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Abstract In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.

Measuring the impact of project portfolio synergy (PPSI) is crucial for making informed decisions to improve the strategic realization of a project portfolio. However, the literature has failed to measure PPSI effectively from the perspective of project element flow and to explore the differences in synergy relationships between homogeneous and heterogeneous projects. Consequently, this study proposes a framework for measuring PPSI from the perspective of project element flow. First, based on analyzing the synergistic relationship between projects, the corresponding project element datum quantity is determined according to the different types of element flow forms. Second, the synergy degree between homogeneous and heterogeneous projects is quantified through project similarity and correlation. Third, a dynamic measurement model is constructed using System Dynamics to solve the complex interactive feedback and dynamics in the PPSI measurement system. Finally, the framework is demonstrated and validated by a numerical example of project management in Xi'an, Shaanxi Province. The results indicate that the model can effectively measure PPSI and identify the key factors generating it. In implementing the portfolio, managers must focus on sharing and integrating newly developed technologies and information, thus facilitating the generation of PPSI. This study extends the boundary of project portfolio synergy theory and contributes to the literature on measuring PPSI by focusing on the elements flow within portfolios. Additionally, the model provides an effective tool for managers to forecast PPSI and determine appropriate optimization strategies.

Background: The Juan-Bi decoction (JBD) is a classic traditional Chinese medicines (TCMs) prescription for the treatment of rheumatoid arthritis (RA). However, the active compounds of the JBD in RA treatment remain unclear.Aim: The aim of this study is to screen effective compounds in the JBD for RA treatment using systems pharmacology and experimental approaches.Method: Botanical drugs and compounds in the JBD were acquired from multiple public TCM databases. All compounds were initially screened using absorption, distribution, metabolism, excretion, and toxicity (ADMET) and physicochemical properties, and then a target prediction was performed. RA pathological genes were acquired from the DisGeNet database. Potential active compounds were screened by constructing a compound–target–pathogenic gene (C-T-P) network and calculating the cumulative interaction intensity of the compounds on pathogenic genes. The effectiveness of the compounds was verified using lipopolysaccharide (LPS)-induced RAW.264.7 cells and collagen-induced arthritis (CIA) mouse models.Results: We screened 15 potentially active compounds in the JBD for RA treatment. These compounds primarily act on multiple metabolic pathways, immune pathways, and signaling transduction pathways. Furthermore, in vivo and in vitro experiments showed that bornyl acetate (BAC) alleviated joint damage, and inflammatory cells infiltrated and facilitated a smooth cartilage surface via the suppression of the steroid hormone biosynthesis.Conclusion: We screened potential compounds in the JBD for the treatment of RA using systems pharmacology approaches. In particular, BAC had an anti-rheumatic effect, and future studies are required to elucidate the underlying mechanisms.

To improve the effectiveness of external stakeholder risks (ESRs) management in project portfolios (PPs), a portfolio-wide risk response approach is required. However, current research is inadequate to effectively identify response strategies for ESRs, which brings challenges to managing ESRs in PPs. In this context, the purpose of this study is to select an appropriate combination of response strategies for ESRs by considering interactions among ESRs, projects, and response strategies in the PP. A Bayesian influence diagram (BID) coupled with a multi-objective optimization model is deemed suitable for this context. Firstly, a probability-sensitivity matrix is established to determine the key ESRs. Then, a BID is constructed to calculate the expected values of different combinations of response strategies. Finally, integrating stakeholder satisfaction and strategy cost, an optimization model for risk response strategy selection is established to obtain candidate combinations. By combining expected values and candidate combinations, the optimal strategy combination is selected. The proposed model comprehensively considers and evaluates the interactions between risks, projects, and risk responses. This enhances the desirability of expected outcomes and reduces project execution costs.

Abstract Antibody-drug conjugates (ADCs) represent an important class of cancer therapies that have revolutionized the treatment paradigm of solid tumors. To date, many ongoing studies of ADC combinations with a variety of anticancer drugs, encompassing chemotherapy, molecularly targeted agents, and immunotherapy, are being rigorously conducted in both preclinical studies and clinical trial settings. Nevertheless, combination therapy does not always guarantee a synergistic or additive effect and may entail overlapping toxicity risks. Therefore, understanding the current status and underlying mechanisms of ADC combination therapy is urgently required. This comprehensive review analyzes existing evidence concerning the additive or synergistic effect of ADCs with other classes of oncology medicines. Here, we discuss the biological mechanisms of different ADC combination therapy strategies, provide prominent examples, and assess their benefits and challenges. Finally, we discuss future opportunities for ADC combination therapy in clinical practice.

Background: Gastric cancer (GC) is among the prevalent gastrointestinal malignancies and is the leading reason for cancer-related mortality. There is an urgent need for novel anti-cancer drugs that are highly active against GC. Parthenolide extracted from the traditional medicines houpo and xinyi is a potential small molecular agent against GC. Methods: GC cell proliferation was assessed in vitro and in vivo using Cell Counting Kit-8 (CCK-8), the colony formation assay, and a xenograft tumor model. Cell death, the cytoskeleton, and mitochondrial membrane potential were examined using TUNEL, phalloidin, and JC-1 staining, respectively. Reactive oxygen and nitrogen species (ROS and RNS, respectively) were investigated using DCFH-DA and DAF-FM DA probes. Cell metabolites were discovered using metabonomics. The degree of DNA damage in GC cells was described using the comet assay and γ-H2AX immunofluorescence staining. Subsequently, protein levels were determined using western blotting. Furthermore, DNA agarose gel electrophoresis was performed to clarify whether parthenolide bound directly to DNA. Results: The potent anti-cancer mechanisms of parthenolide were attributed to its ability to interfere with microtubule assembly, trigger an oxidative stress response, damage DNA by directly binding to DNA or generating excessive free radicals, and ultimately activate the mitochondrial pathway, leading to GC cell death. This investigation determined that parthenolide exhibited a suppressive effect on GC cells in vitro and in vivo. Conclusion: Parthenolide is a potential GC therapeutic agent.

This Letter presents, for the first time, direct constraints on the black hole–halo mass relation using weak gravitational-lensing measurements. We construct type I and type II active galactic nucleus (AGN) samples from the Sloan Digital Sky Survey, with a mean redshift of 0.4 (0.1) for type I (type II) AGNs. This sample is cross correlated with weak-lensing shear from the Ultraviolet Near Infrared Optical Northern Survey. We compute the excess surface mass density of the halos associated with 36,181 AGNs from 94,308,561 lensed galaxies and fit the halo mass in bins of black hole mass. We find that more massive AGNs reside in more massive halos. The relation between halo mass and black hole mass is well described by a power law of slope 0.6 for both type I and type II samples, in agreement with models that link black hole growth to baryon feedback. We see no dependence on AGN type or redshift in the black hole–halo mass relation below a black hole mass of 10 ^8.5 M _⊙ . Above that mass, we find more massive halos for the low- z type II sample compared to the high- z type I sample, but this difference may be interpreted as systematic error in the black hole mass measurements. Our results are consistent with previous measurements for non-AGN galaxies. At a fixed black hole mass, our weak-lensing halo masses are consistent with galaxy rotation curves but significantly lower than galaxy-clustering measurements. Finally, our results are broadly consistent with state-of-the-art hydrodynamical cosmological simulations, providing a new constraint for black hole masses in simulations.

Blast furnace (BF) ironmaking is a key process in iron and steel production. Because BF ironmaking is a dynamic time series process, it is more appropriate to use a recurrent neural network for modeling. The long short-term memory (LSTM) network is commonly used to model time series data. However, its model performance and generalization ability heavily depend on the parameter configuration. Therefore, it is necessary to study parameter optimization for the LSTM model. The sparrow search algorithm (SSA) holds advantages over traditional optimization algorithms in several aspects, such as no need for prior knowledge, fewer parameters, fast convergence, and high scalability. However, the algorithm still faces some challenges, such as the tendency to become trapped in the local optimum and the imbalance between global search ability and local search ability. Therefore, on the basis of SSA, this study examined the Levy flight strategy, sine search strategy, and step size factor adjustment strategy to improve it. This algorithm, improved by three strategies, is called the improved sparrow search algorithm (ISSA). Then, the ISSA-LSTM model was established. Furthermore, considering the limitations of SSA in dealing with multi-objective problems, the fast non-dominated sorting genetic algorithm (NSGAII) was introduced, and the ISSA-NSGAII model was established. Finally, experimental validation was performed using real blast furnace operation data, which demonstrated the proposed algorithm’s superiority in parameter optimization for the LSTM model and prediction for real industrial data.

Chiral α-alkyl α-glycine acids are prevalent structural motifs found in pharmaceuticals and natural products, making them a significant dimension for exploration in modern pharmaceutical and medicinal chemistry. However, the current synthesis methods for these compounds face numerous environmental and practical challenges, highlighting the critical need to establish more sustainable approaches. Herein, we report the first liquid assisted grinding (LAG)-facilitated aerobic asymmetric cross-dehydrogenative coupling reaction involving glycine esters/amides and simple ketones/aldehydes, using a chiral amine catalyst under metal-free and solvent-minimal conditions. A wide variety of α-alkyl α-amino acid derivatives, containing two or three stereocenters, were generated in good yields with remarkable enantioselectivity and diastereoselectivity. Moreover, we propose a new enantioselective control model for mechanical ball milling, suggesting that stoichiometric EtOH may enhance the enantioselectivity by potentially interacting with both the catalyst and the imine ion intermediate. This method, closely aligned with the principles of green chemistry, offers a recoverable catalyst/grinding-aid system, has broad substrate compatibility, and enables the synthesis of multifunctionalized products suitable for diverse transformations, notably including the production of the unnatural amino acid L-cyclohexylglycine (a key intermediate in telaprevir synthesis). [ABSTRACT FROM AUTHOR]

This document is a correction notice for an article titled "Ethanol-assisted mechanochemical asymmetric cross-dehydrogenative coupling reaction with recoverable chiral amine/NaCl for accessing chiral α-alkyl α-glycine derivatives" published in the journal Green Chemistry. The authors acknowledge inaccuracies in the mechanism and associated discussion in the original article. They provide a corrected scheme and clarify a sentence within the main article. The Royal Society of Chemistry apologizes for the errors. The document also includes information about the authors' affiliations and contact details. [Extracted from the article]