Spectral Time-Varying Pattern Causality and Its Application.

In this paper, a new method based on delayed pattern causality is proposed, called spectral time-varying pattern causality. Specifically, this method uses symbolic dynamics and phase space reconstruction to infer causality, systematically quantifies the causal relationship between different frequency components of the signal, and the generated spectrum provides a rich information representation of the time-varying potential causality. The causal intensity at different times is quantified by a sliding window, providing a dynamic perspective for the study of causality in complex systems. Through the simulation data, we verified the effectiveness of the method and its robustness to noise, and then applied it to physiological data to compare the differences in coupling between electrodes in different brain regions between normal and Parkinson's patients in the resting state. The study of causality in complex systems provides a new perspective to better capture the latent and elusive dynamic structures.

Exploring the Relationship among Predictability, Prediction Accuracy and Data Frequency of Financial Time Series.

In this paper, we aim to reveal the connection between the predictability and prediction accuracy of stock closing price changes with different data frequencies. To find out whether data frequency will affect its predictability, a new information-theoretic estimator Plz, which is derived from the Lempel-Ziv entropy, is proposed here to quantify the predictability of five-minute and daily price changes of the SSE 50 index from the Chinese stock market. Furthermore, the prediction method EEMD-FFH we proposed previously was applied to evaluate whether financial data with higher sampling frequency leads to higher prediction accuracy. It turns out that intraday five-minute data are more predictable and also have higher prediction accuracy than daily data, suggesting that the data frequency of stock returns affects its predictability and prediction accuracy, and that higher frequency data have higher predictability and higher prediction accuracy. We also perform linear regression for the two frequency data sets; the results show that predictability and prediction accuracy are positive related.

Incidence and Predictors of Diabetes Mellitus after a Diagnosis of Early-Stage Breast Cancer in the Elderly Using Real-World Data.

PURPOSE: The incidence and predictors of diabetes (DM) in patients with breast cancer (BC) were evaluated. We compared DM incidence and physician access in BC patients to matched controls. METHODS: We identified women with stage I-III BC diagnosed from 2005 to 2013 in the SEER-Medicare database, with ≥ 2 years of follow-up after diagnosis, without previous DM claims. Incident DM was determined by ≥ 1 DM claims after BC diagnosis. Multivariable analysis was used to identify factors associated with incident DM. Age- and race-matched non-cancer controls were obtained from a 5% random sample and assigned an index date. Physician and PCP visits per-patient-per-year were compared between cases and controls in the two-year period prior to and after the index date. RESULTS: Among 14,506 eligible BC patients, 3234 (22.3%) developed DM versus 16.5% of controls. Among BC patients, factors associated with incident DM included race (Black OR 1.63 95% CI 1.39-1.93, Hispanic OR 3.03 95% CI 1.92-4.81; vs. Caucasians), SES (Quintile 0 vs. Quintile 4 OR 1.55 95% CI 1.33-1.78), and receipt of chemotherapy (vs. none OR 1.19 95% CI 1.08-1.31). Among cases and controls, respectively, median physician visits per-patient-per-year were 19 and 17 prior to the index date, and 46 and 19 after the index date; median PCP visits were 2 for both groups in both periods. CONCLUSION: About 22% of BC patients developed DM, more than controls in the same period. While there were differences in healthcare access, there weren't differences in PCP access between groups. This represents an opportunity for better comorbidity management in BC patients.

Author Correction: Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Dynamic network interactions among distinct brain rhythms as a hallmark of physiologic state and function.

Brain rhythms are associated with a range of physiologic states, and thus, studies have traditionally focused on neuronal origin, temporal dynamics and fundamental role of individual brain rhythms, and more recently on specific pair-wise interactions. Here, we aim to understand integrated physiologic function as an emergent phenomenon of dynamic network interactions among brain rhythms. We hypothesize that brain rhythms continuously coordinate their activations to facilitate physiologic states and functions. We analyze healthy subjects during sleep, and we demonstrate the presence of stable interaction patterns among brain rhythms. Probing transient modulations in brain wave activation, we discover three classes of interaction patterns that form an ensemble representative for each sleep stage, indicating an association of each state with a specific network of brain-rhythm communications. The observations are universal across subjects and identify networks of brain-rhythm interactions as a hallmark of physiologic state and function, providing new insights on neurophysiological regulation with broad clinical implications.

Use of Bevacizumab for Elderly Patients With Stage IV Colon Cancer: Analysis of SEER-Medicare Data.

BACKGROUND: Bevacizumab is used for the treatment of metastatic colon cancer in conjunction with first-line chemotherapy. In this study, we examined receipt of first-line bevacizumab and predictors of its use among older patients with stage IV colon cancer. MATERIALS AND METHODS: We used data from the Surveillance, Epidemiology, and End Results-Medicare dataset to identify patients with stage IV colon cancer diagnosed from 2005 to 2013 who received FOLFOX (5-fluorouracil/leucovorin/oxaliplatin) or FOLFIRI (5-fluorouracil/leucovorin/irinotecan) as first-line therapy. We used multivariable regression analysis to determine demographic and clinical factors associated with use of concomitant bevacizumab. RESULTS: We identified 3785 patients with stage IV colon cancer who met our eligibility criteria. Of these, 2352 (62.1%) received bevacizumab. Bevacizumab use has decreased over time from 68.2% in 2005 to 57.6% in 2013 (odds ratio [OR], 0.94; 95% confidence interval [CI], 0.91-0.97). Patients were less likely to receive bevacizumab if they were older (compared with 65-69 years, ≥ 80 years: OR, 0.64; 95% CI, 0.52-0.80), or had multiple comorbidities (compared with comorbidity score of 0, score of 1: OR, 0.73; 95% CI, 0.60-0.89). CONCLUSION: Over one-half of elderly patients received bevacizumab as part of their first-line therapy for stage IV colon cancer. Bevacizumab use has been slowly decreasing since 2005. Newer anti-epidermal growth factor receptor treatments have not been supplanting bevacizumab, as first-line biologic use in general has also decreased during this time period.

FOLFOX and FOLFIRI Use in Stage IV Colon Cancer: Analysis of SEER-Medicare Data.

BACKGROUND: Shortly after the year 2000, randomized trials demonstrated that patients with metastatic colon cancer treated with infusional 5-fluorouracil (5-FU)/leucovorin with either oxaliplatin (FOLFOX) or irinotecan (FOLFIRI) had a comparable progression-free survival benefit, superior to patients who received 5-FU/leucovorin alone. Factors associated with the initial receipt of the FOLFOX or FOLFIRI regimen are unknown. Our goal was to investigate the patterns and predictors of use for first-line FOLFOX and FOLFIRI. PATIENTS AND METHODS: We used the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked data set to identify patients with newly diagnosed stage IV colon cancer between the years 2005 and 2013 who received either first-line FOLFOX or FOLFIRI. We used logistic regression to assess demographic and clinical predictors for FOLFOX versus FOLFIRI. Survival was compared by Kaplan-Meier models. RESULTS: Overall, 3000 patients (79.3%) received FOLFOX and 785 (20.7%) FOLFIRI. FOLFOX was associated with later year of diagnosis (odds ratio [OR] = 0.66, 95% confidence interval [CI], 0.54 to 0.82 for 2011-2013 vs. 2005-2007), being female (OR = 0.82; 95% CI 0.69 to 0.98), and living in the southern region of the United States. FOLFIRI was associated with having a higher comorbidity index (OR = 1.33; 95% CI, 1.07 to 1.67 for >1 comorbidity score vs. 0). There was no survival difference observed between the two treatments. CONCLUSION: The majority of SEER-Medicare patients received FOLFOX and not FOLFIRI as a first-line treatment for stage IV colon cancer. Several demographic and clinical factors were associated with the use of each specific regimen. No survival difference was detected for the 2 groups.

Delay-correlation landscape reveals characteristic time delays of brain rhythms and heart interactions.

Within the framework of 'Network Physiology', we ask a fundamental question of how modulations in cardiac dynamics emerge from networked brain-heart interactions. We propose a generalized time-delay approach to identify and quantify dynamical interactions between physiologically relevant brain rhythms and the heart rate. We perform empirical analysis of synchronized continuous EEG and ECG recordings from 34 healthy subjects during night-time sleep. For each pair of brain rhythm and heart interaction, we construct a delay-correlation landscape (DCL) that characterizes how individual brain rhythms are coupled to the heart rate, and how modulations in brain and cardiac dynamics are coordinated in time. We uncover characteristic time delays and an ensemble of specific profiles for the probability distribution of time delays that underly brain-heart interactions. These profiles are consistently observed in all subjects, indicating a universal pattern. Tracking the evolution of DCL across different sleep stages, we find that the ensemble of time-delay profiles changes from one physiologic state to another, indicating a strong association with physiologic state and function. The reported observations provide new insights on neurophysiological regulation of cardiac dynamics, with potential for broad clinical applications. The presented approach allows one to simultaneously capture key elements of dynamic interactions, including characteristic time delays and their time evolution, and can be applied to a range of coupled dynamical systems.

Plasticity of brain wave network interactions and evolution across physiologic states.

Neural plasticity transcends a range of spatio-temporal scales and serves as the basis of various brain activities and physiologic functions. At the microscopic level, it enables the emergence of brain waves with complex temporal dynamics. At the macroscopic level, presence and dominance of specific brain waves is associated with important brain functions. The role of neural plasticity at different levels in generating distinct brain rhythms and how brain rhythms communicate with each other across brain areas to generate physiologic states and functions remains not understood. Here we perform an empirical exploration of neural plasticity at the level of brain wave network interactions representing dynamical communications within and between different brain areas in the frequency domain. We introduce the concept of time delay stability (TDS) to quantify coordinated bursts in the activity of brain waves, and we employ a system-wide Network Physiology integrative approach to probe the network of coordinated brain wave activations and its evolution across physiologic states. We find an association between network structure and physiologic states. We uncover a hierarchical reorganization in the brain wave networks in response to changes in physiologic state, indicating new aspects of neural plasticity at the integrated level. Globally, we find that the entire brain network undergoes a pronounced transition from low connectivity in Deep Sleep and REM to high connectivity in Light Sleep and Wake. In contrast, we find that locally, different brain areas exhibit different network dynamics of brain wave interactions to achieve differentiation in function during different sleep stages. Moreover, our analyses indicate that plasticity also emerges in frequency-specific networks, which represent interactions across brain locations mediated through a specific frequency band. Comparing frequency-specific networks within the same physiologic state we find very different degree of network connectivity and link strength, while at the same time each frequency-specific network is characterized by a different signature pattern of sleep-stage stratification, reflecting a remarkable flexibility in response to change in physiologic state. These new aspects of neural plasticity demonstrate that in addition to dominant brain waves, the network of brain wave interactions is a previously unrecognized hallmark of physiologic state and function.

From DVD to dendritic nanostructure silver electrode for hydrogen peroxide detection.

A facile method of multi-potential step-scan has been employed to fabricate the dendritic silver nanostructures on a silver thin film based digital video disc (DVD) electrode. The morphologies of the nanostructures, the chemical composition, and the crystal structure have been characterized by field-emission scanning electron microscopy, energy-dispersed x-ray spectroscopy, and x-ray diffraction, respectively. The electrocatalytic activity of the resulting electrode toward the electro-reduction of hydrogen peroxide (H(2)O(2)) has been examined via cyclic voltammetry and amperometric analysis. The excellent linear relationship between current response and H(2)O(2) concentration is observed in the range from 5.88×10(-7) to 6.73×10(-5) mol L(-1) and the detection limit is 2×10(-7) mol L(-1) (S/N=3). The as-developed method has been employed to determine H(2)O(2) concentration in real samples.