The effects of Tai Chi on standing balance control in older adults may be attributed to the improvement of sensory reweighting and complexity rather than reduced sway velocity or amplitude.

Introduction: Tai Chi has proved to be an effective therapy for balance performance and cognition. However, non-consistency exists in the results of the effect of Tai Chi training on standing balance control in older adults. This study aimed to use traditional and non-traditional methods to investigate the effect of Tai Chi on standing balance in older adults. Methods: Thirty-six Tai Chi practitioners (TC group) and thirty-six older adults with no Tai Chi practice (control group) were recruited in this study. A Nintendo Wii Balance Board was used to record the center of pressure (COP) during standing balance over 20 s in the condition of eyes closed with three repetitions. The wavelet analysis, multiscale entropy, recurrence quantification analysis, and traditional methods were used to evaluate the standing balance control in the anterior-posterior (AP) and mediolateral (ML) directions. Results: (1) Greater sway mean velocity in the AP direction and sway Path length were found in the TC group compared with the control group; (2) lower Very-low frequency band (0.10-0.39 Hz) and higher Moderate frequency band (1.56-6.25 Hz) in the AP and ML directions were found in the TC group compared with the control group; (3) greater complexity index (CI) and lower determinism (DET) in the AP and ML directions were observed in the TC group compared with control group; (4) greater path length linked with smaller Very-low frequency band in the AP and ML directions and higher Moderate frequency band in the AP direction in both groups; (5) greater path length linked with lower DET and higher CI in the AP direction only in the TC group. Conclusion: Long-term Tai Chi practice improved sensory reweighting (more reliance on the proprioception system and less reliance on the vestibular system) and complexity of standing balance control in older adults. In addition, greater sway velocity may be as an exploratory role in standing balance control of TC older adults, which correlated with greater complexity, but no such significant relationship in the control group. Therefore, the effects of Tai Chi practice on standing balance control in older adults may be attributed to the improvement of sensory reweighting and complexity rather than reduced sway velocity or amplitude.

3D polarization-interference holographic histology for wavelet-based differentiation of the polycrystalline component of biological tissues with different necrotic states. Forensic applications.

Significance: The interference-holographic method of phase scanning of fields of scattered laser radiation is proposed. The effectiveness of this method for the selection of variously dispersed components is demonstrated. This method made it possible to obtain polarization maps of biological tissues at a high level of depolarized background. The scale-selective analysis of such maps was used to determine necrotic changes in the optically anisotropic architectonics of biological tissues. Objective: Development and experimental approbation of layered phase polarimetry of repeatedly scattered fields in diffuse layers of biological tissues. Application of scale-selective processing of the found coordinate distributions of polarization states in various phase sections of object fields. Determination of criteria (markers) for histological differential diagnosis of the causes of necrotic changes in optical anisotropy of biological tissues. Approach: We used a synthesis of three instrumental and analytical methods. Polarization-interference registration of laser radiation scattered by a sample of biological tissue. Digital holographic reconstruction and layered phase scanning of distributions of complex amplitudes of the object field. Analytical determination of polarization maps of various phase cross-sections of repeatedly scattered radiation. Application of wavelet analysis of the distributions of polarization states in the phase plane of a single scattered component of an object field. Determination of criteria (markers) for differential diagnosis of necrotic changes in biological tissues with different morphological structure. Two cases are considered. The first case is the myocardium of those who died as a result of coronary heart disease and acute coronary insufficiency. The second case is lung tissue samples of deceased with bronchial asthma and fibrosis. Results: A method of polarization-interference mapping of diffuse object fields of biological tissues has been developed and experimentally implemented. With the help of digital holographic reconstruction of the distributions of complex amplitudes, polarization maps in various phase sections of a diffuse object field are found. The wavelet analysis of azimuth and ellipticity distributions of polarization in the phase plane of a single scattered component of laser radiation is used. Scenarios for changing the amplitude of the wavelet coefficients for different scales of the scanning salt-like MHAT function are determined. Statistical moments of the first to fourth orders are determined for the distributions of the amplitudes of the wavelet coefficients of the azimuth maps and the ellipticity of polarization. As a result, diagnostic markers of necrotic changes in the myocardium and lung tissue were determined. The statistical criteria found are the basis for determining the accuracy of their differential diagnosis of various necrotic states of biological tissues. Conclusions: Necrotic changes caused by "coronary artery disease-acute coronary insufficiency" and "asthma-pulmonary fibrosis" were demonstrated by the method of wavelet differentiation with polarization interference with excellent accuracy.

Periodic repolarization dynamics: Different methods for quantifying low-frequency oscillations of repolarization.

BACKGROUND: Periodic repolarization dynamics (PRD) is an electrocardiographic biomarker that quantifies low-frequency (LF) instabilities of repolarization. PRD is a strong predictor of mortality in patients with ischaemic and non-ischaemic cardiomyopathy. Until recently, two methods for calculating PRD have been proposed. The wavelet analysis has been widely tested and quantifies PRD in deg2 units by application of continuous wavelet transformation (PRDwavelet). The phase rectified signal averaging method (PRDPRSA) is an algebraic method, which quantifies PRD in deg. units. The correlation, as well as a conversion formula between the two methods remain unknown. METHOD: The first step for quantifying PRD is to calculate the beat-to-beat change in the direction of repolarization, called dT°. PRD is subsequently quantified by means of either wavelet or PRSA-analysis. We simulated 1.000.000 dT°-signals. For each simulated signal we calculated PRD using the wavelet and PRSA-method. We calculated the ratio between PRDwavelet and PRDPRSA for different values of dT° and RR-intervals and applied this ratio in a real-ECG validation cohort of 455 patients after myocardial infarction (MI). We finally calculated the correlation coefficient between real and calculated PRDwavelet. PRDwavelet was dichotomized at the established cut-off value of ≥5.75 deg2. RESULTS: The ratio between PRDwavelet and PRDPRSA increased with increasing heart-rate and mean dT°-values (p < 0.001 for both). The correlation coefficient between PRDwavelet and PRDPRSA in the validation cohort was 0.908 (95% CI 0.891-0.923), which significantly (p < 0.001) improved to 0.945 (95% CI 0.935-0.955) after applying the formula considering the ratio between PRDwavelet and PRDPRSA obtained from the simulation cohort. The calculated PRDwavelet correctly classified 98% of the patients as low-risk and 87% of the patients as high-risk and correctly identified 97% of high-risk patients, who died within the follow-up period. CONCLUSION: This is the first analytical investigation of the different methods used to calculate PRD using simulated and clinical data. In this article we propose a novel algorithm for converting PRDPRSA to the widely validated PRDwavelet, which could unify the calculation methods and cut-offs for PRD.

Improved assessment sensitivity of time-varying cavitation events based on wavelet analysis.

Ultrasonic cavitation, characterized by the oscillation or abrupt collapse of cavitation nuclei in response to ultrasound stimulation, plays a significant role in various applications within both industrial and biomedical sectors. In particular, inertial cavitation (IC) has garnered considerable attention due to the resulting mechanical, chemical, and thermal effects. Passive cavitation detection (PCD) has emerged as a valuable technique for monitoring this procedure. While the fast Fourier transform (FFT) is a widely used algorithm to analyze IC-induced broadband noise detected by PCD system, it may not adequately capture the time-varying instability of cavitation due to potential nuclei collapse during ultrasound irradiation. In contrast, the continuous wavelet transform offers a more flexible approach, enabling more sensitive analysis of signals with varying frequencies over time. In this study, nanodiamond (ND) and its derivative, nitro-doped nanodiamond (N-AND), known to possess cavitation potential from previous research, were chosen as the source of cavitation nuclei. The cavitation signals detected by PCD were subjected to both FFT and wavelet analyses, with their results comprehensively compared. This research showcased the feasibility of employing wavelet analysis for effective inertial cavitation evaluation. It provided the advantage of monitoring the temporal evolution of cavitation events in real-time, enhancing sensitivity to weak and unstable cavitation signals, especially those in higher order components (3rd and 4th order). Additionally, it yielded a higher level of precision in determining IC thresholds and doses. Furthermore, the inclusion of time information through wavelet analysis offered insights into the limitations of low-cycle ultrasound in inducing IC. This study introduces a novel perspective for more sensitive and precise cavitation assessment, leveraging time and frequency data from wavelet analysis, and holds promise for effective utilization of cavitation effects while minimizing losses and damages resulting from unintended cavitation events.

Muscle fatigue tracking during dynamic elbow flexion-extension movements with a varying hand load.

Muscle fatigue monitoring, an important element in a fatigue risk management process, can help optimize work intensity and reduce risks for musculoskeletal injuries. An experiment was conducted to determine whether myoelectric manifestations of muscle fatigue can reflect the pace of fatigue development associated with varying load intensity. Twenty male participants performed elbow flexion-extension movements with alternating hand loads (2 kg vs. 1 kg) for 16 min. The pace of fatigue in the biceps brachii in response to load variation was quantified by electromyographic (EMG) fatigue measures collected during the dynamic elbow flexion-extension movements and periodic submaximal isometric elbow flexion trials. The isometric and dynamic EMG measures, except for the amplitude of dynamic EMG, indicated fatigue development during the 2-kg isotonic movements and partial recovery with the 1 kg load. Study results suggest the potential of EMG measures for fatigue monitoring during dynamic work tasks with varying load intensity.

Evaluating transient phenomena by wavelet analysis: early recovery to exercise.

Wavelet analysis (WA) provides superior time-frequency decomposition of complex signals than conventional spectral analysis tools. To illustrate its usefulness in assessing transient phenomena, we applied a custom-developed WA algorithm to laser-Doppler (LD) signals of the cutaneous microcirculation measured at glabrous (finger pulp) and nonglabrous (forearm) sites during early recovery after dynamic exercise. This phase, importantly contributing to the establishment of thermal homeostasis after exercise cessation, has not been adequately explored because of its complex, transient form. Using WA, we decomposed the LD signals measured during the baseline and early recovery into power spectra of characteristic frequency intervals corresponding to endothelial nitric oxide (NO)-dependent, neurogenic, myogenic, respiratory, and cardiac physiological influence. Assessment of relative power (RP), defined as the ratio between the median power in the frequency interval and the median power of the total spectrum, revealed that endothelial NO-dependent (5.87 early recovery; 1.53 baseline; P = 0.005; Wilcoxon signed-rank test) and respiratory (0.71 early recovery; 0.40 baseline; P = 0.001) components were significantly increased, and myogenic component (1.35 early recovery; 1.83 baseline; P = 0.02) significantly decreased during early recovery in the finger pulp. In the forearm, only the RP of the endothelial NO-dependent (1.90 early recovery; 0.94 baseline; P = 0.009) component was significantly increased. WA presents an irreplaceable tool for the assessment of transient phenomena. The relative contribution of the physiological mechanisms controlling the microcirculatory response in the early recovery phase appears to differ in glabrous and nonglabrous skin when compared with baseline; moreover, the endothelial NO-dependent influence seems to play an important role.NEW & NOTEWORTHY We address the applicability of wavelet analysis (WA) in evaluating transient phenomena on a model of early recovery to exercise, which is the only exercise-associated phase characterized by a distinct transient shape and as such cannot be assessed using conventional tools. Our WA-based algorithm provided a reliable spectral decomposition of laser-Doppler (LD) signals in early recovery, enabling us to speculate roughly on the mechanisms involved in the regulation of skin microcirculation in this phase.

Comparing the Effects of Static Stretching Alone and in Combination with Post-Activation Performance Enhancement on Squat Jump Performance at Different Knee Starting Angles.

We aimed to investigate the impact of isolated static stretching (4 sets of 30 seconds) and its combined form with 10 repetitive drop jumps on lower limb performance during squat jumps at different knee joint starting angles (60°, 90°, and 120°). Thirteen participants completed three randomly ordered experimental visits, each including a standardized warm-up and squat jumps at three angles, apart from the intervention or control. Information was gathered through a three-dimensional movement tracking system, electromyography system, and force platform. The electromyography data underwent wavelet analysis to compute the energy values across the four wavelet frequency bands. The average power (Pavg), peak power (Ppeak), peak ground reaction force (GRFpeak), peak center of mass velocity (Vpeak), and force-velocity relationship at peak power (SFv) were extracted from the force and velocity-time data. The results revealed no significant influence of isolated static stretching, or its combined form with drop jumps, on the energy values across the frequency bands of the gastrocnemius, biceps femoris and rectus femoris, or the Pavg or Ppeak (P > 0.05). However, at 120°, static stretching reduced the GRFpeak (P = 0.001, d = 0.86) and SFv (P < 0.001, d = 1.12), and increased the Vpeak (P = 0.001, d = 0.5). The GRFpeak, Pavg, Ppeak, and SFv increased with an increase in the joint angle (P < 0.05), whereas the Vpeak decreased (P < 0.05). These findings suggest that static stretching does not diminish power output during squat jumps at the three angles; however, it alters GRFpeak, Vpeak, and the relative contributions of force and velocity to peak power at 120°, which can be eliminated by post-activation performance enhancement. Moreover, compared to 60° and 90°, 120° was more favorable for power and peak force output.

Studying Erythromelalgia Using Doppler Flowmetry Perfusion Signals and Wavelet Analysis-An Exploratory Study.

Erythromelalgia (EM) is a rare disease, which is still poorly characterized. In the present paper, we compared the hand perfusion of one female EM patient, under challenges, with a healthy control group. Using a laser Doppler flowmeter (LDF) with an integrated thermal probe, measurements were taken in both hands at rest (Phase I) and after two separate challenges-post-occlusive hyperemia (PORH) in one arm (A) and reduction of skin temperature (cooling) with ice in one hand (B) (Phase II). The final measurement periods corresponded to recovery (Phases III and IV). The control group involved ten healthy women (27.3 ± 7.9 years old). A second set of measurements was taken in the EM patient one month after beginning a new therapeutic approach with beta-blockers (6.25 mg carvedilol twice daily). Z-scores of the patient's LDF and temperature fluctuations compared to the control group were assessed using the Wavelet transform (WT) analysis. Here, fluctuations with |Z| > 1.96 were considered significantly different from healthy values, whereas positive or negative Z values indicated higher or lower deviations from the control mean values. Cooling elicited more measurable changes in LDF and temperature fluctuations, especially in higher frequency components (cardiac, respiratory, and myogenic), whereas PORH notably evoked changes in lower frequency components (myogenic, autonomic, and endothelial). No significant Z-score deviations were observed in the second measurement, which might signify a stabilization of the patient's distal perfusion following the new therapeutic approach. This analysis involving one EM patient, while clearly exploratory, has shown significant deviations in WT-derived physiological components' values in comparison with the healthy group, confirming the interest in using cold temperature as a challenger. The apparent agreement achieved with the clinical evaluation opens the possibility of expanding this approach to other patients and pathologies in vascular medicine.

[Scale Dependence Between PM2.5 and Meteorological Factors and Its Influencing Factors in "2+26" Cities].

Based on the PM2.5 concentration and meteorological data of "2+26" cities, the variations in PM2.5 time series were analyzed by the continuous wavelet transform(CWT) and discrete wavelet transform(DWT). Wavelet coherence(WTC) and multiple wavelet coherence(MWC) were used to quantify the response relationship between PM2.5 and single/multiple meteorological factors in the time-frequency domain. Partial wavelet coherence(PWC) was used to quantitatively evaluate the influence of atmospheric teleconnection factors on the response relationship. The results showed that:① the concentration of PM2.5 in the "2+26" cities had the spatial distribution characteristics of high in the middle area and low in the peripheral area. The PM2.5 mutation events were mainly concentrated before 2018 and mostly occurred in winter when the meteorological conditions were stable. The annual scale period of 256-512 d was relatively stable, and it was also the dominant period of the PM2.5 time series. ② The coherences between PM2.5 and meteorological factors depended on the time-frequency scale and variable combination. At all time-frequency scales, PM2.5 had strong coherences with relative humidity and temperature. At small and medium time-frequency scales, PM2.5 had strong coherences with wind speed. At large scales, PM2.5 had strong coherences with temperature. The combination of precipitation, temperature, and relative humidity could explain the variation in PM2.5 at all time-frequency scales. ③ At different time-frequency scales, the enhancement/weakening effects of atmospheric teleconnection factors on the response relationship were not the same. At all time-frequency scales, the El Niño-Southern Oscillation(ENSO) had a greater impact on the response relationship between PM2.5 and precipitation/temperature, and the Pacific decadal oscillation(PDO) had a greater impact on the response relationship between PM2.5 and relative humidity/wind speed. These results provide reference for regional air pollution control.

3D digital polarization-holographic wavelet histology in determining the duration of mechanical damage to the myocardium.

We aimed developing and experimentally validating methods for 3D scale-selective polarimetry of multiply scattered fields in diffuse myocardium layers for mechanical myocardial injury prescription histological differential diagnostics. We used the synthesis of diffuse object field polarization-interference registration and polarization-inhomogeneous field digital holographic reconstruction and layer-by-layer complex amplitudes distributions The method for selection single and diffuse object field multiply scattered components polarization maps is proposed. The conditions for eliminating the distorting influence of a depolarized background high level are found. On the basis of еру object field single scattered component polarization maps a large-scale selective wavelet analysis the criteria (markers) for mechanical myocardial injury different prescription diagnosis was determinate. Excellent accuracy mechanical injury myocardium necrotic changes with different duration using polarization-interference wavelet differentiation were achieved.

Enhancing Electroretinogram Classification with Multi-Wavelet Analysis and Visual Transformer.

The electroretinogram (ERG) is a clinical test that records the retina's electrical response to light. Analysis of the ERG signal offers a promising way to study different retinal diseases and disorders. Machine learning-based methods are expected to play a pivotal role in achieving the goals of retinal diagnostics and treatment control. This study aims to improve the classification accuracy of the previous work using the combination of three optimal mother wavelet functions. We apply Continuous Wavelet Transform (CWT) on a dataset of mixed pediatric and adult ERG signals and show the possibility of simultaneous analysis of the signals. The modern Visual Transformer-based architectures are tested on a time-frequency representation of the signals. The method provides 88% classification accuracy for Maximum 2.0 ERG, 85% for Scotopic 2.0, and 91% for Photopic 2.0 protocols, which on average improves the result by 7.6% compared to previous work.

Navigating the interconnected risks in currency valuation: unveiling the role of climate policy uncertainty.

Given the significance of fostering sustainable climate conditions for long-term economic stability and financial resilience, this study probes the connection between climate-related policy ambiguity and its implications for currency valuation. In doing so, the current study investigates the interconnected effects of climate policy on economic policy uncertainty and geopolitical risk with the currency valuation in ASEAN countries. Employing wavelet coherence analysis and partial wavelet coherence analysis, the paper highlights the complex relationships among these factors and their implications for exchange rate fluctuations. Using data from 2000 to 2022, the findings reveal that climate policy uncertainty is an important driver of exchange rate movements, amplifying the impact of economic policy uncertainty and geopolitical risk. Furthermore, the study identifies a vicious cycle between climate policy uncertainty and exchange rates, potentially impacting the region's macroeconomic stability and long-term economic growth. The study presents several policy recommendations to address economic and climate policy uncertainties comprehensively based on the findings. These recommendations include establishing national frameworks for climate risk management, enhancing policy credibility and macroeconomic stability, and promoting regional integration to mitigate the influence of geopolitical risk on exchange rates.

Definition of thermal indicators for the study of thermoregulation alterations in the foot of people living within diabetic peripheral neuropathy: A proof of concept.

AIMS: This study investigates how diabetic peripheral neuropathy is linked to impairment of thermoregulatory mechanisms using a thermal camera, spectral thermal analysis and a physical test. METHODS: The plantar skin temperature of all participants was measured using a thermal camera following a 6-min walking exercise. The data were subjected to frequency decomposition, resulting in two frequency ranges corresponding to endothelial and neurogenic mechanisms. Then, 40 thermal indicators were evaluated for each participant. ROC curve and statistical tests allowed to identify indicators able to detect the presence or absence of diabetic peripheral neuropathy. RESULTS: The study included 33 participants living with diabetes. The results revealed that a 6-min walk exercise increased plantar foot temperature and highlighted a significant difference between people living with diabetes with and without peripheral neuropathy (p < 0.01). The results also revealed the advantages of using thermal images rather than single point measurements. CONCLUSIONS: Diabetic peripheral neuropathy is linked to impairment of thermoregulatory mechanisms. This link can be highlighted after a dedicated 6-min walk exercise, enabling to activate these mechanisms, and measuring with a thermal camera the temporal plantar skin temperature. Assessment of this link gave best results by filtering the thermal signal in the neurogenic range.

Understanding Collective Human Behavior in Social Media Networks Via the Dynamical Hypothesis: Applications to Radicalization and Conspiratorial Beliefs.

The dynamical hypothesis has served to explore the ways in which cognitive agents can be understood dynamically and considered dynamical systems. Originally used to explain simple physical systems as a metaphor for cognition (i.e., the Watt governor) and eventually more complex animal systems (e.g., bird flocks), we argue that the dynamical hypothesis is among the most viable approaches to understanding pressing modern-day issues that arise from collective human behavior in online social networks. First, we discuss how the dynamical hypothesis is positioned to describe, predict, and explain the time-evolving nature of complex systems. Next, we adopt an interdisciplinary perspective to describe how online social networks are appropriately understood as dynamical systems. We introduce a dynamical modeling approach to reveal information about emergent properties in social media, where radicalized conspiratorial beliefs arise via coordination between user-level and community-level comments. Lastly, we contrast how the dynamical hypothesis differs from alternatives in explaining collective human behavior in social networks.

Time-series modelling of harmful cyanobacteria blooms by convolutional neural networks and wavelet generated time-frequency images of environmental driving variables.

Early warning systems for harmful cyanobacterial blooms (HCBs) that enable precautional control measures within water bodies and in water works are largely based on inferential time-series modelling. Among deep learning techniques, convolutional neural networks (CNNs) are widely applied for recognition of pictorial, acoustic and thermal images. Time-frequency images of environmental drivers generated by wavelets may provide crucial signals for modelling of HCBs to be recognized by CNNs. This study applies CNNs for time-series modelling of HCBs of Microcystis sp. in four South Korean rivers between 2016 and 2022 by means of time-frequency images of environmental drivers within the lead time of HCBs. After estimating the cardinal dates of beginning, peak, and ending of HCBs, wavelet analysis identified key drivers by phase analysis and generated time-frequency images of the drivers within the cardinal dates for 3, 4 and 5 years. Performances of CNNs were compared in terms of four determinants of input images: methods of estimating critical timings, the number of segments, time-series continuity, and image size. The resulting CNNs predicted high or low intensities of HCBs with a mean accuracy of 97.79 ± 0.06% and F1-score 97.49 ± 0.06% for training dataset, and a mean accuracy of 95.01 ± 0.06% and F1-score 93.30 ± 0.07% for testing dataset. Predictions of Microcystis abundances by CNNs achieved a mean MSE of 2.58 ± 2.46 and a mean R2 of 0.78 ± 0.20 for training, and a mean MSE of 2.76 ± 2.42 and a mean R2 of 0.55 ± 0.20 for testing dataset. Precipitation and discharge appeared to be the best performing drivers for qualitative and quantitative predictions of HCBs pointing at the nonstationary nature of river habitats. This study highlights the opportunities of time-series modelling by CNNs driven by wavelet generated time-frequency images of key environmental variables for forecasting of HCBs.

Spectral Analysis of Heart Rate Variability Based on the Hilbert-Huang Method.

Analysis of heart rate variability (HRV) is widely used for noninvasive assessment of the state of the regulation systems of the heart. The aim of this study was to evaluate the capabilities of the Hilbert-Huang method for calculating spectral parameters of HRV in comparison with the commonly used Fourier analysis. Fourier analysis allows estimation of averaged spectral amplitudes and power of HRV oscillations in fixed frequency intervals, which are associated with the activity of sympathetic, parasympathetic, and humoral regulation systems. Using the Hilbert-Huang method, we revealed four spectral components, described by Gaussian functions, in which HRV oscillations are concentrated, and showed the absence of fixed boundaries between them. The obtained energy quantitative characteristics of the spectral components of heart rhythm oscillations can serve as the basis for diagnostic methods of heart rhythm regulation, supplementing the commonly used ones.

BaroWavelet: An R-based tool for dynamic baroreflex evaluation through wavelet analysis techniques.

BACKGROUND AND OBJECTIVE: Baroreflex sensitivity constitutes an indicator of the function of the baroreceptor control mechanism of blood pressure levels. It can be computed after estimating heart rate and blood pressure variability. We propose a novel tool for the evaluation of baroreflex sensitivity using wavelet analysis methods. This tool, known as BaroWavelet, incorporates an algorithm proposal based on the analysis methodology of the RHRV software package, as well as other conventional techniques. Our objectives are to develop and evaluate the tool, by testing its ability to detect changes in baroreflex sensitivity in humans. METHODS: The code for this tool was designed in the R programming environment and was organized into two analysis routines and a graphical interface. Simulated recordings of blood pressure and inter-beat intervals were employed for an initial evaluation of the tool in a controlled environment. Finally, similar recordings obtained during supine and orthostatic postural evaluations, from patients that belonged to the open-access EUROBAVAR data set, were analyzed. RESULTS: BaroWavelet identified the scripted changes of the baroreflex sensitivity in the simulated data. The algorithm proposal was also able to better retain additional information regarding the dynamics of the baroreflex. In the EUROBAVAR subjects, baroreflex sensitivity components were significantly smaller during orthostatism when compared with the supine position. CONCLUSIONS: BaroWavelet managed to characterize baroreflex dynamics from the recordings, which were consistent with the findings reported in the literature. This demonstrates its effectiveness to perform these analyses. We suggest that this tool may be of use in research and for the evaluation of baroreflex sensitivity with clinical and therapeutic purposes. The new tool is available at the official GitHub repository of the Autonomic Nervous System Unit of the University of Málaga (https://github.com/CIMES-USNA-UMA/BaroWavelet).

The changing relationship between Cholera and interannual climate variables in Kolkata over the past century.

BACKGROUND: In the Bengal Delta, research has shown that climate and cholera are linked. One demonstration of this is the relationship between interannual ocean-atmospheric oscillations such as the El Niño Southern Oscillation (ENSO) and the Indian Ocean Dipole (IOD). What remains unclear in the present literature is the nature of this relationship in the specific context of Kolkata, and how this relationship may have changed over time. RESULTS: In this study, we analyse the changing relationship between ENSO and IOD with cholera in Kolkata over recent (1999-2019) and historical (1897-1941) time intervals. Wavelet coherence analysis revealed significant non-stationary association at 2-4 year and 4-8 year periods between cholera and both interannual timeseries during both time intervals. However, coherence was notably weakened in the recent interval, particularly with regards to ENSO, a result supported by a complementary SARIMA analysis. Similar coherence patterns with temperature indicate it could be an important mediating factor in the relationship between cholera and oscillating climate phenomena in Kolkata. CONCLUSIONS: This study reveals a shifting relationship between cholera and climate variables (ENSO and IOD) in Kolkata, suggesting a decoupling between environmental influences and cholera transmission in recent years. Our results therefore do not suggest that an intensification of ENSO is likely to significantly influence cholera in the region. We also find that the relationship between cholera and interannual climate variables is distinct to Kolkata, highlighting the spatial heterogeneity of the climate-cholera relationship even within the Bengal Delta.

Dataset of quality assurance measurements of rhythmic movements.

A low-cost quantitative structured office measurement of movements in the extremities of people with Parkinson's disease [1,2] was performed on participants with Parkinson's disease and multiple system atrophy as well as age- and sex-matched healthy participants with typical development. Participants underwent twelve videotaped procedures rated by a trained examiner while connected to four accelerometers [1,2] generating a trace of the three location dimensions expressed as spreadsheets [3,4]. The signals of the five repetitive motion items (3.4 Finger tapping, 3.5 Hand movements, 3.6 Pronation-supination movements of hands, 3.7 Toe tapping, and 3.8 Leg agility) [1] underwent processing to fast Fourier [5] and amor and bump continuous wavelet transforms [6], [7], [8], [9], [10], [11], [12], [13]. Images of the signals and their transforms [4], [5], [6] of the five repetitive tasks of each participant were randomly expressed as panels on an electronic framework for rating by 35 trained examiners who did not know the source of the original output [14]. The team of international raters completed ratings of the signals and their transforms independently using criteria like the scoring systems for live assessments of movements in human participants [1,2]. The raters scored signals and transforms for deficits in the sustained performance of rhythmic movements (interruptions, slowing, and amplitude decrements) often observed in people with Parkinson's disease [15], [16], [17], [18], [19], [20]. Raters were first presented the images of the signals and transforms of a man with multiple system atrophy as a test and a retest in a different random order. After the raters completed the assessments of the man with multiple system atrophy, they were presented random test and retest panels of the images of signals and transforms of ten participants with Parkinson's disease who completed a single rating session. After the raters completed the assessments of the participants with Parkinson's disease who completed one set of ratings, they were presented random test and retest panels of the images of signals and transforms of (A) ten participants with Parkinson's disease and (B) eight age- and sex-match healthy participants with typical development who completed two rating session separated by a month or more [15], [16], [17], [18], [19], [20]. The data provide a framework for further analysis of the acquired information. Additionally, the data provide a template for the construction of electronic frameworks for the remote analysis by trained raters of signals and transforms of rhythmic processes to verify that the systems are operating smoothly without interruptions or changes in frequency and amplitude. Thus, the data provide the foundations to construct electronic frameworks for the virtual quality assurance of a vast spectrum of rhythmic processes. The dataset is a suitable template for solving unsupervised and supervised machine learning algorithms. Readers may utilize this procedure to assure the quality of rhythmic processes by confirming the absence of deviations in rate and rhythm. Thus, this procedure provides the means to confirm the quality of the vast spectrum of rhythmic processes.

Predicting balance impairments in older adults: a wavelet-based center of pressure classification approach.

BACKGROUND: Aging is associated with a decline in postural control and an increased risk of falls. The Center of Pressure (CoP) trajectory analysis is a commonly used method to assess balance. In this study, we proposed a new method to identify balance impairments in older adults by analyzing their CoP trajectory frequency components, sensory inputs, reaction time, motor functions, and Fall-related Concerns (FrC). METHODS: The study includes 45 older adults aged [Formula: see text] years who were assessed for sensory and motor functions. FrC and postural control in a quiet stance with open and closed eyes on stable and unstable surfaces. A Discrete Wavelet Transform (DWT) was used to detect features in frequency scales, followed by the K-means algorithm to detect different clusters. The multinomial logistic model was used to identify and predict the association of each group with the sensorimotor tests and FrC. RESULTS: The study results showed that by DWT, three distinct groups of subjects could be revealed. Group 2 exhibited the broadest use of frequency scales, less decline in sensorimotor functions, and lowest FrC. The study also found that a decline in sensorimotor functions and fall-related concern may cause individuals to rely on either very low-frequency scales (group 1) or higher-frequency scales (group 3) and that those who use lower-frequency scales (group 1) can manage their balance more successfully than group 3. CONCLUSIONS: Our study provides a new, cost-effective method for detecting balance impairments in older adults. This method can be used to identify people at risk and develop interventions and rehabilitation strategies to prevent falls in this population.