Localization of adaptive variants in human genomes using averaged one-dependence estimation.

Statistical methods for identifying adaptive mutations from population genetic data face several obstacles: assessing the significance of genomic outliers, integrating correlated measures of selection into one analytic framework, and distinguishing adaptive variants from hitchhiking neutral variants. Here, we introduce SWIF(r), a probabilistic method that detects selective sweeps by learning the distributions of multiple selection statistics under different evolutionary scenarios and calculating the posterior probability of a sweep at each genomic site. SWIF(r) is trained using simulations from a user-specified demographic model and explicitly models the joint distributions of selection statistics, thereby increasing its power to both identify regions undergoing sweeps and localize adaptive mutations. Using array and exome data from 45 ‡Khomani San hunter-gatherers of southern Africa, we identify an enrichment of adaptive signals in genes associated with metabolism and obesity. SWIF(r) provides a transparent probabilistic framework for localizing beneficial mutations that is extensible to a variety of evolutionary scenarios

Verbal learning impairment in euthymic bipolar disorder: BDI v BDII

AbstractObjectivesCognitive impairment is known to occur in bipolar disorder (BD), even in euthymic patients, with largest effect sizes often seen in Verbal Learning and Memory Tasks (VLT). However, comparisons between BD Type-I and Type-II have produced inconsistent results partly due to low sample sizes.MethodsThis study compared the performance of 183 BDI with 96 BDII out-patients on an adapted version of the Rey Verbal Learning Task. Gender, age, years of education, mood scores and age at onset were all used as covariates. Current medication and a variety of illness variables were also investigated for potential effects on VLT performance.ResultsBDI patients were significantly impaired relative to BDII patients on all five VLT outcome measures after controlling for the other variables [Effect Sizes=.13–.17]. The impairments seem to be unrelated to drug treatment and largely unrelated to illness variables, although age of onset affected performance on three outcome measures and number of episodes of mood elevation affected performance on one.LimitationsThis study used historical healthy controls. Analysis of potential drug effects was limited by insufficient participants not being drug free. Cross-sectional nature of the study limited the analysis of the potential effect of illness variables.ConclusionsThis study replicates earlier findings of increased verbal learning impairment in BDI patients relative to BDII in a substantially larger sample. Such performance cannot be wholly explained by medication effects or illness variables. Thus, the cognitive impairment is likely to reflect a phenotypic difference between bipolar sub-types

The GLM-spectrum:A multilevel framework for spectrum analysis with covariate and confound modelling

The frequency spectrum is a central method for representing the dynamics within electrophysiological data. Some widely used spectrum estimators make use of averaging across time segments to reduce noise in the final spectrum. The core of this approach has not changed substantially since the 1960s, though many advances in the field of regression modelling and statistics have been made during this time. Here, we propose a new approach, the General Linear Model (GLM) Spectrum, which reframes time averaged spectral estimation as multiple regression. This brings several benefits, including the ability to do confound modelling, hierarchical modelling, and significance testing via non-parametric statistics. We apply the approach to a dataset of EEG recordings of participants who alternate between eyes-open and eyes-closed resting state. The GLM-Spectrum can model both conditions, quantify their differences, and perform denoising through confound regression in a single step. This application is scaled up from a single channel to a whole head recording and, finally, applied to quantify age differences across a large group-level dataset. We show that the GLM-Spectrum lends itself to rigorous modelling of within- and between-subject contrasts as well as their interactions, and that the use of model-projected spectra provides an intuitive visualisation. The GLM-Spectrum is a flexible framework for robust multilevel analysis of power spectra, with adaptive covariate and confound modelling

MRI T2 and T1ρ relaxation in patients at risk for knee osteoarthritis: A systematic review and meta-analysis

© 2019 The Author(s). Background: Magnetic resonance imaging (MRI) T2 and T1ρ relaxation are increasingly being proposed as imaging biomarkers potentially capable of detecting biochemical changes in articular cartilage before structural changes are evident. We aimed to: 1) summarize MRI methods of published studies investigating T2 and T1ρ relaxation time in participants at risk for but without radiographic knee OA; and 2) compare T2 and T1ρ relaxation between participants at-risk for knee OA and healthy controls. Methods: We conducted a systematic review of studies reporting T2 and T1ρ relaxation data that included both participants at risk for knee OA and healthy controls. Participant characteristics, MRI methodology, and T1ρ and T2 relaxation data were extracted. Standardized mean differences (SMDs) were calculated within each study. Pooled effect sizes were then calculated for six commonly segmented knee compartments. Results: 55 articles met eligibility criteria. There was considerable variability between scanners, coils, software, scanning protocols, pulse sequences, and post-processing. Moderate risk of bias due to lack of blinding was common. Pooled effect sizes indicated participants at risk for knee OA had lengthened T2 relaxation time in all compartments (SMDs from 0.33 to 0.74; p \u3c 0.01) and lengthened T1ρ relaxation time in the femoral compartments (SMD from 0.35 to 0.40; p \u3c 0.001). Conclusions: T2 and T1ρ relaxation distinguish participants at risk for knee OA from healthy controls. Greater standardization of MRI methods is both warranted and required for progress towards biomarker validation

Exploring differences in adverse symptom event grading thresholds between clinicians and patients in the clinical trial setting

Symptomatic adverse event (AE) monitoring is essential in cancer clinical trials to assess patient safety, as well as inform decisions related to treatment and continued trial participation. As prior research has demonstrated that conventional concordance metrics (e.g., intraclass correlation) may not capture nuanced aspects of the association between clinician and patient-graded AEs, we aimed to characterize differences in AE grading thresholds between doctors (MDs), registered nurses (RNs), and patients using the Bayesian Graded Item Response Model (GRM)

Pre-admission interventions to improve outcome after elective surgery-protocol for a systematic review

BACKGROUND: Poor physical health and fitness increases the risk of death and complications after major elective surgery. Pre-admission interventions to improve patients’ health and fitness (referred to as prehabilitation) may reduce postoperative complications, decrease the length of hospital stay and facilitate the patient’s recovery. We will conduct a systematic review of RCTs to examine the effectiveness of different types of prehabilitation interventions in improving the surgical outcomes of patients undergoing elective surgery. METHODS: This review will be conducted and reported according to the Cochrane and PRISMA reporting guidelines. MEDLINE, EMBASE, CENTRAL, CINAHL, PsycINFO, ISI Web of Science and clinical trial registers will be searched for any intervention administered before any elective surgery (including physical activity, nutritional, educational, psychological, clinical or multicomponent), which aims to improve postoperative outcomes. Reference lists of included studies will be searched, and grey literature including conference proceedings, theses, dissertations and preoperative assessment protocols will be examined. Study quality will be assessed using Cochrane’s risk of bias tool, and meta-analyses for trials that use similar interventions and report similar outcomes will be undertaken where possible. DISCUSSION: This systematic review will determine whether different types of interventions administered before elective surgery are effective in improving postoperative outcomes. It will also determine which components or combinations of components would form the most effective prehabilitation intervention. SYSTEMATIC REVIEW REGISTRATION: PROSPERO CRD4201501919

Trajectories of distress from pregnancy to 15-months post-partum during the COVID-19 pandemic

BackgroundThe COVID-19 pandemic has particularly burdened pregnant and postpartum women. It remains unclear how distress levels of pregnant and postpartum people have changed (or persisted) as the pandemic continues on and which factors may contribute to these trajectories of distress.MethodsThis longitudinal study included 304 pregnant people, who were followed during pregnancy, 6-weeks, 6-months and 15-months postpartum. At each time point, a latent “distress” factor was estimated using self-reported depressive symptoms, anxiety symptoms, and stress. Reported negative impact of COVID-19 and social support were assessed during pregnancy as risk and protective factors related to distress. Second-order latent growth curve modeling with a piecewise growth function was used to estimate initial levels and changes in distress over time.ResultsMean distress was relatively stable from the pregnancy to 6-weeks postpartum and then declined from 6-weeks to 15-months postpartum. Higher education, greater social support, and lower negative impact of COVID-19 were associated with a lower distress during pregnancy. Unexpectedly, negative impact of COVID-19 was associated with a faster decrease in distress and more social support was associated with a greater increase in distress from pregnancy to 6-weeks postpartum. However, these effects became non-significant after controlling for distress during pregnancy.ConclusionFindings indicate high but declining levels of distress from pregnancy to the postpartum period. Changes in distress are related to social support and the negative impact of the pandemic in pregnancy. Findings highlight the continued impact of COVID-19 on perinatal mental health and the need for support to limit the burden of this pandemic on pregnant people and families

ARC OF RECREATION 2.0 Connecting the McKnight Rail Trail From Mason Square to Union Station, Springfield MA

ARC OF RECREATION 2.0 Connecting the McKnight Rail Trail From Mason Square to Union Station, Springfield MA ARC OF RECREATION 2.0 is a Senior Urban Design Studio that created design concepts to envision the McKnight Rail Trail on an abandoned railroad corridor as a place to walk, bike, recreate, and congregate and to connect Mason Square to Union Station. Arc of Recreation was a name that was coined over 10 years ago through a different project at UMass. Finally realization is within reach. The City published a feasibility study in 2014 and has freed a construction budget of $430,000 for the first phases.The McKnight neighborhood hosts a diverse demographic that would benefit to an implemented rail trail in their community. With health complications including obesity and diabetes, the trail can provide a free means of transportation to the metro center that encourages an active lifestyle and increased employment in the neighborhood. Children will be able to utilize a safe passage to school, such as the nearby Rebecca M. Johnson Middle School, as well as young adults working part time jobs in the neighborhood and surrounding areas. The trail will connect from Union Station in downtown to Mason Square in the east. Access along the trail route will enable more community members to enter the trail and use it. Therefore it creates a more equitable transportation network that can be shared by all residents. The Studio created design concepts including first phase tactical interventions that make the street and public space experience enticing to visitors and engaging for the local residents, workers, and their families. The designs reflect the feedback of local community members through community meetings and a design charrette. In conclusion, the envisioned trail will foster community engagement and interaction to steer the future development of the neighborhood. The following measures are crucial for a successful rail trail: • Improvement of major street crossings to enable safe use of the trail during all day and nighttime hours. • Integration of on street bike lanes and the trail to create a bicycle loop around the neighborhood. Expand the downtown bike-share stations for entry points to the trail. • Create accessibility to public parks in the City through a network of safe streets with extensive tree plantings. Envision a trail that allows for access to the Connecticut River Walk and Bikeway. • Embrace the industrial and underused architecture through public art. • Protect existing wetlands on the trail through bypasses and boardwalks. • Raise awareness for the rich ecology on the trail through signage and education. • Create diverse opportunities to play for all ages and demographics. • Create areas for urban gardening to connect trail and community. • Creation of spaces on the trail where the community comes together and hold events and meetings. • Design for activities throughout the year including the winter season. • Rezone the trail to Open Space to guide its future development as a functioning rail trail

Feasibility and clinical impact of sharing patient-reported symptom toxicities and performance status with clinical investigators during a phase 2 cancer treatment trial

Clinicians can miss up to half of patients’ symptomatic toxicities in cancer clinical trials and routine practice. Although patient-reported outcome questionnaires have been developed to capture this information, it is unclear whether clinicians will make use of patient-reported outcomes to inform their own toxicity documentation, or to prompt symptom management activities

The association between clinician-based common terminology criteria for adverse events (CTCAE) and patient-reported outcomes (PRO): a systematic review

Symptomatic adverse events (AEs) are monitored by clinicians as part of all US-based clinical trials in cancer via the U.S. National Cancer Institute’s Common Terminology Criteria for Adverse Events (CTCAE) for the purposes of ensuring patient safety. Recently there has been a charge toward capturing the patient perspective for those AEs amenable to patient self-reporting via patient-reported outcomes (PRO). The aim of this review was to summarize the empirically reported association between analogous CTCAE and PRO ratings