Benchmarking Transfer Entropy Methods for the Study of Linear and Nonlinear Cardio-Respiratory Interactions

Transfer entropy (TE) has been used to identify and quantify interactions between physiological systems. Different methods exist to estimate TE, but there is no consensus about which one performs best in specific applications. In this study, five methods (linear, k-nearest neighbors, fixed-binning with ranking, kernel density estimation and adaptive partitioning) were compared. The comparison was made on three simulation models (linear, nonlinear and linear + nonlinear dynamics). From the simulations, it was found that the best method to quantify the different interactions was adaptive partitioning. This method was then applied on data from a polysomnography study, specifically on the ECG and the respiratory signals (nasal airflow and respiratory effort around the thorax). The hypothesis that the linear and nonlinear components of cardio-respiratory interactions during light and deep sleep change with the sleep stage, was tested. Significant differences, after performing surrogate analysis, indicate an increased TE during deep sleep. However, these differences were found to be dependent on the type of respiratory signal and sampling frequency. These results highlight the importance of selecting the appropriate signals, estimation method and surrogate analysis for the study of linear and nonlinear cardio-respiratory interactions.

A Lymph Node Classification-based cancer-specific Survival Predicting Model in Patients with Ampullary cancer: A Derivation and Validation Study

Background: Current lymph node (LN) staging is controversial in predicting the survival of ampullary cancer (AC). We aimed to develop an alternative LN-classification-based nomogram to individualize AC prognosis.Methods: Using the data of patients diagnosed with AC between 2004 and 2015 from the SEER database, we determined the cut-off values for the number of LNs examined via the K-adaptive partitioning algorithm. A nomogram predicting the survival of AC patients was performed, internally and externally validated, and evaluated by calibration plot, C-index, and decision curve analysis (DCA), and was compared to the 7th TNM stage.Results: We included 2341 patients with detailed information. The optimal cut-off for examined LN number was 12, while the cut-off value for positive LNs was 0 and 4. The C-index for the nomogram was higher than that of the 7th TNM staging (internal: 0.686; 95% CI, 0.584-0.773 vs. 0.616; 95% CI, 0.533-0.754, P < 0.001; external: 0.713; 95% CI, 0.651-0.784 vs. 0.647; 95% CI, 0.551-0.719, P < 0.001). Additionally, the nomogram showed good agreement between internal and external validation. DCA analysis showed no matter in the internal cohort or external cohort; the nomogram showed a greater benefit across the period of follow-up than did the 7th TNM stage.Conclusion: We found that examined LNs that were more than 12 were beneficial for prognosis of patients. We also modified the current N staging into three groups based on number of metastatic LNs: N0, no LN metastasis; N1, 1–4 metastatic LNs; N2, >=5 metastatic LNs. A nomogram with greater benefit for predicting the survival of patients with AC than TNM staging was constructed.

Speed-dependent adaptive partitioning in QM/MM MD simulations of displacement damage in solid-state systems

Solids receive displacement damages (DD) when interacting with energetic particles, which may happen during the fabrication of semiconductor devices, in harsh environments and in certain analysis techniques. Simulations of the...