Stephen V. Faraone

Bio: Stephen V. Faraone is an academic researcher from State University of New York Upstate Medical University. The author has contributed to research in topics: Attention deficit hyperactivity disorder & Bipolar disorder. The author has an hindex of 188, co-authored 1427 publications receiving 140298 citations. Previous affiliations of Stephen V. Faraone include University of Bergen & National Institute for Health Research.

Diagnoses of Attention-deficit Hyperactivity Disorder from Parent R Predict Diagnoses Based on Teacher Reports

Abstract: Objective For DSM-III attention deficit disorder (ADD), it was previously reported that, when a parent report leads to a diagnosis of ADD, it is highly likely that the teacher report will also be positive. This report seeks to generalize that finding to DSM-III-R attention-deficit hyperactivity disorder (ADHD). Method In a population of 34 children meeting clinical criteria for DSM-III-R ADHD, parents and teachers independently responded to questions about individual ADHD symptoms. Results Correlations between parents and teachers for individual symptoms were low to moderate; however, there was a 77% probability that the teacher report would result in a positive diagnosis given a positive parent diagnosis. This probability increased to 88% if "broad" teacher diagnoses of ADHD, defined by 35% of the 14 DSM-III-R symptoms, were included. Conclusions In clinically-referred children, a clinical diagnosis of ADHD based on parent report is likely to be corroborated by a teacher report.

Child behavior checklist clinical scales discriminate referred youth with autism spectrum disorder: a preliminary study.

Abstract: Objective To evaluate the properties of clinical scales of the Child Behavior Checklist in discriminating referred children with autism spectrum disorders (ASDs) (autistic disorder, Asperger's disorder, and pervasive developmental disorder not otherwise specified) from psychiatrically referred children without ASDs. Method Comparisons were made between children with ASDs (n = 65) with intelligence quotient >70 and children without ASDs (N = 83) on the clinical scales of the Child Behavior Checklist. Stepwise logistic regression was used to identify those scales that best predicted ASDs when compared with the non-ASD comparison group. Receiver operating characteristic curves examined the ability of the significant predictor T-scores to identify ASDs versus the non-ASD subjects. Results Withdrawn, Social Problems, and Thought Problems T-scores were the best independent predictors of ASD status. The Withdrawn + Social + Thought Problems T-scores yielded an area under the curve of 0.86, indicating an 86% chance that a randomly selected sample of ASD subject will have abnormal scores on these scales than a randomly selected sample of non-ASD subjects. Conclusion These findings suggest that a new Child Behavior Checklist-ASD profile consisting of the Child Behavior Checklist-Withdrawn, Social, and Thought Problems scales could serve as a rapid and cost-effective screening instrument to help identify cases likely to meet clinical criteria for ASDs in the clinical setting.

Report from the 4th international meeting of the attention deficit hyperactivity disorder molecular genetics network.

Abstract: Investigators from around the world have convened annually since 1999 to share their work on the molecular genetic basis of attention deficit hyperactivity disorder (ADHD). The 4th meeting of the ADHD Molecular Genetics Network was held in May 2002. This conference began with a discussion of ongoing and proposed collaborative projects. Ongoing collaborations include the International Multi-Center ADHD Genetics (IMAGE) project, a study of additive and interactive effects of DAT1 and DRD4 on ADHD and a meta-analysis of association studies of ADHD and the DRD5 gene. A new collaborative project regarding DNA pooling was also proposed. Over 20 investigators presented recent findings from their individual research sites. Participants also broke up into working groups to discuss additional projects and topics of mutual interest, including population genetic studies using dimensional phenotypic data and a QTL approach, endophenotypes for ADHD and the genetics of ADHD in adults. Finally, participant feedback was obtained about the utility of the ADHD Molecular Genetics Network conferences and the e-mail network. Although technological and statistical advances in the field of molecular genetics have allowed researchers to begin to identify specific genes that are associated with ADHD, continued collaborative efforts are needed to fully elucidate the genetic underpinnings of this complex phenotype. Data collected at the current conference indicate that the ADHD Molecular Genetics Network has facilitated new collaborative as well as single-site projects among ADHD researchers.

ADHD Symptoms vs. Impairment: Revisited

Abstract: We concluded that symptoms and impairment were somewhat distinct dimensions that should be considered as such in the diagnostic process. We further concluded that there appeared to be a weak relationship between symptoms and impairment such that clinical cases of ADHD could display the full range of ADHD‐type symptoms without necessarily displaying significant impairment. The prior article also highlighted limitations in current approaches to the assessment of impairment and statistical methods used to evaluate its relationship to symptom severity. The present article illustrates this issue by examining the relationship of ADHD symptom severity to impairment using different means of defining impairment and of analyzing the risk for impairment in major life activities. Specifically, we report on the relationship of ADHD symptoms to several investigator‐created omnibus impairment indexes. These are largely drawn from the types of specific measures of impairment used in our earlier paper. In this case, however, we convert these largely dimensional measures into discrete dichotomous categories (being impaired or not) on each specific measure. The omnibus index is then created by summing across these discrete measures to achieve a count of the total number of different measures on which impairment could be said to exist. We also examine self, other, employer, and clinician ratings of impairment across a number of domains of major life activities (work, school, family, social, driving,

Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US

Abstract: Short-term probabilistic forecasts of the trajectory of the COVID-19 pandemic in the United States have served as a visible and important communication channel between the scientific modeling community and both the general public and decision-makers Forecasting models provide specific, quantitative, and evaluable predictions that inform short-term decisions such as healthcare staffing needs, school closures, and allocation of medical supplies In 2020, the COVID-19 Forecast Hub (https://covid19forecasthuborg/) collected, disseminated, and synthesized hundreds of thousands of specific predictions from more than 50 different academic, industry, and independent research groups This manuscript systematically evaluates 23 models that regularly submitted forecasts of reported weekly incident COVID-19 mortality counts in the US at the state and national level One of these models was a multi-model ensemble that combined all available forecasts each week The performance of individual models showed high variability across time, geospatial units, and forecast horizons Half of the models evaluated showed better accuracy than a naive baseline model In combining the forecasts from all teams, the ensemble showed the best overall probabilistic accuracy of any model Forecast accuracy degraded as models made predictions farther into the future, with probabilistic accuracy at a 20-week horizon more than 5 times worse than when predicting at a 1-week horizon This project underscores the role that collaboration and active coordination between governmental public health agencies, academic modeling teams, and industry partners can play in developing modern modeling capabilities to support local, state, and federal response to outbreaks

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Using Multivariate Statistics

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Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.