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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.

Abstract Formic acid (FA) has emerged as a promising one-carbon feedstock for biorefinery. However, developing efficient microbial hosts for economically competitive FA utilization remains a grand challenge. Here, we discover that the bacterium Vibrio natriegens has exceptional FA tolerance and metabolic capacity natively. This bacterium is remodeled by rewiring the serine cycle and the TCA cycle, resulting in a non-native closed loop (S-TCA) which as a powerful metabolic sink, in combination with laboratory evolution, enables rapid emergence of synthetic strains with significantly improved FA-utilizing ability. Further introduction of a foreign indigoidine-forming pathway into the synthetic V. natriegens strain leads to the production of 29.0 g · L−1 indigoidine and consumption of 165.3 g · L−1 formate within 72 h, achieving a formate consumption rate of 2.3 g · L−1 · h−1. This work provides an important microbial chassis as well as design rules to develop industrially viable microorganisms for FA biorefinery.

Liver disease affects millions of people in the world, and China has the highest prevalence of liver disease in the world. Small ubiquitin-related modifier (SUMO) modification is a highly conserved post-translational modification of proteins. They are widely expressed in a variety of tissues, including the heart, liver, kidney and lung. SUMOylation of protein plays a key role in the occurrence and development of liver disease. Therefore, this study reviewed the effects of SUMO protein on non-alcoholic fatty liver disease (NAFLD), alcoholic liver disease (ALD), viral hepatitis, hepatic fibrosis (HF), hepatocellular carcinoma (HCC), and other liver diseases to provide novel strategies for targeted treatment of liver disease.

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.

Benign prostatic hyperplasia (BPH) is a common urinary disease among the elderly, characterized by abnormal prostatic cell proliferation. Neferine is a dibenzyl isoquinoline alkaloid extracted from Nelumbo nucifera and has antioxidant, anti-inflammatory and anti-prostate cancer effects. The beneficial therapeutic effects and mechanism of action of neferine in BPH remain unclear.A mouse model of BPH was generated by subcutaneous injection of 7.5 mg/kg testosterone propionate (TP) and 2 or 5 mg/kg neferine was given orally for 14 or 28 days. Pathological and morphological characteristics were evaluated. Prostate weight, prostate index (prostate/body weight ratio), expression of type Ⅱ 5α-reductase, androgen receptor (AR) and prostate specific antigen were all decreased in prostate tissue of BPH mice after administration of neferine. Neferine also downregulated the expression of pro-caspase-3, uncleaved PARP, TGF-β1, TGF-β receptor Ⅱ (TGFBR2), p-Smad2/3, N-cadherin and vimentin. Expression of E-cadherin, cleaved PARP and cleaved caspase-3 was increased by neferine treatment.1–100 μM neferine with 1 μM testosterone or 10 nM TGF-β1 were added to the culture medium of the normal human prostate stroma cell line, WPMY-1, for 24 h or 48 h. Neferine inhibited cell growth and production of reactive oxygen species (ROS) in testosterone-treated WPMY-1 cells and regulated the expression of androgen signaling pathway proteins and those related to epithelial-mesenchymal transition (EMT). Moreover, TGF-β1, TGFBR2 and p-Smad2/3, N-cadherin and vimentin expression were increased but E-cadherin was decreased after 24 h TGF-β1 treatment in WPMY-1 cells. Neferine reversed the effects of TGF-β1 treatment in WPMY-1 cells. Neferine appeared to suppress prostate growth by regulating the EMT, AR and TGF-β/Smad signaling pathways in the prostate and is suggested as a potential agent for BPH treatment.

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.

Taipu River is one of the important river channels of Taihu Lake, and also an important water body in the demonstration zone of green and integrated ecological development of the Yangtze River Delta. In this study, we collect the water quality monitoring data of Taipu River and related research results of Taipu River water quality, and analyze the water environment characteristics of Taipu River by analyzing the water quality status of Taipu River main stream and East Taihu Lake. The results indicate that from 2016 to 2018, the annual mean values of conventional indexes in main sections of the Taipu River main stream were maintained in Class II to III. The incoming water from the Taipu River mainly includes the East Taihu Lake and its tributaries. The water quality of the East Taihu Lake is superior to that of the Taipu River. The water quality of the Jinze section is mainly affected by the inflow from the East Taihu Lake. Increase the discharge under Taipu sluice to supply high-quality water, which can effectively guarantee the water quality of the section. Then we identify the main water environmental problems in Taipu River basin. The results of our analysis can provide support for the water ecological environment protection of Taipu River.

The physicochemical properties of sweet potato flour (SPF) can be modified by annealing. Native SPF was annealed in deionized water at a flour-to-water ratio of 1:3 (w/v) and temperatures of 50, 55, 60, or 65 °C for either 12 or 24 h. Annealed SPF maintained the A-type crystalline region and displayed increased relative crystallinity, increased pasting temperature, and decreased breakdown. SPF gels showed enhanced hardness together with better springiness when SPF was annealed at low temperature/long time or high temperature/short time. Annealed SPF hydrogel sheets contained larger, more uniform, and smoother pores than native ones. Noticeably, hydrogel sheets made of SPF annealed at 50 °C for 24 h exhibited advanced fracture strain from 93% to 176%. Overall, this work showed that annealing could modulate the characteristics of SPR hydrogels, which may widen the extent of applications in food industries. However, the annealing conditions need to be optimized.

ObjectiveThe purpose of this study was to examine the association between the VAI (visceral adiposity index) and stroke prevalence and age at stroke in US adults.MethodsWe examined the association between VAI and stroke prevalence and age at stroke using logistic regression, subgroup analysis, and dose-response curves using participants from the National Health and Nutrition Examination Survey (NHANES) database from 2007-2018.ResultsThis study ultimately included 29,337 participants aged >20 years, of whom 1022 self-reported a history of stroke, and after adjusting for all confounders, each unit increase in corrected VAI was associated with a 12% increase in the prevalence of stroke (OR= 1.12, 95% CI: 1.01, 1.24) along with an earlier age at stroke 1.64 years (β= -1.64, 95% CI: -2.84, -0.45), stratified analysis showed that the prevalence of stroke was 20% higher in the female group (OR= 1.20, 95% CI: 1.04, 1.39), black group (OR= 1.22, 95% CI: 1.01, 1.48), age ≤60 years group (OR= 1.25, 95% CI: 1.05, 1.48), hypertensive group (OR=1.15, 95% CI:1.01, 1.31), and diabetic group (OR=1.23, 95% CI:1.02, 1.48) VAI increase was positively correlated with stroke prevalence increase. The dose-response curves showed a positive linear correlation between increased VAI and stroke prevalence, while a negative linear correlation was observed between increased VAI and age at stroke.ConclusionAlthough a causal relationship cannot be proven, higher VAI was positively associated with stroke prevalence and can lead to earlier stroke onset.

In this study, the characteristics of indica rice kernels (IRK) and flour (IRF) annealed in different conditions were evaluated, and the quality of rice noodles made with these IRK and IRF was determined. Native IRK and IRF were annealed in deionized water at a kernel or flour to water ratio of 1:3 (w/v) and temperatures of 50, 55, 60, and 65 °C for 12 and 24 h. Annealing increased the paste viscosity of IRK while decreasing that of IRF. Both annealed IRK and IRF exhibited increases in the gelatinization enthalpy change and relative crystallinity. Annealed IRK gel showed higher hardness, and annealed IRF gel displayed greater springiness. Unlike native rice noodles, annealed IRK noodles exhibited denser pores, while annealed IRF noodles exhibited a looser microstructure. With increasing annealing temperature and time, both annealed IRK and IRF noodles showed enhanced tensile properties. Rice noodles made from IRF annealed at 65 °C for 12 h exhibited a fracture strain of 2.7 times that of native rice noodles. In brief, IRK and IRF exhibited different degrees of susceptibility to annealing. Annealing had more significant effects on IRF than IRK. This study highlights the possibility of using annealed IRK and IRF in rice noodles.

Ionic liquids (ILs) are promising electrolytes or solvents for numerous applications owing to their unique properties. However, it is a challenge to design the ideal IL with the required properties. Variational autoencoders (VAEs) trained by significantly large datasets have shown good performance in drug discovery. However, low generation efficiency and small sparse datasets prevent their application on IL. In this work, we propose a high generation efficiency molecular design model for IL, which realizes multi-objective optimization on a small dataset. The model combines VAE, multilayer perceptron, and particle swarm optimization for property prediction and molecule optimization. The thermal conductivity and heat capacity of the ILs are chosen as a case to verify the advantages of our model. The results shows that by setting molecular validity judgments to optimization target, 98% output of our method are valid molecules. Besides, the heat capacity and thermal conductivity are improved by 39% and 15%, respectively. Our model improves the applicability to small sparse datasets and the generation efficiency of VAE-like generation model. By multi-objective design ILs for given properties, our model can provide guidance for the design and application of ILs.

AbstractEarthquake disasters are frequent, and the seismic intensity is large in Northeast China. Earthquake activity research is an important aspect of earthquake disaster management. We chose some unconventional means to study fault activity, to find updated activity evidence. The Ms 5.3 earthquake occurred near the Fuyu North Fault (FNF) of China on May 27, 2018. Using the Sentinel-1B descending orbit data from 2016 to 2019, the line-of-sight (LOS) surface deformation in the study area was calculated by using the small baseline subset (SBAS) method. After transforming to the horizontal EW deformation, the variance component estimation method was used for fusion reconstruction with the EW data of the surrounding GNSS stations. The polynomial least square method is used to fit the fault slip rate of three EW data on the surface trace of the FNF. The fitting results of the three regions show that the horizontal eastward distribution rate of the upper plate is significantly greater than that of the lower plate, which is left-lateral clockwise torsion. The vertical structural deformation caused by the growth strata of the upper and lower plates of the upper SYT2 seismic profile of the FNF is quantitatively calculated, and the thrust rate of the upper plate is 0.2 mm/y relative to that of the lower plate. Based on the Li Siguang chessboard structure model, we found that the compression stress in the north-south direction is gradually weakened, and the compression stress in the east-west direction is gradually enhanced. Through the Coulomb stress analysis, the three events of CMT only induced the historical focal location of the surrounding part. The events of 2017 did not induce the events of 2018, but the events of 2019 were related to the induced effects of 2017 and 2018.

In this study, a rapid fluorescent and colorimetric dual-mode detection strategy for Hg2+ in seafoods was developed based on the cyclic binding of the organic fluorescent dye rhodamine 6G hydrazide (R6GH) to Hg2+. The luminescence properties of the fluorescent R6GH probe in different systems were investigated in detail. Based on the UV and fluorescence spectra, it was determined that the R6GH has good fluorescence intensity in acetonitrile and good selective recognition of Hg2+. Under optimal conditions, the R6GH fluorescent probe showed a good linear response to Hg2+ (R2 = 0.9888) in the range of 0–5 μM with a low detection limit of 2.5 × 10−2 μM (S/N = 3). A paper-based sensing strategy based on fluorescence and colorimetric analysis was developed for the visualization and semiquantitative analysis of Hg2+ in seafoods. The LAB values of the paper-based sensor impregnated with the R6GH probe solution showed good linearity (R2 = 0.9875) with Hg2+ concentration in the range of 0–50 μM, which means that the sensing paper can be combined with smart devices to provide reliable and efficient Hg2+ detection.

Body size is an important biological phenotypic trait that has attracted substantial attention. Small domestic pigs can serve as excellent animal models for biomedicine and also help meet sacrificial culture needs in human societies. Although the mechanisms underlying vertebral development regulating body size variation in domestic pigs during the embryonic period have been well described, few studies have examined the genetic basis of body size variation in post embryonic developmental stages. In this study, seven candidate genes—PLIN1, LIPE, PNPLA1, SCD, FABP5, KRT10 and IVL—significantly associated with body size were identified in Min pigs, on the basis of weighted gene co-expression network analysis (WGCNA), and most of their functions were found to be associated with lipid deposition. Six candidate genes except for IVL were found to have been subjected to purifying selection. PLIN1 had the lowest ω value (0.139) and showed heterogeneous selective pressure among domestic pig lineages with different body sizes (p < 0.05). These results suggested that PLIN1 is an important genetic factor regulating lipid deposition and consequently affecting body size variation in pigs. The culture of whole pig sacrifice in Manchu during the Qing Dynasty in China might have contributed to the strong artificial domestication and selection of Hebao pigs.

Rail surface inspection using a visual inspection system is an important task in the maintenance of railway networks. Recent inspection approaches have attempted to harness low-level features to identify visual defects. These methods' main issues are the limitations of low-level features and prior information about rail surfaces that show a wide variety of visual appearances on dynamic backgrounds. To overcome these problems, we propose a deep extractor (DE) that combines the strengths of fully convolutional networks and conditional random fields (CRFs), so that the deep network can learn the abstract features needed for the specified inspection task. Specifically, motivated by fully convolutional networks, a bilateral fully convolutional network containing two branches is proposed here: one encode-decode branch focuses semantic meaning; the other decode-encode branch encodes information on tiny defects. The two branches are aggregated to obtain a high-level feature map. Furthermore, the mean-field inference of a dense CRF with Gaussian pairwise potentials formulated as a recurrent neural network (CRF-RNN) is introduced to contribute to smoothing constraints and obtaining a fine-grained inspection result. By doing so, it can avoid offline post-processing and further lead the whole deep architecture to achieve end-to-end deep learning. Compared with classical approaches, our approach outperforms state-of-art approaches in analyzing two publicly available datasets.

Judging imaging quality is an important part of the maintenance of visual intelligent monitoring systems for electrical power scenes. However, accurate and efficient identification of possible abnormalities in imaging quality remains challenging. This paper proposes a novel multi-region two-stream deep architecture to improve judging abnormalities. The proposed architecture incorporates two-stream scheme and multi-region strategy to identify relevant information and explore hidden details. More specifically, in addition to color and intensity in the original images, the two-stream scheme uses high-frequency structure information from gradient images to enhance its performance. The multi-region strategy employs spatial pyramid random cropping and region fusion to handle locally non-uniform changes among categories: spatial pyramid random cropping characterizes images at different spatial pyramid levels, while region fusion focuses attention on cropped regions relevant to quality perception by using adaptive learning weights in a fully connected layer. In this way, the proposed strategy guides the framework to adequately and adaptively explore the discriminative regions hidden in the input images, and provides an end-to-end learning procedure. Experimental results demonstrate its strong performance for judging abnormalities, and the proposed method can be easily extended to the entire surveillance system.

Imaging quality judgement provides useful meaning to benefit intelligent visual power systems. However, accurate identification of possible failures of the monitoring equipment remains challenging. This paper proposes a novel deep architecture to improve the assessment of abnormalities by considering a novel mutual-guided convolutional attention network (MGCAN) and a multi-region scheme. More specifically, the MGCAN exploits various adequate, low-level information to generate spatial and structural flows. Accordingly, it further extends attention mechanisms to model both inter-flow correlations and inter-channel relationships in the network design. The multi-region scheme features a spatial pyramid random-crop strategy and a region-fusion strategy to handle locally non-uniform characteristics among categories. In this way, the whole architecture provides an end-to-end and adaptive learning procedure relevant to quality perception that focuses on learning important features and mining discriminative regions. Experimental results demonstrate its superiority to prior methods for judging abnormalities. The proposed method can be easily extended to an entire surveillance system.

To discover novel scaffolds as leads against dementia, a series of δ-aryl-1,3-dienesulfonyl fluorides with α-halo, α-aryl and α-alkynyl were assayed for ChE inhibitory activity, in which compound A10 was identified as a selective BuChE inhibitor (IC50 = 0.021 μM for eqBChE, 3.62 μM for hBuChE). SAR of BuChE inhibition showed: (i) o- > m- > p-; –OCH3 > –CH3 > –Cl (–Br) for δ-aryl; (ii) α-Br > α-Cl, α-I. Compound A10 exhibited neuroprotective, BBB penetration, mixed competitive inhibitory effect on BuChE (Ki = 29 nM), and benign neural and hepatic safety. Treatment with A10 could almost entirely recover the Aβ1-42-induced cognitive dysfunction to the normal level, and the assessment of total amount of Aβ1-42 confirmed its anti-amyloidogenic profile. Therefore, the potential BuChE inhibitor A10 is a promising effective lead for the treatment of AD.

Based on the DECaLS shear catalog, we study the scaling relations between halo mass ( M _h ) and various proxies for Sloan Digital Sky Survey central galaxies, including stellar mass ( M _* ), stellar velocity dispersion ( σ _* ), abundance-matching halo mass ( M _AM ), and satellite velocity dispersion ( σ _s ), and their dependencies on galaxy and group properties. In general, these proxies all have strong positive correlations with M _h , consistent with previous studies. We find that the M _h – M _* and M _h – σ _* relations depend strongly on group richness ( N _sat ), while the M _h – M _AM and M _h – σ _s relations are independent of it. Moreover, the dependence on the star formation rate (SFR) is rather weak in the M _h – σ _* and M _h – σ _s relations, but very prominent in the other two. σ _s is thus the best proxy among them, and its scaling relation is in good agreement with hydrodynamical simulations. However, estimating σ _s accurately for individual groups/clusters is challenging because of interlopers and the requirement for sufficient satellites. We construct new proxies by combining M _* , σ _* , and M _AM , and find that the proxy with 30% contribution from M _AM and 70% from σ _* can minimize the dependence on N _sat and the SFR. We obtain the M _h –supermassive black hole (SMBH) mass relation via the SMBH scaling relation and find indications for rapid and linear growth phases for the SMBH. We also find that correlations among M _h , M _* , and σ _* change with M _* , indicating that different processes drive the growth of galaxies and SMBHs at different stages.

A stellar system is dynamically hot when its kinetic energy is dominated by random motion represented by the velocity dispersion σ _hot . We use MaNGA data to obtain the inner and outer dispersion of a galaxy, σ _in and σ _out , to characterize its dynamical status and study its connection with star formation quenching and the growth of its supermassive black hole (SMBH). We divide galaxies into fully quenched (FQGs), partially quenched (PQGs), and fully star-forming (FSGs) populations, and identify quenched central cores (QCCs) in PQGs. The galaxy distribution in ( σ _in / σ _hot )–( σ _out / σ _hot ) diagram is L-shaped, consisting of a horizontal sequence ( σ _out / σ _hot ∼ 0) and a vertical sequence ( σ _in / σ _hot ∼ 1). FQGs and QCCs are located at the top of the vertical sequence, σ _out / σ _hot ∼ 1, and are thus dynamically hot over their entire bodies. PQGs reside along the vertical sequence, so they have hot centers but cold outskirts. FSGs are diverse and can be found in both sequences. Galaxy structural properties, star formation, and AGN activities make a transition along the horizontal sequence at σ _in / σ _hot ∼ 0.5, and along the vertical sequence at σ _out / σ _hot ∼ 0.5. The fractions of optical AGNs and barred galaxies increase rapidly in the first transition and decline rapidly in the second; radio galaxies are located at the top of the vertical sequence. Our results demonstrate that star formation quenching and SMBH growth are effective only in dynamically hot systems. A simple model along this line can reproduce the observed SMBH scaling relations. We discuss how secular processes and strong interactions can make a system dynamically hot, and lead to the SMBH growth and star formation quenching.

As an active microwave coherent imaging technology, synthetic aperture radar (SAR) images suffer from severe speckle noise and low-resolution problems due to the limitations of the imaging system, which cause difficulties in image interpretation and target detection. However, the existing SAR super-resolution (SR) methods usually reconstruct the images by a determined degradation model and hardly consider multiplicative speckle noise, meanwhile, most SR models are trained with synthetic datasets in which the low-resolution (LR) images are down-sampled from their high-resolution (HR) counterparts. These constraints cause a serious domain gap between the synthetic and real SAR images. To solve the above problems, this paper proposes an unsupervised blind SR method for SAR images by introducing SAR priors in a cycle-GAN framework. First, a learnable probabilistic degradation model combined with SAR noise priors was presented to satisfy various SAR images produced from different platforms. Then, a degradation model and a SR model in a unified cycle-GAN framework were trained simultaneously to learn the intrinsic relationship between HR–LR domains. The model was trained with real LR and HR SAR images instead of synthetic paired images to conquer the domain gap. Finally, experimental results on both synthetic and real SAR images demonstrated the high performance of the proposed method in terms of image quality and visual perception. Additionally, we found the proposed SR method demonstrates the tremendous potential for target detection tasks by reducing missed detection and false alarms significantly.

In view of the inaccuracy of rock movement observation data and the inaccuracy of mining subsidence prediction parameters, a prediction model of mining subsidence parameters based on fuzzy clustering is proposed. Through the analysis of the main geological and mineral characteristics of mining subsidence, the geological and mineral characteristics are simplified according to the third similar theorem. The feature equation is obtained by using the equation analysis method and dimension analysis method. The original fuzzy clustering method is improved, and the IWFCM_CCS algorithm based on competitive merger strategy is obtained. The data of rock movement observation are analyzed by fuzzy clustering. The membership matrix and clustering center of observation station data are obtained, and the regression model based on the weight of membership degree is established. The accuracy and feasibility of the parameter prediction model are verified by analyzing and comparing the actual measurement data and the predicted results of the model. The method reduces the error of the predicted parameters caused by the observation data and provides a method for the future calculation of the predicted parameters.

As a powerful tool to solve nonlinear problems, artificial neural network method (ANN) gets a wide range of applications in data regression. However, the overfitting often occurs during the ANN training process, which results in high accuracy of correlating the training data but poor prediction performance. At the same time, the principle of k-Nearest Neighbor method (kNN) makes it impossible to make an accurate prediction exceeding the range of the training data, but it can confine the overfitting of ANN. In this work, combining ANN and kNN, a new machine learning method called ANN-kNN combination method (AKC) for thermophysical property prediction of material is proposed. To evaluate the performance of AKC, we take the thermophysical properties of natural gas as an example. The inputs of AKC are temperature, pressure and the components of natural gas, the outputs are the compressibility factor, speed of sound and viscosity. AKC not only overcomes the overfitting problem but also needs less training data than ANN. The average absolute relative deviation of AKC for prediction are 2.5%, which are better than ANN (5.9%) and kNN (19.2%).

Background: This study aimed to investigate the TP53 mutation, its potential immune features, its prognostic value, and its impact on immune infiltration in patients with breast cancer (BC).Methods: We downloaded the somatic mutation data and clinicopathologic features of BC patients from the TCGA GDC database, UCSC Xena platform, and International Cancer Genome Consortium (ICGC) database. The association between the TP53 mutation, clinicopathology features, and overall survival (OS) in BC patients was analyzed. We evaluated the potential role of the TP53 mutation in the immune therapy response, including the tumor mutation burden (TMB), microsatellite instability (MSI), and tumor immune dysfunction and exclusion (TIDE). Moreover, ESTIMATE was employed to assess the ImmuneScore and StromalScore in BC patients. We also explored immunocyte infiltration related to the TP53 mutation and its potential mechanism. Immunohistochemistry (IHC) was performed to validate the association between the expression of CXCL1, CXCL10, and CCL20 and TP53 status.Results: We found that the TP53 mutation was significantly associated with the shorter OS (p = 0.038) and was also an independent predictive factor of OS for BC patients (p < 0.001). Compared to that in the wild type group, the TP53-mutant group showed a higher TMB value (P< 0.001), MSI value (p = 0.077), and TIDE value (p < 0.001) with respect to BC patient immunotherapy. In addition, the ImmuneScore and StromalScore were both significantly increased in the TP53-mutant group (ImmuneScore: p < 0.001; StromalScore: p = 0.003). The results of CIBERSORT suggested that the TP53 mutation significantly promoted the infiltration of Tregs, T helper cells, and M0-type macrophages. KEGG and GSEA enrichment results suggested that the IL-17 signaling pathway and antigen processing and presentation pathways were significantly enriched in the TP53-mutant group. Importantly, based on IHC results of immune-related hub-genes, the chemokines CXCL1, CXCL10, and CCL20 were significantly upregulated in the TP53-mutant group in BC patients.Conclusion: These results indicate that a TP53 mutation might serve as a biomarker for BC prognosis and is related to immunocyte infiltration in the tumor microenvironment.

Abstract Background The toxicity and side effects caused by adjuvant chemotherapy (ACT) after radical surgery for lung adenocarcinoma (LAC) lead to early termination frequently. This study was conducted to provide an objective basis for the effect of Chinese herbal medicine formulas (CHMFs) combined with chemotherapy in reducing toxicity and enhancing efficacy of ACT. Method From February 17th, 2012 to March 20th, 2015, 233 patients from 7 hospitals diagnosed with LAC in IB~IIIA stage were randomly assigned into ACT + CHMF group (116 patients) and ACT + placebo group (117 patients). CHMF was taken orally until the end of chemotherapy. Chemotherapy-related toxic, side effects were investigated as the primary outcome. Disease-free survival (DFS) and overall survival (OS) were used as the secondary outcome. Results At one week following chemotherapy, the incidence of dry mouth, diarrhea and thrombocytopenia significantly decreased in CHMF group (P = 0.017, P = 0.033, P = 0.019, respectively). At two weeks following chemotherapy, fatigue and diarrhea were more obvious in the placebo group (P = 0.028, P = 0.025, respectively). In addition, patients in the CHMF group showed an increase in median DFS from 37.1 to 51.5 months compared with placebo group although there was no statistical significance (P = 0.16). In the stage IB subgroup, the CHMF group had a significantly better DFS (HR (95% CI) = 0.53 (0.28–0.99), P = 0.046). There was no significant difference in OS between the groups (P = 0.72). Conclusion For patients with LAC, ACT combined with CHMF after radical surgery can prolong the DFS time especially in the early stage, and reduces the chemotherapy-related toxic and side effects. Trial Registration NCT 01441752 . Registered 14 July, 2011.

Hibernators are a natural model of vascular ischemia–reperfusion injury; however, the protective mechanisms involved in dealing with such an injury over the torpor–arousal cycle are unclear. The present study aimed to clarify the changes in the thoracic aorta and serum in summer-active (SA), late-torpor (LT) and interbout-arousal (IBA) Daurian ground squirrels (Spermophilus dauricus). The results show that total antioxidant capacity (TAC) was unchanged, but malondialdehyde (MDA), hydrogen peroxide (H2O2), interleukin-1β (IL-1β) and tumor necrosis factor α (TNFα) were significantly increased for the LT group, whereas the levels of superoxide dismutase (SOD) and interleukin-10 (IL-10) were significantly reduced in the LT group as compared with the SA group. Moreover, the levels of MDA and IL-1β were significantly reduced, whereas SOD and IL-10 were significantly increased in the IBA group as compared with the SA group. In addition, the lumen area of the thoracic aorta and the expression of the smooth muscle cells (SMCs) contractile marker protein 22α (SM22α) were significantly reduced, whereas the protein expression of the synthetic marker proteins osteopontin (OPN), vimentin (VIM) and proliferating cell nuclear antigen (PCNA) were significantly increased in the LT group as compared with the SA group. Furthermore, the smooth muscle layer of the thoracic aorta was significantly thickened, and PCNA protein expression was significantly reduced in the IBA group as compared with the SA group. The contractile marker proteins SM22α and synthetic marker protein VIM underwent significant localization changes in both LT and IBA groups, with localization of the contractile marker protein α-smooth muscle actin (αSMA) changing only in the IBA group as compared with the SA group. In tunica intima, the serum levels of heparin sulfate (HS) and syndecan-1 (Sy-1) in the LT group were significantly reduced, but the serum level of HS in the IBA group increased significantly as compared with the SA group. Protein expression and localization of endothelial nitric oxide synthase (eNOS) was unchanged in the three groups. In summary, the decrease in reactive oxygen species (ROS) and pro-inflammatory factors and increase in SOD and anti-inflammatory factors during the IBA period induced controlled phenotypic switching of thoracic aortic SMCs and restoration of endothelial permeability to resist ischemic and hypoxic injury during torpor of Daurian ground squirrels.

Background: Both pathological conditions and hibernation can affect the barrier function of small intestine mucosa. However, the effect of hibernation on the barrier function of colonic mucosa remains unclear. Methods: We investigated morphological changes in colonic mucosa, the concentrations of specific proteins and molecules, and the enzymatic activity of diamine oxidase (DAO), in serum and colonic tissue; the expression of tight junction proteins and mucin, and the changes in inflammatory, farnesoid X receptor (FXR)–small heterodimer partner (SHP), and apoptosis-related molecules that could play a role in gut permeability changes in Daurian ground squirrels in summer active (SA), late torpor (LT), and interbout arousal (IBA) periods. Results: The results show that hibernation reduced the thickness of the colonic mucosa and the depth of the crypt, decreased the number of goblet cells (GCs), and damaged the structure of some microvilli. The concentrations of proteins and molecules, and the enzymatic activity of DAO, were all increased in the serum and colon, and the localization of tight junction proteins and mucin in the colonic mucosa were altered (compensatory response). Although the ground squirrels ate during the interbout arousal period, the changes remained similar to the response to torpor. Inflammation, apoptosis–anti-apoptosis, and FXR–SHP signaling may be involved in the possible changes in intestinal gut permeability during the torpor–arousal cycle in Daurian ground squirrels. In addition, periodic interbout arousal may play an inflammation-correcting role during the long hibernation season of Daurian ground squirrels.

Sulfonyl drugs are widely used to treat various types of diseases, in which sultones have several desirable properties that have led to their increased use, including improving its simple and effective synthetic methods and/or potentially enhancing potency of drugs. This review focuses on the recent progress in typical synthesis of sultones and the latest developments in various therapeutic applications, including carbonic anhydrase inhibitory activity, HIV-I and HIV-II inhibitory activity, human cytomegalovirus inhibitory activity, and cholinesterase inhibitory activity and their structure–activity relationship. We hope that this review will encourage medicinal chemists to consider the potential benefits to incorporating sultone scaffold into future drug discovery.

Background: Long noncoding RNA (lncRNA) ST7-AS1 can be observed in various cancers, but its role in breast cancer (BRC) remains unclear. Our aim is to, on the basis of The Cancer Genome Atlas (TCGA) database, prove the correlation between lncRNA ST7-AS1 and BRC.Methods: The lncRNA ST7-AS1 expression and its roles in the prognosis of BRC were explored using data from the TCGA database. The expression level of lncRNA ST7-AS1 in BRC samples was detected using RT-PCR. The 1-, 3-, or 5-year survival rate was predicted using a nomogram established through Cox proportional hazard regression. At last, the biological function was explored through gene ontology (GO) analysis and gene set enrichment analysis (GSEA). The hallmark pathways significantly involved in hub genes were described through functional enrichment analysis. The correlation between lncRNA ST7-AS1 expression and immune infiltration was analyzed through single-sample GSEA (ssGSEA).Results: LncRNA ST7-AS1 expression was downregulated in BRC. Decreased lncRNA ST7-AS1 expression in BRC was correlated with advanced clinical pathologic characteristics (high grade, histological type, age, menopause status, and HER2 status), survival time, and poor prognosis. The nomogram was established for using lncRNA ST7-AS1 to predict 1-, 3-, or 5-year survival in patients with BRC. In addition, GO and pathway analyses suggested the involvement of lncRNA ST7-AS1 in cell cycle, DNA repair, and immune cell infiltration in the BRC immune microenvironment. We found the correlation of lncRNA ST7-AS1 with T helper cells and DC cells.Conclusion: Low expression of lncRNA ST7-AS1 indicates poor prognosis and has an impact on cell cycle, DNA repair, and proportion of infiltrating immune cells in the BRC microenvironment. Therefore, lncRNA ST7-AS1 can be used as a protective prognostic marker and a potential treatment target for BRC.

Abstract With the gradual increase in the number of GNSS systems and the improvement of functions, in addition to the single-system navigation and timing service, the integrated navigation and positioning service among multiple systems can provide users with more accurate and stable positioning results, arousing more attention from the workers in GNSS field. Compatibility and interoperability among different systems has become a trend in the development of GNSS. Compatibility and interoperability between systems require a uniform time scale. Therefore, the measurement and forecasting of time deviations in GNSS systems is particularly important. This paper first studies the multi-system fusion location model and proposes an adaptive GNSS fusion PPP algorithm based on parameter equivalent reduction. Then, the method of fusion PPP is used to monitor the time difference of GNSS. Finally, the effectiveness of the improved algorithm and time difference monitoring method is verified by practical examples.

Image colorization is a creative process of reasonably adding colors on gray-scale images to generate well-pleasing colorized images. The existing colorization methods normally require user-supplied hints of color points and doodles, or handpicked color reference images for transferring colors, or diverse color images for predicting the colorized results; but the final colorized results generated by most of them may seem unnatural as a consequence of the unprofessional users' skills, inaccurately color transferring, or limited scale of color image collection. To overcome these limitations, a hybrid scheme consisting of two modules is proposed for images' region colorization by combining semantic segmentation and seamless fusion techniques in this paper. In the first module, the masks and category of input image's regions and background are derived from a Mask R-CNN model, and the corresponding reference images of each region are selected from a pre-classified color image database. In the second module, the background and various regions of an image are colorized by a U-Net model and a VGG model respectively. Then, the Poisson editing technique is applied for fusing all the colorized results to generate the final whole colorized image. The experiments show that our scheme can not only flexibly select the appropriate reference images for different regions of the image according their semantic information, but also effectively merge colorized results to generate a plausible colorful image. By reasonably combining different CNN-based models according to their superiority, our scheme avoids the limitation of failures caused by single-step methods, and achieves richer artistic visual effect compared with other existing methods.

Neural style transfer recently has become one of the most popular topics in academic research and industrial application. The existing methods can generate synthetic images by transferring different styles of some images to another given content images, but they mainly focus on learning low-level features of images with losses of content and style, leading to greatly alter the salient information of content images in the semantic level. In this paper, an improved scheme is proposed to keep the salient regions of the transferred image the same with that of content image. By adding the region loss calculated from a localization network, the synthetic image can almost keep the main salient regions consistent with that of original content image, which helps for saliency-based tasks such as object localization and classification. In addition, the transferred effect is more natural and attractive, avoiding simple texture overlay of the style image. Furthermore, our scheme can also extend to remain other semantic information (such as shape, edge, and color) of the image with the corresponding estimation networks.

Smoothness and discontinuous (SD) oscillator is a nonlinear oscillator with the variable frequency, whose frequency can be varied with the smoothing parameter. However, how to adjust the smoothing parameter has not been solved in the actual device. In this paper, the shape memory alloy (SMA) is introduced into the SD oscillator to form the SMA-SD oscillator to adjust the smoothing parameters. Combining the SMA-SD oscillator with MRF, a nonlinear dynamic vibration absorber (DVA) with variable frequency and damping is designed. The structure and control principle of the designed DVA is studied to achieve the two variable characteristics simultaneously by adjusting the current intensity. Numerical results on a two-degree-of-freedom coupled system show that the proposed DVA can adapt to different working conditions only by adjusting the current intensity.

Epilepsy is one of the most common disorders of the brain. Clinically, to corroborate an epileptic seizure-like symptom and to find the seizure localization, electroencephalogram (EEG) data are often visually examined by a clinical doctor to detect the presence of epileptiform discharges. Epileptiform discharges are transient waveforms lasting for several tens to hundreds of milliseconds and are mainly divided into seven types. It is important to develop systematic approaches to accurately distinguish these waveforms from normal control ones. This is a difficult task if one wishes to develop first principle rather than black-box based approaches, since clinically used scalp EEGs usually contain a lot of noise and artifacts. To solve this problem, we analyzed 640 multi-channel EEG segments, each 4s long. Among these segments, 540 are short epileptiform discharges, and 100 are from healthy controls. We have proposed two approaches for distinguishing epileptiform discharges from normal EEGs. The first method is based on Signal Range and EEGs' long range correlation properties characterized by the Hurst parameter H extracted by applying adaptive fractal analysis (AFA), which can also maximally suppress the effects of noise and various kinds of artifacts. Our second method is based on networks constructed from three aspects of the scalp EEG signals, the Signal Range, the energy of the alpha wave component, and EEG's long range correlation properties. The networks are further analyzed using singular value decomposition (SVD). The square of the first singular value from SVD is used to construct features to distinguish epileptiform discharges from normal controls. Using Random Forest Classifier (RF), our approaches can achieve very high accuracy in distinguishing epileptiform discharges from normal control ones, and thus are very promising to be used clinically. The network-based approach is also used to infer the localizations of each type of epileptiform discharges, and it is found that the sub-networks representing the most likely location of each type of epileptiform discharges are different among the seven types of epileptiform discharges.

Sperm-associated antigen 11A (SPAG11A) is expressed exclusively in the epididymis, which can specifically regulate sperm maturation. The aim of this study was to investigate the seasonal expressions of beta-defensin (SPAG11A) and androgen receptor (AR) in the epididymis of the wild ground squirrels (Citellus dauricus Brandt). Morphologically, the results showed that epididymis length and weight in the breeding season were significantly higher than those of the non-breeding season. Histologically, the results revealed that enlarged lumen diameters, thickened epithelial and abundant sperm in the breeding season while reduced lumen diameters and epithelial with no sperm in the non-breeding season. SPAG11A was intensely expressed in cytoplasm and nucleus of epithelial cells in the breeding season, and weaker staining in the non-breeding season. The immunostaining of AR was only presented in nucleus of smooth muscle cells and epithelial cells in the whole epididymis with stronger staining in the breeding season. The results of real-time quantitative PCR also showed that the mRNA levels of SPAG11A and AR in the epididymis during the breeding season were significantly higher than those of the non-breeding season. In addition, the levels of testosterone (T), follicle-stimulating hormone (FSH) and luteinizing hormone (LH) in the serum were higher during the breeding season. Taken together, these results suggested that SPAG11A might play an important role to regulate seasonal changes in epididymal function of the wild ground squirrels.

Abstract Background Hyaluronan synthases (HAS) control the biosynthesis of hyaluronan (HA) and critically modulate the tumor microenviroment. Cancer-associated fibroblasts (CAFs) affect the progression of a tumor by remolding the matrix. However, little is known about the role of HAS from CAFs in this process. This study aimed to determine the role of hyaluronan synthase 2 (HAS2) from CAFs in the progression of oral squamous cell carcinoma (OSCC) invasion. Methods HAS isoforms 1, 2, and 3 in paired sets of CAFs and normal fibroblasts (NFs) were examined by real-time PCR, and the expression of HAS2 and α-SMA in OSCC tissue sections was further evaluated using immunohistochemical staining. Furthermore, we used a conditioned culture medium model to evaluate the effects of HAS2 from CAFs on the invasion and epithelial-mesenchymal transition (EMT) of the oral cancer cells Cal27. Finally, we compared the expression of matrix metalloproteinases (MMPs) and tissue inhibitors of metalloproteinases (TIMPs) between CAFs and NF, and between CAFs with or without HAS2 knockdown using an antibody array and western blotting. Results CAFs expressed higher levels of HAS2 than the paired NFs. HAS2 expression was consistent with α-SMA-positive myofibroblasts in the stroma of OSCC, and these were significantly correlated advanced clinical stages and cervical lymph node metastasis. Knocking down HAS2 with a specific siRNA or treatment with a HAS inhibitor markedly attenuated CAF-induced invasion and EMT of Cal27 cells. Higher MMP1 and lower TIMP1 levels were detected in the supernatants of CAFs relative to NFs. Knocking down HAS2 could decrease the expression of MMP1 and increase that of TIMP1 in CAFs. Conclusions HAS2 is one of the key regulators responsible for CAF-mediated OSCC progression and acts by modulating the balance of MMP1 and TIMP1.

Background: To determine the clinical activity and safety of Chinese herbal medicine (CHM) combined with epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKI) in patients with advanced pulmonary adenocarcinoma (ADC) and the ability of CHM combined with EGFR-TKI to activate EGFR mutations.Methods: Three hundred and fifty-four patients were randomly assigned to EGFR-TKI (erlotinib 150 mg/d, gefitinib 250 mg/d, or icotinib 125 mg tid/d) plus CHM (TKI+CHM, N = 185) or EGFR-TKI plus placebo (TKI+placebo, N = 169). Progression-free survival (PFS) was the primary end point; the secondary end points were overall survival (OS), objective response rate (ORR), disease control rate (DCR), quality of life [Functional Assessment of Cancer Therapy-Lung (FACT-L) and Lung Cancer Symptom Scale (LCSS)], and safety.Results: The median PFS was significantly longer for the TKI+CHM group (13.50 months; 95% CI, 11.20–16.46 months) than with the EGFR-TKI group (10.94 months; 95% CI, 8.97–12.45 months; hazard ratio, 0.68; 95% CI, 0.51–0.90; P = 0.0064). The subgroup analyses favored TKI+CHM as a first-line treatment (15.97 vs. 10.97 months, P = 0.0447) rather than as a second-line treatment (11.43 vs. 9.23 months, P = 0.0530). Patients with exon 19 deletion had a significantly longer PFS than with 21 L858R. The addition of CHM to TKI significantly improved the ORR (64.32% vs. 52.66%, P = 0.026) and QoL. Drug-related grade 1–2 adverse events were less common with TKI+CHM.Conclusions: TKI+CHM improved PFS when compared with TKI alone in patients with EGFR mutation-positive advanced non-small-cell lung cancer (NSCLC).Clinical Trial Registration: www.ClinicalTrials.gov, identifier NCT01745302.

Background: Chinese Herb Medicine Formulas (CHMF) was reported to improve the quality of life (QoL) in advanced NSCLC patients. The present study was designed to investigate whether maintenance chemotherapy plus CHMF in patients would improve QoL and progression-free survival (PFS).Methods: Seventy-one patients were enrolled from 8 medical centers in China, and were randomly assigned to a maintenance chemotherapy plus CHMF group (n = 35) or a maintenance chemotherapy plus placebo group (n = 36). The outcome measures included PFS, Karnofsky performance status (KPS) scores, QoL (assessed with the lung cancer symptom scale (LCSS) questionnaire), and adverse events (AEs).Results: Patients in the CHMF group showed significant improvements in median PFS (HR = 0.55, 95% CI 0.28–0.88, P = 0.019), KPS scores (P = 0.047), fatigue (cycle [C] 3: P = 0.03), interference with daily activities (C3: P = 0.04) and dyspnea (C2: P = 0.03) compared with patients in the placebo group. Compared with the placebo group, the incidence of AEs decreased in the CHMF group, including loss of appetite (C2: P = 0.011, C4: P = 0.004) and dry mouth (C4: P = 0.011).Conclusion: The essential finding of our study is that maintenance chemotherapy combined with CHMF may prolong PFS, relieve symptoms, improve QoL and alleviate the side effects.

The symmetrical difference kernel SAR image edge detection algorithm based on the Canny operator can usually achieve effective edge detection of a single view image. When detecting a multi-view SAR image edge, it has the disadvantage of a low detection accuracy. An edge detection algorithm for a symmetric difference nuclear SAR image based on the GAN network model is proposed. Multi-view data of a symmetric difference nuclear SAR image are generated by the GAN network model. According to the results of multi-view data generation, an edge detection model for an arbitrary direction symmetric difference nuclear SAR image is constructed. A non-edge is eliminated by edge post-processing. The Hough transform is used to calculate the edge direction to realize the accurate detection of the edge of the SAR image. The experimental results show that the average classification accuracy of the proposed algorithm is 93.8%, 96.85% of the detection edges coincide with the correct edges, and 97.08% of the detection edges fall into the buffer of three pixel widths, whichshows that the proposed algorithm has a high accuracy of edge detection for kernel SAR images.

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]