The Crystal Structures of Some Mercury (II) Complexes and a Trihalide Compound

Chapter I reviews relevant structural knowledge of compounds containing mercury (II). Much of this material is to be found in a review by Deacon (1963), and will appear in a review by Grdenic (1965)

Diagnosing Autism Spectrum Disorder in community settings using the development and well-being assessment: validation in a UK population-based twin sample

Background: Increasing numbers of people are being referred for assessment for autism spectrum disorder (ASD). The NICE (UK) and the American Academy of Pediatrics recommend gathering a developmental history using a tool that operationalises ICD/DSM criteria. However the best-established diagnostic interview instruments are time-consuming, costly and rarely used outside national specialist centres. What is needed is a brief, cost-effective measure validated in community settings. We tested the Development and Well-Being Assessment (DAWBA) for diagnosing ASD in a sample of children/adolescents representative of those presenting in community mental health settings. Methods: A general population sample of twins (TEDS) was screened and 276 adolescents were selected as at low (CAST score<12; n=164) or high risk for ASD (CAST score≥15 and/or parent reported that ASD suspected/previously diagnosed; n=112). Parents completed the ASD module of the DAWBA interview by telephone or online. Families were visited at home: the ADI-R and ADOS were completed to allow a best-estimate diagnosis of ASD to be made. Results: DAWBA ASD symptom scores correlated highly with ADI-R algorithm scores (rho=.82, p<.001). Good sensitivity (0.88) and specificity (0.85) were achieved using DAWBA computerised algorithms. Clinician review of responses to DAWBA questions minimally changed sensitivity (0.86) and specificity (0.87). Positive (0.82-0.95) and negative (0.90) predictive values were high. Eighty-six percent of children were correctly classified. Performance was improved by using it in conjunction with the ADOS. Conclusions: The DAWBA is a brief structured interview that showed good sensitivity and specificity in this general population sample. It requires little training, is easy to administer (online or by interview), and diagnosis is aided by an algorithm. It holds promise as a tool for assisting with assessment in community settings and may help services implement the recommendations made by NICE and the American Academy of Pediatrics regarding diagnosis of young people on the autism spectru

The Politics of Environmental Dispute Resolution

Also PCMA Working Paper #17.http://deepblue.lib.umich.edu/bitstream/2027.42/51148/1/380.pd

Oral Abstracts 7: RA ClinicalO37. Long-Term Outcomes of Early RA Patients Initiated with Adalimumab Plus Methotrexate Compared with Methotrexate Alone Following a Targeted Treatment Approach

Background: This analysis assessed, on a group level, whether there is a long-term advantage for early RA patients treated with adalimumab (ADA) + MTX vs those initially treated with placebo (PBO) + MTX who either responded to therapy or added ADA following inadequate response (IR). Methods: OPTIMA was a 78- week, randomized, controlled trial of ADA + MTX vs PBO + MTX in MTX-naïve early (<1 year) RA patients. Therapy was adjusted at week 26: ADA + MTX-responders (R) who achieved DAS28 (CRP) <3.2 at weeks 22 and 26 (Period 1, P1) were re-randomized to withdraw or continue ADA and PBO + MTX-R continued randomized therapy for 52 weeks (P2); IR-patients received open-label (OL) ADA + MTX during P2. This post hoc analysis evaluated the proportion of patients at week 78 with DAS28 (CRP) <3.2, HAQ-DI <0.5, and/or ΔmTSS ≤0.5 by initial treatment. To account for patients who withdrew ADA during P2, an equivalent proportion of R was imputed from ADA + MTX-R patients. Results: At week 26, significantly more patients had low disease activity, normal function, and/or no radiographic progression with ADA + MTX vs PBO + MTX (Table 1). Differences in clinical and functional outcomes disappeared following additional treatment, when PBO + MTX-IR (n = 348/460) switched to OL ADA + MTX. Addition of OL ADA slowed radiographic progression, but more patients who received ADA + MTX from baseline had no radiographic progression at week 78 than patients who received initial PBO + MTX. Conclusions: Early RA patients treated with PBO + MTX achieved comparable long-term clinical and functional outcomes on a group level as those who began ADA + MTX, but only when therapy was optimized by the addition of ADA in PBO + MTX-IR. Still, ADA + MTX therapy conferred a radiographic benefit although the difference did not appear to translate to an additional functional benefit. Disclosures: P.E., AbbVie, Merck, Pfizer, UCB, Roche, BMS—Provided Expert Advice, Undertaken Trials, AbbVie—AbbVie sponsored the study, contributed to its design, and participated in the collection, analysis, and interpretation of the data, and in the writing, reviewing, and approval of the final version. R.F., AbbVie, Pfizer, Merck, Roche, UCB, Celgene, Amgen, AstraZeneca, BMS, Janssen, Lilly, Novartis—Research Grants, Consultation Fees. S.F., AbbVie—Employee, Stocks. A.K., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, Pfizer, Roche, UCB—Research Grants, Consultation Fees. H.K., AbbVie—Employee, Stocks. S.R., AbbVie—Employee, Stocks. J.S., AbbVie, Amgen, AstraZeneca, BMS, Celgene, Centocor-Janssen, GlaxoSmithKline, Lilly, Pfizer (Wyeth), MSD (Schering-Plough), Novo-Nordisk, Roche, Sandoz, UCB—Research Grants, Consultation Fees. R.V., AbbVie, BMS, GlaxoSmithKline, Human Genome Sciences, Merck, Pfizer, Roche, UCB Pharma—Consultation Fees, Research Support. Table 1.Week 78 clinical, functional, and radiographic outcomes in patients who received continued ADA + MTX vs those who continued PBO + MTX or added open-label ADA following an inadequate response ADA + MTX, n/N (%)a PBO + MTX, n/N (%)b Outcome Week 26 Week 52 Week 78 Week 26 Week 52 Week 78 DAS28 (CRP) <3.2 246/466 (53) 304/465 (65) 303/465 (65) 139/460 (30)*** 284/460 (62) 300/460 (65) HAQ-DI <0.5 211/466 (45) 220/466 (47) 224/466 (48) 150/460 (33)*** 203/460 (44) 208/460 (45) ΔmTSS ≤0.5 402/462 (87) 379/445 (86) 382/443 (86) 330/459 (72)*** 318/440 (72)*** 318/440 (72)*** DAS28 (CRP) <3.2 + ΔmTSS ≤0.5 216/462 (47) 260/443 (59) 266/443 (60) 112/459 (24)*** 196/440 (45) 211/440 (48)*** DAS28 (CRP) <3.2 + HAQ-DI <0.5 + ΔmTSS ≤0.5 146/462 (32) 168/443 (38) 174/443 (39) 82/459 (18)*** 120/440 (27)*** 135/440 (31)** aIncludes patients from the ADA Continuation (n = 105) and OL ADA Carry On (n = 259) arms, as well as the proportional equivalent number of responders from the ADA Withdrawal arm (n = 102). bIncludes patients from the MTX Continuation (n = 112) and Rescue ADA (n = 348) arms. Last observation carried forward: DAS28 (CRP) and HAQ-DI; Multiple imputations: ΔmTSS. ***P < 0.001 and **iP < 0.01, respectively, for differences between initial treatments from chi-squar

The genetic architecture of the human cerebral cortex

The cerebral cortex underlies our complex cognitive capabilities, yet little is known about the specific genetic loci that influence human cortical structure. To identify genetic variants that affect cortical structure, we conducted a genome-wide association meta-analysis of brain magnetic resonance imaging data from 51,665 individuals. We analyzed the surface area and average thickness of the whole cortex and 34 regions with known functional specializations. We identified 199 significant loci and found significant enrichment for loci influencing total surface area within regulatory elements that are active during prenatal cortical development, supporting the radial unit hypothesis. Loci that affect regional surface area cluster near genes in Wnt signaling pathways, which influence progenitor expansion and areal identity. Variation in cortical structure is genetically correlated with cognitive function, Parkinson's disease, insomnia, depression, neuroticism, and attention deficit hyperactivity disorder

Heritability of autism spectrum disorder in a UK population-based twin sample

Importance Most evidence to date highlights the importance of genetic influences on the liability to autism and related traits. However, most of these findings are derived from clinically ascertained samples, possibly missing individuals with subtler manifestations, and obtained estimates may not be representative of the population. Objectives To establish the relative contributions of genetic and environmental factors in liability to autism spectrum disorder (ASD) and a broader autism phenotype in a large population-based twin sample and to ascertain the genetic/environmental relationship between dimensional trait measures and categorical diagnostic constructs of ASD. Design, Setting, and Participants We used data from the population-based cohort Twins Early Development Study, which included all twin pairs born in England and Wales from January 1, 1994, through December 31, 1996. We performed joint continuous-ordinal liability threshold model fitting using the full information maximum likelihood method to estimate genetic and environmental parameters of covariance. Twin pairs underwent the following assessments: the Childhood Autism Spectrum Test (CAST) (6423 pairs; mean age, 7.9 years), the Development and Well-being Assessment (DAWBA) (359 pairs; mean age, 10.3 years), the Autism Diagnostic Observation Schedule (ADOS) (203 pairs; mean age, 13.2 years), the Autism Diagnostic Interview–Revised (ADI-R) (205 pairs; mean age, 13.2 years), and a best-estimate diagnosis (207 pairs). Main Outcomes and Measures Participants underwent screening using a population-based measure of autistic traits (CAST assessment), structured diagnostic assessments (DAWBA, ADI-R, and ADOS), and a best-estimate diagnosis. Results On all ASD measures, correlations among monozygotic twins (range, 0.77-0.99) were significantly higher than those for dizygotic twins (range, 0.22-0.65), giving heritability estimates of 56% to 95%. The covariance of CAST and ASD diagnostic status (DAWBA, ADOS and best-estimate diagnosis) was largely explained by additive genetic factors (76%-95%). For the ADI-R only, shared environmental influences were significant (30% [95% CI, 8%-47%]) but smaller than genetic influences (56% [95% CI, 37%-82%]). Conclusions and Relevance The liability to ASD and a more broadly defined high-level autism trait phenotype in this large population-based twin sample derives primarily from additive genetic and, to a lesser extent, nonshared environmental effects. The largely consistent results across different diagnostic tools suggest that the results are generalizable across multiple measures and assessment methods. Genetic factors underpinning individual differences in autismlike traits show considerable overlap with genetic influences on diagnosed ASD