Towards understanding the myometrial physiome: approaches for the construction of a virtual physiological uterus

Premature labour (PTL) is the single most significant factor contributing to neonatal morbidity in Europe with enormous attendant healthcare and social costs. Consequently, it remains a major challenge to alleviate the cause and impact of this condition. Our ability to improve the diagnosis and treatment of women most at risk of PTL is, however, actually hampered by an incomplete understanding of the ways in which the functions of the uterine myocyte are integrated to effect an appropriate biological response at the multicellular whole organ system. The level of organization required to co-ordinate labouring uterine contractile effort in time and space can be considered immense. There is a multitude of what might be considered mini-systems involved, each with their own regulatory feedback cycles, yet they each, in turn, will influence the behaviour of a related system. These include, but are not exclusive to, gestational-dependent regulation of transcription, translation, post-translational modifications, intracellular signaling dynamics, cell morphology, intercellular communication and tissue level morphology. We propose that in order to comprehend how these mini-systems integrate to facilitate uterine contraction during labour (preterm or term) we must, in concert with biological experimentation, construct detailed mathematical descriptions of our findings. This serves three purposes: firstly, providing a quantitative description of series of complex observations; secondly, proferring a database platform that informs further testable experimentation; thirdly, advancing towards the establishment of a virtual physiological uterus and in silico clinical diagnosis and treatment of PTL

Tissue Compatibility of Two Biodegradable Tubular Scaffolds Implanted Adjacent to Skin or Buccal Mucosa in Mice

Radiation therapy for cancer in the head and neck region leads to a marked loss of salivary gland parenchyma, resulting in a severe reduction of salivary secretions. Currently, there is no satisfactory treatment for these patients. To address this problem, we are using both tissue engineering and gene transfer principles to develop an orally implantable, artificial fluid-secreting device. In the present study, we examined the tissue compatibility of two biodegradable substrata potentially useful in fabricating such a device. We implanted in Balb/c mice tubular scaffolds of poly-L-lactic acid (PLLA), poly-glycolic acid coated with PLLA (PGA/PLLA), or nothing (sham-operated controls) either beneath the skin on the back, a site widely used in earlier toxicity and biocompatibility studies, or adjacent to the buccal mucosa, a site quite different functionally and immunologically. At 1, 3, 7, 14, and 28 days postimplantation, implant sites were examined histologically, and systemic responses were assessed by conventional clinical chemistry and hematology analyses. Inflammatory responses in the connective tissue were similar regardless of site or type of polymer implant used. However, inflammatory reactions were shorter and without epithelioid and giant cells in sham-operated controls. Also, biodegradation proceeded more slowly with the PLLA tubules than with the PGA/PLLA tubules. No significant changes in clinical chemistry and hematology were seen due to the implantation of tubular scaffolds. These results indicate that the tissue responses to PLLA and PGA/PLLA scaffolds are generally similar in areas subjacent to skin in the back and oral cavity. However, these studies also identified several potentially significant concerns that must be addressed prior to initiating any clinical applications of this device.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/63126/1/107632702760240562.pd

Overview of NSTX Upgrade initial results and modelling highlights

The National Spherical Torus Experiment (NSTX) has undergone a major upgrade, and the NSTX Upgrade (NSTX-U) Project was completed in the summer of 2015. NSTX-U first plasma was subsequently achieved, diagnostic and control systems have been commissioned, the H-mode accessed, magnetic error fields identified and mitigated, and the first physics research campaign carried out. During ten run weeks of operation, NSTX-U surpassed NSTX record pulse-durations and toroidal fields (TF), and high-performance similar to 1 MA H-mode plasmas comparable to the best of NSTX have been sustained near and slightly above the n = 1 no-wall stability limit and with H-mode confinement multiplier H-98y,H-2 above 1. Transport and turbulence studies in L-mode plasmas have identified the coexistence of at least two ion-gyro-scale turbulent micro-instabilities near the same radial location but propagating in opposite (i.e. ion and electron diamagnetic) directions. These modes have the characteristics of ion-temperature gradient and micro-tearing modes, respectively, and the role of these modes in contributing to thermal transport is under active investigation. The new second more tangential neutral beam injection was observed to significantly modify the stability of two types of Alfven eigenmodes. Improvements in offline disruption forecasting were made in the areas of identification of rotating MHD modes and other macroscopic instabilities using the disruption event characterization and forecasting code. Lastly, the materials analysis and particle probe was utilized on NSTX-U for the first time and enabled assessments of the correlation between boronized wall conditions and plasma performance. These and other highlights from the first run campaign of NSTX-U are described

de Branges-Rovnyak spaces: basics and theory

For $S$ a contractive analytic operator-valued function on the unit disk ${\mathbb D}$, de Branges and Rovnyak associate a Hilbert space of analytic functions ${\mathcal H}(S)$ and related extension space ${\mathcal D(S)}$ consisting of pairs of analytic functions on the unit disk ${\mathbb D}$. This survey describes three equivalent formulations (the original geometric de Branges-Rovnyak definition, the Toeplitz operator characterization, and the characterization as a reproducing kernel Hilbert space) of the de Branges-Rovnyak space ${\mathcal H}(S)$, as well as its role as the underlying Hilbert space for the modeling of completely non-isometric Hilbert-space contraction operators. Also examined is the extension of these ideas to handle the modeling of the more general class of completely nonunitary contraction operators, where the more general two-component de Branges-Rovnyak model space ${\mathcal D}(S)$ and associated overlapping spaces play key roles. Connections with other function theory problems and applications are also discussed. More recent applications to a variety of subsequent applications are given in a companion survey article

Transcriptomes of the Anther Sporophyte: Availability and Uses

An anther includes sporophytic tissues of three outer cell layers and an innermost layer, the tapetum, which encloses a locule where the gametophytic microspores mature to become pollen. The sporophytic tissues also comprise some vascular cells and specialized cells of the stomium aligning the long anther axis for anther dehiscence. Studies of the anther sporophytic cells, especially the tapetum, have recently expanded from the use of microscopy to molecular biology and transcriptomes. The available sequencing technologies, plus the use of laser microdissection and in silico subtraction, have produced high-quality anther sporophyte transcriptomes of rice, Arabidopsis and maize. These transcriptomes have been used for research discoveries and have potential for future discoveries in diverse areas, including developmental gene activity networking and changes in enzyme and metabolic domains, prediction of protein functions by quantity, secretion, antisense transcript regulation, small RNAs and promoters for generating male sterility. We anticipate that these studies with rice and other transcriptomes will expand to encompass other plants, whose genomes will be sequenced soon, with ever-advancing sequencing technologies. In comprehensive gene activity profiling of the anther sporophyte, studies involving transcriptomes will spearhead investigation of the downstream gene activity with proteomics and metabolomics

Cell autonomous expression of inflammatory genes in biologically aged fibroblasts associated with elevated NF-kappaB activity

<p>Abstract</p> <p>Background</p> <p>Chronic inflammation is a well-known corollary of the aging process and is believed to significantly contribute to morbidity and mortality of many age-associated chronic diseases. However, the mechanisms that cause age-associated inflammatory changes are not well understood. Particularly, the contribution of cell stress responses to age-associated inflammation in 'non-inflammatory' cells remains poorly defined. The present cross-sectional study focused on differences in molecular signatures indicative of inflammatory states associated with biological aging of human fibroblasts from donors aged 22 to 92 years.</p> <p>Results</p> <p>Gene expression profiling revealed elevated steady-state transcript levels consistent with a chronic inflammatory state in fibroblast cell-strains obtained from older donors. We also observed enhanced NF-κB DNA binding activity in a subset of strains, and the NF-κB profile correlated with mRNA expression levels characteristic of inflammatory processes, which include transcripts coding for cytokines, chemokines, components of the complement cascade and MHC molecules. This intrinsic low-grade inflammatory state, as it relates to aging, occurs in cultured cells irrespective of the presence of other cell types or the <it>in vivo </it>context.</p> <p>Conclusion</p> <p>Our results are consistent with the view that constitutive activation of inflammatory pathways is a phenomenon prevalent in aged fibroblasts. It is possibly part of a cellular survival process in response to compromised mitochondrial function. Importantly, the inflammatory gene expression signature described here is cell autonomous, i.e. occurs in the absence of prototypical immune or pro-inflammatory cells, growth factors, or other inflammatory mediators.</p

Biomarker discovery in heterogeneous tissue samples -taking the in-silico deconfounding approach

<p>Abstract</p> <p>Background</p> <p>For heterogeneous tissues, such as blood, measurements of gene expression are confounded by relative proportions of cell types involved. Conclusions have to rely on estimation of gene expression signals for homogeneous cell populations, e.g. by applying micro-dissection, fluorescence activated cell sorting, or <it>in-silico </it>deconfounding. We studied feasibility and validity of a non-negative matrix decomposition algorithm using experimental gene expression data for blood and sorted cells from the same donor samples. Our objective was to optimize the algorithm regarding detection of differentially expressed genes and to enable its use for classification in the difficult scenario of reversely regulated genes. This would be of importance for the identification of candidate biomarkers in heterogeneous tissues.</p> <p>Results</p> <p>Experimental data and simulation studies involving noise parameters estimated from these data revealed that for valid detection of differential gene expression, quantile normalization and use of non-log data are optimal. We demonstrate the feasibility of predicting proportions of constituting cell types from gene expression data of single samples, as a prerequisite for a deconfounding-based classification approach.</p> <p>Classification cross-validation errors with and without using deconfounding results are reported as well as sample-size dependencies. Implementation of the algorithm, simulation and analysis scripts are available.</p> <p>Conclusions</p> <p>The deconfounding algorithm without decorrelation using quantile normalization on non-log data is proposed for biomarkers that are difficult to detect, and for cases where confounding by varying proportions of cell types is the suspected reason. In this case, a deconfounding ranking approach can be used as a powerful alternative to, or complement of, other statistical learning approaches to define candidate biomarkers for molecular diagnosis and prediction in biomedicine, in realistically noisy conditions and with moderate sample sizes.</p