Design and control of a sit-to-stand assistive device based on analysis of kinematics and dynamics

Sit-to-stand is a common activity in daily life. It is difficult for the elderly and patients with lower limb disorders to complete this motion due to limb pain, muscle weakness, partial loss of motor control function, and physical defects in joints. An STS assistive device is a piece of automated medical equipment that can facilitate rehabilitation training for patients with lower limb disorders and improve their lower limb function. In this paper, we introduce a 3-DOF series type STS assistive device. First, we selected 26 healthy adults to carry out an STS transfer experiment, and we obtained the trajectory and velocity of each joint and the law of plantar pressure during STS motion. Second, based on the above kinematics and dynamics law, a 3-DOF series mechanism was designed. Through forward and inverse kinematics analysis, the relationship between the end-effector and the linear actuator was established. The trajectory planning of the end-effector was carried out according to the natural STS transfer trajectory, and the law of the linear actuator was obtained. The trajectory planning was verified by ADAMS. Finally, the Arduino controller was used to build the control system of the STS assistive device, and the prototype experiment was carried out

OFDI of Chinese Private Enterprises and Deindustrialization

Outward Foreign Direct Investment（OFDI）has a profound impact on the adjustment of industrial structure, and OFDI of Chinese private enterprises is the most important part of OFDI of Chinese enterprises. What is its connection with the deindustrialization process of China’s economy? This paper adopts the data of OFDI of Chinese private enterprises as well as the data of China’s cities at the prefecture level and above from 2005 to 2016 for empirical analysis, and the results show that OFDI of private enterprises significantly promotes China’s deindustrialization process. The results of further heterogeneity test show that compared with greenfield investment, the deindustrialization effect of OFDI of private enterprises in the form of mergers and acquisitions（M&A）is greater; OFDI of private enterprises in the eastern region has the strongest deindustrialization effect, followed by the western region; and compared with OFDI of private enterprises in developing countries, OFDI of private enterprises in developed countries has a stronger negative impact on the level of industrialization. The conclusion of this paper may provide enlightenment for China to promote the new pattern of all-round opening and industrial adjustment and upgrading

Genome-wide and expression analysis of protein phosphatase 2C in rice and Arabidopsis

<p>Abstract</p> <p>Background</p> <p>The protein phosphatase 2Cs (PP2Cs) from various organisms have been implicated to act as negative modulators of protein kinase pathways involved in diverse environmental stress responses and developmental processes. A genome-wide overview of the PP2C gene family in plants is not yet available.</p> <p>Results</p> <p>A comprehensive computational analysis identified 80 and 78 PP2C genes in <it>Arabidopsis thaliana </it>(AtPP2Cs) and <it>Oryza sativa </it>(OsPP2Cs), respectively, which denotes the PP2C gene family as one of the largest families identified in plants. Phylogenic analysis divided PP2Cs in Arabidopsis and rice into 13 and 11 subfamilies, respectively, which are supported by the analyses of gene structures and protein motifs. Comparative analysis between the PP2C genes in Arabidopsis and rice identified common and lineage-specific subfamilies and potential 'gene birth-and-death' events. Gene duplication analysis reveals that whole genome and chromosomal segment duplications mainly contributed to the expansion of both OsPP2Cs and AtPP2Cs, but tandem or local duplication occurred less frequently in Arabidopsis than rice. Some protein motifs are widespread among the PP2C proteins, whereas some other motifs are specific to only one or two subfamilies. Expression pattern analysis suggests that 1) most PP2C genes play functional roles in multiple tissues in both species, 2) the induced expression of most genes in subfamily A by diverse stimuli indicates their primary role in stress tolerance, especially ABA response, and 3) the expression pattern of subfamily D members suggests that they may constitute positive regulators in ABA-mediated signaling pathways. The analyses of putative upstream regulatory elements by two approaches further support the functions of subfamily A in ABA signaling, and provide insights into the shared and different transcriptional regulation machineries in dicots and monocots.</p> <p>Conclusion</p> <p>This comparative genome-wide overview of the PP2C family in Arabidopsis and rice provides insights into the functions and regulatory mechanisms, as well as the evolution and divergence of the PP2C genes in dicots and monocots. Bioinformatics analyses suggest that plant PP2C proteins from different subfamilies participate in distinct signaling pathways. Our results have established a solid foundation for future studies on the functional divergence in different PP2C subfamilies.</p

Tetris-inspired detector with neural network for radiation mapping

In recent years, radiation mapping has attracted widespread research attention and increased public concerns on environmental monitoring. In terms of both materials and their configurations, radiation detectors have been developed to locate the directions and positions of the radiation sources. In this process, algorithm is essential in converting detector signals to radiation source information. However, due to the complex mechanisms of radiation-matter interaction and the current limitation of data collection, high-performance, low-cost radiation mapping is still challenging. Here we present a computational framework using Tetris-inspired detector pixels and machine learning for radiation mapping. Using inter-pixel padding to increase the contrast between pixels and neural network to analyze the detector readings, a detector with as few as four pixels can achieve high-resolution directional mapping. By further imposing Maximum a Posteriori (MAP) with a moving detector, further radiation position localization is achieved. Non-square, Tetris-shaped detector can further improve performance beyond the conventional grid-shaped detector. Our framework offers a new avenue for high quality radiation mapping with least number of detector pixels possible, and is anticipated to be capable to deploy for real-world radiation detection with moderate validation.Comment: 29 pages, 20 figures. Ryotaro Okabe and Shangjie Xue contributed equally to this wor

Characterization and Genome Sequence of Marine Alteromonas gracilis Phage PB15 Isolated from the Yellow Sea, China

A novel marine Alteromonas gracilis siphovirus, phage PB15, was isolated from the surface water of the Yellow Sea in August 2015. It has a head diameter of 58 ± 5 nm head and a contractile tail approximately 105 ± 10 nm in length, and overall the morphology suggests that PB15 belongs to the family Siphoviridae. PB15 phage is stable at over the temperature range 0-60oC. The best MOI of these phage was 0.1 and infectivity decreased above 60oC. The results suggest that phage is stable at pH value ranging between 3.0 and 11.0. Chloroform test shows that PB15 is not a lipid-containing phage. A one-step growth curve with a strain of A. gracilis gave a latent period of 16 minutes and rise period of 24 minutes and burst size of 60 PFU/cell. Genomic analysis of PB15 reveals a genome size of 37,333bp with 45.52% G+C content, and 61 ORFs. ORF sequences accounted for 30.36% of the genome sequence. There is no obvious similarity between PB15 and other known phages by genomic comparison using the BLASTN tool in the NCBI database

Considering Genetic Heterogeneity in the Association Analysis Finds Genes Associated With Nicotine Dependence

While substantial progress has been made in finding genetic variants associated with nicotine dependence (ND), a large proportion of the genetic variants remain undiscovered. The current research focuses have shifted toward uncovering rare variants, gene-gene/gene-environment interactions, and structural variations predisposing to ND, the impact of genetic heterogeneity in ND has been nevertheless paid less attention. The study of genetic heterogeneity in ND not only could enhance the power of detecting genetic variants with heterogeneous effects in the population but also improve our understanding of genetic etiology of ND. As an initial step to understand genetic heterogeneity in ND, we applied a newly developed heterogeneity weighted U (HWU) method to 26 ND-related genes, investigating heterogeneous effects of these 26 genes in ND. We found no strong evidence of genetic heterogeneity in genes such as CHRNA5. However, results from our analysis suggest heterogeneous effects of CHRNA6 and CHRNB3 on nicotine dependence in males and females. Following the gene-based analysis, we further conduct a joint association analysis of two gene clusters, CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6. While both CHRNA5-CHRNA3-CHRNB4 and CHRNB3-CHRNA6 clusters are significantly associated with ND, there is a much stronger association of CHRNB3-CHRNA6 with ND when considering heterogeneous effects in gender (p-value = 2.11E-07)

Deep learning assisted diagnosis system: improving the diagnostic accuracy of distal radius fractures

ObjectivesTo explore an intelligent detection technology based on deep learning algorithms to assist the clinical diagnosis of distal radius fractures (DRFs), and further compare it with human performance to verify the feasibility of this method.MethodsA total of 3,240 patients (fracture: n = 1,620, normal: n = 1,620) were included in this study, with a total of 3,276 wrist joint anteroposterior (AP) X-ray films (1,639 fractured, 1,637 normal) and 3,260 wrist joint lateral X-ray films (1,623 fractured, 1,637 normal). We divided the patients into training set, validation set and test set in a ratio of 7:1.5:1.5. The deep learning models were developed using the data from the training and validation sets, and then their effectiveness were evaluated using the data from the test set. Evaluate the diagnostic performance of deep learning models using receiver operating characteristic (ROC) curves and area under the curve (AUC), accuracy, sensitivity, and specificity, and compare them with medical professionals.ResultsThe deep learning ensemble model had excellent accuracy (97.03%), sensitivity (95.70%), and specificity (98.37%) in detecting DRFs. Among them, the accuracy of the AP view was 97.75%, the sensitivity 97.13%, and the specificity 98.37%; the accuracy of the lateral view was 96.32%, the sensitivity 94.26%, and the specificity 98.37%. When the wrist joint is counted, the accuracy was 97.55%, the sensitivity 98.36%, and the specificity 96.73%. In terms of these variables, the performance of the ensemble model is superior to that of both the orthopedic attending physician group and the radiology attending physician group.ConclusionThis deep learning ensemble model has excellent performance in detecting DRFs on plain X-ray films. Using this artificial intelligence model as a second expert to assist clinical diagnosis is expected to improve the accuracy of diagnosing DRFs and enhance clinical work efficiency

Incentive Mechanisms for Carbon Emission Abatement Considering Consumers&rsquo; Low-Carbon Awareness under Cap-and-Trade Regulation

In the era of sustainable development, reducing carbon emissions and achieving carbon neutrality are gradually becoming a consensus for our society. This study explores firms&rsquo; incentive mechanisms for carbon emission abatement in a two-echelon supply chain under cap-and-trade regulation, where consumers exhibit low-carbon awareness. To boost the manufacturer&rsquo;s motivation for abatement, the retailer can provide four incentive strategies, i.e., price-only (PO), cost-sharing (CS), revenue-sharing (RS), and both (cost and revenue) sharing (BS). The equilibrium decisions under the four incentive strategies are obtained by establishing and solving game models. A two-part tariff contract is also proposed to coordinate the low-carbon supply chain. Finally, through comparisons and analyses, we find that: (1) Consumers&rsquo; high low-carbon awareness can boost the manufacturer&rsquo;s incentive for carbon emission abatement (CEA), thus increasing supply chain members&rsquo; profits. (2) It is more effective for the retailer to share its revenue to incentivize the manufacturer for abatement than to bear the investment cost of CEA. Thus, Strategy RS is better than Strategy CS and equivalent to Strategy BS. (3) The manufacturer and retailer have consistent incentive strategy preference under cap-and-trade regulation. Both firms prefer the incentive strategy with a higher cooperation level. (4) The incentive strategy with a higher cooperation level can also bring higher eco-social welfare under certain conditions