A Note on Vectorial AdS5_5/CFT4_4 Duality for Spin-jj Boundary Theory

The vectorial holographic correspondences between higher-spin theories in AdS$_5$ and free vector models on the boundary are extended to the cases where the latter is described by free massless spin-$j$ field. The dual higher-spin theory in the bulk does not include gravity and can only be defined on rigid AdS$_5$ background with $S^4$ boundary. We discuss various properties of these rather special higher-spin theories and calculate their one-loop free energies. We show that the result is proportional to the same quantity for spin-$j$ doubleton treated as if it is a AdS$_5$ field. Finally, we consider even more special case where the boundary theory itself is given by an infinite tower of massless higher-spin fields.Comment: 27 pages, version to appear in JHE

Disruption of asxl1 results in myeloproliferative neoplasms in zebrafish

Somatic loss-of-function mutations of the additional sex combs-like transcriptional regulator 1 (ASXL1) gene are common genetic abnormalities in human myeloid malignancies and induce clonal expansion of mutated hematopoietic stem cells (HSCs). To understand how ASXL1 disruption leads to myeloid cell transformation, we generated asxl1 haploinsufficient and null zebrafish lines using genome-editing technology. Here, we show that homozygous loss of asxl1 leads to apoptosis of newly formed HSCs. Apoptosis occurred via the mitochondrial apoptotic pathway mediated by upregulation of bim and bid Half of the asxl1+/ - zebrafish had myeloproliferative neoplasms (MPNs) by 5 months of age. Heterozygous loss of asxl1 combined with heterozygous loss of tet2 led to a more penetrant MPN phenotype, while heterozygous loss of asxl1 combined with complete loss of tet2 led to acute myeloid leukemia (AML). These findings support the use of asxl1+/ - zebrafish as a strategy to identify small-molecule drugs to suppress the growth of asxl1 mutant but not wild-type HSCs in individuals with somatically acquired inactivating mutations of ASXL1

Identification of microRNA-mRNA modules using microarray data

<p>Abstract</p> <p>Background</p> <p>MicroRNAs (miRNAs) are post-transcriptional regulators of mRNA expression and are involved in numerous cellular processes. Consequently, miRNAs are an important component of gene regulatory networks and an improved understanding of miRNAs will further our knowledge of these networks. There is a many-to-many relationship between miRNAs and mRNAs because a single miRNA targets multiple mRNAs and a single mRNA is targeted by multiple miRNAs. However, most of the current methods for the identification of regulatory miRNAs and their target mRNAs ignore this biological observation and focus on miRNA-mRNA pairs.</p> <p>Results</p> <p>We propose a two-step method for the identification of many-to-many relationships between miRNAs and mRNAs. In the first step, we obtain miRNA and mRNA clusters using a combination of miRNA-target mRNA prediction algorithms and microarray expression data. In the second step, we determine the associations between miRNA clusters and mRNA clusters based on changes in miRNA and mRNA expression profiles. We consider the miRNA-mRNA clusters with statistically significant associations to be potentially regulatory and, therefore, of biological interest.</p> <p>Conclusions</p> <p>Our method reduces the interactions between several hundred miRNAs and several thousand mRNAs to a few miRNA-mRNA groups, thereby facilitating a more meaningful biological analysis and a more targeted experimental validation.</p

Changes over time in the effect of marital status on cancer survival

<p>Abstract</p> <p>Background</p> <p>Rates of all-cause and cause-specific mortality are higher among unmarried than married individuals. Cancer survival is also poorer in the unmarried population. Recently, some studies have found that the excess all-cause mortality of the unmarried has increased over time, and the same pattern has been shown for some specific causes of death. The objective of this study was to investigate whether there has been a similar change over time in marital status differences in cancer survival.</p> <p>Methods</p> <p>Discrete-time hazard regression models for cancer deaths among more than 440 000 women and men diagnosed with cancer 1970-2007 at age 30-89 were estimated, using register data encompassing the entire Norwegian population. More than 200 000 cancer deaths during over 2 million person-years of exposure were analyzed.</p> <p>Results</p> <p>The excess mortality of the never-married compared to the married has increased steadily for men, in particular the elderly. Among elderly women, the excess mortality of the never-married compared to the married has increased, and there are indications of an increasing excess mortality of the widowed. The excess mortality of divorced men and women, however, has been stable.</p> <p>Conclusions</p> <p>There is no obvious explanation for the increasing disadvantage among the never-married. It could be due to a relatively poorer general health at time of diagnosis, either because of a more protective effect of partnership in a society that may have become less cohesive or because of more positive selection into marriage. Alternatively, it could be related to increasing differentials with respect to treatment. Today's complex cancer therapy regimens may be more difficult for never-married to follow, and health care interventions directed and adapted more specifically to the broad subgroup of never-married patients might be warranted.</p

Thermodynamics-Based Models of Transcriptional Regulation by Enhancers: The Roles of Synergistic Activation, Cooperative Binding and Short-Range Repression

Quantitative models of cis-regulatory activity have the potential to improve our mechanistic understanding of transcriptional regulation. However, the few models available today have been based on simplistic assumptions about the sequences being modeled, or heuristic approximations of the underlying regulatory mechanisms. We have developed a thermodynamics-based model to predict gene expression driven by any DNA sequence, as a function of transcription factor concentrations and their DNA-binding specificities. It uses statistical thermodynamics theory to model not only protein-DNA interaction, but also the effect of DNA-bound activators and repressors on gene expression. In addition, the model incorporates mechanistic features such as synergistic effect of multiple activators, short range repression, and cooperativity in transcription factor-DNA binding, allowing us to systematically evaluate the significance of these features in the context of available expression data. Using this model on segmentation-related enhancers in Drosophila, we find that transcriptional synergy due to simultaneous action of multiple activators helps explain the data beyond what can be explained by cooperative DNA-binding alone. We find clear support for the phenomenon of short-range repression, where repressors do not directly interact with the basal transcriptional machinery. We also find that the binding sites contributing to an enhancer's function may not be conserved during evolution, and a noticeable fraction of these undergo lineage-specific changes. Our implementation of the model, called GEMSTAT, is the first publicly available program for simultaneously modeling the regulatory activities of a given set of sequences