Building and sharing the cognition model of the environment with collaborative service robots

This research presents a shared cognition concept for building a cognition model collaboratively by robot and human. The proposed concept is based on an object-oriented approach that abstracts robot's perception, cognition, and knowledge to separate but interconnected elements. The shared cognition concept allows the human and robot share information related to objects in the environment for effective task execution and information exchange. The concept enables building a model using sensors, as well as inputting data from the user. User can utilize also robot's sensor data for inputting information. This utilizes the robot's perception of the environment in conjunction with human's cognitive understanding. The proposed concept can be used for exchanging information on the objects, their classes, identities, appearances, locations, structures, and states. Representation of perceived entities is done through observed objects, robot's cognitive understanding of the objects is represented by real objects, and the knowledge of objects' classes by meta-objects. Observed objects are created with segmentation which can be autonomous or assisted by human. Real objects are used to transfer the actual object information between human and robot. Recognition of objects is done by matching real and observed objects. Uncertainties of recognition and localization are handled by probabilistic approach. The model enables learning from perceived information allowing the model to improve its performance as more data is gathered. The cognition model can be used for collaborative task execution. Several aspects of the model are validated with two different platforms, WorkPartner service robot and Avant machine based semi-autonomous robot. Based on the tests, the proposed model is an effective mechanism for collaboratively exchanging spatial information between a robot and a human

Association between 5-HT2A, TPH1 and GNB3 genotypes and response to typical neuroleptics: a serotonergic approach

<p>Abstract</p> <p>Background</p> <p>Schizophrenia is a common psychiatric disease affecting about 1% of population. One major problem in the treatment is finding the right the drug for the right patients. However, pharmacogenetic results in psychiatry can seldom be replicated.</p> <p>Methods</p> <p>We selected three candidate genes associated with serotonergic neurotransmission for the study: serotonin 2A (<it>5-HT2A</it>) receptor gene, tryptophan hydroxylase 1 (<it>TPH1</it>) gene, and G-protein beta-3 subunit (<it>GNB3</it>) gene. We recruited 94 schizophrenia patients representing extremes in treatment response to typical neuroleptics: 43 were good responders and 51 were poor responders. The control group consisted of 392 healthy blood donors.</p> <p>Results</p> <p>We do, in part, replicate the association between <it>5-HT2A </it>T102C polymorphism and response to typical neuroleptics. In female patients, C/C genotype was significantly more common in non-responders than in responders [OR = 6.04 (95% Cl 1.67–21.93), p = 0.005] or in the control population [OR = 4.16 (95% CI 1.46–11.84), p = 0.005]. <it>TPH1 </it>A779C C/A genotype was inversely associated with good treatment response when compared with non-responders [OR = 0.59 (95% Cl 0.36–0.98), p = 0.030] or with the controls [OR = 0.44 (95% CI 0.23–0.86, p = 0.016], and <it>GNB3 </it>C825T C/T genotype showed a trend-like positive association among the male patients with a good response compared with non-responders [OR = 3.48 (95% Cl 0.92–13.25), p = 0.061], and a clearer association when compared with the controls [OR = 4.95 (95% CI 1.56–15.70), p = 0.004].</p> <p>Conclusion</p> <p>More findings on the consequences of functional polymorphisms for the role of serotonin in the development of brain and serotonergic neurotransmission are needed before more detailed hypotheses regarding susceptibility and outcome in schizophrenia can be formulated. The present results may highlight some of the biological mechanisms in different courses of schizophrenia between men and women.</p

InDEx – Industrial Data Excellence

InDEx, the Industrial Data Excellence program, was created to investigate what industrial data can be collected, shared, and utilized for new intelligent services in high-performing, reliable and secure ways, and how to accomplish that in practice in the Finnish manufacturing industry.InDEx produced several insights into data in an industrial environment, collecting data, sharing data in the value chain and in the factory environment, and utilizing and manipulating data with artificial intelligence. Data has an important role in the future in an industrial context, but data sources and utilization mechanisms are more diverse than in cases related to consumer data. Experiences in the InDEx cases showed that there is great potential in data utili zation.Currently, successful business cases built on data sharing are either company-internal or utilize an existing value chain. The data market has not yet matured, and third-party offerings based on public and private data sources are rare. In this program, we tried out a framework that aimed to securely and in a controlled manner share data between organizations. We also worked to improve the contractual framework needed to support new business based on shared data, and we conducted a study of applicable business models. Based on this, we searched for new data-based opportunities within the project consortium. The vision of data as a tradeable good or of sharing with external partners is still to come true, but we believe that we have taken steps in the right direction.The program started in fall 2019 and ended in April 2022. The program faced restrictions caused by COVID-19, which had an effect on the intensity of the work during 2020 and 2021, and the program was extended by one year. Because of meeting restrictions, InDEx collaboration was realized through online meetings. We learned to work and collaborate using digital tools and environments. Despite the mentioned hindrances, and thanks to Business Finland’s flexibility, the extension time made it possible for most of the planned goals to be achieved.This report gives insights in the outcomes of the companies’ work within the InDEx program. DIMECC InDEx is the first finalized program by the members of the Finnish Advanced Manufacturing Network (FAMN, www.famn.fi).</p

Multi-ancestry genome-wide association study accounting for gene-psychosocial factor interactions identifies novel loci for blood pressure traits

Psychological and social factors are known to influence blood pressure (BP) and risk of hypertension and associated cardiovascular diseases. To identify novel BP loci, we carried out genome-wide association meta-analyses of systolic, diastolic, pulse, and mean arterial BP, taking into account the interaction effects of genetic variants with three psychosocial factors: depressive symptoms, anxiety symptoms, and social support. Analyses were performed using a two-stage design in a sample of up to 128,894 adults from five ancestry groups. In the combined meta-analyses of stages 1 and 2, we identified 59 loci (p value &lt; 5e−8), including nine novel BP loci. The novel associations were observed mostly with pulse pressure, with fewer observed with mean arterial pressure. Five novel loci were identified in African ancestry, and all but one showed patterns of interaction with at least one psychosocial factor. Functional annotation of the novel&nbsp;loci supports a major role for genes implicated in the immune response (PLCL2), synaptic function and neurotransmission (LIN7A and PFIA2), as well as genes previously implicated in neuropsychiatric or stress-related disorders (FSTL5 and CHODL). These findings underscore the importance of considering psychological and social factors in gene discovery for BP, especially in non-European populations

Multi-ancestry sleep-by-SNP interaction analysis in 126,926 individuals reveals lipid loci stratified by sleep duration.

Both short and long sleep are associated with an adverse lipid profile, likely through different biological pathways. To elucidate the biology of sleep-associated adverse lipid profile, we conduct multi-ancestry genome-wide sleep-SNP interaction analyses on three lipid traits (HDL-c, LDL-c and triglycerides). In the total study sample (discovery + replication) of 126,926 individuals from 5 different ancestry groups, when considering either long or short total sleep time interactions in joint analyses, we identify 49 previously unreported lipid loci, and 10 additional previously unreported lipid loci in a restricted sample of European-ancestry cohorts. In addition, we identify new gene-sleep interactions for known lipid loci such as LPL and PCSK9. The previously unreported lipid loci have a modest explained variance in lipid levels: most notable, gene-short-sleep interactions explain 4.25% of the variance in triglyceride level. Collectively, these findings contribute to our understanding of the biological mechanisms involved in sleep-associated adverse lipid profiles

Gene-educational attainment interactions in a multi-population genome-wide meta-analysis identify novel lipid loci

Introduction: Educational attainment, widely used in epidemiologic studies as a surrogate for socioeconomic status, is a predictor of cardiovascular health outcomes. Methods: A two-stage genome-wide meta-analysis of low-density lipoprotein cholesterol (LDL), high-density lipoprotein cholesterol (HDL), and triglyceride (TG) levels was performed while accounting for gene-educational attainment interactions in up to 226,315 individuals from five population groups. We considered two educational attainment variables: “Some College” (yes/no, for any education beyond high school) and “Graduated College” (yes/no, for completing a 4-year college degree). Genome-wide significant (p &lt; 5 × 10−8) and suggestive (p &lt; 1 × 10−6) variants were identified in Stage 1 (in up to 108,784 individuals) through genome-wide analysis, and those variants were followed up in Stage 2 studies (in up to 117,531 individuals). Results: In combined analysis of Stages 1 and 2, we identified 18 novel lipid loci (nine for LDL, seven for HDL, and two for TG) by two degree-of-freedom (2 DF) joint tests of main and interaction effects. Four loci showed significant interaction with educational attainment. Two loci were significant only in cross-population analyses. Several loci include genes with known or suggested roles in adipose (FOXP1, MBOAT4, SKP2, STIM1, STX4), brain (BRI3, FILIP1, FOXP1, LINC00290, LMTK2, MBOAT4, MYO6, SENP6, SRGAP3, STIM1, TMEM167A, TMEM30A), and liver (BRI3, FOXP1) biology, highlighting the potential importance of brain-adipose-liver communication in the regulation of lipid metabolism. An investigation of the potential druggability of genes in identified loci resulted in five gene targets shown to interact with drugs approved by the Food and Drug Administration, including genes with roles in adipose and brain tissue. Discussion: Genome-wide interaction analysis of educational attainment identified novel lipid loci not previously detected by analyses limited to main genetic effects.</p

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity.

Many genetic loci affect circulating lipid levels, but it remains unknown whether lifestyle factors, such as physical activity, modify these genetic effects. To identify lipid loci interacting with physical activity, we performed genome-wide analyses of circulating HDL cholesterol, LDL cholesterol, and triglyceride levels in up to 120,979 individuals of European, African, Asian, Hispanic, and Brazilian ancestry, with follow-up of suggestive associations in an additional 131,012 individuals. We find four loci, in/near CLASP1, LHX1, SNTA1, and CNTNAP2, that are associated with circulating lipid levels through interaction with physical activity; higher levels of physical activity enhance the HDL cholesterol-increasing effects of the CLASP1, LHX1, and SNTA1 loci and attenuate the LDL cholesterol-increasing effect of the CNTNAP2 locus. The CLASP1, LHX1, and SNTA1 regions harbor genes linked to muscle function and lipid metabolism. Our results elucidate the role of physical activity interactions in the genetic contribution to blood lipid levels

Multi-ancestry study of blood lipid levels identifies four loci interacting with physical activity

The present work was largely supported by a grant from the US National Heart, Lung, and Blood Institute (NHLBI) of the National Institutes of Health (R01HL118305). The full list of acknowledgments appears in the Supplementary Notes 3 and 4.Peer reviewedPublisher PD