Strobe training as a visual training method that improves performance in climbing.

Introduction: Strobe training is a form of visual training where the athlete has to practice during intermittently dark conditions. Strobe training improves visual, perceptual, and cognitive skills, which will enhance athletic performance. Strobe training can influence multiple training components in climbing: psychological, tactical, physical, and technical training. Materials and methods: The study was conducted on 17 elite climbers from Romania (10 male and 7 female), representing the entire National Youth Climbing Team. The research group was divided into a control group (n = 8) and an experimental group (n = 9). The used instruments were the Cognitrom battery (for cognitive skills, such as spatial skills and reactivity), the Witty SEM system (for motor-cognitive skills, such as cognitive agility, visual processing speed, and visual memory), and the International Rock Climbing Research Association (IRCRA) performance-related test battery for climbers (climbing-specific motor skills). The experimental group had 20 strobe training sessions, which took place during one calendar year, as an additional session to their climbing schedule done with their principal trainer. The strobe session was once a week, depending on the periodization of the macrocycle (preparatory, competitional, and transition periods). The control group and the experimental group had similar climbing training sessions during the 1-year macrocycle in terms of intensity and volume of their training. Results: Strobe training improved on-sight performance (d = 0.38) and red-point performance (d = 0.36). Strobe training improved the majority of cognitive skills [all spatial skills (d = 1.27 for mental image transformation; d = 1.14 for spatial orientation; d = 1.59 for image generation) and simple reaction time (d = 0.99)]. Strobe training improved all motor-cognitive skills (d = 0.16 for visual memory; d = 1.96 for visual memory errors; d = 1.39 for visual processing speed; d = 1.94 for visual processing errors; d = 1.30 for cognitive agility). Strobe training improved many climbing-specific parameters (flexibility and upper body strength) (d = 0.44 and d = 0.47 for flexibility parameters; d = 0.50 to 0.73 for upper body strength parameters). Discussion: Strobe training is an effective training method for enhancing performance that should be used on more experienced climbers. It acts more on spatial skills, rather than on reactivity skills, developing the visual-motor coordination system. Strobe training has greater effects on climbers aged below 16 years, as youth athletes rely more on visual input compared to adults. The improvement in climbing-specific variables was due to the additional climbing session done weekly. Strobe training acts more on the cognitive component of training than on the motor component of training in climbing.

Dynamical Reweighting for Biased Rare Event Simulations.

Dynamical reweighting techniques aim to recover the correct molecular dynamics from a simulation at a modified potential energy surface. They are important for unbiasing enhanced sampling simulations of molecular rare events. Here, we review the theoretical frameworks of dynamical reweighting for modified potentials. Based on an overview of kinetic models with increasing level of detail, we discuss techniques to reweight two-state dynamics, multistate dynamics, and path integrals. We explore the natural link to transition path sampling and how the effect of nonequilibrium forces can be reweighted. We end by providing an outlook on how dynamical reweighting integrates with techniques for optimizing collective variables and with modern potential energy surfaces.

Inequality of opportunity in the double burden of malnutrition in Mexico.

This paper proposes a pseudo-birth-cohort approach to deal with a lack of longitudinal data to measure health inequities over time. Using Roemer's framework for inequality of opportunity, this study measures ex-ante and ex-post inequalities in malnutrition, a concept that spans both sides of the nutrition continuum. The total contribution of observed circumstances and the direct contribution of observed efforts to the variation of malnutrition are disentangled for people born between 1983 and 1988 in Mexico. Results indicate that inequality of opportunity has been persistent across this 30-year lifespan for that cohort. Some evidence suggests that a lack of opportunities has been transmitted from parents to children and that people's circumstances account for most of the explained variation in the double burden of malnutrition. However, stratifying the analysis by sex shows that efforts account for more of the explained variation of inequality of opportunity for women in their middle adulthood than for men in most of the outcomes analyzed.

Robust scalar-on-function partial quantile regression.

Compared with the conditional mean regression-based scalar-on-function regression model, the scalar-on-function quantile regression is robust to outliers in the response variable. However, it is susceptible to outliers in the functional predictor (called leverage points). This is because the influence function of the regression quantiles is bounded in the response variable but unbounded in the predictor space. The leverage points may alter the eigenstructure of the predictor matrix, leading to poor estimation and prediction results. This study proposes a robust procedure to estimate the model parameters in the scalar-on-function quantile regression method and produce reliable predictions in the presence of both outliers and leverage points. The proposed method is based on a functional partial quantile regression procedure. We propose a weighted partial quantile covariance to obtain functional partial quantile components of the scalar-on-function quantile regression model. After the decomposition, the model parameters are estimated via a weighted loss function, where the robustness is obtained by iteratively reweighting the partial quantile components. The estimation and prediction performance of the proposed method is evaluated by a series of Monte-Carlo experiments and an empirical data example. The results are compared favorably with several existing methods. The method is implemented in an R package robfpqr.

Repeated exposure to virtual reality decreases reliance on visual inputs for balance control in healthy adults.

Postural control may encounter acute challenges when individuals are immersed in a virtual reality (VR) environment, making VR a potential pertinent tool for enhancing balance capacity. Nonetheless, the effects of repeated exposure to VR on balance control remain to be fully elucidated. Fifty-five healthy participants stood upright for six bouts of 90 s each in an immersive virtual reality (VR) environment using a head-mounted display (repeated VR exposure). During these bouts, participants experienced simulated forward and backward displacements. Before and after the repeated VR exposure, the center of pressure mean velocity (VELCOP) was measured in response to simulated forward and backward displacement in VR, as well as during quiet upright standing with eyes open (EO) and closed (EC) in the real environment. The results revealed a significant decrease in VELCOP for forward and backward simulated displacements in both antero-posterior and medio-lateral directions (p < 0.01) after compared to before repeated VR exposure. Furthermore, VELCOP significantly decreased when participants stood upright in EC (-5%; p = 0.004), but not EO (+3%; p > 0.05) in the real environment after repeated VR exposure. The Romberg ratio (EC/EO) was reduced in both antero-posterior and medio-lateral directions (p < 0.05) after VR exposure. This study indicates that repeated exposure to VR induces changes in balance control in both virtual and real environments. These changes may be attributed, in part, to a reduction in the weighting of visual inputs in the multisensory integration process occurring during upright standing. Accordingly, these findings highlight VR as a potentially effective tool for balance rehabilitation. SIGNIFICANCE STATEMENT: This study indicates that repeated exposure to VR induces changes in balance control in both virtual and real environments that can rely, in part, on a reduction in the weighting of visual inputs in the multisensory integration process occurring during upright standing.

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.

The Effect of Stroboscopic Vision Training on Blind-folded Straight-line Walking.

Stroboscopic vision training has shown to improve visual-motor control and dynamic visual acuity in sport performance; however, no studies have considered using this training to enhance kinesthetic awareness during walking, applicable to high fall-risk populations. Purpose: The purpose of this study was to assess the effect of stroboscopic vision training on blind-folded straight-line walking. Methods: Thirty-seven college-aged healthy participants (age: 20.14 ± 1.23 years; females: N = 32, males: N = 5) completed this study. In this pre-posttest quasi-experimental investigation, participants with no epileptic or balance disorder history completed a four-week progressive stroboscopic vision training protocol. To assess sensorimotor feedback participants were instructed to walk a 27.5 m straight line while blindfolded. PRE and POST blind-folded straight line walk tests were completed and deviations from endpoint were measured. A paired-samples t-test was used to analyze the calculated deviation angles. Results: Significant difference was noted from PRE (14.48 ± 5.95) to POST (11.60 ± 6.78) deviation angles (t(36) = 2.71, p = 0.01). Conclusions: This is the first study to examine the effects of stroboscopic training on a vision restricted walking task, which demands feedback re-weighting. These findings may be valuable for clinical settings or performance where reliance on non-visual systems may be beneficial.

Theoretical investigation on the solid-liquid phase transition of gallium through free energy analysis.

CONTEXT: Gallium, renowned for its notably low melting point and unique property of becoming liquid at room temperature, is a valuable constituent in phase change materials. In this study, we investigate the solid-liquid phase transition of gallium using the modified embedded atom method (MEAM) potential. It addresses the technique to compute the free energy difference between the solid and liquid without using a reference state. We examine various thermodynamic and dynamic properties, including density, specific heat capacity, diffusivity, and radial distribution functions. We compute the coexistence temperature of the solid-liquid phase transitions of gallium from free energy analysis. This information is crucial for understanding the behavior of the material under different pressure conditions and can be valuable for various applications, such as materials processing and high-pressure studies. The analysis, findings, and insights of the present work will be of great significance to the broad scientific and engineering communities in the field of phase transformation of materials. METHODS: A series of molecular dynamics(MD) simulations were conducted using the LAMMPS software packages. The gallium atoms are modeled using the modified embedded atom method (MEAM) potential. To accurately predict the solid-liquid phase transitions of gallium, we calculated free energy by employing the "constrained λ integration" method, coupled with multiple histogram reweighting (MHR). The solid-liquid coexistence line is determined through the Gibbs-Duhem integration technique.

Characterization of Posttranslationally Modified PHF-1 Tau Peptides Using Gaussian Accelerated Molecular Dynamics Simulation.

The microtubule-associated protein, Tau, is an intrinsically disordered protein that plays a crucial role in neurodegenerative diseases like Alzheimer's disease. The posttranslational modifications across the Tau protein domains are involved in regulating Tau protein's function and disease onset. Of the various posttranslational modifications at Ser, Thr, and Tyr sites, O-GlcNAcylation and phosphorylation are the most critical ones, playing a vital role in Tau aggregation and tauopathies. To understand the function, it is essential to characterize the structural changes associated with Tau modification. Previous experimental studies have focused on high-resolution nuclear magnetic resonance techniques to structurally characterize the effect of phosphorylation, O-GlcNAcylation, and combination of both PTMs on Tau conformation in small peptides centered on the PHF-1 epitope from amino acid 392 to 411. The structural characterization using atomistic molecular dynamics simulation of such disordered peptides requires long simulation time, proper sampling method, and utilization of appropriate force fields for accurate determination of conformational ensembles, resembling the experimental data. This chapter details the protocol for the structural characterization of modified Tau peptides using the CHARMM36m force field and enhanced sampling methods like Gaussian accelerated molecular dynamics (GaMD) simulation. We have focused on a detailed explanation of the GaMD method and analyses of molecular dynamics trajectories to explain the relationship between two modifications, phospho- and glyco-, at C-terminus of Tau protein and its stable conformation over the longer simulation timeframes. The analyses involve energetics reweighting, clustering of simulation trajectories, and characterization of secondary structure using circular dichroism data from the simulation. The reader can utilize this protocol to investigate the structures of complex proteins, especially the disordered ones.

Chronic back pain prevalence at small area level in England - the design and validation of a 2-stage static spatial microsimulation model.

Spatially disaggregated estimates provide valuable insights into the nature of a disease. They highlight inequalities, aid public health planning and identify avenues for further research. Spatial microsimulation is advantageous in that it can be used to create large microdata sets with intact microlevel relationships between variables, which allows analysis of relationships between variables locally. This methodological paper outlines the design and validation of a 2-stage static spatial microsimulation model for chronic back pain prevalence across England, suitable for policy modelling. Data used was obtained from the Health Survey for England and the 2011 Census. Microsimulation was performed using SimObesity, a previously validated static deterministic program, and the synthetic chronic back pain microdataset was internally validated. The paper also highlights modelling considerations for researchers embarking on similar work, as well as future directions for research in this area of microsimulation.

Sensory conflicts through short, discrete visual input manipulations: Identification of balance responses to varied input characteristics.

Human balance control relies on various sensory modalities, and conflict of sensory input may result in postural instability. Virtual reality (VR) technology allows to train balance under conflicting sensory information by decoupling visual from somatosensory and vestibular systems, creating additional demands on sensory reweighting for balance control. However, there is no metric for the design of visual input manipulations that can induce persistent sensory conflicts to perturb balance. This limits the possibilities to generate sustained sensory reweighting processes and design well-defined training approaches. This study aimed to investigate the effects that different onset characteristics, amplitudes and velocities of visual input manipulations may have on balance control and their ability to create persistent balance responses. Twenty-four young adults were recruited for the study. The VR was provided using a state-of-the-art head-mounted display and balance was challenged in two experiments by rotations of the visual scene in the frontal plane with scaled constellations of trajectories, amplitudes and velocities. Mean center of pressure speed was recorded and revealed to be greater when the visual input manipulation had an abrupt onset compared to a smooth onset. Furthermore, the balance response was greatest and most persistent when stimulus velocity was low and stimulus amplitude was large. These findings show clear dissociation in the state of the postural system for abrupt and smooth visual manipulation onsets with no indication of short-term adaption to abrupt manipulations with slow stimulus velocity. This augments our understanding of how conflicting visual information affect balance responses and could help to optimize the conceptualization of training and rehabilitation interventions.

Multisensory and biomechanical influences on postural control in children.

Children's ability to maintain balance requires effective integration of multisensory and biomechanical information. The current project examined the interaction between such sensory inputs, manipulating visual input (presence vs. absence), haptic (somatosensory) input (presence vs. absence of contact with a stable or unstable finger support surface), and biomechanical (sensorimotor) input (varying stance widths). Analyses of mean velocity of the center of pressure and the percentage stability gain highlighted the role of varying multisensory inputs in postural control. Developmentally, older children (6-11 years) showed a multisensory integration advantage compared with their younger counterparts (3-5.9 years), with the impact of varying sensory inputs more closely akin to that seen in adults. Subsequent analyses of the impact of anthropometric individual difference parameters (e.g., height, leg length, weight, areas of base of support) revealed a shifting pattern across development. For younger children, these parameters were positively related to postural stability across experimental conditions (i.e., increasing body size was related to increasing postural control). This pattern transitioned for older children, who showed a nonsignificant relation between body size and balance. Interestingly, because adults show a negative relation between anthropometric factors and stability (i.e., increasing body size is related to decreasing postural control), this shift for the older children can be seen as a developmental transition from child-like to adult-like balance control.

HybridGCN for protein solubility prediction with adaptive weighting of multiple features.

The solubility of proteins stands as a pivotal factor in the realm of pharmaceutical research and production. Addressing the imperative to enhance production efficiency and curtail experimental costs, the demand arises for computational models adept at accurately predicting solubility based on provided datasets. Prior investigations have leveraged deep learning models and feature engineering techniques to distill features from raw protein sequences for solubility prediction. However, these methodologies have not thoroughly delved into the interdependencies among features or their respective magnitudes of significance. This study introduces HybridGCN, a pioneering Hybrid Graph Convolutional Network that elevates solubility prediction accuracy through the combination of diverse features, encompassing sophisticated deep-learning features and classical biophysical features. An exploration into the intricate interplay between deep-learning features and biophysical features revealed that specific biophysical attributes, notably evolutionary features, complement features extracted by advanced deep-learning models. Augmenting the model's capability for feature representation, we employed ESM, a substantial protein language model, to derive a zero-shot learning feature capturing comprehensive and pertinent information concerning protein functions and structures. Furthermore, we proposed a novel feature fusion module termed Adaptive Feature Re-weighting (AFR) to integrate multiple features, thereby enabling the fine-tuning of feature importance. Ablation experiments and comparative analyses attest to the efficacy of the HybridGCN approach, culminating in state-of-the-art performances on the public eSOL and S. cerevisiae datasets.

Sensory Reweighting for Postural Control in Older Adults with Age-Related Hearing Loss.

There is growing evidence linking hearing impairments and the deterioration of postural stability in older adults. To our knowledge, however, no study to date has investigated the effect of age-related hearing loss on the sensory reweighting process during postural control. In the absence of data, much is unknown about the possible mechanisms, both deleterious and compensatory, that could underly the deterioration of postural control following hearing loss in the elderly. The aim of this study was to empirically examine sensory reweighting for postural control in older adults with age-related hearing loss as compared to older adults with normal hearing. The center of pressure of all participants was recorded using a force platform and the modified clinical test of sensory interaction and balance protocol. The results suggest that individuals with age-related hearing loss displayed increased somatosensory reliance relative to normal hearing younger adults. This increased reliance on somatosensory input does not appear to be effective in mitigating the loss of postural control, probably due to the concomitant deterioration of tactile and proprioceptive sensitivity and acuity associated with aging. Beyond helping to further define the role of auditory perception in postural control, these results further the understanding of sensory-related mechanisms associated with postural instability in older adults.

Effects of postural-control training with different sensory reweightings in a patient with body lateropulsion: a single-subject design study.

INTRODUCTION: Body lateropulsion (BL) is an active lateral tilt of the body during standing or walking that is thought to be affected by a lesion of the vestibulospinal tract (VST) and the subjective visual vertical (SVV) tilt. Interventions for BL have not been established. OBJECTIVE: We examined the effects of postural-control training with different sensory reweighting on standing postural control in a patient with BL. METHODS: The patient had BL to the left when standing or walking due to a left-side medullary and cerebellar infarct. This study was a single-subject A-B design with follow-up: Phase A was postural-control training with visual feedback; phase B provided reweighting plantar somatosensory information. Postural control, VST excitability, and SVV were measured. RESULTS: At baseline and phase A, the patient could not stand with eyes-closed on a rubber mat, but became able to stand in phase B. The mediolateral center of pressure (COP) position did not change significantly, but the COP velocity decreased significantly during phase B and the follow-up on the firm surface. VST excitability was lower on the BL versus the non-BL side, and the SVV deviated to the right throughout the study. CONCLUSION: Postural-control training with reweighting somatosensory information might improve postural control in a patient with BL.

Insufficiencies in sensory systems reweighting is associated with walking impairment severity in chronic stroke: an observational cohort study.

Background: Walking and balance impairment are common sequelae of stroke and significantly impact functional independence, morbidity, and mortality. Adequate postural stability is needed for walking, which requires sufficient integration of sensory information between the visual, somatosensory, and vestibular centers. "Sensory reweighting" describes the normal physiologic response needed to maintain postural stability in the absence of sufficient visual or somatosensory information and is believed to play a critical role in preserving postural stability after stroke. However, the extent to which sensory reweighting successfully maintains postural stability in the chronic stages of stroke and its potential impact on walking function remains understudied. Methods: In this cross-sectional study, fifty-eight community-dwelling ambulatory chronic stroke survivors underwent baseline postural stability testing during quiet stance using the modified Clinical test of Sensory Interaction in Balance (mCTSIB) and assessment of spatiotemporal gait parameters. Results: Seventy-six percent (45/58) of participants showed sufficient sensory reweighting with visual and somatosensory deprivation for maintaining postural stability, albeit with greater postural sway velocity indices than normative data. In contrast, survivors with insufficient reweighting demonstrated markedly slower overground walking speeds, greater spatiotemporal asymmetry, and limited acceleration potential. Conclusion: Adequate sensory system reweighting is essential for chronic stroke survivors' postural stability and walking independence. Greater emphasis should be placed on rehabilitation strategies incorporating multisensory system integration testing and strengthening as part of walking rehabilitation protocols. Given its potential impact on outcomes, walking rehabilitation trials may benefit from incorporating formal postural stability testing in design and group stratification.

LncRNA-protein interaction prediction with reweighted feature selection.

LncRNA-protein interactions are ubiquitous in organisms and play a crucial role in a variety of biological processes and complex diseases. Many computational methods have been reported for lncRNA-protein interaction prediction. However, the experimental techniques to detect lncRNA-protein interactions are laborious and time-consuming. Therefore, to address this challenge, this paper proposes a reweighting boosting feature selection (RBFS) method model to select key features. Specially, a reweighted apporach can adjust the contribution of each observational samples to learning model fitting; let higher weights are given more influence samples than those with lower weights. Feature selection with boosting can efficiently rank to iterate over important features to obtain the optimal feature subset. Besides, in the experiments, the RBFS method is applied to the prediction of lncRNA-protein interactions. The experimental results demonstrate that our method achieves higher accuracy and less redundancy with fewer features.

Visual and vestibular integration in Parkinson's disease while walking.

Postural control requires effective sensory integration. People with Parkinson's disease (PD) are reported to have impaired visual and vestibular perception. While self-motion perception is a key aspect of locomotion, visual-vestibular integration has not been directly characterized in people with PD during gait. We compared the ability of people with PD and healthy older adults (OA) to integrate multi-sensory information during straight-line walking in response to visual and vestibular perturbations, using continuous translations of the visual surround and galvanic vestibular stimulation within a virtual reality environment. We measured their endpoint deviations from midline and changes in gait parameters. We found that people with PD deviated more than OA when walking in a dark environment but did not show differences in deviations when walking in a virtual room with visual information. With visual and vestibular perturbations, people with PD did not differ from OA in endpoint deviations nor variabilities. However, people with PD did not adopt a more cautious gait when GVS was applied in a virtual room, unlike OA. Overall, we showed that people with mild PD did not perform worse than OA but did show differences in gait patterns, suggesting that visual-vestibular integration is relatively preserved during gait in PD.

Stable clinical risk prediction against distribution shift in electronic health records.

The availability of large-scale electronic health record datasets has led to the development of artificial intelligence (AI) methods for clinical risk prediction that help improve patient care. However, existing studies have shown that AI models suffer from severe performance decay after several years of deployment, which might be caused by various temporal dataset shifts. When the shift occurs, we have access to large-scale pre-shift data and small-scale post-shift data that are not enough to train new models in the post-shift environment. In this study, we propose a new method to address the issue. We reweight patients from the pre-shift environment to mitigate the distribution shift between pre- and post-shift environments. Moreover, we adopt a Kullback-Leibler divergence loss to force the models to learn similar patient representations in pre- and post-shift environments. Our experimental results show that our model efficiently mitigates temporal shifts, improving prediction performance.