Extracellular vesicles (EVs) contain specific proteins, lipids, and nucleic acids that can be passed to other cells as signal molecules to alter their function. However, there are many problems and ...challenges in the conversion and clinical application of EVs. Storage and protection of EVs is one of the issues that need further research. To adapt to potential clinical applications, this type of problem must be solved. This review summarizes the storage practices of EVs in recent years, and explains the impact of temperature on the quality and stability of EVs during storage based on current research, and explains the potential mechanisms involved in this effect as much as possible.
Extracellular vesicles (EVs) contain specific proteins, lipids, and nucleic acids that can be passed to other cells as signal molecules to alter their function. However, there are many problems and challenges in the conversion and clinical application of EVs. Storage and protection of EVs is one of the issues that need further research. To adapt to potential clinical applications, this type of problem must be solved. Here, we briefly review EVs' biogenesis, contents, subtypes and effect of storage temperature on its quality and stability, as well as application of cryoprotectants in EVs cryopreservation, and specifically focus on the mechanism by which storage temperature affects the quality and stability of EVs.
Learning 3D global features by aggregating multiple views has been introduced as a successful strategy for 3D shape analysis. In recent deep learning models with end-to-end training, pooling is a ...widely adopted procedure for view aggregation. However, pooling merely retains the max or mean value over all views, which disregards the content information of almost all views and also the spatial information among the views. To resolve these issues, we propose Sequential Views To Sequential Labels (SeqViews2SeqLabels) as a novel deep learning model with an encoder-decoder structure based on recurrent neural networks (RNNs) with attention. SeqViews2SeqLabels consists of two connected parts, an encoder-RNN followed by a decoder-RNN, that aim to learn the global features by aggregating sequential views and then performing shape classification from the learned global features, respectively. Specifically, the encoder-RNN learns the global features by simultaneously encoding the spatial and content information of sequential views, which captures the semantics of the view sequence. With the proposed prediction of sequential labels, the decoder-RNN performs more accurate classification using the learned global features by predicting sequential labels step by step. Learning to predict sequential labels provides more and finer discriminative information among shape classes to learn, which alleviates the overfitting problem inherent in training using a limited number of 3D shapes. Moreover, we introduce an attention mechanism to further improve the discriminative ability of SeqViews2SeqLabels. This mechanism increases the weight of views that are distinctive to each shape class, and it dramatically reduces the effect of selecting the first view position. Shape classification and retrieval results under three large-scale benchmarks verify that SeqViews2SeqLabels learns more discriminative global features by more effectively aggregating sequential views than state-of-the-art methods.
The task of point cloud upsampling aims to acquire dense and uniform point sets from sparse and irregular point sets. Although significant progress has been made with deep learning models, ...state-of-the-art methods require ground-truth dense point sets as the supervision, which makes them limited to be trained under synthetic paired training data and not suitable to be under real-scanned sparse data. However, it is expensive and tedious to obtain large numbers of paired sparse-dense point sets as supervision from real-scanned sparse data. To address this problem, we propose a self-supervised point cloud upsampling network, named SPU-Net, to capture the inherent upsampling patterns of points lying on the underlying object surface. Specifically, we propose a coarse-to-fine reconstruction framework, which contains two main components: point feature extraction and point feature expansion, respectively. In the point feature extraction, we integrate the self-attention module with the graph convolution network (GCN) to capture context information inside and among local regions simultaneously. In the point feature expansion, we introduce a hierarchically learnable folding strategy to generate upsampled point sets with learnable 2D grids. Moreover, to further optimize the noisy points in the generated point sets, we propose a novel self-projection optimization associated with uniform and reconstruction terms as a joint loss to facilitate the self-supervised point cloud upsampling. We conduct various experiments on both synthetic and real-scanned datasets, and the results demonstrate that we achieve comparable performances to state-of-the-art supervised methods.
Fine-grained 3D shape classification is important for shape understanding and analysis, which poses a challenging research problem. However, the studies on the fine-grained 3D shape classification ...have rarely been explored, due to the lack of fine-grained 3D shape benchmarks. To address this issue, we first introduce a new 3D shape dataset (named FG3D dataset) with fine-grained class labels, which consists of three categories including airplane, car and chair. Each category consists of several subcategories at a fine-grained level. According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category. To resolve this problem, we further propose a novel fine-grained 3D shape classification method named FG3D-Net to capture the fine-grained local details of 3D shapes from multiple rendered views. Specifically, we first train a Region Proposal Network (RPN) to detect the generally semantic parts inside multiple views under the benchmark of generally semantic part detection. Then, we design a hierarchical part-view attention aggregation module to learn a global shape representation by aggregating generally semantic part features, which preserves the local details of 3D shapes. The part-view attention module hierarchically leverages part-level and view-level attention to increase the discriminability of our features. The part-level attention highlights the important parts in each view while the view-level attention highlights the discriminative views among all the views of the same object. In addition, we integrate a Recurrent Neural Network (RNN) to capture the spatial relationships among sequential views from different viewpoints. Our results under the fine-grained 3D shape dataset show that our method outperforms other state-of-the-art methods. The FG3D dataset is available at https://github.com/liuxinhai/FG3D-Net.
The key to fully leveraging the potential of the electrochemical CO2 reduction reaction (CO2RR) to achieve a sustainable solar‐power‐based economy is the development of high‐performance ...electrocatalysts. The development process relies heavily on trial and error methods due to poor mechanistic understanding of the reaction. Demonstrated here is that ionic liquids (ILs) can be employed as a chemical trapping agent to probe CO2RR mechanistic pathways. This method is implemented by introducing a small amount of an IL (BMImNTf2) to a copper foam catalyst, on which a wide range of CO2RR products, including formate, CO, alcohols, and hydrocarbons, can be produced. The IL can selectively suppress the formation of ethylene, ethanol and n‐propanol while having little impact on others. Thus, reaction networks leading to various products can be disentangled. The results shed new light on the mechanistic understanding of the CO2RR, and provide guidelines for modulating the CO2RR properties. Chemical trapping using an IL adds to the toolbox to deduce the mechanistic understanding of electrocatalysis and could be applied to other reactions as well.
The presence of a small amount of ionic liquid significantly alters the product spectrum of CO2 reduction over a Cu catalyst. The ionic liquid acts as a chemical trapping agent, selectively suppressing the formation of C2+ products that involve carbene as a key intermediate. The response in product distribution to ionic liquid modification offers a new way to disentangle the complex reaction network of CO2 reduction by Cu catalysts.
Efficient electron transfer from photosensitizer to catalytic sites is crucial for effective artificial photosynthesis, yet it remains a significant challenge. Herein, it is reported that simple ...fluorination of the organic linkers in the MIL‐101(Fe) photocatalyst results in a remarkable threefold increase in the photocatalytic CO2‐to‐CO conversion rate (688 µmol g−1 h−1) compared to the pristine counterpart (230 µmol g−1 h−1). It is unveiled that, instead of directly modifying the electron structure of MIL‐101(Fe), the fluorinated linkers enhance the interaction between the discrete photocatalyst and photosensitizer (Ru(bpy)32+, bpy = 2,2'‐bipyridine) via hydrogen bonding, thereby facilitating their intermolecular electron transfer. Most importantly, it is also demonstrated that this performance boosting strategy can be applied to other Fe‐based metal–organic frameworks (MOFs) photocatalysts such as MIL‐53(Fe) and MIL‐88(Fe). The present work not only underscores the fluorination of organic linkers as a generic promising approach to enhance the photocatalytic performance of MOF‐based catalysts, but also holds significant implications for photosynthesis and catalytic processes reliant on intermolecular electron transfer as an important step.
This study demonstrates a threefold increase in the photocatalytic CO2‐to‐CO conversion rate by incorporating fluorinated linkers in Fe‐based metal organic framework photocatalysts. The enhanced electron transfer is attributed to strengthened interaction via hydrogen bonding between the photocatalyst and photosensitizer. This strategy holds promise for developing high‐performance photocatalysts.
3D shape reconstruction from multiple hand-drawn sketches is an intriguing way to 3D shape modeling. Currently, state-of-the-art methods employ neural networks to learn a mapping from multiple ...sketches from arbitrary view angles to a 3D voxel grid. Because of the cubic complexity of 3D voxel grids, however, neural networks are hard to train and limited to low resolution reconstructions, which leads to a lack of geometric detail and low accuracy. To resolve this issue, we propose to reconstruct 3D shapes from multiple sketches using direct shape optimization (DSO), which does not involve deep learning models for direct voxel-based 3D shape generation. Specifically, we first leverage a conditional generative adversarial network (CGAN) to translate each sketch into an attenuance image that captures the predicted geometry from a given viewpoint. Then, DSO minimizes a project-and-compare loss to reconstruct the 3D shape such that it matches the predicted attenuance images from the view angles of all input sketches. Based on this, we further propose a progressive update approach to handle inconsistencies among a few hand-drawn sketches for the same 3D shape. Our experimental results show that our method significantly outperforms the state-of-the-art methods under widely used benchmarks and produces intuitive results in an interactive application.
Learning discriminative shape representation directly on point clouds is still challenging in 3D shape analysis and understanding. Recent studies usually involve three steps: first splitting a point ...cloud into some local regions, then extracting the corresponding feature of each local region, and finally aggregating all individual local region features into a global feature as shape representation using simple max-pooling. However, such pooling-based feature aggregation methods do not adequately take the spatial relationships (e.g. the relative locations to other regions) between local regions into account, which greatly limits the ability to learn discriminative shape representation. To address this issue, we propose a novel deep learning network, named Point2SpatialCapsule, for aggregating features and spatial relationships of local regions on point clouds, which aims to learn more discriminative shape representation. Compared with the traditional max-pooling based feature aggregation networks, Point2SpatialCapsule can explicitly learn not only geometric features of local regions but also the spatial relationships among them. Point2SpatialCapsule consists of two main modules. To resolve the disorder problem of local regions, the first module, named geometric feature aggregation , is designed to aggregate the local region features into the learnable cluster centers, which explicitly encodes the spatial locations from the original 3D space. The second module, named spatial relationship aggregation , is proposed for further aggregating the clustered features and the spatial relationships among them in the feature space using the spatial-aware capsules developed in this article. Compared to the previous capsule network based methods, the feature routing on the spatial-aware capsules can learn more discriminative spatial relationships among local regions for point clouds, which establishes a direct mapping between log priors and the spatial locations through feature clusters. Experimental results demonstrate that Point2SpatialCapsule outperforms the state-of-the-art methods in the 3D shape classification, retrieval and segmentation tasks under the well-known ModelNet and ShapeNet datasets.
Tetraarylethylenes exhibit intriguing photophysical properties and sulfur atom frequently play a vital role in organic photoelectric materials and biologically active compounds. Tetrasubstituted ...vinyl sulfides, which include both sulfur atom and tetrasubstituted alkenes motifs, might be a suitable skeleton for the discovery of the new material molecules and drug with unique functions and properties. However, how to modular synthesis these kinds of compounds is still challenging. Herein, a chemo- and stereo-selective Rh(II)-catalyzed 1,4-acyl rearrangements of α-diazo carbonyl compounds and thioesters has been developed, providing a modular strategy to a library of 63 tetrasubstituted vinyl sulfides. In this transformation, the yield is up to 95% and the turnover number is up to 3650. The mechanism of this reaction is investigated by combining experiments and density functional theory calculation. Moreover, the "aggregation-induced emission" effect of tetrasubstituted vinyl sulfides were also investigated, which might useful in functional material, biological imaging and chemicalnsing via structural modification.
In this paper, we first study the multimodal interaction activities in higher education management from the two points of multimodal interaction types and multimodal interaction activity evaluation ...and propose the perfect way of digital construction of multimodal higher education management from the perspective of students, teachers and higher education institutions. Then, the higher education management system is constructed based on semi-supervised fusion feature learning and homogeneous multimodal features of multimodal machines, and the system architecture and database design are explained in detail. Next, the research literature statistics and system performance of ‘Digitalization of Higher Education Management’ are analyzed through experimental research. The results show that the periodicals’ literature statistics show a rapid increase in articles, mainly from 2003 to 2004 and 2006-2007. The higher education management system maintained an accuracy rate of 80.49% regarding system performance. It is confirmed that the management system constructed in this study can predict the learners’ status more accurately and predictably, contributing to the development of intelligent education management in higher education institutions.
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