Underground engineering often passes through water-rich fractured rock masses, which are prone to fracture and instability under the long-term coupling of in-situ stress field and pore water(P-W) pressure, ultimately ...Underground engineering often passes through water-rich fractured rock masses, which are prone to fracture and instability under the long-term coupling of in-situ stress field and pore water(P-W) pressure, ultimately threatening the stability of underground structures. In order to explore the mechanical properties of rocks under H-M coupling, the corresponding damage constitutive(D-C) model has become the focus of attention. Considering the inadequacy of the current research on rock strength parameters,energy evolution characteristics and D-C model under H-M coupling, the mechanical properties of typical sandstone samples are discussed based on laboratory tests. The results show that the variation of characteristic stresses of sandstone under H-M coupling conforms to the normalized attenuation equation and Mohr-Coulomb(M-C) criterion. The P-W pressure mechanism of sandstone exhibits a dynamic change from softening effect to H-M fracturing effect. The closure stress is mainly provided by cohesive strength, while the initiation stress, damage stress, and peak stress are jointly dominated by cohesive strength and friction strength. In addition, residual stress is attributed to the friction strength formed by the bite of the fracture surface. Subsequently, the energy evolution characteristics of sandstone under H-M coupling were studied, and it was found that P-W pressure weakened the energy storage capacity and energy dissipation capacity of sandstone, and H-M fracturing was an important factor in reducing its energy storage efficiency. Finally, combined with energy dissipation theory and statistical damage theory, two types of D-C models considering P-W pressure are proposed accordingly, and the model parameters can be determined by four methods. The application results indicate that the proposed and modified D-C models have high reliability, and can characterize the mechanical behavior of sandstone under H-M coupling, overcome the inconvenience of existing D-C models due to excessive mechanical parameters,and can be applied to the full-range stress–strain process. The results are conducive to revealing the deformation and damage mechanisms of rocks under H-M coupling, and can provide theoretical guidance for related engineering problems.展开更多
Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing...Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.展开更多
Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive c...Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.展开更多
Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of ...Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.展开更多
In this paper,the hydrogeological characteristics in the southern coalfields of China are first briefly outlined.Then,taking the Meitanba mine as an example,the evolution and modeling of mine water inflow are studied....In this paper,the hydrogeological characteristics in the southern coalfields of China are first briefly outlined.Then,taking the Meitanba mine as an example,the evolution and modeling of mine water inflow are studied.Finally,the hazard characteristics related to mine water and mud inrush are analyzed.The results show that the main mine water sources in the Meitanba mine area are groundwater,surface water and precipitation.The evolution of mine water inflow with time indicates that the water inflow is closely related to the development of karst structures,the amount of water from rainfall infiltration,and the scope of groundwater depression cone.The mine water inflow increases with time due to the increase in mining depth and the expansion of groundwater depression cone.Using the big well method and following the potential superposition principle,a hydrogeological model considering multi-well interactions has been developed to predict the mine water inflow.Based on the monitored data in the Meitanba mine area over a period of nearly 60 years,it is found that with increasing mining depth,the number of water and mud inrush points tended to decrease.However,the average water and mud flow rate per point tended to increase.展开更多
Waste pollution is a significant environmental problem worldwide.With the continuous improvement in the living standards of the population and increasing richness of the consumption structure,the amount of domestic wa...Waste pollution is a significant environmental problem worldwide.With the continuous improvement in the living standards of the population and increasing richness of the consumption structure,the amount of domestic waste generated has increased dramatically,and there is an urgent need for further treatment.The rapid development of artificial intelligence has provided an effective solution for automated waste classification.However,the high computational power and complexity of algorithms make convolutional neural networks unsuitable for real-time embedded applications.In this paper,we propose a lightweight network architecture called Focus-RCNet,designed with reference to the sandglass structure of MobileNetV2,which uses deeply separable convolution to extract features from images.The Focus module is introduced to the field of recyclable waste image classification to reduce the dimensionality of features while retaining relevant information.To make the model focus more on waste image features while keeping the number of parameters small,we introduce the SimAM attention mechanism.In addition,knowledge distillation was used to further compress the number of parameters in the model.By training and testing on the TrashNet dataset,the Focus-RCNet model not only achieved an accuracy of 92%but also showed high deployment mobility.展开更多
AIM: To study a new imaging equipment, highresolution micro-endoscopy(HRME), in the diagnosis and pathological classification of colon polyps.METHODS: We selected 114 specimens of colon polyps, 30 of which were colon ...AIM: To study a new imaging equipment, highresolution micro-endoscopy(HRME), in the diagnosis and pathological classification of colon polyps.METHODS: We selected 114 specimens of colon polyps, 30 of which were colon polyps with known pathological types and 84 that were prospective polyp specimens; 10 normal colon mucosa specimens served as controls. We obtained images of 30 colon polyp specimens with known pathological types using HRME and analyzed the characteristics of these images to develop HRME diagnostic criteria for different pathological types of colon polyps. Based on these criteria, we performed a prospective study of 84 colon polyp specimens using HRME and compared the results with those of the pathological examination to evaluate the diagnostic value of HRME in the pathological classification of different types of colon polyps. RESULTS: In the 30 cases of known pathological type of colon polyp samples, there were 21 cases of adenomatous polyps, which comprised nine cases of tubular adenoma, seven cases of villous adenoma and five cases of mixed adenomas. The nine cases of non-adenomatous polyps included four cases of inflammatory polyps and five cases of hyperplastic polyps five. Ten cases of normal colonic mucosa were confirmed pathologically. In a prospective study of 84 cases using HRME, 23 cases were diagnosed as inflammatory polyps, 11 cases as hyperplastic polyps, 18 cases as tubular adenoma, eight cases as villous adenoma and 24 cases as mixed adenomas. After pathological examination, 24 cases were diagnosed as inflammatory polyps, 11 cases as hyperplastic polyps, 19 cases as tubular adenoma, eight cases as villous adenoma and 22 cases as mixed adenomas. Compared with the pathological examinations, the sensitivities, specificities, accuracies, and positive and negative predictive values of HRME in diagnosing inflammatory polyps(87.5%, 96.7%, 94.0%, 91.3% and 95.1%), hyperplastic polyps(72.7%, 95.9%, 92.9%, 72.7% and 95.9%), tubular adenomas(73.7%, 93.8%, 89.3%, 77.8% and 92.4%), villous adenomas(75.0%, 97.4%, 95.2%, 75.0% and 97.4%), and mixed adenomas(75.0%, 93.3%, 88.1%, 81.8% and 90.3%) were relatively high.CONCLUSION: HRME has a relatively high diagnostic value in the pathological classification of colon polyps. Thus, it may be an alternative to confocal microendoscopy in lower-resource or community-based settings.展开更多
Dear Editor, In the central nervous system, cell migration and accumulation occur widely to construct a complex neural network during mammalian development [1]. Previous studies reported that both the gaseous messeng...Dear Editor, In the central nervous system, cell migration and accumulation occur widely to construct a complex neural network during mammalian development [1]. Previous studies reported that both the gaseous messenger molecule nitric oxide （NO） [2-4] and widely studied axon guidance/migration cues such as Netrins [5, 6] regulated the migratory behavior of cells in the nervous system.展开更多
In this article,we study optimal reinsurance design.By employing the increasing convex functions as the admissible ceded loss functions and the distortion premium principle,we study and obtain the optimal reinsurance ...In this article,we study optimal reinsurance design.By employing the increasing convex functions as the admissible ceded loss functions and the distortion premium principle,we study and obtain the optimal reinsurance treaty by minimizing the VaR(value at risk)of the reinsurer's total risk exposure.When the distortion premium principle is specified to be the expectation premium principle,we also obtain the optimal reinsurance treaty by minimizing the CTE(conditional tail expectation)of the reinsurer's total risk exposure.The present study can be considered as a complement of that of Cai et al.[5].展开更多
Multidrug resistance(MDR) remains a major clinical obstacle to successful cancer treatment.Although diverse mechanisms of MDR have been well elucidated, such as dysregulation of drugs transporters, defects of apoptosi...Multidrug resistance(MDR) remains a major clinical obstacle to successful cancer treatment.Although diverse mechanisms of MDR have been well elucidated, such as dysregulation of drugs transporters, defects of apoptosis and autophagy machinery, alterations of drug metabolism and drug targets, disrupti on of redox homeostasis, the exact mechanisms of MDR in a specific cancer patient and the cross-talk among these different mechanisms and how they are regulated are poorly understood.Micro RNAs(mi RNAs) are a new class of small noncoding RNAs that could control the global activity of the cell by post-transcriptionally regulating a large variety of target genes and proteins expression.Accumulating evidence shows that mi RNAs play a key regulatory role in MDR through modulating various drug resistant mechanisms mentioned above, thereby holding much promise for developing novel and more effective individualized therapies for cancer treatment. This review summarizes the various MDR mechanisms and mainly focuses on the role of mi RNAs in regulating MDR in cancer treatment.展开更多
Mitochondrial diseases are maternally inherited hetero- geneous disorders that are primarily caused by mitochondrial DNA （mtDNA） mutations. Depending on the ratio of mutant to wild-type mtDNA, known as heteroplasmy,...Mitochondrial diseases are maternally inherited hetero- geneous disorders that are primarily caused by mitochondrial DNA （mtDNA） mutations. Depending on the ratio of mutant to wild-type mtDNA, known as heteroplasmy, mitochondrial defects can result in a wide spectrum of clinical manifestations. Mitochondria-targeted endonucleases provide an alternative avenue for treating mitochondrial disorders via targeted destruc- tion of the mutant mtDNA and induction of heteroplasmic shifting. Here, we generated mitochondrial disease patient-specific induced pluripotent stem cells （MiPSCs） that harbored a high proportion of m.3243A〉G mtDNA mutations and caused mitochondrial encephalomyopathy and stroke-like episodes （MELAS）. We engineered mitochondrial-targeted transcription activator-like effector nucleases （mitoTALENs） and successfully eliminated the m.3243A〉G mutation in MiPSCs. Off-target mutagenesis was not detected in the targeted MiPSC clones. Utilizing a dual fluorescence iPSC reporter cell line expressing a 3243G mutant mtDNA sequence in the nuclear genome, mitoTALENs displayed a significantly limited ability to target the nuclear genome compared with nuclear-localized TALENs. Moreover, genetically rescued MiPSCs displayed normal mitochondrial respiration and energy production. Moreover, neuronal progenitor cells differentiated from the rescued MiPSCs also demonstrated normal metabolic profiles. Further- more, we successfully achieved reduction in the human m.3243A〉G mtDNA mutation in porcine oocytes via injection of mitoTALEN mRNA. Our study shows the great potential for using mitoTALENs for specific targeting of mutant mtDNA both in iPSCs and mammalian oocytes, which not only provides a new avenue for studying mitochondrial biology and disease but also suggests a potential therapeutic approach for the treatment of mitochondrial disease, as well as the prevention of germline transmission of mutant mtDNA.展开更多
The limited penetration of nanoparticles and their poor accessibility to cancer cell fractions in tumor remain essential challenges for effective anticancer therapy.Herein,we designed a targeting peptide-decorated bio...The limited penetration of nanoparticles and their poor accessibility to cancer cell fractions in tumor remain essential challenges for effective anticancer therapy.Herein,we designed a targeting peptide-decorated biomimetic lipoprotein(termed as BL-RD)to enable their deep penetration and efficient accessibility to cancer cell fractions in a tumor,thereby improving the combinational chemophotodynamic therapy of triple negative breast cancer.BL-RD was composed of phospholipids,apolipoprotein A1 mimetic peptide(PK22),targeting peptide-conjugated cytotoxic mertansine(RM)and photodynamic agents of DiIC18(5)(DiD).The counterpart biomimetic lipoprotein system without RM(termed as BL-D)was fabricated as control.Both BL-D and BL-RD were nanometer-sized particles with a mean diameter of less than 30 nm and could be efficiently internalized by cancer cells.After intravenous injection,they can be specifically accumulated at tumor sites.When comparing to the counterpart BLD,BL-RD displayed superior capability to permeate across the tumor mass,extravasate from tumor vasculature to distant regions and efficiently access the cancer cell fractions in a solid tumor,thus producing noticeable depression of the tumor growth.Taken together,BL-RD can be a promising delivery nanoplatform with prominent tumor-penetrating and cancer cells-accessing capability for effective tumor therapy.展开更多
Dear Editor, Polycystic ovary syn drome (PCOS) is a comm on female reproductive endocrinopathy that afflicts up to 10%-15% of women in reproductive age worldwide (Nestler, 2016). Women with PCOS exhibit hyperandrogeni...Dear Editor, Polycystic ovary syn drome (PCOS) is a comm on female reproductive endocrinopathy that afflicts up to 10%-15% of women in reproductive age worldwide (Nestler, 2016). Women with PCOS exhibit hyperandrogenism, intermittent/ absent menstrual cycles, and polycystic ovaries on ultrasound (Rotterdam, 2004). The pathophysiology of PCOS extends beyond infertility and hirsutism to hypothalamic neuroendocrine dysfunotion (Goodarzi et al., 2011). Most wome n with PCOS exhibit in creased luteinizing horm one (LH) levels, resulting from high-frequency gonadotropin-releasing hormone (GnRH) secretion (Cimino et al., 2016). Pren ata I testostero ne (T) treatment in sheep results in disrupted steroid feedback on gonadotropin release, which in creases pituitary sen sitivity to GnRH and subseque ntly leads to LH hypersecretion (Sullivan and Moenter, 2004;Cardoso et al., 2016). A recent study shows that GnRHdependent LH pulsatility and secretion are elevated by anti- Mullerian hormone (AMH) in PCOS disease. The increased prenatal AMH reprograms fetus and induces PCOS in adults (Tata et al., 2018). Furthermore, the androgen receptor (AR) plays a role in hyperandrogenism and ovarian folliculoge esis in PCOS (Wang et al., 2015;Abbott, 2017). However, the disease mechanism behind PCOS remains unclear, and current management focuses on treating the symptoms but not the mechanism (Chen et al., 2016;Shi et al., 2018). A further understanding of this disease is necessary to uncover the pathology of PCOS and develop new potential therapeutic avenues and drugs.展开更多
基金funding support from the National Natural Science Foundation of China(Nos.52174088 and 42277154)the Independent Innovation Research Fund Graduate Free Exploration Project(No.104972024JYS0007)supported by Wuhan University of Technology.
文摘Underground engineering often passes through water-rich fractured rock masses, which are prone to fracture and instability under the long-term coupling of in-situ stress field and pore water(P-W) pressure, ultimately threatening the stability of underground structures. In order to explore the mechanical properties of rocks under H-M coupling, the corresponding damage constitutive(D-C) model has become the focus of attention. Considering the inadequacy of the current research on rock strength parameters,energy evolution characteristics and D-C model under H-M coupling, the mechanical properties of typical sandstone samples are discussed based on laboratory tests. The results show that the variation of characteristic stresses of sandstone under H-M coupling conforms to the normalized attenuation equation and Mohr-Coulomb(M-C) criterion. The P-W pressure mechanism of sandstone exhibits a dynamic change from softening effect to H-M fracturing effect. The closure stress is mainly provided by cohesive strength, while the initiation stress, damage stress, and peak stress are jointly dominated by cohesive strength and friction strength. In addition, residual stress is attributed to the friction strength formed by the bite of the fracture surface. Subsequently, the energy evolution characteristics of sandstone under H-M coupling were studied, and it was found that P-W pressure weakened the energy storage capacity and energy dissipation capacity of sandstone, and H-M fracturing was an important factor in reducing its energy storage efficiency. Finally, combined with energy dissipation theory and statistical damage theory, two types of D-C models considering P-W pressure are proposed accordingly, and the model parameters can be determined by four methods. The application results indicate that the proposed and modified D-C models have high reliability, and can characterize the mechanical behavior of sandstone under H-M coupling, overcome the inconvenience of existing D-C models due to excessive mechanical parameters,and can be applied to the full-range stress–strain process. The results are conducive to revealing the deformation and damage mechanisms of rocks under H-M coupling, and can provide theoretical guidance for related engineering problems.
基金Macao Polytechnic University Grant(RP/FCSD-01/2022RP/FCA-05/2022)Science and Technology Development Fund of Macao(0105/2022/A).
文摘Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.
基金Research Fund of Macao Polytechnic University(RP/FCSD-01/2022).
文摘Background Magnetic resonance imaging(MRI)has played an important role in the rapid growth of medical imaging diagnostic technology,especially in the diagnosis and treatment of brain tumors owing to its non invasive characteristics and superior soft tissue contrast.However,brain tumors are characterized by high non uniformity and non-obvious boundaries in MRI images because of their invasive and highly heterogeneous nature.In addition,the labeling of tumor areas is time-consuming and laborious.Methods To address these issues,this study uses a residual grouped convolution module,convolutional block attention module,and bilinear interpolation upsampling method to improve the classical segmentation network U-net.The influence of network normalization,loss function,and network depth on segmentation performance is further considered.Results In the experiments,the Dice score of the proposed segmentation model reached 97.581%,which is 12.438%higher than that of traditional U-net,demonstrating the effective segmentation of MRI brain tumor images.Conclusions In conclusion,we use the improved U-net network to achieve a good segmentation effect of brain tumor MRI images.
基金the Science and Technology Funding Project of Hunan Province,China(2023JJ50410)(HX)Key Laboratory of Tumor Precision Medicine,Hunan colleges and Universities Project(2019-379)(QL).
文摘Background The prognosis and survival of patients with lung cancer are likely to deteriorate with metastasis.Using deep-learning in the detection of lymph node metastasis can facilitate the noninvasive calculation of the likelihood of such metastasis,thereby providing clinicians with crucial information to enhance diagnostic precision and ultimately improve patient survival and prognosis.Methods In total,623 eligible patients were recruited from two medical institutions.Seven deep learning models,namely Alex,GoogLeNet,Resnet18,Resnet101,Vgg16,Vgg19,and MobileNetv3(small),were utilized to extract deep image histological features.The dimensionality of the extracted features was then reduced using the Spearman correlation coefficient(r≥0.9)and Least Absolute Shrinkage and Selection Operator.Eleven machine learning methods,namely Support Vector Machine,K-nearest neighbor,Random Forest,Extra Trees,XGBoost,LightGBM,Naive Bayes,AdaBoost,Gradient Boosting Decision Tree,Linear Regression,and Multilayer Perceptron,were employed to construct classification prediction models for the filtered final features.The diagnostic performances of the models were assessed using various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value.Calibration and decision-curve analyses were also performed.Results The present study demonstrated that using deep radiomic features extracted from Vgg16,in conjunction with a prediction model constructed via a linear regression algorithm,effectively distinguished the status of mediastinal lymph nodes in patients with lung cancer.The performance of the model was evaluated based on various metrics,including accuracy,area under the receiver operating characteristic curve,sensitivity,specificity,positive predictive value,and negative predictive value,which yielded values of 0.808,0.834,0.851,0.745,0.829,and 0.776,respectively.The validation set of the model was assessed using clinical decision curves,calibration curves,and confusion matrices,which collectively demonstrated the model's stability and accuracy.Conclusion In this study,information on the deep radiomics of Vgg16 was obtained from computed tomography images,and the linear regression method was able to accurately diagnose mediastinal lymph node metastases in patients with lung cancer.
基金This research is supported by the National Natural Science Foundation of China(Nos.51774131,51874133)Construction Project of Chenzhou National Sustainable Development Agenda Innovation Demonstration Zone(2021sfQ18).
文摘In this paper,the hydrogeological characteristics in the southern coalfields of China are first briefly outlined.Then,taking the Meitanba mine as an example,the evolution and modeling of mine water inflow are studied.Finally,the hazard characteristics related to mine water and mud inrush are analyzed.The results show that the main mine water sources in the Meitanba mine area are groundwater,surface water and precipitation.The evolution of mine water inflow with time indicates that the water inflow is closely related to the development of karst structures,the amount of water from rainfall infiltration,and the scope of groundwater depression cone.The mine water inflow increases with time due to the increase in mining depth and the expansion of groundwater depression cone.Using the big well method and following the potential superposition principle,a hydrogeological model considering multi-well interactions has been developed to predict the mine water inflow.Based on the monitored data in the Meitanba mine area over a period of nearly 60 years,it is found that with increasing mining depth,the number of water and mud inrush points tended to decrease.However,the average water and mud flow rate per point tended to increase.
文摘Waste pollution is a significant environmental problem worldwide.With the continuous improvement in the living standards of the population and increasing richness of the consumption structure,the amount of domestic waste generated has increased dramatically,and there is an urgent need for further treatment.The rapid development of artificial intelligence has provided an effective solution for automated waste classification.However,the high computational power and complexity of algorithms make convolutional neural networks unsuitable for real-time embedded applications.In this paper,we propose a lightweight network architecture called Focus-RCNet,designed with reference to the sandglass structure of MobileNetV2,which uses deeply separable convolution to extract features from images.The Focus module is introduced to the field of recyclable waste image classification to reduce the dimensionality of features while retaining relevant information.To make the model focus more on waste image features while keeping the number of parameters small,we introduce the SimAM attention mechanism.In addition,knowledge distillation was used to further compress the number of parameters in the model.By training and testing on the TrashNet dataset,the Focus-RCNet model not only achieved an accuracy of 92%but also showed high deployment mobility.
基金Supported by Capital Clinical Characteristics Application Research(Z141107002514099)
文摘AIM: To study a new imaging equipment, highresolution micro-endoscopy(HRME), in the diagnosis and pathological classification of colon polyps.METHODS: We selected 114 specimens of colon polyps, 30 of which were colon polyps with known pathological types and 84 that were prospective polyp specimens; 10 normal colon mucosa specimens served as controls. We obtained images of 30 colon polyp specimens with known pathological types using HRME and analyzed the characteristics of these images to develop HRME diagnostic criteria for different pathological types of colon polyps. Based on these criteria, we performed a prospective study of 84 colon polyp specimens using HRME and compared the results with those of the pathological examination to evaluate the diagnostic value of HRME in the pathological classification of different types of colon polyps. RESULTS: In the 30 cases of known pathological type of colon polyp samples, there were 21 cases of adenomatous polyps, which comprised nine cases of tubular adenoma, seven cases of villous adenoma and five cases of mixed adenomas. The nine cases of non-adenomatous polyps included four cases of inflammatory polyps and five cases of hyperplastic polyps five. Ten cases of normal colonic mucosa were confirmed pathologically. In a prospective study of 84 cases using HRME, 23 cases were diagnosed as inflammatory polyps, 11 cases as hyperplastic polyps, 18 cases as tubular adenoma, eight cases as villous adenoma and 24 cases as mixed adenomas. After pathological examination, 24 cases were diagnosed as inflammatory polyps, 11 cases as hyperplastic polyps, 19 cases as tubular adenoma, eight cases as villous adenoma and 22 cases as mixed adenomas. Compared with the pathological examinations, the sensitivities, specificities, accuracies, and positive and negative predictive values of HRME in diagnosing inflammatory polyps(87.5%, 96.7%, 94.0%, 91.3% and 95.1%), hyperplastic polyps(72.7%, 95.9%, 92.9%, 72.7% and 95.9%), tubular adenomas(73.7%, 93.8%, 89.3%, 77.8% and 92.4%), villous adenomas(75.0%, 97.4%, 95.2%, 75.0% and 97.4%), and mixed adenomas(75.0%, 93.3%, 88.1%, 81.8% and 90.3%) were relatively high.CONCLUSION: HRME has a relatively high diagnostic value in the pathological classification of colon polyps. Thus, it may be an alternative to confocal microendoscopy in lower-resource or community-based settings.
文摘Dear Editor, In the central nervous system, cell migration and accumulation occur widely to construct a complex neural network during mammalian development [1]. Previous studies reported that both the gaseous messenger molecule nitric oxide （NO） [2-4] and widely studied axon guidance/migration cues such as Netrins [5, 6] regulated the migratory behavior of cells in the nervous system.
基金the Natural Science Foundation of Xinjiang Province(2018D01C074)the National Natural Science Foundation of China(11861064,11771343,61563050)。
文摘In this article,we study optimal reinsurance design.By employing the increasing convex functions as the admissible ceded loss functions and the distortion premium principle,we study and obtain the optimal reinsurance treaty by minimizing the VaR(value at risk)of the reinsurer's total risk exposure.When the distortion premium principle is specified to be the expectation premium principle,we also obtain the optimal reinsurance treaty by minimizing the CTE(conditional tail expectation)of the reinsurer's total risk exposure.The present study can be considered as a complement of that of Cai et al.[5].
基金supported by U. S. National Institute of Health Grants R01 HL124122, AR067766American Heart Association Grant 12SDG12070174supported by the National Natural Science Foundation of China (Grant No. 81401155)
文摘Multidrug resistance(MDR) remains a major clinical obstacle to successful cancer treatment.Although diverse mechanisms of MDR have been well elucidated, such as dysregulation of drugs transporters, defects of apoptosis and autophagy machinery, alterations of drug metabolism and drug targets, disrupti on of redox homeostasis, the exact mechanisms of MDR in a specific cancer patient and the cross-talk among these different mechanisms and how they are regulated are poorly understood.Micro RNAs(mi RNAs) are a new class of small noncoding RNAs that could control the global activity of the cell by post-transcriptionally regulating a large variety of target genes and proteins expression.Accumulating evidence shows that mi RNAs play a key regulatory role in MDR through modulating various drug resistant mechanisms mentioned above, thereby holding much promise for developing novel and more effective individualized therapies for cancer treatment. This review summarizes the various MDR mechanisms and mainly focuses on the role of mi RNAs in regulating MDR in cancer treatment.
基金This work was supported in part by the ＂Reproductive health and major birth defects prevention and control research＂ Key Special Fund （No. 2016YFC1000601）, the National Natural Science Foundation of China （Grant Nos. 31371521, 81370766, 81401254, 81570101, 81671121, 31601187, 81521002）, the Guangdong Province Science and Technology Project （2014TQ01R683, 2017A020 214005, 2016A020216023, 2015A030310119, 2016B030229008）, the Bureau of Science and Technology of Guangzhou Municipality （201505011111498）, the ＂Reproductive health and major birth defects prevention and control research＂ Key Special Fund （Nos. 2016YFC1000201 and 2016YFC1000302）, the Ministry of Science and Technology of China Grants （973 program 2014CB943203）, and the Beijing Nova Program （xxjh2015011）.
文摘Mitochondrial diseases are maternally inherited hetero- geneous disorders that are primarily caused by mitochondrial DNA （mtDNA） mutations. Depending on the ratio of mutant to wild-type mtDNA, known as heteroplasmy, mitochondrial defects can result in a wide spectrum of clinical manifestations. Mitochondria-targeted endonucleases provide an alternative avenue for treating mitochondrial disorders via targeted destruc- tion of the mutant mtDNA and induction of heteroplasmic shifting. Here, we generated mitochondrial disease patient-specific induced pluripotent stem cells （MiPSCs） that harbored a high proportion of m.3243A〉G mtDNA mutations and caused mitochondrial encephalomyopathy and stroke-like episodes （MELAS）. We engineered mitochondrial-targeted transcription activator-like effector nucleases （mitoTALENs） and successfully eliminated the m.3243A〉G mutation in MiPSCs. Off-target mutagenesis was not detected in the targeted MiPSC clones. Utilizing a dual fluorescence iPSC reporter cell line expressing a 3243G mutant mtDNA sequence in the nuclear genome, mitoTALENs displayed a significantly limited ability to target the nuclear genome compared with nuclear-localized TALENs. Moreover, genetically rescued MiPSCs displayed normal mitochondrial respiration and energy production. Moreover, neuronal progenitor cells differentiated from the rescued MiPSCs also demonstrated normal metabolic profiles. Further- more, we successfully achieved reduction in the human m.3243A〉G mtDNA mutation in porcine oocytes via injection of mitoTALEN mRNA. Our study shows the great potential for using mitoTALENs for specific targeting of mutant mtDNA both in iPSCs and mammalian oocytes, which not only provides a new avenue for studying mitochondrial biology and disease but also suggests a potential therapeutic approach for the treatment of mitochondrial disease, as well as the prevention of germline transmission of mutant mtDNA.
基金supported by the National Natural Science Foundation of China(82171620 and 81830043)the National Key R&D Program of China(2021YFC2701403 and 2018YFC2002201)the National High Level Hospital Clinical Research Funding(2022-PUMCH-A-205 and 2022-PUMCH-A-114)。
基金financially supported by the National Basic Research Program of China(2015CB932103)the National Natural Science Foundation of China(31771092,81521005,81690265)the Youth Innovation Promotion Association of Chinese Academy of Sciences and Fudan-SIMM Joint Research Fund(FU-SIMM20182005,China).
文摘The limited penetration of nanoparticles and their poor accessibility to cancer cell fractions in tumor remain essential challenges for effective anticancer therapy.Herein,we designed a targeting peptide-decorated biomimetic lipoprotein(termed as BL-RD)to enable their deep penetration and efficient accessibility to cancer cell fractions in a tumor,thereby improving the combinational chemophotodynamic therapy of triple negative breast cancer.BL-RD was composed of phospholipids,apolipoprotein A1 mimetic peptide(PK22),targeting peptide-conjugated cytotoxic mertansine(RM)and photodynamic agents of DiIC18(5)(DiD).The counterpart biomimetic lipoprotein system without RM(termed as BL-D)was fabricated as control.Both BL-D and BL-RD were nanometer-sized particles with a mean diameter of less than 30 nm and could be efficiently internalized by cancer cells.After intravenous injection,they can be specifically accumulated at tumor sites.When comparing to the counterpart BLD,BL-RD displayed superior capability to permeate across the tumor mass,extravasate from tumor vasculature to distant regions and efficiently access the cancer cell fractions in a solid tumor,thus producing noticeable depression of the tumor growth.Taken together,BL-RD can be a promising delivery nanoplatform with prominent tumor-penetrating and cancer cells-accessing capability for effective tumor therapy.
文摘Dear Editor, Polycystic ovary syn drome (PCOS) is a comm on female reproductive endocrinopathy that afflicts up to 10%-15% of women in reproductive age worldwide (Nestler, 2016). Women with PCOS exhibit hyperandrogenism, intermittent/ absent menstrual cycles, and polycystic ovaries on ultrasound (Rotterdam, 2004). The pathophysiology of PCOS extends beyond infertility and hirsutism to hypothalamic neuroendocrine dysfunotion (Goodarzi et al., 2011). Most wome n with PCOS exhibit in creased luteinizing horm one (LH) levels, resulting from high-frequency gonadotropin-releasing hormone (GnRH) secretion (Cimino et al., 2016). Pren ata I testostero ne (T) treatment in sheep results in disrupted steroid feedback on gonadotropin release, which in creases pituitary sen sitivity to GnRH and subseque ntly leads to LH hypersecretion (Sullivan and Moenter, 2004;Cardoso et al., 2016). A recent study shows that GnRHdependent LH pulsatility and secretion are elevated by anti- Mullerian hormone (AMH) in PCOS disease. The increased prenatal AMH reprograms fetus and induces PCOS in adults (Tata et al., 2018). Furthermore, the androgen receptor (AR) plays a role in hyperandrogenism and ovarian folliculoge esis in PCOS (Wang et al., 2015;Abbott, 2017). However, the disease mechanism behind PCOS remains unclear, and current management focuses on treating the symptoms but not the mechanism (Chen et al., 2016;Shi et al., 2018). A further understanding of this disease is necessary to uncover the pathology of PCOS and develop new potential therapeutic avenues and drugs.
