Finer discrimination of brain activation with local multivariate distance

The organization of human brain function is diverse on different spatial scales. Various cognitive states are always represented as distinct activity patterns across the specific brain region on fine scales. Conventional univariate analysis of functional MRI data seeks to determine how a particular cognitive state is encoded in brain activity by analyzing each voxel separately without considering the fine-scale patterns information contained in the local brain regions. In this paper, a local multivariate distance mapping (LMDM) technique is proposed to detect the brain activation and to map the fine-scale brain activity patterns. LMDM directly represents the local brain activity with the patterns across multiple voxels rather than individual voxels, and it employs the multivariate distance between different patterns to discriminate the brain state on fine scales. Experiments with simulated and real fMRI data demonstrate that LMDM technique can dramatically increase the sensitivity of the detection for the fine-scale brain activity patterns which contain the subtle information of the experimental conditions.     

A three-dimensional spatio-temporal EEG pattern analyzing system

Spatio-temporal pattern analysis of EEG is an important tool in brain research. An EEG pattern analysis system based on a hierarchical multi-method approach is proposed here. The system consists of multiple steps including extraction of target signal, acquisition of intracranial electric activity distribution, adaptive segmentation of EEG and spatio-temporal pattern recognition. Some modern signal processing methods such as common spatial subspace decomposition, hidden Markov model are adopted. This paper also proposes an algorithm named LORETA-FOCUSS to estimate the current density inside the brain with a high spatial resolution. Microstate analysis of EEG is extended to the 3-D situation. The system was applied to the brain computer interface problem and achieved the highest accuracy of 88.89% with an average accuracy of 81.48% when classifying two imaginary movement tasks, while the data were not manually pre-selected. The result has proved spatio-temporal EEG pattern analysis is an efficient way in brain research.     

Big data resource service platform for the internet financial industry

It is now the era of big data. Massive volumes of complex and dynamic data exist in a variety of indus- tries, including internet finance. This paper briefly describes the current big data situation and its significance, discusses the big data resource service platform architec- ture that includes three levels： data resource identification and access, data resources storage and analysis, and net- work information service platform, and then introduces some work undertaken by our project team on big data analysis research and application of trusted transactions for internet finance.     

Watching cartoons activates the medial prefrontal cortex in children

The medial prefrontal cortex (MPFC) of human adults is involved in attributing mental states to real human agents but not to virtual artificial characters. This study examined whether such differential MPFC activity can be observed in children who are more fascinated by cartoons than adults. We measured brain activity using functional magnetic resonance imaging (fMRI) while 10-year-old children watched movie and cartoon clips, simulating real and virtual visual worlds, respectively. We showed neuroimaging evidence that, in contrast to adults, the MPFC of children was activated when perceiving both human agents and artificial characters in coherent visual events. Our findings suggest that, around the age of 10 years, the MPFC activity in children is different from that in adults in that it can be spontaneously activated by non-human agents in a virtual visual world.     

A template of rat brain based on fMRI T*2 imaging

The development of functional magnetic resonance imaging (fMRI) technology has made it possible to carry out functional brain imaging experiments in small animals. Usually, group data is required to form the assessment of population, which can not only increase the sensitivity of the overall experiment, but also allow the generalization of the conclusion to the whole population. In order to average the signals of functional brain images from different subjects, it is necessary to put all the mapping images into the same standard space (template image). However, up to now, most animal brain templates remain unavailable and it must be done by ourselves. In this study, a template image based on the brains of eight male Wistar rats is obtained, and it is successfully used in our present Alzheimer disease (AD)-like rat model studies as template for spatially normalizing images to the same stereotaxical space. The fMRI results processed with statistical parametric mapping (SPM99) software are in agreement with the results from immunohistochemical experiment, which proves that this method is universally applicable to the pathologic models of other small animals and to human brain lesion studies.     

fMRI time series analysis based on stationary wavelet and spectrum analysis

The low signal to noise ratio (SNR) of functional MRI (fMRI) prefers more sensitive data analysis methods. Based on stationary wavelet transform and spectrum analysis, a new method with high detective sensitivity was developed for analyzing fMRI time series, which does not require any prior assumption of the characteristics of noises. In the proposed method, every component of fMRI time series in the different time-frequency scales of stationary wavelet transform was discerned by the spectrum analysis, then the components from noises were removed using the stationary wavelet transform, finally the components of real brain activation were detected by cross-correlation analysis. The results obtained from both simulated and in vivo visual experiments illustrated that the proposed method has much higher sensitivity than the traditional cross-correlation method.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental “rest” conditions. The mental activity that occurs during “rest” might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

Resting state brain activity and functional brain mapping

Functional brain imaging studies commonly use either resting or passive task states as their control conditions, and typically identify the activation brain region associated with a specific task by subtracting the resting from the active task conditions. Numerous studies now suggest, however, that the resting state may not reflect true mental "rest" conditions. The mental activity that occurs during "rest" might therefore greatly influence the functional neuroimaging observations that are collected through the usual subtracting analysis strategies. Exploring the ongoing mental processes that occur during resting conditions is thus of particular importance for deciphering functional brain mapping results and obtaining a more comprehensive understanding of human brain functions. In this review article, we will mainly focus on the discussion of the current research background of functional brain mapping at resting state and the physiological significance of the available neuroimaging data.     

Effects of sleep deprivation on gamma oscillation of waking human EEG

This study compares and evaluates the effect of sleep deprivation （SD） on human brain cognition by analyzing the recorded EEG data under normal and 24 h sleep deprived states. EEG auditory event-related potentials were collected from 14 healthy volunteers, and the statistical values of wavelet-transformed EEG in gamma band were decomposed by parallel factor analysis （PARAFAC） to identify where the differences appeared in the time, frequency and spatial domains. The results showed that the changes of brain states caused by SD appeared around 40 Hz at about 400 ms after stimulation on prefrontal and frontal lobes. Negative effects of SD on neuronal activity and oscillation were observed. The analysis of the EEG data by the wavelet transform and PARAFAC can be an integrated way to estimate the change of brain states in the three domains.     

Microstructural evolution during ultra-rapid annealing of severely deformed low-carbon steel: strain,temperature, and heating rate effects

An interaction between ferrite recrystallization and austenite transformation in low-carbon steel occurs when recrystallization is delayed until the intercritical temperature range by employing high heating rate. The kinetics of recrystallization and transformation is affected by high heating rate and such an interaction. In this study, different levels of strain are applied to low-carbon steel using a severe plastic deformation method. Then, ultra-rapid annealing is performed at different heating rates of 200–1100℃/s and peak temperatures of near critical temperature. Five regimes are proposed to investigate the effects of heating rate, strain, and temperature on the interaction between recrystallization and transformation. The microstructural evolution of severely deformed low-carbon steel after ultra-rapid annealing is investigated based on the proposed regimes. Regarding the intensity and start temperature of the interaction, different microstructures consisting of ferrite and pearlite/martensite are formed. It is found that when the interaction is strong, the microstructure is refined because of the high kinetics of transformation and recrystallization. Moreover, strain shifts an interaction zone to a relatively higher heating rate. Therefore, severely deformed steel should be heated at relatively higher heating rates for it to undergo a strong interaction.     

In vivo Dynamic Studies of Brain Metabolism

Nuclear magnetic resonance （NMR） can noninvasively monitor intracellular concentrations and kinetic properties of numerous inorganic and organic compounds. A ^31p NMR surface coil was used in vivo to dynamically measure phosphocreatine （PCr）, adenosine triphosphate （ATP）, and intracellular inorganic phosphate （Pi） levels in mouse brain during ischemia-reperfusion to study the damage of cerebral tissues caused by ischemia and effects of herbs on cerebral energy metabolism during ischemia-reperfusion. The study provides dynamic brain energy metabolism data during different periods. The data show that some herbs more rapidly increase the PCr level during the recovery phase than in the control group.     

Numerical and experimental study on coke size distribution in bell-type charging in the CDQ shaft

The mechanism of coke size segregation in the radial direction of pre-chamber in coke dry quenching (CDQ) shaft was investigated by experiment and numerical analysis. The experimental apparatus was a cold three-dimensional semicircle model for bell-type charging, which was used to study the influences of different kinds of bells and different stock line levels on the radial distribution of coke size in the radial direction of a pre-chamber. A mathematical model was used, which is capable of estimating the radial average size distribution as well as the radial deposit distribution of each particle size for multiple size coke with a few fixed parameters. The calculated results agree well with the experimental data, which proves the reliability of both experimental study and numerical analysis.     

Recent advances in the data analysis method of functional magnetic resonance imaging and its applications in neuroimaging

Functional magnetic resonance imaging (fMRI) has opened a new area to explore the human brain. The fMRI can reveal the deep insights of spatial and temporal changes underlying a broad range of brain function, such as motor, vision, memory and emotion, all of which are helpful in the clinical investigation. In this paper, we introduce some recent-developed algorithms for fMRI signal detection such as model-driven method (general linear model, deconvolution model, non-linear model, etc.) and data-driven method (principle component analysis, independent component analysis, self-organization mapping, clustered constrained non-negative matrix factorization, etc.). We also propose several important applications of neuroimaging and point out their shortcomings and future perspectives.     

A Data Treatment Method of Carbon Saturated Solubility in Fe-C-Cr Melt

Based on the current situation of studying the thermodynamic property of Fe-C-Cr melt using the carbon saturated solubility, an experimental data treatment method of the carbon saturated solubility was put forward. With this method a linear relationship ex-pression of the carbon saturated solubility in Fe-C-Cr melt was obtained, which intercept is dependent on temperature and independent of third component[Cr], but which slope is dependent on third component[Cr] and independent of temperature. Through this expression activity interaction coefficients at different temperatures were calculated and the relationship between activity interaction coefficients and temperature is also obtained.     

A new method for simulating human emotions

How to make machines express emotions would be instrumental in establishing a completely new paradigm for man machine interaction. A new method for simulating and assessing artificial psychology has been developed for the research of the emotion robot. The human psychology activity is regarded as a Markov process. An emotion space and psychology model is constructed based on Markov process. The conception of emotion entropy is presented to assess the artificial emotion complexity. The simulating results play up to human psychology activity. This model can also be applied to consumer-friendly human-computer interfaces, and interactive video etc.     

The synthesis of a fluorescent chemosensor containing two thiourea groups and its recognition behavior for dicarboxylate anions

A novel anion receptor 2 bearing anthracene flurophore and thiourea was synthesized and identified by ^1H NMR,MS,IR and elemental analysis. The interaction between receptor 2 and various α,ω-dicarboxylate anions was studied by fluorescence spectrum. The obtained fluorescence data indicate that 1：1 stoichiometry complex is formed between receptor 2 with diferent dicarboxylate anions through a hydrogen-bonging interaction. The selectivity of 2 for recognition of different dicarboxylates deponds on chain length of the anionic species.     

Human Body Image Edge Detection Based on Wavelet Transform

Human dresses are different in thousands way. Human body image signals have big noise, a poor light and shade contrast and a narrow range of gray gradation distribution. The application of a traditional grads method or gray method to detect human body image edges can't obtain satisfactory results because of false detections and missed detections. According to the peculiarity of human body image, dyadic wavelet transform of cubic spline is successfully applied to detect the face and profile edges of human body image and Mallat algorithm is used in the wavelet decomposition in this paper.     

Multimodal Brain-Computer Interfaces

A critical parameter of brain-computer interfaces(BCIs) is the number of dimensions a user can control independently.One way to increment this number without increasing the mental effort required to operate the system is to stimulate several sensory modalities simultaneously,and to distinguish brain activity patterns when the user focuses attention to different elements of this multisensory input.In this article we show how shifting attention between simultaneously presented tactile and visual stimuli affects the electrical brain activity of human subjects,and that this signal can be used to augment the control information from the two uni-modal BCI subsystems.     

Universal Rendering Mechanism Supporting Dual-Mode Presentation

XML is a standard for the exchange of business data that is completely platform and vendor neutral. Because XML data comes in many forms, one of the most important technologies needed for XML applications is the ability to convert the data into visible renderings. This paper focuses on the rendering of XML/XSL documents into a readable and printable format by means of a platform-independent process that enables high-quality printing of the product. This paper introduces the core components in the data rendering engine, the X2P server and different levels of object abstraction. The design pattern and the complete formatting and representation of the XSL stylesheet into different types of output formats in the X2P server are also given. The results show that the X2P sever simultaneously constructs the formatting object tree and the area tree in a very efficient design that saves execution time and memory.     

Neurobiological disruptions induced in brains of the rats fed with mercury contaminated rice collected from experimental fields in Guizhou Province, China

Guizhou Province, located in the southwestern China, is an important mercury （Hg） production center. High levels of Hg were found in local environmental and biomass samples. The neurobiological disruptions induced in brains of rats fed with Hg polluted rice collected from two experimental plots in Guizhou Province were studied. The plots are close to the Hg mine of Wanshang area and a chemical plant （where Hg is used as a catalyst for the production of acetaldehyde） of Qingzhen area. Rats were fed for 30 d before toxicological end points were tested. The results showed that the accumulations of Hg in rat＇s brain of Qingzhen chemical plant group and Wanshang mine group were significantly higher than those of Shanghai group （control group） （P〈0.01）. The neural transmitters, including acetylcholine （Ach）, acetylcholine enzyme （AchE）, nitric oxide and nitricoxide synthase （NOS）, in rat＇s brain were significantly affected. Oxidative stress was observed in the exposure groups. The expression levels of the immediately early gene （lEG）, c-fos and c-FOS proteins were induced by the Hg polluted rice. It was noted that the lEG （c-fos） participated in the neurotoxic process induced by the Hg polluted rice. lEG response to Hg might be dependent on interactions between neural transmitter and oxidative damage, c.fos could be used as an effective index of detecting and assessing the neurotoxicity of Hg. The results suggested that it should be alert to mental health problem in human beings eating Hg-polluted food. More efforts are necessary to protect the local ecosystem and human health in the Hg polluted areas such as Wangshan and Qingzhen in Guizhou Province of China.