OsbZIP18 Is a Positive Regulator of Phenylpropanoid and Flavonoid Biosynthesis under UV-B Radiation in Rice.

In plants exposed to ultraviolet B radiation (UV-B; 280-315 nm), metabolic responses are activated, which reduce the damage caused by UV-B. Although several metabolites responding to UV-B stress have been identified in plants, the accumulation of these metabolites at different time points under UV-B stress remains largely unclear, and the transcription factors regulating these metabolites have not been well characterized. Here, we explored the changes in metabolites in rice after UV-B treatment for 0 h, 6 h, 12 h, and 24 h and identified six patterns of metabolic change. We show that the rice transcription factor OsbZIP18 plays an important role in regulating phenylpropanoid and flavonoid biosynthesis under UV-B stress in rice. Metabolic profiling revealed that the contents of phenylpropanoid and flavonoid were significantly reduced in osbzip18 mutants compared with the wild-type plants (WT) under UV-B stress. Further analysis showed that the expression of many genes involved in the phenylpropanoid and flavonoid biosynthesis pathways was lower in osbzip18 mutants than in WT plants, including OsPAL5, OsC4H, Os4CL, OsCHS, OsCHIL2, and OsF3H. Electrophoretic mobility shift assays (EMSA) revealed that OsbZIP18 bind to the promoters of these genes, suggesting that OsbZIP18 function is an important positive regulator of phenylpropanoid and flavonoid biosynthesis under UV-B stress. In conclusion, our findings revealed that OsbZIP18 is an essential regulator for phenylpropanoid and flavonoid biosynthesis and plays a crucial role in regulating UV-B stress responses in rice.

Markerless gait analysis through a single camera and computer vision.

The assessment of gait performance using quantitative measures can yield crucial insights into an individual's health status. Recently, computer vision-based human pose estimation has emerged as a promising solution for markerless gait analysis, as it allows for the direct extraction of gait parameters from videos. This study aimed to compare the lower extremity kinematics and spatiotemporal gait parameters obtained from a single-camera-based markerless method with those acquired from a marker-based motion tracking system across a healthy population. Additionally, we investigated the impact of camera viewing angles and distances on the accuracy of the markerless method. Our findings demonstrated a robust correlation and agreement (Rxy > 0.75, Rc > 0.7) between the markerless and marker-based methods for most spatiotemporal gait parameters. We also observed strong correlations (Rxy > 0.8) between the two methods for hip flexion/extension, knee flexion/extension, hip abduction/adduction, and hip internal/external rotation. Statistical tests revealed significant effects of viewing angles and distances on the accuracy of the identified gait parameters. While the markerless method offers an alternative for general gait analysis, particularly when marker use is impractical, its accuracy for clinical applications remains insufficient and requires substantial improvement. Future investigations should explore the potential of the markerless system to measure gait parameters in pathological gaits.

Factors Affecting Workers' Mental Stress in Handover Activities During Human-Robot Collaboration.

OBJECTIVE: This study investigated the effects of different approach directions, movement speeds, and trajectories of a co-robot's end-effector on workers' mental stress during handover tasks. BACKGROUND: Human-robot collaboration (HRC) is gaining attention in industry and academia. Understanding robot-related factors causing mental stress is crucial for designing collaborative tasks that minimize workers' stress. METHODS: Mental stress in HRC tasks was measured subjectively through self-reports and objectively through galvanic skin response (GSR) and electromyography (EMG). Robot-related factors including approach direction, movement speed, and trajectory were analyzed. RESULTS: Movement speed and approach direction had significant effects on subjective ratings, EMG, and GSR. High-speed and approaching from one side consistently resulted in higher fear, lower comfort, and predictability, as well as increased EMG and GSR signals, indicating higher mental stress. Movement trajectory affected GSR, with the sudden stop condition eliciting a stronger response compared to the constrained trajectory. Interaction effects between speed and approach direction were observed for "surprise" and "predictability" subjective ratings. At high speed, approach direction did not significantly differ, but at low speeds, approaching from the side was found to be more surprising and unpredictable compared to approaching from the front. CONCLUSION: The mental stress of workers during HRC is lower when the robot's end effector (1) approaches a worker within the worker's field of view, (2) approaches at a lower speed, or (3) follows a constrained trajectory. APPLICATION: The outcome of this study can serve as a guide to design HRC tasks with a low level of workers' mental stress.

An Image-Based Human-Robot Collision Avoidance Scheme: A Proof of Concept.

OCCUPATIONAL APPLICATIONSIn modern industrial plants, collisions between humans and robots pose a significant risk to occupational safety. To address this concern, we sought to devise a reliable system for human-robot collision avoidance system employing computer vision. This system enables the proactive prevention of dangerous collisions between humans and robots. In contrast to previous approaches, we used a standard RGB camera, making implementation more convenient and cost-effective. Furthermore, the proposed method greatly extends the effective detection range compared to previous studies, thereby enhancing its utility for monitoring large-scale workplaces.

A comparison of the psychological effects of robot motion in physical and virtual environments.

A mixed-methods approach was used to assess the fidelity of virtual environments as ergonomic assessment tools for human-robot interaction. Participants performed a visual search task in the physical environment while a nearby collaborative robot repeatedly extended its arm toward them. This scenario was reconstructed in two virtual environments with different levels of graphical detail. Measures of presence, task performance, workload, and anxiety were taken to determine the effect of robot motion in all three environments. Task performance decreased in response to robot motion in the physical environment, while workload and anxiety increased. This simple effect of motion was consistent across environments for measures of task performance and anxiety. However, people performed faster overall in virtual reality, and the effect of motion on workload was greatly reduced in virtual reality. Results in the virtual environments suggest that people were distracted by the sound of the robot, and that presence was affected by manipulations of immersion and coherence.

Integrated Metabolome and Transcriptome during Fruit Development Reveal Metabolic Differences and Molecular Basis between Lycium barbarum and Lycium ruthenicum.

Wolfberry (Lycium barbarum) is a traditional cash crop in China and is well-known worldwide for its outstanding nutritional and medicinal value. Lycium ruthenicum is a close relative of Lycium barbarum but differs significantly in size, color, flavor and nutritional composition. To date, the metabolic differences between the fruits of the two wolfberry varieties and the genetic basis behind them are unclear. Here, we compared metabolome and transcriptome data of two kinds of wolfberry fruits at five stages of development. Metabolome results show that amino acids, vitamins and flavonoids had the same accumulation pattern in various developmental stages of fruit but that Lycium ruthenicum accumulated more metabolites than Lycium barbarum during the same developmental stage, including L-glutamate, L-proline, L-serine, abscisic acid (ABA), sucrose, thiamine, naringenin and quercetin. Based on the metabolite and gene networks, many key genes that may be involved in the flavonoid synthesis pathway in wolfberry were identified, including PAL, C4H, 4CL, CHS, CHI, F3H, F3'H and FLS. The expression of these genes was significantly higher in Lycium ruthenicum than in Lycium barbarum, indicating that the difference in the expression of these genes was the main reason for the variation in flavonoid accumulation between Lycium barbarum and Lycium ruthenicum. Taken together, our results reveal the genetic basis of the difference in metabolomics between Lycium barbarum and Lycium ruthenicum and provide new insights into the flavonoid synthesis of wolfberry.

Improving Workers' Musculoskeletal Health During Human-Robot Collaboration Through Reinforcement Learning.

OBJECTIVE: This study aims to improve workers' postures and thus reduce the risk of musculoskeletal disorders in human-robot collaboration by developing a novel model-free reinforcement learning method. BACKGROUND: Human-robot collaboration has been a flourishing work configuration in recent years. Yet, it could lead to work-related musculoskeletal disorders if the collaborative tasks result in awkward postures for workers. METHODS: The proposed approach follows two steps: first, a 3D human skeleton reconstruction method was adopted to calculate workers' continuous awkward posture (CAP) score; second, an online gradient-based reinforcement learning algorithm was designed to dynamically improve workers' CAP score by adjusting the positions and orientations of the robot end effector. RESULTS: In an empirical experiment, the proposed approach can significantly improve the CAP scores of the participants during a human-robot collaboration task when compared with the scenarios where robot and participants worked together at a fixed position or at the individual elbow height. The questionnaire outcomes also showed that the working posture resulted from the proposed approach was preferred by the participants. CONCLUSION: The proposed model-free reinforcement learning method can learn the optimal worker postures without the need for specific biomechanical models. The data-driven nature of this method can make it adaptive to provide personalized optimal work posture. APPLICATION: The proposed method can be applied to improve the occupational safety in robot-implemented factories. Specifically, the personalized robot working positions and orientations can proactively reduce exposure to awkward postures that increase the risk of musculoskeletal disorders. The algorithm can also reactively protect workers by reducing the workload in specific joints.

The Effect of Rhenium Content on Microstructural Changes and Irradiated Hardening in W-Re Alloy under High-Dose Ion Irradiation.

An amount of 100 dpa Si2+ irradiation was used to study the effect of transmutation rhenium content on irradiated microscopic defects and hardening in W-xRe (x = 0, 1, 3, 5 and 10 wt.%) alloys at 550 °C. The increase in Re content could significantly refine the grain in the W-xRe alloys, and no obvious surface topography change could be found after high-dose irradiation via the scanning electron microscope (SEM). The micro defects induced by high-dose irradiation in W and W-3Re alloys were observed using a transmission electron microscope (TEM). Dislocation loops with a size larger than 10 nm could be found in both W and W-3Re alloy, but the distribution of them was different. The distribution of the dislocation loops was more uniform in pure W, while they seemed to be clustered around some locations in W-3Re alloy. Voids (~2.4 nm) were observed in W-3Re alloy, while no void was investigated in W. High-dose irradiation induced obvious hardening with the hardening rate between 75% and 155% in all W-xRe alloys, but W-3Re alloy had the lowest hardening rate (75%). The main reasons might be related to the smallest grain size in W-3Re alloy, which suppressed the formation of defect clusters and induced smaller hardening than that in other samples.

Natural variations of OsAUX5, a target gene of OsWRKY78, control the neutral essential amino acid content in rice grains.

Grain essential amino acid (EAA) levels contribute to rice nutritional quality. However, the molecular mechanisms underlying EAA accumulation and natural variation in rice grains remain unclear. Here we report the identification of a previously unrecognized auxin influx carrier subfamily gene, OsAUX5, which encodes an amino acid transporter that functions in uptake of multiple amino acids. We identified an elite haplotype of Pro::OsAUX5Hap2 that enhances grain EAA accumulation without an apparent negative effect on agronomic traits. Natural variations of OsAUX5 occur in the cis elements of its promoter, which are differentially activated because of the different binding affinity between OsWRKY78 and the W-box, contributing to grain EAA variation among rice varieties. The two distinct haplotypes were shown to have originated from different Oryza rufipogon progenitors, which contributed to the divergence between japonica and indica. Introduction of the indica-type Pro::OsAUX5Hap2 genotype into japonica could significantly increase EAA levels, indicating that indica-type Pro::OsAUX5Hap2 can be utilized to increase grain EAAs of japonica varieties. Collectively, our study uncovers an WRKY78-OsAUX5-based regulatory mechanism controlling grain EAA accumulation and provides a potential target for breeding EAA-rich rice.

A mobile platform-based app to assist undergraduate learning of human kinematics in biomechanics courses.

Whole-body biomechanics examines different physical characteristics of the human body movement by applying principles of Newtonian mechanics. Therefore, undergraduate biomechanics courses are highly demanding in mathematics and physics. While the inclusion of laboratory experiences can augment student comprehension of biomechanics concepts, the cost and the required expertise associated with experiment equipment can be a burden of offering laboratory sessions. In this study, we developed a mobile app to facilitate learning human kinematics in biomechanics curriculums. First, a mobile-based computer-vision algorithm that is based on Convolutional pose machine (CPM), MobileNet V2, and TensorFlow Lite framework is adopted to reconstruct 2D human poses from the images collected by a mobile device camera. Key joint locations are then applied to the human kinematics variable estimator for human kinematics analysis. Simultaneously, students can view various kinematics data for a selected joint or body segment in real-time through the user interface of the mobile device. The proposed app can serve as a potential instructional tool to assist in conducting human motion experiments in biomechanics courses.

Mental stress and safety awareness during human-robot collaboration - Review.

Human-robot collaboration (HRC) is an emerging research area that has gained tremendous attention in both academia and industry. Yet, the feature that humans and robots sharing the workplace has led to safety concerns. In particular, the mental stress or safety awareness of human teammates during HRC remains unclear but is also of great importance to workplace safety. In this manuscript, we reviewed twenty-five studies for understanding the relationships between HRC and workers' mental stress or safety awareness. Specifically, we aimed to understand: (1) robot-related factors that may affect human workers' mental stress or safety awareness, (2) a number of measurements that could be used to evaluate workers' mental stress in HRC, and (3) various methods for measuring safety awareness that had been adopted or could be applied in HRC. According to our literature review, robot-related factors including robot characteristics, social touching and trajectory have relationships with workers' mental stress or safety awareness. For the measurement of mental stress and safety awareness, each method mentioned has its validity and rationality. Additionally, a discussion related to the potential co-robot actions to lower mental stress or improve safety awareness as well as future implications were provided.

High-Performance Narrowband Pure-Red OLEDs with External Quantum Efficiencies up to 36.1% and Ultralow Efficiency Roll-Off.

High-color-purity blue and green organic light-emitting diodes (OLEDs) have been resolved thanks to the development of B/N-based polycyclic multiple resonance (MR) emitters. However, due to the derivatization limit of B/N polycyclic structures, the design of red MR emitters remains challenging. Herein, a series of novel red MR emitters is reported by para-positioning N-π-N, O-π-O, B-π-B pairs onto a benzene ring to construct an MR central core. These emitters can be facilely and modularly synthesized, allowing for easy fine-tuning of emission spectra by peripheral groups. Moreover, these red MR emitters display excellent photophysical properties such as near-unity photoluminescence quantum yield (PLQY), fast radiative decay rate (kr ) up to 7.4 × 107 s-1 , and most importantly, narrowband emission with full-width at half-maximum (FWHM) of 32 nm. Incorporating these MR emitters, pure red OLEDs sensitized by phosphor realize state-of-the-art device performances with external quantum efficiency (EQE) exceeding 36%, ultralow efficiency roll-off (EQE remains as high as 25.1% at the brightness of 50 000 cd m-2 ), ultrahigh brightness over 130 000 cd m-2 , together with good device lifetime.

Narrowband blue emission with insensitivity to the doping concentration from an oxygen-bridged triarylboron-based TADF emitter: nondoped OLEDs with a high external quantum efficiency up to 21.4.

Blue thermally activated delayed fluorescence (TADF) emitters that can simultaneously achieve narrowband emission and high efficiency in nondoped organic light-emitting diodes (OLEDs) remain a big challenge. Herein, we successfully design and synthesize two blue TADF emitters by directly incorporating carbazole fragments into an oxygen-bridged triarylboron acceptor. Depending on the linking mode, the two emitters show significantly different photophysical properties. Benefitting from the bulky steric hindrance between the acceptor and terminal pendants, the blue emitter TDBA-Cz exhibited a high photoluminescence quantum yield (PLQY) of 88% in neat films and narrowband emission. The corresponding non-doped blue device exhibited a maximum external quantum efficiency (EQE) of 21.4%, with a full width at half maximum (FWHM) of only 45 nm. This compound is the first blue TADF emitter that can concurrently achieve narrow bandwidth and high electroluminescence (EL) efficiency in nondoped blue TADF-OLEDs.

Real-Time Driving Distraction Recognition Through a Wrist-Mounted Accelerometer.

OBJECTIVE: We propose a method for recognizing driver distraction in real time using a wrist-worn inertial measurement unit (IMU). BACKGROUND: Distracted driving results in thousands of fatal vehicle accidents every year. Recognizing distraction using body-worn sensors may help mitigate driver distraction and consequently improve road safety. METHODS: Twenty participants performed common behaviors associated with distracted driving while operating a driving simulator. Acceleration data collected from an IMU secured to each driver's right wrist were used to detect potential manual distractions based on 2-s long streaming data. Three deep neural network-based classifiers were compared for their ability to recognize the type of distractive behavior using F1-scores, a measure of accuracy considering both recall and precision. RESULTS: The results indicated that a convolutional long short-term memory (ConvLSTM) deep neural network outperformed a convolutional neural network (CNN) and recursive neural network with long short-term memory (LSTM) for recognizing distracted driving behaviors. The within-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.82, and 0.82, respectively. The between-participant F1-scores for the ConvLSTM, CNN, and LSTM were 0.87, 0.76, and 0.85, respectively. CONCLUSION: The results of this pilot study indicate that the proposed driving distraction mitigation system that uses a wrist-worn IMU and ConvLSTM deep neural network classifier may have potential for improving transportation safety.

A Biaxially Stretchable and Self-Sensing Textile Heater Using Silver Nanowire Composite.

Wearable heaters have garnered significant attention from academia and industry for their great potential in thermotherapy. Silver nanowire (AgNW) is a promising conductive material for flexible and stretchable electrodes. Here, a resistive, biaxially stretchable heater based on AgNW composite is reported for the first time, where a AgNW percolation network is encased in a thin polyimide (PI) film and integrated with a highly stretchable textile. AgNW/PI is patterned with a 2D Kirigami structure, which enables constant resistance under a large tensile strain (up to uniaxial 100% strain and 50% biaxial strain). The heater can achieve a high temperature of ∼140 °C with a low current of 0.125 A, fast heating and cooling rates of ∼16.5 and ∼14.1 °C s-1, respectively, and stable performance over 400 heating cycles. A feedback control system is developed to provide constant heating temperature under a temperature change of the surrounding environment. Demonstrated applications in applying thermotherapy at the curvilinear surface of the knee using the stretchable heater illustrate its promising potential for wearable applications.

A computer-vision method to estimate joint angles and L5/S1 moments during lifting tasks through a single camera.

Weight lifting is a risk factor of work-related low-back musculoskeletal disorders (MSD). From the ergonomics perspective, it is important to measure workers' body motion during a lifting task and estimate low-back joint moments to ensure the low-back biomechanical loadings are within the failure tolerance. With the recent development of advanced deep neural networks, an increasing number of computer vision algorithms have been presented to estimate 3D human poses through videos. In this study, we first performed a 3D pose estimation of lifting tasks using a single RGB camera and VideoPose3D, an open-source library with a fully convolutional model. Joint angle trajectories and L5/S1 joint moment were then calculated following a top-down inverse dynamic biomechanical model. To evaluate the accuracy of the computer-vision-based angular trajectories and L5/S1 joint moments, we conducted an experiment in which participants performed a variety of lifting tasks. The body motions of the participants were concurrently captured by an RGB camera and a laboratory-grade motion tracking system. The body joint angles and L5/S1 joint moments obtained from the camera were compared with those obtained from the motion tracking system. The results showed a strong correlation (r > 0.9, RMSE < 10°) between the two methods for shoulder flexion, trunk flexion, trunk rotation, and elbow flexion. The computer-vision-based method also yielded a good estimate for the total L5/S1 moment and the L5/S1 moment in the sagittal plane (r > 0.9, RMSE < 20 N·m). This study showed computer vision could facilitate safety practitioners to quickly identify the jobs with high MSD risks through field survey videos.

Different Time Scale Distribution of Negative Air Ions Concentrations in Mount Wuyi National Park.

The concentration of negative air ions (NAIs) is an important indicator of air quality. Here, we analyzed the distribution patterns of negative air ion (NAI) concentrations at different time scales using statistical methods; then described the contribution of meteorological factors of the different season to the concentration of NAIs using correlation analysis and regression analysis; and finally made the outlook for the trends of NAI concentrations in the prospective using the auto regressive integrated moving average (ARIMA) models. The dataset of NAI concentrations and meteorological factors measured at the fixed stations in the Mountain Wuyi National Park were obtained from the Fujian Provincial Meteorological Bureau. The study showed that NAI concentrations were correlated with relative humidity spanning all seasons. Water was an important factor affecting the distribution of NAI concentrations in different time series. Compared with other ARIMA models, the outlook value of the ARIMA (0,1, 1) model was closer to the original data and the errors were smaller. This article provided a unique perspective on the study of the distribution of negative air oxygen ions over time series.

MOPED25: A multimodal dataset of full-body pose and motion in occupational tasks.

In recent years, there has been a trend of using images and deep neural network-based computer vision algorithms to perform postural evaluation in workplace safety and ergonomics community. The performance of the computer vision algorithms, however, heavily relies on the generalizability of the posture dataset that was used for algorithm training. Current open-access posture datasets from the computer vision community mainly focus on the pose and motion of daily activities and lack the context in workplaces. In this study, a new posture dataset named, MOPED25 (Multimodal Occupational Posture Dataset with 25 tasks) is presented. This dataset includes full-body kinematics data and the synchronized videos of 11 participants, performing commonly seen tasks at workplaces. All the data has been made publicly available online. This dataset can serve as a benchmark for developing more robust computer vision algorithms for postural evaluation at workplaces.

The Correlation-Base-Selection Algorithm for Diagnostic Schizophrenia Based on Blood-Based Gene Expression Signatures.

Microarray analysis of gene expression is often used to diagnose different types of disease. Many studies report remarkable achievements in nervous system disease. Clinical diagnosis of schizophrenia (SCZ) still depends on doctors' experience, which is unreliable and needs to be more objective and quantified. To solve this problem, we collected whole blood gene expression data from four studies, including 152 individuals with schizophrenia (SCZ) and 138 normal controls in different regions. The correlation-based feature selection (CFS, one of the machine learning methods) algorithm was applied in this study, and 103 significantly differentially expressed genes between patients and controls, called "feature genes," were selected; then, a model for SCZ diagnosis was built. The samples were subdivided into 10 groups, and cross-validation showed that the model we constructed achieved nearly 100% classification accuracy. Mathematical evaluation of the datasets before and after data processing proved the effectiveness of our algorithm. Feature genes were enriched in Parkinson's disease, oxidative phosphorylation, and TGF-beta signaling pathways, which were previously reported to be associated with SCZ. These results suggest that the analysis of gene expression in whole blood by our model could be a useful tool for diagnosing SCZ.