Development of the neonatal pain response variable set: a mixed methods consensus process.

While over 40 neonatal pain assessment scales have been published, owing to a lack of consensus and standardized metrics, there are more than 100 assessment indicators with varying descriptors and quality differences. This study aims to reach a consensus on optimal and comprehensive variables for neonatal pain assessment, leading to the development of a multidimensional neonatal pain response variable set. This study consisted of three phases: (1) A literature review was conducted to identify influencing factors and assessment indicators of neonatal pain response. (2) Panel meetings involving neonatal healthcare professionals evaluated and screened factors and indicators to develop an initial draft of the variable set. (3) Through two rounds of Delphi study achieved consensus, and determined the neonatal pain response variable set. Through a literature review and a panel meeting, the identified factors and indicators were categorized into contextual, physiological, and behavioral variables, forming an initial draft of the variable set. Sixteen professionals participated in two rounds of the Delphi study, with response rates exceeding 70%, and authority coefficients surpassing 0.7 in both rounds. The final iteration of the variable set includes 9 contextual variables, 2 physiological variables, and 5 behavioral variables.   Conclusion: Neonatal pain response variable set developed in this study is scientific, comprehensive, and multidimensional, aligning with the characteristics of neonatal pain response and clinically applicable. The inclusion of contextual variables enhances the ability to confront the complexity of clinical environments and individual differences. It can provide a practical and theoretical basis for clinical research on neonatal pain assessment. What is Known: • Neonatal pain assessment relies on scales used by healthcare professionals currently. But there is no "gold standard" for neonatal pain assessment. • While over 40 neonatal pain assessment scales have been published, owing to a lack of consensus and standardized metrics, there are more than 100 assessment indicators with varying descriptors and quality differences. Most of scales overlook the clinical environment complexity individual differences in pain responses, diminishing the accuracy and applicability. What is New: • In addition to the commonly used physiological and behavioral variables in the scales, we have incorporated contextual variables to better address the complexity of clinical environments and individual differences in pain responses. • Through an evidence-based approach, developed a neonatal pain response variable set comprising 9 contextual variables, 2 physiological variables, and 5 behavioral variables.

Comparison of clinical outcomes of a corneal wavefront- and topography-guided platforms for laser in situ keratomileusis on virgin eyes: an expanded cohort study.

PURPOSE: To compare the clinical outcomes of myopiacorrected with corneal-wavefront-guided (CWG) laser in situ keratomileusis (LASIK) with AMARIS 1050S (SCHWIND eye-tech-solutions GmbH & Co. KG) and corneal-topography-guided (CTG) LASIK with WaveLight EX500 (Alcon Laboratories, Fort Worth, TX). METHODS: In this prospective, pseudo-randomized expanded cohort study, a total of 266 patients were subjected to binocular LASIK surgery, either with WaveLight EX500 (WaveLight group) or Amaris 1050S (AMARIS group) platforms. Data related to right eyes were selected for analysis. Corneal higher-order aberration (HOA) was selected as the primary endpoint; while visual acuity and refraction were the secondary endpoints. All the endpoints were assessed at 3 months postoperatively. RESULTS: There were 134 eyes in the AMARIS group and 132 eyes in the WaveLight group. After 3 months of postoperative follow-up, spherical and coma aberrations were significantly lower (P < 0.05) in the WaveLight group (spherical aberration: - 0.104 ± 0.199 µm; coma aberration: - 0.117 ± 0.202 µm) in comparison with the AMARIS group (spherical aberrations: 0.254 ± 0.146 µm; coma aberrations: 0.316 ± 0.297 µm). In the AMARIS group, 96.3% of the eyes achieved an uncorrected distance visual acuity (UDVA) of 20/20 while in the WaveLight group, 96.2% of the eyes achieved an UDVA of 20/20. Furthermore, the mean postoperative manifest refraction spherical equivalent (MRSE) was - 0.02 ± 0.28 in the AMARIS group and - 0.05 ± 0.21 in the WaveLight group (P = 0.34). CONCLUSIONS: Both WaveLight EX500 and Amaris 1050S LASIK showed excellent refractive and visual outcomes. In addition, the WaveLight group showed minimal spherical and coma aberrations when compared to the AMARIS group.

Helicobacter macacae MazF interplays with Escherichia coli homologs and enhances antibiotic tolerance.

BACKGROUND: Toxin-antitoxin systems are highly variable, even among strains of the same bacterial species. The MazEF toxin-antitoxin system is found in many bacteria and plays important roles in various biological processes such as antibiotic tolerance and phage defense. However, no interplay of MazEF systems between different species was reported. MATERIALS AND METHODS: MazEF toxin-antitoxin system of Helicobacter macacae was examined in three Escherichia coli strains with and without endogenous MazEF knockout. In vivo toxicity, antibiotic tolerance, and live/dead staining followed by flowcytometry analysis were performed to evaluate the functionality and interplay of the toxin-antitoxin system between the two species. RESULTS: Controlled ectopic expression of MazF of H. macacae (MazFhm) in E. coli did not affect its growth. However, in endogenous MazEF knockout E. coli strains, MazFhm expression caused a sharp growth arrest. The toxicity of MazFhm could be neutralized by both the antitoxin of MazE homolog of H.macacae and the antitoxin of MazE of E. coli, indicating interplay of MazEF toxin-antitoxin systems between the two species. Induced expression of MazFhm enhanced tolerance to a lethal dose of levofloxacin, suggesting enhanced persister formation, which was further confirmed by live/dead cell staining. CONCLUSIONS: The MazEF toxin-antitoxin system of H. macace enhances persister formation and thus antibiotic tolerance in E. coli. Our findings reveal an interplay between the MazEF systems of H. macacae and E. coli, emphasizing the need to consider this interaction while evaluating the toxicity and functionality of MazF homologs from different species in future studies.

Quality of vision after myopic refractive surgeries: SMILE, FS-LASIK, and ICL.

BACKGROUND: To characterize the quality of vision after SMILE, FS-LASIK, and ICL implantation and evaluate the related factors. METHODS: 131 eyes of 131 myopic patients (90 female, 41 male) who underwent refractive surgeries including SMILE (35 patients), FS-LASIK (73 patients), and ICL implantation (23 patients) were analyzed. The Quality of Vision questionnaires were completed 3 months after surgery, and the results were characterized and analyzed with baseline characteristics, treatment parameters, and postoperative refractive outcomes using logistic regression analysis to find out predicted factors. RESULTS: Mean age was 26.5 ± 4.6 years (range: 18 to 39 years) and mean preoperative spherical equivalent was - 4.95 ± 2.04 diopters (D) (range: -1.5 to -13.5). Safety and efficacy index was comparable between different techniques: the safety index was 1.21 ± 0.18, 1.22 ± 0.18, and 1.22 ± 0.16 and the efficacy index were 1.18 ± 0.20, 1.15 ± 0.17, 1.17 ± 0.15 for SMILE, FS-LASIK and ICL respectively. The mean overall QoV score was 13.40 ± 9.11, with mean frequency, severity, and bothersome score of 5.40 ± 3.29, 4.53 ± 3.04, and 3.48 ± 3.18 respectively, and there was no significant difference between different techniques. Overall, the symptom with the highest scores was glare, following fluctuation in vision and halos. Only the scores of halos were significantly different among different techniques (P < 0.000). Using ordinal regression analysis, mesopic pupil size was identified as a risk factor (OR = 1.63, P = 0.037), while postoperative UDVA was a protective factor (OR = 0.036, P = 0.037) for overall QoV scores. Using binary logistic regression analysis, we found that patients with larger mesopic pupil size had an increased risk to experience glare postoperatively; compared to ICL, patients who underwent SMILE or FS-LASIK tended to report fewer halos; patients with better postoperative UDVA were less likely to report blurred vision and focusing difficulty; with larger residual myopic sphere postoperatively, patients experienced focusing difficulties and difficulty judging distance or depth perception more frequently. CONCLUSIONS: SMILE, FS-LASIK, and ICL had comparable visual outcomes. Overall, glare, fluctuation in vision, and halos were the most frequently experienced visual symptoms 3 months postoperatively. Patients with ICL implanted tended to report halos more frequently compared with SMILE and FS-LASIK. Mesopic pupil size, postoperative UDVA, and postoperative residual myopic sphere were predicted factors for reported visual symptoms.

Comparison of clinical and microbiological characteristics of community-acquired hypervirulent Klebsiella pneumoniae liver abscess in diabetic and non-diabetic patients.

Invasive infection caused by hypervirulent Klebsiella pneumoniae (HvKP) has been reported worldwide. Most of the patients are community population, related to diabetes mellitus (DM), chronic liver disease and other basic diseases, which prone to systemic migratory infection. In this study, we collected 377 patients with community acquired Klebsiella pneumoniae liver abscess in our hospital from January 2013 to December 2018, 65.8% of whom were male, and 49.6% had DM. Patients with DM are prone to eye and central nervous system (CNS) infection, which need continuous local abscess drainage during treatment. Among them, patients with poor blood glucose control have a higher rate of blood stream infections (BSI). 219 strains of HvKP were obtained, with K1/K2 Serotype accounted for 81.7%. The incidence of BSI in K2 patients was higher than that in K1 patients. The PCR results indicate that the carrying rate of virulence genes (rmpA、areo、kfu、allS、iroN、magA、uge、wcaG) in K1/K2 type strains is significantly higher than that in non K1/K2 type strains. ST23 and ST65 are the most common multilocus sequence typing (MLST), which belong to K1 and K2 Serotype respectively. All of HvKP strains showed high sensitivity to commonly used clinical antibiotics other than ampicillin, with 54.3% of the strains exhibiting high viscosity characteristics. Meanwhile, 35 classic Klebsiella pneumoniae (cKP) strains were collected, and their serum typing is mainly non K1/K2. The carrying rate of virulence genes and viscosity degree in HvKP are significantly higher than those in cKP. Primary liver abscess caused by HvKP is prone to multiple tissue and organ infections, but it shows higher sensitivity to most commonly used antibiotics in clinical practice except for ampicillin. After effective treatment, the overall prognosis of patients is better. This study analyzes the pathogenic characteristics of HvKP and elaborates on the clinical characteristics of patients, which can provide reference for clinical and scientific research work.

[The Occurrence and Risk Factors of Stroke Complicated by Venous Thromboembolism].

Objective: To investigate the occurrence and the risk factors of stroke complicated by venous thromboembolism. Methods: A total of 2709 stroke patients who received treatment at our hospital between January 2018 and June 2021 were selected. The incidence of stroke complicated by venous thromboembolism was analyzed and the risk factors of stroke complicated by venous thromboembolism were investigated by logistic regression. Results: Among the 2709 stroke patients, 390 had venous thromboembolism, resulting in 14.39% incidence of venous thromboembolism. Among them, 383 patients (14.14%) had deep venous thrombosis (DVT), 4 patients (0.15%) had pulmonary thromboembolism (PTE), and 3 cases (0.11%) had DVT combined with PTE. According to the analysis of unconditional multivariate logistic regression model, age>60 years, concomitant hypertension, concomitant diabetes, bedrest time after admission≥3 days, D-dimer (D-D)≥0.95 mg/mL, triglyceride (TG)≥1.83 mmol/L, Barthel Index (BI) score≤9 points, and Padua score>4 points after admission were independent risk factors for stroke complicated by venous thromboembolism ( P<0.05), while anticoagulation therapy was a protective factor ( P<0.05). Conclusion: Stroke complicated by venous thromboembolism is associated, to some degree, with age, concomitant hypertension, concomitant diabetes, bedrest time, D-D, TG, BI score, Padua score, and whether anticoagulant therapy is administered or not. Interventions in line with relevant risk factors should be strengthened to effectively reduce the risk of stroke complicated by venous thromboembolism.

Frailty is an independent determinant of COVID-19 vaccine hesitancy in the elderly: a cross-sectional study.

COVID-19 vaccines have become an important hope for slowing down or stopping the pandemic. As the population ages, older adults will comprise a greater proportion of the vaccinated population. We aimed to assess influencing factors of COVID-19 vaccine hesitancy in older adults. For this aim, We conducted a cross-sectional study on a questionnaire survey of the elderly over 65 years living in the community of Haikou City from August 1st to September 30th, 2021. Univariate and multivariate Logistic regression analyses were performed to identify factors related to vaccine hesitancy. We analyzed completed questionnaires from 225 respondents (42.2% women, mean age 73.4±6.2 years). There were 99 people in the vaccine hesitation group and 126 people in the vaccine acceptance group, the incidence of vaccine hesitation in the elderly population is about44%（99/225）. The incidence of frailty in the vaccine hesitation group was much higher than that in the vaccine trust group (62.63 vs. 30.95%, P＜0.001). The risk factors of vaccine hesitancy in the elderly aged 70-75years and over 75 years were 2.987 times and 3.587 times higher than that in the population aged 65-70 years (OR=2.987,95%CI: 1.424-6.265, P＜0.001; OR=3.587,95% CI:1.804-7.131, P＜0.001). Frailty is also an independent risk factor of vaccine hesitancy in the elderly population (OR=2.624,95%CI: 1.447-4.757, P＜0.001). Then the vaccination rate of the COVID-19 vaccine is far from reaching the requirements of herd immunity, and more flexible and comprehensive efforts are needed to increase the vaccination willingness of the frail elderly.

Insight into the Pathogenic Mechanism of Mycoplasma pneumoniae.

Mycoplasma pneumoniae, an obligate parasitic pathogen without cell wall, can cause severe upper and lower respiratory tract symptoms. It is the pathogen of human bronchitis and walking pneumonia, and named community-acquired pneumonia. In addition to severe respiratory symptoms, there are clinical extrapulmonary manifestations in the skin, brain, kidney, musculoskeletal, digestive system, and even blood system after M. pneumoniae infection. Hereby, we comprehensively summarized and reviewed the intrapulmonary and extrapulmonary pathogenesis of M. pneumoniae infection. The pathogenesis of related respiratory symptoms caused by M. pneumoniae is mainly adhesion damage, direct damage including nutrient predation, invasion and toxin, cytokine induced inflammation damage and immune evasion effect. The pathogenesis of extrapulmonary manifestations includes direct damage mediated by invasion and inflammatory factors, indirect damage caused by host immune response, and vascular occlusion. The intrapulmonary and extrapulmonary pathogenic mechanisms of M. pneumoniae infection are independent and interrelated, and have certain commonalities. In fact, the pathogenic mechanisms of M. pneumoniae are complicated, and the specific content is still not completely clear, further researches are necessary for determining the detailed pathogenesis of M. pneumoniae. This review can provide certain guidance for the effective prevention and treatment of M. pneumoniae infection.

Surgical vs. drug therapy in patients with patent foramen ovale and cryptogenic stroke.

OBJECTIVE: This study aimed to investigate the effectiveness of risk scoring for predicting stroke recurrence after percutaneous patent foramen ovale (PFO) closure or medication therapy in patients with PFO and a history of cryptogenic stroke. METHODS: This study included 559 patients with PFO and cryptogenic stroke who were treated in our hospital from January 2013 to January 2018 and were followed up for 15-72 months. After calculating the risk scores for stroke recurrence, we randomly divided the patients into two groups (ratio, 1:1): Patients in one group underwent PFO closure and those in the other received drug therapy. RESULTS: Patients in the PFO closure group had a lower risk of recurrent stroke than those in the drug therapy group (1.1% vs. 4.2%). Moreover, serious bleeding was less frequent in the PFO closure group than in the drug therapy group (0% vs. 3.2%), although the incidence of atrial fibrillation or flutter did not significantly differ between the groups (p = 0.67). Interestingly, a subgroup analysis revealed no inter-treatment group difference in the rate of cryptogenic stroke recurrence among patients with risk scores of 0-1. By contrast, PFO closure yielded superior outcomes among patients with risk scores of ≥2. CONCLUSION: Compared with drug therapy, PFO closure reduced the risk of recurrent stroke among patients with a risk score of ≥2 and reduced the incidence of serious bleeding without increasing the risk of new-onset atrial fibrillation or atrial flutter.

An Optimized Trajectory Planner and Motion Controller Framework for Autonomous Driving in Unstructured Environments.

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.

Joint Unsupervised Learning of Depth, Pose, Ground Normal Vector and Ground Segmentation by a Monocular Camera Sensor.

We propose a completely unsupervised approach to simultaneously estimate scene depth, ego-pose, ground segmentation and ground normal vector from only monocular RGB video sequences. In our approach, estimation for different scene structures can mutually benefit each other by the joint optimization. Specifically, we use the mutual information loss to pre-train the ground segmentation network and before adding the corresponding self-learning label obtained by a geometric method. By using the static nature of the ground and its normal vector, the scene depth and ego-motion can be efficiently learned by the self-supervised learning procedure. Extensive experimental results on both Cityscapes and KITTI benchmark demonstrate the significant improvement on the estimation accuracy for both scene depth and ego-pose by our approach. We also achieve an average error of about 3° for estimated ground normal vectors. By deploying our proposed geometric constraints, the IOUaccuracy of unsupervised ground segmentation is increased by 35% on the Cityscapes dataset.

Design, Validation and Comparison of Path Following Controllers for Autonomous Vehicles.

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.

Traffic Intersection Re-Identification Using Monocular Camera Sensors.

Perception of road structures especially the traffic intersections by visual sensors is an essential task for automated driving. However, compared with intersection detection or visual place recognition, intersection re-identification (intersection re-ID) strongly affects driving behavior decisions with given routes, yet has long been neglected by researchers. This paper strives to explore intersection re-ID by a monocular camera sensor. We propose a Hybrid Double-Level re-identification approach which exploits two branches of Deep Convolutional Neural Network to accomplish multi-task including classification of intersection and its fine attributes, and global localization in topological maps. Furthermore, we propose a mixed loss training for the network to learn the similarity of two intersection images. As no public datasets are available for the intersection re-ID task, based on the work of RobotCar, we propose a new dataset with carefully-labeled intersection attributes, which is called "RobotCar Intersection" and covers more than 30,000 images of eight intersections in different seasons and day time. Additionally, we provide another dataset, called "Campus Intersection" consisting of panoramic images of eight intersections in a university campus to verify our updating strategy of topology map. Experimental results demonstrate that our proposed approach can achieve promising results in re-ID of both coarse road intersections and its global pose, and is well suited for updating and completion of topological maps.

HFO-LADRC Lateral Motion Controller for Autonomous Road Sweeper.

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.

Multi-sensor Fusion Road Friction Coefficient Estimation During Steering with Lyapunov Method.

The road friction coefficient is a key parameter for autonomous vehicles and vehicle dynamic control. With the development of autonomous vehicles, increasingly, more environmental perception sensors are being installed on vehicles, which means that more information can be used to estimate the road friction coefficient. In this paper, a nonlinear observer aided by vehicle lateral displacement information for estimating the road friction coefficient is proposed. First, the tire brush model is modified to describe the tire characteristics more precisely in high friction conditions using tire test data. Then, on the basis of vehicle dynamics and a kinematic model, a nonlinear observer is designed, and the self-aligning torque of the wheel, lateral acceleration, and vehicle lateral displacement are used to estimate the road friction coefficient during steering. Finally, slalom tests and DLC (Double Line Change) tests in high friction conditions are conducted to verify the proposed estimation algorithm. Test results showed that the proposed method performs well during steering and the estimated road friction coefficient converges to the reference value rapidly.

Visual Semantic Landmark-Based Robust Mapping and Localization for Autonomous Indoor Parking.

Autonomous parking in an indoor parking lot without human intervention is one of the most demanded and challenging tasks of autonomous driving systems. The key to this task is precise real-time indoor localization. However, state-of-the-art low-level visual feature-based simultaneous localization and mapping systems (VSLAM) suffer in monotonous or texture-less scenes and under poor illumination or dynamic conditions. Additionally, low-level feature-based mapping results are hard for human beings to use directly. In this paper, we propose a semantic landmark-based robust VSLAM for real-time localization of autonomous vehicles in indoor parking lots. The parking slots are extracted as meaningful landmarks and enriched with confidence levels. We then propose a robust optimization framework to solve the aliasing problem of semantic landmarks by dynamically eliminating suboptimal constraints in the pose graph and correcting erroneous parking slots associations. As a result, a semantic map of the parking lot, which can be used by both autonomous driving systems and human beings, is established automatically and robustly. We evaluated the real-time localization performance using multiple autonomous vehicles, and an repeatability of 0.3 m track tracing was achieved at a 10 kph of autonomous driving.

Reinforcement Learning-Based End-to-End Parking for Automatic Parking System.

According to the existing mainstream automatic parking system (APS), a parking path is first planned based on the parking slot detected by the sensors. Subsequently, the path tracking module guides the vehicle to track the planned parking path. However, since the vehicle is non-linear dynamic, path tracking error inevitably occurs, leading to inclination and deviation of the parking. Accordingly, in this paper, a reinforcement learning-based end-to-end parking algorithm is proposed to achieve automatic parking. The vehicle can continuously learn and accumulate experience from numerous parking attempts and then learn the command of the optimal steering wheel angle at different parking slots. Based on this end-to-end parking, errors caused by path tracking can be avoided. Moreover, to ensure that the parking slot can be obtained continuously in the process of learning, a parking slot tracking algorithm is proposed based on the combination of vision and vehicle chassis information. Furthermore, given that the learning network output is hard to converge, and it is easy to fall into local optimum during the parking process, several reinforcement learning training methods in terms of parking conditions are developed. Lastly, by the real vehicle test, it is proved that using the proposed method can achieve a better parking attitude than using the path planning and path tracking-based method.

IMU-Based Automated Vehicle Slip Angle and Attitude Estimation Aided by Vehicle Dynamics.

The slip angle and attitude are vital for automated driving. In this paper, a systematic inertial measurement unit (IMU)-based vehicle slip angle and attitude estimation method aided by vehicle dynamics is proposed. This method can estimate the slip angle and attitude simultaneously and autonomously. With accurate attitude, the slip angle can be estimated precisely even though the vehicle dynamic model (VDM)-based velocity estimator diverges for a short time. First, the longitudinal velocity, pitch angle, lateral velocity, and roll angle were estimated by two estimators based on VDM considering the lever arm between the IMU and rotation center. When this information was in high fidelity, it was applied to aid the IMU-based slip angle and attitude estimators to eliminate the accumulated error correctly. Since there is a time delay in detecting the abnormal estimation results from VDM-based estimators during critical steering, a novel delay estimation and prediction structure was proposed to avoid the outlier feedback from vehicle dynamics estimators for the IMU-based slip angle and attitude estimators. Finally, the proposed estimation method was validated under large lateral excitation experimental tests including double lane change (DLC) and slalom maneuvers.

Metabolism of SKLB-TB1001, a Potent Antituberculosis Agent, in Animals.

Tuberculosis is a major global health problem, and the emergence of multidrug-resistant and extensively drug-resistant strains has increased the difficulty of treating this disease. Among the novel antituberculosis drugs in the pipeline, decaprenylphosphoryl-beta-d-ribose-2-epimerase (DprE1) inhibitors such as BTZ043 and pBTZ169 exhibited extraordinary antituberculosis potency. Here, the metabolites of the new DprE1 inhibitor SKLB-TB1001 in vivo and its inhibition of cytochrome P450 isoforms and plasma protein binding (PPB) in vitro were studied. The results showed that rapid transformation and high PPB resulted in inadequate exposure in vivo and thus led to the moderate potency of SKLB-TB1001 in vivo This study provided explanations for the discrepant potency of this scaffold in vivo and in vitro Meanwhile, it also provides a rationale for lead optimization of this very promising scaffold of antituberculosis agents to prevent them from being metabolized, thus improving their exposure in vivo.