Comprehensive review of models and methods for inferences in bio-chemical reaction networks

The key processes in biological and chemical systems are described by networks of chemical reactions. From molecular biology to biotechnology applications, computational models of reaction networks are used extensively to elucidate their non-linear dynamics. The model dynamics are crucially dependent on the parameter values which are often estimated from observations. Over the past decade, the interest in parameter and state estimation in models of (bio-) chemical reaction networks (BRNs) grew considerably. The related inference problems are also encountered in many other tasks including model calibration, discrimination, identifiability, and checking, and optimum experiment design, sensitivity analysis, and bifurcation analysis. The aim of this review paper is to examine the developments in literature to understand what BRN models are commonly used, and for what inference tasks and inference methods. The initial collection of about 700 documents concerning estimation problems in BRNs excluding books and textbooks in computational biology and chemistry were screened to select over 270 research papers and 20 graduate research theses. The paper selection was facilitated by text mining scripts to automate the search for relevant keywords and terms. The outcomes are presented in tables revealing the levels of interest in different inference tasks and methods for given models in the literature as well as the research trends are uncovered. Our findings indicate that many combinations of models, tasks and methods are still relatively unexplored, and there are many new research opportunities to explore combinations that have not been considered—perhaps for good reasons. The most common models of BRNs in literature involve differential equations, Markov processes, mass action kinetics, and state space representations whereas the most common tasks are the parameter inference and model identification. The most common methods in literature are Bayesian analysis, Monte Carlo sampling strategies, and model fitting to data using evolutionary algorithms. The new research problems which cannot be directly deduced from the text mining data are also discussed

The effects of morphology instruction on literacy outcomes for children in English-speaking countries: A systematic review and meta-analysis

In this pre-registered meta-analysis, we investigated the effectiveness of morphology instruction on literacy outcomes for primary school children in English-speaking countries. We were interested in overall reading and spelling outcomes, but we also looked separately at results for trained and untrained words in order to determine whether there was evidence of transfer to untrained words. We were also interested in whether results transferred beyond the word level to reading comprehension outcomes. Our screening process revealed 28 eligible studies, which contributed 177 effect sizes to the analyses. Robust variance estimation methods were used to account for dependence between effect sizes. Overall, effect sizes on reading and spelling outcomes were small to moderate. Effect sizes were larger for trained words than untrained words. There was evidence of transfer to untrained words for spelling outcomes, but not for reading outcomes. There was also no clear evidence of effects on reading comprehension outcomes. In general, the evidence was characterised by large amounts of heterogeneity and imprecision, which was reflective of the wide variety within and between studies in terms of intervention content, outcome measures, intervention dosage and type of control group. We discuss the limitations of the current literature and make recommendations for future research and practice in the field of morphology instruction

Proliferation of Tail Risks and Policy Responses in EU Financial Markets

This study draws attention to the proliferation of tail risks in financial markets prior to and during the course of the recent global financial crisis. It examines the level of tail risks in selected equity, interbank lending and foreign exchange markets in selected EU Member States in relation to the United States. The extent of tail risks is assessed by applying general error distribution (GED) parameterization in GARCH volatility tests of the examined variables. The empirical tests prove that tail risks were pronounced across all of the examined European financial markets throughout the crisis. They were also significant prior to the crisis outbreak. The analyzed interbank lending markets exhibited more extreme volatility outbursts than the equity and foreign exchange markets. Several countercyclical monetary and macroprudential policies aimed at abating tail risks are identified and discussed. Flexible capital adequacy and contingent capital requirements for financial institutions are advocated