Determination of Acquisition Frequency for Intrafractional Motion of Pancreas in CyberKnife Radiotherapy

Purpose. To report the characteristics of pancreas motion as tracked using implanted fiducials during radiotherapy treatments with CyberKnife. Methods and Materials. Twenty-nine patients with pancreas cancer treated using CyberKnife system were retrospectively selected for this study. During the treatment, the deviation is examined every 3-4 nodes (~45 s interval) and compensated by the robot. The pancreas displacement calculated from X-ray images acquired within the time interval between two consecutive couch motions constitute a data set. Results. A total of 498 data sets and 4302 time stamps of X-ray images were analyzed in this study. The average duration for each data set is 634 s. The location of the pancreas becomes more dispersed as the time elapses. The acquisition frequency depends on the prespecified movement distance threshold of pancreas. If the threshold between two consecutive images is 1 mm, the acquisition frequency should be less than 30 s, while if the threshold is 2 mm, the acquisition frequency can be around 1 min. Conclusions. The pancreas target moves significantly and unpredictably during treatment. Effective means of compensating the intrafractional movement is critical to ensure adequate dose coverage of the tumor target

An investigation into the bilateral functional differences of the lower limb muscles in standing and walking

To date, most studies use surface electromyographic (sEMG) signals as the control source on active rehabilitation robots, and unilateral data are collected based on the gait symmetry hypothesis, which has caused much controversy. The purpose of this study is to quantitatively evaluate the sEMG activity asymmetry of bilateral muscles in lower extremities during functional tasks. Nine participants were instructed to perform static and dynamic steady state tests. sEMG signals from the tibialis anterior, soleus, medial gastrocnemius and lateral gastrocnemius muscles of bilateral lower extremities were recorded in the experiments. Muscle activities are quantified in terms of sEMG amplitude. We investigated whether characteristics of left limb and the one of the right limb have the same statistical characteristics during functional tasks using The Wilcoxon rank-sum test, and studied dynamic signal irregularity degree for sEMG activities via sample entropy. The total of muscle activities showed significant differences between left limb and right limb during the static steady state (p = 0.000). For dynamic steady states, there were significant differences for most muscle activities between left limb and right limb at different speeds (p = 0.000). Nevertheless, there was no difference between the lateral gastrocnemius for bilateral limb at 2.0 kilometers per hour (p = 0.060). For medial gastrocnemius, differences were not found between left limb and right limb at 1.0 and 3.0 kilometers per hours (p = 0.390 and p = 0.085, respectively). Similarly, there was no difference for soleus at 3.0 kilometers per hour (p = 0.115). The importance of the differences in muscle activities between left limb and right limb were found. These results can potentially be used for evaluating lower limb extremity function of special populations (elderly people or stroke patients) in an objective and simple method

Polyploidization Genetic Mechanism of Sugarcane Genome

The sugarcane genome polyploidization can reduce the pressure of gene evolution selection, promote the fixation of fine traits, and increase the biomass and economic value of sugarcane. This paper mainly introduced the origin of the sugarcane genome, the chromosome composition, the research progress of polyploidization genetic mechanism, in the hope of providing theoretical reference for sugarcane polyploidization breeding

A quaternion-based Attitude Estimate System Based on a Low Power Consumption Inertial Measurement Unit

Accurate and real-time tracking of the orientation or attitude of rigid bodies has traditional applications in robotics, aerospace, underwater vehicles, human body motion capture, etc. Towards human body motion capture, especially wearable devices, the use of a longer time has always been a challenge for several weeks or several months continuously, so a low-cost chip and a low computational cost algorithm are necessary .The paper presented a quaternion-based algorithm that integrated the sensor output with the Kalman filtering algorithm, and a low power consumption Inertial Measurement Unit (IMU) for the attitude estimation. The low power consumption IMU with an inner Digital Motion Processor(DMP) from InvenSense Inc. called MPU9150, which contains triaxial accelerometers, triaxial gyroscopes, triaxial magnetometers and inner DMP. Firstly, we got attitude quaternion from DMP, and used the factored quaternion algorithm (FQA) to calculate course angle quaternion component. Then the Kalman Filtering algorithm was used to mix them together to acquire the accurate and good real-time performance attitude .The experimental results showed that Kalman filtering algorithm to mix DMP output and magnetometers data have better performance than gradient descent algorithm and complementary filter algorithm even in static performance and dynamic performance and power consumption

Photopatternable Magnetic Hollowbots by Nd-Fe-B Nanocomposite for Potential Targeted Drug Delivery Applications

In contrast to traditional drug administration, targeted drug delivery can prolong, localize, target and have a protected drug interaction with the diseased tissue. Drug delivery carriers, such as polymeric micelles, liposomes, dendrimers, nanotubes, and so on, are hard to scale-up, costly, and have short shelf life. Here we show the novel fabrication and characterization of photopatternable magnetic hollow microrobots that can potentially be utilized in microfluidics and drug delivery applications. These magnetic hollowbots can be fabricated using standard ultraviolet (UV) lithography with low cost and easily accessible equipment, which results in them being easy to scale up, and inexpensive to fabricate. Contact-free actuation of freestanding magnetic hollowbots were demonstrated by using an applied 900 G external magnetic field to achieve the movement control in an aqueous environment. According to the movement clip, the average speed of the magnetic hollowbots was estimated to be 1.9 mm/s

On the Detection of a Non-Cooperative Beam Signal Based on Wireless Sensor Networks

With the extensive research of multiantenna technology, beamforming (BF) will play an important role in the future communication systems due to its high transmission gain and satisfying directivity. If we can detect the non-cooperative beams, it is of great significance in counter reconnaissance, beam tracking, and spectrum sensing of multiantenna transmitters. This paper investigates the wireless sensor networks (WSNs), which is used to detect the unknown non-cooperative beam signal. In order to perceive the presence of beam signals without the prior information, we first derive the detection probability based on the sensors’ received signal strength (RSS). Then, based on the strong directivity of the beam signal, we propose an improved “k rank” fusion algorithm by jointly exploiting the energy detection (ED) information and location information of the sensors. Finally, the beam detection performance of different fusion algorithms is compared in simulation, and we find that our proposed algorithm showed better detection probability and lower error probability. The simulation results verify the correctness and effectiveness of the proposed algorithm

Feature Selection and Predictors of Falls with Foot Force Sensors Using KNN-Based Algorithms

The aging process may lead to the degradation of lower extremity function in the elderly population, which can restrict their daily quality of life and gradually increase the fall risk. We aimed to determine whether objective measures of physical function could predict subsequent falls. Ground reaction force (GRF) data, which was quantified by sample entropy, was collected by foot force sensors. Thirty eight subjects (23 fallers and 15 non-fallers) participated in functional movement tests, including walking and sit-to-stand (STS). A feature selection algorithm was used to select relevant features to classify the elderly into two groups: at risk and not at risk of falling down, for three KNN-based classifiers: local mean-based k-nearest neighbor (LMKNN), pseudo nearest neighbor (PNN), local mean pseudo nearest neighbor (LMPNN) classification. We compared classification performances, and achieved the best results with LMPNN, with sensitivity, specificity and accuracy all 100%. Moreover, a subset of GRFs was significantly different between the two groups via Wilcoxon rank sum test, which is compatible with the classification results. This method could potentially be used by non-experts to monitor balance and the risk of falling down in the elderly population