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Abstract Coffin-Siris syndrome (CSS) is a rare autosomal dominant inheritance disorder characterized by distinctive facial features, hypoplasia of the distal phalanx or nail of the fifth and additional digits, developmental or cognitive delay of varying degree, hypotonia, hirsutism/hypertrichosis, sparse scalp hair and varying kind of congenital anomalies. CSS can easily be misdiagnosed as other syndromes or disorders with a similar clinical picture because of their genetic and phenotypic heterogeneity. We describde the genotype-phenotype correlation of one patient from a healthy Chinese family with a novel genotype underlying CSS, who was first diagnosed in the ophthalmology department as early-onset high myopia (eoHM). Comprehensive ophthalmic tests as well as other systemic examinations were performed on participants to confirm the phenotype. The genotype was identified using whole exome sequencing, and further verified the results among other family members by Sanger sequencing. Real-time quantitative PCR (RT-qPCR) technology was used to detect the relative mRNA expression levels of candidate genes between proband and normal family members. The pathogenicity of the identified variant was determined by The American College of Medical Genetics and Genomics (ACMG) guidelines. STRING protein-protein interactions (PPIs) network analysis was used to detect the interaction of candidate gene-related proteins with high myopia gene-related proteins. The patient had excessive eoHM, cone-rod dystrophy, coarse face, excessive hair growth on the face, sparse scalp hair, developmental delay, intellectual disability, moderate hearing loss, dental hypoplasia, patent foramen ovale, chronic non-atrophic gastritis, bilateral renal cysts, cisterna magna, and emotional outbursts with aggression. The genetic assessment revealed that the patient carries a de novo heterozygous frameshift insertion variant in the ARID1B c.3981dup (p.Glu1328ArgfsTer5), which are strongly associated with the typical clinical features of CSS patients. The test results of RT-qPCR showed that mRNA expression of the ARID1B gene in the proband was approximately 30% lower than that of the normal control in the family, suggesting that the variant had an impact on the gene function at the level of mRNA expression. The variant was pathogenic as assessed by ACMG guidelines. Analysis of protein interactions in the STRING online database revealed that the ARID1A protein interacts with the high myopia gene-related proteins FGFR3, ASXL1, ERBB3, and SOX4, whereas the ARID1A protein antagonizes the ARID1B protein. Therefore, in this paper, we are the first to report a de novo heterozygous frameshift insertion variant in the ARID1B gene causing CSS with excessive eoHM. Our study extends the genotypic and phenotypic spectrums for ARID1B-CSS and supplies evidence of significant association of eoHM with variant in ARID1B gene. As CSS has high genetic and phenotypic heterogeneity, our findings highlight the importance of molecular genetic testing and an interdisciplinary clinical diagnostic workup to avoid misdiagnosis as some disorders with similar manifestations of CSS.

IntroductionJoubert syndrome a rare genetic disorder, is characterized by abnormalities in the development of the central nervous system with “molar signs” on magnetic resonance imaging of the brain and accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. Keratoconus (KC) is a kind of genetically predisposed eye disease that causes blindness characterized by a dilated thinning of the central or paracentral cornea conically projected forward, highly irregular astigmatism, and severe visual impairment. Klinefelter syndrome is caused by an extra X chromosome in the cells of male patients, and the main phenotype is tall stature and dysplasia with secondary sex characteristics. This study was intended to identify the genetic etiology and determine the clinical diagnosis of one Han Chinese family with specific clinical manifestations of keratoconus and multiorgan involvement.MethodsA comprehensive ocular and related general examination was performed on one patient and his asymptomatic parents and brother. Pathogenic genes were tested by exome sequencing. CNV-seq was used to verify the copy number variation, and peripheral blood was cultured for karyotype analysis. The pathogenicity of the identified variant was determined subject to ACMG guidelines. The Gene Expression Omnibus (GEO) dataset of keratoconus-related genes in the NCBI database was obtained to analyze the differentially expressed genes in corneal tissues of the keratoconus group and the normal control group, and analysis of protein-protein interaction networks (PPI) was performed.ResultsProband, a 25-year-old male, had sudden loss of vision in the left eye for 1 week. Best corrected visual acuity (BCVA): 0.5 (−1.00DS/-5.00DC*29°) in the right eye, counting fingers/40 cm in the left eye. Slit-lamp microscopy of the right eye showed mild anterior protrusion of the cornea and thinning of the cone-topped cornea. The left eye showed marked thinning of the central region of the cornea, rounded edema in the form of a cone-like bulge, epithelial bullae, edema and turbidity of the stroma, and bulging of the Descemet’s membrane. Cranial magnetic resonance imaging (MRI) revealed changes in the midbrain and cerebellum, with a “molar sign” and a “bat-winged” ventriculus quartus cerebri. General check-up: 168 cm in height, decreased muscle tone in all four limbs, knee jerk elicited, negative Babinski sign, abdominal reflexes elicited, finger-to-nose test positive, intentional tremor evident in both hands, positive Romberg’s sign, instability of gait, level I intellectual disability, poor adaptive behavior, communication disorders, teeth all dentures, a peculiar face with blepharophimosis, wide inner canthus distance, mild ptosis, severe positive epicanthus, high palatal arches, exotropia, hypotrichosis of beard and face, inconspicuous prominentia laryngea, and short upper and lower limbs. Exome sequencing detected compound heterozygous frameshift variants M1:c.9279dup:p.His3094Thrfs*18 and M2:c.6515_6522del:p.Lys2172Thrfs*37 in the patient’s CPLANE1 gene and the presence of duplication-type CNV on the X chromosome. Sanger sequencing showed that the mother and father carried the M1 and M2 variants, respectively, and the younger brother carried the M2 variant, which was a novel variant. CNV-seq analysis showed the presence of a duplication-type CNV Xp22.33-Xq28 (2757837-156030895) of approximately 155 Mb on the X chromosome of the proband, which was a de novo variant and carried by neither of the parents. The two heterozygous frameshift variants and duplication-type CNV were pathogenic according to the ACMG guidelines. Differential expression analysis of keratoconus-related genes showed that CPLANE1 was upregulated in the corneal tissues of keratoconus patients compared with normal controls, and such a difference was statistically significant (p = 0.000515,

Xp21 DNA microdeletion syndrome is a very rare disease characterized by retinitis pigmentosa (RP), chronic granulomatous disease (CGD), and McLeod syndrome (MLS). Due to the complex and diverse clinical manifestations, early diagnosis remains a challenge for many physicians. In this study, for the purpose of determining the pathogenic gene variants and definitive diagnosis in a patient medically backgrounded with RP and CGD from a normal Chinese family, whole-exome sequencing (WES) was performed in this proband and copy number variation (CNV) was further verified in other family members by qPCR. A genetic evaluation revealed that the short arm of the X chromosome in the proband had a deletion CNV Xp21.1p11.4 (37431123–38186681) of approximately 0.755 Mb in size, and contained three contiguous OMIM genes as X-linked Kx blood group antigen (XK), cytochrome b-245 beta chain (CYBB), and RP GTPase regulator (RPGR). The qPCR results confirmed the copy number loss in Xp21.1p11.4 present in the proband and his unaffected mother. According to the American College of Medical Genetics and Genomics (ACMG) guidelines for the CNV interpretation, the deletion of this segment was a pathogenic variant. Our results provided evidence that CNV deletion of Xp21.1p11.4 in the short arm of the X chromosome was a pathogenic variant in such Chinese RP and CGD family, and the McLeod phenotype was not yet available. This study suggests that genetic testing is essential for a definitive diagnosis, which should better assist physicians in prediction, diagnosis, genetic counseling, and guidance for Xp21 DNA microdeletion syndrome.

To compare the contents of active ingredients and antioxidant activity of Rosa roxburghii Tratt juice (RRTJ), pomegranate juice (PJ) and blueberry juice (BJ) and investigate the protective effects of three kinds of fruit juices against dextran sulfate sodium (DSS)-induced oxidative damage in human normal colonic epithelial cells NCM460. There were 28 bioactive components in the three juices. The total polyphenols and total flavonoids of RRTJ were significantly higher than those in the PJ and BJ (P

Aiming at the safety problems of flight avoidance caused by multiple aircraft performing tasks together in the mixed airspace in the future,a priority operation strategy of UAV avoidance based on improved grey correlation projection method with combination weighting was proposed.Firstly,this paper analyzed the factors affecting the collision avoidance process from the perspectives of airspace management,aviation regulations and risk assessment,and constructed the evaluation index system of UAV collision avoidance.Secondly,the analytic hierarchy process (AHP) and rough entropy method were used to assign subjective and objective weights,and the deviation and minimum objective optimization function were constructed by the cooperative game method to fuse subjective and objective weights to determine the final weight,so as to overcome the disadvantages of the arbitrary subjective weight and objective weight relying too much on data.Thirdly,the fusion algorithm of TOPSIS and gray correlation projection method were used to comprehensively evaluate the UAV avoidance object,so as to obtain the UAV priority avoidance operation strategy.Finally,the fusion algorithm was compared with TOPSIS algorithm.The results show that compared with TOPSIS method,the acquisition strategy of the fusion algorithm is consistent with the airspace operation practice,and the sensitivity is improved by 17.08%.The algorithm avoids the data loss of single algorithm and improves the accuracy of acquisition strategy,which provides a new idea for the acquisition of UAV operation strategy in mixed airspace.

Seashore paspalum is a halophytic, warm-season grass with wide applications. It is noted for its superior salt tolerance in saline environments; however, the nutritive value of seashore paspalum and the effect of salinity remains to be determined. Therefore, this study aimed to evaluate the relationship between agronomic traits and forage quality and identified the effects of short-term high-salt stress (1 week, 700 mM NaCl) on the growth and forage nutritive value of 16 ecotypes of seashore paspalum. The salt and cold tolerances of the seashore paspalum ecotypes were assessed based on the survival rate following long-term high-salt stress (7 weeks, 700 mM NaCl) and exposure to natural low temperature stress. There were significant genetic (ecotype-specific) effects on plant height, leaf–stem ratio, and survival rate of seashore paspalum following salt or low temperature stress. Plant height was significantly negatively correlated with the leaf–stem ratio (r = −0.63, P

Chlamydial infection, caused by Chlamydia trachomatis, is the most common bacterial sexually transmitted infection and remains a major public health problem worldwide, particularly in underdeveloped regions. Developing a rapid and sensitive point-of-care (POC) testing for accurate screening of C. trachomatis infection is critical for earlier treatment to prevent transmission. In this study, a novel diagnostic assay, loop-mediated isothermal amplification integrated with gold nanoparticle-based lateral flow biosensor (LAMP-LFB), was devised and applied for diagnosis of C. trachomatis in clinical samples. A set of LAMP primers based on the ompA gene from 14 C. trachomatis serological variants (serovar A-K, L1, L2, L3) was successfully designed and used for the development of C. trachomatis-LAMP-LFB assay. The optimal reaction system can be performed at a constant temperature of 67°C for 35 min. The total assay process, including genomic DNA extraction (~15 min), LAMP reaction (35 min), and LFB readout (~2 min), could be finished within 60 min. The C. trachomatis-LAMP-LFB could detect down to 50 copies/ml, and the specificity was 100%, no cross-reactions with other pathogens were observed. Hence, our C. trachomatis-LAMP-LFB was a rapid, reliable, sensitive, cost-effective, and easy-to-operate assay, which could offer an attractive POC testing tool for chlamydial infection screening, especially in resource starvation settings.

Aiming at the large amount of clutter and noise interference(mainly direct waves on the surface) in the original image of ground penetrating radar,an adaptive bilateral filtering method based on wavelet transform and K-Means singular value decomposition was proposed in order to effectively extract the target signal.The original radar data was decomposed by wavelet,and then the decomposed data was sparsely coded by K-SVD transform and updated atoms.The updated sparse coefficients and dictionary were used to reconstruct small blocks,and the small blocks were subjected to inverse wavelet transform to reconstruct the image.The reconstructed image was processed by adaptive bilateral filtering to obtain the processed image,so as to realize the complementary advantages of wavelet transform,K-SVD algorithm and adaptive bilateral filtering method.Compared with other methods,the experimental results show that the proposed method has a good clutter suppression effect and better performance in peak signal-to-noise ratio and target image entropy.The research results have certain application value in the actual radar survey tasks.

In order to solve the problem that ant colony algorithm is easy to fall into local optimum in UAV route planning, an improved ant colony algorithm was proposed. The upper and lower limits of pheromone volatilization factor and pheromone were set to prevent ants from falling into local optimum because pheromone on short path was too high or pheromone on long path was too low. At the same time, under the influence of multiple heuristic factors, the overall length of the path was taken as an adaptive heuristic function factor to determine the state transition probability. When the path length was large, the adaptive heuristic function factor was small, which reduced the probability of choosing the path by the ant colony. The experimental results show that the improved algorithm reduces the path length by 6.4% and the variance of the optimal path length by 85.78%, which increases the consideration of environmental integrity, shortens the path length, reduces the number of iterations, and jumps out of the local optimum. In the case of increasing environmental complexity, the algorithm can effectively choose a better path and provide a theoretical basis for UAV route planning after introducing the adaptive heuristic function factor.

The path length of multiple unmanned aerial vehicle (multi-UAV) has a certain impact on the task allocation of multi-UAV. In order to improve the efficiency of multi-UAV and reduce the loss of multi-UAV during the process of performing tasks, this paper takes the path length as one of the influencing factors of the evaluation function. The UAV path length, UAV performance, and task characteristics are taken as the influencing factors of multi-UAV task allocation evaluation function. In addition, in order to improve the efficiency of genetic algorithm (GA) in solving multi-UAV task allocation problem, this paper proposes a fusion genetic algorithm based on improved simulated annealing (ISAFGA). In order to improve the population diversity of GA, the second selection operation of GA is carried out and the improved simulated annealing algorithm (SA) is used in the second selection operation. The threshold is set to improve the acceptance criteria of new solutions in SA, and then the promotion of secondary selection operation on population diversity is improved. The simulation results showed that the improved algorithm could improve the diversity of the population and improve the global search ability, and verified the effectiveness of the improved algorithm.

Considering the possible flight issues due to multiple aircrafts performing tasks in the same airspace in the future, an evaluation index system for the optimal avoidance scheme of large UAVs in mixed airspaces was constructed by studying various scenarios of possible collisions, and a hybrid airspace strategy of multi-scale analytic hierarchy process was proposed by analyzing the pros and cons of different scale methods. First, a large-scale unmanned aerial vehicle was used as a research object to establish a hybrid airspace model, which classified various birds or aircraft that have the possibility of conflict with the UAV in the mixed airspace, and considered the factors of collision risk level and the target execution task level. Secondly, the multi-scale analytic hierarchy process was used to obtain the weights of various avoidance targets in different situations, and the consistency was determined. The membership function was further introduced, and the final result was further weighted by analyzing the obtained weights. Finally, the airspace environment that large drones may encounter in the airspace is analyzed to verify whether the drone avoidance decision plan is effective. The verification results show that the determined multi-scale analytic hierarchy process can meet the establishment of the avoidance strategy in a complex airspace environment, and has a dynamic effect to meet the needs of UAVs to construct and adjust the avoidance strategy in real time. The research result provides new ideas for drones to complete avoidance strategies and safe flight under complex airspace conditions.

For large-scale Unmanned Aerial Vehicle (UAV), it is a challenging problem to automatically generate an optimal path considering the multi-constraints. In order to improve the efficiency and optimality of conventional A* algorithm, Bi-directional adaptive A* algorithm is proposed in this paper. Regarding to the defect of expansion efficiency for conventional A* algorithm based on multi-direction, directional search strategy is adopted to improve the efficiency of expansion process and guarantee the smooth of the path. Adaptive step strategy and adaptive weight strategy are used to improve the exploration speed and the accuracy of the proposed algorithm. By adopting bi-directional search strategy, the exploration ability of the algorithm can be improved in some complex environment. Finally, rewiring process is introduced to optimize the path length. The simulation for UAV path planning under the multi-constraints conditions is carried out and the simulation results show that Bi-directional adaptive A* algorithm has the superiority in run time and path quality.

Aiming at the problem of traditional UAV trajectory planning which falls into local optimum easily, and the problem of UAV trajectory planning in complex terrain, a trajectory planning method based on adaptive polymorphic fusion ant colony algorithm is proposed. This study describes the problem of route planning, establishes a mathematical model, and combines the adaptive ant colony algorithm with the polymorphic ant colony algorithm to form a global and local parallel search mode, which improves the ability of the algorithm to find the global optimal value. An adaptive parallel strategy and an adaptive information update strategy are proposed to improve the global search ability. Simulation results show that this method has better performance than the other two traditional ant colony algorithm and polymorphic ant colony algorithms. It can effectively improve the length and convergence speed of the search path and avoid falling into local optimum in the solution process. Therefore, the adaptive polymorphic fusion ant colony algorithm has a good application prospect in solving the optimal track planning problem.

To solve the problems of traditional RRT algorithm, such as slow convergence and long path length, the dynamic step BI-RRT algorithm is proposed. The sampling efficiency of the RRT algorithm is improved by using a target-oriented sampling strategy. The dynamic step algorithm improves the local smoothness of the path in the obstacle environment. A new tree cutting strategy is proposed to make the growth trend of random trees more obvious and improve the convergence rate of the algorithm. For the initial path, the path nodes are filtered by applying greedy algorithm to reduce the invalid nodes and path length of the unmanned aerial vehicle (UAV). Finally, a feasible smooth path is obtained by using B-Spline. The simulation maps in 2D and 3D environment are built to verify the effectiveness of the algorithm. Compared to the segmentated optimization RRT algorithm, the dynamic step BI-RRT algorithm proposed in this paper improves the convergence rate and reduces the path distance, while ensuring that the number of path nodes is optimal. This path planning algorithm has some advantages such as good real-time performance and smoothness of path.

In order to improve the accuracy of identifying the target image while the drone is sailing, a hybrid model combining convolutional neural network (CNN) and probabilistic neural network (PNN) is proposed. The model uses CNN to extract multi-layer image representations and uses PNN extraction features to classify images. In order to improve the generalization and robustness of CNN, CNN-PNN model replaces BP neural network inside CNN with PNN, and trains the model by mean square error and gradient reduction method. The pre-processed image is transmitted to the CNN-PNN model to classify and identify the texture and contour of the image, and the simulation results of this model are compared with the results of convolutional neural network model and convolutional neural network-support vector machine model. The simulation results show that the CNN-PNN model has better accuracy compared with the two models, and the recognition rate is as high as 96.30%. The improved model improves the generalization and robustness of CNN, and can enhance the accuracy of image recognition in all aspects, and has high real-time performance.

Melatonin acts both as an antioxidant and as a growth regulatory substance in plants. Pseudomonas fluorescens endophytic bacterium has been shown to produce melatonin and increase plant resistance to abiotic stressors through increasing endogenous melatonin. However, in bacteria, genes are still not known to be melatonin-related. Here, we reported that the bacterial phenylalanine 4-hydroxylase (PAH) may be involved in the 5-hydroxytryptophan (5-HTP) biosynthesis and further influenced the subsequent production of melatonin in P. fluorescens. The purified PAH protein of P. fluorescens not only hydroxylated phenylalanine but also exhibited l-tryptophan (l-Trp) hydroxylase activity by converting l-Trp to 5-HTP in vitro. However, bacterial PAH displayed lower activity and affinity for l-Trp than l-phenylalanine. Notably, the PAH deletion of P. fluorescens blocked melatonin production by causing a significant decline in 5-HTP levels and thus decreased the resistance to abiotic stress. Overall, this study revealed a possible role for bacterial PAH in controlling 5-HTP and melatonin biosynthesis in bacteria, and expanded the current knowledge of melatonin production in microorganisms.

Hepatitis B virus (HBV) is one of the most dangerous and prevalent agents that causes acute and chronic liver diseases in humans. Genotyping plays an important role in determining clinical outcomes and response to antiviral treatment in HBV–infected patients. Here, we first devised a CRISPR–based testing platform, termed “CRISPR-HBV,” for ultrasensitive, highly specific, and rapid detection of two major HBV genotypes (HBV-B and HBV-C) in clinical application. The CRISPR-HBV employed multiple cross displacement amplification (MCDA) for rapid preamplification and then Cas12b–based detection for decoding the targets. Finally, the detection result was read out with real-time fluorescence and a lateral flow biosensor. The sensitivity of CRISPR-HBV was 10 copies per test. The specificity was one hundred percent, and no cross reactions were observed in other HBV genotypes and pathogens. The whole detection process, including DNA template extraction (15 min), preamplification reaction of MCDA (30 min at 65°C), CRISPR-Cas12b–based detection (5 min at 37°C), and results readout (∼2 min), could be completed within 1 h. The feasibility of the CRISPR-HBV assay for genotyping HBV-B and -C as successfully validated with clinical samples. Hence, the CRISPR-HBV assay has remarkable potential to develop a point-of-care testing for identifying and distinguishing HBV genotypes B and C in clinical settings, especially in resource-scarcity countries.

Neisseriagonorrhoeae is a host-adapted human pathogen that causes sexually transmitted gonorrhea and remains to be a serious global public health challenge, especially in low- and middle-income regions. It is vital to devise a reliable, simple, cost-saving, and easy-to-use assay for detecting the N. gonorrhoeae agent. In the current study, we firstly report a novel approach, loop-mediated isothermal amplification linked with a polymer nanoparticle–based biosensor (LAMP-PNB), that was used for identifying N. gonorrhoeae in clinical samples. The results showed that the LAMP primers based on the orf1 gene were valid for development of the N. gonorrhoeae-LAMP-PNB assay. The detection system with optimal conditions could be performed at a fixed temperature of 64°C for 40 min. The whole process, including genomic DNA preparation (approximately 10 min), LAMP reaction (40 min), and PNB reporting (approximately 2 min), could be accomplished within 60 min. The limit of detection (LoD) of the N. gonorrhoeae-LAMP-PNB assay was 50 copies per test. The specificity of the current assay was 100%, and no cross-reactions to non–N. gonorrhoeae isolates were observed. These results confirmed that the N. gonorrhoeae-LAMP-PNB technique is a reliable, specific, sensitive, rapid, low-cost, and easy-to-use method for detecting gonococci isolates. More importantly, this assay has great potential to develop a point-of-care (POC) testing method in clinical practice, especially in resource-constrained regions.

Electrocatalytic conversion of carbon dioxide (CO2) into specific renewable fuels is an attractive way to mitigate the greenhouse effect and solve the energy crisis. AunCu100-n/C alloy nanoparticles (AunCu100−n/C NPs) with tunable compositions, a highly active crystal plane and a strained lattice were synthesized by the thermal solvent co-reduction method. Transmission electron microscopy (TEM) and X-ray diffraction (XRD) results show that AunCu100−n/C catalysts display a subtle lattice strain and dominant (111) crystal plane, which can be adjusted by the alloy composition. Electrochemical results show that AunCu100−n/C alloy catalysts for CO2 reduction display high catalytic activity; in particular, the Faradaic efficiency of Au75Cu25/C is up to 92.6% for CO at −0.7 V (vs. the reversible hydrogen electrode), which is related to lattice shrinkage and the active facet. This research provides a new strategy with which to design strong and active nanoalloy catalysts with lattice mismatch and main active surfaces for CO2 reduction reaction.

In order to suppress the strong direct wave in the original image of GPR, highlight the weak target signal and realize the accurate location of the hidden danger, a Stolt migration method combining with robust principal component analysis (RPCA) is proposed.Firstly, the synthetic data are generated by using the GPR analysis program package matGPR3.1 to simulate the dam-void model, and the original image of the GPR is obtained through scanning processing.Then the RPCA method is used to extract the hyperbolic signal of the target.Finally, the target signal is focused by Stolt migration technique, and the optimal migration effect is obtained by selecting the appropriate migration velocity through minimum entropy velocity estimation.The experimental results show that the proposed method can effectively suppress the clutter and identify the location of the target void more clearly.Compared with the previous methods, the image obtained by the method has higher resolution and better performance, and has a certain application value in the detection of hidden dangers in water conservancy project.

In order to monitor human blood flow rate and flow velocity distribution, a blood flow meter based on multi-electrode electromagnetic measurement has been proposed to measure the flow velocity of the arteriovenous blood by obtaining the induced voltage on the skin surface. The COMSOL 3-D model has been built to simulate the physical structure of the human limb, including the size and relative position of skin, fat, bones, muscles, arteries and veins. Based on the Helmholtz coil and C-type coil, the electromagnetic induction intensity distribution of excitation structures and methods is simulated which is imposed through the body measurement section. The optimal design scheme of the excitation coil is proposed. Different excitation systems of different structures and different incentive methods are compared. The result shows that the two pairs of Helmholtz coils of orthogonal layout with syntroph excitation current has better uniformity, field center symmetry and stronger magnetic field strength, and effectively enhances the value of induction electromotive force, showing it is more suitable for multi-electrode electromagnetic measurement of non-axisymmetric flow. The research result proves that the optimization scheme of excitation system is feasible, and its uniformity plays a positive role in improving the accuracy of velocity reconstruction.

In order to measure the velocity profile of arterial and venous blood flow of human body and predict major cardiovascular diseases, the multi-electrode electromagnetic flow meter based on region weight function theory is proposed in this paper. The region weight function theory has been derived from traditional Shercliff theory and COMSOL model has been established to simulate the human limb according to the layout and relative position of skin, fat, bone, muscle, artery and vein. The cross section has been divided in to several regions and the induced voltages at different positions along the skin surface have been obtained by the electrode array. The local mean axial velocity profile of the vessel cross section is calculated, and the velocity information of the reverse flow in the arteries and veins is reconstructed. The results of three-dimensional finite element model simulation demonstrate that multi-electrode electromagnetic flow meter based on region weight function is feasible to characterize the velocity profile of arteries and veins with high accuracy. Region weight function based multi-electrode electromagnetic measurement method has some reference value for vessel blood flow velocity measurement and hemorheology anomaly monitoring of human body.

To understand how light intensity influences plant morphology and photosynthesis in the forage crop alfalfa (Medicago sativa L. cv. Zhongmu 1), we investigated changes in leaf angle orientation, chlorophyll fluorescence, parameters of photosynthesis and expression of genes related to enzymes involved in photosynthesis, the Calvin cycle and carbon metabolism in alfalfa seedlings exposed to five light intensities (100, 200, 300, 400 and 500 μmol m−2 s−1) under hydroponic conditions. Seedlings grown under low light intensities had significantly increased plant height, leaf hyponasty, specific leaf area, photosynthetic pigments, leaf nitrogen content and maximal PSII quantum yield, but the increased light-capturing capacity generated a carbon resource cost (e.g., decreased carbohydrates and biomass accumulation). Increased light intensity significantly improved leaf orientation toward the sun and upregulated the genes for Calvin cycle enzymes, thereby increasing photosynthetic capacity. Furthermore, high light (400 and 500 μmol m−2 s−1) significantly enhanced carbohydrate accumulation, accompanied by gene upregulation and increased activity of sucrose and starch-synthesis-related enzymes and those involved in carbon metabolism. Together, these results advance our understanding of morphological and physiological regulation in shade avoidance in alfalfa, which would guide the identification of suitable spatial planting patterns in the agricultural system.

Airplane control system is a typical multitude input and multitude output (MIMO) nonlinear system with strong coupling and uncertainties, which would increase the controlling difficulty. The tracking control problem has been investigated for a class of airplane attitude control systems in this paper. Firstly, a new higher-order differential sliding mode surface is presented and its stability of the sliding mode surface is proved by coordinate transformation of airplane attitude control system and considering the existing of the input uncertainties. Then, a smooth adaptive sliding mode controller is design based on this higher-order differential sliding mode surface and the approximation ability of the neural network. Based on Lyapunov stability theory, it is proved that the designed controller not only ensures the airplane attitude control system with good tracking performance, but also avoids the appearance of the chattering phenomenon of traditional sliding mode controllers. Finally, the effectiveness of the proposed controller is verified by simulation results. The controller proposed in this paper has very fine application prospects in the field of airplane attitude control comparing the traditional one.

With the gradual opening of the low-altitude airspace and the rapid development of Unmanned Aerial Vehicle(UAV) industry, the users of UAV are increasing continuously and the conflicts could occur at any time. It is necessary to develop a reliable UAV conflict resolution algorithm to avoid the danger. This paper proposes an UAV conflict resolution algorithm based on the improved ant colony algorithm with two advantages. Firstly, the algorithm adopts adaptive parameters adjustment strategy, which adjusts the parameters value dynamically according to the quality of the solution, prevents the algorithm premature convergence and improves the accuracy. In addition, the disturbance factors is introduced to the state transition rules of random selected path in order to accelerate the initial convergence. The simulation results have shown that the improved algorithm displays obvious superiority in convergence precision, helping the two UAVs avoiding dangers in time. The algorithm described in this paper could be applied to target identification, path planning and other issues as a general optimized algorithm, which is of great significance and wide application.

Abstract Background Whole-exome sequencing (WES) over the last few years has been increasingly employed for clinical diagnosis. However, one caveat with its use is that it inevitably fails to detect disease-causative variants that occur within noncoding RNA genes. Our experience in identifying pathogenic variants in the noncoding RNU4ATAC gene, in a Chinese family where two successive foetuses had been affected by severe microcephaly, is a case in point. These foetuses exhibited remarkably similar phenotypes in terms of their microcephaly and brain abnormalities; however, the paucity of other characteristic phenotypic features had made a precise diagnosis impossible. Given that no external causative factors had been reported/identified during the pregnancies, we sought a genetic cause for the phenotype in the proband, the second affected foetus. Results A search for chromosomal abnormalities and pathogenic copy number variants proved negative. WES was also negative. These initial failures prompted us to consider the potential role of RNU4ATAC, a noncoding gene implicated in microcephalic osteodysplastic primordial dwarfism type-1 (MOPD1), a severe autosomal recessive disease characterised by dwarfism, severe microcephaly and neurological abnormalities. Subsequent targeted sequencing of RNU4ATAC resulted in the identification of compound heterozygous variants, one being the most frequently reported MOPD1-causative mutation (51G>A), whereas the other was a novel 29T>A variant. Four distinct lines of evidence (allele frequency in normal populations, evolutionary conservation of the affected nucleotide, occurrence within a known mutational hotspot for MOPD1-causative variants and predicted effect on RNA secondary structure) allowed us to conclude that 29T>A is a new causative variant for MOPD1. Conclusions Our findings highlight the limitations of WES in failing to detect variants within noncoding RNA genes and provide support for a role for whole-genome sequencing as a first-tier genetic test in paediatric medicine. Additionally, the identification of a novel RNU4ATAC variant within the mutational hotspot for MOPD1-causative variants further strengthens the critical role of the 5′ stem-loop structure of U4atac in health and disease. Finally, this analysis enabled us to provide prenatal diagnosis and genetic counselling for the mother’s third pregnancy, the first report of its kind in the context of inherited RNU4ATAC variants.

The risk of the reservoir dam seepage will bring the waste of water resources and the loss of life and property. The ground penetrating radar (GPR) is designed as a daily inspection system of dams to improve the existing technology which can't determine the actual situation of the dam seepage tunnel coordinates. The finite difference time domain (FDTD) is used to solve the Yee's grids discreatization in two-dimensional space, and its electromagnetic distribution equation is obtained as well. Based on the actual structure of reservoir dam foundation, the ideal model of air layer, concrete layer, clay layer and two water seepage holes is described in detail, and the concrete layer interference model with limestone interference point is established. The system architecture is implemented by using MATLAB, and the forward modeling is performed. The results indicate that ground penetrating radar can be used for deep target detection. Through comparing the detection spectrum of three kinds of frequency electromagnetic wave by changing the center frequency of the GPR electromagnetic wave of 50 MHz, 100 MHz and 200 MHz, it is concluded that the scanning result is more accurate at 100 MHz. At the same time, the simulation results of the interference model show that this method can be used for the detection of complex terrain.

In order to improve the precision and accuracy of artificial potential field, and avoid the situation of hovering on the end point and being caught in minimum value, a path planning algorithm based on artificial potential algorithm for unmanned aerial vehicle is presented. The traditional artificial potential field method is improved: chaos theory is used to improve artificial potential field calculation formula, which changes the potential field coefficients of each barrier and target point, resulting in the best screened out route. The simulation experimental analysis and result show that the optimized algorithm considers the influence of obstacles to the optimization process, ruling out the situation of hovering on the end point in unmanned aerial vehicle route planning, and through iterative method, the algorithm has the ability to adapt to different maps. The improved artificial potential field method is better than the traditional artificial potential field method in speed and precision aspects.

The problem of longitudinal safety interval of aircraft during parallel air route flight is discussed in order to solve the problem of flight safety and improve the accuracy of the collision risk. The traditional EVENT model is improved by using elliptic cylinder as the collision template. The performance and application field of broadcast automatic monitoring (ADS-B) are introduced, and based on the improved EVENT model, a method for calculating the parameters through using ADS-B monitoring technology is put forward. Taking E500 aircraft as a model example, the minimum safety distance is calculated. The results are in line with the international civil aviation organization's flight safety index, which provides an effective method for the study of risk classification based on ADS-B technology.

Conflict resolution requires the aircraft to adopt an effective method to meet the constraints of the working space. Aiming at the problem of conflict resolution in multi obstacle environment, a novelty algorithm based on particle swarm optimization (PSO) is proposed for conflict resolution which meets the mentioned constrains. The proposed DARPSO is similar to PSO with two modifications: firstly, it takes into account the obstacle avoidance; secondly, it has a mechanism to escape from local optima. Various experimental results obtained in simulations show that the proposed DARPSO is much more superior than PSO. The DARPSO is applied to the process of conflict resolution of aircrafts, and it is simulated. The simulation results show that the proposed algorithm can avoid the obstacles and complete conflict resolution effectively.

This paper proposes a new scheme for solving finite time neural networks adaptive tracking control issue for the nonaffine pure-feedback nonlinear system. The procedure, based on homeomorphism mapping and backstepping, effectively deals with constraint control and design difficulty induced by pure-feedback structure. The most outstanding novelty is that finite time adaptive law is proposed for training weights of neural networks. Furthermore, by combining finite time adaptive law and Lyapunov-based arguments, a valid finite time adaptive neural networks controller design algorithm is presented to ensure that system is practical finite stable (PFS) rather than uniformly ultimately bounded (UUB). Because of using the finite time adaptive law to training weights of neural networks, the closed-loop error system signals are in assurance of bounded in finite time. Benchmark simulations have well demonstrated effectiveness and efficiency of the proposed approach.

The Super-Twisting control algorithm is adopted to analyze the U model based nonlinear control system in order to solve the controller design problems of non-affine nonlinear systems. The non-affine nonlinear systems are studied, the neural network approximation of the nonlinear function is performed, and the Super-Twisting control algorithm is used to control. The convergence of the Super-Twisting algorithm is proved by selecting an appropriate Lyapunov function. The Matlab simulation is carried out to verify the feasibility and effectiveness of the described method. The result shows that the output of the controlled system can be tracked in a very short time by using the designed Super-Twisting controller, and the robustness of the controlled system is significantly improved as well.

A U model based Super-Twisting synchronization control method for chaotic systems is proposed. The chaos control of chaotic systems is prescribed, then, based on the current research status of chaotic systems and some useful research results in nonlinear system design, some new methods for chaos control and synchronization are provided, and the controller is designed to achieve the finite time chaos synchronization. The numerical simulations are carried out for Lorenz system and Chen system, and the result proves the effectiveness of the method.

This paper presents a new algorithm for the consistency of information based on the average state, and it is applied in first-order system and second-order system. Frequency domain analysis method is used to obtain sufficient conditions for the convergence rate. Due to the inevitable presence of the communication network itself and the other individual time lag problems between individuals, the paper further analyzes the stability of the system under time delay which uses the proposed consistency algorithm. For second-order multi-agent system, firstly, consensus algorithm of average state under the undirected network is proposed and based on the given conditions, sufficient conditions are obtained under which the system achieves static consistent and dynamic consistent. Finally, the algorithm is applied in a multi-aircraft formation control groups.

Seashore paspalum (Paspalum vaginatum O. Swartz) is an important warm-season turfgrass species. In this study we generated transgenic seashore paspalum overexpressing CdtNF-YC1, a nuclear factor Y transcription factor from hybrid bermudagrass (Cynodon dactylon × Cynodon transvaalensis). DNA blot hybridization and qRT-PCR analysis showed that CdtNF-YC1 was integrated into the genomes of transgenic seashore paspalum plants and expressed. Reduced relative water content (RWC) and survival rate and increased ion leakage were observed in both wild type (WT) and transgenic plants after drought stress, while transgenic plants had higher levels of RWC and survival rate and lower ion leakage than the WT. Maximal photochemical efficiency of photosystem II (Fv/Fm), chlorophyll concentration and survival rate were decreased after salt stress, while higher levels were maintained in transgenic plants than in WT. In addition, an increased Na+ content and decreased or unaltered K+ in leaves and roots were observed after salt treatment, while lower level of Na+ and higher levels of K+ and K+/ Na+ ratio were maintained in transgenic plants than in WT. The results indicated that overexpressing CdtNF-YC1 resulted in enhanced drought and salt tolerance in transgenic plants. Transcript levels of stress responsive genes including PvLEA3, PvP5CS1, PvABI2, and PvDREB1B were induced in response to drought and salt stress, and higher levels were observed in transgenic seashore paspalum than in WT. The results suggest that the enhanced drought and salt tolerance in transgenic seashore paspalum is associated with induction of a series of stress responsive genes as a result of overexpression of CdtNF-YC1.

Aiming at the flight path project in low complicated airspace, the influence of terrain conditions and surface threatening to aircraft flight are studied. Through the analysis of digital map and static threat, the paper explores the processing method of the digital map, and uses the Hermite function to process the map smoothly, reducing the searching range of optimal trajectory. By designing the terrain following, terrain avoidance and the way of avoiding a threat, the safety of aircraft can be guaranteed. In-depth analysis of particle swarm optimization (PSO) algorithm realizes the three dimensional paths project before the aircraft performs a task. Through simulation, the difference of the maps before and after processing is shown, and offline programming of the three dimensional optimal path is achieved.

Balanced energy depletion is an important way to prolong network lifetime for wireless sensor networks. Traditional algorithms mainly aim at maximizing network coverage rate with uniform sensor node distribution. However, wireless sensor network is characterized by many-to-one traffic pattern and multihop communication, which usually lead to the energy hole problem in the region around the sink node. In this paper, we investigate the problem of joint placement for both relays and sensors to eliminate energy hole. We first theoretically conclude that balanced energy depletion is achievable with rational designed deployment density for relay nodes. We then propose a novel relay deploying strategy as well as a data routing scheme to eliminate uneven energy depletion. Extensive simulations are presented to verify that our approach outperforms other schemes in terms of both network lifetime and unused energy ratio.