Computing and Visualization in Science最新文献

The brain is a complex organ operating on multiple scales. From molecular events that inform electrical and biochemical cellular responses, the brain interconnects processes all the way up to the massive network size of billions of brain cells. This strongly coupled, nonlinear, system has been subject to research that has turned increasingly multidisciplinary. The seminal work of Hodgkin and Huxley in the 1950s made use of experimental data to derive a coherent physical model of electrical signaling in neurons, which can be solved using mathematical and computational methods, thus bringing together neuroscience, physics, mathematics, and computer science. Over the last decades numerous projects have been dedicated to modeling and simulation of specific parts of molecular dynamics, neuronal signaling, and neural network behavior. Simulators have been developed around a specific objective and scale, in order to cope with the underlying computational complexity. Often times a dimension reduction approach allows larger scale simulations, this however has the inherent drawback of losing insight into structure-function interplay at the cellular level. This paper gives an overview of the project NeuroBox that has the objective of integrating multiple brain scales and associated physical models into one unified framework. NeuroBox hosts geometry and anatomical reconstruction methods, such that detailed three-dimensional domains can be integrated into numerical simulations of models based on partial differential equations. The project further focusses on deriving numerical methods for handling complex computational domains, and to couple multiple spatial dimensions. The latter allows the user to specify in which parts of the biological problem high-dimensional representations are necessary and where low-dimensional approximations are acceptable. NeuroBox offers workflow user interfaces that are automatically generated with VRL-Studio and can be controlled by non-experts. The project further uses uG4 as the numerical backend, and therefore accesses highly advanced discretization methods as well as hierarchical and scalable numerical solvers for very large neurobiological problems.

Background: Children with asthma (subjects), just as their non asthmatic colleagues, are expected to perform optimally academically and with a good intelligent score. A number of activities in children with asthma may be affected by asthmatic attacks including their education/academic performance. Report on academic performance and IQ of school children with asthma and comparisons with those without asthma (controls) within and outside Nigeria are scanty.

Aims: This study compared the academic performance and IQ of asthmatics and nonasthmatic school children.

Materials and methods: One hundred and twenty children with asthma aged 5-11 years were consecutively recruited at the asthma clinic of UNTH Enugu and their age, gender and socio-economic class-matched normal classmates were enrolled as controls. Academic performance of the children with asthma was studied using the overall scores achieved in the three term examinations in the preceding academic year (2012/2013), while their IQ was determined using the Draw-A-Person-test. The findings were compared with that of the 120 controls.

Results: The median (range) overall academic scores for the subjects 79.04% (36.08%-99.57%) was similar to that of controls 80.01% (50.65%-97.47%) (U = 6804, P = 0.461). However, a significant number of subjects compared to controls had poor academic performance. The mean IQ scores for subjects (123.28 ± 21.45) and controls (118.41 ± 19.87) did not differ significantly (t = 1.83; P = 0.069). There was also a significant and negative correlation (Pearson's) between age and mean DAPQ in both the subjects and controls (r = -0.377, P < 0.001; r = -0.492, P < 0.001 respectively.

Conclusions: The intelligence scores and overall academic performance of children with asthma compares favorably with that of children without asthma.