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The European Community’s Seventh Framework Programme under grant agreement No. HEALTH-F2-2010-241909 (EUGEI); The Spanish sample was supported by the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/024) (...), Velthorst, E., Mollon, J., Murray, R.M., de Haan, L., Germeys, I.M., Glahn, D.C., Arango, C., van der Ven, E., Di Forti, M., Bernardo, M., Guloksuz, S., Delespaul, P., Mezquida, G., Amoretti, S., Bobes, J., Saiz, P.A., García-Portilla, M.P., Santos, J.L., Jiménez-López, E., Sanjuan, J., Aguilar, E.J., Arrojo, M., Carracedo, A., López, G., González-Peñas, J., Parellada, M., Atbaşoğlu, C., Saka, M.C., Üçok, A., Alptekin, K., Akdede, B., Binbay, T., Altınyazar, V., Ulaş, H., Yalınçetin, B., Gümüş-Akay, G., Beyaz, B.C., Soygür, H., Cankurtaran, E.Ş., Kaymak, S.U., Maric, N.P., Mihaljevic, M.M., Petrovic, S.A., Mirjanic, T., Del-Ben, C.M., Ferraro, L., Gayer-Anderson, C., Jones, P.B., Jongsma, H.E., Kirkbride, J.B., La Cascia, C., Lasalvia, A., Tosato, S., Llorca, P.-M., Menezes, P.R., Morgan, C., Quattrone, D., Menchetti, M., Selten, J.-P., Szöke, A., Tarricone, I., Tortelli, A., McGuire, P., Valmaggia, L., Kempton, M.J., van der Gaag, M., Riecher-Rössler, A., Bressan, R.A., Barrantes-Vidal, N., Nelson, B., McGorry, P., Pantelis, C., Krebs, M.-O., Ruhrmann, S., Sachs, G., Rutten, B.P.F., van Os, J., Alizadeh, B.Z., van Amelsvoort, T., Bartels-Velthuis, A.A., Bruggeman, R., van Beveren, N.J., Luykx, J.J., Cahn, W., Simons, C.J.P., Kahn, R.S., Schirmbeck, F., van Winkel, R., Calem, M., Tognin, S., Modinos, G., Pisani, S., Kraan, T.C., van Dam, D.S., Burger, N., Amminger, G.P., Politis, A., Goodall, J., Borgwardt, S., Studerus, E., Gadelha, A., Brietzke, E., Asevedo, G., Asevedo, E., Zugman, A., Domínguez-Martínez, T., Monsonet, M., Cristóbal-Narváez, P., Racioppi, A., Kwapil, T.R., Kazes, M., Daban, C., Bourgin, J., Gay, O., Mam-Lam-Fook, C., Nordholm, D., Rander, L., Krakauer, K., Glenthøj, L.B., Glenthøj, B., Gebhard, D., Arnhold, J., Klosterkötter, J., Lasser, I., Winklbaur, B., Reichenberg, A., EU-GEI High Risk Study

Background First-degree relatives of patients with psychotic disorder have higher levels of polygenic risk (PRS) for schizophrenia and higher levels of intermediate phenotypes. Methods We conducted, using two different samples for discovery (n= 336 controls and 649 siblings of patients with psychotic disorder) and replication (n= 1208 controls and 1106 siblings), an analysis of association between PRS on the one hand and psychopathological and cognitive intermediate phenotypes of schizophrenia on the other in a sample at average genetic risk (healthy controls) and a sample at higher than average risk (healthy siblings of patients). Two subthreshold psychosis phenotypes, as well as a standardised measure of cognitive ability, based on a short version of the WAIS-III short form, were used. In addition, a measure of jumping to conclusion bias (replication sample only) was tested for association with PRS. Results In both discovery and replication sample, evidence for an association between PRS and subthreshold psychosis phenotypes was observed in the relatives of patients, whereas in the controls no association was observed. Jumping to conclusion bias was similarly only associated with PRS in the sibling group. Cognitive ability was weakly negatively and non-significantly associated with PRS in both the sibling and the control group. Conclusions The degree of endophenotypic expression of schizophrenia polygenic risk depends on having a sibling with psychotic disorder, suggestive of underlying gene-environment interaction. Cognitive biases may better index genetic risk of disorder than traditional measures of neurocognition, which instead may reflect the population distribution of cognitive ability impacting the prognosis of psychotic disorder.

Aims Psychosis spectrum disorder has a complex pathoetiology characterised by interacting environmental and genetic vulnerabilities. The present study aims to investigate the role of gene-environment interaction using aggregate scores of genetic (polygenic risk score for schizophrenia (PRS-SCZ)) and environment liability for schizophrenia (exposome score for schizophrenia (ES-SCZ)) across the psychosis continuum. Methods The sample consisted of 1699 patients, 1753 unaffected siblings, and 1542 healthy comparison participants. The Structured Interview for Schizotypy-Revised (SIS-R) was administered to analyse scores of total, positive, and negative schizotypy in siblings and healthy comparison participants. The PRS-SCZ was trained using the Psychiatric Genomics Consortiums results and the ES-SCZ was calculated guided by the approach validated in a previous report in the current data set. Regression models were applied to test the independent and joint effects of PRS-SCZ and ES-SCZ (adjusted for age, sex, and ancestry using 10 principal components). Results Both genetic and environmental vulnerability were associated with case-control status. Furthermore, there was evidence for additive interaction between binary modes of PRS-SCZ and ES-SCZ (above 75% of the control distribution) increasing the odds for schizophrenia spectrum diagnosis (relative excess risk due to interaction = 6.79, [95% confidential interval (CI) 3.32, 10.26], p < 0.001). Sensitivity analyses using continuous PRS-SCZ and ES-SCZ confirmed gene-environment interaction (relative excess risk due to interaction = 1.80 [95% CI 1.01, 3.32], p = 0.004). In siblings and healthy comparison participants, PRS-SCZ and ES-SCZ were associated with all SIS-R dimensions and evidence was found for an interaction between PRS-SCZ and ES-SCZ on the total (B = 0.006 [95% CI 0.003, 0.009], p < 0.001), positive (B = 0.006 [95% CI, 0.002, 0.009], p = 0.002), and negative (B = 0.006, [95% CI 0.004, 0.009], p < 0.001) schizotypy dimensions. Conclusions The interplay between exposome load and schizophrenia genetic liability contributing to psychosis across the spectrum of expression provide further empirical support to the notion of aetiological continuity underlying an extended psychosis phenotype.

Exposures constitute a dense network of the environment: exposome. Here, we argue for embracing the exposome paradigm to investigate the sum of nongenetic "risk" and show how predictive modeling approaches can be used to construct an exposome score (ES; an aggregated score of exposures) for schizophrenia. The training dataset consisted of patients with schizophrenia and controls, whereas the independent validation dataset consisted of patients, their unaffected siblings, and controls. Binary exposures were cannabis use, hearing impairment, winter birth, bullying, and emotional, physical, and sexual abuse along with physical and emotional neglect. We applied logistic regression (LR), Gaussian Naive Bayes (GNB), the least absolute shrinkage and selection operator (LASSO), and Ridge penalized classification models to the training dataset. ESs, the sum of weighted exposures based on coefficients from each model, were calculated in the validation dataset. In addition, we estimated ES based on meta-analyses and a simple sum score of exposures. Accuracy, sensitivity, specificity, area under the receiver operating characteristic, and Nagelkerke's R-2 were compared. The ESMeta-analyses performed the worst, whereas the sum score and the ESGNB were worse than the ESLR that performed similar to the ESLASSO and ESRIDGE. The ESLR distinguished patients from controls (odds ratio [OR] = 1.94, P < .001), patients from siblings (OR = 1.58, P < .001), and siblings from controls (OR = 1.21, P= .001). An increase in ESLR was associated with a gradient increase of schizophrenia risk. In reference to the remaining fractions, the ESLR at top 30%, 20%, and 10% of the control distribution yielded ORs of 3.72, 3.74, and 4.77, respectively. Our findings demonstrate that predictive modeling approaches can be harnessed to evaluate the exposome.

A novel nonlinear impact angle guidance law for stationary targets, based on Lyapunov stability theory, is proposed in this work. Unlike the previous works, proposed guidance law generates necessary acceleration commands perpendicular to the velocity vector based on controlling nonlinear line-of-sight dynamics between target and pursuer. The nonlinear dynamics of the line-of-sight angle and its rate are derived in the planar engagement geometry. The line-of-sight angle is aimed to be controlled to satisfy impact angle constraints. Qualitative studies show that pursuer follows a straight line trajectory at the end of the flight for stationary targets. Thus, the impact angle constraints can be satisfied via controlling line-of-sight angle. A sliding manifold which reduces the order of the system and controls both line-of-sight angle and rate with exponential stability is introduced. Then, the system is forced to slide on defined manifold based on Lyapunov stability theory. The necessary commands are analytically calculated. Finally the comparison with different impact angle control guidance laws are performed to summarize the proposed guidance law performance.

Getting an unbiased result is a remarkably long standing problem ofcollective observation/measurement. It is pointed out that quantum coin tossingcan generate unbiased result defeating dishonesty.

Getting an unbiased result is a remarkably long standing problem ofcollective observation/measurement. It is pointed out that quantum coin tossingcan generate unbiased result defeating dishonesty.

A novel nonlinear impact angle guidance law for stationary targets, based on Lyapunov stability theory, is proposed in this work. In the proposed guidance law, nonlinear line-ofsight dynamics of the engagement geometry between the pursuer and stationary target is studied and the state equations for the line-of-sight angle and its rate are derived. The lineof-sight angle is aimed to be controlled to satisfy impact angle constraints. Then a sliding manifold which drives the line-of-sight angle and its rate to exponential stability is constituted. Robust Control Lyapunov Function is introduced to the system to control the line-of-sight dynamics to head it towards defined sliding manifold. The necessary commands are analytically calculated. Finally the comparison with a different impact angle control guidance law is performed to summarize the proposed guidance law performance.

The treatment gap for people with mental disorders exceeds 50% in all countries of the world, approaching astonishingly high rates of 90% in the least resourced countries. We report the findings of the first systematic survey of leaders of psychiatry in nearly 60 countries on the strategies for reducing the treatment gap. We sought to elicit the views of these representatives on the roles of different human resources and health care settings in delivering care and on the importance of a range of strategies to increase the coverage of evidence-based treatments for priority mental disorders for each demographic stage (childhood, adolescence, adulthood and old age). Our findings clearly indicate three strategies for reducing the treatment gap: increasing the numbers of psychiatrists and other mental health professionals; increasing the involvement of a range of appropriately trained non-specialist providers; and the active involvement of people affected by mental disorders. This is true for both high income and low/middle income countries, though relatively of more importance in the latter. We view this survey as a critically important first step in ascertaining the position of psychiatrists, one of the most influential stakeholder communities in global mental health, in addressing the global challenge of scaling up mental health services to reduce the treatment gap.

In this experimental study, the 50CrV4 (SAE 6150) steel was subjected to the machining tests with coated carbide and cermet cutting tools in a turning operation. The tests were carried out at various cutting speeds, feed rates and cutting depths. In the light of these parameters, cutting forces and surface-roughness values were determined. Three components (F-a, F-r, and F-c) of the cutting forces were measured during the tests using a dynamometer, while the machined surface-roughness values were determined using a surface roughness measuring unit. A multiple regression analysis and experimental design were performed statistically. The measured surface-roughness values were used for the modeling with an artificial neural network system (ANNS). The relations between the cutting forces and the surface-roughness values were also defined.