How active perception and attractor dynamics shape perceptual categorization: A computational model

We propose a computational model of perceptual categorization that fuses elements of grounded and sensorimotor theories of cognition with dynamic models of decision-making. We assume that category information consists in anticipated patterns of agent–environment interactions that can be elicited through overt or covert (simulated) eye movements, object manipulation, etc. This information is firstly encoded when category information is acquired, and then re-enacted during perceptual categorization. The perceptual categorization consists in a dynamic competition between attractors that encode the sensorimotor patterns typical of each category; action prediction success counts as ‘‘evidence’’ for a given category and contributes to falling into the corresponding attractor. The evidence accumulation process is guided by an active perception loop, and the active exploration of objects (e.g., visual exploration) aims at eliciting expected sensorimotor patterns that count as evidence for the object category. We present a computational model incorporating these elements and describing action prediction, active perception, and attractor dynamics as key elements of perceptual categorizations. We test the model in three simulated perceptual categorization tasks, and we discuss its relevance for grounded and sensorimotor theories of cognition.Peer reviewe

Development and Psychometric Properties of the Standardized Assessment of Severity of Personality Disorder (SASPD)

Personality disorder (PD) is increasingly categorized according to its severity, but there is no simple way to screen for severity according to ICD-11 criteria. We set out to develop the Standardized Assessment of Severity of Personality Disorder (SASPD). A total of 110 patients completed the SASPD together with a clinical assessment of the severity of personality disorder. We examined the predictive ability of the SASPD using the area under the ROC curve (AUC). Two to four weeks later, 43 patients repeated the SASPD to examine reliability. The SASPD had good predictive ability for determining mild (AUC = 0.86) and moderate (AUC = 0.84) PD at cut points of 8 and 10, respectively. Test-retest reliability of the SASPD was high (intraclass correlation coefficient = 0.93, 95% CI [0.88, 0.96]). The SASPD thus provides a simple, brief, and reliable indicator of the presence of mild or moderate PD according to ICD-11 criteria. </jats:p

Effects of copy number variations on brain structure and risk for psychiatric illness: large-scale studies from the ENIGMA working groups on CNVs

The Enhancing NeuroImaging Genetics through Meta-Analysis copy number variant (ENIGMA-CNV) and 22q11.2 Deletion Syndrome Working Groups (22q-ENIGMA WGs) were created to gain insight into the involvement of genetic factors in human brain development and related cognitive, psychiatric and behavioral manifestations. To that end, the ENIGMA-CNV WG has collated CNV and magnetic resonance imaging (MRI) data from ~49,000 individuals across 38 global research sites, yielding one of the largest studies to date on the effects of CNVs on brain structures in the general population. The 22q-ENIGMA WG includes 12 international research centers that assessed over 533 individuals with a confirmed 22q11.2 deletion syndrome, 40 with 22q11.2 duplications, and 333 typically developing controls, creating the largest-ever 22q11.2 CNV neuroimaging data set. In this review, we outline the ENIGMA infrastructure and procedures for multi-site analysis of CNVs and MRI data. So far, ENIGMA has identified effects of the 22q11.2, 16p11.2 distal, 15q11.2, and 1q21.1 distal CNVs on subcortical and cortical brain structures. Each CNV is associated with differences in cognitive, neurodevelopmental and neuropsychiatric traits, with characteristic patterns of brain structural abnormalities. Evidence of gene-dosage effects on distinct brain regions also emerged, providing further insight into genotype-phenotype relationships. Taken together, these results offer a more comprehensive picture of molecular mechanisms involved in typical and atypical brain development. This "genotype-first" approach also contributes to our understanding of the etiopathogenesis of brain disorders. Finally, we outline future directions to better understand effects of CNVs on brain structure and behavior.Funding information: European Union's Horizon2020 Research and Innovation Programme, Grant/Award Number: CoMorMent project; Grant #847776; KG Jebsen Stiftelsen; National Institutes of Health, Grant/Award Number: U54 EB020403; Norges Forskningsråd, Grant/Award Number: #223273; South-Eastern Norway Regional Health Authority, Grant/Award Number: #2020060ACKNOWLEDGMENTS: The ENIGMA Consortium is supported by the NIH Big Data to Knowledge (BD2K) program under consortium grant number U54 EB020403 (PI: Thompson). OAA is supported by the Research Council of Norway, South East Norway Health Authority, KG Jebsen Stiftelsen, EU H2020. C. A. has been funded by the Spanish Ministry of Science and Innovation; Instituto de Salud Carlos III (SAM16PE07CP1, PI16/02012, PI19/ 024), co-financed by ERDF Funds from the European Commission, “A way of making Europe”, CIBERSAM; Madrid Regional Government (B2017/BMD-3740 AGES-CM-2), European Union Structural Funds; European Union Seventh Framework Program under grant agreements FP7-4-HEALTH-2009-2.2.1-2-241,909 (Project EU-GEI), FP7- HEALTH-2013-2.2.1-2-603,196 (Project PSYSCAN) and FP7- HEALTH-2013- 2.2.1-2-602,478 (Project METSY); and European Union H2020 Program under the Innovative Medicines Initiative two Joint Undertaking (grant agreement No 115916, Project PRISM, and grant agreement No 777394, Project AIMS-2-TRIALS), Fundación Familia Alonso and Fundación Alicia Koplowitz. R. A-A is funded by a Miguel Servet contract from the Carlos III Health Institute (CP18/00003). G. B. is supported by the Dutch Organization for Health Research and Development ZonMw (grants 91112002 & 91712394). A. S. B. is supported by the Dalglish Family Chair in 22q11.2 Deletion Syndrome, Canadian Institutes of Health Research (CIHR) grants MOP-79518, MOP89066, MOP-97800 and MOP-111238, and NIMH grant number U01 MH101723–01(3/5). C. E. B. is also supported by the National Institute of Mental Health: RO1 MH085953, R01 MH100900 and 1U01MH119736. N. E. B. is granted the KNAW Academy Professor Award (PAH/6635). V. D. C. is supported by NIH R01 MH094524. S. C. is supported by the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3); Helmholtz Initiative and Networking Fund. C. R. K. C. is supported by NIA T32AG058507. E. W. C. C. is supported by the Canadian Institutes of Health Research, Ontario Mental Health Foundation grant MOP-74631 and NIMH grant U01MH101723–01(3/5). S. Ci. has received funding from the European Union's Horizon 2020 Framework Programme for Research and Innovation under the Specific Grant Agreement No. 945539 (Human Brain Project SGA3). M. C. C. is supported by the Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London. N. A. C. is supported by Agencia Nacional de Investigación y Desarrollo (ANID Chile) PIA ACT192064. GId. Z. is supported by the NHMRC. J. L. D. and D. E. J. L. are supported by the Wellcome Trust. T. B. C. is supported by NICHD grant PO1-HD070454, NIH grant UO1-MH191719, and NIMH grant R01 MH087636-01A1. AMD is supported by U24DA041147. B. D. is supported by the Swiss National Science Foundation (NCCR Synapsy, project grant numbers 32003B_135679, 32003B_159780, 324730_192755 and CRSK3_190185), the Leenaards Foundation and the Roger De Spoelberch Foundation. SE is supported by the NARSAD-Young Investigator Grant “Epigenetic Regulation of Intermediate Phenotypes in Schizophrenia”. B. E. S. is supported by the NIH (NIMH). D. C. G. is supported by NIH grant numbers MH078143, MH083824, AG058464. W. R. K. is supported by NIH/MH R0106824. R. E. G. is supported by NIH/NIMH grant numbers MH087626, MH119737. DMMcD-McG is supported by National Institutes of Mental Health (NIMH), grant numbers MH119737-02; MH191719; and MH087636-01A1. S. E. M. is supported by NHMRC grants APP1103623; APP1158127; APP1172917. TM is supported by Research Council of Norway - grant number 273345. D. G. M. is supported by the National Institute for Health Research Mental Health Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London and S (European Autism Interventions)/EU AIMS-2-TRIALS, a European Innovative Medicines Initiative Joint Undertaking under grant agreements 115300 and 777394. T. N. was supported by Stiftelsen KG Jebsen under grant number SKGJ-MED-021. R. A. O. is supported by NIMH R01 MH090553. S. Y. S. has been funded by the Canadain Institutes of Health Research. M. J. O. is supported by MRC Centre grant MR/L010305/1 and Wellcome Trust grant 100,202/Z/12/Z; Dr. Owen has received research support from Takeda. Z. P. is supported by CIHR, CFI, HSFC. B. G. P. is supported by CIHR FDN 143290 and CAIP Chair. G. M. R. is supported by Fondecyt-Chile #1171014 and ANID-Chile ACT192064. A. Re. was supported by a grant from the Swiss National Science Foundation (31003A_182632). DRR is supported by R01 MH120174 (PI: Roalf). This report represents independent research funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King's College London (to J. J. R). PSS is supported by NHMRC (Australia) program grant 1093083. J. E. S. is supported by NIH K01-ES026840. S. M. S. is supported by the Epilepsy Society. T. J. S. is supported by NIH grants R01MH107108, R01HD042794, and HDU54079125. I. E. S. is supported by South-Eastern Norway Regional Health Authority (#2020060), European Union's Horizon2020 Research and Innovation Programme (CoMorMent project; grant #847776) and the KG Jebsen Foundation (SKGJ-MED-021). V. M. S. is supported by Research Council of Norway (CoE funding scheme, grant number 223273). D. J. S. is supported by the SA MRC. C. K. T. is supported by Research Council of Norway (#230345, #288083, #223273) and South-Eastern Norway Regional Health Authority (#2019069, #2021070, #500189). D. T.-G. was supported by the Instituto de Salud Carlos III (PI14/00639 and PI14/00918) and Fundación Instituto de Investigación Marqués de Valdecilla (NCT0235832 and NCT02534363). Dvd. M. is supported by Research Council of Norway #276082. F. V. R. is supported by the Michael Smith Foundation for Health Research Scholar Award. deCODE genetics has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreements' no. 115008 (NEWMEDS) and no. 115300 (EUAIMS), of which resources are composed of EFPIA in-kind contribution and financial contribution from the European Union's Seventh Framework Programme (EU-FP7/ 2007–2013). L. T. W. is supported by Research Council of Norway, European Research Council. The IDIVAL neuroimage unit is supported by Instituto de Salud Carlos III PI020499, research funding SCIII-INT13/0014, MICINN research funding SAF2010-20840-C02- 02, SAF2013-46292-R. The TOP/NORMENT study are supported by the Research Council of Norway (#223273). The GOBS study data collection was supported in part by the National Institutes of Health (NIH) grants: R01 MH078143, R01 MH078111, and R01 MH083824 with work conducted in part in facilities constructed under the support of NIH grant number C06 RR020547. The Sydney Memory and Ageing Study has been funded by three National Health & Medical Research Council (NHMRC) Program Grants (ID No. ID350833, ID568969, and APP1093083). We thank the participants and their informants for their time and generosity in contributing to this research. We also acknowledge the MAS research team: https://cheba.unsw.edu.au/researchprojects/sydney-memory-and-ageing-study. We acknowledge the contribution of the OATS research team (https://cheba.unsw.edu.au/ project/older-australian-twins-study) to this study. The OATS study has been funded by a National Health & Medical Research Council (NHMRC) and Australian Research Council (ARC) Strategic Award Grant of the Aging Well, Aging Productively Program (ID No. 401162); NHMRC Project (seed) Grants (ID No. 1024224 and 1025243); NHMRC Project Grants (ID No. 1045325 and 1085606); and NHMRC Program Grants (ID No. 568969 and 1093083). We thank the participants for their time and generosity in contributing to this research. This research was facilitated through access to Twins Research Australia, a national resource supported by a Centre of Research Excellence Grant (ID No. 1079102) from the National Health and Medical Research Council. The NCNG sample collection was supported by grants from the Bergen Research Foundation and the University of Bergen, the Dr Einar Martens Fund, the KG Jebsen Foundation, the Research Council of Norway, to S. L. H., V. M. S., A. J. L., and T. E. The authors thank Dr. Eike Wehling for recruiting participants in Bergen, and Professor Jonn-Terje Geitung and Haraldplass Deaconess Hospital for access to the MRI facility. Additional support by RCN grants 177458/V50 and 231286/F20. The Betula study was supported by a Wallenberg Scholar Grant (KAW). The HUNT Study is a collaboration between HUNT Research Centre (Faculty of Medicine and Health Sciences, NTNU—Norwegian University of Science and Technology), Nord-Trøndelag County Council, Central Norway Health Authority, and the Norwegian Institute of Public Health. HUNT-MRI was funded by the Liaison Committee between the Central Norway Regional Health Authority and the Norwegian University of Science and Technology, and the Norwegian National Advisory Unit for functional MRI. Research for the GAP cohort was supported by the Department of Health via the National Institute for Health Research (NIHR) Specialist Biomedical Research Center for Mental Health award to South London and Maudsley NHS Foundation Trust (SLaM) and the Institute of Psychiatry at King's College London, London. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. S.J. is supported by Calcul Quebec (http:// www.calculquebec.ca), Compute Canada (http://www.computecanada. ca), the Brain Canada Multi investigator research initiative (MIRI), the Institute of Data Valorization (Canada First Research Excellence Fund), CHIR, Canada Research Chairs and the Jeanne et Jean Louis Levesque Foundation. The NTR cohort was supported by the Netherlands Organization for Scientific Research (NWO), MW904-61-193 (de Geus & Boomsma), MaGWnr: 400-07-080 (van 't Ent), MagW 480-04-004 (Boomsma), NWO/SPI 56-464-14,192 (Boomsma), the European Research Council, ERC-230374 (Boomsma), and Amsterdam Neuroscience. Funding for genotyping was obtained from the National Institutes of Health (NIMH U24 MH068457-06; Grand Opportunity grants 1RC2 MH089951, and 1RC2 MH089995); the Avera Institute for Human Genetics, Sioux Falls, South Dakota (USA). Part of the genotyping and analyses were funded by the Genetic Association Information Network (GAIN) of the Foundation for the National Institutes of Health. The Brainscale study was supported by the Netherlands Organization for Scientific Research MagW 480-04-004 (Boomsma), 51.02.060 (Hilleke Hulshoff Pol), 668.772 (Boomsma & Hulshoff Pol); NWO/SPI 56-464-14192 (Boomsma), the European Research Council (ERC230374) (Boomsma), High Potential Grant Utrecht University (Hulshoff Pol), NWO Brain and Cognition 433-09-220 (Hulshoff Pol). SHIP is part of the Community Medicine Research net of the University of Greifswald, Germany, which is funded by the Federal Ministry of Education and Research (grants no. 01ZZ9603, 01ZZ0103, and 01ZZ0403), the Ministry of Cultural Affairs and the Social Ministry of the Federal State of Mecklenburg-West Pomerania. Genome-wide SNP typing in SHIP and MRI scans in SHIP and SHIP-TREND have been supported by a joint grant from Siemens Healthcare, Erlangen, Germany and the Federal State of Mecklenburg-West Pomerania. The ENIGMA-22q11.2 Deletion Syndrome Working Group wishes to acknowledge our dear colleague Dr. Clodagh Murphy, who sadly passed away in April 2020. Open access funding enabled and organized by Projekt DEAL

Perturbations in cosmologies with a scalar field and a perfect fluid

We study the properties of cosmological density perturbations in a multi-component system consisting of a scalar field and a perfect fluid. We discuss the number of degrees of freedom completely describing the system, introduce a full set of dynamical gauge-invariant equations in terms of the curvature and entropy perturbations, and display an efficient formulation of these equations as a first-order system linked by a fairly sparse matrix. Our formalism includes spatial gradients, extending previous formulations restricted to the large-scale limit, and fully accounts for the evolution of an isocurvature mode intrinsic to the scalar field. We then address the issue of the adiabatic condition, in particular demonstrating its preservation on large scales. Finally, we apply our formalism to the quintessence scenario and clearly underline the importance of initial conditions when considering late-time perturbations. In particular, we show that entropy perturbations can still be present when the quintessence field energy density becomes non-negligible.Comment: RevTex4, 9 pages, 3 figures. Significant additions on the quintessence scenario (new appendix and additional numerical example). Conclusions unchanged, but more robus

A manually annotated Actinidia chinensis var. chinensis (kiwifruit) genome highlights the challenges associated with draft genomes and gene prediction in plants

Most published genome sequences are drafts, and most are dominated by computational gene prediction. Draft genomes typically incorporate considerable sequence data that are not assigned to chromosomes, and predicted genes without quality confidence measures. The current Actinidia chinensis (kiwifruit) 'Hongyang' draft genome has 164\ua0Mb of sequences unassigned to pseudo-chromosomes, and omissions have been identified in the gene models

A low energy dense diet in the context of a weight management program improves appetite control in overweight and obese women

Background: Low energy density foods (LED) reduce energy intake (EI); whether this effect is sustained over time and during weight loss is unknown. Objective: This trial examined the effects of LED compared to high energy density (HED) meals on appetite, EI and control over eating in the laboratory and during a weight management program that encourages unrestricted intake of LED foods [Slimming World, UK (SW)] compared to a self-led Standard Care program [NHS weight loss plan (SC)]. Methods: Overweight and obese women (n=96;age:41.03±12.61 years; BMI:34.00±3.61 kg/m2) were recruited from SW- or SC-program. Primary outcomes included appetite, food preferences (liking and wanting for LED and HED foods), cravings and evening meal EI (LED, HED) in response to calorie-matched LED (≤0.8 kcal/g) and HED (≥2.5 kcal/g) breakfast and lunch meals. Probe day tests were conducted at weeks 3 and 4 and repeated at weeks 12 and 13 in a within-day cross-over design. Secondary outcomes including body weight and program experience were measured from week 1 to 14 in a parallel-group design. Dietary compliance was monitored using weighed food diaries at weeks 3 and 12. Results: Intention-to-treat (ITT) and completers-analyses showed SW lost more weight compared to SC [ITT:-5.9% (95%CI:-4.7, -7.2) versus -3.5% (-2.3,-4.8), p<0.05; completers:-6.2% (-4.8,-7.6) versus 3.9% (-2.5,-5.2), p<0.05]. SW reported greater control over eating and more motivation to continue the program compared to SC. LED meals increased sensations of fullness and reduced hunger on probe days (p<0.001). Total-day-EI was 1057±73 kcal less (95% CI:912, 1203;36%) under LED compared to HED (p<.001). Liking for LED and HED foods and wanting for HED foods were lower pre-lunch under LED compared to HED conditions and liking decreased to a greater extent after the LED-lunch. SW reported fewer cravings under LED compared to HED conditions (p<0.05). On probe days, appetite and EI outcomes did not differ between weeks 3 and 12 or SW- and SC-groups. Conclusion: LED meals improve appetite control in women attempting weight loss and the effect is sustainable. Consumption of LED meals likely contributed to weight loss in the SW-program.ClinicalTrials.gov #NCT02012426

High mortality rates in men initiated on anti-retroviral treatment in KwaZulu-Natal, South Africa.

CAPRISA, 2017.Abstract available in pdf

Robust Estimation of Wage Dispersion with Censored Data: An Application to Occupational Earnings Risk and Risk Attitudes

We present a semiparametric method to estimate group-level dispersion, which is particularly effective in the presence of censored data. We apply this procedure to obtain measures of occupation-specific wage dispersion using top-coded administrative wage data from the German IAB Employment Sample (IABS). We then relate these robust measures of earnings risk to the risk attitudes of individuals working in these occupations. We find that willingness to take risk is positively correlated with the wage dispersion of an individual's occupation