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Abstract Background Methods for estimating variance components (VC) using restricted maximum likelihood (REML) typically require elements from the inverse of the coefficient matrix of the mixed model equations (MME). As genomic information becomes more prevalent, the coefficient matrix of the MME becomes denser, presenting a challenge for analyzing large datasets. Thus, computational algorithms based on iterative solving and Monte Carlo approximation of the inverse of the coefficient matrix become appealing. While the standard average information REML (AI-REML) is known for its rapid convergence, its computational intensity imposes limitations. In particular, the standard AI-REML requires solving the MME for each VC, which can be computationally demanding, especially when dealing with complex models with many VC. To bridge this gap, here we (1) present a computationally efficient and tractable algorithm, named the augmented AI-REML, which facilitates the AI-REML by solving an augmented MME only once within each REML iteration; and (2) implement this approach for VC estimation in a general framework of a multi-trait GBLUP model. VC estimation was investigated based on the number of VC in the model, including a two-trait, three-trait, four-trait, and five-trait GBLUP model. We compared the augmented AI-REML with the standard AI-REML in terms of computing time per REML iteration. Direct and iterative solving methods were used to assess the advances of the augmented AI-REML. Results When using the direct solving method, the augmented AI-REML and the standard AI-REML required similar computing times for models with a small number of VC (the two- and three-trait GBLUP model), while the augmented AI-REML demonstrated more notable reductions in computing time as the number of VC in the model increased. When using the iterative solving method, the augmented AI-REML demonstrated substantial improvements in computational efficiency compared to the standard AI-REML. The elapsed time of each REML iteration was reduced by 75%, 84%, and 86% for the two-, three-, and four-trait GBLUP models, respectively. Conclusions The augmented AI-REML can considerably reduce the computing time within each REML iteration, particularly when using an iterative solver. Our results demonstrate the potential of the augmented AI-REML as an appealing approach for large-scale VC estimation in the genomic era.

Transfer RNAs (tRNAs) are highly abundant species and, along their biosynthetic and functional path, they establish interactions with a plethora of proteins. The high number of nucleobase modifications in tRNAs renders conventional RNA quantification approaches unsuitable to study protein-tRNA interactions and their associated functional roles in the cell. We present an immunoprecipitation-based approach to quantify tRNA bound to its interacting protein partner(s). The tRNA-protein complexes are immunoprecipitated from cells or tissues and tRNAs are identified by northern blot and quantified by tRNA-specific fluorescent labeling. The tRNA interacting protein is quantified by an automated western blot and the tRNA amount is presented per unit of the interacting protein. We tested the approach to quantify tRNAGly associated with mutant glycyl-tRNA-synthetase implicated in Charcot-Marie-Tooth disease. This simple and versatile protocol can be easily adapted to any other tRNA binding proteins.Graphic abstract: Figure 1. Schematic of the tRNA-Immunoprecipitation approach.

Abstract The successful development of an economic model for the evaluation of future Alzheimer's disease (AD) interventions is critical to accurately inform policy makers and payers. As our understanding of AD expands, this becomes an increasingly complex and challenging goal. Advances in diagnostic techniques for AD and the prospect of disease‐modifying treatments raise an urgent need to define specifications for future economic models and to ensure that the necessary data to populate them are available. This Perspective article provides expert opinions from health economists and governmental agency representatives on how future economic models for AD might be structured, validated, and reported. We aim to stimulate much‐needed discussion about the detailed specification of future health economic models for AD.

Aims: To facilitate regulatory learning, we evaluated similarities and differences in evidence requirements between regulatory and health technology assessment (HTA) bodies of Alzheimer's disease (AD) approved products.Methods: The European marketing authorisation application dossiers and European public assessment reports (EPARs) of the licensed AD drugs were screened to identify the phase III randomised controlled trials (RCTs) and outcomes used. We also screened the assessment reports of the National Institute of Health and Care Excellence (NICE, England) and the National Health Care Institute (ZiN, the Netherlands) to identify the studies and outcomes used in HTA assessments.Results: The application dossiers of donepezil, galantamine, rivastigmine, and memantine contained 16 phase III RCTs in total. These trials were also included in HTA assessments except that NICE excluded studies that were not published (n = 2) or trials that included patients with other types of dementia (n = 3). In the regulatory assessments the focus was on cognitive and global outcomes, and to some extent on function. In the HTA assessments of clinical effectiveness other domains were also covered including: function, behaviour and mood, and, occasionally, quality of life. In the economic analyses of NICE the domains cognition, function, and quality of life were included.Conclusion: There was a large overlap in inclusion of trials in regulatory and HTA assessments, although the focus on specific outcomes slightly differed. Understanding the methods and perceptions of both authorities can stimulate regulatory and HTA cross-talk and further alignment, and therefore more rapid patient access to new treatments.

Genomic selection in crop breeding introduces modeling challenges not found in animal studies. These include the need to accommodate replicate plants for each line, consider spatial variation in field trials, address line by environment interactions, and capture nonadditive effects. Here, we propose a flexible single-stage genomic selection approach that resolves these issues. Our linear mixed model incorporates spatial variation through environment-specific terms, and also randomization-based design terms. It considers marker, and marker by environment interactions using ridge regression best linear unbiased prediction to extend genomic selection to multiple environments. Since the approach uses the raw data from line replicates, the line genetic variation is partitioned into marker and nonmarker residual genetic variation (i.e., additive and nonadditive effects). This results in a more precise estimate of marker genetic effects. Using barley height data from trials, in 2 different years, of up to 477 cultivars, we demonstrate that our new genomic selection model improves predictions compared to current models. Analyzing single trials revealed improvements in predictive ability of up to 5.7%. For the multiple environment trial (MET) model, combining both year trials improved predictive ability up to 11.4% compared to a single environment analysis. Benefits were significant even when fewer markers were used. Compared to a single-year standard model run with 3490 markers, our partitioned MET model achieved the same predictive ability using between 500 and 1000 markers depending on the trial. Our approach can be used to increase accuracy and confidence in the selection of the best lines for breeding and/or, to reduce costs by using fewer markers.

The use of social media tools by individuals and organizations to radicalize individuals for political and social change has become increasingly popular as the Internet penetrates more of the world and mobile computing devices are more accessible. To establish a construct for radicalization,the power and reach of social media will be described so there is common understanding of what social media is and how it is utilized by various individuals and groups. The second section will answer the question of why social media applications are the perfect platform for the radical voice. Finally, the use of social media and its influence in radicalizing populations in Northern Africa and the Middle East during 2011 will be analyzed and recommendations proposed.

Estimation of variance components by Monte Carlo (MC) expectation maximization (EM) restricted maximum likelihood (REML) is computationally efficient for large data sets and complex linear mixed effects models. However, efficiency may be lost due to the need for a large number of iterations of the EM algorithm. To decrease the computing time we explored the use of faster converging Newton-type algorithms within MC REML implementations. The implemented algorithms were: MC Newton-Raphson (NR), where the information matrix was generated via sampling; MC average information(AI), where the information was computed as an average of observed and expected information; and MC Broyden's method, where the zero of the gradient was searched using a quasi-Newton-type algorithm. Performance of these algorithms was evaluated using simulated data. The final estimates were in good agreement with corresponding analytical ones. MC NR REML and MC AI REML enhanced convergence compared to MC EM REML and gave standard errors for the estimates as a by-product. MC NR REML required a larger number of MC samples, while each MC AI REML iteration demanded extra solving of mixed model equations by the number of parameters to be estimated. MC Broyden's method required the largest number of MC samples with our small data and did not give standard errors for the parameters directly. We studied the performance of three different convergence criteria for the MC AI REML algorithm. Our results indicate the importance of defining a suitable convergence criterion and critical value in order to obtain an efficient Newton-type method utilizing a MC algorithm. Overall, use of a MC algorithm with Newton-type methods proved feasible and the results encourage testing of these methods with different kinds of large-scale problem settings.

Current views about language are dominated by the idea of arbitrary connections between linguistic form and meaning. However, if we look beyond the more familiar Indo-European languages and also include both spoken and signed language modalities, we find that motivated, iconic form-meaning mappings are, in fact, pervasive in language. In this paper, we review the different types of iconic mappings that characterize languages in both modalities, including the predominantly visually iconic mappings in signed languages. Having shown that iconic mapping are present across languages, we then proceed to review evidence showing that language users (signers and speakers) exploit iconicity in language processing and language acquisition. While not discounting the presence and importance of arbitrariness in language, we put forward the idea that iconicity need also be recognized as a general property of language, which may serve the function of reducing the gap between linguistic form and conceptual representation to allow the language system to hook up to motor and perceptual experience.

The representation of agreement is a crucial aspect of current syntactic theory, and therefore should apply in both signed and spoken languages. Neidle et al. (2000) claim that all verb types in American Sign Language (agreeing, spatial, and plain) can occur with abstract syntactic agreement for subject and object. On this view, abstract agreement can be marked with either manual agreement morphology (verb directed toward locations associated with the subject/object) or non-manual agreement (eye gaze toward the object/head tilt toward the subject). Non-manual agreement is claimed to function independently as a feature-checking mechanism since it can occur with plain verbs not marked with overt morphological agreement. We conducted a language production experiment using head-mounted eye-tracking to directly measure signers’ eye gaze. The results were inconsistent with Neidle et al.’s claims. While eye gaze accompanying (manually/morphologically) agreeing verbs was most frequently directed toward the location of the syntactic object, eye gaze accompanying plain verbs was rarely directed toward the object. Further, eye gaze accompanying spatial verbs was toward the locative argument, rather than toward the object of transitive verbs or the subject of intransitive verbs as predicted by Neidle et.al. Additionally, we found a consistent difference in the height of directed eye gaze between spatial and agreeing verbs. Gaze was directed lower in signing space for locative marking than for object marking, thus clearly distinguishing these two argument types. Plain verbs occurring with null object pronouns were not marked by gaze toward the location of the object and always occurred with an overt object topic. Thus, Neidle et.al.’s analysis of null objects as licensed by agreement (manual or non-manual) was not supported. Rather, the data substantiated Lillo-Martin’s (1986) claim that null arguments for plain verbs are licensed by topics. To account for the observed patterns of eye gaze, we propose an analysis of eye gaze agreement for agreeing and spatial verbs as marking the ‘lowest’ available argument on a noun phrase accessibility hierarchy. [ABSTRACT FROM AUTHOR]

This paper seeks to analyse the findings of an extensive household survey, uncovering interesting evidence of variation in health-seeking behaviour across rural and urban areas due, it is suggested, to differences in real costs, quality of care, and perceptions of the value of health and health care. It is shown that, ceteris paribus, urban households in Kazakhstan are more likely to consult, to be admitted to hospital, to report illness, and will spend relatively more on health care. The data suggest the need for further qualitative study into the factors underlying these patterns leading to strategies to increase the quality, acceptability and affordability of rural health services. This is important given the decline in health indicators such as life expectancy in Kazakhstan and the increased burden on households of funding health care in a time of economic insecurity and deterioration of public services. Copyright © 2002 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Speech?sign or ?bimodal? bilingualism is exceptional because distinct modalities allow for simultaneous production of two languages. We investigated the ramifications of this phenomenon for models of language production by eliciting language mixing from eleven hearing native users of American Sign Language (ASL) and English. Instead of switching between languages, bilinguals frequently produced code-blends (simultaneously produced English words and ASL signs). Code-blends resembled co-speech gesture with respect to synchronous vocal?manual timing and semantic equivalence. When ASL was the Matrix Language, no single-word code-blends were observed, suggesting stronger inhibition of English than ASL for these proficient bilinguals. We propose a model that accounts for similarities between co-speech gesture and code-blending and assumes interactions between ASL and English Formulators. The findings constrain language production models by demonstrating the possibility of simultaneously selecting two lexical representations (but not two propositions) for linguistic expression and by suggesting that lexical suppression is computationally more costly than lexical selection. [ABSTRACT FROM AUTHOR]

When robots have multiple tasks to perform, they must determine the order in which to complete them. Interleaving tasks is efficient for the robot trying to finish its to-do list, but it may be less satisfying for a human whose request was delayed in favor of schedule efficiency. Following online research that examined delays with various motivations, we created two in-person studies in which participants' tasks were impacted by the robot's other tasks. In the first, participants either requested a task for the robot to complete on their behalf or watched the robot performing tasks for other people. We measured how their opinions changed depending on whether their task's completion was delayed due to another participant's task or they were observing without a task of their own. In the second, participants had a robot walk them to an office and became delayed as the robot detoured to another location. We measured how opinions of the robot changed depending on who requested the detour task and the length of the detour. Overall, participants positively viewed task interleaving as long as the delay and inconvenience imposed by someone else's task were small and the task was well-justified. Also, observers often had lower opinions of the robot than participants who requested tasks, highlighting a concern for online research. [ABSTRACT FROM AUTHOR]

Background: Methods for estimating variance components (VC) using restricted maximum likelihood (REML) typically require elements from the inverse of the coefficient matrix of the mixed model equations (MME). As genomic information becomes more prevalent, the coefficient matrix of the MME becomes denser, presenting a challenge for analyzing large datasets. Thus, computational algorithms based on iterative solving and Monte Carlo approximation of the inverse of the coefficient matrix become appealing. While the standard average information REML (AI-REML) is known for its rapid convergence, its computational intensity imposes limitations. In particular, the standard AI-REML requires solving the MME for each VC, which can be computationally demanding, especially when dealing with complex models with many VC. To bridge this gap, here we (1) present a computationally efficient and tractable algorithm, named the augmented AI-REML, which facilitates the AI-REML by solving an augmented MME only once within each REML iteration; and (2) implement this approach for VC estimation in a general framework of a multi-trait GBLUP model. VC estimation was investigated based on the number of VC in the model, including a two-trait, three-trait, four-trait, and five-trait GBLUP model. We compared the augmented AI-REML with the standard AI-REML in terms of computing time per REML iteration. Direct and iterative solving methods were used to assess the advances of the augmented AI-REML. Results: When using the direct solving method, the augmented AI-REML and the standard AI-REML required similar computing times for models with a small number of VC (the two- and three-trait GBLUP model), while the augmented AI-REML demonstrated more notable reductions in computing time as the number of VC in the model increased. When using the iterative solving method, the augmented AI-REML demonstrated substantial improvements in computational efficiency compared to the standard AI-REML. The elapsed time of each REML iteration was reduced by 75%, 84%, and 86% for the two-, three-, and four-trait GBLUP models, respectively. Conclusions: The augmented AI-REML can considerably reduce the computing time within each REML iteration, particularly when using an iterative solver. Our results demonstrate the potential of the augmented AI-REML as an appealing approach for large-scale VC estimation in the genomic era. [ABSTRACT FROM AUTHOR]

Through his own research contributions on the modelling and genetic analysis of quantitative traits and through his former students and postdocs, Robin Thompson has indirectly left a major legacy in human genetics. In this short note, we highlight examples of the long‐lasting relevance and impact of Robin's work in human genetics. A lone early study of marker‐assisted selection developed many of the tools and approaches later exploited (often after reinvention) by the human genetics community in GWAS studies and for prediction. Furthermore, a particularly clear example of the pervasive impact of Robin's work is that REML has become the default method to estimate variance components and that genetic predictions exploiting linkage disequilibrium in the population are starting to become used in precision medicine applications. [ABSTRACT FROM AUTHOR]