Owing to the extreme rainfall and evaporation events under the changing climate,coastal zones are experiencing salinity fluctuations that stress aquatic organisms.However,the biological consequences of ongoing alterat...Owing to the extreme rainfall and evaporation events under the changing climate,coastal zones are experiencing salinity fluctuations that stress aquatic organisms.However,the biological consequences of ongoing alteration in salinity levels on euryhaline organisms remain inconclusive.Herein,we sought to uncover how variation in salinity level adversely alters the bacterioplankton community,the gut microbiota of euryhaline shrimp Penaeus monodon,and subsequent shrimp disease risk.To mimic the extreme weather that induces abrupt changes in coastal water salinity,three salinity levels(10,20,and 30)were selected to investigate the differences in shrimp gut microbiota using bacterial 16S rRNA gene sequencing.Results shows that salinity level and days post experiment(dpe)respectively constrained 45.9%and 13.0%of the variance in the gut bacterial communities.Particularly,abnormal salinity levels accelerated temporal turnover rate,disrupted gut network stability,augmented average variation degree,and increased pathogenic potential in the gut microbiota of shrimp reared at 10 and 30 salinities compared with 20 salinity controls.These changes were accompanied with the shifts in the gut microbiota-mediated functions,especially the compromised immunity and elevated infectious diseases potentials,thereby increasing shrimp disease risk.In addition,abnormal salinity levels increased the role of homogeneous selection governing the gut microbiota.After excluding the dpe-effect,we screened 35 gut salinity-discriminatory taxa that quantitatively discriminated the salinity levels where shrimp were reared,with overall accuracy of 91.1%.Collectively,abnormal salinity levels profoundly disrupt the structure,stability,assembly,and functions of the gut microbiota,which in turn increased disease risk in shrimp.In addition,gut symbionts sensitively responded to the changes in external salinity level.These findings deepened our understanding on the biological consequence of abnormal salinity levels on shrimp health.展开更多
Two great earthquakes occurred in the sea northwest of Sumatra,Indonesia,on December 26,2004 and March 29,2005.The observation of water levels in Yunnan yielded abundant information about the two earthquakes.This pape...Two great earthquakes occurred in the sea northwest of Sumatra,Indonesia,on December 26,2004 and March 29,2005.The observation of water levels in Yunnan yielded abundant information about the two earthquakes.This paper presents the water level response to the two earthquakes in Yunnan and makes a preliminary analysis.It is observed that the large earthquake-induced abnormal water level change could be better recorded by analog recording than by digital recording.The large earthquake-caused water level rise or decline may be attributed to the effect of seismic waves that change the stress in tectonic units,and is correlated with the geological structure where the well is located.The water level response mode in a well is totally the same for earthquakes occurring on the same fault and with the same fracture mode.The only difference is that the response amplitude increases with the growth of the earthquake magnitude.展开更多
With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is vio...With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.展开更多
基金Supported by the National Natural Science Foundation of China(Nos.32371596,32071549)the Zhejiang Provincial Key Natural Science Foundation of China(No.Z25C030002)+2 种基金the Science and Technology Innovation Yongjiang 2035 Key Research and Development Project of Ningbo(No.2024Z279)the One Health Interdisciplinary Research Project(No.HZ202404)the K.C.Wong Magna Fund in Ningbo University。
文摘Owing to the extreme rainfall and evaporation events under the changing climate,coastal zones are experiencing salinity fluctuations that stress aquatic organisms.However,the biological consequences of ongoing alteration in salinity levels on euryhaline organisms remain inconclusive.Herein,we sought to uncover how variation in salinity level adversely alters the bacterioplankton community,the gut microbiota of euryhaline shrimp Penaeus monodon,and subsequent shrimp disease risk.To mimic the extreme weather that induces abrupt changes in coastal water salinity,three salinity levels(10,20,and 30)were selected to investigate the differences in shrimp gut microbiota using bacterial 16S rRNA gene sequencing.Results shows that salinity level and days post experiment(dpe)respectively constrained 45.9%and 13.0%of the variance in the gut bacterial communities.Particularly,abnormal salinity levels accelerated temporal turnover rate,disrupted gut network stability,augmented average variation degree,and increased pathogenic potential in the gut microbiota of shrimp reared at 10 and 30 salinities compared with 20 salinity controls.These changes were accompanied with the shifts in the gut microbiota-mediated functions,especially the compromised immunity and elevated infectious diseases potentials,thereby increasing shrimp disease risk.In addition,abnormal salinity levels increased the role of homogeneous selection governing the gut microbiota.After excluding the dpe-effect,we screened 35 gut salinity-discriminatory taxa that quantitatively discriminated the salinity levels where shrimp were reared,with overall accuracy of 91.1%.Collectively,abnormal salinity levels profoundly disrupt the structure,stability,assembly,and functions of the gut microbiota,which in turn increased disease risk in shrimp.In addition,gut symbionts sensitively responded to the changes in external salinity level.These findings deepened our understanding on the biological consequence of abnormal salinity levels on shrimp health.
基金sponsored by the "Personnel Training" of Yunnan Province (2006PY0139)the National Key Technology R & D Program for the 11th "Five-Year Plan"(Grant No.2006BAC01B020302),China
文摘Two great earthquakes occurred in the sea northwest of Sumatra,Indonesia,on December 26,2004 and March 29,2005.The observation of water levels in Yunnan yielded abundant information about the two earthquakes.This paper presents the water level response to the two earthquakes in Yunnan and makes a preliminary analysis.It is observed that the large earthquake-induced abnormal water level change could be better recorded by analog recording than by digital recording.The large earthquake-caused water level rise or decline may be attributed to the effect of seismic waves that change the stress in tectonic units,and is correlated with the geological structure where the well is located.The water level response mode in a well is totally the same for earthquakes occurring on the same fault and with the same fracture mode.The only difference is that the response amplitude increases with the growth of the earthquake magnitude.
基金supported by National Natural Science Foundation of China(No.52277083).
文摘With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft detection.However,the imbalance of electricity consumption data is violent,which makes the training of detection model challenging.In this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal level.In this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption data.After calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown samples.In the meanwhile,through training the network by different balanced subsets of training set,the prototype is representative.Compared with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal data.The results show that the proposed method outperforms other state-of-the-art methods.
