BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influ...BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influence and respond to a variety of behavioral and mental conditions.AIM To explore the correlation between cognitive impairment and gut microbiota imbalance in patients with schizophrenia.METHODS A total of 498 untreated patients with schizophrenia admitted to our hospital from July 2020 to July 2022 were selected as the case group,while 498 healthy volunteers who underwent physical examinations at our hospital during the same period were selected as a control group.Fluorescence in situ hybridization was employed to determine the total number of bacteria in the feces of the two groups.The cognitive function test package was used to assess the score of cognitive function in each dimension.Then,the relationship between gut microbiota and cognitive function was analyzed.RESULTS There were statistically significant differences in the relative abundance of gut microbiota at both phylum and class levels between the case group and the control group.In addition,the scores of cognitive function,such as attention/alertness and learning ability,were significantly lower in the case group than in the control group(all P<0.05).The cognitive function was positively correlated with Actinomycetota,Bacteroidota,Euryarchaeota,Fusobacteria,Pseudomonadota,and Saccharibacteria,while negatively correlated with Bacillota,Tenericutes,and Verrucomicrobia at the phylum level.While at the class level,the cognitive function was positively correlated with Class Actinobacteria,Bacteroidia,Betaproteobacteria,Proteobacteria,Blastomycetes,and Gammaproteobacteria,while negatively correlated with Bacilli,Clostridia,Coriobacteriia,and Verrucomicrobiae.CONCLUSION There is a relationship between the metabolic results of gut microbiota and cognitive function in patients with schizophrenia.When imbalances occur in the gut microbiota of patients,it leads to more severe cognitive impairment.展开更多
Based on annual pork yield of each province in 1996-2014,using Gini coefficient and spatial polarization index model,empirical analysis was carried out for spatial imbalance and polarization degree of swine production...Based on annual pork yield of each province in 1996-2014,using Gini coefficient and spatial polarization index model,empirical analysis was carried out for spatial imbalance and polarization degree of swine production distribution. Results indicate that there is high imbalance in spatial distribution of swine production. The imbalance takes on first rise then declines with fluctuation,largely because of inter-group gap. From the perspective of regions,the regions with balanced grain production have the highest imbalance in swine production,followed by main sales regions,and the main grain production regions have the lowest imbalance. The polarization index indicates that the overall polarization degree of the spatial distribution of swine production takes on a growing trend,and that of each functional region of grain production is also growing.展开更多
With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the ba...With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction.However,the fraud cases have also increased causing the loss of money to the consumers.Hence,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time.Generally,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem.In this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets.Also,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.展开更多
文摘BACKGROUND The gut microbiome interacts with the central nervous system through the gutbrain axis,and this interaction involves neuronal,endocrine,and immune mechanisms,among others,which allow the microbiota to influence and respond to a variety of behavioral and mental conditions.AIM To explore the correlation between cognitive impairment and gut microbiota imbalance in patients with schizophrenia.METHODS A total of 498 untreated patients with schizophrenia admitted to our hospital from July 2020 to July 2022 were selected as the case group,while 498 healthy volunteers who underwent physical examinations at our hospital during the same period were selected as a control group.Fluorescence in situ hybridization was employed to determine the total number of bacteria in the feces of the two groups.The cognitive function test package was used to assess the score of cognitive function in each dimension.Then,the relationship between gut microbiota and cognitive function was analyzed.RESULTS There were statistically significant differences in the relative abundance of gut microbiota at both phylum and class levels between the case group and the control group.In addition,the scores of cognitive function,such as attention/alertness and learning ability,were significantly lower in the case group than in the control group(all P<0.05).The cognitive function was positively correlated with Actinomycetota,Bacteroidota,Euryarchaeota,Fusobacteria,Pseudomonadota,and Saccharibacteria,while negatively correlated with Bacillota,Tenericutes,and Verrucomicrobia at the phylum level.While at the class level,the cognitive function was positively correlated with Class Actinobacteria,Bacteroidia,Betaproteobacteria,Proteobacteria,Blastomycetes,and Gammaproteobacteria,while negatively correlated with Bacilli,Clostridia,Coriobacteriia,and Verrucomicrobiae.CONCLUSION There is a relationship between the metabolic results of gut microbiota and cognitive function in patients with schizophrenia.When imbalances occur in the gut microbiota of patients,it leads to more severe cognitive impairment.
基金Supported by Key Project of National Social Science Foundation(12&ZD048)
文摘Based on annual pork yield of each province in 1996-2014,using Gini coefficient and spatial polarization index model,empirical analysis was carried out for spatial imbalance and polarization degree of swine production distribution. Results indicate that there is high imbalance in spatial distribution of swine production. The imbalance takes on first rise then declines with fluctuation,largely because of inter-group gap. From the perspective of regions,the regions with balanced grain production have the highest imbalance in swine production,followed by main sales regions,and the main grain production regions have the lowest imbalance. The polarization index indicates that the overall polarization degree of the spatial distribution of swine production takes on a growing trend,and that of each functional region of grain production is also growing.
基金This research was supported by Korea Institute for Advancement of Technology(KIAT)grant funded by the Korea Government(MOTIE)(P0012724,The Competency Development Program for Industry Specialist)and the Soonchunhyang University Research Fund.
文摘With the rise of internet facilities,a greater number of people have started doing online transactions at an exponential rate in recent years as the online transaction system has eliminated the need of going to the bank physically for every transaction.However,the fraud cases have also increased causing the loss of money to the consumers.Hence,an effective fraud detection system is the need of the hour which can detect fraudulent transactions automatically in real-time.Generally,the genuine transactions are large in number than the fraudulent transactions which leads to the class imbalance problem.In this research work,an online transaction fraud detection system using deep learning has been proposed which can handle class imbalance problem by applying algorithm-level methods which modify the learning of the model to focus more on the minority class i.e.,fraud transactions.A novel loss function named Weighted Hard-Reduced Focal Loss(WH-RFL)has been proposed which has achieved maximum fraud detection rate i.e.,True PositiveRate(TPR)at the cost of misclassification of few genuine transactions as high TPR is preferred over a high True Negative Rate(TNR)in fraud detection system and same has been demonstrated using three publicly available imbalanced transactional datasets.Also,Thresholding has been applied to optimize the decision threshold using cross-validation to detect maximum number of frauds and it has been demonstrated by the experimental results that the selection of the right thresholding method with deep learning yields better results.
