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Resting-state functional magnetic resonance imaging (fMRI) provides an efficient way to analyze the functional connectivity between brain regions. A comprehensive understanding of brain functionality requires a unified description of multi-scale layers of neural structure. However, existing brain network modeling methods often simplify this property by averaging Blood oxygen level dependent (BOLD) signals at the brain region level for fMRI-based analysis with the assumption that BOLD signals are homogeneous within each brain region, which ignores the heterogeneity of voxels within each Region of Interest (ROI). This study introduces a novel multi-stage self-supervised learning framework for multiscale brain network analysis, which effectively delineates brain functionality from voxel to ROIs and up to sample level. A Contrastive Voxel Clustering (CVC) module is proposed to simultaneously learn the voxel-level features and clustering assignments, which ensures the retention of informative clustering features at the finest voxel-level and concurrently preserves functional connectivity characteristics. Additionally, based on the extracted features and clustering assignments at the voxel level by CVC, a Brain ROI-based Graph Neural Network (BR-GNN) is built to extract functional connectivity features at the brain ROI-level and used for sample-level prediction, which integrates the functional clustering maps with the pre-established structural ROI maps and creates a more comprehensive and effective analytical tool. Experiments are performed on two datasets, which illustrate the effectiveness and generalization ability of the proposed method by analyzing voxel-level clustering results and brain ROIs-level functional characteristics. The proposed method provides a multiscale modeling framework for brain functional connectivity analysis, which will be further used for other brain disease identification. Code is available at https://github.com/yanliugroup/fmri-cvc.
Subject(s)
Brain , Magnetic Resonance Imaging , Nerve Net , Humans , Magnetic Resonance Imaging/methods , Brain/diagnostic imaging , Brain/physiology , Cluster Analysis , Nerve Net/diagnostic imaging , Nerve Net/physiology , Image Processing, Computer-Assisted/methods , Brain Mapping/methods , Neural Networks, Computer , Connectome/methods , Models, NeurologicalABSTRACT
Understanding Li+ ion diffusion pathways in Li-rich layered transition metal (TM) oxides is crucial for understanding the sluggish kinetics in anionic O2- redox. Although Li diffusion within the alkali layers undergoes a low-barrier octahedral-tetrahedral-octahedral pathway, it is less clear how Li diffuses in and out of the TM layers, particularly given the complex structural rearrangements that take place during the oxidation of O2-. Here, we develop simultaneous electron ptychography and annular dark field imaging methods to unlock the Li migration pathways in Li1.2Ni0.13Mn0.54Co0.13O2 associated with structural changes in the charge-discharge cycle. At the end of TM oxidation and before the high-voltage O oxidation plateau, we show that the Li migrating out of the TM layers occupies the alkali-layer tetrahedral sites on opposite sides of the TM layers, forming Li-Li dumbbell configurations, consistent with the density functional theory calculations. Also occurring are the TM migration and phase transition from O3 to O1 stacking, leading to unstable tetrahedral Li and the absence of Li contrast in imaging. Upon further Li deintercalation to 4.8 V, most of the tetrahedral Li are removed. After discharging to 2 V, we did not identify the reformation of tetrahedral Li but observed permanently migrated TMs at the alkali-layer sites, disfavoring the Li occupying the tetrahedral sites for diffusion. Our findings suggest a landscape of Li diffusion pathways in Li-rich layered oxides and strategies for minimizing the disruption of Li diffusion.
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An anode material is one of the key factors affecting the capacity, cycle, and rate (fast charge) performance of lithium-ion batteries. Using the adaptive genetic algorithm, we found a new ground-state Li2CoB and two metastable states LiCoB and LiCo2B2 in the Li-Co-B system. The Li2CoB phase is a lithium-rich layered structure, and it has an equivalent lithium-ion migration barrier (0.32 eV) in addition to the lower voltage platform (0.05 V) than graphite, which is the most important commercial anode material at present. Moreover, we analyzed the mechanism of delithiation for Li2CoB and found that it maintained metallicity in the process of delithiation, indicating its good conductivity as an electrode material. Therefore, it is an excellent potential anode material for lithium-ion batteries. Our work provides a promising theoretical basis for the experimental synthesis of Li-Co-B and similar new materials.
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Programmable assembly of nanoparticles (NPs) into well-defined architectures has attracted attention because of tailored properties resulting from coupling effects. However, general and precise approaches to control binding modes between NPs remain a challenge owing to the difficulty in manipulating the accurate positions of the functional patches on the surface of NPs. Here, a strategy is developed to encage spherical NPs into pre-designed octahedral DNA origami frames (DOFs) through DNA base-pairings. The DOFs logically define the arrangements of functional patches in three dimensions, owing to the programmability of DNA hybridization, and thus control the binding modes of the caged nanoparticle with designed anisotropy. Applying the node-and-spacer approach that was widely used in crystal engineering to design coordination polymers, patchy NPs could be rationally designed with lower symmetry encoded to assemble a series of nano-architectures with high-order geometries.
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We report a method for quantitative phase recovery and simultaneous electron energy loss spectroscopy analysis using ptychographic reconstruction of a data set of "hollow" diffraction patterns. This has the potential for recovering both structural and chemical information at atomic resolution with a new generation of detectors.
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BACKGROUND AND OBJECTIVE: Lung cancer continues to be a leading cause of cancer-related mortality worldwide, with immunotherapy emerging as a promising therapeutic strategy for advanced non-small cell lung cancer (NSCLC). Despite its potential, not all patients experience benefits from immunotherapy, and the current biomarkers used for treatment selection possess inherent limitations. As a result, the implementation of imaging-based biomarkers to predict the efficacy of lung cancer treatments offers a promising avenue for improving therapeutic outcomes. METHODS: This study presents an automatic system for immunotherapy efficacy prediction on the subjects with lung cancer, facilitating significant clinical implications. Our model employs an advanced 2.5D neural network that incorporates 2D intra-slice feature extraction and 3D inter-slice feature aggregation. We further present a lesion-focused prior to guide the re-calibration for intra-slice features, and a attention-based re-calibration for the inter-slice features. Finally, we design an accumulated back-propagation strategy to optimize network parameters in a memory-efficient fashion. RESULTS: We demonstrate that the proposed method achieves impressive performance on an in-house clinical dataset, surpassing existing state-of-the-art models. Furthermore, the proposed model exhibits increased efficiency in inference for each subject on average. To further validate the effectiveness of our model and its components, we conducted comprehensive and in-depth ablation experiments and discussions. CONCLUSION: The proposed model showcases the potential to enhance physicians' diagnostic performance due to its impressive performance in predicting immunotherapy efficacy, thereby offering significant clinical application value. Moreover, we conduct adequate comparison experiments of the proposed methods and existing advanced models. These findings contribute to our understanding of the proposed model's effectiveness and serve as motivation for future work in immunotherapy efficacy prediction.
Subject(s)
Carcinoma, Non-Small-Cell Lung , Lung Neoplasms , Humans , Carcinoma, Non-Small-Cell Lung/therapy , Lung Neoplasms/therapy , Immunotherapy , Neural Networks, Computer , BiomarkersABSTRACT
The use of amino-terminated siloxanes as coupling interface for perovskite single crystals (PSCs)/silicon integrated devices has been demonstrated to be an effective method toward CMOS compatible optoelectronics; however, it suffers from the coupling stability against the hydrophilicity of the exposed terminal amino groups. In this work, a phenyl-terminated interfacial molecule, anilino-methyl-triethoxysilane (AMTES), is proposed to achieve the effectively galvanic coupling between PSCs and silicon, which can not only improve the device environmental reliability but also lower the surface energy of the silicon substrate so as to facilitate the epitaxial growth of PSCs. Benefiting from the interfacial coupling of AMTES, the obtained MAPbI3 SC/silicon integrated device possesses highly efficient multiwavelength photodetection properties across the X-ray and NIR range, which exhibits a specific detectivity D* of 3.84 × 1013 cm Hz1/2 W-1 in the visible-NIR region and an X-ray sensitivity of 1.18 × 104 µC Gyair-1 cm-2 with the lowest detection limit of 49.6 nGyair s-1. The ultra wide -3 dB bandwidth of 67,300 Hz and the linear dynamic range (LDR) of 112 dB also prove its impressive dynamic response capabilities. Moreover, the AMTES modified integrated device almost maintains 96% of the initial photodetection performance even after keeping in the atmosphere environment for 28 days. This work opens a new avenue for interfacial engineering toward the development of on-chip PSC integrated silicon optoelectronic devices.
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2D polymers have emerged as one of the most promising classes of organic photocatalysts for solar fuel production due to their tunability, charge-transport properties, and robustness. They are however difficult to process and so there are limited studies into the formation of heterojunction materials incorporating these components. In this work, a novel templating approach is used to combine an imine-based donor polymer and an acceptor polymer formed through Knoevenagel condensation. Heterojunction formation is shown to be highly dependent on the topological match of the donor and acceptor polymers with the most active templated material found to be between three and nine times more active for photocatalysis than its constituent components. Transient absorption spectroscopy reveals that this improvement is due to faster charge separation and more efficient charge extraction in the templated heterojunction. The templated material shows a very high hydrogen evolution rate of >20 mmol h-1 m-2 with an ascorbic acid hole scavenger but also produces hydrogen in the presence of only water and a cobalt-based redox mediator. This suggests the improved charge-separation interface and reduced trapping accessed through this approach could be suitable for Z-scheme formation.
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Herein, facet-engineered Cu2 O nanostructures are synthesized by wet chemical methods for electrocatalytic HER, and it is found that the octahedral Cu2 O nanostructures with exposed crystal planes of (111) (O-Cu2 O) has the best hydrogen evolution performance. Operando Raman spectroscopy and ex-situ characterization techniques showed that Cu2 O is reduced during HER, in which Cu dendrites are grown on the surface of the Cu2 O nanostructures, resulting in the better HER performance of O-Cu2 O after HER (O-Cu2 O-A) compared with that of the as-prepared O-Cu2 O. Under illumination, the onset potential of O-Cu2 O-A is ca. 52 mV positive than that of O-Cu2 O, which is induced by the plasmon-activated electrochemical system consisting of Cu2 O and the in-situ generated Cu dendrites. Incident photon-to-current efficiency (IPCE) measurements and the simulated UV-Vis spectrum demonstrate the hot electron injection (HEI) from Cu dendrites to Cu2 O. Ab initio nonadiabatic molecular dynamics (NAMD) simulations revealed the transfer of photogenerated electrons (27 fs) from Cu dendrites to Cu2 O nanostructures is faster than electron relaxation (170 fs), enhancing its surface plasmons activity, and the HEI of Cu dendrites increases the charge density of Cu2 O. These make the energy level of the catalyst be closer to that of H+ /H2 , evidenced by the plasmon-enhanced HER electrocatalytic activity.
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Background: Age-induced sarcopenia negatively affects walking stability and increases the risk of falls, which is the leading cause of accidental death in the elderly. Objective: This study aimed to analyze and contrast body composition and gait characteristics in those with sarcopenia in relation to healthy controls to shed some light on the prevention of falls in elderly patients with sarcopenia. Materials and Methods: In this study, 68 community dwellers were scanned by the Hologic QDR-4500A Dual-energy X-ray absorptiometry (DXA). The appendicular lean mass index (ALMI) results were used to distinguish the normal participants from those with sarcopenia: 24 in the sarcopenia group, and 44 into the normal group. The participants were asked to undergo gait analysis on a plantar pressure measurement system. Statistical analysis was conducted to contrast both groups' gait and butterfly parameters from their gait test, and then a gait forward dynamics method was performed to quantify the analysis for both groups. Results: The ALMI of the female was not related to their age (r = 0.06) while that of the male was weakly related (r = 0.17). Body mass index (BMI) from both groups was normal, although with a statistically greater BMI from the normal group compared with sarcopenia (p < 0.001). Greater values and significant differences were found in step length and stride length from the normal elderly group (p < 0.01), and so was the length of the gait line and single support line (p < 0.05). Gait forward dynamics analysis results showed no motor neural or musculoskeletal disorders in their gait performance from the sarcopenia group. Conclusion: For the elderly, age did not largely affect the ALMI, BMI, or T-score, but BMI and ALMI were strongly correlated. In this study, significant differences were found in certain gait parameters between the elderly with sarcopenia and the normal elderly, which were related to absolute muscle strength, suggesting that sarcopenia was a disease mainly caused by decreased muscle mass. In addition, when abnormities were identified in step length, stride length, length of gait line, or length of single support line, it is proposed to take a DXA scan to confirm whether the elderly suffer from sarcopenia.
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BACKGROUND AND OBJECTIVE: Alzheimer's Disease (AD) is a progressive irreversible neurodegeneration disease and thus timely identification is critical to delay its progression. METHODS: In this work, we focus on the traditional branch to design discriminative feature extraction and selection strategies to achieve explainable AD identification. Specifically, a spatial pyramid based three-dimensional histogram of oriented gradient (3D-HOG) feature learning method is proposed. Both global and local texture changes are included in spatial pyramid 3D-HOG (SPHOG) features for comprehensive analysis. Then a modified wrapper-based feature selection algorithm is introduced to select the discriminative features for AD identification while reduce feature dimensions. RESULTS: Discriminative SPHOG histograms with various resolutions are selected, which can represent the atrophy characteristics of cerebral cortex with promising performance. As subareas corresponding to selected histograms are consistent with clinical experience, explanatory is emphasized and illustrated with Hippocampus. CONCLUSION: Experimental results illustrate the effectiveness of the proposed method on feature learning based on samples obtained from common dataset and a clinical dataset. The proposed method will be useful for further medical analysis as its explanatory on other region-of-interests (ROIs) of the brain for early diagnosis of AD.
Subject(s)
Alzheimer Disease , Cognitive Dysfunction , Algorithms , Alzheimer Disease/diagnostic imaging , Brain , Humans , Magnetic Resonance Imaging , NeuroimagingABSTRACT
Three dimensional scaffolded DNA origami with inorganic nanoparticles has been used to create tailored multidimensional nanostructures. However, the image contrast of DNA is poorer than those of the heavy nanoparticles in conventional transmission electron microscopy at high defocus so that the biological and non-biological components in 3D scaffolds cannot be simultaneously resolved using tomography of samples in a native state. We demonstrate the use of electron ptychography to recover high contrast phase information from all components in a DNA origami scaffold without staining. We further quantitatively evaluate the enhancement of contrast in comparison with conventional transmission electron microscopy. In addition, We show that for ptychography post-reconstruction focusing simplifies the workflow and reduces electron dose and beam damage.
Subject(s)
Electrons , Nanostructures , DNA/chemistry , Microscopy, Electron, Transmission , Nanostructures/chemistryABSTRACT
A glassy carbon electrode (GCE) modified by a hybrid, macroporous carbon (MPC) functionalized with triazine bridged covalent organic polycalix[4]arenes (CalCOP) (CalCOP-MPC), has been fabricated and utilized for simultaneous detection of nitrophenols (NP). The obtained CalCOP-MPC were characterized by scanning electron microscope (SEM), transmission electron microscope (TEM), thermogravimetric analysis (TGA), Fourier transform infrared spectroscopy (FTIR), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), cyclic voltammetry (CV), and electrochemical impedance spectroscopy (EIS), which confirmed that MPC had functionalized with CalCOP successfully. Benefiting from the synergistic supramolecular effect of macrocyclic receptor of CalCOP and the excellent electrical properties of MPC, the anodic peaks of o-nitrophenol (o-NP), m-nitrophenol (m-NP), and p-nitrophenol (p-NP) in their mixture can be well separated by the prepared electrode. Differential pulse voltammetry (DPV) measurements at CalCOP-MPC/GCE revealed that the linear ranges of NP isomers were all 1-400 µM, and the detection limit limits were 0.383 µM, 0.122 µM, and 0.212 µM for o-NP, m-NP, and p-NP, respectively. Moreover, the prepared modified electrodes showed a relatively good selectivity and stability, implying the prospect for detecting NP in real environmental samples.
Subject(s)
Carbon , Nitrophenols , Dielectric Spectroscopy , Electrochemical Techniques , Electrodes , Limit of DetectionABSTRACT
PURPOSE: This paper aims to measure the technical efficiency of China's medical and health institutions from 2012 to 2017 and outline the path to achieve high-quality development. METHODS: The DEA-Malmquist was used to evaluate the total factor productivity of medical and health institutions in 31 provinces. A fuzzy set Qualitative Comparative Analysis (fsQCA) was used for configuration analysis of determinants affecting technical efficiency. RESULTS: The average total factor productivity (TFP) of those institutions was 0.965, namely TFP declined averagely by 3.5% annually. The efficiency change and the technical change were 0.998 and 0.967, respectively. The realization paths of high technical efficiency are composed of high fatality rate and high financial allocation-led, high population density and high GDP-led. Low dependency ratio and low financial allocation-led, low fatality rate and low financial allocation-led are the main reasons for low technical efficiency. CONCLUSION: Due to advanced medical technology and economic development, major cities like Beijing, Shanghai, and Guangdong have attracted a large number of high-level health personnel, achieving long-term and stable health business growth. Hubei, Anhui, and Sichuan also have made rapid development of health care through appropriate financial subsidies and policy supports. The technical changes in Qinghai, Yunnan, and Inner Mongolia are higher than the national average, but the operation and management level of the medical and health institutions is relatively weak. Henan, Jiangxi, and Heilongjiang have a prominent performance in the efficiency change, but the technical change is weaker than the national average.
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The present study reports the development of an electrochemical sensor based on sulfobutylether-ß-cyclodextrin modified reduced graphene oxide hybrid (SBCD-rGO) for simultaneous detection of nitrophenol isomers. First, SBCD-rGO hybrid was synthesized and detailed characterized. Afterwards, a sensor was fabricated via the modification of glassy carbon electrode (GCE) with SBCD-rGO, and its electrochemical detection performances were also investigated. Then, the constructed electrochemical sensor was applied to detect nitrophenol isomers by voltammetry analysis. The results suggested that the sensitivities were 389.26, 280.88 and 217.19 µA/mM for p-nitrophenol (p-NP), m-nitrophenol (m-NP), and o-nitrophenol (o-NP), respectively, and their corresponding detection limits were all about 0.05 µM. Significantly, the combination of voltammetry analysis with the constructed sensor and data analysis by multiple linear regression realized the simultaneous detection of nitrophenol isomers.
Subject(s)
Electrochemical Techniques/methods , Graphite/chemistry , Nitrophenols/analysis , beta-Cyclodextrins/chemistry , Carbon/chemistry , Electrochemical Techniques/instrumentation , Electrodes , Isomerism , Limit of Detection , Nanostructures/chemistry , Nitrophenols/chemistry , Reproducibility of ResultsABSTRACT
PURPOSE: This paper aims to measure the operating efficiency of China's basic pension insurance from 2014 to 2019 in 31 provinces. METHODS: The three-stage DEA model was used to evaluate the operating efficiency of basic pension insurance in 31 provinces. RESULTS: On the whole, the operating efficiency of China's basic pension insurance was at a high level, but there was still room for improvement. GDP, urbanization level and scale of government public expenditure had a positive impact on the operating efficiency of regional basic pension insurance. In contrast, the old-age dependency ratio had a significant negative effect. There were noticeable regional differences in the operating efficiency of China's basic pension insurance, which showed a pattern of Central (0.742) >Eastern (0.689) >Western (0.505) after removing the influence of environmental variables. CONCLUSION: This study systematically analyzes the impact of external environmental regulations on China's basic pension insurance's operating efficiency and provides decision-making references for further improving the operating efficiency of China's basic pension insurance. In order to further optimize the allocation of basic endowment insurance, this paper proposes the following countermeasures and suggestions: (1) broaden investment channels and improve the investment efficiency; (2) increase supervision of social security departments and strengthen information disclosure; (3) unify the management of social basic pension insurance.
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DNA origami technology has proven to be an excellent tool for precisely manipulating molecules and colloidal elements in a three-dimensional manner. However, fabrication of single crystals with well-defined facets from highly programmable, complex DNA origami units is a great challenge. Here, we report the successful fabrication of DNA origami single crystals with Wulff shapes and high yield. By regulating the symmetries and binding modes of the DNA origami building blocks, the crystalline shapes can be designed and well-controlled. The single crystals are then used to induce precise growth of an ultrathin layer of silica on the edges, resulting in mechanically reinforced silica-DNA hybrid structures that preserve the details of the single crystals without distortion. The silica-infused microcrystals can be directly observed in the dry state, which allows meticulous analysis of the crystal facets and tomographic 3D reconstruction of the single crystals by high-resolution electron microscopy.
Subject(s)
Crystallization/methods , DNA/chemistry , Heterocyclic Compounds/chemistry , Genetic Techniques , Imaging, Three-Dimensional , Models, Molecular , Nanotechnology/methods , Nucleic Acid Conformation , Silicon DioxideABSTRACT
Hydrogen production is the key step for the future hydrogen economy. As a promising H2 production route, electrolysis of water suffers from high overpotentials and high energy consumption. This study proposes an N-doped CoP as the novel and effective electrocatalyst for hydrogen evolution reaction (HER) and constructs a coupled system for simultaneous hydrogen and sulfur production. Nitrogen doping lowers the d-band of CoP and weakens the H adsorption on the surface of CoP because of the strong electronegativity of nitrogen as compared to phosphorus. The H adsorption that is close to thermos-neutral states enables the effective electrolysis of the HER. Only -42 mV is required to drive a current density of -10 mA cm-2 for the HER. The oxygen evolution reaction in the anode is replaced by the oxidation reaction of Fe2+ , which is regenerated by a coupled H2 S absorption reaction. The coupled system can significantly reduce the energy consumption of the HER and recover useful sulfur sources.