Examining the effects of emotional valence and arousal on takeover performance in conditionally automated driving

In conditionally automated driving, drivers have difficulty in takeover transitions as they become increasingly decoupled from the operational level of driving. Factors influencing takeover performance, such as takeover lead time and the engagement of non-driving-related tasks, have been studied in the past. However, despite the important role emotions play in human-machine interaction and in manual driving, little is known about how emotions influence drivers’ takeover performance. This study, therefore, examined the effects of emotional valence and arousal on drivers’ takeover timeliness and quality in conditionally automated driving. We conducted a driving simulation experiment with 32 participants. Movie clips were played for emotion induction. Participants with different levels of emotional valence and arousal were required to take over control from automated driving, and their takeover time and quality were analyzed. Results indicate that positive valence led to better takeover quality in the form of a smaller maximum resulting acceleration and a smaller maximum resulting jerk. However, high arousal did not yield an advantage in takeover time. This study contributes to the literature by demonstrating how emotional valence and arousal affect takeover performance. The benefits of positive emotions carry over from manual driving to conditionally automated driving while the benefits of arousal do not

The effectiveness of a sustained nurse home visiting intervention for Aboriginal infants compared with non-Aboriginal infants and with Aboriginal infants receiving usual child health care : a quasi-experimental trial : the Bulundidi Gudaga study

Background: In Australia there is commitment to developing interventions that will 'Close the Gap' between the health and welfare of Indigenous and non-Indigenous Australians and recognition that early childhood interventions offer the greatest potential for long term change. Nurse led sustained home visiting programs are considered an effective way to deliver a health and parenting service, however there is little international or Australian evidence that demonstrates the effectiveness of these programs for Aboriginal infants. This protocol describes the Bulundidi Gudaga Study, a quasi-experimental design, comparing three cohorts of families from the Macarthur region in south western Sydney to explore the effectiveness of the Maternal Early Childhood Sustained Home-visiting (MECSH) program for Aboriginal families. Methods: Mothers were recruited when booking into the local hospital for perinatal care and families are followed up until child is age 4 years. Participants are from three distinct cohorts: Aboriginal MECSH intervention cohort (Group A), Non-Aboriginal MECSH intervention cohort (Group B) and Aboriginal non-intervention cohort (Group C). Eligible mothers were those identified as at risk during the Safe Start assessment conducted by antenatal clinic midwives. Mothers in Group A were eligible if they were pregnant with an Aboriginal infant. Mothers in Group B were eligible if they were pregnant with a non-Aboriginal infant. Mothers in Group C are part of the Gudaga descriptive cohort study and were recruited between October 2005 and May 2007. The difference in duration of breastfeeding, child body mass index, and child development outcomes at 18 months and 4 years of age will be measured as primary outcomes. We will also evaluate the intervention effect on secondary measures including: child dental health; the way the program is received; patterns of child health and illness; patterns of maternal health, health knowledge and behaviours; family and environmental conditions; and service usage for mothers and families. Discussion: Involving local Aboriginal research and intervention staff and investing in established relationships between the research team and the local Aboriginal community is enabling this study to generate evidence regarding the effectiveness of interventions that are feasible to implement and sustainable in the context of Aboriginal communities and local service systems. Trial registration: Australian New Zealand Clinical Trials Registry ACTRN12616001721493 Registered 14 Dec 2016. Retrospectively registered

Characterization of the U.S. Gulf of Mexico and South Atlantic penaeid and rock shrimp fisheries based on observer data

In July 2007, a mandatory Federal observer program was implemented to characterize the U.S. Gulf of Mexico penaeid shrimp (Farfantepenaeus aztecus, F. duorarum, and Litopenaeus setiferus) fishery. In June 2008, the program expanded to include the South Atlantic penaeid and rock shrimp, Sicyonia spp., fisheries. Data collected from 10,206 tows during 5,197 sea days of observations were analyzed by geographical area and target species. The majority of tows (~70%) sampled were off the coasts of Texas and Louisiana. Based on total hours towed, the highest concentrated effort occurred off South Texas and southwestern Florida. Gear information, such as net characteristics, bycatch reduction devices, and turtle excluder devices were fairly consistent among areas and target species. By species categories, finfish comprised the majority (≥57%) of the catch composition in the Gulf of Mexico and South Atlantic penaeid shrimp fisheries, while in the South Atlantic rock shrimp fishery the largest component (41%) was rock shrimp. Bycatch to shrimp ratios were lower than reported in previous studies for the Gulf of Mexico penaeid shrimp fishery. These decreased ratios may be attributed to several factors, notably decreased shrimp effort and higher shrimp catch per unit of effort (CPUE) in recent years. CPUE density surface plots for several species of interest illustrated spatial differences in distribution. Hot Spot Analyses for shrimp (penaeid and rock) and bycatch species identified areas with significant clustering of high or low CPUE values. Spatial and temporal distribution of protected species interactions were documented

Improvement of maternal Aboriginality in NSW birth data

<p>Abstract</p> <p>Background</p> <p>The Indigenous population of Australia was estimated as 2.5% and under-reported. The aim of this study is to improve statistical ascertainment of Aboriginal women giving birth in New South Wales.</p> <p>Methods</p> <p>This study was based on linked birth data from the Midwives Data Collection (MDC) and the Registry of Births Deaths and Marriages (RBDM) of New South Wales (NSW). Data linkage was performed by the Centre for Health Record Linkage (CHeReL) for births in NSW for the period January 2001 to December 2005. The accuracy of maternal Aboriginal status in the MDC and RBDM was assessed by consistency, sensitivity and specificity. A new statistical variable, ASV, or Aboriginal Statistical Variable, was constructed based on Indigenous identification in both datasets. The ASV was assessed by comparing numbers and percentages of births to Aboriginal mothers with the estimates by capture-recapture analysis.</p> <p>Results</p> <p>Maternal Aboriginal status was under-ascertained in both the MDC and RBDM. The ASV significantly increased ascertainment of Aboriginal women giving birth and decreased the number of missing cases. The proportion of births to Aboriginal mothers in the non-registered birth group was significantly higher than in the registered group.</p> <p>Conclusions</p> <p>Linking birth data collections is a feasible method to improve the statistical ascertainment of Aboriginal women giving birth in NSW. This has ramifications for the ascertainment of babies of Aboriginal mothers and the targeting of appropriate services in pregnancy and early childhood.</p

Contribution of copy number variants to schizophrenia from a genome-wide study of 41,321 subjects

Copy number variants (CNVs) have been strongly implicated in the genetic etiology of schizophrenia (SCZ). However, genome-wide investigation of the contribution of CNV to risk has been hampered by limited sample sizes. We sought to address this obstacle by applying a centralized analysis pipeline to a SCZ cohort of 21,094 cases and 20,227 controls. A global enrichment of CNV burden was observed in cases (OR=1.11, P=5.7×10−15), which persisted after excluding loci implicated in previous studies (OR=1.07, P=1.7 ×10−6). CNV burden was enriched for genes associated with synaptic function (OR = 1.68, P = 2.8 ×10−11) and neurobehavioral phenotypes in mouse (OR = 1.18, P= 7.3 ×10−5). Genome-wide significant evidence was obtained for eight loci, including 1q21.1, 2p16.3 (NRXN1), 3q29, 7q11.2, 15q13.3, distal 16p11.2, proximal 16p11.2 and 22q11.2. Suggestive support was found for eight additional candidate susceptibility and protective loci, which consisted predominantly of CNVs mediated by non-allelic homologous recombination

No Reliable Association between Runs of Homozygosity and Schizophrenia in a Well-Powered Replication Study

It is well known that inbreeding increases the risk of recessive monogenic diseases, but it is less certain whether it contributes to the etiology of complex diseases such as schizophrenia. One way to estimate the effects of inbreeding is to examine the association between disease diagnosis and genome-wide autozygosity estimated using runs of homozygosity (ROH) in genome-wide single nucleotide polymorphism arrays. Using data for schizophrenia from the Psychiatric Genomics Consortium (n = 21,868), Keller et al. (2012) estimated that the odds of developing schizophrenia increased by approximately 17% for every additional percent of the genome that is autozygous (β = 16.1, CI(β) = [6.93, 25.7], Z = 3.44, p = 0.0006). Here we describe replication results from 22 independent schizophrenia case-control datasets from the Psychiatric Genomics Consortium (n = 39,830). Using the same ROH calling thresholds and procedures as Keller et al. (2012), we were unable to replicate the significant association between ROH burden and schizophrenia in the independent PGC phase II data, although the effect was in the predicted direction, and the combined (original + replication) dataset yielded an attenuated but significant relationship between Froh and schizophrenia (β = 4.86,CI(β) = [0.90,8.83],Z = 2.40,p = 0.02). Since Keller et al. (2012), several studies reported inconsistent association of ROH burden with complex traits, particularly in case-control data. These conflicting results might suggest that the effects of autozygosity are confounded by various factors, such as socioeconomic status, education, urbanicity, and religiosity, which may be associated with both real inbreeding and the outcome measures of interest

Age at first birth in women is genetically associated with increased risk of schizophrenia

Prof. Paunio on PGC:n jäsenPrevious studies have shown an increased risk for mental health problems in children born to both younger and older parents compared to children of average-aged parents. We previously used a novel design to reveal a latent mechanism of genetic association between schizophrenia and age at first birth in women (AFB). Here, we use independent data from the UK Biobank (N = 38,892) to replicate the finding of an association between predicted genetic risk of schizophrenia and AFB in women, and to estimate the genetic correlation between schizophrenia and AFB in women stratified into younger and older groups. We find evidence for an association between predicted genetic risk of schizophrenia and AFB in women (P-value = 1.12E-05), and we show genetic heterogeneity between younger and older AFB groups (P-value = 3.45E-03). The genetic correlation between schizophrenia and AFB in the younger AFB group is -0.16 (SE = 0.04) while that between schizophrenia and AFB in the older AFB group is 0.14 (SE = 0.08). Our results suggest that early, and perhaps also late, age at first birth in women is associated with increased genetic risk for schizophrenia in the UK Biobank sample. These findings contribute new insights into factors contributing to the complex bio-social risk architecture underpinning the association between parental age and offspring mental health.Peer reviewe

Genetic correlation between amyotrophic lateral sclerosis and schizophrenia

A. Palotie on työryhmän Schizophrenia Working Grp Psychiat jäsen.We have previously shown higher-than-expected rates of schizophrenia in relatives of patients with amyotrophic lateral sclerosis (ALS), suggesting an aetiological relationship between the diseases. Here, we investigate the genetic relationship between ALS and schizophrenia using genome-wide association study data from over 100,000 unique individuals. Using linkage disequilibrium score regression, we estimate the genetic correlation between ALS and schizophrenia to be 14.3% (7.05-21.6; P = 1 x 10(-4)) with schizophrenia polygenic risk scores explaining up to 0.12% of the variance in ALS (P = 8.4 x 10(-7)). A modest increase in comorbidity of ALS and schizophrenia is expected given these findings (odds ratio 1.08-1.26) but this would require very large studies to observe epidemiologically. We identify five potential novel ALS-associated loci using conditional false discovery rate analysis. It is likely that shared neurobiological mechanisms between these two disorders will engender novel hypotheses in future preclinical and clinical studies.Peer reviewe

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Estimation of Genetic Correlation via Linkage Disequilibrium Score Regression and Genomic Restricted Maximum Likelihood

J. Lönnqvist on työryhmän Psychiat Genomics Consortium jäsen.Genetic correlation is a key population parameter that describes the shared genetic architecture of complex traits and diseases. It can be estimated by current state-of-art methods, i.e., linkage disequilibrium score regression (LDSC) and genomic restricted maximum likelihood (GREML). The massively reduced computing burden of LDSC compared to GREML makes it an attractive tool, although the accuracy (i.e., magnitude of standard errors) of LDSC estimates has not been thoroughly studied. In simulation, we show that the accuracy of GREML is generally higher than that of LDSC. When there is genetic heterogeneity between the actual sample and reference data from which LD scores are estimated, the accuracy of LDSC decreases further. In real data analyses estimating the genetic correlation between schizophrenia (SCZ) and body mass index, we show that GREML estimates based on similar to 150,000 individuals give a higher accuracy than LDSC estimates based on similar to 400,000 individuals (from combinedmeta-data). A GREML genomic partitioning analysis reveals that the genetic correlation between SCZ and height is significantly negative for regulatory regions, which whole genome or LDSC approach has less power to detect. We conclude that LDSC estimates should be carefully interpreted as there can be uncertainty about homogeneity among combined meta-datasets. We suggest that any interesting findings from massive LDSC analysis for a large number of complex traits should be followed up, where possible, with more detailed analyses with GREML methods, even if sample sizes are lesser.Peer reviewe