3D Object Detection under Urban Road Traffic Scenarios Based on Dual-Layer Voxel Features Fusion Augmentation.

To enhance the accuracy of detecting objects in front of intelligent vehicles in urban road scenarios, this paper proposes a dual-layer voxel feature fusion augmentation network (DL-VFFA). It aims to address the issue of objects misrecognition caused by local occlusion or limited field of view for targets. The network employs a point cloud voxelization architecture, utilizing the Mahalanobis distance to associate similar point clouds within neighborhood voxel units. It integrates local and global information through weight sharing to extract boundary point information within each voxel unit. The relative position encoding of voxel features is computed using an improved attention Gaussian deviation matrix in point cloud space to focus on the relative positions of different voxel sequences within channels. During the fusion of point cloud and image features, learnable weight parameters are designed to decouple fine-grained regions, enabling two-layer feature fusion from voxel to voxel and from point cloud to image. Extensive experiments on the KITTI dataset demonstrate the significant performance of DL-VFFA. Compared to the baseline network Second, DL-VFFA performs better in medium- and high-difficulty scenarios. Furthermore, compared to the voxel fusion module in MVX-Net, the voxel feature fusion results in this paper are more accurate, effectively capturing fine-grained object features post-voxelization. Through ablative experiments, we conducted in-depth analyses of the three voxel fusion modules in DL-VFFA to enhance the performance of the baseline detector and achieved superior results.

Genetic characterization of dilated cardiomyopathy patients undergoing heart transplantation in the Chinese population by whole-exome sequencing.

BACKGROUND: Dilated cardiomyopathy (DCM) is one of the most frequent causes of heart failure and heart transplantation (HTx). The genetic basis of DCM among patients undergoing HTx remains to be further studied. This study aimed to characterize the genetic basis of DCM HTx in the Chinese population. METHODS: In total, 208 unrelated DCM patients who underwent HTx at Fuwai Hospital between June 2004 and June 2017 were included in this study. Whole-exome sequencing (WES) was performed for all patients. Gene burden analysis, variant classification, and genotype-phenotype correlation analysis were subsequently performed. RESULTS: After completing the bioinformatics analysis, gene burden analysis suggested that titin (TTN), filamin C (FLNC) and lamin A/C (LMNA) were significantly enriched with rare protein-altering variants. The frequencies of TTN and FLNC truncating variants in our cohort were 18.8% and 8.7%, respectively. Among the 165 rare variants in high evidence DCM-related genes, 27 (16.4%) and 59 (35.8%) were interpreted as pathogenic (P) and likely pathogenic (LP), respectively. In addition, 41 (47.7%) and 16 (18.6%) of these 86 P/LP variants are located in TTN and FLNC, respectively. The FLNC group contained more patients with NYHA class IV than the P/LP-negative group (FLNC, 16/18 vs. P/LP-negative, 81/123, P = 0.049). CONCLUSIONS: Based on WES, we provided a primary genetic spectrum of DCM patients undergoing HTx in the Chinese population. TTN and FLNC harbour the most P/LP variants. FLNC truncation may lead to severe clinical symptoms in DCM patients.

EZH2 controls epicardial cell migration during heart development.

Enhancer of zeste homolog 2 (EZH2) is an important transcriptional regulator in development that catalyzes H3K27me3. The role of EZH2 in epicardial development is still unknown. In this study, we show that EZH2 is expressed in epicardial cells during both human and mouse heart development. Ezh2 epicardial deletion resulted in impaired epicardial cell migration, myocardial hypoplasia, and defective coronary plexus development, leading to embryonic lethality. By using RNA sequencing, we identified that EZH2 controls the transcription of tissue inhibitor of metalloproteinase 3 (TIMP3) in epicardial cells during heart development. Loss-of-function studies revealed that EZH2 promotes epicardial cell migration by suppressing TIMP3 expression. We also found that epicardial Ezh2 deficiency-induced TIMP3 up-regulation leads to extracellular matrix reconstruction in the embryonic myocardium by mass spectrometry. In conclusion, our results demonstrate that EZH2 is required for epicardial cell migration because it blocks Timp3 transcription, which is vital for heart development. Our study provides new insight into the function of EZH2 in cell migration and epicardial development.

meta-Selective O-Arylation of Cyclic Diaryliodonium Salts with Phenols via Aryne Intermediates.

Herein, we report a base-mediated, highly meta-selective O-arylation process of phenols and cyclic diaryliodonium salts without usage of transition metals. This novel and practical method was proved to be useful for the synthesis of iodine-containing meta-functionalized biaryl ethers in a broad functional group tolerance and environmentally friendly manner. Diversity-oriented transformations of the products were carried out to give various valuable functionalized biaryls. Preliminary mechanistic studies support the proposed aryne generation mechanism.

Research on a Cooperative Adaptive Cruise Control (CACC) Algorithm Based on Frenet Frame with Lateral and Longitudinal Directions.

Research on the cooperative adaptive cruise control (CACC) algorithm is primarily concerned with the longitudinal control of straight scenes. In contrast, the lateral control involved in certain traffic scenes such as lane changing or turning has rarely been studied. In this paper, we propose an adaptive cooperative cruise control (CACC) algorithm that is based on the Frenet frame. The algorithm decouples vehicle motion from complex motion in two dimensions to simple motion in one dimension, which can simplify the controller design and improve solution efficiency. First, the vehicle dynamics model is established based on the Frenet frame. Through a projection transformation of the vehicles in the platoon, the movement state of the vehicles is decomposed into the longitudinal direction along the reference trajectory and the lateral direction away from the reference trajectory. The second is the design of the longitudinal control law and the lateral control law. In the longitudinal control, vehicles are guaranteed to track the front vehicle and leader by satisfying the exponential convergence condition, and the tracking weight is balanced by a sigmoid function. Laterally, the nonlinear group dynamics equation is converted to a standard chain equation, and the Lyapunov method is used in the design of the control algorithm to ensure that the vehicles in the platoon follow the reference trajectory. The proposed control algorithm is finally verified through simulation, and validation results prove the effectiveness of the proposed algorithm.

Single Cell Sequencing Reveals Mechanisms of Persistent Truncus Arteriosus Formation after PDGFRα and PDGFRß Double Knockout in Cardiac Neural Crest Cells.

Persistent truncus arteriosus (PTA) is an uncommon and complex congenital cardiac malformation accounting for about 1.2% of all congenital heart diseases (CHDs), which is caused by a deficiency in the embryonic heart outflow tract's (OFT) septation and remodeling. PDGFRα and PDGFRß double knockout (DKO) in cardiac neural crest cells (CNCCs) has been reported to cause PTA, but the underlying mechanisms remain unclear. Here, we constructed a PTA mouse model with PDGFRα and PDGFRß double knockout in Pax3+ CNCCs and described the condensation failure into OFT septum of CNCC-derived cells due to disturbance of cell polarity in the DKO group. In addition, we further explored the mechanism with single-cell RNA sequencing. We found that two main cell differentiation trajectories into vascular smooth muscle cells (VSMCs) from cardiomyocytes (CMs) and mesenchymal cells (MSs), respectively, were interrupted in the DKO group. The process of CM differentiation into VSMC stagnated in a transitional CM I-like state, which contributed to the failure of OFT remodeling and muscular septum formation. On the other hand, a Penk+ transitional MS II cluster closely related to cell condensation into the OFT septum disappeared, which led to the OFT's septation absence directly. In conclusion, the disturbance of CNCC-derived cells caused by PDGFRα and PDGFRß knockout can lead to the OFT septation disorder and the occurrence of PTA.

An interconnected NaTi2(PO4)3/carbon composite from an all-integrated framework with chelating Ti in a cross-linked citric acid-organic phosphonic acid skeleton for high-performance sodium storage.

Finding suitable hosts for Na+ ion storage holds the key to achieving large-scale applications of sodium-ion batteries (SIBs). NaTi2(PO4)3 is widely considered to be an advanced anode material for SIBs, because of its 3D open framework, high theoretical capacity, and good thermodynamic stability. However, the instability of electrolyte/electrode interface and intrinsic low electronic conductivity of NaTi2(PO4)3 lead to the poor cycling and rate performance. In this work, an all-integrated framework with chelating Ti in a cross-linked citric acid-organic phosphonic acid skeleton is fabricated as a precursor for the synthesis of NaTi2(PO4)3/carbon composite. The generated interconnected carbon provides extensive support for NaTi2(PO4)3 crystal. These unique structure delivers a high reversible capacity (225.8 mAh g-1 at 0.2 A g-1), good rate performance (219.7 mAh g-1 at 0.4 A g-1, and 189.6 mAh g-1 at 1 A g-1), and superb long-term cycling stability (156.0 mAh g-1 at 2 A g-1 after 4000 cycles). It is believed that this facile and effective strategy can shed light on the development of advanced phosphate electrode materials for SIBs.

Transfer learning-based highway crash risk evaluation considering manifold characteristics of traffic flow.

The main objective of this study is to evaluate highway crash risk and improve the spatial and temporal transferability of crash risk models. The predictive performance is affected by the difficulty of existing models to quantify crash risk from historical traffic flow data at different locations and times and may not fully capture the complex nonlinear relationships between high-dimensional factors in traffic flow states. Oregon US26W freeway data from 2016 and 2017 and I5N freeway data from 2017 were used. Raw detector data collected from two consecutive detector stations upstream-downstream detector stations were converted into 30 traffic variables. The averages, standard deviations, and coefficients of variation were obtained by aggregating traffic values using each lane. Candidate variables for traffic flow were extracted, and the importance of each variable was calculated using LightGBM, which reveals that variable differences between lanes contributed more. The manifold distance was then applied to quantify the crash risk and classify traffic crashes or not. When the manifold distance is 0.4, it could effectively distinguish traffic crashes. TransferBoost was further employed to build a crash risk model. Modeling using 2016 and 2017 data from the US26W freeway revealed a significant decrease in AUC and a gradual decrease in the model's sensitivity. However, the crash risk prediction performance of TransferBoost improved by 5.2% when modeling using 2017 data from US26W and I5N freeways. The results show that the model developed for one time period cannot be directly used to predict crash risk for another period on the same freeway. However, the model developed for one highway cannot be directly used to predict the crash risk of another highway either, maintaining some transferability at low false alarm rates. TransferBoost provides a fresh perspective on the transferability of the model. The findings of this study could facilitate more accurate proactive safety management and improvement countermeasure development.

Electrochemical Difunctionalization of Styrenes via Chemoselective Oxo-Azidation or Oxo-Hydroxyphthalimidation.

Atom- and step-economic oxo-azidation and oxo-hydroxyphthalimidation of styrenes have been developed under mild electrolytic conditions, respectively. Various valuable alpha-azido or hydroxyphthalimide aromatic ketones were synthesized efficiently from readily available styrenes, azides, and N-hydroxyphthalimides. Mechanism studies show that two different pathways involved in these two transformations.

Establishment and characterization of an immortalized epicardial cell line.

Recently, the increasing significance of the epicardium in cardiac development and regeneration is beginning to be recognized. However, because of the small proportion of primary epicardial cells and the limited cell culture time, further research on the mechanism of epicardial cells is hindered. Here, we transfected simian virus 40 Large T (SV40-LT) into primary epicardial cells to establish an immortalized cell line, named EpiSV40. We further demonstrated that EpiSV40 can be easy to culture and has the proliferation, migration and differentiation capacities comparable to primary epicardial cells. EpiSV40 can serve as an ideal in vitro model for epicardial cell research, which will booster the study of the epicardium in cardiac development and heart regeneration.

Ultrafine molybdenum oxycarbide nanodots encapsulated in N,P co-doped carbon nanofibers as an advanced anode material for lithium-ion batteries.

Molybdenum oxycarbide (MoOC) is a single-phase compound, which can serve as a potential anode for Li-ion batteries (LIBs) that integrates the merits of the high specific capacity of MoO2and high conductivity of Mo2C. Herein, a novel architecture with N,P co-doped C nanofibers and MoOC nanodots is constructed from a one-step phosphorization of MoOx/aniline organic-inorganic hybrid. Ultrafine MoOC nanodots are well confined by N,P co-doped C nanofibers, which ensures fast Li+/electron transfer and good stability of the structure under repeated charge/discharge processes. When this unique hybrid is employed as an anode material for LIBs, promising Li+storage properties are gained in terms of high specific capacity, superb rate and long-term cycling performance. The remarkable capacitive contribution facilitates the fast Li+uptake/release. This work may shed light on the development of well-defined Mo-based anodes for advanced LIBs.

Modeling vehicle ownership with machine learning techniques in the Greater Tamale Area, Ghana.

Vehicle ownership modeling and prediction is a crucial task in the transportation planning processes which, traditionally, uses statistical models in the modeling process. However, with the advancement in computing power of computers and Artificial Intelligence, Machine Learning (ML) algorithms are becoming an alternative or a complement to the statistical models in modeling the transportation planning processes. Although the application of ML algorithms to the transportation planning processes-like mode choice, and traffic forecasting and demand modeling-have received much attention in research and abound in literature, scanty attention is paid to its application to vehicle ownership modeling especially in the context of small to medium cities in developing countries. Therefore, this study attempts to fill this gap by modeling vehicle ownership in the Greater Tamale Area (GTA), a typically small to medium city in Ghana. Using a cross sectional survey of formal sectors workers, data was collected between June-August 2018. The study applied nine different ML classification algorithms to the dataset using 10-fold cross-validation technique/s and the Cohen-Kappa static/statistic to evaluate the predictive performance of each of the algorithms, and the Permutation Feature Importance to examine the features that contribute significantly to the prediction of vehicle ownership in GTA. The results showed that Linear Support Vector Classification (LinearSVC) classifier performed well in comparison with the other classifiers with regards to the overall predictive ability of the classifiers. In terms of class predictions, K- Nearest Neighbors (KNN) classifier performs well for no-vehicle class whiles Linear Support Vector Classification (LinearSVC) and GaussianNB classifiers performs well for motorcycle ownership. LinearSVC and Logistic Regression classifiers performed well on the car ownership class. Also, the results indicated that travel mode choice, average monthly income, average travel distance to workplace, average monthly expenditure on transport, duration of travel to workplace, occupational rank, age, household size and marital status were significant in predicting vehicle ownership for most of the classifiers. These findings could help policies makers carve out strategies that would reduce vehicle ownership but improve personal mobility.

Expected yaw rate-based trajectory tracking control with vision delay for intelligent vehicle.

Accurate and real-time position of preview point is significant to trajectory tracking control of vision-guided intelligent vehicle. The unavoidable delay of road automatic identification system weakens trajectory tracking control performance, and even deteriorates the vehicle stability. Therefore, a compensator for the delay of road automatic identification system was proposed which combines the current statistical model and adaptive Kalman predictor to estimate the state of preview point position. The trajectory tracking sliding mode controller of intelligent vehicle is established through a 2-degrees of freedom vehicle dynamic model and motion model by using MATLAB/Simulink and CarSim. The trajectory tracking performance under 20-100 ms delay is analyzed. The simulation results show that the trajectory tracking performance of intelligent vehicle will be affected by the delay of road automatic identification system, reducing tracking accuracy. And when the delay is too large, it will deteriorate the vehicle stability and safety. In addition, the simulation results also verify the effectiveness of current statistical-adaptive Kalman predictor compensator at different delays.

CACCT: An Automated Tool of Detecting Complicated Cardiac Malformations in Mouse Models.

Congenital heart disease (CHD) is the major cause of morbidity/mortality in infancy and childhood. Using a mouse model to uncover the mechanism of CHD is essential to understand its pathogenesis. However, conventional 2D phenotyping methods cannot comprehensively exhibit and accurately distinguish various 3D cardiac malformations for the complicated structure of heart cavity. Here, a new automated tool based on microcomputed tomography (micro-CT) image data sets known as computer-assisted cardiac cavity tracking (CACCT) is presented, which can detect the connections between cardiac cavities and identify complicated cardiac malformations in mouse hearts automatically. With CACCT, researchers, even those without expert training or diagnostic experience of CHD, can identify complicated cardiac malformations in mice conveniently and precisely, including transposition of the great arteries, double-outlet right ventricle and atypical ventricular septal defect, whose accuracy is equivalent to senior fetal cardiologists. CACCT provides an effective approach to accurately identify heterogeneous cardiac malformations, which will facilitate the mechanistic studies into CHD and heart development.

Surface modification of coordination polymers to enable the construction of CoP/N,P-codoped carbon nanowires towards high-performance lithium storage.

A one-dimensional hybrid with N,P co-doped carbon nanowires threaded CoP nanoparticles is rationally fabricated by employing surface modified coordination polymers as a precursor. Ultrasmall CoP nanoparticlesare well encapsulated in N,P co-doped carbon nanowires, which can effectively buffer the volume expansion of active CoP and facilitate fast lithium-ion/electron transfer during charge/discharge processes. Moreover, N,P co-doped carbon with high defect density and graphitic-N content are obtained, which facilitates high lithium storage capacity and fast electron transfer. As a result, attractive lithium storage properties are gained by employing this unique architecture as an anode material for lithium-ion batteries, including high reversible charge/discharge capacities, good rate capability, and excellent long-term cycling stability. Kinetic investigation shows that the fast lithium ion uptake/release is related to the remarkable capacitive contribution. This work may offer an effective way for design well-defined transition metal phosphide-based anodes for advanced lithium-ion batteries.

A multinomial logit analysis of factors associated with severity of motorcycle crashes in Ghana.

Objective: The objective of this study was to explore the factors affecting motorcycle crash severity in Ghana. Methods: A retrospective analysis of motorcycle crash data between 2011 and 2015 was conducted using a motorcycle crash data set extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. Injury severity was classified into 4 categories: Fatal, hospitalized, injured, and damage only. A multinomial logit modeling framework was used to identify the possible determinants of motorcycle crash severity. Results: During the study period, a total of 8,516 motorcycle crashes were recorded, of which 22.9% were classified as fatal, 42.1% were classified as hospitalized injuries, 29.4% were classified as slight injuries, and 5.6% were classified as damage-only crashes. The estimation results indicate that the following factors increase the probability of fatal injuries: At a junction; weekend; signage; poor road shoulder; village settlement; tarred and good road surface; and collision between motorcycle and heavy goods vehicle (HGV). Motorcycle crashes occurring during the daytime and on the weekend increases the probability of hospitalized injury. The results also suggest that motorcycle crashes occurring during the daytime, in curves or inclined portions of roads, or in unclear weather conditions decrease the probability of fatal injury. Conclusions: This study provides further empirical evidence to support motorcycle crash modeling research, which is lacking in developing countries. The ability to understand the various factors that influence motorcycle crash severity is a step forward in providing an appropriate basis upon which informed motorcycle crash policies can be developed. Particular attention should be given to the provision of road signage at junctions and speed humps and controlling traffic during the weekend. In addition, road maintenance should be carried out periodically to address motorcycle safety in Ghana.

A comparative study on machine learning based algorithms for prediction of motorcycle crash severity.

Motorcycle crash severity is under-researched in Ghana. Thus, the probable risk factors and association between these factors and motorcycle crash severity outcomes is not known. Traditional statistical models have intrinsic assumptions and pre-defined correlations that, if flouted, can generate inaccurate results. In this study, machine learning based algorithms were employed to predict and classify motorcycle crash severity. Machine learning based techniques are non-parametric models without the presumption of relationships between endogenous and exogenous variables. The main aim of this research is to evaluate and compare different approaches to modeling motorcycle crash severity as well as investigating the effect of risk factors on the injury outcomes of motorcycle crashes. Motorcycle crash dataset between 2011 and 2015 was extracted from the National Road Traffic Crash Database at the Building and Road Research Institute (BRRI) in Ghana. The dataset was classified into four injury severity categories: fatal, hospitalized, injured, and damage-only. Three machine learning based models were developed: J48 Decision Tree Classifier, Random Forest (RF) and Instance-Based learning with parameter k (IBk) were employed to model the severity of injury in a motorcycle crash. These machine learning algorithms were validated using 10-fold cross-validation technique. The three machine learning based algorithms were compared with one another and the statistical model: multinomial logit model (MNLM). Also, the relative importance analysis of the attribute was conducted to determine the impact of these attributes on injury severity outcomes. The results of the study reveal that the predictions of machine learning algorithms are superior to the MNLM in accuracy and effectiveness, and the RF-based algorithms show the overall best agreement with the experimental data out of the three machine learning algorithms, for its global optimization and extrapolation ability. Location type, time of the crash, settlement type, collision partner, collision type, road separation, road surface type, the day of the week, and road shoulder condition were found as the critical determinants of motorcycle crash injury severity.

Edible fungi-assisted harvesting system for efficient microalgae bio-flocculation.

Conventional flocculants, commonly used to improve harvesting efficiency, can contaminate the broth and cause microalgae not suitable for food or feed production. In the present study, Pleurotus ostreatus, an edible fungal strain, was developed to improve the harvesting efficiency of microalgae. The results show that Pleurotus ostreatus pellets cultured under 100 rpm agitation resulted in higher harvesting efficiency than pellets cultured under 0 rpm and 150 rpm agitation. Lower pH of the Chlorella sp. suspension resulted in higher harvesting efficiency. The maximum recovery efficiency reached 64.86% in 150 mins. The above process could be used to achieve low cost, flocculant-free harvesting of microalgae as feedstock for feed or food production.

Estimation of Longitudinal Force and Sideslip Angle for Intelligent Four-Wheel Independent Drive Electric Vehicles by Observer Iteration and Information Fusion.

Exact estimation of longitudinal force and sideslip angle is important for lateral stability and path-following control of four-wheel independent driven electric vehicle. This paper presents an effective method for longitudinal force and sideslip angle estimation by observer iteration and information fusion for four-wheel independent drive electric vehicles. The electric driving wheel model is introduced into the vehicle modeling process and used for longitudinal force estimation, the longitudinal force reconstruction equation is obtained via model decoupling, the a Luenberger observer and high-order sliding mode observer are united for longitudinal force observer design, and the Kalman filter is applied to restrain the influence of noise. Via the estimated longitudinal force, an estimation strategy is then proposed based on observer iteration and information fusion, in which the Luenberger observer is applied to achieve the transcendental estimation utilizing less sensor measurements, the extended Kalman filter is used for a posteriori estimation with higher accuracy, and a fuzzy weight controller is used to enhance the adaptive ability of observer system. Simulations and experiments are carried out, and the effectiveness of proposed estimation method is verified.