INTRODUCTIONThe liver is one of the organs,which have potentialregenerative capability in mammalian animal.The study of the canine model indicated that theliver could regenerate to original size after 70%hepatectomy i...INTRODUCTIONThe liver is one of the organs,which have potentialregenerative capability in mammalian animal.The study of the canine model indicated that theliver could regenerate to original size after 70%hepatectomy in only two weeks.So it is a hotresearch topic for the cellular and molecularmechanism of liver regeneration.展开更多
Hepatic stimulator substance （HSS） has been referred to as a liver-specific but species non-specific growth factor. Gradient purification and sequence analysis of HSS protein indicated that it contained the augmente...Hepatic stimulator substance （HSS） has been referred to as a liver-specific but species non-specific growth factor. Gradient purification and sequence analysis of HSS protein indicated that it contained the augmenter of liver regeneration （ALR）, also known as hepatopoietin （HPO）. ALR, acting as a hepatotrophic growth factor, specifically stimulated proliferation of cultured hepatocytes as well as hepatoma cells in vitro, promoted liver regeneration and recovery of damaged hepatocytes and rescued acute hepatic failure in vivo. ALR belongs to the new Erv1/Alr protein family, members of which are found in lower and higher eukaryotes from yeast to man and even in some double-stranded DNA viruses. The present review article focuses on the molecular biology of ALR, examining the ALR gene and its expression from yeast to man and the biological function of ALR protein. ALR protein seems to be non-liver-specific as was previously believed, increasing the necessity to extend research on mammalian ALR protein in different tissues, organs and developmental stages in conditions of normal and abnormal cellular growth.展开更多
AIM： To observe the effects of augmenter of liver regeneration （ALR） on Kupffer cells and to determine whether ALR promotes hepatocyte proliferation induced by Kupffer cells. METHODS： Kupffer cells and hepatocytes...AIM： To observe the effects of augmenter of liver regeneration （ALR） on Kupffer cells and to determine whether ALR promotes hepatocyte proliferation induced by Kupffer cells. METHODS： Kupffer cells and hepatocytes were cultured in vitro and various concentrations of recombinant rat ALR （rrALR） were added. ^3H-thymidine, BrdU and ^3H-leucine incorporation was determined in cultured Kupffer cells and hepatocytes, in hepatocytes conditioned by Kupffer cells, and in associated medium, rrALR was labeled by iodination and used to determine its binding activity by Scatchard analysis in Kupffer cells and primarily cultured rat hepatocytes. RESULTS： rrALR stimulated DNA replication in Kupffer cells and protein synthesis both in cells and in medium in a non-concentration-dependent manner. The effect was significant at the concentration of 1μg/L ALR. However, rrALR had no effect on primarily cultured hepatocytes, when hepatocytes were cultured with the Kupffer cell medium conditioned by ALR, DNA replication and protein synthesis in hepatocytes increased significantly at the concentration of 1μg/L ALR. When the ALR concentration was increased, its effect on hepatocyte proliferation decreased to the basal level. Scatchard analysis indicated the presence of a single class of high affinity receptors with a dissociation constant （Kd） of 0.883 nmol/L and a maximum binding capacity （Bmax） of 126.1 pmol/g protein in the rat Kupffer cells. CONCLUSION： ALR can promote hepatocyte proliferation induced by Kupffer cells, which is associated with the concentration of ALR, suggesting that Kupffer cells play a dual role in liver regeneration.展开更多
AIM:To investigate the role of autophagy in the antiapoptotic effect of augmenter of liver regeneration(ALR).METHODS:Autophagy was induced through serum deprivation.An ALR-expressing plasmid was transfected into HepG2...AIM:To investigate the role of autophagy in the antiapoptotic effect of augmenter of liver regeneration(ALR).METHODS:Autophagy was induced through serum deprivation.An ALR-expressing plasmid was transfected into HepG2 cells,and autophagic flux was determined using fluorescence microscopy,electron microscopy,Western blot and quantitative polymerase chain reaction(q PCR) assays.After ALR-expressing plasmid transfection,an autophagy inhibitor [3-methyladenine(3-MA)] was added to HepG2 cells,and apoptosis was observed using fluorescence microscopy and flow cytometry.RESULTS:Autophagy was activated in HepG2 cells,peaking at 24 h after serum deprivation.Microtubuleassociated protein light chain three-II levels were higher in HepG2 cells treated with ALR than in control cells,fluorescence microscopy,electron microscopy and q PCR studies showed the similar trend,and p62 levels showed the opposite trend,which indicated that ALR may play an important role in increasing autophagy flux.The numbers of apoptotic cells were substantially higher in HepG2 cells treated with both ALR and 3-MA than in cells treated with ALR alone.Therefore,the protective effect of ALR was significantly attenuated or abolished when autophagy was inhibited,indicating that the anti-apoptotic effect of ALR may be related to autophagy.CONCLUSION:ALR protects cells from apoptosis partly through increased autophagy in HepG2 cells and may be valuable as a new therapeutic treatment for liver disease.展开更多
AIM： To construct the expression vectors for prokaryotic and eukaryotic human augmenter of liver regeneration （hALR） and to study their biological activity. METHODS： hALRcDNA clone was obtained from plasmid pGEM-T...AIM： To construct the expression vectors for prokaryotic and eukaryotic human augmenter of liver regeneration （hALR） and to study their biological activity. METHODS： hALRcDNA clone was obtained from plasmid pGEM-T-hALR, and cDNA was subcloned into the prokatyotic expression vector pGEX-4T-2. The recombinant vector and pGEX-4T-2hALR were identified by enzyme digestion and DNA sequencing and transformed into E coli JM109. The positively selected clone was induced by the expression of GST-hALR fusion protein with IPTG, then the fusion protein was purified by glutathine s-transferase （GST） sepharose 4B affinity chromatography, cleaved by thrombin and the hALR monomer was obtained and detected by measuring H thymidine incorporation. RESULTS： The product of PCR from plasmid pGEM-T- hALR was examined by 1.5% sepharose electrophoresis. The specific strap was coincident with the theoretical one. The sequence was accurate and pGEX-4T-hALP digested by enzymes was coincident with the theoretical one. The sequence was accurate and the fragment was inserted in the positive direction. The recombinant vector was transformed into E coli JM109. SDS-PAGE proved that the induced expressive fusion protein showed a single band with a molecular weight of 41 kDa. The product was purified and cleaved. The molecular weights of GST and hALR were 26 kDa, 15 kDa respectively. The recombinant fusion protein accounted for 31% of the total soluble protein of bacterial lysate. HALR added to the culture medium of adult rat hepatocytes in primary culture and HepG2 cell line could significantly enhance the rate of DNA synthesis compared to the relevant control groups （P 〈 0.01）.CONCLUSION： Purified hALR has the ability to stimulate DNA synthesis of adult rat hepatocytes in primary culture and HepG2 cells in vitro, and can provide evidence for its clinical application.展开更多
Objective: To study the function of augmenter of liverregeneration (ALR) as a regulatory factor that specif-ically stimulates hepatic cell regeneration, we con-structed yeast expressive vector of ALR and expressedit i...Objective: To study the function of augmenter of liverregeneration (ALR) as a regulatory factor that specif-ically stimulates hepatic cell regeneration, we con-structed yeast expressive vector of ALR and expressedit in yeast cells.Methods: Total RNA was extracted from HepG2 cells,and reverse transcription polymerase chain reaction(RT-PCR) was performed to amplify the coding re-gion of ALR. The products were cloned into pGEM-Tvector and sequenced, then cloned into pGBKT7 vec-tor. The recombinant plasmid pGBKT7-ALR wastransformed into yeast AH109. The yeast protein wasextracted and analyzed by SDS-polyacrylamide gelelectrophoresis (SDS-PAGE) and Western blottinghybridization technique.Results: DNA sequencing results confirmed that thecoding region of ALR was correctly inserted into theyeast expression vector, and Western blotting assayshowed that recombinant ALR was successfully ex-pressed in yeast. Its molecular weight was identical tothe theoretical value of 15,000 Da; the protein wasfound inside the yeast cells.Conclusion: The successful expression of ALR in yeastcells makes it possible to study further on its biologicalfunction.展开更多
OBJECTIVE: To investigate the biological function of augmenter of liver regeneration (ALR), we usedyeast-two hybrid technique to detect proteins in hepatocytes interacting with ALR.METHODS: ALR bait plasmid was constr...OBJECTIVE: To investigate the biological function of augmenter of liver regeneration (ALR), we usedyeast-two hybrid technique to detect proteins in hepatocytes interacting with ALR.METHODS: ALR bait plasmid was constructed by using yeast-two hybrid system 3, then transformedinto yeast AH109. The transformed yeast was mated with yeast Y187 containing liver cDNA libraryplasmid in a 2×YPDA medium. Diploid yeast was plated on a synthetic dropout nutrient medium(SD/-Trp-Leu-His-Ade) containing x-α-gal for selection and screening. After extracting and sequencingof the plasmid from blue colonies. Analysis was performed by bioinformatics.RESULTS: Of 36 colonies sequenced, 14 are metallothionein, 12 albumin, and 3 selenoprotein P. Onecolony is a new gene with unknown function.CONCLUSION: The successful cloning of gene of ALR interacting protein has paved the way forstudying the physiological function of ALR and associated proteins.展开更多
Experimental evidence has been presented to suggest that the human augmenter of liver regeneration （hALR） serves as a hepatotruphic growth factor during liver regeneration and as a generalized growth factor during p...Experimental evidence has been presented to suggest that the human augmenter of liver regeneration （hALR） serves as a hepatotruphic growth factor during liver regeneration and as a generalized growth factor during pancreas transplant/regeneration. A prokaryotic expression plasmid, pRSET/6his-c-myc-hALR was constructed, by cloning synthesized hALR cDNA into pRSET/6his-c-myc that was improved on the basis of pRSET B by the group. As a result, the protein was highly expressed in E. coli BL21. The recombinant hALR was over 60% of the total protein in E. coli. Its validity was confirmed by means of Western Blotting. The protein was purified by Ni-NTA affinity chrumatography and this FAD-dependent sulthydryl oxidase activity was measured.展开更多
Background and Aims: Hepatocellular carcinoma (HCC) is one of the most common types of cancer, often resulting in death. Augmenter of liver regeneration (ALR), a widely expressed multifunctional protein, has roles in ...Background and Aims: Hepatocellular carcinoma (HCC) is one of the most common types of cancer, often resulting in death. Augmenter of liver regeneration (ALR), a widely expressed multifunctional protein, has roles in liver dis-ease. In our previous study, we reported that ALR knock-down inhibited cell proliferation and promoted cell death. However, there is no study on the roles of ALR in HCC. Methods: We used in vitro and in vivo models to inves-tigate the effects of ALR in HCC as well as its mechanism of action. We produced and characterized a human ALR-specific monoclonal antibody (mAb) and investigated the effects of the mAb in HCC cells. Results: The purified ALR-specific mAb matched the predicted molecular weight of IgG heavy and light chains. Thereafter, we used the ALR-specific mAb as a therapeutic strategy to suppress tumor growth in nude mice. Additionally, we assessed the prolif-eration and viability of three HCC cell lines, Hep G2, Huh-7, and MHC97-H, treated with the ALR-specific mAb. Com-pared with controls, tumor growth was inhibited in mice treated with the ALR-specific mAb at 5 mg/kg, as shown by hematoxylin and eosin staining and terminal deoxynu-cleotidyl transferase dUTP nick end labeling. Simultaneous treatment with the ALR-specific mAb and adriamycin pro-moted apoptosis, whereas treatment with the ALR-specific mAb alone inhibited cell proliferation. Conclusions: The ALR-specific mAb might be a novel therapy for HCC by blocking extracellular ALR.展开更多
Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods...Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.展开更多
Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While suc...Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.展开更多
Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many met...Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many methods with varying effects.This article starts with the application research of extended reality technology in PLP treatment,through describing the application of its branch technologies(virtual reality,augmented reality,and mixed reality technology),to lay the foundation for subsequent research,in the hope of finding advanced and effective treatment methods,and providing a basis for future product transformation.展开更多
Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginn...Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.展开更多
Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)t...Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.展开更多
Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of suc...Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.展开更多
Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in ed...Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.展开更多
The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and hist...The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.展开更多
Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited t...Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.展开更多
文摘INTRODUCTIONThe liver is one of the organs,which have potentialregenerative capability in mammalian animal.The study of the canine model indicated that theliver could regenerate to original size after 70%hepatectomy in only two weeks.So it is a hotresearch topic for the cellular and molecularmechanism of liver regeneration.
文摘Hepatic stimulator substance （HSS） has been referred to as a liver-specific but species non-specific growth factor. Gradient purification and sequence analysis of HSS protein indicated that it contained the augmenter of liver regeneration （ALR）, also known as hepatopoietin （HPO）. ALR, acting as a hepatotrophic growth factor, specifically stimulated proliferation of cultured hepatocytes as well as hepatoma cells in vitro, promoted liver regeneration and recovery of damaged hepatocytes and rescued acute hepatic failure in vivo. ALR belongs to the new Erv1/Alr protein family, members of which are found in lower and higher eukaryotes from yeast to man and even in some double-stranded DNA viruses. The present review article focuses on the molecular biology of ALR, examining the ALR gene and its expression from yeast to man and the biological function of ALR protein. ALR protein seems to be non-liver-specific as was previously believed, increasing the necessity to extend research on mammalian ALR protein in different tissues, organs and developmental stages in conditions of normal and abnormal cellular growth.
基金Supported by the National High Technology Research and Development Program of China (863 Program), No. 2003 AA208106Medical Outstanding Talent Foundation of the Army, No. 04J020
文摘AIM： To observe the effects of augmenter of liver regeneration （ALR） on Kupffer cells and to determine whether ALR promotes hepatocyte proliferation induced by Kupffer cells. METHODS： Kupffer cells and hepatocytes were cultured in vitro and various concentrations of recombinant rat ALR （rrALR） were added. ^3H-thymidine, BrdU and ^3H-leucine incorporation was determined in cultured Kupffer cells and hepatocytes, in hepatocytes conditioned by Kupffer cells, and in associated medium, rrALR was labeled by iodination and used to determine its binding activity by Scatchard analysis in Kupffer cells and primarily cultured rat hepatocytes. RESULTS： rrALR stimulated DNA replication in Kupffer cells and protein synthesis both in cells and in medium in a non-concentration-dependent manner. The effect was significant at the concentration of 1μg/L ALR. However, rrALR had no effect on primarily cultured hepatocytes, when hepatocytes were cultured with the Kupffer cell medium conditioned by ALR, DNA replication and protein synthesis in hepatocytes increased significantly at the concentration of 1μg/L ALR. When the ALR concentration was increased, its effect on hepatocyte proliferation decreased to the basal level. Scatchard analysis indicated the presence of a single class of high affinity receptors with a dissociation constant （Kd） of 0.883 nmol/L and a maximum binding capacity （Bmax） of 126.1 pmol/g protein in the rat Kupffer cells. CONCLUSION： ALR can promote hepatocyte proliferation induced by Kupffer cells, which is associated with the concentration of ALR, suggesting that Kupffer cells play a dual role in liver regeneration.
基金National Natural Science Foundation of China,No.81300349 and No.81270532Beijing Natural Science Foundation,No.7144216+2 种基金Beijing Nova Program,No.Z131107000413016Project of Science and Technology Activities of Preferred Overseas Personnel of Beijing(2014)Project of Cultivation of High Level Medical Technical Personnel in Health System of Beijing
文摘AIM:To investigate the role of autophagy in the antiapoptotic effect of augmenter of liver regeneration(ALR).METHODS:Autophagy was induced through serum deprivation.An ALR-expressing plasmid was transfected into HepG2 cells,and autophagic flux was determined using fluorescence microscopy,electron microscopy,Western blot and quantitative polymerase chain reaction(q PCR) assays.After ALR-expressing plasmid transfection,an autophagy inhibitor [3-methyladenine(3-MA)] was added to HepG2 cells,and apoptosis was observed using fluorescence microscopy and flow cytometry.RESULTS:Autophagy was activated in HepG2 cells,peaking at 24 h after serum deprivation.Microtubuleassociated protein light chain three-II levels were higher in HepG2 cells treated with ALR than in control cells,fluorescence microscopy,electron microscopy and q PCR studies showed the similar trend,and p62 levels showed the opposite trend,which indicated that ALR may play an important role in increasing autophagy flux.The numbers of apoptotic cells were substantially higher in HepG2 cells treated with both ALR and 3-MA than in cells treated with ALR alone.Therefore,the protective effect of ALR was significantly attenuated or abolished when autophagy was inhibited,indicating that the anti-apoptotic effect of ALR may be related to autophagy.CONCLUSION:ALR protects cells from apoptosis partly through increased autophagy in HepG2 cells and may be valuable as a new therapeutic treatment for liver disease.
基金Supported by National "863" Program of China , No. 2002AA214011
文摘AIM： To construct the expression vectors for prokaryotic and eukaryotic human augmenter of liver regeneration （hALR） and to study their biological activity. METHODS： hALRcDNA clone was obtained from plasmid pGEM-T-hALR, and cDNA was subcloned into the prokatyotic expression vector pGEX-4T-2. The recombinant vector and pGEX-4T-2hALR were identified by enzyme digestion and DNA sequencing and transformed into E coli JM109. The positively selected clone was induced by the expression of GST-hALR fusion protein with IPTG, then the fusion protein was purified by glutathine s-transferase （GST） sepharose 4B affinity chromatography, cleaved by thrombin and the hALR monomer was obtained and detected by measuring H thymidine incorporation. RESULTS： The product of PCR from plasmid pGEM-T- hALR was examined by 1.5% sepharose electrophoresis. The specific strap was coincident with the theoretical one. The sequence was accurate and pGEX-4T-hALP digested by enzymes was coincident with the theoretical one. The sequence was accurate and the fragment was inserted in the positive direction. The recombinant vector was transformed into E coli JM109. SDS-PAGE proved that the induced expressive fusion protein showed a single band with a molecular weight of 41 kDa. The product was purified and cleaved. The molecular weights of GST and hALR were 26 kDa, 15 kDa respectively. The recombinant fusion protein accounted for 31% of the total soluble protein of bacterial lysate. HALR added to the culture medium of adult rat hepatocytes in primary culture and HepG2 cell line could significantly enhance the rate of DNA synthesis compared to the relevant control groups （P 〈 0.01）.CONCLUSION： Purified hALR has the ability to stimulate DNA synthesis of adult rat hepatocytes in primary culture and HepG2 cells in vitro, and can provide evidence for its clinical application.
文摘Objective: To study the function of augmenter of liverregeneration (ALR) as a regulatory factor that specif-ically stimulates hepatic cell regeneration, we con-structed yeast expressive vector of ALR and expressedit in yeast cells.Methods: Total RNA was extracted from HepG2 cells,and reverse transcription polymerase chain reaction(RT-PCR) was performed to amplify the coding re-gion of ALR. The products were cloned into pGEM-Tvector and sequenced, then cloned into pGBKT7 vec-tor. The recombinant plasmid pGBKT7-ALR wastransformed into yeast AH109. The yeast protein wasextracted and analyzed by SDS-polyacrylamide gelelectrophoresis (SDS-PAGE) and Western blottinghybridization technique.Results: DNA sequencing results confirmed that thecoding region of ALR was correctly inserted into theyeast expression vector, and Western blotting assayshowed that recombinant ALR was successfully ex-pressed in yeast. Its molecular weight was identical tothe theoretical value of 15,000 Da; the protein wasfound inside the yeast cells.Conclusion: The successful expression of ALR in yeastcells makes it possible to study further on its biologicalfunction.
文摘OBJECTIVE: To investigate the biological function of augmenter of liver regeneration (ALR), we usedyeast-two hybrid technique to detect proteins in hepatocytes interacting with ALR.METHODS: ALR bait plasmid was constructed by using yeast-two hybrid system 3, then transformedinto yeast AH109. The transformed yeast was mated with yeast Y187 containing liver cDNA libraryplasmid in a 2×YPDA medium. Diploid yeast was plated on a synthetic dropout nutrient medium(SD/-Trp-Leu-His-Ade) containing x-α-gal for selection and screening. After extracting and sequencingof the plasmid from blue colonies. Analysis was performed by bioinformatics.RESULTS: Of 36 colonies sequenced, 14 are metallothionein, 12 albumin, and 3 selenoprotein P. Onecolony is a new gene with unknown function.CONCLUSION: The successful cloning of gene of ALR interacting protein has paved the way forstudying the physiological function of ALR and associated proteins.
文摘Experimental evidence has been presented to suggest that the human augmenter of liver regeneration （hALR） serves as a hepatotruphic growth factor during liver regeneration and as a generalized growth factor during pancreas transplant/regeneration. A prokaryotic expression plasmid, pRSET/6his-c-myc-hALR was constructed, by cloning synthesized hALR cDNA into pRSET/6his-c-myc that was improved on the basis of pRSET B by the group. As a result, the protein was highly expressed in E. coli BL21. The recombinant hALR was over 60% of the total protein in E. coli. Its validity was confirmed by means of Western Blotting. The protein was purified by Ni-NTA affinity chrumatography and this FAD-dependent sulthydryl oxidase activity was measured.
文摘Background and Aims: Hepatocellular carcinoma (HCC) is one of the most common types of cancer, often resulting in death. Augmenter of liver regeneration (ALR), a widely expressed multifunctional protein, has roles in liver dis-ease. In our previous study, we reported that ALR knock-down inhibited cell proliferation and promoted cell death. However, there is no study on the roles of ALR in HCC. Methods: We used in vitro and in vivo models to inves-tigate the effects of ALR in HCC as well as its mechanism of action. We produced and characterized a human ALR-specific monoclonal antibody (mAb) and investigated the effects of the mAb in HCC cells. Results: The purified ALR-specific mAb matched the predicted molecular weight of IgG heavy and light chains. Thereafter, we used the ALR-specific mAb as a therapeutic strategy to suppress tumor growth in nude mice. Additionally, we assessed the prolif-eration and viability of three HCC cell lines, Hep G2, Huh-7, and MHC97-H, treated with the ALR-specific mAb. Com-pared with controls, tumor growth was inhibited in mice treated with the ALR-specific mAb at 5 mg/kg, as shown by hematoxylin and eosin staining and terminal deoxynu-cleotidyl transferase dUTP nick end labeling. Simultaneous treatment with the ALR-specific mAb and adriamycin pro-moted apoptosis, whereas treatment with the ALR-specific mAb alone inhibited cell proliferation. Conclusions: The ALR-specific mAb might be a novel therapy for HCC by blocking extracellular ALR.
基金supported by the project“Romanian Hub for Artificial Intelligence-HRIA”,Smart Growth,Digitization and Financial Instruments Program,2021–2027,MySMIS No.334906.
文摘Objective expertise evaluation of individuals,as a prerequisite stage for team formation,has been a long-term desideratum in large software development companies.With the rapid advancements in machine learning methods,based on reliable existing data stored in project management tools’datasets,automating this evaluation process becomes a natural step forward.In this context,our approach focuses on quantifying software developer expertise by using metadata from the task-tracking systems.For this,we mathematically formalize two categories of expertise:technology-specific expertise,which denotes the skills required for a particular technology,and general expertise,which encapsulates overall knowledge in the software industry.Afterward,we automatically classify the zones of expertise associated with each task a developer has worked on using Bidirectional Encoder Representations from Transformers(BERT)-like transformers to handle the unique characteristics of project tool datasets effectively.Finally,our method evaluates the proficiency of each software specialist across already completed projects from both technology-specific and general perspectives.The method was experimentally validated,yielding promising results.
基金National Natural Science Foundation of China(62171305,62405206,62004135,62001317,62111530301)Natural Science Foundation of Jiangsu Province(BK20240778,BK20241917)+3 种基金State Key Laboratory of Advanced Optical Communication Systems and Networks,China(2023GZKF08)China Postdoctoral Science Foundation(2024M752314)Postdoctoral Fellowship Program of CPSF(GZC20231883)Innovative and Entrepreneurial Talent Program of Jiangsu Province(JSSCRC2021527).
文摘Photonic platforms are gradually emerging as a promising option to encounter the ever-growing demand for artificial intelligence,among which photonic time-delay reservoir computing(TDRC)is widely anticipated.While such a computing paradigm can only employ a single photonic device as the nonlinear node for data processing,the performance highly relies on the fading memory provided by the delay feedback loop(FL),which sets a restriction on the extensibility of physical implementation,especially for highly integrated chips.Here,we present a simplified photonic scheme for more flexible parameter configurations leveraging the designed quasi-convolution coding(QC),which completely gets rid of the dependence on FL.Unlike delay-based TDRC,encoded data in QC-based RC(QRC)enables temporal feature extraction,facilitating augmented memory capabilities.Thus,our proposed QRC is enabled to deal with time-related tasks or sequential data without the implementation of FL.Furthermore,we can implement this hardware with a low-power,easily integrable vertical-cavity surface-emitting laser for high-performance parallel processing.We illustrate the concept validation through simulation and experimental comparison of QRC and TDRC,wherein the simpler-structured QRC outperforms across various benchmark tasks.Our results may underscore an auspicious solution for the hardware implementation of deep neural networks.
文摘Phantom limb pain(PLP)is not only a physical pain experience but also poses a significant challenge to mental health and quality of life.Currently,the mechanism of PLP treatment is still unclear,and there are many methods with varying effects.This article starts with the application research of extended reality technology in PLP treatment,through describing the application of its branch technologies(virtual reality,augmented reality,and mixed reality technology),to lay the foundation for subsequent research,in the hope of finding advanced and effective treatment methods,and providing a basis for future product transformation.
文摘Objective The study of medicine formulas is a core component of traditional Chinese medicine(TCM),yet traditional learning methods often lack interactivity and contextual understanding,making it challenging for beginners to grasp the intricate composition rules of formulas.To address this gap,we introduce Formula-S,a situated visualization method for TCM formula learning in augmented reality(AR)and evaluate its performance.This study aims to evaluate the effectiveness of Formula-S in enhancing TCM formula learning for beginners by comparing it with traditional text-based formula learning and web-based visualization.Methods Formula-S is an interactive AR tool designed for TCM formula learning,featuring three modes(3D,Web,and Table).The dataset included TCM formulas and herb properties extracted from authoritative references,including textbook and the SymMap database.In Formula-S,the hierarchical visualization of the formulas as herbal medicine compositions,is linked to the multidimensional herb attribute visualization and embedded in the real world,where real herb samples are presented.To evaluate its effectiveness,a controlled study(n=30)was conducted.Participants who had no formal TCM knowledge were tasked with herbal medicine identification,formula composition,and recognition.In the study,participants interacted with the AR tool through HoloLens 2.Data were collected on both task performance(accuracy and response time)and user experience,with a focus on task efficiency,accuracy,and user preference across the different learning modes.Results The situated visualization method of Formula-S had comparable accuracy to other methods but shorter response time for herbal formula learning tasks.Regarding user experience,our new approach demonstrated the highest system usability and lowest task load,effectively reducing cognitive load and allowing users to complete tasks with greater ease and efficiency.Participants reported that Formula-S enhanced their learning experience through its intuitive interface and immersive AR environment,suggesting this approach offers usability advantages for TCM education.Conclusions The situated visualization method in Formula-S offers more efficient and accurate searching capabilities compared to traditional and web-based methods.Additionally,it provides superior contextual understanding of TCM formulas,making it a promising new solution for TCM learning.
基金supported by the Natural Science Foundation of China(No.41804112,author:Chengyun Song).
文摘Existing semi-supervisedmedical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch.However,current copy-paste methods have three limitations:(1)training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information;(2)low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data;(3)the segmentation performance in low-contrast and local regions is less than optimal.We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy(SADT),which enhances feature diversity and learns high-quality features to overcome these problems.To be more precise,SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data,which prevents the loss of rare labeled data.We introduce a bi-directional copy-pastemask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision.For the mixed images,Deep-Shallow Spatial Contrastive Learning(DSSCL)is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas.In this procedure,the features retrieved by the Student Network are subjected to a random feature perturbation technique.On two openly available datasets,extensive trials show that our proposed SADT performs much better than the state-ofthe-art semi-supervised medical segmentation techniques.Using only 10%of the labeled data for training,SADT was able to acquire a Dice score of 90.10%on the ACDC(Automatic Cardiac Diagnosis Challenge)dataset.
文摘Lung cancer continues to be a leading cause of cancer-related deaths worldwide,emphasizing the critical need for improved diagnostic techniques.Early detection of lung tumors significantly increases the chances of successful treatment and survival.However,current diagnostic methods often fail to detect tumors at an early stage or to accurately pinpoint their location within the lung tissue.Single-model deep learning technologies for lung cancer detection,while beneficial,cannot capture the full range of features present in medical imaging data,leading to incomplete or inaccurate detection.Furthermore,it may not be robust enough to handle the wide variability in medical images due to different imaging conditions,patient anatomy,and tumor characteristics.To overcome these disadvantages,dual-model or multi-model approaches can be employed.This research focuses on enhancing the detection of lung cancer by utilizing a combination of two learning models:a Convolutional Neural Network(CNN)for categorization and the You Only Look Once(YOLOv8)architecture for real-time identification and pinpointing of tumors.CNNs automatically learn to extract hierarchical features from raw image data,capturing patterns such as edges,textures,and complex structures that are crucial for identifying lung cancer.YOLOv8 incorporates multiscale feature extraction,enabling the detection of tumors of varying sizes and scales within a single image.This is particularly beneficial for identifying small or irregularly shaped tumors that may be challenging to detect.Furthermore,through the utilization of cutting-edge data augmentation methods,such as Deep Convolutional Generative Adversarial Networks(DCGAN),the suggested approach can handle the issue of limited data and boost the models’ability to learn from diverse and comprehensive datasets.The combined method not only improved accuracy and localization but also ensured efficient real-time processing,which is crucial for practical clinical applications.The CNN achieved an accuracy of 97.67%in classifying lung tissues into healthy and cancerous categories.The YOLOv8 model achieved an Intersection over Union(IoU)score of 0.85 for tumor localization,reflecting high precision in detecting and marking tumor boundaries within the images.Finally,the incorporation of synthetic images generated by DCGAN led to a 10%improvement in both the CNN classification accuracy and YOLOv8 detection performance.
文摘Augmented reality(AR)is an emerging dynamic technology that effectively supports education across different levels.The increased use of mobile devices has an even greater impact.As the demand for AR applications in education continues to increase,educators actively seek innovative and immersive methods to engage students in learning.However,exploring these possibilities also entails identifying and overcoming existing barriers to optimal educational integration.Concurrently,this surge in demand has prompted the identification of specific barriers,one of which is three-dimensional(3D)modeling.Creating 3D objects for augmented reality education applications can be challenging and time-consuming for the educators.To address this,we have developed a pipeline that creates realistic 3D objects from the two-dimensional(2D)photograph.Applications for augmented and virtual reality can then utilize these created 3D objects.We evaluated the proposed pipeline based on the usability of the 3D object and performance metrics.Quantitatively,with 117 respondents,the co-creation team was surveyed with openended questions to evaluate the precision of the 3D object created by the proposed photogrammetry pipeline.We analyzed the survey data using descriptive-analytical methods and found that the proposed pipeline produces 3D models that are positively accurate when compared to real-world objects,with an average mean score above 8.This study adds new knowledge in creating 3D objects for augmented reality applications by using the photogrammetry technique;finally,it discusses potential problems and future research directions for 3D objects in the education sector.
文摘The integration of image analysis through deep learning(DL)into rock classification represents a significant leap forward in geological research.While traditional methods remain invaluable for their expertise and historical context,DL offers a powerful complement by enhancing the speed,objectivity,and precision of the classification process.This research explores the significance of image data augmentation techniques in optimizing the performance of convolutional neural networks(CNNs)for geological image analysis,particularly in the classification of igneous,metamorphic,and sedimentary rock types from rock thin section(RTS)images.This study primarily focuses on classic image augmentation techniques and evaluates their impact on model accuracy and precision.Results demonstrate that augmentation techniques like Equalize significantly enhance the model's classification capabilities,achieving an F1-Score of 0.9869 for igneous rocks,0.9884 for metamorphic rocks,and 0.9929 for sedimentary rocks,representing improvements compared to the baseline original results.Moreover,the weighted average F1-Score across all classes and techniques is 0.9886,indicating an enhancement.Conversely,methods like Distort lead to decreased accuracy and F1-Score,with an F1-Score of 0.949 for igneous rocks,0.954 for metamorphic rocks,and 0.9416 for sedimentary rocks,exacerbating the performance compared to the baseline.The study underscores the practicality of image data augmentation in geological image classification and advocates for the adoption of DL methods in this domain for automation and improved results.The findings of this study can benefit various fields,including remote sensing,mineral exploration,and environmental monitoring,by enhancing the accuracy of geological image analysis both for scientific research and industrial applications.
文摘Retinal blood vessel segmentation is crucial for diagnosing ocular and cardiovascular diseases.Although the introduction of U-Net in 2015 by Olaf Ronneberger significantly advanced this field,yet issues like limited training data,imbalance data distribution,and inadequate feature extraction persist,hindering both the segmentation performance and optimal model generalization.Addressing these critical issues,the DEFFA-Unet is proposed featuring an additional encoder to process domain-invariant pre-processed inputs,thereby improving both richer feature encoding and enhanced model generalization.A feature filtering fusion module is developed to ensure the precise feature filtering and robust hybrid feature fusion.In response to the task-specific need for higher precision where false positives are very costly,traditional skip connections are replaced with the attention-guided feature reconstructing fusion module.Additionally,innovative data augmentation and balancing methods are proposed to counter data scarcity and distribution imbalance,further boosting the robustness and generalization of the model.With a comprehensive suite of evaluation metrics,extensive validations on four benchmark datasets(DRIVE,CHASEDB1,STARE,and HRF)and an SLO dataset(IOSTAR),demonstrate the proposed method’s superiority over both baseline and state-of-the-art models.Particularly the proposed method significantly outperforms the compared methods in cross-validation model generalization.
