The increase of the functional entropy of the human brain with age

We use entropy to characterize intrinsic ageing properties of the human brain. Analysis of fMRI data from a large dataset of individuals, using resting state BOLD signals, demonstrated that a functional entropy associated with brain activity increases with age. During an average lifespan, the entropy, which was calculated from a population of individuals, increased by approximately 0.1 bits, due to correlations in BOLD activity becoming more widely distributed. We attribute this to the number of excitatory neurons and the excitatory conductance decreasing with age. Incorporating these properties into a computational model leads to quantitatively similar results to the fMRI data. Our dataset involved males and females and we found significant differences between them. The entropy of males at birth was lower than that of females. However, the entropies of the two sexes increase at different rates, and intersect at approximately 50 years; after this age, males have a larger entropy

How Does Spatial Study Design Influence Density Estimates from Spatial Capture-Recapture Models?

When estimating population density from data collected on non-invasive detector arrays, recently developed spatial capture-recapture (SCR) models present an advance over non-spatial models by accounting for individual movement. While these models should be more robust to changes in trapping designs, they have not been well tested. Here we investigate how the spatial arrangement and size of the trapping array influence parameter estimates for SCR models. We analysed black bear data collected with 123 hair snares with an SCR model accounting for differences in detection and movement between sexes and across the trapping occasions. To see how the size of the trap array and trap dispersion influence parameter estimates, we repeated analysis for data from subsets of traps: 50% chosen at random, 50% in the centre of the array and 20% in the South of the array. Additionally, we simulated and analysed data under a suite of trap designs and home range sizes. In the black bear study, we found that results were similar across trap arrays, except when only 20% of the array was used. Black bear density was approximately 10 individuals per 100 km2. Our simulation study showed that SCR models performed well as long as the extent of the trap array was similar to or larger than the extent of individual movement during the study period, and movement was at least half the distance between traps. SCR models performed well across a range of spatial trap setups and animal movements. Contrary to non-spatial capture-recapture models, they do not require the trapping grid to cover an area several times the average home range of the studied species. This renders SCR models more appropriate for the study of wide-ranging mammals and more flexible to design studies targeting multiple species

Exploring Cell Tropism as a Possible Contributor to Influenza Infection Severity

Several mechanisms have been proposed to account for the marked increase in severity of human infections with avian compared to human influenza strains, including increased cytokine expression, poor immune response, and differences in target cell receptor affinity. Here, the potential effect of target cell tropism on disease severity is studied using a mathematical model for in-host influenza viral infection in a cell population consisting of two different cell types. The two cell types differ only in their susceptibility to infection and rate of virus production. We show the existence of a parameter regime which is characterized by high viral loads sustained long after the onset of infection. This finding suggests that differences in cell tropism between influenza strains could be sufficient to cause significant differences in viral titer profiles, similar to those observed in infections with certain strains of influenza A virus. The two target cell mathematical model offers good agreement with experimental data from severe influenza infections, as does the usual, single target cell model albeit with biologically unrealistic parameters. Both models predict that while neuraminidase inhibitors and adamantanes are only effective when administered early to treat an uncomplicated seasonal infection, they can be effective against more severe influenza infections even when administered late

A Comprehensive Map of Mobile Element Insertion Polymorphisms in Humans

As a consequence of the accumulation of insertion events over evolutionary time, mobile elements now comprise nearly half of the human genome. The Alu, L1, and SVA mobile element families are still duplicating, generating variation between individual genomes. Mobile element insertions (MEI) have been identified as causes for genetic diseases, including hemophilia, neurofibromatosis, and various cancers. Here we present a comprehensive map of 7,380 MEI polymorphisms from the 1000 Genomes Project whole-genome sequencing data of 185 samples in three major populations detected with two detection methods. This catalog enables us to systematically study mutation rates, population segregation, genomic distribution, and functional properties of MEI polymorphisms and to compare MEI to SNP variation from the same individuals. Population allele frequencies of MEI and SNPs are described, broadly, by the same neutral ancestral processes despite vastly different mutation mechanisms and rates, except in coding regions where MEI are virtually absent, presumably due to strong negative selection. A direct comparison of MEI and SNP diversity levels suggests a differential mobile element insertion rate among populations

Assessment of regional best‐fit probability density function of annual maximum rainfall using CFSR precipitation data

The upper Cross River basin (UCRB) fits a true description of a data scarce watershed in respect of climatic data. This paper seeks to determine the best‐fit probability density function (PDF) of annual maximum rainfall for the UCRB using the Climate Forecast System Reanalysis (CFSR) precipitation data. Also, to evaluate the performance of the Intergovernmental Panel on Climate Change (IPCC) Coupled Model Inter‐comparison Project (CMIP3) Fourth Assessment Report (AR4) Global Circulation Models (GCMs) in simulating the monthly precipitation in the UCRB considering 1979–2014 data. For the determination of the best‐fit PDF, the models under review included the generalized extreme value (GEV), normal, gamma, Weibull and log‐normal (LN) distributions. Twenty‐four weather station datasets were obtained and subjected to frequency distribution analysis on per station basis, and subsequently fitted to the respective PDFs. Also, simulated monthly precipitation data obtained from 16 AR4 GCMs, for weather station p6191, were subjected to frequency distribution analysis. The results showed the percentages of best‐fit to worst‐fit PDFs, considering the total number of stations, as follows: 54.17%, 45.83%, 37.50%, 45.83%, and 50%/50%. These percentages corresponded to GEV, Weibull, gamma, gamma, and LN/normal, respectively. The comparison of the predicted and observed values using the Chi‐square goodness‐of‐fit test revealed that the GEV PDF is the best‐fit model for the UCRB. The correlation coefficient values further corroborated the correctness of the test. The PDF of the observed data (weather station p6191) and the simulations of the 16 GCMs computed using monthly rainfall datasets were compared using a mean square error (MSE) dependent skill score. The result from this study suggested that the CGCM3.1 (T47) and MRI‐CGCM2.3.2 provide the best representations of precipitation, considering about 36 years trend for station p6191. The results have no influence on how well the models perform in other geographical locations

Transcriptome study and identification of potential marker genes related to the stable expression of recombinant proteins in CHO clones

BACKGROUND: Chinese hamster ovary (CHO) cells have become the host of choice for the production of recombinant proteins, due to their capacity for correct protein folding, assembly, and posttranslational modifications. The most widely used system for recombinant proteins is the gene amplification procedure that uses the CHO-Dhfr expression system. However, CHO cells are known to have a very unstable karyotype. This is due to chromosome rearrangements that can arise from translocations and homologous recombination, especially when cells with the CHO-Dhfr expression system are treated with methotrexate hydrate. The present method used in the industry for testing clones for their long-term stability of recombinant protein production is empirical, and it involves their cultivation over extended periods of time prior to the selection of the most suitable clone for further bioprocess development. The aim of the present study was the identification of marker genes that can predict stable expression of recombinant genes in particular clones early in the development stage. RESULTS: The transcriptome profiles of CHO clones with stable and unstable recombinant protein production were investigated over 10-weeks of cultivation, using a DNA microarray. We identified 14 genes that were differentially expressed between the stable and unstable clones already at 2 weeks from the beginning of the cultivation. Their expression was validated by reverse-transcription quantitative real-time PCR (RT-qPCR). Furthermore, the k-nearest neighbour algorithm approach shows that the combination of the gene expression patterns of only five of these 14 genes is sufficient to predict stable recombinant protein production in clones in the early phases of cell-line development. CONCLUSIONS: The exact molecular mechanisms that cause unstable recombinant protein production are not fully understood. However, the expression profiles of some genes in clones with stable and unstable recombinant protein production allow prediction of such instability early in the cell-line development stage. We have thus developed a proof-of-concept for a novel approach to eliminate unstable clones in the CHO-Dhfr expression system, which saves time and labour-intensive work in cell-line development. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s12896-015-0218-9) contains supplementary material, which is available to authorized users

Variability of structurally constrained and unconstrained functional connectivity in schizophrenia

Spatial variation in connectivity is an integral aspect of the brain\u27s architecture. In the absence of this variability, the brain may act as a single homogenous entity without regional specialization. In this study, we investigate the variability in functional links categorized on the basis of the presence of direct structural paths (primary) or indirect paths mediated by one (secondary) or more (tertiary) brain regions ascertained by diffusion tensor imaging. We quantified the variability in functional connectivity using an unbiased estimate of unpredictability (functional connectivity entropy) in a neuropsychiatric disorder where structure-function relationship is considered to be abnormal; 34 patients with schizophrenia and 32 healthy controls underwent DTI and resting state functional MRI scans. Less than one-third (27.4% in patients, 27.85% in controls) of functional links between brain regions were regarded as direct primary links on the basis of DTI tractography, while the rest were secondary or tertiary. The most significant changes in the distribution of functional connectivity in schizophrenia occur in indirect tertiary paths with no direct axonal linkage in both early (P=0.0002, d=1.46) and late (P=1 × 10-17, d=4.66) stages of schizophrenia, and are not altered by the severity of symptoms, suggesting that this is an invariant feature of this illness. Unlike those with early stage illness, patients with chronic illness show some additional reduction in the distribution of connectivity among functional links that have direct structural paths (P=0.08, d=0.44). Our findings address a critical gap in the literature linking structure and function in schizophrenia, and demonstrate for the first time that the abnormal state of functional connectivity preferentially affects structurally unconstrained links in schizophrenia. It also raises the question of a continuum of dysconnectivity ranging from less direct (structurally unconstrained) to more direct (structurally constrained) brain pathways underlying the progressive clinical staging and persistence of schizophrenia