Healthy systems exhibit complex dynamics on the changing of information embedded in physiologic signals on multiple time scales that can be quantified by employing multiscale entropy (MSE) analysis. Here, we propose a measure of complexity, called entropy of entropy (EoE) analysis. The analysis combines the features of MSE and an alternate measure of information, called superinformation, useful for DNA sequences. In this work, we apply the hybrid analysis to the cardiac interbeat interval time series. We find that the EoE value is significantly higher for the healthy than the pathologic groups. Particularly, short time series of 70 heart beats is sufficient for EoE analysis with an accuracy of 81% and longer series of 500 beats results in an accuracy of 90%. In addition, the EoE versus Shannon entropy plot of heart rate time series exhibits an inverted U relationship with the maximal EoE value appearing in the middle of extreme order and disorder.

Static standing postural stability has been measured by multiscale entropy (MSE), which is used to measure complexity. In this study, we used the average entropy (AE) to measure the static standing postural stability, as AE is a good measure of disorder. The center of pressure (COP) trajectories were collected from 11 subjects under four kinds of balance conditions, from stable to unstable: bipedal with open eyes, bipedal with closed eyes, unipedal with open eyes, and unipedal with closed eyes. The AE, entropy of entropy (EoE), and MSE methods were used to analyze these COP data, and EoE was found to be a good measure of complexity. The AE of the 11 subjects sequentially increased by 100% as the balance conditions progressed from stable to unstable, but the results of EoE and MSE did not follow this trend. Therefore, AE, rather than EoE or MSE, is a good measure of static standing postural stability. Furthermore, the comparison of EoE and AE plots exhibited an inverted U curve, which is another example of a complexity versus disorder inverted U curve.

Static standing postural stability has been measured by multiscale entropy (MSE), which is used to measure complexity. In this study, we used the average entropy (AE) to measure the static standing postural stability, as AE is a good measure of disorder. The center of pressure (COP) trajectories were collected from 11 subjects under four kinds of balance situation, from stable to unstable: bipedal with open eyes, bipedal with closed eyes, unipedal with open eyes, and unipedal with closed eyes. The AE, entropy of entropy (EoE), and MSE methods were used to analyze these COP data, and EoE was found to be a good measure of complexity. The AE of the 11 subjects sequentially increased by 100%as the balance situations progressed from stable to unstable, but the results of EoE and MSE did not follow this trend. Therefore, AE, rather than EoE or MSE, is a good measure of static standing postural stability. Furthermore, the comparison of EoE and AE plots exhibited an inverted U curve, which is another example of a complexity versus disorder inverted U curve.

This paper reports the platform which combines organic photocoductor-based optoelectrowetting (OEW) and optoelectronic tweezers (OET) by spin-coating titanium oxide phthalocyanine (TiOPc) as a photoconductive layer for realizing the manipulation of microparticles and droplets in a single chip. By producing specific image-induced virtual electrodes and modulating the frequency of the electric field on this device, dielectrophoresis (DEP) and electrowetting (EW) can be selected to become dominant force acting on microparticles and droplets. We demonstrate two kinds of operation modes: (1) In OET mode, micropaticles are concentrated by using strip-shaped optical images and the applied voltage in the high frequency range. (2) In OEW mode, droplets are actuated by employing square-shaped optical images and the applied voltage in the low frequency range.

Macrophages are members of the leukocyte family. Tissue damage causes inflammation and release of vasoactive and chemotactic factors, which trigger a local increase in blood flow and capillary permeability. Then, leukocytes accumulate quickly to the infection site. The leukocyte extravasation process takes place according to a sequence of events that involve tethering, activation by a chemoattractant stimulus, adhesion by integrin binding, and migrating to the infection site. The leukocyte extravasation process reveals that adhesion is an important part of the immune system. Optical tweezers have become a useful tool with broad applications in biology and physics. In force measurement, the trapped bead as a probe usually uses a polystyrene bead of 1 μm diameter to measure adhesive force between the trapped beads and cell by optical tweezers. In this paper, using the ray-optics model calculated trapping stiffness and defined the linear displacement ranges. By the theoretical values of stiffness and linear displacement ranges, this study attempted to obtain a proper trapped particle size in measuring adhesive force. Finally, this work investigates real-time adhesion force measurements between human macrophages and trapped beads coated with lipopolysaccharides using optical tweezers with backscattered detection.

The complexity of biological signals has been proposed to reflect the adaptability of a given biological system to different environments. Two measures of complexity—multiscale entropy (MSE) and entropy of entropy (EoE)—have been proposed, to evaluate the complexity of heart rate signals from different perspectives. The MSE evaluates the information content of a long time series across multiple temporal scales, while the EoE characterizes variation in amount of information, which is interpreted as the “state changing,” of segments in a time series. However, both are problematic when analyzing white noise and are sensitive to data size. Therefore, based on the concept of “state changing,” we propose state change probability (SCP) as a measure of complexity. SCP utilizes a statistical hypothesis test to determine the physiological state changes between two consecutive segments in heart rate signals. The SCP value is defined as the ratio of the number of state changes to total number of consecutive segment pairs. Two common statistical tests, the t-test and Wilcoxon rank–sum test, were separately used in the SCP algorithm for comparison, yielding similar results. The SCP method is capable of reasonably evaluating the complexity of white noise and other signals, including 1/f noise, periodic signals, and heart rate signals, from healthy subjects, as well as subjects with congestive heart failure or atrial fibrillation. The SCP method is also insensitive to data size. A universal SCP threshold value can be applied, to differentiate between healthy and pathological subjects for data sizes ranging from 100 to 10,000 points. The SCP algorithm is slightly better than the EoE method when differentiating between subjects, and is superior to the MSE method.