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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.

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]

The full-scale invasion of Ukraine caused an unprecedented number of refugees after 24 February 2022. To estimate the influence of this humanitarian disaster on the COVID-19 pandemic dynamics, the smoothed daily numbers of cases in Ukraine, the UK, Poland, Germany, the Republic of Moldova, and in the whole world were calculated and compared with values predicted by the generalized SIR model. In March 2022, the increase in the smoothed number of new cases in the UK, Germany, and worldwide was visible. A simple formula to estimate the effective reproduction number based on the smoothed accumulated numbers of cases is proposed. The results of calculations agree with the figures presented by John Hopkins University and demonstrate a short-term growth in the reproduction number in the UK, Poland, Germany, Moldova, and worldwide in March 2022. [ABSTRACT FROM AUTHOR]

Current World Health Organization (WHO) reports claim a decline in COVID-19 testing and reporting of new infections. To discuss the consequences of ignoring severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) infection, the endemic characteristics of the disease in 2023 with the ones estimated before using 2022 data sets are compared. The accumulated numbers of cases and deaths reported to the WHO by the 10 most infected countries and global figures were used to calculate the average daily numbers of cases DCC and deaths DDC per capita and case fatality rates (CFRs = DDC/DCC) for two periods in 2023. In some countries, the DDC values can be higher than the upper 2022 limit and exceed the seasonal influenza mortality. The increase in CFR in 2023 shows that SARS-CoV-2 infection is still dangerous. The numbers of COVID-19 cases and deaths per capita in 2022 and 2023 do not demonstrate downward trends with the increase in the percentages of fully vaccinated people and boosters. The reasons may be both rapid mutations of the coronavirus, which reduced the effectiveness of vaccines and led to a large number of re-infections, and inappropriate management. [ABSTRACT FROM AUTHOR]

The descriptive contribution of this paper is a typology of mouthing constructions in 37 signed languages and an analysis of the ideologies and resources that affect their documentation. The languages are divided into two categories labelled deaf (n=26) and rural (n=11), that are defined based on socio-historical/-cultural properties, but which have been problematised for their conflation of multiple linguistic ecologies (Kusters, 2009: 200; Nyst, 2012; Reed, 2019; Safar, 2020). The analysis is used to argue that phenomena like mouthing are marginalised in documentation, as opposed to marginal, and that the deaf-rural divide tracks differences that are more related to issues of documentation than contexts of language use and emergence. The Semiotic Repertoires approach (Kusters et al., 2017) is used to motivate the unit of analysis, the mouthing construction, and Uniformitarianism in Creole linguistics (Mufwene, 2000; DeGraff, 2005) is used as a lens onto signed language typology. [ABSTRACT FROM AUTHOR]

This pilot study explored effects of hearing loss on deaf children's reading comprehension in an online context. Eighteen deaf secondary students, ages 12–14 years (11 with British Sign Language as their dominant language, seven with English as their dominant language) engaged in an online research comprehension task. Six age-matched hearing spoken language bilingual students served as a comparison group. All participants were identified as confident readers by their teachers. Participants were asked to "think aloud" during an online search task to provide insights into their strategies. Additionally, participants completed a battery of assessments related to reading comprehension, vocabulary, non-verbal IQ, and working memory. Overall results showed similar use of strategies across all students. Strategies applied by the most skilled readers involved drawing on prior knowledge sources, e.g. informational websites or search engines, prior knowledge of the topic, and taking the time to read and evaluate website headings before deciding which one to use as source. Participants also made use of working memory skills. Findings highlight the importance of teaching online search and evaluation skills as part of the reading curriculum in schools. [ABSTRACT FROM AUTHOR]

This essay outlines the kinds of evidence available (and not available) for studies of ancient Mediterranean pandemics, the scholarship on the subject so far, and some reflections on the relationship between the two. The focus is on the three largescale epidemic episodes that have attracted the most scholarly attention: the "Plague of Athens" in the fifth century BCE; the "Antonine Plague," which spread across the Roman Empire in the late second century CE; and the "Justinianic Plague," which first engulfed the Mediterranean in the sixth century CE. All present considerable evidential challenges. There are traditional resources and the rich remains of ancient medical writings, but we also now have at our disposal new archaeological and genetic evidence. The research questions on these pandemics must be expanded beyond asking what disease was implicated and what political and economic impact its Mediterranean outbreaks had, sometimes within quite a restricted geographical area. Interdisciplinary research will be required to answer these wider enquiries fully. All the salient disciplines need to be involved, with due attention to their methodologies, if the field is to move forward and make the most of its resources. [ABSTRACT FROM AUTHOR]

Cassava (Manihot esculenta Crantz) is a staple food and generates income for smallholder farmers in southern Ethiopia. The performance of cassava genotypes varies in different growing environments; thus, the evaluation of genotypes tested in various environments plays an essential role in developing strategies to delineate environments, explore unstable genotypes in target environments, and identify stable genotypes for multiple environments. In this regard, there needs to be more information on the identification of mega-environments and stable genotypes with high yields for wide adaptation. Thus, this study aimed to identify mega-environment and high-yielding cassava genotypes for multiple environments using AMMI and GGE biplots. A total of 25 genotypes were evaluated in six environments using a RCBD during the 2020–2021 cropping season. The AMMI analysis of variances revealed that environments, genotypes, and genotype-environment interaction had a significant (P ≤ 0.001) influence on cassava fresh storage root yield (t·ha−1), showing genetic variability among genotypes by changing environments. The genotype-by-environment interaction showed a 61.36% contribution to the total treatment SS variation, while the environment and genotype effects explained 28.16% and 10.48% of the total treatment SS, respectively. IPCA1 and IPCA2 accounted for 33.42% and 23.5% of the GE interactions SS, respectively. The GGE biplot showed that the six environments used in this study were delineated into three mega-environments, namely, the first (Tarcha and Disa), the second (Wara and Areka), and the third (Jimma and Bonbe). Those mega-environments could be helpful for genotype evaluation and effective breeding. The GGE biplot indicated that the vertex genotypes were G16, G17, and G25. They are regarded as specifically adapted genotypes since they are more responsive to environmental change. The GGE biplot also revealed that Tarcha was ideal, having the most discriminating and representative environment, while G10 was the ideal and the overall winning genotype for the current study. Moreover, the genotypes G10 and G14 were identified as being the most stable, with a higher fresh storage root yield than the grand mean. Thus, G10 and G14 were selected as superior genotypes that could be promoted to advanced yield trials to develop stable cultivars with better storage root yield of cassava. [ABSTRACT FROM AUTHOR]

Nuts from hybrid hazels (Corylus americana Walter × Corylus avellana L.) are a burgeoning, novel industry for the Great Lakes region of the United States. As such, scant information exists about the pest complex, although stem-boring beetles in the genus Agrilus Curtis (Coleoptera: Buprestidae) have been noted by growers as potential pests. Here, we report results of a comprehensive survey of 186 plants in a hybrid hazel planting consisting of nine cultivars in southeastern Minnesota affected by stem borers. Sixteen percent of the hybrid hazel plants exhibited between 25 and 100% buprestid associated dieback. Morphological and molecular identification of the adult and larval buprestids colonizing hybrid hazel plants revealed that the hazel stem borer, Agrilus pseudocoryli Fisher, was the only species present. Adult A. pseudocoryli were observed on plants between 8 June and 13 July 2021. Plants with higher damage ratings from the hazel stem borer were more likely to exhibit signs of eastern filbert blight (Anisogramma anomala Peck), a fungal pathogen virulent to C. avellana. Some hazel cultivars were more susceptible to damage from stem borers than others. Taken together, our findings suggest that the hazel stem borer has the potential to impact hybrid hazelnut yield. We speculate that a regional drought may have promoted increased populations of the hazel stem borer, as native Agrilus species are often secondary pests that become problematic when environmental stresses decrease host resistance. More research is needed to elucidate interactions between hazels, buprestid pests, and environmental conditions to predict impacts on crop yields. [ABSTRACT FROM AUTHOR]

Background: Recovery Housing (RH), an important resource for substance use disorder (SUD) recovery, centers on shared lived experience. Program evaluation considers the contribution of environmental factors to outcomes, yet most research on outcomes has focused on patient factors and fidelity to protocols. Investigations of process measures reflecting the dynamic interplay between patient factors and the treatment program are limited. Alliance, one's perceived connection with others, is a process measure associated with mental health outcomes and includes domains "tasks," "goals," and "bonds." We posit that alliance serves as a proxy construct to measure the impact of shared experience in RH. Objectives: Develop and assess the psychometric properties of the Fletcher Recovery Housing Alliance Measure (FRHAM-12) for RH. Methods: A cross-sectional survey with the 12-item FRHAM-12 was administered to 271 individuals (60% men, 39% women, 1% other) within six RH centers in Kentucky. Item-total correlations, internal consistency reliability, exploratory factor analysis (EFA), and confirmatory factor analysis (CFA) were conducted. Results: The FRHAM-12 was found to have a strong internal consistency (0.924 alpha coefficient) and the EFA yielded a single component (56.38% of cumulative scale variance). CFA indicated acceptable levels of absolute and relative fit of a unidimensional scale with values of 0.67 and 0.976 for the standardized root mean square residual and relative fit index. Conclusion: This study aimed to construct and validate an initial measure for RH alliance resulted in the brief, FRHAM-12; a tool with strong internal and factor validity. Future research should examine the measure's predictive and concurrent validity. [ABSTRACT FROM AUTHOR]

Social media platforms continue to be vastly utilized in university contexts as channels for the dissemination of zealous religious ideas. As a group, students are vulnerable to radicalism and are easily affected by extremist perceptions. This study investigated how the LINE Official account could be utilized to spread moderate Islamic perspectives in the Muslim student community in Indonesia’s universities. It also examined how such an account could contribute to the mitigation of radicalism among students. This study contributes significantly to the literature because most extant research on counterradicalism movements remains centered on actions involving numerous stakeholders. It is hence necessary to initiate individual-focused movements that employ technology. The data used for the present study were obtained by analyzing conversations and comments available on the LINE platform. The study’s findings suggest that LINE Official accounts can be employed as tools to oppose radicalism and spread moderate views among university students in Indonesia. [ABSTRACT FROM AUTHOR]

The coronavirus disease 2019 (COVID-19) is a recent outbreak of respiratory infections that have affected millions of humans all around the world. Initially, the major intervention strategies used to combat the infection were the basic public health measure, nevertheless, vaccination is an effective strategy and has been used to control the incidence of many infectious diseases. Currently, few safe and effective vaccines have been approved to control the inadvertent transmission of COVID-19. In this paper, the modeling approach is adopted to investigate the impact of currently available anti-COVID vaccines on the dynamics of COVID-19. A new fractional-order epidemic model by incorporating the vaccination class is presented. The fractional derivative is considered in the well-known Caputo sense. Initially, the proposed vaccine model for the dynamics of COVID-19 is developed via integer-order differential equations and then the Caputo-type derivative is applied to extend the model to a fractional case. By applying the least square method, the model is fitted to the reported cases in Pakistan and some of the parameters involved in the models are estimated from the actual data. The threshold quantity (R 0) is computed by the Next-generation method. A detailed analysis of the fractional model, such as positivity of model solution, equilibrium points, and stabilities on both disease-free and endemic states are discussed comprehensively. An efficient iterative method is utilized for the numerical solution of the proposed model and the model is then simulated in the light of vaccination. The impact of important influential parameters on the pandemic dynamics is shown graphically. Moreover, the impact of different intervention scenarios on the disease incidence is depicted and it is found that the reduction in the effective contact rate (up to 30%) and enhancement in vaccination rate (up to 50%) to the current baseline values significantly reduced the disease new infected cases. [ABSTRACT FROM AUTHOR]

The critical stage for any breeder is the selection of crossing parents to drive improved inbred for subsequent breeding cycles. In our study, we estimate breeding parameters such as mid-parent value (MPV), variances among and within crosses, the heritability of relevant traits and their correlations, the usefulness of crosses, and regression of cross means on MPV. 900 F4:5 Recombinant inbred lines (RILs) derived from 30 crosses were tested together with their parental lines in a modified split-plot p-rep design at two locations. The analysis revealed significant genetic variation among parents, crosses, and RIL for almost all traits. Heritability for parents ranged from 49.50% (malt extract) to 93.60% (plant height) and heritability for crosses ranged from 29.52% (grain protein concentration) to 87.0% (days to maturity), whereas heritability for RIL was the lowest with 27.40% for beta-glucan and the highest to 73.60% for thousand kernel weight, respectively. Significant (P < 0. 01) genotypic correlations with high impact for practical breeding were found between malting traits. Accordingly, the genotypic correlation ranged from −0.73 to 0.78 whereas the phenotypic correlation ranged from −0.60 to 0.65, respectively. Significant (P < 0. 01) regression of cross-mean on MPV where R2 ranges from 0.27 to 0.70 and is higher than 0.5 for most of the traits demonstrates that cross means can accurately be predicted from MPV and selection among crosses at an early stage is highly effective. Based on the usefulness criterion, 16 superior crosses were identified compared to the planet as the actual leading malt variety. Starting from a simple additive genetic model with random mating, we discuss deviations from the initial model and their impact on the actual estimates implying how to design a state-of-the-art cereal breeding program. [ABSTRACT FROM AUTHOR]

Normally, science proceeds following a well-established set of principles. Studies are done with an emphasis on correctness, are submitted to a journal editor who evaluates their relevance, and then undergo anonymous peer review by experts before publication in a journal and acceptance by the scientific community via the open literature. This process is slow, but its accuracy has served all fields of science well. In an emergency situation, different priorities come to the fore. Research and review need to be conducted quickly, and the target audience consists of policymakers. Scientists must jostle for the attention of non-specialists without sacrificing rigour, and must deal not only with peer assessment but also with media scrutiny by journalists who may have agendas other than ensuring scientific correctness. Here, we describe how the Royal Society coordinated efforts of diverse scientists to help model the coronavirus epidemic. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'. [ABSTRACT FROM AUTHOR]

Recovery housing provides substance-free living environments that use peer-support to empower individuals in recovery from substance use disorder. This study estimated the total revenue of the recovery housing industry from recovery houses, nationwide. Using survey data collected in June and July of 2020, we calculate the total revenue of the recovery housing industry and determine the share of revenue that comes from different sources for rural and nonrural houses. We find that individual recovery houses operate with an annual revenue of $250,000 and the whole industry accounts for $4.5 billion annually. COVID-19 has reduced industry revenue by 4%. Rural and nonrural houses differ significantly in their sources of revenue. Our results suggest that COVID-19 reduced the size of the recovery housing industry. Houses in nonrural regions may need more federal support due to the relatively high reliance on resident fees, which may be unreliable during COVID-19. [ABSTRACT FROM AUTHOR]

Background: Through the extensive use of public media, the government of England was heavily involved in encouraging and instructing people on how to manage their life during coronavirus disease 2019 (COVID-19). This model of health emergency governance replicates the practice of 'calculative technologies' and 'bio-politics' embedded in population management. Previous research on COVID-19 governance both in the United Kingdom and beyond provides varied revelations on broader 'technologies of government' and bio-politics by numerous governments. However, rarely have any studies explicitly and distinctively highlighted the unique 'calculative technologies' mobilised by governments within their bio-politically designed "technologies of government" to compel the populations to manage their lives under their COVID-19 guidance. The paper therefor examines how the UK government deployed "calculative technologies," as part of its strategies of health governance and governmentality during the first wave of COVID-19 in England. Methods: This study uses document analysis as its data collection method. Its review includes documents, press releases, social media disclosures and health guidance issued by the UK government from March to December, 2020. The data are analysed employing the Foucault's governmentality and bio-political scholarship. Results: The paper's findings reveal the UK government's use of integrated calculative technologies of self-governance in the form of risk calculations and metrices/statistics (eg, death tolls, infection rates), performance management (eg, two metre social distancing, and hand washing for twenty seconds) and discipline and control (eg, fourteen days selfisolation), in addition to a more conventional top-down, managerial decision-making process adopted in the past. By these newly initiated "calculative technologies," the government has "bio-politically" governed the behaviours and lifestyles of vulnerable community members, health workers and general public at a distance, inculcating selfmanagement and individualisation of responsibility. Conclusion: The newly adopted calculative technologies used by the UK government created a multi-faceted discourse of obligations, entitlements and scale of engagement, and facilitated directions about what people should do to protect themselves and others from the spread of the virus. Overall, the overtly and idiosyncratically used calculative technologies resemble a unique 'art of government' and produce a set of 'bio-political' interventions enforcing the populations to manage their own wellbeing and governing them at a distance during COVID-19. [ABSTRACT FROM AUTHOR]

The widespread, and in many countries unprecedented, use of non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic has highlighted the need for mathematical models which can estimate the impact of these measures while accounting for the highly heterogeneous risk profile of COVID-19. Models accounting either for age structure or the household structure necessary to explicitly model many NPIs are commonly used in infectious disease modelling, but models incorporating both levels of structure present substantial computational and mathematical challenges due to their high dimensionality. Here we present a modelling framework for the spread of an epidemic that includes explicit representation of age structure and household structure. Our model is formulated in terms of tractable systems of ordinary differential equations for which we provide an open-source Python implementation. Such tractability leads to significant benefits for model calibration, exhaustive evaluation of possible parameter values, and interpretability of results. We demonstrate the flexibility of our model through four policy case studies, where we quantify the likely benefits of the following measures which were either considered or implemented in the UK during the current COVID-19 pandemic: control of within- and between-household mixing through NPIs; formation of support bubbles during lockdown periods; out-of-household isolation (OOHI); and temporary relaxation of NPIs during holiday periods. Our ordinary differential equation formulation and associated analysis demonstrate that multiple dimensions of risk stratification and social structure can be incorporated into infectious disease models without sacrificing mathematical tractability. This model and its software implementation expand the range of tools available to infectious disease policy analysts. Author summary: Non-pharmaceutical interventions have seen widespread use during the COVID-19 pandemic. Some of the most prominent such interventions act at the household level, with isolation measures confining individuals to their own home and measures such as work and school closure seeking to prevent transmission between members of different households. In this study we develop a mathematical model of COVID-19 transmission in a population of households which accounts for age-specific variation in behaviour. We demonstrate how to perform simulations with our model as well as how to calibrate it to empirical estimates of epidemic growth rate. We apply our model to four specific policy questions, including the impact of within-household controls on transmission, the effect of support bubble exemptions to between-household mixing controls, the effect of temporary relaxations to non-pharmaceutical interventions, and the possible impact of out-of-household isolation measures. We provide an open source software implementation of our model so that it can be used by researchers and policy experts interested in planning interventions during subsequent stages of the COVID-19 pandemic and other future pandemics. [ABSTRACT FROM AUTHOR]

In the recent COVID-19 pandemic, a wide range of epidemiological modelling approaches were used to predict the effective reproduction number, R(t), and other COVID-19-related measures such as the daily rate of exponential growth, r(t). These candidate models use different modelling approaches or differing assumptions about spatial or age-mixing, and some capture genuine uncertainty in scientific understanding of disease dynamics. Combining estimates using appropriate statistical methodology from multiple candidate models is important to better understand the variation of these outcome measures to help inform decision-making. In this paper, we combine estimates for specific UK nations/regions using random-effects meta-analyses techniques, utilising the restricted maximum-likelihood (REML) method to estimate the heterogeneity variance parameter, and two approaches to calculate the confidence interval for the combined estimate: the standard Wald-type and the Knapp and Hartung (KNHA) method. As estimates in this setting are derived using model predictions, each with varying degrees of uncertainty, equal-weighting is favoured over the standard inverse-variance weighting to avoid potential up-weighting of models providing estimates with lower levels of uncertainty that are not fully accounting for inherent uncertainties. Both equally-weighted models using REML alone and REML+KNHA approaches were found to provide similar variation for R(t) and r(t), with both approaches providing wider, and therefore more conservative, confidence intervals around the combined estimate compared to the standard inverse-variance weighting approach. Utilising these meta-analysis techniques has allowed for statistically robust combined estimates to be calculated for key COVID-19 outcome measures. This in turn allows timely and informed decision-making based on all available information. [ABSTRACT FROM AUTHOR]

Since the beginning of the COVID-19 pandemic, the reproduction number R has become a popular epidemiological metric used to communicate the state of the epidemic. At its most basic, R is defined as the average number of secondary infections caused by one primary infected individual. R seems convenient, because the epidemic is expanding if R > 1 and contracting if R < 1. The magnitude of R indicates by how much transmission needs to be reduced to control the epidemic. Using R in a naïve way can cause new problems. The reasons for this are threefold: (1) There is not just one definition of R but many, and the precise definition of R affects both its estimated value and how it should be interpreted. (2) Even with a particular clearly defined R , there may be different statistical methods used to estimate its value, and the choice of method will affect the estimate. (3) The availability and type of data used to estimate R vary, and it is not always clear what data should be included in the estimation. In this review, we discuss when R is useful, when it may be of use but needs to be interpreted with care, and when it may be an inappropriate indicator of the progress of the epidemic. We also argue that careful definition of R , and the data and methods used to estimate it, can make R a more useful metric for future management of the epidemic. [ABSTRACT FROM AUTHOR]

Iconic signs are overrepresented in the vocabularies of young deaf children, but it is unclear why. It is possible that iconic signs are easier for children to learn, but it is also possible that adults use iconic signs in child-directed signing in ways that make them more learnable, either by using them more often than less iconic signs or by lengthening them. We analyzed videos of naturalistic play sessions between parents and deaf children (n = 24 dyads) aged 9-60 months. To determine whether iconic signs are overrepresented during child-directed signing, we compared the iconicity of actual parent productions to the iconicity of simulated vocabularies designed to estimate chance levels of iconicity. For almost all dyads, parent sign types and tokens were not more iconic than the simulated vocabularies, suggesting that parents do not select more iconic signs during child-directed signing. To determine whether iconic signs are more likely to be lengthened, we ran a linear regression predicting sign duration, and found an interaction between age and iconicity: while parents of younger children produced non-iconic and iconic signs with similar durations, parents of older children produced non-iconic signs with shorter durations than iconic signs. Thus, parents sign more quickly with older children than younger children, and iconic signs appear to resist that reduction in sign length. It is possible that iconic signs are perceptually available longer, and their availability is a candidate hypothesis as to why iconic signs are overrepresented in children's vocabularies. [ABSTRACT FROM AUTHOR]

There is a lack of knowledge of the arthropod communities in both wild hazelnut and cultivated hybrid hazelnut ecosystems in the Midwestern United States. Our goal was to characterize the composition of these arthropod communities in hazelnut plantings. We surveyed six experimental plantings of hazelnuts in Wisconsin and two in Minnesota during growing seasons, from May 2017 to August 2021. We used four methods to survey the arthropod community of these plantings: ad libitum survey of specimen observations and collection, dissection of hazelnut buds and nuts, cone traps placed on the ground, and beat sheet sampling of plants. We registered 116 different morphospecies of five classes and 83 families. Arthropods with known feeding habits were grouped into four guilds: 59 herbivores, 36 carnivores (predators, parasites, and parasitoids), 4 decomposers (detritivores and scavengers), and 4 omnivores. While we registered 12 herbivorous species that could potentially cause damage to hazelnuts, we directly observed nine of them feeding upon or damaging plant parts: Phytoptus avellanae s.l. Nalepa (Eriophyoidea: Phytoptidae), Curculio obtusus Blanchard and Strophosoma melanogrammum Förster (Coleoptera: Curculionidae), Popillia japonica Newman (Coleoptera: Scarabeidae), Parthenolecanium sp. (Hemiptera: Coccidae), Euschistus servus euschistoides Say (Hemiptera: Pentatomidae), aphids (Hemiptera: Aphididae), Acleris sp. and Choristoneura rosaceana Harris (Lepidoptera: Tortricidae). We also registered eight species of parasites or parasitoids that could offer biocontrol services in the cropping system. These findings provide a foundation for future targeted studies on potential pests and beneficial arthropods, as well as ecological interactions within the hybrid hazelnut ecosystem in the Midwest. [ABSTRACT FROM AUTHOR]

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 themare available. ThisPerspective 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. [ABSTRACT FROM AUTHOR]

Introduction: The daily escalation in incidence and mortality caused by Coronavirus disease (COVID-19) has mandated forced curfew in our country (same as many other countries) to limit the spread of infection. This is predicted to have a more negative impact on cochlear implant (CI) patients since this group of patients needs a unique type of psychological, medical, and technical care in addition to a daily rehabilitation program. Methods: A cross-sectional study based on Arabic questionnaire that looked into the collateral consequences of COVID-19 on the pediatric CI patients. The questionnaire was designed to highlight different problems such as exposure to head trauma or ear infection, difficulties in device maintenance and getting spare parts, impacts of the delay of programing or switch on appointments, and the impacts of missing rehabilitation sessions. Different ways of management of these problems are presented and discussed. Results: A total of 174 parents responded to the questionnaire. The main problem met by the patients was missing their device programing and rehabilitation sessions. Many children had device maintenance and spare parts problems. Virtual clinics were helpful in solving different problems. Additionally, children who needed device programing were scheduled for remote programing sessions. Conclusion: Although the inevitable consequences of the COVID-19 pandemic are catastrophic, they are forcing the medical field to explore new opportunities by sitting up an infrastructure for future usage of telemedicine. Telemedicine is cost-effective and more convenient and enables health-care providers to be immune to future circumstances. [ABSTRACT FROM AUTHOR]