Treatment Outcome in Patients Receiving Assertive Community Treatment

In an observational study of severely mentally ill patients treated in assertive community treatment (ACT) teams, we investigated how treatment outcome was associated with demographic factors, clinical factors, and motivation for treatment. To determine psychosocial outcome, patients were routinely assessed using the Health of the Nation Outcome Scales (HoNOS). Trends over time were analyzed using a mixed model with repeated measures. The HoNOS total score was modeled as a function of treatment duration and patient-dependent covariates. Data comprised 637 assessments of 139 patients; mean duration of follow-up was 27.4 months (SD = 5.4). Substance abuse, higher age, problems with motivation, and lower educational level were associated with higher HoNOS total scores (i.e., worse outcome). To improve treatment outcome, we recommend better implementation of ACT, and also the implementation of additional programs targeting subgroups which seem to benefit less from ACT

Prime movers : mechanochemistry of mitotic kinesins

Mitotic spindles are self-organizing protein machines that harness teams of multiple force generators to drive chromosome segregation. Kinesins are key members of these force-generating teams. Different kinesins walk directionally along dynamic microtubules, anchor, crosslink, align and sort microtubules into polarized bundles, and influence microtubule dynamics by interacting with microtubule tips. The mechanochemical mechanisms of these kinesins are specialized to enable each type to make a specific contribution to spindle self-organization and chromosome segregation

Genetics of Microenvironmental Sensitivity of Body Weight in Rainbow Trout (Oncorhynchus mykiss) Selected for Improved Growth

Microenvironmental sensitivity of a genotype refers to the ability to buffer against non-specific environmental factors, and it can be quantified by the amount of residual variation in a trait expressed by the genotype’s offspring within a (macro)environment. Due to the high degree of polymorphism in behavioral, growth and life-history traits, both farmed and wild salmonids are highly susceptible to microenvironmental variation, yet the heritable basis of this characteristic remains unknown. We estimated the genetic (co)variance of body weight and its residual variation in 2-year-old rainbow trout (Oncorhynchus mykiss) using a multigenerational data of 45,900 individuals from the Finnish national breeding programme. We also tested whether or not microenvironmental sensitivity has been changed as a correlated genetic response when genetic improvement for growth has been practiced over five generations. The animal model analysis revealed the presence of genetic heterogeneity both in body weight and its residual variation. Heritability of residual variation was remarkably lower (0.02) than that for body weight (0.35). However, genetic coefficient of variation was notable in both body weight (14%) and its residual variation (37%), suggesting a substantial potential for selection responses in both traits. Furthermore, a significant negative genetic correlation (−0.16) was found between body weight and its residual variation, i.e., rapidly growing genotypes are also more tolerant to perturbations in microenvironment. The genetic trends showed that fish growth was successfully increased by selective breeding (an average of 6% per generation), whereas no genetic change occurred in residual variation during the same period. The results imply that genetic improvement for body weight does not cause a concomitant increase in microenvironmental sensitivity. For commercial production, however, there may be high potential to simultaneously improve weight gain and increase its uniformity if both criteria are included in a selection index

Delinquent Behavior of Dutch Rural Adolescents

This article compares Dutch rural and non-rural adolescents’ delinquent behavior and examines two social correlates of rural delinquency: communal social control and traditional rural culture. The analyses are based on cross-sectional data, containing 3,797 participants aged 13–18 (48.7% females). The analyses show that rural adolescents are only slightly less likely to engage in delinquent behavior. Furthermore, while rural adolescents are exposed more often to communal social control, this does not substantially reduce the likelihood that they engage in delinquent behavior. Concerning rural culture, marked differences appeared between rural and non-rural adolescents. First, alcohol use and the frequency of visiting pubs were more related to rural adolescents’ engagement in delinquent behavior. Second, the gender gap in delinquency is larger among rural adolescents: whereas rural boys did not differ significantly from non-rural boys, rural girls were significantly less likely to engage in delinquent behavior than non-rural girls. However, the magnitude of the effects of most indicators was rather low. To better account for the variety of rural spaces and cultures, it is recommended that future research into antisocial and criminal behavior of rural adolescents should adopt alternative measurements of rurality, instead of using an indicator of population density only

BAC array CGH in patients with Velocardiofacial syndrome-like features reveals genomic aberrations on chromosome region 1q21.1

<p>Abstract</p> <p>Background</p> <p>Microdeletion of the chromosome 22q11.2 region is the most common genetic aberration among patients with velocardiofacial syndrome (VCFS) but a subset of subjects do not show alterations of this chromosome region.</p> <p>Methods</p> <p>We analyzed 18 patients with VCFS-like features by comparative genomic hybridisation (aCGH) array and performed a face-to-face slide hybridization with two different arrays: a whole genome and a chromosome 22-specific BAC array. Putative rearrangements were confirmed by FISH and MLPA assays.</p> <p>Results</p> <p>One patient carried a combination of rearrangements on 1q21.1, consisting in a microduplication of 212 kb and a close microdeletion of 1.15 Mb, previously reported in patients with variable phenotypes, including mental retardation, congenital heart defects (CHD) and schizophrenia. While 326 control samples were negative for both 1q21.1 rearrangements, one of 73 patients carried the same 212-kb microduplication, reciprocal to TAR microdeletion syndrome. Also, we detected four copy number variants (CNVs) inherited from one parent (a 744-kb duplication on 10q11.22; a 160 kb duplication and deletion on 22q11.21 in two cases; and a gain of 140 kb on 22q13.2), not present in control subjects, raising the potential role of these CNVs in the VCFS-like phenotype.</p> <p>Conclusions</p> <p>Our results confirmed aCGH as a successful strategy in order to characterize additional submicroscopic aberrations in patients with VCF-like features that fail to show alterations in 22q11.2 region. We report a 212-kb microduplication on 1q21.1, detected in two patients, which may contribute to CHD.</p

The CanOE Strategy: Integrating Genomic and Metabolic Contexts across Multiple Prokaryote Genomes to Find Candidate Genes for Orphan Enzymes

Of all biochemically characterized metabolic reactions formalized by the IUBMB, over one out of four have yet to be associated with a nucleic or protein sequence, i.e. are sequence-orphan enzymatic activities. Few bioinformatics annotation tools are able to propose candidate genes for such activities by exploiting context-dependent rather than sequence-dependent data, and none are readily accessible and propose result integration across multiple genomes. Here, we present CanOE (Candidate genes for Orphan Enzymes), a four-step bioinformatics strategy that proposes ranked candidate genes for sequence-orphan enzymatic activities (or orphan enzymes for short). The first step locates “genomic metabolons”, i.e. groups of co-localized genes coding proteins catalyzing reactions linked by shared metabolites, in one genome at a time. These metabolons can be particularly helpful for aiding bioanalysts to visualize relevant metabolic data. In the second step, they are used to generate candidate associations between un-annotated genes and gene-less reactions. The third step integrates these gene-reaction associations over several genomes using gene families, and summarizes the strength of family-reaction associations by several scores. In the final step, these scores are used to rank members of gene families which are proposed for metabolic reactions. These associations are of particular interest when the metabolic reaction is a sequence-orphan enzymatic activity. Our strategy found over 60,000 genomic metabolons in more than 1,000 prokaryote organisms from the MicroScope platform, generating candidate genes for many metabolic reactions, of which more than 70 distinct orphan reactions. A computational validation of the approach is discussed. Finally, we present a case study on the anaerobic allantoin degradation pathway in Escherichia coli K-12

Heat or Insulation: Behavioral Titration of Mouse Preference for Warmth or Access to a Nest

In laboratories, mice are housed at 20–24°C, which is below their lower critical temperature (≈30°C). This increased thermal stress has the potential to alter scientific outcomes. Nesting material should allow for improved behavioral thermoregulation and thus alleviate this thermal stress. Nesting behavior should change with temperature and material, and the choice between nesting or thermotaxis (movement in response to temperature) should also depend on the balance of these factors, such that mice titrate nesting material against temperature. Naïve CD-1, BALB/c, and C57BL/6 mice (36 male and 36 female/strain in groups of 3) were housed in a set of 2 connected cages, each maintained at a different temperature using a water bath. One cage in each set was 20°C (Nesting cage; NC) while the other was one of 6 temperatures (Temperature cage; TC: 20, 23, 26, 29, 32, or 35°C). The NC contained one of 6 nesting provisions (0, 2, 4, 6, 8, or 10g), changed daily. Food intake and nest scores were measured in both cages. As the difference in temperature between paired cages increased, feed consumption in NC increased. Nesting provision altered differences in nest scores between the 2 paired temperatures. Nest scores in NC increased with increasing provision. In addition, temperature pairings altered the difference in nest scores with the smallest difference between locations at 26°C and 29°C. Mice transferred material from NC to TC but the likelihood of transfer decreased with increasing provision. Overall, mice of different strains and sexes prefer temperatures between 26–29°C and the shift from thermotaxis to nest building is seen between 6 and 10 g of material. Our results suggest that under normal laboratory temperatures, mice should be provided with no less than 6 grams of nesting material, but up to 10 grams may be needed to alleviate thermal distress under typical temperatures

Bayesian Markov Random Field Analysis for Protein Function Prediction Based on Network Data

Inference of protein functions is one of the most important aims of modern biology. To fully exploit the large volumes of genomic data typically produced in modern-day genomic experiments, automated computational methods for protein function prediction are urgently needed. Established methods use sequence or structure similarity to infer functions but those types of data do not suffice to determine the biological context in which proteins act. Current high-throughput biological experiments produce large amounts of data on the interactions between proteins. Such data can be used to infer interaction networks and to predict the biological process that the protein is involved in. Here, we develop a probabilistic approach for protein function prediction using network data, such as protein-protein interaction measurements. We take a Bayesian approach to an existing Markov Random Field method by performing simultaneous estimation of the model parameters and prediction of protein functions. We use an adaptive Markov Chain Monte Carlo algorithm that leads to more accurate parameter estimates and consequently to improved prediction performance compared to the standard Markov Random Fields method. We tested our method using a high quality S.cereviciae validation network with 1622 proteins against 90 Gene Ontology terms of different levels of abstraction. Compared to three other protein function prediction methods, our approach shows very good prediction performance. Our method can be directly applied to protein-protein interaction or coexpression networks, but also can be extended to use multiple data sources. We apply our method to physical protein interaction data from S. cerevisiae and provide novel predictions, using 340 Gene Ontology terms, for 1170 unannotated proteins and we evaluate the predictions using the available literature