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Abstract Background Interactions between alien plants and insect herbivores in introduced ranges may determine their invasion success. However, few studies have investigated whether alien plants respond differently to native and introduced herbivores in their introduced ranges and whether genotypes of alien plants matter. We conducted a greenhouse experiment to examine the effects of herbivory by a native insect (Spodoptera litura), by an introduced insect (S. frugiperda), and simultaneously by both insect species on growth, morphology, and biomass allocation of 17 genotypes of an invasive alien clonal plant Hydrocotyle verticillata, and used selection gradient analysis to test which herbivory conditions favor selection of a specific leaf or root trait value. Results Different genotypes of H. verticillata showed significant variation in growth, morphology, and biomass allocation, but their responses to herbivory were relatively consistent. All three herbivory treatments significantly decreased total mass and stolon mass, but herbivory of S. frugiperda increased specific leaf area. Herbivory of S. litura and simultaneous herbivory of both insect species also decreased leaf mass, petiole mass, root mass, and ramet mass. Selection gradient analysis showed that leaf and root traits varied under different herbivory treatments. To achieve greater fitness, as measured by total mass and/or number of ramets, H. verticillata favored larger leaf area under herbivory by S. frugiperda, larger leaf area and lower specific leaf area under herbivory by S. litura, and larger leaf area, lower specific leaf area, and lower root-to-shoot ratio under simultaneous herbivory. Conclusions H. verticillata demonstrated contrasting responses to herbivory by native and introduced insects, showing a remarkable ability to coordinate leaf trait plasticity and optimize biomass allocation. This strategy allows H. verticillata to achieve greater fitness under various herbivory conditions, potentially contributing to its invasion success. These findings highlight the importance of plant–herbivore interactions in shaping invasion dynamics and underscore the complex adaptive mechanism that enables invasive plants to establish and spread in introduced ranges.

The food infected by Alternaria species can produce Alternaria toxins with genotoxicity, cytotoxicity, reproductive toxicity, and developmental toxicity. Due to the low content of Alternaria toxins with greatly different chemical structures in food, it is necessary to establish a reliable and highly sensitive method for Alternaria toxins residues analysis in food. Sample pretreatment is not only an important part but also a bottleneck in the process of residue analysis. This article reviewed the sample pretreatment technology of Alternaria toxins in food, specifically focusing on the application of QuEChERs, dispersed liquid-liquid microextraction, molecular imprinting solid phase extraction, and direct dilution method in Alternaria toxins extraction, as well as the applications of solid-phase extraction, dispersed solid-phase extraction, low- temperature freezing, and liquid-liquid distribution in the purification process of Alternaria toxins in food matrices. The advantages and disadvantages of different extraction and purification technologies were summarized. The advantages and limitations of existing pre-treatment technologies were analyzed. The development prospects of pre-treatment technologies were discussed, such as preparing new molecularly imprinted solid-phase extraction materials and purification materials with good performance and establishing an integrated detection system for extracting and analyzing Alternaria toxins in food.

Abstract Exposure to multiple mosquito-borne flaviviruses within a lifetime is not uncommon; however, how sequential exposures to different flaviviruses shape the cross-reactive humoral response against an antigen from a different serocomplex has yet to be explored. Here, we report that dengue-infected individuals initially primed with the Japanese encephalitis virus (JEV) showed broad, highly neutralizing potencies against Zika virus (ZIKV). We also identified a rare class of ZIKV-cross-reactive human monoclonal antibodies with increased somatic hypermutation and broad neutralization against multiple flaviviruses. One huMAb, K8b, binds quaternary epitopes with heavy and light chains separately interacting with overlapping envelope protein dimer units spanning domains I, II, and III through cryo-electron microscopy and structure-based mutagenesis. JEV virus-like particle immunization in mice further confirmed that such cross-reactive antibodies, mainly IgG3 isotype, can be induced and proliferate through heterologous dengue virus (DENV) serotype 2 virus-like particle stimulation. Our findings highlight the role of prior immunity in JEV and DENV in shaping the breadth of humoral response and provide insights for future vaccination strategies in flavivirus-endemic countries.

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications.

Thanks to the successful performance of the James Webb Space Telescope, our understanding of the epoch of reionization of the Universe has been advanced. The ultraviolet luminosity functions (UV LFs) of galaxies span a wide range of redshifts, not only revealing the connection between galaxies and dark matter (DM) halos but also providing information during reionization. In this work, we develop a model connecting galaxy counts and apparent magnitude based on UV LFs, which incorporates redshift-dependent star formation efficiency (SFE) and corrections for dust attenuation. By synthesizing some observations across the redshift range of 4 ≤ z ≤ 10 from various galaxy surveys, we discern the evolving SFE with increasing redshift and DM halo mass through model fitting. Subsequent analyses indicate that the Thomson scattering optical depth was ${\tau }_{{\rm{e}}}={0.054}_{-0.003}^{+0.001}$ and the epoch of reionization started (ended) at $z={18.8}_{-6.0}^{+7.2}$ ( $z={5.3}_{-1.0}^{+0.8}$ ), which is insensitive to the choice of the truncated magnitude of the UV LFs. Incorporating additional data sets and some reasonable constraints, the amplitude of matter perturbation is found to be σ _8 = 0.80 ± 0.05, which is consistent with the standard ΛCDM model. Future galaxy surveys and the dynamical simulations of galaxy evolution will break the degeneracy between SFE and cosmological parameters, improving the accuracy and the precision of the UV LF model further.

Machine Learning (ML) has become one of the important tools for High Energy Physics analysis. As the size of the dataset increases at the Large Hadron Collider (LHC), and at the same time the search spaces become bigger and bigger in order to exploit the physics potentials, more and more computing resources are required for processing these ML tasks. In addition, complex advanced ML workflows are developed in which one task may depend on the results of previous tasks. How to make use of vast distributed CPUs/GPUs in WLCG for these big complex ML tasks has become a popular research area. In this paper, we present our efforts enabling the execution of distributed ML workflows on the Production and Distributed Analysis (PanDA) system and intelligent Data Delivery Service (iDDS). First, we describe how PanDA and iDDS deal with large-scale ML workflows, including the implementation to process workloads on diverse and geographically distributed computing resources. Next, we report real-world use cases, such as HyperParameter Optimization, Monte Carlo Toy confidence limits calculation, and Active Learning. Finally, we conclude with future plans.

The Vera C. Rubin Observatory will produce an unprecedented astronomical data set for studies of the deep and dynamic universe. Its Legacy Survey of Space and Time (LSST) will image the entire southern sky every three to four days and produce tens of petabytes of raw image data and associated calibration data over the course of the experiment’s run. More than 20 terabytes of data must be stored every night, and annual campaigns to reprocess the entire dataset since the beginning of the survey will be conducted over ten years. The Production and Distributed Analysis (PanDA) system was evaluated by the Rubin Observatory Data Management team and selected to serve the Observatory’s needs due to its demonstrated scalability and flexibility over the years, for its Directed Acyclic Graph (DAG) support, its support for multi-site processing, and its highly scalable complex workflows via the intelligent Data Delivery Service (iDDS). PanDA is also being evaluated for prompt processing where data must be processed within 60 seconds after image capture. This paper will briefly describe the Rubin Data Management system and its Data Facilities (DFs). Finally, it will describe in depth the work performed in order to integrate the PanDA system with the Rubin Observatory to be able to run the Rubin Science Pipelines using PanDA.

In recent years, advanced and complex analysis workflows have gained increasing importance in the ATLAS experiment at CERN, one of the large scientific experiments at LHC. Support for such workflows has allowed users to exploit remote computing resources and service providers distributed worldwide, overcoming limitations on local resources and services. The spectrum of computing options keeps increasing across the Worldwide LHC Computing Grid (WLCG), volunteer computing, high-performance computing, commercial clouds, and emerging service levels like Platform-as-a-Service (PaaS), Container-as-a-Service (CaaS) and Function-as-a-Service (FaaS), each one providing new advantages and constraints. Users can significantly benefit from these providers, but at the same time, it is cumbersome to deal with multiple providers, even in a single analysis workflow with fine-grained requirements coming from their applications’ nature and characteristics. In this paper, we will first highlight issues in geographically-distributed heterogeneous computing, such as the insulation of users from the complexities of dealing with remote providers, smart workload routing, complex resource provisioning, seamless execution of advanced workflows, workflow description, pseudointeractive analysis, and integration of PaaS, CaaS, and FaaS providers. We will also outline solutions developed in ATLAS with the Production and Distributed Analysis (PanDA) system and future challenges for LHC Run4.

The copolymerization of CO2 and epoxides presents a transformative approach to converting greenhouse gases into aliphatic polycarbonates (CO2-PCs), thereby reducing the polymer industry's dependence on fossil resources. Over the past 50 years, a wide array of metallic catalysts, both heterogeneous and homogeneous, have been developed to achieve precise control over polymer selectivity, sequence, regio-, and stereoselectivity. This review details the evolution of metal-based catalysts, with a particular focus on the emergence of organoborane catalysts, and explores how these catalysts effectively address kinetic and thermodynamic challenges in CO2/epoxides copoly2merization. Advances in the synthesis of CO2-PCs with varied sequence and chain architectures through diverse polymerization protocols are examined, alongside the applications of functional CO2-PCs produced by incorporating different epoxides. The review also underscores the contributions of computational techniques to our understanding of copolymerization mechanisms and highlights recent advances in the closed-loop chemical recycling of CO2-sourced polycarbonates. Finally, the industrialization efforts of CO2-PCs are discussed, offering readers a comprehensive understanding of the evolution and future potential of epoxide copolymerization with CO2. [ABSTRACT FROM AUTHOR]

Objective: Hepatocellular carcinoma (HCC) is a highly lethal cancer with significant mortality, primarily attributed to metastasis. Although Protocadherin Gamma Subfamily A, 9 (PCDHGA9) has been identified as a tumor suppressor gene in cancer metastasis, its role in HCC remains ambiguous. This study clarifies the role of PCDHGA9 in HCC by examining its expression, clinical significance, and molecular activities. Methods: Tissue microarray immunofluorescence analysis evaluated the expression of PCDHGA9 and its clinical relevance. In vitro experiments involved manipulating PCDHGA9 levels in SK-HEP-1 cells to assess migration through wound-healing and transwell assays. In vivo, shPCDHGA9 cell injections were utilized to observe effects on tumor growth and metastasis. Protein analysis and Western Blot validated epithelial-mesenchymal transition (EMT)-related proteins. Subsequent to TGF-β treatment, cell proliferation and apoptosis were quantified using cell counting kit-8 and flow cytometry, respectively, followed by investigation of TGF-β effects on PCDHGA9 N6-methyladenosine (m6A) modification via Methylated RNA immunoprecipitation, RT-qPCR, and Western blot analysis. Results: Downregulation of PCDHGA9 expression in HCC tissues is correlated with poor prognosis. In vitro experiments demonstrated that modulating PCDHGA9 expression influenced HCC cell migration. In vivo, PCDHGA9 knockdown is correlated with increased metastasis. Furthermore, TGF-β stimulation promoted cell proliferation and inhibited apoptosis. Mechanistically, TGF-β-mediated m6A modification led to PCDHGA9 decay, promoting EMT in HCC cells. Conclusion: PCDHGA9 serves as a potential tumor suppressor in HCC by inhibiting EMT. During this process, TGF-β is observed to exert regulatory control over m6A modifications of PCDHGA9. [ABSTRACT FROM AUTHOR]

Background: Interactions between alien plants and insect herbivores in introduced ranges may determine their invasion success. However, few studies have investigated whether alien plants respond differently to native and introduced herbivores in their introduced ranges and whether genotypes of alien plants matter. We conducted a greenhouse experiment to examine the effects of herbivory by a native insect (Spodoptera litura), by an introduced insect (S. frugiperda), and simultaneously by both insect species on growth, morphology, and biomass allocation of 17 genotypes of an invasive alien clonal plant Hydrocotyle verticillata, and used selection gradient analysis to test which herbivory conditions favor selection of a specific leaf or root trait value. Results: Different genotypes of H. verticillata showed significant variation in growth, morphology, and biomass allocation, but their responses to herbivory were relatively consistent. All three herbivory treatments significantly decreased total mass and stolon mass, but herbivory of S. frugiperda increased specific leaf area. Herbivory of S. litura and simultaneous herbivory of both insect species also decreased leaf mass, petiole mass, root mass, and ramet mass. Selection gradient analysis showed that leaf and root traits varied under different herbivory treatments. To achieve greater fitness, as measured by total mass and/or number of ramets, H. verticillata favored larger leaf area under herbivory by S. frugiperda, larger leaf area and lower specific leaf area under herbivory by S. litura, and larger leaf area, lower specific leaf area, and lower root-to-shoot ratio under simultaneous herbivory. Conclusions: H. verticillata demonstrated contrasting responses to herbivory by native and introduced insects, showing a remarkable ability to coordinate leaf trait plasticity and optimize biomass allocation. This strategy allows H. verticillata to achieve greater fitness under various herbivory conditions, potentially contributing to its invasion success. These findings highlight the importance of plant–herbivore interactions in shaping invasion dynamics and underscore the complex adaptive mechanism that enables invasive plants to establish and spread in introduced ranges. [ABSTRACT FROM AUTHOR]

This study aims to improve the helicopter electric power inspection process by using the feature embedding convolution (FEC) model to solve the problems of small scope and poor real-time inspection. First, simulation experiments and model analysis determine the keyframe and flight trajectory. Second, an improved FEC model is proposed, extracting features from aerial images in large ranges in real time and accurately identifying and classifying electric power inspection targets. In the simulation experiment, the accuracy of the model in electric power circuit and equipment detection is improved by 30% compared with the traditional algorithm, and the inspection range is expanded by 26%. In addition, this study further optimizes the model with reinforcement learning technology, conducts a comparative analysis of different flight environments and facilities, and reveals the diversity and complexity of inspection objectives. The performance of the optimized model in fault detection is increased by more than 36%. In conclusion, the proposed model improves the accuracy and scope of inspection, provides a more scientific strategy for electric power inspection, and ensures inspection efficiency. [ABSTRACT FROM AUTHOR]

When multiple objects are positioned close together or stacked, pre-grasp operations such as pushing objects can be used to create space for the grasp, thereby improving the grasping success rate. This study develops a model based on a deep Q-learning network architecture and introduces a fully convolutional network to accurately identify pixels in the workspace image that correspond to target locations for exploration. In addition, this study incorporates image masking to limit the exploration area of the robotic arm, ensuring that the agent consistently explores regions containing objects. This approach effectively addresses the sparse reward problem and improves the convergence rate of the model. Experimental results from both simulated and real-world environments show that the proposed method accelerates the learning of effective grasping strategies. When image masking is applied, the success rate in the grasping task reaches 80% after 600 iterations. The time required to reach 80% success rate is 25% shorter when image masking is used compared to when it is not used. The main finding of this study is the direct integration of image masking technique with a deep reinforcement learning (DRL) algorithm, which offers significant advancement in robotic arm control. Furthermore, this study shows that image masking technique can substantially reduce training time and improve the object grasping success rate. This innovation enables the robotic arm to better adapt to scenarios that conventional DRL methods cannot handle, thereby improving training efficiency and performance in complex and dynamic industrial applications. [ABSTRACT FROM AUTHOR]

Pore-filling hydrates are the main occurrence forms of marine gas hydrates. Pore characteristics are a vital factor affecting the thermodynamic properties of hydrates and their distribution in sediments. Currently, the characterization of the pore system for hydrate-bearing reservoirs are little reported. Therefore, this paper focuses on the Krishna-Godavari Basin, via various methods to characterize the hydrate-bearing sediments in the region. The results showed that X-ray diffraction (XRD) combined with scanning electron microscopy (SEM) and cast thin section (CTS) can better characterize the mineral composition in the reservoir, high-pressure mercury injection (HPMI) focused on the contribution of pore size to permeability, constant-rate mercury injection (CRMI) had the advantage of distinguishing between the pore space and pore throat, and nuclear magnetic resonance cryoporometry (NMRC) technique can not only obtain the pore size distribution of nanopores with a characterization range greater than nitrogen gas adsorption (N2GA), but also quantitatively describe the trend of fluids in the pore system with temperature. In terms of the pore system, the KG Basin hydrate reservoir develops nanopores, with a relatively dispersed mineral distribution and high content of pyrite. Rich pyrite debris and foraminifera-rich paleontological shells are observed, which leads to the development of intergranular pores and provides more nanopores. The pore throat concentration and connectivity of the reservoir are high, and the permeability of sediments in the same layer varies greatly. The reason for this phenomenon is the significant difference in average pore radius and pore size contribution to pore permeability. This article provides a reference and guidance for exploring the thermodynamic stability of hydrates in sediments and the exploration and development of hydrates by characterizing the pores of hydrate reservoirs. [ABSTRACT FROM AUTHOR]

Seeking high‐performance photoresists is an important item for semiconductor industry due to the continuous miniaturization and intelligentization of integrated circuits. Polymer resin containing carbonate group has many desirable properties, such as high transmittance, acid sensitivity and chemical formulation, thus serving as promising photoresist material. In this work, a series of aqueous developable CO2‐sourced polycarbonates (CO2‐PCs) were produced via alternating copolymerization of CO2 and epoxides bearing acid‐cleavable cyclic acetal groups in the presence of tetranuclear organoborane catalyst. The produced CO2‐PCs were investigated as chemical amplification resists in deep ultraviolet (DUV) lithography. Under the catalysis of photogenerated acid, the acetal (ketal) groups in CO2‐PC were hydrolysed into two equivalents of hydroxyl groups, which change the exposed area from hydrophobicity to hydrophilicity, thus enabling the exposed area to be developed with water. Through normalized remaining thickness analysis, the optimal CO2‐derived resist achieved a remarkable sensitivity of 1.9 mJ/cm2, a contrast of 7.9, a favorable resolution (750 nm, half pitch), and a good etch resistance (38 % higher than poly(tert‐butyl acrylate)). Such performances outperform commercial KrF and ArF chemical amplification resists (i.e. polyhydroxystyrene‐derived and polymethacrylate‐based resists), which endows broad application prospects in the field of DUV (KrF and ArF) and extreme ultraviolet (EUV) lithography for nanomanufacturing. [ABSTRACT FROM AUTHOR]

Alkyl borane compounds‐mediated polymerizations have expanded to Lewis pair polymerization, free radical polymerization, ionic ring‐opening polymerization, and polyhomologation. The bifunctional organoborane catalysts that contain the Lewis acid and ammonium or phosphonium salt in one molecule have demonstrated superior catalytic performance for ring‐opening polymerization of epoxides and ring‐opening copolymerization of epoxides and CO2 than their two‐component analogues, i.e. the blend of organoborane and ammonium or phosphonium salt. To explore the origin of the differences of the one‐component and two‐component organoborane catalysts, here we conducted a systematic investigation on the catalytic performances of these two kinds of organoborane catalysts via terpolymerization of epoxide, carbon dioxide and anhydride. The resultant terpolymers produced independently by bifunctional and binary organoborane catalyst exhibited distinct microstructures, where a series of gradient polyester‐polycarbonate terpolymers with varying polyester content were afforded using the bifunctional catalyst, while tapering diblock terpolymers were obtained using the binary system. The bifunctional catalyst enhances the competitiveness of CO2 insertion than anhydride, which leads to the premature incorporation of CO2 into the polymer chains and ultimately results in the formation of gradient terpolymers. DFT calculations revealed the role of electrostatic interaction and charge distribution caused by intramolecular synergistic effect for bifunctional organoborane catalyst. [ABSTRACT FROM AUTHOR]

The complex pore structure of unconventional oil and gas reservoirs is one of the reasons for the difficulties in resource evaluation and development. Therefore, it is crucial to comprehensively characterize the pore structure, understand reservoir heterogeneity from multiple perspectives, and gain an in-depth understanding of fluid migration and accumulation mechanisms. This review outlines the methods and basic principles for characterizing microporous systems in unconventional reservoirs, summarizes the fractal analysis corresponding to the different methods, sorts out the relationship between the fractals and reservoir macroscopic physical properties (porosity, permeability, etc.) with the reservoir microscopic pore structures (pore structure parameters, pore connectivity, etc.). The research focuses on cutting-edge applications of characterization techniques, such as improved characterization accuracy, calibration of PSD ranges, and identification of different hydrogen compositions in pore systems for dynamic assessment of unconventional reservoirs. Fractal dimension analysis can effectively identify the quality level of the reservoir; complex pore-throat structures reduce permeability and destroy free fluid storage space, and the saturation of removable fluids is negatively correlated with Df. As for the mineral composition, the fractal dimension is positively correlated with quartz, negatively correlated with feldspar, and weakly correlated with clay mineral content. In future qualitative characterization studies, the application and combination of contrast agents, molecular dynamics simulations, artificial intelligence techniques, and 4D imaging techniques can effectively improve the spatial resolution of the images and explore the adsorption/desorption of gases within the pores, and also help to reduce the computational cost of these processes; these could also attempt to link reservoir characterization to research on supercritical carbon dioxide-enhanced integrated shale gas recovery, carbon geological sequestration, and advanced underground hydrogen storage. [ABSTRACT FROM AUTHOR]