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system offers full control over the exploration process, by providing direct access to the specification of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces

Reexamination of gene targeting frequency as a function of the extent of homology between the targeting vector and the target locus.

Journal ArticleMutations were targeted to the Hprt locus of mouse embryo-derived stem cells by using 22 different sequence replacement and sequence insertion vectors. The targeting frequency was examined at two sites within the Hprt locus as a function of the extent of homology between the targeting vector and the target locus. The targeting frequency was also compared by using vectors prepared from isogenic and nonisogenic DNA sources. With one exception, all of the vectors showed the same exponential dependence of targeting efficiency on the extent of homology between the targeting vector and the target locus. This was true regardless of whether they were sequence replacement or sequence insertion vectors, whether they were directed toward either of the two different sites within the Hprt locus, or whether they were prepared from isogenic or nonisogenic DNA sources. Vectors prepared from isogenic DNA targeted four to five times more efficiently than did the corresponding vectors prepared from nonisogenic DNA. The single case of unexpectedly low targeting efficiency involved one of the vectors prepared from nonisogenic DNA and could be attributed to an unfavorable distribution of heterology between the Hprt sequences present in the targeting vector and the endogenous Hprt gene

Changes in Heart Rate Variability During Heartfulness Meditation: A Power Spectral Analysis Including the Residual Spectrum

Background: Meditation refers to a group of practices commonly proposed to treat stress-related conditions and improve overall wellness. In particular, meditation might exert beneficial actions on heart rate variability (HRV) by acting on autonomic tone with an increase in the vagal activity. The effects of heartfulness meditation (HM) on HRV remain poorly defined.Methods: We investigated the effects of HM on HRV in a group of 26 healthy subjects. Subjects were regularly practicing this form of meditation on a daily basis. We assessed the HRV and residual HRV (rHRV) at rest and during meditation. We also used as control a period of respiratory rhythm imposed by an auditory signal, with the imposed breathing rhythm being identical to the spontaneous rhythm recorded during meditation.Results: During deep meditation period, the standard deviation of RR intervals (SDRR), coefficient of variation of RR intervals (CVRR), and total power (TP) were decreased while the low-frequency power (LFP), normalized LFP (nLFP), and normalized residual LFP (nrLFP) were increased as compared with those at rest, suggesting that the global vagal modulation was suppressed while the baroreflex was increased during deep medication. At the end of meditation, the LFP, residual LFP (rLFP), nLFP, nrLFP, low-/high-frequency power ratio (LHR), and residual LHR (rLHR) were increased while the residual very low-frequency power (rVLFP), normalized high-frequency power (nHFP), and normalized residual HFP (nrHFP) were decreased, as compared with those during paced breathing, suggesting that the vagal modulation was decreased while the sympathetic modulation was increased by deep meditation. During paced breathing period, the SDRR, CVRR, TP, LFP, rLFP, nLFP, nrLFP, LHR, and rLHR were decreased while nHFP and nrHFP were increased as compared with at rest, suggesting that paced breathing could suppress the sympathetic modulation and enhance the vagal modulation.Conclusion: HM can induce a suppression of global vagal modulation and increased the sympathetic modulation and baroreflex. In addition, paced breathing can suppress the sympathetic modulation and enhance the vagal modulation. Unlike studies using other types of meditation, we did not identify evidence of increased vagal tone during HM

Exploring the linkages between education sector governance, inequity, conflict, and peacebuilding in South Sudan

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Gene expression profiling to study racial differences after heart transplantation.

BackgroundThe basis for increased mortality after heart transplantation in African Americans and other non-Caucasian racial groups is poorly defined. We hypothesized that increased risk of adverse events is driven by biologic factors. To test this hypothesis in the Invasive Monitoring Attenuation through Gene Expression (IMAGE) study, we determined whether the event rate of the primary outcome of acute rejection, graft dysfunction, death, or retransplantation varied by race as a function of calcineurin inhibitor (CNI) levels and gene expression profile (GEP) scores.MethodsWe determined the event rate of the primary outcome, comparing racial groups, stratified by time after transplant. Logistic regression was used to compute the relative risk across racial groups, and linear modeling was used to measure the dependence of CNI levels and GEP score on race.ResultsIn 580 patients monitored for a median of 19 months, the incidence of the primary end point was 18.3% in African Americans, 22.2% in other non-Caucasians, and 8.5% in Caucasians (p < 0.001). There were small but significant correlations of race and tacrolimus trough levels to the GEP score. Tacrolimus levels were similar among the races. Of patients receiving tacrolimus, other non-Caucasians had higher GEP scores than the other racial groups. African American recipients demonstrated a unique decrease in expression of the FLT3 gene in response to higher tacrolimus levels.ConclusionsAfrican Americans and other non-Caucasian heart transplant recipients were 2.5-times to 3-times more likely than Caucasians to experience outcome events in the Invasive Monitoring Attenuation through Gene Expression study. The increased risk of adverse outcomes may be partly due to the biology of the alloimmune response, which is less effectively inhibited at similar tacrolimus levels in minority racial groups

Gene Expression Signatures of Peripheral Blood Mononuclear Cells during the Early Post-Transplant Period in Patients Developing Cardiac Allograft Vasculopathy

Background. Cardiac allograft vasculopathy (CAV) is a major cause of graft loss and death after heart transplantation. Currently, no diagnostic methods are available during the early post-transplant period to accurately identify patients at risk of CAV. We hypothesized that PBMC gene expression profiles (GEP) can identify patients at risk of CAV. Methods. We retrospectively analyzed a limited set of whole-genome PBMC microarrays from 10 post-transplant patients who did (n = 3) or did not (n = 7) develop advanced grade CAV during their long-term follow-up. We used significance analysis of microarrays to identify differentially expressed genes and High-Throughput GoMiner to assess gene ontology (GO) categories. We corroborated our findings by retrospective analysis of PBMC real-time PCR data from 33 patients. Results. Over 300 genes were differentially expressed (FDR < 5%), and 18 GO-categories including “macrophage activation”, “Interleukin-6 pathway”, “NF-KappaB cascade”, and “response to virus” were enriched by these genes (FDR < 5%). Out of 8 transcripts available for RT-PCR analysis, we confirmed 6 transcripts (75.0%) including FPRL1, S100A9, CXCL10, PRO1073, and MMP9 (P < .05). Conclusion. Our pilot data suggest that GEP of PBMC may become a valuable tool in the evaluation of patients at risk of CAV. Larger prospectively designed studies are needed to corroborate our hypothesis

APPAS: A Privacy-Preserving Authentication Scheme Based on Pseudonym Ring in VSNs

Vehicular social networks (VSNs) provide a variety of services for users based on social relationships through vehicular ad hoc networks (VANETs). During the communication in VSNs, vehicles are at risk of exposure to privacy information. Consequently, how to guarantee the security and privacy of vehicles is a critical issue. Ring signature is an effective mechanism to achieve anonymous authentication and communication. However, how to establish rings and how to select ring members become open problems. In this paper, a privacy-preserving scheme based on the pseudonym ring in VSNs is proposed. Hierarchical network architecture and trust model are established. A series of authentication protocols are then elaborated. According to the security and performance analysis, the proposed scheme is more robust and efficient compared with the typical ones

Interactive design exploration for constrained meshes

In architectural design, surface shapes are commonly subject to geometric con- straints imposed by material, fabrication or assembly. Rationalization algo- rithms can convert a freeform design into a form feasible for production, but often require design modi�cations that might not comply with the design intent. In addition, they only o�er limited support for exploring alternative feasible shapes, due to the high complexity of the optimization algorithm. We address these shortcomings and present a computational framework for interactive shape exploration of discrete geometric structures in the context of freeform architectural design. Our method is formulated as a mesh optimiza- tion subject to shape constraints. Our formulation can enforce soft constraints and hard constraints at the same time, and handles equality constraints and inequality constraints in a uni�ed way. We propose a novel numerical solver that splits the optimization into a sequence of simple subproblems that can be solved e�ciently and accurately. Based on this algorithm, we develop a system that allows the user to explore designs satisfying geometric constraints. Our system o�ers full control over the exploration process, by providing direct access to the speci�cation of the design space. At the same time, the complexity of the underlying optimization is hidden from the user, who communicates with the system through intuitive interfaces