Quantitative Susceptibility Mapping by Inversion of a Perturbation Field Model: Correlation With Brain Iron in Normal Aging

There is increasing evidence that iron deposition occurs in specific regions of the brain in normal aging and neurodegenerative disorders such as Parkinson's, Huntington's, and Alzheimer's disease. Iron deposition changes the magnetic susceptibility of tissue, which alters the MR signal phase, and allows estimation of susceptibility differences using quantitative susceptibility mapping (QSM). We present a method for quantifying susceptibility by inversion of a perturbation model, or “QSIP.” The perturbation model relates phase to susceptibility using a kernel calculated in the spatial domain, in contrast to previous Fourier-based techniques. A tissue/air susceptibility atlas is used to estimate B[subscript 0] inhomogeneity. QSIP estimates in young and elderly subjects are compared to postmortem iron estimates, maps of the Field-Dependent Relaxation Rate Increase, and the L1-QSM method. Results for both groups showed excellent agreement with published postmortem data and in vivo FDRI: statistically significant Spearman correlations ranging from Rho=0.905 to Rho=1.00 were obtained. QSIP also showed improvement over FDRI and L1-QSM: reduced variance in susceptibility estimates and statistically significant group differences were detected in striatal and brainstem nuclei, consistent with age-dependent iron accumulation in these regions.National Institutes of Health (U.S.) (Grant P41EB015902)National Institutes of Health (U.S.) (Grant P41RR013218)National Institutes of Health (U.S.) (Grant P41EB015898)National Institutes of Health (U.S.) (Grant P41RR019703)National Institutes of Health (U.S.) (Grant T32EB0011680-06)National Institutes of Health (U.S.) (Grant K05AA017168)National Institutes of Health (U.S.) (Grant R01AA012388

Multiscale Kinetic Monte-Carlo for Simulating Epitaxial Growth

We present a fast Monte-Carlo algorithm for simulating epitaxial surface growth, based on the continuous-time Monte-Carlo algorithm of Bortz, Kalos and Lebowitz. When simulating realistic growth regimes, much computational time is consumed by the relatively fast dynamics of the adatoms. Continuum and continuum-discrete hybrid methods have been developed to approach this issue; however in many situations, the density of adatoms is too low to efficiently and accurately simulate as a continuum. To solve the problem of fast adatom dynamics, we allow adatoms to take larger steps, effectively reducing the number of transitions required. We achieve nearly a factor of ten speed up, for growth at moderate temperatures and large D/F.Comment: 7 pages, 6 figures; revised text, accepted by PR

Single cells from human primary colorectal tumors exhibit polyfunctional heterogeneity in secretions of ELR+ CXC chemokines

Cancer is an inflammatory disease of tissue that is largely influenced by the interactions between multiple cell types, secreted factors, and signal transduction pathways. While single-cell sequencing continues to refine our understanding of the clonotypic heterogeneity within tumors, the complex interplay between genetic variations and non-genetic factors ultimately affects therapeutic outcome. Much has been learned through bulk studies of secreted factors in the tumor microenvironment, but the secretory behavior of single cells has been largely uncharacterized. Here we directly profiled the secretions of ELR+ CXC chemokines from thousands of single colorectal tumor and stromal cells, using an array of subnanoliter wells and a technique called microengraving to characterize both the rates of secretion of several factors at once and the numbers of cells secreting each chemokine. The ELR+ CXC chemokines are highly redundant, pro-angiogenic cytokines that signal via the CXCR1 and CXCR2 receptors, influencing tumor growth and progression. We find that human primary colorectal tumor and stromal cells exhibit polyfunctional heterogeneity in the combinations and magnitudes of secretions for these chemokines. In cell lines, we observe similar variance: phenotypes observed in bulk can be largely absent among the majority of single cells, and discordances exist between secretory states measured and gene expression for these chemokines among single cells. Together, these measures suggest secretory states among tumor cells are complex and can evolve dynamically. Most importantly, this study reveals new insight into the intratumoral phenotypic heterogeneity of human primary tumors.Janssen Pharmaceutical Ltd.National Cancer Institute (U.S.) (Cancer Center Support (Core) Grant P30-CA14051)National Science Foundation (U.S.). Graduate Research FellowshipSingapore. Agency for Science, Technology and ResearchSwiss National Science Foundation (Fellowship for Advanced Researchers PA00P3 139659

A new ghost cell/level set method for moving boundary problems:application to tumor growth

In this paper, we present a ghost cell/level set method for the evolution of interfaces whose normal velocity depend upon the solutions of linear and nonlinear quasi-steady reaction-diffusion equations with curvature-dependent boundary conditions. Our technique includes a ghost cell method that accurately discretizes normal derivative jump boundary conditions without smearing jumps in the tangential derivative; a new iterative method for solving linear and nonlinear quasi-steady reaction-diffusion equations; an adaptive discretization to compute the curvature and normal vectors; and a new discrete approximation to the Heaviside function. We present numerical examples that demonstrate better than 1.5-order convergence for problems where traditional ghost cell methods either fail to converge or attain at best sub-linear accuracy. We apply our techniques to a model of tumor growth in complex, heterogeneous tissues that consists of a nonlinear nutrient equation and a pressure equation with geometry-dependent jump boundary conditions. We simulate the growth of glioblastoma (an aggressive brain tumor) into a large, 1 cm square of brain tissue that includes heterogeneous nutrient delivery and varied biomechanical characteristics (white matter, gray matter, cerebrospinal fluid, and bone), and we observe growth morphologies that are highly dependent upon the variations of the tissue characteristics—an effect observed in real tumor growth

Provenancing Archaeological Wool Textiles from Medieval Northern Europe by Light Stable Isotope Analysis (δ13C, δ15N, δ2H)

We investigate the origin of archaeological wool textiles preserved by anoxic waterlogging from seven medieval archaeological deposits in north-western Europe (c. 700-1600 AD), using geospatial patterning in carbon (δ13C), nitrogen (δ15N) and non-exchangeable hydrogen (δ2H) composition of modern and ancient sheep proteins. δ13C, δ15N and δ2H values from archaeological wool keratin (n = 83) and bone collagen (n = 59) from four sites were interpreted with reference to the composition of modern sheep wool from the same regions. The isotopic composition of wool and bone collagen samples clustered strongly by settlement; inter-regional relationships were largely parallel in modern and ancient samples, though landscape change was also significant. Degradation in archaeological wool samples, examined by elemental and amino acid composition, was greater in samples from Iceland (Reykholt) than in samples from north-east England (York, Newcastle) or northern Germany (Hessens). A nominal assignment approach was used to classify textiles into local/non-local at each site, based on maximal estimates of isotopic variability in modern sheep wool. Light element stable isotope analysis provided new insights into the origins of wool textiles, and demonstrates that isotopic provenancing of keratin preserved in anoxic waterlogged contexts is feasible. We also demonstrate the utility of δ2H analysis to understand the location of origin of archaeological protein samples

Cascading signaling pathways improve the fidelity of a stochastically and deterministically simulated molecular RS latch

<p>Abstract</p> <p>Background</p> <p>While biological systems have often been compared with digital systems, they differ by the strong effect of crosstalk between signals due to diffusivity in the medium, reaction kinetics and geometry. Memory elements have allowed the creation of autonomous digital systems and although biological systems have similar properties of autonomy, equivalent memory mechanisms remain elusive. Any such equivalent memory system, however, must silence the effect of crosstalk to maintain memory fidelity.</p> <p>Results</p> <p>Here, we present a system of enzymatic reactions that behaves like an RS latch (a simple memory element in digital systems). Using both a stochastic molecular simulator and ordinary differential equation simulator, we showed that crosstalk between two latches operating in the same spatial localization disrupts the memory fidelity of both latches. Crosstalk was reduced or silenced when simple reaction loops were replaced with multiple step or cascading reactions, showing that cascading signaling pathways are less susceptible to crosstalk.</p> <p>Conclusion</p> <p>Thus, the common biological theme of cascading signaling pathways is advantageous for maintaining the fidelity of a memory latch in the presence of crosstalk. The experimental implementation of such a latch system will lead to novel approaches to cell control using synthetic proteins and will contribute to our understanding of why cells behave differently even when given the same stimulus.</p

Fire as a fundamental ecological process: Research advances and frontiers

© 2020 The Authors. Journal of Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society Fire is a powerful ecological and evolutionary force that regulates organismal traits, population sizes, species interactions, community composition, carbon and nutrient cycling and ecosystem function. It also presents a rapidly growing societal challenge, due to both increasingly destructive wildfires and fire exclusion in fire-dependent ecosystems. As an ecological process, fire integrates complex feedbacks among biological, social and geophysical processes, requiring coordination across several fields and scales of study. Here, we describe the diversity of ways in which fire operates as a fundamental ecological and evolutionary process on Earth. We explore research priorities in six categories of fire ecology: (a) characteristics of fire regimes, (b) changing fire regimes, (c) fire effects on above-ground ecology, (d) fire effects on below-ground ecology, (e) fire behaviour and (f) fire ecology modelling. We identify three emergent themes: the need to study fire across temporal scales, to assess the mechanisms underlying a variety of ecological feedbacks involving fire and to improve representation of fire in a range of modelling contexts. Synthesis: As fire regimes and our relationships with fire continue to change, prioritizing these research areas will facilitate understanding of the ecological causes and consequences of future fires and rethinking fire management alternatives

Widespread recovery of methylation at gametic imprints in hypomethylated mouse stem cells following rescue with DNMT3A2

BACKGROUND: Imprinted loci are paradigms of epigenetic regulation and are associated with a number of genetic disorders in human. A key characteristic of imprints is the presence of a gametic differentially methylated region (gDMR). Previous studies have indicated that DNA methylation lost from gDMRs could not be restored by DNMT1, or the de novo enzymes DNMT3A or 3B in stem cells, indicating that imprinted regions must instead undergo passage through the germline for reprogramming. However, previous studies were non-quantitative, were unclear on the requirement for DNMT3A/B and showed some inconsistencies. In addition, new putative gDMR has recently been described, along with an improved delineation of the existing gDMR locations. We therefore aimed to re-examine the dependence of methylation at gDMRs on the activities of the methyltransferases in mouse embryonic stem cells (ESCs). RESULTS: We examined the most complete current set of imprinted gDMRs that could be assessed using quantitative pyrosequencing assays in two types of ESCs: those lacking DNMT1 (1KO) and cells lacking a combination of DNMT3A and DNMT3B (3abKO). We further verified results using clonal analysis and combined bisulfite and restriction analysis. Our results showed that loss of methylation was approximately equivalent in both cell types. 1KO cells rescued with a cDNA-expressing DNMT1 could not restore methylation at the imprinted gDMRs, confirming some previous observations. However, nearly all gDMRs were remethylated in 3abKO cells rescued with a DNMT3A2 expression construct (3abKO + 3a2). Transcriptional activity at the H19/Igf2 locus also tracked with the methylation pattern, confirming functional reprogramming in the latter. CONCLUSIONS: These results suggested (1) a vital role for DNMT3A/B in methylation maintenance at imprints, (2) that loss of DNMT1 and DNMT3A/B had equivalent effects, (3) that rescue with DNMT3A2 can restore imprints in these cells. This may provide a useful system in which to explore factors influencing imprint reprogramming. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1186/s13072-016-0104-2) contains supplementary material, which is available to authorized users

Bistability versus Bimodal Distributions in Gene Regulatory Processes from Population Balance

In recent times, stochastic treatments of gene regulatory processes have appeared in the literature in which a cell exposed to a signaling molecule in its environment triggers the synthesis of a specific protein through a network of intracellular reactions. The stochastic nature of this process leads to a distribution of protein levels in a population of cells as determined by a Fokker-Planck equation. Often instability occurs as a consequence of two (stable) steady state protein levels, one at the low end representing the “off” state, and the other at the high end representing the “on” state for a given concentration of the signaling molecule within a suitable range. A consequence of such bistability has been the appearance of bimodal distributions indicating two different populations, one in the “off” state and the other in the “on” state. The bimodal distribution can come about from stochastic analysis of a single cell. However, the concerted action of the population altering the extracellular concentration in the environment of individual cells and hence their behavior can only be accomplished by an appropriate population balance model which accounts for the reciprocal effects of interaction between the population and its environment. In this study, we show how to formulate a population balance model in which stochastic gene expression in individual cells is incorporated. Interestingly, the simulation of the model shows that bistability is neither sufficient nor necessary for bimodal distributions in a population. The original notion of linking bistability with bimodal distribution from single cell stochastic model is therefore only a special consequence of a population balance model