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1.
J Phys Chem A ; 2024 Jul 15.
Article in English | MEDLINE | ID: mdl-39008894

ABSTRACT

We demonstrate the use of gradient-boosted ensemble models that accurately predict emission wavelengths in benzobis[1,2-d:4,5-d']oxazole (BBO) based fluorescent emitters. We have curated a database of 50 molecules from previously published data by the Jeffries-EL group using density functional theory (DFT) computed ground and excited state features. We consider two machine learning (ML) models based on (i) whole cruciform molecules and (ii) their constituent fragment molecules. Both ML models provide accurate predictions with root-mean-square errors between 30 and 36 nm, competitive with state-of-the-art deep learning models trained on orders of magnitude more molecules, and this accuracy holds even when tested on four new BBO emitters unseen by the models. We also provide an interpretable feature importance analysis and discuss the relevant relationships between DFT and changes in predicted emission wavelength.

2.
PLoS Comput Biol ; 19(6): e1011188, 2023 Jun.
Article in English | MEDLINE | ID: mdl-37327238

ABSTRACT

In clinical neuroscience, epileptic seizures have been associated with the sudden emergence of coupled activity across the brain. The resulting functional networks-in which edges indicate strong enough coupling between brain regions-are consistent with the notion of percolation, which is a phenomenon in complex networks corresponding to the sudden emergence of a giant connected component. Traditionally, work has concentrated on noise-free percolation with a monotonic process of network growth, but real-world networks are more complex. We develop a class of random graph hidden Markov models (RG-HMMs) for characterizing percolation regimes in noisy, dynamically evolving networks in the presence of edge birth and edge death. This class is used to understand the type of phase transitions undergone in a seizure, and in particular, distinguishing between different percolation regimes in epileptic seizures. We develop a hypothesis testing framework for inferring putative percolation mechanisms. As a necessary precursor, we present an EM algorithm for estimating parameters from a sequence of noisy networks only observed at a longitudinal subsampling of time points. Our results suggest that different types of percolation can occur in human seizures. The type inferred may suggest tailored treatment strategies and provide new insights into the fundamental science of epilepsy.


Subject(s)
Epilepsy , Seizures , Humans , Brain , Phase Transition , Algorithms
3.
Environ Res ; 225: 115584, 2023 05 15.
Article in English | MEDLINE | ID: mdl-36868447

ABSTRACT

Aircraft emissions contribute to overall ambient air pollution, including ultrafine particle (UFP) concentrations. However, accurately ascertaining aviation contributions to UFP is challenging due to high spatiotemporal variability along with intermittent aviation emissions. The objective of this study was to evaluate the impact of arrival aircraft on particle number concentration (PNC), a proxy for UFP, across six study sites 3-17 km from a major arrival aircraft flight path into Boston Logan International Airport by utilizing real-time aircraft activity and meteorological data. Ambient PNC at all monitoring sites was similar at the median but had greater variation at the 95th and 99th percentiles with more than two-fold increases in PNC observed at sites closer to the airport. PNC was elevated during the hours with high aircraft activity with sites closest to the airport exhibiting stronger signals when downwind from the airport. Regression models indicated that the number of arrival aircraft per hour was associated with measured PNC at all six sites, with a maximum contribution of 50% of total PNC at a monitor 3 km from the airport during hours with arrival activity on the flight path of interest (26% across all hours). Our findings suggest strong but intermittent contributions from arrival aircraft to ambient PNC in communities near airports.


Subject(s)
Air Pollutants , Air Pollution , Particulate Matter/analysis , Airports , Air Pollutants/analysis , Boston , Aircraft , Air Pollution/analysis , Massachusetts , Vehicle Emissions/analysis , Environmental Monitoring
4.
Clin Infect Dis ; 76(3): e400-e408, 2023 02 08.
Article in English | MEDLINE | ID: mdl-35616119

ABSTRACT

BACKGROUND: The Omicron variant of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is highly transmissible in vaccinated and unvaccinated populations. The dynamics that govern its establishment and propensity toward fixation (reaching 100% frequency in the SARS-CoV-2 population) in communities remain unknown. Here, we describe the dynamics of Omicron at 3 institutions of higher education (IHEs) in the greater Boston area. METHODS: We use diagnostic and variant-specifying molecular assays and epidemiological analytical approaches to describe the rapid dominance of Omicron following its introduction into 3 IHEs with asymptomatic surveillance programs. RESULTS: We show that the establishment of Omicron at IHEs precedes that of the state and region and that the time to fixation is shorter at IHEs (9.5-12.5 days) than in the state (14.8 days) or region. We show that the trajectory of Omicron fixation among university employees resembles that of students, with a 2- to 3-day delay. Finally, we compare cycle threshold values in Omicron vs Delta variant cases on college campuses and identify lower viral loads among college affiliates who harbor Omicron infections. CONCLUSIONS: We document the rapid takeover of the Omicron variant at IHEs, reaching near-fixation within the span of 9.5-12.5 days despite lower viral loads, on average, than the previously dominant Delta variant. These findings highlight the transmissibility of Omicron, its propensity to rapidly dominate small populations, and the ability of robust asymptomatic surveillance programs to offer early insights into the dynamics of pathogen arrival and spread.


Subject(s)
COVID-19 , Humans , COVID-19/epidemiology , SARS-CoV-2/genetics , Universities , Boston
5.
Nucleic Acids Res ; 51(1): e6, 2023 01 11.
Article in English | MEDLINE | ID: mdl-36395816

ABSTRACT

With more and more data being collected, modern network representations exploit the complementary nature of different data sources as well as similarities across patients. We here introduce the Variation of information fused Layers of Networks algorithm (ViLoN), a novel network-based approach for the integration of multiple molecular profiles. As a key innovation, it directly incorporates prior functional knowledge (KEGG, GO). In the constructed network of patients, patients are represented by networks of pathways, comprising genes that are linked by common functions and joint regulation in the disease. Patient stratification remains a key challenge both in the clinic and for research on disease mechanisms and treatments. We thus validated ViLoN for patient stratification on multiple data type combinations (gene expression, methylation, copy number), showing substantial improvements and consistently competitive performance for all. Notably, the incorporation of prior functional knowledge was critical for good results in the smaller cohorts (rectum adenocarcinoma: 90, esophageal carcinoma: 180), where alternative methods failed.


Subject(s)
Algorithms , Esophageal Neoplasms , Humans , Esophageal Neoplasms/drug therapy , Esophageal Neoplasms/genetics , Esophageal Neoplasms/pathology , Gene Regulatory Networks , Cohort Studies
6.
PLoS Comput Biol ; 18(9): e1010434, 2022 09.
Article in English | MEDLINE | ID: mdl-36048890

ABSTRACT

The reproductive number is an important metric that has been widely used to quantify the infectiousness of communicable diseases. The time-varying instantaneous reproductive number is useful for monitoring the real-time dynamics of a disease to inform policy making for disease control. Local estimation of this metric, for instance at a county or city level, allows for more targeted interventions to curb transmission. However, simultaneous estimation of local reproductive numbers must account for potential sources of heterogeneity in these time-varying quantities-a key element of which is human mobility. We develop a statistical method that incorporates human mobility between multiple regions for estimating region-specific instantaneous reproductive numbers. The model also can account for exogenous cases imported from outside of the regions of interest. We propose two approaches to estimate the reproductive numbers, with mobility data used to adjust incidence in the first approach and to inform a formal priori distribution in the second (Bayesian) approach. Through a simulation study, we show that region-specific reproductive numbers can be well estimated if human mobility is reasonably well approximated by available data. We use this approach to estimate the instantaneous reproductive numbers of COVID-19 for 14 counties in Massachusetts using CDC case report data and the human mobility data collected by SafeGraph. We found that, accounting for mobility, our method produces estimates of reproductive numbers that are distinct across counties. In contrast, independent estimation of county-level reproductive numbers tends to produce similar values, as trends in county case-counts for the state are fairly concordant. These approaches can also be used to estimate any heterogeneity in transmission, for instance, age-dependent instantaneous reproductive number estimates. As people are more mobile and interact frequently in ways that permit transmission, it is important to account for this in the estimation of the reproductive number.


Subject(s)
COVID-19 , Communicable Diseases , Bayes Theorem , COVID-19/epidemiology , Humans , Reproduction , SARS-CoV-2
7.
Philos Trans A Math Phys Eng Sci ; 380(2233): 20210303, 2022 Oct 03.
Article in English | MEDLINE | ID: mdl-35965456

ABSTRACT

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia. This article is part of the theme issue 'Technical challenges of modelling real-life epidemics and examples of overcoming these'.


Subject(s)
COVID-19 , Communicable Diseases , Bayes Theorem , COVID-19/epidemiology , Communicable Diseases/epidemiology , Disease Outbreaks , Humans , Reproduction
8.
Am J Public Health ; 112(2): 277-283, 2022 02.
Article in English | MEDLINE | ID: mdl-35080960

ABSTRACT

Objectives. To develop an approach to project quarantine needs during an outbreak, particularly for communally housed individuals who interact with outside individuals. Methods. We developed a method that uses basic surveillance data to do short-term projections of future quarantine needs. The development of this method was rigorous, but it is conceptually simple and easy to implement and allows one to anticipate potential superspreading events. We demonstrate how this method can be used with data from the fall 2020 semester of a large urban university in Boston, Massachusetts, that provided quarantine housing for students living on campus in response to the COVID-19 pandemic. Our approach accounted for potentially infectious interactions between individuals living in university housing and those who did not. Results. Our approach was able to accurately project 10-day-ahead quarantine utilization for on-campus students in a large urban university. Our projections were most accurate when we anticipated weekend superspreading events around holidays. Conclusions. We provide an easy-to-use software tool to project quarantine utilization for institutions that can account for mixing with outside populations. This software tool has potential application for universities, corrections facilities, and the military. (Am J Public Health. 2022;112(2):277-283. https://doi.org/10.2105/AJPH.2021.306573).


Subject(s)
Forecasting/methods , Quarantine/trends , Software , Boston/epidemiology , Health Services Needs and Demand/trends , Housing/trends , Humans , Universities
9.
medRxiv ; 2022 Apr 28.
Article in English | MEDLINE | ID: mdl-33948612

ABSTRACT

A valuable metric in understanding local infectious disease dynamics is the local time-varying reproduction number, i.e. the expected number of secondary local cases caused by each infected individual. Accurate estimation of this quantity requires distinguishing cases arising from local transmission from those imported from elsewhere. Realistically, we can expect identification of cases as local or imported to be imperfect. We study the propagation of such errors in estimation of the local time-varying reproduction number. In addition, we propose a Bayesian framework for estimation of the true local time-varying reproduction number when identification errors exist. And we illustrate the practical performance of our estimator through simulation studies and with outbreaks of COVID-19 in Hong Kong and Victoria, Australia.

10.
JAMA Netw Open ; 4(6): e2116425, 2021 06 01.
Article in English | MEDLINE | ID: mdl-34170303

ABSTRACT

Importance: The COVID-19 pandemic has severely disrupted US educational institutions. Given potential adverse financial and psychosocial effects of campus closures, many institutions developed strategies to reopen campuses in the fall 2020 semester despite the ongoing threat of COVID-19. However, many institutions opted to have limited campus reopening to minimize potential risk of spread of SARS-CoV-2. Objective: To analyze how Boston University (BU) fully reopened its campus in the fall of 2020 and controlled COVID-19 transmission despite worsening transmission in Boston, Massachusetts. Design, Setting, and Participants: This multifaceted intervention case series was conducted at a large urban university campus in Boston, Massachusetts, during the fall 2020 semester. The BU response included a high-throughput SARS-CoV-2 polymerase chain reaction testing facility with capacity to deliver results in less than 24 hours; routine asymptomatic screening for COVID-19; daily health attestations; adherence monitoring and feedback; robust contact tracing, quarantine, and isolation in on-campus facilities; face mask use; enhanced hand hygiene; social distancing recommendations; dedensification of classrooms and public places; and enhancement of all building air systems. Data were analyzed from December 20, 2020, to January 31, 2021. Main Outcomes and Measures: SARS-CoV-2 diagnosis confirmed by reverse transcription-polymerase chain reaction of anterior nares specimens and sources of transmission, as determined through contact tracing. Results: Between August and December 2020, BU conducted more than 500 000 COVID-19 tests and identified 719 individuals with COVID-19, including 496 students (69.0%), 11 faculty (1.5%), and 212 staff (29.5%). Overall, 718 individuals, or 1.8% of the BU community, had test results positive for SARS-CoV-2. Of 837 close contacts traced, 86 individuals (10.3%) had test results positive for COVID-19. BU contact tracers identified a source of transmission for 370 individuals (51.5%), with 206 individuals (55.7%) identifying a non-BU source. Among 5 faculty and 84 staff with SARS-CoV-2 with a known source of infection, most reported a transmission source outside of BU (all 5 faculty members [100%] and 67 staff members [79.8%]). A BU source was identified by 108 of 183 undergraduate students with SARS-CoV-2 (59.0%) and 39 of 98 graduate students with SARS-CoV-2 (39.8%); notably, no transmission was traced to a classroom setting. Conclusions and Relevance: In this case series of COVID-19 transmission, BU used a coordinated strategy of testing, contact tracing, isolation, and quarantine, with robust management and oversight, to control COVID-19 transmission in an urban university setting.


Subject(s)
COVID-19/prevention & control , Infection Control/standards , Universities/trends , Urban Population/statistics & numerical data , Boston/epidemiology , COVID-19/epidemiology , COVID-19/transmission , Contact Tracing/instrumentation , Contact Tracing/methods , Hand Hygiene/methods , Humans , Infection Control/methods , Infection Control/statistics & numerical data , Quarantine/methods , Universities/organization & administration
11.
PLoS Comput Biol ; 17(1): e1008545, 2021 01.
Article in English | MEDLINE | ID: mdl-33503024

ABSTRACT

We investigate the source detection problem in epidemiology, which is one of the most important issues for control of epidemics. Mathematically, we reformulate the problem as one of identifying the relevant component in a multivariate Gaussian mixture model. Focusing on the study of cholera and diseases with similar modes of transmission, we calibrate the parameters of our mixture model using human mobility networks within a stochastic, spatially explicit epidemiological model for waterborne disease. Furthermore, we adopt a Bayesian perspective, so that prior information on source location can be incorporated (e.g., reflecting the impact of local conditions). Posterior-based inference is performed, which permits estimates in the form of either individual locations or regions. Importantly, our estimator only requires first-arrival times of the epidemic by putative observers, typically located only at a small proportion of nodes. The proposed method is demonstrated within the context of the 2000-2002 cholera outbreak in the KwaZulu-Natal province of South Africa.


Subject(s)
Disease Transmission, Infectious , Epidemics , Models, Statistical , Public Health Surveillance/methods , Bayes Theorem , Cholera/epidemiology , Cholera/prevention & control , Cholera/transmission , Computational Biology , Contact Tracing , Disease Outbreaks/prevention & control , Disease Outbreaks/statistics & numerical data , Disease Transmission, Infectious/prevention & control , Disease Transmission, Infectious/statistics & numerical data , Epidemics/prevention & control , Epidemics/statistics & numerical data , Humans , Population Dynamics , South Africa , Travel
12.
Genet Epidemiol ; 44(4): 352-367, 2020 06.
Article in English | MEDLINE | ID: mdl-32100372

ABSTRACT

We propose a novel variant set test for rare-variant association studies, which leverages multiple single-nucleotide variant (SNV) annotations. Our approach optimizes a convex combination of different sequence kernel association test (SKAT) statistics, where each statistic is constructed from a different annotation and combination weights are optimized through a multiple kernel learning algorithm. The combination test statistic is evaluated empirically through data splitting. In simulations, we find our method preserves type I error at α=2.5×10-6 and has greater power than SKAT(-O) when SNV weights are not misspecified and sample sizes are large ( N≥5,000 ). We utilize our method in the Framingham Heart Study (FHS) to identify SNV sets associated with fasting glucose. While we are unable to detect any genome-wide significant associations between fasting glucose and 4-kb windows of rare variants ( p<10-7 ) in 6,419 FHS participants, our method identifies suggestive associations between fasting glucose and rare variants near ROCK2 ( p=2.1×10-5 ) and within CPLX1 ( p=5.3×10-5 ). These two genes were previously reported to be involved in obesity-mediated insulin resistance and glucose-induced insulin secretion by pancreatic beta-cells, respectively. These findings will need to be replicated in other cohorts and validated by functional genomic studies.


Subject(s)
Models, Genetic , Polymorphism, Single Nucleotide , Adaptor Proteins, Vesicular Transport/genetics , Algorithms , Blood Glucose/analysis , Genome-Wide Association Study , Humans , Insulin Resistance , Insulin-Secreting Cells/cytology , Insulin-Secreting Cells/metabolism , Longitudinal Studies , Models, Statistical , Nerve Tissue Proteins/genetics , Obesity/genetics , Obesity/pathology , rho-Associated Kinases/genetics
13.
Nat Commun ; 11(1): 635, 2020 01 31.
Article in English | MEDLINE | ID: mdl-32005814

ABSTRACT

Multipotent Nkx2-1-positive lung epithelial primordial progenitors of the foregut endoderm are thought to be the developmental precursors to all adult lung epithelial lineages. However, little is known about the global transcriptomic programs or gene networks that regulate these gateway progenitors in vivo. Here we use bulk RNA-sequencing to describe the unique genetic program of in vivo murine lung primordial progenitors and computationally identify signaling pathways, such as Wnt and Tgf-ß superfamily pathways, that are involved in their cell-fate determination from pre-specified embryonic foregut. We integrate this information in computational models to generate in vitro engineered lung primordial progenitors from mouse pluripotent stem cells, improving the fidelity of the resulting cells through unbiased, easy-to-interpret similarity scores and modulation of cell culture conditions, including substratum elastic modulus and extracellular matrix composition. The methodology proposed here can have wide applicability to the in vitro derivation of bona fide tissue progenitors of all germ layers.


Subject(s)
Epithelial Cells/cytology , Lung/cytology , Mice/genetics , Pluripotent Stem Cells/cytology , Animals , Cell Culture Techniques , Cell Differentiation , Epithelial Cells/metabolism , Extracellular Matrix/genetics , Extracellular Matrix/metabolism , Female , Germ Layers/embryology , Germ Layers/metabolism , Lung/embryology , Lung/metabolism , Male , Mice/embryology , Mice/metabolism , Mice, Inbred C57BL , Mice, Transgenic , Pluripotent Stem Cells/metabolism , Signal Transduction , Thyroid Nuclear Factor 1/genetics , Thyroid Nuclear Factor 1/metabolism , Transcriptome , Transforming Growth Factor beta/genetics , Transforming Growth Factor beta/metabolism
14.
Am J Orthopsychiatry ; 89(4): 462-474, 2019.
Article in English | MEDLINE | ID: mdl-31305114

ABSTRACT

To our knowledge, Asian Women's Action for Resilience and Empowerment (AWARE) is the first gender- and culture-specific and trauma-informed group psychotherapy intervention designed for Asian-American young women with histories of interpersonal violence and trauma and/or Post-Traumatic Stress Disorder (PTSD) diagnosis. We employed a 2-arm randomized controlled trial. Sixty-three women who met clinical criteria for trauma were randomized to the intervention (n = 32) or waitlist control (n = 31) group. We documented retention rates, preliminary efficacy for sexual risk behaviors and depressive symptoms (overall and stratified by PTSD at baseline), and safety in terms of suicidality at baseline, postintervention, and 3-month follow-up. AWARE demonstrated high retention rates, in that 87.50% of those enrolled in the program completed at least 6 out of the 8 sessions. Although there were no differences overall for sexual risk behaviors or depressive symptoms, among women with PTSD, significant reductions in depressive symptoms were observed in treatment compared to control, with an effect size of .84. Suicidal ideation and intent were reduced in both the treatment and control groups, with no attempts during the trial. AWARE is uniquely tailored to serve a pressing clinical need. These results support its feasibility and safety. A large-scale trial targeted at women with PTSD is recommended to further explore the efficacy of AWARE. (PsycINFO Database Record (c) 2019 APA, all rights reserved).


Subject(s)
Asian/psychology , Empowerment , Psychotherapy, Group , Resilience, Psychological , Adult , Culturally Competent Care , Female , Humans , Risk-Taking , Sexual Behavior/psychology , Stress Disorders, Post-Traumatic/therapy , Violence/psychology , Young Adult
15.
Netw Sci (Camb Univ Press) ; 7(2): 196-214, 2019 Jun.
Article in English | MEDLINE | ID: mdl-33312566

ABSTRACT

The study of complex brain networks, where structural or functional connections are evaluated to create an interconnected representation of the brain, has grown tremendously over the past decade. Much of the statistical network science tools for analyzing brain networks have been developed for cross-sectional studies and for the analysis of static networks. However, with both an increase in longitudinal study designs, as well as an increased interest in the neurological network changes that occur during the progression of a disease, sophisticated methods for longitudinal brain network analysis are needed. We propose a paradigm for longitudinal brain network analysis over patient cohorts, with the key challenge being the adaptation of Stochastic Actor-Oriented Models (SAOMs) to the neuroscience setting. SAOMs are designed to capture network dynamics representing a variety of influences on network change in a continuous-time Markov chain framework. Network dynamics are characterized through both endogenous (i.e., network related) and exogenous effects, where the latter include mechanisms conjectured in the literature. We outline an application to the resting-state fMRI setting with data from the Alzheimers Disease Neuroimaging Initiative (ADNI) study. We draw illustrative conclusions at the subject level and make a comparison between elderly controls and individuals with AD.

16.
Biometrics ; 74(4): 1351-1361, 2018 12.
Article in English | MEDLINE | ID: mdl-29772079

ABSTRACT

Cellular mechanism-of-action is of fundamental concern in many biological studies. It is of particular interest for identifying the cause of disease and learning the way in which treatments act against disease. However, pinpointing such mechanisms is difficult, due to the fact that small perturbations to the cell can have wide-ranging downstream effects. Given a snapshot of cellular activity, it can be challenging to tell where a disturbance originated. The presence of an ever-greater variety of high-throughput biological data offers an opportunity to examine cellular behavior from multiple angles, but also presents the statistical challenge of how to effectively analyze data from multiple sources. In this setting, we propose a method for mechanism-of-action inference by extending network filtering to multi-attribute data. We first estimate a joint Gaussian graphical model across multiple data types using penalized regression and filter for network effects. We then apply a set of likelihood ratio tests to identify the most likely site of the original perturbation. In addition, we propose a conditional testing procedure to allow for detection of multiple perturbations. We demonstrate this methodology on paired gene expression and methylation data from The Cancer Genome Atlas (TCGA).


Subject(s)
Biometry/methods , Computer Simulation/statistics & numerical data , Systems Biology/methods , Cell Physiological Phenomena , Computational Biology/methods , DNA Methylation , Data Interpretation, Statistical , Gene Expression Profiling , Humans , Neoplasms/genetics , Regression Analysis
17.
Dev Med Child Neurol ; 60(8): 801-809, 2018 08.
Article in English | MEDLINE | ID: mdl-29528103

ABSTRACT

AIM: Project TEAM (Teens making Environment and Activity Modifications) teaches transition-age young people with developmental disabilities, including those with co-occurring intellectual or cognitive disabilities, to identify and resolve environmental barriers to participation. We examined its effects on young people's attainment of participation goals, knowledge, problem-solving, self-determination, and self-efficacy. METHOD: We used a quasi-experimental, repeated measures design (initial, outcome, 6-week follow-up) with two groups: (1) Project TEAM (28 males, 19 females; mean age 17y 6mo); and (2) goal-setting comparison (21 males, 14 females; mean age 17y 6mo). A matched convenience sample was recruited in two US states. Attainment of participation goals and goal attainment scaling (GAS) T scores were compared at outcome. Differences between groups for all other outcomes were analyzed using linear mixed effects models. RESULTS: At outcome, Project TEAM participants demonstrated greater knowledge (estimated mean difference: 1.82; confidence interval [CI]: 0.90, 2.74) and ability to apply knowledge during participation (GAS: t[75]=4.21; CI: 5.21, 14.57) compared to goal-setting. While both groups achieved significant improvements in knowledge, problem-solving, and self-determination, increases in parent reported self-determination remained at 6-week follow-up only for Project TEAM (estimated mean difference: 4.65; CI: 1.32, 7.98). Significantly more Project TEAM participants attained their participation goals by follow-up (Project TEAM=97.6%, goal-setting=77.1%, p=0.009). INTERPRETATION: Both approaches support attainment of participation goals. Although inconclusive, Project TEAM may uniquely support young people with developmental disabilities to act in a self-determined manner and apply an environmental problem-solving approach over time. WHAT THIS PAPER ADDS: Individualized goal-setting, alone or during Project TEAM (Teens making Environment and Activity Modifications) appears to support attainment of participation goals. Project TEAM appears to support young people with developmental disabilities to apply an environmental problem-solving approach to participation barriers. Parents of young people with developmental disabilities report sustained changes in self-determination 6 weeks after Project TEAM.


Subject(s)
Cognitive Remediation/methods , Developmental Disabilities/rehabilitation , Intellectual Disability/rehabilitation , Occupational Therapy/methods , Outcome Assessment, Health Care , Problem Solving , Adolescent , Adult , Comorbidity , Developmental Disabilities/epidemiology , Female , Follow-Up Studies , Goals , Humans , Intellectual Disability/epidemiology , Male , Personal Autonomy , Self Efficacy , Social Participation , Young Adult
18.
Cell Rep ; 21(10): 2688-2695, 2017 Dec 05.
Article in English | MEDLINE | ID: mdl-29212017

ABSTRACT

Alteration of corticostriatal glutamatergic function is an early pathophysiological change associated with Huntington's disease (HD). The factors that regulate the maintenance of corticostriatal glutamatergic synapses post-developmentally are not well understood. Recently, the striatum-enriched transcription factor Foxp2 was implicated in the development of these synapses. Here, we show that, in mice, overexpression of Foxp2 in the adult striatum of two models of HD leads to rescue of HD-associated behaviors, while knockdown of Foxp2 in wild-type mice leads to development of HD-associated behaviors. We note that Foxp2 encodes the longest polyglutamine repeat protein in the human reference genome, and we show that it can be sequestered into aggregates with polyglutamine-expanded mutant Huntingtin protein (mHTT). Foxp2 overexpression in HD model mice leads to altered expression of several genes associated with synaptic function, genes that present additional targets for normalization of corticostriatal dysfunction in HD.


Subject(s)
Corpus Striatum/metabolism , Forkhead Transcription Factors/metabolism , Huntington Disease/metabolism , Repressor Proteins/metabolism , Animals , Blotting, Western , Disease Models, Animal , Fluorescent Antibody Technique, Indirect , Forkhead Transcription Factors/genetics , Gene Expression Regulation/genetics , Gene Expression Regulation/physiology , Humans , Huntington Disease/genetics , Male , Mice , Phenotype , Repressor Proteins/genetics
19.
Hum Hered ; 81(3): 142-149, 2016.
Article in English | MEDLINE | ID: mdl-28002817

ABSTRACT

OBJECTIVE: Penalized regression has been successfully applied in genome-wide association studies. While meta-analysis is often conducted to increase power and protect patients' confidentiality, methods for meta-analyzing results of penalized regression in multi-cohort setting are still under development. METHODS: We propose to use a data-splitting method to obtain valid p values (or equivalently, coefficient estimates and standard errors) for meta-analysis across multiple cohorts. We examine two ways of splitting data in multi-cohort setting and propose three methods to conduct meta-analysis based on p values. We compare the three meta-analysis methods to mega-analysis, which consists of pooling individual level data. We also apply our proposed meta-analysis approaches to the Framingham Heart Study data, where we divide the original dataset into four parts to create a multi-cohort scenario. RESULTS: The simulations suggest that splitting cohorts has better performance than splitting data within each cohort. The real data application also shows that this method provides results that are similar to the mega-analysis. CONCLUSION: After comparing the three methods that we proposed to conduct meta-analysis, we recommend splitting cohorts rather than datasets to obtain valid p values for meta-analysis of results from penalized regression in multi-cohort setting.


Subject(s)
Genome-Wide Association Study , Cohort Studies , Computer Simulation , Humans , Regression Analysis
20.
Diabetes ; 65(12): 3794-3804, 2016 Dec.
Article in English | MEDLINE | ID: mdl-27625022

ABSTRACT

Genome-wide association studies (GWAS) have successfully identified genetic loci associated with glycemic traits. However, characterizing the functional significance of these loci has proven challenging. We sought to gain insights into the regulation of fasting insulin and fasting glucose through the use of gene expression microarray data from peripheral blood samples of participants without diabetes in the Framingham Heart Study (FHS) (n = 5,056), the Rotterdam Study (RS) (n = 723), and the InCHIANTI Study (Invecchiare in Chianti) (n = 595). Using a false discovery rate q <0.05, we identified three transcripts associated with fasting glucose and 433 transcripts associated with fasting insulin levels after adjusting for age, sex, technical covariates, and complete blood cell counts. Among the findings, circulating IGF2BP2 transcript levels were positively associated with fasting insulin in both the FHS and RS. Using 1000 Genomes-imputed genotype data, we identified 47,587 cis-expression quantitative trait loci (eQTL) and 6,695 trans-eQTL associated with the 433 significant insulin-associated transcripts. Of note, we identified a trans-eQTL (rs592423), where the A allele was associated with higher IGF2BP2 levels and with fasting insulin in an independent genetic meta-analysis comprised of 50,823 individuals. We conclude that integration of genomic and transcriptomic data implicate circulating IGF2BP2 mRNA levels associated with glucose and insulin homeostasis.


Subject(s)
Blood Glucose/metabolism , Fasting/blood , Insulin/blood , Transcriptome/genetics , Adult , Aged , Female , Genetic Predisposition to Disease/genetics , Genome-Wide Association Study , Genotype , Humans , Male , Middle Aged , Quantitative Trait Loci/genetics , RNA, Messenger/genetics , RNA-Binding Proteins/genetics
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