Legitimising data-driven models: exemplification of a new data-driven mechanistic modelling framework

In this paper the difficult problem of how to legitimisedata-driven hydrological models is addressed using an example of a simple artificial neural network modelling problem. Many data-driven models in hydrology have been criticised for their black-box characteristics, which prohibit adequate understanding of their mechanistic behaviour and restrict their wider heuristic value. In response, presented here is a new generic data-driven mechanistic modelling framework. The framework is significant because it incorporates an evaluation of the legitimacy of a data-driven model’s internal modelling mechanism as a core element in the modelling process. The framework’s value is demonstrated by two simple artificial neural network river forecasting scenarios. We develop a novel adaptation of first-order partial derivative, relative sensitivity analysis to enable each model’s mechanistic legitimacy to be evaluated within the framework. The results demonstrate the limitations of standard, goodness-of-fit validation procedures by highlighting how the internal mechanisms of complex models that produce the best fit scores can have lower mechanistic legitimacy than simpler counterparts whose scores are only slightly inferior. Thus, our study directly tackles one of the key debates in data-driven, hydrological modelling: is it acceptable for our ends (i.e. model fit) to justify our means (i.e. the numerical basis by which that fit is achieved)

Ideal point error for model assessment in data-driven river flow forecasting

When analysing the performance of hydrological models in river forecasting, researchers use a number of diverse statistics. Although some statistics appear to be used more regularly in such analyses than others, there is a distinct lack of consistency in evaluation, making studies undertaken by different authors or performed at different locations difficult to compare in a meaningful manner. Moreover, even within individual reported case studies, substantial contradictions are found to occur between one measure of performance and another. In this paper we examine the ideal point error (IPE) metric – a recently introduced measure of model performance that integrates a number of recognised metrics in a logical way. Having a single, integrated measure of performance is appealing as it should permit more straightforward model inter-comparisons. However, this is reliant on a transferrable standardisation of the individual metrics that are combined to form the IPE. This paper examines one potential option for standardisation: the use of naive model benchmarking

Sensitivity analysis for comparison, validation and physical-legitimacy of neural network-based hydrological models

This paper addresses the difficult question of how to perform meaningful comparisons between neural network-based hydrological models and alternative modelling approaches. Standard, goodness-of-fit metric approaches are limited since they only assess numerical performance and not physical legitimacy of the means by which output is achieved. Consequently, the potential for general application or catchment transfer of such models is seldom understood. This paper presents a partial derivative, relative sensitivity analysis method as a consistent means by which the physical legitimacy of models can be evaluated. It is used to compare the behaviour and physical rationality of a generalised linear model and two neural network models for predicting median flood magnitude in rural catchments. The different models perform similarly in terms of goodness-of-fit statistics, but behave quite distinctly when the relative sensitivities of their inputs are evaluated. The neural solutions are seen to offer an encouraging degree of physical legitimacy in their behaviour, over that of a generalised linear modelling counterpart, particularly when overfitting is constrained. This indicates that neural models offer preferable solutions for transfer into ungauged catchments. Thus, the importance of understanding both model performance and physical legitimacy when comparing neural models with alternative modelling approaches is demonstrated

The EBV-encoded latent membrane proteins, LMP2A and LMP2B, limit the actions of interferon by targeting interferon receptors for degradation

Although frequently expressed in Epstein–Barr virus (EBV)-positive malignancies, the role that latent membrane protein 2A and 2B (LMP2A and LMP2B) have in the oncogenic process remains obscure. Here we show a novel function for these proteins in epithelial cells, namely, their ability to modulate signalling from type I/II interferon receptors (IFNRs). We show that LMP2A- and LMP2B-expressing epithelial cells show decreased responsiveness to interferon (IFN)α and IFNγ, as assessed by STAT1 phosphorylation, ISGF3 and GAF-mediated binding to IFN-stimulated response element and IFNγ-activated factor sequence elements and luciferase reporter activation. Transcriptional profiling highlighted the extent of this modulation, with both viral proteins impacting ‘globally’ on IFN-stimulated gene expression. Although not affecting the levels of cell-surface IFNRs, LMP2A and LMP2B accelerated the turnover of IFNRs through processes requiring endosome acidification. This function may form part of EBV's strategy to limit anti-viral responses and define a novel function for LMP2A and LMP2B in modulating signalling from receptors that participate in innate immune responses

Beam divergence measurements of InGaN/GaN micro-array light-emitting diodes using confocal microscopy

The recent development of high-density, two-dimensional arrays of micrometer-sized InGaN/GaN light-emitting diodes (micro-LEDs) with potential applications from scientific instrumentation to microdisplays has created an urgent need for controlled manipulation of the light output from these devices. With directed light output these devices can be used in situations where collimated beams or light focused onto several thousand matrix points is desired. In order to do this effectively, the emission characteristics of the devices must be fully understood and characterized. Here we utilize confocal microscopy to directly determine the emission characteristics and angular beam divergences from the individual micro-LED elements. The technique is applied to both top (into air) and bottom (through substrate) emission in arrays of green (540 nm), blue (470 nm), and UV (370 nm) micro-LED devices, at distances of up to 50 µm from the emission plane. The results are consistent with simple optical modeling of the expected beam profiles

Improved validation framework and R-package for artificial neural network models

Validation is a critical component of any modelling process. In artificial neural network (ANN) modelling, validation generally consists of the assessment of model predictive performance on an independent validation set (predictive validity). However, this ignores other aspects of model validation considered to be good practice in other areas of environmental modelling, such as residual analysis (replicative validity) and checking the plausibility of the model in relation to a priori system understanding (structural validity). In order to address this shortcoming, a validation framework for ANNs is introduced in this paper that covers all of the above aspects of validation. In addition, the validann R-package is introduced that enables these validation methods to be implemented in a user-friendly and consistent fashion. The benefits of the framework and R-package are demonstrated for two environmental modelling case studies, highlighting the importance of considering replicative and structural validity in addition to predictive validity

The EBV-encoded oncoprotein, LMP1, induces an epithelial-to-mesenchymal transition (EMT) via Its CTAR1 domain through integrin-mediated ERK-MAPK signalling

The Epstein⁻Barr virus (EBV)-encoded latent membrane protein 1 (LMP1) oncogene can induce profound effects on epithelial growth and differentiation including many of the features of the epithelial-to-mesenchymal transition (EMT). To better characterise these effects, we used the well-defined Madin Darby Canine Kidney (MDCK) epithelial cell model and found that LMP1 expression in these cells induces EMT as defined by characteristic morphological changes accompanied by loss of E-cadherin, desmosomal cadherin and tight junction protein expression. The induction of the EMT phenotype required a functional CTAR1 domain of LMP1 and studies using pharmacological inhibitors revealed contributions from signalling pathways commonly induced by integrin⁻ligand interactions: extracellular signal-regulated kinases/mitogen-activated protein kinases (ERK-MAPK), PI3-Kinase and tyrosine kinases, but not transforming growth factor beta (TGFβ). More detailed analysis implicated the CTAR1-mediated induction of Slug and Twist in LMP1-induced EMT. A key role for β1 integrin signalling in LMP1-mediated ERK-MAPK and focal adhesion kianse (FAK) phosphorylation was observed, and β1 integrin activation was found to enhance LMP1-induced cell viability and survival. These findings support an important role for LMP1 in disease pathogenesis through transcriptional reprogramming that enhances tumour cell survival and leads to a more invasive, metastatic phenotype

The Critical Role of Spreading Depolarizations in Early Brain Injury: Consensus and Contention

Background: When a patient arrives in the emergency department following a stroke, a traumatic brain injury, or sudden cardiac arrest, there is no therapeutic drug available to help protect their jeopardized neurons. One crucial reason is that we have not identified the molecular mechanisms leading to electrical failure, neuronal swelling, and blood vessel constriction in newly injured gray matter. All three result from a process termed spreading depolarization (SD). Because we only partially understand SD, we lack molecular targets and biomarkers to help neurons survive after losing their blood flow and then undergoing recurrent SD. Methods: In this review, we introduce SD as a single or recurring event, generated in gray matter following lost blood flow, which compromises the Na/K pump. Electrical recovery from each SD event requires so much energy that neurons often die over minutes and hours following initial injury, independent of extracellular glutamate. Results: We discuss how SD has been investigated with various pitfalls in numerous experimental preparations, how overtaxing the Na/K ATPase elicits SD. Elevated K or glutamate are unlikely natural activators of SD. We then turn to the properties of SD itself, focusing on its initiation and propagation as well as on computer modeling. Conclusions: Finally, we summarize points of consensus and contention among the authors as well as where SD research may be heading. In an accompanying review, we critique the role of the glutamate excitotoxicity theory, how it has shaped SD research, and its questionable importance to the study of early brain injury as compared with SD theory.This work was supported by grants from the Heart and Stroke Foundation of Canada and the National Science and Engineering Research Council of Canada to RDA, an NIH grant (NS106901) to CWS, a National Research, Development and Innovation Office of Hungary grant (K1343777) and EU Horizon 2020 research and innovation program (739953) to EF and from DFG Deutsche Forschungsgemeinschaft (German Research Council) (DFG DR 323/5-1), DFG DR 323/10-1, and BMBF Bundesministerium fuer Bildung und Forschung (EraNet Neuron EBio2, with funds from BMBF 01EW2004) to JPD

In vivo confocal microscopy in scarring trachoma.

OBJECTIVE: To characterize the tissue and cellular changes found in trachomatous scarring (TS) and inflammation using in vivo confocal microscopy (IVCM). DESIGN: Two complimentary case-control studies. PARTICIPANTS: The first study included 363 cases with TS (without trichiasis), of whom 328 had IVCM assessment, and 363 control subjects, of whom 319 had IVCM assessment. The second study included 34 cases with trachomatous trichiasis (TT), of whom 28 had IVCM assessment, and 33 control subjects, of whom 26 had IVCM assessment. METHODS: All participants were examined with ×2.5 loupes. The IVCM examination of the upper tarsal conjunctiva was carried out with a Heidelberg Retina Tomograph 3 with the Rostock Cornea Module (Heidelberg Engineering GmbH, Dossenheim, Germany). MAIN OUTCOME MEASURES: The IVCM images were graded in a masked manner using a previously published grading system evaluating the inflammatory infiltrate density; the presence or absence of dendritiform cells (DCs), tissue edema, and papillae; and the level of subepithelial connective tissue organization. RESULTS: Subjects with clinical scarring had a characteristic appearance on IVCM of well-defined bands and sheets of scar tissue visible. Similar changes were also seen in some clinically normal subjects consistent with subclinical scarring. Scarred subjects had more DCs and an elevated inflammatory infiltrate, even after adjusting for other factors, including the level of clinical inflammation. Cellular activity was usually seen only in or just below the epithelium, rarely being seen deeper than 30 μm from the surface. The presence of tissue edema was strongly associated with the level of clinical inflammation. CONCLUSIONS: In vivo confocal microscopy can be quantitatively used to study inflammatory and scarring changes in the conjunctiva. Dendritic cells seem to be closely associated with the scarring process in trachoma and are likely to be an important target in antifibrotic therapies or the development of a chlamydial vaccine. The increased number of inflammatory cells seen in scarred subjects is consistent with the immunopathologic nature of the disease. The localization of cellular activity close to the conjunctival surface supports the view that the epithelium plays a central role in the pathogenesis of trachoma. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article

Spatial and temporal scaling of sub-daily extreme rainfall for data sparse places

Global efforts to upgrade water, drainage, and sanitation services are hampered by hydrometeorological data-scarcity plus uncertainty about climate change. Intensity–duration–frequency (IDF) tables are used routinely to design water infrastructure so offer an entry point for adapting engineering standards. This paper begins with a novel procedure for guiding downscaling predictor variable selection for heavy rainfall simulation using media reports of pluvial flooding. We then present a three-step workflow to: (1) spatially downscale daily rainfall from grid-to-point resolutions; (2) temporally scale from daily series to sub-daily extreme rainfalls and; (3) test methods of temporal scaling of extreme rainfalls within Regional Climate Model (RCM) simulations under changed climate conditions. Critically, we compare the methods of moments and of parameters for temporal scaling annual maximum series of daily rainfall into sub-daily extreme rainfalls, whilst accounting for rainfall intermittency. The methods are applied to Kampala, Uganda and Kisumu, Kenya using the Statistical Downscaling Model (SDSM), two RCM simulations covering East Africa (CP4 and P25), and in hybrid form (RCM-SDSM). We demonstrate that Gumbel parameters (and IDF tables) can be reliably scaled to durations of 3 h within observations and RCMs. Our hybrid RCM-SDSM scaling reduces errors in IDF estimates for the present climate when compared with direct RCM output. Credible parameter scaling relationships are also found within RCM simulations under changed climate conditions. We then discuss the practical aspects of applying such workflows to other city-regions