Congruity of genomic and epidemiological data in modelling of local cholera outbreaks.

Cholera continues to be a global health threat. Understanding how cholera spreads between locations is fundamental to the rational, evidence-based design of intervention and control efforts. Traditionally, cholera transmission models have used cholera case-count data. More recently, whole-genome sequence data have qualitatively described cholera transmission. Integrating these data streams may provide much more accurate models of cholera spread; however, no systematic analyses have been performed so far to compare traditional case-count models to the phylodynamic models from genomic data for cholera transmission. Here, we use high-fidelity case-count and whole-genome sequencing data from the 1991 to 1998 cholera epidemic in Argentina to directly compare the epidemiological model parameters estimated from these two data sources. We find that phylodynamic methods applied to cholera genomics data provide comparable estimates that are in line with established methods. Our methodology represents a critical step in building a framework for integrating case-count and genomic data sources for cholera epidemiology and other bacterial pathogens.

Influence of Perinatal Factors on Gene Expression of IAPs Family and Main Factors of Pluripotency: OCT4 and SOX2 in Human Breast Milk Stem Cells-A Preliminary Report.

Due to their therapeutic potential, mesenchymal stem cells are the subject of intensive research on the use of their potential in the treatment of, among others, neurodegenerative diseases or immunological diseases. They are among the newest in the field of medicine. The presented study aimed to evaluate the expression of eight genes from the IAP family and the gene regulating IAP-XAF1-in stem cells derived from human milk, using the qPCR method. The relationships between the expression of genes under study and clinical data, such as maternal age, maternal BMI, week of pregnancy in which the delivery took place, bodyweight of the newborn, the number of pregnancies and deliveries, and the time elapsed since delivery, were also analyzed. The research was carried out on samples of human milk collected from 42 patients hospitalized in The Clinic of Obstetrics and Perinatology of the Independent Public Clinical Hospital No. 4, in Lublin. The conducted research confirmed the expression of the following genes in the tested material: NAIP, BIRC2, BIRC3, BIRC5, BIRC6, BIRC8, XIAP, XAF1, OCT4 and SOX2. Moreover, several dependencies of the expression of individual genes on the maternal BMI (BIRC5, XAF1 and NAIP), the time since childbirth (BIRC5, BIRC6, XAF1 and NAIP), the number of pregnancies and deliveries (BIRC2, BIRC5, BIRC6 and XAF1), the manner of delivery (XAF1 and OCT4), preterm labor (BIRC6 and NAIP) were demonstrated. Additionally, we found positive relationships between gene expression of BIRC7, BIRC8 and XAF1 and the main factors of pluripotency: SOX2 and OCT4. This work is the first to investigate the expression of genes from the IAPs family in mother's milk stem cells.

Detecting hidden layers from spreading dynamics on complex networks.

When dealing with spreading processes on networks it can be of the utmost importance to test the reliability of data and identify potential unobserved spreading paths. In this paper we address these problems and propose methods for hidden layer identification and reconstruction. We also explore the interplay between difficulty of the task and the structure of the multilayer network describing the whole system where the spreading process occurs. Our methods stem from an exact expression for the likelihood of a cascade in the susceptible-infected model on an arbitrary graph. We then show that by imploring statistical properties of unimodal distributions and simple heuristics describing joint likelihood of a series of cascades one can obtain an estimate of both existence of a hidden layer and its content with success rates far exceeding those of a null model. We conduct our analyses on both synthetic and real-world networks providing evidence for the viability of the approach presented.

Detectability of macroscopic structures in directed asymmetric stochastic block model.

We study the problem of identifying macroscopic structures in networks, characterizing the impact of introducing link directions on the detectability phase transition. To this end, building on the stochastic block model, we construct a class of nontrivially detectable directed networks. We find closed-form solutions by using the belief propagation method, showing how the transition line depends on the assortativity and the asymmetry of the network. Finally, we numerically identify the existence of a hard phase for detection close to the transition point.