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Simple slopes, regions of significance, and confidence bands are commonly used to evaluate interactions in multiple linear regression (MLR) models, and the use of these techniques has recently been extended to multilevel or hierarchical linear modeling (HLM) and latent curve analysis (LCA). However, conducting these tests and plotting the conditional relations is often a tedious and error-prone task. This article provides an overview of methods used to probe interaction effects and describes a unified collection of freely available online resources that researchers can use to obtain significance tests for simple slopes, compute regions of significance, and obtain confidence bands for simple slopes across the range of the moderator in the MLR, HLM, and LCA contexts. Plotting capabilities are also provided.

BackgroundThe burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May–June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records.MethodsAfter identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period.ResultsWe collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020.DiscussionTo our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome-based transgenic mouse models (Ube3aOE) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, Ube3aOE mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2-q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.

Most drugs are developed through iterative rounds of chemical synthesis and biochemical testing to optimize the affinity of a particular compound for a protein target of therapeutic interest. This process is challenging because candidate molecules must be selected from a chemical space of more than 10 60 drug-like possibilities 1 , and a single reaction used to synthesize each molecule has more than 10 7 plausible permutations of catalysts, ligands, additives and other parameters 2 . The merger of a method for high-throughput chemical synthesis with a biochemical assay would facilitate the exploration of this enormous search space and streamline the hunt for new drugs and chemical probes. Miniaturized high-throughput chemical synthesis 3 – 7 has enabled rapid evaluation of reaction space, but so far the merger of such syntheses with bioassays has been achieved with only low-density reaction arrays, which analyse only a handful of analogues prepared under a single reaction condition 8 – 13 . High-density chemical synthesis approaches that have been coupled to bioassays, including on-bead 14 , on-surface 15 , on-DNA 16 and mass-encoding technologies 17 , greatly reduce material requirements, but they require the covalent linkage of substrates to a potentially reactive support, must be performed under high dilution and must operate in a mixture format. These reaction attributes limit the application of transition-metal catalysts, which are easily poisoned by the many functional groups present in a complex mixture, and of transformations for which the kinetics require a high concentration of reactant. Here we couple high-throughput nanomole-scale synthesis with a label-free affinity-selection mass spectrometry bioassay. Each reaction is performed at a 0.1-molar concentration in a discrete well to enable transition-metal catalysis while consuming less than 0.05 milligrams of substrate per reaction. The affinity-selection mass spectrometry bioassay is then used to rank the affinity of the reaction products to target proteins, removing the need for time-intensive reaction purification. This method enables the primary synthesis and testing steps that are critical to the invention of protein inhibitors to be performed rapidly and with minimal consumption of starting materials. A system that combines nanoscale synthesis and affinity ranking enables high-throughput screening of reaction conditions and bioactivity for a given protein target, accelerating the process of drug discovery.

While past research has suggested possible seasonal trends in crime rates, this study employs a novel methodology that directly models these changes and predicts them with explanatory variables. Using a nonlinear latent curve model, seasonal fluctuations in crime rates are modeled for a large number of communities in the U.S. over a three-year period with a focus on testing the theoretical predictions of two key explanations for seasonal changes in crime rates: the temperature/aggression and routine activities theories. Using data from 8,460 police units in the U.S. over the 1990 to 1992 period, we found that property crime rates are primarily driven by pleasant weather, consistent with the routine activities theory. Violent crime exhibited evidence in support of both theories.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Although there is empirical evidence supporting associations between exposure to violence and engaging in physically aggressive behavior during adolescence, there is limited longitudinal research to determine the extent to which exposure to violence is a cause or a consequence of physical aggression, and most studies have not addressed the influence of other negative life events experienced by adolescents. This study examined bidirectional relations between physical aggression, two forms of exposure to violence—witnessing violence and victimization, and other negative life events. Participants were a sample of 2568 adolescents attending three urban public middle schools who completed measures of each construct every 3 months during middle school. Their mean age was 12.76 (SD = 0.98); 52% were female. The majority were African American (89%); 17% were Hispanic or Latino/a. Cross-lagged regression analyses across four waves of data collected within the same grade revealed bidirectional relations between witnessing violence and physical aggression, and between witnessing violence and negative life events. Although physical aggression predicted subsequent changes in victimization, victimization predicted changes in physical aggression only when witnessing violence was not taken into account. Findings were consistent across sex and grades. Overall, these findings highlight the need for interventions that break the connection between exposure to violence and aggression during adolescence.

The effectiveness of a prevention or intervention program has traditionally been assessed using time‐specific comparisons of mean levels between the treatment and the control groups. However, many times the behavior targeted by the intervention is naturally developing over time, and the goal of the treatment is to alter this natural or normative developmental trajectory. Examining time‐specific mean levels can be both limiting and potentially misleading when the behavior of interest is developing systematically over time. It is argued here that there are both theoretical and statistical advantages associated with recasting intervention treatment effects in terms of normative and altered developmental trajectories. The recently developed technique of latent curve (LC) analysis is reviewed and extended to a true experimental design setting in which subjects are randomly assigned to a treatment intervention or a control condition. LC models are applied to both artificially generated and real intervention data sets to evaluate the efficacy of an intervention program. Not only do the LC models provide a more comprehensive understanding of the treatment and control group developmental processes compared to more traditional fixed‐effects models, but LC models have greater statistical power to detect a given treatment effect. Finally, the LC models are modified to allow for the computation of specific power estimates under a variety of conditions and assumptions that can provide much needed information for the planning and design of more powerful but cost‐efficient intervention programs for the future.

We examined two hypotheses about the developmental relation between substance abuse and individual differences in desistance from antisocial behavior during young adulthood. The “snares” hypothesis posits that substance abuse should result in time-specific elevations in antisocial behavior relative to an individual's own developmental trajectory of antisocial behavior, whereas the “launch” hypothesis posits that substance abuse early in young adulthood slows an individual's overall pattern of crime desistance relative to the population norm during this developmental period. We conducted latent trajectory analyses to test these hypotheses using interview data about antisocial behaviors and substance abuse assessed at ages 18, 21, and 26 in men from the Dunedin Multidisciplinary Health and Development Study (N = 461). We found significant individual variability in initial levels and rates of change in antisocial behavior over time as well as support for both the snares hypothesis and the launch hypothesis as explanations for the developmental relation between substance abuse and crime desistance in young men.We thank the Dunedin Study members, Dunedin Unit Director Richie Poulton, Unit research staff, and Study founder Phil Silva. Research assistance was provided by HonaLee Harrington. The Dunedin Multidisciplinary Health and Development Research Unit is supported by the New Zealand Health Research Council. We also thank Alex Piquero for his helpful comments. This research received support from the NIDA (Grant DA15398 and DA13148), NIMH (Grants MH45070 and MH49414), William T. Grant Foundation, and Air New Zealand.

Adopting a developmental epidemiology perspective, the current study examines sources of risk heterogeneity for internalizing symptomatology among children of alcoholic parents (COAs). Parent-based factors, including comorbid diagnoses and the number of alcoholic parents in the family, as well as child-based factors, namely child gender, formed the indicators of heterogeneity. Following a novel approach to cross-study methods, we present a three-stage analysis involving measurement development using item response theory, examination of study effects on latent trajectories over time using latent curve modeling, and prediction of these latent trajectories testing our theoretically derived hypotheses in two longitudinal investigations across both mother- and self-reported symptomatology. Specifically, we replicated previous findings that parent alcoholism has a unique effect on child internalizing symptoms, above and beyond those of both parent depression and antisocial personality disorder. However, we also found important subgroup differences, explaining heterogeneity within COAs' risk profile in terms of the number of alcoholic parents in the family, comorbid diagnoses for the alcoholic parent and, for self-reported symptoms, child gender. Such factors serve to refine the definition of risk among COAs, suggesting a more severely impaired target group for preventive interventions, identifying the significance of familial alcoholism in individual differences underlying internalizing symptoms over time, and further specifying the distal risk matrix for an internalizing pathway to alcohol involvement.