Supplementary document for VoxDMRN: A voxelwise Deep Max-Pooling residual network for Bioluminescence tomography Reconstruction - 5730042.pdf

Supplemental Documen

Additional file 1: of Destabilization of Surfactant-Dispersed Carbon Nanotubes by Anions

Experimental Results. (DOCX 636 kb

Sorption/Desorption Behavior and Mechanism of NH₄⁺ by Biochar as a Nitrogen Fertilizer Sustained-Release Material

Biochar, the pyrolysis product of biomass material with limited oxygen, has the potential to increase crop production and sustained-release fertilizer, but the understanding of the reason for improving soil fertility is insufficient, especially the behavior and mechanism of ammonium sulfate. In this study, the sorption/desorption effect of NH₄⁺ by biochar deriving from common agricultural wastes under different preparation temperatures from 200 to 500 °C was studied and its mechanism was discussed. The results showed that biochar displayed excellent retention ability in holding NH₄⁺ above 90% after 21 days under 200 °C preparation temperature, and it can be deduced that the oxygen functional groups, such as carboxyl and keto group, played the primary role in adsorbing NH₄⁺ due to hydrogen bonding and electrostatic interaction. The sorption/desorption effect and mechanism were studied for providing an optional way to dispose of agricultural residues into biochar as a nitrogen fertilizer sustained-release material under suitable preparation temperature

Supplementary document for Total Variation Constrained Graph Manifold Learning Strategy for Cerenkov Luminescence Tomography - 5542390.pdf

Supplemental Document

Data_Sheet_1_Hierarchical Spatio-Temporal Modeling of Naturalistic Functional Magnetic Resonance Imaging Signals via Two-Stage Deep Belief Network With Neural Architecture Search.docx

Naturalistic functional magnetic resonance imaging (NfMRI) has become an effective tool to study brain functional activities in real-life context, which reduces the anxiety or boredom due to difficult or repetitive tasks and avoids the problem of unreliable collection of brain activity caused by the subjects’ microsleeps during resting state. Recent studies have made efforts on characterizing the brain’s hierarchical organizations from fMRI data by various deep learning models. However, most of those models have ignored the properties of group-wise consistency and inter-subject difference in brain function under naturalistic paradigm. Another critical issue is how to determine the optimal neural architecture of deep learning models, as manual design of neural architecture is time-consuming and less reliable. To tackle these problems, we proposed a two-stage deep belief network (DBN) with neural architecture search (NAS) combined framework (two-stage NAS-DBN) to model both the group-consistent and individual-specific naturalistic functional brain networks (FBNs), which reflected the hierarchical organization of brain function and the nature of brain functional activities under naturalistic paradigm. Moreover, the test-retest reliability and spatial overlap rate of the FBNs identified by our model reveal better performance than that of widely used traditional methods. In general, our model provides a promising method for characterizing hierarchical spatiotemporal features under the natural paradigm.</p

Supplementary document for A correntropy-induced metric with laplacian kernel for robust fluorescence molecular tomography - 5422868.pdf

434679 Properties and proofs of propertie

Supplementary document for FSMN-Net: A Free Space Matching Network based on Manifold Convolution for Optical Molecular Tomography - 6830118.pdf

supplementar

Data_Sheet_1_Mapping the time-varying functional brain networks in response to naturalistic movie stimuli.docx

One of human brain’s remarkable traits lies in its capacity to dynamically coordinate the activities of multiple brain regions or networks, adapting to an externally changing environment. Studying the dynamic functional brain networks (DFNs) and their role in perception, assessment, and action can significantly advance our comprehension of how the brain responds to patterns of sensory input. Movies provide a valuable tool for studying DFNs, as they offer a naturalistic paradigm that can evoke complex cognitive and emotional experiences through rich multimodal and dynamic stimuli. However, most previous research on DFNs have predominantly concentrated on the resting-state paradigm, investigating the topological structure of temporal dynamic brain networks generated via chosen templates. The dynamic spatial configurations of the functional networks elicited by naturalistic stimuli demand further exploration. In this study, we employed an unsupervised dictionary learning and sparse coding method combing with a sliding window strategy to map and quantify the dynamic spatial patterns of functional brain networks (FBNs) present in naturalistic functional magnetic resonance imaging (NfMRI) data, and further evaluated whether the temporal dynamics of distinct FBNs are aligned to the sensory, cognitive, and affective processes involved in the subjective perception of the movie. The results revealed that movie viewing can evoke complex FBNs, and these FBNs were time-varying with the movie storylines and were correlated with the movie annotations and the subjective ratings of viewing experience. The reliability of DFNs was also validated by assessing the Intra-class coefficient (ICC) among two scanning sessions under the same naturalistic paradigm with a three-month interval. Our findings offer novel insight into comprehending the dynamic properties of FBNs in response to naturalistic stimuli, which could potentially deepen our understanding of the neural mechanisms underlying the brain’s dynamic changes during the processing of visual and auditory stimuli.</p

Efficient and Green Fabrication of Porous Magnetic Chitosan Particles Based on a High-Adhesive Superhydrophobic Polyimide Fiber Mat

In this paper, an efficient and green strategy was developed to synthesize porous magnetic chitosan (PMCS) particles via a special superhydrophobic effect of a porous fluorinated polyimide (PFPI) fiber mat with a petal effect. By controlling the fiber morphology and porous structures on the fiber surface, the water contact angle on the fiber mat reached as high as 155.3° and the adhesion to a water droplet was up to 236.4 μN, indicating that the PMCS droplets could be pinned on the fiber surface steadily. Then PMCS particles can be obtained after evaporation, exfoliation, lavation, and desiccation processes. Morphologies and porous structures of PMCS particles were investigated. Cu­(II) adsorption ability of PMCS particles have been characterized, and the effects of different experimental conditions like adsorbent dosage, pH, initial Cu­(II) concentration, and contact time on the adsorption capacity were also examined. Field emission scanning electron microscopy (FE-SEMs) showed that PMCS particles presented a stable morphology and adjustable porous structures. The adsorption isotherm was better fitted with the Langmuir isotherm model, and the adsorption kinetics followed the pseudo-second-order kinetic model. The maximum adsorption capacity of PMCS particles was 188.68 mg/g. Even after eight cycles, 85% adsorption capacity was still retained. These results suggested that the obtained PMCS particles exhibited excellent Cu­(II) adsorption capacity and reusability. Moreover, compared with traditional methods, the mentioned fabrication approach of PMCS particles was more effective, saves energy, and was environmentally friendly

Carbon-Based Porous Bimetallic Phosphide in CNT Network as a Separator for Li–S Batteries

Lithium–sulfur (Li–S) batteries have received continuous attention due to their high theoretical specific capacity. However, the shuttle effect of lithium polysulfides (LiPSs) severely limits its development. Modification of commercial polypropylene (PP) separators is regarded as an effective strategy to suppress the shuttle effect. In this paper, NiFe-PBA-derived carbon-based porous bimetallic phosphide (NFP) was inserted into the carbon nanotube (CNT) networks, and these composites were utilized to modify the PP separator. CNT networks provide abundant pathways of electron transport to improve the conductivity of S cathode and Li2S. Ni2P and Fe2P in NFP exhibit robust chemisorption and catalytic effects on LiPSs. Moreover, porous structures and networks were beneficial for Li+ immigration. The synergistic effects of NFP and CNT networks enable the modified separator to obtain a high electrical conductivity, superior adsorption capability, and enhanced catalytic performance. Assembled Li–S batteries show a high initial discharge specific capacity of 1244 mAh g–1 at 0.2 C. Notably, the highest discharge-specific capacity reached up to 938.9 mAh g–1 at 1 C, and the average capacity decay rate per cycle after 300 cycles was a mere 0.067%. This study paves the way for a robust research pathway in harnessing transition metal phosphides and carbon materials for modified separators, offering an effective strategy to realize high-performance Li–S batteries