Modulation Of Balance Research Articles

Omni-Domain Feature Extraction Method for Gait Recognition

As a biological feature with strong spatio-temporal correlation, the current difficulty of gait recognition lies in the interference of covariates (viewpoint, clothing, etc.) in feature extraction. In order to weaken the influence of extrinsic variable changes, we propose an interval frame sampling method to capture more information about joint dynamic changes, and an Omni-Domain Feature Extraction Network. The Omni-Domain Feature Extraction Network consists of three main modules: (1) Temporal-Sensitive Feature Extractor: injects key gait temporal information into shallow spatial features to improve spatio-temporal correlation. (2) Dynamic Motion Capture: extracts temporal features of different motion and assign weights adaptively. (3) Omni-Domain Feature Balance Module: balances fine-grained spatio-temporal features, highlight decisive spatio-temporal features. Extensive experiments were conducted on two commonly used public gait datasets, showing that our method has good performance and generalization ability. In CASIA-B, we achieved an average rank-1 accuracy of 94.2% under three walking conditions. In OU-MVLP, we achieved a rank-1 accuracy of 90.5%.

CO2 and non-CO2 balanced Environmental Scores Module for flight performance evaluation and optimisation

The SESAR2020 exploratory research (ER4) programme CREATE (Grant 890898) developed a climate and weather aware Concept of Operations (ConOps) which encompasses a multi-aircraft 4D trajectory optimisation framework, which utilises a CO2 and non-CO2 balanced Environmental Scores Module (ESM) for the en-route flight phase. The ESM provides a computational method to evaluate the “greenness” of aircraft trajectories. Some components related to the internal ESM scoring are based on expert judgement, which is in line with the technology readiness level (TRL) 1 of the solution. Fast-time simulations were performed to demonstrate the proof-of-concept of the ESM in a multi-aircraft tactical optimisation scenario in the North-Atlantic region. The results show that, because of the simplicity of the metric, the ESM could be well used for trajectory optimisation and tactical replanning, and most likely as well as flight and ATC sector environmental performance evaluations.

The Contribution of PGPR in Salt Stress Tolerance in Crops: Unravelling the Molecular Mechanisms of Cross-Talk between Plant and Bacteria.

Soil salinity is a major abiotic stress in global agricultural productivity with an estimated 50% of arable land predicted to become salinized by 2050. Since most domesticated crops are glycophytes, they cannot be cultivated on salt soils. The use of beneficial microorganisms inhabiting the rhizosphere (PGPR) is a promising tool to alleviate salt stress in various crops and represents a strategy to increase agricultural productivity in salt soils. Increasing evidence underlines that PGPR affect plant physiological, biochemical, and molecular responses to salt stress. The mechanisms behind these phenomena include osmotic adjustment, modulation of the plant antioxidant system, ion homeostasis, modulation of the phytohormonal balance, increase in nutrient uptake, and the formation of biofilms. This review focuses on the recent literature regarding the molecular mechanisms that PGPR use to improve plant growth under salinity. In addition, very recent -OMICs approaches were reported, dissecting the role of PGPR in modulating plant genomes and epigenomes, opening up the possibility of combining the high genetic variations of plants with the action of PGPR for the selection of useful plant traits to cope with salt stress conditions.

Investigating the modulatory effects of Moringa oleifera on the gut microbiota of chicken model through metagenomic approach.

This study aimed to assess the effects of supplementing chicken feed with Moringa oleifera leaf powder, a phytobiotic, on the gastrointestinal microbiota. The objective was to examine the microbial changes induced by the supplementation. A total of 40, one-day-old chickens were fed their basal diet for 42 days and then divided into two groups: SG1 (basal diet) and SG2 (basal diet + 10 g/kg Moringa oleifera leaf powder). Metagenomics analysis was conducted to analyze operational taxonomic units (OTUs), species annotation, and biodiversity. Additionally, 16S rRNA sequencing was performed for molecular characterization of isolated gut bacteria, identified as Enterococcus faecium. The isolated bacteria were tested for essential metabolites, demonstrating antibacterial, antioxidant, and anticancer activities. The analysis revealed variations in the microbial composition between the control group (SG1) and the M. oleifera-treated group (SG2). SG2 showed a 47% increase in Bacteroides and a 30% decrease in Firmicutes, Proteobacteria, Actinobacteria, and Tenericutes compared to SG1. TM7 bacteria were observed exclusively in the M. oleifera-treated group. These findings suggest that Moringa oleifera leaf powder acts as a modulator that enhances chicken gut microbiota, promoting the colonization of beneficial bacteria. PICRUSt analysis supported these findings, showing increased carbohydrate and lipid metabolism in the M.oleifera-treated gut microbiota. This study indicates that supplementing chicken feed with Moringa oleifera leaf powder as a phytobiotic enhances the gut microbiota in chicken models, potentially improving overall health. The observed changes in bacterial composition, increased presence of Bacteroides, and exclusive presence of TM7 bacteria suggest a positive modulation of microbial balance. The essential metabolites from isolated Enterococcus faecium bacteria further support the potential benefits of Moringa oleifera supplementation.

Residual balanced attention network for real-time traffic scene semantic segmentation

<span lang="EN-US">Intelligent transportation systems (ITS) are among the most focused research in this century. Actually, autonomous driving provides very advanced tasks in terms of road safety monitoring which include identifying dangers on the road and protecting pedestrians. In the last few years, deep learning (DL) approaches and especially convolutional neural networks (CNNs) have been extensively used to solve ITS problems such as traffic scene semantic segmentation and traffic signs classification. Semantic segmentation is an important task that has been addressed in computer vision (CV). Indeed, traffic scene semantic segmentation using CNNs requires high precision with few computational resources to perceive and segment the scene in real-time. However, we often find related work focusing only on one aspect, the precision, or the number of computational parameters. In this regard, we propose RBANet, a robust and lightweight CNN which uses a new proposed balanced attention module, and a new proposed residual module. Afterward, we have simulated our proposed RBANet using three loss functions to get the best combination using only 0.74M parameters. The RBANet has been evaluated on CamVid, the most used dataset in semantic segmentation, and it has performed well in terms of parameters’ requirements and precision compared to related work.</span>

VLT: Vision-Language Transformer and Query Generation for Referring Segmentation.

We propose a Vision-Language Transformer (VLT) framework for referring segmentation to facilitate deep interactions among multi-modal information and enhance the holistic understanding to vision-language features. There are different ways to understand the dynamic emphasis of a language expression, especially when interacting with the image. However, the learned queries in existing transformer works are fixed after training, which cannot cope with the randomness and huge diversity of the language expressions. To address this issue, we propose a Query Generation Module, which dynamically produces multiple sets of input-specific queries to represent the diverse comprehensions of language expression. To find the best among these diverse comprehensions, so as to generate a better mask, we propose a Query Balance Module to selectively fuse the corresponding responses of the set of queries. Furthermore, to enhance the model's ability in dealing with diverse language expressions, we consider inter-sample learning to explicitly endow the model with knowledge of understanding different language expressions to the same object. We introduce masked contrastive learning to narrow down the features of different expressions for the same target object while distinguishing the features of different objects. The proposed approach is lightweight and achieves new state-of-the-art referring segmentation results consistently on five datasets.

Host-diet-gut microbiome interactions influence human energy balance: a randomized clinical trial

The gut microbiome is emerging as a key modulator of human energy balance. Prior studies in humans lacked the environmental and dietary controls and precision required to quantitatively evaluate the contributions of the gut microbiome. Using a Microbiome Enhancer Diet (MBD) designed to deliver more dietary substrates to the colon and therefore modulate the gut microbiome, we quantified microbial and host contributions to human energy balance in a controlled feeding study with a randomized crossover design in young, healthy, weight stable males and females (NCT02939703). In a metabolic ward where the environment was strictly controlled, we measured energy intake, energy expenditure, and energy output (fecal and urinary). The primary endpoint was the within-participant difference in host metabolizable energy between experimental conditions [Control, Western Diet (WD) vs. MBD]. The secondary endpoints were enteroendocrine hormones, hunger/satiety, and food intake. Here we show that, compared to the WD, the MBD leads to an additional 116 ± 56 kcals (P < 0.0001) lost in feces daily and thus, lower metabolizable energy for the host (89.5 ± 0.73%; range 84.2-96.1% on the MBD vs. 95.4 ± 0.21%; range 94.1-97.0% on the WD; P < 0.0001) without changes in energy expenditure, hunger/satiety or food intake (P > 0.05). Microbial 16S rRNA gene copy number (a surrogate of biomass) increases (P < 0.0001), beta-diversity changes (whole genome shotgun sequencing; P = 0.02), and fermentation products increase (P < 0.01) on an MBD as compared to a WD along with significant changes in the host enteroendocrine system (P < 0.0001). The substantial interindividual variability in metabolizable energy on the MBD is explained in part by fecal SCFAs and biomass. Our results reveal the complex host-diet-microbiome interplay that modulates energy balance.

PPA-Net: Pyramid Pooling Attention Network for Multi-Scale Ship Detection in SAR Images

In light of recent advances in deep learning and Synthetic Aperture Radar (SAR) technology, there has been a growing adoption of ship detection models that are based on deep learning methodologies. However, the efficiency of SAR ship detection models is significantly impacted by complex backgrounds, noise, and multi-scale ships (the number of pixels occupied by ships in SAR images varies significantly). To address the aforementioned issues, this research proposes a Pyramid Pooling Attention Network (PPA-Net) for SAR multi-scale ship detection. Firstly, a Pyramid Pooled Attention Module (PPAM) is designed to alleviate the influence of background noise on ship detection while its parallel component favors the processing of multiple ship sizes. Different from the previous attention module, the PPAM module can better suppress the background noise in SAR images because it considers the saliency of ships in SAR images. Secondly, an Adaptive Feature Balancing Module (AFBM) is developed, which can automatically balance the conflict between ship semantic information and location information. Finally, the detection capabilities of the ship detection model for multi-scale ships are further improved by introducing the Atrous Spatial Pyramid Pooling (ASPP) module. This innovative module enhances the detection model’s ability to detect ships of varying scales by extracting features from multiple scales using atrous convolutions and spatial pyramid pooling. PPA-Net achieved detection accuracies of 95.19% and 89.27% on the High-Resolution SAR Images Dataset (HRSID) and the SAR Ship Detection Dataset (SSDD), respectively. The experimental results demonstrate that PPA-Net outperforms other ship detection models.

Reductions towards a characteristic free proof of the Canonical Element Theorem

We reduce Hochster's Canonical Element Conjecture (theorem since 2016) to a localization problem in a characteristic free way. We prove the validity of a new variant of the Canonical Element Theorem (CET) and explain how a characteristic free deduction of the new variant from the original CET would provide us with a characteristic free proof of the CET. We also show that the Balanced Big Cohen-Macaulay Module Theorem can be settled by a characteristic free proof if the big Cohen-Macaulayness of Hochster's modification module can be deduced from the existence of a maximal Cohen-Macaulay complex in a characteristic free way.

CCN2/CTGF tip the balance of growth factors towards TGF-β2 in primary open-angle glaucoma.

TGF-β2 is the predominant TGF-β isoform within the eye. One function of TGF-β2 is to provide the eye with immune protection against intraocular inflammation. The beneficial function of TGF-β2 within the eye must be under tight control of a network of different factors. A disbalance of the network can result in different eye diseases. In Primary Open-Angle Glaucoma (POAG), one of the leading causes of irreversible blindness worldwide, TGF-β2 is significantly elevated in the aqueous humor and antagonistic molecules like BMPs are reduced. The changes provoke an altering of the quantity and quality of the extracellular matrix and the actin cytoskeleton in the outflow tissues, leading to an increased outflow resistance and thereby to an increased intraocular pressure (IOP), the major risk factor for primary open-angle glaucoma. The pathologic effect of TGF-β2 in primary open-angle glaucoma is mainly meditated by CCN2/CTGF. CCN2/CTGF can modulate TGF-β and BMP signaling by direct binding. The eye specific overexpression of CCN2/CTGF caused an increase in IOP and led to a loss of axons, the hallmark of primary open-angle glaucoma. CCN2/CTGF appears to play a critical role in the homeostatic balance of the eye, so we investigated if CCN2/CTGF can modulate BMP and TGF-β signaling pathways in the outflow tissues. To this end, we analyzed the direct effect of CCN2/CTGF on both signaling pathways in two transgenic mouse models with a moderate (βB1-CTGF1) and a high CCN2/CTGF (βB1-CTGF6) overexpression and in immortalized human trabecular meshwork (HTM) cells. Additionally, we investigate whether CCN2/CTGF mediates TGF-β effects via different pathways. We observed developmental malformations in the ciliary body in βB1-CTGF6 caused by an inhibition of the BMP signaling pathway. In βB1-CTGF1, we detected a dysregulation of the BMP and TGF-β signaling pathways, with reduced BMP activity and increased TGF-β signaling. A direct CCN2/CTGF effect on BMP and TGF-β signaling was shown in immortalized HTM cells. Finally, CCN2/CTGF mediated its effects on TGF-β via the RhoA/ROCK and ERK signaling in immortalized HTM cells. We conclude that CCN2/CTGF functions as a modulator of the homeostatic balance of BMP and TGF-β signaling pathways, which is shifted in primary open-angle glaucoma.

Multi-contact planning and control for humanoid robots: Design and validation of a complete framework

In this paper, we consider the problem of generating appropriate motions for a torque-controlled humanoid robot that is assigned a multi-contact loco-manipulation task, i.e., a task that requires the robot to move within the environment by repeatedly establishing and breaking multiple, non-coplanar contacts. To this end, we present a complete multi-contact planning and control framework for multi-limbed robotic systems, such as humanoids. The planning layer works offline and consists of two sequential modules: first, a stance planner computes a sequence of feasible contact combinations; then, a whole-body planner finds the sequence of collision-free humanoid motions that realize them while respecting the physical limitations of the robot. For the challenging problem posed by the first stage, we propose a novel randomized approach that does not require the specification of pre-designed potential contacts or any kind of pre-computation. The control layer produces online torque commands that enable the humanoid to execute the planned motions while guaranteeing closed-loop balance. It relies on two modules, i.e., the stance switching and reactive balancing module; their combined action allows it to withstand possible execution inaccuracies, external disturbances, and modeling uncertainties. Numerical and experimental results obtained on COMAN+, a torque-controlled humanoid robot designed at Istituto Italiano di Tecnologia, validate our framework for loco-manipulation tasks of different complexity.

AFSFusion: An Adjacent Feature Shuffle Combination Network for Infrared and Visible Image Fusion

To obtain fused images with excellent contrast, distinct target edges, and well-preserved details, we propose an adaptive image fusion network called the adjacent feature shuffle-fusion network (AFSFusion). The proposed network adopts a UNet-like architecture and incorporates key refinements to enhance network architecture and loss functions. Regarding the network architecture, the proposed two-branch adjacent feature fusion module, called AFSF, expands the number of channels to fuse the feature channels of several adjacent convolutional layers in the first half of the AFSFusion, enhancing its ability to extract, transmit, and modulate feature information. We replace the original rectified linear unit (ReLU) with leaky ReLU to alleviate the problem of gradient disappearance and add a channel shuffling operation at the end of AFSF to facilitate information interaction capability between features. Concerning loss functions, we propose an adaptive weight adjustment (AWA) strategy to assign weight values to the corresponding pixels of the infrared (IR) and visible images in the fused images, according to the VGG16 gradient feature response of the IR and visible images. This strategy efficiently handles different scene contents. After normalization, the weight values are used as weighting coefficients for the two sets of images. The weighting coefficients are applied to three loss items simultaneously: mean square error (MSE), structural similarity (SSIM), and total variation (TV), resulting in clearer objects and richer texture detail in the fused images. We conducted a series of experiments on several benchmark databases, and the results demonstrate the effectiveness of the proposed network architecture and the superiority of the proposed network compared to other state-of-the-art fusion methods. It ranks first in several objective metrics, showing the best performance and exhibiting sharper and richer edges of specific targets, which is more in line with human visual perception. The remarkable enhancement in performance is ascribed to the proposed AFSF module and AWA strategy, enabling balanced feature extraction, fusion, and modulation of image features throughout the process.

Modeling Task Relationships in Multivariate Soft Sensor With Balanced Mixture-of-Experts

Accurate estimation of multiple quality variables is critical for building industrial soft sensor models, which have long been confronted with data efficiency and negative transfer issues. Methods sharing backbone parameters among tasks address the data efficiency issue; however, they still fail to mitigate the negative transfer problem. To address this issue, a balanced Mixture-of-Experts (BMoE) is proposed in this work, which consists of a multi-gate mixture of experts (MMoE) module and a task gradient balancing (TGB) module. The MoE module aims to portray task relationships, while the TGB module balances the gradients among tasks dynamically. Both of them cooperate to mitigate the negative transfer problem. Experiments on the typical sulfur recovery unit demonstrate that BMoE models task relationship and balances the training process effectively, and achieves better performance than baseline models significantly.>

Short-term water stress responses of grafted pepper plants are associated with changes in the hormonal balance.

Phytohormones play an important role in regulating the plant behavior to drought. In previous studies, NIBER® pepper rootstock showed tolerance to drought in terms of production and fruit quality compared to ungrafted plants. In this study, our hypothesis was that short-term exposure to water stress in young, grafted pepper plants would shed light on tolerance to drought in terms of modulation of the hormonal balance. To validate this hypothesis, fresh weight, water use efficiency (WUE) and the main hormone classes were analyzed in self-grafted pepper plants (variety onto variety, V/V) and variety grafted onto NIBER® (V/N) at 4, 24, and 48h after severe water stress was induced by PEG addition. After 48h, WUE in V/N was higher than in V/V, due to major stomata closure to maintain water retention in the leaves. This can be explained by the higher abscisic acid (ABA) levels observed in the leaves of V/N plants. Despite the interaction between ABA and the ethylene precursor, 1-aminocyclopropane-1-carboxylic acid (ACC), in relation to stomata closure is controversial, we observed an important increase of ACC at the end of the experiment in V/N plants coinciding with an important rise of the WUE and ABA. The maximum concentration of jasmonic acid and salicylic acid after 48h was found in the leaves of V/N, associated with their role in abiotic stress signaling and tolerance. Respect to auxins and cytokinins, the highest concentrations were linked to water stress and NIBER®, but this effect did not occur for gibberellins. These results show that hormone balance was affected by water stress and rootstock genotype, where NIBER® rootstock displayed a better ability to overcome short-term water stress.

Magnetic state balance technology of transformer based on magnetic field modulation strategy

The transformer core is easy to saturate in the half cycle under the influence of the remanent magnetism (RM) and the bias magnetism (BM), then the excitation surge current (ESC) and the DC bias will be generated, causing the transformer excitation current (TEC) distort, leading the winding temperature rise and decreasing the transformer insulation. In this paper, one kind of magnetic balance modulation method for the transformer core is proposed. TEC distortion could be improved by adjusting the transformer magnetic field distribution. Firstly, the AC/DC/DC converter was designed and connected to the auxiliary winding according to the hybrid transformer topology. Second, the specific modulated signal for the magnetic field modulation (MFM) was generated and output through the AC/DC/DC. The modulated forward and reverse DC current or the stepped AC current would be injected into the transformer winding to achieve the balance of the transformer magnetic state. Finally, in order to verify the correctness of the design scheme, a 1 kVA/380 V/220 V/38 V transformer experiment platform was built. The results showed that this proposed MFM strategy could effectively solve the impact of the transformer ESC, DC bias and prolong the transformer service life, which provides a novel idea to solve the transformer fault.

Geneless optical control of cell redox balance in HL-1 cardiac muscle cells

Photoactivation of conjugated polymers has been shown to be an attractive approach to modulate biological functions in several cell models; it is characterized by minimal invasiveness, high modulability, spatial selectivity at the level of single cells and even intracellular compartments. Reactive oxygen species produced by polymer photostimulation, in particular, were reported to trigger cell proliferation and recently proposed to finely modulate the cell cycle. Light-activated proliferation is extremely attractive for cardiac muscle cells, to overcome pathological conditions as heart failure and cardiovascular diseases with minimal invasiveness.Here, we specifically address the target to optically modulate the cell redox balance of HL-1 cells, a cardiac muscle cell model, by localized photoexcitation of a light sensitive polymer, namely poly-3-hexyl-thiophene. Scanning electrochemical microscopy is employed to quantify the changes of the cellular redox balance. Both extracellular and intracellular production of reactive oxygen species upon illumination is investigated, by employing poly-3-hexyl-thiophene respectively in the form of thin films or nanoparticles. Our results show that light induced, spatially controlled production of reactive oxygen species by poly-3-hexyl-thiophene films in the extracellular compartment determines the shift of HL-1 redox balance towards more reducing values. Thus, highly resolved spatial control of the illuminated area enables modulation of HL-1 redox balance at the single cell level. The effect on cell redox balance of cytosol internalized poly-3-hexyl-thiophene nanoparticles is also presented.Our work shows that photostimulation of conjugated polymers can be employed as a wireless, geneless technique to modulate on demand the intracellular redox balance, with high spatial resolution and minimal invasiveness, in a biologically relevant model of contractile, functioning cardiomyocytes. In perspective, this approach may be easily extended to other cell systems, wherever a fine control of the cell redox state is desirable, and open the path to innovative therapeutic tools.

The Influence of Platelet-Derived Growth Factors on the Proliferation of Germinal Epithelium After Local Irradiation with Electrons.

At present, the damage of male reproductive function caused by electron irradiation, as well as the development of methods for its correction are the relevant topics for further research. In fact, the effect of leukocyte-poor platelet-rich plasma (LP-PRP) growth factors are poorly investigated on different aspects of male fertility. In this study, Wistar rats were divided into four groups; I) control which were injected with saline; II and III) groups (n=65) whose testes were locally irradiated with electrons to a dose of 2 Gy (linear accelerator "NOVAC-11"); III) the group (n=30) which received LP-PRP for 11 weeks after irradiation; and IV) animals (n=30) which received only LP-PRP (conditional control). The testes were studied by histological, immunohistochemical (IHC), western blotting, and TUNEL methods using Ki-67, Bcl-2, and p53. Comparison between groups was performed and p<0.01 was set as the level of significance. The results showed a decrease in the expression levels of Ki-67 and Bcl-2 besides an increase in p53-positive cells by the end of the experiment (p<0.01). After injection of LP-PRP, a gradual restoration of the proliferative activity of gametes was noted, which was confirmed by an increase in the proportion of Ki-67- and Bcl-2-positive germ cells (46.4±2.3, p<0.01 and 23.5±1.1, respectively, p<0.01). Ki-67 expression and TUNEL analysis in the testes revealed a modulation of the proliferative-apoptotic balance towards apoptosis of germ cells after 2 Gy local electron irradiation. A tendency to restore the proliferative-apoptotic balance was noted after LP-PRP injections as indicated by increase in Ki-67-positive germ cells.

Can the response to dialysis treatment be predicted by using patient-specific modeling of fluid and solute exchanges? A multicentric evaluation.

Parametric multipool kinetic models were used to describe the intradialytic trends of electrolytes, breakdown products, and body fluids volumes during hemodialysis. Therapy customization can be achieved by the identification of parameters, allowing patient-specific modulation of mass and fluid balance across dialyzer, capillary, and cell membranes. This study wants to evaluate the possibility to use this approach to predict the patient's intradialytic response. 6 sessions of 68 patients (DialysIS© project) were considered. Data from the first three sessions were used to train the model, identifying the patient-specific parameters, that, together with the treatment settings and the patient's data at the session start, could be used for predicting the patient's specific time course of solutes and fluids along the sessions. Na+ , K+ , Cl- , Ca2+ , HCO3 - , and urea plasmatic concentrations and hematic volume deviations from clinical data were evaluated. nRMSE predictive error is on average equal to 4.76% when describing the training sessions, and only increases by 0.97 percentage points on average in independent sessions of the same patient. The proposed predictive approach represents a first step in the development of tools to support the clinician in tailoring the patient's prescription.

Independent sequences and freeness criteria

Let M be a module over a Noetherian local ring A. We study M-independent sequences of elements of the maximal ideal mA in the sense of Lech and Hanes. The main tool is a new characterization of the M-independence of a sequence in terms of the associated Koszul complex. As applications, we give a result in linkage theory, a freeness criterion for M in terms of the existence of a strongly M-independent sequence of length equal to the embedding dimension edim(A) of A, and another freeness criterion inspired from the patching method of Calegari and Geraghty for balanced modules in their 2018 paper.

Balance Disorder Trends in US Adults 2008-2016: Epidemiology and Functional Impact.

To quantify the changes in prevalence and impact of dizziness and balance disorders in adults from 2008 to 2016. Epidemiological survey analysis. United States. The balance modules of the adult 2008 and 2016 National Health Interview Surveys were examined, and persons reporting dizziness or balance problems identified. The prevalence of balance problems was determined and compared over time, adjusting for age and sex. Among those with balance problems, associated symptoms and self-reported functional limitations were quantified and compared over time. In 2016, 36.8 ± 1.0 million (15.5% ± 0.3%) adults reported a balance problem in the past year, versus 24.2 ± 0.7 million (11.% ± 0.3%) in 2008 (p < .001). After adjustment for age and sex, this percentage increase remained significant (odds ratio 1.435 [1.332-1.546], p < .001). Among those with balance problems, significantly more patients reported specific issues with feeling: off-balance (69.4% vs. 65.4%; p = .005), faint (48.5% vs. 40.3%; p < .001), or vertiginous (45.9% vs. 39.3%; p < .001) in 2016 than 2008. More adults experienced anxiety (29.4% vs. 19.4%; p < .001) and depression (16.3% vs. 12.9%; p = .002) with their balance problems in 2016 than in 2008. In 2016, adults with balance problems were limited in ability to drive motor vehicles (13.0%), exercise (14.4%), or walk downstairs (12.8%). These rates were not significantly different from 2008 (all p > .05). In this nationally representative analysis, we found a significantly increasing prevalence of balance problems and associated psychiatric symptom burden. This merits attention with respect to present and future health care resource allocation.