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In vitro investigations on the effects of graphene and graphene oxide on polycaprolactone bone tissue engineering scaffolds

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Bio-Design and Manufacturing 2024年第5期7卷 651-669页

作者： Yanhao hou Weiguang Wang Paulo Bartolo School of Engineering Faculty of Science and EngineeringThe University of ManchesterManchesterUK Singapore Centre for 3D Printing School of Mechanical and Aerospace EngineeringNanyang Technological UniversitySingaporeSingapore

Polycaprolactone(PCL)scaffolds that are produced through additive manufacturing are one of the most researched bone tissue engineering structures in the *** to the intrinsic limitations of PCL,carbon nanomaterials are often investigated to reinforce the PCL *** several studies that have been conducted on carbon nanomaterials,such as graphene(G)and graphene oxide(GO),certain challenges remain in terms of the precise design of the biological and nonbiological properties of the *** paper addresses this limitation by investigating both the nonbiological(element composition,surface,degradation,and thermal and mechanical properties)and biological characteristics of carbon nanomaterial-reinforced PCL scaffolds for bone tissue engineering *** showed that the incorporation of G and GO increased surface properties(reduced modulus and wettability),material crystallinity,crystallization temperature,and degradation ***,the variations in compressive modulus,strength,surface hardness,and cell metabolic activity strongly depended on the type of ***,a series of phenomenological models were developed based on experimental results to describe the variations of scaffold’s weight,fiber diameter,porosity,and mechanical properties as functions of degradation time and carbon nanomaterial *** results presented in this paper enable the design of three-dimensional(3D)bone scaffolds with tuned properties by adjusting the type and concentration of different functional fillers.

关键词： Additive manufacturing Bone tissue engineering Carbon nanomaterial Graphene Graphene oxide Scaffold

Dual Frequency Transformer for Efficient SDR-to-HDR Translation

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Machine Intelligence Research 2024年第3期21卷 538-548页

作者： Gang Xu Qibin hou Ming-Ming Cheng Tianjin Media Computing Center College of Computer ScienceNankai UniversityTianjin300000China

The SDR-to-HDR translation technique can convert the abundant standard-dynamic-range (SDR) media resources to high-dynamic-range (HDR) ones, which can represent high-contrast scenes, providing more realistic visual experiences. While recent vision Transformers have achieved promising performance in many low-level vision tasks, there are few works attempting to leverage Transformers for SDR-to-HDR translation. In this paper, we are among the first to investigate the performance of Transformers for SDR-to-HDR translation. We find that directly using the self-attention mechanism may involve artifacts in the results due to the inappropriate way to model long-range dependencies between the low-frequency and high-frequency components. Taking this into account, we advance the self-attention mechanism and present a dual frequency attention (DFA), which leverages the self-attention mechanism to separately encode the low-frequency structural information and high-frequency detail information. Based on the proposed DFA, we further design a multi-scale feature fusion network, named dual frequency Transformer (DFT), for efficient SDR-to-HDR translation. Extensive experiments on the HDRTV1. dataset demonstrate that our DFT can achieve better quantitative and qualitative performance than the recent state-of-the-art methods. The code of our DFT is made publicly available at https://***/CS-GangXu/DFT.

关键词： Standard-dynamic-range to high-dynamic-range(SDR-to-HDR)translation Transformer dual frequency attention(DFA) frequency-aware feature decomposition efficient model

Hierarchical vectorization for facial images

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Computational Visual Media 2024年第1期10卷 97-118页

作者： Qian Fu Linlin Liu Fei hou Ying He School of Computer Science and Engineering Nanyang Technological University639798SingaporeSingapore Data61 Commonwealth Scientific and Industrial Research OrganisationSydney2015Australia Interdisciplinary Graduate School Nanyang Technological University and Alibaba Group639798SingaporeSingapore State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing100190China University of Chinese Academy of Sciences Beijing100049China

The explosive growth of social media means portrait editing and retouching are in high *** portraits are commonly captured and stored as raster images,editing raster images is non-trivial and requires the user to be highly *** at developing intuitive and easy-to-use portrait editing tools,we propose a novel vectorization method that can automatically convert raster images into a 3-tier hierarchical *** base layer consists of a set of sparse diffusion curves(DCs)which characterize salient geometric features and low-frequency colors,providing a means for semantic color transfer and facial expression *** middle level encodes specular highlights and shadows as large,editable Poisson regions(PRs)and allows the user to directly adjust illumination by tuning the strength and changing the shapes of *** top level contains two types of pixel-sized PRs for high-frequency residuals and fine details such as pimples and *** train a deep generative model that can produce high-frequency residuals *** to the inherent meaning in vector primitives,editing portraits becomes easy and *** particular,our method supports color transfer,facial expression editing,highlight and shadow editing,and automatic *** quantitatively evaluate the results,we extend the commonly used FLIP metric(which measures color and feature differences between two images)to consider *** new metric,illumination-sensitive FLIP,can effectively capture salient changes in color transfer results,and is more consistent with human perception than FLIP and other quality measures for portrait *** evaluate our method on the FFHQR dataset and show it to be effective for common portrait editing tasks,such as retouching,light editing,color transfer,and expression editing.

关键词： face editing vectorization Poisson editing color transfer illumination editing expression editing

Transcriptomic and machine learning analyses identify hub genes of metabolism and host immune response that are associated with the progression of breast capsular contracture

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Genes & Diseases 2024年第3期11卷 427-437页

作者： Yukun Mao Xueying hou Su Fu Jie Luan Breast Plastic and Reconstructive Surgery Center Plastic Surgery HospitalChinese Academy of Medical SciencesPeking Union Medical CollegeBeijing 100144China

Capsular contracture is a prevalent and severe complication that affects the post-operative outcomes of patients who receive silicone breast *** present,prosthesis replacement is the major treatment for capsular contracture after both breast augmentation procedures and breast reconstruction following breast cancer ***,the mecha-nism(s)underlying breast capsular contracture remains *** study aimed to identify the biological features of breast capsular contracture and reveal the potential underlying mechanism using RNA *** tissues from 1. female patients(1. breast capsules)were divided into low capsular contracture(LCC)and high capsular contracture(HCC)groups based on the Baker ***,41.lipid metabolism-related genes were identified through enrichment analysis,and three of these genes were identified as candidate genes by SVM-RFE and LASSO *** then compared the proportions of the 22 types of im-mune cells between the LCC and HCC groups using a CIBERSORT analysis and explored the Cor-relation between the candidate hub features and immune ***,PRKAR2B was positively correlated with the differentially clustered immune cells,which were M1.macro-phages and follicular helper T cells(area under the ROC=0.786).In addition,the expression of PRKAR2B at the mRNA or protein level was lower in the HCC group than in the LCC *** molecular mechanisms were identified based on the expression levels in the high and low PRKAR2B *** findings indicate that PRKAR2B is a novel diagnostic biomarker for breast capsular contracture and might also influence the grade and progression of capsularcontracture.

关键词： Biomarker Breastcapsular contracture Immune infiltration Machinelearning algorithms PRKAR2B

Low Resource Chinese Geological Text Named Entity Recognition Based on Prompt Learning

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Journal of Earth Science 2024年第3期35卷 1035-1043页

作者： Hang He Chao Ma Shan Ye Wenqiang Tang Yuxuan Zhou Zhen Yu Jiaxin Yi Li hou Mingcai hou State Key Laboratory of Oil and Gas Reservoir Geology and Exploitation Institute of Sedimentary GeologyCollege of Computer Science and Cyber SecurityChengdu University of TechnologyChengdu 610059China Key Laboratory of Deep-Time Geography and Environment Reconstruction and Applications of Ministry of Natural Resources Chengdu University of TechnologyChengdu 610059China School of Information Engineering China University of Geosciences(Beijing)Beijing 100083China

Geological reports are a significant accomplishment for geologists involved in geological investigations and scientific research as they contain rich data and textual *** the rapid development of science and technology,a large number of textual reports have accumulated in the field of ***,many non-hot topics and non-English speaking regions are neglected in mainstream geoscience databases for geological information mining,making it more challenging for some researchers to extract necessary information from these *** Language Processing(NLP)has obvious advantages in processing large amounts of textual *** objective of this paper is to identify geological named entities from Chinese geological texts using NLP *** propose the RoBERTa-Prompt-Tuning-NER method,which leverages the concept of Prompt Learning and requires only a small amount of annotated data to train superior models for recognizing geological named entities in low-resource dataset *** RoBERTa layer captures context-based information and longer-distance dependencies through dynamic word ***,we conducted experiments on the constructed Geological Named Entity Recognition(GNER)*** experimental results show that the proposed model achieves the highest F1.score of 80.64%among the four baseline algorithms,demonstrating the reliability and robustness of using the model for Named Entity Recognition of geological texts.

关键词： Prompt Learning Named Entity Recognition(NER) low resource geological text text information mining big data geology.

Thick Electrodes of a Self-Assembled MXene Hydrogel Composite for High-Rate Energy Storage

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Energy & Environmental Materials 2024年第4期7卷 255-261页

作者： Leiqiang Qin Jianxia Jiang Lintao hou Fengling Zhang Johanna Rosen Department of Physics Chemistry and Biology (IFM) Link?ping University Flexible Electronics Innovation Institute (FEII) Jiangxi Key Laboratory of Flexible Electronics Jiangxi Science and Technology Normal University Guangzhou Key Laboratory of Vacuum Coating Technologies and New Energy Materials Physics Department Jinan University

Supercapacitors based on two-dimensional MXene （Ti3C2Tz） have shown extraordinary performance in ultrathin electrodes with low mass loading,but usually there is a significant reduction in high-rate performance as the thickness increases,caused by increasing ion diffusion *** limitations include restacking of the nanosheets,which makes it challenging to realize the full potential of these electrode materials.

关键词： energy storage high-rate hydrogel MXene self-assemble

Wind disturbance compensated path-following control for fixed-wing UAVs in arbitrarily strong winds

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Chinese Journal of Aeronautics 2024年第2期37卷 431-445页

作者： Hao LU Lan GAO Yunda YAN Mingzhe hou Chenliang WANG Hangzhou Innovation Institute Beihang UniversityHangzhou 310051China School of Information Science and Technology Hangzhou Normal UniversityHangzhou 311121China Department of Computer Science University College LondonLondon WC1E 6BTUnited Kingdom Center for Control Theory and Guidance Technology Harbin Institute of TechnologyHarbin 150001China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

Wind is the primary challenge for low-speed fixed-wing unmanned aerial vehicles to follow a predefined flight *** cope with various wind conditions,this paper proposes a wind disturbance compensated path following control strategy where the wind disturbance estimate is incorporated with the nominal guiding vector field to provide the desired airspeed direction for the *** the control input vector for the outer-loop kinematic subsystem needs to satisfy a magnitude constraint,a scaling mechanism is introduced to tune the proportions of the compensation and nominal ***,an optimization problem is formulated to pursue a maximum wind compensation in strong winds,which can be solved analytically to yield two scaling factors.A cascaded inner-loop tracking controller is also designed to fulfill the outer-loop wind disturbance compensated guiding vector ***-fidelity simulation results under sensor noises and realistic winds demonstrate that the proposed path following algorithm is less sensitive to sensor noises,achieves promising accuracy in normal winds,and mitigates the deviation from a desired path in wild winds.

关键词： Path following Guiding vector field Wind disturbance Disturbance rejection Fixed wings

GRIK1.promotes glioblastoma malignancy and is a novel prognostic factor of poor prognosis

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Oncology Research 2024年第4期32卷 727-736页

作者： GUOQIANG hou XINHANG XU WEIXING HU Department of Neurosurgery First Affiliated Hospital of Nanjing Medical UniversityNanjingChina Department of Neurosurgery Renji HospitalShanghai Jiao Tong UniversityShanghaiChina

Primary tumors of the central nervous system(CNS)are classified into over 1.0 different histological *** most common type of glioma is derived from astrocytes,and the most invasive glioblastoma(WHO IV)accounts for over 57%of these ***(GBM)is the most common and fatal tumor of the CNS,with strong growth and invasion capabilities,which makes complete surgical resection almost *** various treatment methods such as surgery,radiotherapy,and chemotherapy,glioma is still an incurable disease,and the median survival time of patients with GBM is shorter than 1. ***,molecular mechanisms of GBM characteristic invasive growth need to be clarified to improve the poor *** ionotropic receptor kainate type subunit 1.GRIK1.is essential for brain function and is involved in many mental and neurological ***,GRIK1.s pathogenic roles and mechanisms in GBM are still ***-nuclear RNA sequencing of primary and recurrent GBM samples revealed that GRIK1.expression was noticeably higher in the recurrent ***,immunohistochemical staining of an array of GBM samples showed that high levels of GRIK1.correlated with poor prognosis of GBM,consistent with The Cancer Genome Atlas *** of GRIK1.retarded GBM cells growth,migration,and *** together,these findings show that GRIK1.is a unique and important component in the development of GBM and may be considered as a biomarker for the diagnosis and therapy in individuals with GBM.

关键词： Glioblastoma GRIK1 Invasion Proliferation Prognosis

Machine Learning Analysis of Impact of Western US Fires on Central US Hailstorms

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Advances in Atmospheric Sciences 2024年第7期41卷 1450-1462页

作者： Xinming LIN Jiwen FAN Yuwei ZHANG ZJason hou Pacific Northwest National Laboratory RichlandWA 99354USA Argonne National Laboratory LemontIL 60439USA

Fires,including wildfires,harm air quality and essential public services like transportation,communication,and *** fires can also influence atmospheric conditions,including temperature and aerosols,potentially affecting severe convective ***,we investigate the remote impacts of fires in the western United States(WUS)on the occurrence of large hail(size:≥2.54 cm)in the central US(CUS)over the 20-year period of 2001.20 using the machine learning(ML),Random Forest(RF),and Extreme Gradient Boosting(XGB)*** developed RF and XGB models demonstrate high accuracy(>90%)and F1.scores of up to 0.78 in predicting large hail occurrences when WUS fires and CUS hailstorms coincide,particularly in four states(Wyoming,South Dakota,Nebraska,and Kansas).The key contributing variables identified from both ML models include the meteorological variables in the fire region(temperature and moisture),the westerly wind over the plume transport path,and the fire features(i.e.,the maximum fire power and burned area).The results confirm a linkage between WUS fires and severe weather in the CUS,corroborating the findings of our previous modeling study conducted on case simulations with a detailed physics model.

关键词： wildfire severe convective storm hailstorm machine learning