From primordial germ cells to spermatids in Caenorhabditis elegans.

Development of a syncytial germline for gamete formation requires complex regulation of cytokinesis and cytoplasmic remodeling. Recently, several uncovered cellular events have been investigated in the Caenorhabditis elegans (C. elegans) germline. In these cellular processes, the factors involved in contractility are highly conserved with those of mitosis and meiosis. However, the underlying regulatory mechanisms are far more complicated than previously thought, likely due to the single syncytial germline structure. In this review, we highlight how the proteins involved in contractility ensure faithful cell division in different cellular contexts and how they contribute to maintaining intercellular bridge stability. In addition, we discuss the current understanding of the cellular events of cytokinesis and cytoplasmic remodeling during the development of the C. elegans germline, including progenitor germ cells, germ cells, and spermatocytes. Comparisons are made with relevant systems in Drosophila melanogaster (D. melanogaster) and other animal models.

Wearable Sensors Assess the Effects of Human-Robot Collaboration in Simulated Pollination.

Pollination for indoor agriculture is hampered by environmental conditions, requiring farmers to pollinate manually. This increases the musculoskeletal illness risk of workers. A potential solution involves Human-Robot Collaboration (HRC) using wearable sensor-based human motion tracking. However, the physical and biomechanical aspects of human interaction with an advanced and intelligent collaborative robot (cobot) during pollination remain unknown. This study explores the impact of HRC on upper body joint angles during pollination tasks and plant height. HRC generally resulted in a significant reduction in joint angles with flexion decreasing by an average of 32.6 degrees (p ≤ 0.001) for both shoulders and 30.5 degrees (p ≤ 0.001) for the elbows. In addition, shoulder rotation decreased by an average of 19.1 (p ≤ 0.001) degrees. However, HRC increased the left elbow supination by 28.3 degrees (p ≤ 0.001). The positive effects of HRC were reversed when the robot was unreliable (i.e., missed its target), but this effect was not applicable for the left elbow. The effect of plant height was limited with higher plant height increasing right shoulder rotation but decreasing right elbow pronation. These findings aim to shed light on both the benefits and challenges of HRC in agriculture, providing valuable insights before deploying cobots in indoor agricultural settings.

Evaluation of Biomechanical and Mental Workload During Human-Robot Collaborative Pollination Task.

OBJECTIVE: The purpose of this study is to identify the potential biomechanical and cognitive workload effects induced by human robot collaborative pollination task, how additional cues and reliability of the robot influence these effects and whether interacting with the robot influences the participant's anxiety and attitude towards robots. BACKGROUND: Human-Robot Collaboration (HRC) could be used to alleviate pollinator shortages and robot performance issues. However, the effects of HRC for this setting have not been investigated. METHODS: Sixteen participants were recruited. Four HRC modes, no cue, with cue, unreliable, and manual control were included. Three categories of dependent variables were measured: (1) spine kinematics (L5/S1, L1/T12, and T1/C7), (2) pupillary activation data, and (3) subjective measures such as perceived workload, robot-related anxiety, and negative attitudes towards robotics. RESULTS: HRC reduced anxiety towards the cobot, decreased joint angles and angular velocity for the L5/S1 and L1/T12 joints, and reduced pupil dilation, with the "with cue" mode producing the lowest values. However, unreliability was detrimental to these gains. In addition, HRC resulted in a higher flexion angle for the neck (i.e., T1/C7). CONCLUSION: HRC reduced the physical and mental workload during the simulated pollination task. Benefits of the additional cue were minimal compared to no cues. The increased joint angle in the neck and unreliability affecting lower and mid back joint angles and workload requires further investigation. APPLICATION: These findings could be used to inform design decisions for HRC frameworks for agricultural applications that are cognizant of the different effects induced by HRC.

Exploring the effect of human-drone communication modality on safety and balance control in virtual construction environments.

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers' safety and balance control within virtual environments. Utilising virtual reality (VR) simulations, the study explored how gesture and speech-based communications influence workers' physical postures and balance, contrasting these modalities with a non-interactive control group. One hundred participants were recruited, and their movements and balance control were tracked using motion sensors while they interacted with virtual drones through either gesture, speech, or without communication. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls, suggesting that advanced HDI can enhance safety on construction sites. However, speech-based interaction increased cognitive workload, highlighting a trade-off between physical safety and mental strain. These findings underscore the potential of integrating intuitive communication methods into construction operations, although further research is needed to optimise these interactions for long-term use and in diverse noise environments.

This study examines the impact of Human-Drone Interaction (HDI) modalities on construction workers' safety and balance control within virtual environments with a human subject experiment. Results showed that interactive modalities significantly improved balance control and reduced the risk of falls.

Salient Targets and Fear of Falling Changed the Gait Pattern and Joint Kinematic of Older Adults.

BACKGROUND: Fear of falling and environmental barriers in the home are two major factors that cause the incidence of falling. Poor visibility at night is one of the key environmental barriers that contribute to falls among older adult residents. Ensuring their visual perception of the surroundings, therefore, becomes vital to prevent falling injuries. However, there are limited works in the literature investigating the impact of the visibility of the target on older adults' walking destinations and how that impact differs across them with different levels of fear of falling. OBJECTIVE: The purpose of the study was to examine the effects of target salience on older adults' walking performance and investigate whether older adults with varying levels of fear of falling behave differently. METHODS: The salient target was constructed with LED strips around the destination of walking. Fifteen older adults (aged 75 years old and above), seven with low fear of falling and eight with high fear of falling, volunteered for the study. Participants walked from the designated origin (i.e., near their beds) to the destination (i.e., near the bathroom entrance), with the target turned on or off around the destination of the walking trials. Spatiotemporal gait variables and lower-body kinematics were recorded by inertial sensors and compared by using analysis of variance methods. RESULTS: Data from inertial sensors showed that a more salient target at the destination increased older adults' gait speed and improved their walking stability. These changes were accompanied by less hip flexion at heel strikes and toe offs during walking. In addition, older adults with low fear of falling showed more substantial lower-body posture adjustments with the salient target presented in the environment. CONCLUSIONS: Older adults with a low fear of falling can potentially benefit from a more salient target at their walking destination, whereas those with a high fear of falling were advised to implement a more straightforward falling intervention in their living areas.

Going Short: The Effects of Short-Travel Key Switches on Typing Performance, Typing Force, Forearm Muscle Activity, and User Experience.

This study examined the effects of 4 micro-travel keyboards on forearm muscle activity, typing force, typing performance, and self-reported discomfort and difficulty. A total of 20 participants completed typing tasks on 4 commercially available devices with different key switch characteristics (dome, scissors, and butterfly) and key travels (0.55, 1.3, and 1.6 mm). The device with short-travel (0.55 mm) and a dome-type key switch mechanism was associated with higher muscle activities (6%-8%, P < .01), higher typing force (12%, P < .01), slower typing speeds (8%, P < .01), and twice as much discomfort (P < .05), compared with the other 3 devices: short-travel (0.55 mm) and butterfly switch design and long travel (1.3 and 1.6 mm) with scissor key switches. Participants rated the devices with larger travels (1.3 and 1.6 mm) with least discomfort (P = .02) and difficulty (P < .01). When stratified by sex/gender, these observed associations were larger and more significant in the female participants compared with male participants. The devices with similar travel but different key switch designs had difference in outcomes and devices with different travel were sometimes not different. The results suggest that key travel alone does not predict typing force or muscle activity.

Using a deep learning network to recognise low back pain in static standing.

Low back pain (LBP) remains one of the most prevalent musculoskeletal disorders, while algorithms that able to recognise LBP patients from healthy population using balance performance data are rarely seen. In this study, human balance and body sway performance during standing trials were utilised to recognise chronic LBP populations using deep neural networks. To be specific, 44 chronic LBP and healthy individuals performed static standing tasks, while their spine kinematics and centre of pressure were recorded. A deep learning network with long short-term memory units was used for training, prediction and implementation. The performance of the model was evaluated by: (a) overall accuracy, (b) precision, (c) recall, (d) F1 measure, (e) receiver-operating characteristic and (f) area under the curve. Results indicated that deep neural networks could recognise LBP populations with precision up to 97.2% and recall up to 97.2%. Meanwhile, the results showed that the model with the C7 sensor output performed the best. Practitioner summary: Low back pain (LBP) remains the most common musculoskeletal disorder. In this study, we investigated the feasibility of applying artificial intelligent deep neural network in detecting LBP population from healthy controls with their kinematics data. Results showed a deep learning network can solve the above classification problem with both promising precision and recall performance.

The changes of lumbar muscle flexion-relaxation phenomenon due to antero-posteriorly slanted ground surfaces.

Uneven ground surface is a common occupational injury risk factor in industries such as agriculture, fishing, transportation and construction. Studies have shown that antero-posteriorly slanted ground surfaces could reduce spinal stability and increase the risk of falling. In this study, the influence of antero-posteriorly slanted ground surfaces on lumbar flexion-relaxation responses was investigated. Fourteen healthy participants performed sagittally symmetric and asymmetric trunk bending motions on one flat and two antero-posteriorly slanted surfaces (-15° (uphill facing) and 15° (downhill facing)), while lumbar muscle electromyography and trunk kinematics were recorded. Results showed that standing on a downhill facing slanted surface delays the onset of lumbar muscle flexion-relaxation phenomenon (FRP), while standing on an uphill facing ground causes lumbar muscle FRP to occur earlier. In addition, compared to symmetric bending, when performing asymmetric bending, FRP occurred earlier on the contralateral side of lumbar muscles and significantly smaller maximum lumbar flexion and trunk inclination angles were observed. Practitioner Summary: Uneven ground surface is a common risk factor among a number of industries. In this study, we investigated the influence of antero-posteriorly slanted ground surface on trunk biomechanics during trunk bending. Results showed the slanted surface alters the lumbar tissue load-sharing mechanism in both sagittally symmetric and asymmetric bending.

The influence of lumbar extensor muscle fatigue on lumbar-pelvic coordination during weightlifting.

Lumbar muscle fatigue is a potential risk factor for the development of low back pain. In this study, we investigated the influence of lumbar extensor muscle fatigue on lumbar-pelvic coordination patterns during weightlifting. Each of the 15 male subjects performed five repetitions of weightlifting tasks both before and after a lumbar extensor muscle fatiguing protocol. Lumbar muscle electromyography was collected to assess fatigue. Trunk kinematics was recorded to calculate lumbar-pelvic continuous relative phase (CRP) and CRP variability. Results showed that fatigue significantly reduced the average lumbar-pelvic CRP value (from 0.33 to 0.29 rad) during weightlifting. The average CRP variability reduced from 0.17 to 0.15 rad, yet this change ws statistically not significant. Further analyses also discovered elevated spinal loading during weightlifting after the development of lumbar extensor muscle fatigue. Our results suggest that frequently experienced lumbar extensor muscle fatigue should be avoided in an occupational environment. PRACTITIONER SUMMARY: Lumbar extensor muscle fatigue generates more in-phase lumbar-pelvic coordination patterns and elevated spinal loading during lifting. Such increase in spinal loading may indicate higher risk of back injury. Our results suggest that frequently experienced lumbar muscle fatigue should be avoided to reduce the risk of LBP.

The changes of lumbar muscle flexion-relaxation response due to laterally slanted ground surfaces.

Lifting tasks performed on uneven ground surfaces are common in outdoor industries. Previous studies have demonstrated that lifting tasks performed on laterally slanted ground surfaces influence lumbar muscle activation and trunk kinematics. In this study, the effect of laterally slanted ground surfaces on the lumbar muscle flexion-relaxation responses was investigated. Fourteen participants performed sagittal plane, trunk flexion-extension tasks on three laterally slanted ground surfaces (0° (flat ground), 15° and 30°), while lumbar muscle activities and trunk kinematics were recorded. Results showed that flexion-relaxation occurred up to 6.2° earlier among ipsilateral lumbar muscles with an increase in laterally slanted ground angle; however, the contralateral side was not affected as much. Our findings suggest that uneven ground alters the lumbar tissue load-sharing mechanism and creates unbalanced lumbar muscle activity, which may increase the risk of low back pain with repeated exposure to lifting on variable surfaces. PRACTITIONER SUMMARY: Uneven ground surfaces are ubiquitous in agriculture, construction, fishing and other outdoor industries. A better understanding of the effects of laterally slanted ground surfaces on the interaction between passive and active lumbar tissues during lifting tasks could provide valuable knowledge in the design of preventive strategies for low back injuries.

Human activity recognition in an end-of-life consumer electronics disassembly task.

The production of electronic waste, also known as e-waste, has risen with the growing reliance on electronic products. To reduce negative environmental impact and achieve sustainable industrial processes, recovering and reusing products is crucial. Advances in AI and robotics can help in this effort by reducing workload for human workers and allowing them to stay away from hazardous materials. However, autonomous human motion/intention perception is a primary barrier in e-waste remanufacturing. To address the research gap, this study combined experimental data collection with deep learning models for accurate disassembly task recognition. Over 570,000 frames of motion data were collected from inertial measurement units (IMU) worn by 22 participants. A novel sequence-based correction (SBC) algorithm was also proposed to further improve the accuracy of the overall pipeline. Results showed that models (CNN, LSTM, and GoogLeNet) had an overall accuracy of 88-92%. The proposed SBC algorithm improved accuracy to 95%.

Manifold-Constrained Geometric Optimization via Local Parameterizations.

Many geometric optimization problems contain manifold constraints that restrict the optimized vertices on some specified manifold surface. The constraints are highly nonlinear and non-convex, therefore existing methods usually suffer from a breach of condition or low optimization quality. In this article, we present a novel divide-and-conquer methodology for manifold-constrained geometric optimization problems. Central to our methodology is to use local parameterizations to decouple the optimization with hard constraints, which transforms nonlinear constraints into linear constraints. We decompose the input mesh into a set of developable or nearly-developable overlapping patches with disc topology, then flatten each patch into the planar domain with very low isometric distortion, optimize vertices with linear constraints and recover the patch. Finally, we project it onto the constrained manifold surface. We demonstrate the applicability and robustness of our methodology through a variety of geometric optimization tasks. Experimental results show that our method performs much better than existing methods.

Socially Aware Robot Obstacle Avoidance Considering Human Intention and Preferences.

In order to navigate safely and effectively with humans in close proximity, robots must be capable of predicting the future motions of humans. This study first consolidates human studies in motion, intention, and preference into a discretized human model that can readily be used in robotics decision making algorithms. Cooperative Markov Decision Process (Co-MDP), a novel framework that improves upon Multiagent MDPs, is then proposed for enabling socially aware robot obstacle avoidance. Utilizing the consolidated and discretized human model, Co-MDP allows the system to (1) approximate rational human behavior and intention, (2) generate socially-aware robotic obstacle avoidance behavior, and (3) remain robust to the uncertainty of human intention and motion variance. Simulations of a human-robot co-populated environment verify Co-MDP as a feasible obstacle avoidance algorithm. In addition, the anthropomorphic behavior of Co-MDP was assessed and confirmed with a human-in-the-loop experiment. Results reveal that participants can not directly differentiate agents that were controlled by human operators from Co-MDP, and the reported confidences of their choices indicates that the predictions from participants were backed by behavioral evidence rather than random guesses. Thus the main contributions for this paper are: consolidating past human studies of rational human behavior and intention into a simple, discretized model; the development of Co-MDP: a robotic decision framework that can utilize this human model and maximize the joint utility between the human and robot; and an experimental design for evaluation of the human acceptance of obstacle avoidance algorithms.

Distraction From Smartphones Changed Pedestrians' Walking Behaviors in Open Areas.

The prevalence of phone use has become a major concern for pedestrian safety. Using smartphones while walking reduces pedestrians' ability to perceive the environment by increasing their cognitive, manual, and visual demands. The purpose of this study was to investigate the effect of common phone tasks (i.e., reading, tapping, gaming) on walking behaviors during outdoor walking. Nineteen young adults were instructed to complete four walking conditions (walking only, walking-reading, walking-tapping, and walking-gaming) along an open corridor. Results showed that all three phone tasks increased participants' neck flexion (i.e., neck kyphosis) during walking. Meanwhile, the reading task showed a greater influence on the temporal aspect during the early phases of a gait cycle. The tapping task lowered the flexion angles of the middle and lower back (i.e., torso lordosis) and induced a longer terminal double support. And the gaming task resulted in a decrease in middle back flexion, a shorter stride length, and a longer terminal double support while walking. Findings from the study confirmed our hypothesis that phone tasks changed pedestrians' physical responses to smartphone distraction while walking. To avoid potential risks caused by the observed posture and gait adaptations, safety precautions (e.g., roadside/electronic warning signals) might be imposed depending on the workload expected by different phone tasks.

Effects of Tai Chi on insomnia in elderly people with chronic non-specific low back pain: A study protocol for a randomized controlled trial.

Introduction: Chronic non-specific low back pain (CNLBP) is a complex condition characterized by pain, dysfunction, disturbed sleep, anxiety, and depression, all of which impair the quality of life. Previous studies showed that practicing Tai Chi had effects on chronic low back pain. However, there is a lack of evidence on its impact on sleep. The trial will evaluate the use of Tai Chi as a treatment for insomnia in elderly people with CNLBP. Methods: The study design will be a randomized, controlled, open-label trial. Participants (n = 106) will be recruited from the Hospital of Chengdu University of Traditional Chinese Medicine, Qing Yang District University for the Elderly, and Ci Tang Street Community. Participants will be randomly assigned to the Tai Chi group (n = 53) and the control group (n = 53). The Tai Chi group will undergo a Yang-style 24-form Tai Chi program for 8 weeks. The control group will have a waiting period of 8 weeks, followed by 8 weeks of Tai Chi practice. The primary outcomes of this study will be changes in sleep quality and pain intensity. Secondary outcomes of interest will include changes in the quality of pain, range of motion, physical performance, social support, and overall quality of life. Any adverse events and attendance rates will also be reported in this study. Clinical trial registration: ChiCTR2200064977.

Anti-cancer effect of targeting fibroblast activation protein alpha in glioblastoma through remodeling macrophage phenotype and suppressing tumor progression.

INTRODUCTION: Glioblastoma (GBM) is the most malignant form of glioma and has a poor median survival time. Fibroblast activation protein alpha (FAP) is a dual-specificity serine protease that is strongly associated with the development and progression of human carcinomas. However, relatively little is known about the function of FAP and its potential as a therapeutic target in GBMs. AIMS: In this study, we aimed to explore the role of FAP in GBM through a series of experiments and to evaluate the therapeutic effect of PT100, a small molecule inhibitor of FAP, on GBM. RESULTS: Increased FAP expression was associated with poor survival in glioma. In vitro, FAP knockdown inhibited the process of EMT and caused a decrease in the number of M2 macrophages. In vivo, PT100 was confirmed to suppress the progression of GBMs significantly. CONCLUSIONS: FAP could serve as a biomarker and novel therapeutic target for the treatment of GBM and that PT100 is a promising drug for the treatment of GBM.

Exploring Older Adults' Nighttime Trips to the Bathroom Under Different Lighting Conditions: An Exploratory Field Study.

PURPOSE: The field study was to understand older adults' reactions to and use of different low-light conditions while walking to bathrooms in the dark in their homes. Low-light conditions included participants' usual nightlights and a destination-based LED strip lighting system. BACKGROUND: Older adults encounter fall accidents while going to bathrooms at night due to low illuminance levels. They also fear falling due to previous fall histories or visual impairments. This field study tested and compared a destination-based LED strip lighting system with their usual nightlights on their movement and fear of falls. METHODS: Fifteen older adults from an independent living facility participated in the within-subject design experiment, walking under two scenarios in random order: with usual nightlights turned on or with the destination-based LED strip lighting system turned on. Body-worn sensors were used to collect participants' movement behaviors, and subjective questionnaires were used to understand participants' anxiety under the two low-light conditions. Further, semi-structured interviews were conducted to understand their nightlight usage patterns and their evaluations of the destination-based LED strip lighting system. RESULTS: Participants walked more smoothly under the destination-based LED strip lighting system scenario. However, the anxiety states were not statistically different between the two scenarios. CONCLUSION: Visual cues in the dark can benefit older adults' safe movement. However, the application of the lab-effective LED strip lighting system in home settings should consider older adults' floor plans and their furniture layout, both indoor and outdoor ambient lighting sources, and their lifestyles.

Effect of Flocculants Residue on Rheological Properties of Ultra-Fine Argillaceous Backfilling Slurry.

Tailings concentration is indispensable for backfilling. Additionally, the residual flocculants in the concentration process affect the rheological properties of ultra-fine argillaceous backfilling slurry (e.g., viscosity and yield stress), resulting in a great effect on the fluidity and resistance of pipeline transportation. In this study, to explore the effect of flocculants residue on the rheological properties of the slurry, a series of rheological tests (constant shear rate test and variable shear rate test) were performed by changing the type, dosage, stirring time, temperature of flocculants addition and the amount of binder added. The results showed that the addition of flocculants increased the viscosity and yield stress of slurry. At a certain amount of flocculants additive, the flocculant network structure reached the best development state, which had a positive effect on increasing slurry viscosity and yield stress. As the stirring time increased, the scale of damage to the flocculant network structure became larger, which had a negative effect on increasing slurry viscosity and yield stress. Low temperature weakened the adsorption and bridging effect of polymeric chains, resulting in a poorly developed flocculant network structure, which had a negative effect on increasing slurry viscosity and yield stress. Caused by hydration products, the viscosity and yield stress of slurry with binder further increased. This study is significant for an in-depth study of the rheological and pipeline transport characteristics of ultra-fine argillaceous backfilling slurry, optimising the selection of flocculants for ultrafine particles, guiding backfill parameters and improving the reliability of pipeline transport.

Load Carriage Modes and Limb Crossing Patterns Altered Gait during Obstacle Negotiation.

Personal and environmental factors both increase the likelihood of falling injuries while negotiating obstacles. Eighteen male participants (seven older, eleven young) were recruited to walk over an obstacle with and without loads on their hands to study the effects of age, load carriage modes, and limb crossing patterns on gait during obstacle negotiation. Participants initiated tasks with either their dominant or non-dominant leg. Step length (SL), toe clearance (TC), step velocity (V), and step width (SW) were extracted from four critical steps. Results showed that during obstacle negotiation (1) older adults had more TC than younger adults, (2) hand loads affected SL and TC, (3) gait parameters are dissimilar between the dominant limb and non-dominant limb.

IMU-Based Monitoring for Assistive Diagnosis and Management of IoHT: A Review.

With the rapid development of Internet of Things (IoT) technologies, traditional disease diagnoses carried out in medical institutions can now be performed remotely at home or even ambient environments, yielding the concept of the Internet of Health Things (IoHT). Among the diverse IoHT applications, inertial measurement unit (IMU)-based systems play a significant role in the detection of diseases in many fields, such as neurological, musculoskeletal, and mental. However, traditional numerical interpretation methods have proven to be challenging to provide satisfying detection accuracies owing to the low quality of raw data, especially under strong electromagnetic interference (EMI). To address this issue, in recent years, machine learning (ML)-based techniques have been proposed to smartly map IMU-captured data on disease detection and progress. After a decade of development, the combination of IMUs and ML algorithms for assistive disease diagnosis has become a hot topic, with an increasing number of studies reported yearly. A systematic search was conducted in four databases covering the aforementioned topic for articles published in the past six years. Eighty-one articles were included and discussed concerning two aspects: different ML techniques and application scenarios. This review yielded the conclusion that, with the help of ML technology, IMUs can serve as a crucial element in disease diagnosis, severity assessment, characteristic estimation, and monitoring during the rehabilitation process. Furthermore, it summarizes the state-of-the-art, analyzes challenges, and provides foreseeable future trends for developing IMU-ML systems for IoHT.