Towards Hand-over-Face Gesture Detection

Facial microexpressions are immediately appearing reactions on the face that indicate various details about people's mental and emotional states. Their most important property is that their interpretation is identical or very similar for people all over the world. At present, their identification requires a psychologist expert. Thus automating this task would enable a broader application. The goal of this research is the detection of microexpressions using hybrid expert algorithms. Our algorithms mainly rely on landmark point detectors. Based on their output, several expert algorithms are utilized to extract key features and changes appearing on the face of a subject. These algorithms usually include several steps of image processing and time series analysis algorithms. In this paper, a component responsible for detecting hand gestures and hand pose is introduced. This component helps other algorithms to eliminate false positive detections by detecting the hands over the face. In addition, the recognizability of hand-over-face gestures is investigated. Finally, the implemented face occlusion detector method is evaluated on videos

Structural and parametric uncertainties in full Bayesian and graphical lasso based approaches: beyond edge weights in psychological networks

Uncertainty over model structures poses a challenge for many approaches exploring effect strength parameters at system-level. Monte Carlo methods for full Bayesian model averaging over model structures require considerable computational resources, whereas bootstrapped graphical lasso and its approximations offer scalable alternatives with lower complexity. Although the computational efficiency of graphical lasso based approaches has prompted growing number of applications, the restrictive assumptions of this approach are frequently ignored, such as its lack of coping with interactions. We demonstrate using an artificial and a real-world example that full Bayesian averaging using Bayesian networks provides detailed estimates through posterior distributions for structural and parametric uncertainties and it is a feasible alternative, which is routinely applicable in mid-sized biomedical problems with hundreds of variables. We compare Bayesian estimates with corresponding frequentist quantities from bootstrapped graphical lasso using pairwise Markov Random Fields, discussing also their interpretational differences. We present results using synthetic data from an artificial model and using the UK Biobank data set to explore a psychopathological network centered around depression (this research has been conducted using the UK Biobank Resource under Application Number 1602)

Beyond Structural Equation Modeling: model properties and effect size from a Bayesian viewpoint. An example of complex phenotype - genotype associations in depression.

Despite the rapid evolution of measurement technologies in biomedicine and genetics, most of the recent studies aiming to explore the genetic background of multifactorial diseases were only moderately successful. One of the causes of this phenomenon is that the bottleneck of genetic research is no longer the measurement process related to various laboratory technologies, but rather the analysis and interpretation of results. The commonly applied univariate methods are inadequate for exploring complex dependency patterns of multifactorial diseases which includes nearly all common diseases, such as depression, hypertension, and asthma. A comprehensive investigation requires multivariate modeling methods that enable the analysis of interactions between factors, and allow a more detailed interpretation of studies measuring complex phenotype descriptors. In this paper we discuss various aspects of multivariate modeling through a case study analyzing the effect of the single nucleotide polymorphism rs6295 in the HTR1A gene on depression and impulsivity. We overview basic concepts related to multivariate modeling and compare the properties of two investigated modeling techniques: Structural Equation Modeling and Bayesian network based learning algorithms. The resulting models demonstrate the advantages of the Bayesian approach in terms of model properties and effect size as it allows coherent handling of the weakly significant effect of rs6295. Results also confirm the mediating role of impulsivity between the SNP rs6295 of HTR1A and depression

Investigating the combined application of Mendelian Randomization and constraint-based causal discovery methods

Mendelian randomization (MR) is often used in medical studies and biostatistics, to reveal direct causation effects between exposures and diseases, typically the effect of some exposure (like chemicals, habits and other factors) to a known disease or disorder. However, this procedure has some strict prerequisites, which often do not comply with the known variables, or the exact causal structure of the variables is not known in advance. In this study, we investigate the use of constraint-based causal discovery algorithms (PC, FCI and RFCI) to produce a sufficient causal structure from the known observations, to aid us in finding variable triplets, upon which MR can be performed. In addition, we show that the validity of MR cannot always be determined based on its results alone. Finally, we investigate the application of the MR principle to determine the direction of causality between variable-pairs, which is a problem most constraintbased causal discovery methods struggle with

The UKB envirome of depression

Major depressive disorder is a result of the complex interplay between a large number of environmental and genetic factors but the comprehensive analysis of contributing environmental factors is still an open challenge. The primary aim of this work was to create a Bayesian dependency map of environmental factors of depression, including life stress, social and lifestyle factors, using the UK Biobank data to determine direct dependencies and to characterize mediating or interacting effects of other mental health, metabolic or pain conditions. As a complementary approach, we also investigated the non-linear, synergistic multi-factorial risk of the UKB envirome on depression using deep neural network architectures. Our results showed that a surprisingly small number of core factors mediate the effects of the envirome on lifetime depression: neuroticism, current depressive symptoms, parental depression, body fat, while life stress and household income have weak direct effects. Current depressive symptom showed strong or moderate direct relationships with life stress, pain conditions, falls, age, insomnia, weight change, satisfaction, confiding in someone, exercise, sports and Townsend index. In conclusion, the majority of envirome exerts their effects in a dynamic network via transitive, interactive and synergistic relationships explaining why environmental effects may be obscured in studies which consider them individually

A depresszió környezeti faktorainak vizsgálata oksági elemzési módszerekkel

Kutatómunkánk során globális és lokális oksági feltáró algoritmusokat alkalmazunk a depresszióhoz kapcsolódó környezeti és egyéb tényezőket közötti oksági kapcsolatok azonosítására

Roles of genetic polymorphisms in the folate pathway in childhood acute lymphoblastic leukemia evaluated by bayesian relevance and effect size analysis.

In this study we investigated whether polymorphisms in the folate pathway influenced the risk of childhood acute lymphoblastic leukemia (ALL) or the survival rate of the patients. For this we selected and genotyped 67 SNPs in 15 genes in the folate pathway in 543 children with ALL and 529 controls. The results were evaluated by gender adjusted logistic regression and by the Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA) methods. Bayesian structure based odds ratios for the relevant variables and interactions were also calculated. Altogether 9 SNPs in 8 genes were associated with altered susceptibility to ALL. After correction for multiple testing, two associations remained significant. The genotype distribution of the MTHFD1 rs1076991 differed significantly between the ALL and control population. Analyzing the subtypes of the disease the GG genotype increased only the risk of B-cell ALL (p = 3.52x10(-4); OR = 2.00). The GG genotype of the rs3776455 SNP in the MTRR gene was associated with a significantly reduced risk to ALL (p = 1.21x10(-3); OR = 0.55), which resulted mainly from the reduced risk to B-cell and hyperdiploid-ALL. The TC genotype of the rs9909104 SNP in the SHMT1 gene was associated with a lower survival rate comparing it to the TT genotype (80.2% vs. 88.8%; p = 0.01). The BN-BMLA confirmed the main findings of the frequentist-based analysis and showed structural interactional maps and the probabilities of the different structural association types of the relevant SNPs especially in the hyperdiploid-ALL, involving additional SNPs in genes like TYMS, DHFR and GGH. We also investigated the statistical interactions and redundancies using structural model properties. These results gave further evidence that polymorphisms in the folate pathway could influence the ALL risk and the effectiveness of the therapy. It was also shown that in gene association studies the BN-BMLA could be a useful supplementary to the traditional frequentist-based statistical method

Novel genes in Human Asthma Based on a Mouse Model of Allergic Airway Inflammation and Human Investigations

PURPOSE: Based on a previous gene expression study in a mouse model of asthma, we selected 60 candidate genes and investigated their possible roles in human asthma. METHODS: In these candidate genes, 90 SNPs were genotyped using MassARRAY technology from 311 asthmatic children and 360 healthy controls of the Hungarian (Caucasian) population. Moreover, gene expression levels were measured by RT PCR in the induced sputum of 13 asthmatics and 10 control individuals. t-tests, chi-square tests, and logistic regression were carried out in order to assess associations of SNP frequency and expression level with asthma. Permutation tests were performed to account for multiple hypothesis testing. RESULTS: The frequency of 4 SNPs in 2 genes differed significantly between asthmatic and control subjects: SNPs rs2240572, rs2240571, rs3735222 in gene SCIN, and rs32588 in gene PPARGC1B. Carriers of the minor alleles had reduced risk of asthma with an odds ratio of 0.64 (0.51-0.80; P=7×10(-5)) in SCIN and 0.56 (0.42-0.76; P=1.2×10(-4)) in PPARGC1B. The expression levels of SCIN, PPARGC1B and ITLN1 genes were significantly lower in the sputum of asthmatics. CONCLUSIONS: Three potentially novel asthma-associated genes were identified based on mouse experiments and human studies

Evaluation of a Partial Genome Screening of Two Asthma Susceptibility Regions Using Bayesian Network Based Bayesian Multilevel Analysis of Relevance

Genetic studies indicate high number of potential factors related to asthma. Based on earlier linkage analyses we selected the 11q13 and 14q22 asthma susceptibility regions, for which we designed a partial genome screening study using 145 SNPs in 1201 individuals (436 asthmatic children and 765 controls). The results were evaluated with traditional frequentist methods and we applied a new statistical method, called Bayesian network based Bayesian multilevel analysis of relevance (BN-BMLA). This method uses Bayesian network representation to provide detailed characterization of the relevance of factors, such as joint significance, the type of dependency, and multi-target aspects. We estimated posteriors for these relations within the Bayesian statistical framework, in order to estimate the posteriors whether a variable is directly relevant or its association is only mediated. With frequentist methods one SNP (rs3751464 in the FRMD6 gene) provided evidence for an association with asthma (OR = 1.43(1.2–1.8); p = 3×10−4). The possible role of the FRMD6 gene in asthma was also confirmed in an animal model and human asthmatics. In the BN-BMLA analysis altogether 5 SNPs in 4 genes were found relevant in connection with asthma phenotype: PRPF19 on chromosome 11, and FRMD6, PTGER2 and PTGDR on chromosome 14. In a subsequent step a partial dataset containing rhinitis and further clinical parameters was used, which allowed the analysis of relevance of SNPs for asthma and multiple targets. These analyses suggested that SNPs in the AHNAK and MS4A2 genes were indirectly associated with asthma. This paper indicates that BN-BMLA explores the relevant factors more comprehensively than traditional statistical methods and extends the scope of strong relevance based methods to include partial relevance, global characterization of relevance and multi-target relevance