Impaired object tracking in children with chromosome 22q11.2 deletion syndrome

Background: Chromosome 22q11.2 Deletion Syndrome (22q11.2DS) occurs in approximately 1:4,000 live births with a complex and variable presentation that includes medical, socioemotional and psychological symptoms with intellectual impairment. Cognitive impairments in spatiotemporal and visuospatial attention have also been reported. However, maintenance of selective attention to dynamic and interacting objects has not been systematically investigated in children with 22q11.2DS. Methods: We used a multiple object tracking task to assay capacity and resolution performance of children with 22q11.2DS aged 7 to 14 years versus age-matched typically developing (TD) peers. Results: Children with 22q11.2DS but not TD children demonstrated impaired performance when task demands increased due to an increase in the number of targets presented, but not from an increase in object speed. Task performance in children with 22q11.2DS was also unrelated to intelligence or measures of attention deficit hyperactivity disorder. Conclusions: These findings suggest that children with 22q11.2DS may be particularly susceptible to dynamic crowding of objects with increasing cognitive demands related to monitoring multiple targets reflecting a reduced acuity in spatiotemporal cognitive representation

Impaired object tracking in children with chromosome 22q11.2 deletion syndrome

Background: Chromosome 22q11.2 Deletion Syndrome (22q11.2DS) occurs in approximately 1:4,000 live births with a complex and variable presentation that includes medical, socioemotional and psychological symptoms with intellectual impairment. Cognitive impairments in spatiotemporal and visuospatial attention have also been reported. However, maintenance of selective attention to dynamic and interacting objects has not been systematically investigated in children with 22q11.2DS. Methods: We used a multiple object tracking task to assay capacity and resolution performance of children with 22q11.2DS aged 7 to 14 years versus age-matched typically developing (TD) peers. Results: Children with 22q11.2DS but not TD children demonstrated impaired performance when task demands increased due to an increase in the number of targets presented, but not from an increase in object speed. Task performance in children with 22q11.2DS was also unrelated to intelligence or measures of attention deficit hyperactivity disorder. Conclusions: These findings suggest that children with 22q11.2DS may be particularly susceptible to dynamic crowding of objects with increasing cognitive demands related to monitoring multiple targets reflecting a reduced acuity in spatiotemporal cognitive representation

Disordered Regimes of the one-dimensional complex Ginzburg-Landau equation

I review recent work on the ``phase diagram'' of the one-dimensional complex Ginzburg-Landau equation for system sizes at which chaos is extensive. Particular attention is paid to a detailed description of the spatiotemporally disordered regimes encountered. The nature of the transition lines separating these phases is discussed, and preliminary results are presented which aim at evaluating the phase diagram in the infinite-size, infinite-time, thermodynamic limit.Comment: 14 pages, LaTeX, 9 figures available by anonymous ftp to amoco.saclay.cea.fr in directory pub/chate, or by requesting them to [email protected]

Efficient MaxCount and threshold operators of moving objects

Calculating operators of continuously moving objects presents some unique challenges, especially when the operators involve aggregation or the concept of congestion, which happens when the number of moving objects in a changing or dynamic query space exceeds some threshold value. This paper presents the following six d-dimensional moving object operators: (1) MaxCount (or MinCount), which finds the Maximum (or Minimum) number of moving objects simultaneously present in the dynamic query space at any time during the query time interval. (2) CountRange, which finds a count of point objects whose trajectories intersect the dynamic query space during the query time interval. (3) ThresholdRange, which finds the set of time intervals during which the dynamic query space is congested. (4) ThresholdSum, which finds the total length of all the time intervals during which the dynamic query space is congested. (5) ThresholdCount, which finds the number of disjoint time intervals during which the dynamic query space is congested. And (6) ThresholdAverage, which finds the average length of time of all the time intervals when the dynamic query space is congested. For these operators separate algorithms are given to find only estimate or only precise values. Experimental results from more than 7,500 queries indicate that the estimation algorithms produce fast, efficient results with error under 5%