Abstract
Background: Understanding how alternative phenotypes arise from the same genome is a major challenge in modern biology. Eusociality in insects requires the evolution of two alternative phenotypes - workers, who sacrifice personal reproduction, and queens, who realize that reproduction. Extensive work on honeybees and ants has revealed the molecular basis of derived queen and worker phenotypes in highly eusocial lineages, but we lack equivalent deep-level analyses of wasps and of primitively eusocial species, the latter of which can reveal how phenotypic decoupling first occurs in the early stages of eusocial evolution. Results: We sequenced 20 Gbp of transcriptomes derived from brains of different behavioral castes of the primitively eusocial tropical paper wasp Polistes canadensis. Surprisingly, 75% of the 2,442 genes differentially expressed between phenotypes were novel, having no significant homology with described sequences. Moreover, 90% of these novel genes were significantly upregulated in workers relative to queens. Differential expression of novel genes in the early stages of sociality may be important in facilitating the evolution of worker behavioral complexity in eusocial evolution. We also found surprisingly low correlation in the identity and direction of expression of differentially expressed genes across similar phenotypes in different social lineages, supporting the idea that social evolution in different lineages requires substantial de novo rewiring of molecular pathways. Conclusions: These genomic resources for aculeate wasps and first transcriptome-wide insights into the origin of castes bring us closer to a more general understanding of eusocial evolution and how phenotypic diversity arises from the same genome.

Abstract
Genome sequencing projects are discovering millions of genetic variants in humans, and interpretation of their functional effects is essential for understanding the genetic basis of variation in human traits. Here we report sequencing and deep analysis of messenger RNA and microRNA from lymphoblastoid cell lines of 462 individuals from the 1000 Genomes Project-the first uniformly processed high-throughput RNA-sequencing data from multiple human populations with high-quality genome sequences. We discover extremely widespread genetic variation affecting the regulation of most genes, with transcript structure and expression level variation being equally common but genetically largely independent. Our characterization of causal regulatory variation sheds light on the cellular mechanisms of regulatory and loss-of-function variation, and allows us to infer putative causal variants for dozens of disease-associated loci. Altogether, this study provides a deep understanding of the cellular mechanisms of transcriptome variation and of the landscape of functional variants in the human genome.

Abstract

Abstract
More than half of mammalian genes generate multiple messenger RNA isoforms that differ in their 3' untranslated regions (3' UTRs) and therefore in regulatory sequences(1), often associated with cell proliferation and cancer(2,3); however, the mechanisms coordinating alternative 3'-UTR processing for specific mRNA populations remain poorly defined. Here we report that the cytoplasmic-polyadenylation element binding protein 1 (CPEB1), an RNA-binding protein that regulates mRNA translation(4), also controls alternative 3'-UTR processing. CPEB1 shuttles to the nudeus(5,6), where it co-localizes with splicing factors and mediates shortening of hundreds of mRNA 3' UTRs, thereby modulating their translation efficiency in the cytoplasm. CPEB1-mediated 3'-UTR shortening correlates with cell proliferation and tumorigenesis. CPEB1 binding to pre-mRNAs not only directs the use of alternative polyadenylation sites, but also changes alternative splicing by preventing U2AF65 recruitment. Our results reveal a novel function of CPEB1 in mediating alternative 3'-UTR processing, which is coordinated with regulation of mRNA translation, through its dual nuclear and cytoplasmic functions.

Abstract
The characterization of heart rate variability (HRV) series has become important for clinical diagnosis. These series are non-stationary and exhibit long and short-range correlations. The non-parametric methodology detrended fluctuation analysis (DFA) has become widely used for the detection of these correlations. The standard procedure is to apply DFA to the RR series, estimating the desired scaling exponents. In this work we pursue an alternative approach which consists in applying DFA to the fractionally differenced RR series, Delta(RR)-R-d, where 0 < d < 1 is the long-range correlation parameter. Both methodologies are applied to 24 hour HRV series from the Noltisalis data base. We conclude that changes in HRV are better quantified by DFA scaling exponents calculated over fractionally differenced RR series than by the standard procedure. The results indicate that the scaling exponent corresponding to high frequencies obtained from Delta(RR)-R-d increases the discriminatory power among the groups: from 60% to 87% during the day period and 57% to 77% during the night period.

Abstract
This study aimed to find parameters to characterize heart rate variability (HRV) and discriminate healthy subjects and patients with heart diseases. The parameters used for discrimination characterize the different components of HRV memory (short and long) and are extracted from HRV recordings using parametric as well as non parametric methods. Thus, the parameters are: spectral components at low frequencies (LH) and high frequencies (HF) which are associated with the short memory of HRV and the long memory parameter (d) obtained from autoregressive fractionally integrated moving average (ARFIMA) models. In the non parametric context, short memory (a1) and long memory (a2) parameters are obtained from detrended fluctuation analysis (DFA). The sample used in this study contains 24-hour Holter HRV recordings of 30 subjects: 10 healthy individuals, 10 patients suffering from congestive heart failure and 10 heart transplanted patients from the Noltisalis database. It was found that short memory parameters present higher values for the healthy individuals whereas long memory parameters present higher values for the diseased individuals. Moreover, there is evidence that ARFIMA modeling allows the discrimination between the 3 groups under study, being advantageous over DFA.