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The work of Ed Zigler spans decades of research all singularly dedicated to using science to improve the lives of children facing different challenges. The focus of this article is on one of Zigler's numerous lines of work: advocating for the practice of mental age (MA) matching in empirical research, wherein groups of individuals are matched on the basis of developmental level, rather than chronological age. While MA matching practices represented a paradigm shift that provided the seeds from which the developmental approach to developmental disability sprouted, it is not without its own limits. Here, we examine and test the underlying assumption of linearity inherent in MA matching using three commonly used IQ measures. Results provide practical constraints of using MA matching, a solution which we hope refines future clinical and empirical practices, furthering Zigler's legacy of continued commitment to compassionate, meaningful, and rigorous science in the service of children.

The persistent organ shortage has led to the consideration of “marginal” donors, including those of advanced age, but the outcomes of using living kidney donors aged 70 years or older remain debated. This multicenter retrospective cohort study aimed to evaluate the propriety of kidney donation from older adult donors by analyzing the association between transplant outcomes and recipient age. Using data from 633 adult living-donor kidney transplants, we compared an elderly donor group (age ≥ 70 years, n  = 75) with a non-elderly donor group (age < 70 years, n  = 558). After 1:1 propensity score matching, the elderly donor group showed significantly lower death-censored graft survival. However, this disadvantage disappeared entirely in recipients aged 50 years or older, who exhibited comparable death-censored graft survival to those with non-elderly donors ( p  = 0.743). In contrast, recipients younger than 50 years who received grafts from elderly donors had markedly inferior death-censored graft survival ( p  = 0.008). In conclusion, using living kidney donors aged ≥ 70 years is a viable strategy to expand the donor pool, but its success is influenced by recipient age. These results support an age-sensitive approach in counseling and decision-making for kidney transplantation.

Technologies for multi-atlas brain segmentation of T1-weighted MRI images have rapidly progressed in recent years, with highly promising results. This approach, however, relies on a large number of atlases with accurate and consistent structural identifications. Here, we introduce our atlas inventories (n=90), which cover ages 4–82years with unique hierarchical structural definitions (286 structures at the finest level). This multi-atlas library resource provides the flexibility to choose appropriate atlases for various studies with different age ranges and structure-definition criteria. In this paper, we describe the details of the atlas resources and demonstrate the improved accuracy achievable with a dynamic age-matching approach, in which atlases that most closely match the subject's age are dynamically selected. The advanced atlas creation strategy, together with atlas pre-selection principles, is expected to support the further development of multi-atlas image segmentation. •We introduce our T1 multi-atlas inventories (n=90) covering ages 4–82years.•The atlases are defined with hierarchical ontology and comprehensive online repository.•This rich resource provides flexibility to pre-select atlases for various studies.•Atlas pre-selection using dynamic age-matching improves segmentation accuracy.

Background: Both the genetic and pathological studies link Alzheimer’s disease (AD) to the triggering receptor expressed on myeloid cells 2 (TREM2). A large number of studies have explored the value of cerebrospinal fluid (CSF) soluble TREM2 (sTREM2) levels as a biomarker for the diagnosis and prediction of AD; however, the findings are inconsistent. We aimed to review the studies that investigated the association of CSF sTREM2 levels and AD risk, and to provide the recommendations for future research. Methods and Results: A systematic literature search was performed using the MEDLINE, EMBASE, and Web of Science (all databases) databases. The meta-analysis for the association between the CSF sTREM2 levels and AD risk included 15 studies (17 comparisons) with a total of 1,153 cases and 1,626 controls. The total results showed that the higher CSF sTREM2 levels and AD risk were associated [standardized mean difference (SMD) = 0.428, 95% CI (0.213, 0.643), I 2 = 81.1%]. However, the analysis of the subgroup of “age difference ≤ 2 years” indicated that sTREM2 was not associated with AD [SMD = 0.090, 95% CI (−0.092, 0.272), I 2 = 27.4%] and had a significantly lower heterogeneity. Combining the results of the “age difference of 5–10 years” [SMD = 0.497, 95% CI (0.139, 0.855), I 2 = 82.5%] and “age difference > 10 years” [SMD = 0.747, 95% CI (0.472, 1.023), I 2 = 50.0%] subgroups showed that the difference in CSF sTREM2 between the AD and control groups was positively correlated with the age difference. A meta-regression analysis showed that the age difference can explain 33.4% of the between-study variance. By conducting further subgroup analyses of the five age-matched studies (495 cases and 364 controls) according to the measurement method, and whether inclusion criteria containing the requirement for pathological evidence of AD, no changes were observed in the corresponding pooled SMD or in the significance of the results. The meta-analysis result of “age difference ≤ 2 years” group was robust in the sensitivity analysis. Conclusion: The available high-quality evidence does not yet support an association between the CSF sTREM2 levels and AD risk. Age matching between the patients with AD and cognitively unimpaired controls was a major influencing factor in the results.

The paper proposes a simple approach for matching the ages of humans and mammals (some laboratory animals are considered to be mammals). A mathematical and simulation model has been built based on an analysis of the time of the onset of individual ontogenic events (such as the emergence of the first molars, first ovulation, almost complete cessation of growth, and menopause for female individuals). The choice of the these events is due to the fact that the time of the onset of the same events can be individually fixed in the human. Age-matching is important in giving each person the opportunity to make an individual choice in the regulation of a drug application that corresponds to his or her biological age.

Recent data show that, despite a long period during which few elderly patients in end-stage renal failure received grafts, there are neither medical nor ethical grounds for avoiding kidney transplantation, at least in those aged under 70 or even 75 years of age. Units in which transplantation in older recipients is routine show a good survival of recipients, and comparable survival of grafts to those placed in younger recipients. This equality of graft survival with age arises because, although death with a functioning graft is more common in the elderly (principally from cardiovascular disease and infections, with malignant diseases becoming more important with time), graft losses from rejection are lower, and so overall outcomes are similar. Long-term patient survival is better, quality of life is improved and treatment is cheaper than in comparable elderly patients maintained on hemodialysis or chronic ambulatory peritoneal dialysis. In terms of allocation to older recipients, this success presents major practical and ethical difficulties given the shortage of cadaver organs. Data do not support the idea of 'age-matching' older or marginal kidneys to older recipients: like their younger counterparts, older recipients do better with organs from younger donors. Living donors can be used successfully even in those over 70, and elderly living donors have a place in the treatment of the elderly. The optimum immunosuppressive regimes for elderly recipients have not been determined, given their poorer immune responsiveness and lower rejection rates compared with younger individuals.

Toward understanding assortative matching, this is a self-contained introduction to research on search and matching. We first explore the nontransferable and perfectly transferable utility matching paradigms, and then a unifying imperfectly transferable utility matching model. Motivated by some unrealistic predictions of frictionless matching, we flesh out the foundational economics of search theory. We then revisit the original matching paradigms with search frictions. We finally allow informational frictions that often arise, such as in college-student sorting.

This paper specifies, designs and critically evaluates two tools for the automated identification of demographic data (age, occupation and social class) from the profile descriptions of Twitter users in the United Kingdom (UK). Meta-data data routinely collected through the Collaborative Social Media Observatory (COSMOS: http://www.cosmosproject.net/) relating to UK Twitter users is matched with the occupational lookup tables between job and social class provided by the Office for National Statistics (ONS) using SOC2010. Using expert human validation, the validity and reliability of the automated matching process is critically assessed and a prospective class distribution of UK Twitter users is offered with 2011 Census baseline comparisons. The pattern matching rules for identifying age are explained and enacted following a discussion on how to minimise false positives. The age distribution of Twitter users, as identified using the tool, is presented alongside the age distribution of the UK population from the 2011 Census. The automated occupation detection tool reliably identifies certain occupational groups, such as professionals, for which job titles cannot be confused with hobbies or are used in common parlance within alternative contexts. An alternative explanation on the prevalence of hobbies is that the creative sector is overrepresented on Twitter compared to 2011 Census data. The age detection tool illustrates the youthfulness of Twitter users compared to the general UK population as of the 2011 Census according to proportions, but projections demonstrate that there is still potentially a large number of older platform users. It is possible to detect \"signatures\" of both occupation and age from Twitter meta-data with varying degrees of accuracy (particularly dependent on occupational groups) but further confirmatory work is needed.

Despite being one of the wealthiest nations, disparities in adverse birth outcomes persist across racial and ethnic lines in the United States. We studied the association between historical redlining and preterm birth, low birth weight (LBW), small-for-gestational age (SGA), and perinatal mortality over a ten-year period (2006-2015) in Los Angeles, Oakland, and San Francisco, California. We used birth outcomes data from the California Office of Statewide Health Planning and Development between January 1, 2006 and December 31, 2015. Home Owners' Loan Corporation (HOLC) Security Maps developed in the 1930s assigned neighborhoods one of four grades that pertained to perceived investment risk of borrowers from that neighborhood: green (grade A) were considered \"Best\", blue (grade B) \"Still Desirable\", yellow (grade C) \"Definitely Declining\", and red (grade D, hence the term \"redlining\") \"Hazardous\". Geocoded residential addresses at the time of birth were superimposed on HOLC Security Maps to assign each birth a HOLC grade. We adjusted for potential confounders present at the time of Security Map creation by assigning HOLC polygons areal-weighted 1940s Census measures. We then employed propensity score matching methods to estimate the association of historical HOLC grades on current birth outcomes. Because tracts graded A had almost no propensity of receiving grade C or D and because grade B tracts had low propensity of receiving grade D, we examined birth outcomes in the three following comparisons: B vs. A, C vs. B, and D vs. C. The prevalence of preterm birth, SGA and mortality tended to be higher in worse HOLC grades, while the prevalence of LBW varied across grades. Overall odds of mortality and preterm birth increased as HOLC grade worsened. Propensity score matching balanced 1940s census measures across contrasting groups. Logistic regression models revealed significantly elevated odds of preterm birth (odds ratio (OR): 1.02, 95% confidence interval (CI): 1.00-1.05), and SGA (OR: 1.03, 95% CI: 1.00-1.05) in the C vs. B comparison and significantly reduced odds of preterm birth (OR: 0.93, 95% CI: 0.91-0.95), LBW (OR: 0.94-95% CI: 0.92-0.97), and SGA (OR: 0.94, 95% CI: 0.92-0.96) in the D vs. C comparison. Results differed by metropolitan area and maternal race. Similar to prior studies on redlining, we found that worsening HOLC grade was associated with adverse birth outcomes, although this relationship was less clear after propensity score matching and stratifying by metropolitan area. Higher odds of preterm birth and SGA in grade C versus grade B neighborhoods may be caused by higher-stress environments, racial segregation, and lack of access to resources, while lower odds of preterm birth, SGA, and LBW in grade D versus grade C neighborhoods may due to population shifts in those neighborhoods related to gentrification.

Functional connectomes (FCs), represented by networks or graphs that summarize coactivation patterns between pairs of brain regions, have been related at a population level to age, sex, cognitive/behavioral scores, life experience, genetics, and disease/disorders. However, quantifying FC differences between individuals also provides a rich source of information with which to map to differences in those individuals' biology, experience, genetics or behavior. In this study, graph matching is used to create a novel inter‐individual FC metric, called swap distance, that quantifies the distance between pairs of individuals' partial FCs, with a smaller swap distance indicating the individuals have more similar FC. We apply graph matching to align FCs between individuals from the the Human Connectome Project N=997 and find that swap distance (i) increases with increasing familial distance, (ii) increases with subjects' ages, (iii) is smaller for pairs of females compared to pairs of males, and (iv) is larger for females with lower cognitive scores compared to females with larger cognitive scores. Regions that contributed most to individuals' swap distances were in higher‐order networks, that is, default‐mode and fronto‐parietal, that underlie executive function and memory. These higher‐order networks' regions also had swap frequencies that varied monotonically with familial relatedness of the individuals in question. We posit that the proposed graph matching technique provides a novel way to study inter‐subject differences in FC and enables quantification of how FC may vary with age, relatedness, sex, and behavior. We use a novel graph‐matching metric, swap distance, to quantify differences between FCs of pairs of individuals and look at how this pairwise metric varies with age, sex, cognitive scores, and familial relationships. This metric highlights similarity of FCs between pairs of individuals, increases monotonically along with familial distance, increases with subjects' ages, is smaller for pairs of females compared to pairs of males, and is larger for females with lower cognitive scores compared to females with higher cognitive scores. Furthermore, higher‐order association regions like those in the frontoparietal and default mode network show more variability across individuals compared to lower regions belong to lower order networks.