Inferring Deep Brain Stimulation Induced Short-term Synaptic Plasticity Using Novel Dual Optimization Algorithm

Experimental evidence in both human and animal studies demonstrated that deep brain stimulation (DBS) can induce short term synaptic plasticity (STP) in the stimulated nucleus. Given that DBS induced STP may be connected to the therapeutic effects of DBS, we sought to develop an appropriate computational predictive model that infers the dynamics of STP in response to DBS at different frequencies. Existing methods for estimating STP either model based or model free approaches require access to presynaptic spiking activity. However, in the context of DBS, extracellular stimulation (e.g. DBS) can be used to elicit presynaptic activations directly. We present a model based approach that integrates multiple individual frequencies of DBS like electrical stimulation as presynaptic spikes and infers parameters of the Tsodyks Markram (TM) model from post-synaptic currents of the stimulated nucleus. By distinguishing between the steady-state and transient responses of the TM model, we develop a novel dual optimization algorithm that infers the model parameters in two steps. First, the TM model parameters are calculated by integrating multiple frequencies of stimulation to estimate the steady state response of postsynaptic current through a closed form analytical solution. The results of this step are utilized as the initial values for the second step in which a nonderivative optimization algorithm is used to track the transient response of the postsynaptic potential across different individual frequencies of stimulation. Moreover, we apply our algorithm to empirical data recorded from acute rodent brain slices of the subthalamic nucleus (STN) during DBS like stimulation to infer dynamics of STP for inhibitory synaptic inputs.

Quantitative CT texture-based method to predict diagnosis and prognosis of fibrosing interstitial lung disease patterns

Objectives To utilize high-resolution quantitative CT (QCT) imaging features for prediction of diagnosis and prognosis in fibrosing interstitial lung diseases (ILD). Methods 40 ILD patients (20 usual interstitial pneumonia (UIP), 20 non-UIP pattern ILD) were classified by expert consensus of 2 radiologists and followed for 7 years. Clinical variables were recorded. Following segmentation of the lung field, a total of 26 texture features were extracted using a lattice-based approach (TM model). The TM model was compared with previously histogram-based model (HM) for their abilities to classify UIP vs non-UIP. For prognostic assessment, survival analysis was performed comparing the expert diagnostic labels versus TM metrics. Results In the classification analysis, the TM model outperformed the HM method with AUC of 0.70. While survival curves of UIP vs non-UIP expert labels in Cox regression analysis were not statistically different, TM QCT features allowed statistically significant partition of the cohort. Conclusion TM model outperformed HM model in distinguishing UIP from non-UIP patterns. Most importantly, TM allows for partitioning of the cohort into distinct survival groups, whereas expert UIP vs non-UIP labeling does not. QCT TM models may improve diagnosis of ILD and offer more accurate prognostication, better guiding patient management.

Neural Network-Based Models for Estimating Weighted Mean Temperature in China and Adjacent Areas

The weighted mean temperature (Tm) is a key parameter when converting the zenith wet delay (ZWD) to precipitation water vapor (PWV) in ground-based Global Navigation Satellite System (GNSS) meteorology. Tm can be calculated via numerical integration with the atmospheric profile data measured along the zenith direction, but this method is not practical in most cases because it is not easy for general users to get real-time atmospheric profile data. An alternative method to obtain an accurate Tm value is to establish regional or global models on the basis of its relations with surface meteorological elements as well as the spatiotemporal variation characteristics of Tm. In this study, the complex relations between Tm and some of its essentially associated factors including the geographic position and terrain, surface temperature and surface water vapor pressure were considered to develop Tm models, and then a non-meteorological-factor Tm model (NMFTm), a single-meteorological-factor Tm model (SMFTm) and a multi-meteorological-factor Tm model (MMFTm) applicable to China and adjacent areas were established by adopting the artificial neural network technique. The generalization performance of new models was strengthened with the help of an ensemble learning method, and the model accuracies were compared with several representative published Tm models from different perspectives. The results show that the new models all exhibit consistently better performance than the competing models under the same application conditions tested by the data within the study area. The NMFTm model is superior to the latest non-meteorological model and has the advantages of simplicity and utility. Both the SMFTm model and MMFTm model show higher accuracy than all the published Tm models listed in this study; in particular, the MMFTm model is about 14.5% superior to the first-generation neural network-based Tm (NN-I) model, with the best accuracy so far in terms of the root-mean-square error.

A REFINING METHOD OF NON-LINEAR REGIONAL TM MODEL BASED ON RANDOM FOREST

Weighted mean temperature (Tm) is a critical parameters in GNSS technology to retrieve precipitable water vaper (PWV). By obtaining high-precision Tm, it can provide an important reference data source for regional strong convective weather and large-scale climate anomalies. The high-precision Tm of most areas can be obtained by using the BEVIS model and the surface temperature (Ts). The eastern coastal areas of China are affected by the monsoon climate, which makes the applicability of the method in this area to be improved. The research shows that the Tm which calculated by Fourier series analysis (FTm model) has better applicability in the region than the BEVIS model. However, the method has a single modeling factor, and the precision improvement effect in some area is not obvious. By using the observation data of 13 radiosonde stations in the eastern coastal areas of China from 2010 to 2015. Tm which calculated by numerical integration is used as the reference of the true value. Four of the observation data are selected by the method of random forest (RF). The eigenvalues include the pressure、surface temperature、water vapor pressure and specific humidity are used as input factors. The prediction corrections are added to the deviation of FTm model, and a new Tm is applied to the eastern coast of China which called RFF Tm. Taking the observation data from 2010 to 2014 as the training database, the research area is divided into three areas from south to north according to the latitude. The prediction results of different time scales are studied by the clamping criterion, and then the prediction of random forest is discussed. The correction effect is adaptable in the eastern coast areas of China. The results show that: (1) The RFF Tm model refinement method based on random forest has better adaptability in eastern coastal areas of China, and the applicability of first area is more stable with the prediction time scale than the FTm model. (2) On the time scale with a forecast period of one year, MAE and RMS are 4.7 and 4.6 in third area, 3.2 and 3.8 in second area, and 2.6 and 2.5 in first area. (3) The improvement effect of random forests in the eastern coastal areas of China gradually increases with the prediction period becoming shorter. The predicted deviation values of the eastern coast areas of China reach a steady state when the period is one month. The correction deviations is within 1.5K. The correction range of the third area is better than the second area and first area, which makes up for the shortcomings of the FTm model with low precision in the region. It can be used as a new multi-factor prediction and correction Tm model for GNSS remote sensing water vapor in the eastern coastal areas of China.

ATMOSPHERIC WEIGHTED MEAN TEMPERATURE MODEL IN GUILIN

Atmospheric water vapor is an important part of the earth's atmosphere, and it has a great relationship with the formation of precipitation and climate change. In CNSS-derived precipitable water vapor (PWV), atmospheric weighted mean temperature, Tm, is the key factor in the progress of retrieving PWV. In this study, using the profiles of Guilin radiosonde station in 2017, the spatiotemporal variation characteristics and relationships between Tm and surface temperature (Ts) are analyzed in Guilin, an empirical Tm model suitable for Guilin is constructed by regression analysis. Comparing the Tm values calculated from Bevis model, Li Jianguo model and new model, it is found that the root mean square error (RMSE) of new model is 2.349 K, which are decreased by 14% and 19%, respectively. Investigating the impact of different Tm models on GNSS-PWV, the Tm-induced error from new model has a smaller impact on PWV than other two models. The results show that the new Tm model in Guilin has a relatively good performance and it can improve the reliability of the regional GNSS water vapor retrieval to some extent.

A Transfer Matrix Model of the IEC 60318-4 Ear Simulator: Application to the Simulation of Earplug Insertion Loss

The IEC 60318-4 ear simulator is used to measure the insertion loss (IL) of earplugs in the ear canal of an acoustical test fixture (ATF) and is designed to represent an average acoustic impedance (in a reference plane) of the human ear. The ear simulator is usually modeled using a lumped parameter model (LPM) which has frequency limitations and inadequately accounts for the thermo-viscous effects in the simulator. The simulator numerical models that can better deal with the thermo-viscous phenomena often lack essential geometric details. Most related studies also suffer from the lack of experimental validation of the models. Therefore, a transfer matrix (TM) model of the IEC 60318-4 simulator is proposed based on a direct assessment of its geometric dimensions. Such a model is of particular interest for designing artificial ear simulators. The variability in the simulator impedance due to the geometric uncertainties is quantified using the Monte Carlo method. The TM model is validated using i) a finite element (FE) model of the simulator and ii) impedance measurements with a sound intensity probe. It is found to better describe the simulator impedance above 3 kHz compared to the LPM. The TM model is then coupled to a FE model of an occluded ATF ear canal to simulate the IL of an earplug in the frequency range [100 Hz, 10 kHz]. In the model, the simulator is considered as a cylindrical cavity terminated by an equivalent tympanic impedance which is determined from the TM model to simulate the sound pressure measured at the real microphone position (not at the reference plane) in the ATF ear canal. The simulated IL is validated against i) that obtained with a complete FE model of the corresponding system and ii) measurements using an ATF. The TM model is shown to better agree with the simulator FE model than the LPM above 6 kHz regarding the earplug IL simulated using this method.