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The RNA chaperone Hfq is involved in the riboregulation of diverse genes via small RNAs. Recent studies have demonstrated that Hfq contributes to the stress response and the virulence of several pathogens, and the roles of Hfq vary among bacterial species. Here, we attempted to elucidate the role of Hfq in Acinetobacter baumannii ATCC 17978. In the absence of hfq, A. baumannii exhibited retarded cell growth and was highly sensitive to environmental stress, including osmotic and oxidative pressure, pH, and temperature. Compared to the wild-type, the Hfq mutant had reduced outer membrane vesicles secretion and fimbriae production as visualized by atomic force microscopy. The absence of hfq reduced biofilm formation, airway epithelial cell adhesion and invasion, and survival in macrophage. Further, the hfq mutant induced significantly higher IL-8 levels in airway epithelial cells, which would promote bacterial clearance by the host. In addition to results similar to those reported for other bacteria, our findings demonstrate that Hfq is required in the regulation of the iron-acquisition system via downregulating the bauA and basD genes, the stress-related outer membrane proteins carO, A1S_0820, ompA, and nlpE, and the stress-related cytosolic proteins uspA and groEL. Our data indicate that Hfq plays a critical role in environmental adaptation and virulence in A. baumannii by modulating stress responses, surface architectures, and virulence factors. This study is the first to illustrate the functional role of Hfq in A. baumannii.

High-quality orthorhombic (OT) TmMnO3 (TMO) thin films with a-axis perpendicular to the film surface are grown epitaxially on Nb-doped SrTiO3(110) substrates using pulsed laser deposition. The structural, magnetic, and electric properties of OT-TMO films are measured. We found that a strong coupling between the magnetic structure and the electric polarization. Our experimental results also show that ferroelectricity in OT-TMO thin films below 32 K. Furthermore, the large electric polarization up to 0.45 μC/cm2 is observed at 10 K, supporting a theoretical prediction of large polarization in the E-type spin structure in this system.

Multiscale entropy (MSE) was used to analyze electroencephalography (EEG) signals to differentiate patients with Alzheimer’s disease (AD) from healthy subjects. It was found that the MSE values of the EEG signals from the healthy subjects are higher than those of the AD ones at small time scale factors in the MSE algorithm, while lower than those of the AD patients at large time scale factors. Based on the finding, we applied the linear discriminant analysis (LDA) to optimize the differentiating performance by comparing the resulting weighted sum of the MSE values under some specific time scales of each subject. The EEG data from 15 healthy subjects, 69 patients with mild AD, and 15 patients with moderate to severe AD were recorded. As a result, the weighted sum values are significantly higher for the healthy than the patients with moderate to severe AD groups. The optimal testing accuracy under five specific scales is 100% based on the EEG signals acquired from the T4 electrode. The resulting weighted sum value for the mild AD group is in the middle of those for the healthy and the moderate to severe AD groups. Therefore, the MSE-based weighted sum value can potentially be an index of severity of Alzheimer’s disease.

Mode mixing is a limitation of the empirical mode decomposition (EMD) method appropriate for physiological signal analysis. In 2008, boundary condition map presented by Rilling and Flandrin provided the efficiency of separating the two components of a two-tone signal as a function of their amplitude and frequency ratios. Until 2019, their findings were still applied. However, their maps only give an uncertainty-like efficiency of mode mixing separation for two-tone signals. In this paper, we propose a criterion for mode mixing separation in EMD, which provides a binary judgment on mode mixing separation instead of the above-mentioned efficiency. By comparing the slopes of the two components, we found that the phenomenon of mode mixing occurs as the extrema of the high-tone component are suppressed by the low-tone component. Under this condition, the criterion shows the relation among their amplitude ratio, frequency ratio, and relative phase between the two components. Given with the values of the three parameters, one can affirm whether the two components are mixed according to the criterion. Accordingly, we derive a black/white three-dimensional (3D) map that plots the binary result of mode mixing in black or white as a function of the three parameters. Our map agrees with Rilling's map and the results obtained from our gait analysis. Among the 23 sets of center-of-mass trajectory signals, six sets encountered the mode mixing problem and their coordinates of the three parameters were found in the black region of the map, while the other 17 sets were in the white region.INDEX TERMS Empirical mode decomposition, mode mixing separation, improved EMD.

Multiscale entropy (MSE) is widely used to analyze heartbeat signals. Even though cardiologists do not use MSE to diagnose heart failure at present, these studies are of importance and have potential clinical applications. In previous studies, MSE discrimination between old congestive heart failure (CHF) and healthy individuals has remained controversial. Few studies have been published on the discrimination between them, using only MSE with machine learning for automatic multidimensional analysis, with reported testing accuracies of less than 86%. In this study, we determined the optimal MSE scales for discrimination by using a low-dimensional exhaustive search along with three classifiers—linear discriminant analysis (LDA), support vector machine (SVM), and k-nearest neighbor (KNN). In younger people (<55 years), the results showed an accuracy of up to 95.5% with two optimal MSE scales (2D) and up to 97.7% with four optimal MSE scales (4D) in discriminating between young CHF and healthy participants. In older people (≥55 years), the discrimination accuracy reached 90.1% using LDA in 2D, SVM in 3D (three optimal MSE scales), and KNN in 5D (five optimal MSE scales). LDA with a 3D exhaustive search also achieved 94.4% accuracy in older people. Therefore, the results indicate that MSE analysis can differentiate between CHF and healthy individuals of any age.

BackgroundTotal motile sperm count (TMSC) and curvilinear velocity (VCL) are two important parameters in preliminary semen analysis for male infertility. Traditionally, both parameters are evaluated manually by embryologists or automatically using an expensive computer-assisted sperm analysis (CASA) instrument. The latter applies a point-tracking method using an image processing technique to detect, recognize and classify each of the target objects, individually, which is complicated. However, as semen is dense, manual counting is exhausting while CASA suffers from severe overlapping and heavy computation.MethodsWe proposed a simple frame-differencing method that tracks motile sperms collectively and treats their overlapping with a statistical occupation probability without heavy computation. The proposed method leads to an overall image of all of the differential footprint trajectories (DFTs) of all motile sperms and thus the overall area of the DFTs in a real-time manner. Accordingly, a theoretical DFT model was also developed to formulate the overall DFT area of a group of moving beads as a function of time as well as the total number and average speed of the beads. Then, using the least square fitting method, we obtained the optimal values of the TMSC and the average VCL that yielded the best fit for the theoretical DFT area to the measured DFT area.ResultsThe proposed method was used to evaluate the TMSC and the VCL of 20 semen samples. The maximum TMSC evaluated using the method is more than 980 sperms per video frame. The Pearson correlation coefficient (PCC) between the two series of TMSC obtained using the method and the CASA instrument is 0.946. The PCC between the two series of VCL obtained using the method and CASA is 0.771. As a consequence, the proposed method is as accurate as the CASA method in TMSC and VCL evaluations.ConclusionIn comparison with the individual point-tracking techniques, the collective DFT tracking method is relatively simple in computation without complicated image processing. Therefore, incorporating the proposed method into a cell phone equipped with a microscopic lens can facilitate the design of a simple sperm analyzer for clinical or household use without advance dilution.

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.