Burden of Liver Cancer Attributable to Hepatitis B and Alcohol Globally, in China, and for Five Sociodemographic Index Regions from 1990 to 2021: A Population-based Study.

Background And Aims: Liver cancer is a digestive system malignancy that poses a significant public health challenge globally. This study aimed to analyze and compare the epidemiological trends of liver cancer attributed to hepatitis B (LCHB) and alcohol use (LCAL) over the past 32 years.

Methods: Data on mortality and disability-adjusted life years for LCHB and LCAL in China, globally, and across five sociodemographic index regions were obtained from the Global Burden of Disease 2021 database and comprehensively analyzed.

Analysis of nonstationarity in renal autoregulation mechanisms using time-varying transfer and coherence functions.

The extent to which renal blood flow dynamics vary in time and whether such variation contributes substantively to dynamic complexity have emerged as important questions. Data from Sprague-Dawley rats (SDR) and spontaneously hypertensive rats (SHR) were analyzed by time-varying transfer functions (TVTF) and time-varying coherence functions (TVCF). Both TVTF and TVCF allow quantification of nonstationarity in the frequency ranges associated with the autoregulatory mechanisms.

Limbic dysregulation is associated with lowered heart rate variability and increased trait anxiety in healthy adults.

Objectives: We tested whether dynamic interaction between limbic regions supports a control systems model of excitatory and inhibitory components of a negative feedback loop, and whether dysregulation of those dynamics might correlate with trait differences in anxiety and their cardiac characteristics among healthy adults.

Experimental Design: Sixty-five subjects received fMRI scans while passively viewing angry, fearful, happy, and neutral facial stimuli. Subjects also completed a trait anxiety inventory, and were monitored using ambulatory wake ECG.

Representation of time-varying nonlinear systems with time-varying principal dynamic modes.

System identification of nonlinear time-varying (TV) systems has been a daunting task, as the number of parameters required for accurate identification is often larger than the number of data points available, and scales with the number of data points. Further, a 3-D graphical representation of TV second-order nonlinear dynamics without resorting to taking slices along one of the four axes has been a significant challenge to date. In this paper, we present a TV principal dynamic mode (TVPDM) method which overcomes these deficiencies.

Frequency modulation between low- and high-frequency components of the heart rate variability spectrum may indicate sympathetic-parasympathetic nonlinear interactions.

Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate, to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological mechanisms.

Autonomic nervous nonlinear interactions lead to frequency modulation between low- and high-frequency bands of the heart rate variability spectrum.

Cardiac sympathetic and parasympathetic neural activities have been found to interact with each other to efficiently regulate the heart rate and maintain homeostasis. Quantitative and noninvasive methods used to detect the presence of interactions have been lacking, however. This may be because interactions among autonomic nervous systems are nonlinear and nonstationary.

Frequency modulation between low- and high-frequency components of the heart rate variability spectrum.

Interactions among physiological mechanisms are abundant in biomedical signals, and they may exist to maintain efficient homeostasis. For example, sympathetic and parasympathetic neural activities interact to either elevate or depress the heart rate to maintain homeostasis. There has been considerable effort devoted to developing algorithms that can detect interactions between various physiological mechanisms.

Quantifying cardiac sympathetic and parasympathetic nervous activities using principal dynamic modes analysis of heart rate variability.

The ratio between low-frequency (LF) and high-frequency (HF) spectral power of heart rate has been used as an approximate index for determining the autonomic nervous system (ANS) balance. An accurate assessment of the ANS balance can only be achieved if clear separation of the dynamics of the sympathetic and parasympathetic nervous activities can be obtained, which is a daunting task because they are nonlinear and have overlapping dynamics. In this study, a promising nonlinear method, termed the principal dynamic mode (PDM) method, is used to separate dynamic components of the sympathetic and parasympathetic nervous activities on the basis of ECG signal, and the results are compared with the power spectral approach to assessing the ANS balance.

Nonlinear analysis of the separate contributions of autonomic nervous systems to heart rate variability using principal dynamic modes.

This paper introduces a modified principal dynamic modes (PDM) method, which is able to separate the dynamics of sympathetic and parasympathetic nervous activities. The PDM is based on the principle that among all possible choices of expansion bases, there are some that require the minimum number of basis functions to achieve a given mean-square approximation of the system output. Such a minimum set of basis functions is termed PDMs of the nonlinear system.