Endometriosis is one of the refractory and common disease in gynaecology, which the main manifestations are dysmenorrhea and infertility. Professor Cheng considers it's the deficiency-excess mixing illness whose p...Endometriosis is one of the refractory and common disease in gynaecology, which the main manifestations are dysmenorrhea and infertility. Professor Cheng considers it's the deficiency-excess mixing illness whose pathological base is blood stasis and root is the deficiency of Yin and Yang, Qi and blood. So in terms of treatment, Professor Cheng praises highly the way of strengthening the body resistance to eliminate pathogenic factors. Strengthening the body resistance and expelling evil at the same time can achieve the purpose of treatment. In the adjustment of Yin and Yang ,Qi and blood, Professor Cheng attaches importance to treating middle burner. On the treatment of dysmenorrhea, Professor Cheng is able to capture xuefu zhuyu decoction and bazhen decoction and pays attention to menstrual cycle therapy. In addition, Professor Cheng often uses jingui wenjing decoction for infertility and re-understands the decoction by meridians theory and circular motion theory. Furthermore, Professor Cheng's treatment in reserving follicle, promoting ovulation and developing follicle is distinctive by traditional Chinese medicine.展开更多
To determine the value of dissecting the recurrent laryngeal nerve during thyroid surgery with respect to preventing recurrent laryngeal nerve injury, we retrospectively analyzed clinical data from 5 344 patients unde...To determine the value of dissecting the recurrent laryngeal nerve during thyroid surgery with respect to preventing recurrent laryngeal nerve injury, we retrospectively analyzed clinical data from 5 344 patients undergoing thyroidectomy. Among these cases, 548 underwent dissection of the recurrent laryngeal nerve, while 4 796 did not. There were 12 cases of recurrent laryngeal nerve injury following recurrent laryngeal nerve dissection （injury rate of 2.2%） and 512 cases of recurrenl laryngeal nerve injury in those not undergoing nerve dissection （injury rate of 10.7%）. This difference remained statistically significant between the two groups in terms of type of thyroid disease, type of surgery, and number of surgeries. Among the 548 cases undergoing recurrent laryngeal nerve dissection, 128 developed anatomical variations of the recurrent laryngeal nerve （incidence rate of 23.4%）, but no recurrent laryngeal nerve injury was found. In addition, the incidence of recurrent laryngeal nerve injury was significantly lower in patients with the infedor parathyroid gland and middle thyroid veins used as landmarks for locating the recurrent laryngeal nerve compared with those with the entry of the recurrent laryngeal nerve into the larynx as a landmark. These findings indicate that anatomical variations of the recurrent laryngeal nerve are common, and that dissecting the recurrent laryngeal nerve during thyroid surgery is an effective means of preventing nerve injury.展开更多
The objective of this study was to evaluate the diagnostic value of 128-slice spiral CT combined with virtual colonoscopy in diagnosis of colorectal cancer.We retrospectively analyzed 45 patients of colorectal disease...The objective of this study was to evaluate the diagnostic value of 128-slice spiral CT combined with virtual colonoscopy in diagnosis of colorectal cancer.We retrospectively analyzed 45 patients of colorectal diseases who underwent definition AS+128-slice spiral CT combined with virtual colonoscopy after bowel preparation and gas injection to evaluate the clinical diagnostic value of this technology.All the patients received electronic colonoscopy and were confirmed by pathology.In total,colorectal cancer was confirmed in 42 cases and inflammation in 3 cases.Diagnostic results shows:there were 17 cases of lump,10 cases of infiltration,6 cases of ulcer,9 cases of mixed type,4 cases of liver metastases,and 36 cases of lymph node metastasis.There was no significant difference between 128-slice spiral CT combined with virtual colonoscopy and electronic colonoscopy in detection,localization and characterization of colorectal tumors.CT virtual endoscopy has great advantages in observing the invasion around the lesion and the presence or absence of metastasis in distant organs and lymph node metastasis.It is also possible to understand the shape of the lesion in the intestinal lumen and the length of the lesion involving the lumen of the intestine.展开更多
Combinatorial optimization(CO)on graphs is a classic topic that has been extensively studied across many scientific and industrial fields.Recently,solving CO problems on graphs through learning methods has attracted g...Combinatorial optimization(CO)on graphs is a classic topic that has been extensively studied across many scientific and industrial fields.Recently,solving CO problems on graphs through learning methods has attracted great attention.Advanced deep learning methods,e.g.,graph neural networks(GNNs),have been used to effectively assist the process of solving COs.However,current frameworks based on GNNs are mainly designed for certain CO problems,thereby failing to consider their transferable and generalizable abilities among different COs on graphs.Moreover,simply using original graphs to model COs only captures the direct correlations among objects,which does not consider the mathematical logicality and properties of COs.In this paper,we propose a unified pre-training and adaptation framework for COs on graphs with the help of the maximum satisfiability(Max-SAT)problem.We first use Max-SAT to bridge different COs on graphs since they can be converted to Max-SAT problems represented by standard formulas and clauses with logical information.Then we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.In the pre-training stage,Max-SAT instances are generated to initialize the parameters of the model.In the fine-tuning stage,instances from CO and Max-SAT problems are used for adaptation so that the transferable ability can be further improved.Numerical experiments on several datasets show that features extracted by our framework exhibit superior transferability and Max-SAT can boost the ability to solve COs on graphs.展开更多
With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intri...With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.展开更多
This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson’s disease(PD)reflected by fractional anisotropy(FA),addressing clinical profiles and methodology-related heterogeneit...This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson’s disease(PD)reflected by fractional anisotropy(FA),addressing clinical profiles and methodology-related heterogeneity.Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls(HC)using the anisotropic effect size–signed differential mapping.A total of 808 patients with PD and 760 HC coming from 27 databases were finally included.Subgroup analyses were conducted considering heterogeneity with respect to medication status,disease stage,analysis methods,and the number of diffusion directions in acquisition.Compared with HC,patients with PD had decreased FA in the left middle cerebellar peduncle,corpus callosum(CC),left inferior fronto-occipital fasciculus,and right inferior longitudinal fasciculus.Most of the main results remained unchanged in subgroup metaanalyses of medicated patients,early stage patients,voxel-based analysis,and acquisition with˂30 diffusion directions.The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex.The cerebellum and CC,associated with typical motor impairment,showed the most consistent FA decreases in PD.Medication status,analysis approaches,and the number of diffusion directions have an important impact on the findings,needing careful evaluation in future meta-analyses.展开更多
Dear Editor Combinatorial immunotherapy has provided patients with advanced hepatocellular carcinoma(HCC)the potential for long-term survival.However,sustained responses are seen only in a minority of patients[1].Thus...Dear Editor Combinatorial immunotherapy has provided patients with advanced hepatocellular carcinoma(HCC)the potential for long-term survival.However,sustained responses are seen only in a minority of patients[1].Thus,there is an unmet need for precision modeling to understand the different responses and uncover predictive biomarkers for treatment stratification.展开更多
基金Scientific Research Project of Guangdong bureau of traditional Chinese medicine(No.20171106).
文摘Endometriosis is one of the refractory and common disease in gynaecology, which the main manifestations are dysmenorrhea and infertility. Professor Cheng considers it's the deficiency-excess mixing illness whose pathological base is blood stasis and root is the deficiency of Yin and Yang, Qi and blood. So in terms of treatment, Professor Cheng praises highly the way of strengthening the body resistance to eliminate pathogenic factors. Strengthening the body resistance and expelling evil at the same time can achieve the purpose of treatment. In the adjustment of Yin and Yang ,Qi and blood, Professor Cheng attaches importance to treating middle burner. On the treatment of dysmenorrhea, Professor Cheng is able to capture xuefu zhuyu decoction and bazhen decoction and pays attention to menstrual cycle therapy. In addition, Professor Cheng often uses jingui wenjing decoction for infertility and re-understands the decoction by meridians theory and circular motion theory. Furthermore, Professor Cheng's treatment in reserving follicle, promoting ovulation and developing follicle is distinctive by traditional Chinese medicine.
基金supported by the National Natural Science Foundation of China, No. 81271088the Natural Science Foundation of Shanghai, No. 11ZR1423600
文摘To determine the value of dissecting the recurrent laryngeal nerve during thyroid surgery with respect to preventing recurrent laryngeal nerve injury, we retrospectively analyzed clinical data from 5 344 patients undergoing thyroidectomy. Among these cases, 548 underwent dissection of the recurrent laryngeal nerve, while 4 796 did not. There were 12 cases of recurrent laryngeal nerve injury following recurrent laryngeal nerve dissection （injury rate of 2.2%） and 512 cases of recurrenl laryngeal nerve injury in those not undergoing nerve dissection （injury rate of 10.7%）. This difference remained statistically significant between the two groups in terms of type of thyroid disease, type of surgery, and number of surgeries. Among the 548 cases undergoing recurrent laryngeal nerve dissection, 128 developed anatomical variations of the recurrent laryngeal nerve （incidence rate of 23.4%）, but no recurrent laryngeal nerve injury was found. In addition, the incidence of recurrent laryngeal nerve injury was significantly lower in patients with the infedor parathyroid gland and middle thyroid veins used as landmarks for locating the recurrent laryngeal nerve compared with those with the entry of the recurrent laryngeal nerve into the larynx as a landmark. These findings indicate that anatomical variations of the recurrent laryngeal nerve are common, and that dissecting the recurrent laryngeal nerve during thyroid surgery is an effective means of preventing nerve injury.
文摘The objective of this study was to evaluate the diagnostic value of 128-slice spiral CT combined with virtual colonoscopy in diagnosis of colorectal cancer.We retrospectively analyzed 45 patients of colorectal diseases who underwent definition AS+128-slice spiral CT combined with virtual colonoscopy after bowel preparation and gas injection to evaluate the clinical diagnostic value of this technology.All the patients received electronic colonoscopy and were confirmed by pathology.In total,colorectal cancer was confirmed in 42 cases and inflammation in 3 cases.Diagnostic results shows:there were 17 cases of lump,10 cases of infiltration,6 cases of ulcer,9 cases of mixed type,4 cases of liver metastases,and 36 cases of lymph node metastasis.There was no significant difference between 128-slice spiral CT combined with virtual colonoscopy and electronic colonoscopy in detection,localization and characterization of colorectal tumors.CT virtual endoscopy has great advantages in observing the invasion around the lesion and the presence or absence of metastasis in distant organs and lymph node metastasis.It is also possible to understand the shape of the lesion in the intestinal lumen and the length of the lesion involving the lumen of the intestine.
基金supported by National Natural Science Foundation of China(Grant Nos.11991021,11991020 and 12271503)。
文摘Combinatorial optimization(CO)on graphs is a classic topic that has been extensively studied across many scientific and industrial fields.Recently,solving CO problems on graphs through learning methods has attracted great attention.Advanced deep learning methods,e.g.,graph neural networks(GNNs),have been used to effectively assist the process of solving COs.However,current frameworks based on GNNs are mainly designed for certain CO problems,thereby failing to consider their transferable and generalizable abilities among different COs on graphs.Moreover,simply using original graphs to model COs only captures the direct correlations among objects,which does not consider the mathematical logicality and properties of COs.In this paper,we propose a unified pre-training and adaptation framework for COs on graphs with the help of the maximum satisfiability(Max-SAT)problem.We first use Max-SAT to bridge different COs on graphs since they can be converted to Max-SAT problems represented by standard formulas and clauses with logical information.Then we further design a pre-training and domain adaptation framework to extract the transferable and generalizable features so that different COs can benefit from them.In the pre-training stage,Max-SAT instances are generated to initialize the parameters of the model.In the fine-tuning stage,instances from CO and Max-SAT problems are used for adaptation so that the transferable ability can be further improved.Numerical experiments on several datasets show that features extracted by our framework exhibit superior transferability and Max-SAT can boost the ability to solve COs on graphs.
基金supported by the National Natural Science Foundation China under Grants number 62073232,and the Science and Technology Project of Shenzhen,China(KCXST20221021111402006,JSGG20220831105800002)and the“Nanling Team Project”of Shaoguan city,and the Science and Technology project of Tianjin,China(22YFYSHZ00330)+1 种基金and Shenzhen Excellent Innovative Talents RCYX20221008093036022,Shenzhen-HongKong joint funding project(A)(SGDX20230116092053005)the Shenzhen Undertaking the National Major Science and Technology Program,China(CJGJZD20220517141405012).
文摘With the rapid development of urban power grids and the large-scale integration of renewable energy, traditional power grid fault diagnosis techniques struggle to address the complexities of diagnosing faults in intricate power grid systems. Although artificial intelligence technologies offer new solutions for power grid fault diagnosis, the difficulty in acquiring labeled grid data limits the development of AI technologies in this area. In response to these challenges, this study proposes a semi-supervised learning framework with self-supervised and adaptive threshold (SAT-SSL) for fault detection and classification in power grids. Compared to other methods, our method reduces the dependence on labeling data while maintaining high recognition accuracy. First, we utilize frequency domain analysis on power grid data to filter abnormal events, then classify and label these events based on visual features, to creating a power grid dataset. Subsequently, we employ the Yule–Walker algorithm extract features from the power grid data. Then we construct a semi-supervised learning framework, incorporating self-supervised loss and dynamic threshold to enhance information extraction capabilities and adaptability across different scenarios of the model. Finally, the power grid dataset along with two benchmark datasets are used to validate the model’s functionality. The results indicate that our model achieves a low error rate across various scenarios and different amounts of labels. In power grid dataset, When retaining just 5% of the labels, the error rate is only 6.15%, which proves that this method can achieve accurate grid fault detection and classification with a limited amount of labeled data.
基金supported by the National Natural Science Foundation(Nos.81621003,81761128023,81220108013,81227002,and 81030027)the Program for Innovative Research Team in University(No.IRT16R52)of China+1 种基金the Professorship Award(No.T2014190)of Chinathe CMB Distinguished Professorship Award(No.F510000/G16916411)administered by the Institute of International Education.
文摘This study aimed to define the most consistent white matter microarchitecture pattern in Parkinson’s disease(PD)reflected by fractional anisotropy(FA),addressing clinical profiles and methodology-related heterogeneity.Web-based publication databases were searched to conduct a meta-analysis of whole-brain diffusion tensor imaging studies comparing PD with healthy controls(HC)using the anisotropic effect size–signed differential mapping.A total of 808 patients with PD and 760 HC coming from 27 databases were finally included.Subgroup analyses were conducted considering heterogeneity with respect to medication status,disease stage,analysis methods,and the number of diffusion directions in acquisition.Compared with HC,patients with PD had decreased FA in the left middle cerebellar peduncle,corpus callosum(CC),left inferior fronto-occipital fasciculus,and right inferior longitudinal fasciculus.Most of the main results remained unchanged in subgroup metaanalyses of medicated patients,early stage patients,voxel-based analysis,and acquisition with˂30 diffusion directions.The subgroup meta-analysis of medication-free patients showed FA decrease in the right olfactory cortex.The cerebellum and CC,associated with typical motor impairment,showed the most consistent FA decreases in PD.Medication status,analysis approaches,and the number of diffusion directions have an important impact on the findings,needing careful evaluation in future meta-analyses.
基金supported by the National Institutes of Health grant R01-CA-249929-06A1 to KHKsupport from the Molecular Pathology&Imaging Core(MPIC)of the UPenn Center for Molecular Studies in Digestive and Liver Diseases(P30 DK050306)the Comparative Pathology Core,the Wistar Institute Genomics Facility,the Functional Genomics Core of the UPenn Diabetes Research Center(P30 DK019525).
文摘Dear Editor Combinatorial immunotherapy has provided patients with advanced hepatocellular carcinoma(HCC)the potential for long-term survival.However,sustained responses are seen only in a minority of patients[1].Thus,there is an unmet need for precision modeling to understand the different responses and uncover predictive biomarkers for treatment stratification.
