Optimal stopping with f-expectations: The irregular case

We consider the optimal stopping problem with non-linear f-expectation (induced by a BSDE) without making any regularity assumptions on the payoff process ξ and in the case of a general filtration. We show that the value family can be aggregated by an optional process Y. We characterize the process Y as the Ef-Snell envelope of ξ. We also establish an infinitesimal characterization of the value process Y in terms of a Reflected BSDE with ξ as the obstacle. To do this, we first establish some useful properties of irregular RBSDEs, in particular an existence and uniqueness result and a comparison theorem

On the strict value of the non-linear optimal stopping problem

We address the non-linear strict value problem in the case of a general filtration and a completely irregular pay-off process (ξt). While the value process (Vt) of the non-linear problem is only right-uppersemicontinuous, we show that the strict value process (V+t) is necessarily right-continuous. Moreover, the strict value process (V+t) coincides with the process of right-limits (Vt+) of the value process. As an auxiliary result, we obtain that a strong non-linear f-supermartingale is right-continuous if and only if it is right-continuous along stopping times in conditional f-expectation

Applying process mining techniques and neural networks to creating and assessment of business process models

The article presents an approach for automated generation of business process models by applying process mining techniques to event logs created during the operation of information systems used in an organization. Existing algorithms for process mining are discussed. Criteria for performing a comparative analysis of these algorithms are specified. А framework is proposed in which to build and analyze business process models. The framework includes tools for initial analysis of the event log file, extracting elements of a business process model, and composing a new model by applying a trained neural network

Discovering study-specific gene regulatory networks

This article has been made available through the Brunel Open Access Publishing Fund.Microarrays are commonly used in biology because of their ability to simultaneously measure thousands of genes under different conditions. Due to their structure, typically containing a high amount of variables but far fewer samples, scalable network analysis techniques are often employed. In particular, consensus approaches have been recently used that combine multiple microarray studies in order to find networks that are more robust. The purpose of this paper, however, is to combine multiple microarray studies to automatically identify subnetworks that are distinctive to specific experimental conditions rather than common to them all. To better understand key regulatory mechanisms and how they change under different conditions, we derive unique networks from multiple independent networks built using glasso which goes beyond standard correlations. This involves calculating cluster prediction accuracies to detect the most predictive genes for a specific set of conditions. We differentiate between accuracies calculated using cross-validation within a selected cluster of studies (the intra prediction accuracy) and those calculated on a set of independent studies belonging to different study clusters (inter prediction accuracy). Finally, we compare our method's results to related state-of-the art techniques. We explore how the proposed pipeline performs on both synthetic data and real data (wheat and Fusarium). Our results show that subnetworks can be identified reliably that are specific to subsets of studies and that these networks reflect key mechanisms that are fundamental to the experimental conditions in each of those subsets

Assessment of the relationship between the vaginal microecosystem in teenage girls with reproductive disorders

Background. Violations of indigenous microflora composition associates with a wide variety of gynecological complications. Thus, the qualitative and quantitative composition of lactobacilli and their associative capacity and functional activity may serve as a criterion of microecological well-being in the body. Aim. To study species diversity of lactobacilli in vaginal biotope teenage girls with gynecological pathologies and to assess the extent of their relationship with the combination of diversity profiles associated opportunistic pathogens. Materials and methods. The study included 107 adolescents with reproductive system disorders. The study was conducted with the use of gynecological and general microbiological methods. Results. It was revealed that in vaginal biocenosis of the studied group of teenage girls dominating lactobacilli were Lactobacillus plantarum and L. crispatus, the incidence of other species did not exceed 21 %. Among the representatives of opportunistic pathogenic microflora dominated coccal microflora and Corynebacterium spp., being the part of normal flora of vaginal mucosa. Analysis of species composition revealed a statistically significant relationship between certain types of lactobacilli and opportunistic microorganisms, i.e. lactobacilli showed no antagonistic activity towards the opportunistic microorganisms, and formed symbiotic relationships with them. Conclusions. Most commonness was found among the minor species of lactobacilli (L. iners, L. gasseri, L. jensenii), coagulase-negative staphylococci and fungi of Candida genus, and that increases the risk of transformation of normal microflora in the pathological one

Intriguing Balancing Selection on the Intron 5 Region of LMBR1 in Human Population

Background: The intron 5 of gene LMBR1 is the cis-acting regulatory module for the sonic hedgehog (SHH) gene. Mutation in this non-coding region is associated with preaxial polydactyly, and may play crucial roles in the evolution of limb and skeletal system. Methodology/Principal Findings: We sequenced a region of the LMBR1 gene intron 5 in East Asian human population, and found a significant deviation of Tajima’s D statistics from neutrality taking human population growth into account. Data from HapMap also demonstrated extended linkage disequilibrium in the region in East Asian and European population, and significantly low degree of genetic differentiation among human populations. Conclusion/Significance: We proposed that the intron 5 of LMBR1 was presumably subject to balancing selection during the evolution of modern human

Copy number rather than epigenetic alterations are the major dictator of imprinted methylation in tumors

It has been postulated that imprinting aberrations are common in tumors. To understand the role of imprinting in cancer, we have characterized copy-number and methylation in over 280 cancer cell lines and confirm our observations in primary tumors. Imprinted differentially methylated regions (DMRs) regulate parent-of-origin monoallelic expression of neighboring transcripts in cis. Unlike single-copy CpG islands that may be prone to hypermethylation, imprinted DMRs can either loose or gain methylation during tumorigenesis. Here, we show that methylation profiles at imprinted DMRs often not represent genuine epigenetic changes but simply the accumulation of underlying copy-number aberrations (CNAs), which is independent of the genome methylation state inferred from cancer susceptible loci. Our results reveal that CNAs also influence allelic expression as loci with copy-number neutral loss-of-heterozygosity or amplifications may be expressed from the appropriate parental chromosomes, which is indicative of maintained imprinting, although not observed as a single expression foci by RNA FISH