Reversible Constrained Dissociation and Reassembly of MXene Films.

Enabling materials to undergo reversible dynamic transformations akin to the behaviors of living organisms represents a critical challenge in the field of material assembly. The pursuit of such capabilities using conventional materials has largely been met with limited success. Herein, the discovery of reversible constrained dissociation and reconfiguration in MXene films, offering an effective solution to overcome this obstacle is reported. Specifically, MXene films permit rapid intercalation of water molecules between their distinctive layers, resulting in a significant expansion and exhibiting confined dissociation within constrained spaces. Meanwhile, the process of capillary compression driven by water evaporation reinstates the dissociated MXene film to its original compact state. Further, the adhesive properties emerging from the confined disassociation of MXene films can spontaneously induce fusion between separate films. Utilizing this attribute, complex structures of MXene films can be effortlessly foamed and interlayer porosity precisely controlled, using only water as the inducer. Additionally, a parallel phenomenon has been identified in graphene oxide films. This work not only provides fresh insights into the microscopic mechanisms of 2D materials such as MXene but also paves a transformative path for their macroscopic assembly applications in the future.

Characteristics and variability of functional brain networks.

Functional brain networks were constructed from functional magnetic resonance imaging (fMRI) data originating from 96 healthy adults. These networks possessed a total of 360 nodes, derived from the latest multi-modal brain parcellation method. A novel group network (overlay network) analysis model is proposed to study common attributes as well as differences found in the human brain by analysis of the functional brain network. Currently, the mean network is generally used to represent the group network. But mean networks have a modularity problem making them distinct from real networks. The overlay network is constructed by calculating the connections between the whole brain network regions, and then filtering the connections by limiting the threshold value. We find that the overlay network is closer to the real network condition of the group in terms of network characteristics related to modularity. Multiple network features are applied to investigate the discrepancies between the new group network and the mean network. Individual divergences between brain regions of everyone are also explored. Results show that the brain network of different people has a high consistency in the global measures, while there exist great differences for local measures in brain regions. Some brain regions show variability over other brain regions on most measures. In addition, we explored the impact of different thresholds on the overlay network and find that different thresholds have a greater impact on the clustering coefficient, maximized modularity, strength, and global efficiency.

A novel joint HCPMMP method for automatically classifying Alzheimer's and different stage MCI patients.

A 360-area surface-based cortical parcellation was recently generated using multimodal data in a group average of 210 healthy young adults from the Human Connectome Project (HCP). In order to automatically and accurately identify mild cognitive impairment (MCI) at its two levels (early MCI and late MCI), Alzheimer's disease (AD) and healthy control (HC), a novel joint HCP MMP method was first proposed to delineate the cortical architecture and function connectivity in a group of non healthy adults. The proposed method was applied to register a dataset of 96 resting-state functional connectomes from the Alzheimer's Disease Neuroimaging Initiative (ADNI) to Connectivity Informatics Technology Initiative (CIFTI) space and parcellated brain into human connectome project multi-modal parcellation (HCPMMP) with 360 areas. Various network features in each node of the connectivity network were considered as the candidate features for classification.The fine-grained multi-modal based on HCP-MMP combined with machine learning in identification for EMCI, LMCI, AD and HC. Applying various network features, including strength, betweenness centrality, clustering coefficient, local efficiency, eigenvector centrality, etc, we trained and tested several machine learning models. Thousands of features were processed by filter and wrapper feature selection procedures, and finally there were thirty features to be selected to achieve classification accuracies of 93.8% for EMCI vs. HC, 95.8% for LMCI vs. HC, 95.8% for AD vs. HC, and 91.7% for LMCI vs. AD, respectively by using support vector machine (SVM) algorithm. Most of the selected features locate in the region of temporal or cingulate cortex. Compared with previous studies, our results demonstrate the superiority of the proposed method over existing techniques.