Comparisons of the Nonlinear Relationship of Cerebral Blood Flow Response and Cerebral Vasomotor Reactivity to Carbon Dioxide under Hyperventilation between Postural Orthostatic Tachycardia Syndrome Patients and Healthy Subjects.

Postural orthostatic tachycardia syndrome (POTS) typically occurs in youths, and early accurate POTS diagnosis is challenging. A recent hypothesis suggests that upright cognitive impairment in POTS occurs because reduced cerebral blood flow velocity (CBFV) and cerebrovascular response to carbon dioxide (CO2) are nonlinear during transient changes in end-tidal CO2 (PETCO2). This novel study aimed to reveal the interaction between cerebral autoregulation and ventilatory control in POTS patients by using tilt table and hyperventilation to alter the CO2 tension between 10 and 30 mmHg. The cerebral blood flow velocity (CBFV), partial pressure of end-tidal carbon dioxide (PETCO2), and other cardiopulmonary signals were recorded for POTS patients and two healthy groups including those aged >45 years (Healthy-Elder) and aged <45 years (Healthy-Youth) throughout the experiment. Two nonlinear regression functions, Models I and II, were applied to evaluate their CBFV-PETCO2 relationship and cerebral vasomotor reactivity (CVMR). Among the estimated parameters, the curve-fitting Model I for CBFV and CVMR responses to CO2 for POTS patients demonstrated an observable dissimilarity in CBFVmax (p = 0.011), mid-PETCO2 (p = 0.013), and PETCO2 range (p = 0.023) compared with those of Healthy-Youth and in CBFVmax (p = 0.015) and CVMRmax compared with those of Healthy-Elder. With curve-fitting Model II for POTS patients, the fit parameters of curvilinear (p = 0.036) and PETCO2 level (p = 0.033) displayed significant difference in comparison with Healthy-Youth parameters; range of change (p = 0.042), PETCO2 level, and CBFVmax also displayed a significant difference in comparison with Healthy-Elder parameters. The results of this study contribute toward developing an early accurate diagnosis of impaired CBFV responses to CO2 for POTS patients.

Design and Implementation of a Smart Home System Using Multisensor Data Fusion Technology.

This paper aims to develop a multisensor data fusion technology-based smart home system by integrating wearable intelligent technology, artificial intelligence, and sensor fusion technology. We have developed the following three systems to create an intelligent smart home environment: (1) a wearable motion sensing device to be placed on residents' wrists and its corresponding 3D gesture recognition algorithm to implement a convenient automated household appliance control system; (2) a wearable motion sensing device mounted on a resident's feet and its indoor positioning algorithm to realize an effective indoor pedestrian navigation system for smart energy management; (3) a multisensor circuit module and an intelligent fire detection and alarm algorithm to realize a home safety and fire detection system. In addition, an intelligent monitoring interface is developed to provide in real-time information about the smart home system, such as environmental temperatures, CO concentrations, communicative environmental alarms, household appliance status, human motion signals, and the results of gesture recognition and indoor positioning. Furthermore, an experimental testbed for validating the effectiveness and feasibility of the smart home system was built and verified experimentally. The results showed that the 3D gesture recognition algorithm could achieve recognition rates for automated household appliance control of 92.0%, 94.8%, 95.3%, and 87.7% by the 2-fold cross-validation, 5-fold cross-validation, 10-fold cross-validation, and leave-one-subject-out cross-validation strategies. For indoor positioning and smart energy management, the distance accuracy and positioning accuracy were around 0.22% and 3.36% of the total traveled distance in the indoor environment. For home safety and fire detection, the classification rate achieved 98.81% accuracy for determining the conditions of the indoor living environment.

The analysis of cardio-respiratory signals and cerebral autoregulation based on CO2 reactivity with healthy subjects and Parkinson's patients.

Current paper focus on Parkinson's patients with autonomic dysfunction and how their interactions between cerebral autoregulation and ventilatory control are affected. The experimental data of dynamic CA assessment from the ANS Laboratory of CCGH was accessed for further processing and analysis. The subjects were classified into the groups of healthy and with Parkinson's disease. Based on the accessed ventilation and CBF data, the percentage changes in ventilation and CBF responses to PETCO2 were examined. To minimize effects of changes in ABP on cerebral vasomotor reactivity (CVMR) estimation, cerebrovascular conductance index (CVCi) was calculated, and CBFV-PETCO2 and CVCi-PETCO2 relationships were quantified by nonlinear logistic regression. The interaction between ventilation responses and CBF autoregulation will be modeled and parameters will be validated.

Automatic microfluidic fluorescence-array measurement system for detecting organic phosphate.

In this study, an automatic microfluidic fluorescence-array measurement system is developed to detect the concentration of organic phosphate based on the luminol-hydrogen peroxide catalytic fluorescent mechanism. Not only sample quantity and cost can be reduced, but also detection time, accuracy and precision can be improved in the system. The system is composed of a CCD image module, a stepper motor with driver, a microfluidic fluorescence array, a background light elimination module, and a dynamic image-analyzed interface. The pesticides of chlorpyrifos and fenitrothion of organic phosphate are chosen as experimental samples. Only a 2.5 µ l quantity of sample is required to have a fast response time of 1.4 second. Experimental results show that the sensitivities of chlorpyrifos and fenitrothion are 1.88 V/ppm in the range of 0.166 ∼ 10 ppm with averaged error of 1.66% and 0.32 V/ppm in the range of 0.03 ∼ 10 ppm with averaged error of 1.68% respectively. The organophosphorus effective detection range of the developed system covers the legal prescription for pesticide residues.

The analysis of cardio-respiratory signals and cerebral autoregulation based on CO2 reactivity with healthy subjects and Parkinson's patients.

Current paper focus on Parkinson's patients with autonomic dysfunction and how their interactions between cerebral autoregulation and ventilatory control are affected. The experimental data of dynamic CA assessment from the ANS Laboratory of CCGH was accessed for further processing and analysis. The subjects were classified into the groups of healthy and with Parkinson's disease. Based on the accessed ventilation and CBF data, the percentage changes in ventilation and CBF responses to PETCO2 were examined. To minimize effects of changes in ABP on cerebral vasomotor reactivity (CVMR) estimation, cerebrovascular conductance index (CVCi) was calculated, and CBFV-PETCO2 and CVCi-PETCO2 relationships were quantified by nonlinear logistic regression. The interaction between ventilation responses and CBF autoregulation will be modeled and parameters will be validated.