The discrepancy between social isolation and loneliness as a clinically meaningful metric: findings from the Irish and English longitudinal studies of ageing (TILDA and ELSA)

OBJECTIVE: Scant evidence is available on the discordance between loneliness and social isolation among older adults. We aimed to investigate this discordance and any health implications that it may have. METHOD: Using nationally representative datasets from ageing cohorts in Ireland (TILDA) and England (ELSA), we created a metric of discordance between loneliness and social isolation, to which we refer as Social Asymmetry. This metric was the categorised difference between standardised scores on a scale of loneliness and a scale of social isolation, giving categories of: Concordantly Lonely and Isolated, Discordant: Robust to Loneliness, or Discordant: Susceptible to Loneliness. We used regression and multilevel modelling to identify potential relationships between Social Asymmetry and cognitive outcomes. RESULTS: Social Asymmetry predicted cognitive outcomes cross-sectionally and at a two-year follow-up, such that Discordant: Robust to Loneliness individuals were superior performers, but we failed to find evidence for Social Asymmetry as a predictor of cognitive trajectory over time. CONCLUSIONS: We present a new metric and preliminary evidence of a relationship with clinical outcomes. Further research validating this metric in different populations, and evaluating its relationship with other outcomes, is warranted. Copyright © 2016 John Wiley & Sons, Ltd

Across-country genetic evaluation of meat sheep from Ireland and the United Kingdom

Genetic evaluations in sheep have proven to be an effective way of increasing farm profitability. Much research has previously been conducted on producing within‐country genetic evaluations; however, to date, no across‐country sheep genetic evaluations have been produced between Ireland and the UK. The objective of the present study was to examine the feasibility of an across‐country genetic evaluation of live body weight and carcass composition traits for Texel sheep raised in Ireland and the UK. The benefit of genetic selection based on across‐country genetic evaluations, in comparison with within‐country genetic evaluations, was also quantified. Animal traits included early‐life and postweaning live body weights, and muscle and fat depth ultrasound measurements. Irish and UK data were combined, common animals with progeny with records in both countries were identified and a series of bivariate analyses were performed separately for each trait to produce across‐country genetic evaluations. Fixed effects included contemporary group, age at first lambing of the dam, parity of the dam (Ireland), dam age at lamb's birth (UK), a gender by age of the lamb interaction, a birth type by rearing type of the lamb interaction and country of birth of the lamb. Random effects included the animal additive genetic, dam maternal, litter common environment and residual effect. The model for postweaning weight, muscle depth and fat depth included only the animal additive genetic and litter common environmental random effects. Genetic correlations between the two countries ranged from 0.82 to 0.88 for the various traits. Across‐country breeding values were estimated for all animals and response to selection was predicted using the top 10 and top 20 sires in both within‐ and across‐country analyses for the two countries. Overall, results showed that rates of genetic gain could potentially increase from between 2.59% and 19.63% from selection based on across‐country genetic evaluations compared to within‐country evaluations alone. Across‐country evaluations are feasible and would be of significant benefit to both the Irish and UK sheep industries. In order to realize these potential gains though, there would need to be a switch in emphasis by sheep breeders towards using objective traits as their primary selection criteria

Genetic analyses of live weight and carcass composition traits in purebred Texel, Suffolk and Charollais lambs

peer-reviewedLamb live weight is one of the key drivers of profitability on sheep farms. Previous studies in Ireland have estimated genetic parameters for live weight and carcass composition traits using a multi-breed population rather than on an individual breed basis. The objective of the present study was to undertake genetic analyses of three lamb live weight and two carcass composition traits pertaining to purebred Texel, Suffolk and Charollais lambs born in the Republic of Ireland between 2010 and 2017, inclusive. Traits (with lamb age range in parenthesis) considered in the analyses were: pre-weaning weight (20 to 65 days), weaning weight (66 to 120 days), post-weaning weight (121 to 180 days), muscle depth (121 to 180 days) and fat depth (121 to 180 days). After data edits, 137 402 records from 50 372 lambs across 416 flocks were analysed. Variance components were derived using animal linear mixed models separately for each breed. Fixed effects included for all traits were contemporary group, age at first lambing of the dam, parity of the dam, a gender by age of the lamb interaction and a birth type by rearing type of the lamb interaction. Random effects investigated in the pre-weaning and weaning weight analyses included animal direct additive genetic, dam maternal genetic, litter common environment, dam permanent environment and residual variances. The model of analysis for post-weaning, muscle and fat depth included an animal direct additive genetic and litter common environment effect only. Significant direct additive genetic variation existed in all cases. Direct heritability for pre-weaning weight ranged from 0.14 to 0.30 across the three breeds. Weaning weight had a direct heritability ranging from 0.17 to 0.27 and post-weaning weight had a direct heritability ranging from 0.15 to 0.27. Muscle and fat depth heritability estimates ranged from 0.21 to 0.31 and 0.15 to 0.20, respectively. Positive direct correlations were evident for all traits. Results revealed ample genetic variation among animals for the studied traits and significant differences between breeds to suggest that genetic evaluations could be conducted on a per-breed basis

Loneliness and social engagement in older adults: A bivariate dual change score analysis

Few longitudinal studies have explored the impact of loneliness on social engagement. We investigated whether loneliness would result in decreased social engagement over time among older adults and also whether the converse, that low levels of social engagement would predict increases in loneliness, held. Additionally, we explored potential mechanisms (specifically, memory and depressive symptomatology as mediators) in the bidirectional relationship(s) between loneliness and social engagement. Data from 4,714 adults over 50 years of age, participating in Waves 3, 4, and 5 of the English Longitudinal Study of Ageing (between 2006 and 2011), were analyzed using bivariate dual change scores within structural equation models. Higher levels of loneliness were inversely associated with social engagement over time, and high levels of social engagement were inversely associated with loneliness over time. To address the 2nd aim, we used structural equation modeling to evaluate potential mediators of the bidirectional relationships between loneliness and changes in social engagement. Depressive symptomatology, semantic memory, and episodic memory were found to partially mediate the relationship between loneliness measured at baseline and social engagement 4 years later. In addition, these variables also partially mediated the relationship between social engagement at baseline and loneliness 4 years later. A comparison of the 2 models revealed that the model proposing a pathway from loneliness to social engagement (as mediated by depressive symptoms and memory) provided a better fit to the data. Implications for theories of loneliness are discussed. (PsycINFO Database Recor

Harvest: an open-source tool for the validation and improvement of peptide identification metrics and fragmentation exploration

<p>Abstract</p> <p>Background</p> <p>Protein identification using mass spectrometry is an important tool in many areas of the life sciences, and in proteomics research in particular. Increasing the number of proteins correctly identified is dependent on the ability to include new knowledge about the mass spectrometry fragmentation process, into computational algorithms designed to separate true matches of peptides to unidentified mass spectra from spurious matches. This discrimination is achieved by computing a function of the various features of the potential match between the observed and theoretical spectra to give a numerical approximation of their similarity. It is these underlying "metrics" that determine the ability of a protein identification package to maximise correct identifications while limiting false discovery rates. There is currently no software available specifically for the simple implementation and analysis of arbitrary novel metrics for peptide matching and for the exploration of fragmentation patterns for a given dataset.</p> <p>Results</p> <p>We present Harvest: an open source software tool for analysing fragmentation patterns and assessing the power of a new piece of information about the MS/MS fragmentation process to more clearly differentiate between correct and random peptide assignments. We demonstrate this functionality using data metrics derived from the properties of individual datasets in a peptide identification context. Using Harvest, we demonstrate how the development of such metrics may improve correct peptide assignment confidence in the context of a high-throughput proteomics experiment and characterise properties of peptide fragmentation.</p> <p>Conclusions</p> <p>Harvest provides a simple framework in C++ for analysing and prototyping metrics for peptide matching, the core of the protein identification problem. It is not a protein identification package and answers a different research question to packages such as Sequest, Mascot, X!Tandem, and other protein identification packages. It does not aim to maximise the number of assigned peptides from a set of unknown spectra, but instead provides a method by which researchers can explore fragmentation properties and assess the power of novel metrics for peptide matching in the context of a given experiment. Metrics developed using Harvest may then become candidates for later integration into protein identification packages.</p

Tutorial : applying machine learning in behavioral research

Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets

ZyFISH: A Simple, Rapid and Reliable Zygosity Assay for Transgenic Mice

Microinjection of DNA constructs into fertilized mouse oocytes typically results in random transgene integration at a single genomic locus. The resulting transgenic founders can be used to establish hemizygous transgenic mouse lines. However, practical and experimental reasons often require that such lines be bred to homozygosity. Transgene zygosity can be determined by progeny testing assays which are expensive and time-consuming, by quantitative Southern blotting which is labor-intensive, or by quantitative PCR (qPCR) which requires transgene-specific design. Here, we describe a zygosity assessment procedure based on fluorescent in situ hybridization (zyFISH). The zyFISH protocol entails the detection of transgenic loci by FISH and the concomitant assignment of homozygosity using a concise and unbiased scoring system. The method requires small volumes of blood, is scalable to at least 40 determinations per assay, and produces results entirely consistent with the progeny testing assay. This combination of reliability, simplicity and cost-effectiveness makes zyFISH a method of choice for transgenic mouse zygosity determinations

NIK Stabilization in Osteoclasts Results in Osteoporosis and Enhanced Inflammatory Osteolysis

Maintenance of healthy bone requires the balanced activities of osteoclasts (OCs), which resorb bone, and osteoblasts, which build bone. Disproportionate action of OCs is responsible for the bone loss associated with postmenopausal osteoporosis and rheumatoid arthritis. NF-κB inducing kinase (NIK) controls activation of the alternative NF-κB pathway, a critical pathway for OC differentiation. Under basal conditions, TRAF3-mediated NIK degradation prevents downstream signaling, and disruption of the NIK:TRAF3 interaction stabilizes NIK leading to constitutive activation of the alternative NF-κB pathway.Using transgenic mice with OC-lineage expression of NIK lacking its TRAF3 binding domain (NT3), we now find that alternative NF-κB activation enhances not only OC differentiation but also OC function. Activating NT3 with either lysozyme M Cre or cathepsinK Cre causes high turnover osteoporosis with increased activity of OCs and osteoblasts. In vitro, NT3-expressing precursors form OCs more quickly and at lower doses of RANKL. When cultured on bone, they exhibit larger actin rings and increased resorptive activity. OC-specific NT3 transgenic mice also have an exaggerated osteolytic response to the serum transfer model of arthritis.Constitutive activation of NIK drives enhanced osteoclastogenesis and bone resorption, both in basal conditions and in response to inflammatory stimuli