PEP-ASSOCIATED PROTEIN 3 regulates rice tiller formation and grain yield by controlling chloroplast biogenesis.

Plastid-encoded RNA polymerase (PEP) plays a pivotal role in chloroplast development by governing the transcription of chloroplast genes, and PEP-associated proteins (PAPs) modulate PEP transcriptional activity. Therefore, PAPs provide an intriguing target for those efforts to improve yield, by enhancing chloroplast development. In this study, we identified the rice (Oryza sativa) OsPAP3 gene and characterized its function in chloroplast development. OsPAP3 expression was light-dependent and leaf-specific, similar to the PEP-dependent chloroplast gene RUBISCO LARGE SUBUNIT (OsRbcL), and OsPAP3 protein localized to chloroplast nucleoids where PEP functions. Analysis of loss-of-function and gain-of-function mutants showed that the expression of OsPAP3 is tightly linked to chloroplast gene expression and chloroplast biogenesis in rice. Homozygous knockout mutants of OsPAP3 had fewer chloroplasts than wild type, whereas plants overexpressing OsPAP3 had more chloroplasts. Also, OsPAP3 knockout suppressed the PEP-dependent expression of chloroplast genes, but OsPAP3 overexpression increased their expression. These findings indicate that OsPAP3 regulates chloroplast biogenesis in rice by controlling the PEP-dependent expression of chloroplast genes. More importantly, data from 3 seasons of field cultivation revealed that the overexpression of OsPAP3 improves rice grain yield by approximately 25%, largely due to increased tiller formation. Collectively, these observations suggest that OsPAP3 regulates rice growth and productivity by promoting chloroplast development.

Study protocol for "In-vehicle sensors to detect changes in cognition of older drivers".

BACKGROUND: Driving is a complex behavior that may be affected by early changes in the cognition of older individuals. Early changes in driving behavior may include driving more slowly, making fewer and shorter trips, and errors related to inadequate anticipation of situations. Sensor systems installed in older drivers' vehicles may detect these changes and may generate early warnings of possible changes in cognition. METHOD: A naturalistic longitudinal design is employed to obtain continuous information on driving behavior that will be compared with the results of extensive cognitive testing conducted every 3 months for 3 years. A driver facing camera, forward facing camera, and telematics unit are installed in the vehicle and data downloaded every 3 months when the cognitive tests are administered. RESULTS: Data processing and analysis will proceed through a series of steps including data normalization, adding information on external factors (weather, traffic conditions), and identifying critical features (variables). Traditional prediction modeling results will be compared with Recurring Neural Network (RNN) approach to produce Driver Behavior Indices (DBIs), and algorithms to classify drivers within age, gender, ethnic group membership, and other potential group characteristics. CONCLUSION: It is well established that individuals with progressive dementias are eventually unable to drive safely, yet many remain unaware of their cognitive decrements. Current screening and evaluation services can test only a small number of individuals with cognitive concerns, missing many who need to know if they require treatment. Given the increasing number of sensors being installed in passenger vehicles and pick-up trucks and their increasing acceptability, reconfigured in-vehicle sensing systems could provide widespread, low-cost early warnings of cognitive decline to the large number of older drivers on the road in the U.S. The proposed testing and evaluation of a readily and rapidly available, unobtrusive in-vehicle sensing system could provide the first step toward future widespread, low-cost early warnings of cognitive change for this large number of older drivers in the U.S. and elsewhere.

Origami and Kirigami Structure for Impact Energy Absorption: Its Application to Drone Guards.

As the use of drones grows, so too does the demand for physical protection against drone damage resulting from collisions and falls. In addition, as the flight environment becomes more complicated, a shock absorption system is required, in which the protective structure can be deformed based on the circumstances. Here, we present an origami- and kirigami-based structure that provides protection from various directions. This research adds a deformation capacity to existing fixed-shape guards; by using shape memory alloys, the diameter and height of the protective structure are controlled. We present three protective modes (1: large diameter/low height; 2: small diameter/large height; and 3: lotus shaped) that mitigate drone falls and side collisions. From the result of the drop impact test, mode 2 showed a 78.2% reduction in the maximum impact force at side impact. We incorporated kirigami patterns into the origami structures in order to investigate the aerodynamic effects of the hollow patterns. Airflow experiments yielded a macro understanding of flow-through behaviors on each kirigami pattern. In the wind speed experiment, the change in airflow velocity induced by the penetration of the kirigami pattern was measured, and in the force measurement experiment, the air force applied to the structure was determined.

Smart Active Vibration Control System of a Rotary Structure Using Piezoelectric Materials.

A smart active vibration control (AVC) system containing piezoelectric (PZT) actuators, jointly with a linear quadratic regulator (LQR) controller, is proposed in this article to control transverse deflections of a wind turbine (WT) blade. In order to apply controlling rules to the WT blade, a state-of-the-art semi-analytical solution is developed to obtain WT blade lateral displacement under external loadings. The proposed method maps the WT blade to a Euler-Bernoulli beam under the same conditions to find the blade's vibration and dynamic responses by solving analytical vibration solutions of the Euler-Bernoulli beam. The governing equations of the beam with PZT patches are derived by integrating the PZT transducer vibration equations into the vibration equations of the Euler-Bernoulli beam structure. A finite element model of the WT blade with PZT patches is developed. Next, a unique transfer function matrix is derived by exciting the structures and achieving responses. The beam structure is projected to the blade using the transfer function matrix. The results obtained from the mapping method are compared with the counter of the blade's finite element model. A satisfying agreement is observed between the results. The results showed that the method's accuracy decreased as the sensors' distance from the base of the wind turbine increased. In the designing process of the LQR controller, various weighting factors are used to tune control actions of the AVC system. LQR optimal control gain is obtained by using the state-feedback control law. The PZT actuators are located at the same distance from each other an this effort to prevent neutralizing their actuating effects. The LQR shows significant performance by diminishing the weights on the control input in the cost function. The obtained results indicate that the proposed smart control system efficiently suppresses the vibration peaks along the WT blade and the maximum flap-wise displacement belonging to the tip of the structure is successfully controlled.

A Numerical Study of an Ellipsoidal Nanoparticles under High Vacuum Using the DSMC Method.

The semiconductor and display manufacturing process requires high precision. Therefore, inside the equipment, fine impurity particles affect the yield rate of production. However, since most manufacturing processes are performed under high-vacuum conditions, it is difficult to estimate particle flow with conventional analytical tools. In this study, high-vacuum flow was analyzed using the direct simulation Monte Carlo (DSMC) method, and various forces acting on fine particles in a high-vacuum flow field were calculated. To compute the computationally intensive DSMC method, GPU-based computer unified device architecture (CUDA) technology was used. The force acting on the particles in the high-vacuum rarefied gas region was verified using the results of previous studies, and the results were derived for the difficult-to-experiment region. An ellipsoid shape with an aspect ratio rather than a spherical shape was also analyzed. The change in drag force according to various aspect ratios was analyzed and compared with the results of the spherical shape under the same flow conditions.

Driver behavior indices from large-scale fleet telematics data as surrogate safety measures.

Large-scale telematics data enable a high-resolution inference of road network's safety conditions and driver behavior. Although many researchers have investigated how to define meaningful safety surrogates and crash predictors from telematics, no comprehensive study analyzes the driver behavior derived from large-scale telematics data and relates them to crash data and the road networks in metropolitan cities. This study extracts driver behavior indices (e.g., speed, speed variation, hard braking rate, and hard acceleration rate) from large-scale telematics data, collected from 4000 vehicles in New York City five boroughs. These indices are compared to collision frequencies and collision rates at the street level. Moderate correlations were found between the safety surrogate measures and collision rates, summarized as follows: (i) When normalizing crash frequencies with traffic volume, using a traffic AADT model, safety-critical regions almost remain the same. (ii) The correlation magnitude of hard braking and hard acceleration varies by road types: hard braking clusters are more indicative of higher collision rates on highways, whereas hard acceleration is a stronger hazard indicator on non-highway urban roads. (iii) Locations with higher travel times coincide with locations of high crash incidence on non-highway roads. (iv) However, speeding on highways is indicative of collision risks. After establishing the spatial correlation between the driver behavior indices and crash data, two prototype safety metrics are proposed: speed corridor maps and hard braking and hard acceleration hot-spots. Overall, this paper shows that data-driven network screening enabled by telematics has great potential to advance our understanding of road safety assessment.

NON-INTRUSIVE DROWSINESS DETECTION TECHNIQUES AND THEIR APPLICATION IN DETECTING EARLY DEMENTIA IN OLDER DRIVERS.

Drowsy drivers cause the most car accidents thus, adopting an efficient drowsiness detection system can alert the driver promptly and precisely which will reduce the numbers of accidents and also save a lot of money. This paper discusses many tactics and methods for drowsy driving warning. The non-intrusive nature of most of the strategies mentioned and contrasted means both vehicular and behavioural techniques are examined here. Thus, the latest strategies are studied and discussed for both groups, together with their benefits and drawbacks. The goal of this review was to identify a practical and low-cost approach for analysing elder drivers' behaviour.

Abnormal Driving Detection using GPS Data.

Given a GPS dataset comprising driving records captured at one-second intervals, this research addresses the challenge of Abnormal Driving Detection (ADD). The study introduces an integrated approach that leverages data preprocessing, dimensionality reduction, and clustering techniques. Speed Over Ground (SOG), Course Over Ground (COG), longitude (lon), and latitude (lat) data are aggregated into minute-level segments. We use Singular Value Decomposition (SVD) to reduce dimensionality, enabling K-means clustering to identify distinctive driving patterns. Results showcase the methodology's effectiveness in distinguishing normal from abnormal driving behaviors, offering promising insights for driver safety, insurance risk assessment, and personalized interventions.

Anomalous Behavior Detection in Trajectory Data of Older Drivers.

Given a road network and a set of trajectory data, the anomalous behavior detection (ABD) problem is to identify drivers that show significant directional deviations, hard-brakings, and accelerations in their trips. The ABD problem is important in many societal applications, including Mild Cognitive Impairment (MCI) detection and safe route recommendations for older drivers. The ABD problem is computationally challenging due to the large size of temporally-detailed trajectories dataset. In this paper, we propose an Edge-Attributed Matrix that can represent the key properties of temporally-detailed trajectory datasets and identify abnormal driving behaviors. Experiments using real-world datasets demonstrated that our approach identifies abnormal driving behaviors.

In-vehicle Sensing and Data Analysis for Older Drivers with Mild Cognitive Impairment.

Driving is a complex daily activity indicating age and disease-related cognitive declines. Therefore, deficits in driving performance compared with ones without mild cognitive impairment (MCI) can reflect changes in cognitive functioning. There is increasing evidence that unobtrusive monitoring of older adults' driving performance in a daily-life setting may allow us to detect subtle early changes in cognition. The objectives of this paper include designing low-cost in-vehicle sensing hardware capable of obtaining high-precision positioning and telematics data, identifying important indicators for early changes in cognition, and detecting early-warning signs of cognitive impairment in a truly normal, day-to-day driving condition with machine learning approaches. Our statistical analysis comparing drivers with MCI to those without reveals that those with MCI exhibit smoother and safer driving patterns. This suggests that drivers with MCI are cognizant of their condition and tend to avoid erratic driving behaviors. Furthermore, our Random Forest models identified the number of night trips, number of trips, and education as the most influential factors in our data evaluation.

Division of cortical cells is regulated by auxin in Arabidopsis roots.

The root cortex transports water and nutrients absorbed by the root epidermis into the vasculature and stores substances such as starch, resins, and essential oils. The cortical cells are also deeply involved in determining epidermal cell fate. In Arabidopsis thaliana roots, the cortex is composed of a single cell layer generated by a single round of periclinal division of the cortex/endodermis initials. To further explore cortex development, we traced the development of the cortex by counting cortical cells. Unlike vascular cells, whose number increased during the development of root apical meristem (RAM), the number of cortical cells did not change, indicating that cortical cells do not divide during RAM development. However, auxin-induced cortical cell division, and this finding was confirmed by treatment with the auxin transport inhibitor N-1-naphthylphthalamic acid (NPA) and examining transgenic plants harboring CO2::ΔARF5, in which cortical expression of truncated AUXIN RESPONSE FACTOR5 (ΔARF5) induces auxin responses. NPA-induced cortical auxin accumulation and CO2::ΔARF5-mediated cortical auxin response induced anticlinal and periclinal cell divisions, thus increasing the number of cortical cells. These findings reveal a tight link between auxin and cortical cell division, suggesting that auxin is a key player in determining root cortical cell division.

Isolation of high-purity and high-stability exosomes from ginseng.

Exosomes are nano-sized extracellular vesicles that regulate cell growth and defense by delivering bioactive cellular constituents. They are a promising material for biomedical and cosmetic utilization, especially in medicinal crops such as ginseng. One main hurdle to their usage is the need for a method to isolate stable exosomes with high purity. In this study, we first tested two methods to isolate exosomes from ginseng: ultracentrifugation, the most widely used method; and the ExoQuick system, a polymer-based exosome precipitation approach. We also designed and tested a third method in which we combined ultracentrifugation and ExoQuick methods. Size distribution analysis revealed that the exosome isolation purity by the ultracentrifugation and ExoQuick methods alone were 34.1% and 59.7%, respectively, while the combination method greatly improved exosome isolation purity (83.3%). Furthermore, we found that the combination method also increases the colloidal stability of isolated ginseng exosomes, and the increase was almost double that of the ultracentrifugation method. Lastly, we showed that the combination method can also be used to isolate high-purity and high-stability exosomes from the model plant Arabidopsis. Overall, our findings indicate that the combination method is suitable to isolate high-purity and high-stability exosomes from plants including ginseng.

Methods and Tools for Monitoring Driver's Behavior.

In-vehicle sensing technology has gained tremendous attention due to its ability to support major technological developments, such as connected vehicles and self-driving cars. In-vehicle sensing data are invaluable and important data sources for traffic management systems. In this paper we propose an innovative architecture of unobtrusive in-vehicle sensors and present methods and tools that are used to measure the behavior of drivers. The proposed architecture including methods and tools are used in our NIH project to monitor and identify older drivers with early dementia.

Optimized Expression, Purification, and Rapid Detection of Recombinant Influenza Nucleoproteins Expressed in Sf9 Insect Cells.

Accurate and rapid diagnosis of influenza infection is essential to enable early antiviral treatment and reduce the mortality associated with seasonal and epidemic infections. Immunochromatography is one of the most common methods used for the diagnosis of seasonal human influenza; however, it is less effective in diagnosing pandemic influenza virus. Currently, rapid diagnostic kits for pandemic influenza virus rely on the detection of nucleoprotein (NP) or hemagglutinin (HA). NP detection shows higher specificity and is more sensitive than HA detection. In this study, we time-dependently screened expression conditions, and herein report optimal conditions for the expression of recombinant nucleoprotein (rNP), which was 48 h after infection. In addition, we report the use of the expressed rNP in a rapid influenza diagnostic test (SGT i-flex Influenza A&B Test). We constructed expression vectors that synthesized rNP (antigen) of influenza A and B in insect cells (Sf9 cells), employed the purified rNP to the immunoassay test kit, and clearly distinguished NPs of influenza A and influenza B using this rapid influenza diagnostic kit. This approach may improve the development of rapid test kits for influenza using NP.