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Ying-Xue Sun,1,* Tian Tang,1,* Jin-Yu Zou,1,* Qian-Qian Yue,1 Li-Feng Hu,1 Tong Peng,1 Xin-Ru Meng,1 Ge-Hui Feng,1 Li-Li Huang,1 Ying Zeng1– 3 1Department of International and Humanistic Nursing, School of Nursing, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China; 2Hunan Province Key Laboratory of Tumor Cellular & Molecular Pathology, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China; 3Hunan Engineering Research Center for Early Diagnosis and Treatment of Liver Cancer, Cancer Research Institute, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China*These authors contributed equally to this workCorrespondence: Ying Zeng, Department of International and Humanistic Nursing, School of Nursing, Hengyang Medical School, University of South China, Hengyang, People’s Republic of China, Tel +86 17773486339, Fax + 86 0734-8281344, Email zengying2003@126.comBackground: Colorectal, and gastric cancers have the second, and fourth mortality rates worldwide, respectively. Endoscopic screening is a crucial diagnostic tool for colorectal, and gastric cancers. Effective interventions can improve adherence to endoscopic screening in high-risk populations, which is important for cancer prevention and mortality reduction. This study aimed to identify interventions that could improve adherence to endoscopic screening for cancer in high-risk populations.Methods: Combination keywords including colorectal cancer, gastric cancer, screening adherence, and interventions were used to search for articles in PubMed, Web of Science, Cochrane Library, and MEDLINE Complete. The review methodology was guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-SCR).Results: A total of 12 articles were included in this review: 9 randomized controlled trials(RC

Existing oriented object detection methods commonly use metric AP$_{50}$ to measure the performance of the model. We argue that AP$_{50}$ is inherently unsuitable for oriented object detection due to its large tolerance in angle deviation. Therefore, we advocate using high-precision metric, e.g. AP$_{75}$, to measure the performance of models. In this paper, we propose an Aspect Ratio Sensitive Oriented Object Detector with Transformer, termed ARS-DETR, which exhibits a competitive performance in high-precision oriented object detection. Specifically, a new angle classification method, calling Aspect Ratio aware Circle Smooth Label (AR-CSL), is proposed to smooth the angle label in a more reasonable way and discard the hyperparameter that introduced by previous work (e.g. CSL). Then, a rotated deformable attention module is designed to rotate the sampling points with the corresponding angles and eliminate the misalignment between region features and sampling points. Moreover, a dynamic weight coefficient according to the aspect ratio is adopted to calculate the angle loss. Comprehensive experiments on several challenging datasets show that our method achieves competitive performance on the high-precision oriented object detection task., Comment: 15 pages, 13 figures, 13 tables, the source code is available at https://github.com/httle/ARS-DETR

Radiology report generation, as a key step in medical image analysis, is critical to the quantitative analysis of clinically informed decision-making levels. However, complex and diverse radiology reports with cross-source heterogeneity pose a huge generalizability challenge to the current methods under massive data volume, mainly because the style and normativity of radiology reports are obviously distinctive among institutions, body regions inspected and radiologists. Recently, the advent of large language models (LLM) offers great potential for recognizing signs of health conditions. To resolve the above problem, we collaborate with the Second Xiangya Hospital in China and propose ChatRadio-Valuer based on the LLM, a tailored model for automatic radiology report generation that learns generalizable representations and provides a basis pattern for model adaptation in sophisticated analysts' cases. Specifically, ChatRadio-Valuer is trained based on the radiology reports from a single institution by means of supervised fine-tuning, and then adapted to disease diagnosis tasks for human multi-system evaluation (i.e., chest, abdomen, muscle-skeleton, head, and maxillofacial $\&$ neck) from six different institutions in clinical-level events. The clinical dataset utilized in this study encompasses a remarkable total of \textbf{332,673} observations. From the comprehensive results on engineering indicators, clinical efficacy and deployment cost metrics, it can be shown that ChatRadio-Valuer consistently outperforms state-of-the-art models, especially ChatGPT (GPT-3.5-Turbo) and GPT-4 et al., in terms of the diseases diagnosis from radiology reports. ChatRadio-Valuer provides an effective avenue to boost model generalization performance and alleviate the annotation workload of experts to enable the promotion of clinical AI applications in radiology reports.

Learning effective joint embedding for cross-modal data has always been a focus in the field of multimodal machine learning. We argue that during multimodal fusion, the generated multimodal embedding may be redundant, and the discriminative unimodal information may be ignored, which often interferes with accurate prediction and leads to a higher risk of overfitting. Moreover, unimodal representations also contain noisy information that negatively influences the learning of cross-modal dynamics. To this end, we introduce the multimodal information bottleneck (MIB), aiming to learn a powerful and sufficient multimodal representation that is free of redundancy and to filter out noisy information in unimodal representations. Specifically, inheriting from the general information bottleneck (IB), MIB aims to learn the minimal sufficient representation for a given task by maximizing the mutual information between the representation and the target and simultaneously constraining the mutual information between the representation and the input data. Different from general IB, our MIB regularizes both the multimodal and unimodal representations, which is a comprehensive and flexible framework that is compatible with any fusion methods. We develop three MIB variants, namely, early-fusion MIB, late-fusion MIB, and complete MIB, to focus on different perspectives of information constraints. Experimental results suggest that the proposed method reaches state-of-the-art performance on the tasks of multimodal sentiment analysis and multimodal emotion recognition across three widely used datasets. The codes are available at \url{https://github.com/TmacMai/Multimodal-Information-Bottleneck}., Comment: This paper is accepted by IEEE Transactions on Multimedia. This version addresses some mistakes and typos in the original paper. The appendix is available at https://github.com/TmacMai/Multimodal-Information-Bottleneck/blob/main/appendix.pdf

To protect structural steel in the event of a hydrocarbon fire, epoxy-based intumescent coatings, which expand to form a multicellular char layer, are increasingly used. Consequently, to improve formulations and understand the performance of such coatings, it is of great industrial and scientific interest to establish relationships between the char properties and the critical heating times of coated steel plates. In this work, a so-called Stratification Parameter (SP) that combines the relative expansions and insulation efficiencies of individual char layers (termed the sponge-like, the macroporous, and the compact phase, respectively), is introduced to characterize the properties of the multilayered intumescent coating char. An approximate linear relationship was revealed between the SP of the char and the fire-resistance performance (i.e. critical times to 400 and 550 oC) of the intumescent coating. Furthermore, owing to the mapped-out exponential relationship between the dynamic viscosity minimum and the char appearance, it was discovered that the SP of the char can be effectively used to point out the required dynamic viscosity change of an intumescent coating to form a given char. Finally, results of thermogravimetric analyses suggest that constraining the coating mass loss during char degradation (to less than 28 wt.% for the coatings of the present work) may prevent the formation of the less efficient sponge-like phase. The stratification methodology developed, can be used to estimate expected thermal insulation properties of non-uniform hydrocarbon intumescent coating chars

For high performance of hydrocarbonintumescent coatings, interactions between the acid source ammoniumpolyphosphate (APP) and inorganic fillers can be critical. In the present work,low-borate concentration (i.e., 1 wt.% zinc borate) intumescent coatings with different concentrationratios of CaCO3 to APP were studied in an attempt to map thereaction mechanisms. The analysis of the coating performance was conductedusing thermal insulation assessment according to the UL1709 fire curve(disregarding substrate load, geometrical diversity, and other full-scaleimplications of the complete standard), rheological measurements,thermogravimetric analyses (TGA), Fourier transform infrared spectrometer(ATR-FTIR), and X-ray diffraction (XRD). Results show that a coating with a CaCO3/APPmass ratio of 0.3 gives the best performance (i.e., the longest critical time ofsteel substrates to reach both 400 and 550 oC in the thermalinsulation assessment). The dependency on the CaCO3/APP ratio of thedynamic viscosity minimum points to significant interactions between CaCO3and APP during the softening state (i.e., the intumescence process) of thecoating. Due to an enhanced anti-oxidation property, the degradation zone ofthe intumescent char and the residual weight of the coating after exposure wereboth improved when increasing the CaCO3/APP ratio. The compositionaldevelopment of the intumescent char showed that CaCO3 and APP reactto form a new polymeric phase, calcium catena-polyphosphate (CaP2O6).This polyphosphate may contribute to the formation of a compact phase in theintumescent char and benefit the thermal insulation performance of low-borateconcentration hydrocarbon intumescent coatings.

Yi-Yu Qi,1,* Xia Heng,1,* Zeng-Ying Yao,2 Shu-Yue Qu,1 Ping-Yuan Ge,1 Xin Zhao,1 Sai-jia Ni,2 Rui Guo,3 Nian-Yun Yang,1 Qi-Chun Zhang,2 Hua-Xu Zhu1 1Department of Traditional Chinese Medicine Processing and Preparation, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 2Department of Pharmacology, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China; 3Department of Physiological, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qi-Chun Zhang; Hua-Xu Zhu, Email zhangqichun@njucm.edu.cn; zhuhx@njucm.edu.cnBackground: Abnormal sphingolipid metabolism is closely related to the occurrence and development of Alzheimer’s disease (AD). With heat-clearing and detoxifying effects, Huanglian Jiedu decoction (HLJDD) has been used to treat dementia and improve learning and memory impairments.Purpose: To study the therapeutic effect of HLJDD on AD as it relates to sphingolipid metabolism.Methods: The level of sphingolipids in the brains of APP/PS1 mice and in the supernatant of β-amyloid (Aβ)25– 35-induced BV2 microglia was detected by HPLC-QTOF-MS and HPLC-QTRAP-MS techniques, respectively. The co-expression of ionized calcium-binding adapter molecule 1 (Iba1) and Aβ as well as four enzymes related to sphingolipid metabolism, including serine palmitoyltransferase 2 (SPTLC2), cer synthase 2 (CERS2), sphingomyelin phosphodiesterase 1 (SMPD1), and sphingomyelin synthase 1 (SGMS1), in the brains of APP/PS1 mice were evaluated by immunofluorescence double labelling. In addition, real-time quantitative reverse transcription-polymerase chain reaction was conducted to determine the mRNA expression of SPTLC2, CERS2, SMPD1, SGMS1, galactosylceramidase (GALC), and sphingosine kinase 2 (SPHK2) in Aβ25-35-stimulated BV2 microglia.Results: Abnor

This paper examines the impacts of COVID-19 on Chinese nationals' tourism preferences. Employing a mixed-method research design, two rounds of nation-wide online surveys were conducted, one in February 2020 when COVID-19 cases started to peak in China and another one in June 2020 when COVID-19 was a global pandemic; both survey studies were accompanied with semi-structured in-depth interviews and altogether 37 interviews were conducted in two stages. Based on both quantitative survey data and qualitative interview data, the research identified that: 1) COVID-19 significantly reduced Chinese nationals' preferences to travel to countries with high infection numbers, and geographically faraway, administratively and culturally distant outbound destinations; 2) Chinese nationals reduced their preferences in all travel modes and most of the tourism forms, but most of them would prefer nature-based, rural, and cultural destinations after COVID-19; and 3) shortened trips in short travel distance are preferred after COVID-19. The findings offer rich insights and practical implications for governments, industry organisations, and tourism operators to formulate tourism recovery strategies toward Chinese tourists.

This paper examines the impacts of COVID-19 on Chinese nationals' tourism preferences. Employing a mixed-method research design, two rounds of nation-wide online surveys were conducted, one in February 2020 when COVID-19 cases started to peak in China and another one in June 2020 when COVID-19 was a global pandemic; both survey studies were accompanied with semi-structured in-depth interviews and altogether 37 interviews were conducted in two stages. Based on both quantitative survey data and qualitative interview data, the research identified that: 1) COVID-19 significantly reduced Chinese nationals' preferences to travel to countries with high infection numbers, and geographically faraway, administratively and culturally distant outbound destinations; 2) Chinese nationals reduced their preferences in all travel modes and most of the tourism forms, but most of them would prefer nature-based, rural, and cultural destinations after COVID-19; and 3) shortened trips in short travel distance are preferred after COVID-19. The findings offer rich insights and practical implications for governments, industry organisations, and tourism operators to formulate tourism recovery strategies toward Chinese tourists.

Multimodal sentiment analysis (MSA) draws increasing attention with the availability of multimodal data. The boost in performance of MSA models is mainly hindered by two problems. On the one hand, recent MSA works mostly focus on learning cross-modal dynamics, but neglect to explore an optimal solution for unimodal networks, which determines the lower limit of MSA models. On the other hand, noisy information hidden in each modality interferes the learning of correct cross-modal dynamics. To address the above-mentioned problems, we propose a novel MSA framework \textbf{M}odulation \textbf{M}odel for \textbf{M}ultimodal \textbf{S}entiment \textbf{A}nalysis ({$ M^3SA $}) to identify the contribution of modalities and reduce the impact of noisy information, so as to better learn unimodal and cross-modal dynamics. Specifically, modulation loss is designed to modulate the loss contribution based on the confidence of individual modalities in each utterance, so as to explore an optimal update solution for each unimodal network. Besides, contrary to most existing works which fail to explicitly filter out noisy information, we devise a modality filter module to identify and filter out modality noise for the learning of correct cross-modal embedding. Extensive experiments on publicly datasets demonstrate that our approach achieves state-of-the-art performance., Comment: Updated version

© 2020 Xing et al. Joint profiling of transcriptome and chromatin accessibility within single cells allows for the deconstruction of the complex relationship between transcriptional states and upstream regulatory programs determining different cell fates. Here, we developed an automated method with high sensitivity, assay for single-cell transcriptome and accessibility regions (ASTARseq), for simultaneous measurement of whole-cell transcriptome and chromatin accessibility within the same single cell. To show the utility of ASTAR-seq, we profiled 384 mESCs under naive and primed pluripotent states as well as a two-cell like state, 424 human cells of various lineage origins (BJ, K562, JK1, and Jurkat), and 480 primary cord blood cells undergoing erythroblast differentiation. With the joint profiles, we configured the transcriptional and chromatin accessibility landscapes of discrete cell states, uncovered linked sets of cis-regulatory elements and target genes unique to each state, and constructed interactome and transcription factor (TF)-centered upstream regulatory networks for various cell states.

The wide application of smart devices enables the availability of multimodal data, which can be utilized in many tasks. In the field of multimodal sentiment analysis (MSA), most previous works focus on exploring intra- and inter-modal interactions. However, training a network with cross-modal information (language, visual, audio) is still challenging due to the modality gap, and existing methods still cannot ensure to sufficiently learn intra-/inter-modal dynamics. Besides, while learning dynamics within each sample draws great attention, the learning of inter-class relationships is neglected. Moreover, the size of datasets limits the generalization ability of existing methods. To address the afore-mentioned issues, we propose a novel framework HyCon for hybrid contrastive learning of tri-modal representation. Specifically, we simultaneously perform intra-/inter-modal contrastive learning and semi-contrastive learning (that is why we call it hybrid contrastive learning), with which the model can fully explore cross-modal interactions, preserve inter-class relationships and reduce the modality gap. Besides, a refinement term is devised to prevent the model falling into a sub-optimal solution. Moreover, HyCon can naturally generate a large amount of training pairs for better generalization and reduce the negative effect of limited datasets. Extensive experiments on public datasets demonstrate that our proposed method outperforms existing works., Comment: Under Review

Automatically constructing taxonomy finds many applications in e-commerce and web search. One critical challenge is as data and business scope grow in real applications, new concepts are emerging and needed to be added to the existing taxonomy. Previous approaches focus on the taxonomy expansion, i.e. finding an appropriate hypernym concept from the taxonomy for a new query concept. In this paper, we formulate a new task, "taxonomy completion", by discovering both the hypernym and hyponym concepts for a query. We propose Triplet Matching Network (TMN), to find the appropriate pairs for a given query concept. TMN consists of one primal scorer and multiple auxiliary scorers. These auxiliary scorers capture various fine-grained signals (e.g., query to hypernym or query to hyponym semantics), and the primal scorer makes a holistic prediction on triplet based on the internal feature representations of all auxiliary scorers. Also, an innovative channel-wise gating mechanism that retains task-specific information in concept representations is introduced to further boost model performance. Experiments on four real-world large-scale datasets show that TMN achieves the best performance on both taxonomy completion task and the previous taxonomy expansion task, outperforming existing methods., Comment: AAA1 2021

Ping-Yuan Ge,1 Yi-Yu Qi,1 Shu-Yue Qu,1 Xin Zhao,1 Sai-jia Ni,2 Zeng-Ying Yao,2 Rui Guo,3 Nian-Yun Yang,2 Qi-Chun Zhang,1,2 Hua-Xu Zhu2 1Jiangsu Key Laboratory for High Technology Research of TCM Formulae, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 2Jiangsu Key Laboratory for Pharmacology and Safety Evaluation of Chinese Materia Medica, School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of China; 3School of Medicine and Holistic Integrative Medicine, Nanjing University of Chinese Medicine, Nanjing, People’s Republic of ChinaCorrespondence: Qi-Chun Zhang; Hua-Xu Zhu Email zhangqichun@njucm.edu.cn; zhuhx@njucm.edu.cnBackground: S. baicalensis, a traditional herb, has great potential in treating diseases associated with aberrant lipid metabolism, such as inflammation, hyperlipidemia, atherosclerosis and Alzheimer’s disease.Aim of the Study: To elucidate the mechanism by which S. baicalensis modulates lipid metabolism and explore the medicinal effects of S. baicalensis at a holistic level.Materials and Methods: The potential active ingredients of S. baicalensis and targets involved in regulating lipid metabolism were identified using a network pharmacology approach. Metabolomics was utilized to compare lipids that were altered after S. baicalensis treatment in order to identify significantly altered metabolites, and crucial targets and compounds were validated by molecular docking.Results: Steroid biosynthesis, sphingolipid metabolism, the PPAR signaling pathway and glycerolipid metabolism were enriched and predicted to be potential pathways upon which S. baicalensis acts. Further metabolomics assays revealed 14 significantly different metabolites were identified as lipid metabolism-associated elements. After the pathway enrichment analysis of the metabolites, cholesterol metabolism and sphingolipid metabolism were identified as the most relevant pathways. Based

This paper proposes the building of Xiaomingbot, an intelligent, multilingual and multimodal software robot equipped with four integral capabilities: news generation, news translation, news reading and avatar animation. Its system summarizes Chinese news that it automatically generates from data tables. Next, it translates the summary or the full article into multiple languages, and reads the multilingual rendition through synthesized speech. Notably, Xiaomingbot utilizes a voice cloning technology to synthesize the speech trained from a real person's voice data in one input language. The proposed system enjoys several merits: it has an animated avatar, and is able to generate and read multilingual news. Since it was put into practice, Xiaomingbot has written over 600,000 articles, and gained over 150,000 followers on social media platforms., Comment: Accepted to ACL 2020 - system demonstration

In this paper, we study the task of multimodal sequence analysis which aims to draw inferences from visual, language and acoustic sequences. A majority of existing works generally focus on aligned fusion, mostly at word level, of the three modalities to accomplish this task, which is impractical in real-world scenarios. To overcome this issue, we seek to address the task of multimodal sequence analysis on unaligned modality sequences which is still relatively underexplored and also more challenging. Recurrent neural network (RNN) and its variants are widely used in multimodal sequence analysis, but they are susceptible to the issues of gradient vanishing/explosion and high time complexity due to its recurrent nature. Therefore, we propose a novel model, termed Multimodal Graph, to investigate the effectiveness of graph neural networks (GNN) on modeling multimodal sequential data. The graph-based structure enables parallel computation in time dimension and can learn longer temporal dependency in long unaligned sequences. Specifically, our Multimodal Graph is hierarchically structured to cater to two stages, i.e., intra- and inter-modal dynamics learning. For the first stage, a graph convolutional network is employed for each modality to learn intra-modal dynamics. In the second stage, given that the multimodal sequences are unaligned, the commonly considered word-level fusion does not pertain. To this end, we devise a graph pooling fusion network to automatically learn the associations between various nodes from different modalities. Additionally, we define multiple ways to construct the adjacency matrix for sequential data. Experimental results suggest that our graph-based model reaches state-of-the-art performance on two benchmark datasets., Comment: preprint, work in progress

Mapping the performance of intumescent coatings as a function of concentration of ingredients is of great significance for formulation optimization. In this work, the effects of ammonium polyphosphate (APP), melamine (MEL), TiO2, CaCO3, and vitreous silicate fiber, were investigated on the fire-resistance performance of selected zinc borate (ZB)-containing and ZB-free hydrocarbon intumescent coatings with exposure to the hydrocarbon fire testing curve UL 1709. Owing to the promoted interactions of APP with epoxy binder and zinc borate, increasing levels of APP in the ZB-containing coatings enhanced the critical times of steel substrate and the formation of macroporous and compact char layers. In contrast, when increasing the content of MEL, the performance of the ZB-containing coatings generally declined, due mainly to the restricted char formation resulting from the decreased binder content. Among the investigated formulations, the coating with 25 wt.% APP or 5 wt.% MEL obtained the best performance. The oxides TiO2 and CaCO3, and the fiber barely had effects on the performance of the ZB-containing intumescent coatings, but the critical times and char appearance were strongly affected for the ZB-free coatings. Increasing the content of any of the three inorganics led to reduced expansion of the ZB-free coatings, but improved the chars with more mechanically stable structures. The longest critical times (e.g. to 550 oC) were found with the ZB-free formulations containing 1.5 wt.% TiO2 (87.7 min), 2.5 wt.% CaCO3 (86.3 min), and 5 wt% vitreous silicate fiber (85.1 min).

Due to increasing demands for fire protection in high-risk environments, such as petrochemical processes and offshore platforms, so-called hydrocarbon intumescent coatings are increasingly used to protect structural steel in the event of a hydrocarbon fire. For these coatings, the fire-resistance performance is typically evaluated in a series of costly experiments with industrial-scale (i.e. 1-10 m3) furnaces, programmed to follow a standard hydrocarbon fire test curve. In the present work, we propose a laboratory-scale furnace for coating evaluation, which can simulate the conditions of a typical hydrocarbon fire curve, i.e. the standard UL1709. In a case study with five hydrocarbon intumescent coating formulations, the correlation between the laboratory- and the industrial-scale furnace was investigated and a good agreement was found for the temperature progression of the coated steel plates. The physical and chemical properties of the intumescent coating chars were also similar for the two furnaces. In summary, the low-cost, time-efficient laboratory-scale furnace can provide reliable screening of hydrocarbon intumescent coatings, and is recommended as a complementary tool for industrial fire tests.

Ying Zeng,1,2 Xiangfeng Zhu,2 Yali Wang,1 Bo Liu,1 Xin Yang,2 Qiushi Wang,2 Juan Du,2 Yu Ma,2 Li Lin,2 Ping Fu,2 Hualiang Xiao,2 Qiao-Nan Guo1 1Department of Pathology, Second Affiliated Hospital, Amy Medical University (Third Military Medical University), Chongqing 400037, People’s Republic of China; 2Department of Pathology, Daping Hospital, Amy Medical University (Third Military Medical University), Chongqing 400042, People’s Republic of ChinaCorrespondence: Qiao-Nan Guo; Hualiang Xiao Email qiaonan85@263.net; dpbl_xhl@126.comPurpose: To observe the clinicopathological, immunohistochemical, and molecular genetic features of epithelioid glioblastoma (E-GBM), and identify tumor-associated prognostic factors.Patients and Methods: The clinical and radiological data of fifteen cases of E-GBM were collected, and their pathological, immunohistochemical, and molecular features were examined. A 1p/19q analysis via FISH, MGMT promoter methylation by MS-PCR, and IDH1 and BRAF V600E mutation analysis by HRM-PCR were performed. The level of EZH2 expression was valuated by immunohistochemistry in 15 E-GBM cases, and the prognostic factors were analyzed in E-GBM patients. Fifteen non-E-GBM cases were used as a control.Results: The fifteen cases of E-GBM included twelve males and three females, with fourteen cases supratentorially located. Headache was the main symptom. Microscopy revealed that the tumors were composed of epithelioid cells and some rhabdoid cells. The epithelioid and rhabdoid cells displayed focal discohesion, scant intervening neuropil, a distinct cell membrane, eosinophilic cytoplasm, and a laterally positioned nucleus. Most tumors showed high mitosis, zonal necrosis, and microvascular hyperplasia. Immunohistochemical findings included epithelioid cells positive for GFAP, vimentin, nestin, S-100, and INI-1. The molecular findings included no deletions of 1p/19q, EGFR amplifications, or IDH1 mutations in any case, a methylated MGMT promoter

Increased use of pyrethroids and the exposure to pyrethroids for pregnant women and children have raised the concerns over the potential effect of pyrethroids on developmental cardiotoxicity and other abnormalities. The purpose of this study was to investigate whether long term perinatal deltamethrin exposure altered embryonic cardiac electrophysiology in mice. Pregnant mice were administered with 0 or 3 mg/kg of deltamethrin by gavage daily from gestational day (gd) 10.5 to gd 17. 5. Whole cell patch-clamp technique was used in electrophysiological study, and real time RT-PCR was applied to analyze the molecular changes for the electrophysiological properties. Deltamethrin exposure resulted in increased mortality of pregnant mice and decreased viability of embryos. Moreover, deltamethrin slowed the maximum depolarization velocity (Vmax), prolonged the action potential duration (APD) and depolarized the maximum diastolic potential (MDP) of embryonic cardiomyocytes. Additionally, perinatal deltamethrin exposure decreased the mRNA expression of Na+ channel regulatory subunit Nav1, inward rectifier K+ channel subunit Kir2.1, and delayed rectifier K+ channel subunit MERG while the L-type Ca2+ channel subunit, Cav1.2 expression was increased. On the contrary, deltamethrin administration did not significantly alter the regulation of -adrenergic or muscarinic receptor on embryonic cardiomyocytes. In conclusion, deltamethrin exposure at perinatal stage significantly alters mRNA expression of embryonic cardiac ion channels and therefore influences embryonic cardiac electrophysiological properties. This highlights the need to understand the persistent effects of pyrethroid exposure on cardiac function during embryonic development due to potential for cardiac arrhythmogenicity.

Increased use of pyrethroids and the exposure to pyrethroids for pregnant women and children have raised the concerns over the potential effect of pyrethroids on developmental cardiotoxicity and other abnormalities. The purpose of this study was to investigate whether long term perinatal deltamethrin exposure altered embryonic cardiac electrophysiology in mice. Pregnant mice were administered with 0 or 3 mg/kg of deltamethrin by gavage daily from gestational day (gd) 10.5 to gd 17. 5. Whole cell patch-clamp technique was used in electrophysiological study, and real time RT-PCR was applied to analyze the molecular changes for the electrophysiological properties. Deltamethrin exposure resulted in increased mortality of pregnant mice and decreased viability of embryos. Moreover, deltamethrin slowed the maximum depolarization velocity (Vmax), prolonged the action potential duration (APD) and depolarized the maximum diastolic potential (MDP) of embryonic cardiomyocytes. Additionally, perinatal deltamethrin exposure decreased the mRNA expression of Na+ channel regulatory subunit Nav1, inward rectifier K+ channel subunit Kir2.1, and delayed rectifier K+ channel subunit MERG while the L-type Ca2+ channel subunit, Cav1.2 expression was increased. On the contrary, deltamethrin administration did not significantly alter the regulation of -adrenergic or muscarinic receptor on embryonic cardiomyocytes. In conclusion, deltamethrin exposure at perinatal stage significantly alters mRNA expression of embryonic cardiac ion channels and therefore influences embryonic cardiac electrophysiological properties. This highlights the need to understand the persistent effects of pyrethroid exposure on cardiac function during embryonic development due to potential for cardiac arrhythmogenicity.

Many social media news writers are not professionally trained. Therefore, social media platforms have to hire professional editors to adjust amateur headlines to attract more readers. We propose to automate this headline editing process through neural network models to provide more immediate writing support for these social media news writers. To train such a neural headline editing model, we collected a dataset which contains articles with original headlines and professionally edited headlines. However, it is expensive to collect a large number of professionally edited headlines. To solve this low-resource problem, we design an encoder-decoder model which leverages large scale pre-trained language models. We further improve the pre-trained model's quality by introducing a headline generation task as an intermediate task before the headline editing task. Also, we propose Self Importance-Aware (SIA) loss to address the different levels of editing in the dataset by down-weighting the importance of easily classified tokens and sentences. With the help of Pre-training, Adaptation, and SIA, the model learns to generate headlines in the professional editor's style. Experimental results show that our method significantly improves the quality of headline editing comparing against previous methods., Comment: AAAI 2020

Hydrocarbon intumescent coatings are an efficient means to passive fire protection of steel structures in high-risk environments such as petrochemical plants and offshore platforms. In the present study, the effects of zinc borate on the performance of hydrocarbon intumescent coatings were investigated. The analyses include steel temperature-time relationships and char expansion, as well as structural investigations and compositional profiles of the char. Furthermore, transient rheological coating behaviors and thermal degradation of the coatings were measured and analyzed. Results of furnace experiments, conducted under a standard hydrocarbon fire curve (UL1709), showed that a formulation containing 15 wt.% zinc borate gave the best performance. Fully expanded char measurements and the associated rheological recordings, evidenced that adjusting the zinc borate content from 0 to 20 wt.% increased the dynamic viscosity minimum of the incipient char from 6.2 to 5671.4 Pa·s. The increased dynamic viscosity resulted in a more uniform, but less expanded char layer. Compositional profiles of intumescent chars, measured using X-ray Diffraction, showed an increasing phosphate (especially BPO4) content with increasing levels of zinc borate in the intumescent coatings. Finally, the thermogravimetric analyses revealed that zinc borate can help to reduce epoxy binder degradation and oxidation of the carbon-inorganic residue. Due to the health risks associated with borate and its compounds, there is a requirement to find alternatives. The present work has mapped out some of the beneficial properties of zinc borate, and the results may provide some insight as to how to formulate without these compounds in the future.

Lu Zhou,1 Huiping Shi,2 Wenyu Shi,1 Li Yang,1 Yaping Zhang,1 Mengqi Xu,1 Xiufang Chen,1 Yanv Zhu,1 Hui Mu,1 Xiaochun Wan,3 Zhonghua Yang,3 Ying Zeng,3,* Hong Liu1,* 1Hematology Department, Affiliated Hospital of Nantong University, Nantong, People’s Republic of China; 2Cardiovascular Biology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, Oklahoma, USA; 3Shenzhen Bin De Bio Tech Co. Lid, Shenzhen, People’s Republic of China*These authors contributed equally to this workCorrespondence: Hong LiuHematology Department, Affiliated Hospital of Nantong University, No. 20 Xisi Road, Nantong, Jiangsu 226001, People’s Republic of ChinaEmail Hongliu63@126.comYing ZengShenzhen Bin De Bio Tech Co. Lid, Rich Industrial Park, Shenzhen 518055, People’s Republic of ChinaEmail ying.zeng@bindebio.comAbstract: Despite the prominent effects of BCR-ABL tyrosine kinase inhibitors (TKI) therapy in patients with chronic phase-chronic myeloid leukemia (CP-CML) and thus low incidence of blastic transformation, blast phase (BP)-CML remains a major therapeutic challenge in the TKI era. The “gatekeeper” mutation T315I in BCR-ABL1 kinase, which often coupled with a poor prognosis, is quite common and resistant to all TKIs except for ponatinib. The occurrence of T315I mutation in BP-CML makes the situation more complex. Anti-CD19 chimeric antigen receptor T cell (CAR-T) technology is a new immunotherapy which has significantly improved the efficacy of B cell hematologic malignances. Here we report a lymphoid BP-CML patient harboring T315I mutation who achieved complete molecular remission and returned to chronic phase by anti-CD19 CAR-T therapy. Our study provides a new therapeutic strategy for patients in BP-CML.Keywords: anti-CD19 CAR-T, BP-CML, BCR-ABL1, T315I

The protection of structural steel on exposure to a fire has become paramount to prevent the loss of lives and assets with increasing use of structural steel as a material to create the shape of construction projects. Structural steel loses bearing ability when its temperature exceeds 500 ºC (critical temperature), and an efficient way to prolong the time before the critical temperature is reached is to use intumescent coatings. At elevated temperature, intumescent coatings swell, via a complex chemical mechanism, to a porous multicellular char layer that acts as a thermal shield protecting the underlying substrate. The focus of the present work is the so-called hydrocarbon intumescent coatings, as it is the most challenging type used for fire scenarios in high-risk environments such as petrochemical complexes and offshore platforms. Zinc borate, an effective but suspiciously toxic compound in hydrocarbon intumescent formulations, was investigated with respect to its effects on the performance parameters (e.g. fire-resistance performance, thermal degradation, dynamic viscosity of incipient char, and char expansion and structure) of the coatings. With the understanding of the mechanism behind the positive effects of zinc borate, one can get a scientific basis for the replacement of zinc borate.

Hydrocarbon intumescent coatings are an efficient way to protect steel structures during fires in high-risk environments such as offshore oil rigs. The fire-resistance performance evaluation of a hydrocarbon intumescent coating, according to standard hydrocarbon fire test methods (e.g. UL 1709), is typically conducted using industrial furnaces, because regular laboratory equipment, such as Bunsen burners, gas lamps, or cone calorimeters, cannot provide the required fast heating curves. These constraints of experimental facilities have greatly limited the understanding of the behavior of hydrocarbon intumescent coatings at conditions of industrial relevance. Consequently, in the present PhD project it is has been one of the goals to develop a laboratory-scale setup that can simulate the standard fire test curve UL 1709. Using this setup, a mapping of the performance of hydrocarbon intumescent coatings as a function of the concentration of coating ingredients has been undertaken. The first ingredient studied was zinc borate and the fire-resistance experiments showed that the best performance (i.e. longest critical times of coated steel plates to reach 400 and 550 oC) was obtained when using a hydrocarbon intumescent coating with 15 wt.% zinc borate. Due to the strong influence of zinc borate on the rheological behavior of the coatings, increasing the level of zinc borate resulted in a more uniform, but less expanded char layer. The compositional profiles of the char layers indicated that the yield of phosphates (especially BPO4) was enhanced by raising the zinc borate content. Analyses on thermal degradation of the coatings suggested that zinc borate can improve the thermal stability of the epoxy binder and the anti-oxidation properties of the char layer. Before proceeding the investigation further to other ingredients, the initial laboratory-scale setup was upgraded with better insulation to the backside of steel plates to give a more clear distinc

Ying-Ying Zeng,1 Wei-Ping Hu,1 Yi-Hui Zuo,1 Xiao-Ru Wang,2 Jing Zhang1 1Department of Pulmonary Medicine, Zhongshan Hospital, Fudan University, Shanghai, China; 2Department of Pulmonary Medicine, Dahua Hospital, Shanghai, China Background: COPD, characterized by chronic inflammation and airway remodeling, has significant pathological alterations in composition and deposition of the extracellular matrix. The expression of procollagen 1 C-terminal peptide (PICP) and collagen type 1 C-terminal telopeptide (ICTP), two major by-products in the synthesis and degradation of collagen, was shown to be positively correlated with inflammatory mediator levels in previous studies. Purpose: In this study, we investigated whether the serum concentrations of PICP and ICTP were associated with the inflammation level for patients with stable COPD. Patients and methods: We collected serum samples from 25 control subjects and 20 patients with stable COPD from December 2011 to October 2012 in Shanghai Zhongshan Hospital and Shanghai Dahua Hospital. We determined concentrations of PICP, ICTP, C-reactive protein (CRP), IL-6, IL-8, and tumor necrosis factor (TNF)-α by using enzyme-linked immunosorbent assay methods. Results: Demographic characteristics were comparable between the two groups. In patients with stable COPD, serum levels of CRP, IL-6, IL-8, and TNF-α were all elevated compared to control subjects, but only changes of IL-6 achieved statistical significance. Serum concentration of PICP was significantly elevated in patients with COPD, and level of ICTP was slightly decreased. Moreover, serum concentrations of PICP were positively correlated with the levels of both IL-6 and IL-8. Conclusion: The increased levels of serum PICP in COPD might indicate the condition of airway remodeling, and IL-6 and/or IL-8 might play an important role in stimulating collagen synthesis. Keywords: chronic obstructive pulmonary disease, COPD, procollagen type I C-terminal peptide

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of highquality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events.

The protection of structural steel on exposure to a fire has become paramount to prevent the loss of lives and assets with increasing use of structural steel as a material to create the shape of construction projects. Structural steel loses bearing ability when its temperature exceeds 500 C (failure temperature), and an efficient way to prolong the time before the failure temperature is reached is to use intumescent coatings. At elevated temperature, intumescent coatings swell, via a complex chemical mechanism, to a porous multicellular char layer that acts as a thermal shield protecting the underlying substrate. For the special case of hydrocarbon fires on oil rigs, so-called hydrocarbon intumescent coatings are used, which must also be able to withstand jet fires after expansion. The crucial assessment of intumescent coatings is fire testing that is usually done with big industrial furnaces under standard temperature-time curves. However, the industrial fire tests have shortcomings of high costs, poor repeatability, and longtime cooling demand between two tests. These factors limit standard fire tests, especially for hydrocarbon fires. Therefore, a small-scale and high-throughput fire testing is badly needed to favor the assessment and development of effective intumescent coatings. The fire testing that we have developed is based on a lab-scale oven that can be rapidly heated up to 1600 C within 10 minutes. The standard fire curve mimicked by this oven is UL 1709, which is a widely accepted hydrocarbon fire-testing curve for intumescent coatings. The samples for tests are prepared by applying about 6 mm intumescent coating on the surface of a grit-blasted steel plate. A thermocouple is attached to the back of the plate to record the temperature-time (T-t) relationship during the fire testing. According to the chamber temperature recorded during each fire test, this oven can adequately simulate the T-t curve defined in the standard. The

In recent years, research on decoding brain activity based on functional magnetic resonance imaging (fMRI) has made remarkable achievements. However, constraint-free natural image reconstruction from brain activity is still a challenge. The existing methods simplified the problem by using semantic prior information or just reconstructing simple images such as letters and digitals. Without semantic prior information, we present a novel method to reconstruct nature images from fMRI signals of human visual cortex based on the computation model of convolutional neural network (CNN). Firstly, we extracted the units output of viewed natural images in each layer of a pre-trained CNN as CNN features. Secondly, we transformed image reconstruction from fMRI signals into the problem of CNN feature visualizations by training a sparse linear regression to map from the fMRI patterns to CNN features. By iteratively optimization to find the matched image, whose CNN unit features become most similar to those predicted from the brain activity, we finally achieved the promising results for the challenging constraint-free natural image reconstruction. As there was no use of semantic prior information of the stimuli when training decoding model, any category of images (not constraint by the training set) could be reconstructed theoretically. We found that the reconstructed images resembled the natural stimuli, especially in position and shape. The experimental results suggest that hierarchical visual features can effectively express the visual perception process of human brain.

Wei-Ping Hu, Ying-Ying Zeng, Yi-Hui Zuo, Jing Zhang Department of Pulmonary and Critical Care Medicine, Zhongshan Hospital, Shanghai Medical College, Fudan University, Shanghai, China Purpose: By reanalyzing the gene expression profile GSE76925 in the Gene Expression Omnibus database using bioinformatic methods, we attempted to identify novel candidate genes promoting the development of emphysema in patients with COPD.Patients and methods: According to the Quantitative CT data in GSE76925, patients were divided into mild emphysema group (%LAA-950<20%, n=12) and severe emphysema group (%LAA-950.50%, n=11). Differentially expressed genes (DEGs) were identified using Agilent GeneSpring GX v11.5 (corrected P-value <0.05 and |Fold Change|>1.3). Known driver genes of COPD were acquired by mining literatures and retrieving databases. Direct protein–protein interaction network (PPi) of DEGs and known driver genes was constructed by STRING.org to screen the DEGs directly interacting with driver genes. In addition, we used STRING.org to obtain the first-layer proteins interacting with DEGs’ products and constructed the indirect PPi of these interaction proteins. By merging the indirect PPi with driver genes’ PPi using Cytoscape v3.6.1, we attempted to discover potential pathways promoting emphysema’s development.Results: All the patients had COPD with severe airflow limitation (age=62±8, FEV1%=28±12). A total of 57 DEGs (including 12 pseudogenes) and 135 known driving genes were identified. Direct PPi suggested that GPR65, GNB4, P2RY13, NPSR1, BCR, BAG4, and IMPDH2 were potential pathogenic genes. GPR65 could regulate the response of immune cells to the acidic microenvironment, and NPSR1’s expression on eosinophils was associated with asthma’s severity and IgE level. Indirect merging PPi demonstrated that the interacting network of TP53, IL8, CCR2, HSPA1A, ELANE, PIK3CA was

There is wide belief that a student's behavior and academic outcome are affected by her/his fellow students. This peer effect lies at the center of the debate about education policy. My dissertation focuses on measuring peer effects in academic outcomes. It consists of a theoretical part and an empirical part. In the theoretical part, I study a peer effects model with group-wise equal interactions and random group effects. Identifying peer effects is notoriously challenging due to the reflection problem. Common shocks to the groups also generate spurious peer effects. My model, therefore, controls for the common shocks to the whole group with random group effects. My estimation strategy overcomes the identification problem with spatial econometrics techniques. I develop a quasi-maximum likelihood estimator of the model. Monte-Carlo simulations show that the bias of the estimator decreases with the number of groups and the variation in group size, and increases with group size. Finally, I prove the consistency and asymptotic normality of the estimator under standard assumptions. In the empirical part, I apply the model to Project STAR data to study the peer effects among kindergarten students. Peers constitute an important context for children's academic development. This empirical study measures peer effects on math and reading scores of kindergarten children using data from Project STAR, an experiment in Tennessee that randomly assigned both children and teachers to classrooms of different sizes. It estimates the impact of peers’ scores and characteristics on children’s individual scores, controlling among other things for random class effects. In contrast to most existing studies, the estimated peer effects in the empirical part are small and insignificant. The results are robust when allowing peer effects to be heterogeneous by gender, using data from higher grades or considering alternative specifications. The findings of the empirical study cast doubt on the ef

There is wide belief that a student's behavior and academic outcome are affected by her/his fellow students. This peer effect lies at the center of the debate about education policy. My dissertation focuses on measuring peer effects in academic outcomes. It consists of a theoretical part and an empirical part. In the theoretical part, I study a peer effects model with group-wise equal interactions and random group effects. Identifying peer effects is notoriously challenging due to the reflection problem. Common shocks to the groups also generate spurious peer effects. My model, therefore, controls for the common shocks to the whole group with random group effects. My estimation strategy overcomes the identification problem with spatial econometrics techniques. I develop a quasi-maximum likelihood estimator of the model. Monte-Carlo simulations show that the bias of the estimator decreases with the number of groups and the variation in group size, and increases with group size. Finally, I prove the consistency and asymptotic normality of the estimator under standard assumptions. In the empirical part, I apply the model to Project STAR data to study the peer effects among kindergarten students. Peers constitute an important context for children's academic development. This empirical study measures peer effects on math and reading scores of kindergarten children using data from Project STAR, an experiment in Tennessee that randomly assigned both children and teachers to classrooms of different sizes. It estimates the impact of peers’ scores and characteristics on children’s individual scores, controlling among other things for random class effects. In contrast to most existing studies, the estimated peer effects in the empirical part are small and insignificant. The results are robust when allowing peer effects to be heterogeneous by gender, using data from higher grades or considering alternative specifications. The findings of the empirical study cast doubt on the ef

The task of event extraction has long been investigated in a supervised learning paradigm, which is bound by the number and the quality of the training instances. Existing training data must be manually generated through a combination of expert domain knowledge and extensive human involvement. However, due to drastic efforts required in annotating text, the resultant datasets are usually small, which severally affects the quality of the learned model, making it hard to generalize. Our work develops an automatic approach for generating training data for event extraction. Our approach allows us to scale up event extraction training instances from thousands to hundreds of thousands, and it does this at a much lower cost than a manual approach. We achieve this by employing distant supervision to automatically create event annotations from unlabelled text using existing structured knowledge bases or tables.We then develop a neural network model with post inference to transfer the knowledge extracted from structured knowledge bases to automatically annotate typed events with corresponding arguments in text.We evaluate our approach by using the knowledge extracted from Freebase to label texts from Wikipedia articles. Experimental results show that our approach can generate a large number of high quality training instances. We show that this large volume of training data not only leads to a better event extractor, but also allows us to detect multiple typed events., Comment: 8 pages, accepted by AAAI 2018

Thymosin beta 4 (T beta 4) is a key factor in cardiac development, growth, disease, epicardial integrity, blood vessel formation and has cardio-protective properties. However, its role in murine embryonic stem cells (mESCs) proliferation and cardiovascular differentiation remains unclear. Thus we aimed to elucidate the influence of T beta 4 on mESCs. Target genes during mESCs proliferation and differentiation were detected by real-time PCR or Western blotting, and patch clamp was applied to characterize the mESCs-derived cardiomyocytes. It was found that T beta 4 decreased mESCs proliferation in a partial dose-dependent manner and the expression of cell cycle regulatory genes c-myc, c-fos and c-jun. However, mESCs self-renewal markers Oct4 and Nanog were elevated, indicating the maintenance of self-renewal ability in these mESCs. Phosphorylation of STAT3 and Akt was inhibited by T beta 4 while the expression of RAS and phosphorylation of ERK were enhanced. No significant difference was found in BMP2/BMP4 or their downstream protein smad. Wnt3 and Wnt11 were remarkably decreased by T beta 4 with upregulation of Tcf3 and constant beta-catenin. Under mESCs differentiation, T beta 4 treatment did not change the expression of cardiovascular cell markers alpha-MHC, PECAM, and alpha-SMA. Neither the electrophysiological properties of mESCs-derived cardiomyocytes nor the hormonal regulation by Iso/Cch was affected by T beta 4. In conclusion, T beta 4 suppressed mESCs proliferation by affecting the activity of STAT3, Akt, ERK and Wnt pathways. However, T beta 4 did not influence the in vitro cardiovascular differentiation.

Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling.

Chinese event extraction is a challenging task in information extraction. Previous approaches highly depend on sophisticated feature engineering and complicated natural language processing (NLP) tools. In this paper, we first come up with the language specific issue in Chinese event extraction, and then propose a convolution bidirectional LSTM neural network that combines LSTM and CNN to capture both sentence-level and lexical information without any hand-craft features. Experiments on ACE 2005 dataset show that our approaches can achieve competitive performances in both trigger labeling and argument role labeling.

Thymosin beta 4 (T beta 4) is a key factor in cardiac development, growth, disease, epicardial integrity, blood vessel formation and has cardio-protective properties. However, its role in murine embryonic stem cells (mESCs) proliferation and cardiovascular differentiation remains unclear. Thus we aimed to elucidate the influence of T beta 4 on mESCs. Target genes during mESCs proliferation and differentiation were detected by real-time PCR or Western blotting, and patch clamp was applied to characterize the mESCs-derived cardiomyocytes. It was found that T beta 4 decreased mESCs proliferation in a partial dose-dependent manner and the expression of cell cycle regulatory genes c-myc, c-fos and c-jun. However, mESCs self-renewal markers Oct4 and Nanog were elevated, indicating the maintenance of self-renewal ability in these mESCs. Phosphorylation of STAT3 and Akt was inhibited by T beta 4 while the expression of RAS and phosphorylation of ERK were enhanced. No significant difference was found in BMP2/BMP4 or their downstream protein smad. Wnt3 and Wnt11 were remarkably decreased by T beta 4 with upregulation of Tcf3 and constant beta-catenin. Under mESCs differentiation, T beta 4 treatment did not change the expression of cardiovascular cell markers alpha-MHC, PECAM, and alpha-SMA. Neither the electrophysiological properties of mESCs-derived cardiomyocytes nor the hormonal regulation by Iso/Cch was affected by T beta 4. In conclusion, T beta 4 suppressed mESCs proliferation by affecting the activity of STAT3, Akt, ERK and Wnt pathways. However, T beta 4 did not influence the in vitro cardiovascular differentiation.

战后日本经济的崛起被称为奇迹，日本汽车工业在此其中起了非常重要的作用。可以说，汽车工业是日本经济的重要组成部分，没有汽车工业的迅速发展，就没有今天日本经济的繁荣和国际地位。現在，日本是全球汽车工业最发达的国家之一，其产销規模，技术水平，质量水平都居世界先进水平。但是，战前和战争结束时的日本汽车工业，与欧美的汽车产业相比，处于不可否认的落后状态。日本的汽车产业发展到今天经历了许多危机，但毎次都取得了奇迹性的发展。其取得今天的成就原因是多方面的，既有内因也有外因，内因是起决定作用的因素，而成功的产业政策是最重要的因素之一。

This is an author's accepted manuscript of: Huang, S., Shao, Y., Zeng, Y., Liu, X., & Li, Z. (2021). Impacts of COVID-19 on Chinese nationals' tourism preferences. Tourism Management Perspectives, 40, article 100895.https://doi.org/10.1016/j.tmp.2021.100895

Samples of serpentine were pre-processed by ball milling and calcining to destroy the crystal structure and improve the physical and chemical properties. The ore samples were then leached using different inorganic acids. Optimum conditions were determined to be ball milling for 1 h, calcining at 700 degrees C for 1 h followed by mixing with 3 mol/l H2SO4 and stirring at room temperature at a solid/liquid ratio of 1: 15. MgO leaching rates up to 93 % were obtained., Samples of serpentine were pre-processed by ball milling and calcining to destroy the crystal structure and improve the physical and chemical properties. The ore samples were then leached using different inorganic acids. Optimum conditions were determined to be ball milling for 1 h, calcining at 700 degrees C for 1 h followed by mixing with 3 mol/l H2SO4 and stirring at room temperature at a solid/liquid ratio of 1: 15. MgO leaching rates up to 93 % were obtained.