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Abstract Obesity is a prevalent chronic disease that has significant negative impacts on humans and our companion animals, including dogs and cats. Obesity occurs with multiple comorbidities, such as diabetes, hypertension, heart disease and osteoarthritis in dogs and cats. A direct link between lipid metabolism dysregulation and obesity-associated diseases has been implicated. However, the understanding of such pathophysiology in companion animals is limited. This review aims to address the role of lipid metabolism in various metabolic disorders associated with obesity, emphasizing the involvement of the gut microbiota. Furthermore, we also discuss the management of obesity, including approaches like nutritional interventions, thus providing novel insights into obesity prevention and treatment for canines and felines.

Abstract Background Patients who are recovering from general anesthesia commonly exhibit symptoms such as dry lips, throat irritation, and thirst, prompting a desire to drink water in the post-anesthesia care unit (PACU). In this study, we aimed to evaluate the therapeutic effects and any potential complications of administering varying quantities of water to such patients. The primary objectives are to assess the safety and feasibility of early water intake after general anesthesia, specifically in the context of daytime surgery. Methods A total of 200 nongastrointestinal patients who underwent outpatient surgery were randomly assigned to four groups: Group A (drinking 2 ml/kg), and Group D (no water intake). We monitored changes in the assessment parameters before and after water consumption, as well as the incidence of post-drinking nausea and vomiting, and compared these outcomes among the four groups. Results Water intake led to a significant reduction in thirst, oropharyngeal discomfort, and pain scores and a notable increase in the gastric antrum motility index (MI), exhibiting statistical significance compared to the values before drinking (p 0.05). Early water consumption enhanced patient satisfaction with medical care, significantly varying from Group D (p

In October 2017, a small outbreak of echovirus 30 (E30) associated with aseptic meningitis in nine cases occurred at a primary school in the Ningxia Hui Autonomous Region. That year, we observed a significant increase in E30 levels in an acute flaccid paralysis (AFP) case surveillance system. To investigate their phylogenetic relationships, we determined the whole genomic sequences of 12 strains isolated from aseptic meningitis cases, AFP cases, and healthy children. We found that the E30 strains circulating in Ningxia belong to two lineages (H and J). The strains isolated in 2010, 2012, and 2016 belonged to the H lineage. In 2017, a new lineage, J, emerged as the dominant lineage. Phylogenetic trees were constructed based on the whole genome and P1, P2, and P3 regions; clustering with other types of enterovirus species B was found, suggesting that recombination events had occurred. The recombination sites were mainly in regions 2B, 2C, and 3D. This study confirmed that the E30 strains in Ningxia in 2010, 2012, and 2016 had different recombination patterns and were recombined with different enteroviruses. The 2017 epidemic E30 originated from another new lineage with a complex recombination pattern and formed an independent transmission chain in Ningxia.

Abstract Controlling an autonomous vehicle's unprotected left turn at an intersection is a challenging task. Traditional rule‐based autonomous driving decision and control algorithms struggle to construct accurate and trustworthy mathematical models for such circumstances, owing to their considerable uncertainty and unpredictability. To overcome this problem, a rule‐constrained reinforcement learning (RCRL) control method is proposed in this work for autonomous driving. To train a reinforcement learning controller with rule constraints, outcomes of the path planning module are used as a goal condition in the reinforcement learning framework. Since they include vehicle dynamics, the proposed approach is safer and more reliable compared to end‐to‐end learning, thereby ensuring that the generated trajectories are locally optimal while adjusting to unpredictable situations. In the experiments, a highly randomized two‐way four‐lane intersection is established based on the CARLA simulator to verify the effectiveness of the proposed RCRL control method. Accordingly, the results show that the proposed method can provide real‐time safe planning and ensure high passing efficiency for autonomous vehicles in the unprotected left turn task.

The study proposes an active trailer steering control method for tractor semitrailers to promote the path tracking effect of the trailer portion as well as lateral stability during lane changing. Firstly, a simplified model of a tractor semitrailer is constructed, and the MAP map is formed based on the genetic algorithm for the identification of the key parameters, which improves the model’s accuracy. Then the tractor and trailer’s yaw rate and sideslip angle at CG are tracked as the control objective and the trailer angle distribution strategy is given. Then the LQR-based corner controller is designed to control the steering actuators of each axle of the trailer. Finally, the effectiveness of the designed control strategy is verified based on the Trucksim/Simulink joint simulation platform and the semi-physical HiL test platform. The simulation results show that the designed controller can effectively improve the path tracking effect of the tractor and the trailer, and at the same time, the lateral stability parameters of the tractor and the trailer are also significantly improved, which improves the driving stability of the tractor semitrailer.

Abstract Cooperative adaptive cruise control (CACC) realizes efficient, intelligent control of vehicle acceleration, deceleration, and steering, through inter‐vehicle communication and cooperative control. However, the close combination of the platoon makes it difficult for other vehicles to cut‐in, which can lead to severe traffic jams on certain sections of the road. The control effect of the CACC depends on the platoon penetration rate, which is the percentage of connected and autonomous vehicles (CAVs) in the total number of platoon members. There is no quantitative control method for different penetration rates, and it is difficult to quantify the impact of CACC vehicles on traffic. Therefore, this paper proposes an innovative CACC control method based on deep reinforcement learning (DRL). First, the altruism control and the quantitative control of the car‐following strategy are realized by the virtual car‐following distance method to reduce the exclusivity of the CACC platoon or improve the road utilization efficiency. Second, a more appropriate platoon reward function and collision avoidance method are proposed. Finally, the Car Learning to Act (CARLA) simulator is used. The obtained results confirm that the CACC control of CAVs based on DRL can absorb speed oscillation and improve fuel economy.

When an ultrasonic pulse propagates in a thin plate, nonlinear Lamb waves with higher harmonics and a zero-frequency component (ZFC) will be generated because of the nonlinearity of materials. The ZFC, also known as the static displacement or static component, has its unique application on the evaluation of early-stage damages in the elastic symmetrical undulated plate. In this study, analysis of the excitation mechanism of the ZFC and the second harmonic component (SHC) was theoretically and numerically investigated, and the material early-stage damage of a symmetrical undulated was characterized by studying the propagation of nonlinear Lamb waves. Both the ZFC and SHC can be effectively employed in monitoring the material damages of the undulated plate in its early stage. However, several factors must be considered for the propagation of the SHC in an undulated plate because of the geometric curvature and interference between the second harmonics during propagation, preventing efficient application of this technique. If the fundamental wave can propagate in the plate regardless of the plate boundary conditions, an accumulative effect always exists for the ZFC in a thin plate, indicating that the ZFC is independent of the structural geometry. This study reveals that the ZFC-based inspection technique is more efficient and powerful in characterizing the damages of a symmetrical undulated plate in the early stage of service compared to the second harmonic method.

The mode rotator is an important component in a PLC-based mode-division multiplexing (MDM) system, which is used to implement high-order modes with vertical intensity peaks, such as LP11b mode conversions from LP11a in PLC chips. In this paper, an LP11 mode rotator based on a polymer/silica hybrid inverted ridge waveguide is demonstrated. The proposed mode rotator is composed of an asymmetrical waveguide with a trench. According to the simulation results, the broadband conversion efficiency between the LP11a and LP11b modes is greater than 98.5%, covering the C-band after optimization. The highest mode conversion efficiency (MCE) is 99.2% at 1550 nm. The large fabrication tolerance of the proposed rotator enables its wide application in on-chip MDM systems.

Recent advancements in spatial transcriptomics (ST) technologies offer unprecedented opportunities to unveil the spatial heterogeneity of gene expression and cell states within tissues. Despite these capabilities of the ST data, accurately dissecting spatiotemporal structures (e.g., spatial domains, temporal trajectories, and functional interactions) remains challenging. Here, we introduce a computational framework, PearlST (partial differential equation [PDE]-enhanced adversarial graph autoencoder of ST), for accurate inference of spatiotemporal structures from the ST data using PDE-enhanced adversarial graph autoencoder. PearlST employs contrastive learning to extract histological image features, integrates a PDE-based diffusion model to enhance characterization of spatial features at domain boundaries, and learns the latent low-dimensional embeddings via Wasserstein adversarial regularized graph autoencoders. Comparative analyses across multiple ST datasets with varying resolutions demonstrate that PearlST outperforms existing methods in spatial clustering, trajectory inference, and pseudotime analysis. Furthermore, PearlST elucidates functional regulations of the latent features by linking intercellular ligand–receptor interactions to most contributing genes of the low-dimensional embeddings, as illustrated in a human breast cancer dataset. Overall, PearlST proves to be a powerful tool for extracting interpretable latent features and dissecting intricate spatiotemporal structures in ST data across various biological contexts.

Recent mechanistic studies have indicated that combinations of radiotherapy (RT) plus immunotherapy (via CSF-1R inhibition) can serve as a strategy to overcome RT resistance and improve the survival of glioma mice. Given the high mortality rate for glioma, including low-grade glioma (LGG) patients, it is of critical importance to investigate the mechanism of the combination of RT and immunotherapy and further translate the mechanism from mouse studies to improve survival of RT-treated human glioma patients. Using the RNA-seq data from a glioma mouse study, 874 differentially expressed genes (DEGs) between the group of RT-treated mice at glioma recurrence and the group of mice with combination treatment (RT plus CSF-1R inhibition) were translated to the human genome to identify significant molecular pathways using the KEGG enrichment analysis. The enrichment analysis yields statistically significant signaling pathways, including the phosphoinositide 3-kinase (PI3K)/AKT pathway, Hippo pathway, and Notch pathway. Within each pathway, a candidate gene set was selected by Cox regression models as genetic biomarkers for resistance to RT and response to the combination of RT plus immunotherapies. Each Cox model is trained using a cohort of 295 RT-treated LGG patients from The Cancer Genome Atlas (TCGA) database and validated using a cohort of 127 RT-treated LGG patients from the Chinese Glioma Genome Atlas (CGGA) database. A four-DEG signature (ITGB8, COL9A3, TGFB2, JAG1) was identified from the significant genes within the three pathways and yielded the area under time-dependent ROC curve AUC = 0.86 for 5-year survival in the validation set, which indicates that the selected DEGs have strong prognostic value and are potential intervention targets for combination therapies. These findings may facilitate future trial designs for developing combination therapies for glioma patients.

Drug resistance is a prominent impediment to the efficacy of targeted therapies across various cancer types, including glioblastoma (GBM). However, comprehending the intricate intracellular and extracellular mechanisms underlying drug resistance remains elusive. Empirical investigations have elucidated that genetic aberrations, such as gene mutations, along with microenvironmental adaptation, notably angiogenesis, act as pivotal drivers of tumor progression and drug resistance. Nonetheless, mathematical models frequently compartmentalize these factors in isolation. In this study, we present a multiscale agent-based model of GBM, encompassing cellular dynamics, intricate signaling pathways, gene mutations, angiogenesis, and therapeutic interventions. This integrative framework facilitates an exploration of the interplay between genetic mutations and the vascular microenvironment in shaping the dynamic evolution of tumors during treatment with tyrosine kinase inhibitor. Our simulations unveil that mutations influencing the migration and proliferation of tumor cells expedite the emergence of phenotype heterogeneity, thereby exacerbating tumor invasion under both treated and untreated conditions. Moreover, angiogenesis proximate to the tumor fosters a protumoral milieu, augmenting mutation-induced drug resistance by increasing the survival rate of tumor cells. Collectively, our findings underscore the dual roles of intrinsic genetic mutations and extrinsic microenvironmental adaptations in steering tumor growth and drug resistance. Finally, we substantiate our model predictions concerning the impact of gene mutations and angiogenesis on the responsiveness of targeted therapies by integrating single-cell RNA-seq, spatial transcriptomics, bulk RNA-seq, and clinical data from GBM patients. The multidimensional approach enhances our understanding of the complexities governing drug resistance in glioma and offers insights into potential therapeutic strategies.

Abstract The combination of reinforcement learning and platooning control has been widely studied, which has been considered to be altruistic on the basis of safety. However, the platooning control method based on reinforcement learning is not mature. This paper proposes a platoon sharing deep deterministic policy gradient algorithm (PSDDPG) for a multi‐vehicle network which overcomes the problem of low efficiency of continuous action space exploration. On the basis of the noise of deep deterministic policy gradient (DDPG) algorithm, additional platoon noise is added to enhance the diversity of training samples during exploration and avoid using a single‐vehicle data, which could weaken model robustness. The method of using the time sequence information as an input and replay buffer backup is proposed to prevent the problems of insufficient exploration and low sampling efficiency from deteriorating the training effect. The robustness of the proposed algorithm is verified by tests using the Carla simulator. Also, the platoon merging, overtaking, cruising following and obstacle avoidance control of a vehicle platoon is realized under typical traffic flow conditions. The experimental results show that the PSDDPG algorithm can provide an efficient control strategy and thus has the potential to reduce energy consumption and improve road efficiency.

Aims: Upregulation of Ca2+/calmodulin-dependent protein kinase II (CaMKII) contributes to the pathogenesis of cardiovascular disease, including hypertension. Transgenic rats expressing the human angiotensinogen gene [TGR (hAGT)L1623] are a new novel humanized model of hypertension that associates with declines in cardiac contractile function and β-adrenergic receptor (AR) reserve. The molecular mechanisms are unclear. We tested the hypothesis that in TGR (hAGT)L1623 rats, left ventricular (LV) myocyte CaMKIIδ and β3-AR are upregulated, but β1-AR is down-regulated, which are important causes of cardiac dysfunction and β-AR desensitization. Main methods: We compared LV myocyte CaMKIIδ, CaMKIIδ phosphorylation (at Thr287) (pCaMKIIδ), and β1-and β3-AR expressions and determined myocyte functional and [Ca2+]I transient ([Ca2+]iT) responses to β-AR stimulation with and without pretreatment of myocytes using an inhibitor of CaMKII, KN-93 (10−6 M, 30 min) in male Sprague Dawley (SD; N = 10) control and TGR (hAGT)L1623 (N = 10) adult rats. Key findings: Hypertension in TGR (hAGT)L1623 rats was accompanied by significantly increased LV myocyte β3-AR protein levels and reduced β1-AR protein levels. CaMKIIδ phosphorylation (at Thr287), pCaMKIIδ was significantly increased by 35%. These changes were followed by significantly reduced basal cell contraction (dL/dtmax), relaxation (dR/dtmax), and [Ca2+]iT. Isoproterenol (10−8 M) produced significantly smaller increases in dL/dtmax, dR/dtmax, and [Ca2+]iT. Moreover, only in TGR (hAGT)L1623 rats, pretreatment of LV myocytes with KN-93 (10−6 M, 30 min) fully restored normal basal and isoproterenol-stimulated myocyte contraction, relaxation, and [Ca2+]iT. Significance: LV myocyte CaMKIIδ overactivation with associated contrast changes in β3-AR and β1-AR may be the key molecular mechanism for the abnormal contractile phenotype and β-AR desensitization in this humanized model of hypertension.

Mode-division multiplexing (MDM) is a promising multiplexing technique to further improve the transmission capacity of optical communication and on-chip optical interconnection systems. Furthermore, the multimode optical switch is of great importance in the MDM system, since it makes the MDM system more flexible by directly switching multiple spatial signals simultaneously. In this paper, we proposed a mode-independent optical switch based on the graphene–polymer hybrid waveguide platform that could process the TE11, TE12, TE21 and TE22 modes in a few-mode waveguide. The presented switch is independent of the four guided modes, optimizing the buried position of graphene capacitors in the polymer waveguide to regulate the coplanar interaction between the graphene capacitors and spatial modes. The TE11, TE12, TE21 and TE22 modes can be regulated simultaneously by changing the chemical potential of graphene capacitors in a straight waveguide. Our presented switch can enable the independent management of the spatial modes to be more flexible and efficient and has wide application in the MDM transmission systems.

In this paper, we present a novel robust adaptive neural network-based control framework to address the ride height tracking control problem of active air suspension systems with magnetorheological fluid damper (MRD-AAS) subject to uncertain mass and time-varying input delay. First, a radial basis function neural network (RBFNN) approximator is designed to compensate for unmodeled dynamics of the MRD. Then, a projector-based estimator is developed to estimate uncertain parameter variation (sprung mass). Additionally, to deal with the effect of input delay, a time-delay compensator is integrated in the adaptive control law to enhance the transient response of MRD-AAS system. By introducing a Lyapunov–Krasovskii (LK) functional, both ride height tracking and estimator errors can robustly converge towards the neighborhood of the desired values, achieving uniform ultimate boundness. Finally, comparative simulation results based on a dynamic co-simulator built in AMESim 2021.2 and Matlab/Simulink 2019(b) are given to illustrate the validity of the proposed control framework, showing its effectiveness to operate ride height regulation with MRD-AAS systems accurately and reliably under random road excitations.

Background: Diabetic cardiomyopathy is a multifaceted complication of diabetes that lacks effective treatments. Berberine (BBR), a bioactive compound from Rhizoma coptidis , has potential therapeutic implications, but its precise role in diabetic cardiomyopathy remains to be defined. Methods: In this study, a diabetic cardiomyopathy model was established by administration of a high-fat diet and streptozotocin injection to C57BL/6J mice. Concurrently, the mice received BBR treatment daily for a duration of 8 weeks. After the treatment period, myocardial injury, cardiac function, and the levels of oxidative stress and apoptosis were assessed. Results: BBR significantly ameliorated cardiac dysfunction and histopathological damage caused by diabetic cardiomyopathy. This treatment also elevated serum superoxide dismutase levels while decreasing malondialdehyde levels. The anti-apoptotic activity of BBR was evidenced by a decrease in TUNEL-positive cells and the percentage of apoptotic cells, as determined by flow cytometry, in conjunction with diminished levels of BCL2-associated X protein/B cell lymphoma 2 (BAX/BCL2) in heart tissues. Mechanistically, BBR was found to ameliorate diabetic cardiomyopathy by upregulating the expression of myocardial methionine sulfoxide reductase A (MsrA) and concurrently suppressing cardiac CaMKII oxidation. Conclusions: BBR alleviates diabetic cardiomyopathy by inhibiting myocardial apoptosis and oxidative stress through the MsrA and CaMKII signaling pathways.

Mode multiplexers/demultiplexers (MUX/deMUX) are key components in mode division multiplexing. A silica waveguide mode MUX consisting of four cascaded directional couplers is experimentally demonstrated. The beam propagation method is used in the device design and optimization. Thermal oxidation, plasma-enhanced chemical vapor deposition, and ultraviolet photolithography are adopted in the silica waveguide mode MUX fabrication. The measurement results prove that the input E00 mode can be selectively converted to E10 mode, E20 mode, and E30 mode. Within the wavelength range of 1500 to 1620 nm, the insertion loss is less than 12.2 dB. The proposed mode MUX has good potential in on-chip MDM applications.

Abstract Major Depressive Disorder (MDD) is characterized by increased stress sensitivity. Emerging findings in healthy adults suggest that stress responses within limbic/striatal-prefrontal regions are moderated by sex and unfold over time. Thus, we hypothesized that stress response abnormalities in MDD might be affected by sex and stress exposure time. The Montreal Imaging Stress Task was administered to 124 unmedicated patients with first-episode MDD (76 females) and 243 healthy controls (HC; 137 females) during functional magnetic resonance imaging (fMRI). Based on prior studies, amygdala, hippocampus, medial orbitofrontal cortex (mOFC), nucleus accumbens (NAc) and dorsolateral prefrontal cortex (dlPFC) were selected as a priori regions of interest. In a complementary approach, we probed the effects of stress on the frontoparietal network (FPN) and a network including the amygdala, NAc and anterior cingulate cortex (ACC). Across groups, males exhibited higher dlPFC activity and right FPN amplitude than females. Relative to female HCs, the female MDD group had less deactivation in limbic/striatal regions (amygdala, NAc, hippocampus, Amygdala-NAc-ACC network). Furthermore, unlike female HCs, the female MDD group failed to show a significant increase of deactivation over stress exposure time in the amygdala, mOFC and NAc. Our findings confirm the importance of considering sex differences when investigating neural stress responses. Case-control differences in neural stress responses observed in females (but not males) provide insights into sex differences in the etiology and pathophysiology of depression. The failure to deactivate limbic/NAc regions in depressed females point to dysfunction of adaptive stress responses over stress exposure time.

Micro-ring resonator (MRR) is a key element in integrated optics. The mutual-coupling in dual-concentric MRR has great influence on the resonance output. In this work, high-Q silicon dual-concentric MRR and racetrack resonator have been investigated by the coupled mode theory. The theoretical model is built to explain and alleviate the phenomenon of resonance splitting. CMOS fabrication is adopted for the preparation of dual-concentric MRR and racetrack resonators. The highest Q-factors of dual-concentric MRR and racetrack resonator are measured to be ∼9.00 × 104 at 1530.783 nm and ∼7.32 × 104 at 1536.596 nm, respectively. The notch depth improvement over 20 dB has been demonstrated on the 5-μm-radius double-ring structure. The experimental results prove that the asymmetry of resonance splitting can be tuned by adjusting the distance between the inner-ring and outer-ring, as well as the waveguide width. The proposed work has potentials in the design and optimization of dual-concentric ring resonators.

Cardiovascular and cerebrovascular diseases are leading causes of death worldwide, accounting for more than 40% of all deaths in China. Acute myocardial infarction (AMI) is a common cardiovascular disease and traditionally divided into ST-segment (STEMI) and non-ST-segment elevation myocardial infarction (NSTEMI), which are known with different prognoses and treatment strategies. However, key regulatory genes and pathways involved in AMI that may be used as potential biomarker for prognosis are unknown. In this study, we employed both bulk and single-cell RNA-seq to construct gene regulatory networks and cell-cell communication networks. We first constructed weighted gene co-expression networks for differential expressed genes between STEMI and NSTEMI patients based on whole-blood RNA-seq transcriptomics. Network topological attributes (e.g., node degree, betweenness) were analyzed to identify key genes involved in different functional network modules. Furthermore, we used single-cell RNA-seq data to construct multilayer signaling network to infer regulatory mechanisms of the above key genes. PLAUR (receptor for urokinase plasminogen activator) was found to play a vital role in transducing inter-cellular signals from endothelial cells and fibroblast cells to intra-cellular pathways of myocardial cells, leading to gene expression involved in cellular response to hypoxia. Our study sheds lights on identifying molecular biomarkers for diagnosis and prognosis of AMI, and provides candidate key regulatory genes for further experimental validation.

Abstract Due to the bus characteristics of large quality, high center of gravity and narrow wheelbase, the research of its yaw stability control (YSC) system has become the focus in the field of vehicle system dynamics. However, the tire nonlinear mechanical properties and the effectiveness of the YSC control system are not considered carefully in the current research. In this paper, a novel adaptive nonsingular fast terminal sliding mode (ANFTSM) control scheme for YSC is proposed to improve the bus curve driving stability and safety on slippery roads. Firstly, the STI (Systems Technologies Inc.) tire model, which can effectively reflect the nonlinear coupling relationship between the tire longitudinal force and lateral force, is established based on experimental data and firstly adopted in the bus YSC system design. On this basis, a more accurate bus lateral dynamics model is built and a novel YSC strategy based on ANFTSM, which has the merits of fast transient response, finite time convergence and high robustness against uncertainties and external disturbances, is designed. Thirdly, to solve the optimal allocation problem of the tire forces, whose objective is to achieve the desired direct yaw moment through the effective distribution of the brake force of each tire, the robust least-squares allocation method is adopted. To verify the feasibility, effectiveness and practicality of the proposed bus YSC approach, the TruckSim-Simulink co-simulation results are finally provided. The co-simulation results show that the lateral stability of bus under special driving conditions has been significantly improved. This research proposes a more effective design method for bus YSC system based on a more accurate tire model.

Summary: Due to a lack of explicit temporal information, it can be challenging to infer gene regulatory networks from clinical transcriptomic data. Here, we describe the protocol of PROB_R for inferring latent temporal disease progression and reconstructing gene regulatory networks from cross-sectional clinical transcriptomic data. We illustrate the protocol by applying it to a breast cancer dataset to demonstrate its use in recovering pseudo-temporal dynamics of gene expression alongside disease progression, reconstructing gene regulatory networks, and identifying key regulatory genes.For complete details on the use and execution of this protocol, please refer to Sun et al. (2021). : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

This paper proposes a novel vehicle state estimation (VSE) method that combines a physics-informed neural network (PINN) and an unscented Kalman filter on manifolds (UKF-M). This VSE aimed to achieve inertial measurement unit (IMU) calibration and provide comprehensive information on the vehicle’s dynamic state. The proposed method leverages a PINN to eliminate IMU drift by constraining the loss function with ordinary differential equations (ODEs). Then, the UKF-M is used to estimate the 3D attitude, velocity, and position of the vehicle more accurately using a six-degrees-of-freedom vehicle model. Experimental results demonstrate that the proposed PINN method can learn from multiple sensors and reduce the impact of sensor biases by constraining the ODEs without affecting the sensor characteristics. Compared to the UKF-M algorithm alone, our VSE can better estimate vehicle states. The proposed method has the potential to automatically reduce the impact of sensor drift during vehicle operation, making it more suitable for real-world applications.

Mode converters is a key component in mode-division multiplexing (MDM) systems, which plays a key role in signal processing and multi-mode conversion. In this paper, we propose an MMI-based mode converter on 2%-Δ silica PLC platform. The converter transfers E00 mode to E20 mode with high fabrication tolerance and large bandwidth. The experimental results show that the conversion efficiency can exceed −1.741 dB with the wavelength range of 1500 nm to 1600 nm. The measured conversion efficiency of the mode converter can reach −0.614 dB at 1550 nm. Moreover, the degradation of conversion efficiency is less than 0.713 dB under the deviation of multimode waveguide length and phase shifter width at 1550 nm. The proposed broadband mode converter with high fabrication tolerance is promising for on-chip optical network and commercial applications.

The current vehicle stability judgment methods in the stability control of distributed drive electric vehicles focuses on using the phase plane method, but the existing phase plane stability boundary delineation methods ignore the influence of the front wheel rotation angle. Therefore, a new phase plane partitioning method (NPPPM) is proposed in this paper, and a new phase plane stability domain library is established. Based on this, the direct yaw moment control of distributed drive electric vehicle through phase plane analysis is established. Firstly, the influence of the front wheel turning angle on the phase plane is considered in the design of the phase plane stability boundary, and a new stability boundary function is fitted by the least squares method. This approach avoids the lateral instability caused by too large a front-wheel turning angle and improves the accuracy of vehicle stability judgment. Next, a hierarchical direct transverse moment controller is constructed using Matlab/Simulink. Among them, the upper layer controller adopts sliding mode control with the sideslip angle error of the center of mass as the tracking target. The lower controller adopts the optimal configuration algorithm with the optimal road adhesion utilization as the objective function. Finally, the effectiveness and superiority of the proposed phase plane partitioning method (NPPPM) for distributed drive electric vehicles with sinusoidal and angular order inputs for the front wheel rotation angle are verified by simulation.

Wideband and polarization-insensitive operation are key issues for electro-optic modulators. An electro-absorption modulator based on graphene-silicon nitride hybrid waveguide has been proposed. Geometric parameters are investigated through finite element method to enhance the interaction between the optical mode and the graphene. For a 200-µm-long hybrid waveguide, a modulation depth (MD) of 20 dB can be obtained when Femi level varies from 0.2 to 0.6 eV. The MD difference between the transversal electric polarization and transversal magnetic polarization (ΔMD) can be restrained to 4 × 10−3 dB at 1550 nm. A low average ΔMD of 0.2 dB within the wavelength range from 1100 to 1645 nm can be obtained. The 3 dB-bandwidth of 53 GHz and a power consumption is 0.687 pJ/bit can be obtained. The proposed modulator has potentials for on-chip signal processing.

Optical mode switch is a key device for reconfigurable mode-division-multiplexing (MDM) technique. Here, an ultra-broadband 1×2 mode switch based on cascaded Mach-Zehnder interferometer and asymmetric Y-branch is proposed. The input E00 or E10 mode will be converted to E00 mode and switched between two output ports by changing the working state of electrode. When E00 mode is launched, the measured insertion loss of fabricated switch is 3.3 dB (off-state)/3.4 dB (on-state), and corresponding crosstalk is −18.8 dB (off-state)/−16.1 dB (on-state) at 1550 nm, respectively. Due to the adoption of polymer with high thermo-optic coefficient as the waveguide material, the measured switching power is 10.3 mW. The captured far-field output mode patterns prove the wavelength independence of proposed mode switch within the range from 1500 to 1630 nm. This broadband operation mode switch has potentials in wavelength-division-multiplexing & MDM complex systems, as well as cost-effective low-end switching applications.

An electrical absorption graphene modulator based on hybrid plasmonic waveguide is theoretically demonstrated. The sidewall scattering loss is mostly restrained by the vertical metal-insulator-silicon (MIS) waveguide structure. The patterned Si3N4 (SiN) can both serve as the mask and the insulation layer, which offers the direct formation of silicon stripe by thermal oxidization. The metal cap layer utilized as an electrode is favorable to improve the frequency response. By finite element method (FEM) optimization of waveguide dimensions, the 700 nm-wide optical 3 dB-bandwidth that ranges from 1200 to 1900 nm can be obtained at the modulation length of 30 μm. The propagation loss is reduced to 0.088 dB/μm. The modulation depth and power consumption are calculated to be 0.509 dB/μm and 61.3 fJ/bit, respectively. The estimated electrical 3 dB-bandwidth of 330 GHz promises the proposed modulator potentials in next generation on-chip optical signal processing.

Inferring dynamic regulatory networks that rewire at different stages is a reasonable way to understand the mechanisms underlying cancer development. In this study, we reconstruct the stage-specific gene regulatory networks (GRNs) for colorectal cancer to understand dynamic changes of gene regulations along different disease stages. We combined multiple sets of clinical transcriptomic data of colorectal cancer patients and employed a supervised approach to select initial gene set for network construction. We then developed a dynamical system-based optimization method to infer dynamic GRNs by incorporating mutual information-based network sparsification and a dynamic cascade technique into an ordinary differential equations model. Dynamic GRNs at four different stages of colorectal cancer were reconstructed and analyzed. Several important genes were revealed based on the rewiring of the reconstructed GRNs. Our study demonstrated that reconstructing dynamic GRNs based on clinical transcriptomic profiling allows us to detect the dynamic trend of gene regulation as well as reveal critical genes for cancer development which may be important candidates of master regulators for further experimental test.

Microorganisms in the complex root canal system and the extraradicular regions, including the periapical lesions and extraradicular biofilm may cause root canal treatment failures. However, few studies described the difference between the intraradicular and extraradicular infections from the same tooth associated with persistent apical periodontitis. This study aimed to characterize the microbiome present in the root canal, extraradicular biofilm, and periapical lesions associated with persistent apical periodontitis. The microbial communities in the root canal, extraradicular biofilm, and periapical lesions were investigated by Illumina high-throughput sequencing using Illumina Hiseq 2500 platform. The dominant phyla in the extraradicular and intraradicular infections associated with persistent apical periodontitis were Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, and the genera Fusobacterium, Morganella, Porphyromonas, Streptococcus, and Bifidobacterium dominated across all samples. Although extraradicular infection sites showed higher OTU richness and β-diversity compared to intraradicular samples, the occurrence of sinus tract rather than the sampling sites demarcated the microbial communities in the infections associated with persistent apical periodontitis. PERMANOVA analysis confirmed that the samples with or without sinus tracts contained significantly different microbial communities. Porphyromonas, Eubacterium, Treponema, and Phocaeicola were found in significantly higher levels with sinus tracts, whilst Microbacterium and Enterococcus were more abundant in samples without sinus tracts. In conclusion, diverse bacteria were detected in both intraradicular and extraradicular infections associated with persistent apical periodontitis, which might be influenced by the occurrence of the sinus tract. The results may provide new insight into the pathogenesis of persistent apical periodontitis.

β cell dysfunction and failure are driving forces of type 2 diabetes mellitus (T2DM) pathogenesis. Investigating the underlying mechanisms of β cell dysfunction may provide novel targets for the development of next generation therapy for T2DM. Epigenetics is the study of gene expression changes that do not involve DNA sequence changes, including DNA methylation, histone modification, and non-coding RNAs. Specific epigenetic signatures at all levels, including DNA methylation, chromatin accessibility, histone modification, and non-coding RNA, define β cell identity during embryonic development, postnatal maturation, and maintain β cell function at homeostatic states. During progression of T2DM, overnutrition, inflammation, and other types of stress collaboratively disrupt the homeostatic epigenetic signatures in β cells. Dysregulated epigenetic signatures, and the associating transcriptional outputs, lead to the dysfunction and eventual loss of β cells. In this review, we will summarize recent discoveries of the establishment and disruption of β cell-specific epigenetic signatures, and discuss the potential implication in therapeutic development.

Mode division multiplexing (MDM) is a promising technology for the capacity enlargement of the optical transmission network. As a key element in the MDM system, the mode converter plays an important role in signal processing. In this work, a wideband E00-E10 silicon mode converter constructed by Y-branch and cascaded multimode interference coupler is demonstrated. The theoretical mode crosstalk is less than –29.2 dB within the wavelength range from 1540 nm to 1600 nm. By 180 nm standard CMOS fabrication, the tested mode conversion efficiency of 91.5% and the crosstalk of −10.3 dB can be obtained at 1575.9 nm. The 3 dB bandwidth is over 60 nm. The proposed E00-E10 silicon mode converter is applicable in mode multiplexing.

A polarization-insensitive graphene-assisted electro-optic modulator is proposed. The orthogonal T-shaped metal slot hybrid plasmonic waveguide allows the polarization-independent propagation of transverse electric field mode and complex mode. By the introduction of dual-layer graphene on the ridge waveguide, the polarization-insensitive modulation depths of the TE mode and complex mode are 0.511 dB/µm and 0.502 dB/µm, respectively. The 3 dB bandwidth of the modulator we have proposed is about 127 GHz at the waveguide length of 20 μm. The power consumption of 72 fJ/bit promised potential graphene electro-optic modulator applications for on-chip interconnected information transfer and processing.

Angiostrongylus cantonensis (AC), which parasitizes in the brain of the non-permissive host, such as mouse and human, is an etiologic agent of eosinophilic meningitis. Excretory-secretory (ES) products play an important role in the interaction between parasites and hosts’ immune responses. Inflammatory macrophages are responsible for eosinophilic meningitis induced by AC, and the soluble antigens of Angiostrongylus cantonensis fourth stage larva (AC L4), a mimic of dead AC L4, aggravate eosinophilic meningitis in AC-infected mice model via promoting alternative activation of macrophages. In this study, we investigated the key molecules in the ES products of AC L4 on macrophages and observed the relationship between metabolic reprogramming and the PI3K-Akt pathway. First, a co-culture system of macrophage and AC L4 was established to define the role of AC L4 ES products on macrophage polarization. Then, AC L4 exosome and exosome-depleted excretory-secretory products (exofree) were separated from AC L4 ES products using differential centrifugation, and their distinct roles on macrophage polarization were confirmed using qPCR and ELISA experiments. Moreover, AC L4 exofree induced alternative activation of macrophages, which is partially associated with metabolic reprogramming by the PI3K-Akt pathway. Next, lectin blot and deglycosylation assay were done, suggesting the key role of N-linked glycoproteins in exofree. Then, glycoproteomic analysis of exofree and RNA-seq analysis of exofree-treated macrophage were performed. Bi-layer PPI network analysis based on these results identified macrophage-related protein Hexa as a key molecule in inducing alternative activation of macrophages. Our results indicate a great value for research of helminth-derived immunoregulatory molecules, which might contribute to drug development for immune-related diseases.

Abstract Background The tumor-associated microenvironment plays important roles in tumor progression and drug resistance. However, systematic investigations of macrophage–tumor cell interactions to identify novel macrophage-related gene signatures in gliomas for predicting patient prognoses and responses to targeted therapies are lacking. Methods We developed a multicellular gene network approach to investigating the prognostic role of macrophage–tumor cell interactions in tumor progression and drug resistance in gliomas. Multicellular gene networks connecting macrophages and tumor cells were constructed from re-grouped drug-sensitive and drug-resistant samples of RNA-seq data in mice gliomas treated with BLZ945 (a CSF1R inhibitor). Subsequently, a differential network-based COX regression model was built to identify the risk signature using a cohort of 310 glioma samples from the Chinese Glioma Genome Atlas database. A large independent validation set of 690 glioma samples from The Cancer Genome Atlas database was used to test the prognostic significance and accuracy of the gene signature in predicting prognosis and targeted therapeutic response of glioma patients. Results A macrophage-related gene signature was developed consisting of twelve genes (ANPEP, DPP4, PRRG1, GPNMB, TMEM26, PXDN, CDH6, SCN3A, SEMA6B, CCDC37, FANCA, NETO2), which was tested in the independent validation set to examine its prognostic significance and accuracy. The generation of 1000 random gene signatures by a bootstrapping scheme justified the non-random nature of the macrophage-related gene signature. Moreover, the discovered gene signature was verified to be predictive of the sensitivity or resistance of glioma patients to molecularly targeted therapeutics and outperformed other existing gene signatures. Additionally, the macrophage-related gene signature was an independent and the strongest prognostic factor when adjusted for clinicopathologic risk factors and other existing gene signatures. Conclusion The multicellular gene network approach developed herein indicates profound roles of the macrophage-mediated tumor microenvironment in the progression and drug resistance of gliomas. The identified macrophage-related gene signature has good prognostic value for predicting resistance to targeted therapeutics and survival of glioma patients, implying that combining current targeted therapies with new macrophage-targeted therapy may be beneficial for the long-term treatment outcomes of glioma patients.

Abstract Background Experimental studies have demonstrated that both the extracellular vasculature or microenvironment and intracellular molecular network (e.g., epidermal growth factor receptor (EGFR) signaling pathway) are important for brain tumor growth. Additionally, some drugs have been developed to inhibit EGFR signaling pathways. However, how angiogenesis affects the response of tumor cells to drug treatment has rarely been mechanistically studied. Therefore, a multiscale model is required to investigate such complex biological systems that contain interactions and feedback among multiple levels. Results In this study, we developed a single cell-based multiscale spatiotemporal model to simulate vascular tumor growth and the drug response based on the vascular endothelial growth factor receptor (VEGFR) signaling pathway, the EGFR signaling pathway and the cell cycle as well as several microenvironmental factors that determine cell fate switches in a temporal and spatial context. By incorporating the EGFRI treatment effect, the model showed an interesting phenomenon in which the survival rate of tumor cells decreased in the early stage but rebounded in a later stage, revealing the emergence of drug resistance. Moreover, we revealed the critical role of angiogenesis in acquired drug resistance, since inhibiting blood vessel growth using a VEGFR inhibitor prevented the recovery of the survival rate of tumor cells in the later stage. We further investigated the optimal timing of combining VEGFR inhibition with EGFR inhibition and predicted that the drug combination targeting both the EGFR pathway and VEGFR pathway has a synergistic effect. The experimental data validated the prediction of drug synergy, confirming the effectiveness of our model. In addition, the combination of EGFR and VEGFR genes showed clinical relevance in glioma patients. Conclusions The developed multiscale model revealed angiogenesis-induced drug resistance mechanisms of brain tumors to EGFRI treatment and predicted a synergistic drug combination targeting both EGFR and VEGFR pathways with optimal combination timing. This study explored the mechanistic and functional mechanisms of the angiogenesis underlying tumor growth and drug resistance, which advances our understanding of novel mechanisms of drug resistance and provides implications for designing more effective cancer therapies.

Compact high-performance electro-optic (EO) modulators are key elements for optical communications. An electro-absorption modulator is theoretically designed. Due to the introduction of graphene film over the silicon slot waveguide, the modulation efficiency is strongly improved. The effect of waveguide dimensions, dielectric insulator thickness, and carrier mobility on performances of the proposed EO modulator have been comprehensively investigated. For the 120-μm-long graphene-on-silicon waveguide modulator, the extinction ratio 28 dB and insertion loss 1.28 dB can be obtained at 1550 nm when drive voltage is 1.91 and 0.59 V, respectively. The 3 dB bandwidth of 117 GHz can be obtained at a small power consumption of 212 fJ/bit. The proposed modulator possesses a high figure-of-merit of 20.5. Its benefits of small size, power saving, and process compatible promises possible applications in on-chip signal processing.

Accurate and reliable vehicle state estimation results are very significant to the active safety, energy optimization, and the intelligent control of vehicles. In this paper, to improve the accuracy and adaptability of vehicle running state estimation, the vehicle running states fused estimation strategy is presented for in-wheel motor drive electric vehicle using the Kalman filters and tire force compensation method. The concept of electric drive wheel model (EDWM) is developed and deduced, and then, considering that the EDWM is a nonlinear model with an unknown input, the design concept of high-order sliding mode observer is used to construct the state space equation of longitudinal force. To improve the accuracy and the reliability of vehicle state estimation, an overall estimation strategy with information fusion and tire force compensation is designed, in which a weighted square-root cubature Kalman filter with an adaptive covariance matrix of measurement noise is developed for observer design. Finally, the simulations in CarSim-Simulink co-simulation model and experiments are carried out, and the effectiveness of the designed estimation strategy is validated.

Longitudinal dynamics control is the basis for autonomous driving of intelligent vehicles, which have great significance to the development of intelligent transportation system (ITS). To solve the problems of traditional sliding mode control method when applied to intelligent vehicle longitudinal dynamics, such as large velocity tracking errors, strong chattering phenomenon and so on, a new sliding mode control strategy based on RBF (Radical Basis Function) neural network is presented in this paper. Firstly, a nonlinear mathematical model of the intelligent vehicle longitudinal motion is established by considering the dynamics of the engine, the torque converter, the automatic transmission and the brake system. On the basis of the system model, a variable structure control system with sliding mode is introduced to design a sliding mode variable controller with RBF neural network. This controller can adaptively adjust the switching gain and its stability is proved based on the Lyapunov theory. Finally, the effectiveness of the designed longitudinal velocity control strategy is verified by simulation under typical driving conditions. The simulation results show that the improved control algorithm can effectively suppress chattering, obtain the higher precision and stronger robustness than the traditional sliding mode control. Thus, the longitudinal motion control performance of intelligent vehicles is improved effectively.

A narrow-band Bragg grating filter based on a long-range surface plasmon polariton (LRSPP) waveguide is theoretically demonstrated. The three-dimensional Au stripe that is embedded in polymer SU-8 acts as both the waveguide and the heating electrode. With the eigen mode expansion and finite element method optimizations, the proposed filter shows a reflectivity of 0.578 and a 3 dB bandwidth of 1.1 nm. The central wavelength can be tuned from 1549.9 nm to 1544.3 nm by varying temperature from 25 °C to 75 °C, while maintaining the optical return loss at −2.5 dB. This proposed tunable filter has potential in on-chip light signal processing.

To improve the curve driving stability and safety under critical maneuvers for four-wheel-independent drive autonomous electric vehicles, a three-stage direct yaw moment control (DYC) strategy design procedure is proposed in this work. The first stage conducts the modeling of the tire nonlinear mechanical properties, i.e. the coupling relationship between the tire longitudinal force and the tire lateral force, which is crucial for the DYC strategy design, in the STI (Systems Technologies Inc.) form based on experimental data. On this basis, a 7-DOF vehicle dynamics model is established and the direct yaw moment calculation problem of the four-wheel-independent drive autonomous electric vehicle is solved through the nonsingular fast terminal sliding mode (NFTSM) control method, thus the optimal direct yaw moment can be obtained. To achieve this direct yaw moment, an optimal allocation problem of the tire forces is further solved by using the trust-region interior-point method, which can effectively guarantee the solving efficiency of complex optimization problem like the tire driving and braking forces allocation of four wheels in this work. Finally, the effectiveness of the DYC strategy proposed for the autonomous electric vehicles is verified through the CarSim-Simulink co-simulation results.

Due to the coexistence of continuity and discreteness, energy management of a multi-mode power split hybrid electric vehicle (HEV) can be considered a typical hybrid system. Therefore, the hybrid system theory is applied to investigate the optimum energy distribution strategy of a power split multi-mode HEV. In order to obtain a unified description of the continuous/discrete dynamics, including both the steady power distribution process and mode switching behaviors, mixed logical dynamical (MLD) modeling is adopted to build the control-oriented model. Moreover, linear piecewise affine (PWA) technology is applied to deal with nonlinear characteristics in MLD modeling. The MLD model is finally obtained through a high level modeling language, i.e. HYSDEL. Based on the MLD model, hybrid model predictive control (HMPC) strategy is proposed, where a mixed integer quadratic programming (MIQP) problem is constructed for optimum power distribution. Simulation studies under different driving cycles demonstrate that the proposed control strategy can have a superior control effect as compared with the rule-based control strategy.

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

It has been proposed recently that major depressive disorder (MDD) could represent an adaptation to conserve energy after the perceived loss of an investment in a vital source, such as group identity, personal assets, or relationships. Energy conserving behaviors associated with MDD may form a persistent marker in brain regions and networks involved in cognition and emotion regulation. In this study, we examined whether subcortical regions and volume-based structural covariance networks (SCNs) have state-independent alterations (trait markers).First-episode drug-naïve currently depressed (cMDD) patients (N = 131), remitted MDD (RD) patients (N = 67), and healthy controls (HCs, N = 235) underwent structural magnetic resonance imaging (MRI). Subcortical gray matter volumes (GMVs) were calculated in FreeSurfer software, and group differences in GMVs and SCN were analyzed.Compared to HCs, major findings were decreased GMVs of left pallidum and pulvinar anterior of thalamus in the cMDD and RD groups, indicative of a trait marker. Relative to HCs, subcortical SCNs of both cMDD and RD patients were found to have reduced small-world-ness and path length, which together may represent a trait-like topological feature of depression.In sum, the left pallidum, left pulvinar anterior of thalamus volumetric alterations may represent trait marker and reduced small-world-ness, path length may represent trait-like topological feature of MDD.

Background: Childhood trauma is a major social public-health problem worldwide. Previous literature suggests childhood trauma is associated with the development of psychiatric disorders and maladaptive behaviours later in life, but little is known about the neural basis underlying these associations. Objective: The aim of the current study was to investigate intrinsic brain network alterations in non-clinical adults with childhood trauma. Methods: Resting-state functional magnetic resonance imaging (fMRI) data were collected from 65 non-clinical adults with moderate or severe childhood trauma (CT group), according to the international demarcation criteria of the Childhood Trauma Questionnaire (CTQ), and 73 socio-demographically matched non-clinical controls without childhood trauma (no-CT group). Independent component analysis (ICA) was used to extract subnetworks of the default mode network (DMN), salience network (SN), and central executive network (CEN). Results: ICA revealed that the CT group had increased FC of the left medial prefrontal cortex (mPFC) in the anterior DMN (aDMN), increased functional connectivity (FC) of the left anterior insula in the SN, and decreased FC of the inferior parietal gyrus of the right CEN (rCEN). Compared to the controls, the CT group had decreased inter-network FCs between the SN and posterior DMN (pDMN), as well as between the pDMN and rCEN. Conclusions: Impaired FC within the three key brain networks, decreased inter-FC between SN and rCEN, and decreased inter-FC between pDMN and rCEN may reflect biomarkers of childhood trauma.

Deconvolution of heterogeneous bulk tumor samples into distinct cellular populations is an important yet challenging problem, particularly when only partial references are available. A common approach to dealing with this problem is to deconvolve the mixed signals using available references and leverage the remaining signal as a new cell component. However, as indicated in our simulation, such an approach tends to over-estimate the proportions of known cell types and fails to detect novel cell types. Here, we propose PREDE, a partial reference-based deconvolution method using an iterative non-negative matrix factorization algorithm. Our method is verified to be effective in estimating cell proportions and expression profiles of unknown cell types based on simulated datasets at a variety of parameter settings. Applying our method to TCGA tumor samples, we found that proportions of pure cancer cells better indicate different subtypes of tumor samples. We also detected several cell types for each cancer type whose proportions successfully predicted patient survival. Our method makes a significant contribution to deconvolution of heterogeneous tumor samples and could be widely applied to varieties of high throughput bulk data. PREDE is implemented in R and is freely available from GitHub (https://xiaoqizheng.github.io/PREDE).

This article introduces an efficient and high-precision estimation framework for four-wheel independently actuated (FWIA) autonomous vehicles based on a novel tire model and adaptive square-root cubature Kalman filter (SCKF) estimation strategy. Firstly, a reliable and concise tire model that considers the tire’s nonlinear mechanics characteristics under combined conditions through the piecewise affine (PWA) identification method is established to improve the accuracy of the lateral dynamics model of FWIA autonomous vehicles. On this basis, the longitudinal relaxation length of each tire is integrated into the lateral dynamics modeling of FWIA autonomous vehicle. A novel nonlinear state function, including the PWA tire model, is proposed in this paper. To reduce the impact of the uncertainty of noise statistics on the estimation accuracy, an adaptive SCKF estimation algorithm based on the maximum a posteriori (MAP) criterion is proposed in the estimation framework. Finally, the estimation accuracy and stability of the adaptive SCKF algorithm are verified by the co-simulation of CarSim and Simulink. The simulation results show that when the statistical characteristics of noise are unknown and the target state changes suddenly under critical maneuvers, the estimation framework proposed in this paper still maintains high accuracy and stability.

During actual operations, Automatic Guided Vehicles (AGV) will inevitably encounter the phenomena of overexposure or shadowy areas, and unclear or even damaged guide wires, which interfere with the identification of guide wires. Therefore, this paper aims to solve the shortcomings of existing technology at the software level. Firstly, a Fast Guide Filter (FGF) is adopted with the two-dimensional gamma function with variable parameters, and an image preprocessing algorithm in a complex illumination environment is designed to get rid of the interference of illumination. Secondly, an ant colony edge detection algorithm is proposed, and the guide wire is accurately extracted by secondary screening combined with the guide wire characteristics; A variable universe Fuzzy Sliding Mode Control (FSMC) algorithm is designed as a lateral motion control method to realize the accurate tracking of AGV. Finally, the experimental platform is used to comprehensively verify the series of algorithms designed in this paper. The experimental results show that the maximum deviation can be limited to 1.2 mm, and the variance of the deviation is less than 0.2688 mm2.

In this work, we have proposed and designed a 1 × 1 optical switch based on the optical phase-change material, Ge2Sb2Se4Te1 (GSST), for GSST-assisted silicon racetrack microring. Its optical power can periodically be exchanged between the straight silicon waveguide and the GSST/Si hybrid racetrack waveguide due to the formed directional coupling structure. By changing GSST from the crystalline state to the amorphous state, the switch shifts from the ON state to the OFF state, and vice versa. With finite-difference time-domain method optimization, the proposed switch shows an extinction ratio of 18 dB at 1547.4 nm. The insert losses at the ON and OFF states are both less than 1 dB. The proposed switch unit has the potential to build an N × N switch matrix.

The stress response is regulated by many mechanisms. Monoamine oxidase A (MAOA) has been related to many mental illnesses. However, few studies have explored the relationship between MAOA and acute laboratory-induced psychosocial stress with functional magnetic resonance imaging (fMRI). In the current study, the Montreal Imaging Stress Task (MIST) and fMRI were used to investigate how MAOA influences the stress response. Increased cortisol concentrations were observed after the task; functional connectivity between the bilateral anterior hippocampus and other brain regions was reduced during stress. MAOA-H allele carriers showed greater deactivation of the right anterior hippocampus and greater cortisol response after stress than did MAOH-L allele carriers. Hippocampal deactivation may lead to disinhibition of the hypothalamic-pituitary-adrenal (HPA) axis and the initiation of stress hormone release under stress. Our results suggest that the MAOA gene regulates the stress response by influencing the right anterior hippocampus.