Evaluation of walking activity and gait to identify physical and mental fatigue in neurodegenerative and immune disorders: preliminary insights from the IDEA-FAST feasibility study.

BACKGROUND: Many individuals with neurodegenerative (NDD) and immune-mediated inflammatory disorders (IMID) experience debilitating fatigue. Currently, assessments of fatigue rely on patient reported outcomes (PROs), which are subjective and prone to recall biases. Wearable devices, however, provide objective and reliable estimates of gait, an essential component of health, and may present objective evidence of fatigue. This study explored the relationships between gait characteristics derived from an inertial measurement unit (IMU) and patient-reported fatigue in the IDEA-FAST feasibility study. METHODS: Participants with IMIDs and NDDs (Parkinson's disease (PD), Huntington's disease (HD), rheumatoid arthritis (RA), systemic lupus erythematosus (SLE), primary Sjogren's syndrome (PSS), and inflammatory bowel disease (IBD)) wore a lower-back IMU continuously for up to 10 days at home. Concurrently, participants completed PROs (physical fatigue (PF) and mental fatigue (MF)) up to four times a day. Macro (volume, variability, pattern, and acceleration vector magnitude) and micro (pace, rhythm, variability, asymmetry, and postural control) gait characteristics were extracted from the accelerometer data. The associations of these measures with the PROs were evaluated using a generalised linear mixed-effects model (GLMM) and binary classification with machine learning. RESULTS: Data were recorded from 72 participants: PD = 13, HD = 9, RA = 12, SLE = 9, PSS = 14, IBD = 15. For the GLMM, the variability of the non-walking bouts length (in seconds) with PF returned the highest conditional R2, 0.165, and with MF the highest marginal R2, 0.0018. For the machine learning classifiers, the highest accuracy of the current analysis was returned by the micro gait characteristics with an intrasubject cross validation method and MF as 56.90% (precision = 43.9%, recall = 51.4%). Overall, the acceleration vector magnitude, bout length variation, postural control, and gait rhythm were the most interesting characteristics for future analysis. CONCLUSIONS: Counterintuitively, the outcomes indicate that there is a weak relationship between typical gait measures and abnormal fatigue. However, factors such as the COVID-19 pandemic may have impacted gait behaviours. Therefore, further investigations with a larger cohort are required to fully understand the relationship between gait and abnormal fatigue.

A study protocol to investigate if acipimox improves muscle function and sarcopenia: an open-label, uncontrolled, before-and-after experimental medicine feasibility study in community-dwelling older adults.

INTRODUCTION: Sarcopenia is the age-associated loss of muscle mass and strength. Nicotinamide adenine dinucleotide (NAD) plays a central role in both mitochondrial function and cellular ageing processes implicated in sarcopenia. NAD concentrations are low in older people with sarcopenia, and increasing skeletal muscle NAD concentrations may offer a novel therapy for this condition. Acipimox is a licensed lipid-lowering agent known to act as an NAD precursor. This open-label, uncontrolled, before-and-after proof-of-concept experimental medicine study will test whether daily supplementation with acipimox improves skeletal muscle NAD concentrations. METHODS AND ANALYSIS: Sixteen participants aged 65 and over with probable sarcopenia will receive acipimox 250 mg and aspirin 75 mg orally daily for 4 weeks, with the frequency of acipimox administration being dependent on renal function. Muscle biopsy of the vastus lateralis and MRI scanning of the lower leg will be performed at baseline before starting acipimox and after 3 weeks of treatment. Adverse events will be recorded for the duration of the trial. The primary outcome, analysed in a per-protocol population, is the change in skeletal muscle NAD concentration between baseline and follow-up. Secondary outcomes include changes in phosphocreatine recovery rate by 31P magnetic resonance spectroscopy, changes in physical performance and daily activity (handgrip strength, 4 m walk and 7-day accelerometry), changes in skeletal muscle mitochondrial respiratory function, changes in skeletal muscle mitochondrial DNA copy number and changes in NAD concentrations in whole blood as a putative biomarker for future participant selection. ETHICS AND DISSEMINATION: The trial is approved by the UK Medicines and Healthcare Products Regulatory Agency (EuDRACT 2021-000993-28) and UK Health Research Authority and Northeast - Tyne and Wear South Research Ethics Committee (IRAS 293565). Results will be made available to participants, their families, patients with sarcopenia, the public, regional and national clinical teams, and the international scientific community. PROTOCOL: Acipimox feasibility study Clinical Trial Protocol V.2 2/11/21. TRIAL REGISTRATION NUMBER: The ISRCTN trial database (ISRCTN87404878).

Identification of Fatigue and Sleepiness in Immune and Neurodegenerative Disorders from Measures of Real-World Gait Variability.

Current assessments of fatigue and sleepiness rely on patient reported outcomes (PROs), which are subjective and prone to recall bias. The current study investigated the use of gait variability in the "real world" to identify patient fatigue and daytime sleepiness. Inertial measurement units were worn on the lower backs of 159 participants (117 with six different immune and neurodegenerative disorders and 42 healthy controls) for up to 20 days, whom completed regular PROs. To address walking bouts that were short and sparse, four feature groups were considered: sequence-independent variability (SIV), sequence-dependant variability (SDV), padded SDV (PSDV), and typical gait variability (TGV) measures. These gait variability measures were extracted from step, stride, stance, and swing time, step length, and step velocity. These different approaches were compared using correlations and four machine learning classifiers to separate low/high fatigue and sleepiness.Most balanced accuracies were above 50%, the highest was 57.04% from TGV measures. The strongest correlation was 0.262 from an SDV feature against sleepiness. Overall, TGV measures had lower correlations and classification accuracies.Identifying fatigue or sleepiness from gait variability is extremely complex and requires more investigation with a larger data set, but these measures have shown performances that could contribute to a larger feature set.Clinical relevance- Gait variability has been repeatedly used to assess fatigue in the lab. The current study, however, explores gait variability for fatigue and daytime sleepiness in real-world scenarios with multiple gait-impacted disorders.

Walking Bout Detection for People Living in Long Residential Care: A Computationally Efficient Algorithm for a 3-Axis Accelerometer on the Lower Back.

Accurate and reliable measurement of real-world walking activity is clinically relevant, particularly for people with mobility difficulties. Insights on walking can help understand mobility function, disease progression, and fall risks. People living in long-term residential care environments have heterogeneous and often pathological walking patterns, making it difficult for conventional algorithms paired with wearable sensors to detect their walking activity. We designed two walking bout detection algorithms for people living in long-term residential care. Both algorithms used thresholds on the magnitude of acceleration from a 3-axis accelerometer on the lower back to classify data as "walking" or "non-walking". One algorithm had generic thresholds, whereas the other used personalized thresholds. To validate and evaluate the algorithms, we compared the classifications of walking/non-walking from our algorithms to the real-time research assistant annotated labels and the classification output from an algorithm validated on a healthy population. Both the generic and personalized algorithms had acceptable accuracy (0.83 and 0.82, respectively). The personalized algorithm showed the highest specificity (0.84) of all tested algorithms, meaning it was the best suited to determine input data for gait characteristic extraction. The developed algorithms were almost 60% quicker than the previously developed algorithms, suggesting they are adaptable for real-time processing.

Can Digital Mobility Assessment Enhance the Clinical Assessment of Disease Severity in Parkinson's Disease?

BACKGROUND: Real-world walking speed (RWS) measured using wearable devices has the potential to complement the Movement Disorder Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS III) for motor assessment in Parkinson's disease (PD). OBJECTIVE: Explore cross-sectional and longitudinal differences in RWS between PD and older adults (OAs), and whether RWS was related to motor disease severity cross-sectionally, and if MDS-UPDRS III was related to RWS, longitudinally. METHODS: 88 PD and 111 OA participants from ICICLE-GAIT (UK) were included. RWS was evaluated using an accelerometer at four time points. RWS was aggregated within walking bout (WB) duration thresholds. Between-group-comparisons in RWS between PD and OAs were conducted cross-sectionally, and longitudinally with mixed effects models (MEMs). Cross-sectional association between RWS and MDS-UPDRS III was explored using linear regression, and longitudinal association explored with MEMs. RESULTS: RWS was significantly lower in PD (1.04 m/s) in comparison to OAs (1.10 m/s) cross-sectionally. RWS significantly decreased over time for both cohorts and decline was more rapid in PD by 0.02 m/s per year. Significant negative relationship between RWS and the MDS-UPDRS III only existed at a specific WB threshold (30 to 60 s, ß= - 3.94 points, p = 0.047). MDS-UPDRS III increased significantly by 1.84 points per year, which was not related to change in RWS. CONCLUSION: Digital mobility assessment of gait may add unique information to quantify disease progression remotely, but further validation in research and clinical settings is needed.

High-Speed Network DDoS Attack Detection: A Survey.

Having a large number of device connections provides attackers with multiple ways to attack a network. This situation can lead to distributed denial-of-service (DDoS) attacks, which can cause fiscal harm and corrupt data. Thus, irregularity detection in traffic data is crucial in detecting malicious behavior in a network, which is essential for network security and the integrity of modern Cyber-Physical Systems (CPS). Nevertheless, studies have shown that current techniques are ineffective at detecting DDoS attacks on networks, especially in the case of high-speed networks (HSN), as detecting attacks on the latter is very complex due to their fast packet processing. This review aims to study and compare different approaches to detecting DDoS attacks, using machine learning (ML) techniques such as k-means, K-Nearest Neighbors (KNN), and Naive Bayes (NB) used in intrusion detection systems (IDSs) and flow-based IDSs, and expresses data paths for packet filtering for HSN performance. This review highlights the high-speed network accuracy evaluation factors, provides a detailed DDoS attack taxonomy, and classifies detection techniques. Moreover, the existing literature is inspected through a qualitative analysis, with respect to the factors extracted from the presented taxonomy of irregular traffic pattern detection. Different research directions are suggested to support researchers in identifying and designing the optimal solution by highlighting the issues and challenges of DDoS attacks on high-speed networks.

Validation of an Algorithm for Measurement of Sedentary Behaviour in Community-Dwelling Older Adults.

Accurate measurement of sedentary behaviour in older adults is informative and relevant. Yet, activities such as sitting are not accurately distinguished from non-sedentary activities (e.g., upright activities), especially in real-world conditions. This study examines the accuracy of a novel algorithm to identify sitting, lying, and upright activities in community-dwelling older people in real-world conditions. Eighteen older adults wore a single triaxial accelerometer with an onboard triaxial gyroscope on their lower back and performed a range of scripted and non-scripted activities in their homes/retirement villages whilst being videoed. A novel algorithm was developed to identify sitting, lying, and upright activities. The algorithm's sensitivity, specificity, positive predictive value, and negative predictive value for identifying scripted sitting activities ranged from 76.9% to 94.8%. For scripted lying activities: 70.4% to 95.7%. For scripted upright activities: 75.9% to 93.1%. For non-scripted sitting activities: 92.3% to 99.5%. No non-scripted lying activities were captured. For non-scripted upright activities: 94.3% to 99.5%. The algorithm could, at worst, overestimate or underestimate sedentary behaviour bouts by ±40 s, which is within a 5% error for sedentary behaviour bouts. These results indicate good to excellent agreement for the novel algorithm, providing a valid measure of sedentary behaviour in community-dwelling older adults.

The Role of Wearable Sensors to Monitor Physical Activity and Sleep Patterns in Older Adult Inpatients: A Structured Review.

Low levels of physical activity (PA) and sleep disruption are commonly seen in older adult inpatients and are associated with poor health outcomes. Wearable sensors allow for objective continuous monitoring; however, there is no consensus as to how wearable sensors should be implemented. This review aimed to provide an overview of the use of wearable sensors in older adult inpatient populations, including models used, body placement and outcome measures. Five databases were searched; 89 articles met inclusion criteria. We found that studies used heterogenous methods, including a variety of sensor models, placement and outcome measures. Most studies reported the use of only one sensor, with either the wrist or thigh being the preferred location in PA studies and the wrist for sleep outcomes. The reported PA measures can be mostly characterised as the frequency and duration of PA (Volume) with fewer measures relating to intensity (rate of magnitude) and pattern of activity (distribution per day/week). Sleep and circadian rhythm measures were reported less frequently with a limited number of studies providing both physical activity and sleep/circadian rhythm outcomes concurrently. This review provides recommendations for future research in older adult inpatient populations. With protocols of best practice, wearable sensors could facilitate the monitoring of inpatient recovery and provide measures to inform participant stratification and establish common objective endpoints across clinical trials.

Assessing fatigue and sleep in chronic diseases using physiological signals from wearables: A pilot study.

Problems with fatigue and sleep are highly prevalent in patients with chronic diseases and often rated among the most disabling symptoms, impairing their activities of daily living and the health-related quality of life (HRQoL). Currently, they are evaluated primarily via Patient Reported Outcomes (PROs), which can suffer from recall biases and have limited sensitivity to temporal variations. Objective measurements from wearable sensors allow to reliably quantify disease state, changes in the HRQoL, and evaluate therapeutic outcomes. This work investigates the feasibility of capturing continuous physiological signals from an electrocardiography-based wearable device for remote monitoring of fatigue and sleep and quantifies the relationship of objective digital measures to self-reported fatigue and sleep disturbances. 136 individuals were followed for a total of 1,297 recording days in a longitudinal multi-site study conducted in free-living settings and registered with the German Clinical Trial Registry (DRKS00021693). Participants comprised healthy individuals (N = 39) and patients with neurodegenerative disorders (NDD, N = 31) and immune mediated inflammatory diseases (IMID, N = 66). Objective physiological measures correlated with fatigue and sleep PROs, while demonstrating reasonable signal quality. Furthermore, analysis of heart rate recovery estimated during activities of daily living showed significant differences between healthy and patient groups. This work underscores the promise and sensitivity of novel digital measures from multimodal sensor time-series to differentiate chronic patients from healthy individuals and monitor their HRQoL. The presented work provides clinicians with realistic insights of continuous at home patient monitoring and its practical value in quantitative assessment of fatigue and sleep, an area of unmet need.

Sugar and acid profile of loquat (Eriobotrya japonica Lindl.), enzymes assay and expression profiling of their metabolism-related genes as influenced by exogenously applied boron.

Soluble sugars and organic acids are the most abundant components in ripe fruits, and they play critical roles in the development of fruit flavor and taste. Some loquat cultivars have high acid content which seriously affect the quality of fruit and reduce the value of commodity. Consequently, studying the physiological mechanism of sugar-acid metabolism in loquat can clarify the mechanism of their formation, accumulation and degradation in the fruit. Minerals application has been reported as a promising way to improve sugar-acid balance of the fruits. In this study, loquat trees were foliar sprayed with 0.1, 0.2 and 0.3% borax, and changes in soluble sugars and organic acids were recorded. The contents of soluble sugars and organic acids were determined using HPLC-RID and UPLC-MS, respectively. The activities of enzymes responsible for the metabolism of sugars and acids were quantified and expressions of related genes were determined using quantitative real-time PCR. The results revealed that 0.2% borax was a promising treatment among other B applications for the increased levels of soluble sugars and decreased acid contents in loquats. Correlation analysis showed that the enzymes i.e., SPS, SS, FK, and HK were may be involved in the regulation of fructose and glucose metabolism in the fruit pulp of loquat. While the activity of NADP-ME showed negative and NAD-MDH showed a positive correlation with malic acid content. Meanwhile, EjSPS1, EjSPS3, EjSS3, EjHK1, EjHK3, EjFK1, EjFK2, EjFK5, and EjFK6 may play an important role in soluble sugars metabolism in fruit pulp of loquat. Similarly, EjPEPC2, EjPEPC3, EjNAD-ME1, EjNAD-MDH1, EjNAD-MDH5-8, EjNAD-MDH10, and EjNAD-MDH13 may have a vital contribution to malic acid biosynthesis in loquat fruits. This study provides new insights for future elucidation of key mechanisms regulating soluble sugars and malic acid biosynthesis in loquats.

LR-AKAP: A Lightweight and Robust Security Protocol for Smart Home Environments.

For the betterment of human life, smart Internet of Things (IoT)-based systems are needed for the new era. IoT is evolving swiftly for its applications in the smart environment, including smart airports, smart buildings, smart manufacturing, smart homes, etc. A smart home environment includes resource-constrained devices that are interlinked, monitored, controlled, and analyzed with the help of the Internet. In a distributed smart environment, devices with low and high computational power work together and require authenticity. Therefore, a computationally efficient and secure protocol is needed. The authentication protocol is employed to ensure that authorized smart devices communicate with the smart environment and are accessible by authorized personnel only. We have designed a novel, lightweight secure protocol for a smart home environment. The introduced novel protocol can withstand well-known attacks and is effective with respect to computation and communication complexities. Comparative, formal, and informal analyses were conducted to draw the comparison between the introduced protocol and previous state-of-the-art protocols.

Investigating the Impact of Environment and Data Aggregation by Walking Bout Duration on Parkinson's Disease Classification Using Machine Learning.

Parkinson's disease (PD) is a common neurodegenerative disease. PD misdiagnosis can occur in early stages. Gait impairment in PD is typical and is linked with an increased fall risk and poorer quality of life. Applying machine learning (ML) models to real-world gait has the potential to be more sensitive to classify PD compared to laboratory data. Real-world gait yields multiple walking bouts (WBs), and selecting the optimal method to aggregate the data (e.g., different WB durations) is essential as this may influence classification performance. The objective of this study was to investigate the impact of environment (laboratory vs. real world) and data aggregation on ML performance for optimizing sensitivity of PD classification. Gait assessment was performed on 47 people with PD (age: 68 ± 9 years) and 52 controls [Healthy controls (HCs), age: 70 ± 7 years]. In the laboratory, participants walked at their normal pace for 2 min, while in the real world, participants were assessed over 7 days. In both environments, 14 gait characteristics were evaluated from one tri-axial accelerometer attached to the lower back. The ability of individual gait characteristics to differentiate PD from HC was evaluated using the Area Under the Curve (AUC). ML models (i.e., support vector machine, random forest, and ensemble models) applied to real-world gait showed better classification performance compared to laboratory data. Real-world gait characteristics aggregated over longer WBs (WB 30-60 s, WB > 60 s, WB > 120 s) resulted in superior discriminative performance (PD vs. HC) compared to laboratory gait characteristics (0.51 ≤ AUC ≤ 0.77). Real-world gait speed showed the highest AUC of 0.77. Overall, random forest trained on 14 gait characteristics aggregated over WBs > 60 s gave better performance (F1 score = 77.20 ± 5.51%) as compared to laboratory results (F1 Score = 68.75 ± 12.80%). Findings from this study suggest that the choice of environment and data aggregation are important to achieve maximum discrimination performance and have direct impact on ML performance for PD classification. This study highlights the importance of a harmonized approach to data analysis in order to drive future implementation and clinical use. Clinical Trial Registration: [09/H0906/82].

Carboxymethyl cellulose coating delays ripening of harvested mango fruits by regulating softening enzymes activities.

The effect of carboxymethyl cellulose [(1%) CMC] was evaluated on mango fruits under storage at 20 ± 1 °C for 20 days. The CMC coating noticeably reduced weight loss and disease incidence. Application of CMC delayed climacteric peak of ethylene and respiration rate with significantly reduced relative ion leakage, malondialdehyde, superoxide anion and hydrogen peroxide content. The treated mangoes showed significantly lower L*, a*, b* and total carotenoids. The CMC treatment reduced the increase in cellulase, pectin methylesterase and polygalacturonase activity that delayed softening of mango fruits. In addition, activities of peroxidase, catalase, ascorbate peroxidase and superoxide dismutase were substantially higher in CMC-treated mango fruits. The treated fruits showed significantly lower soluble solids and higher titratable acidity which thereby reduced the ripening index of mangoes. In conclusion, CMC treatment could be considered a potential pre-storage treatment to delay postharvest ripening and to conserve the eating quality of ambient stored mango fruits.

Predicting the Progression of Parkinson's Disease MDS-UPDRS-III Motor Severity Score from Gait Data using Deep Learning.

Parkinson's disease (PD) is a common neurodegenerative disease presenting with both motor and non-motor symptoms. Among PD motor symptoms, gait impairments are common and evolve over time. PD motor symptoms severity can be evaluated using clinical scales such as the Movement Disorder Society Unified Parkinson's Rating Scale part III (MDS-UPDRS-III), which depend on the patient's status at the time of assessment and are limited by subjectivity. Objective quantification of motor symptoms (i.e. gait) with wearable technology paired with Deep Learning (DL) techniques could help assess motor severity. The aims of this study were to: (i) apply DL techniques to wearable-based gait data to estimate MDS-UPDRS-III scores; (ii) test the DL approach on longitudinal dataset to predict the progression of MDS-UPDRSIII scores. PD gait was measured in the laboratory, during a 2 minute continuous walk, with a sensor positioned on the lower back. A DL Convolutional Neural Network (CNN) was trained on 70 PD subjects (mean disease duration: 3.5 years), validated on 58 subjects (mean disease duration: 5 years) and tested on 46 subjects (mean disease duration: 6.5 years). Model performance was evaluated on longitudinal data by quantifying the association (Pearson correlation (r)), absolute agreement (Intraclass correlation (ICC)) and mean absolute error between the predicted and true MDS-UPDRS-III. Results showed that MDS-UPDRS-III scores predicted with the proposed model, strongly correlated (r=0.82) and had a good agreement (ICC(2,1)=0.76) with true values; the mean absolute error for the predicted MDS-UPDRS-III scores was 6.29 points. The results from this study are encouraging and show that a DL-CNN model trained on baseline wearable-based gait data could be used to assess PD motor severity after 3 years.Clinical Relevance-Gait assessed with wearable technology paired with DL-CNN can estimate PD motor symptom severity and progression to support clinical decision making.

Addressing the challenge of cold stress resilience with the synergistic effect of Rhizobium inoculation and exogenous melatonin application in Medicago truncatula.

Cold stress is an adverse environmental condition that limits the growth and yield of leguminous plants. Thus, discovering an effective way of ameliorating cold-mediated damage is important for sustainable legume production. In this study, the combined use of Rhizobium inoculation (RI) and melatonin (MT) pretreatment was investigated in Medicago truncatula plants under cold stress. Eight-week-old seedlings were divided into eight groups: (i) CK (no stress, noninoculated, no MT), (ii) RI (Rhizobium inoculated), (iii) MT (75 µM melatonin), (iv) RI+MT, (v) CS (cold stress at 4 °C without Rhizobium inoculation and melatonin), (vi) CS+RI, (vii) CS+MT, and (viii) CS+RI+MT. Plants were exposed to cold stress for 24 hrs. Cold stress decreased photosynthetic pigments and increased oxidative stress. Pretreatment with RI and MT alone or combined significantly improved root activity and plant biomass production under cold stress. Interestingly, chlorophyll contents increased by 242.81% and MDA levels dramatically decreased by 34.22% in the CS+RI+MT treatment compared to the CS treatment. Moreover, RI+MT pretreatment improved the antioxidative ability by increasing the activities of peroxidase (POD; 8.45%), superoxide dismutase (SOD; 50.36%), catalase (CAT; 140.26%), and ascorbate peroxidase (APX; 42.63%) over CS treated plants. Additionally, increased osmolyte accumulation, nutrient uptake, and nitrate reductase activity due to the combined use of RI and MT helped the plants counteract cold-mediated damage by strengthening the nonenzymatic antioxidant system. These data indicate that pretreatment with a combined application of RI and MT can attenuate cold damage by enhancing the antioxidation ability of legumes.

Postharvest Aloe vera gel coating application maintains the quality of harvested green chilies during cold storage.

The effect of Aloe vera (ALV) coating was studied on chillies at 10 ± 1°C for 28 days. ALV gel-coated chillies showed reduced weight loss, disease incidence, red chili percentage, respiration rate, electrolyte leakage, hydrogen peroxide, and superoxide anion contents. The ALV coating maintained general acceptance in terms of visual quality and marketability index with higher chlorophyll contents, ascorbic acid contents, total phenolic contents, and total antioxidants. In addition, ascorbate peroxidase, catalase, superoxide dismutase, and peroxidase activities were markedly higher in coated chillies compared to control. The biochemical attributes such as soluble solids content, acidity, sugar: acid ratio, and juice pH were non-significantly affected by ALV application; however, the said attributes were comparatively higher in contrast to control. In conclusion, ALV edible coating could be used as an eco-friendly approach for delaying senescence and maintaining the postharvest quality of green chillies up to 28 days. PRACTICAL APPLICATIONS: Green chilies being highly perishable exhibit limited postharvest life with rapid loss of water, shrivelling, wilting, disease incidence, and reduced consumer acceptability. ALV gel coating significantly delayed postharvest senescence, reduced disease spread, maintained higher antioxidant activities of green chilies during cold storage. Therefore, ALV coating [50%] would be the suitable alternative to synthetic preservatives for extending the storage life and conserving the quality of green chilies.

Potential of Aloe vera gel coating for storage life extension and quality conservation of fruits and vegetables: An overview.

Aloe vera (ALV) with its unique nutritional profile is being used for food, health, and nutraceutical industries globally. Due to its organic nature, ALV gel coating has created lot of interest for exploring its potential in extending the shelf and storage life of fresh produce. ALV gel coating plays imperative role in delaying fruit ripening by lowering ethylene biosynthesis, respiration rate, and internal metabolic activities associated with fruit softening, color development, enzymatic browning, and decay. ALV gel coating reduces the microbial spoilage due to its antifungal properties and maintains visual appearance, firmness, sugar: acid ratio, total antioxidants, and phenolic contents with conserved eating quality. ALV coated fruits and vegetables showed reduced weight loss, superoxide ion ( O2-∙ ), hydrogen peroxide (H2 O2 ), ion leakage, and soluble solids content and exhibited higher acidity, anthocyanins, ascorbic acid, catalase (CAT), superoxide dismutase (SOD), and ascorbate peroxidase (APX) activities. It also delayed the enzymatic browning by inducing peroxidase (POD) activity during storage. Recent local studies also revealed that ALV gel coating markedly conserved higher consuming quality and extended storage period (>1.34-fold) of different fruits and vegetables. Overall, Aloe vera gel coating alone or in combination with other organic compounds has shown great potential as a food-safe and eco-friendly coating for maintaining the quality of fruits and vegetables over extended period and reducing postharvest losses in the supply chain. PRACTICAL APPLICATIONS: ALV gel is a plant-based natural coating of eco-friendly nature. The present review summarizes the updated information of ALV gel coating application, methods of extraction, combinations with other postharvest coatings, and its impact on quality of various fruits and vegetables. It also provides future insights for the development of commercially applicable ALV gel coating protocols through simulation studies. So, being a natural coating, ALV gel has tremendous potential to be used in fruit and vegetable industries around the globe.

Gait Analysis with Wearables Can Accurately Classify Fallers from Non-Fallers: A Step toward Better Management of Neurological Disorders.

Falls are the leading cause of mortality, morbidity and poor quality of life in older adults with or without neurological conditions. Applying machine learning (ML) models to gait analysis outcomes offers the opportunity to identify individuals at risk of future falls. The aim of this study was to determine the effect of different data pre-processing methods on the performance of ML models to classify neurological patients who have fallen from those who have not for future fall risk assessment. Gait was assessed using wearables in clinic while walking 20 m at a self-selected comfortable pace in 349 (159 fallers, 190 non-fallers) neurological patients. Six different ML models were trained on data pre-processed with three techniques such as standardisation, principal component analysis (PCA) and path signature method. Fallers walked more slowly, with shorter strides and longer stride duration compared to non-fallers. Overall, model accuracy ranged between 48% and 98% with 43-99% sensitivity and 48-98% specificity. A random forest (RF) classifier trained on data pre-processed with the path signature method gave optimal classification accuracy of 98% with 99% sensitivity and 98% specificity. Data pre-processing directly influences the accuracy of ML models for the accurate classification of fallers. Using gait analysis with trained ML models can act as a tool for the proactive assessment of fall risk and support clinical decision-making.

Turning Detection During Gait: Algorithm Validation and Influence of Sensor Location and Turning Characteristics in the Classification of Parkinson's Disease.

Parkinson's disease (PD) is a common neurodegenerative disorder resulting in a range of mobility deficits affecting gait, balance and turning. In this paper, we present: (i) the development and validation of an algorithm to detect turns during gait; (ii) a method to extract turn characteristics; and (iii) the classification of PD using turn characteristics. Thirty-seven people with PD and 56 controls performed 180-degree turns during an intermittent walking task. Inertial measurement units were attached to the head, neck, lower back and ankles. A turning detection algorithm was developed and validated by two raters using video data. Spatiotemporal and signal-based characteristics were extracted and used for PD classification. There was excellent absolute agreement between the rater and the algorithm for identifying turn start and end (ICC ≥ 0.99). Classification modeling (partial least square discriminant analysis (PLS-DA)) gave the best accuracy of 97.85% when trained on upper body and ankle data. Balanced sensitivity (97%) and specificity (96.43%) were achieved using turning characteristics from the neck, lower back and ankles. Turning characteristics, in particular angular velocity, duration, number of steps, jerk and root mean square distinguished mild-moderate PD from controls accurately and warrant future examination as a marker of mobility impairment and fall risk in PD.