A transcriptomic time-series reveals differing trajectories during pre-floral development in the apex and leaf in winter and spring varieties of Brassica napus

Oilseed rape (Brassica napus) is an important global oil crop, with spring and winter varieties grown commercially. To understand the transcriptomic differences between these varieties, we collected transcriptomes from apex and leaf tissue from a spring variety, Westar, and a winter variety, Tapidor, before, during, and after vernalisation treatment, until the plants flowered. Large transcriptomic differences were noted in both varieties during the vernalisation treatment because of temperature and day length changes. Transcriptomic alignment revealed that the apex transcriptome reflects developmental state, whereas the leaf transcriptome is more closely aligned to the age of the plant. Similar numbers of copies of genes were expressed in both varieties during the time series, although key flowering time genes exhibited expression pattern differences. BnaFLC copies on A2 and A10 are the best candidates for the increased vernalisation requirement of Tapidor. Other BnaFLC copies show tissue-dependent reactivation of expression post-cold, with these dynamics suggesting some copies have retained or acquired a perennial nature. BnaSOC1 genes, also related to the vernalisation pathway, have expression profiles which suggest tissue subfunctionalisation. This understanding may help to breed varieties with more consistent or robust vernalisation responses, of special importance due to the milder winters resulting from climate change

Incorporating prior knowledge improves detection of differences in bacterial growth rate

BACKGROUND: Robust statistical detection of differences in the bacterial growth rate can be challenging, particularly when dealing with small differences or noisy data. The Bayesian approach provides a consistent framework for inferring model parameters and comparing hypotheses. The method captures the full uncertainty of parameter values, whilst making effective use of prior knowledge about a given system to improve estimation. RESULTS: We demonstrated the application of Bayesian analysis to bacterial growth curve comparison. Following extensive testing of the method, the analysis was applied to the large dataset of bacterial responses which are freely available at the web-resource, ComBase. Detection was found to be improved by using prior knowledge from clusters of previously analysed experimental results at similar environmental conditions. A comparison was also made to a more traditional statistical testing method, the F-test, and Bayesian analysis was found to perform more conclusively and to be capable of attributing significance to more subtle differences in growth rate. CONCLUSIONS: We have demonstrated that by making use of existing experimental knowledge, it is possible to significantly improve detection of differences in bacterial growth rate

Astro2020 APC White Paper: The MegaMapper: a z > 2 spectroscopic instrument for the study of Inflation and Dark Energy

MegaMapper is a proposed ground-based experiment to measure Inflation parameters and Dark Energy from galaxy redshifts at

Bayesian model comparison and parameter inference in systems biology using nested sampling.

Inferring parameters for models of biological processes is a current challenge in systems biology, as is the related problem of comparing competing models that explain the data. In this work we apply Skilling's nested sampling to address both of these problems. Nested sampling is a Bayesian method for exploring parameter space that transforms a multi-dimensional integral to a 1D integration over likelihood space. This approach focuses on the computation of the marginal likelihood or evidence. The ratio of evidences of different models leads to the Bayes factor, which can be used for model comparison. We demonstrate how nested sampling can be used to reverse-engineer a system's behaviour whilst accounting for the uncertainty in the results. The effect of missing initial conditions of the variables as well as unknown parameters is investigated. We show how the evidence and the model ranking can change as a function of the available data. Furthermore, the addition of data from extra variables of the system can deliver more information for model comparison than increasing the data from one variable, thus providing a basis for experimental design

CNS Elevation of Vascular and Not Mucosal Addressin Cell Adhesion Molecules in Patients with Multiple Sclerosis

The mucosal addressin cell adhesion molecule (MAdCAM) and vascular cell adhesion molecule (VCAM) appear to play roles in the recruitment of leukocytes to specialized endothelium lining the gastrointestinal tract. The purpose of this study was to clarify the role of MAdCAM and VCAM in the central nervous system by comparing protein expression in patients with multiple sclerosis (MS) and control subjects by immunohistochemistry. Specific antibodies to human VCAM and MAdCAM were used to confirm expression in control and MS nervous system specimens by immunohistochemistry. VCAM immunoreactivity was detected in endothelial cells, perivascular tissue, and in some cases, leukocytes within the meninges, gray, and white matter, of both controls and MS patients. VCAM immunoreactivity was maximal in a patient with acute active plaques, but of lower intensity and reduced distribution in controls and those with chronic active or inactive MS plaques. In contrast, MAdCAM immunoreactivity could not be detected in brain tissue from unaffected or MS patients. Taken together, these data support a role of VCAM, but not MAdCAM in the development of MS

The migration of objects to higher likelihood regions.

<p>From an initial uniform parameter distribution (A), nested sampling selects points that are in regions of higher likelihood. The sample sets are shown after (B) 1800, (C) 2700, (D) 5400 and (E) 9000 sampling iterations, with (F) a close-up of the final (9000 iterations) sample set and the true value of the parameters indicated with a red circle ( and ). In this case, disconnected regions of high likelihood (B,C,D) are first explored before the sampling ends up in a single region of high probability (E,F). The underlying model is the repressilator, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088419#pone.0088419.e076" target="_blank">Equation (11)</a>, and the samples are from the posterior over the respressilator parameters and .</p

Evidence changes as a function of data quantity.

<p>As the resolution of the time course improves the Goodwin model (skyblue, diamonds) and the Schnakenberg model (green, circles) lose support faster than the Lotka-Volterra (orange, squares) and repressilator (black, triangles) systems. The known model—the repressilator—gains preference only for a larger number of data points (500 points with a time gap of 0.1), even when using noiseless data.</p

Fit to noisy data of four different oscillatory models.

<p>Clockwise from top left: Lotka-Volterra, repressilator, Goodwin and Schnakenberg models. Using the same noisy data (diamonds) 3000 equally-weighted samples (purple) were drawn from the posterior distribution of each model (except the Goodwin where we show a representative sample as all solutions were similar). The mean of the Lotka-Volterra system's posterior is not a good summary statistic for this distribution due to its non-unimodality (Figures S9 & S10 in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0088419#pone.0088419.s001" target="_blank">File S1</a>). The best-fit solution, dashed yellow line; solution using mean parameters, black dotted line.</p