We propose a hierarchical process for inferring the 3D pose of a person from monocular images. Fi... more We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time.
We present a probabilistic framework for component-based automatic detection and tracking of obje... more We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, where each node corresponds to an object or component of an object at a given time, and the edges correspond to learned spatial and temporal constraints. Object detection and tracking is formulated as inference over a directed loopy graph, and is solved with non-parametric belief propagation. This type of object model allows object-detection to make use of temporal consistency (over an arbitrarily sized temporal window), and facilitates robust tracking of the object. The two layer structure of the graphical model allows inference over the entire object as well as individual components. AdaBoost detectors are used to define the likelihood and form proposal distributions for components. Proposal distributions provide 'bottomup' information that is incorporated into the inference process, enabling automatic object detection and tracking. We illustrate our method by detecting and tracking two classes of objects, vehicles and pedestrians, in video sequences collected using a single grayscale uncalibrated carmounted moving camera.
ABSTRACT Ground truth optical flow is difficult to measure in real scenes with natural motion. As... more ABSTRACT Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.
ABSTRACT With the MPI-Sintel Flow dataset, we introduce a naturalistic dataset for optical flow e... more ABSTRACT With the MPI-Sintel Flow dataset, we introduce a naturalistic dataset for optical flow evaluation derived from the open source CGI movie Sintel. In contrast to the well-known Middlebury dataset, the MPI-Sintel Flow dataset contains longer and more varied sequences with image degradations such as motion blur, defocus blur, and atmospheric effects. Animators use a variety of techniques that produce pleasing images but make the raw animation data inappropriate for computer vision applications if used "out of the box". Several changes to the rendering software and animation files were necessary in order to produce data for flow evaluation and similar changes are likely for future efforts to construct a scientific dataset from an animated film. Here we distill our experience with Sintel into a set of best practices for using computer animation to generate scientific data for vision research.
Adipose stem cell (ASC) differentiation is necessary for the proper maintenance and function of a... more Adipose stem cell (ASC) differentiation is necessary for the proper maintenance and function of adipose tissue. The procurement and characterization of multipotent ASCs has enabled investigation into the molecular determinants driving human adipogenesis. Here, the transcription factor MYC was identified as a significant regulator of ASC differentiation. Expression of MYC transcript and protein was found to accumulate during the initial course of differentiation. Loss-of-function analysis using siRNA mediated knockdown of MYC demonstrated inhibition of hormonally stimulated adipogenesis. MYC exhibited an early and sustained expression pattern that preceded down regulation of key suppressor genes, as well as induction of transcriptional and functional effectors. Glucocorticoid stimulation was identified as a necessary component for MYC induction and was found to impact adipogenesis in a concentration-dependent manner. Global gene expression analysis of MYC knockdown in ASC enriched fo...
From the outside it may not be apparent that Brown University has a large, interdisciplinary, and... more From the outside it may not be apparent that Brown University has a large, interdisciplinary, and vibrant computer vision community. Despite being a small school (5,674 undergraduate and 1,343 graduate students), Brown has a tightly knit community of vision researchers in various departments. This can be partly seen in this special issue which has contributions from researchers in the Division of Applied Mathematics, the Department of Computer Science, the Division of Engineering, and the Department of Cognitive and Linguistic Sciences. What we find amazing and wonderful about Brown is the high degree of interaction among researchers from these different disciplines.
The analysis of extra-cellular neural recordings typically begins with careful spike sorting and ... more The analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. In many situations this is unproblematic as experimental and spike sorting procedures often focus on well isolated units. There is evidence in the literature, however, that errors in spike sorting can occur even with carefully collected and selected data. Additionally, chronically implanted electrodes and arrays with fixed electrodes cannot be easily adjusted to provide well isolated units. In these situations, multiple units may be recorded and the assignment of waveforms to units may be ambiguous. At the same time, analysis of such data may be both scientifically important and clinically relevant. In this paper we address this issue using a novel probabilistic model that accounts for several important sources of uncertainty and error in spike sorting. In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. We show how such a distribution over spike sortings can support standard neuroscientific questions while providing a representation of uncertainty in the analysis. As a representative illustration of the approach, we analyzed primary motor cortical tuning with respect to hand movement in data recorded with a chronic multi-electrode array in non-human primates. We found that the probabilistic analysis generally agrees with human sorters but suggests the presence of tuned units not detected by humans.
The symbiotic relationship between legumes and nitrogen fixing bacteria is critical for agricultu... more The symbiotic relationship between legumes and nitrogen fixing bacteria is critical for agriculture, as it may have profound impacts on lowering costs for farmers, on land sustainability, on soil quality, and on mitigation of greenhouse gas emissions. However, despite the importance of the symbioses to the global nitrogen cycling balance, very few rhizobial genomes have been sequenced so far, although there are some ongoing efforts in sequencing elite strains. In this study, the genomes of fourteen selected strains of the order Rhizobiales, all previously fully sequenced and annotated, were compared to assess differences between the strains and to investigate the feasibility of defining a core 'symbiome'-the essential genes required by all rhizobia for nodulation and nitrogen fixation. Comparison of these whole genomes has revealed valuable information, such as several events of lateral gene transfer, particularly in the symbiotic plasmids and genomic islands that have contributed to a better understanding of the evolution of contrasting symbioses. Unique genes were also identified, as well as omissions of symbiotic genes that were expected to be found. Protein comparisons have also allowed the identification of a
2007 IEEE 11th International Conference on Computer Vision, 2007
Strong lighting is common in natural scenes yet is often viewed as a nuisance for object pose est... more Strong lighting is common in natural scenes yet is often viewed as a nuisance for object pose estimation and tracking. In human shape and pose estimation, cast shadows can be confused with foreground structure while self shadowing and shading variation on the body cause the appearance of the person to change with pose. Rather than attempt to minimize the effects of lighting and shadows, we show that strong lighting in a scene actually makes pose and shape estimation more robust. Additionally, by recovering multiple body poses we are able to automatically estimate the lighting in the scene and the albedo of the body. Our approach makes use of a detailed 3D body model, the parameters of which are directly recovered from image data. We provide a thorough exploration of human pose estimation under strong lighting conditions and show: 1. the estimation of the light source from cast shadows; 2. the estimation of the light source and the albedo of the body from multiple body poses; 3. that a point light and cast shadows on the ground plane can be treated as an additional "shadow camera" that improves pose and shape recovery, particularly in monocular scenes. Additionally we introduce the notion of albedo constancy which employs lighting normalized image data for matching. Our experiments with multiple subjects show that rather than causing problems, strong lighting improves human pose and shape estimation.
A direct neural interface system (NIS) promises to provide communication and independence to pers... more A direct neural interface system (NIS) promises to provide communication and independence to persons with paralysis by harnessing intact motor cortical signals to enable controlling prosthetic devices. An intracortical NIS aims to achieve this by sensing extracellular neuronal signals through chronically implanted microelectrodes and by decoding the spiking activity of neurons into prosthetic control signals. In non-human primate studies, decoding
Toxicological sciences : an official journal of the Society of Toxicology, 2014
Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcr... more Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the -log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was appro...
2009 IEEE 12th International Conference on Computer Vision, 2009
We describe a solution to the challenging problem of estimating human body shape from a single ph... more We describe a solution to the challenging problem of estimating human body shape from a single photograph or painting. Our approach computes shape and pose parameters of a 3D human body model directly from monocular image cues and advances the state of the art in several directions. First, given a user-supplied estimate of the subject's height and a few clicked points on the body we estimate an initial 3D articulated body pose and shape. Second, using this initial guess we generate a tri-map of regions inside, outside and on the boundary of the human, which is used to segment the image using graph cuts. Third, we learn a low-dimensional linear model of human shape in which variations due to height are concentrated along a single dimension, enabling height-constrained estimation of body shape. Fourth, we formulate the problem of parametric human shape from shading. We estimate the body pose, shape and reflectance as well as the scene lighting that produces a synthesized body that robustly matches the image evidence. Quantitative experiments demonstrate how smooth shading provides powerful constraints on human shape. We further demonstrate a novel application in which we extract 3D human models from archival photographs and paintings.
this paper we provide an overview of recent research conducted at the Universityof Maryland&a... more this paper we provide an overview of recent research conducted at the Universityof Maryland's Computer Vision Laboratory on problems related to surveillanceof human activities. Our research is motivated by considerations of aground-based mobile surveillance system that monitors an extended area forhuman activity. During motion, the surveillance system must detect other movingobjects and identify them as humans, animals, vehicles. When one or morepersons are detected, their movements need...
Consumption of high fructose corn syrup (HFCS)-sweetened beverages increases serum urate and risk... more Consumption of high fructose corn syrup (HFCS)-sweetened beverages increases serum urate and risk of incident gout. Genetic variants in SLC2A9, that exchanges uric acid for glucose and fructose, associate with gout. We tested association between sugar (sucrose)-sweetened beverage (SSB) consumption and prevalent gout. We also tested the hypothesis that SLC2A9 genotype and SSB consumption interact to determine gout risk. Participants were 1634 New Zealand (NZ) European Caucasian, Ma¯ori and Pacific Island people and 7075 European Caucasians from the Atherosclerosis Risk in Communities (ARIC) study. NZ samples were genotyped for rs11942223 and ARIC for rs6449173. Effect estimates were multivariate adjusted. SSB consumption increased gout risk. The OR for four drinks/day relative to zero was 6.89 (p=0.045), 5.19 (p=0.010) and 2.84 (p=0.043) for European Caucasian, Ma¯ori and Pacific Islanders, respectively. With each extra daily SSB serving, carriage of the gout-protective allele of SLC...
Thalassaemia and sickle cell disease have been recognized by the World Health Organization as imp... more Thalassaemia and sickle cell disease have been recognized by the World Health Organization as important inherited disorders principally impacting on the populations of low income countries. To create a national and regional profile of β-thalassaemia mutations in the population of India, a meta-analysis was conducted on 17 selected studies comprising 8,505 alleles and offering near-national coverage for the disease. At the national level 52 mutations accounted for 97.5% of all β-thalassaemia alleles, with IVSI-5(G>C) the most common disease allele (54.7%). Population stratification was apparent in the mutation profiles at regional level with, for example, the prevalence of IVSI-5(G>C) varying from 44.8% in the North to 71.4% in the East. A number of major mutations, such as Poly A(T>C), were apparently restricted to a particular region of the country, although these findings may in part reflect the variant test protocols adopted by different centres. Given the size and genet...
Recent advances in high-throughout sequencing technologies have made it possible to accurately as... more Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralog ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compa...
We propose a hierarchical process for inferring the 3D pose of a person from monocular images. Fi... more We propose a hierarchical process for inferring the 3D pose of a person from monocular images. First we infer a learned view-based 2D body model from a single image using non-parametric belief propagation. This approach integrates information from bottom-up body-part proposal processes and deals with self-occlusion to compute distributions over limb poses. Then, we exploit a learned Mixture of Experts model to infer a distribution of 3D poses conditioned on 2D poses. This approach is more general than recent work on inferring 3D pose directly from silhouettes since the 2D body model provides a richer representation that includes the 2D joint angles and the poses of limbs that may be unobserved in the silhouette. We demonstrate the method in a laboratory setting where we evaluate the accuracy of the 3D poses against ground truth data. We also estimate 3D body pose in a monocular image sequence. The resulting 3D estimates are sufficiently accurate to serve as proposals for the Bayesian inference of 3D human motion over time.
We present a probabilistic framework for component-based automatic detection and tracking of obje... more We present a probabilistic framework for component-based automatic detection and tracking of objects in video. We represent objects as spatio-temporal two-layer graphical models, where each node corresponds to an object or component of an object at a given time, and the edges correspond to learned spatial and temporal constraints. Object detection and tracking is formulated as inference over a directed loopy graph, and is solved with non-parametric belief propagation. This type of object model allows object-detection to make use of temporal consistency (over an arbitrarily sized temporal window), and facilitates robust tracking of the object. The two layer structure of the graphical model allows inference over the entire object as well as individual components. AdaBoost detectors are used to define the likelihood and form proposal distributions for components. Proposal distributions provide 'bottomup' information that is incorporated into the inference process, enabling automatic object detection and tracking. We illustrate our method by detecting and tracking two classes of objects, vehicles and pedestrians, in video sequences collected using a single grayscale uncalibrated carmounted moving camera.
ABSTRACT Ground truth optical flow is difficult to measure in real scenes with natural motion. As... more ABSTRACT Ground truth optical flow is difficult to measure in real scenes with natural motion. As a result, optical flow data sets are restricted in terms of size, complexity, and diversity, making optical flow algorithms difficult to train and test on realistic data. We introduce a new optical flow data set derived from the open source 3D animated short film Sintel. This data set has important features not present in the popular Middlebury flow evaluation: long sequences, large motions, specular reflections, motion blur, defocus blur, and atmospheric effects. Because the graphics data that generated the movie is open source, we are able to render scenes under conditions of varying complexity to evaluate where existing flow algorithms fail. We evaluate several recent optical flow algorithms and find that current highly-ranked methods on the Middlebury evaluation have difficulty with this more complex data set suggesting further research on optical flow estimation is needed. To validate the use of synthetic data, we compare the image- and flow-statistics of Sintel to those of real films and videos and show that they are similar. The data set, metrics, and evaluation website are publicly available.
ABSTRACT With the MPI-Sintel Flow dataset, we introduce a naturalistic dataset for optical flow e... more ABSTRACT With the MPI-Sintel Flow dataset, we introduce a naturalistic dataset for optical flow evaluation derived from the open source CGI movie Sintel. In contrast to the well-known Middlebury dataset, the MPI-Sintel Flow dataset contains longer and more varied sequences with image degradations such as motion blur, defocus blur, and atmospheric effects. Animators use a variety of techniques that produce pleasing images but make the raw animation data inappropriate for computer vision applications if used "out of the box". Several changes to the rendering software and animation files were necessary in order to produce data for flow evaluation and similar changes are likely for future efforts to construct a scientific dataset from an animated film. Here we distill our experience with Sintel into a set of best practices for using computer animation to generate scientific data for vision research.
Adipose stem cell (ASC) differentiation is necessary for the proper maintenance and function of a... more Adipose stem cell (ASC) differentiation is necessary for the proper maintenance and function of adipose tissue. The procurement and characterization of multipotent ASCs has enabled investigation into the molecular determinants driving human adipogenesis. Here, the transcription factor MYC was identified as a significant regulator of ASC differentiation. Expression of MYC transcript and protein was found to accumulate during the initial course of differentiation. Loss-of-function analysis using siRNA mediated knockdown of MYC demonstrated inhibition of hormonally stimulated adipogenesis. MYC exhibited an early and sustained expression pattern that preceded down regulation of key suppressor genes, as well as induction of transcriptional and functional effectors. Glucocorticoid stimulation was identified as a necessary component for MYC induction and was found to impact adipogenesis in a concentration-dependent manner. Global gene expression analysis of MYC knockdown in ASC enriched fo...
From the outside it may not be apparent that Brown University has a large, interdisciplinary, and... more From the outside it may not be apparent that Brown University has a large, interdisciplinary, and vibrant computer vision community. Despite being a small school (5,674 undergraduate and 1,343 graduate students), Brown has a tightly knit community of vision researchers in various departments. This can be partly seen in this special issue which has contributions from researchers in the Division of Applied Mathematics, the Department of Computer Science, the Division of Engineering, and the Department of Cognitive and Linguistic Sciences. What we find amazing and wonderful about Brown is the high degree of interaction among researchers from these different disciplines.
The analysis of extra-cellular neural recordings typically begins with careful spike sorting and ... more The analysis of extra-cellular neural recordings typically begins with careful spike sorting and all analysis of the data then rests on the correctness of the resulting spike trains. In many situations this is unproblematic as experimental and spike sorting procedures often focus on well isolated units. There is evidence in the literature, however, that errors in spike sorting can occur even with carefully collected and selected data. Additionally, chronically implanted electrodes and arrays with fixed electrodes cannot be easily adjusted to provide well isolated units. In these situations, multiple units may be recorded and the assignment of waveforms to units may be ambiguous. At the same time, analysis of such data may be both scientifically important and clinically relevant. In this paper we address this issue using a novel probabilistic model that accounts for several important sources of uncertainty and error in spike sorting. In lieu of sorting neural data to produce a single best spike train, we estimate a probabilistic model of spike trains given the observed data. We show how such a distribution over spike sortings can support standard neuroscientific questions while providing a representation of uncertainty in the analysis. As a representative illustration of the approach, we analyzed primary motor cortical tuning with respect to hand movement in data recorded with a chronic multi-electrode array in non-human primates. We found that the probabilistic analysis generally agrees with human sorters but suggests the presence of tuned units not detected by humans.
The symbiotic relationship between legumes and nitrogen fixing bacteria is critical for agricultu... more The symbiotic relationship between legumes and nitrogen fixing bacteria is critical for agriculture, as it may have profound impacts on lowering costs for farmers, on land sustainability, on soil quality, and on mitigation of greenhouse gas emissions. However, despite the importance of the symbioses to the global nitrogen cycling balance, very few rhizobial genomes have been sequenced so far, although there are some ongoing efforts in sequencing elite strains. In this study, the genomes of fourteen selected strains of the order Rhizobiales, all previously fully sequenced and annotated, were compared to assess differences between the strains and to investigate the feasibility of defining a core 'symbiome'-the essential genes required by all rhizobia for nodulation and nitrogen fixation. Comparison of these whole genomes has revealed valuable information, such as several events of lateral gene transfer, particularly in the symbiotic plasmids and genomic islands that have contributed to a better understanding of the evolution of contrasting symbioses. Unique genes were also identified, as well as omissions of symbiotic genes that were expected to be found. Protein comparisons have also allowed the identification of a
2007 IEEE 11th International Conference on Computer Vision, 2007
Strong lighting is common in natural scenes yet is often viewed as a nuisance for object pose est... more Strong lighting is common in natural scenes yet is often viewed as a nuisance for object pose estimation and tracking. In human shape and pose estimation, cast shadows can be confused with foreground structure while self shadowing and shading variation on the body cause the appearance of the person to change with pose. Rather than attempt to minimize the effects of lighting and shadows, we show that strong lighting in a scene actually makes pose and shape estimation more robust. Additionally, by recovering multiple body poses we are able to automatically estimate the lighting in the scene and the albedo of the body. Our approach makes use of a detailed 3D body model, the parameters of which are directly recovered from image data. We provide a thorough exploration of human pose estimation under strong lighting conditions and show: 1. the estimation of the light source from cast shadows; 2. the estimation of the light source and the albedo of the body from multiple body poses; 3. that a point light and cast shadows on the ground plane can be treated as an additional "shadow camera" that improves pose and shape recovery, particularly in monocular scenes. Additionally we introduce the notion of albedo constancy which employs lighting normalized image data for matching. Our experiments with multiple subjects show that rather than causing problems, strong lighting improves human pose and shape estimation.
A direct neural interface system (NIS) promises to provide communication and independence to pers... more A direct neural interface system (NIS) promises to provide communication and independence to persons with paralysis by harnessing intact motor cortical signals to enable controlling prosthetic devices. An intracortical NIS aims to achieve this by sensing extracellular neuronal signals through chronically implanted microelectrodes and by decoding the spiking activity of neurons into prosthetic control signals. In non-human primate studies, decoding
Toxicological sciences : an official journal of the Society of Toxicology, 2014
Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcr... more Relative to microarrays, RNA-seq has been reported to offer higher precision estimates of transcript abundance, a greater dynamic range, and detection of novel transcripts. However, previous comparisons of the 2 technologies have not covered dose-response experiments that are relevant to toxicology. Male F344 rats were exposed for 13 weeks to 5 doses of bromobenzene, and liver gene expression was measured using both microarrays and RNA-seq. Multiple normalization methods were evaluated for each technology, and gene expression changes were statistically analyzed using both analysis of variance and benchmark dose (BMD). Fold-change values were highly correlated between the 2 technologies, whereas the -log p values showed lower correlation. RNA-seq detected fewer statistically significant genes at lower doses, but more significant genes based on fold change except when a negative binomial transformation was applied. Overlap in genes significant by both p value and fold change was appro...
2009 IEEE 12th International Conference on Computer Vision, 2009
We describe a solution to the challenging problem of estimating human body shape from a single ph... more We describe a solution to the challenging problem of estimating human body shape from a single photograph or painting. Our approach computes shape and pose parameters of a 3D human body model directly from monocular image cues and advances the state of the art in several directions. First, given a user-supplied estimate of the subject's height and a few clicked points on the body we estimate an initial 3D articulated body pose and shape. Second, using this initial guess we generate a tri-map of regions inside, outside and on the boundary of the human, which is used to segment the image using graph cuts. Third, we learn a low-dimensional linear model of human shape in which variations due to height are concentrated along a single dimension, enabling height-constrained estimation of body shape. Fourth, we formulate the problem of parametric human shape from shading. We estimate the body pose, shape and reflectance as well as the scene lighting that produces a synthesized body that robustly matches the image evidence. Quantitative experiments demonstrate how smooth shading provides powerful constraints on human shape. We further demonstrate a novel application in which we extract 3D human models from archival photographs and paintings.
this paper we provide an overview of recent research conducted at the Universityof Maryland&a... more this paper we provide an overview of recent research conducted at the Universityof Maryland's Computer Vision Laboratory on problems related to surveillanceof human activities. Our research is motivated by considerations of aground-based mobile surveillance system that monitors an extended area forhuman activity. During motion, the surveillance system must detect other movingobjects and identify them as humans, animals, vehicles. When one or morepersons are detected, their movements need...
Consumption of high fructose corn syrup (HFCS)-sweetened beverages increases serum urate and risk... more Consumption of high fructose corn syrup (HFCS)-sweetened beverages increases serum urate and risk of incident gout. Genetic variants in SLC2A9, that exchanges uric acid for glucose and fructose, associate with gout. We tested association between sugar (sucrose)-sweetened beverage (SSB) consumption and prevalent gout. We also tested the hypothesis that SLC2A9 genotype and SSB consumption interact to determine gout risk. Participants were 1634 New Zealand (NZ) European Caucasian, Ma¯ori and Pacific Island people and 7075 European Caucasians from the Atherosclerosis Risk in Communities (ARIC) study. NZ samples were genotyped for rs11942223 and ARIC for rs6449173. Effect estimates were multivariate adjusted. SSB consumption increased gout risk. The OR for four drinks/day relative to zero was 6.89 (p=0.045), 5.19 (p=0.010) and 2.84 (p=0.043) for European Caucasian, Ma¯ori and Pacific Islanders, respectively. With each extra daily SSB serving, carriage of the gout-protective allele of SLC...
Thalassaemia and sickle cell disease have been recognized by the World Health Organization as imp... more Thalassaemia and sickle cell disease have been recognized by the World Health Organization as important inherited disorders principally impacting on the populations of low income countries. To create a national and regional profile of β-thalassaemia mutations in the population of India, a meta-analysis was conducted on 17 selected studies comprising 8,505 alleles and offering near-national coverage for the disease. At the national level 52 mutations accounted for 97.5% of all β-thalassaemia alleles, with IVSI-5(G>C) the most common disease allele (54.7%). Population stratification was apparent in the mutation profiles at regional level with, for example, the prevalence of IVSI-5(G>C) varying from 44.8% in the North to 71.4% in the East. A number of major mutations, such as Poly A(T>C), were apparently restricted to a particular region of the country, although these findings may in part reflect the variant test protocols adopted by different centres. Given the size and genet...
Recent advances in high-throughout sequencing technologies have made it possible to accurately as... more Recent advances in high-throughout sequencing technologies have made it possible to accurately assign copy number (CN) at CN variable loci. However, current analytic methods often perform poorly in regions in which complex CN variation is observed. Here we report the development of a read depth-based approach, CNVrd2, for investigation of CN variation using high-throughput sequencing data. This methodology was developed using data from the 1000 Genomes Project from the CCL3L1 locus, and tested using data from the DEFB103A locus. In both cases, samples were selected for which paralog ratio test data were also available for comparison. The CNVrd2 method first uses observed read-count ratios to refine segmentation results in one population. Then a linear regression model is applied to adjust the results across multiple populations, in combination with a Bayesian normal mixture model to cluster segmentation scores into groups for individual CN counts. The performance of CNVrd2 was compa...
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