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| 1 | Ferroptosis as a novel form of regulated cell death:Implications in the pathogenesis,oncometabolism and treatment of human cancer显示文摘The treatment of cancer mainly involves surgical excision supplemented by radiotherapy and chemotherapy.Chemotherapy drugs act by interfering with tumor growth and inducing the death of cancer cells.Anti-tumor drugs were developed to induce apoptosis,but some patient’s show apoptosis escape and chemotherapy resistance.Therefore,other forms of cell death that can overcome the resistance of tumor cells are important in the context of cancer treatment.Ferroptosis is a newly discovered iron-dependent,non-apoptotic type of cell death that is highly negatively correlated with cancer development.Ferroptosis is mainly caused by the abnormal increase in iron-dependent lipid reactive oxygen species and the imbalance of redox homeostasis.This review summarizes the progression and regulatory mechanism of ferroptosis in cancer and discusses its possible clinical applications in cancer diagnosis and treatment. | Feifei Pu Fengxia Chen Zhicai Zhang Deyao Shi Binlong Zhong Xiao Lv Andrew Blake Tucker Jiaming Fan Alexander J.Li Kevin Qin Daniel Hu Connie Chen Hao Wang Fang He Na Ni Linjuan Huang Qing Liu William Wagstaff Hue H.Luu Rex C.Haydon Le Shen Tong-Chuan He Jianxiang Liu Zengwu Shao | 2022 | Genes & Diseases2022，9，2： | 6 |
| 2 | Effect of Ningmitai capsule plus sertraline on patients with premature ejaculation and enlarged seminal vesicles:A randomized clinical trial显示文摘OBJECTIVE: To investigate the effects of Ningmitai capsule combined with sertraline on patients with premature ejaculation(PE) and an increased anterior-posterior diameter(APD) of the seminal vesicles(SVs).METHODS: Sixty men with acquired PE were enrolled and randomly divided into two groups. The combined group was treated with Ningmitai capsule and sertraline, while the control group was treated with sertraline alone. Main outcomes were measured using the premature ejaculation diagnostic tool(PEDT), APD of SVs, and Clinical Global Impression of Change questionnaire and compared before and after 3 months of treatment.RESULTS: Comparing after treatment with before treatment outcomes within each group, the PEDT score was significantly reduced in the combined group(12.1 ± 2.5 vs 8.6 ± 3.2, P < 0.001, respectively) and control group(12.9 ± 2.6 vs 10.3 ± 1.6, P <0.001, respectively). Furthermore, the PEDT score after treatment was significantly lower in the combined compared with control group(8.6 ± 3.2 vs10.3 ± 1.6, P = 0.011, respectively). The APD of SVs in the combined group was significantly decreased after treatment [(10.8 ± 2.4) vs(12.9 ± 2.2) mm, P =0.001], while the APD of SVs in the control group was equivalent before and after treatment. The treatment response rate was not significantly higher in the combined compared with control group.CONCLUSION: These results indicated that the effect of Ningmitai capsule combined with sertraline was better than that of sertraline alone for the treatment of PE patients exhibiting an increased APD of SVs. The therapeutic effect found for the combined treatment may be due to antibacterial and anti-inflammatory activity reported for Ningmitai capsule,and may suggest that seminal vesiculitis is a potential pathophysiological factor in acquired PE. | Peng Longping Hong Zhiwei Shen Jiaming Hu Xuechun Shao Yong Jing Jun Lu Jinchun Yao Bing | 2018 | Journal of Traditional Chinese Medicine2018，38，2： | 3 |
| 3 | Intermedin/adrenomedullin 2 protects against tubular cell hypoxia‐reoxygenation injury in vitro by promoting cell proliferation and upregulating cyclin D 1 expression显示文摘 | Yanhong Wang Rongshan Li Xi Qiao Jihua Tian Xiaole Su Ruiping Wu Ruijing Zhang Xiaoshuang Zhou Jiaming Li Shan Shao | 2013 | Nephrology2013，，9： | 1 |
| 4 | An internal-external optimized convolutional neural network for arbitrary orientated object detection from optical remote sensing images显示文摘Due to the bird’s eye view of remote sensing sensors,the orientational information of an object is a key factor that has to be considered in object detection.To obtain rotating bounding boxes,existing studies either rely on rotated anchoring schemes or adding complex rotating ROI transfer layers,leading to increased computational demand and reduced detection speeds.In this study,we propose a novel internal-external optimized convolutional neural network for arbitrary orientated object detection in optical remote sensing images.For the internal opti-mization,we designed an anchor-based single-shot head detector that adopts the concept of coarse-to-fine detection for two-stage object detection networks.The refined rotating anchors are generated from the coarse detection head module and fed into the refining detection head module with a link of an embedded deformable convolutional layer.For the external optimiza-tion,we propose an IOU balanced loss that addresses the regression challenges related to arbitrary orientated bounding boxes.Experimental results on the DOTA and HRSC2016 bench-mark datasets show that our proposed method outperforms selected methods. | Sihang Zhang Zhenfeng Shao Xiao Huang Linze Bai Jiaming Wang | 2021 | Geo-Spatial Information Science2021，24，4： | 1 |
| 5 | Effects of innovativeness and trust on web survey participation显示文摘 | Fang Jiaming Shao Peiji George Lan | 2009 | Computers in Human Behavior2009，25，： | 1 |
| 6 | Intensified inactivation of model and environmental bacteria by an atmospheric-pressure air-liquid discharge plasma compared with chlorination显示文摘Water-borne pathogenic bacteria are always the top priority to be removed through disinfection process in water treatment due to their threat to human health. It was necessary to develop novel disinfection methods since the conventional chlorine disinfection was inefficient in inactivating chlorine-resistant bacteria, inducing the viable but non-culturable(VBNC) bacteria and forming disinfection by-products(DBPs). In this study, the inactivation of four model strains including Gram-negative(G), Gram-positive(G) and environmental samples by atmospheric-pressure air-liquid discharge plasma(ALDP) was assessed systematically. The results showed that ALDP was superior in inactivating all of the samples compared with chlorination. During 10 min ALDP treatment, the Gbacteria were completely inactivated, and the Gone was inactivated by more than 4.61 logs. The inactivation of bacteria from a campus lake and a wastewater treatment plant effluent exceeded 99.82% and 97.78%, respectively. For G-bacteria, ALDP resulted in a much lower(10~2~10~3 times) levels of VBNC cells than chlorination. ALDP could effectively remove the chlorine-resistant bacteria. More than 96.41% of the intracellular DNA and 99.99% of the extracellular DNA were removed, whereas it was only 56.35% and 12.82% for chlorination. ALDP had a stronger ability to destroy cell structure than chlorination, presumably due to the existence of ROS( ·OH, ~1Oand O). GC-MS analysis showed that ALDP produced less DBPs than chlorination. These findings provided new insights for the application of discharge plasma in water disinfection, which could be complemental or alternative to the conventional disinfection methods. | Mingli Shao Chengsong Ye Ting Li Jiaming Gan Xin Yu Lei Wang | 2022 | Journal of Environmental Sciences2022，34，7： | 1 |
| 7 | Dual-Branch Multi-Level Feature Aggregation Network for Pansharpening显示文摘Dear Editor,In pansharpening task,the most existing deep-learning-based pansharpening methods fail to fully utilize the different level features,inevitably leading to spectral or spatial distortions.To address this challenge,in this letter,we propose a dual-branch multi-level feature aggregation network for pansharpening(DMFANet). | Gui Cheng Zhenfeng Shao Jiaming Wang Xiao Huang Chaoya Dang | 2022 | IEEE/CAA Journal of Automatica Sinica2022，9，11： | 1 |
| 8 | Fault Diagnosis of Industrial Motors with Extremely Similar Thermal Images Based on Deep Learning-Related Classification Approaches显示文摘Induction motors(IMs)typically fail due to the rate of stator short-circuits.Because of the similarity of the thermal images produced by various instances of short-circuit and the minor interclass distinctions between categories,non-destructive fault detection is universally perceived as a difficult issue.This paper adopts the deep learning model combined with feature fusion methods based on the image’s low-level features with higher resolution and more position and details and high-level features with more semantic information to develop a high-accuracy classification-detection approach for the fault diagnosis of IMs.Based on the publicly available thermal images(IRT)dataset related to condition monitoring of electrical equipment-IMs,the proposed approach outperforms the highest training accuracy,validation accuracy,and testing accuracy,i.e.,99%,100%,and 94%,respectively,compared with 8 benchmark approaches based on deep learning models and 3 existing approaches in the literature for 11-class IMs faults.Even the training loss,validation loss,and testing loss of the eleven deployed deep learning models meet industry standards. | Hong Zhang Qi Wang Lixing Chen Jiaming Zhou Haijian Shao | 2023 | Energy Engineering2023，120，8： | 0 |
| 9 | Novel tools for early diagnosis and precision treatment based on artificial intelligence显示文摘Lung cancer has the highest mortality rate among all cancers in the world.Hence,early diagnosis and personal-ized treatment plans are crucial to improving its 5-year survival rate.Chest computed tomography(CT)serves as an essential tool for lung cancer screening,and pathology images are the gold standard for lung cancer diagnosis.However,medical image evaluation relies on manual labor and suffers from missed diagnosis or misdiagnosis,and physician heterogeneity.The rapid development of artificial intelligence(AI)has brought a whole novel op-portunity for medical task processing,demonstrating the potential for clinical application in lung cancer diagnosis and treatment.AI technologies,including machine learning and deep learning,have been deployed extensively for lung nodule detection,benign and malignant classification,and subtype identification based on CT images.Furthermore,AI plays a role in the non-invasive prediction of genetic mutations and molecular status to provide the optimal treatment regimen,and applies to the assessment of therapeutic efficacy and prognosis of lung cancer patients,enabling precision medicine to become a reality.Meanwhile,histology-based AI models assist patholo-gists in typing,molecular characterization,and prognosis prediction to enhance the efficiency of diagnosis and treatment.However,the leap to extensive clinical application still faces various challenges,such as data sharing,standardized label acquisition,clinical application regulation,and multimodal integration.Nevertheless,AI holds promising potential in the field of lung cancer to improve cancer care. | Jun Shao Jiaming Feng Jingwei Li Shufan Liang Weimin Li Chengdi Wang | 2023 | Chinese Medical Journal Pulmonary and Critical Care Medicine2023，1，3： | 0 |
| 10 | Three-dimensional porous In_(2)O_(3) arrays for self-powered transparent solar-blind photodetectors with high responsivity and excellent spectral selectivity显示文摘Transparent solar-blind ultraviolet photodetectors(SBUV PDs)have extensive applications in versatile scenarios,such as optical communication.However,it is still challenging to simultaneously achieve high responsivity,high transparency,and satisfying self-powered capability.Here,we demonstrated high-performance,transparent,and self-powered photoelectrochemical-type(PEC)SBUV PDs based on vertically grown ultrathin In_(2)O_(3) nanosheet arrays(NAs)with a three-dimensional(3D)porous structure.The 3D porous structure simultaneously improves the transmittance in the visible light region,accelerates interfacial reaction kinetics,and promotes photogenerated carrier transport.The performance of In_(2)O_(3) NAs photoanodes exceeds most reported self-powered PEC SBUV PDs,exhibiting a high transmittance of approximately 80%in the visible light region,a high responsivity of 86.15 mA/W for 254 nm light irradiation,a fast response speed of 15/18 ms,and good multicycle stability.The In_(2)O_(3) NAs also show excellent spectral selectivity with an ultrahigh solar-blind rejection ratio of 1319.30,attributed to the quantum confinement effect induced by the ultrathin feature(2-3 nm).Furthermore,In_(2)O_(3) NAs photoanodes show good capability in underwater optical communication.Our work demonstrated that a 3D porous structure is a powerful strategy to synchronously achieve high responsivity and transparency and provides a new perspective for designing high-performance,transparent,and self-powered PEC SBUV PDs. | Nana Zhang Xinyu Gao Haoran Guan Simin Sun Jiaming Liu Zhitao Shao Qiyue Gao Yuan Zhang Ruyu Sun Guang Yang Feng Gao Wei Feng | 2024 | Nano Research2024，17，5： | 0 |
| 11 | Co-pyrolysis of oil sludge with hydrogen-rich plastics in a vertical stirring reactor:Kinetic analysis,emissions,and products显示文摘Pyrolysis is an effective method to treat oily sludge(OS)due to its balance between oil recovery and nonhazardous disposal.However,tank bottom OS contains a high content of heavy fractions,which creates obstacles for pyrolysis due to the high activation energy.The incomplete cracking of macromolecules and secondary polymerization decreases the oil quality and causes coking during the operation process.This study introduced polyethylene(PE)into OS to deploy the H/Ceff ratio of feedstocks for pyrolysis A strong interaction between OS and PE during copyrolysis could be observed from the TG/DTG curves.PE tightly participated in OS degradation,while OS also promoted PE degradation at high temperature.Apparent pits were generated in solid residues from copyrolysis,which was attributed to the uniform and violent gas release.In addition to HCN,other nitrogenous and sulphurous pollutants were inhibited.Accordingly,more gas products were attained after PE addition with more value-added compositions of alkanes and alkenes.Although the oil yield decreased after PE addition,the oil products from copyrolysis possessed higher heating values and higher contents of light fractions with short chains as well as paraffins.Consequently,copyrolysis of OS and PE significantly improved the pyrolysis process and resulted in high oil quality. | Lujun Zhao Jiaming Shao Li Xiang Yiping Feng Zhihua Wang Fawei Lin | 2022 | Frontiers of Environmental Science & Engineering2022，16，10： | 0 |
| 12 | Room-temperature multiple ligands-tailored SnO_(2) quantum dots endow in situ dual-interface binding for upscaling efficient perovskite photovoltaics with high V_(OC)显示文摘The benchmark tin oxide(SnO_(2))electron transporting layers(ETLs)have enabled remarkable progress in planar perovskite solar cell(PSCs).However,the energy loss is still a challenge due to the lack of“hidden interface”control.We report a novel ligand-tailored ultrafine SnO_(2) quantum dots(QDs)via a facile rapid room temperature synthesis.Importantly,the ligand-tailored SnO_(2) QDs ETL with multi-functional terminal groups in situ refines the buried interfaces with both the perovskite and transparent electrode via enhanced interface binding and perovskite passivation.These novel ETLs induce synergistic effects of physical and chemical interfacial modulation and preferred perovskite crystallization-directing,delivering reduced interface defects,suppressed non-radiative recombination and elongated charge carrier lifetime.Power conversion efficiency(PCE)of 23.02%(0.04 cm^(2))and 21.6%(0.98 cm^(2),V_(OC) loss:0.336 V)have been achieved for the blade-coated PSCs(1.54 eV E_(g))with our new ETLs,representing a record for SnO_(2) based blade-coated PSCs.Moreover,a substantially enhanced PCE(V_(OC))from 20.4%(1.15 V)to 22.8%(1.24 V,90 mV higher V_(OC),0.04 cm^(2) device)in the blade-coated 1.61 eV PSCs system,via replacing the benchmark commercial colloidal SnO_(2) with our new ETLs. | Zhiwei Ren Kuan Liu Hanlin Hu Xuyun Guo Yajun Gao Patrick W.K.Fong Qiong Liang Hua Tang Jiaming Huang Hengkai Zhang Minchao Qin Li Cui Hrisheekesh Thachoth Chandran Dong Shen Ming-Fai Lo Annie Ng Charles Surya Minhua Shao Chun-Sing Lee Xinhui Lu Frédéric Laquai Ye Zhu Gang Li | 2021 | Light(Science & Applications)2021，10，12： | 0 |
| 13 | A lightweight distillation CNN-transformer architecture for remote sensing image super-resolution显示文摘Remote sensing images exhibit rich texture features and strong autocorrelation.Although the super-resolution(SR)method of remote sensing images based on convolutional neural networks(CNN)can capture rich local information,the limited perceptual field prevents it from establishing long-distance dependence on global information,leading to the low accuracy of remote sensing image reconstruction.Furthermore,it is difficult for existing SR methods to be deployed in mobile devices due to their large network parameters and high computational demand.In this study,we propose a lightweight distillation CNN-Transformer SR architecture,named DCTA,for remote sensing SR,addressing the aforementioned issues.Specifically,the proposed DCTA first extracts the coarse features through the coarse feature extraction layer and then learns the deep features of remote sensing at different scales by fusing the feature distillation extraction module of CNN and Transformer.In addition,we introduce the feature fusion module at the end of the feature distillation extraction module to control the information propagation,aiming to select the informative components for better feature fusion.The extracted low-resolution(LR)feature maps are reorganized through the up-sampling module to obtain high-resolution(HR)feature maps with high accuracy to generate highquality HR remote sensing images.The experiments comparing different methods demonstrate that the proposed approach performs well on multiple datasets,including NWPU-RESISC45,Draper,and UC Merced.This is achieved by balancing reconstruction performance and network complexity,resulting in both competitive subjective and objective results. | Yu Wang Zhenfeng Shao Tao Lu Lifeng Liu Xiao Huang Jiaming Wang Kui Jiang Kangli Zeng | 2023 | International Journal of Digital Earth2023，16，1： | 0 |
| 14 | Classification-Detection of Metal Surfaces under Lower Edge Sharpness Using a Deep Learning-Based Approach Combined with an Enhanced LoG Operator显示文摘Metal flat surface in-line surface defect detection is notoriously difficult due to obstacles such as high surface reflectivity,pseudo-defect interference,and random elastic deformation.This study evaluates the approach for detecting scratches on a metal surface in order to address a problem in the detection process.This paper proposes an improved Gauss-Laplace(LoG)operator combined with a deep learning technique for metal surface scratch identification in order to solve the difficulties that it is challenging to reduce noise and that the edges are unclear when utilizing existing edge detection algorithms.In the process of scratch identification,it is challenging to differentiate between the scratch edge and the interference edge.Therefore,local texture screening is utilized by deep learning techniques that evaluate and identify scratch edges and interference edges based on the local texture characteristics of scratches.Experiments have proven that by combining the improved LoG operator with a deep learning strategy,it is able to effectively detect image edges,distinguish between scratch edges and interference edges,and identify clear scratch information.Experiments based on the six categories of meta scratches indicate that the proposedmethod has achieved rolled-in crazing(100%),inclusion(94.4%),patches(100%),pitted(100%),rolled(100%),and scratches(100%),respectively. | Hong Zhang Jiaming Zhou Qi Wang Chengxi Zhu Haijian Shao | 2023 | Computer Modeling in Engineering & Sciences2023，，11： | 0 |
| 15 | A novel model for assessing the degree of intelligent manufacturing readiness in the process industry: process-industry intelligent manufacturing readiness index (PIMRI)显示文摘Recently,the implementation of Industry 4.0 has become a new tendency,and it brings both opportunities and challenges to worldwide manufacturing companies.Thus,many manufacturing companies are attempting to find advanced technologies to launch intelligent manufacturing transformation.In this study,we propose a new model to measure the intelligent manufacturing readiness for the process industry,which aims to guide companies in recognizing their current stage and short slabs when carrying out intelligent manufacturing transformation.Although some models have already been reported to measure Industry 4.0 readiness and maturity,there are no models that are aimed at the process industry.This newly proposed model has six levels to describe different development stages for intelligent manufacturing.In addition,the model consists of four races,nine species,and 25 domains that are relevant to the essential businesses of companies’daily operation and capability requirements of intelligent manufacturing.Furthermore,these 25 domains are divided into 249 characteristic items to evaluate the manufacturing readiness in detail.A questionnaire is also designed based on the proposed model to help process-industry companies easily carry out self-diagnosis.Using the new method,a case including 196 real-world process-industry companies is evaluated to introduce the method of how to use the proposed model.Overall,the proposed model provides a new way to assess the degree of intelligent manufacturing readiness for process-industry companies. | Lujun ZHAO Jiaming SHAO Yuqi QI Jian CHU Yiping FENG | 2023 | Frontiers of Information Technology & Electronic Engineering2023，24，3： | 0 |
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