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Background DNA methylation changes at a discrete set of sites in the human genome are predictive of chronological and biological age. However, it is not known whether these changes are causative or a consequence of an underlying ageing process. It has also not been shown whether this epigenetic clock is unique to humans or conserved in the more experimentally tractable mouse. Results We have generated a comprehensive set of genome-scale base-resolution methylation maps from multiple mouse tissues spanning a wide range of ages. Many CpG sites show significant tissue-independent correlations with age which allowed us to develop a multi-tissue predictor of age in the mouse. Our model, which estimates age based on DNA methylation at 329 unique CpG sites, has a median absolute error of 3.33 weeks and has similar properties to the recently described human epigenetic clock. Using publicly available datasets, we find that the mouse clock is accurate enough to measure effects on biological age, including in the context of interventions. While females and males show no significant differences in predicted DNA methylation age, ovariectomy results in significant age acceleration in females. Furthermore, we identify significant differences in age-acceleration dependent on the lipid content of the diet. Conclusions Here we identify and characterise an epigenetic predictor of age in mice, the mouse epigenetic clock. This clock will be instrumental for understanding the biology of ageing and will allow modulation of its ticking rate and resetting the clock in vivo to study the impact on biological age.

Recent work at Vinča-Belo Brdo has combined a total of more than 200 radiocarbon dates with an array of other information to construct much more precise narratives for the structural history of the site and the cultural materials recovered from it. In this paper, we present the results of a recent attempt to construct formal models for the chronology of the wider Vinča potscape, so that we can place our now detailed understanding of changes at Belo Brdo within their contemporary contexts. We present our methodology for assessing the potential of the existing corpus of more than 600 radiocarbon dates for refining the chronology of the five phases of Vinča ceramics proposed by Milojčić across their spatial ranges, including a total of 490 of them in a series of Bayesian chronological models. Then we outline our main results for the development of Vinča pottery. Finally, we discuss some of the major implications for our understanding of the source, character and tempo of material change.

Studies with multiple radiocarbon dates often contain useful information on the relative locations of the dated levels. Such information can be used to obtain robust, integrated site chronologies, with at times more precise ages than those of the individual dates, where outliers can be identified and downweighted, and where the ages of any undated levels can also be estimated. Examples include trees with radiocarbon dates separated by exactly known amounts of yearly tree-rings, or sedimentary sites where ages further down the stratigraphy can be assumed to be older than ages further up. Here we present coffee , an R package for Bayesian models that apply c hronological o rdering f or f ossils and e nvironmental e vents. Coffee runs natively within the popular and versatile R environment, with no need for importing or exporting data or code from other programs, and works with plain-text input files that are relatively easy to read and write. It thus provides a new, transparent and adaptable educational and research platform designed to make chronology building more accessible.

The progressive aging of society, caused by profound demographic changes, brings with it the necessity of confronting the subject of biases against the elderly. Ageism, in fact, can influence society’s attitudes regarding this population, in addition to impacting the self-perception of elderly people. This, in turn, has consequences for positive outcomes during the aging process. The current research aims to investigate the simultaneous relationships between knowledge, age, anxiety about aging, and stereotypes toward the elderly, as well as their predictive roles with respect to ageism. A self-report questionnaire was administered to 886 participants, with an average age of 35.8 years (Standard Deviation—SD = 14.2), predominantly female (64.8%). Descriptive and correlational analyses were performed, along with structural equation modeling. Based on the analyses conducted, anxiety about aging and knowledge are antecedents for stereotypes, which in turn, together with the other variables, influence ageism. Increased education about the aging process could help reduce anxiety and stereotypes against the aging among those who are most responsible for prejudice against the elderly. Knowledge of the antecedents of prejudice toward the elderly is fundamental to promoting positive attitudes toward them.

Dental radiographies have been used for many decades for estimating the chronological age, with a view to forensic identification, migration flow control, or assessment of dental development, among others. This study aims to analyse the current application of chronological age estimation methods from dental X-ray images in the last 6 years, involving a search for works in the Scopus and PubMed databases. Exclusion criteria were applied to discard off-topic studies and experiments which are not compliant with a minimum quality standard. The studies were grouped according to the applied methodology, the estimation target, and the age cohort used to evaluate the estimation performance. A set of performance metrics was used to ensure good comparability between the different proposed methodologies. A total of 613 unique studies were retrieved, of which 286 were selected according to the inclusion criteria. Notable tendencies to overestimation and underestimation were observed in some manual approaches for numeric age estimation, being especially notable in the case of Demirjian (overestimation) and Cameriere (underestimation). On the other hand, the automatic approaches based on deep learning techniques are scarcer, with only 17 studies published in this regard, but they showed a more balanced behaviour, with no tendency to overestimation or underestimation. From the analysis of the results, it can be concluded that traditional methods have been evaluated in a wide variety of population samples, ensuring good applicability in different ethnicities. On the other hand, fully automated methods were a turning point in terms of performance, cost, and adaptability to new populations.

Background Women who experience menopause are at higher cardiometabolic risk and often display adverse changes in metabolic biomarkers compared with pre-menopausal women. It remains elusive whether the changes in cardiometabolic biomarkers during the menopausal transition are due to ovarian aging or chronological aging. Well-conducted longitudinal studies are required to determine this. The aim of this study was to explore the cross-sectional and longitudinal associations of reproductive status, defined according to the 2012 Stages of Reproductive Aging Workshop criteria, with 74 metabolic biomarkers, and establish whether any associations are independent of age-related changes. Methods We determined cross-sectional associations of reproductive status with metabolic profiling in 3,312 UK midlife women. In a subgroup of 1,492 women who had repeat assessments after 2.5 years, we assessed how the change in reproductive status was associated with the changes in metabolic biomarkers. Metabolic profiles were measured by high-throughput quantitative nuclear magnetic resonance metabolomics. In longitudinal analyses, we compared the change in metabolic biomarkers for each reproductive-status category change to that of the reference of being pre-menopausal at both time points. As all women aged by a similar amount during follow-up, these analyses contribute to distinguishing age-related changes from those related to change in reproductive status. Results Consistent cross-sectional and longitudinal associations of menopause with a wide range of metabolic biomarkers were observed, suggesting the transition to menopause induces multiple metabolic changes independent of chronological aging. The metabolic changes included increased concentrations of very small very low-density lipoproteins, intermediate-density lipoproteins, low-density lipoproteins (LDLs), remnant, and LDL cholesterol, and reduced LDL particle size, all toward an atherogenic lipoprotein profile. Increased inflammation was suggested via an inflammatory biomarker, glycoprotein acetyls, but not via C-reactive protein. Also, levels of glutamine and albumin increased during the transition. Most of these metabolic changes seen at the time of becoming post-menopausal remained or became slightly stronger during the post-menopausal years. Conclusions The transition to post-menopause has effects on multiple circulating metabolic biomarkers, over and above the underlying age trajectory. The adverse changes in multiple apolipoprotein-B-containing lipoprotein subclasses and increased inflammation may underlie women’s increased cardiometabolic risk in their post-menopausal years.

Purpose The purpose of this paper is to provide a historical account of organizational citizenship behavior (OCB) based on the existing literature. Design/methodology/approach The paper performs keywords search of published articles from 1930 to 2017 in widely used research databases. Findings The historical review shows that the OCB, as a field of study, was slow to develop. Although it has been introduced in the late 1970s and officially defined in the 1980s, its origins can be traced back to the 1930s. Despite this, OCB is generally regarded as a relatively new construct and has become one of the biggest subjects studied in the literature. OCB has reached far and wide into the business and management domains, supporting the fact that the well-being employees and their behaviors can greatly affect organizations’ effectiveness and performance. Having been the topic of a significant number of studies, there have been inconsistent research findings regarding the concepts. Furthermore, some concepts have been noted to overlap, with several scholars using different terms for essentially similar concepts. Originality/value The advent of technology and globalization has greatly affected organizations today which resulted in increased competition in the global business. Firms have started to look into the behavior exhibited by employees as a means of achieving competitive advantage, such as OCB. Voluminous works have been conducted regarding the study of OCB; however, none have been recorded to make an in-depth exploration of when and how it first surfaced. Since its official introduction, explorations regarding OCB have dramatically increased, most especially in the twenty-first century. Unfortunately, this has resulted in an increasing difficulty to keep up with the theoretical and empirical developments in the literature. As interest in OCB continues to grow, coherent integration of the concept becomes progressively more complex and necessary. This paper looks into the chronological evolution of the OCB, giving precise details of its development from the time it was first conceptualized up until the present wherein OCB has been used to indicate organizational effectiveness and performance.

Usage of the phrase “biological age” has picked up considerably since the advent of aging clocks and it has become commonplace to describe an aging clock's output as biological age. In contrast to this labeling, biological age is also often depicted as a more concept that helps explain how individuals are aging internally, externally, and functionally. Given that the bulk of molecular aging is tissue‐specific and aging itself is a remarkably complex, multifarious process, it is unsurprising that most surveyed scientists agree that aging cannot be quantified via a single metric. We share this sentiment and argue that, just like it would not be reasonable to assume that an individual with an ideal grip strength, VO2 max, or any other aging biomarker is biologically young, we should be careful not to conflate an aging clock with whole‐body biological aging. To address this, we recommend that researchers describe the output of an aging clock based on the type of input data used or the name of the clock itself. Epigenetic aging clocks produce epigenetic age, transcriptomic aging clocks produce transcriptomic age, and so forth. If a clock has a unique name, such as our recently developed epigenetic aging clock CheekAge, the name of the clock can double as the output. As a compromise solution, aging biomarkers can be described as indicators of biological age. We feel that these recommendations will help scientists and the public differentiate between aging biomarkers and the much more elusive concept of biological age. Biological age is an concept that helps explain how an individual is functionally aging. Given that aging is a remarkably complex and multifarious process, it is infeasible to quantify biological age with a single aging biomarker. Rather than conflating a given metric with biological age, it makes more sense to conceptualize aging biomarkers, such as grip strength, VO2 max, or next‐generation aging clocks, as providing unique insights into different aspects of aging.

Bayesian chronological modelling is fast becoming the method of choice for the interpretation of radiocarbon dates in archaeological and palaeoenvironmental studies around the world. Although software enabling the routine application of the method has been available for more than twenty years, more than half of published models have appeared in the past five years. Unfortunately, the pace of development in statistical methodology has not been matched by the increased care in sample selection and reporting that is required for robust modelling. Barely half the applications considered in this article provide the information necessary to assess the models presented critically. This article discusses what information is required to allow the quality of Bayesian chronological models to be assessed, and provides check-lists for authors, editors and referees, in the hope of improving current practice.

Carbon constraints, as well as the growing hazard of greenhouse gas emissions, have accelerated research into all possible renewable energy and fuel sources. Microbial electrolysis cells (MECs), a novel technology able to convert soluble organic matter into energy such as hydrogen gas, represent the most recent breakthrough. While research into energy recovery from wastewater using microbial electrolysis cells is fascinating and a carbon-neutral technology that is still mostly limited to lab-scale applications, much more work on improving the function of microbial electrolysis cells would be required to expand their use in many of these applications. The present limiting issues for effective scaling up of the manufacturing process include the high manufacturing costs of microbial electrolysis cells, their high internal resistance and methanogenesis, and membrane/cathode biofouling. This paper examines the evolution of microbial electrolysis cell technology in terms of hydrogen yield, operational aspects that impact total hydrogen output in optimization studies, and important information on the efficiency of the processes. Moreover, life-cycle assessment of MEC technology in comparison to other technologies has been discussed. According to the results, MEC is at technology readiness level (TRL) 5, which means that it is ready for industrial development, and, according to the techno-economics, it may be commercialized soon due to its carbon-neutral qualities.