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Based on the sample of A-share listed companies in China from 2013 to 2021, this paper explores the influence of economic policy uncertainty on cash dividend policy through random effect panel regression, and draws the following conclusions: First, the increase of economic policy uncertainty will make enterprises pay more cash dividends; Secondly, compared with enterprises with low financing constraints, enterprises with high financing constraints are more willing to pay more cash dividends in the face of increasing economic policy uncertainty; Finally, the higher the growth of enterprises, the more significant the impact of economic policy uncertainty on cash dividends of enterprises. The research of this paper not only further expands the research direction of economic policy uncertainty and cash dividend policy, but also has certain guiding role for enterprises to formulate more reasonable cash dividend policy.

As new ways to solve partial differential equations (PDEs), physics-informed neural network (PINN) algorithms have received widespread attention and have been applied in many fields of study. However, the standard PINN framework lacks sufficient seepage head data, and the method is difficult to apply effectively in seepage analysis with complex boundary conditions. In addition, the differential type Neumann boundary makes the solution more difficult. This study proposed an improved prediction method based on a PINN with the aim of calculating PDEs with complex boundary conditions such as Neumann boundary conditions, in which the spatial distribution characteristic information is increased by a small amount of measured data and the loss equation is dynamically adjusted by loss weighting coefficients. The measured data are converted into a quadratic regular term and added to the loss function as feature data to guide the update process for the weight and bias coefficient of each neuron in the neural network. A typical geotechnical problem concerning seepage phreatic line determination in a rectangular dam is analyzed to demonstrate the efficiency of the improved method. Compared with the standard PINN algorithm, due to the addition of measurement data and dynamic loss weighting coefficients, the improved PINN algorithm has better convergence and can handle more complex boundary conditions. The results show that the improved method makes it convenient to predict the phreatic line in seepage analysis for geotechnical engineering projects with measured data.

Introduction: In view of the vital implication of long noncoding RNAs in tumorigenesis, we possess the aim to determine the action effects and mechanisms of LINC01002 in prostate cancer (PCa). Methods: Expression level of LINC01002, miR-650, or filamin A (FLNA) in PCa tissues and cells was assessed using quantitative real-time PCR or Western blotting. Cell proliferative and migratory capacities were investigated by Cell Counting Kit-8 (CCK-8) and wound healing assays. Cell apoptosis was investigated by the levels of Bax and Bcl-2. Xenograft models were constructed to testify the role of LINC01002 in vivo. The anticipated binding of miR-650 to LINC01002 or FLNA was confirmed by dual-luciferase reporter or RNA binding protein immunoprecipitation assays. Results: Relatively poor expression of LINC01002 and FLNA, and high expression of miR-650 were identified in PCa tumor specimens and cells. Ectopic LINC01002 expression restrained PCa cell proliferation/migration and provoked apoptosis in vitro, and blocked solid tumor growth in Xenograft models. MiR-650 was directly targeted by LINC01002, and it also directly bound to FLNA. MiR-650 reintroduction in PCa cells overexpressing LINC01002 or FLNA partly reversed the anticancer effects of LINC01002 or FLNA overexpression, thus recovering PCa cell proliferation/migration and repressing apoptosis. Conclusion: LINC01002 deregulation was linked to PCa development. LINC01002 exerted potential anticancer effects in PCa via targeting the miR-650/FLNA pathway, which, at least in part, provided a basis for the involvement of LINC01002 as a therapeutic target in PCa.

The powder diameter affects the heat generation and further influences the quality of weldments when the MZA powder is used as laser-absorbents to carry out laser transmission welding (LTW). It is difficult to measure the temperature due to its extremely short duration. A mathematical model considering the multiple reflections of metal powder is established to predict heat generation. The mathematical model is verified and then used to describe the influence of line energy density and powder diameter on the temperature profile of the LTW process. The results demonstrate that the temperature from experiments and simulation are consistent. Therefore, the established model is suitable to predict the heat generation of metal powder laser-absorbents in the LTW process.

Increasing evidences showed that ovulatory dysfunction, possibly caused by luteinized unruptured follicular follicle syndrome (LUFS), is one of the reasons for endometriosis-related infertility. The present study was conducted to explore the potential effect of elevated progesterone in follicular fluid (FF) on ovulation in endometriosis. A prospective study including 50 ovarian endometriosis patients and 50 control patients with matched pairs design was conducted with alterations in FF and peritoneal fluid (PF) components identified by metabolomics analyses and differentially expressed genes in granulosa cells (GCs) identified by transcriptome analysis. Patients with endometriosis exhibited a significantly higher progesterone level in serum, FF, and PF. Granulosa cells from endometriosis patients revealed decreased expression of HPGD, COX-2, and suppressed NF-ĸB signaling. Similarly, progesterone treatment in vitro downregulated HPGD and COX2 expression and suppressed NF-ĸB signaling in granulosa tumor-like cell line KGN (Bena Culture Collection, China) and primarily cultured GCs, as manifested by decreased expressions of IL1R1, IRAK3, reduced pIĸBα/IĸBα ratio, and nucleus translocation of p65. On the contrary, TNF-α treatment increased expression of IL1R1, IRAK3, pIĸBα, p65, and HPGD in GCs. One potential p65 binding site was identified in the promoter region of HPGD by chromatin immunoprecipitation. In conclusion, we found that intrafollicular progesterone might downregulate HPGD and COX-2 in GCs via suppressing the NF-ĸB signaling pathway, shedding light on the mechanism underlying the endometriosis-related ovulatory dysfunction.

DHA-enriched phosphatidylserine could likely be used as a functional food supplement for the prevention of high fat diet-induced jejunum injury in mice by modulating gut microbiota.

With the development of economy and society, cast-in-place buildings have brought many unexpected side effects, which affect our living environment. The emergence of "assembled" building makes up for the shortcomings of cast-in-place building such as high cost, unfriendly materials, long construction period and short life span, etc. With the support of BIM technology (BIM+assembly), it makes up for the shortcomings of assembled building which requires secondary pouring and large workload. BIM technology simplifies the design and review of drawings in the early stage of the assembled building; the assembled building shortens the construction and acceptance cycle in the later stage. The combination of "BIM+assembly" is a form of construction worth studying and promoting. It can better realize efficient and accurate design, green, low-carbon, energy-saving, environmental protection, time-saving and intelligent construction.

Based on the limitations of traditional soil analyzers, this paper proposes an auxiliary feeding device and feeding method of soil element analyzer, including an outer cylinder with a discharge port and a hollow structure, and a material conveying device in the outer cylinder. The outer cylinder is also provided with a feeding device coaxially, and the feeding device is provided with a plurality of storage silos, and the feeding device rotates the materials placed in the storage silo into the material conveying device in turn, and is transported to the discharge port by the material conveying device; The feeding device is set on the sliding bracket, the outer cylinder is set on the base, and the sliding bracket is sliding connected with the base. This method can realize a large number of automatic feeding at one time, which greatly reduces the labor intensity of the staff.

PurposeThe existing consensus reaching mechanisms ignore the influence of social triangle structure on the decision-makers’ (DMs') weights, and the consensus reaching process (CRP) fails to fully reflect the DMs' subjectivity and can be time consuming and costly. To solve these issues, a novel CRP for multi-criteria group decision-making (MCGDM) problems with intuitionistic grey linguistic numbers (IGLNs) is proposed in this paper.Design/methodology/approachFirst, a weight calculation method is proposed by analysing the triangle structure of DMs' social network and scale of adjacent nodes. Then, a consensus degree index based on three-level polygon area is defined and applied to identify the inconsistent DMs. Finally, the feedback mechanism based on particle swarm optimisation (PSO) algorithm under grey linguistic environment is developed, where subjective trust relationships in social network is utilised to determine the adjustment coefficient.FindingsThe advantages of the proposed method are highlighted by two practical applications of the evaluation of tunnel construction method and the selection of a hotel for the centralised isolation. Comparision analysis and numerical simulation are performed to reveal the effectiveness and applicability of the method.Practical implicationsThe proposed model can not only reflect the effect of triangle structure in social network on DMs' weights, but also reduce the time and cost of decision-making.Originality/valueThe main contribution of this paper is to propose a new MCGDM model based on intuitionistic grey linguistic numbers, which can handle the problem of inconsistency of information more effectively.

Qinbo Wang,1– 3,&ast; Zongyan Li,1,&ast; Xiaojia Huang,1 Xiaoyan Fu,1 Qian Li,1 Haiyan Li1,3 1Department of Breast Surgery, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 2Department of Pharmacy, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 3Department of Graceland Medical Center, The Sixth Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Haiyan Li, Department of Breast Surgery, the Sixth Affiliated Hospital, Sun Yat-Sen University, 26# Erheng Road, Yuan Village, Tianhe District, Guangzhou, 510655, People’s Republic of China, Email lihy27@mail.sysu.edu.cnPurpose: Traditional standardized training in ultrasound-guided minimally invasive breast surgery (UMIBS) focused on lecture-based learning (LBL) resulted in students’ insufficient analysis, limited spatial visual conversion ability, and poor practical application. This study examined the effects of the step-by-step (SBS) method combined with a simulation model in UMIBS education.Subjects and Methods: A total of 84 residents participated in this study. The residents were divided into the SBS group (experience group, n=42) and the LBL group (control group, n=42), and the same teacher taught the two groups to ensure a comparable result. Based on the pork simulation model, two experts evaluated student performance scores, and the total time taken by each student was also counted. The participants were surveyed with 7 questions after the training, and each answer was assigned a score of 1, 2 or 3 to compare the participants’ satisfaction.Results: The average value of the surgical skills for SBS group were significantly higher than LBS group, which was 82.8± 4.4 and 72.7± 4.0 (t=4.27, P< 0.001), the time spend of neoplasm localization by the experience group was significantly less than the control group, which was 17.9± 1.6 and 20.9± 1.2 secs, (t=1.58, P< 0.001), and there were significant differences in puncture accuracy and excision integrity between the two groups (P< 0.05). In addition, the results of the questionnaire survey showed that learning interest, surgical ability and satisfaction were better in the SBS group than in the LBS group (P< 0.05), and there were no significant differences in clinical thinking and learning pressure between the two groups.Conclusion: The SBS teaching method may help to improve the surgical skills and learning interest, as well as reduce adverse reactions and cultivate clinical thinking of the students in UMIBS training. Future studies could consider multicenter clinical research to further confirm the practicality of this teaching method and reduce the risk of deviation.Keywords: ultrasound, minimally invasive, step by step, lecture-based learning, simulation

International audience; This paper proposes BPNet, a novel end-to-end deep learning framework to learn Bézier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from Bézier decomposition on NURBS models, we transfer it to guide point cloud segmentation casting off primitive types. A joint optimization framework is proposed to learn Bézier primitive segmentation and geometric fitting simultaneously on a cascaded architecture. Specifically, we introduce a soft voting regularizer to improve primitive segmentation and propose an auto-weight embedding module to cluster point features, making the network more robust and generic. We also introduce a reconstruction module where we successfully process multiple CAD models with different primitives simultaneously. We conducted extensive experiments on the synthetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline methods. Experiments show superior performance over previous work in terms of segmentation, with a substantially faster inference speed.

Fairness-aware recommendation eliminates discrimination issues to build trustworthy recommendation systems.Explaining the causes of unfair recommendations is critical, as it promotes fairness diagnostics, and thus secures users' trust in recommendation models. Existing fairness explanation methods suffer high computation burdens due to the large-scale search space and the greedy nature of the explanation search process. Besides, they perform score-based optimizations with continuous values, which are not applicable to discrete attributes such as gender and race. In this work, we adopt the novel paradigm of counterfactual explanation from causal inference to explore how minimal alterations in explanations change model fairness, to abandon the greedy search for explanations. We use real-world attributes from Heterogeneous Information Networks (HINs) to empower counterfactual reasoning on discrete attributes. We propose a novel Counterfactual Explanation for Fairness (CFairER) that generates attribute-level counterfactual explanations from HINs for recommendation fairness. Our CFairER conducts off-policy reinforcement learning to seek high-quality counterfactual explanations, with an attentive action pruning reducing the search space of candidate counterfactuals. The counterfactual explanations help to provide rational and proximate explanations for model fairness, while the attentive action pruning narrows the search space of attributes. Extensive experiments demonstrate our proposed model can generate faithful explanations while maintaining favorable recommendation performance.

Graph collaborative filtering (GCF) has gained considerable attention in recommendation systems by leveraging graph learning techniques to enhance collaborative filtering (CF) models. One classical approach in GCF is to learn user and item embeddings by modeling complex graph relations and utilizing these embeddings for CF models. However, the quality of the embeddings significantly impacts the recommendation performance of GCF models. In this paper, we argue that existing graph learning methods are insufficient in generating satisfactory embeddings for CF models. This is because they aggregate neighboring node messages directly, which can result in incorrect estimations of user-item correlations. To overcome this limitation, we propose a novel approach that incorporates causal modeling to explicitly encode the causal effects of neighboring nodes on the target node. This approach enables us to identify spurious correlations and uncover the root causes of user preferences. We introduce Causal Neural Graph Collaborative Filtering (CNGCF), the first causality-aware graph learning framework for CF. CNGCF integrates causal modeling into the graph representation learning process, explicitly coupling causal effects between node pairs into the core message-passing process of graph learning. As a result, CNGCF yields causality-aware embeddings that promote robust recommendations. Our extensive experiments demonstrate that CNGCF provides precise recommendations that align with user preferences. Therefore, our proposed framework can address the limitations of existing GCF models and offer a more effective solution for recommendation systems.

The gray-body factor and the absorption cross section of the 4D ESTGB gravity with a mode of nonlinear electrodynamics for the massless Dirac field are studied in this paper. The magnetic charge value varies between $-2^{(\frac{5}{3})}/3$ and $0$ as well as the ADM mass is set to $1$, which corresponds to a non-extreme black hole. The gray-body factor is obtained using the semi-analytic WKB method after solving the massless Dirac equation. When the absolute value of magnetic charge is increasing, the gray-body factor $\gamma(\omega)$ is decreasing. In addition, the partial absorption cross section and the total absorption cross section are calculated by using the partial wave method. We find that the maximum value of partial absorption cross section decreases as $\kappa$ increases. And the existence of magnetic charge causes the diminishing of the total absorption cross section. Finally, we find that the absorption cross section of the Dirac field is more sensitive to electric charge than magnetic charge by comparing the absorption cross section of the Reissner-Nordstr$\rm\ddot{o}$m and ESTGB-NLED black holes., Comment: 11 pages, 7 figures

This paper proposes BPNet, a novel end-to-end deep learning framework to learn B\'ezier primitive segmentation on 3D point clouds. The existing works treat different primitive types separately, thus limiting them to finite shape categories. To address this issue, we seek a generalized primitive segmentation on point clouds. Taking inspiration from B\'ezier decomposition on NURBS models, we transfer it to guide point cloud segmentation casting off primitive types. A joint optimization framework is proposed to learn B\'ezier primitive segmentation and geometric fitting simultaneously on a cascaded architecture. Specifically, we introduce a soft voting regularizer to improve primitive segmentation and propose an auto-weight embedding module to cluster point features, making the network more robust and generic. We also introduce a reconstruction module where we successfully process multiple CAD models with different primitives simultaneously. We conducted extensive experiments on the synthetic ABC dataset and real-scan datasets to validate and compare our approach with different baseline methods. Experiments show superior performance over previous work in terms of segmentation, with a substantially faster inference speed.

With the wide availability of remotely sensed data from various sensors, fusion-based tree species classification approaches have emerged as a prominent and ongoing research topic. However, most recent studies primarily focused on combining multisource data at the feature level, while few systematically examined their positive or negative contributions to tree species classification. This study aimed to investigate fusion approaches at the feature and decision levels deployed with support vector machine and random forest algorithms to classify five dominant tree species: Norway maple, honey locust, Austrian pine, white spruce, and blue spruce in individual crowns. Spectral, textural, and structural features derived from multispectral imagery (MSI), a very high-resolution panchromatic image (PAN), and LiDAR data were systematically exploited to assess their contributions to accurate classifications. Among the various classification schemes that were explored, both feature- and decision-level fusion approaches demonstrated significant improvements in tree species classification compared with the utilization of MSI (0.7), PAN (0.74), or LiDAR (0.8) in isolation. Notably, the decision-level fusion approach achieved the highest overall accuracies (0.86 for SVM and 0.84 for RF) and kappa coefficients (0.82 for SVM and 0.79 for RF). The misclassification analysis of fusion approaches highlighted the potential and flexibility of decision-level fusion in tree species classification.

Thermal infrared technology is utilized for detecting mastitis in cows owing to its non-invasive and efficient characteristics. However, the presence of surrounding regions and obstacles can impede accurate temperature measurement, thereby compromising the effectiveness of dairy mastitis detection. To address these problems, we proposed the CLE-UNet (Centroid Loss Ellipticization UNet) semantic segmentation algorithm. The algorithm consists of three main parts. Firstly, we introduced the efficient channel attention (ECA) mechanism in the feature extraction layer of UNet to improve the segmentation accuracy by focusing on more useful channel features. Secondly, we proposed a new centroid loss function to facilitate the network’s output to be closer to the position of the real label during the training process. Finally, we used a cow’s eye ellipse fitting operation based on the similarity between the shape of the cow’s eye and the ellipse. The results indicated that the CLE-UNet model obtained a mean intersection over union (MIoU) of 89.32% and an average segmentation speed of 0.049 s per frame. Compared to somatic cell count (SCC), this method achieved an accuracy, sensitivity, and F1 value of 86.67%, 82.35%, and 87.5%, respectively, for detecting mastitis in dairy cows. In conclusion, the innovative use of the CLE-UNet algorithm has significantly improved the segmentation accuracy and has proven to be an effective tool for accurately detecting cow mastitis.

An imbalance between excitation (E) and inhibition (I) in the brain has been identified as a key pathophysiology of epilepsy over the years. The hippocampus and amygdala in the limbic system play a crucial role in the initiation and conduction of epileptic seizures and are often referred to as the transfer station and amplifier of seizure activities. Existing animal and imaging studies reveal that the hippocampus and amygdala, which are significant parts of the vagal afferent network, can be modulated in order to generate an antiepileptic effect. Using stereo-electroencephalography (SEEG) data, we examined the E/I imbalance in the hippocampus and amygdala of ten drug-resistant epilepsy children treated with acute vagus nerve stimulation (VNS) by estimating the 1/f power slope of hippocampal and amygdala signals in the range of 1–80 Hz. While the change in the 1/f power slope from VNS-BASE varied between different stimulation amplitudes and brain regions, it was more prominent in the hippocampal region. In the hippocampal region, we found a flatter 1/f power slope during VNS-ON in patients with good responsiveness to VNS under the optimal stimulation amplitude, indicating that the E/I imbalance in the region was improved. There was no obvious change in 1/f power slope for VNS poor responders. For VNS non-responders, the 1/f power slope slightly increased when the stimulation was applied. Overall, this study implies that the regulation of E/I imbalance in the epileptic brain, especially in the hippocampal region, may be an acute intracranial effect of VNS.

Multi-Modal Relation Extraction (MMRE) aims at identifying the relation between two entities in texts that contain visual clues. Rich visual content is valuable for the MMRE task, but existing works cannot well model finer associations among different modalities, failing to capture the truly helpful visual information and thus limiting relation extraction performance. In this paper, we propose a novel MMRE framework to better capture the deeper correlations of text, entity pair, and image/objects, so as to mine more helpful information for the task, termed as DGF-PT. We first propose a prompt-based autoregressive encoder, which builds the associations of intra-modal and inter-modal features related to the task, respectively by entity-oriented and object-oriented prefixes. To better integrate helpful visual information, we design a dual-gated fusion module to distinguish the importance of image/objects and further enrich text representations. In addition, a generative decoder is introduced with entity type restriction on relations, better filtering out candidates. Extensive experiments conducted on the benchmark dataset show that our approach achieves excellent performance compared to strong competitors, even in the few-shot situation.

It is pointed out that with the economic development and social progress, China's historical cultural heritage has been gradually discovered and slowly changed by modern civilization and the general environment. Through the people's brainstorming, China has made great efforts in the protection of cultural heritage and promoted the development of various heritage protection measures. The contradictions between cultural heritage conservation and construction are analyzed, and measures for active conservation, holistic creation, and renovation and improvement of old cities are proposed in order to provide a reference for the conservation of heritage and innovation of cultural environment.

The present study sought to investigate the amelioration effects of enzymatically synthesized docosahexaenoic acid-enriched phosphatidylserine (DHA-PS) on the high-fat diet (HFD)-induced kidney injury in mice. After 6 weeks of DHA-PS intervention, the mice's body weight in the 20 and 40 mg/kg DHA-PS groups decreased by 7.09% and 9.71%, respectively, compared to the HFD group. Especially, compared to the HFD group, 40 mg/kg DHA-PS treatment effectively reduced the levels of serum urea nitrogen by 68.48%, creatinine by 38.98%, kidney lipid accumulation (total cholesterol, triglycerides, and nonesterified fatty acids levels by 26.19%, 51.00%, and 26.11%), kidney or serum proinflammatory cytokines and enhanced the levels of kidney or serum oxidative stress parameters, except for malondialdehyde (MDA). Moreover, 40 mg/kg DHA-PS treatment decreased the expression levels of toll-like receptor 4 (TLR4) by 18.63%, IKKα by 31.81%, and p-p65 by 40.73% in the nuclear factor kappa-B pathway, thereby upregulating the expression levels of p-AMPKα by 64.93%, HSL by 99.60%, ATGL by 344.50%, PPARα by 162.02%, CPT1 by 167.95%, p-ACC1 by 144.92%, and p-SREBP1 by 1172.95%, and downregulating the expression levels of SREBP1 by 38.80%, ACC1 by 18.10%, and FAS by 82.28% in the AMPK pathway. Furthermore, our results also suggested that improving serum or kidney parameters and regulating intestinal microbial could affect each other after DHA-PS treatment. These results elucidated that DHA-PS could be a potential dietary supplement to alleviate HFD-induced kidney injury. PRACTICAL APPLICATION: Our results elucidated that DHA-PS could be a potential dietary supplement to alleviate HFD-induced kidney injury.

This paper has collected more than one hundred papers about resilience at home and abroad, summarised and analysed the meaning and evaluation methods of resilience and then summarised the key issues. Through research on these papers, we found that the current evaluation method for the water supply system seismic resilience is divided into two aspects: on the one hand, it’s based on the seismic performance of water supply system, where the system itself evaluates the resilience absorbs the earthquake resistance level. On the other hand, the assessment is based on the post-earthquake recovery ability of the water supply system, namely the recovery time, recovery degree, recovery path and the resilience level of resources input. Based on past seismic safety of the water supply system and research on post-earthquake recovery ability, the meaning of water supply system in seismic resilience should include two aspects of seismic safety and post-earthquake recovery ability. From using the seismic safety evaluation method also and post-earthquake recovery ability in two angles, this paper gives a comprehensive multiple evaluation index framework of the evaluation model and points out that water system earthquake state resilience evaluative criteria and the requirements of the current seismic code should match.

The algorithm used is based on the Faster R-CNN network. It uses resnet101_vd as the backbone network to extract navel orange image features. It minimizes the error between the inferred bounding box and the actual labeled bounding box through the RPN network and the ROI Pooling layer and non-maximum suppression method. The AP during model training reached 92.34% on sunny days, 96.84% on cloudy days, and 90.05% on foggy days. When evaluating the model, it reached 92.34% on sunny days, 96.89% on cloudy days, and 89.2% on foggy days. The processing time of this model is 63.84fps on sunny days, 68.6fps on cloudy days, and 56.29fps on foggy days. It meets the requirements of rapid and accurate identification in actual picking. In addition, it compares this model with the Faster RCNN with vgg16 as the backbone network and YOLO-v4 models. It effectively improves detection accuracy and speed. Moreover, it reduces the number of false detection and missed detection of navel orange detection. It im-proves position accuracy. This paper realizes the efficient detection of ripe navel orange fruits on trees in various weather. &nbsp

Two new phenolic glycosides (1 and 2), one known analogue (3), along with a new diterpene glucoside (4) were obtained from ethanolic extract of the stems of Eurya chinensis R. Br. The structures of these isolated compounds were identified by extensive analysis of HRESIMS and NMR spectroscopic data. The cytotoxicities of these compounds were evaluated on MCF-7, A549, HepG2, CaCo2 and 5-8 F cell lines by MTT method, but no obvious activities were observed.

Two new withaphysalin-type withanolides (18-O-ethylwithaphysalin R and 5-O-ethylphysaminimin C, 1 and 2), along with twelve known withanolides (3–14), were purified and identified from Physalis peruviana L. The chemical structures of these new isolates were elucidated through analyzing spectroscopic and HRESIMS data. All the obtained metabolites were appraised for their potential antiproliferative activity against the human breast cancer cell line MCF-7. Compound 7 was discovered to exhibit potent activity with an IC50 value of 3.51 µM and compounds 2, 6 and 14 showed weak cytotoxic effect.

Knowledge Graph Embedding (KGE) aims to learn representations for entities and relations. Most KGE models have gained great success, especially on extrapolation scenarios. Specifically, given an unseen triple (h, r, t), a trained model can still correctly predict t from (h, r, ?), or h from (?, r, t), such extrapolation ability is impressive. However, most existing KGE works focus on the design of delicate triple modeling function, which mainly tells us how to measure the plausibility of observed triples, but offers limited explanation of why the methods can extrapolate to unseen data, and what are the important factors to help KGE extrapolate. Therefore in this work, we attempt to study the KGE extrapolation of two problems: 1. How does KGE extrapolate to unseen data? 2. How to design the KGE model with better extrapolation ability? For the problem 1, we first discuss the impact factors for extrapolation and from relation, entity and triple level respectively, propose three Semantic Evidences (SEs), which can be observed from train set and provide important semantic information for extrapolation. Then we verify the effectiveness of SEs through extensive experiments on several typical KGE methods. For the problem 2, to make better use of the three levels of SE, we propose a novel GNN-based KGE model, called Semantic Evidence aware Graph Neural Network (SE-GNN). In SE-GNN, each level of SE is modeled explicitly by the corresponding neighbor pattern, and merged sufficiently by the multi-layer aggregation, which contributes to obtaining more extrapolative knowledge representation. Finally, through extensive experiments on FB15k-237 and WN18RR datasets, we show that SE-GNN achieves state-of-the-art performance on Knowledge Graph Completion task and performs a better extrapolation ability. Our code is available at https://github.com/renli1024/SE-GNN., Comment: Accepted by AAAI'22

Analyzing topic evolution is an effective way to monitor the overview of topic spreading. Existing methods have focused either on the intensity evolution of topics along a timeline or the topic evolution path of technical literature. In this paper, we aim to study topic evolution from a micro perspective, which not only captures the topic timeline but also reveals the topic status and the directed evolutionary path among topics. Firstly, we construct a word network by co-occurrence relationship between feature words. Secondly, Latent Dirichlet allocation (LDA) model is used to automatically extract topics and capture the mapping relationship between words and topics, and then a 'word-topic' coupling network is built. Thirdly, based on the 'word-topic' coupling network, we describe the topic intensity evolution over time and measure topic status considering the contribution of feature words to a topic. The concept of topic drifting probability is proposed to identify the evolutionary path. Experimental results conducted on two real-world data sets of "COVID-19" demonstrate the effectiveness of our proposed method.

In situ grazing experiments were carried out using fluorescent tracer, microscopy and flow cytometry to retrieve active bacterivores in situ, including mixotrophic and heterotrophic eukaryotes.18S rRNA gene metabarcoding was applied to retrieve overall eukaryotic community structure and taxa with mixotrophic potentials in the studied region of estuary-coastal east China Sea. This dataset accompanies the manuscript ‘Relative importance of bacterivorous mixotrophs in an estuary-coast environment’ by Li et al.

We provide an European option pricing formula written in the form of an infinite series of Black Scholes type terms under double Levy jumps model, where both the interest rate and underlying price are driven by Levy process. The series solution converges with a radius of convergence, and it is complemented by some numerical experiments to demonstrate its speed of convergence., 14 pages, 4 figures

Overexpression of centromere protein H (CENPH) promotes cancer growth and progression. However, the roles and underlying mechanisms have not been elucidated. Therefore, we aim to explore the roles and mechanisms of CENPH in lung adenocarcinoma (LUAD) progression by using comprehensive data analysis and cell experiments. In this study, the relationship between CENPH expression, which was obtained from the TCGA, and GTEx databases, and the prognosis and clinical characteristics of LUAD patients was analyzed, and the diagnostic values of CENPH was evaluated. CENPH-related risk models and nomograms were constructed to evaluate the prognosis of LUAD via Cox and LASSO regression analysis. The roles and mechanisms of CENPH in LUAD cells were studied using CCK-8 assay, wound healing and migration tests, and western blotting. The relationship between CENPH expression and immune microenvironment and RNA modifications was explored through correlation analysis. We found that CENPH was overexpressed in LUAD tissues, and tumors with diameter >3 cm, lymph node metastasis, distant metastasis, late stage, men, and dead cancer patients. Increased expression of CENPH was related to the diagnosis, poor survival rate, disease specific survival rate, and progression of LUAD. CENPH-related nomograms and risk models could predict the survival rate of LUAD patients. Inhibiting the expression of CENPH in LUAD cells decreased their migration, proliferation, and invasion, and promoted their sensitivity to cisplatin, which was related to the downregulation of p-AKT, p-ERK, and p-P38. However, there was no effect on AKT, ERK, and P38. Enhanced expression of CENPH was significantly correlated with immune score, immune cells, cell markers, and RNA modifications. In conclusion, CENPH was strongly expressed in LUAD tissues and was associated with poor prognosis, immune microenvironment, and RNA modifications. CENPH overexpression could enhance cell growth and metastasis and promote resistance to cisplatin via the AKT and ERK/P38 pathways, indicating its potential as a biomarker for the prognosis of LUAD.

Background To determine whether transrectal ultrasound and urologist_dually guided pelvic floor muscle exercise is associated with immediate, early and long-term urinary continence after radical prostatectomy. Materials and methods Data from 114 patients with localized prostate cancer (PC) who underwent RP at Henan Cancer Hospital from November 2018 to April 2021 were included in the retrospective study. Of the 114 patients, 50 patients in the observation group underwent transrectal ultrasound and urologist_dually guided PFME, and 64 patients in the control group underwent verbally_guided PFME. Contractile function of the external urinary sphincter was in the observation group was evaluated. The immediate, early and long-term urinary continence rates were assessed in both groups, and the factors affecting urinary continence were analyzed. Results The urinary continence rate at 2 weeks and 1, 3, 6 and 12 months in the observation group after RP was significantly higher than that in the control group (52.0% vs. 29.7%, 70.0% vs. 39.1%, 82% vs. 57.8, 88% vs. 70.3%, 98.0 vs. 84.4%, p Conclusions Transrectal ultrasound and urologist_dually guided PFME had a significant role in improving immediate, early and long-term urinary continence after RP and acted as an independent prognostic factor.

Bone metastasis is one of the main complications of lung cancer and most important factors that lead to poor life quality and low survival rate in lung cancer patients. However, the regulatory mechanisms underlying lung cancer bone metastasis are still poor understood. Here, we report that microRNA-182 (miR-182) plays a critical role in regulating osteoclastic metastasis of lung cancer cells. We found that miR-182 was significantly upregulated in both bone-metastatic human non–small cell lung cancer (NSCLC) cell line and tumor specimens. We further demonstrated that miR-182 markedly enhanced the ability of NSCLC cells for osteolytic bone metastasis in nude mice. Mechanistically, miR-182 promotes NSCLC cells to secrete Interleukin-8 (IL-8) and in turn facilitates osteoclastogenesis via activating STAT3 signaling in osteoclast progenitor cells. Importantly, systemically delivered IL-8 neutralizing antibody inhibits NSCLC bone metastasis in nude mice. Collectively, our findings identify the miR-182/IL-8/STAT3 axis as a key regulatory pathway in controlling lung cancer cell-induced osteolytic bone metastasis and suggest a promising therapeutic strategy that targets this regulatory axis to interrupt lung cancer bone metastasis.

Overly restricted and poorly designed eligibility criteria reduce the generalizability of the results from clinical trials. We conducted a study to identify and quantify the impacts of study traits extracted from eligibility criteria on the age of study populations in Alzheimer’s Disease (AD) clinical trials. Using machine learning methods and SHapley Additive exPlanation (SHAP) values, we identified 30 and 34 study traits that excluded older patients from AD trials in our 2 generated target populations respectively. We also found that study traits had different magnitudes of impacts on the age distributions of the generated study populations across racial-ethnic groups. To our best knowledge, this was the first study that quantified the impact of eligibility criteria on the age of AD trial participants. Our research is a first step in addressing the overly restrictive eligibility criteria in AD clinical trials.

PURPOSE: To investigate morphofunctional chest MRI for the detection and management of incidental pulmonary nodules in participants with chronic obstructive pulmonary disease (COPD). MATERIALS AND METHODS: In this prospective study, 567 participants (mean age, 66 years ± 9 [SD]; 340 men) underwent same-day contrast-enhanced MRI and nonenhanced low-dose CT (LDCT) in a nationwide multicenter trial (clinicaltrials.gov: NCT01245933). Nodule dimensions, morphologic features, and Lung Imaging Reporting and Data System (Lung-RADS) category were assessed at MRI by two blinded radiologists, and consensual LDCT results served as the reference standard. Comparisons were performed using the Student t test, and agreements were assessed using the Cohen weighted κ. RESULTS: A total of 525 nodules larger than 3 mm in diameter were detected at LDCT in 178 participants, with a mean diameter of 7.2 mm ± 6.1 (range, 3.1–63.1 mm). Nodules were not detected in the remaining 389 participants. Sensitivity and positive predictive values with MRI for readers 1 and 2, respectively, were 63.0% and 84.8% and 60.2% and 83.9% for solid nodules (n = 495), 17.6% and 75.0% and 17.6% and 60.0% for part-solid nodules (n = 17), and 7.7% and 100% and 7.7% and 50.0% for ground-glass nodules (n = 13). For nodules 6 mm or greater in diameter, sensitivity and positive predictive values were 73.3% and 92.2% for reader 1 and 71.4% and 93.2% for reader 2, respectively. Readers underestimated the long-axis diameter at MRI by 0.5 mm ± 1.7 (reader 1) and 0.5 mm ± 1.5 (reader 2) compared with LDCT (P < .001). For Lung-RADS categorization per nodule using MRI, there was substantial to perfect interreader agreement (κ = 0.75–1.00) and intermethod agreement compared with LDCT (κ = 0.70–1.00 and 0.69–1.00). CONCLUSION: In a multicenter setting, morphofunctional MRI showed moderate sensitivity for detection of incidental pulmonary nodules in participants with COPD but high agreement with LDCT for Lung-RADS classification of nodules. Clinical trial registration no. NCT01245933 and NCT02629432 Keywords: MRI, CT, Thorax, Lung, Chronic Obstructive Pulmonary Disease, Screening © RSNA, 2023 Supplemental material is available for this article.

We study the event horizon of a rotating Bardeen black hole surrounded by perfect fluid dark matter and the black hole as a particle accelerator. The black hole is represented by four parameters: mass $M$, rotation parameter $a$, dark matter parameter $\alpha$ and magnetic charge $g$. It is interesting that when we determine the values of magnetic charge $g$ and dark matter parameters $\alpha$ we can get a critical rotation parameter $a_E$ and then we get a contour plane with $\Delta$= 0 taking three parameters as coordinates. We also derive the effective potential of the particle and the centre-of-mass (CM) energy of the two particles outside the black hole by using the motion equation of the particle in the equatorial plane of the black hole. We find that the CM energy depends not only on the rotation parameter $a$, but also on the parameters $g$ and $\alpha$. We discuss the CM energy for two particles colliding at the black hole horizon in the extreme and non-extreme cases, respectively. It is found that the CM energy can be arbitrarily high when the angular momentum of one of the two particles is the critical angular momentum under the background of extreme black holes and there is no such result for non-extreme black holes, because the particles do not reach the black hole horizon when the angular momentum of the particles is critical angular momentum. Therefore, we prove that the rotating Bardeen black hole surrounded by perfect fluid dark matter can be used as a particle accelerator., Comment: 10 pages, 7 figures, 2 Tables