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The façades of photogrammetric building mesh models frequently lack detailed semantics, such as windows, doors, and balconies. To address this, we introduce SemanticMesh, a methodology designed to enrich façades with detailed semantics by integrating discrete components seamlessly. The process begins by transforming a localized area of the building surface into a 2D planar mesh using geodesic vectors, based on the component poses. This is followed by the application of 2D constrained triangulation to map the component boundaries onto the parameterized plane, after which the mesh is reconverted into 3D space. To achieve a smooth and aesthetically pleasing integration, we employ a Laplacian mesh deformation technique along the 3D embedding boundary. Our experiments across three distinct datasets – featuring near-planar, non-planar, and noisy surfaces – demonstrate that SemanticMesh provides superior modeling outcomes compared to conventional approaches.

Abstract The economic outcomes of U.S.–China trade conflicts on Chinese enterprises remain ambiguous. Using the staggered export control lists released by the Bureau of Industry and Security (BIS) of the U.S. Department of Commerce in conjunction with data from Chinese publicly listed manufacturing firms, we identify Chinese entities subjected to these export controls. Our Difference-in-Differences (DID) estimates show that export restrictions induced by the entity lists enhance the R&D investment intensity among Chinese firms by 16.58% in the next year. The mechanism is that controls increase firms’ R&D investment through government subsidies, firms’ inventory adjustment and firms’ risk-taking. Heterogeneity tests show that the policy effect is more significant for State-owned enterprises (SOEs), firms with executives with foreign experience. In addition, the R&D promotion of firms supported by China’s industrial policy is more pronounced. Although controls significantly increase R&D inputs, they have little or negative effect on firms’ innovation outputs. Our study provides micro-level causal evidence on the economic impacts of the U.S.–China trade war.

The utilization of ultrasonic guided wave technology for detecting cracks in railway tracks involves analyzing echo signals produced by the interaction of cracks with guided wave modes to achieve precise crack localization, which is extremely important in a real-time railway crack robotic detection system. Addressing the challenge of selecting the optimal detection mode for cracks in various regions of railway tracks, this paper presents a method for optimal crack detection mode selection. This method is based on the sensitivity of guided wave modes to cracks. By examining the frequency dispersion characteristics and mode shapes of guided wave modes, we establish indicators for crack zone energy and crack reflection intensity. Our focus is on the railhead of the railway track, selecting guided wave modes characterized by specific cracks for detection purposes. Experimental findings validate the accuracy of our proposed mode selection method in detecting cracks in railway tracks. This research not only enhances crack detection but also lays the groundwork for exploring advanced detection and localization techniques for cracks in railway tracks.

Modulation of immune microenvironment is critical for inflammatory bowel disease (IBD) intervention. Epigallocatechin gallate (EGCG), as a natural low toxicity product, has shown promise in treating IBD. However, whether and how EGCG regulates the intestinal microenvironment is not fully understood. Here we report that EGCG lessens colitis by orchestrating Th1 polarization and self-amplification in a novel manner that required multilevel-regulated intestinal microecosystem. Mechanistically, EGCG activates GPR43 on IEC to inhibit Th1 polarization dependently of short chain fatty acid (SCFA)-producing gut microbiota. Inhibition of GPR43 activity weakens the protective effects of EGCG on colitis development. Moreover, we confirm that fecal SCFAs and/or intestinal GPR43 are limited in patients with colitis and are correlated with Th1 cell number. Taken together, our study reveals an intestinal microenvironment-dependent immunoregulatory effects of EGCG in treating IBD and provides insight into mechanisms of EGCG-based novel immunotherapeutic strategies for IBD.

Boiling is a high-performance heat dissipation process that is central to electronics cooling and power generation. However, there exists a practical limit of boiling heat transfer known as the critical heat flux (CHF), beyond which significant performance degradation is observed. Understanding the physical mechanism that triggers CHF is essential to meet the increasing cooling demands driven by power densification and device miniaturization. However, the high dimensionality, stochasticity, and dynamicity of the boiling process have led to strong challenges in the experimental characterization and modeling of boiling CHF. As such, high-frame rate, high-resolution, multi-physics boiling datasets are critical to advancing the fundamental understanding of boiling heat transfer. To this end, this paper presents a multimodal boiling dataset consisting of synchronized thermal, acoustic, and optical signals collected from five different heater surfaces under two distinct heat load conditions. With its high sampling frequency, diverse signal types, large data volume, and detailed recorded information, this dataset provides valuable ''data blood'' for the field of thermal crisis monitoring. This dataset will not only promote fundamental research on bubble dynamics during boiling but also support the implementation of advanced monitoring technologies in industrial applications such as power electronics, motors, data centers, and power plants.

In Beyond the Fifth Generation (B5G) heterogeneous edge networks, numerous users are multiplexed on a channel or served on the same frequency resource block, in which case the transmitter applies coding and the receiver uses interference cancellation. Unfortunately, uncoordinated radio resource allocation can reduce system throughput and lead to user inequity, for this reason, in this paper, channel allocation and power allocation problems are formulated to maximize the system sum rate and minimum user achievable rate. Since the construction model is non-convex and the response variables are high-dimensional, a distributed Deep Reinforcement Learning (DRL) framework called distributed Proximal Policy Optimization (PPO) is proposed to allocate or assign resources. Specifically, several simulated agents are trained in a heterogeneous environment to find robust behaviors that perform well in channel assignment and power allocation. Moreover, agents in the collection stage slow down, which hinders the learning of other agents. Therefore, a preemption strategy is further proposed in this paper to optimize the distributed PPO, form DP-PPO and successfully mitigate the straggler problem. The experimental results show that our mechanism named DP-PPO improves the performance over other DRL methods.

Reconstruction and expansion, as well as asset management, of highways necessitate the development of a current and highly precise 3D pavement model. Current inverse modeling methods with point clouds are laborious, time-consuming, and limited in precision. This article introduces an alternative framework for parametric inverse procedural modeling of highway pavement with standardized alignments seamlessly integrated with off-the-shelf modeling software. It comprises three key steps. (1) Extraction of highway pavement boundaries and lane markings: Initially, we combine grid-based and model-driven methods, followed by line structure-based clustering, to accurately generate road centerlines and layouts. (2) Road centerline generation: The centerline, derived from lane markings, informs highway alignments and parameters based on geometric characteristics such as curvature and slope. We utilize cost functions to facilitate this process. (3) Novel inverse procedural assembly: This innovative step integrates off-the-shelf modeling software. This approach involves extracting vector lines from point clouds and applying constraints at pivotal points on highway pavement cross-sections. Our focus is on refined component-level modeling, allowing for the assembly of diverse highway elements. This method significantly reduces human intervention and achieves high precision. In tests on two highway datasets from Sichuan Province, China, our method achieved excellent results. It attained an average correctness of 98.63% and completeness of 99.66% within a 10 cm error margin. A comparison with the intersection point method indicated minimal errors, with maximum values below 1.2%. The resultant 3D highway pavement model is modular and highly accurate at the centimeter level.

ABSTRACT Autism spectrum disorder (ASD) is a heterogeneous neurodevelopmental condition characterized by social communication deficiencies and stereotypic behaviors influenced by hereditary and/or environmental risk factors. There are currently no approved medications for treating the core symptoms of ASD. Human fecal microbiota transplantation (FMT) has emerged as a potential intervention to improve autistic symptoms, but the underlying mechanisms are not fully understood. In this study, we evaluated the effects of human-derived FMT on behavioral and multi-omics profiles of the BTBR mice, an established model for ASD. FMT effectively alleviated the social deficits in the BTBR mice and normalized their distinct plasma metabolic profile, notably reducing the elevated long-chain acylcarnitines. Integrative analysis linked these phenotypic changes to specific Bacteroides species and vitamin B6 metabolism. Indeed, vitamin B6 supplementation improved the social behaviors in BTBR mice. Collectively, these findings shed new light on the interplay between FMT and vitamin B6 metabolism and revealed a potential mechanism underlying the therapeutic role of FMT in ASD.IMPORTANCEAccumulating evidence supports the beneficial effects of human fecal microbiota transplantation (FMT) on symptoms associated with autism spectrum disorder (ASD). However, the precise mechanism by which FMT induces a shift in the microbiota and leads to symptom improvement remains incompletely understood. This study integrated data from colon-content metagenomics, colon-content metabolomics, and plasma metabolomics to investigate the effects of FMT treatment on the BTBR mouse model for ASD. The analysis linked the amelioration of social deficits following FMT treatment to the restoration of mitochondrial function and the modulation of vitamin B6 metabolism. Bacterial species and compounds with beneficial roles in vitamin B6 metabolism and mitochondrial function may further contribute to improving FMT products and designing novel therapies for ASD treatment.

Improving the heat resistance of bio-based and biodegradable polyesters is of great significance to extend their applications. Herein, N,N’-trans-1,4-cyclohexane-bis(pyrrolidone-4-methyl carboxylate) (T-CBPMC) was prepared through efficient Michael-addition reaction between dimethyl itaconate and trans-1,4-cyclohexanediamine. The obtained T-CBPMC was copolymerized into aliphatic poly(butylene succinate) (PBS), and a series of PBSPs copolymers with T-CBPMC (BP) molar percentages between 41−80 mol % and weight average molecular weight (Mw) values ranging between 5.77*104 and 6.67*104 g/mol were prepared. BP units efficiently facilitated the melting temperature (203-251 °C) and isothermal-crystallization rate (t1/2 < 20 s) of PBSPs, endowing the highest heat resistance among commercial biodegradable polyesters, which helps maintain stable in pasteurization, high-temperature disinfection, and microwave environments. Moreover, these copolymers displayed remarkable mechanical, gas barrier properties and degradability. PBSP40-PBSP60 obtained high elastic modulus (335–872 MPa) and tensile strength (24.7–31.5 MPa), and good toughness simultaneously. Multiple-ring structures and large steric hindrance of BP units resulted in superior O2 barrier performance than that of non-degradable PET films. Importantly, the PBSP copolyesters showed obvious degradation in water environments and relatively better enzymatic degradation. It was interesting to find that even with 70 % of the BP units, the PBSP copolyesters still retained hydrolysis ability. The resulting PBSP copolyesters open the way for alternative candidates of biodegradable packaging materials with rapid crystallization, high heat-resistance and gas barrier.

BackgroundRemimazolam is a new ultrashort-acting benzodiazepine for sedation and anesthesia. The effects of remimazolam and the mechanism by which it functions in cancer cells have not been determined. This research aimed to explore the mechanism of remimazolam action in colon cancer treatment, using bioinformatics analysis and in vitro experiments.MethodsCell cycle progression, colony formation, self-renewal capacity, and apoptosis detection were performed in HCT8 cells treated with or without remimazolam. Transcriptome sequencing, Gene Ontology, Kyoto Encyclopedia of Genes and Genome, Protein–Protein Interaction, Gene Set Enrichment Analysis, Western blotting, and qPCR were performed to investigate the mechanism of action of remimazolam in HCT8 colon cancer cells.ResultsRemimazolam promoted proliferation and cell-cycle progression of HCT8 cells. After remimazolam treatment, a total of 1,096 differentially expressed genes (DEGs) were identified: 673 genes were downregulated, and 423 genes were upregulated. The DEGs were enriched mainly in “DNA replication“, “cell cycle”, and “G1/S transition” related pathways. There were 15 DEGs verified by qPCR, and representative biomarkers were detected by Western Bloting. The remimazolam-mediated promotion of cell proliferation and cell cycle was reversed by G1T28, a CDK4/6 inhibitor.ConclusionRemimazolam promoted cell-cycle progression and proliferation in HCT8 colon cancer cells, indicating that the long-term use of remimazolam has potential adverse effects in the anesthesia of patients with colon cancer.

In the United States, coronavirus disease 2019 (COVID-19) cases have consistently been linked to the prevailing variant XBB.1.5 of SARS-CoV-2 since late 2022. A system has been developed for producing and infecting cells with a pseudovirus (PsV) of SARS-CoV-2 to investigate the infection in a Biosafety Level 2 (BSL-2) laboratory. This system utilizes a lentiviral vector carrying ZsGreen1 and Firefly luciferase (Fluc) dual reporter genes, facilitating the analysis of experimental results. In addition, we have created a panel of PsV variants that depict both previous and presently circulating mutations found in circulating SARS-CoV-2 strains. A series of PsVs includes the prototype SARS-CoV-2, Delta B.1.617.2, BA.5, XBB.1, and XBB.1.5. To facilitate the study of infections caused by different variants of SARS-CoV-2 PsV, we have developed a HEK-293T cell line expressing mCherry and human angiotensin converting enzyme 2 (ACE2). To validate whether different SARS-CoV-2 PsV variants can be used for neutralization assays, we employed serum from rats immunized with the PF-D-Trimer protein vaccine to investigate its inhibitory effect on the infectivity of various SARS-CoV-2 PsV variants. According to our observations, the XBB variant, particularly XBB.1.5, exhibits stronger immune evasion capabilities than the prototype SARS-CoV-2, Delta B.1.617.2, and BA.5 PsV variants. Hence, utilizing the neutralization test, this study has the capability to forecast the effectiveness in preventing future SARS-CoV-2 variants infections.

Abstract High‐performance organic neuromorphic devices with miniaturized device size and computing capability are essential elements for developing brain‐inspired humanoid intelligence technique. However, due to the structural inhomogeneity of most organic materials, downscaling of such devices to nanoscale and their high‐density integration into compact matrices with reliable device performance remain challenging at the moment. Herein, based on the design of a semicrystalline polymer PBFCL10 with ordered structure to regulate dense and uniform formation of conductive nanofilaments, we realize an organic synapse with the smallest device dimension of 50 nm and highest integration size of 1 Kb reported thus far. The as‐fabricated PBFCL10 synapses can switch between 32 conductance states linearly with a high cycle‐to‐cycle uniformity of 98.89% and device‐to‐device uniformity of 99.71%, which are the best results of organic devices. A mixed-signal neuromorphic hardware system based on the organic neuromatrix and FPGA controller is implemented to execute spiking‐plasticity‐related algorithm for decision-making tasks.

Abstract The significance of subducted organic carbon for the deep carbon cycle has been demonstrated by the presence of ultra-deep diamonds and arc emissions. However, there is no convincing evidence that organic carbon can be subducted to subarc depths. This study provides the evidence for deep subduction of sedimentary organic carbon to mantle depths through petrological observation, Raman and isotopic analyses of ultrahigh-pressure (UHP) coesite- and graphite-bearing pelitic schists. The analyzed graphite shows light δ13C values (−24.7 to −22.5‰), indicating a sedimentary organic carbon source. Petrological characteristics of graphite co-existing with coesite in garnet and P–T calculations suggest that organic graphite was subducted to the subarc depths exceeding 90 km. This research represents the petrological evidence for the deep subduction of organic carbon to mantle depths and may provide insight into the origin of diamonds with light δ13C values and contribute as key evidence for the Lomagundi event.

To address the challenges posed by the complex shapes of hollow parts, this study examined the ultrasonic-assisted granular medium hydroforming (UGMF) process for tubular components. The dynamics of the deformation behavior and deformation control during 6063-T5 aluminum alloy tube free forming by UGMF were studied via simulations and experiments. Based on the ABAQUS software platform, a coupled method based on finite element (FE) simulation analysis and discrete element (DE) analysis for the UGMF free forming process was used. The results showed that ultrasonic vibration (UV) could reduce the forming force required for expansion and promote the flow of material at the end to the forming area as well as inhibit the decrease in the wall thickness. The accuracy of the FE-DE coupled simulation model and a parabolic geometric model was verified by testing. The results found that UV enhances material flow, decreases the forming force needed, and minimizes damage to the granular surface.

Abstract Developing efficient metal‐nitrogen‐carbon (M‐N‐C) single‐atom catalysts for oxygen reduction reaction (ORR) is significant for the widespread implementation of Zn‐air batteries, while the synergic design of the matrix microstructure and coordination environment of metal centers remains challenges. Herein, a novel salt effect‐induced strategy is proposed to engineer N and P coordinated atomically dispersed Fe atoms with extra‐axial Cl on interlinked porous carbon nanosheets, achieving a superior single‐atom Fe catalyst (denoted as Fe‐NP‐Cl‐C) for ORR and Zn‐air batteries. The hierarchical porous nanosheet architecture can provide rapid mass/electron transfer channels and facilitate the exposure of active sites. Experiments and density functional theory (DFT) calculations reveal the distinctive Fe‐N2P2‐Cl active sites afford significantly reduced energy barriers and promoted reaction kinetics for ORR. Consequently, the Fe‐NP‐Cl‐C catalyst exhibits distinguished ORR performance with a half‐wave potential (E1/2) of 0.92 V and excellent stability. Remarkably, the assembled Zn‐air battery based on Fe‐NP‐Cl‐C delivers an extremely high peak power density of 260 mW cm−2 and a large specific capacity of 812 mA h g−1, outperforming the commercial Pt/C and most reported congeneric catalysts. This study offers a new perspective on structural optimization and coordination engineering of single‐atom catalysts for efficient oxygen electrocatalysis and energy conversion devices.

Tibetan antelopes (Pantholops hodgsonii) migrate great distances to specific delivery and calving areas. In the current study, we investigated calving site selection and vigilance behaviour during delivery and nursing in migratory female Tibetan antelopes at Zonag Lake. According to observations and analysis, the females were distributed south of Zonag Lake, where vegetation was abundant. We determined their dates of migration (crossing the Qinghai-Tibet Highway observation site), showing a shift of one month during the period from June in 2008 to May 2021. Results also showed that 81.4% of females expressed high vigilance behaviour during calving and nursing compared to those without calves (7.1%). From delivery until calf standing, females were highly vigilant and spent considerable time scanning, with 96% of females showing vigilance behaviour. Females with calves (average 9.94 ± 0.62 s) spent more time on vigilance behaviour than females without calves (average 6.25 ± 1.38 s). Females with newborns spent the greatest amount of time being vigilant (average 51.63 ± 4.24 s). These results not only identify basic Tibetan antelope calving behaviour, but also provide scientific analysis and evidence for further ethological research on female Tibetan antelopes.

Abstract Angiosperms became the dominant plant group in early to middle Cretaceous terrestrial ecosystems, coincident with the timing of the earliest pulse of bird diversification. While living birds and angiosperms exhibit strong interactions across pollination/nectivory, seed dispersal/frugivory, and folivory, documentation of the evolutionary origins and construction of that ecological complexity remains scarce in the Mesozoic. Through the first study of preserved in situ dietary derived phytoliths in a nearly complete skeleton of the early diverging avialan clade Jeholornithidae, we provide direct dietary evidence that Jeholornis consumed leaves likely from the magnoliid angiosperm clade, and these results lend further support for early ecological connections among the earliest birds and angiosperms. The broad diet of the early diverging avialan Jeholornis including at least fruits and leaves marks a clear transition in the early evolution of birds in the establishment of an arboreal (angiosperm) herbivore niche in the Early Cretaceous occupied largely by birds today. Morphometric reanalysis of the lower jaw of Jeholornis further supports a generalized morphology shared with other herbivorous birds, including an extant avian folivore, the hoatzin.

Temperature rise of the hub motor in distributed drive electric vehicles (DDEVs) under long-time and overload operating conditions brings parameter drift and degrades the performance of the motor. A novel online parameter identification method based on improved teaching-learning-based optimization (ITLBO) is proposed to estimate the stator resistance, ��-axis inductance, ��-axis inductance, and flux linkage of the hub motor with respect to temperature rise. The effect of temperature rise on the stator resistance, ��-axis inductance, ��-axis inductance, and magnetic flux linkage is analysed. The hub motor parameters are identified offline. The proposed ITLBO algorithm is introduced to estimate the parameters online. The Gaussian perturbation function is employed to optimize the TLBO algorithm and improve the identification speed and accuracy. The mechanisms of group learning and low-ranking elimination are established. After that, the proposed ITLBO algorithm for parameter identification is employed to identify the hub motor parameters online on the test bench. Compared with other parameter identification algorithms, both simulation and experimental results show the proposed ITLBO algorithm has rapid convergence and a higher convergence precision, by which the robustness of the algorithm is effectively verified. Keywords: parameters identification, teaching–learning-based optimization, hub motor, temperature rise.

Abstract Background Immunotherapy is currently applied in the first-line treatment regimens for numerous advanced cancers, especially advanced lung cancer. Immune-related adverse events (irAEs) resulting from immunotherapy can vary in severity and cause a substantial symptom burden to patients. However, there are limited data on symptom burden in patients with advanced lung cancer following immunotherapy. To address this deficit, this study aims to provide insight into the symptom burden and severity through patient-reported outcome measurements and conduct an analysis of temporal trends and clinical consequences of symptom burden in patients with advanced lung cancer receiving combination immunotherapy. Methods We will prospectively recruit 168 eligible patients from 14 hospitals in China. Eligible patients will be aged ≥ 18 years, pathologically diagnosed with locally advanced or stage IV primary lung cancer without surgical indications, and agreed to receive immunotherapy in combination with other therapies. The primary outcome of this study is the symptom burden of patients during the immunotherapy course. Longitudinal symptom data will be collected using the MD Anderson Symptom Inventory–Lung Cancer module (MDASI-LC) and the symptomatic irAEs scale at baseline (once before treatment) and weekly after treatment, until 1 month after the last treatment cycle has been completed. The trajectory of symptom burden following combination immunotherapy will be depicted, and by linking it to clinical outcomes (the secondary outcome and exploratory outcome of this study), the consequence of symptom burden in patients with advanced lung cancer receiving combination immunotherapy will be examined further. Discussion This study intends to establish longitudinal symptom trajectories in patients with lung cancer receiving immunotherapy, and explore its association with clinical outcomes. These findings may serve as an important reference for clinicians in the symptomatic management of patients with lung cancer receiving immunotherapy. Trial registration number ChiCTR2200061540. Registered on June 28, 2022.

Planting under plastic-film mulches is widely used in spring maize production in arid-cold regions for water conservation and warming the soil. To ameliorate the associated issues such as plastic-film residues and additional labor during the “seedling release” in spring maize production, we have developed a plastic-film-side seeding (PSS) technology with the supporting machinery. In the semi-arid regions of Northwest China, a 7-year trial demonstrated that PSS increased plant number per hectare by 6 547 and maize yield by 1 686 kg ha–1 compared with the traditional method of seeding under plastic-film mulch (PM). Two-year experiments were conducted in two semi-arid regions to further understand the effects of PSS on three important aspects of production: (i) the moisture and temperature of soil, (ii) maize development, yield output, and water use efficiency (WUE), and (iii) the revenue and plastic-film residuals in comparison with that of flat planting (CK) and PM. Continuous monitoring of the soil status demonstrated that, compared with CK, the PSS treatment significantly increased the temperature and moisture of the 0–20 cm soil in the seeding row at the early stage of maize development, and it also promoted grain yield (at 884–1 089 kg ha–1) and WUE, achieving a similar effect as the PM treatment. Economically, the labor inputs of PSS were equal to CK, whereas the PM cost an additional 960 CNY ha–1 in labor for releasing the seedlings from below the film. Overall, the PSS system increased profits by 5.83% (547 CNY ha–1 yr–1) and 8.16% (748 CNY ha–1 yr–1) compared with CK and PM, respectively. Environmentally, PSS achieved a residual film recovery rate of nearly 100% and eliminated 96 to 130 kg ha–1 of residual plastic-film in PM in 3–5 years of maize production. Collectively, these results show that PSS is an eco-friendly technique for improving yield stability and incomes for the sustainable production of maize in semi-arid regions.

Most urban applications necessitate building footprints in the form of concise vector graphics with sharp boundaries rather than pixel-wise raster images. This need contrasts with the majority of existing methods, which typically generate over-smoothed footprint polygons. Editing these automatically produced polygons can be inefficient, if not more time-consuming than manual digitization. This paper introduces a semi-automatic approach for building footprint extraction through semantically-sensitive superpixels and neural graph networks. We first learn to generate superpixels that are not only boundary-preserving but also semantically-sensitive. The superpixels respond exclusively to building boundaries rather than other natural objects. These intermediate superpixel representations can be naturally considered as nodes within a graph. Consequently, graph neural networks are employed to model the global interactions among all superpixels and enhance the representativeness of node features for building segmentation, which also enables efficient editing of segmentation results. Classical approaches are utilized to extract and regularize boundaries for the vectorized building footprints. We efficiently accomplish accurate segmentation outcomes by utilizing minimal clicks and straightforward strokes, eliminating the necessity for editing polygon vertices. A significant improvement of 8% in AP50 was observed in vector graphics evaluation, surpassing established techniques. Additionally, we have devised an optimized and sophisticated pipeline for interactive editing, poised to further augment the overall quality of the results. The code for training the superpixel and graph networks will be made publicly available upon publication.11 https://vrlab.org.cn/~hanhu/projects/spgraph/.

Point-based and voxel-based methods can learn the local features of point clouds. However, although point-based methods are geometrically precise, the discrete nature of point clouds negatively affects feature learning performance. Moreover, although voxel-based methods can exploit the learning power of convolutional neural networks, their resolution and detail extraction may be inadequate. Therefore, in this study, point-based and voxel-based methods are combined to enhance localization precision and matching distinctiveness. The core procedure is embodied in V2PNet, an innovative fused neural network that we design to perform voxel-to-pixel propagation and fusion, which seamlessly integrates the two encoder–decoder branches. Experiments are conducted on indoor and outdoor benchmark datasets with different platforms and sensors, i.e., the 3DMatch and Karlsruhe Institute of Technology and Toyota Technological Institute datasets, with the registration recall of 89.4% and the success rate of 99.86%, respectively. Qualitative and quantitative evaluations demonstrate that V2PNet has shown improvements in semantic awareness, geometric structure discernment, and other performance metrics.

BackgroundHepatocellular carcinoma (HCC) represents a prominent gastrointestinal malignancy with a grim clinical outlook. In this regard, the discovery of novel early biomarkers holds substantial promise for ameliorating HCC-associated mortality. Efferocytosis, a vital immunological process, assumes a central position in the elimination of apoptotic cells. However, comprehensive investigations exploring the role of efferocytosis-related genes (EFRGs) in HCC are sparse, and their regulatory influence on HCC immunotherapy and targeted drug interventions remain poorly understood.MethodsRNA sequencing data and clinical characteristics of HCC patients were acquired from the TCGA database. To identify prognostically significant genes in HCC, we performed the limma package and conducted univariate Cox regression analysis. Subsequently, machine learning algorithms were employed to identify hub genes. To assess the immunological landscape of different HCC subtypes, we employed the CIBERSORT algorithm. Furthermore, single-cell RNA sequencing (scRNA-seq) was utilized to investigate the expression levels of ERFGs in immune cells and to explore intercellular communication within HCC tissues. The migratory capacity of HCC cells was evaluated using CCK-8 assays, while drug sensitivity prediction reliability was determined through wound-healing assays.ResultsWe have successfully identified a set of nine genes, termed EFRGs, that hold significant potential for the establishment of a hepatocellular carcinoma-specific prognostic model. Furthermore, leveraging the individual risk scores derived from this model, we were able to stratify patients into two distinct risk groups, unveiling notable disparities in terms of immune infiltration patterns and response to immunotherapy. Notably, the model’s capacity to accurately predict drug responses was substantiated through comprehensive experimental investigations, encompassing wound-healing assay, and CCK8 experiments conducted on the HepG2 and Huh7 cell lines.ConclusionsWe constructed an EFRGs model that serves as valuable tools for prognostic assessment and decision-making support in the context of immunotherapy and chemotherapy.

Nanoscale hierarchically porous metal–organic frameworks (NHP-MOFs) have received unprecedented attention in many fields owing to their integration of the strengths of nanoscale size (

Abstract Background Exosomes derived from bone marrow mesenchymal stem cells (BMSC-exos) have been shown triggering osteogenic differentiation and mineralization of MSCs, but exosomes administered via bolus injections are rapidly sequestered and cleared. Therefore, we considered the implant as a new organ of patient’s body and expected to find a method to treat implant with BMSC-exos in vivo directly. Methods A fusion peptide (PEP), as a drug delivery system (DDS) which contained a titanium-binding peptide (TBP) possessing the ability to selectively bind to the titanium surface and another peptide CP05 being able to capture exosomes expertly, is constructed to modify the titanium surface. Results Both in vitro and in vivo experiments prove PEP retains the ability to bind titanium and exosome simultaneously, and the DDS gain the ability to target exosomes to titanium implants surface following enhancing osseointegration post-implantation. Moreover, the DDS constructed by exosomes of diverse origins shows the similar combination rate and efficiency of therapy. Conclusion This drug delivery system demonstrates the concept that EXO-PEP system can offer an accurate and efficient therapy for treating implants with long-term effect.

Abstract The human gut microbiome has been extensively explored, while the archaeal viruses remain largely unknown. Here, we present a comprehensive analysis of the archaeal viruses from the human gut metagenomes and the existing virus collections using the CRISPR spacer and viral signature-based approach. This results in 1279 viral species, of which, 95.2% infect Methanobrevibacteria_A, 56.5% shared high identity (>95%) with the archaeal proviruses, 37.2% have a host range across archaeal species, and 55.7% are highly prevalent in the human population (>1%). A methanogenic archaeal virus-specific gene for pseudomurein endoisopeptidase (PeiW) frequently occurs in the viral sequences (n = 150). Analysis of 33 Caudoviricetes viruses with a complete genome often discovers the genes (integrase, n = 29; mazE, n = 10) regulating the viral lysogenic-lytic cycle, implying the dominance of temperate viruses in the archaeal virome. Together, our work uncovers the unexplored diversity of archaeal viruses, revealing the novel facet of the human gut microbiome.

Abstract Due to the urgent demand for more anti-cancer methods, the new applications of metal ions in cancer have attracted increasing attention. Especially the three kinds of the new mode of cell death, including ferroptosis, calcicoptosis, and cuproptosis, are of great concern. Meanwhile, many metal ions have been found to induce cell death through different approaches, such as interfering with osmotic pressure, triggering biocatalysis, activating immune pathways, and generating the prooxidant effect. Therefore, varieties of new strategies based on the above approaches have been studied and applied for anti-cancer applications. Moreover, many contrast agents based on metal ions have gradually become the core components of the bioimaging technologies, such as MRI, CT, and fluorescence imaging, which exhibit guiding significance for cancer diagnosis. Besides, the new nano-theranostic platforms based on metal ions have experimentally shown efficient response to endogenous and exogenous stimuli, which realizes simultaneous cancer therapy and diagnosis through a more controlled nano-system. However, most metal-based agents have still been in the early stages, and controlled clinical trials are necessary to confirm or not the current expectations. This article will focus on these new explorations based on metal ions, hoping to provide some theoretical support for more anti-cancer ideas.

Abstract Here, we investigate the precoding schemes for the downlink data transmission of a time‐division duplex cell‐free massive multiple‐input multiple‐output (MIMO) system with channel aging, which arises from the user mobility. Closed‐form spectral efficiency (SE) expressions of the downlink with the normalized conjugate beamforming (NCB), and the full‐pilot zero‐forcing (FZF), are derived, which are used for the analytical system performance evaluation. Then, a novel combinatorial precoding scheme with enhanced system SE performance, which adopts either NCB or FZF according to each user channel aging condition, is proposed. Moreover, a pilot allocation strategy is proposed to alleviate the extra interference brought by the combinatorial precoding scheme. Also, a statistical channel cooperative power control is employed to further improve the performance for all the above precoding schemes. Numerical results show that the proposed precoding scheme can substantially improve the average downlink SE.

To realize the autonomous navigation and automatic target spraying of intelligent plant protect machinery in orchard, in this study, an autonomous navigation and automatic target spraying robot for orchards was developed. Firstly, a single 3D light detection and ranging (LiDAR) was used to collect fruit trees and other information around the robot. The region of interest (ROI) was determined using information on the fruit trees in the orchard (plant spacing, plant height, and row spacing), as well as the fundamental LiDAR parameters. Additionally, it must be ensured that LiDAR was used to detect the canopy information of a whole fruit tree in the ROI. Secondly, the point clouds within the ROI was two-dimension processing to obtain the fruit tree center of mass coordinates. The coordinate was the location of the fruit trees. Based on the location of the fruit trees, the row lines of fruit tree were obtained by random sample consensus (RANSAC) algorithm. The center line (navigation line) of the fruit tree row within ROI was obtained through the fruit tree row lines. The robot was controlled to drive along the center line by the angular velocity signal transmitted from the computer. Next, the ATRS's body speed and position were determined by encoders and the inertial measurement unit (IMU). And the collected fruit tree zoned canopy information was corrected by IMU. The presence or absence of fruit tree zoned canopy was judged by the logical algorithm designed. Finally, the nozzles were controlled to spray or not according to the presence or absence of corresponding zoned canopy. The conclusions were obtained. The maximum lateral deviation of the robot during autonomous navigation was 21.8 cm, and the maximum course deviation angle was 4.02°. Compared with traditional spraying, the automatic target spraying designed in this study reduced pesticide volume, air drift and ground loss by 20.06%, 38.68% and 51.40%, respectively. There was no significant difference between the automatic target spraying and the traditional spraying in terms of the percentage of air drift. In terms of the percentage of ground loss, automatic target spraying had 43% at the bottom of the test fruit trees and 29% and 28% at the middle of the test fruit trees and the left and right neighboring fruit trees. But in traditional spraying, the percentage of ground loss was, in that sequence, 25%, 38%, and 37%. The robot developted can realize autonomous navigation while ensuring the spraying effect, reducing the pesticides volume and loss.

While liquid-liquid interface offers better contact and charge transfer potential than solid-based counterparts, fluidity still poses challenges for their application. Here, authors show that charge transfer exists in aqueous two-phase systems and propose a nanogenerator design based on the immiscible aqueous-aqueous interface.

The effects of different particle sizes of purple sweet potato flour (PSPF) on the structure and quality of noodles and the diffusion kinetics of anthocyanins during cooking were studied. As the particle size of the PSPF decreased (from 269 to 66 μm), the adverse effects of the addition of PSPF on the quality of noodles were reduced. The smaller particle size of PSPF was beneficial for the secondary structure orderliness and the tighter microstructure of PSP noodles. The diffusion of anthocyanins in noodles to the soup during cooking could be fitted well with Fick's second law, and diffusion coefficients were in the range of 8.3248–14.0893 × 10-9 m2/s. The noodles with 15% 66 μm PSPF showed the best cooking properties, the highest sensory score, the highest anthocyanin retention ability and a compact and orderly microstructure. Thus, they could be considered as noodles rich in anthocyanins for commercial application.

Abstract For a “Carbon Neutrality” society, electrochemical energy storage and conversion (EESC) devices are urgently needed to facilitate the smooth utilization of renewable and sustainable energy where the electrode materials and catalysts play a decisive role. However, the efficiency of the current trial‐and‐error research paradigm largely lags behind the imminent demands of EESC requiring increasingly improved performance. The emerged machine learning (ML), a subfield of artificial intelligence, is capable of evaluating and analyzing big data for hidden rules. In this regard, the relationships between the structure and performance of the key materials can be more efficiently revealed, which fundamentally revolutionizes the material research manner of the current EESC devices. In this review, the typical ML algorithms utilized in EESC development are first introduced. Then, focused attention has been paid to multiple aspects of applying ML to reshape the materials research for EESC. In addition to highlighting the emerging prospect, the challenges which are still hindering the further development of this emerging field are also discussed.

Mobile mapping systems, which are equipped with a laser scanner, panoramic camera, and inertial navigation system, can flexibly and efficiently obtain precision point clouds. However, these systems require re-installation for each mission, which causes displacement errors in the instruments, resulting in discrepancies between multiple collections of data. The existing calibration methods usually require additional control targets settled in a managed field survey. Furthermore, the point clouds obtained with mobile mapping systems do not follow the common assumption of rigid transformation. To alleviate the above problems, we propose a flexible in-situ calibration method that only requires data collected in a round-trip survey pattern and conducts a self-calibration of the placement parameters with meticulously designed exact point matching. Specifically, because the accuracy of the existing feature-based registration method is not sufficient to acquire the exact correspondences of point clouds and to prune the outliers in correspondence searching. We propose novel pruning and refining approaches to obtain exact point-to-point correspondences between the round-trip point clouds. From systematic analyses of the coupling between different factors, we then construct a simplified error model that takes into account the a priori rotation- and translation-weighted observations. Finally, to alleviate the correlation effects between different parameters, we decouple the least-squares solver into several stages and obtain robust self-calibration parameters. Surveying experiments show that the flexible calibration method proposed in this paper is both effective and feasible in improving the accuracy of point clouds in overlapping areas, and can reduce the inconsistency of the calibrated point clouds by 37.02%.

Due to the irregularity and complexity of ground and non-ground objects, filtering non-ground data from airborne LiDAR point clouds to create Digital Elevation Models (DEMs) remains a longstanding and unresolved challenge. Recent advancements in deep learning have offered effective solutions for understanding three-dimensional semantic scenes. However, existing studies lack the capability to model global semantic relationships and fail to integrate global and local semantic information effectively, which are crucial for the ground filtering of point cloud data, especially for larger objects. This study focuses on ground filtering challenges in large scenes and introduces an elevation offset-attention (E-OA) module, which considers global semantic features and integrates them into existing network frameworks. The performance of this module has been validated on three classic benchmark models (RandLA-Net, point transformer, and PointMeta-L). It was compared with two traditional filtering methods and the advanced CDFormer model. Additionally, the E-OA module was compared with three state-of-the-art attention frameworks. Experiments were conducted on two distinct data sources. The results show that our proposed E-OA module improves the filtering performance of all three benchmark models across both data sources, with a maximum improvement of 6.15%. The performance of models was enhanced with the E-OA module, consistently exceeding that of traditional methods and all competing attention frameworks. The proposed E-OA module can serve as a plug-and-play component, compatible with existing networks featuring local feature extraction capabilities.

Recent studies have introduced transformer modules into convolutional neural networks (CNNs) to solve the inherent limitations of CNNs in global modeling and have achieved impressive performance. However, some challenges have yet to be addressed: first, networks with simple connections between the CNN and transformer perform poorly in small change areas; second, networks that only use transformer structures are prone to attaining coarse detection results and excessively generalizing feature boundaries. In addition, the methods of fusing the CNN and transformer have the issue of a unilateral flow of feature information and inter-scale communication, leading to a loss of change information across different scales. To mitigate these problems, this study proposes a full-scale connected CNN–Transformer network, which incorporates the Siamese structure, Unet3+, and transformer structure, used for change detection in remote sensing images, namely SUT. A progressive attention module (PAM) is adopted in SUT to deeply integrate the features extracted from both the CNN and the transformer, resulting in improved global modeling, small target detection capacities, and clearer feature boundaries. Furthermore, SUT adopts a full-scale skip connection to realize multi-directional information flow from the encoder to decoder, enhancing the ability to extract multi-scale features. Experimental results demonstrate that the method we designed performs best on the CDD, LEVIR-CD, and WHU-CD datasets with its concise structure. In particular, based on the WHU-CD dataset, SUT upgrades the F1-score by more than 4% and the intersection over union (IOU) by more than 7% compared with the second-best method.

Many cities worldwide have large amounts of industrial vacant land (IVL) due to development and transformation, posing a growing problem. However, the large-scale identification of IVL is hindered by obstacles such as high cost, high variability, and closed-source data. Moreover, it is difficult to distinguish industrial vacant land from operational industrial land based solely upon image features. To address these issues, we propose a method for the large-scale automatic identification of IVL. The framework uses deep learning to train remote-sensing images of potential industrial vacant land to generate a semantic segmentation model and further use population density and surface temperature data to filter model predictions. The feasibility of the proposed methodology was validated through a case study in Tangshan City, Hebei Province, China. The study indicates two major conclusions: (1) The proposed IVL identification framework can efficiently generate industrial vacant land mapping. (2) HRNet exhibits the highest accuracy and strongest robustness after training compared with other semantic segmentation backbone networks, ensuring high-quality performance and stability, as evidenced by a model accuracy of 97.84%. Based on the above advantages, the identification framework provides a reference method for various countries and regions to identify industrial vacant land on a large scale, which is of great significance for advancing the research and transformation of industrial vacant land worldwide.

The selective hydrogenation of 1, 3-butadiene over Pd-Sn/Al2O3 bimetallic catalysts was investigated by heterogeneous parahydrogen-induced polarization (PHIP). A series of Pd1-Snx/Al2O3 bimetallic catalysts with different Pd/Sn ratios were synthesized by incipient wetness co-impregnation method. It was observed that the Pd/Sn ratios of the catalysts had a significant effect on the reaction activity of 1, 3-butadiene and the selectivity to butene. A monotonic decrease of reaction conversion was observed as the Pd/Sn ratio decreased, while the selectivity to butene showed an opposite trend. This can be attributed to the ensemble effect and the ligand effect caused by the increasing tin component content: as the Pd/Sn ratio decreases, less palladium component was exposed on the surface, leading to low activity. The electronic properties of the Pd atoms were altered, which led to a weaker adsorption strength of the semi-hydrogenation product butene and an increased selectivity. Smaller Pd ensembles were exposed on Sn-rich catalysts surface, which favored a higher ratio of pairwise addition process, leading to a stronger PHIP effect. The isomerization measurements by PASADENA (parahydrogen and synthesis allow for dramatically enhanced nuclear alignment) showed that the isomerization process between 1-butene and 2-butene decreased with the increase of Sn content. This can be accounted for by the easier desorption of 1-butene on Sn-rich catalysts.

AIM: To study the protective effect of astragalus-containing serum on cobalt chloride(CoCl2)-induced hypoxia injury of human retinal pigment epithelial cells(ARPE-19), so as to explore whether astragalus can improve diabetic retinopathy(DR)by anti-oxidative stress.METHODS: The ARPE-19 hypoxia model induced by CoCl2 was established and divided into the following 5 groups: normal group(cells were cultured normally without any treatment), hypoxia model group(200μmol/L CoCl2), blank serum group(200μmol/L CoCl2+blank serum), low-dose drug-containing serum group(200μmol/L CoCl2+10% medicated serum)and high-dose drug-containing serum group(200μmol/L CoCl2+20% medicated serum); CCK-8 detects cell viability; Detect the levels of reduced glutathione(GSH)and malondialdehyde(MDA)in the cell supernatant with a kit; ELISA was used to detect the content of hypoxia-inducible factor-1α(HIF-1α)and vascular endothelial growth factor(VEGF)in cell culture medium; Real-time quantitative PCR(qPCR)to detect the mRNA levels of VEGF, HIF-1α and Prolyl hydroxylase-2(PHD-2); The expressions of VEGF, HIF-1α and PHD-2 were detected by Western Blot.RESULTS: Hypoxia model of ARPE-19 can successfully establish by CoCl2 at 200μmol/L. Low-dose and high-dose astragalus-containing serum could inhibit hypoxia-induced ARPE-19 proliferation(P

Primary angle-closure glaucoma(PACG)is the most common type of glaucoma in clinical. It is characterized by progressive and irreversible development with high global incidence and high possibility of blindness. The pathogenesis of PACG is complicated. It is not only associated with the abnormality of the ocular anatomy, such as the small cornea, short eye axis and shallow anterior chamber, but also found that the abnormality of the lens, iris, ciliary body and choroid are related to the pathogenesis of PACG in recent years. Moreover, the occurrence of PACG is also closely related to genetic and psychological stress factors. Our study summarized some literatures and made a brief discussion on the pathogenesis of PACG from the three aspects of anatomical characteristics, genetic factors, and psychological stress. We hope to provide useful theoretical support for the early diagnosis and treatment of glaucoma.

Abstract Background Selectively replicating herpes simplex virus-2 (HSV-2) vector is a promising treatment for cancer therapy. The insertion of multiple transgenes into the viral genome has been performed to improve its oncolytic activity. Methods Herein, we simultaneously constructed five “armed” oncolytic viruses (OVs), designated oHSV2-IL12, -IL15, GM-CSF, -PD1v, and IL7 × CCL19. These OVs delete the ICP34.5 and ICP47 genes with the insertion of transgenes into the deleted ICP34.5 locus. The anti-tumor efficacy in vivo was tested in the syngeneic 4T1 and CT26 tumor-bearing mice model. Results The OVs showed comparable oncolytic capability in vitro. The combination therapy of oHSV2-IL12, -IL15, GM-CSF, -PD1v, and IL7 × CCL19 exhibited the highest tumor inhibition efficacy compared with the treatment of single OV or two OVs combination. Conclusions The OVs armed with different transgenes combination therapy also named 5-valent oHSV2 (also called cocktail therapy) might be an effective therapeutic strategy for solid tumors.

The Longipterygidae are a unique clade among the enantiornithines in that they exhibit elongate rostra (≥60% total skull length) with dentition restricted to the distal tip of the rostrum, and pedal morphologies suited for an arboreal lifestyle (as in other enantiornithines). This suite of features has made interpretations of this group’s diet and ecology difficult to determine due to the lack of analogous taxa that exhibit similar morphologies together. Many extant bird groups exhibit rostral elongation, which is associated with several disparate ecologies and diets (e.g., aerial insectivory, piscivory, terrestrial carnivory). Thus, the presence of rostral elongation in the Longipterygidae only somewhat refines trophic predictions of this clade. Anatomical morphologies do not function singularly but as part of a whole and thus, any dietary or ecological hypothesis regarding this clade must also consider other features such as their unique dentition. The only extant group of dentulous volant tetrapods are the chiropterans, in which tooth morphology and enamel thickness vary depending upon food preference. Drawing inferences from both avian bill proportions and variations in the dental morphology of extinct and extant taxa, we provide quantitative data to support the hypothesis that the Longipterygidae were animalivorous, with greater support for insectivory.

The death of Sertoli cells (SCs) under condition of heat stress (HS) affects spermatogenesis and is associated with impaired function of the blood-testis barrier (BTB). The fatty acid arachidonic acid (AA) is essential for the maintenance of cellular function. However, excessive release of AA during HS may adversely affect the reproductive function. The molecular mechanisms through which AA modulates the BTB in SCs are unclear. In this study, we found that 100 µM AA damaged testicular morphology and accelerated SC apoptosis during HS, reducing the stability of tight junction proteins (TJPs), shown by measurement of the levels of Claudin 11, 5, Occludin, and trans-epithelial electrical resistance (TEER). It was also found that AA adversely affected TJPs by increasing the accumulation of reactive oxygen species (ROS) and malondialdehyde (MDA), activating p38 mitogen-activated protein kinases (P38 MAPK) and reducing mitochondria DNA (mtDNA) and the expression of mitochondrial complexes I and III. In contrast, pretreatment with SB203508 (a P38 MAPK inhibitor), Rotenone (an inhibitor of complex I) and Antimycin A1 (an inhibitor of complex III) reversed TJPs degradation induced by AA. Interestingly, pretreatment of cells with 10 µM Baicalein, a 12/15 lipoxygenase (12/15-LOX) -dependent inhibitor of AA production, protected against AA-induced TJPs degradation, restored mitochondrial function, and reduced apoptosis. These results suggested an intriguing link between the induction of TJPs degradation induced by AA overload and mitochondrial antioxidant function during HS, which was found to be regulated by the mitochondrial complex-ROS-P38 MAPK axis.

Many methods have been proposed to extract line segment (LS) correspondences for images with viewpoint variations. However, the matching performance on images with viewpoint variations is still limited due to the influence of discontinuous parallax and poor textures on images. Existing line segment matching (LSM) methods rarely consider the coexistence of these two situations. This study attempts to address this problem by proposing an adaptive feature region (FR)-based LSM method based on the observation that different FRs are suitable for different image scenes under image viewpoint change. In the proposed method, LSs are paired, and two types of FRs, symmetric FR centered on the line-pair intersection and asymmetric FR with the intersection as a vertex, are constructed for every line pair. The asymmetric FR of a line pair is a subregion of its symmetric FR. And the complement region of asymmetric FR in symmetric FR is called complement FR. To choose a suitable FR for the matching of each line pair adaptively, the asymmetric FR-based feature descriptor similarity and epipolar line-based constraints are combined to determine candidate line pair matches firstly. Then, the candidate matches with similar complement FR-based feature descriptors are considered initial matches and used to construct a topological constraint to check other candidates. Finally, we estimate an adaptive influence region-based local homography for each matched line pair to constrain the matching of unmatched individual LSs. The experimental results on the test images with viewpoint changes show that our approach outperforms the state-of-the-art methods in terms of Recall, matching precision (MP), and F-Measure (reflecting the overall matching performance of Recall and MP). Especially, it improves the average Recall and average F-Measure of the best method among the comparison methods by 45.20% and 25.83%, respectively.

In recent years, an increasing amount of interest has been dedicated to the synthesis and application of ZIF-67-based materials due to their exceptionally high surface area, tunable porosity, and excellent thermal and chemical stabilities. This review summarizes the latest strategies of synthesizing ZIF-67-based materials by exploring the prominent examples. Then, the recent progress in the applications of ZIF-67-based materials in heterogeneous catalysis, including catalysis of the redox reactions, addition reactions, esterification reactions, Knoevenagel condensations, and hydrogenation–dehydrogenation reactions, has been elaborately discussed. Finally, we end this work by shedding some light on the large-scale industrial production of ZIF-67-based materials and their applications in the future.

Biodiversity monitoring is an important means by which to evaluate management effectiveness and develop sound conservation plans. In this study, 52 cameras were installed in the study area of Tianzhushan to assess wildlife diversity and elevational patterns from 2018 to 2019. In total, 9 541 independent photos were collected within 26 565 camera-days. We analysed the relative abundance index (RAI), activity patterns and seasonal activity rhythms of the five most abundant species at 200-m elevational intervals throughout the study area (elevation range 1 400–2 000 m a.s.l.). Based on RAI results, the activity patterns of the five most abundant species (i.e. Sus scrofa, Naemorhedus griseus, Muntiacus reevesi, Arctonyx collaris and Capricornis milneedwardsii) were analysed. Amongst the detected wild mammals, Sus scrofa had the highest RAI value of 9.91, while the occurrence of Tamiops swinhoei had the lowest RAI at 0.004. In addition, there were significant differences in species activity during the daytime and night-time. RAI of the ungulate community peaked at the 1 600–1 800 m a.s.l. elevational range, thus showing a mid-elevational peak pattern. Differences in vertical distribution patterns were detected for the five most abundant species. Mainland serows and wild boars preferred mid-elevation habitats (1 600–1 800 m a.s.l.), long-tailed gorals preferred mid- and high altitudes (1 600–2 000 m a.s.l.) elevation habitats and Chinese muntjacs and hog badgers preferred low elevation habitats (1 400–1 600 m a.s.l.). Taken together, this is the first study to reveal the five dominant species activity patterns in Tianzhushan, which is of importance for wildlife conservation.

Image dense matching plays a crucial role in the reconstruction of three-dimensional models of buildings. However, large variations in target heights and serious occlusion lead to obvious mismatches in areas with discontinuous depths, such as building edges. To solve this problem, the present study mines the geometric and semantic information of line segments to produce a constraint for the dense matching process. First, a disparity consistency-based line segment matching method is proposed. This method correctly matches line segments on building structures in discontinuous areas based on the assumption that, within the corresponding local areas formed by two corresponding line pairs, the disparity obtained by the coarse-level matching of the hierarchical dense matching is similar to that derived from the local homography estimated from the corresponding line pairs. Second, an adaptive guide parameter is designed to constrain the cost propagation between pixels in the neighborhood of line segments. This improves the rationality of cost aggregation paths in discontinuous areas, thereby enhancing the matching accuracy near building edges. Experimental results using satellite and aerial images show that the proposed method efficiently obtains reliable line segment matches at building edges with a matching precision exceeding 97%. Under the constraint of the matched line segments, the proposed dense matching method generates building edges that are visually clearer, and achieves higher accuracy around edges, than without the line segment constraint.

This work assesses the flexural performance of prestressed concrete beams with external carbon fiber-reinforced polymer (CFRP) tendons, focusing on tendon-related variables. A finite element analysis (FEA) method is verified. A numerical parametric analysis of prestressed concrete beams with external CFRP tendons is carried out. Four tendon-related variables are considered, namely, the area, initial prestress, depth and elastic modulus of tendons. The analysis shows that flexural ductility decreases as the tendon area, initial prestress or elastic modulus increases but is insensitive to the tendon depth. The ultimate tendon stress increment (Δσp) is influenced by all of the four variables investigated. JGJ 92-2016 (Chinese technical specification for concrete structures prestressed with unbonded tendons) significantly underestimates Δσp and, hence, is over-conservative for the strength design of these beams. An equation is proposed for calculating Δσp, taking into account all four variables investigated. An analytical model is then developed to estimate the flexural strength (Mu) of prestressed concrete beams with external CFRP tendons. The proposed analytical model shows good agreement with FEA, i.e., the mean discrepancy for Δσp is 0.9% with a standard deviation of 11.1%; and the mean discrepancy for Mu is −1.6% with a standard deviation of 2.1%.

The OsPIN1 paralogous genes (OsPIN1a-1d) are important for root and panicle development in rice (Oryza sativa L.). However, the specific role of OsPIN1 paralogous genes is still not clear. To understand the specific roles of PIN1 paralogs in rice, we generated pin1 triple and quadruple mutants by crossing the pin1a pin1b and pin1c pin1d double mutants which we previously created. Compared with the 7-day-old wild type, the pin1a pin1c pin1d and pin1b pin1c pin1d triple mutants showed no obvious phenotype variation except that the pin1a pin1c pin1d triple mutant had shorter primary root and shoot. The pin1a pin1b pin1c and pin1a pin1b pin1d triple mutants exhibited a series of developmental abnormalities, including shorter primary roots, longer root hairs, fewer crown roots and lateral roots, shorter and curved shoots. Furthermore, the pin1a pin1b pin1c pin1d quadruple mutant displayed more severe phenotypic defects which was lethal. In addition, the expression levels of some hormone signal transduction and crown root development related genes, such as OsIAAs, OsARFs, OsRRs, and OsCRLs, were significantly altered in the stem base of all examined pin1 multiple mutants. Taken together, our results demonstrated that the four OsPIN1 paralogous genes function redundantly in regulating rice growth and development.

Reproduction involves the investment of resources into offspring. Although variation in reproductive effort often affects the number of offspring, adjustments of propagule size are also found in numerous species, including the Western honey bee, Apis mellifera. However, the proximate causes of these adjustments are insufficiently understood, especially in oviparous species with complex social organization in which adaptive evolution is shaped by kin selection. Here, we show in a series of experiments that queens predictably and reversibly increase egg size in small colonies and decrease egg size in large colonies, while their ovary size changes in the opposite direction. Additional results suggest that these effects cannot be solely explained by egg-laying rate and are due to the queens’ perception of colony size. Egg-size plasticity is associated with quantitative changes of 290 ovarian proteins, most of which relate to energy metabolism, protein transport, and cytoskeleton. Based on functional and network analyses, we further study the small GTPase Rho1 as a candidate regulator of egg size. Spatio-temporal expression analysis via RNAscope and qPCR supports an important role of Rho1 in egg-size determination, and subsequent RNAi-mediated gene knockdown confirmed that Rho1 has a major effect on egg size in honey bees. These results elucidate how the social environment of the honey bee colony may be translated into a specific cellular process to adjust maternal investment into eggs. It remains to be studied how widespread this mechanism is and whether it has consequences for population dynamics and epigenetic influences on offspring phenotype in honey bees and other species.

Electrodeposition in electrochemical cells is one of the leading causes of its performance deterioration. The prediction of electrodeposition growth demands a good understanding of the complex physics involved, which can lead to the fabrication of a probabilistic mathematical model. As an alternative, a convolutional Long short-term memory architecture-based image analysis approach is presented herein. This technique can predict the electrodeposition growth of the electrolytes, without prior detailed knowledge of the system. The captured images of the electrodeposition from the experiments are used to train and test the model. A comparison between the expected output image and predicted image on a pixel level, percentage mean squared error, absolute percentage error, and pattern density of the electrodeposit are investigated to assess the model accuracy. The randomness of the electrodeposition growth is outlined by investigating the fractal dimension and the interfacial length of the electrodeposits. The trained model predictions show a significant promise between all the experimentally obtained relevant parameters with the predicted one. It is expected that this deep learning-based approach for predicting random electrodeposition growth will be of immense help for designing and optimizing the relevant experimental scheme in near future without performing multiple experiments.