Search

Xin-Mei Hu,1,* Can-Can Wang,1,* Yu Xiao,1 Yu Liu,1 Hong-Ri Huang,2 Peng Jiang,1 Ying-Kai Wang,1 Yun-Jin Lin,1 Liang-Cheng Li,3 Zhong-Quan Qi1 1Medical College, Guangxi University, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 2Department of Research and Development, Guangxi Taimei Rensheng Biotechnology Co., Ltd, Nanning, Guangxi Zhuang Autonomous Region, People’s Republic of China; 3School of Pharmaceutical Sciences, Xiamen University, Xiamen, Fujian Province, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zhong-Quan Qi, Guangxi University, Da-Xue-Dong Road No. 100, Xixiangtang District, Nanning, Guangxi Zhuang Autonomous Region, 530004, People’s Republic of China, Tel +8613806001271, Email yxyyz@gxu.edu.cn Liang-Cheng Li, Xiamen University, No. 4221-115, Rm 355, Xiang’an District, Xiang’an, Xiamen Fujian Province, 361102, People’s Republic of China, Tel +8618359206906, Email lchli2013@xmu.edu.cnPurpose: In recent years, exosomes have been proved to be used to treat many diseases. However, due to the lack of uniform quality control standards for exosomes, the safety of exosomes is still a problem to be solved, especially now more and more exosomes are used in clinical trials, and its non-clinical safety evaluation is particularly important. However, there is no safety evaluation standard for exosomes at present. Therefore, this study will refer to the evaluation criteria of therapeutic biological products, adopt non-human primates to evaluate the non-clinical safety of human umbilical cord mesenchymal stem cell exosomes from the general pharmacology and immunotoxicity, aiming at establishing a safety evaluation system of exosomes and providing reference for the clinical application of exosomes in the future.Methods: 3.85 × 1012 exosomes derived from human umbilical cord mesenchymal stem cells were injected into cynomolgus monkeys intravenously. The changes of g

In the modern "omics" era, measurement of the human exposome is a critical missing link between genetic drivers and disease outcomes. High-resolution mass spectrometry (HRMS), routinely used in proteomics and metabolomics, has emerged as a leading technology to broadly profile chemical exposure agents and related biomolecules for accurate mass measurement, high sensitivity, rapid data acquisition, and increased resolution of chemical space. Non-targeted approaches are increasingly accessible, supporting a shift from conventional hypothesis-driven, quantitation-centric targeted analyses toward data-driven, hypothesis-generating chemical exposome-wide profiling. However, HRMS-based exposomics encounters unique challenges. New analytical and computational infrastructures are needed to expand the analysis coverage through streamlined, scalable, and harmonized workflows and data pipelines that permit longitudinal chemical exposome tracking, retrospective validation, and multi-omics integration for meaningful health-oriented inferences. In this article, we survey the literature on state-of-the-art HRMS-based technologies, review current analytical workflows and informatic pipelines, and provide an up-to-date reference on exposomic approaches for chemists, toxicologists, epidemiologists, care providers, and stakeholders in health sciences and medicine. We propose efforts to benchmark fit-for-purpose platforms for expanding coverage of chemical space, including gas/liquid chromatography-HRMS (GC-HRMS and LC-HRMS), and discuss opportunities, challenges, and strategies to advance the burgeoning field of the exposome.

By utilizing the AdS/CFT correspondence, we investigate the holographic image of an AdS black hole in Einstein-power-Yang-Mills gravity. The AdS boundary hosts a Gaussian oscillation source, which induces a lensed response on the opposite side of the boundary during propagation through bulk spacetime. The optical system assists observers at the north pole to continuously capture holographic images that show an axisymmetric bright ring known as the Einstein ring. As the observation position shifted, the bright ring gradually transformed into a luminous arc and eventually transitioned into a light point. Simultaneously, we examine the impact of variations in relevant physical quantity on the ring, and present the corresponding brightness curve. The results indicate that as the temperature $T$ and nonlinear Yang Mills charge parameter $q$ increase, the ring radius also increases, while an increase in the chemical potential $u$ leads to a decrease. However, the peak brightness curve of the ring invariably decreases as the values of $T$, $u$, and $q$ increase, albeit to varying degrees. Upon comparing the outcomes of geometric optics, it can be observed that the position of the ring in holography images is consistent with that of the photon sphere., Comment: 23pages, 19 figures

Mrk 501 is a prototypical high-synchrotron-peaked blazar (HBL) and serves as one of the primary targets for the Imaging X-ray Polarimetry Explorer (IXPE). In this study, we report X-ray polarization measurements of Mrk 501 based on six IXPE observations. The $>99\%$ confidence detection of X-ray polarization is obtained in four out of six observations in the entire energy band (2--8 keV) of IXPE, with a maximum polarization degree ($\Pi_{\rm X}$) of $15.8\%\pm2.8\%$ and a polarization angle ($\psi_{\rm X}$) of $98.0^{\circ}\pm5.1^{\circ}$ at a confidence level of $5.2\sigma$. During the other two observations, the source only exhibits the $>99\%$ confidence detection of X-ray polarization within an energy bin of 1 keV. Interestingly, both the second and sixth IXPE observations present energy-dependent variability in X-ray polarization, which is also observed in our optical spectropolarimetry. The chromatic behavior of $\Pi$ and the consistent values of $\psi$ across different frequencies, along with the agreement between $\psi$ and jet position angle, strongly support the interpretation of the energy-stratified model with shock-accelerated particles in the jet of Mrk 501. Additionally, the possibility of the presence of a global helical magnetic field in the jet of Mrk 501 is discussed., Comment: 19 pages, 8 figures, 5 tables, submitted

We report on the detection of extended $\gamma$-ray emission from lobes in the radio galaxy NGC 6251 using observation data of Fermi Large Area Telescope (Fermi-LAT). The maximum likelihood analysis results show that a radio morphology template provides a better fit than a point-like source description for the observational data at a confidence level of 8.1$\sigma$, and the contribution of lobes accounts for more than 50\% of the total $\gamma$-ray flux. Furthermore, the $\gamma$-ray energy spectra show a significant disparity in shape between the core and lobe regions, with a curved log-parabola shape observed in core region and a power-law form observed in lobes. Neither the core region nor the northwest lobe displays the significant flux variations in the long-term $\gamma$-ray light curves. The broadband spectral energy distributions of both core region and northwest lobe can be will explained with a single-zone leptonic model. The $\gamma$-rays of core region are due to the synchrotron-self-Compton process while the $\gamma$-rays from northwest lobe are interpreted as inverse Compton emission of the cosmic microwave background., Comment: 13 pages,6 figures,3 tables, the Astrophysical Journal, Accepted

Deep learning-based image restoration methods generally struggle with faithfully preserving the structures of the original image. In this work, we propose a novel Residual-Conditioned Optimal Transport (RCOT) approach, which models image restoration as an optimal transport (OT) problem for both unpaired and paired settings, introducing the transport residual as a unique degradation-specific cue for both the transport cost and the transport map. Specifically, we first formalize a Fourier residual-guided OT objective by incorporating the degradation-specific information of the residual into the transport cost. We further design the transport map as a two-pass RCOT map that comprises a base model and a refinement process, in which the transport residual is computed by the base model in the first pass and then encoded as a degradation-specific embedding to condition the second-pass restoration. By duality, the RCOT problem is transformed into a minimax optimization problem, which can be solved by adversarially training neural networks. Extensive experiments on multiple restoration tasks show that RCOT achieves competitive performance in terms of both distortion measures and perceptual quality, restoring images with more faithful structures as compared with state-of-the-art methods., Comment: ICML 2024

BACKGROUND: Genomic DNA reference materials are widely recognized as essential for ensuring data quality in omics research. However, relying solely on reference datasets to evaluate the accuracy of variant calling results is incomplete, as they are limited to benchmark regions. Therefore, it is important to develop DNA reference materials that enable the assessment of variant detection performance across the entire genome. RESULTS: We established a DNA reference material suite from four immortalized cell lines derived from a family of parents and monozygotic twins. Comprehensive reference datasets of 4.2 million small variants and 15,000 structural variants were integrated and certified for evaluating the reliability of germline variant calls inside the benchmark regions. Importantly, the genetic built-in-truth of the Quartet family design enables estimation of the precision of variant calls outside the benchmark regions. Using the Quartet reference materials along with study samples, batch effects are objectively monitored and alleviated by training a machine learning model with the Quartet reference datasets to remove potential artifact calls. Moreover, the matched RNA and protein reference materials and datasets from the Quartet project enables cross-omics validation of variant calls from multiomics data. CONCLUSIONS: The Quartet DNA reference materials and reference datasets provide a unique resource for objectively assessing the quality of germline variant calls throughout the whole-genome regions and improving the reliability of large-scale genomic profiling.

With the help of AdS/CFT correspondence, the Einstein ring of a charged black hole in conformal gravity has been studied. Imposing an oscillating Gauss source on one side of the AdS boundary which propagates in the bulk, we derive the response function on the other side of the boundary. With the proposed wave optics system, we observe the Einstein ring as expect. The results reveal that when the observer locates at the north pole, the Einstein ring and surrounding concentric stripes always exist. While the observer departs away from the north pole, the ring becomes into a luminosity-deformed ring or light spot. We also investigate the effect of temperature $T$, chemical potential $u$ and gravity-related parameters $c_0$ on the ring radius. We find the ring radius increases with the decrease of the temperature, increase of the chemical potential, and increase of the gravity-related parameters respectively. To check the results in the framework of holography, we investigate the ingong angle of photon at the photon ring via geometric optics and find it is consistent with the angle of Einstein ring obtained via holography., Comment: many figures. arXiv admin note: text overlap with arXiv:2309.07404

In the context of holography, the Einstein ring of an AdS black hole (BH) in massive gravity (MG) is depicted. An oscillating Gaussian source on one side of the AdS boundary propagates in bulk, and we impose a response function to explain it. Using a wave optics imaging system, we obtain the optical appearance of the Einstein ring. Our research reveals that the ring can change into a luminosity-deformed ring or light spots depending on the variation of parameters and observational positions. When observers are positioned at the north pole, the holographic profiles always appear as a ring with concentric stripe surroundings, and a bright ring appears at the location of the photon sphere of the BH. These findings are consistent with the radius of the photon sphere of the BH, which is calculated in geometrical optics. Our study contributes to a better understanding of the analytical studies of holographic theory, which can be used to evaluate different types of BHs for a fixed wave source and optical system., Comment: 15 pages, 14 figures

Daiqin Zhang,* Wenting Li,* Xin Hu, Hongyu Huang, Xiaobing Zhang Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Xiaobing Zhang, Department of Laboratory Medicine, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Tel +86-15123967161, Fax +86-23-89012742, Email xhpg85@aliyun.comPurpose: The purpose of this study is to examine the variations between extra-intestinal and intestinal infections of Aeromonas in terms of strain types, risk factors, drug susceptibility results, and the distribution of drug resistance and virulence genes.Patients and Methods: A total of 188 Aeromonas strains were identified to the species level using housekeeping genes (rpoD, gyrB, and gyrA). The risk factors for Aeromonas extra-intestinal and intestinal infection, as well as mortality, were retrospectively examined in this study. The broth microdilution method was used to investigate the antimicrobial susceptibility profiles. Touchdown polymerase chain reaction (PCR) assays and DNA sequencing were employed to confirm virulence and the presence of drug resistance genes.Results: The housekeeping genes identified 188 strains into 7 species. Extra-intestinal isolates generally contained A. caviae and A. hydrophila, while intestinal were A. veronii (p=0.0001). Extra-intestinal infections (158/188) were the main type and accounted for 24/27 of all fatalities. Malignant tumors, hepatobiliary diseases, anemia, and hypoproteinemia were linked to infections. Poor results were associated with septic shock. Using the broth microdilution method, over 80% isolates were susceptible to most antimicrobials, except for ceftazidime (79.8%) and ceftriaxone (69.7%). Except for imipenem, intestinal strains were more susceptible to other medic

Due to the growth of the mobile phone application market, which development method can efficiently develop high-performance applications has become a problem. This experimental study compares the rendering performance of applications developed through native development and React Native on the Android platform. By developing an application with the ability to render pictures, and measuring the rendering performance of the application. The measurement results show that natively developed applications usually have high stability, while the average rendering time of React Native applications is similar to that of natively developed applications as the rendering performance pressure gradually increases. In future work, other image types may be measured, or text and video may be measured. It is also possible to run multiple different experiments to collect data to improve development tools or develop new performance analysis tools., Det finns övrigt digitalt material (t.ex. film-, bild- eller ljudfiler) eller modeller/artefakter tillhörande examensarbetet som ska skickas till arkivet.There are other digital material (eg film, image or audio files) or models/artifacts that belongs to the thesis and need to be archived.

The Venezuelan equine encephalitis virus (VEEV) nonstructural protein 2 (nsP2) cysteine protease (EC 3.4.22.B79) is essential for viral replication. High throughput in silico/in vitro screening using a focused set of known cysteine protease inhibitors identified two epoxysuccinyl prodrugs, E64d and CA074 methyl ester (CA074me) and a reversible oxindole inhibitor. Here, we determined the X-ray crystal structure of the CA074-inhibited nsP2 protease and compared it with our E64d-inhibited structure. We found that the two inhibitors occupy different locations in the protease. We designed hybrid inhibitors with improved potency. Virus yield reduction assays confirmed that the viral titer was reduced by >5 logs with CA074me. Cell-based assays showed reductions in viral replication for CHIKV, VEEV, and WEEV, and weaker inhibition of EEEV by the hybrid inhibitors. The most potent was NCGC00488909-01 which had an EC50 of 1.76 µM in VEEV-Trd-infected cells; the second most potent was NCGC00484087 with an EC50 = 7.90 µM. Other compounds from the NCATS libraries such as the H1 antihistamine oxatomide (>5-log reduction), emetine, amsacrine an intercalator (NCGC0015113), MLS003116111-01, NCGC00247785-13, and MLS00699295-01 were found to effectively reduce VEEV viral replication in plaque assays. Kinetic methods demonstrated time-dependent inhibition by the hybrid inhibitors of the protease with NCGC00488909-01 (Ki = 3 µM) and NCGC00484087 (Ki = 5 µM). Rates of inactivation by CA074 in the presence of 6 mM CaCl2, MnCl2, or MgCl2 were measured with varying concentrations of inhibitor, Mg2+ and Mn2+ slightly enhanced inhibitor binding (3 to 6-fold). CA074 inhibited not only the VEEV nsP2 protease but also that of CHIKV and WEEV.

Diese Sammlung enthält die Daten, die in einem Experiment zur gleichzeitigen Streuung von zwei Photonen an einem Zwei-Niveau Atom verwendet wurden. Weitere Informationen finden Sie in der folgenden Publikation: [https://doi.org/10.1038/s41566-023-01260-7] [https://doi.org/10.48550/arXiv.2209.02547], This repository contains the data used in an experiment concerning the simultaneous scattering of two photons by a single two-level atom. For more information, please see the following publication: [https://doi.org/10.1038/s41566-023-01260-7] [https://doi.org/10.48550/arXiv.2209.02547]

We report the development of GPT-4, a large-scale, multimodal model which can accept image and text inputs and produce text outputs. While less capable than humans in many real-world scenarios, GPT-4 exhibits human-level performance on various professional and academic benchmarks, including passing a simulated bar exam with a score around the top 10% of test takers. GPT-4 is a Transformer-based model pre-trained to predict the next token in a document. The post-training alignment process results in improved performance on measures of factuality and adherence to desired behavior. A core component of this project was developing infrastructure and optimization methods that behave predictably across a wide range of scales. This allowed us to accurately predict some aspects of GPT-4's performance based on models trained with no more than 1/1,000th the compute of GPT-4., Comment: 100 pages; updated authors list; fixed author names and added citation

Humans no doubt use language to communicate about their emotional experiences, but does language in turn help humans understand emotions, or is language just a vehicle of communication? This study used a form of artificial intelligence (AI) known as large language models (LLMs) to assess whether language-based representations of emotion causally contribute to the AI's ability to generate inferences about the emotional meaning of novel situations. Fourteen attributes of human emotion concept representation were found to be represented by the LLM's distinct artificial neuron populations. By manipulating these attribute-related neurons, we in turn demonstrated the role of emotion concept knowledge in generative emotion inference. The attribute-specific performance deterioration was related to the importance of different attributes in human mental space. Our findings provide a proof-in-concept that even a LLM can learn about emotions in the absence of sensory-motor representations and highlight the contribution of language-derived emotion-concept knowledge for emotion inference., Comment: 44 pages, 14 figures, 2 tables

Recent advancements in Natural Language Processing have opened up new possibilities for the development of large language models like ChatGPT, which can facilitate knowledge management in the design process by providing designers with access to a vast array of relevant information. However, integrating ChatGPT into the design process also presents new challenges. In this paper, we provide a concise review of the classification and representation of design knowledge, and past efforts to support designers in acquiring knowledge. We analyze the opportunities and challenges that ChatGPT presents for knowledge management in design and propose promising future research directions. A case study is conducted to validate the advantages and drawbacks of ChatGPT, showing that designers can acquire targeted knowledge from various domains, but the quality of the acquired knowledge is highly dependent on the prompt., Comment: This is a conference paper, 8 pages, 4 figures

The high-energy-peaked BL Lacertae objects (HBLs) are the main targets of the Imaging X-ray Polarimetry Explorer (IXPE) for investigating the mechanisms of radiation and particle acceleration in jets. In this paper, we report the first IXPE observations of two HBLs, 1ES 1959+650 and PKS 2155--304. Both sources exhibit X-ray polarization with a confidence level exceeding 99\%, as well as significant variability in polarization across different time intervals and energy ranges. Notably, PKS 2155--304 demonstrates the highest X-ray polarization among all blazars detected by IXPE within its entire energy band (2--8 keV), with a polarization degree of $\Pi_{\rm X}=21.9\%\pm1.9\%$ (MDP$_{99}\sim$6.0\%). An even higher polarization is observed in the 3--4 keV band, reaching $\Pi_{\rm X}=28.6\%\pm2.7\%$ (MDP$_{99}\sim$8.1\%) with a confidence level of 10.8$\sigma$. Furthermore, no polarization is detected above 5 keV energy band. For 1ES 1959+650, the highest detected polarization degree in the 2--8 keV band is $\Pi_{\rm X}=12.4\%\pm0.7\%$ (MDP$_{99}\sim$2.2\%), with an electric vector position angle (EVPA) of $\psi_{\rm X}=19.7^{\circ}\pm1.6^{\circ}$. The X-ray polarization of 1ES 1959+650 exhibits evident variability, accompanied by the variations of $\psi_{\rm X}$, flux, spectrum, and energy bin. We discuss possible implications of these observational findings, including the variability in polarization, rotation of EVPA, and transition between synchrotron and synchrotron-self-Compton. We speculate that the X-rays observed during different IXPE observations originate from distinct regions in the jet and may involve diverse mechanisms for particle acceleration., Comment: The Astrophysical Journal Letters, In Press

BackgroundUtilization of basic public health services (BPHS) allows for disease prevention and management and is an essential component for protecting health. Disparities in utilization exist between rural-to-urban migrants and their local counterparts in China. This study sought to determine the frequency of BPHS utilization and whether social support, discrimination, and sociodemographic features were risk factors for low BPHS utilization by Chinese female migrants.MethodsData were derived from a survey of female rural-to-urban migrants at nine work sites in Changsha, China. The association between social support, discrimination, sociodemographic factors and BPHS utilization was obtained using Chi-square and logistic regression analysis.ResultsBetween December 2017 and April 2018, 307 female participants completed the survey. A total of 24.7% reported having had health education, 26.1% had breast and cervical cancer screening, 27.2% had established a health care record, and 40.9% had received basic contraceptive services. Two factors were associated with the reduced likelihood of BPHS utilization: Length of migration and health record establishment (OR = 0.53; 95% CI = 0.31, 0.92) and years of education and basic contraceptive service use (OR = 0.36; 95% CI = 0.20, 0.67). The remaining six factors were associated with an increased likelihood of BPHS utilization: Living circumstances and health record establishment (OR = 2.11; 95% CI = 1.17, 3.80), health education (OR = 2.71; 95% CI = 1.51, 4.87) and cancer screening (OR = 2.38; 95% CI = 1.30, 4.36). Utilization of social support was associated with health record establishment (OR = 1.24; 95% CI = 1.06, 1.44), basic contraceptive service use (OR = 1.21; 95% CI = 1.04, 1.42) and cancer screening (OR = 1.29; 95% CI = 1.10, 1.51). Objective social support was associated with health education utilization (OR = 1.15; 95% CI = 1.04, 1.26), while subjective social support was associated with basic contraceptive service u

Omics-based technologies have enabled comprehensive characterization of our exposure to environmental chemicals (chemical exposome) as well as assessment of the corresponding biological responses at the molecular level (eg, metabolome, lipidome, proteome, and genome). By systematically measuring personal exposures and linking these stimuli to biological perturbations, researchers can determine specific chemical exposures of concern, identify mechanisms and biomarkers of toxicity, and design interventions to reduce exposures. However, further advancement of metabolomics and exposomics approaches is limited by a lack of standardization and approaches for assigning confidence to chemical annotations. While a wealth of chemical data is generated by gas chromatography high-resolution mass spectrometry (GC-HRMS), incorporating GC-HRMS data into an annotation framework and communicating confidence in these assignments is challenging. It is essential to be able to compare chemical data for exposomics studies across platforms to build upon prior knowledge and advance the technology. Here, we discuss the major pieces of evidence provided by common GC-HRMS workflows, including retention time and retention index, electron ionization, positive chemical ionization, electron capture negative ionization, and atmospheric pressure chemical ionization spectral matching, molecular ion, accurate mass, isotopic patterns, database occurrence, and occurrence in blanks. We then provide a qualitative framework for incorporating these various lines of evidence for communicating confidence in GC-HRMS data by adapting the Schymanski scoring schema developed for reporting confidence levels by liquid chromatography HRMS (LC-HRMS). Validation of our framework is presented using standards spiked in plasma, and confident annotations in outdoor and indoor air samples, showing a false-positive rate of 12% for suspect screening for chemical identifications assigned as Level 2 (when structurally similar

The construction industry is a high-risk industry with many safety accidents. The popularity of Internet information technology has led to an explosion in the amount of data obtained in various engineering fields, and it is of necessary significance to explore the current situation of the application of big data technology in construction safety management. This paper systematically reviews 66 articles closely related to the research topic and objectives, describes the current status of big data application to various construction safety issues from the perspectives of both big data collection and big data analysis for engineering and construction projects, and categorically lists the breakthrough results of big data analysis technology in improving construction safety. Finally, the trends and challenges of big data in the field of construction safety are discussed in three directions: the application of big data to worker behavior, the prospect of integrating big data technologies, and the integration of big data technologies with construction management. The aim of this paper is to demonstrate the current state of research on big data technology fueling construction safety management, providing valuable insight into improving safety at engineering construction sites and providing guidance for future research in this field.

BackgroundAlthough many young Chinese women migrate to urban regions for better opportunities, little is known about the prevalence and awareness of having high blood pressure (HBP) in this population. This study investigated the prevalence, awareness, and factors associated with HBP among young female migrant workers in Central South China.MethodsWe conducted a cross-sectional study to identify HBP (2017 ACC/AHA guidelines) among female migrant workers aged 18-45 years in Central South China. Demographics, anthropometric measurements, hypertension-related lifestyle, awareness of HBP, and blood pressure were recorded. Logistic regression analysis was used to identify the factors associated with HBP (blood pressure ≥ 130/80 mmHg).ResultsOverall, 232 female migrants participated in the study (mean age 34.4; standard deviation: 6.4 years). The prevalence of HBP was 27.2% (95% CI [21.6-33.2]), and 88.9% of the participants were unaware of their HBP status. Having rural medical insurance (odds ratio [OR] = 20.7; 95% confidence interval 95% CI [2.1-204.8]), awareness of having HBP (OR = 5.1; 95% CI [1.4-18.5]), physical inactivity (OR = 2.9; 95% CI [1.1-7.9]), and being overweight/obese (OR = 2.7; 95% CI [1.3-6.1]) were independently associated with HBP.ConclusionsThis study revealed a high prevalence of HBP among young Chinese female migrant workers, as well as a high frequency of being unaware of their condition and some associated factors (rural medical insurance, awareness of having HBP, physical inactivity, and overweight/obesity). The uncontrolled HBP among young Chinese female migrant workers suggested that health education needs further promotion in such a population.

Sha Lv,1,* Xin Hu,1,2,* Zhe Liu,1 Yitong Lin,1 Hanfei Wu,3 Fuqiu Li1 1Department of Dermatology, the Second Hospital of Jilin University, Changchun, 130041, People’s Republic of China; 2Department of Microbiology, Graduate School of Medicine, Kyoto University, Kyoto, 606-8510, Japan; 3Department of General Surgery, The First Clinical Hospital of Academy of Science of TCM in Jilin Province, Changchun, 130041, People’s Republic of China*These authors contributed equally to this workCorrespondence: Fuqiu Li, Department of Dermatology, the Second Hospital of Jilin University, 218 Ziqiang Street, Changchun, 130041, People’s Republic of China, Email lifuqiu1234@126.comIntroduction: This study was aimed to examine the clinical and epidemiological characteristics of sporotrichosis in China and specifically Jilin Province, which is one of the areas with the highest incidence worldwide, and to provide data support for the global prevalence of sporotrichosis.Methods: A total of 4969 cases of sporotrichosis diagnosed at the Second Hospital of Jilin University from January 1, 1990 to December 31, 2019 were collected.Results: In Jilin Province, the male-to-female ratio was 1:2, the average age at onset was 48 ± 1 years, and the average disease duration was 4.8 ± 2.7 months. The most susceptible individuals were farmers. Cases occurred more commonly in the winter and spring (71.5%) than in the summer and autumn (28.5%). The fixed type infection was more prevalent. Among the cases, 64.8% showed typical mycological changes, and 77.6% showed atypical pathological changes. Regarding the epidemiological characteristics of sporotrichosis in China, 6565 cases were retrieved from the literature from January 1, 2010 to December 31, 2019. Among them, the most affected area was Jilin Province, followed by Heilongjiang Province, and Liaoning Province. The male-to-female ratio was 1:1.46. The fixed type infection was the most common. A total of 241 strains were

Jin Wu,1– 3,* Tianyi Zhang,1– 3,* Haofeng Xiong,1– 4 Liujun Zeng,1– 3 Zijia Wang,1– 3 Ying Peng,1– 3 Weijun Chen,1– 3 Xin Hu,1– 3 Tong Su1– 3 1Department of Oral and Maxillofacial Surgery, Center of Stomatology, Xiangya Hospital of Central South University, Changsha, Hunan, 410008, People’s Republic of China; 2Research Center of Oral and Maxillofacial Tumor, Xiangya Hospital of Central South University, Changsha, Hunan, 410008, People’s Republic of China; 3Institute of Oral Cancer and Precancerous Lesions, Central South University, Changsha, Hunan, 410008, People’s Republic of China; 4Center for Medical Genetics & Hunan Key Laboratory of Medical Genetics, School of Life Sciences, Central South University, Changsha, Hunan, 410008, People’s Republic of China*These authors contributed equally to this workCorrespondence: Tong Su; Xin HuCenter Department of Oral and Maxillofacial Surgery, of Stomatology, Xiangya Hospital of Central South University, No. 87 Xiangya Road, Changsha, Hunan, 410008, People’s Republic of ChinaEmail sutong@csu.edu.cn; huxin_666@qq.comObjective: Oral squamous cell carcinoma (OSCC) is the most frequent oral malignancy with a poor prognosis, in which tumor-infiltrating immune cells may play a critical role. Therefore, our study aims to screen potential immune cells and immune-related genes for predicting OSCC prognosis.Methods: A total of 310 OSCC patients with full transcriptional data and clinical characteristics were extracted from the TCGA database. Then, we obtained their abundance of tumor-infiltrating immune cells on TIMER 2.0 and analyzed them using xCell method. Univariate and multivariate Cox regressions were applied successively to identify the immune cells associated with overall survival of OSCC patients. Furthermore, we screened the prognostic genes that related to the identified immune cells and validated their expressions by immunohistochemistry.Results: CD4+ central memory T

Diagram object detection is the key basis of practical applications such as textbook question answering. Because the diagram mainly consists of simple lines and color blocks, its visual features are sparser than those of natural images. In addition, diagrams usually express diverse knowledge, in which there are many low-frequency object categories in diagrams. These lead to the fact that traditional data-driven detection model is not suitable for diagrams. In this work, we propose a gestalt-perception transformer model for diagram object detection, which is based on an encoder-decoder architecture. Gestalt perception contains a series of laws to explain human perception, that the human visual system tends to perceive patches in an image that are similar, close or connected without abrupt directional changes as a perceptual whole object. Inspired by these thoughts, we build a gestalt-perception graph in transformer encoder, which is composed of diagram patches as nodes and the relationships between patches as edges. This graph aims to group these patches into objects via laws of similarity, proximity, and smoothness implied in these edges, so that the meaningful objects can be effectively detected. The experimental results demonstrate that the proposed GPTR achieves the best results in the diagram object detection task. Our model also obtains comparable results over the competitors in natural image object detection.

This paper focuses on discrete-time wireless sensor networks with privacy-preservation. In practical applications, information exchange between sensors is subject to attacks. For the information leakage caused by the attack during the information transmission process, privacy-preservation is introduced for system states. To make communication resources more effectively utilized, a dynamic event-triggered set-membership estimator is designed. Moreover, the privacy of the system is analyzed to ensure the security of the real data. As a result, the set-membership estimator with differential privacy is analyzed using recursive convex optimization. Then the steady-state performance of the system is studied. Finally, one example is presented to demonstrate the feasibility of the proposed distributed filter containing privacy-preserving analysis.

The low earth orbit (LEO) satellite system is one of the promising solutions to provide broadband services to a wide-coverage area for future integrated LEO-6G networks, where users' demands vary with time and geographical locations. Conventional satellites with fixed beam pattern and footprint planning may not be capable of meeting such dynamic requests and irregular traffic distributions. As the development of flexible satellite payload with beamforming capabilities, spot beams with flexible size and shape are considered potential solutions to this issue. As an early investigation, in this paper, we consider the scenarios where satellite payloads are equipped with multiple beam patterns and study the optimal beam pattern selection. We exploit the potential synergies of joint resource optimization between adaptive beam patterns and non-orthogonal multiple access (NOMA) in a LEO satellite system, where NOMA is employed to reduce intra-beam interference and flexible beam pattern is adopted to mitigate inter-satellite interference. The formulated problem is to minimize the capacity-demand gap of terminals, which falls into mixed-integer nonconvex pro-gramming (MINCP). To tackle the discrete variables and non-convexity, we design a joint approach to allocate power and select beam patterns. Numerical results show that the proposed scheme achieves capacity-demand gap reduction of 37.8% over conventional orthogonal multiple access (OMA) and 42.5% over the fixed-beam-pattern scheme., This work has been supported by the Luxembourg national research fund (FNR) under the project ROSETTA (C17/IS/11632107) and MegaLEO (C20/IS/14767486)., Peer Reviewed, Postprint (author's final draft)

Video action detection (spatio-temporal action localization) is usually the starting point for human-centric intelligent analysis of videos nowadays. It has high practical impacts for many applications across robotics, security, healthcare, etc. The two-stage paradigm of Faster R-CNN inspires a standard paradigm of video action detection in object detection, i.e., firstly generating person proposals and then classifying their actions. However, none of the existing solutions could provide fine-grained action detection to the "who-when-where-what" level. This paper presents a tracking-based solution to accurately and efficiently localize predefined key actions spatially (by predicting the associated target IDs and locations) and temporally (by predicting the time in exact frame indices). This solution won first place in the UAV-Video Track of 2021 Low-Power Computer Vision Challenge (LPCVC).

Imagery collected from outdoor visual environments is often degraded due to the presence of dense smoke or haze. A key challenge for research in scene understanding in these degraded visual environments (DVE) is the lack of representative benchmark datasets. These datasets are required to evaluate state-of-the-art vision algorithms (e.g., detection and tracking) in degraded settings. In this paper, we address some of these limitations by introducing the first realistic hazy image benchmark, from both aerial and ground view, with paired haze-free images, and in-situ haze density measurements. This dataset was produced in a controlled environment with professional smoke generating machines that covered the entire scene, and consists of images captured from the perspective of both an unmanned aerial vehicle (UAV) and an unmanned ground vehicle (UGV). We also evaluate a set of representative state-of-the-art dehazing approaches as well as object detectors on the dataset. The full dataset presented in this paper, including the ground truth object classification bounding boxes and haze density measurements, is provided for the community to evaluate their algorithms at: https://a2i2-archangel.vision. A subset of this dataset has been used for the ``Object Detection in Haze'' Track of CVPR UG2 2022 challenge at http://cvpr2022.ug2challenge.org/track1.html., Comment: This paper has been ACCEPTED for publication as a REGULAR paper in the IEEE Transactions on Image Processing (TIP)

The advancement of high-resolution metabolomics (HRM) and metabolome-wide-association study (MWAS) enables the readout of environmental effects in human specimens. We used HRM to understand DDT-induced alterations of in utero environment and potential health effects. Endogenous metabolites were measured in 397 maternal perinatal serum samples collected during 1959-1967 in the Child Health and Development Studies (CHDS) and in 16 maternal postnatal serum samples of mice treated with or without DDT. MWAS was performed to assess associations between metabolites and p,p'-DDT, o,p'-DDT and p,p'-DDE levels, followed by pathway analysis. Distinct metabolic profiles were found with p,p'-DDT and p,p'-DDE. Amino acids such arginine had a strong association with p,p'-DDT and o,p'-DDT in both women and mice, whereas lipids and acyl-carnitine intermediates were found exclusively associated with p,p'-DDE in CHDS women indicating mitochondrial impairment. It suggests that the role of serine and fatty acid metabolism on the causal disease pathway should be examined.

Small-cell lung cancer (SCLC) is speculated to harbor complex genomic intratumor heterogeneity (ITH) associated with high recurrence rate and suboptimal response to immunotherapy. Here, using multi-region whole exome/T cell receptor (TCR) sequencing as well as immunohistochemistry, we reveal a rather homogeneous mutational landscape but extremely cold and heterogeneous TCR repertoire in limited-stage SCLC tumors (LS-SCLCs). Compared to localized non-small cell lung cancers, LS-SCLCs have similar predicted neoantigen burden and genomic ITH, but significantly colder and more heterogeneous TCR repertoire associated with higher chromosomal copy number aberration (CNA) burden. Furthermore, copy number loss of IFN-gamma pathway genes is frequently observed and positively correlates with CNA burden. Higher mutational burden, higher T cell infiltration and positive PD-L1 expression are associated with longer overall survival (OS), while higher CNA burden is associated with shorter OS in patients with LS-SCLC. Small-cell lung cancer (SCLC) is an aggressive disease with limited therapeutic options. Here the authors perform an immunogenomic analysis of limited-stage SCLC, revealing a homogeneous mutational landscape, but limited T-cell infiltration and a cold and heterogeneous T cell repertoire.

China is the largest producer of carbon in the world. China’s construction industry has received widespread attention in recent years due to its environmental issues. However, little research has been conducted to investigate the environmental efficiency of the domestic part of this industry. As the foreign contribution is beyond China’s control, identification of domestic carbon emissions is necessary to formulate effective policy interventions. Based on a multi-regional input‐output model, this study attempts to reduce the statistical bias associated with international trade, thereby obtaining a more accurate indicator of domestic carbon emission intensity. This study aims to reveal the change in the domestic carbon emission intensity of China’s construction industry during 2000–2014 and analyze the reason behind it. The results show that, first, both the constructed intensity indicator and commonly used measures of carbon emission intensity have exhibited a decreasing trend over the study period. However, the former has been consistently larger than the latter. Moreover, this difference first increased and then suddenly decreased after a particular year. Second, although the domestic carbon emission intensity shows a gradually declining trend, it has moved from second to first in global rankings, implying that China’s domestic construction industry’s carbon emission efficiency, while falling, lags behind other major economies. Third, the structural decomposition results reveal that changes in direct production emission intensity are the leading causes of the decline in domestic carbon emission intensity. In contrast, a change in the intermediate input structure led to an increase in the emission intensity in China’s construction industry. In addition, the enormous gaps of domestic carbon emission intensity in the construction industry between China and the selected countries are mainly attributable to the difference in the intermediate input structure. The study

Unsafe behaviors of construction workers are one of the main causes of accidents at construction sites. The research on unsafe behaviors of workers helps to reduce the incidence of accidents and has attracted much attention. However, a systematic literature review in this field is still lacking, which hinders stakeholders’ comprehensive understanding of the unsafe behaviors of construction workers. Therefore, the aim of this study is to address this research gap based on retrieved literature from the Web of Science. First, the study conducted a descriptive analysis of the year, quantity, publishing organization, and keywords of the literature. In addition, three research topics were identified and discussed, including the influencing factors of construction workers’ unsafe behaviors, the formation mechanism of unsafe behaviors, and the pre-control methods of unsafe behaviors. Moreover, a research framework was proposed and future research directions were also suggested. The research findings promote stakeholders’ understanding of the influencing factors, formation mechanism, and pre-control methods of construction workers’ unsafe behaviors, and lead to future research directions in the studied field.

The implementation of precursor management can improve safety performance of construction projects through effectively managing the correlations between construction accidents and their precursors. However, a system of comprehensive knowledge about what precursors mean within the context of construction safety is still lacking. This study aims to capture the nature of precursors in the construction industry and explore the process of a precursor event evolving into a construction accident to fill this gap. Based on 135 construction accident reports in China, this study adopts grounded theory to identify different types of accident precursors and explore their interactions with the development of the accident. An indicator system of precursors for construction accidents was developed, which included two major categories of precursors: behavioral factors and physical factors and five minor categories of precursors: individual behavior factors, organizational driving factors, objective physical factors, construction environmental factors, mechanical equipment factors. In addition, a precursor management strategy that includes the three stages of identification, response and effectiveness testing was established. The results of the study reveal the correlations between precursors and construction accidents, which can promote construction professionals’ better understanding about precursors and improve their capabilities of managing precursors in practice.

Background Chronic exposure to certain metals plays a role in disease development. Integrating untargeted metabolomics with urinary metallome data may contribute to better understanding the pathophysiology of diseases and complex molecular interactions related to environmental metal exposures. To discover novel associations between urinary metal biomarkers and metabolism networks, we conducted an integrative metallome-metabolome analysis using a panel of urinary metals and untargeted blood metabolomic data from the Strong Heart Family Study (SHFS). Methods The SHFS is a prospective family-based cohort study comprised of American Indian men and women recruited in 2001–2003. This nested case-control analysis of 145 participants of which 50 developed incident diabetes at follow up in 2006–2009, included participants with urinary metal and untargeted metabolomic data. Concentrations of 8 creatinine-adjusted urine metals/metalloids [antimony (Sb), cadmium (Cd), lead (Pb), molybdenum (Mo), selenium (Se), tungsten (W), uranium (U) and zinc (Zn)], and 4 arsenic species [inorganic arsenic (iAs), monomethylarsonate (MMA), dimethylarsinate (DMA), and arsenobetaine (AsB)] were measured. Global metabolomics was performed on plasma samples using high-resolution Orbitrap mass spectrometry. We performed an integrative network analysis using xMWAS and a metabolic pathway analysis using Mummichog. Results 8,810 metabolic features and 12 metal species were included in the integrative network analysis. Most metal species were associated with distinct subsets of metabolites, forming single-metal-multiple-metabolite clusters (|r|>0.28, p-value < 0.001). DMA (clustering with W), iAs (clustering with U), together with Mo and Se showed modest interactions through associations with common metabolites. Pathway enrichment analysis of associated metabolites (|r|>0.17, p-value < 0.1) showed effects in amino acid metabolism (AsB, Sb, Se and U), fatty acid and lipid metabolism (iAs, Mo, W, Sb, Pb, Version of record

In the originally published version, this manuscript included an error related to indicating the corresponding author within the author list. This has now been corrected online to reflect the fact that author Jun Luo is the corresponding author of the article.

Relation reasoning in knowledge graphs (KGs) aims at predicting missing relations in incomplete triples, whereas the dominant paradigm is learning the embeddings of relations and entities, which is limited to a transductive setting and has restriction on processing unseen entities in an inductive situation. Previous inductive methods are scalable and consume less resource. They utilize the structure of entities and triples in subgraphs to own inductive ability. However, in order to obtain better reasoning results, the model should acquire entity-independent relational semantics in latent rules and solve the deficient supervision caused by scarcity of rules in subgraphs. To address these issues, we propose a novel graph convolutional network (GCN)-based approach for interpretable inductive reasoning with relational path contrast, named RPC-IR. RPC-IR firstly extracts relational paths between two entities and learns representations of them, and then innovatively introduces a contrastive strategy by constructing positive and negative relational paths. A joint training strategy considering both supervised and contrastive information is also proposed. Comprehensive experiments on three inductive datasets show that RPC-IR achieves outstanding performance comparing with the latest inductive reasoning methods and could explicitly represent logical rules for interpretability., Comment: This work is going to be submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible

TianQin is a proposed space-borne gravitational wave (GW) observatory composed of three identical satellites orbiting around the geocenter with a radius of $10^5$ km. It aims at detecting GWs in the frequency range of 0.1 mHz -- 1 Hz. The detection of GW relies on the high precision measurement of optical path length at $10^{-12}$~m level. The dispersion of space plasma can lead to the optical path difference (OPD, $\Delta l$) along the propagation of laser beams between any pair of satellites. Here, we study the OPD noises for TianQin. The Space Weather Modeling Framework is used to simulate the interaction between the Earth magnetosphere and solar wind. From the simulations, we extract the magnetic field and plasma parameters on the orbits of TianQin at four relative positions of the satellite constellation in the Earth magnetosphere. We calculate the OPD noise for single link, Michelson combination, and Time-Delay Interferometry (TDI) combinations ($\alpha$ and $X$). For single link and Michelson interferometer, the maxima of $|\Delta l|$ are on the order of 1 pm. For the TDI combinations, these can be suppressed to about 0.004 and 0.008 pm for $\alpha$ and $X$. The OPD noise of the Michelson combination is colored in the concerned frequency range; while the ones for the TDI combinations are approximately white. Furthermore, we calculate the ratio of the equivalent strain of the OPD noise to that of TQ, and find that the OPD noises for the TDI combinations can be neglected in the most sensitive frequency range of TQ., Comment: 19 pages, 10 figures

The proposed space-borne laser interferometric gravitational wave (GW) observatory TianQin adopts a geocentric orbit for its nearly equilateral triangular constellation formed by three identical drag-free satellites. The geocentric distance of each satellite is $\approx 1.0 \times 10^{5} ~\mathrm{km}$, which makes the armlengths of the interferometer be $\approx 1.73 \times 10^{5} ~\mathrm{km}$. It is aimed to detect the GWs in $0.1 ~\mathrm{mHz}-1 ~\mathrm{Hz}$. For space-borne detectors, the armlengths are unequal and change continuously which results in that the laser frequency noise is nearly $7-8$ orders of magnitude higher than the secondary noises (such as acceleration noise, optical path noise, etc.). The time delay interferometry (TDI) that synthesizes virtual interferometers from time-delayed one-way frequency measurements has been proposed to suppress the laser frequency noise to the level that is comparable or below the secondary noises. In this work, we evaluate the performance of various data combinations for both first- and second-generation TDI based on the five-year numerically optimized orbits of the TianQin's satellites which exhibit the actual rotating and flexing of the constellation. We find that the time differences of symmetric interference paths of the data combinations are $\sim 10^{-8}$ s for the first-generation TDI and $\sim 10^{-12}$ s for the second-generation TDI, respectively. While the second-generation TDI is guaranteed to be valid for TianQin, the first-generation TDI is possible to be competent for GW signal detection with improved stabilization of the laser frequency noise in the concerned GW frequencies., Comment: 18 pages, 13 figures, 1 table. Revised version, to be published in Phys. Rev. D

EEG signals have been reported to be informative and reliable for emotion recognition in recent years. However, the inter-subject variability of emotion-related EEG signals still poses a great challenge for the practical applications of EEG-based emotion recognition. Inspired by recent neuroscience studies on inter-subject correlation, we proposed a Contrastive Learning method for Inter-Subject Alignment (CLISA) to tackle the cross-subject emotion recognition problem. Contrastive learning was employed to minimize the inter-subject differences by maximizing the similarity in EEG signal representations across subjects when they received the same emotional stimuli in contrast to different ones. Specifically, a convolutional neural network was applied to learn inter-subject aligned spatiotemporal representations from EEG time series in contrastive learning. The aligned representations were subsequently used to extract differential entropy features for emotion classification. CLISA achieved state-of-the-art cross-subject emotion recognition performance on our THU-EP dataset with 80 subjects and the publicly available SEED dataset with 15 subjects. It could generalize to unseen subjects or unseen emotional stimuli in testing. Furthermore, the spatiotemporal representations learned by CLISA could provide insights into the neural mechanisms of human emotion processing., Comment: 23 pages, 13 figures, journal paper. IEEE Transactions on Affective Computing, 2022

Convolutional neural networks (CNNs) have been applied to learn spatial features for high-resolution (HR) synthetic aperture radar (SAR) image classification. However, there has been little work on integrating the unique statistical distributions of SAR images which can reveal physical properties of terrain objects, into CNNs in a supervised feature learning framework. To address this problem, a novel end-to-end supervised classification method is proposed for HR SAR images by considering both spatial context and statistical features. First, to extract more effective spatial features from SAR images, a new deep spatial context encoder network (DSCEN) is proposed, which is a lightweight structure and can be effectively trained with a small number of samples. Meanwhile, to enhance the diversity of statistics, the nonstationary joint statistical model (NS-JSM) is adopted to form the global statistical features. Specifically, SAR images are transformed into the Gabor wavelet domain and the produced multi-subbands magnitudes and phases are modeled by the log-normal and uniform distribution. The covariance matrix is further utilized to capture the inter-scale and intra-scale nonstationary correlation between the statistical subbands and make the joint statistical features more compact and distinguishable. Considering complementary advantages, a feature fusion network (Fusion-Net) base on group compression and smooth normalization is constructed to embed the statistical features into the spatial features and optimize the fusion feature representation. As a result, our model can learn the discriminative features and improve the final classification performance. Experiments on four HR SAR images validate the superiority of the proposed method over other related algorithms., Comment: 17 pages,11 figures

Recent studies on computer vision mainly focus on natural images that express real-world scenes. They achieve outstanding performance on diverse tasks such as visual question answering. Diagram is a special form of visual expression that frequently appears in the education field and is of great significance for learners to understand multimodal knowledge. Current research on diagrams preliminarily focuses on natural disciplines such as Biology and Geography, whose expressions are still similar to natural images. Another type of diagrams such as from Computer Science is composed of graphics containing complex topologies and relations, and research on this type of diagrams is still blank. The main challenges of graphic diagrams understanding are the rarity of data and the confusion of semantics, which are mainly reflected in the diversity of expressions. In this paper, we construct a novel dataset of graphic diagrams named Computer Science Diagrams (CSDia). It contains more than 1,200 diagrams and exhaustive annotations of objects and relations. Considering the visual noises caused by the various expressions in diagrams, we introduce the topology of diagrams to parse topological structure. After that, we propose Diagram Parsing Net (DPN) to represent the diagram from three branches: topology, visual feature, and text, and apply the model to the diagram classification task to evaluate the ability of diagrams understanding. The results show the effectiveness of the proposed DPN on diagrams understanding.

Ziyu Liu,1,2 Leilei Huang,3,4 Liwei Sun,1 Hui Nie,3,4 Yuqi Liang,1,2 Jinwen Huang,3,4 Fanhong Wu,3,4 Xin Hu1 1The Laboratory of Cancer Biology, China-Japan Union Hospital of Jilin University, Jilin University, Changchun, Jilin, People’s Republic of China; 2Department of Biochemistry and Molecular Biology, School of Life Science, Jilin University, Changchun, Jilin, People’s Republic of China; 3Shanghai Engineering Research Center of Green Fluoropharmaceutical Technology, Shanghai, People’s Republic of China; 4Department of Pharmaceutical Engineering, School of Chemical and Environmental Engineering, Shanghai Institute of Technology, Shanghai, People’s Republic of ChinaCorrespondence: Xin Hu; Fanhong Wu Email huxin@jlu.edu.cn; wfh@sit.edu.cnPurpose: Erianin is a small chemical compound extracted from Dendrobium chrysotoxum and has excellent antineoplastic effects against a variety of cancers. Combretastatin A-4 (CA4) is the most effective member of natural phenolic stilbene compounds isolated from the African willow tree Combretum caffrum. Ecust004 (Chemical Formula: C18H21NO7S) is a drug candidate optimized from structure–activity relationship studies of the sulfamate derivatives of Erianin and CA4, which has better bioavailability and pharmacokinetic profiles than Erianin and CA4.Methods: To investigate the antitumor activity of Ecust004 in different types of breast cancer cells, MDA-MB-231 and MCF7 cells were treated with Ecust004. MTT and CCK8 were used to determine the effects of Ecust004 on cell proliferation. Wound-healing and Transwell assays were used to evaluate the migration and invasion level of cells treated with Ecust004. The expression of genes and proteins associated with epithelial–mesenchymal transition was detected by RT-PCR and Western blotting. In vivo studies further clarified the functional effects of Ecust004.Results: Ecust004 treatment decreased the growth and proliferation of MDA-MB-231 and MCF7 cells at a lower dosage than Eriani

The first law of black hole thermodynamics in the extended phase space is prevailing recently. However, the second law as well as the weak cosmic censorship conjecture has not been investigated extensively. In this paper, we investigate the laws of thermodynamics and the weak cosmic censorship conjecture of an AdS black hole with a global monopole in the extended phase space under a charged particle absorption. It is shown that the first law of thermodynamics is valid, while the second law is violated for the extremal and near-extremal black holes. Moreover, we find the weak cosmic censorship conjecture is valid only for the extremal black hole, but is violable for the near-extremal black hole. It is different from the previous results where the high order corrections were neglected., Comment: 8 pages, accepted by Chinese Physcis C

Adhering a cobalt porphyrin (Co(TPP)) catalyst on a carbon nanotube (CNT) supporting material greatly enhances its reactivity and enables catalysis in water, which is otherwise impossible. However, the effect of solvent as well as supporting materials on catalysis is still elusive. On the basis of computational results we found that water as a reaction medium lowers the reductive potential required due to the stabilization of intermediates and transition states, and provides higher availability of protons. To understand the effect of the support materials, we combine computations and experiments and illustrate that the curvature of the nanotubes plays an essential role in aggregation through the competition between the Ï-πinteractions between the porphyrin rings as well as between the Co(TPP) and the nanotube, providing an insight into lessening the degree of aggregation. Copyright © 2020 American Chemical Society., QC 20200617

BACKGROUND: Endocrine disrupting chemicals (EDCs) are xenobiotics that mimic the interaction of natural hormones and alter synthesis, transport, or metabolic pathways. The prospect of EDCs causing adverse health effects in humans and wildlife has led to the development of scientific and regulatory approaches for evaluating bioactivity. This need is being addressed using high-throughput screening (HTS) in vitro approaches and computational modeling. OBJECTIVES: In support of the Endocrine Disruptor Screening Program, the U.S. Environmental Protection Agency (EPA) led two worldwide consortiums to virtually screen chemicals for their potential estrogenic and androgenic activities. Here, we describe the Collaborative Modeling Project for Androgen Receptor Activity (CoMPARA) efforts, which follows the steps of the Collaborative Estrogen Receptor Activity Prediction Project (CERAPP). METHODS: The CoMPARA list of screened chemicals built on CERAPP's list of 32,464 chemicals to include additional chemicals of interest, as well as simulated ToxCast (TM) metabolites, totaling 55,450 chemical structures. Computational toxicology scientists from 25 international groups contributed 91 predictive models for binding, agonist, and antagonist activity predictions. Models were underpinned by a common training set of 1,746 chemicals compiled from a combined data set of 11 ToxCast (TM)/Tox21 HTS in vitro assays. RESULTS: The resulting models were evaluated using curated literature data extracted from different sources. To overcome the limitations of single-model approaches, CoMPARA predictions were combined into consensus models that provided averaged predictive accuracy of approximately 80% for the evaluation set. DISCUSSION: The strengths and limitations of the consensus predictions were discussed with example chemicals; then, the models were implemented into the free and open-source OPERA application to enable screening of new chemicals with a defined applicability domain and accura