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Background Gestational diabetes can lead to increased blood pressure in offspring, accompanied by impaired renal sodium excretion function and vasoconstriction and diastole dysfunction. However, there are few studies on whether it is accompanied by increased sympathetic nerve activity.Methods Pregnant C57BL/6 mice were intraperitoneally injected with streptozotocin (35 mg/kg) or citrate buffer at day 0 of gestation. The mice of control mother offspring (CMO) and diabetic mother offspring (DMO) at 16 weeks of age were infused with vehicle (artificial cerebrospinal fluid, aCSF, 0.4 μL/h) or tempol (1 mmol/L, 0.4 μL/h) into the bilateral paraventricular nucleus (PVN) of mice for 4 weeks, respectively.Results Compared with CMO group, SBP and peripheral sympathetic nerve activity (increased heart rate, LF/HF and plasma norepinephrine and decreased SDNN and RMSSD) were increased in DMO group, which was accompanied by increased angiotensin II type-1 receptor (AT1R) expression and function in PVN. The increase in AT1R expression levels was attributed to a decrease in the methylation level of the AT1R promoter region, resulting in an increase in AT1R mRNA levels in PVN of DMO. Moreover, compared with CMO group, the levels of oxidative stress were increased and DNMT1 expression was decreased in PVN of DMO. Bilateral PVN infusion of tempol attenuated oxidative stress increased the level of DNMT1 expression and the binding of DNMT1 to the AT1R promoter region, which reduced mRNA and protein expression level of AT1R, heart rate and SBP in DMO, but not in CMO.Conclusions The present study provides evidence for overactive sympathetic nervous systems in the pathogenesis of gestational diabetes-induced hypertension in offspring. Central antioxidant intervention in the PVN may be an important treatment strategy for fetal-programmed hypertension.

Background Irisin, as a myokine, plays a protective role against cardiovascular disease, including myocardial infarction, atherosclerosis and hypertension. However, whether irisin attenuates salt-sensitive hypertension and the related underlying mechanisms is unknown.Methods Male Dahl salt-resistant (DSR) and Dahl salt-sensitive (DSS) (12 weeks) rats were fed a high salt diet (8% NaCl) with or without irisin treatment by intraperitoneal injection for 8 weeks.Results Compared with DSR rats, DSS rats showed higher systolic blood pressure (SBP), impaired natriuresis and diuresis and renal dysfunction. In addition, it was accompanied by downregulation of renal p-AMPKα and upregulation of renal RAC1 and nuclear mineralocorticoid receptor (MR). Irisin intervention could significantly up-regulated renal p-AMPKα level and down-regulated renal RAC1-MR signal, thereby improving renal sodium excretion and renal function, and ultimately reducing blood pressure in DSS rats. Ex vivo treatment with irisin reduced the expression of RAC1 and nuclear MR in primary renal distal convoluted tubule cells from DSS rats and the effects of irisin were abolished by cotreatment of compound C (AMPK inhibitor), indicating that the regulation of RAC1-MR signals by irisin depended on the activation of AMPK.Conclusions Irisin administration lowered salt-sensitive hypertension through regulating RAC1-MR signaling via activation of AMPK, which may be a promising therapeutic approach for salt-sensitive hypertension.

In this study, the mesoporous material NCNT was prepared by treating carbon nanotubes (CNT) with hydrazine and subsequently loaded with Cu-Fe layered double hydroxide (CuFeLDH) to create a multiphase catalyst (CuFeLDH-NCNT). Its application as a multiphase catalyst was investigated in an ultrasound-assisted Fenton process for ciprofloxacin (CIP) degradation in aqueous solution. In addition, the impacts of catalyst dosage, ultrasonic power, H2O2 dosage, and beginning pH on CIP removal efficiency were carefully evaluated to maximize the removal efficiency of CIP. The findings indicated that the elimination rate of the initial CIP concentration of 20 mg/L surpassed 94.66% after a mere 100 min, while the TOC degradation rate was 70.4%. The high removal rate was due to the synergistic action between the nanoparticles, H2O2, and ultrasonography. The degradation intermediates of CIP were examined, and putative degradation pathways and mechanisms were postulated. HIGHLIGHTS Preparation of CuFeLDH–NCNT material as an efficient catalyst for ultrasound-assisted Fenton-like reaction.; Enhanced catalytic performance of H2O2 by the synergistic effect of CuFeLDH–NCNT.; Effective degradation of CIP by CuFeLDH–NCNT/US/H2O2.; Good stability and reusability of CuFeLDH–NCNT, with a low metal leaching rate.;

ObjectiveTo investigate the knowledge, attitude, and practice (KAP) towards pediatric vitamin D deficiency (VitD) among parents and explore the risk factors of their knowledge, attitude, and practice.MethodsThis cross-sectional study enrolled parents in our Hospital between November 2022 and January 2023.ResultsA total of 621 valid questionnaires were collected in this study. The knowledge, attitude, and practice scores were 6.13 ± 3.07 (theoretical score range: 0–13), 31.13 ± 6.20 (theoretical score range: 9–45), and 27.47 ± 4.21 (theoretical score range: 9–45), respectively; the mean knowledge score was

In recent years, many excellent computational models have emerged in microbe-drug association prediction, but their performance still has room for improvement. This paper proposed the OGNNMDA framework, which applied an ordered message-passing mechanism to distinguish the different neighbor information in each message propagation layer, and it achieved a better embedding ability through deeper network layers. Firstly, the method calculates four similarity matrices based on microbe functional similarity, drug chemical structure similarity, and their respective Gaussian interaction profile kernel similarity. After integrating these similarity matrices, it concatenates the integrated similarity matrix with the known association matrix to obtain the microbe-drug heterogeneous matrix. Secondly, it uses a multi-layer ordered message-passing graph neural network encoder to encode the heterogeneous network and the known association information adjacency matrix, thereby obtaining the final embedding features of the microbe-drugs. Finally, it inputs the embedding features into the bilinear decoder to get the final prediction results. The OGNNMDA method performed comparative experiments, ablation experiments, and case studies on the aBiofilm, MDAD and DrugVirus datasets using 5-fold cross-validation. The experimental results showed that OGNNMDA showed the strongest prediction performance on aBiofilm and MDAD and obtained sub-optimal results on DrugVirus. In addition, the case studies on well-known drugs and microbes also support the effectiveness of the OGNNMDA method. Source codes and data are available at: https://github.com/yyzg/OGNNMDA.

Thanks to the advancements in bioinformatics, drugs, and other interventions that modulate microbes to treat diseases have been emerging continuously. In recent years, an increasing number of databases related to traditional Chinese medicine (TCM) or gut microbes have been established. However, a database combining the two has not yet been developed. To accelerate TCM research and address the traditional medicine and micro ecological system connection between short board, we have developed the most comprehensive micro-ecological database of TCM. This initiative includes the standardization of the following advantages: (1) A repeatable process achieved through the standardization of a retrieval strategy to identify literature. This involved identifying 419 experiment articles from PubMed and six authoritative databases; (2) High-quality data integration achieved through double-entry extraction of literature, mitigating uncertainties associated with natural language extraction; (3) Implementation of a similar strategy aiding in the prediction of mechanisms of action. Leveraging drug similarity, target entity similarity, and known drug-target entity association, our platform enables the prediction of the effects of a new herb or acupoint formulas using the existing data. In total, MicrobeTCM includes 171 diseases, 725 microbes, 1468 herb-formulas, 1032 herbs, 15780 chemical compositions, 35 acupoint-formulas, and 77 acupoints. For further exploration, please visit https://www.microbetcm.com.

The fifth edition of the World Health Organization Classification of Tumors of the Central Nervous System (WHO CNS5) has identified a new classification system for tumors of the brain and spinal cord, highlighting the pivotal role of molecular diagnosis in accurately categorizing neoplasms. In addition to previous classifications, one of the key distinctions lies in categorizing pediatric-type diffuse low-grade gliomas (pDLGGs) and pediatric-type diffuse high-grade gliomas (pDHGGs) as distinct tumor types. Although similar in histology and morphology, pediatric diffuse gliomas are completely different from the adult type with respect to the molecular genetic characteristics, prognosis, and treatment strategies. pDLGG includes four tumor types, namely, diffuse astrocytoma, MYB- or MYBL1-altered; angiocentric glioma; polymorphous low-grade neuroepithelial tumor of the young (PLNTY); and diffuse low-grade glioma, MAPK pathway-altered, three types of which are newly recognized tumor types. Herein, we review the clinical characteristics, histopathological and molecular genetic characteristics, neuroimaging features, and prognosis of pDLGG and summarize the neuroimaging key points in diagnosing different tumor types. This review aims to evaluate and update the relevant pDLGG features and advances in neuroimaging that may assist in differential diagnosis, surgery planning, and prognostic determination of these tumor types and provide accurate diagnostic information for clinical colleagues.

Self-driving General N-trailers (GNT) vehicles are one of the future solutions to build intelligent factory due to its flexibility and large load. Maneuvering of GNT vehicle to its destination requires accurate and robust motion planning. But the narrow operating environment causes nonlinear nonconvex constraints which are challenging. Furthermore, the nonholonomic constraints in GNT kinematics elevate the complexity in state space. Therefore, motion planning of GNT vehicle maneuvering in narrow space within a reasonable time and high success rate is a critical problem. This paper proposes a fast bidirectional motion planning algorithm to generate trajectories for GNT vehicles to maneuver in a narrow space. A coarse-to-fine motion planning paradigm has been proposed to balance the robustness and time. In the coarse step, an initial guess is generated through a bidirectional-sampled closed-loop Rapidly-exploring Random Tree, and a spatial-temporal safety corridor has been constructed to convert nonlinear nonconvex constraints to linear convex constraints. In the fine step, an optimal control problem is defined accordingly and solved to obtain feasible trajectory. Four different scenarios have been conducted with forward and reverse GNT vehicle maneuvering in a narrow environment. The results show that the proposed method outperforms state-of-the-art sampling-based and optimization-based motion planning methods.

In recent years, vehicle detection in intelligent transportation systems using 3D LIDAR point clouds based on deep neural networks has made substantial progress. However, when the point clouds are very sparse, the detection model cannot generate proposals efficiently, resulting in false negative results. Considering that the object tracking technology accurately predicts vehicles based on historical measurements and motion models, and these prediction results can become proposals for object detection. Therefore, this paper proposes a novel object detection paradigm based on tracking feedback to address the false negative problem based on sparse point clouds. According to the distribution of the state vector from the Kalman prediction, multiple proposals are sampled and fed back to the second stage of two-stage detection models. After regression and non-maximum suppression, the false negative results can be effectively reduced. This method enhances the vehicle detection capability of classical neural networks. Comparing the recall metric of multiple detection models at different distances in the public KITTI and nuSences datasets, the proposed method can promote up to 5.31% compared to the previous method, which reflects the effectiveness and versatility of the proposed method.

In the hierarchical evolution framework of cosmology, larger halos grow through matter accretion and halo mergers. To clarify halo evolution, we need to define the halo mass and radius physically. However, the pseudoevolution problem makes the process difficult. Thus, we aim to measure the splash-back radius ( R _sp ), a physically defined halo radius for a large number of halos with various mass and redshift, and to determine the most important parameters that affect it. We use the typical definition of splash-back radius as the radius with the steepest radial density profile. In this work, we measure the splash-back radius of dark matter halos within the mass of 10 ^13 M _⊙ to 3 × 10 ^15 M _⊙ and redshifts spanning 0.08–0.65. This is the measurement of the R _sp in the largest range of halo mass and redshift. Using the shear catalog of the Dark Energy Camera Legacy Survey Data Release 8, we investigate the splash-back radius of halos associated with galaxies and galaxy clusters identified in the various catalogs. Our finding reveals a trend wherein massive halos demonstrate a larger splash-back radius, and the normalized splash-back radius ( R _sp / R _200m ) shows a U-shaped mass evolution. The upturn in these relations mainly comes from the contribution of massive halos with low redshifts. We further find the splash-back radius increases with the peak height, while the normalized splash-back radius has a negative relation with the peak height. We also find that R _sp ≳ R _200m for most halos, indicating their low accretion rates. Our result is consistent with previous literature across a wide range of mass, redshift, and peak height, as well as the simulation work from More et al.

BackgroundImproving the health self-management level of patients with tuberculosis (TB) is significant for reducing drug resistance, improving the cure rate, and controlling the prevalence of TB. Mobile health (mHealth) interventions based on behavioral science theories may be promising to achieve this goal. ObjectiveThis study aims to explore and conduct an mHealth intervention based on the Integrated Theory of Health Behavior Change (ITHBC) in patients with pulmonary TB to increase their ability of self-care management. MethodsA prospective randomized controlled study was conducted from May to November 2020. A total of 114 patients who were admitted consecutively to the TB clinic of Harbin Chest Hospital, China from May 2020 to August 2020 were recruited by convenience sampling. Patients were divided into the control group and intervention group, and all received a 3-month intervention. Patients in the intervention group and the control group received routine medical and nursing care in the TB clinic, including the supervision of their medications. In addition, pharmacist-assisted mHealth (WeChat) intervention based on the ITHBC theory about TB management was provided to the intervention group. The primary outcome was self-management behavior, while the secondary outcomes were TB awareness, self-efficacy, social support, and degree of satisfaction with health education. The outcomes were measured using web-based self-designed and standard questionnaires administered at baseline and at the end point of the study. Intergroup data were assessed using the Mann-Whitney U test, whereas intragroup data were assessed with the Wilcoxon test (for paired samples). ResultsA total of 112 patients (59 in intervention group and 53 in control group) completed the study. After the intervention, a statistically significant increase was noted in the scores of each item of self-care management behaviors compared with the scores at the baseline (P

Abstract Background Advances in single-cell RNA-seq technology have led to great opportunities for the quantitative characterization of cell types, and many clustering algorithms have been developed based on single-cell gene expression. However, we found that different data preprocessing methods show quite different effects on clustering algorithms. Moreover, there is no specific preprocessing method that is applicable to all clustering algorithms, and even for the same clustering algorithm, the best preprocessing method depends on the input data. Results We designed a graph-based algorithm, SC3-e, specifically for discriminating the best data preprocessing method for SC3, which is currently the most widely used clustering algorithm for single cell clustering. When tested on eight frequently used single-cell RNA-seq data sets, SC3-e always accurately selects the best data preprocessing method for SC3 and therefore greatly enhances the clustering performance of SC3. Conclusion The SC3-e algorithm is practically powerful for discriminating the best data preprocessing method, and therefore largely enhances the performance of cell-type clustering of SC3. It is expected to play a crucial role in the related studies of single-cell clustering, such as the studies of human complex diseases and discoveries of new cell types.

As one of the most important tasks in autonomous driving systems, ego-lane detection has been extensively studied and has achieved impressive results in many scenarios. However, ego-lane detection in the missing feature scenarios is still an unsolved problem. To address this problem, previous methods have been devoted to proposing more complicated feature extraction algorithms, but they are very time-consuming and cannot deal with extreme scenarios. Different from others, this paper exploits prior knowledge contained in digital maps, which has a strong capability to enhance the performance of detection algorithms. Specifically, we employ the road shape extracted from OpenStreetMap as lane model, which is highly consistent with the real lane shape and irrelevant to lane features. In this way, only a few lane features are needed to eliminate the position error between the road shape and the real lane, and a search-based optimization algorithm is proposed. Experiments show that the proposed method can be applied to various scenarios and can run in real-time at a frequency of 20 Hz. At the same time, we evaluated the proposed method on the public KITTI Lane dataset where it achieves state-of-the-art performance. Moreover, our code will be open source after publication.

Doping in 2D materials is an important method for tuning of band structures. For this purpose, it is important to develop controllable doping techniques. Here, we demonstrate a substitutional doping strategy by erbium (Er) ions in the synthesis of monolayer WS2 by chemical vapor deposition. Substantial enhancements in photoluminescent and photoresponsive properties are achieved, which indicate a tungsten vacancy suppression mechanism by Er filling. Er ion doping in the monolayer WS2 is proved by X-ray diffraction (XRD) and X-ray photoelectron spectra (XPS), fluorescence, absorption, excitation, and Raman spectra. 11.5 at% of the maximum Er concentration is examined by energy dispersive X-ray spectroscopy (EDX). Over 6 times enhancement of intensities with 7.9 nm redshift in peaks are observed from the fluorescent spectra of Er-doped WS2 monolayers compared with their counterparts of the pristine WS2 monolayers, which agrees well with the density functional theory calculations. In addition, over 11 times of dark current, 469 times of photocurrents, photoresponsivity, and external quantum efficiency, and two orders of photoresponse speed are demonstrated from the Er-doped WS2 photodetector compared with those of the pristine WS2 device. Our findings prove rare-earth doping in 2D materials, the exciting and ideal technique for substantially enhanced photoluminescent and photoresponsive properties.

Abstract Objective To explore shared brain structural alterations in patients diagnosed with mental disorders who experience own‐thought auditory verbal hallucinations (OTAVHs). Methods A cohort of 143 first‐diagnosis, nonmedicated patients with OTAVHs was enrolled: 25 with schizophrenia (FUSCH‐OTAVH), 20 with major depression disorder (FUMDD‐OTAVH), 28 with bipolar disorder (FUBD‐OTAVH), 22 patients with posttraumatic stress disorder (FUPTSD‐OTAVH), 21 with anxiety disorder (FUAD‐OTAVH), and 27 with borderline personality disorder (FUBPD‐OTAVH); 25 healthy controls (HCs) participated. The Auditory Hallucinations Rating Scale (AHRS), multiple psychometric scales, voxel‐based morphometry (VBM), tract‐based spatial statistics (TBSS), and multiple regression were used. Results Compared with HCs, patients had increased occipital cortex, dorsal prefrontal cortex (PFC), and striatum gray matter volumes (GMVs), a reduced insular cortex (IC) GMV, and an impaired frontooccipital fasciculus. The following differences were found versus HCs: FUSCH‐OTAVH, reduced PFC and occipital GMVs, increased striatum and thalamus GMVs, impaired arcuate fasciculus, u‐shaped bundle, optic tract, and upper longitudinal fasciculus (LF); FUMDD‐OTAVH, increased posterior frontotemporal junction and hippocampus GMVs; FUMN‐OTAVH, increased posterior frontotemporal junction and parietal cortex GMVs, reduced hippocampus GMV, impaired upper LF; FUPTSD‐OTAVH, increased temporal, hippocampus, and nucleus accumbens GMVs; FUBPD‐OTAVH, increased frontotemporal junction and hippocampus GMVs, impaired upper/lower LF; and FUAD‐OTAVH, increased frontal and temporal cortex, hippocampus GMVs. Conclusions The present findings provide evidence consistent with a bottom‐up and top‐down reciprocal action dysfunction hypothesis of AVHs and with the dopamine hypothesis of AVHs. We observed specific features related to OTAVHs in patients with different mental disorders. The findings, though complex, provide clues for further studies of specific mental disorders.

This article presents a lane-level localization system adaptive to different driving conditions, such as occlusions, complicated road structures, and lane-changing maneuvers. The system uses surround-view cameras, other low-cost sensors, and a lane-level road map which suits for mass deployment. A map-matching localizer is proposed to estimate the probabilistic lateral position. It consists of a sub-map extraction module, a perceptual model, and a matching model. A probabilistic lateral road feature is devised as a sub-map without limitations of road structures. The perceptual model is a deep learning network that processes raw images from surround-view cameras to extract a local probabilistic lateral road feature. Unlike conventional deep-learning-based methods, the perceptual model is trained by auto-generated labels from the lane-level map to reduce manual effort. The matching model computes the correlation between the sub-map and the local probabilistic lateral road feature to output the probabilistic lateral estimation. A particle-filter-based framework is developed to fuse the output of map-matching localizer with the measurements from wheel speed sensors and an inertial measurement unit. Experimental results demonstrate that the proposed system provides the localization results with submeter accuracy in different driving conditions.

BackgroundPBL approach has been widely used in many Chinese universities over the past decade. However, the effects of PBL approach on medical psychology education in China are inconsistent. The purpose of this study was to ascertain whether the PBL approach was superior to the lecture-based teaching method in the context of the medical psychology curriculum in China.MethodsA systematic review and meta-analysis was performed to confirm the effectiveness of PBL in Chinese medical psychology. Corresponding databases were searched for available studies, where data were extracted to calculated Hedges' g and its 95% confidence interval in total and subgroup analyses. Subgroup analyses were also carried out.ResultsNine studies with 551 cases and 496 controls were identified. The total examination scores of students in the PBL approach group were significantly higher compared with students in the traditional lecture-based teaching group under the random effect model (Hedges' g = 1.510, 95%CI 0.792-2.227, pConclusionsOur study supported the notion that the PBL approach may be applicable to Chinese medical psychology education.

The precursor material of Ni0.8Co0.1Mn0.1(OH)2 was prepared by a co-precipitation method to obtain the layered cathode materials LiNi0.8Co0.1Mn0.1O2 (811) through roasting with LiOH·H2O. Then the SnO2-modified samples were obtained by adding tin oxide into the ball mill. It was found by XRD characterization of the original and modified samples, that the addition of SnO2 did not change the layered structure of the raw materials. Microstructure and distribution of the SnO2 were analyzed with SEM and EDS. Charge/discharge tests indicated that the SnO2-modified phases improved the cycling stability and rate capability. The results of CV test showed that the polarization of SnO2 modified electrode was smaller. AC impedance showed that SnO2/LiNi0.8Co0.1Mn0.1O2 electrode had smaller Rct and Rf value. The above test results indicate that the modification of SnO2 improves the electrochemical performance of cathode material LiNi0.8Co0.1Mn0.1O2.

The vertex PI index $PI(G) = sum_{xy in E(G)} [n_{xy}(x)‎ + ‎n_{xy}(y)]$ is a distance-based molecular structure descriptor‎, ‎where $n_{xy}(x)$ denotes the number of vertices which are closer to the vertex $x$ than to the vertex $y$ and which has been the considerable research in computational chemistry dating back to Harold Wiener in 1947‎. ‎A connected graph is a cactus if any two of its cycles have at most one common vertex‎. ‎In this paper‎, ‎we completely determine the extremal graphs with the greatest and smallest vertex PI indices mong all cacti with a fixed number of vertices‎. ‎As a consequence‎, ‎we obtain the sharp bounds with corresponding extremal cacti and extend a known result‎.

Let A ( G ) be the adjacent matrix and D ( G ) the diagonal matrix of the degrees of a graph G, respectively. For 0 ≤ α ≤ 1 , the A α -matrix is the general adjacency and signless Laplacian spectral matrix having the form of A α ( G ) = α D ( G ) + ( 1 − α ) A ( G ) . Clearly, A 0 ( G ) is the adjacent matrix and 2 A 1 2 is the signless Laplacian matrix. A cactus is a connected graph such that any two of its cycles have at most one common vertex, that is an extension of the tree. The A α -spectral radius of a cactus graph with n vertices and k cycles is explored. The outcomes obtained in this paper can imply some previous bounds from trees to cacti. In addition, the corresponding extremal graphs are determined. Furthermore, we proposed all eigenvalues of such extremal cacti. Our results extended and enriched previous known results.

High-precision lane keeping is essential for the future autonomous driving. However, due to the imbalanced and inaccurate datasets collected by human drivers, current end-to-end driving models have poor lane keeping the effect. To improve the precision of lane keeping, this study presents a novel multi-state model-based end-to-end lane keeping method. First, three driving states will be defined: going straight, turning right and turning left. Second, the finite-state machine (FSM) table as well as three kinds of training datasets will be generated based on the three driving states. Instead of collecting the dataset by human drivers, the accurate dataset will be collected by the high-performance path following controller. Third, three sets of parameters based on 3DCNN-LSTM model will be trained for going straight, turning left and turning right, which will be combined with FSM table to form a multi-state model. This study evaluates the multi-state model by testing it on five tracks and recording the lane keeping error. The result shows the multi-state model-based end-to-end method performs the higher precision of lane keeping than the traditional single end-to-end model.

The root of Prismatomeris connata has been used in China for centuries as the medicinal herb “Huang Gen” (HG), but its phytochemicals or active ingredients are not well understood. In this study, we performed chemical analysis of the ethyl acetate fraction of a HG ethanol extract. We thus isolated seven new tetrahydroanthraquinones, prisconnatanones C–I (compounds 1–7) from the root of P. connata and identified their structures using spectroscopic analyses. Their absolute configurations were established by both modified Mosher’s and Mo2OAc4 methods, and ORD techniques. Their cytotoxicity was tested in a panel of human lung tumor cells (H1229, HTB179, A549 and H520 cell lines). Prisconnatanone I (7) showed the highest activity, with an IC50 value ranging from 2.7 µM to 3.9 µM in the suppression of tumor cell growth, and the others with chelated phenolic hydroxyls exhibited relatively lower activity (IC50: 8–20 µM). In conclusion, these data suggest that some of the natural tetrahydroanthraquinones in HG are bioactive, and hydroxylation at C-1 significantly increases the cytotoxicity of these compounds against lung tumor cell growth.

The A α -matrix is A α ( G ) = α D ( G ) + ( 1 − α ) A ( G ) with α ∈ [ 0 , 1 ] , given by Nikiforov in 2017, where A ( G ) is adjacent matrix, and D ( G ) is its diagonal matrix of the degrees of a graph G. The maximal eigenvalue of A α ( G ) is said to be the A α -spectral radius of G. In this work, we determine the graphs with largest A α ( G ) -spectral radius with fixed vertex or edge connectivity. In addition, related extremal graphs are characterized and equations satisfying A α ( G ) -spectral radius are proposed.

Given a (molecular) graph, the first multiplicative Zagreb index Π 1 is considered to be the product of squares of the degree of its vertices, while the second multiplicative Zagreb index Π 2 is expressed as the product of endvertex degree of each edge over all edges. We consider a set of graphs G n , k having n vertices and k cut edges, and explore the graphs subject to a number of cut edges. In addition, the maximum and minimum multiplicative Zagreb indices of graphs in G n , k are provided. We also provide these graphs with the largest and smallest Π 1 ( G ) and Π 2 ( G ) in G n , k .

There are several chemical indices that have been introduced in theoretical chemistry to measure the properties of molecular topology, such as distance-based topological indices, degree-based topological indices and counting-related topological indices. Among the degree-based topological indices, the atom-bond connectivity ( A B C ) index and geometric–arithmetic ( G A ) index are the most important, because of their chemical significance. Certain physicochemical properties, such as the boiling point, stability and strain energy, of chemical compounds are correlated by these topological indices. In this paper, we study the molecular topological properties of some special graphs. The indices ( A B C ) , ( A B C 4 ) , ( G A ) and ( G A 5 ) of these special graphs are computed.

This paper aims at realizing an automatic parking method through a bird's eye view vision system. With this method, vehicles can make robust and real-time detection and recognition of parking spaces. During parking process, the omnidirectional information of the environment can be obtained by using four on-board fisheye cameras around the vehicle, which are the main part of the bird's eye view vision system. In order to achieve this purpose, a polynomial fisheye distortion model is firstly used for camera calibration. An image mosaicking method based on the Levenberg-Marquardt algorithm is used to combine four individual images from fisheye cameras into one omnidirectional bird's eye view image. Secondly, features of the parking spaces are extracted with a Radon transform based method. Finally, double circular trajectory planning and a preview control strategy are utilized to realize autonomous parking. Through experimental analysis, we can see that the proposed method can get effective and robust real-time results in both parking space recognition and automatic parking.

The study aims to investigate fungal community structures and dynamic changes in forest soil lignocellulose-degrading process. rRNA gene clone libraries for the samples collected in different stages of lignocellulose degradation process were constructed and analyzed. A total of 26 representative RFLP types were obtained from original soil clone library, including Mucoromycotina (29.5%), unclassified Zygomycetes (33.5%), Ascomycota (32.4%), and Basidiomycota (4.6%). When soil accumulated with natural lignocellulose, 16 RFLP types were identified from 8-day clone library, including Basidiomycota (62.5%), Ascomycota (36.1%), and Fungi incertae sedis (1.4%). After enrichment for 15 days, identified 11 RFLP types were placed in 3 fungal groups: Basidiomycota (86.9%), Ascomycota (11.5%), and Fungi incertae sedis (1.6%). The results showed richer, more diversity and abundance fungal groups in original forest soil. With the degradation of lignocellulose, fungal groups Mucoromycotina and Ascomycota decreased gradually, and wood-rotting fungi Basidiomycota increased and replaced the opportunist fungi to become predominant group. Most of the fungal clones identified in sample were related to the reported lignocellulose-decomposing strains. Understanding of the microbial community structure and dynamic change during natural lignocellulose-degrading process will provide us with an idea and a basis to construct available commercial lignocellulosic enzymes or microbial complex.

Existing orthophoto map based on satellite photography and aerial photography is not precise enough for road marking. This paper proposes a new approach to high-accuracy orthophoto mapping. The approach uses inverse perspective transformation to process the image information and generates the orthophoto fragment. The offline interpolation algorithm is used to process the location information. It processes the dead reckoning and the EKF location information, and uses the result to transform the fragments to the global coordinate system. At last it uses wavelet transform to divides the image to two frequency bands and uses weighted median algorithm to deal with them separately. The result of experiment shows that the map produced with this method has high accuracy.

The traffic lights play an indispensable role in urban road safety and researches on intelligent vehicles become more popular recently. In this paper an automatic system for robust and real-time detection and recognition of traffic lights for intelligent vehicles based on vehicle-mounted camera is proposed. The method applying image processing and pattern recognition theory mainly works in three stages. First, the candidate regions of traffic lights are extracted using the color threshold segmentation method. Secondly, noise removal and two types of filtering which take account of shape information are applied to the candidate regions. Thirdly, template matching using normalized cross correlation techniques is adopted to validate the traffic lights candidate. Experimental results show that the proposed algorithm works effectively and robustly for traffic lights recognition in complex urban environments.

Reliable vehicle localization is a basic requirement in many applications in transportation field. GPS-based localization is quite popular nowadays. However, in urban environments applications, signal of GPS is often blocked by surrounding objects like high-rise buildings, tunnels, overhead roads, etc, making localization information unavailable. This paper proposed a laser radar based map matching approach to address this problem, especially when GPS signal blocked area is large. The proposed approach includes mapping and localization. In the mapping, after map initialization sensor data constraints are linearized to formulate an optimal linear estimation based map optimization framework, which can improve map accuracy effectively. In the localization, vehicle pose is estimated by matching the current laser scan with the best submap and by a UKF (Unscented Kalman Filter) based fusion strategy. Results from both synthetic and real experiments show good performance of the proposed approach. Keywords: GPS Signal Blocked, Vehicle Localization, Mapping, Map Optimization

Simulation platforms play an important role in helping intelligent vehicle research, especially for the research of cooperative driving due to the high cost and risk of the real experiments. In order to ease and bring more convenience for cooperative driving tests, we introduce an intelligent vehicle simulation platform, called CyberTORCS, for the research in cooperative driving. Details of the simulator modules including vehicle body control, vehicle visualization modeling and track visualization modeling are presented. Two simulation examples are given to validate the feasibility and effectiveness of the proposed simulation platform.

This paper proposes an improved intelligent vehicle localization method, which integrates the data from the odometry and the magnetic ruler(MR). The MR is used to detect the magnetic markers, which provides the absolute positioning reference. The data from odometry and the MR are used to localize the vehicle by using unscented Kalman filter. The data association (DA) method is adopted to validate the observations from the MR. The experiment results show that the method is a robust way.