Self-supervised pretraining improves the performance of classification of task functional magnetic resonance imaging

IntroductionDecoding brain activities is one of the most popular topics in neuroscience in recent years. And deep learning has shown high performance in fMRI data classification and regression, but its requirement for large amounts of data conflicts with the high cost of acquiring fMRI data.MethodsIn this study, we propose an end-to-end temporal contrastive self-supervised learning algorithm, which learns internal spatiotemporal patterns within fMRI and allows the model to transfer learning to datasets of small size. For a given fMRI signal, we segmented it into three sections: the beginning, middle, and end. We then utilized contrastive learning by taking the end-middle (i.e., neighboring) pair as the positive pair, and the beginning-end (i.e., distant) pair as the negative pair.ResultsWe pretrained the model on 5 out of 7 tasks from the Human Connectome Project (HCP) and applied it in a downstream classification of the remaining two tasks. The pretrained model converged on data from 12 subjects, while a randomly initialized model required 100 subjects. We then transferred the pretrained model to a dataset containing unpreprocessed whole-brain fMRI from 30 participants, achieving an accuracy of 80.2 ± 4.7%, while the randomly initialized model failed to converge. We further validated the model’s performance on the Multiple Domain Task Dataset (MDTB), which contains fMRI data of 26 tasks from 24 participants. Thirteen tasks of fMRI were selected as inputs, and the results showed that the pre-trained model succeeded in classifying 11 of the 13 tasks. When using the 7 brain networks as input, variations of the performance were observed, with the visual network performed as well as whole brain inputs, while the limbic network almost failed in all 13 tasks.DiscussionOur results demonstrated the potential of self-supervised learning for fMRI analysis with small datasets and unpreprocessed data, and for analysis of the correlation between regional fMRI activity and cognitive tasks

Imaging neuropeptide release at synapses with a genetically engineered reporter

Research on neuropeptide function has advanced rapidly, yet there is still no spatio-temporally resolved method to measure the release of neuropeptides in vivo. Here we introduce Neuropeptide Release Reporters (NPRRs): novel genetically-encoded sensors with high temporal resolution and genetic specificity. Using the Drosophila larval neuromuscular junction (NMJ) as a model, we provide evidence that NPRRs recapitulate the trafficking and packaging of native neuropeptides, and report stimulation-evoked neuropeptide release events as real-time changes in fluorescence intensity, with sub-second temporal resolution

The Standardization and Harmonization of Land Cover Classification Systems towards Harmonized Datasets: A Review

A number of national, regional and global land cover classification systems have been developed to meet specific user requirements for land cover mapping exercises, independent of scale, nomenclature and quality. However, this variety of land-cover classification systems limits the compatibility and comparability of land cover data. Furthermore, the current lack of interoperability between different land cover datasets, often stemming from incompatible land cover classification systems, makes analysis of multi-source, heterogeneous land cover data for various applications a very difficult task. This paper provides a critical review of the harmonization of land cover classification systems, which facilitates the generation, use and analysis of land cover maps consistently. Harmonization of existing land cover classification systems is essential to improve their cross-comparison and validation for understanding landscape patterns and changes. The paper reviews major land cover classification standards according to different scales, summarizes studies on harmonizing land cover mapping, and discusses some research problems that need to be solved and some future research directions

A hierarchical spatial unit partitioning approach for fine-grained urban functional region identification

Urban functional regions (UFRs) are formed and developed with human social actions and can reflect urban land use types. Appropriately identifying UFRs helps solve existing urban problems, optimize the spatial structure of cities, and provide a database for sustainable urban development. Most existing studies focus on developing novel methods and fusing multiple data sources, but neglect the impact of heterogeneous spatial units on UFR identification results. In this work, a hierarchical spatial unit partition approach was proposed to split the research area into many hierarchical units whilst considering the mixed degree of each unit. We further explored its capacity for identifying UFRs by integrating it with three widely used UFR identification methods. The results reveal that our proposed approach can correctly identify 10% more UFRs compared with the traditional grid-based method, showing the efficiency of our proposed approach for identifying UFRs at a finer scale. This work provides support for those who are engaged in urban planning and urban policymaking, promoting urban sustainable development

Li₇La₃Zr₂O₁₂-co-LiNbO₃ Surface Modification Improves the Interface Stability between Cathode and Sulfide Solid-State Electrolyte in All-Solid-State Batteries

With the rapid development of energy storage and electric vehicles, thiophosphate-based all-solid-state batteries (ASSBs) are considered the most promising power source. In order to commercialize ASSBs, the interfacial problem between high-voltage cathode active materials and thiophosphate-based solid-state electrolytes needs to be solved in a simple, effective way. Surface coatings are considered the most promising approach to solving the interfacial problem because surface coatings could prevent direct physical contact between cathode active materials and thiophosphate-based solid-state electrolytes. In this work, Li7La3Zr2O12 (LLZO) and LiNbO3 (LNO) coatings for LiCoO2 (LCO) were fabricated by in-situ interfacial growth of two high-Li+ conductive oxide electrolytes on the LCO surface and tested for thiophosphate-based ASSBs. The coatings were obtained from a two-step traditional sol–gel coatings process, the inner coatings were LNO, and the surface coatings were LLZO. Electrochemical evaluations confirmed that the two-layer coatings are beneficial for ASSBs. ASSBs containing LLZO-co-LNO coatings LiCoO2 (LLZO&LNO@LCO) significantly improved long-term cycling performance and discharge capacity compared with those assembled from uncoated LCO. LLZO&LNO@LCO||Li6PS5Cl (LPSC)||Li-In delivered discharge capacities of 138.8 mAh/g, 101.8 mAh/g, 60.2 mAh/g, and 40.2 mAh/g at 0.05 C, 0.1 C, 0.2 C, and 0.5 C under room temperature, respectively, and better capacity retentions of 98% after 300 cycles at 0.05 C. The results highlighted promising low-cost and scalable cathode material coatings for ASSBs

A GIS-Based Web Approach for Serving Land Price Information

Participants in the land market are usually hampered to browse and analyze the land price information due to the lack of information sources and available analysis tools. A service-oriented GIS-based web system was developed to provide a practical solution, its essential data sources contain basic geographic elements and benchmark land price (BLP)-related information. Core models for land price analysis were implemented, including land price index, spatial distribution, and parcel appraisal. The system was developed based on a four-level Browse Server (B/S) architecture using GIS and web service technologies, which enables the publishing, browsing, and analysis of the land price information via the Internet. With effective functionalities, the system has been employed in a project for updating BLP in a case study city located in China. The main advantage of the GIS-based web approach lies in its integration of spatial-temporal analysis models and web GIS technology, which allows more investors and administrators with limited domain knowledge to obtain further understanding on the change pattern and spatial distribution of land price by an online means. The experience in the case study city demonstrates that the approach has strong practicality for land price information services

Context-Aware Matrix Factorization for the Identification of Urban Functional Regions with POI and Taxi OD Data

The identification of urban functional regions (UFRs) is important for urban planning and sustainable development. Because this involves a set of interrelated processes, it is difficult to identify UFRs using only single data sources. Data fusion methods have the potential to improve the identification accuracy. However, the use of existing fusion methods remains challenging when mining shared semantic information among multiple data sources. In order to address this issue, we propose a context-coupling matrix factorization (CCMF) method which considers contextual relationships. This method was designed based on the fact that the contextual relationships embedded in all of the data are shared and complementary to one another. An empirical study was carried out by fusing point-of-interest (POI) data and taxi origin–destination (OD) data in Beijing, China. There are three steps in CCMF. First, contextual information is extracted from POI and taxi OD trajectory data. Second, fusion is performed using contextual information. Finally, spectral clustering is used to identify the functional regions. The results show that the proposed method achieved an overall accuracy (OA) of 90% and a kappa of 0.88 in the study area. The results were compared with the results obtained using single sources of non-fused data and other fusion methods in order to validate the effectiveness of our method. The results demonstrate that an improvement in the OA of about 5% in comparison to a similar method in the literature could be achieved using this method