Efficacy of Integrated Yoga and Naturopathy With Physiotherapy or Acupuncture for Low Back Pain: A Parallel, Two-Arm, Randomized Trial.

INTRODUCTION: Low back pain (LBP) is a musculoskeletal condition that affects many individuals. While physiotherapy and acupuncture are frequently used treatments, determining their specific contributions when used alongside integrated yoga and naturopathy (IYN) therapy for relieving chronic LBP symptoms and enhancing quality of life is important. METHODOLOGY: In the present prospective randomized comparative trial, a total of 60 participants were divided into the following two groups: physiotherapy with IYN and acupuncture with IYN. The treatment duration was 10 days, and assessments were conducted both before (baseline) and after the intervention. Various assessment tools, such as the visual analog scale (VAS), Oswestry Disability Index (ODI), fingertip-to-floor test (FTF), State-Trait Anxiety Inventory (STAI), and Short Form 36 (SF-36), were utilized. The statistical analysis was performed using SPSS version 21.0 (Armonk, NY: IBM Corp). RESULTS: The results revealed significant differences in VAS score, ODI, FTF score, STAI score, and SF-36 score between the physiotherapy and acupuncture groups (p<0.001). A t-test for the equality of means and a Mann-Whitney U test were used to compare the two groups; these tests revealed a significant difference in disability levels, range of motion, and state of anxiety. The acupuncture group showed a significant difference in the ODI (15.9 {12.6, 19.3}; p <0.001) and state anxiety (23.0 {20.0, 26.0}; p<0.001) compared to the physiotherapy ODI (22.4 {18.5, 26.4}) and state anxiety (27.5 {25.0, 30.0}). The physiotherapy group showed a significant difference in range of motion (7.7 {5.7, 9.7}; p<0.001) compared to the acupuncture group (11.6 {9.8, 13.5}). CONCLUSION: The present study findings revealed that both physiotherapy and acupuncture, as interventions along with integrated yoga and naturopathy may be considered an effective treatment strategy for chronic LBP.

Effect of various different pretreatment methods on infrared combined hot air impingement drying behavior and physicochemical properties of strawberry slices.

In current work, the effect of freezing (F), ultrasound (U), and freeze- ultrasound (FU) pretreatment on infrared combined with hot air impingement drying kinetics, cell ultrastructure, enzyme activity, and physicochemical properties of strawberry slices were explored. Results showed that FU pretreatment enhanced cell membrane permeability via forming micropores, altered water status by transforming bound water into free water and thus promoted moisture diffusivity and decreased drying time by 50% compared to the control group. FU pretreatment also extensively decreased pectin methylesterase enzyme activity and maintained quality. The contents of total phenols, anthocyanins, vitamin C, antioxidant activity, and a* value of dried strawberries pretreated by FU were extensively increased compared to the control group. U and FU pretreatments were beneficial for retaining aromatic components and organic sulfides according to e-nose analyses. The findings indicate that FU is a promising pretreatment technique as it enhances drying process and quality of strawberry slices.

State-owned capital and quality of green innovation: Evidence from Chinese listed private firms.

Green innovation is pivotal for global sustainability, with state-owned capital playing a significant role, especially in the Chinese corporate landscape. This study, spanning 2008 to 2020 and leveraging a comprehensive dataset of listed companies, explores the intricate relationship between state-owned capital and the quality of green innovation in Chinese private enterprises. Motivated by the imperative to address crucial issues in green innovation quality in China, this research utilizes empirical data to uncover the mechanisms through which state-owned capital fosters green innovation. The study reveals how state-owned capital optimizes internal governance structures and reinforces environmental consciousness within private firms. Findings underscore the crucial role of state-owned capital in enhancing the quality of green innovation in private enterprises, operating through two primary mechanisms. Firstly, state-owned capital cultivates a heightened inclination towards green innovation within these firms. Secondly, it facilitates the adoption of enhanced internal governance practices, catalyzing the development of high-quality green innovation projects. A battery of mechanism tests provides robust evidence that state-owned capital enhances environmental awareness, restrains self-serving behaviors among major shareholders, mitigates financing constraints, and amplifies the motivation and capability of private enterprises for green innovation. This multifaceted approach ultimately fosters high-quality green innovation within companies. The study reveals the subtle interplay between state capital and private sector green innovation, highlighting its relevance to policymaking and practical considerations. It provides valuable insights into the ongoing pursuit of sustainability and the integration of green practices into the corporate world.

Spectral dynamic causal modeling: A didactic introduction and its relationship with functional connectivity.

We present a didactic introduction to spectral dynamic causal modeling (DCM), a Bayesian state-space modeling approach used to infer effective connectivity from noninvasive neuroimaging data. Spectral DCM is currently the most widely applied DCM variant for resting-state functional MRI analysis. Our aim is to explain its technical foundations to an audience with limited expertise in state-space modeling and spectral data analysis. Particular attention will be paid to cross-spectral density, which is the most distinctive feature of spectral DCM and is closely related to functional connectivity, as measured by (zero-lag) Pearson correlations. In fact, the model parameters estimated by spectral DCM are those that best reproduce the cross-correlations between all measurements-at all time lags-including the zero-lag correlations that are usually interpreted as functional connectivity. We derive the functional connectivity matrix from the model equations and show how changing a single effective connectivity parameter can affect all pairwise correlations. To complicate matters, the pairs of brain regions showing the largest changes in functional connectivity do not necessarily coincide with those presenting the largest changes in effective connectivity. We discuss the implications and conclude with a comprehensive summary of the assumptions and limitations of spectral DCM.

The evolution of whole-brain turbulent dynamics during recovery from traumatic brain injury.

It has been previously shown that traumatic brain injury (TBI) is associated with reductions in metastability in large-scale networks in resting-state fMRI (rsfMRI). However, little is known about how TBI affects the local level of synchronization and how this evolves during the recovery trajectory. Here, we applied a novel turbulent dynamics framework to investigate whole-brain dynamics using an rsfMRI dataset from a cohort of moderate to severe TBI patients and healthy controls (HCs). We first examined how several measures related to turbulent dynamics differ between HCs and TBI patients at 3, 6, and 12 months post-injury. We found a significant reduction in these empirical measures after TBI, with the largest change at 6 months post-injury. Next, we built a Hopf whole-brain model with coupled oscillators and conducted in silico perturbations to investigate the mechanistic principles underlying the reduced turbulent dynamics found in the empirical data. A simulated attack was used to account for the effect of focal lesions. This revealed a shift to lower coupling parameters in the TBI dataset and, critically, decreased susceptibility and information-encoding capability. These findings confirm the potential of the turbulent framework to characterize longitudinal changes in whole-brain dynamics and in the reactivity to external perturbations after TBI.

Resting network architecture of theta oscillations reflects hyper-learning of sensorimotor information in Gilles de la Tourette syndrome.

Gilles de la Tourette syndrome is a neurodevelopmental disorder characterized by motor and vocal tics. It is associated with enhanced processing of stimulus-response associations, including a higher propensity to learn probabilistic stimulus-response contingencies (i.e. statistical learning), the nature of which is still elusive. In this study, we investigated the hypothesis that resting-state theta network organization is a key for the understanding of superior statistical learning in these patients. We investigated the graph-theoretical network architecture of theta oscillations in adult patients with Gilles de la Tourette syndrome and healthy controls during a statistical learning task and in resting states both before and after learning. We found that patients with Gilles de la Tourette syndrome showed a higher statistical learning score than healthy controls, as well as a more optimal (small-world-like) theta network before the task. Thus, patients with Gilles de la Tourette syndrome had a superior facility to integrate and evaluate novel information as a trait-like characteristic. Additionally, the theta network architecture in Gilles de la Tourette syndrome adapted more to the statistical information during the task than in HC. We suggest that hyper-learning in patients with Gilles de la Tourette syndrome is likely a consequence of increased sensitivity to perceive and integrate sensorimotor information leveraged through theta oscillation-based resting-state dynamics. The study delineates the neural basis of a higher propensity in patients with Gilles de la Tourette syndrome to pick up statistical contingencies in their environment. Moreover, the study emphasizes pathophysiologically endowed abilities in patients with Gilles de la Tourette syndrome, which are often not taken into account in the perception of this common disorder but could play an important role in destigmatization.

Optimizing echo state networks for continuous gesture recognition in mobile devices: A comparative study.

Continuous gesture recognition can be used to enhance human-computer interaction. This can be accomplished by capturing human movement with the use of the Inertial Measurement Units in smartphones and using machine learning algorithms to predict the intended gestures. Echo State Networks (ESNs) consist of a fixed internal reservoir that is able to generate rich and diverse nonlinear dynamics in response to input signals that capture temporal dependencies within the signal. This makes ESNs well-suited for time series prediction tasks, such as continuous gesture recognition. However, their application has not been rigorously explored, with regard to gesture recognition. In this study, we sought to enhance the efficacy of ESN models in continuous gesture recognition by exploring diverse model structures, fine-tuning hyperparameters, and experimenting with various training approaches. We used three different training schemes that used the Leave-one-out Cross-validation (LOOCV) protocol to investigate the performance in real-world scenarios with different levels of data availability: Leaving out data from one user to use for testing (F1-score: 0.89), leaving out a fraction of data from all users to use in testing (F1-score: 0.96), and training and testing using LOOCV on a single user (F1-score: 0.99). The obtained results outperformed the Long Short-Term Memory (LSTM) performance from past research (F1-score: 0.87) while maintaining a low training time of approximately 13 seconds compared to 63 seconds for the LSTM model. Additionally, we further explored the performance of the ESN models through behaviour space analysis using memory capacity, Kernel Rank, and Generalization Rank. Our results demonstrate that ESNs can be optimized to achieve high performance on gesture recognition in mobile devices on multiple levels of data availability. These findings highlight the practical ability of ESNs to enhance human-computer interaction.

Altered resting-state functional connectivity and dynamic network properties in cognitive impairment: an independent component and dominant-coactivation pattern analyses study.

Introduction: Cognitive impairment (CI) due to Alzheimer's disease (AD) encompasses a decline in cognitive abilities and can significantly impact an individual's quality of life. Early detection and intervention are crucial in managing CI, both in the preclinical and prodromal stages of AD prior to dementia. Methods: In this preliminary study, we investigated differences in resting-state functional connectivity and dynamic network properties between 23 individual with CI due to AD based on clinical assessment and 15 healthy controls (HC) using Independent Component Analysis (ICA) and Dominant-Coactivation Pattern (d-CAP) analysis. The cognitive status of the two groups was also compared, and correlations between cognitive scores and d-CAP switching probability were examined. Results: Results showed comparable numbers of d-CAPs in the Default Mode Network (DMN), Executive Control Network (ECN), and Frontoparietal Network (FPN) between HC and CI groups. However, the Visual Network (VN) exhibited fewer d-CAPs in the CI group, suggesting altered dynamic properties of this network for the CI group. Additionally, ICA revealed significant connectivity differences for all networks. Spatial maps and effect size analyses indicated increased coactivation and more synchronized activity within the DMN in HC compared to CI. Furthermore, reduced switching probabilities were observed for the CI group in DMN, VN, and FPN networks, indicating less dynamic and flexible functional interactions. Discussion: The findings highlight altered connectivity patterns within the DMN, VN, ECN, and FPN, suggesting the involvement of multiple functional networks in CI. Understanding these brain processes may contribute to developing targeted diagnostic and therapeutic strategies for CI due to AD.

Investigating the Role of Anger and Cognitive Malfunction in Mental Health: A Cross-Sectional Exploration Paving the Way for a Subsequent Experiment.

The functional outcomes associated with subjective well-being (SWB) and the detrimental aspects of psychological distress (PD) make it essential to explore contributing factors. The present study investigated a model about the existing gap in the determining role of trait anger (TA), state anger (SA), cognitive reappraisal (CR), rumination and cognitive failure (CF) as predictors of SWB and PD. The study contributes by exploring the interaction of dispositional, situation factors and emotional regulation strategies in shaping SWB and PD in the Indian Sample. A cross-sectional survey design was employed wherein 600 young adults aged 18-40 (Mage = 22.13, SDage = 4.06) were recruited from Uttar Pradesh, India using a multi-level cluster sampling method. The data were collected using questionnaires in the field setting during August-December 2021. Regression and path analysis revealed that the proposed predictors explained significant variance in SWB and PD, i.e., R2 = 0.24, F (5, 594) = 38.03, p < 0.01, Cohen f2 =0.31 and R2 = 0.35, F (5, 594) = 66.40, p < 0.01, Cohen f2 = 0.53, respectively. The models also fit well with the statistical indices. Except CR, all predictors emerged as significant risk factors. The findings suggest that the interventions to reduce PD and enhance SWB may consider inculcating CR and reducing higher levels of TA, SA, rumination and CF to enhance an individual's adaptive functioning. The findings pave the way for conducting a pre-planned experiment to study the outcomes of various levels of TA in regulating incidental anger (SA) employing CR and rumination.

A chronometric study of the posterior cerebellum's function in emotional processing.

The posterior cerebellum is a recently discovered hub of the affective and social brain, with different subsectors contributing to different social functions. However, very little is known about when the posterior cerebellum plays a critical role in social processing. Due to its location and anatomy, it has been difficult to use traditional approaches to directly study the chronometry of the cerebellum. To address this gap in cerebellar knowledge, here we investigated the causal contribution of the posterior cerebellum to social processing using a chronometric transcranial magnetic stimulation (TMS) approach. We show that the posterior cerebellum is recruited at an early stage of emotional processing (starting from 100 ms after stimulus onset), simultaneously with the posterior superior temporal sulcus (pSTS), a key node of the social brain. Moreover, using a condition-and-perturb TMS approach, we found that the recruitment of the pSTS in emotional processing is dependent on cerebellar activation. Our results are the first to shed light on chronometric aspects of cerebellar function and its causal functional connectivity with other nodes of the social brain.

Metal Valence State Modulation Strategy to Design Core@shell Hollow Carbon Microspheres@MoSe2/MoOx Multicomponent Composites for Anti-Corrosion and Microwave Absorption.

The exploitation of multicomponent composites (MCCs) has become the main pathway for obtaining advanced microwave absorption materials (MAMs). Herein, a metal valence state modulation strategy is proposed to tune the electromagnetic (EM) parameters and improve microwave absorption performances. Core@shell hollow carbon microspheres@MoSe2 and hollow carbon microspheres@MoSe2/MoOx MCCs with various mixed-valence states content are well-designed and produced by a simple hydrothermal reaction or/and heat treatment process. The results reveal that the thermal treatment of hollow carbon microspheres@MoSe2 in Ar and Ar/H2 leads to the in situ formation of MoOx and multivalence state, respectively, and the enhanced content of Mo4+ in the designed MCCs greatly boosts their impedance matching characteristics, polarization, and conduction loss capacities, which lead to their evidently improved EM wave absorption properties. Amongst, the as-prepared hollow carbon microspheres@MoSe2/MoOx MCCs achieve an effective absorption bandwidth of 5.80 GHz under a matching thickness of 1.97 mm and minimum reflection loss of -21.49 dB. Therefore, this work offers a simple and universal method to fabricate core@shell hollow carbon microspheres@MoSe2/MoOx MCCs, and a novel and feasible metal valence state modulation strategy is proposed to develop high-efficiency MAMs.

Determination of ileal endogenous nitrogen losses and true ileal nitrogen digestibility during non-steady-state conditions of the 15N-isotope dilution technique.

The aim was to determine ileal endogenous nitrogen losses (ENL) and true ileal N-digestibility (TD-N) under non-steady-state conditions of the 15N-isotope dilution technique (15N-IDT), using diets generating low and high ENL and compare results to those obtained under steady-state conditions. Twelve growing pigs (mean LW 22.4 kg) fitted with a post-valve T-caecum cannula were fed an enzyme-hydrolysed casein (EHC)-based diet or an EHC diet + 4% quebracho tannins (QT) and were labelled via continuous 15N-leucine i.v. infusion or twice daily oral 15N-leucine administration. Digesta were collected daily over three consecutive hours with blood plasma sampled on the four consecutive days after cessation of 15N-labelling. There was a significant effect of sampling day on the dilution factor. Endogenous N losses were significantly lower for the EHC than the EHC+QT diet (2.41 vs. 8.69 g/kg DMI), while no significant effect of sampling day was observed. The TD-N of the EHC+QT diet did not differ from the TD-N of the EHC diet (95.1 vs. 92.0%). A significant effect of sampling day was observed for TD-N with day 1 and 2, being higher than day 4. Non-steady-state conditions overestimated ENL by 25-28% as compared to 3 h collections in steady-state conditions, but the relative overestimation was similar for the EHC diet as for the EHC+QT diet. TD-N did not differ significantly compared to 12 h steady-state measurements, but comparison to 3 h steady-state measurements showed that non-steady-state conditions overestimated TD-N for the EHC+QT diet by 9%. However, on day 4 this overestimation disappeared. Using the 15N-IDT during non-steady-state conditions can provide valuable additional data on endogenous N losses and TD-N.

Solidarity as a Political Determinant of Health: Insights from EU Competition Policy.

CONTEXT: The connection between law and political determinants of health is not well-understood, but nevertheless it is suggested that the two are inseparable, and that this represents an "upstream" level with scope for influencing other determinants of health (particularly social). Solidarity underpins European healthcare systems and given its clear link with redistribution, can be seen as a means to address health inequities. As such, solidarity may be seen as a political determinant of health in the specific context of EU competition policy. METHODS: A range of EU case law, Treaty provisions, and Commission publications relating to EU competition policy are analysed. FINDINGS: Solidarity is typically juxtaposed as antithetical to competition, thus underpins exceptions to the applicability of the prohibitions on anticompetitive agreements, abuse of dominance, and state aid. Case law indicates a further dynamic between definitions of solidarity at EU and national levels. CONCLUSIONS: This analysis leads to two groups of considerations when framing solidarity as a political determinant of health in the EU competition policy context: firstly, the predominance of solidarity suggests it may shape competition reforms, and secondly the EU-Member State dynamic which indicates less EU-level reach into national competition reforms in healthcare than may be expected.

Is Redistribution Good for Our Health? Examining the Macro-Correlation between Welfare Generosity and Health across EU Nations over the Last 40 Years.

CONTEXT: Social determinants of health are finally getting much needed policy attention. Yet, their political origins remain underexplored. In this paper, we advance a theory of political determinants as accruing along three pathways of welfare state effects (redistribution, poverty reduction and status preservation) and test these assumptions by examining impacts of policy generosity on life expectancy (LE) over the last 40 years. METHODS: We merge new and existing welfare policy generosity data from the Comparative Welfare Entitlement Project (CWEP) with data on LE spanning 1980-2018 across 21 OECD countries. We then examine relationships between five welfare policy generosity measures and LE using cross-sectional differencing and auto-regressive lag models. FINDINGS: We find consistent and positive effects for total generosity (an existing measure of social insurance generosity) on LE at birth across different model specifications in the magnitude of a 0.10-0.15-year increase in LE at birth (p < 0.05) and a measure of status preservation (0.11, p < 0.05). We find less consistent support for our redistribution and poverty reduction measures. CONCLUSIONS: We conclude that in addition to generalized effects of policy generosity on health, status-preserving social insurance may be an important, and relatively overlooked, mechanism in increasing life expectancies over time in advanced democracies.

Tunable Narrow-Band Cyan-Emission of Eu2+-doped Nitridomagnesophosphates Ba3-xSrx[Mg2P10N20]:Eu2+ (x = 0-3).

Tetrahedron-based nitrides offer a wide range of properties and applications. Highly condensed nitridophosphates are examples of nitrides exhibiting fascinating luminescence properties when doped with Eu2+, making them appealing for industrial applications. We present the first nitridomagnesophosphate solid solution series Ba3-xSrx[Mg2P10N20]:Eu2+ (x = 0-3), synthesized by a high-pressure high-temperature approach using the multianvil technique (3 GPa, 1400 °C). Starting from the binary nitrides P3N5 and Mg3N2 and the respective alkaline earth azides, we incorporate Mg into the P/N framework to increase the degree of condensation κ to 0.6, the highest observed value for alkaline earth nitridophosphates. The crystal structure was elucidated by single-crystal X-ray diffraction, powder X-ray diffraction, energy-dispersive X-ray spectroscopy (EDX), and solid-state NMR. DFT calculations were performed on the title compounds and other related highly condensed nitridophosphates to investigate the influence of Mg in the P/N network. Eu2+-doped samples of the solid solution series show a tunable narrow-band emission from cyan to green (492-515 nm), which is attributed to the preferred doping of a single crystallographic site. Experimental confirmation of this assumption was provided by overdoping experiments and STEM-HAADF studies on the series as well on the stoichiometric compound Ba2Eu[Mg2P10N20] with additional atomic resolution energy-dispersive X-ray spectroscopy (EDX) mapping.

Continuity with caveats in anesthesia: state and response entropy of the EEG.

The growing use of neuromonitoring in general anesthesia provides detailed insights into the effects of anesthetics on the brain. Our study focuses on the processed EEG indices State Entropy (SE), Response Entropy (RE), and Burst Suppression Ratio (BSR) of the GE EntropyTM Module, which serve as surrogate measures for estimating the level of anesthesia. While retrospectively analyzing SE and RE index values from patient records, we encountered a technical anomaly with a conspicuous distribution of index values. In this single-center, retrospective study, we analyzed processed intraoperative electroencephalographic (EEG) data from 15,608 patients who underwent general anesthesia. We employed various data visualization techniques, including histograms and heat maps, and fitted custom non-Gaussian curves. Individual patients' anesthetic periods were evaluated in detail. To compare distributions, we utilized the Kolmogorov-Smirnov test and Kullback-Leibler divergence. The analysis also included the influence of the BSR on the distribution of SE and RE values. We identified distinct pillar indices for both SE and RE, i.e., index values with a higher probability of occurrence than others. These pillar index values were not age-dependent and followed a non-equidistant distribution pattern. This phenomenon occurs independently of the BSR distribution. SE and RE index values do not adhere to a continuous distribution, instead displaying prominent pillar indices with a consistent pattern of occurrence across all age groups. The specific features of the underlying algorithm responsible for this pattern remain elusive.

Valorization of sugarcane bagasse for high-yield production of laccase through Aspergillus terreus for effective azo dye decolourization.

Synthetic dyes such as azo dyes are significant pollutants in the wastewater released from various textile industries. The low biodegradability and production from synthetic sources with high shelf life make azo dyes a challenging material for degradation. This study used chemically mutated Aspergillus terrus in the laccase production under solid-state fermentation using sugarcane bagasse. Initially, the wild-type strain produced a laccase activity of 4.12 U/mL. Later, the alkaline pretreatment of sugarcane bagasse showed a significant increase in laccase activity by 38.9%. Further, random mutagenesis treatment with 100 mM EMS generated a hyper laccase-producing strain with a 2.3-fold increment in laccase activity compared to the wild-type strain. The enzyme displayed optimal activity at pH 6.5 and 35 °C. The metal ions such as Fe3+ (29.4 U/mL), Fe2+ (20.8 U/mL) and Cu2+ (18.05 U/mL) showed positive effects on laccase activity. The crude laccase was used to bioremediate Congo red, a prominent azo dye used in textile and pharmaceutical industries. The preliminary studies with a crude enzyme displayed 68.86% dye decolourization after 24 h of incubation. Additionally, with Taguchi orthogonal array optimization experiments, the maximal dye decolorization of 78.24% was achieved by maintaining crude enzyme concentration (20 U), dye concentration (25 mg/L) and pH 4.5.

Effect of Regional Brain Activity Following Repeat Transcranial Magnetic Stimulation in SCA3: A Secondary Analysis of a Randomized Clinical Trial.

Repetitive transcranial magnetic stimulation (rTMS), a noninvasive neuroregulatory technique used to treat neurodegenerative diseases, holds promise for spinocerebellar ataxia type 3 (SCA3) treatment, although its efficacy and mechanisms remain unclear. This study aims to observe the short-term impact of cerebellar rTMS on motor function in SCA3 patients and utilize resting-state functional magnetic resonance imaging (RS-fMRI) to assess potential therapeutic mechanisms. Twenty-two SCA3 patients were randomly assigned to receive actual rTMS (AC group, n = 11, three men and eight women; age 32-55 years) or sham rTMS (SH group, n = 11, three men and eight women; age 26-58 years). Both groups underwent cerebellar rTMS or sham rTMS daily for 15 days. The primary outcome measured was the ICARS scores and parameters for regional brain activity. Compared to baseline, ICARS scores decreased more significantly in the AC group than in the SH group after the 15-day intervention. Imaging indicators revealed increased Amplitude of Low Frequency Fluctuation (ALFF) values in the posterior cerebellar lobe and cerebellar tonsil following AC stimulation. This study suggests that rTMS enhances motor functions in SCA3 patients by modulating the excitability of specific brain regions and associated pathways, reinforcing the potential clinical utility of rTMS in SCA3 treatment. The Chinese Clinical Trial Registry identifier is ChiCTR1800020133.

A Data-Driven Approach to Estimate Human Center of Mass State During Perturbed Locomotion Using Simulated Wearable Sensors.

Center of mass (COM) state, specifically in a local reference frame (i.e., relative to center of pressure), is an important variable for controlling and quantifying bipedal locomotion. However, this metric is not easily attainable in real time during human locomotion experiments. This information could be valuable when controlling wearable robotic exoskeletons, specifically for stability augmentation where knowledge of COM state could enable step placement planners similar to bipedal robots. Here, we explored the ability of simulated wearable sensor-driven models to rapidly estimate COM state during steady state and perturbed walking, spanning delayed estimates (i.e., estimating past state) to anticipated estimates (i.e., estimating future state). We used various simulated inertial measurement unit (IMU) sensor configurations typically found on lower limb exoskeletons and a temporal convolutional network (TCN) model throughout this analysis. We found comparable COM estimation capabilities across hip, knee, and ankle exoskeleton sensor configurations, where device type did not significantly influence error. We also found that anticipating COM state during perturbations induced a significant increase in error proportional to anticipation time. Delaying COM state estimates significantly increased accuracy for velocity estimates but not position estimates. All tested conditions resulted in models with R2 > 0.85, with a majority resulting in R2 > 0.95, emphasizing the viability of this approach. Broadly, this preliminary work using simulated IMUs supports the efficacy of wearable sensor-driven deep learning approaches to provide real-time COM state estimates for lower limb exoskeleton control or other wearable sensor-based applications, such as mobile data collection or use in real-time biofeedback.

Optimizing glutamine concentration enhances ex vivo expansion of natural killer cells through improved redox status.

Amino acids are vital components of the serum-free medium that influence the expansion and function of NK cells. This study aimed to clarify the relationship between amino acid metabolism and expansion and cytotoxicity of NK cells. Based on analyzing the mino acid metabolism of NK-92 cells and Design of Experiments (DOE), we optimized the combinations and concentrations of amino acids in NK-92 cells culture medium. The results demonstrated that NK-92 cells showed a pronounced demand for glutamine, serine, leucine, and arginine, in which glutamine played a central role. Significantly, at a glutamine concentration of 13 mM, NK-92 cells expansion reached 161.9 folds, which was significantly higher than 55.5 folds at 2.5 mM. Additionally, under higher glutamine concentrations, NK-92 cells expressed elevated levels of cytotoxic molecules, the level of cytotoxic molecules expressed by NK-92 cells was increased and the cytotoxic rate was 68.42%, significantly higher than that of 58.08% under low concentration. In view of the close relationship between glutamine metabolism and intracellular redox state, we investigated the redox status within the cells. This study demonstrated that intracellular ROS levels in higher glutamine concentrations were significantly lower than those under lower concentration cultures with decreased intracellular GSH/GSSG ratio, NADPH/NADP+ ratio, and apoptosis rate. These findings indicate that NK-92 cells exhibit improved redox status when cultured at higher glutamine concentrations. Overall, our research provides valuable insights into the development of serum-free culture medium for ex vivo expansion of NK-92 cells.