Candidate Regions Of Interest Research Articles

3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

Animal-Vehicle Collision Mitigation System for Automated Vehicles

Detecting large animals on roadways using automated systems such as robots or vehicles is a vital task. This can be achieved using conventional tools such as ultrasonic sensors, or with innovative technology based on smart cameras. In this paper, we investigate a vision-based solution. We begin the paper by performing a comparative study between three detectors: 1) Haar-AdaBoost; 2) histogram of oriented gradient (HOG)-AdaBoost; and 3) local binary pattern (LBP)-AdaBoost, which were initially developed to detect humans and their faces. These detectors are implemented, evaluated, and compared to each other in terms of accuracy and processing time. Based on our evaluation and comparison results, we design a two-stage architecture which outperforms the aforementioned detectors. The proposed architecture detects candidate regions of interest using LBP-AdaBoost in the first stage, which offers robustness to false positives in real-time conditions. The second stage is based on support vector machine classifiers that were trained using HOG features. The training data are generated from our novel dataset called large animal dataset, which contains common and thermographic images of large road-animals. We emphasize that no such public dataset currently exists.

LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

LIDAR-INCORPORATED TRAFFIC SIGN DETECTION FROM VIDEO LOG IMAGES OF MOBILE MAPPING SYSTEM

Abstract. Mobile Mapping System (MMS) simultaneously collects the Lidar points and video log images in a scenario with the laser profiler and digital camera. Besides the textural details of video log images, it also captures the 3D geometric shape of point cloud. It is widely used to survey the street view and roadside transportation infrastructure, such as traffic sign, guardrail, etc., in many transportation agencies. Although many literature on traffic sign detection are available, they only focus on either Lidar or imagery data of traffic sign. Based on the well-calibrated extrinsic parameters of MMS, 3D Lidar points are, the first time, incorporated into 2D video log images to enhance the detection of traffic sign both physically and visually. Based on the local elevation, the 3D pavement area is first located. Within a certain distance and height of the pavement, points of the overhead and roadside traffic signs can be obtained according to the setup specification of traffic signs in different transportation agencies. The 3D candidate planes of traffic signs are then fitted using the RANSAC plane-fitting of those points. By projecting the candidate planes onto the image, Regions of Interest (ROIs) of traffic signs are found physically with the geometric constraints between laser profiling and camera imaging. The Random forest learning of the visual color and shape features of traffic signs is adopted to validate the sign ROIs from the video log images. The sequential occurrence of a traffic sign among consecutive video log images are defined by the geometric constraint of the imaging geometry and GPS movement. Candidate ROIs are predicted in this temporal context to double-check the salient traffic sign among video log images. The proposed algorithm is tested on a diverse set of scenarios on the interstate highway G-4 near Beijing, China under varying lighting conditions and occlusions. Experimental results show the proposed algorithm enhances the rate of detecting traffic signs with the incorporation of the 3D planar constraint of their Lidar points. It is promising for the robust and large-scale survey of most transportation infrastructure with the application of MMS.

Measurement of longitudinal β-amyloid change with 18F-florbetapir PET and standardized uptake value ratios.

The accurate measurement of β-amyloid (Aβ) change using amyloid PET imaging is important for Alzheimer disease research and clinical trials but poses several unique challenges. In particular, reference region measurement instability may lead to spurious changes in cortical regions of interest. To optimize our ability to measure (18)F-florbetapir longitudinal change, we evaluated several candidate regions of interest and their influence on cortical florbetapir change over a 2-y period in participants from the Alzheimer Disease Neuroimaging Initiative (ADNI). We examined the agreement in cortical florbetapir change detected using 6 candidate reference regions (cerebellar gray matter, whole cerebellum, brain stem/pons, eroded subcortical white matter [WM], and 2 additional combinations of these regions) in 520 ADNI subjects. We used concurrent cerebrospinal fluid Aβ1-42 measurements to identify subgroups of ADNI subjects expected to remain stable over follow-up (stable Aβ group; n = 14) and subjects expected to increase (increasing Aβ group; n = 91). We then evaluated reference regions according to whether cortical change was minimal in the stable Aβ group and cortical retention increased in the increasing Aβ group. There was poor agreement across reference regions in the amount of cortical change observed across all 520 ADNI subjects. Within the stable Aβ group, however, cortical florbetapir change was 1%-2% across all reference regions, indicating high consistency. In the increasing Aβ group, cortical increases were significant with all reference regions. Reference regions containing WM (as opposed to cerebellum or pons) enabled detection of cortical change that was more physiologically plausible and more likely to increase over time. Reference region selection has an important influence on the detection of florbetapir change. Compared with cerebellum or pons alone, reference regions that included subcortical WM resulted in change measurements that are more accurate. In addition, because use of WM-containing reference regions involves dividing out cortical signal contained in the reference region (via partial-volume effects), use of these WM-containing regions may result in more conservative estimates of actual change. Future analyses using different tracers, tracer-kinetic models, pipelines, and comparisons with other biomarkers will further optimize our ability to accurately measure Aβ changes over time.

Multiplexed direct genomic selection (MDiGS): a pooled BAC capture approach for highly accurate CNV and SNP/INDEL detection

Despite declining sequencing costs, few methods are available for cost-effective single-nucleotide polymorphism (SNP), insertion/deletion (INDEL) and copy number variation (CNV) discovery in a single assay. Commercially available methods require a high investment to a specific region and are only cost-effective for large samples. Here, we introduce a novel, flexible approach for multiplexed targeted sequencing and CNV analysis of large genomic regions called multiplexed direct genomic selection (MDiGS). MDiGS combines biotinylated bacterial artificial chromosome (BAC) capture and multiplexed pooled capture for SNP/INDEL and CNV detection of 96 multiplexed samples on a single MiSeq run. MDiGS is advantageous over other methods for CNV detection because pooled sample capture and hybridization to large contiguous BAC baits reduces sample and probe hybridization variability inherent in other methods. We performed MDiGS capture for three chromosomal regions consisting of ∼550 kb of coding and non-coding sequence with DNA from 253 patients with congenital lower limb disorders. PITX1 nonsense and HOXC11 S191F missense mutations were identified that segregate in clubfoot families. Using a novel pooled-capture reference strategy, we identified recurrent chromosome chr17q23.1q23.2 duplications and small HOXC 5′ cluster deletions (51 kb and 12 kb). Given the current interest in coding and non-coding variants in human disease, MDiGS fulfills a niche for comprehensive and low-cost evaluation of CNVs, coding, and non-coding variants across candidate regions of interest.

A Novel Saliency Detection Method for Lunar Remote Sensing Images

The saliency detection provides an alternative methodology to semantic image understanding in many applications, for example, content-based image retrieval. To detect saliency for lunar remote sensing images, this letter proposes a crater feature model by analyzing the relationship between local interest points and saliency of lunar images. Based on the model, we propose a novel saliency detection method for lunar images. Our method merges and combines the speed-up robust feature features of the highlight region and shadow region of an impact crater to get the candidate regions of interest (ROI). Then, a descriptive feature vector is generated for each ROI, and the resulting saliency regions are distinguished from false detected and inconspicuous ones through a support vector machine. The method has been put into test on Chang'e-1 and Chang'e-2 lunar image data, and confirmed to be able to detect the salient region of impact craters correctly, with results much better than those obtained by the classical saliency detection method.

Toward embedded detection of polyps in WCE images for early diagnosis of colorectal cancer

Wireless capsule endoscopy (WCE) is commonly used for noninvasive gastrointestinal tract evaluation, including the detection of mucosal polyps. A new embeddable method for polyp detection in wireless capsule endoscopic images was developed and tested. First, possible polyps within the image were extracted using geometric shape features. Next, the candidate regions of interest were evaluated with a boosting based method using textural features. Each step was carefully chosen to accommodate hardware implementation constraints. The method's performance was evaluated on WCE datasets including 300 images with polyps and 1,200 images without polyps. Hardware implementation of the proposed approach was evaluated to quantitatively demonstrate the feasibility of such integration into the WCE itself. The boosting based polyp classification demonstrated a sensitivity of 91.0 %, a specificity of 95.2 % and a false detection rate of 4.8 %. This performance is close to that reported recently in systems developed for an online analysis of video colonoscopy images. A new method for polyp detection in videoendoscopic WCE examinations was developed using boosting based approach. This method achieved good classification performance and can be implemented in situ with embedded hardware.

Genome-wide association study of insect bite hypersensitivity in two horse populations in the Netherlands

BackgroundInsect bite hypersensitivity is a common allergic disease in horse populations worldwide. Insect bite hypersensitivity is affected by both environmental and genetic factors. However, little is known about genes contributing to the genetic variance associated with insect bite hypersensitivity. Therefore, the aim of our study was to identify and quantify genomic associations with insect bite hypersensitivity in Shetland pony mares and Icelandic horses in the Netherlands.MethodsData on 200 Shetland pony mares and 146 Icelandic horses were collected according to a matched case–control design. Cases and controls were matched on various factors (e.g. region, sire) to minimize effects of population stratification. Breed-specific genome-wide association studies were performed using 70 k single nucleotide polymorphisms genotypes. Bayesian variable selection method Bayes-C with a threshold model implemented in GenSel software was applied. A 1 Mb non-overlapping window approach that accumulated contributions of adjacent single nucleotide polymorphisms was used to identify associated genomic regions.ResultsThe percentage of variance explained by all single nucleotide polymorphisms was 13% in Shetland pony mares and 28% in Icelandic horses. The 20 non-overlapping windows explaining the largest percentages of genetic variance were found on nine chromosomes in Shetland pony mares and on 14 chromosomes in Icelandic horses. Overlap in identified associated genomic regions between breeds would suggest interesting candidate regions to follow-up on. Such regions common to both breeds (within 15 Mb) were found on chromosomes 3, 7, 11, 20 and 23. Positional candidate genes within 2 Mb from the associated windows were identified on chromosome 20 in both breeds. Candidate genes are within the equine lymphocyte antigen class II region, which evokes an immune response by recognizing many foreign molecules.ConclusionsThe genome-wide association study identified several genomic regions associated with insect bite hypersensitivity in Shetland pony mares and Icelandic horses. On chromosome 20, associated genomic regions in both breeds were within 2 Mb from the equine lymphocyte antigen class II region. Increased knowledge on insect bite hypersensitivity associated genes will contribute to our understanding of its biology, enabling more efficient selection, therapy and prevention to decrease insect bite hypersensitivity prevalence.

Discovery of conserved long non-coding RNAs in vertebrates

Motivations. Long non-coding RNAs (lncRNA) have been reported as a major class of novel transcripts related to organism development and early neural expression pattern [1-4]. They are reported to be expressed in large numbers in the mammalian transcriptomes [5,6] and recently reported to be expressed in the teleost fishes [7,8]. Computational identification and characterization of lncRNA from public sequence resources have been performed by different groups [9-11]. The focus of attention has been on the mammalian genomes starting by the assumption that they are not well conserved in term of sequence. However, systematic studies measuring their levels of conservation among vertebrates are lacking. Hence we want to computationally evaluate the existence of vertebrate conserved lncRNAs through systematic conservation analyses of both sequence as well as genomic architecture. Methods. Mouse lncRNAs reported in an earlier study [2] and predicted by the EnsEMBL pipeline were considered as a reference dataset. Homology search of the lncRNAs against the zebrafish conserved phastcons elements was performed with the BLAST program. The phastcons elements are regions of conservation in the zebrafish genome with human, mouse, western clawed frog and two teleost fishes, tetraodon and stickleback. The lack of selection pressure in lncRNAs as compared to the protein-coding genes required a calibration of BLAST parameters to define a cut-off score indicative of significant conservation. Using ROC analyses we calculated the best BLAST parameters able to select regions of lnRNA conserved in vertebrates. The predicted conserved candidates were also evaluated in terms of their RNA secondary structure using the RNAfold software. Gene ontology and expression pattern enrichment of flanking protein-coding genes was performed with DAVID software. Results. Our results show that the usage of the alignment length as cut-off is sufficient to distinguish the conservation of mouse lncRNAs in zebrafish as compared to conservation of random genomic regions. The RNA secondary structure prediction was not able to define any threshold for conservation. From an initial dataset of ~2,800 lncRNAs we could predict that 235 are conserved using the defined cut-off on the alignment length. Gene ontology enrichment analyses, related to the protein-coding genes proximal to the region of conservation in mouse and zebrafish, highlighted corresponding GO classes such as regulation of transcription and central nervous system development. The proximal coding genes exhibited a similar enrichment for their tissue of expression where brain was highly enriched in both mouse as well as zebrafish. Two interesting candidate regions of conservation were chosen for future experimental validation based upon the presence of ESTs overlap and the function of the proximal proteins (in this case the interest being development and functioning of the nervous system). The analysis is poised as an initial pipeline to select interesting candidate lncRNAs conserved among vertebrates.

Clinical assessment and genomic landscape of a consanguineous family with three Kallmann syndrome descendants

Although some genes that cause Kallmann syndrome (KS) have been identified by traditional linkage analysis and candidate gene techniques, the syndrome's molecular etiology in the majority of patients remains poorly understood. In this paper, we present the clinical assessments of a consanguineous Han Chinese family with three KS descendants. To understand the molecular etiology of KS from a genome-wide perspective, we investigated the genome-wide profile of structural variation in this family using the Affymetrix Genome-Wide Human SNP Array 6.0 platform. The results revealed that the three affected individuals had common copy number variants (microdeletions) on chromosomes 1p21.1, 2q32.2, 8q21.13, 14q21.2 and Xp22.31. Moreover, the copy number variants on Xp22.31 were located in the intron of KAL1, which causes X-linked KS. Two PCR assays were performed on these regions to validate the results obtained using the chips. In addition, genomic microdeletions in this region were verified in one of 29 Han Chinese sporadic KS cases and one of four other family cases, but not in 26 Han Chinese sporadic normosmic idiopathic hypogonadotropic hypogonadism cases and 100 unrelated Han Chinese normal controls. Our results provide a novel insight into the relative contributions of certain copy number variants to KS's molecular etiology and generate a list of interesting candidate regions for further studies.

Identification of novel disease gene for primary congenital glaucoma (PCG) through homozygosity mapping and next‐generation sequencing strategies in a large consanguineous pedigree

Abstract Purpose Primary congenital glaucoma (PCG) is caused by developmental anomalies of the trabecular meshwork and the anterior chamber angle resulting in an increased ocular pressure (IOP) and optic nerve damage. In general PCG displays an autosomal recessive inheritance pattern and is genetically heterogeneous. To date, three PCG loci are known, namely GLC3A, GLC3B and GLC3C, and two causal genes have been identified, CYP1B1 located in the GLC3A locus and LTPB2 located at 1.3 MB proximal to the GLC3C locus. The purpose of the current study is to identify the causal disease gene in a large consanguineous family with PCG, originating from Jordany. CYP1B1 mutations and linkage to the LTBP2, GLCB3 and GLCC3 locus were previously excluded. Methods In a first step, DNA from members from the consanguineous family will be genotyped by 250K GeneChip Mapping Affymetrix arrays. Homozygosity mapping will be applied to identify potential disease loci, using a homemade Perl script. Next, microsatellite analysis will be performed in order to confirm findings and to narrow down candidate regions. Subsequently, candidate regions of interest will be captured (Agilent) and sequenced on the Illumina Genome Analyser IIx (GAIIx). Gene and variant prioritization will be done using in‐house developed software, followed by segregation analysis and screening in control individuals. At last, a cohort of 30 molecularly unsolved PCG patients will be screened for mutations in the newly identified disease. Conclusion The identification of a new disease gene for PCG may lead to better insights into the molecular pathogenesis of glaucoma, and might uncover novel therapeutic strategies.

Robust pedestrian detection in thermal infrared imagery using the wavelet transform

A novel and robust pedestrian detection method in thermal infrared images based on the double-density dual-tree complex wavelet transform (DD-DT CWT) and wavelet entropy is presented in this paper. The regions of interest (ROIs) are located first making use of high brightness property of the pedestrian pixels caused by the self-emission of the pedestrians related to the Planck’s law. The candidate ROIs are then decomposed by DD-DT CWT and the wavelet entropy features are extracted from the high frequency subbands. The true pedestrian regions are finally classified and recognized using the support vector machine (SVM) classifier. Comparisons between our approach and traditional approaches are presented and experimental results using several thermal infrared image databases show the proposed scheme to be very promising.

Program Update and Novel Use of the DESPAIR Program to Design a Genome-Wide Linkage Study Using Relative Pairs

DESPAIR (DESign for PAIRs) is a computer program useful for designing a two-stage linkage study using relative pairs for a dichotomous phenotype. It determines the optimal two-stage study design – i.e., for specified power and significance level, how many pairs of relatives should be studied, how many equally spaced markers should be used initially, and what criterion should be used to specify the markers around which further searching should be done at a second stage. The program will calculate either the number of relative pairs required for a given number of first-stage markers or the number of markers required for a given number of relative pairs. We highlight the use of the latest version of DESPAIR to decide to what extent additional fine mapping in a candidate region of interest can lead to an increase in power in a previous linkage study sample. We also discuss new features of the program, such as the mean difference test for affected and discordant relative pairs and estimation of full sibling pair equivalents to design a study when several types of relative pairs are available. DESPAIR is part of the S.A.G.E. program package and is freely available for use online.

Comparative genomic hybridization analysis of genetic aberrations associated with development of esophageal squamous cell carcinoma in Henan, China

To characterize cytogenetic alterations in esophageal squamous cell carcinoma (ESCC) and its metastasis. A total of 37 cases of primary ESCC and 15 pairs of primary ESCC tumors and their matched metastatic lymph nodes cases were enrolled from Linzhou, the high incidence area for ESCC in Henan, northern China. The comparative genomic hybridization (CGH) was applied to determine the chromosomal aberrations on the DNA extracted from the frozen ESCC and metastatic lymph node samples from these patients. CGH showed chromosomal aberrations in all the cases. In 37 cases of primary ESCC, chromosomal profile of DNA copy number was characterized by frequently detected gains at 8q (29/37, 78%), 3q (24/37, 65%), 5p (19/37, 51%); and frequently detected losses at 3p (21/37, 57%), 8p and 9q (14/37, 38%). In 15 pairs of primary ESCC tumors and their matched metastatic lymph node cases, the majority of the chromosomal aberrations in both primary tumor and metastatic lymph node lesions were consistent with the primary ESCC cases, but new candidate regions of interest were also detected. The most significant finding is the gains of chromosome 6p with a minimum high-level amplification region at 6p12-6q12 in 7 metastatic lymph nodes but only in 2 corresponding primary tumors (P = 0.05) and 20p with a minimum high-level amplification region at 20p12 in 11 metastatic lymph nodes but only in 5 corresponding primary tumors (P < 0.05). Another interesting finding is the loss of chromosome 10p and 10q in 8 and 7 metastatic lymph nodes but only in 2 corresponding primary tumors (P < 0.05). Using the CGH technique to detect chromosomal aberrations in both the primary tumor and its metastatic lymph nodes of ESCC, gains of 8q, 3q and 5p and loss of 3p, 8p, 9q and 13q were specifically implicated in ESCC in Linzhou population. Gains of 6p and 20p and loss of 10pq may contribute to the lymph node metastasis of ESCC. These findings suggest that the gains and losses of chromosomal regions may contain ESCC-related oncogenes and tumor suppressor genes and provide important theoretic information for identifying and cloning novel ESCC-related oncogenes and tumor suppressor genes.

Imputation-Based Analysis of Association Studies: Candidate Regions and Quantitative Traits

We introduce a new framework for the analysis of association studies, designed to allow untyped variants to be more effectively and directly tested for association with a phenotype. The idea is to combine knowledge on patterns of correlation among SNPs (e.g., from the International HapMap project or resequencing data in a candidate region of interest) with genotype data at tag SNPs collected on a phenotyped study sample, to estimate (“impute”) unmeasured genotypes, and then assess association between the phenotype and these estimated genotypes. Compared with standard single-SNP tests, this approach results in increased power to detect association, even in cases in which the causal variant is typed, with the greatest gain occurring when multiple causal variants are present. It also provides more interpretable explanations for observed associations, including assessing, for each SNP, the strength of the evidence that it (rather than another correlated SNP) is causal. Although we focus on association studies with quantitative phenotype and a relatively restricted region (e.g., a candidate gene), the framework is applicable and computationally practical for whole genome association studies. Methods described here are implemented in a software package, Bim-Bam, available from the Stephens Lab website http://stephenslab.uchicago.edu/software.html.

Association Cluster Detector: a tool for heuristic detection of significance clusters in whole-genome scans

Whole genome scans analyze large sets of genetic markers, mainly single nucleotide polymorphisms, over the entire genome in order to find variants and regions associated with complex traits so these can be further investigated. Analyzing the results of such scans becomes difficult due to multiple testing problems and to the genomic distributions of recombination, linkage disequilibrium and true associations, which generate an extremely complex network of dependences between markers. Here we present Association Cluster Detector (ACD), a simple tool aiming to ease the analysis of the results of whole genome scans. ACD facilitates correction for multiple tests using several standard procedures and implements a sliding-window heuristic method that helps in detecting potentially interesting candidate regions by exploiting the property of non-random distribution of significantly associated markers. The tool can be downloaded from http://www.upf.es/cexs/recerca/bioevo/softanddata.htm

Some properties of a variance components model for fine-mapping quantitative trait loci.

Identifying etiological variants for multifactorial traits by allelic association holds promise when many markers are available in close proximity. However, evidence for or against association at any particular marker does not provide any direct information about the influence of causal variants or the frequency of the etiologic allele(s). Recently, a variance components model of linkage and association was developed for quantitative traits which is sufficiently flexible to provide some insights into these issues. We show that this combined linkage/association model provides an estimate of the additive genetic variance of a trait that is attributable to disequilibrium between the marker and QTL. We use this estimate to construct approximate boundaries of the minimum level of disequilibrium between an observed marker and unobserved QTL and to delimit the permissible range of allele frequencies at the QTL based on available data at nearby markers. This information may facilitate fine-mapping studies of complex traits that aim to localize QTLs by assessment of association with many markers in a candidate region of interest.

Infantile Autism and Associated Autosomal Chromosome Abnormalities: A Register‐based Study and a Literature Survey

Infantile autism is a heterogenous disorder with unknown aetiology. Evidence from the relatively few family and twin studies suggests a genetic component. Co-occurrence or cosegregation between infantile autism and chromosomal abnormalities may identify candidate regions, which could be tested in linkage or association studies. The purpose of this study was to use the Danish Cytogenetic Central Register in order to detect autosomal chromosome abnormalities associated with infantile autism, and to review the literature for cases of autism associated with autosomal chromosome abnormalities to identify candidate chromosomal regions. The register-based study identified possible candidate regions on chromosome 7q21 and 10q21.2, which have not previously been reported. A few interesting candidate regions, 15q11-13, 16q23, and 17p11.2 were found in the literature survey.

Detection filters and algorithm fusion for ATR

Detection involves locating all candidate regions of interest (objects) in a scene independent of the object class with object distortions and contrast differences, etc., present. It is one of the most formidable problems in automatic target recognition, since it involves analysis of every local scene region. We consider new detection algorithms and the fusion of their outputs to reduce the probability of false alarm P(FA) while maintaining high probability of detection P(D). Emphasis is given to detecting obscured targets in infrared imagery.