Evaluation of an ankle-foot orthosis effect on gait transitional stability during ramp ascent/descent.

Wearable ankle-foot orthoses (AFO) are widely prescribed clinically; however, their effect on balance control during ramp ascent/descent walk remains unknown. This study evaluates walking stability on a ramp during weight loading and unloading transitions of the stance phase with the effect of an adjustable AFO. An AFO is tuned firstly by tuning dorsiflexion only and then combining dorsi-plantarflexion adjustments. Gait stability is assessed from neuromotor input (centre-of-mass) and output (centre-of-pressure) responses obtained through motion-capture system and force platform. Stability margins are quantified from Nyquist and Bode methods illustrating the loading phase as stable and the unloading phase as unstable transition in all walking conditions. Further, a significant decrease in stability (p < 0.05) is observed by wearing AFO in its free mode which gets improved (p < 0.05) by tuning AFO. Results from neuromotor outputs also illustrated a strong interlimb correlation (p < 0.001), which implies a compensatory interaction between opposite limbs loading and unloading transitions. Neuromotor inputs illustrated unstable responses both in loading and unloading transitions and were observed to be greater in magnitudes compared with output margins. The overall results support the hypothesis that a wearable AFO affects gait stability during transitional phases, and by applying AFO adjustments, neuromotor balance control achieves stability margins closer to normal range.

Evaluation of an adjustable ankle-foot orthosis impact on walking stability during gait transitional phases.

Walking stability evaluation during gait transitional phases (loading and unloading) has been remained indistinct mainly due to methodological limitations and multiple biomechanical signals being used. This study introduces Nyquist and Bode methods using resultant neuromechanical output/input (O/I) responses to evaluate gait transitional stabilities. The centre of pressure and ground reaction force data are recorded experimentally as output and somatosensory input responses by the neuromotor. Six different walking conditions are simulated by wearing an adjustable orthosis using eleven healthy subjects. The rate of change in O/Is are modelled in time and frequency domains applying linear regression and stability margins are quantified applying N&B methods. Results from outputs showed loading phase as stable and unloading phase as unstable gait phases whereas the margins quantified from inputs showed unstable response during both phases. Stability margins quantified from outputs are decreased (p < 0.05) and instability margins are increased (p < 0.05) on applying ankle-foot restrictions. Overall, the neuromotor output stability margins are greater than the instabilities quantified from inputs. Furthermore, a strong interlimb negative correlation (p < 0.001) was found between loading and unloading phases computed from outputs. This study introduces new stability assessment methods with applications like evaluating lower limb impairments, varying terrains, and stability impacts of wearable devices.

Detection and Grading of Hypertensive Retinopathy Using Vessels Tortuosity and Arteriovenous Ratio.

Hypertensive retinopathy (HR) refers to changes in the morphological diameter of the retinal vessels due to persistent high blood pressure. Early detection of such changes helps in preventing blindness or even death due to stroke. These changes can be quantified by computing the arteriovenous ratio and the tortuosity severity in the retinal vasculature. This paper presents a decision support system for detecting and grading HR using morphometric analysis of retinal vasculature, particularly measuring the arteriovenous ratio (AVR) and retinal vessel tortuosity. In the first step, the retinal blood vessels are segmented and classified as arteries and veins. Then, the width of arteries and veins is measured within the region of interest around the optic disk. Next, a new iterative method is proposed to compute the AVR from the caliber measurements of arteries and veins using Parr-Hubbard and Knudtson methods. Moreover, the retinal vessel tortuosity severity index is computed for each image using 14 tortuosity severity metrics. In the end, a hybrid decision support system is proposed for the detection and grading of HR using AVR and tortuosity severity index. Furthermore, we present a new publicly available retinal vessel morphometry (RVM) dataset to evaluate the proposed methodology. The RVM dataset contains 504 retinal images with pixel-level annotations for vessel segmentation, artery/vein classification, and optic disk localization. The image-level labels for vessel tortuosity index and HR grade are also available. The proposed methods of iterative AVR measurement, tortuosity index, and HR grading are evaluated using the new RVM dataset. The results indicate that the proposed method gives superior performance than existing methods. The presented methodology is a novel advancement in automated detection and grading of HR, which can potentially be used as a clinical decision support system.

Zinc Plus Biopolymer Coating Slows Nitrogen Release, Decreases Ammonia Volatilization from Urea and Improves Sunflower Productivity.

Currently, the global agriculture productivity is heavily relied on the use of chemical fertilizers. However, the low nutrient utilization efficiency (NUE) is the main obstacle for attaining higher crop productivity and reducing nutrients losses from these fertilizers to the environment. Coating fertilizer with micronutrients and biopolymer can offer an opportunity to overcome these fertilizers associated problems. Here, we coated urea with zinc sulphate (ZnS) and ZnS plus molasses (ZnSM) to control its N release, decrease the ammonia (NH3) volatilization and improve N utilization efficiency by sunflower. Morphological analysis confirmed a uniform coating layer formation of both formulations on urea granules. A slow release of N from ZnS and ZnSM was observed in water. After soil application, ZnSM decreased the NH3 emission by 38% compared to uncoated urea. Most of the soil parameters did not differ between ZnS and uncoated urea treatment. Microbial biomass N and Zn in ZnSM were 125 and 107% higher than uncoated urea, respectively. Soil mineral N in ZnSM was 21% higher than uncoated urea. Such controlled nutrient availability in the soil resulted in higher sunflower grain yield (53%), N (80%) and Zn (126%) uptakes from ZnSM than uncoated fertilizer. Hence, coating biopolymer with Zn on urea did not only increase the sunflower yield and N utilization efficiency but also meet the micronutrient Zn demand of sunflower. Therefore, coating urea with Zn plus biopolymer is recommended to fertilizer production companies for improving NUE, crop yield and reducing urea N losses to the environment in addition to fulfil crop micronutrient demand.

A route pruning algorithm for an automated geographic location graph construction.

Automated construction of location graphs is instrumental but challenging, particularly in logistics optimisation problems and agent-based movement simulations. Hence, we propose an algorithm for automated construction of location graphs, in which vertices correspond to geographic locations of interest and edges to direct travelling routes between them. Our approach involves two steps. In the first step, we use a routing service to compute distances between all pairs of L locations, resulting in a complete graph. In the second step, we prune this graph by removing edges corresponding to indirect routes, identified using the triangle inequality. The computational complexity of this second step is [Formula: see text], which enables the computation of location graphs for all towns and cities on the road network of an entire continent. To illustrate the utility of our algorithm in an application, we constructed location graphs for four regions of different size and road infrastructures and compared them to manually created ground truths. Our algorithm simultaneously achieved precision and recall values around 0.9 for a wide range of the single hyperparameter, suggesting that it is a valid approach to create large location graphs for which a manual creation is infeasible.

Exploring potential of copper and silver nano particles to establish efficient callogenesis and regeneration system for wheat (Triticum aestivum L.).

In vitro recalcitrance of wheat to regeneration is the major bottleneck for its improvement through callus-based genetic transformation. Nanotechnology is one of the most dynamic areas of research, which can transform agriculture and biotechnology to ensure food security on sustainable basis. Present study was designed to investigate effects of CuSO4, AgNO3 and their nanoparticles on tissue culture responses of mature embryo culture of wheat genotypes (AS-2002 and Wafaq-2001). Initially, MS-based callus induction and regeneration medium were optimized for both genotypes using various concentrations of auxin (2,4-D, IAA) and cytokinins (BAP, kinetin). The genotypes differed for embryogenic callus induction and regeneration potential. Genotype AS-2002 yielded maximum embryogenic calli in response to 3.0 mg/l 2,4-D, whereas Wafaq-2001 offered the highest embryogenic calli against 3.5 mg/l 2,4-D supplemented in the induction medium. Genotype AS-2002 showed maximum regeneration (59.33%) in response to regeneration protocol comprising 0.5 mg/l IAA, 0.3 mg/l BAP and 1.0 mg/l Kin, while Wafaq-2001 performed best in response to 0.5 mg/l IAA, 0.3 mg/l BAP and 1.5 mg/l Kin with 55.33% regeneration efficiency. The same optimized basal induction and regeneration medium for both genotypes were further used to study effects of CuSO4, AgNO3 and their nano-particles employing independent experiments. The optimized induction medium fortified with various concentrations of CuSO4 or CuNPs confirmed significant effects on frequency of embryogenic callus. Addition of either 0.020 mg/l or 0.025 mg/l CuSO4, or 0.015 mg/l CNPs showed comparable results for embryogenic callus induction and were statistically at par with embryogenic callus induction of 74.00%, 75.67% and 76.83%, respectively. Significantly higher regeneration was achieved from MS-based regeneration medium supplemented with 0.015 mg/l or 0.020 mg/l CuNPs than standard 0.025 mg/l CuSO4. In another study, the basal induction and regeneration medium were fortified with AgNO3 or AgNPs ranging from 1 to 7 mg/l along with basal regeneration media devoid of AgNO3 or AgNPs (control). The maximum embryogenic calli were witnessed from medium fortified with 3.0 mg/l or 4.0 mg/l AgNPs compared with control and rest of the treatments. The standardized regeneration medium fortified with 5.0 mg/l AgNO3 or 3.0 mg/l AgNPs showed pronounced effect on regeneration of wheat genotypes and offered maximum regeneration compared with control. The individual and combined effect of Cu and Ag nanoparticles along with control (basal regeneration media of each genotype) was also tested. Surprisingly, co-application of metallic NPs showed a significant increase in embryogenic callus formation of genotypes. Induction medium supplemented with 0.015 mg/l CuNPs + 4.0 mg/l AgNPs or 0.020 mg/l CuNPs + 2.0 mg/l AgNPs showed splendid results compared to control and other combination of Cu and Ag nanoparticles. The maximum regeneration was achieved by co-application of 0.015 mg/l CuNP and 4.0 mg/l AgNPs with 21% increment of regeneration over control. It is revealed that CuNPs and AgNPs are potential candidate to augment somatic embryogenesis and regeneration of mature embryo explants of wheat.Abbreviations: 2,4-D (2,4-dichlorophenoxyacetic acid), BAP (6-benzylaminopurine), IAA (Indole-3-acetic acid), AgNPs (silver nanoparticles), CuNPs (copper nanoparticles).

Uncertainty quantification of dynamic earthquake rupture simulations.

We present a tutorial demonstration using a surrogate-model based uncertainty quantification (UQ) approach to study dynamic earthquake rupture on a rough fault surface. The UQ approach performs model calibration where we choose simulation points, fit and validate an approximate surrogate model or emulator, and then examine the input space to see which inputs can be ruled out from the data. Our approach relies on the mogp_emulator package to perform model calibration, and the FabSim3 component from the VECMA toolkit to streamline the workflow, enabling users to manage the workflow using the command line to curate reproducible simulations on local and remote resources. The tools in this tutorial provide an example template that allows domain researchers that are not necessarily experts in the underlying methods to apply them to complex problems. We illustrate the use of the package by applying the methods to dynamic earthquake rupture, which solves the elastic wave equation for the size of an earthquake and the resulting ground shaking based on the stress tensor in the Earth. We show through the tutorial results that the method is able to rule out large portions of the input parameter space, which could lead to new ways to constrain the stress tensor in the Earth based on earthquake observations. This article is part of the theme issue 'Reliability and reproducibility in computational science: implementing verification, validation and uncertainty quantification in silico'.

The impact of uncertainty on predictions of the CovidSim epidemiological code.

Epidemiological modelling has assisted in identifying interventions that reduce the impact of COVID-19. The UK government relied, in part, on the CovidSim model to guide its policy to contain the rapid spread of the COVID-19 pandemic during March and April 2020; however, CovidSim contains several sources of uncertainty that affect the quality of its predictions: parametric uncertainty, model structure uncertainty and scenario uncertainty. Here we report on parametric sensitivity analysis and uncertainty quantification of the code. From the 940 parameters used as input into CovidSim, we find a subset of 19 to which the code output is most sensitive-imperfect knowledge of these inputs is magnified in the outputs by up to 300%. The model displays substantial bias with respect to observed data, failing to describe validation data well. Quantifying parametric input uncertainty is therefore not sufficient: the effect of model structure and scenario uncertainty must also be properly understood.

Evaluation of human brain hyperthermia using exergy balance equation.

Hyperthermia is caused by disturbance in the thermoregulatory system of the human body and requires emergency treatment to prevent disability or possible mortality. To design any therapeutic device for hyperthermia, an exhaustive effort is required to establish the extremities of such thermal traumas. In this context, the authors have incorporated the human-body exergy-balance equation to compute the hyperthermia thresholds. This is a pioneer attempt to model hyperthermia states. An induced-hyperthermia technique is used to evaluate the extremities of metabolic heat generation and other dependent parameters. Moreover, a case study is also presented to calculate the parameters of prime importance i.e. exergy consumption (EC) and entropy generation rate (δSg) to provide the body's accumulative and exhaustive thermal energy maxima, respectively. Furthermore, the thresholds have been evaluated and simulated by the varying body and/or environmental conditions. The resulting states have been analysed to setup critical ranges to provide the guidelines for rehabilitation therapy. A thermal manikin has also been developed, mimicking the blood circulation in humans, to further substantiate the use of an exergy-based approach. The results indicate that the exergy-based approach is well suited to model hyperthermia at pathophysiological boundaries, contrary to existing approaches which predominantly are limited to the physiological domain.

A model identification approach to quantify impact of whole-body vertical vibrations on limb compliant dynamics and walking stability.

Extensive research is ongoing in the field of orthoses/exoskeleton design for efficient lower limbs assistance. However, despite wearable devices reported to improve lower limb mobility, their structural impacts on whole-body vertical dynamics have not been investigated. This study introduced a model identification approach and frequency domain analysis to quantify the impacts of orthosis-generated vibrations on limb stability and contractile dynamics. Experiments were recorded in the motion capture lab using 11 unimpaired subjects by wearing an adjustable ankle-foot orthosis (AFO). The lower limb musculoskeletal structure was identified as spring-mass (SM) and spring-mass-damper (SMD) based compliant models using the whole-body centre-of-mass acceleration data. Furthermore, Nyquist and Bode methods were implemented to quantify stabilities resulting from vertical impacts. Our results illustrated a significant decrease (p < 0.05) in lower limb contractile properties by wearing AFO compared with a normal walk. Also, stability margins quantified by wearing AFO illustrated a significant variance in terms of gain-margins (p < 0.05) for both loading and unloading phases whereas phase-margins decreased (p < 0.05) only for the respective unloading phases. The methods introduced here provide evidence that wearable orthoses significantly affect lower limb vertical dynamics and should be considered when evaluating orthosis/prosthesis/exoskeleton effectiveness.

Evaluation of gait transitional phases using neuromechanical outputs and somatosensory inputs in an overground walk.

In a bipedal walk, the human body experiences continuous changes in stability especially during weight loading and unloading transitions which are reported crucial to avoid fall. Prior stability assessment methods are unclear to quantify stabilities during these gait transitions due to methodological and/or measurement limitations. This study introduces Nyquist and Bode methods to quantify stability gait transitional stabilities using the neuromechanical output (CoP) and somatosensory input (GRF) responses. These methods are implemented for five different walking conditions grouped into walking speed and imitated rotational impairments. The trials were recorded with eleven healthy subjects using motion cameras and force platforms. The time rate of change in O/Is illustrated impulsive responses and modelled in the frequency domain. Nyquist and Bode stability methods are applied to quantify stability margins. Stability margins from outputs illustrated loading phases as stable and unloading phases as unstable in all walking conditions. There was a strong intralimb compensatory interaction (p < .001, Spearman correlation) found between opposite limbs. Overall, both walking groups illustrated a decrease (p < .05, Wilcoxon signed-rank test) in stability margins compared with normal/preferred speed walk. Further, stabilities quantified from outputs were found greater in magnitudes than the instability quantified from inputs illustrating the neuromotor balance control ability. These stability outcomes were also compared by applying extrapolated-CoM method. These methods of investigating gait dynamic stability are considered as having important implications for the assessment of ankle-foot impairments, rehabilitation effectiveness, and wearable orthoses.

Prediction of gait events in walking activities with a Bayesian perception system.

In this paper, a robust probabilistic formulation for prediction of gait events from human walking activities using wearable sensors is presented. This approach combines the output from a Bayesian perception system with observations from actions and decisions made over time. The perception system makes decisions about the current gait events, while observations from decisions and actions allow to predict the most probable gait event during walking activities. Furthermore, our proposed method is capable to evaluate the accuracy of its predictions, which permits to obtain a better performance and trade-off between accuracy and speed. In our work, we use data from wearable inertial measurement sensors attached to the thigh, shank and foot of human participants. The proposed perception system is validated with multiple experiments for recognition and prediction of gait events using angular velocity data from three walking activities; level-ground, ramp ascent and ramp descent. The results show that our method is fast, accurate and capable to evaluate and adapt its own performance. Overall, our Bayesian perception system demonstrates to be a suitable high-level method for the development of reliable and intelligent assistive and rehabilitation robots.