The Study of Rule-Governed Behavior and Derived Stimulus Relations: Bridging the Gap

The concept of rule-governed behavior or instructional control has been widely recognized for many decades within the behavior-analytic literature. It has also been argued that the human capacity to formulate and follow increasingly complex rules may undermine sensitivity to direct contingencies of reinforcement, and that excessive reliance upon rules may be an important variable in human psychological suffering. Although the concept of rules would appear to have been relatively useful within behavior analysis, it seems wise from time to time to reflect upon the utility of even well-established concepts within a scientific discipline. Doing so may be particularly important if it begins to emerge that the existing concept does not readily orient researchers toward potentially important variables associated with that very concept. The primary purpose of this article is to engage in this reflection. In particular, we will focus on the link that has been made between rule-governed behavior and derived relational responding, and consider the extent to which it might be useful to supplement talk of rules or instructions with terms that refer to the dynamics of derived relational responding

Accuracy of genomic breeding values in multi-breed dairy cattle populations

<p>Abstract</p> <p>Background</p> <p>Two key findings from genomic selection experiments are 1) the reference population used must be very large to subsequently predict accurate genomic estimated breeding values (GEBV), and 2) prediction equations derived in one breed do not predict accurate GEBV when applied to other breeds. Both findings are a problem for breeds where the number of individuals in the reference population is limited. A multi-breed reference population is a potential solution, and here we investigate the accuracies of GEBV in Holstein dairy cattle and Jersey dairy cattle when the reference population is single breed or multi-breed. The accuracies were obtained both as a function of elements of the inverse coefficient matrix and from the realised accuracies of GEBV.</p> <p>Methods</p> <p>Best linear unbiased prediction with a multi-breed genomic relationship matrix (GBLUP) and two Bayesian methods (BAYESA and BAYES_SSVS) which estimate individual SNP effects were used to predict GEBV for 400 and 77 young Holstein and Jersey bulls respectively, from a reference population of 781 and 287 Holstein and Jersey bulls, respectively. Genotypes of 39,048 SNP markers were used. Phenotypes in the reference population were de-regressed breeding values for production traits. For the GBLUP method, expected accuracies calculated from the diagonal of the inverse of coefficient matrix were compared to realised accuracies.</p> <p>Results</p> <p>When GBLUP was used, expected accuracies from a function of elements of the inverse coefficient matrix agreed reasonably well with realised accuracies calculated from the correlation between GEBV and EBV in single breed populations, but not in multi-breed populations. When the Bayesian methods were used, realised accuracies of GEBV were up to 13% higher when the multi-breed reference population was used than when a pure breed reference was used. However no consistent increase in accuracy across traits was obtained.</p> <p>Conclusion</p> <p>Predicting genomic breeding values using a genomic relationship matrix is an attractive approach to implement genomic selection as expected accuracies of GEBV can be readily derived. However in multi-breed populations, Bayesian approaches give higher accuracies for some traits. Finally, multi-breed reference populations will be a valuable resource to fine map QTL.</p

Application of site and haplotype-frequency based approaches for detecting selection signatures in cattle

<p>Abstract</p> <p>Background</p> <p>'Selection signatures' delimit regions of the genome that are, or have been, functionally important and have therefore been under either natural or artificial selection. In this study, two different and complementary methods--integrated Haplotype Homozygosity Score (|iHS|) and population differentiation index (F_ST)--were applied to identify traces of decades of intensive artificial selection for traits of economic importance in modern cattle.</p> <p>Results</p> <p>We scanned the genome of a diverse set of dairy and beef breeds from Germany, Canada and Australia genotyped with a 50 K SNP panel. Across breeds, a total of 109 extreme |iHS| values exceeded the empirical threshold level of 5% with 19, 27, 9, 10 and 17 outliers in Holstein, Brown Swiss, Australian Angus, Hereford and Simmental, respectively. Annotating the regions harboring clustered |iHS| signals revealed a panel of interesting candidate genes like SPATA17, MGAT1, PGRMC2 and ACTC1, COL23A1, MATN2, respectively, in the context of reproduction and muscle formation. In a further step, a new Bayesian F_ST-based approach was applied with a set of geographically separated populations including Holstein, Brown Swiss, Simmental, North American Angus and Piedmontese for detecting differentiated loci. In total, 127 regions exceeding the 2.5 per cent threshold of the empirical posterior distribution were identified as extremely differentiated. In a substantial number (56 out of 127 cases) the extreme F_STvalues were found to be positioned in poor gene content regions which deviated significantly (p < 0.05) from the expectation assuming a random distribution. However, significant F_STvalues were found in regions of some relevant genes such as SMCP and FGF1.</p> <p>Conclusions</p> <p>Overall, 236 regions putatively subject to recent positive selection in the cattle genome were detected. Both |iHS| and F_STsuggested selection in the vicinity of the Sialic acid binding Ig-like lectin 5 gene on BTA18. This region was recently reported to be a major QTL with strong effects on productive life and fertility traits in Holstein cattle. We conclude that high-resolution genome scans of selection signatures can be used to identify genomic regions contributing to within- and inter-breed phenotypic variation.</p

Dry weather induces outbreaks of human West Nile virus infections

<p>Abstract</p> <p>Background</p> <p>Since its first occurrence in the New York City area during 1999, West Nile virus (WNV) has spread rapidly across North America and has become a major public health concern in North America. By 2002, WNV was reported in 40 states and the District of Columbia with 4,156 human and 14,539 equine cases of infection. Mississippi had the highest human incidence rate of WNV during the 2002 epidemic in the United States. Epidemics of WNV can impose enormous impacts on local economies. Therefore, it is advantageous to predict human WNV risks for cost-effective controls of the disease and optimal allocations of limited resources. Understanding relationships between precipitation and WNV transmission is crucial for predicting the risk of the human WNV disease outbreaks under predicted global climate change scenarios.</p> <p>Methods</p> <p>We analyzed data on the human WNV incidences in the 82 counties of Mississippi in 2002, using standard morbidity ratio (SMR) and Bayesian hierarchical models, to determine relationships between precipitation and human WNV risks. We also entertained spatial autocorrelations of human WNV risks with conditional autocorrelative (CAR) models, implemented in WinBUGS 1.4.3.</p> <p>Results</p> <p>We observed an inverse relationship between county-level human WNV incidence risk and total annual rainfall during the previous year. Parameters representing spatial heterogeneity in the risk of human exposure to WNV improved model fit. Annual precipitation of the previous year was a predictor of spatial variation of WNV risk.</p> <p>Conclusions</p> <p>Our results have broad implications for risk assessment of WNV and forecasting WNV outbreaks. Assessing risk of vector-born infectious diseases will require understanding of complex ecological relationships. Based on the climatologically characteristic drought occurrence in the past and on climate model predictions for climate change and potentially greater drought occurrence in the future, we suggest that the frequency and relative risk of WNV outbreaks could increase.</p

The power to detect artificial selection acting on single loci in recently domesticated species

<p>Abstract</p> <p>Background</p> <p>An increasing number of aquaculture species are subjected to artificial selection in systematic breeding programs. Rapid improvements of important commercial traits are reported, but little is known about the effects of the strong directional selection applied, on gene level variation. Large numbers of genetic markers are becoming available, making it feasible to detect and estimate these effects. Here a simulation tool was developed in order to explore the power by which single genetic loci subjected to uni-directional selection in parallel breeding populations may be detected.</p> <p>Findings</p> <p>Two simulation models were pursued: 1) screening for loci displaying higher genetic differentiation than expected (high-F_SToutliers), from neutral evolution between a pool of domesticated populations and a pool of wild populations; 2) screening for loci displaying lower genetic differentiation (low-F_SToutliers) between domesticated strains than expected from neutral evolution. The premise for both approaches was that the isolated domesticated strains are subjected to the same breeding goals. The power to detect outlier loci was calculated under the following parameter values: number of populations, effective population size per population, number of generations since onset of selection, initial F_ST, and the selection coefficient acting on the locus. Among the parameters investigated, selection coefficient, the number of generation since onset of selection, and number of populations, had the largest impact on power. The power to detect loci subjected to directional in breeding programmes was high when applying the between farmed and wild population approach, and low for the between farmed populations approach.</p> <p>Conclusions</p> <p>A simulation tool was developed for estimating the power to detect artificial selection acting directly on single loci. The simulation tool should be applicable to most species subject to domestication, as long as a reasonable high accuracy in input parameters such as effective population size, number of generations since the initiation of selection, and initial differentiation (F_ST) can be obtained. Identification of genetic loci under artificial selection would be highly valuable, since such loci could be used to monitor maintenance of genetic variation in the breeding populations and monitoring possible genetic changes in wild populations from genetic interaction between escapees and their wild counterpart.</p

A cluster randomised controlled trial of the community effectiveness of two interventions in rural Malawi to improve health care and to reduce maternal, newborn and infant mortality

<p>Abstract</p> <p>Background</p> <p>The UN Millennium Development Goals call for substantial reductions in maternal and child mortality, to be achieved through reductions in morbidity and mortality during pregnancy, delivery, postpartum and early childhood. The MaiMwana Project aims to test community-based interventions that tackle maternal and child health problems through increasing awareness and local action.</p> <p>Methods/Design</p> <p>This study uses a two-by-two factorial cluster-randomised controlled trial design to test the impact of two interventions. The impact of a community mobilisation intervention run through women's groups, on home care, health care-seeking behaviours and maternal and infant mortality, will be tested. The impact of a volunteer-led infant feeding and care support intervention, on rates of exclusive breastfeeding, uptake of HIV-prevention services and infant mortality, will also be tested. The women's group intervention will employ local female facilitators to guide women's groups through a four-phase cycle of problem identification and prioritisation, strategy identification, implementation and evaluation. Meetings will be held monthly at village level. The infant feeding intervention will select local volunteers to provide advice and support for breastfeeding, birth preparedness, newborn care and immunisation. They will visit pregnant and new mothers in their homes five times during and after pregnancy.</p> <p>The unit of intervention allocation will be clusters of rural villages of 2500-4000 population. 48 clusters have been defined and randomly allocated to either women's groups only, infant feeding support only, both interventions, or no intervention. Study villages are surrounded by 'buffer areas' of non-study villages to reduce contamination between intervention and control areas. Outcome indicators will be measured through a demographic surveillance system. Primary outcomes will be maternal, infant, neonatal and perinatal mortality for the women's group intervention, and exclusive breastfeeding rates and infant mortality for the infant feeding intervention.</p> <p>Structured interviews will be conducted with mothers one-month and six-months after birth to collect detailed quantitative data on care practices and health-care-seeking. Further qualitative, quantitative and economic data will be collected for process and economic evaluations.</p> <p>Trial registration</p> <p>ISRCTN06477126</p

Peer mentorship to promote effective pain management in adolescents: study protocol for a randomised controlled trial

<p>Abstract</p> <p>Background</p> <p>This protocol is for a study of a new program to improve outcomes in children suffering from chronic pain disorders, such as fibromyalgia, recurrent headache, or recurrent abdominal pain. Although teaching active pain self-management skills through cognitive-behavioral therapy (CBT) or a complementary program such as hypnotherapy or yoga has been shown to improve pain and functioning, children with low expectations of skill-building programs may lack motivation to comply with therapists' recommendations. This study will develop and test a new manualized peer-mentorship program which will provide modeling and reinforcement by peers to other adolescents with chronic pain (the mentored participants). The mentorship program will encourage mentored participants to engage in therapies that promote the learning of pain self-management skills and to support the mentored participants' practice of these skills. The study will examine the feasibility of this intervention for both mentors and mentored participants, and will assess the preliminary effectiveness of this program on mentored participants' pain and functional disability.</p> <p>Methods</p> <p>This protocol will recruit adolescents ages 12-17 with chronic pain and randomly assign them to either peer mentorship or a treatment-as-usual control group. Mentored participants will be matched with peer mentors of similar age (ages 14-18) who have actively participated in various treatment modalities through the UCLA Pediatric Pain Program and have learned to function successfully with a chronic pain disorder. The mentors will present information to mentored participants in a supervised and monitored telephone interaction for 2 months to encourage participation in skill-building programs. The control group will receive usual care but without the mentorship intervention. Mentored and control subjects' pain and functioning will be assessed at 2 months (end of intervention for mentored participants) and at 4 month follow-up to see if improvements persist. Measures of treatment adherence, pain, disability, and anxiety and depression will be assessed throughout study participation. Qualitative interviews for mentors, mentored participants, and control subjects will also be administered.</p> <p>Trial registration</p> <p>ClinicalTrials.gov <a href="http://www.clinicaltrials.gov/ct2/show/NCT01118988">NCT01118988</a>.</p

RLIP76, a Glutathione-Conjugate Transporter, Plays a Major Role in the Pathogenesis of Metabolic Syndrome

PURPOSE: Characteristic hypoglycemia, hypotriglyceridemia, hypocholesterolemia, lower body mass, and fat as well as pronounced insulin-sensitivity of RLIP76⁻/⁻ mice suggested to us the possibility that elevation of RLIP76 in response to stress could itself elicit metabolic syndrome (MSy). Indeed, if it were required for MSy, drugs used to treat MSy should have no effect on RLIP76⁻/⁻ mice. RESEARCH DESIGN AND METHODS: Blood glucose (BG) and lipid measurements were performed in RLIP76⁺/⁺ and RLIP76⁻/⁻ mice, using Ascensia Elite Glucometer® for glucose and ID Labs kits for cholesterol and triglycerides assays. The ultimate effectors of gluconeogenesis are the three enzymes: PEPCK, F-1,6-BPase, and G6Pase, and their expression is regulated by PPARγ and AMPK. The activity of these enzymes was tested by protocols standardized by us. Expressions of RLIP76, PPARα, PPARγ, HMGCR, pJNK, pAkt, and AMPK were performed by Western-blot and tissue staining. RESULTS: The concomitant activation of AMPK and PPARγ by inhibiting transport activity of RLIP76, despite inhibited activity of key glucocorticoid-regulated hepatic gluconeogenic enzymes like PEPCK, G6Pase and F-1,6-BP in RLIP76⁻/⁻ mice, is a salient finding of our studies. The decrease in RLIP76 protein expression by rosiglitazone and metformin is associated with an up-regulation of PPARγ and AMPK. CONCLUSIONS/SIGNIFICANCE: All four drugs, rosiglitazone, metformin, gemfibrozil and atorvastatin failed to affect glucose and lipid metabolism in RLIP76⁻/⁻ mice. Studies confirmed a model in which RLIP76 plays a central role in the pathogenesis of MSy and RLIP76 loss causes profound and global alterations of MSy signaling functions. RLIP76 is a novel target for single-molecule therapeutics for metabolic syndrome

The Herbicide Atrazine Activates Endocrine Gene Networks via Non-Steroidal NR5A Nuclear Receptors in Fish and Mammalian Cells

Atrazine (ATR) remains a widely used broadleaf herbicide in the United States despite the fact that this s-chlorotriazine has been linked to reproductive abnormalities in fish and amphibians. Here, using zebrafish we report that environmentally relevant ATR concentrations elevated zcyp19a1 expression encoding aromatase (2.2 µg/L), and increased the ratio of female to male fish (22 µg/L). ATR selectively increased zcyp19a1, a known gene target of the nuclear receptor SF-1 (NR5A1), whereas zcyp19a2, which is estrogen responsive, remained unchanged. Remarkably, in mammalian cells ATR functions in a cell-specific manner to upregulate SF-1 targets and other genes critical for steroid synthesis and reproduction, including Cyp19A1, StAR, Cyp11A1, hCG, FSTL3, LHß, INHα, αGSU, and 11ß-HSD2. Our data appear to eliminate the possibility that ATR directly affects SF-1 DNA- or ligand-binding. Instead, we suggest that the stimulatory effects of ATR on the NR5A receptor subfamily (SF-1, LRH-1, and zff1d) are likely mediated by receptor phosphorylation, amplification of cAMP and PI3K signaling, and possibly an increase in the cAMP-responsive cellular kinase SGK-1, which is known to be upregulated in infertile women. Taken together, we propose that this pervasive and persistent environmental chemical alters hormone networks via convergence of NR5A activity and cAMP signaling, to potentially disrupt normal endocrine development and function in lower and higher vertebrates

Recent artificial selection in U.S. Jersey cattle impacts autozygosity levels of specific genomic regions

Background: Genome signatures of artificial selection in U.S. Jersey cattle were identified by examining changes in haplotype homozygosity for a resource population of animals born between 1953 and 2007. Genetic merit of this population changed dramatically during this period for a number of traits, especially milk yield. The intense selection underlying these changes was achieved through extensive use of artificial insemination (AI), which also increased consanguinity of the population to a few superior Jersey bulls. As a result, allele frequencies are shifted for many contemporary animals, and in numerous cases to a homozygous state for specific genomic regions. The goal of this study was to identify those selection signatures that occurred after extensive use of AI since the 1960, using analyses of shared haplotype segments or Runs of Homozygosity. When combined with animal birth year information, signatures of selection associated with economically important traits were identified and compared to results from an extended haplotype homozygosity analysis. Results: Overall, our results reveal that more recent selection increased autozygosity across the entire genome, but some specific regions increased more than others. A genome-wide scan identified more than 15 regions with a substantial change in autozygosity. Haplotypes found to be associated with increased milk, fat and protein yield in U.S. Jersey cattle also consistently increased in frequency. Conclusions: The analyses used in this study was able to detect directional selection over the last few decades when individual production records for Jersey animals were available