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Fiber length is an important indicator of cotton fiber quality, and the time and rate of cotton fiber cell elongation are key factors in determining the fiber length of mature cotton. To gain insight into the differences in fiber elongation mechanisms in the offspring of backcross populations of Sea Island cotton Xinhai 16 and land cotton Line 9, we selected two groups with significant differences in fiber length (long-fiber group L and short-fiber group S) at different fiber development stages 0, 5, 10 and 15 days post-anthesis (DPA) for transcriptome comparison. A total of 171.74 Gb of clean data was obtained by RNA-seq, and eight genes were randomly selected for qPCR validation. Data analysis identified 6055 differentially expressed genes (DEGs) between two groups of fibers, L and S, in four developmental periods, and gene ontology (GO) term analysis revealed that these DEGs were associated mainly with microtubule driving, reactive oxygen species, plant cell wall biosynthesis, and glycosyl compound hydrolase activity. Kyoto encyclopedia of genes and genomes (KEGG) pathway analysis indicated that plant hormone signaling, mitogen-activated protein kinase (MAPK) signaling, and starch and sucrose metabolism pathways were associated with fiber elongation. Subsequently, a sustained upregulation expression pattern, profile 19, was identified and analyzed using short time-series expression miner (STEM). An analysis of the weighted gene coexpression network module uncovered 21 genes closely related to fiber development, mainly involved in functions such as cell wall relaxation, microtubule formation, and cytoskeletal structure of the cell wall. This study helps to enhance the understanding of the Sea Island–Upland backcross population and identifies key genes for cotton fiber development, and these findings will provide a basis for future research on the molecular mechanisms of fiber length formation in cotton populations.

This paper investigates the frequency connectedness among economic policy uncertainties of G20 countries using the novel frequency connectedness proposed by Barunik and Krehlik (2018) which can depict the dynamic connectedness not only over time but also across different frequencies. The empirical results obtained in this paper demonstrate that, firstly, the connectedness among economic policy uncertainties is significant, and the spillover effects during the financial crisis and the post-financial crisis period are stronger than the pre-financial crisis period. Secondly, the United States, France, and Australia are the main net-transmitters of the economic policy uncertainty spillovers while Brazil, Italy, Mexico, and Russia act as the main net-recipients of the spillovers. Thirdly, the major international events may significantly enhance the spillover transmissions of economic policy uncertainty among different countries, thus increasing the magnitude of the total connectedness. Finally, the economic policy uncertainty spillovers are mainly transmitted in the short term, i.e., 1∼4 months instead of longer time horizons in terms of the magnitude of the frequency connectedness measures. The findings of this paper not only have profound theoretical and practical significance but also provide several significant implications for the policymakers, supervision agents, international traders, and various investors.

The vascular interventional surgery (VIS) is the most popular treatment for the cardiovascular and cerebrovascular diseases. Master-slave VIS robot is a promising technology to further improve the operation accuracy and surgical safety of both the patient and surgeon. The surgical outcome of robot assisted VIS highly depends on surgeon’s operating skills. However, current VIS robot systems affect the exerting of surgeon’s operating skills with different degrees due to the master manipulators with rigid-link structure or low detecting accuracy. To address this issue, a novel vascular interventional surgery robot based on surgeon’s operating skills is developed in this paper. A master manipulator is developed to contactlessly measure the surgeon’s operating motion. The surgeon operates the catheter with no limit, just as the operating manner in conventional VIS. It is enabled by measuring the catheter’s axial motion through tracking the edge of a series of markers with a macro camera, and by measuring the catheter’s rotational motion via a high-precise MEMS (Micro-Electro-Mechanic-System) gyroscope. Then, the developed master manipulator is integrated with a slave robot to construct the master-slave VIS robot system. Experimental results show that the detecting accuracy of the developed master manipulator are 0.71° and 0.104mm. Experimental results of surgical tasks implemented in EVE (endovascular evaluator) indicate that the developed robot system is superior to the system using a phantom as the master manipulator according to the metrics including operating time, path length, maximum speed and maximum accelerate.

Master-slave endovascular interventional surgery (EIS) robots have brought revolutionary advantages to traditional EIS, such as avoiding X-ray radiation to the surgeon and improving surgical precision and safety. However, the master controllers of most of the current EIS robots always lead to bad human-machine interaction, because of the difference in nature between the rigid operating handle and the flexible medical catheter used in EIS. In this paper, a noncontact detection method is proposed, and a novel master controller is developed to realize real-time detection of surgeon's operation without interference to the surgeon. A medical catheter is used as the operating handle. It is enabled by using FAST corner detection algorithm and optical flow algorithm to track the corner points of the continuous markers on a designed sensing pipe. A mathematical model is established to calculate the axial and rotational motion of the sensing pipe according to the moving distance of the corner points in image coordinates. A master-slave EIS robot system is constructed by integrating the proposed master controller and a developed slave robot. Surgical task performance evaluation in an endovascular evaluator (EVE) is conducted, and the results indicate that the proposed detection method breaks through the axial measuring range limitation of the previous marker-based detection method. In addition, the rotational detection error is reduced by 92.5% compared with the previous laser-based detection method. The results also demonstrate the capability and efficiency of the proposed master controller to control the slave robot for surgical task implementation. Graphical abstract A novel master controller is developed to realize real-time noncontact detection of surgeon's operation without interference to the surgeon. The master controller is used to remotely control the slave robot to implement certain surgical tasks.

Vaccines have become one of the most effective strategies to deal with various infectious diseases and chronic noninfectious diseases, such as SARS virus, Novel Coronavirus, cancer, etc. However, recent studies have found that the neutralizing antibody titers induced by vaccines would drop to half level or even lower after vaccination. In this study, we designed a novel small-sized positively charged nanofiber-1 (PEI-CNF-1) as a vaccine carrier, which can induce a high long-term humoral immune response by controlled release of antigen. Further studies showed that PEI-CNF-1 could significantly induce the release of immune response factor IL-1β and bone marrow-derived cell (BMDC) maturation. Moreover, compare to other cellulose nanofibers (CNFs), PEI-CNF-1 combined antigen (ovalbumin, OVA) induced and maintained the highest and longest antibody titers after vaccination. Interestingly, the antibody titers have no significant difference between at 21 and 90 d. Mechanically, we found that PEI-NCF-1 not only could control the slow-release of antigen, but also could be more easily swallowed by macrophages and metabolized by the bodies, thus presenting antigen more effectively. In conclusion, we believe that PEI-CNF-1 have a very high application prospect in inducing long-term humoral immune response, so as to achieve efficient prevention effect to epidemic viruses.

This paper investigates the time-frequency dependence and risk connectedness among oil and stock markets in oil-importing and oil-exporting countries using the wavelet coherence and BK frequency connectedness method. Those two methods allow us to capture the dynamics of the dependence and risk connectedness over time as well as across different frequency bands (i.e. 1–5 days, 5–22 days, and more than 22 days). The empirical results demonstrate that the dependence structures among oil and stock markets are stronger on long-term scales and the lead-lag associations among oil and stock markets are mixed and time-varying. The total risk spillovers among oil and stock markets are mainly transmitted in the long-run (i.e. more than 22 days). The oil market receives much more risk spillovers from the stock markets in the US, EU, Canada, and Russia. The dynamic risk spillovers at diverse frequency bands are time-varying and heterogeneous. Besides, the major international crisis events, such as the GFC, oil price collapse, and the COVID-19 pandemic have greatly intensified the risk spillover magnitude. The time-frequency dependence and risk spillover analysis can offer great benefits for those energy and financial market participants with multivariate time-horizons in asset allocation and risk management practices.

Background: As one of the most important preoperative steps, surgical path planning of passive flexible tool in soft tissue encounters a prohibitive challenge, typically in vascular interventional surgery(VIS). The key problem is that unstructured surgical environment and tools’ unpredictable motion is hard to be explicitly modeled. We propose a generative adversarial networks (GAN)-based framework (defined as surigcal GAN) towards automatic guide wire path planning in real time for VIS. Methods: A novel GAN architecture is proposed by combining convolutional neural networks (CNN) and long short term memory networks (LSTM), which extracts and fuses the spatial features in medical images and temporal features of historical tool path as the conditional information. It inputs the surgical state information and continously outputs local future path of guide wire tip. A training dataset (3.5*105 samples) is collected under laboratory conditions with 10 surgeons. Effects of different CNN architectures and path planning length on network performance are investigated. User experiments are conducted with 10 novice surgeons in an operating room. Findings: The results shows significant reduction of path planning accuracy with surgical GAN comparing with baseline networks (from 46.2%-69.78%) and non-rigid registration method (72.94%). Results of user experiments demonstrate overall better task performance with the guidance of planned surgical path. Interpretation: Collectively, surgical GAN can achieve real-time surgical path planning of passive flexible tools in soft tissue surgery, and holds great potential for novice training and robotic VIS autonomy. Funding Statement: The National High-tech R&D Program of China [No.2015AA043202], National Key Research and Development Program of China [No.2017YFB1304400, 2018YFB1306500], National Nature Science Foundation of China [No.62003128], Natural Science Foundation of Hebei Province of China [No.F2020202053], Natural Science Foundation of Tianjin City of China [No.S20YBE154] and Preferential funding for postdoctoral research projects of Hebei Province of China [No.B2020003020]. Declaration of Interests: The authors declare no potential conflicts of interest.

Existing studies of risk spillovers primarily examine returns and volatility but ignore higher-order moment risks and asymmetrical effects, thereby impeding portfolio optimization and risk management. This study addresses those scholarly deficiencies by investigating the realized higher-order moments (realized skewness and kurtosis) risk spillovers, and asymmetrical spillovers in volatility between WTI and Brent oil and China's commodities spanning 2006–2020. For robustness, we explore the spillovers in signed jump variations, realized hyper-skewness, hyper-kurtosis, and realized jump. Based on the DCC framework and the proposed MVHAR-RS/RK models, we propose a model to quantify the dynamic links in volatility and higher-order moments. Empirical results demonstrate that risk-connectedness varies under different moments. Alongside spillovers in realized volatility, analysis reveals significant spillovers in higher-order moment risks and captures the effects of asymmetries in spillovers of good and bad volatility. WTI and Brent oil act as the net transmitters of risk spillovers in all realized moments considered. The dynamic links in realized kurtosis are relatively more volatile and higher than volatility and skewness links. The dynamic risk spillovers under different realized moments and conditional correlations are time-varying and sensitive to major crises. These findings benefit investors and regulators concerned with cross-commodity risks, portfolio optimization, and policymaking.

This paper investigates the time-frequency dependence, extreme risk spillovers, and dynamic linkages between oil and China’s commodity futures markets, using wavelet coherence, quantile connectedness approach, and the DECO-FIAPARCH (1,d,1) model. The results suggest that the oil market exhibits higher coherence with the copper, natural rubber, and fuel oil futures but low coherence with corn, soybean, soybean meal, and white sugar futures on a long-term scale. The crude oil market leads most of China’s commodity futures. The results of the DECO model point to the time-varying and low average equicorrelations between oil and China’s commodity futures. The total risk connectedness at the extreme lower quantile level (0.01) is higher than the conditional mean and conditional median level. WTI oil, Brent oil, soybean oil, and copper futures are the main spillover net-transmitters whereas the white sugar, soybean, soybean meal, cotton, corn, aluminum, natural rubber, and fuel oil futures are risk spillover net-recipients. The dynamic extreme negative risk spillovers are highly volatile and vulnerable to major international events such as the GFC, oil price plunge, and the COVID-19 pandemic. Finally, Brent oil offers better portfolio diversification benefits than WTI oil and the optimal-weighted portfolio illustrates the highest risk and downside risk reduction effectiveness.

An accurate assessment of surgical operation skills is essential for improving the vascular intervention surgical outcome and the performance of endovascular surgery robots. In existing studies, subjective and objective assessments of surgical operation skills use a variety of indicators, such as the operation speed and operation smoothness. However, the vascular conditions of particular patients have not been considered in the assessment, leading to deviations in the evaluation. Therefore, in this paper, an operation skills assessment method including the vascular difficulty level index for catheter insertion at the aortic arch in endovascular surgery is proposed. First, the model describing the difficulty of the vascular anatomical structure is established with characteristics of different aortic arch branches based on machine learning. Afterwards, the vascular difficulty level is set as an objective index combined with operating characteristics extracted from the operations performed by surgeons to evaluate the surgical operation skills at the aortic arch using machine learning. The accuracy of the assessment improves from 86.67 to 96.67% after inclusion of the vascular difficulty as an evaluation indicator to more objectively and accurately evaluate skills. The method described in this paper can be adopted to train novice surgeons in endovascular surgery, and for studies of vascular interventional surgery robots. Graphical abstract Operation skill assessment with vascular difficulty for vascular interventional surgery.

In the field of rapidly developing endovascular technique and technology, accurate assessment of surgical operation is essential for improving the efficiency of endovascular surgery and the performance of endovascular surgery robots. Existing methods of assessment have taken into consideration of a variety of indicators such as path length of operation, operation time and so on. The indicators that have been considered all come from surgeon’s operation itself. However, the characteristics of specific patients’ blood vessels are not considered for objective assessment. So in this paper, operating difficulty of different blood vessels was described for operation through the aortic arch by machine learning k-means models. Then clustering results were verified with external and internal metrics. Based on this study, difficulty levels of blood vessels can be taken as an important indicator for surgeons’ endovascular operation evaluation in the future research.

Increasingly high incidence of cardiovascular and cerebrovascular diseases, which has become one of the three major causes of human disease death, because of its high incidence, disability, and mortality, it brings tremendous mental stress and heavy weight to individuals, families and society economic burden. Vascular interventional technique is a newly developed treatment for prevention and treatment of cardiovascular and cerebrovascular diseases. Under the guidance of medical imaging, the doctor enters the lumen of the blood vessel through the catheter directly to the blood vessel of the lesion in the body, and then uses the catheter to transport the therapeutic agent or surgical instrument. Minimally invasive diagnosis and treatment of distant lesions in the body. Therefore, it is critical to accurately and stably deliver the catheter to the designated location. This paper proposes a reciprocating towed slave robot, which mainly includes four degrees of freedom: linear motion, torsional motion, catheter front clamping release, and catheter end clamping release. The catheter motion operation is achieved by the four degrees of freedom of the end robot. And the linkage is released by the clamping of the front of the catheter to achieve the operation of transporting the catheter to a further destination. This method was demonstrated to meet clinical operational requirements by experiments in a simulated vascular model.

At present, vascular interventional surgery is still the most popular way to treat cardiovascular and cerebrovascular diseases, and it is currently the most recognized. During the traditional vascular interventional procedure, the surgeon needs to makes a judgment on the contact of the catheter in the human body with the inner wall of the vascular lumen according to the information of the catheter operation force felt by the operation, and then make an adjustment decision for the surgical operation. If the surgeon judges that the operating force is abnormal according to experience, he will stop pushing or twisting the catheter, and then perform the action of retreating to avoid damage to the vessel wall or rupture of the aneurysm. This paper proposes a variable-coefficient follow-up control method based on force and torque identification. By inputting the samples of forces and torques in the previous 50 sampling points into a convolutional neural network, the risk probability of the operating force and torque is obtained, which is used to calculate the following coefficient of master-slave system to ensure the safety of the surgery. Experiments have shown that the operating force and torque during surgery are reduced by 20.8% and 14.2%.

Global climate change caused by human activities has posed a huge threat to the environmental governance and sustainability of human-being. Fortunately, the carbon emission trading scheme, a powerful tool for reducing carbon emissions, has been established in many countries to slow catastrophic climate change thus promoting human well-being. The nexuses between energy and European carbon markets have been extensively studied due to the grim reality of global climate change. However, no literature to date has examined the multi-scale associations between China’s energy and regional CET markets. This paper aims to fill this gap by utilizing a proposed wavelet-based multi-scale investigation framework to explore the information spillovers and dynamic dependence over different time horizons. The original energy and carbon return series are decomposed by the MODWT method. The TVP-VAR-based connectedness and the DECO-FIGARCH model are then built at each selected wavelet component. The wavelet coherence method is also applied to depict the time-frequency dependence. The empirical results demonstrate that the information spillovers at multivariate time-scales are heterogeneous. Generally, GDEA and the crude oil markets act as the information spillover net-transmitters at the raw data level and all wavelet scales. The dynamic spillovers present significant time-varying features and a similar evaluation trajectory. Besides, we offer strong evidence of a low dynamic equicorrelation between China’s energy and regional CET markets, suggesting the portfolio diversification benefits. Furthermore, the information spillover and dependence are proved to be more significant at longer time horizons. Finally, the energy-carbon portfolios offer diversification opportunities, and the risk reduction effectiveness varies with wavelet scales. Moreover, the optimal-weighted portfolio exhibits the best risk management performance.

Performance of robot-assisted endovascular surgery (ES) remains highly dependent on an individual surgeon's skills, due to common adoption of master-slave robotic structure. Surgeons' skill modeling and unstructured surgical state perception pose prohibitive challenges for an autonomous ES robot. In this paper, a novel convolutional neural network (CNN)-based framework is proposed to address these challenges for navigation of an ES robot based on surgeons' skill learning. An operating action probability estimator is proposed by integrating a two-dimensional CNN, with which the features of a surgical state image are extracted and then directly mapped to the action probability. A one-dimensional CNN with multi-input is developed to recognize the guide wire operating force condition. An eye-hand collaborative servoing algorithm is proposed to combine the outputs of these two networks and to control the robot under a closed-loop architecture. A real-world ES robot is employed for data collection and task performance evaluation in laboratory condition. Compared with the state of the art, the CNN-based method shows its capability of adapting to different situations and achieves similar success rate and average operating time. Robotic operation performs similar operating trajectory and maintains similar level of operating force with manual operation. The CNN-based method can be easily extended to many other surgical robots. Graphical abstract A surgeon's guide wire operating skills in endovascular surgery (ES) is learned by the proposed CNN-based method. Then, the learned model is used for autonomous control of a ES robot with surgical state input (images and operating force).

Vascular interventional surgery has played an important role in the treatment of cardiovascular and cerebrovascular diseases. The corresponding vascular interventional robots are also developed. In our previous work, the interventional operation robot was developed to operate catheter or guidewire. However, in the process of conveying the catheter, the surgical accuracy will be affected by the inevitable bending deformation of the catheter in the sleeve. In this paper, in order to improve the accuracy of position control, we simulated the deformation of the catheter and obtains the displacement error caused by the catheter based on the ANSYS software. The results proved that the deformation of the catheter affects the precision of position control, and simulation results are prepared for the later compensation algorithm.

With the development of surgical robotic technology, more and more requirements on safety property were raised upon the surgical robots. For master-slave control surgical robots, it's physiological hand tremor that influences the accuracy and success rate of the robot-assisted surgery. Focusing on the physiological tremor recognition and cancelling, this paper proposes a moving-window-least-square-support-vector-machine-based recognition algorithm and adaptive filter method to rectify the wrong operations caused by physiological tremor. The performance assessment was shown with the indicator of accuracy, which implies that the MWLSSVMAF reduces accuracy error of the tremor signal. The comparison between recognition results and surface electromyographic signal is conducted for assessing the classification accuracy rate. Also, some experiments on correction effects are carried out. The results indicate that the method proposed by our research possesses better classifying accuracy rate of 83% and that the secure property requests of vascular interventional surgical robot have been improved obviously.

Remote-controlled vascular interventional robots (RVIRs) are being developed to increase the accuracy of surgical operations and reduce the number of occupational risks sustained by intervening physicians, such as radiation exposure and chronic neck/back pain. However, complex control of the RVIRs improves the doctor's operation difficulty and reduces the operation efficiency. Furthermore, incomplete sterilization of the RVIRs will increase the risk of infection, or even cause medical accidents. In this study, we introduced a novel method that provides higher operation efficiency than a previous prototype and allows for complete robot sterilization. A prototype was fabricated and validated through laboratory setting experiments and an in-human experiment. The results illustrated that the proposed RVIR has better performance compared with the previous prototype, and preliminarily demonstrated that the proposed RVIR has good safety and reliability and can be used in clinical surgeries.

Microbial fuel cell (MFC) is a new technology that can recover energy from biomass with simultaneous waste treatment. This technique has been developed fast in recent years in combining with environmental techniques such as wastewater treatment, degradation of toxic pollutants and desalination. With the increase of solid waste, applying MFC in composting is promising due to its property of waste disposal with simultaneous energy generation. In this paper, the microbial community of MFCs during composting was summarized. Four major influencing factors including electrodes, separators, oxygen supplement and configurations on the performance of composting MFCs were discussed. The characteristics of composting MFC as a new technique for reducing solid waste were as follows: high microbial biomass resulted in the high current density; adaptable to different environmental conditions; self-adjustable temperature with high energy efficiency; the transportation of proton from anode to cathode were limited by different solid substrates.