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This paper proposes a method for estimating the speed and position of unsampled vehicles using sampled data from connected automated vehicles (CAVs). The determination of vehicle speed and position on the road is a challenging and crucial task, as they can effectively reflect traffic flow characteristics and contribute to traffic state estimation and intersection signal timing optimization. Connected automated vehicles have the capability to upload their own trajectory data while also capturing trajectory data of surrounding vehicles through onboard sensors. Therefore, this paper proposes a novel approach to estimate the speed and position of unsampled vehicles. Firstly, using real vehicle trajectory data, the correlation between the velocity of following vehicles and the velocity of leading vehicles under different densities is analyzed, leading to the development of a velocity estimation model incorporating a speed correction factor. Secondly, the correlation between time headway, the rate of change of following vehicle acceleration, and traffic density is examined. To address the issue of heterogeneous behavior in vehicle following described by the Intelligent Driver Model (IDM), a real-time optimization model for estimating vehicle position by optimizing IDM parameters is proposed. The velocity estimation model and the position estimation model are summarized as two nonlinear optimization problems. Finally, the proposed method is validated using actual vehicle trajectory data. Experimental results demonstrate that when the number of connected automated vehicles (CAVs) is 2, the proposed method reduces the average absolute error by 30.73% and the standard deviation of the average absolute error by 42.8% compared to a linear model-based speed estimation method under different density conditions. Compared to a method that estimates vehicle position by calibrating desired gaps, the proposed method reduces the average absolute error by 38.2% and the standard deviation of the average absolute error by 41.7% under different density conditions. Furthermore, the proposed method exhibits good practicality under different CAV penetration rates., Author(s): HanQing Xia (corresponding author) [1,2]; XiaoMing Liu [1,2]; ZeChao Ma [1,2]; Fang Zhu [1]; Lin Zhang [1]; YingJie Zhao [1]; YuanRong Wang [3] 1. Introduction Vehicle trajectory information accurately [...]

The 22q11.2 deletion syndrome (22q11.2DS) results from non-allelic homologous recombination between low-copy repeats termed LCR22. About 60%-70% of individuals with the typical 3 megabase (Mb) deletion from LCR22A-D have congenital heart disease, mostly of the conotruncal type (CTD), whereas others have normal cardiac anatomy. In this study, we tested whether variants in the hemizygous LCR22A-D region are associated with risk for CTDs on the basis of the sequence of the 22q11.2 region from 1,053 22q11.2DS individuals. We found a significant association (FDR p < 0.05) of the CTD subset with 62 common variants in a single linkage disequilibrium (LD) block in a 350 kb interval harboring CRKL. A total of 45 of the 62 variants were associated with increased risk for CTDs (odds ratio [OR) ranges: 1.64-4.75). Associations of four variants were replicated in a meta-analysis of three genome-wide association studies of CTDs in affected individuals without 22q11.2DS. One of the replicated variants, rs178252, is located in an open chromatin region and resides in the double-elite enhancer, GH22J020947, that is predicted to regulate CRKL (CRK-like proto-oncogene, cytoplasmic adaptor) expression. Approximately 23% of patients with nested LCR22C-D deletions have CTDs, and inactivation of Crkl in mice causes CTDs, thus implicating this gene as a modifier. Rs178252 and rs6004160 are expression quantitative trait loci (eQTLs) of CRKL. Furthermore, set-based tests identified an enhancer that is predicted to target CRKL and is significantly associated with CTD risk (GH22J020946, sequence kernal association test (SKAT) p = 7.21 × 10-5) in the 22q11.2DS cohort. These findings suggest that variance in CTD penetrance in the 22q11.2DS population can be explained in part by variants affecting CRKL expression.

Background: Spinach (Spinacia oleracea L.) is an important leafy vegetable with dioecious and occasional monoecious plants. Monoecious lines are more suitable for hybrid production than dioecious lines due to their extended flowering period. However, genetic research on the sex determination of monoecism remains limited. Methods: In this study, RNA-seq analysis of monoecious and female spinach plants was performed at two distinct flowering stages. In total, we identified 4586 differentially expressed genes (DEGs), which were primarily involved in biological processes such as hormone signaling, cell wall biosynthesis, photosynthesis, and flower development, based on Gene Ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. Results: Among these DEGs, 354 transcription factors, including 27 genes associated with the ABCDE gene, were discovered. Furthermore, a co-expression gene regulatory network was built, identifying nine key genes that play important roles in regulating sex differentiation between female and monoecious plants. Conclusions: Our findings provide crucial molecular insights into the mechanisms of monoecism in spinach and offer a scientific basis for future spinach breeding. [ABSTRACT FROM AUTHOR]

The 3D resistivity imaging surveying is adept at identifying structural details at various subsurface depths, and it can acquire valuable underground information even in complex geological environments. This technique has been widely applied in fields such as geological exploration, hydrogeology, and environmental monitoring. Currently, the 3D electrical resistivity surveys in the field are primarily deployed using regular grid and equidistant electrode layouts. However, when the survey area is influenced by obstacles or when the target being investigated is particularly complex, this may hinder the deployment of the electrode array in accordance with a rectangular grid. In some instances, it may even be impossible to position the electrodes directly above the target of investigation. The only viable option is thus to place electrodes around obstacles or special targets. In view of this, this paper studied unconventional electrode arrays and inversion imaging techniques of 3D electrical resistivity surveying in confined areas. It presents the design of three electrode arrays: "□"-shaped, "U"-shaped, and "L"-shaped. Furthermore, the study explores 3D resistivity finite element numerical simulation and inversion imaging techniques based on continuous electrical media. Finally, model experiments were conducted to verify the feasibility of these three special electrode arrays. We have applied the "□"-shaped electrode array to investigate the foundation structure of an ancient pagoda in Zhejiang Province, achieving promising results. This study provides valuable reference for the research and application of special observation in 3D electrical resistivity exploration. [ABSTRACT FROM AUTHOR]

Rolling bearing plays an important role in carrying and transmitting power in rotating machinery, and the bearing fault is easy to lead to mechanical accidents, resulting in huge losses and casualties. Therefore, the condition monitoring and diagnosis of rolling bearings are very important to improve the safety of equipment. Compound fault is a common fault evolved from the initial defect, which is characterized by randomness, coupling, concealment, and secondary. The existence of these characteristics brings great challenges to the accurate diagnosis of compound faults. In the diagnosis of compound faults, the traditional methods that select the single optimal demodulation frequency band for analysis and identification sometimes cannot completely extract multiple fault components, which are prone to miss diagnosis and misdiagnosis. In order to solve this problem, the SEACKgram method is proposed by constructing a Square Envelope Unbiased Autocorrelation Correlation Kurtosis (SEACK) index. The frequency band of the original signal is divided by the Maximal Overlap Discrete Wavelet Packet Transform, and the SEACK index is used to quantitatively describe the fault signals of different frequency bands. According to the different fault periods, the resonant frequency bands of the maximum SEACK value are selected, then the resonance band signal is analyzed by square envelope spectrum, and the fault type is identified according to the fault characteristic frequency. The simulated and experimental vibration signals of rolling bearings with compound faults are used to verify the feasibility of the proposed method. The results show that the proposed SEACKgram can improve the accuracy of compound faults identification and would be applied in engineering practice to a certain extent. [ABSTRACT FROM AUTHOR]

Gibellulopsis nigrescens, previously classified in the Verticillium genus until 2007, is an attenuated pathogen known to provide cross-protection against Verticillium wilt in various crops. To investigate the potential mechanisms underlying its reduced virulence, we conducted genome sequencing, annotation, and a comparative genome analysis of G. nigrescens GnVn.1 (GnVn.1), an attenuated strain isolated from sunflower. The genome sequencing and annotation results revealed that the GnVn.1 genome consists of 22 contigs, with a total size of 31.79 Mb. We predicted 10,876 genes, resulting in a gene density of 342 genes per Mb. The pathogenicity gene prediction results indicated 1733 high-confidence pathogenicity factors (HCPFs), 895 carbohydrate-active enzymes (CAZys), and 359 effectors. Moreover, we predicted 40 secondary metabolite clusters (SMCs). The comparative genome analysis indicated that GnVn.1 contains more CAZys, SMCs, predicted effectors, and HCPF genes than Verticillium dahliae (VdLs.17) and Verticillium alfalfae (VaMas.102). The core–pan analysis results showed that GnVn.1 had more specific HCPFs, effectors, CAZys, and secreted protein (SP) genes, and lost many critical pathogenic genes compared to VdLs.17 and VaMs.102. Our results indicate that the GnVn.1 genome harbors more pathogenicity-related genes than the VdLs.17 and VaMs.102 genomes. These abundant genes may play critical roles in regulating virulence. The loss of critical pathogenic genes causes weak virulence and confers biocontrol strategies to GnVn.1. [ABSTRACT FROM AUTHOR]

Automatic identification of modal frequencies can be used to directly estimate the real-time tension force of bridge cables and provide early damage alarming. However, a large amount of abnormal monitoring data usually exists in structural health monitoring (SHM) systems. Abnormal monitoring data may lead to faulty results of modal frequency identification and incorrect cable tension force estimation. Then, false or missing alarming of cable damage may arise. An automatic identification method of bridge cable modal frequencies under the influence of abnormal monitoring data is proposed in this study. The peak picking (PP) method is used to automatically obtain the original identification results of cable modal frequencies. To remove faulty frequency identification results, a multidimensional density-based clustering model is established. The cable acceleration data of the Waitan cable-stayed bridge are used to verify the accuracy of the proposed method. The influence of various abnormal monitoring data on frequency identification is investigated, and the accuracy of multidimensional clustering models is verified. The results show that abnormal monitoring data have a harmful influence on automatic modal frequency identification for bridge cables. The accuracy of the multidimensional clustering model for faulty frequency identification results is more than 99%. After removing the faulty frequency identification results, the correlation between the cable modal frequencies and environmental temperature becomes clearer and more reasonable. [ABSTRACT FROM AUTHOR]

Background: It has been reported that COVID-19 patients have an increased risk of developing IBS; however, the underlying genetic mechanisms of these associations remain largely unknown. The aim of this study was to investigate potential shared SNPs, genes, proteins, and biological pathways between COVID-19 and IBS by assessing pairwise genetic correlations and cross-trait genetic analysis. Materials and methods: We assessed the genetic correlation between three COVID-19 phenotypes and IBS using linkage disequilibrium score regression (LDSC) and high-definition likelihood (HDL) methods. Two different sources of IBS data were combined using METAL, and the Multi-trait analysis of GWAS (MTAG) method was applied for multi-trait analysis to enhance statistical robustness and discover new genetic associations. Independent risk loci were examined using genome-wide complex trait analysis (GCTA)-conditional and joint analysis (COJO), multi-marker analysis of genomic annotation (MAGMA), and functional mapping and annotation (FUMA), integrating various QTL information and methods to further identify risk genes and proteins. Gene set variation analysis (GSVA) was employed to compute pleiotropic gene scores, and combined with immune infiltration algorithms, IBS patients were categorized into high and low immune infiltration groups. Results: We found a positive genetic correlation between COVID-19 infection, COVID-19 hospitalization, and IBS. Subsequent multi-trait analysis identified nine significantly associated genomic loci. Among these, eight genetic variants were closely related to the comorbidity of IBS and COVID-19. The study also highlighted four genes and 231 proteins associated with the susceptibility to IBS identified through various analytical strategies and a stratification approach for IBS risk populations. Conclusions: Our study reveals a shared genetic architecture between these two diseases, providing new insights into potential biological mechanisms and laying the groundwork for more effective interventions. [ABSTRACT FROM AUTHOR]

Highlights: Low-temperature fabrication of black-phase CsPbI3 perovskite films is first demonstrated by using diphenylphosphinic chloride additive under 30–50 °C, arising from the steric effect and chloride insertion engineering. Large-area high-quality all-inorganic perovskite films with fewer defects enhanced crystallographic orientation, and excellent environmental stability is fabricated. The record performances are demonstrated for flexible wearable photodetectors with a responsivity of 42.1 A W−1, a detectivity of 1.3 × 1014 Jones, high-fidelity image, photoplethysmography sensor functions, and high mechanical stability. Flexible wearable optoelectronic devices fabricated from organic–inorganic hybrid perovskites significantly accelerate the development of portable energy, biomedicine, and sensing fields, but their poor thermal stability hinders further applications. Conversely, all-inorganic perovskites possess excellent thermal stability, but black-phase all-inorganic perovskite film usually requires high-temperature annealing steps, which increases energy consumption and is not conducive to the fabrication of flexible wearable devices. In this work, an unprecedented low-temperature fabrication of stable black-phase CsPbI3 perovskite films is demonstrated by the in situ hydrolysis reaction of diphenylphosphinic chloride additive. The released diphenyl phosphate and chloride ions during the hydrolysis reaction significantly lower the phase transition temperature and effectively passivate the defects in the perovskite films, yielding high-performance photodetectors with a responsivity of 42.1 A W−1 and a detectivity of 1.3 × 1014 Jones. Furthermore, high-fidelity image and photoplethysmography sensors are demonstrated based on the fabricated flexible wearable photodetectors. This work provides a new perspective for the low-temperature fabrication of large-area all-inorganic perovskite flexible optoelectronic devices. [ABSTRACT FROM AUTHOR]

Aiming at the problem that it is difficult for an orbital photovoltaic panel cleaning robot to span a large distance between photovoltaic panels, a method of designing and optimizing a non-coplanar orbit based on Bezier curves is proposed. Firstly, the robot's motion law is analyzed to obtain trajectory data for a single work cycle. Then, Bezier curves are utilized for trajectory design to ensure a smooth transition during the spanning motion phase. Thirdly, with the average value of the minimum distance between the Bezier curve and the point set data of the spanning motion phase as the optimization objective function, the nonlinear planning based on the SQP algorithm was adopted for the optimization of the upper and lower trajectories. Finally, the results of the case calculations indicate that the standard deviation of the optimized upper and lower trajectories was reduced by 35.63% and 40.57%, respectively. Additionally, the ADAMS simulation validation demonstrates that the trajectory errors of the four wheels decreased by a maximum of 8.79 mm, 23.78 mm, 10.11 mm, and 14.97 mm, respectively, thereby confirming the effectiveness of the trajectory optimization. [ABSTRACT FROM AUTHOR]

In the planning of public charging facilities and the charging activity network of users, there is a decision-making conflict among three stakeholders: the government, charging station enterprises, and electric vehicle users. Previous studies have described the tripartite game relationship in a relatively simplistic manner, and when designing charging facility planning schemes, they did not consider scenarios where users' choice preferences undergo continuous random changes. In order to reduce the impacts of queuing phenomenon and resource idleness on the three participants, we introduce a bilateral matching algorithm combined with the dynamic Huff model as a strategy for EV charging selection in the passenger flow problem based on the three-dimensional activity network of time–space–energy of users. Meanwhile, the Dirichlet distribution is utilized to control the selection preferences on the user side, constructing uncertain scenarios for the choice of user charging activities. In this study, we establish a bilevel programming model that takes into account the uncertainty in social responsibility and user charging selection behavior. Solutions for the activity network and facility planning schemes can be derived based on the collaborative relationships among the three parties. The model employs a robust optimization method to collaboratively design the charging activity network and facility planning scheme. For this mixed-integer nonlinear multi-objective multi-constraint optimization problem, the model is solved by the NSGA-II algorithm, and the optimal compromise scheme is determined by using the EWM-TOPSIS comprehensive evaluation method for the Pareto solution set. Finally, the efficacy of the model and the solution algorithm is illustrated by a simulation example in a real urban space. [ABSTRACT FROM AUTHOR]

Purpose: Colorectal cancer (CRC) is a common malignancy with a low survival rate as well as a low response rate to immunotherapy. This study aims to develop a risk model based on tertiary lymphoid structure (TLS)-associated gene signatures to enhance predictions of prognosis and immunotherapy response. Methods: TLS-associated gene data were obtained from TCGA-CRC and GEO cohorts. A comprehensive analysis using univariate Cox regression identified TLS-associated genes with significant prognostic implications. Subsequently, multiple algorithms were employed to select the most influential genes, and a stepwise Cox regression model was constructed. The model's predictive performance was validated using independent datasets (GSE39582, GSE17536, and GSE38832). To further investigate the immune microenvironment, immune cell infiltration in high-risk (HRG) and low-risk (LRG) groups was assessed using the CIBERSORT and ssGSEA algorithms. Additionally, we evaluated the model's potential to predict immune checkpoint blockade therapy response using data from The Cancer Imaging Archive, the TIDE algorithm, and external immunotherapy cohorts (GSE35640, GSE78200, and PRJEB23709). Immunohistochemistry (IHC) was employed to characterize TLS presence and CCL2 gene expression. Results: A three-gene (CCL2, PDCD1, and ICOS) TLS-associated model was identified as strongly associated with prognosis and demonstrated predictive power for CRC patient outcomes and immunotherapy efficacy. Notably, patients in the low-risk group (LRG) had a higher overall survival rate as well as a higher re-response rate to immunotherapy compared to the high-risk group (HRG). Finally, IHC results confirmed significantly elevated CCL2 expression in the TLS regions. Conclusions: The multi-algorithm-integrated model demonstrated robust performance in predicting patient prognosis and immunotherapy response, offering a novel perspective for assessing immunotherapy efficacy. CCL2 may function as a TLS modulator and holds potential as a therapeutic target in CRC. [ABSTRACT FROM AUTHOR]

Background: Patients with inflammatory bowel disease (IBD) have an increased risk of developing immune-mediated diseases. However, the genetic basis of IBD is complex, and an integrated approach should be used to elucidate the complex genetic relationship between IBD and immune-mediated diseases. Methods: The genetic relationship between IBD and 16 immune-mediated diseases was examined using linkage disequilibrium score regression. GWAS data were synthesized from two IBD databases using the METAL, and multi-trait analysis of genome-wide association studies was performed to enhance statistical robustness and identify novel genetic associations. Independent risk loci were meticulously examined using conditional and joint genome-wide multi-trait analysis, multi-marker analysis of genomic annotation, and functional mapping and annotation of significant genetic loci, integrating the information of quantitative trait loci and different methodologies to identify risk-related genes and proteins. Results: The results revealed four immune-mediated diseases (AS, psoriasis, iridocyclitis, and PsA) with a significant relationship with IBD. The multi-trait analysis revealed 909 gene loci of statistical significance. Of these loci, 28 genetic variants were closely related to IBD, and 7 single-nucleotide polymorphisms represented novel independent risk loci. In addition, 14 genes and 514 proteins were found to be associated with susceptibility to immune-mediated diseases. Notably, IL1RL1 emerged as a key player, present within pleiotropic genes across multiple protein databases, highlighting its potential as a therapeutic target. Conclusions: This study suggests that the common polygenic determinants between IBD and immune-mediated diseases are widely distributed across the genome. The findings not only support a shared genetic relationship between IBD and immune-mediated diseases but also provide novel therapeutic targets for these diseases. [ABSTRACT FROM AUTHOR]

Remote sensing spatiotemporal fusion technology can provide abundant data source information for assimilating crop growth model data, enhancing crop growth monitoring, and providing theoretical support for crop irrigation management. This study focused on the winter wheat planting area in the southeastern part of the Loess Plateau, a typical semi-arid region, specifically the Linfen Basin. The SEBAL and ESTARFM were used to obtain 8 d, 30 m evapotranspiration (ET) for the growth period of winter wheat. Then, based on the 'localization' of the CERES-Wheat model, the fused results were incorporated into the data assimilation process to further determine the optimal assimilation method. The results indicate that (1) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). (2) ESTARFM ET can accurately capture the spatial details of SEBAL ET (R > 0.9, p < 0.01). The calibrated CERES-Wheat ET characteristic curve effectively reflects the ET variation throughout the winter wheat growth period while being consistent with the trend and magnitude of ESTARFM ET variation. (3) The correlation between Ensemble Kalman filter (EnKF) ET and ESTARFM ET (R2 = 0.7119, p < 0.01) was significantly higher than that of Four-Dimensional Variational data assimilation (4DVar) ET (R2 = 0.5142, p < 0.01) and particle filter (PF) ET (R2 = 0.5596, p < 0.01). The results of the study provide theoretical guidance to improve the yield and water use efficiency of winter wheat in the region, which will help promote sustainable agricultural development. [ABSTRACT FROM AUTHOR]

Macroscopic and microscopic mechanical responses, damage behavior and microstructure evolution of NiCoCrFe high entropy alloys (HEAs) during finite deformation under quasi-static loading were investigated by experiments and crystal plasticity finite element method. The microstructure of NiCoCrFe before and after tensile deformation was characterized by electron backscattering diffraction technique(EBSD). The internal state variables of dislocation density and continuum damage factors were introduced into the CPFEM model by modifying the strengthening model and the flow criterion, and the NiCoCrFe related model parameters were determined by combining the stress-strain curves of the tensile test. The results show that the CPFEM model considering the dislocation density and damage can effectively describe the macroscopic and microscopic mechanical responses of NiCoCrFe. CPFEM model can reasonably predict the deformation shape and size of NiCoCrFe necking region, among which, the length of the necking region obtained in the experiment is 7% smaller than the predicted result, and the width of the necking region predicted by CPFEM is 23% larger than the experimental result. The texture evolution predicted by CPFEM model after NiCoCrFe tensile deformation is in good agreement with the results that characterized by EBSD, showing weak (100)//RD and strong (111)//RD fiber texture. In the analysis of the 3D micro- structure damage, the damage predicted by the current CPFEM model appears as an inter-granular damage mechanism at the grain boundary where stress and dislocation density are concentrated, and the damage gradually expands to the grain interior with the increase of deformation.

The 22q11.2 deletion syndrome is caused by non-allelic homologous recombination events during meiosis between low copy repeats (LCR22) termed A, B, C, and D. Most patients have a typical LCR22A-D (AD) deletion of 3 million base pairs (Mb). In this report, we evaluated IQ scores in 1,478 subjects with 22q11.2DS. The mean of full scale IQ, verbal IQ, and performance IQ scores in our cohort were 72.41 (standard deviation-SD of 13.72), 75.91(SD of 14.46), and 73.01(SD of 13.71), respectively. To investigate whether IQ scores are associated with deletion size, we examined individuals with the 3 Mb, AD (n = 1,353) and nested 1.5 Mb, AB (n = 74) deletions, since they comprised the largest subgroups. We found that full scale IQ was decreased by 6.25 points (p = .002), verbal IQ was decreased by 8.17 points (p = .0002) and performance IQ was decreased by 4.03 points (p = .028) in subjects with the AD versus AB deletion. Thus, individuals with the smaller, 1.5 Mb AB deletion have modestly higher IQ scores than those with the larger, 3 Mb AD deletion. Overall, the deletion of genes in the AB region largely explains the observed low IQ in the 22q11.2DS population. However, our results also indicate that haploinsufficiency of genes in the LCR22B-D region (BD) exert an additional negative impact on IQ. Furthermore, we did not find evidence of a confounding effect of severe congenital heart disease on IQ scores in our cohort.

Recurrent, de novo, meiotic non-allelic homologous recombination events between low copy repeats, termed LCR22s, leads to the 22q11.2 deletion syndrome (22q11.2DS; velo-cardio-facial syndrome/DiGeorge syndrome). Although most 22q11.2DS patients have a similar sized 3 million base pair (Mb), LCR22A-D deletion, some have nested LCR22A-B or LCR22A-C deletions. Our goal is to identify additional recurrent 22q11.2 deletions associated with 22q11.2DS, serving as recombination hotspots for meiotic chromosomal rearrangements. Here, using data from Affymetrix 6.0 microarrays on 1680 22q11.2DS subjects, we identified what appeared to be a nested proximal 22q11.2 deletion in 38 (2.3%) of them. Using molecular and haplotype analyses from 14 subjects and their parent(s) with available DNA, we found essentially three types of scenarios to explain this observation. In eight subjects, the proximal breakpoints occurred in a small sized 12 kb LCR distal to LCR22A, referred to LCR22A+, resulting in LCR22A+-B or LCR22A+-D deletions. Six of these eight subjects had a nested 22q11.2 deletion that occurred during meiosis in a parent carrying a benign 0.2 Mb duplication of the LCR22A-LCR22A+ region with a breakpoint in LCR22A+. Another six had a typical de novo LCR22A-D deletion on one allele and inherited the LCR22A-A+ duplication from the other parent thus appearing on microarrays to have a nested deletion. LCR22A+ maps to an evolutionary breakpoint between mice and humans and appears to serve as a local hotspot for chromosome rearrangements on 22q11.2.