Phenotype-based and Self-learning Inter-individual Sleep Apnea Screening with a Level IV Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes, and test it on a Level IV monitoring system. Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV wearable device measuring the thoracic and abdominal movements and the SpO2. Results: With the leave-one cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03. Conclusion: The results confirm the hypothesis and show that the proposed algorithm has great potential to screen patients with SAS

Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System

Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes and test it on a Level IV-like monitoring system.Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV-like wearable device measuring the thoracic and abdominal movements and the SpO2.Results: With the leave-one-subject-out cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03.Conclusion: The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS

Generic parameters of trajectory-extending kinetic Monte Carlo for calculating diffusion coefficients

One of the challenging applications of molecular dynamics (MD) simulations is to determine the dynamic properties such as the diffusion coefficient of the molecule of interest, particularly slow dynamic systems such as hydrogels and polymer melts. Recently, Neyertz et al. proposed a trajectory- extending kinetic Monte Carlo (TEKMC) algorithm combining both MD and kinetic Monte Carlo to probe the penetrant diffusion within the glassy polymer systems (S. Neyertz and D. Brown, Macromolecules 43, 9210, 2010). Yet, the original TEKMC relies on the manual adjustments of the key parameters of the sampling interval τ and the discretizing grid size rgrid, which limits its applicability to systems with unknown kinetic properties. Here, we reviewed the theoretical background of kinetic Monte Carlo to establish the generic criteria for selecting TEKMC parameters. Also, we modified and expanded the TEKMC algorithm for bulk fluid systems. The modified TEKMC algorithm were applied to systems with various kinetic properties, including Lennard Jones liquid, bulk water, Li+ liquid electrolyte, and Li+ polymer electrolyte. The diffusion coefficients obtained from the modified TEKMC and the generic parameter selections were promising and robust compared with the conventional MD results. With the proposed TEKMC approach, one can extend the MD trajectories to unambiguously characterize the diffusion behavior in the long-time diffusive regime

Asian Culturally Specific Predictors in a Large-Scale Land Use Regression Model to Predict Spatial-Temporal Variability of Ozone Concentration

This paper developed a land use regression (LUR) model to study the spatial-temporal variability of O3 concentrations in Taiwan, which has typical Asian cultural characteristics with diverse local emission sources. The Environmental Protection Agency’s (EPA) data of O3 concentrations from 2000 and 2013 were used to develop this model, while observations from 2014 were used as the external data verification to assess model reliability. The distribution of temples, cemeteries, and crematoriums was included for a potential predictor as an Asian culturally specific source for incense and joss money burning. We used stepwise regression for the LUR model development, and applied 10-fold cross-validation and external data for the verification of model reliability. With the overall model R2 of 0.74 and a 10-fold cross-validated R2 of 0.70, this model presented a mid-high prediction performance level. Moreover, during the stepwise selection procedures, the number of temples, cemeteries, and crematoriums was selected as an important predictor. By using the long-term monitoring data to establish an LUR model with culture specific predictors, this model can better depict O3 concentration variation in Asian areas

Presentation_1_Phenotype-Based and Self-Learning Inter-Individual Sleep Apnea Screening With a Level IV-Like Monitoring System.pdf

<p>Purpose: We propose a phenotype-based artificial intelligence system that can self-learn and is accurate for screening purposes and test it on a Level IV-like monitoring system.</p><p>Methods: Based on the physiological knowledge, we hypothesize that the phenotype information will allow us to find subjects from a well-annotated database that share similar sleep apnea patterns. Therefore, for a new-arriving subject, we can establish a prediction model from the existing database that is adaptive to the subject. We test the proposed algorithm on a database consisting of 62 subjects with the signals recorded from a Level IV-like wearable device measuring the thoracic and abdominal movements and the SpO2.</p><p>Results: With the leave-one-subject-out cross validation, the accuracy of the proposed algorithm to screen subjects with an apnea-hypopnea index greater or equal to 15 is 93.6%, the positive likelihood ratio is 6.8, and the negative likelihood ratio is 0.03.</p><p>Conclusion: The results confirm the hypothesis and show that the proposed algorithm has potential to screen patients with SAS.</p

The Effect of Sertoli Cells on Xenotransplantation and Allotransplantation of Ventral Mesencephalic Tissue in a Rat Model of Parkinson’s Disease

Intra-striatal transplantation of fetal ventral mesencephalic (VM) tissue has a therapeutic effect on patients with Parkinson&rsquo;s disease (PD). Sertoli cells (SCs) possess immune-modulatory properties that benefit transplantation. We hypothesized that co-graft of SCs with VM tissue can attenuate rejection. Hemi-parkinsonian rats were generated by injecting 6-hydroxydopamine into the right medial forebrain bundle of Sprague Dawley (SD) rats. The rats were then intrastriatally transplanted with VM tissue from rats or pigs (rVM or pVM), with/without a co-graft of SCs (rVM+SCs or pVM+SCs). Recovery of dopaminergic function and survival of the grafts were evaluated using the apomorphine-induced rotation test and small animal-positron emission tomography (PET) coupled with [18F] DOPA or [18F] FE-PE2I, respectively. Immunohistochemistry (IHC) examination was used to determine the survival of the grafted dopaminergic neurons in the striatum and to investigate immune-modulatory effects of SCs. The results showed that the rVM+SCs and pVM+SCs groups had significantly improved drug-induced rotational behavior compared with the VM alone groups. PET revealed a significant increase in specific uptake ratios (SURs) of [18F] DOPA and [18F] FE-PE2I in the grafted striatum of the rVM+SCs and pVM+SCs groups as compared to that of the rVM and pVM groups. SC and VM tissue co-graft led to better dopaminergic (DA) cell survival. The co-grafted groups exhibited lower populations of T-cells and activated microglia compared to the groups without SCs. Our results suggest that co-graft of SCs benefit both xeno- and allo-transplantation of VM tissue in a PD rat model. Use of SCs enhanced the survival of the grafted dopaminergic neurons and improved functional recovery. The enhancement may in part be attributable to the immune-modulatory properties of SCs. In addition, [18F]DOPA and [18F]FE-PE2I coupled with PET may provide a feasible method for in vivo evaluation of the functional integrity of the grafted DA cell in parkinsonian rats