Exhaustive and Efficient Constraint Propagation: A Semi-Supervised Learning Perspective and Its Applications

This paper presents a novel pairwise constraint propagation approach by decomposing the challenging constraint propagation problem into a set of independent semi-supervised learning subproblems which can be solved in quadratic time using label propagation based on k-nearest neighbor graphs. Considering that this time cost is proportional to the number of all possible pairwise constraints, our approach actually provides an efficient solution for exhaustively propagating pairwise constraints throughout the entire dataset. The resulting exhaustive set of propagated pairwise constraints are further used to adjust the similarity matrix for constrained spectral clustering. Other than the traditional constraint propagation on single-source data, our approach is also extended to more challenging constraint propagation on multi-source data where each pairwise constraint is defined over a pair of data points from different sources. This multi-source constraint propagation has an important application to cross-modal multimedia retrieval. Extensive results have shown the superior performance of our approach.Comment: The short version of this paper appears as oral paper in ECCV 201

Genetic and environmental influences on the stability of withdrawn behavior in children: A longitudinal, multi-informant twin study.

We examined the contribution of genetic and environmental influences on the stability of withdrawn behavior (WB) in childhood using a longitudinal multiple rater twin design. Maternal and paternal ratings on the withdrawn subscale of the Child Behavior Checklist (CBCL) were obtained from 14,889 families when the twins were 3, 7, 10 and 12 years old. A longitudinal psychometric model was fitted to the data and the fit of transmission and common factor models were evaluated for each variance component. WB showed considerable stability throughout childhood, with correlation coefficients ranging from about .30 for the 9-year time interval to .65 for shorter time intervals. Individual differences in WB as observed by the mother and the father were found to be largely influenced by genetic effects at all four time points, in both boys (50–66%) and girls (38–64%). Shared environmental influences explained a small to modest proportion (0–24%) of the variance at all ages and were slightly more pronounced in girls. Non-shared environmental influences were of moderate importance to the variance and slightly increased with age, from 22–28% at age 3 to 35–41% at age 12 years. The stability of WB was largely explained by genetic effects, accounting for 74% of stability in boys and 65% in girls. Shared environmental effects explained 7% (boys) and 17% (girls) of the behavioral stability. Most shared environmental effects were common to both raters, suggesting little influence of rater bias in the assessment of WB. The shared environmental effects common to both raters were best described by a common factor model, indicating that these effects are stable and persistent throughout childhood. Non-shared environmental effects accounted for the remaining covariance over time

Cross-cultural adaptation and clinical validation of the neonatal skin condition score to Brazilian Portuguese

Objective: to describe the process of cross-cultural adaptation and clinical validation of the Neonatal Skin Condition Score. Methods: this methodological cross-cultural adaptation study included five steps: initial translation, synthesis of the initial translation, back translation, review by an Committee of Specialists and testing of the pre-final version, and an observational cross-sectional study with analysis of the psychometric properties using the Adjusted Kappa, Intraclass Correlation Coefficient, and Bland- Altman Method statistical tests. A total of 38 professionals were randomly recruited to review the clarity of the adapted instrument, and 47 newborns hospitalized in the Neonatology Unit of the Clinical Hospital of Porto Alegre were selected by convenience for the clinical validation of the instrument. Results: the adapted scale showed approximately 85% clarity. The statistical tests showed moderate to strong intra and interobserver item to item reliability and from strong to very strong in the total score, with a variation of less than 2 points among the scores assigned by the nurses to the patients. Conclusions: the scale was adapted and validated to Brazilian Portuguese. The psychometric properties of the Brazilian version of the Neonatal Skin Condition Score instrument were similar to the validation results of the original scale

High frequency of CHD7 mutations in congenital hypogonadotropic hypogonadism

Congenital hypogonadotropic hypogonadism (CHH) is characterized by lack of normal pubertal development due to deficient gonadotropin-releasing hormone (GnRH) secretion or action, and is caused by genetic defects in several genes. Mutations in the CHD7 gene cause CHARGE syndrome (Coloboma of the eye, Heart defects, Atresia of the choanae, Retardation of growth and development, Genital hypoplasia and Ear abnormalities), but have also been found in patients with isolated CHH. The aim of this study was to identify CHD7 mutations in patients with CHH. Fifty Portuguese patients with CHH were screened for mutations in the CHD7 gene by DNA sequencing. Eight (16%) patients had CHD7 rare sequence variants that consisted of six missense (p.Gly388Glu, p.His903Pro, p.Thr1082Ile, p.Val1452Leu, p.Asp1854Gly, and p.Arg2065His) and two synonymous (p.Ser559Ser, and p.Ala2785Ala) mutations. Five of these mutations have never been reported before. Three CHD7 mutations occurred in patients that had mutations in additional CHH-genes. This study uncovered novel genetic variants that expand the known spectrum of mutations associated with CHH. The frequency of CHD7 mutations in this cohort was higher than that of other major CHH-genes and confirms the importance of including CHD7 in the genetic testing of CHH, even in the absence of additional CHARGE features.info:eu-repo/semantics/publishedVersio

Removal of emerging contaminants for water reuse by membrane technology

Emerging trace organic contaminants (TrOCs) are broadly defined as any synthetic or naturally occurring chemicals that have not been routinely monitored but have the potential to enter the environment and cause known or suspected adverse ecological or human health effects [1]. Most of these TrOCs are from municipal, agricultural and industrial wastewater sources (see Figure 9.1). Their release to the environment had likely occurred for a long time but was only recently recognised by new and advanced analytical methods [2]. In some cases, emerging contaminants can also result from the synthesis of new chemicals either intentionally as a chemical of industrial interest or unintentionally as a by-product. A notable example of the latter is the N-nitrosamine group that can be formed as by-products due to a range of industrial activities including the production of rocket fuel, rubber and tobacco as well as water or wastewater disinfection by chloramine [3]

Detecting Specific Genotype by Environment Interactions Using Marginal Maximum Likelihood Estimation in the Classical Twin Design

Considerable effort has been devoted to the analysis of genotype by environment (G × E) interactions in various phenotypic domains, such as cognitive abilities and personality. In many studies, environmental variables were observed (measured) variables. In case of an unmeasured environment, van der Sluis et al. (2006) proposed to study heteroscedasticity in the factor model using only MZ twin data. This method is closely related to the Jinks and Fulker (1970) test for G × E, but slightly more powerful. In this paper, we identify four challenges to the investigation of G × E in general, and specifically to the heteroscedasticity approaches of Jinks and Fulker and van der Sluis et al. We propose extensions of these approaches purported to solve these problems. These extensions comprise: (1) including DZ twin data, (2) modeling both A × E and A × C interactions; and (3) extending the univariate approach to a multivariate approach. By means of simulations, we study the power of the univariate method to detect the different G × E interactions in varying situations. In addition, we study how well we could distinguish between A × E, A × C, and C × E. We apply a multivariate version of the extended model to an empirical data set on cognitive abilities

The stability of educational achievement across school years is largely explained by genetic factors.

Little is known about the etiology of developmental change and continuity in educational achievement. Here, we study achievement from primary school to the end of compulsory education for 6000 twin pairs in the UK-representative Twins Early Development Study sample. Results showed that educational achievement is highly heritable across school years and across subjects studied at school (twin heritability ~60%; SNP heritability ~30%); achievement is highly stable (phenotypic correlations ~0.70 from ages 7 to 16). Twin analyses, applying simplex and common pathway models, showed that genetic factors accounted for most of this stability (70%), even after controlling for intelligence (60%). Shared environmental factors also contributed to the stability, while change was mostly accounted for by individual-specific environmental factors. Polygenic scores, derived from a genome-wide association analysis of adult years of education, also showed stable effects on school achievement. We conclude that the remarkable stability of achievement is largely driven genetically even after accounting for intelligence