15 research outputs found

    Hippocampal volume reduction in children with chromosome 22q11.2 deletion syndrome is associated with cognitive impairment

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    <p>Abstract</p> <p>Background</p> <p>Previous investigations of individuals with chromosome 22q11.2 deletion syndrome (DS22q11.2) have reported alterations in both brain anatomy and cognitive function. Neuroanatomical studies have reported multiple abnormalities including changes in both gray and white matter in the temporal lobe, including the amygdala and hippocampus. Separate investigations of cognitive abilities have established the prevalence of general intellectual impairment, although the actual extent to which a single individual is affected varies greatly within the population. The present study was designed to examine structures within the temporal lobe and assess their functional significance in terms of cognition in children with DS22q11.2.</p> <p>Method</p> <p>A total of 72 children (ages 7–14 years) participated in the investigation: 36 children (19 female, 17 male) tested FISH positive for chromosome 22q11.2 deletion (Mean age = 10 years 9 months, ± 2 yr 4 mo) and 36 were age-matched typically developing controls (13 female, 23 male; Mean age = 10 years 6 months, ± 1 yr 11 mo). For each subject, a three-dimensional high-resolution (1 mm isotropic) T1-weighted structural MRI was acquired. Neuroanatomical guidelines were used to define borders of the amygdala and hippocampus bilaterally and volumes were calculated based on manual tracings of the regions. The Wechsler Intelligence Scale for Children (WISC) was also administered.</p> <p>Results</p> <p>Volumetric reductions in total gray matter, white matter, and both the amygdala and hippocampus bilaterally were observed in children with DS22q11.2. Reductions in the left hippocampus were disproportionate to decreases in gray matter after statistically controlling for group differences in total gray matter, age, and data collection site. This specific reduction in hippocampal volume was significantly correlated with performance on standardized measures of intelligence, whereas the other neuroanatomical measures were not (gray/white matter, CSF, and amygdala).</p> <p>Conclusion</p> <p>Results from this study not only contribute to the understanding of the neuroanatomical variation in DS22q11.2, but also provide insight into the nature and source of the cognitive impairments associated with the syndrome. Specifically, we report that decreases in hippocampal volume may serve as an index of severity for cognitive impairments in children with DS22q11.2.</p

    Atypical cortical connectivity and visuospatial cognitive impairments are related in children with chromosome 22q11.2 deletion syndrome

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    BackgroundChromosome 22q11.2 deletion syndrome is one of the most common genetic causes of cognitive impairment and developmental disability yet little is known about the neural bases of those challenges. Here we expand upon our previous neurocognitive studies by specifically investigating the hypothesis that changes in neural connectivity relate to cognitive impairment in children with the disorder.MethodsWhole brain analyses of multiple measures computed from diffusion tensor image data acquired from the brains of children with the disorder and typically developing controls. We also correlated diffusion tensor data with performance on a visuospatial cognitive task that taps spatial attention.ResultsAnalyses revealed four common clusters, in the parietal and frontal lobes, that showed complementary patterns of connectivity in children with the deletion and typical controls. We interpreted these results as indicating differences in connective complexity to adjoining cortical regions that are critical to the cognitive functions in which affected children show impairments. Strong, and similarly opposing patterns of correlations between diffusion values in those clusters and spatial attention performance measures considerably strengthened that interpretation.ConclusionOur results suggest that atypical development of connective patterns in the brains of children with chromosome 22q11.2 deletion syndrome indicate a neuropathology that is related to the visuospatial cognitive impairments that are commonly found in affected individuals

    AI is a viable alternative to high throughput screening: a 318-target study

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    : High throughput screening (HTS) is routinely used to identify bioactive small molecules. This requires physical compounds, which limits coverage of accessible chemical space. Computational approaches combined with vast on-demand chemical libraries can access far greater chemical space, provided that the predictive accuracy is sufficient to identify useful molecules. Through the largest and most diverse virtual HTS campaign reported to date, comprising 318 individual projects, we demonstrate that our AtomNet® convolutional neural network successfully finds novel hits across every major therapeutic area and protein class. We address historical limitations of computational screening by demonstrating success for target proteins without known binders, high-quality X-ray crystal structures, or manual cherry-picking of compounds. We show that the molecules selected by the AtomNet® model are novel drug-like scaffolds rather than minor modifications to known bioactive compounds. Our empirical results suggest that computational methods can substantially replace HTS as the first step of small-molecule drug discovery

    Immunoproteomic to analysis the pathogenicity factors in leukopenia caused by Klebsiella pneumonia bacteremia.

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    Incidences of leukopenia caused by bacteremia have increased significantly and it is associated with prolonged hospital stay and increased cost. Immunoproteomic is a promising method to identify pathogenicity factors of different diseases. In the present study, we used immunoproteomic to analysis the pathogenicity factors in leukopenia caused by Klebsiella Pneumonia bacteremia. Approximately 40 protein spots localized in the 4 to 7 pI range were detected on two-dimensional electrophoresis gels, and 6 differentially expressed protein spots between 10 and 170 kDa were identified. Pathogenicity factors including S-adenosylmethionine synthetase, pyruvate dehydrogenase, glutathione synthetase, UDP-galactose-4-epimerase, acetate kinase A and elongation factor tu (EF-Tu). In validation of the pathogenicity factor, we used western blotting to show that Klebsiella pneumonia had higher (EF-Tu) expression when they accompanied by leukopenia rather than leukocytosis. Thus, we report 6 pathogenicity factors of leukopenia caused by Klebsiella pneumonia bacteremia, including 5 housekeeping enzymes and EF-Tu. We suggest EF-Tu could be a potential pathogenicity factor for leukopenia caused by Klebsiella pneumonia

    A Deep Detection Network Based on Interaction of Instance Segmentation and Object Detection for SAR Images

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    Ship detection is a challenging task for synthetic aperture radar (SAR) images. Ships have arbitrary directionality and multiple scales in SAR images. Furthermore, there is a lot of clutter near the ships. Traditional detection algorithms are not robust to these situations and easily cause redundancy in the detection area. With the continuous improvement in resolution, the traditional algorithms cannot achieve high-precision ship detection in SAR images. An increasing number of deep learning algorithms have been applied to SAR ship detection. In this study, a new ship detection network, known as the instance segmentation assisted ship detection network (ISASDNet), is presented. ISASDNet is a two-stage detection network with two branches. A branch is called an object branch and can extract object-level information to obtain positioning bounding boxes and classification results. Another branch called the pixel branch can be utilized for instance segmentation. In the pixel branch, the designed global relational inference layer maps the features to interaction space to learn the relationship between ship and background. The global reasoning module (GRM) based on global relational inference layers can better extract the instance segmentation results of ships. A mask assisted ship detection module (MASDM) is behind the two branches. The MASDM can improve detection results by interacting with the outputs of the two branches. In addition, a strategy is designed to extract the mask of SAR ships, which enables ISASDNet to perform object detection training and instance segmentation training at the same time. Experiments carried out two different datasets demonstrated the superiority of ISASDNet over other networks

    Orthogonal views for segmenting the amygdala and hippocampus on MRI sections

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    <p><b>Copyright information:</b></p><p>Taken from "Hippocampal volume reduction in children with chromosome 22q11.2 deletion syndrome is associated with cognitive impairment"</p><p>http://www.behavioralandbrainfunctions.com/content/3/1/54</p><p>Behavioral and brain functions : BBF 2007;3():54-54.</p><p>Published online 23 Oct 2007</p><p>PMCID:PMC2173902.</p><p></p> A three dimensional reconstruction of images (a) in which lines indicate the position of the horizontal plane (b), sagittal plane (c), and coronal plane (d) is shown. The arrow in b indicates the best-fit line along the white matter separating the amygdala from the putamen; the arrow in c represents the white matter that forms the ventral border of the rostral amygdala. A, Amygdala; EC, entorhinal cortex; H, hippocampus; PU, putamen; TLV, temporal horn of the lateral ventricle; WM, subamygdaloid white matter. Figure reproduced with permission from [37]. Copyright 2004 by the Society for Neuroscience
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