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When using mathematical models to make quantitative predictions for clinical or industrial use, it is important that predictions come with a reliable estimate of their accuracy (uncertainty quantification). Because models of complex biological systems are always large simplifications, model discrepancy arises—models fail to perfectly recapitulate the true data generating process. This presents a particular challenge for making accurate predictions, and especially for accurately quantifying uncertainty in these predictions. Experimentalists and modellers must choose which experimental procedures (protocols) are used to produce data used to train models. We propose to characterise uncertainty owing to model discrepancy with an ensemble of parameter sets, each of which results from training to data from a different protocol. The variability in predictions from this ensemble provides an empirical estimate of predictive uncertainty owing to model discrepancy, even for unseen protocols. We use the example of electrophysiology experiments that investigate the properties of hERG potassium channels. Here, ‘information-rich’ protocols allow mathematical models to be trained using numerous short experiments performed on the same cell. In this case, we simulate data with one model and fit it with a different (discrepant) one. For any individual experimental protocol, parameter estimates vary little under repeated samples from the assumed additive independent Gaussian noise model. Yet parameter sets arising from the same model applied to different experiments conflict—highlighting model discrepancy. Our methods will help select more suitable ion channel models for future studies, and will be widely applicable to a range of biological modelling problems. [ABSTRACT FROM AUTHOR]

We consider a one‐dimensional stochastic differential equation that is observed on a fine grid of equally spaced time points. A novel approach for approximating the transition density of the stochastic differential equation is presented, which is based on an Itô‐Taylor expansion of the sample path, combined with an application of the so‐called ϵ‐expansion. The resulting approximation is economical with respect to the number of terms needed to achieve a given level of accuracy in a high‐frequency sampling framework. This method of density approximation leads to a closed‐form approximate likelihood function from which an approximate maximum likelihood estimator may be calculated numerically. A detailed theoretical analysis of the proposed estimator is provided and it is shown that it compares favorably to the Gaussian likelihood‐based estimator and does an excellent job of approximating the exact, but usually intractable, maximum likelihood estimator. Numerical simulations indicate that the exact and our approximate maximum likelihood estimator tend to be close, and the latter performs very well relative to other approximate methods in the literature in terms of speed, accuracy, and ease of implementation. [ABSTRACT FROM AUTHOR]

Targeting the potent immunosuppressive properties of FOXP3+ regulatory T cells (Tregs) has substantial therapeutic potential for treating autoimmune and inflammatory diseases. Yet, the molecular mechanisms controlling Treg homeostasis, particularly during inflammation, remain unclear. We report that caspase-8 is a central regulator of Treg homeostasis in a context-specific manner that is decisive during immune responses. In mouse genetic models, targeting caspase-8 in Tregs led to accumulation of effector Tregs resistant to apoptotic cell death. Conversely, inflammation induced the MLKL-dependent necroptosis of caspase-8–deficient lymphoid and tissue Tregs, which enhanced immunity to a variety of chronic infections to promote clearance of viral or parasitic pathogens. However, improved immunity came at the risk of lethal inflammation in overwhelming infections. Caspase-8 inhibition using a clinical-stage compound revealed that human Tregs have heightened sensitivity to necroptosis compared with conventional T cells. These findings reveal a fundamental mechanism in Tregs that could be targeted to manipulate the balance between immune tolerance versus response for therapeutic benefit. [ABSTRACT FROM AUTHOR]

Identifying the most deprived regions of any country or city is key if policy makers are to design successful interventions. However, locating areas with the greatest need is often surprisingly challenging in developing countries. Due to the logistical challenges of traditional household surveying, official statistics can be slow to be updated; estimates that exist can be coarse, a consequence of prohibitive costs and poor infrastructures; and mass urbanization can render manually surveyed figures rapidly out‐of‐date. Comparative judgement models, such as the Bradley–Terry model, offer a promising solution. Leveraging local knowledge, elicited via comparisons of different areas' affluence, such models can both simplify logistics and circumvent biases inherent to household surveys. Yet widespread adoption remains limited, due to the large amount of data existing approaches still require. We address this via development of a novel Bayesian Spatial Bradley–Terry model, which substantially decreases the number of comparisons required for effective inference. This model integrates a network representation of the city or country, along with assumptions of spatial smoothness that allow deprivation in one area to be informed by neighbouring areas. We demonstrate the practical effectiveness of this method, through a novel comparative judgement data set collected in Dar es Salaam, Tanzania. [ABSTRACT FROM AUTHOR]

Network data are becoming increasingly available, and so there is a need to develop a suitable methodology for statistical analysis. Networks can be represented as graph Laplacian matrices, which are a type of manifold‐valued data. Our main objective is to estimate a regression curve from a sample of graph Laplacian matrices conditional on a set of Euclidean covariates, for example, in dynamic networks where the covariate is time. We develop an adapted Nadaraya–Watson estimator which has uniform weak consistency for estimation using Euclidean and power Euclidean metrics. We apply the methodology to the Enron email corpus to model smooth trends in monthly networks and highlight anomalous networks. Another motivating application is given in corpus linguistics, which explores trends in an author's writing style over time based on word co‐occurrence networks. [ABSTRACT FROM AUTHOR]

The stress‐activated protein kinases (SAPKs)/c‐Jun‐N‐terminal‐kinases (JNK) are members of the mitogen‐activated protein kinase family. These kinases are responsible for transducing cellular signals through a phosphorylation‐dependent signaling cascade. JNK activation in immune cells can lead to a range of critical cellular responses that include proliferation, differentiation and apoptosis. MKK4 is a SAPK that can activate both JNK1 and JNK2; however, its role in T‐cell development and function has been controversial. Additionally, loss of either JNK1 or JNK2 has opposing effects in the generation of T‐cell immunity to viral infection and cancer. We used mice with a conditional loss of MKK4 in T cells to investigate the in vivo role of MKK4 in T‐cell development and function during lymphocytic choriomeningitis virus (LCMV) infection. We found no physiologically relevant differences in T‐cell responses or immunity to either acute or chronic LCMV in the absence of MKK4. [ABSTRACT FROM AUTHOR]

Background and Aims Diurnal changes in solar position and intensity combined with the structural complexity of plant architecture result in highly variable and dynamic light patterns within the plant canopy. This affects productivity through the complex ways that photosynthesis responds to changes in light intensity. Current methods to characterize light dynamics, such as ray-tracing, are able to produce data with excellent spatio-temporal resolution but are computationally intensive and the resulting data are complex and high-dimensional. This necessitates development of more economical models for summarizing the data and for simulating realistic light patterns over the course of a day. Methods High-resolution reconstructions of field-grown plants are assembled in various configurations to form canopies, and a forward ray-tracing algorithm is applied to the canopies to compute light dynamics at high (1 min) temporal resolution. From the ray-tracer output, the sunlit or shaded state for each patch on the plants is determined, and these data are used to develop a novel stochastic model for the sunlit–shaded patterns. The model is designed to be straightforward to fit to data using maximum likelihood estimation, and fast to simulate from. Key Results For a wide range of contrasting 3-D canopies, the stochastic model is able to summarize, and replicate in simulations, key features of the light dynamics. When light patterns simulated from the stochastic model are used as input to a model of photoinhibition, the predicted reduction in carbon gain is similar to that from calculations based on the (extremely costly) ray-tracer data. Conclusions The model provides a way to summarize highly complex data in a small number of parameters, and a cost-effective way to simulate realistic light patterns. Simulations from the model will be particularly useful for feeding into larger-scale photosynthesis models for calculating how light dynamics affects the photosynthetic productivity of canopies. [ABSTRACT FROM AUTHOR]

The article focuses on a study related to effect of wind stimulated canopy movement on photosynthesis and light fluctuations including relevance of canopy traits for improvement of crop yield and impact of mechanical canopy excitation on frequency and duration of light patterning.

Summary Plasmacytoid dendritic cells (pDCs) represent a unique immune cell type that responds to viral nucleic acids through the rapid production of type I interferons. Within the hematopoietic system, the transcription factor RUNX2 is exclusively expressed in pDCs and is required for their peripheral homeostasis. Here, we show that RUNX2 plays an essential role in promoting pDC localization and function. RUNX2 is required for the appropriate expression of the integrin-mediated adhesion machinery, as well as for the down-modulation of the chemokine receptor CXCR4, which allows pDC egress into the circulation. RUNX2 also facilitates the robust response to viral infection through the control of IRF7, the major regulator of type I interferon production. Mice lacking one copy of Runx2 have reduced numbers of peripheral pDCs and IFN-α expression, which might contribute to the reported difficulties of individuals with cleidocranial dysplasia, who are haploinsufficient for RUNX2 , to clear viral infections. [ABSTRACT FROM AUTHOR]

Photoinhibition reduces photosynthetic productivity; however, it is difficult to quantify accurately in complex canopies partly because of a lack of high-resolution structural data on plant canopy architecture, which determines complex fluctuations of light in space and time. Here, we evaluate the effects of photoinhibition on long-term carbon gain (over 1 d) in three different wheat (Triticum aestivum) lines, which are architecturally diverse. We use a unique method for accurate digital three-dimensional reconstruction of canopies growing in the field. The reconstruction method captures unique architectural differences between lines, such as leaf angle, curvature, and leaf density, thus providing a sensitive method of evaluating the productivity of actual canopy structures that previously were difficult or impossible to obtain. We show that complex data on light distribution can be automatically obtained without conventional manual measurements. We use a mathematical model of photosynthesis parameterized by field data consisting of chlorophyll fluorescence, light response curves of carbon dioxide assimilation, and manual confirmation of canopy architecture and light attenuation. Model simulations show that photoinhibition alone can result in substantial reduction in carbon gain, but this is highly dependent on exact canopy architecture and the diurnal dynamics of photoinhibition. The use of such highly realistic canopy reconstructions also allows us to conclude that even a moderate change in leaf angle in upper layers of the wheat canopy led to a large increase in the number of leaves in a severely light-limited state. [ABSTRACT FROM AUTHOR]

Plants have evolved complex mechanisms to balance the efficient use of absorbed light energy in photosynthesis with the capacity to use that energy in assimilation, so avoiding potential damage from excess light. This is particularly important under natural light, which can vary according to weather, solar movement and canopy movement. Photosynthetic acclimation is the means by which plants alter their leaf composition and structure over time to enhance photosynthetic efficiency and productivity. However there is no empirical or theoretical basis for understanding how leaves track historic light levels to determine acclimation status, or whether they do this accurately. We hypothesized that in fluctuating light (varying in both intensity and frequency), the light-response characteristics of a leaf should adjust (dynamically acclimate) to maximize daily carbon gain. Using a framework of mathematical modelling based on light-response curves, we have analysed carbon-gain dynamics under various light patterns. The objective was to develop new tools to quantify the precision with which photosynthesis acclimates according to the environment in which plants exist and to test this tool on existing data. We found an inverse relationship between the optimal maximum photosynthetic capacity and the frequency of low to high light transitions. Using experimental data from the literature we were able to show that the observed patterns for acclimation were consistent with a strategy towards maximizing daily carbon gain. Refinement of the model will further determine the precision of acclimation. [ABSTRACT FROM AUTHOR]

The E3 ligase ARIH2 has an unusual structure and mechanism of elongating ubiquitin chains. To understand its physiological role, we generated gene-targeted mice deficient in ARIH2. ARIH2 deficiency resulted in the embryonic death of C57BL/6 mice. On a mixed genetic background, the lethality was attenuated, with some mice surviving beyond weaning and then succumbing to an aggressive multiorgan inflammatory response. We found that in dendritic cells (DCs), ARIH2 caused degradation of the inhibitor I?B? in the nucleus, which abrogated its ability to sequester, protect and transcriptionally coactivate the transcription factor subunit p65 in the nucleus. Loss of ARIH2 caused dysregulated activation of the transcription factor NF-?B in DCs, which led to lethal activation of the immune system in ARIH2-sufficent mice reconstituted with ARIH2-deficient hematopoietic stem cells. Our data have therapeutic implications for targeting ARIH2 function. [ABSTRACT FROM AUTHOR]

We investigate resampling methodologies for testing the null hypothesis that two samples of labelled landmark data in three dimensions come from populations with a common mean reflection shape or mean reflection size-and-shape. The investigation includes comparisons between (i) two different test statistics that are functions of the projection onto tangent space of the data, namely the James statistic and an empirical likelihood statistic; (ii) bootstrap and permutation procedures; and (iii) three methods for resampling under the null hypothesis, namely translating in tangent space, resampling using weights determined by empirical likelihood and using a novel method to transform the original sample entirely within refection shape space. We present results of extensive numerical simulations, on which basis we recommend a bootstrap test procedure that we expect will work well in practise. We demonstrate the procedure using a data set of human faces, to test whether humans in different age groups have a common mean face shape. [ABSTRACT FROM AUTHOR]

The data objects of interest are infinite dimensional covariance operators in a Hilbert space and practical analysis is carried out in the feature space of finite dimensional covariance matrices. The need for the development of I d i -covariance is reminiscent of issues that have arisen previously in statistical shape analysis, where procedures such as Procrustes estimation produced inconsistent estimates in general for population mean shapes from Gaussian landmark models (Lele [8]). Dryden, Koloydenko, and Zhou ([3]) and Pigoli et al. ([11]) considered the power Euclidean distances between pairs of covariance matrices and infinite dimensional covariance operators, respectively. [Extracted from the article]