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Conventional topological photonic insulators typically have narrow nontrivial band gaps truncated by broad dispersive bulk bands, resulting in limited edge mode transmission bandwidths that can be exploited for potential applications. Here, we demonstrate a Floquet–Lieb topological photonic insulator with all flat bands that can support continuous edge mode transmission across multiple Floquet–Brillouin zones. This supercontinuum of edge states results from the coexistence and orthogonality of the localized flat-band modes and the edge states, allowing for continuous excitation of the latter without scattering into the bulk modes. Moreover, we show that these flat bands are perfectly immune to random variations in the on-site potential, regardless of how large the perturbations are, thus ensuring complete robustness of the edge modes to this type of disorder. We realized Floquet–Lieb insulators using 2D microring resonator lattices with perfect nearest-neighbor couplings. Transmission measurements and direct imaging of the scattered light distributions showed an edge mode supercontinuum spanning more than three microring free spectral ranges. The proposed Floquet–Lieb insulator can potentially be used to realize topological photonic devices with wide bandwidths and super robustness for applications in integrated quantum photonics and programmable photonic circuits.

Aluminum alloys with low-weight property are promising structure materials for sports equipment. Alloying element-rich second-phase particles create the risk of localized corrosion and result in failure of sports equipment. Chromate conversion coatings as conventional and successful surface treatments were employed to provide a thin but compact film against corrosion. However, chromate species were toxic and carcinogenic for human beings and this process has been highly restricted. In this sense, alternative processes such as trivalent chromium conversion coating with low environmental risk require better corrosion-resistant performance compared to chromate conversion coating. In addition, the closed-loop system of the chromate electroplating process has been used in Europe and the United States. This is also a sustainable process for surface treatment of aluminum alloys applied in sports equipment. The present paper aims to summarize the methods and types of different aluminum alloy surface treatments and compiles the effects of various surface treatments on the corrosion resistance of aluminum alloys. The eco-friendly application of aluminum alloys in the field of sports equipment may be facilitated in the future.

In response to the design problems of violent vibration and noise when a stranding machine is running at high speed, this project completed a motion simulation and vibration analysis based on the prototype FB-650C-2 bow-type stranding machine produced by Fuchuan Mechanical and Electrical Technology Co. The modal analysis was carried out in ANSYS to obtain the first eight orders of inherent frequencies and vibration patterns, combined with excitation force analysis to verify whether the rotating parts could avoid the resonant frequency when operating. Harmonic response analysis was carried out based on the modal state to calculate the steady-state forced vibration of the structure, and the variation curve of response value (usual deformation) with frequency and the cloud diagrams of stress distribution of each component at the rotation frequency were obtained. Suggestions for improving vibration and reducing noise were made based on the experimental and analytical results.

Abstract Vehicle mass is an important parameter for motion control of intelligent vehicles, but is hard to directly measure using normal sensors. Therefore, accurate estimation of vehicle mass becomes crucial. In this paper, a vehicle mass estimation method based on fusion of machine learning and vehicle dynamic model is introduced. In machine learning method, a feedforward neural network (FFNN) is used to learn the relationship between vehicle mass and other state parameters, namely longitudinal speed and acceleration, driving or braking torque, and wheel angular speed. In dynamics-based method, recursive least square (RLS) with forgetting factor based on vehicle dynamic model is used to estimate the vehicle mass. According to the reliability of each method under different conditions, these two methods are fused using fuzzy logic. Simulation tests under New European Driving Cycle (NEDC) condition are carried out. The simulation results show that the estimation accuracy of the fusion method is around 97%, and that the fusion method performs better stability and robustness compared with each single method.

It is difficult for traditional therapies to further improve the prognosis of hepatocellular carcinoma (LIHC), and immunotherapy is considered to be a promising approach to overcome this dilemma. However, only a minority of patients benefit from immunotherapy, which greatly limits its application. Therefore, it is particularly urgent to elucidate the specific regulatory mechanism of tumor immunity so as to provide a new direction for immunotherapy. NOP2/Sun RNA methyltransferase 3 (NSUN3) is a protein with RNA binding and methyltransferase activity, which has been shown to be involved in the occurrence and development of a variety of tumors. At present, the relationship between NSUN3 and immune implication in LIHC has not been reported. In this study, we first revealed that NSUN3 expression is upregulated in LIHC and that patients with high NSUN3 expression have a poor prognosis through multiple databases. Pathway enrichment analysis demonstrated that NSUN3 may be participated in cell adhesion and cell matrix remodeling. Next, we obtained a set of genes coexpressed with NSUN3 (NCGs). Further LASSO regression was performed based on NCGs, and a risk score model was constructed, which proved to have good predictive power. In addition, Cox regression analysis revealed that the risk score of NCGs model was an independent risk factor for LIHC patients. Moreover, we established a nomogram based on the NCGs-related model, which was verified to have a good predictive ability for the prognosis of LIHC. Furthermore, we investigated the relationship between NCGs-related model and immune implication. The results implied that our model was closely related to immune score, immune cell infiltration, immunotherapy response, and multiple immune checkpoints. Finally, the pathway enrichment analysis of NCGs-related model showed that the model may be involved in the regulation of various immune pathways. In conclusion, our study revealed a novel role of NSUN3 in LIHC. The NSUN3-based prognostic model may be a promising biomarker for inspecting the prognosis and immunotherapy response of LIHC.

BackgroundA pincer nail (PN) is a type of nail deformity. Although it is a minor ailment, it can cause intractable pain, affecting daily work and life. Currently, there is no standard invention for PN. The main purpose of treatment is to correct the curvature of the nail, so we apply double-layer artificial dermis (DLAD), a novel treatment, for PN according to this aim.Case presentationA 40-year-old man suffering from PN was treated with DLAD. After 1 year of follow-up, the patient's great toenail plate of the right foot was completely grown out. His pain was relieved, and the curvature of the toenail was corrected.ConclusionThe innovative method of using DLAD to fill the ingrown nail bed not only solves the problem of recurrent pincer nail deformity but also restores the original appearance of the toenail. It suggested that this simple procedure can be widely applied in clinical practice.

The existence of information silos between vehicles and parking lots means that Unmanned Ground Vehicles (UGVs) repeatedly drive to seek available parking slots, resulting in wasted resources, time consumption and traffic congestion, especially in high-density parking scenarios. To address this problem, a novel UGV parking planning method is proposed in this paper, which consists of cooperative path planning, conflict resolution strategy, and optimal parking slot allocation, intending to avoid ineffective parking seeking by vehicles and releasing urban traffic pressure. Firstly, the parking lot induction model was established and the IACA–IA was developed for optimal parking allocation. The IACA–IA was generated using the improved ant colony algorithm (IACA) and immunity algorithm. Compared with the first-come-first-served algorithm (FCFS), the normal ant colony algorithm (NACA), and the immunity algorithm (IA), the IACA–IA was able to allocate optimal slots at a lower cost and in less time in complex scenarios with multi-entrance parking lots. Secondly, an improved conflict-based search algorithm (ICBS) was designed to efficiently resolve the conflict of simultaneous path planning for UGVs. The dual-layer objective expansion strategy is the core of the ICBS, which takes the total path cost of UGVs in the extended constraint tree as the first layer objective, and the optimal driving characteristics of a single UGV as the second layer objective. Finally, three kinds of load-balancing and unbalanced parking scenarios were constructed to test the proposed method, and the performance of the algorithm was demonstrated from three aspects, including computation, quality and timeliness. The results show that the proposed method requires less computation, has higher path quality, and is less time-consuming in high-density scenarios, which provide a reasonable and efficient solution for innovative urban mobility.

Acquiring road information is important for smart cities and sustainable urban development. In recent years, significant progress has been made in the extraction of urban road information from remote sensing images using deep learning (DL) algorithms. However, due to the complex shape, narrowness, and high span of roads in the images, the results are often unsatisfactory. This article proposes a Seg-Road model to improve road connectivity. The Seg-Road uses a transformer structure to extract the long-range dependency and global contextual information to improve the fragmentation of road segmentation and uses a convolutional neural network (CNN) structure to extract local contextual information to improve the segmentation of road details. Furthermore, a novel pixel connectivity structure (PCS) is proposed to improve the connectivity of road segmentation and the robustness of prediction results. To verify the effectiveness of Seg-Road for road segmentation, the DeepGlobe and Massachusetts datasets were used for training and testing. The experimental results show that Seg-Road achieves state-of-the-art (SOTA) performance, with an intersection over union (IoU) of 67.20%, mean intersection over union (MIoU) of 82.06%, F1 of 91.43%, precision of 90.05%, and recall of 92.85% in the DeepGlobe dataset, and achieves an IoU of 68.38%, MIoU of 83.89%, F1 of 90.01%, precision of 87.34%, and recall of 92.86% in the Massachusetts dataset, which is better than the values for CoANet. Further, it has higher application value for achieving sustainable urban development.

Functional overlaps and conflicts between Electronic Power Steering (EPS) and Torque Vectoring Control (TVC) for Distributed Drive Electric Vehicles (DDEVs) are issues that need to be addressed urgently. This paper deals with the interaction between EPS and TVC from the view of the influence mechanism and control objectives. The disturbance steering torque caused by the differential torque is estimated and a unified steering characteristic is proposed. To validate the analysis results, a torque compensation strategy is modified based on the exiting control system. And a robust vehicle state estimator is built thanks to the aligning torque estimation to provide the necessary information. Typical ground test results show that the torque oscillation on the steering wheel is suppressed. A faster and more linear steering response can be seen, which means that the proposed disturbance compensation strategy can comfort the contradiction between EPS and TVC, also improve the handling performance of the vehicle.

Slip rate control is important in improving vehicle stability and driving efficiency. In this paper, a robust slip rate control system is designed for distributed drive electric vehicles that consists of two slip rate estimators for multi-driving conditions, a vehicle speed estimator, and an anti-windup robust variable structure slip rate tracking controller. Because there is no driven wheel in a four-in-wheel-motor distributed drive electric vehicle, the estimators for small and large slip rates are designed based on dynamic and kinematic methods, respectively, which can switch according to the slip conditions. The convergence of the estimation error is discussed with the Lyapunov stability law and is less than 2% under the condition of acceleration on a low-friction road. The slip rate tracking controller is designed based on the sliding mode control law and the proportional-integral (PI) control method to handle model nonlinearity, modelling and estimation errors, and disturbances and to control the input chattering and saturation. The asymptotic stability of the tracking error is proven by Lyapunov theory. A joint control variable composed of the wheel angular acceleration and slip rate is designed to improve the robustness of the controller against the slip rate estimation error. Simulations and experiments under various conditions are performed to verify the proposed anti-slip control method. The results show that compared with a distributed drive vehicle without a slip rate controller, the controlled vehicle can prevent serious wheel skid on low-adhesion roads and improve the driving performance.

The connected and automated vehicle (CAV) is a promising technology, anticipated to enhance the safety and effectiveness of mobility [...]

Buildings bear much of the damage from natural disasters, and determining the extent of this damage is of great importance to post-disaster emergency relief. The application of deep learning to satellite remote sensing imagery has become more and more mature in monitoring natural disasters, but there are problems such as the small pixel scale of targets and overlapping targets that hinder the effectiveness of the model. Based on the SegFormer semantic segmentation model, this study proposes the SegDetector model for difficult detection of small-scale targets and overlapping targets in target detection tasks. By changing the calculation method of the loss function, the detection of overlapping samples is improved and the time-consuming non-maximum-suppression (NMS) algorithm is discarded, and the horizontal and rotational detection of buildings can be easily and conveniently implemented. In order to verify the effectiveness of the SegDetector model, the xBD dataset, which is a dataset for assessing building damage from satellite imagery, was transformed and tested. The experiment results show that the SegDetector model outperforms the state-of-the-art (SOTA) models such as you-only-look-once (YOLOv3, v4, v5) in the xBD dataset with F1: 0.71, Precision: 0.63, and Recall: 0.81. At the same time, the SegDetector model has a small number of parameters and fast detection capability, making it more practical for deployment.

The tire-road peak adhesion coefficient (TRPAC), which cannot be directly measured by on-board sensors, is essential to road traffic safety. Reliable TRPAC estimation can not only serve the vehicle active safety system, but also benefit the safety of other traffic participants. In this paper, a TRPAC fusion estimation method considering model uncertainty is proposed. Based on virtual sensing theory, an image-based fusion estimator considering the uncertainty of the deep-learning model and the kinematic model is designed to realize the accurate classification of the road surface condition on which the vehicle will travel in the future. Then, a dynamics-image-based fusion estimator considering the uncertainty of visual information is proposed based on gain scheduling theory. The results of simulation and real vehicle experiments show that the proposed fusion estimation method can make full use of multisource sensor information, and has significant advantages in estimation accuracy, convergence speed and estimation robustness compared with other single-source-based estimators.

The study aimed to investigate the effect of combined balance and plyometric training on the change of direction (COD) performance of badminton athletes. Sixteen elite male badminton players volunteered to participate and were randomly assigned to a balance-plyometric group (BP: n = 8) and plyometric group (PL: n = 8). The BP group performed balance combined with plyometric training three times a week over 6 weeks; while the PL group undertook only plyometric training three times a week during the same period. Meanwhile, both groups were given the same technical training. All participants were tested to assess the COD ability before and after the training period: Southeast Missouri (SEMO) test and 5-0-5 test, dynamic balance ability (Y-Balance test, YBT), and reactive strength index (RSI). Repeated-measure ANOVA revealed that after the intervention there was a significant time × group interaction for 5-0-5 COD test, YBT of both legs and RSI (p < 0.05, partial η2 = 0.26–0.58) due to the better performance observed at post-test compared with a pre-test for the BP group [effect size (ES) = 1.20–1.76], and the improvement was higher than that of the PL group. The change in SEMO test did not differ between BP and PL (p < 0.159, partial η2= 0.137), but the magnitude of the with-group improvement for BP (ES = 1.55) was higher than that of PL (ES = 0.81). These findings suggest that combined training could further improve the COD performance of badminton athletes than plyometric training alone and might provide fitness trainers a more efficient COD training alternative.

We consider the interactions in a mesonic system, referred here to as ‘tetron’, consisting of two heavy quarks and two lighter antiquarks (which may still be heavy in the scale of QCD), i.e. generally QaQbq¯cq¯d, and study the existence of bound states below the threshold for decay into heavy meson pairs. At a small ratio of the lighter to heavier quark masses an expansion parameter arises for treatment of the binding in such systems. We find that in the limit where all the quarks and antiquarks are so heavy that a Coulomb-like approximation can be applied to the gluon exchange between all of them, such bound states arise when this parameter is below a certain critical value. We find the parametric dependence of the critical mass ratio on the number of colors Nc, and confirm this dependence by numerical calculations. In particular there are no stable tetrons when all constituents have the same mass. We discuss an application of a similar expansion in the large Nc limit to realistic systems where the antiquarks are light and their interactions are nonperturbative. In this case our findings are in agreement with the recent claims from a phenomenological analysis that a stable bbu¯d¯ tetron is likely to exist, unlike those where one or both bottom quarks are replaced by the charmed quark.

This study explored and investigated how zearalenone (ZEA) affects the morphology of small intestine and the distribution and expression of ghrelin and proliferating cell nuclear antigen (PCNA) in the small intestine of weaned gilts. A total of 20 weaned gilts (42-day-old, D × L × Y, weighing 12.84 ± 0.26 kg) were divided into the control and ZEA groups (ZEA at 1.04 mg/kg in diet) in a 35-d study. Histological observations of the small intestines revealed that villus injuries of the duodenum, jejunum and ileum, such as atrophy, retardation and branching dysfunction, were observed in the ZEA treatment. The villi branch of the ileum in the ZEA group was obviously decreased compared to that of the ileum, jejunum and duodenum, and the number of lymphoid nodules of the ileum was increased. Additionally, the effect of ZEA (1.04 mg/kg) was decreased by the immunoreactivity and distribution of ghrelin and PCNA in the duodenal and jejunal mucosal epithelial cells. Interestingly, ZEA increased the immunoreactivity of ghrelin in the ileal mucosal epithelial cells and decreased the immunoreactivity expression of PCNA in the gland epithelium of the small intestine. In conclusion, ZEA (1.04 mg/kg) had adverse effects on the development and the absorptive capacity of the villi of the intestines; yet, the small intestine could resist or ameliorate the adverse effects of ZEA by changing the autocrine of ghrelin in intestinal epithelial cells.

Stable maneuverability is extremely important for the overall safety and robustness of autonomous vehicles under extreme conditions, and automated drift is able to ensure the widest possible range of maneuverability. However, due to the strong nonlinearity and fast vehicle dynamics occurring during the drift process, drift control is challenging. In view of the drift parking scenario, this paper proposes a segmented drift parking method to improve the handling ability of vehicles under extreme conditions. The whole process is divided into two parts: the location approach part and the drift part. The model predictive control (MPC) method was used in the approach to achieve consistency between the actual state and the expected state. For drift, the open-loop control law was designed on the basis of drift trajectories obtained by professional drivers. The drift monitoring strategy aims to monitor the whole drift process and improve the success rate of the drift. A simulation and an actual vehicle test platform were built, and the test results show that the proposed algorithm can be used to achieve accurate vehicle drift to the parking position.

This paper proposes an optimized trajectory planner and motion planner framework, which aim to deal with obstacle avoidance along a reference road for autonomous driving in unstructured environments. The trajectory planning problem is decomposed into lateral and longitudinal planning sub-tasks along the reference road. First, a vehicle kinematic model with road coordinates is established to describe the lateral movement of the vehicle. Then, nonlinear optimization based on a vehicle kinematic model in the space domain is employed to smooth the reference road. Second, a multilayered search algorithm is applied in the lateral-space domain to deal with obstacles and find a suitable path boundary. Then, the optimized path planner calculates the optimal path by considering the distance to the reference road and the curvature constraints. Furthermore, the optimized speed planner takes into account the speed boundary in the space domain and the constraints on vehicle acceleration. The optimal speed profile is obtained by using a numerical optimization method. Furthermore, a motion controller based on a kinematic error model is proposed to follow the desired trajectory. Finally, the experimental results show the effectiveness of the proposed trajectory planner and motion controller framework in handling typical scenarios and avoiding obstacles safely and smoothly on the reference road and in unstructured environments.

In order to conduct risk assessment for collision-free decision making of intelligent vehicles in a complex road traffic environment, it is essential to conduct stable tracking and state estimation of multiple vehicle targets. Therefore, this paper proposes a multitarget tracking method in line with the priority data association rule. Firstly, a standard coordinate turn process model is deduced as the existence of translation and rotation of the vehicle on the two-dimensional ground plane. Secondly, an unscented Kalman filter algorithm is developed due to the nonlinear radar measurement model. Thirdly, a priority data association rule, which puts the targets in a priority queue according to the number of times they are associated, is designed to filter out noise, given that it is unlikely for a vehicle target as an inertial system to appear or disappear instantly in practice. In addition, the data association rule specifies that the closer the target is to the front of the queue, the more prioritized the association with the newly observed target would be. Finally, the track management algorithm is constructed. On this basis, a series of real vehicle tests were conducted on real roads with four typical scenarios. According to the test results, the proposed algorithm is effective in filtering out noise and is suboptimal as the nearest neighbor data association.

As one of the core issues of autonomous vehicles, vehicle motion control directly affects vehicle safety and user experience. Therefore, it is expected to design a simple, reliable, and robust path following the controller that can handle complex situations. To deal with the longitudinal motion control problem, a speed tracking controller based on sliding mode control with nonlinear conditional integrator is proposed, and its stability is proved by the Lyapunov theory. Then, a linear parameter varying model predictive control (LPV-MPC) based lateral controller is formulated that the optimization problem is solved by CVXGEN. The nonlinear active disturbance rejection control (ADRC) method is applied to the second lateral controller that is easy to be implemented and robust to parametric uncertainties and disturbances, and the pure pursuit algorithm serves as a benchmark. Simulation results in different scenarios demonstrate the effectiveness of the proposed control schemes, and a comparison is made to highlight the advantages and drawbacks. It can be concluded that the LPV-MPC has some trouble to handle uncertainties while the nonlinear ADRC performs slight worse tracking but has strong robustness. With the parallel development of the control theory and computing power, robust MPC may be the future direction.

How to make a controller robust and stable to reject the disturbance of uncertainty is an inevitable challenge. Aiming at addressing the lateral control problem for an autonomous road sweeper, a heading-error-based first order linear active disturbance rejective controller (HFO-LADRC) is proposed in this paper. To eliminate the lateral error and the heading error at the same time, a new model, called the heading-error-based model, is proposed for lateral motion, and the Lyapunov function was employed to explore the convergence ability of the heading error and lateral error. Since the heading-error-based model is first order, the ADRC is designed as first order and linear, and each module of the HFO-LADRC has been devised in detail. To ensure solution accuracy, the fourth order Runge–Kutta method was adopted as the differential system solver, and a typical ring scenario and a double lane-changing scenario were designed referencing the standard. Considering the obvious influence, wheelbase uncertainty, steering ratio uncertainty and Gaussian white noise disturbance were taken into account for the tests. The results illustrate that, in the case of both wheelbase uncertainty and steer ratio uncertainty, the HFO-LADRC has strong robustness and stability compared with a typical pure pursuit controller and classical SO-LADRC.

Sequential cycloisomerizations of diynyl o-benzaldehyde substrates to access novel polycyclic cyclopropanes are reported. The reaction sequence involves initial Cu(I)-mediated cycloisomerization/nucleophilic addition to an isochromene followed by diastereoselective Pt(II)-catalyzed enyne cycloisomerization.

The purpose of the study was to analyze the influence of left-handed athletes on the opponent (right-handed players) of elite badminton women's singles. The study selected a total of 40 women's singles matches played by elite female players: twenty matches (42 games, n = 42) were played between left-handed and right-handed players and twenty matches (44 games, n = 44) were played between two right-handed players. There were significant (p < 0.05) differences in hitting positions, techniques, routes and landing points. No significant (p > 0.05) differences were found in scores per game and frequency distribution of rally outcomes. In conclusion, the details of technical and tactical application were different in two game forms, the main impact of the left-handed player on the opponent's (right-handed player) game was a decrease in the opponent's stroke in the overhead, an increase in the number of drives, predominance of small slashes and a decrease in big slashes. [ABSTRACT FROM AUTHOR]

An endochitinase, Bbchit1, is purified from culture liquid of endochitinase grown in a medium in which colloidal chitin was the sole carbon and nitrogen source and it is examined that endochitinase overproduction can significantly enhance the virulence of B. bassiana. It is found that entomopathogenic fungi can produce a series of chitinases, some of which act synergistically with proteases to degrade insect cuticle.

To study the main active components and antioxidant activity in vitro of extracts from Callisia repens, the contents of main active components such as total flavonoids, total anthocyanin and total sugar in the extracts were studied by spectro photo me try. The components and content of 18 kinds of metals were determined by ICP-MS mass spectrometry. Finally, the oxidative activity of the extract was evaluated by spectro photometry. Results showed that the content of flavonoids, the total protein, the total sugar and the total anthocyanin in C. repens extract powder were 2.04%, 1.83%, 55.2% and 7.2%, respectively. Beneficial trace elements such as Ca, Mn, Mg in C. repens extracts were higher, while harmful heavy metals such as Pb, Hg, Ag, Co, Ge were very tiny or not detected at all. The IC50 of C. repens was 0.265 mg/niL for scavenging DPPH *, and 1.16 mg/mL for scavenging * OH free radical, the total reducing power of 1 mg extract was equivalent to that of 39 µg of Vc, and the extract showed no regular chelating power to ferrous ions. In conclusion, C. repens extracts have high content of natural active components, but extremely low content of the harmful heavy metals, and C. repens extract has good antioxidant capacity. Its antioxidant activity is realized by a variety of active factors through a synergistic mechanism. Thus, C. repens extract has great potential for developing into functional foods. [ABSTRACT FROM AUTHOR]

Entomopathogenic fungi can produce a series of chitinases, some of which act synergistically with proteases to degrade insect cuticle. However, chitinase involvement in insect fungus pathogenesis has not been fully characterized. In this paper, an endochitinase, Bbchit1, was purified to homogeneity from liquid cultures of Beauveria bassiana grown in a medium containing colloidal chitin. Bbchit1 had a molecular mass of about 33 kDa and pI of 5.4. Based on the N-terminal amino acid sequence, the chitinase gene, Bbchit1, and its upstream regulatory sequence were cloned. Bbchit1 was intronless, and there was a single copy in B. bassiana. Its regulatory sequence contained putative CreA/Crel carbon catabolic repressor binding domains, which was consistent with glucose suppression of Bbchit1. At the amino acid level, Bbchit1 showed significant similarity to a Streptomyces avermitilis putative endochitinase, a Streptomyces coelicolor putative chitinase, and Trichoderma harzianum endochitinase Chit36Y. However, Bbchit1 had very low levels of identity to other chitinase genes previously isolated from entomopathogenic fungi, indicating that Bbchit1 was a novel chitinase gene from an insect-pathogenic fungus. A gpd-Bbchit1 construct, in which Bbchit1 was driven by the Aspergiullus nidulans constitutive promoter, was transformed into the genome of B. bassiana, and three transformants that overproduced Bbchit1 were obtained. Insect bioassays revealed that overproduction of Bbchit1 enhanced the virulence of B. bassiana for aphids, as indicated by significantly lower 50% lethal concentrations and 50% lethal times of the transformants compared to the values for the wild-type strain. [ABSTRACT FROM AUTHOR]