Automated Particle Identification through Regression Analysis of Size, Shape and Colour

Rapid point of care diagnostic tests and tests to provide therapeutic information are now available for a range of specific conditions from the measurement of blood glucose levels for diabetes to card agglutination tests for parasitic infections. Due to a lack of specificity these test are often then backed up by more conventional lab based diagnostic methods for example a card agglutination test may be carried out for a suspected parasitic infection in the field and if positive a blood sample can then be sent to a lab for confirmation. The eventual diagnosis is often achieved by microscopic examination of the sample. In this paper we propose a computerized vision system for aiding in the diagnostic process; this system used a novel particle recognition algorithm to improve specificity and speed during the diagnostic process. We will show the detection and classification of different types of cells in a diluted blood sample using regression analysis of their size, shape and colour. The first step is to define the objects to be tracked by a Gaussian Mixture Model for background subtraction and binary opening and closing for noise suppression. After subtracting the objects of interest from the background the next challenge is to predict if a given object belongs to a certain category or not. This is a classification problem, and the output of the algorithm is a Boolean value (true/false). As such the computer program should be able to ”predict” with reasonable level of confidence if a given particle belongs to the kind we are looking for or not. We show the use of a binary logistic regression analysis with three continuous predictors: size, shape and color histogram. The results suggest this variables could be very useful in a logistic regression equation as they proved to have a relatively high predictive value on their own

Continuous cell lysis in microfluidics through acoustic and optoelectronic tweezers

A versatile platform for efficient cell lysis using a combination of acoustic and electric fields in a microchannel is presented. Cell membrane disruption is triggered by electric fields inducing electroporation and then lysis. The principle of optoelectronic tweezers (OET) is applied to control the electric field strength and a surface acoustic wave transducer is attached to an OET chip to implement acoustic tweezing (AT). The system is characterized in terms of spatial control of electric fields, single cell precision and lysi

Portable optoelectronic tweezers (OET), taking optical micromanipulation out of the optics lab

We report the development of a portable optical micromanipulation setup based on Optoelectronic Tweezers (OET). We show multiple microparticle manipulation in a setup that fits within a briefcase and demonstrate its potential for facilitating interdisciplinary science by reducing the effort required to explore new applications

Tuning the polarization states of optical spots at the nanoscale on the poincar´e sphere using a plasmonic nanoantenna

It is shown that the polarization states of optical spots at the nanoscale can be manipulated to various points on the Poincar´e sphere using a plasmonic nanoantenna. Linearly, circularly, and elliptically polarized near-field optical spots at the nanoscale are achieved with various polarization states on the Poincar´e sphere using a plasmonic nanoantenna. A novel plasmonic nanoantenna is illuminated with diffraction-limited linearly polarized light. It is demonstrated that the plasmonic resonances of perpendicular and longitudinal components of the nanoantenna and the angle of incident polarization can be tuned to obtain optical spots beyond the diffraction limit with a desired polarization and handedness

Linkage analysis of smoking initiation and quantity in Dutch sibling pairs.

The heritability of smoking initiation (SI) and number of cigarettes smoked (NC) was determined in 3657 Dutch twin pairs. For SI a heritability of 36% was found and for NC of 51%. Both SI and NC were also significantly influenced by environmental factors shared by family members. The etiological factors that influence these traits partly overlap. Linkage analyses were performed on data of 536 DZ twins and siblings from 192 families, forming 592 sibling pairs. Results suggested QTLs on chromosome 6 (LOD=3.05) and chromosome 14 (LOD=1.66) for SI and on chromosome 3 (LOD=1.98) for NC. Strikingly, on chromosome 10 a peak was found in the same region for both SI (LOD=1.92) and for NC (LOD=2.29) which may partly explain the overlapping etiological factors for SI and N

De Novo Sequence and Copy Number Variants Are Strongly Associated with Tourette Disorder and Implicate Cell Polarity in Pathogenesis.

We previously established the contribution of de novo damaging sequence variants to Tourette disorder (TD) through whole-exome sequencing of 511 trios. Here, we sequence an additional 291 TD trios and analyze the combined set of 802 trios. We observe an overrepresentation of de novo damaging variants in simplex, but not multiplex, families; we identify a high-confidence TD risk gene, CELSR3 (cadherin EGF LAG seven-pass G-type receptor 3); we find that the genes mutated in TD patients are enriched for those related to cell polarity, suggesting a common pathway underlying pathobiology; and we confirm a statistically significant excess of de novo copy number variants in TD. Finally, we identify significant overlap of de novo sequence variants between TD and obsessive-compulsive disorder and de novo copy number variants between TD and autism spectrum disorder, consistent with shared genetic risk

The improved assembly of the European Pear

Apple and Pear diverged from each other between 5.4 and 21.5 MYA and are believed to share a common genome duplication event between 35 and 50 MYA (Velasco et al. 2010, Wu et al. 2012). Size differences have been observed between the Apple and Pear genomes which are estimated at 527Mb (Pyrus x Bretschneideri Rehd) and 700Mb (Malus x Domestica Borkh) respectively (Wu et al. 2013, Li et al. 2016). The difference in genome size has been accounted for primarily by the proliferation of transposable elements, with the gene space thought to be fairly similar between the two species (Wu et al. 2012). Comparative genomics of the lineage has however, been hampered by the fragmented nature of the reference assemblies. A new chromosome scale assembly was recently produced (Daccord et al. 2017) and now also a chromosome scale assmble of the European Pear (this study), which shows strong collinearity with Apple, greatly facilitating the comparative study of these genomes

Synaptic processes and immune-related pathways implicated in Tourette syndrome.

Tourette syndrome (TS) is a neuropsychiatric disorder of complex genetic architecture involving multiple interacting genes. Here, we sought to elucidate the pathways that underlie the neurobiology of the disorder through genome-wide analysis. We analyzed genome-wide genotypic data of 3581 individuals with TS and 7682 ancestry-matched controls and investigated associations of TS with sets of genes that are expressed in particular cell types and operate in specific neuronal and glial functions. We employed a self-contained, set-based association method (SBA) as well as a competitive gene set method (MAGMA) using individual-level genotype data to perform a comprehensive investigation of the biological background of TS. Our SBA analysis identified three significant gene sets after Bonferroni correction, implicating ligand-gated ion channel signaling, lymphocytic, and cell adhesion and transsynaptic signaling processes. MAGMA analysis further supported the involvement of the cell adhesion and trans-synaptic signaling gene set. The lymphocytic gene set was driven by variants in FLT3, raising an intriguing hypothesis for the involvement of a neuroinflammatory element in TS pathogenesis. The indications of involvement of ligand-gated ion channel signaling reinforce the role of GABA in TS, while the association of cell adhesion and trans-synaptic signaling gene set provides additional support for the role of adhesion molecules in neuropsychiatric disorders. This study reinforces previous findings but also provides new insights into the neurobiology of TS

Genome-wide association study identifies 30 Loci Associated with Bipolar Disorder

This paper is dedicated to the memory of Psychiatric Genomics Consortium (PGC) founding member and Bipolar disorder working group co-chair Pamela Sklar. We thank the participants who donated their time, experiences and DNA to this research, and to the clinical and scientific teams that worked with them. We are deeply indebted to the investigators who comprise the PGC. The views expressed are those of the authors and not necessarily those of any funding or regulatory body. Analyses were carried out on the NL Genetic Cluster Computer (http://www.geneticcluster.org ) hosted by SURFsara, and the Mount Sinai high performance computing cluster (http://hpc.mssm.edu).Bipolar disorder is a highly heritable psychiatric disorder. We performed a genome-wide association study including 20,352 cases and 31,358 controls of European descent, with follow-up analysis of 822 variants with P<1x10-4 in an additional 9,412 cases and 137,760 controls. Eight of the 19 variants that were genome-wide significant (GWS, p < 5x10-8) in the discovery GWAS were not GWS in the combined analysis, consistent with small effect sizes and limited power but also with genetic heterogeneity. In the combined analysis 30 loci were GWS including 20 novel loci. The significant loci contain genes encoding ion channels, neurotransmitter transporters and synaptic components. Pathway analysis revealed nine significantly enriched gene-sets including regulation of insulin secretion and endocannabinoid signaling. BDI is strongly genetically correlated with schizophrenia, driven by psychosis, whereas BDII is more strongly correlated with major depressive disorder. These findings address key clinical questions and provide potential new biological mechanisms for BD.This work was funded in part by the Brain and Behavior Research Foundation, Stanley Medical Research Institute, University of Michigan, Pritzker Neuropsychiatric Disorders Research Fund L.L.C., Marriot Foundation and the Mayo Clinic Center for Individualized Medicine, the NIMH Intramural Research Program; Canadian Institutes of Health Research; the UK Maudsley NHS Foundation Trust, NIHR, NRS, MRC, Wellcome Trust; European Research Council; German Ministry for Education and Research, German Research Foundation IZKF of Münster, Deutsche Forschungsgemeinschaft, ImmunoSensation, the Dr. Lisa-Oehler Foundation, University of Bonn; the Swiss National Science Foundation; French Foundation FondaMental and ANR; Spanish Ministerio de Economía, CIBERSAM, Industria y Competitividad, European Regional Development Fund (ERDF), Generalitat de Catalunya, EU Horizon 2020 Research and Innovation Programme; BBMRI-NL; South-East Norway Regional Health Authority and Mrs. Throne-Holst; Swedish Research Council, Stockholm County Council, Söderström Foundation; Lundbeck Foundation, Aarhus University; Australia NHMRC, NSW Ministry of Health, Janette M O'Neil and Betty C Lynch

Improving Genetic Prediction by Leveraging Genetic Correlations Among Human Diseases and Traits

Genomic prediction has the potential to contribute to precision medicine. However, to date, the utility of such predictors is limited due to low accuracy for most traits. Here theory and simulation study are used to demonstrate that widespread pleiotropy among phenotypes can be utilised to improve genomic risk prediction. We show how a genetic predictor can be created as a weighted index that combines published genome-wide association study (GWAS) summary statistics across many different traits. We apply this framework to predict risk of schizophrenia and bipolar disorder in the Psychiatric Genomics consortium data, finding substantial heterogeneity in prediction accuracy increases across cohorts. For six additional phenotypes in the UK Biobank data, we find increases in prediction accuracy ranging from 0.7 for height to 47 for type 2 diabetes, when using a multi-trait predictor that combines published summary statistics from multiple traits, as compared to a predictor based only on one trait. © 2018 The Author(s)