Environmental survival of SARS-CoV-2 - A solid waste perspective.

The advent of COVID-19 has kept the whole world on their toes. Countries are maximizing their efforts to combat the virus and to minimize the infection. Since infectious microorganisms may be transmitted by variety of routes, respiratory and facial protection is required for those that are usually transmitted via droplets/aerosols. Therefore this pandemic has caused a sudden increase in the demand for personal protective equipment (PPE) such as gloves, masks, and many other important items since, the evidence of individual-to-individual transmission (through respiratory droplets/coughing) and secondary infection of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). But the disposal of these personal protective measures remains a huge question mark towards the environmental impact. Huge waste generation demands proper segregation according to waste types, collection, and recycling to minimize the risk of infection spread through aerosols and attempts to implement measures to monitor infections. Hence, this review focuses on the impact of environment due to improper disposal of these personal protective measures and to investigate the safe disposal methods for these protective measures by using the safe, secure and innovative biological methods such as the use of Artificial Intelligence (AI) and Ultraviolet (UV) lights for killing such deadly viruses.

Intrinsically Linear Transistor for Millimeter-Wave Low Noise Amplifiers.

Transistors are the backbone of any electronic and telecommunication system but all known transistors are intrinsically nonlinear introducing signal distortion. Here, we demonstrate a novel transistor with the best linearity achieved to date, attained by sequential turn-on of multiple channels composed of a planar top-gate and several trigate Fin field-effect transistors (FETs), using AlGaN/GaN structures. A highly linearized transconductance plateau of >6 V resulted in a record linearity figure of merit OIP3/PDC of 15.9 dB at 5 GHz and a reduced third-order intermodulation power by 400× in reference to a conventional planar device. The proposed architecture also features an exceptional performance at 30 GHz with an OIP3/PDC of ≥8.2 dB and a minimum noise figure of 2.2 dB. The device demonstrated on a scalable Si substrate paves the way for GaN low noise amplifiers (LNAs) to be utilized in telecommunication systems, and is also translatable to other material systems.

Mitochondrial calcium uniporter as a potential therapeutic strategy for Alzheimer's disease.

Alzheimer's disease (AD), a neurodegenerative disorder, is the leading cause of dementia in the world whose aetiology is still unclear. AD was always related to ageing though there have been instances where people at an early age also succumb to this disease. With medical advancements, the mortality rate has significantly reduced which also makes people more prone to AD. AD is rare, yet the prominent disease has been widely studied with several hypotheses trying to understand the workings of its onset. The most recent and popular hypothesis in AD is the involvement of mitochondrial dysfunction and calcium homeostasis in the development of the disease though their exact roles are not known. With the sudden advent of the mitochondrial calcium uniporter (MCU), many previously known pathological hallmarks of AD may be better understood. Several studies have shown the effect of excess calcium in mitochondria and the influence of MCU complex in mitochondrial function. In this article, we discuss the possible involvement of MCU in AD by linking the uniporter to mitochondrial dysfunction, calcium homeostasis, reactive oxygen species, neurotransmitters and the hallmarks of AD - amyloid plaque formation and tau tangle formation.

Generation of clinical-grade red blood cells from human umbilical cord blood mononuclear cells.

A xeno-free method for ex vivo generation of red blood cells (RBCs) is attempted in order to replicate for large-scale production and clinical applications. An efficient milieu was formulated using injectable drugs substituting the animal-derived components in the culture medium. Unfractionated mononuclear cells isolated from human umbilical cord blood were used hypothesizing that the heterogeneous cell population could effectively contribute to erythroid cell generation. The strategy adopted includes a combination of erythropoietin and other injectable drugs under low oxygen levels, which resulted in an increase in the number of mature RBCs produced in vitro. The novelty in this study is the addition of supplements to the medium in a stage-specific manner for the differentiation of unfractionated umbilical cord blood mononuclear cells (MNCs) into erythropoietic lineage. The erythropoietic lineage was well established by day 21, wherein the mean cell count of RBCs was found to be 21.36 ± 0.9 × 108 and further confirmed by an upregulated expression of CD235a+ specific to RBCs. The rationale was to have a simple method to produce erythroid cells from umbilical cord blood isolates in vitro by mitigating the effects of multiple erythroid-activating agents and batch to batch variability.

Generation of nighttime pedestrian fatal precrash scenarios at junctions in Tamil Nadu, India, using cluster correspondence analysis.

OBJECTIVE: Modern transportation amenities and lifestyles have changed people's behavioral patterns while using the road, specifically at nighttime. Pedestrian and driver maneuver behaviors change based on their exposure to the environment. Pedestrians are more vulnerable to fatal injuries at junctions due to increased conflict points with vehicles. Generation of precrash scenarios allows drivers and pedestrians to understand errors on the road during driver maneuvering and pedestrian walking/crossing. This study aims to generate precrash scenarios using comprehensive nighttime fatal pedestrian crashes at junctions in Tamil Nadu, India. METHODS: Though numerous studies were available on identifying pedestrian crash patterns, only some focused on identifying crash patterns at junctions at night. We used cluster correspondence analysis (CCA) to address this research gap to identify the patterns in nighttime pedestrian fatal crashes at junctions. Further, high-risk precrash scenarios were generated based on the positive residual means available in each cluster. This study used crash data from the Road Accident Database Management System of Tamil Nadu State in India from 2009 to 2018. Characteristics of pedestrians, drivers, vehicles, crashes, light, and roads were input to the CCA to find optimal clusters using the average silhouette width, Calinski-Harabasz measure, and objective values. RESULTS: CCA found 4 clusters with 2 dimensions as optimal clusters, with an objective value of 3.3618 and a valence criteria ratio of 80.03%. Results from the analysis distinctly clustered the pedestrian precrash behaviors: Clusters 1 and 2 on pedestrian walking behaviors and clusters 3 and 4 on crossing behaviors. Moreover, a hidden pattern was observed in cluster 4, such as transgender drivers involved in fatal pedestrian crashes at junctions at night. CONCLUSION: The generated precrash scenarios may be used to train drivers (novice and inexperienced for nighttime driving), test scenario creation for developing advanced driver/rider assistance systems, hypothesis creation for researchers, and planning of effective strategic interventions for engineers and policymakers to change pedestrian and driver behaviors toward sustainable safety on Indian roads.

Visual hazardous models: A hybrid approach to investigate road hazardous events.

Road users (drivers, passengers, pedestrians, and Animals) are exposed to hazardous events during their commute. With 23 % of global fatalities among pedestrians, their safety continues to be a principal interest for policymakers worldwide. Owing to limited budgets available, there is a growing emphasis on data-driven stochastic models to decide on policies. However, statistical models have limitations due to crash data having redundant features, inherent heterogeneity, and unobserved characteristics. The random parameter model framework addresses the unobserved heterogeneity, but redundant features and inherent heterogeneity among the data's characteristics still compute the biased estimates. This is further complicated if the data has spatiotemporal attributes. To address this, we developed two visual hazardous (VH) models: (i) addresses the unobserved heterogeneity in the data, and (ii) addresses the dimensionality, inherent heterogeneity among the characteristics and unobserved heterogeneity in the collected data after spatiotemporal pattern identification. The feature selection model reduces the dimensionality, whereas latent class clustering classifies the data into maximum heterogeneity between classes. This integration reduces bias in the estimates. As a use-case, pedestrian crosswalk crashes for a decade (2009-2018) in the Indian state of Tamil Nadu extracted from the Road Accident Database Management System (RADMS) was used to understand model performance. This data comprises the crash location, road, vehicle, driver, pedestrian, and environment details. Results show that visual hazardous model 2 allows for generating crash scenarios with five homogeneous sub-classes and the magnitude with marginal effects of contributing factors impacting it. For example, pedestrians during their crosswalks are likely to sustain 82% more chance of fatal/grievous injuries on expressways (posted speed limit: 100 km per hour) in annual hazardous zone locations. Working pedestrian age group (25-64 years), an older pedestrian (>64 years), the pedestrian position on a pedestrian crossing and not in the centre of the road, pedestrian action: walking along the edge of the road, multiple lanes, two lanes, paved shoulder, straight and flat road, motorcycle, bus, truck, medium-duty vehicle, illegal driver (<=17 years), going ahead/ overtaking, high speed, expressways, and rural region were statistically significant (positively) contributing to the fatal/grievous injury pedestrian crashes during their crosswalk. This technique serves as a structure for engineers, researchers, and policymakers to formulate effective countermeasures that enhance road safety.

Influence of occupant collision state parameters on the lumbar spinal injury during frontal crash.

INTRODUCTION: Developed a detailed finite element model of spine and validated with the experimental or cadaveric tests to gain insight on occupant safety. OBJECTIVES: This study evaluates the influence of occupant collision state parameters such as height of the drop, occupant seating posture (occupant posture angle) and mass of the upper body on the risk of lumbar spinal injury during a frontal crash. METHODS: This parametric evaluation utilizing response surface methodology (RSM) performed. ANOVA was used to test the significance of parameters. RESULTS: Higher axial force of 3547 N is observed with higher dropping distance of 1500 mm. Similarly, higher strain and energy absorption were observed for the same dropping condition respectively. CONCLUSION: The result shows that all the factors considered in the experiment contribute to the risk of spinal lumbar injury during the frontal crash. Among all, height of the drop and the occupant posture angle are the most significant parameters in determining the lumbar spinal injury of occupant. It is observed that the injury criteria are directly proportional to the posture angle of the seat and height of drop.

Investigation of factors contributing to injury severity in single vehicle motorcycle crashes in India.

Single-vehicle crashes are of major concern in both developed as well as in Low Middle Income Countries due to the severity of injuries, particularly fatal accidents. In India, a significant proportion of crashes are single-vehicle crashes. The vehicles which are involved in accidents due to causes such as self skidding, hitting stationary objects, trees that are simply contributed by the drivers themselves are referred to as out-of-control single-vehicle crashes. The main objective of this study is to evaluate the risk factors associated with single-vehicle motorcycle collisions in Tamilnadu, India and identifies the unique characteristics and injury outcomes associated with these collisions. Crash dataset for the present study was prepared from the police-reported crashes for the past nine years that occurred within the state of Tamilnadu between 2009 and 2017. The identified contributory factors which influence injury severity include driver characteristics, crash-related factors, traffic-related factors, vehicle and environment-related factors. In this study, injury severity is classified into three categories, i.e. fatal, serious, and minor injuries. Since the outcome of the injury severity could be measured on an ordinal scale, a discrete ordered outcome model, an ordered logit model is applied. To summarise the results, thirteen of the studied factors are found to have a significant influence on the injury severity of drivers. Results show that the likelihood of fatal injuries increases in crashes where motorcyclists hit stationary fixed objects, hit trees, ran-off road, inclement weather conditions, urban areas. It is also found that winter season, north districts of Tamilnadu, single and two-lane roads, highways, village roads and, other district roads, daylight conditions, drivers who are younger and working-age group, overtaking from left, taking u-turn are associated with less likelihood of fatal crashes. To increase the overall safety of the roads, targeted countermeasures may be designed in light of injury severity of the drivers with respect to single-vehicle crashes also. This study provides useful insights for reducing injury severity in single-vehicle motorcycle crashes.

Applying fast and frugal tree heuristic algorithm to identify factors influencing crash severity of bicycle-vehicle crashes in Tamilnadu.

Though bicycle as a mode of transport has many environmental and societal benefits as well as health benefits, bicyclists are one of the most vulnerable road users. According to the report by the Ministry of Road Transport and Highways (MoRTH, 2017), there is a sharp increase in the number of fatal victims in respect of bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2585 in 2016 to 3559 in 2017, a 37.7% increase. In the present study, we present the analysis of the effect of the crash, geometric, environmental and cyclist characteristics on the bicycle-vehicle involved collisions by using the crash dataset of nine years (2009-2017) from Tamilnadu RADMS (Road Accident Data Management System) database with the application of fast and frugal tree (FFT) heuristic algorithm. The complete dataset (9978 crashes) was divided into two separate datasets: training data (6984 crashes) for the development of model and testing data (2984 crashes) for the performance evaluation. FFT algorithm identifies five major hues or variable attributes that influence the severity of bicycle crashes. The five major hues include the number of lanes, road separation, intersection, colliding vehicle type and road category. From the results of the present study, FFT acts as a complementary tool to other complex machine learning algorithms such as support vector machines, random forest, logistic regression and CART. The findings of the present study provide important insights for reducing the severity of bicycle-involved crashes at the planning and operations levels.

Exploring the severity of bicycle-vehicle crashes using latent class clustering approach in India.

INTRODUCTION: Bicyclists are vulnerable users in the shared asset like roadways. However, people still prefer to use bicycles for environmental, societal, and health benefits. In India, the bicycle plays a role in supporting the mobility to more people at lower cost and are often associated with the urban poor. Bicyclists represents one of the road user categories with highest risk of injuries and fatalities. According to the report by the Ministry of Road Transport and Highways (Accidents, 2017) in India, there is a sharp increase in the number of fatal victims for bicyclists in 2017 over 2016. The number of cyclists killed jumped from 2,585 in 2016 to 3,559 in 2017, a 37.7% increase. METHOD: Few studies have only investigated the crash risk perceived by the bicyclists while interacting with other road users. The present paper investigates the injury severity of bicyclists in bicycle-vehicle crashes that occurred in the state of Tamilnadu, India during the nine year period (2009-2017). The analyses demonstrate that dividing bicycle-vehicle collision data into five clusters helps in reducing the systematic heterogeneity present in the data and identify the hidden relationship between the injury severity levels of bicyclists and cyclists demographics, vehicle, environmental, temporal cause for the crashes. RESULTS: Latent Class Clustering (LCC) approach was used in the present study as a preliminary tool for the segmentation of 9,978 crashes. Later, logistic regression analysis was used to identify the factors that influence bicycle crash severity for the whole dataset as well as for the clusters that were obtained from the LCC model. Results of this study show that combined use of both techniques reveals further information that wouldn't be obtained without prior segmentation of the data. Few variables such as season, weather conditions, and light conditions were significant for certain clusters that were hidden in the whole dataset. This study can help domain experts or traffic safety researchers to segment traffic crashes and develop targeted countermeasures to mitigate injury severity.

Developing a heuristic relationship to predict the spinal injury during vertical impact for autonomous vehicle and bio environment.

BACKGROUND AND OBJECTIVE: Recent research and tested data suggested that spinal injuries occur more often in a frontal impact. Most of the published information is focused on the lumbar spinal injury with respect to axial compression force by varying the height of drops. Parametric studies on the lumbar spinal injury are very scanty. Therefore, the present investigation aimed to optimize the effects of drop height, torso weight and seat angle on the characterization of lumbar injury criteria METHODS: A detailed finite element model of a spine with multi-segmented spinal columns is developed and validated with the experimental or cadaveric tests using CORA evaluation. Hence, Dynamic loading studies or weight drop techniques were used to characterize the effect of drop height, seat angle and torso weight of the upper body on the lumbar spinal injury during a frontal impact. Parametric simulations were carried out using response surface methodology (RSM). Test of significance (p < 0.05) on the parameters was carried out using ANOVA. Desirability Function Approach is used to optimize the parameters for better safety design. RESULTS: The result shows that all the factors considered in the experiment are related to the risk of lumbar spinal injury during the frontal impact. All the factors selected, the drop height, torso weight and the seat angle were the most prominent element in determining the lumbar spinal injury. The injury increased with the increase in the posture angle of the seat. Optimal parameters were determined for the better safety of the occupants as seat angle of 105°, drop height 500 mm and torso weight of 25 kg in vehicle design. During vertical impact, posterior undergoes maximum impact in the portions of vertebra and confirmed with the patient case study fracture of vertical drop incident. CONCLUSIONS: This research insight gives an improved understanding of the parametric influence of design alternatives to minimize the risk of lumbar spinal injury in automotive vehicles. The optimal combination of drop height and the seat angle provides futuristic view on autonomous vehicle seat design.

Investigation of pedestrian crashes using multiple correspondence analysis in India.

Pedestrian safety is of growing concern with an increasing number of traffic accidents, especially in developing economies like India. In 2017, there were 20,457 pedestrian fatalities in India. Pedestrian crashes have also become a key concern in the state of Tamilnadu, India, due to the high percentage of deaths. If the available datasets are large and complex, identifying key factors is a challenging task. In this study, Multiple Correspondence Analysis (MCA), an exploratory data analysis technique was used to explore the roadway, traffic, crash, and pedestrian-related variables influencing pedestrian crashes. This study used the data from Government of Tamilnadu Road Accident Traffic Management System (RADMS) database, to analyse accident data of nine years (2009-2017) related to pedestrian crashes. The results of the study show that crashes occurring on the express highways on a multilane road are often associated with hit-and-run behaviour among drivers. Factors such as lighting conditions, location, pedestrian behaviour, crossings, and physical separation are also significantly contributing to pedestrian crashes. The key advantage of MCA is that it identifies a possible association between various contributing factors. The findings from this study will be useful for state transport authorities to improve countermeasures for mitigating pedestrian crashes and fatalities.

Unraveling correlative roles of dopamine transporter (DAT) and Parkin in Parkinson's disease (PD) - A road to discovery?

Parkinson's disease (PD) is a neurodegenerative disorder accompanied by depletion of dopamine(DA) and loss of dopaminergic (DAergic) neurons in the brain that is believed to be responsible for the motor and non-motor symptoms of PD. Dopamine Transporter (DAT) is essential for reuptake of DA into the presynaptic terminal, thereby controlling the availability and spatial activity of released DA. Parkin interacts with proteins involved in the endosomal pathway, suggesting that presynaptic Parkin could regulate the expression of DAT in the plasma membrane. Parkin mutations lead to early synaptic damage and it appears as a crucial gene having a vast functioning area. PD-specific induced pluripotent stem cells (iPSCs) derived DA neurons exist as a potential tool for in-vitro modeling of PD, as they can recapitulate the pathological features of PD. The exact mechanism of PARKIN influenced DAT variations and changes in DA reuptake by DAT remain unknown. Hence, DAT and PARKIN mutated PD-specific iPSCs-derived DA neurons could provide important clues for elucidating the pathogenesis and mechanism of PD. This mysterious and hidden connection may prove to be a boon in disguise, hence, here we review the influence of PARKIN and DAT on DA mechanism and will discuss how these findings underpin the concept of how downregulation or upregulation of DAT is influenced by PARKIN. We conclude that the establishment of new model for PD with a combination of DAT and PARKIN would have a high translational potential, which includes the identification of drug targets and testing of known and novel therapeutic agents.

The potential role of rhythmic entrainment and music therapy intervention for individuals with autism spectrum disorders.

Autism spectrum disorder (ASD) is characterized by social and interpersonal communication disabilities and repetitive motor activities. A deficit in social interaction may be due to motor and synchronization disabilities in individuals with ASD. These disabilities serve as a hindrance for the progression of day-to-day life. ASD individuals are known to have variations in the neural network contributing to changes in their oral-motor activity. As the brain has experience-dependent structural plasticity, these changes in the neural network can probably be reversed with appropriate treatment Music playing a universal role in human life has been studied for its therapeutic potential in rehabilitation of ASD individuals. Music and rhythm have shown a significant potential in improving the oral-motor activities of people affected by ASD. Music based interventions are being used for children diagnosed with ASDs to improve their social communication and motor skills. This article represents the possible role of rhythmic cueing for sensorimotor regulation in ASD individuals. This can serve as a base for further research for the impact of musical therapy on coordination and oral-motor synchronization of individuals diagnosed with ASD.

mTOR signalling pathway - A root cause for idiopathic autism?

Autism spectrum disorder (ASD) is a complex neurodevelopmental monogenic disorder with a strong genetic influence. Idiopathic autism could be defined as a type of autism that does not have a specific causative agent. Among signalling cascades, mTOR signalling pathway plays a pivotal role not only in cell cycle, but also in protein synthesis and regulation of brain homeostasis in ASD patients. The present review highlights, underlying mechanism of mTOR and its role in altered signalling cascades as a triggering factor in the onset of idiopathic autism. Further, this review discusses how distorted mTOR signalling pathway stimulates truncated translation in neuronal cells and leads to downregulation of protein synthesis at dendritic spines of the brain. This review concludes by suggesting downstream regulators such as p70S6K, eIF4B, eIF4E of mTOR signalling pathway as promising therapeutic targets for idiopathic autistic individuals. [BMB Reports 2019; 52(7): 424-433].

Pedestrians' perception and response towards vehicles during road-crossing at nighttime.

Pedestrian being involved in road traffic accidents (RTA) is about 22% of all road traffic related deaths. In this study, we have estimated the pedestrian's response towards an approaching vehicle and the time taken to correctly recognize it while they crossed the road in dim-light nighttime conditions. This is also extendable to cycles and other low powered vehicles. Thirty volunteers participated in this study. A collection of six videos, which comprised of different vehicle scenarios were shown to each of the participants. It was observed that correct identification and time to recognize the vehicle was fastest when light emitting diode (LED) strip was fixed between headlights of a four-wheeler. Average time to recognize a low beam car and a high beam car with an LED strip was 7.62±2.39s and 11.23±2.94s respectively, whereas correct identification rates of the said low beam and high beam cars with LED strips were 93.33% and 86.67% respectively. Earlier when no LED was used, time to recognize low beam car and high beam car without LED strip were 20.55±3.50s and 25.57±4.14s respectively whereas correct identification of low beam car without LED strip and high beam car without LED strip were 90.00% and 56.67% respectively. Pedestrians are therefore less confused and can take right decision while crossing the road - particularly in a poor lighting environment - when there is a demarcating illumination between headlights of vehicle.

Correlation of elution and sensitivity of cell lines to dental composites.

OBJECTIVES: The aim of the present study was to understand the role played by composition on the elution of dental composites and the subsequent cytotoxicity, the sensitivity of different cell lines to eluates on a temporal basis and the correlation of the two parameters elution and cytotoxicity. METHODS: LC-MS was done to analyze the eluates. MTT assay was done to assess cytotoxicity on two cell lines. RESULTS: Eluates were found to have matrix monomers, photoinitiators and their degradation products. The short-term viability of other mammalian cell line was inferior to human cell line. However human cell line became more sensitive to long-term incubation with composites. There was a strong inverse correlation to elution of monomers and photoinitiators and cell viability for both cell lines. Bisphenol A elution did not correlate to cell viability. SIGNIFICANCE: Other mammalian cell lines may be more sensitive to acute toxin build-up than human cell line while the latter may be more sensitive to prolonged toxin exposure. Dimethacrylate based composites elute more and exert strong cytotoxicity than Ormocer and Silorane based composites. Most of the eluates correlated linearly to cytotoxicity.

Assessment of early onset of driver fatigue using multimodal fatigue measures in a static simulator.

Driver fatigue is an important contributor to road accidents. This paper reports a study that evaluated driver fatigue using multimodal fatigue measures, i.e., surface electromyography (sEMG), electroencephalography (EEG), seat interface pressure, blood pressure, heart rate and oxygen saturation level. Twenty male participants volunteered in this study by performing 60 min of driving on a static simulator. Results from sEMG showed significant physical fatigue (ρ < 0.05) in back and shoulder muscle groups. EEG showed significant (ρ < 0.05) increase of alpha and theta activities and a significant decrease of beta activity during monotonous driving. Results also showed significant change in bilateral pressure distribution on thigh and buttocks region during the study. These findings demonstrate the use of multimodal measures to assess early onset of fatigue. This will help us understand the influence of physical and mental fatigue on driver during monotonous driving.

Analysis of muscle fatigue in helicopter pilots.

Helicopter pilots espouse ergonomically unfavourable postures and endure vibration which result in low back pain. The objective of this study was to investigate the effects of a helicopter flight on pilots back and shoulder muscles using surface Electromyography (sEMG) analysis. This study also correlates low back pain symptoms from Rehabilitation Bioengineering Group Pain Scale (RBGPS) questionnaire with muscle fatigue rates obtained. RBGPS was administered on 20 Coast Guard helicopter pilots. sEMG was acquired before and after flight from erector spinae and trapezius muscles in 8 of these 20 pilots. Statistical analysis of time and frequency domain parameters indicated significant fatigue in right trapezius muscle due to flying. Muscle fatigue correlated with average duration of flight (r² = 0.913), total service as pilot (r² = 0.825), pain (r² = 0.463) and total flying hours (r² = 0.507). However, muscle fatigue weakly correlated with Body Mass Index (BMI) (r² = 0.000144) and age (r² = 0.033).

Surface EMG based muscle activity analysis for aerobic cyclist.

In this study, we determined the muscle activity of aerobic cyclist on biceps brachii medial, trapezius medial, latissimus dorsi medial, and erector spinae muscles bilaterally during 30 min of cycling. Thirteen male volunteers were chosen and placed in two groups (with and without low back pain (LBP)). Surface electromyography (sEMG) was recorded bilaterally from selected muscle groups for 30 min of cycling for each subject. Statistical tests were performed to determine the difference in fatigue, using mean power frequency difference. LBP group showed a significantly higher fatigue (p<0.05) in left biceps brachii medial when compared to the control group. High fatigue in the back muscles in the LBP group was not found; however, when linear regression was performed for these individuals, the data showed a possibility of worsening in their condition due to 30 min of cycling.