Proactively detecting falls and preventing injuries are among the primary keys to a healthy life for the elderly. Near-fall remote monitoring in daily life could provide key information to prevent future falls and obtain quantitative rehabilitation status for patients with weak balance ability. In this study, we developed a deep learning-based novel classification algorithm to precisely categorize three classes (falls, near-falls, and activities of daily living (ADLs)) using a single inertial measurement unit (IMU) device attached to the waist. A total of 34 young participants were included in this study. An IMU containing accelerometer and gyroscope sensors was fabricated to acquire acceleration and angular velocity signals. A comprehensive experiment including thirty-six types of activities (10 types of falls, 10 types of near-falls, and 16 types of ADLs) was designed based on previous studies. A modified directed acyclic graph-convolution neural network (DAG-CNN) architecture with hyperparameter optimization was proposed to predict fall, near-fall, and ADLs. Prediction results of the modified DAG-CNN structure were found to be approximately 7% more accurate than the traditional CNN structure. For the case of near-falls, the modified DAG-CNN demonstrated excellent prediction performance with accuracy of over 98% by combining gyroscope and accelerometer features. Additionally, by combining acceleration and angular velocity the trained model showed better performance than each model of acceleration and angular velocity. It is believed that information to preemptively handle the risk of falls and quantitatively evaluate the rehabilitation status of the elderly with weak balance will be provided by monitoring near-falls.

The aim of this study was to quantitatively analyze quite standing postural stability of adolescent idiopathic scoliosis (AIS) patients in respect to three sensory systems (visual, vestibular, and somatosensory). In this study, we analyzed the anterior-posterior center of pressure (CoP) signal using discrete wavelet transform (DWT) between AIS patients (n = 32) and normal controls (n = 25) during quiet standing. The energy rate (∆E _EYE %) of the CoP signal was significantly higher in the AIS group than that in the control group at levels corresponding to vestibular and somatosensory systems (p < 0.01). This implies that AIS patients use strategies to compensate for possible head position changes and spinal asymmetry caused by morphological deformations of the spine through vestibular and somatosensory systems. This could be interpreted that such compensation could help them maintain postural stability during quiet standing. The interpretation of CoP signal during quiet standing in AIS patients will improve our understanding of changes in physical exercise ability due to morphological deformity of the spine. This result is useful for evaluating postural stability before and after treatments (spinal fusion, bracing, rehabilitation, and so on).

The CRISPR-based genome editing technology, known as clustered regularly interspaced short palindromic repeats (CRISPR), has sparked renewed interest in gene therapy. This interest is accompanied by the development of single-guide RNAs (sgRNAs), which enable the introduction of desired genetic modifications at the targeted site when used alongside the CRISPR components. However, the efficient delivery of CRISPR/Cas remains a challenge. Successful gene editing relies on the development of a delivery strategy that can effectively deliver the CRISPR cargo to the target site. To overcome this obstacle, researchers have extensively explored non-viral, viral, and physical methods for targeted delivery of CRISPR/Cas9 and a guide RNA (gRNA) into cells and tissues. Among those methods, liposomes offer a promising approach to enhance the delivery of CRISPR/Cas and gRNA. Liposomes facilitate endosomal escape and leverage various stimuli such as light, pH, ultrasound, and environmental cues to provide both spatial and temporal control of cargo release. Thus, the combination of the CRISPR-based system with liposome delivery technology enables precise and efficient genetic modifications in cells and tissues. This approach has numerous applications in basic research, biotechnology, and therapeutic interventions. For instance, it can be employed to correct genetic mutations associated with inherited diseases and other disorders or to modify immune cells to enhance their disease-fighting capabilities. In summary, liposome-based CRISPR genome editing provides a valuable tool for achieving precise and efficient genetic modifications. This review discusses future directions and opportunities to further advance this rapidly evolving field.

Xenon (Xe), a noble gas, has promising neuroprotective properties with no proven adverse side-effects. We evaluated neuroprotective effects of Xe delivered by Xe-containing echogenic liposomes (Xe-ELIP) via ultrasound-controlled cerebral drug release on early brain injury following subarachnoid hemorrhage (SAH). The Xe-ELIP structure was evaluated by ultrasound imaging, electron microscopy and gas chromatography-mass spectroscopy. Animals were randomly divided into five groups: Sham, SAH, SAH treated with Xe-ELIP, empty ELIP, or Xe-saturated saline. Treatments were administrated intravenously in combination with ultrasound application over the common carotid artery to trigger Xe release from circulating Xe-ELIP. Hematoma development was graded by SAH scaling and quantitated by a colorimetric method. Neurological evaluation and motor behavioral tests were conducted for three days following SAH injury. Ultrasound imaging and electron microscopy demonstrated that Xe-ELIP have a unique two-compartment structure, which allows a two-stage Xe release profile. Xe-ELIP treatment effectively reduced bleeding, improved general neurological function, and alleviated motor function damage in association with reduced apoptotic neuronal death and decreased mortality. Xe-ELIP alleviated early SAH brain injury by inhibiting neuronal death and bleeding. This novel approach provides a noninvasive strategy of therapeutic gas delivery for SAH treatment.

This study aims to design, develop, and evaluate the traction performance of an electric all-wheel-drive (AWD) tractor based on the power transmission and electric systems. The power transmission system includes the electric motor, helical gear reducer, planetary gear reducer, and tires. The electric system consists of a battery pack and charging system. An engine-generator and charger are installed to supply electric energy in emergency situations. The load measurement system consists of analog (current) and digital (battery voltage and rotational speed of the electric motor) components using a controller area network (CAN) bus. A traction test of the electric AWD tractor was performed towing a test vehicle. The output torques of the tractor motors during the traction test were calculated using the current and torque curves provided by the motor manufacturer. The agricultural work performance is verified by comparing the torque and rpm (T–N) curve of the motor with the reduction ratio applied. The traction is calculated using torque and specifications of the wheel, and traction performance is evaluated using tractive efficiency (TE) and dynamic ratio (DR). The results suggest a direction for the improvement of the electric drive system in agricultural research by comparison with the conventional tractor through the analysis of the agricultural performance and traction performance of the electric AWD tractor.

Machine vision with deep learning is a promising type of automatic visual perception for detecting and segmenting an object effectively; however, the scarcity of labelled datasets in agricultural fields prevents the application of deep learning to agriculture. For this reason, this study proposes weakly supervised crop area segmentation (WSCAS) to identify the uncut crop area efficiently for path guidance. Weakly supervised learning has advantage for training models because it entails less laborious annotation. The proposed method trains the classification model using area-specific images so that the target area can be segmented from the input image based on implicitly learned localization. This way makes the model implementation easy even with a small data scale. The performance of the proposed method was evaluated using recorded video frames that were then compared with previous deep-learning-based segmentation methods. The results showed that the proposed method can be conducted with the lowest inference time and that the crop area can be localized with an intersection over union of approximately 0.94. Additionally, the uncut crop edge could be detected for practical use based on the segmentation results with post-image processing such as with a Canny edge detector and Hough transformation. The proposed method showed the significant ability of using automatic perception in agricultural navigation to infer the crop area with real-time level speed and have localization comparable to existing semantic segmentation methods. It is expected that our method will be used as essential tool for the automatic path guidance system of a combine harvester.