[Objective] The aim was to study the relevance of grassland temperature and ground net radiation in Guilin.[Method] By dint of ground observation data and net radiation of national benchmark climate station in Guilin ...[Objective] The aim was to study the relevance of grassland temperature and ground net radiation in Guilin.[Method] By dint of ground observation data and net radiation of national benchmark climate station in Guilin from 2007 to 2009,the changes of grassland temperature and ground net radiation were expounded and their relations were pointed out.[Result] The annual changes trends of grassland temperature and ground net radiation in Guilin were basically the same.Monthly average maximum value all appeared in summer(July to August).Monthly average lowest value appeared in winter(December to next January);monthly average grassland temperature and monthly average ground net radiation had positive relation.Grassland temperature and ground net radiation had basically same distribution in four seasons.The average largest value of ground net radiation in summer was the largest and average smallest value was the smallest;the average largest value of ground net radiation in cloudy days was the smallest and average minimum value was the largest;the daily difference was the largest in sunny day and daily difference was the smallest in cloudy day.The daily changes trend of grassland temperature and ground net radiation in different weather state were basically the same;when it was sunny or cloudy,the daily largest value of grassland temperature and ground net radiation occurred between 15:00 and 19:00;when it was overcast,there was no distinct peak and daily changes.The largest value of the daily extreme value of grassland temperature and ground net radiation took place from 12:00 to 15:00.The daily lowest value took place from 20:00 to 07:00 on the next day.[Conclusion] The study provided reference for the analysis of temperature changes in Guilin.展开更多
In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyze...In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyzed by using the quantitative analysis of grey relation degree by using the grey system theory.The relevancy degree among the primary industry,the secondary industry and the tertiary industry and living energy consumption are obtained,and then the trend of energy consumption in the following several years can be predicted.The results show that the secondary industry has the largest relevancy degree to the total energy consumption.In the end,according to the results of the research,several suggestions on how to saving energy are put forward.Firstly,the government should improve the high-tech industry and restrict the development of high-consumption and high-pollution industries.Secondly,the government should promote the low-carbon way of life;promote energy saving and control the energy consumption of the department of life.Thirdly,clean production should be actively promoted in the tertiary industry and the circular economy should be vigorously expanded.展开更多
Objective To explore the relevant risk factors of tramatic arthritis resulting from the surgery of acetabular fractures. Methods From January 2000 to January 2009,88 patients aged from 20 to 60 years old with acetabul...Objective To explore the relevant risk factors of tramatic arthritis resulting from the surgery of acetabular fractures. Methods From January 2000 to January 2009,88 patients aged from 20 to 60 years old with acetabular fractures展开更多
·AIM: To explore the correlation between the retinal nerve fiber layer (RNFL) thickness by using optical coherence tomography (OCT) and by histological measurements in normal adult rats and optic nerve transected...·AIM: To explore the correlation between the retinal nerve fiber layer (RNFL) thickness by using optical coherence tomography (OCT) and by histological measurements in normal adult rats and optic nerve transected rats. · METHODS: The RNFL thickness of 36 rats was scanned in a circle 3.46mm far from the optic disc by OCT. The two experimental groups were the normal group ( =20 rats) and the optic nerve transected group ( =16 rats). The latter group included 4 groups ( =4 /group) surviving for 1 day, 3, 5 and 7 days. Then the RNFL thickness of the same retina area was also measured by NF -200 immunohistochemical staining method. Linear regression was used to analyze the correlation between the data obtained from these two methods. ·RESULTS: The RNFL thickness of normal right eyes around optic disc by OCT was 72.35 ±5.71μm and that of the left eyes was 72.65 ±5.88μm ( =0.074). The RNFL thickness of the corresponding histological section by immunohistochemistry was 37.54 ±4.05μm (right eyes) and 37.38 ±4.23μm (left eyes) ( =0.059). There was a good correlation between the RNFL thickness measured by OCT and that measured by histology (R 2 =0.8131). After optic nerve transection, the trend of the RNFL thickness was thinner with the prolonged survival time. The correlation of the thickness detected by the above two methods was approximately (R 2 =0.8265). Value of the RNFL thickness in rats around optic disc measured by OCT was obviously higher than that measured by common histological measurement in normal adult rats and optic nerve transected rats. ·CONCLUSION: The RNFL thickness measured by OCT has a strong correlation with that measured by histological method. Through OCT scanning, we found that the thickness of RNFL gradually becomes thinner in a time-dependent manner.展开更多
Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer ...Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.展开更多
文摘[Objective] The aim was to study the relevance of grassland temperature and ground net radiation in Guilin.[Method] By dint of ground observation data and net radiation of national benchmark climate station in Guilin from 2007 to 2009,the changes of grassland temperature and ground net radiation were expounded and their relations were pointed out.[Result] The annual changes trends of grassland temperature and ground net radiation in Guilin were basically the same.Monthly average maximum value all appeared in summer(July to August).Monthly average lowest value appeared in winter(December to next January);monthly average grassland temperature and monthly average ground net radiation had positive relation.Grassland temperature and ground net radiation had basically same distribution in four seasons.The average largest value of ground net radiation in summer was the largest and average smallest value was the smallest;the average largest value of ground net radiation in cloudy days was the smallest and average minimum value was the largest;the daily difference was the largest in sunny day and daily difference was the smallest in cloudy day.The daily changes trend of grassland temperature and ground net radiation in different weather state were basically the same;when it was sunny or cloudy,the daily largest value of grassland temperature and ground net radiation occurred between 15:00 and 19:00;when it was overcast,there was no distinct peak and daily changes.The largest value of the daily extreme value of grassland temperature and ground net radiation took place from 12:00 to 15:00.The daily lowest value took place from 20:00 to 07:00 on the next day.[Conclusion] The study provided reference for the analysis of temperature changes in Guilin.
基金Supported by Qinghai Provincial Department of Land and Resources
文摘In view of the complexity and non-linearity of energy consumption system and the status quo of the development of energy in Qinghai Province,the relations between energy consumption and industrial structure is analyzed by using the quantitative analysis of grey relation degree by using the grey system theory.The relevancy degree among the primary industry,the secondary industry and the tertiary industry and living energy consumption are obtained,and then the trend of energy consumption in the following several years can be predicted.The results show that the secondary industry has the largest relevancy degree to the total energy consumption.In the end,according to the results of the research,several suggestions on how to saving energy are put forward.Firstly,the government should improve the high-tech industry and restrict the development of high-consumption and high-pollution industries.Secondly,the government should promote the low-carbon way of life;promote energy saving and control the energy consumption of the department of life.Thirdly,clean production should be actively promoted in the tertiary industry and the circular economy should be vigorously expanded.
文摘Objective To explore the relevant risk factors of tramatic arthritis resulting from the surgery of acetabular fractures. Methods From January 2000 to January 2009,88 patients aged from 20 to 60 years old with acetabular fractures
基金National Natural Science Foundation of China (No.81070729,No.81100663)Doctoral Foundation of Ministry of Education of China (No.20100162110067)+1 种基金Natural Science Foundation of Hunan Province (No.11JJ2020)Young Teachers Training Program of University of Hunan Province
文摘·AIM: To explore the correlation between the retinal nerve fiber layer (RNFL) thickness by using optical coherence tomography (OCT) and by histological measurements in normal adult rats and optic nerve transected rats. · METHODS: The RNFL thickness of 36 rats was scanned in a circle 3.46mm far from the optic disc by OCT. The two experimental groups were the normal group ( =20 rats) and the optic nerve transected group ( =16 rats). The latter group included 4 groups ( =4 /group) surviving for 1 day, 3, 5 and 7 days. Then the RNFL thickness of the same retina area was also measured by NF -200 immunohistochemical staining method. Linear regression was used to analyze the correlation between the data obtained from these two methods. ·RESULTS: The RNFL thickness of normal right eyes around optic disc by OCT was 72.35 ±5.71μm and that of the left eyes was 72.65 ±5.88μm ( =0.074). The RNFL thickness of the corresponding histological section by immunohistochemistry was 37.54 ±4.05μm (right eyes) and 37.38 ±4.23μm (left eyes) ( =0.059). There was a good correlation between the RNFL thickness measured by OCT and that measured by histology (R 2 =0.8131). After optic nerve transection, the trend of the RNFL thickness was thinner with the prolonged survival time. The correlation of the thickness detected by the above two methods was approximately (R 2 =0.8265). Value of the RNFL thickness in rats around optic disc measured by OCT was obviously higher than that measured by common histological measurement in normal adult rats and optic nerve transected rats. ·CONCLUSION: The RNFL thickness measured by OCT has a strong correlation with that measured by histological method. Through OCT scanning, we found that the thickness of RNFL gradually becomes thinner in a time-dependent manner.
基金supported by grants from the National Natural Science Foundation of P.R.China(62276081 and 62106113)Guangdong Basic and Applied Basic Research Foundation(2023A1515010792 and 2023B1515120065)Shenzhen Science and Technology Program(GXWD20231129121139001 and JCYJ20240813110522029).
文摘Importance:Precisely decoding brain dysfunction from high-dimensional functional recordings is crucial for advancing our understanding of brain dysfunction in brain disorders.Self-supervised learning(SSL)models offer a transformative approach for mapping dependencies in functional neuroimaging data.Leveraging the intrinsic organization of brain signals for comprehensive feature extraction,these models enable the analysis of critical neurofunctional features within a clinically relevant framework,overcoming challenges related to data heterogeneity and the scarcity of labeled data.Highlight:This paper provides a comprehensive overview of SSL techniques applied to functional neuroimaging data,such as functional magnetic resonance imaging and electroencephalography,with a specific focus on their applications in various neuropsychiatric disorders.We discuss 3 main categories of SSL methods:contrastive learning,generative learning,and generative-contrastive methods,outlining their basic principles and representative methods.Critically,we highlight the potential of SSL in addressing data scarcity,multimodal integration,and dynamic network modeling for disease detection and prediction.We showcase successful applications of these techniques in understanding and classifying conditions such as Alzheimer’s disease,Parkinson’s disease,and epilepsy,demonstrating their potential in downstream neuropsychological applications.Conclusion:SSL models provide a scalable and effective methodology for individual detection and prediction in brain disorders.Despite current limitations in interpretability and data heterogeneity,the potential of SSL for future clinical applications,particularly in the areas of transdiagnostic psychosis subtyping and decoding task-based brain functional recordings,is substantial.
