Search

C22H26N6NiO8, monoclinic, P21/n (no. 14), a = 10.9372(7) Å, b = 10.4567(6) Å, c = 11.7618(7) Å, β = 116.5552(19)°, V = 1203.25(13) Å3, Z = 2, Rgt(F) = 0.0406, wRref(F2) = 0.1089, T = 297(2) K.

C36H44Co4N8O26, triclinic, P1‾$‾{1}$ (no. 2), a = 6.6434(4) Å, b = 9.3788(5) Å, c = 18.5581(10) Å, α = 86.262(2)°, β = 89.642(2)°, γ = 84.726(2)°, V = 1148.96(11) Å3, Z = 1, Rgt(F) = 0.0258, ωRref(F2) = 0.0679, T = 298(2) K.

With the increasing scale of cloud computing applications of next-generation embedded systems, a major challenge that domain scientists are facing is how to efficiently store and analyze the vast volume of output data. Compression can reduce the amount of data that needs to be transferred and stored. However, most of the large datasets are in floating-point format, which exhibits high entropy. As a result, existing lossless compressors cannot provide enough performance for such applications. To address this problem, we propose a total variation reduction method for improving the compression ratio of lossless compressors (namely, FPC+ and FPZIP+), which employs a median-based hyperplane to precondition the data. In particular, we first try to exploit the space-filling curve (SFC), a well-known technique to preserve data locality for a multi-dimensional dataset. We show and explain why a raw SFC, such as Hilbert and Z-order curves, cannot improve the compression ratio. Then, we explore the opportunity and theoretical feasibility of the proposed total variation reduction-based algorithm. The experiment results show the effectiveness of the proposed method. The compression ratios are improved up to 48.2% (20.6% on average) for FPZIP and 42.4% (18.4% on average) for FPC. Moreover, through observing the time composition of the proposed method, it is found that the median finding holds a high percentage of the execution time. Hence, we further introduce an approximate median finding algorithm, providing a linear-time overhead reduction scheme. The experiment results clearly demonstrate that this algorithm reduces execution time by an average of 56.7% and 40.7% compared to FPC+ and FPZIP+, respectively. [ABSTRACT FROM AUTHOR]

Acute liver failure (ALF) involves extensive necrosis of liver cells and severe impairment of liver function. Hepatocyte transplantation holds promise for treating ALF by swiftly supporting liver functions and promoting liver regeneration. However, the scarcity of suitable cell sources requires strategies to obtain enough functional hepatocyte‐like cells (HLCs) and optimize their in vivo transplantation efficiency. A DNA hexahedral nanostructure (DHN) is developed loaded with microRNA‐122 to efficiently induce hepatic differentiation of human adipose‐derived mesenchymal stem cells into HLCs. These HLCs can serve as alternative hepatocyte sources, as confirmed by expression of liver‐specific genes and proteins, and the restoration of liver functions. To enhance in vivo survival efficiency of HLCs, a versatile scaffold is also created by 3D printing the calcium‐cross‐linked mixture bioink composed of sodium alginate, gelatin, and silk fibroin with excellent ROS scavenging capabilities. The scaffold is infused with chitosan‐DHN hydrogel containing HLCs for tissue engineering orthotopic transplantation in CCl4‐induced ALF mice. The transplanted composite scaffold‐HLCs successfully repair tissue necrosis in the liver injury area of mice and regulate expressions of genes and proteins associated with inflammation, oxidative stress, and hepatocyte function. Collectively, this study offers a novel approach and strategy for identifying alternative hepatocyte sources and treating ALF. [ABSTRACT FROM AUTHOR]

Liver fibrosis is a chronic pathological condition lacking specific clinical treatments. Stem cells, with notable potential in regenerative medicine, offer promise in treating liver fibrosis. However, stem cell therapy is hindered by potential immunological rejection, carcinogenesis risk, efficacy variation, and high cost. Stem cell secretome‐based cell‐free therapy offers potential solutions to address these challenges, but it is limited by low delivery efficiency and rapid clearance. Herein, an innovative approach for in situ implantation of smart microneedle (MN) arrays enabling precisely controlled delivery of multiple therapeutic agents directly into fibrotic liver tissues is developed. By integrating cell‐free and platinum‐based nanocatalytic combination therapy, the MN arrays can deactivate hepatic stellate cells. Moreover, they promote excessive extracellular matrix degradation by more than 75%, approaching normal levels. Additionally, the smart MN arrays can provide hepatocyte protection while reducing inflammation levels by ≈70–90%. They can also exhibit remarkable capability in scavenging almost 100% of reactive oxygen species and alleviating hypoxia. Ultimately, this treatment strategy can effectively restrain fibrosis progression. The comprehensive in vitro and in vivo experiments, supplemented by proteome and transcriptome analyses, substantiate the effectiveness of the approach in treating liver fibrosis, holding immense promise for clinical applications. [ABSTRACT FROM AUTHOR]

Featured Application: This research has important applications in areas such as smart furniture and human-computer interaction. It will bring people a more efficient and comfortable living experience as well as a new smart experience.  Human action recognition has many application prospects in human-computer interactions, innovative furniture, healthcare, and other fields. The traditional human motion recognition methods have limitations in privacy protection, complex environments, and multi-person scenarios. Millimeter-wave radar has attracted attention due to its ultra-high resolution and all-weather operation. Many existing studies have discussed the application of millimeter-wave radar in single-person scenarios, but only some have addressed the problem of action recognition in multi-person scenarios. This paper uses a commercial millimeter-wave radar device for human action recognition in multi-person scenarios. In order to solve the problems of severe interference and complex target segmentation in multiplayer scenarios, we propose a filtering method based on millimeter-wave inter-frame differences to filter the collected human point cloud data. We then use the DBSCAN algorithm and the Hungarian algorithm to segment the target, and finally input the data into a neural network for classification. The classification accuracy of the system proposed in this paper reaches 92.2% in multi-person scenarios through experimental tests with the five actions we set. [ABSTRACT FROM AUTHOR]

In view of the problem that the public data in the field of mine hoist fault is less and the fault knowledge is difficult to be effectively utilized, this paper proposes a method for constructing a mine hoist fault knowledge graph. This method firstly introduces a fault text classification process to deal with the information redundancy problem existing in the target corpus. Then it uses dictionary embedding BERT and BiLSTM-CRF combination for entity recognition, uses ERNIE for entity relation extraction, and stores the extracted triples in Neo4j graph database. On this basis, an intelligent question answering system based on mine hoist fault knowledge graph is realized. This knowledge graph can reveal the complex correlation between mine hoist faults, realize the root cause analysis of related faults, and provide support for mine hoist fault diagnosis. [ABSTRACT FROM AUTHOR]

Breathing is one of the body's most basic functions and abnormal breathing can indicate underlying cardiopulmonary problems. Monitoring respiratory abnormalities can help with early detection and reduce the risk of cardiopulmonary diseases. In this study, a 77 GHz frequency-modulated continuous wave (FMCW) millimetre-wave (mmWave) radar was used to detect different types of respiratory signals from the human body in a non-contact manner for respiratory monitoring (RM). To solve the problem of noise interference in the daily environment on the recognition of different breathing patterns, the system utilised breathing signals captured by the millimetre-wave radar. Firstly, we filtered out most of the static noise using a signal superposition method and designed an elliptical filter to obtain a more accurate image of the breathing waveforms between 0.1 Hz and 0.5 Hz. Secondly, combined with the histogram of oriented gradient (HOG) feature extraction algorithm, K-nearest neighbours (KNN), convolutional neural network (CNN), and HOG support vector machine (G-SVM) were used to classify four breathing modes, namely, normal breathing, slow and deep breathing, quick breathing, and meningitic breathing. The overall accuracy reached up to 94.75%. Therefore, this study effectively supports daily medical monitoring. [ABSTRACT FROM AUTHOR]

Supramolecular glass is a non-covalently cross-linked amorphous material that exhibits excellent optical properties and unique intrinsic structural features. Compared with artificial inorganic/organic glass, which has been extensively developed, supramolecular glass is still in the infancy stage, and itself is rarely recognized and studied thus far. Herein, we present the development of the host–guest molecular recognition motifs between methyl-β-cyclodextrin and para-hydroxybenzoic acid as the building blocks of supramolecular glass. Non-covalent polymerization resulting from the host–guest complexation and hydrogen bonding formation enables high transparency and bulk state to supramolecular glass. Various advantages, including recyclability, compatibility, and thermal processability, are associated with dynamic assembly pattern. Short-range order (host–guest complexation) and long-range disorder (three dimensional polymeric network) structures are identified simultaneously, thus demonstrating the typical structural characteristics of glass. This work provides a supramolecular strategy for constructing transparent materials from organic components. Strategies to produce supramolecular glass and the study of its intrinsic structure and mechanical properties remains largely unexplored. Here, the authors prepare a supramolecular glass via the host–guest recognition between methyl-β-cyclodextrin and para-hydroxybenzoic acid with recyclability, compatibility, and thermal processability. [ABSTRACT FROM AUTHOR]

Cells in the brain are surrounded by extracellular space (ECS), which forms porous nets and interconnected routes for molecule transportation. Our view of brain ECS has changed from a largely static compartment to dynamic and diverse structures that actively regulate neural activity and brain states. Emerging evidence supports that dysregulation of brain ECS contributes to the pathogenesis and development of many neurological disorders, highlighting the importance of therapeutic modulation of brain ECS function. Here, we aim to provide an overview of the regulation and dysfunction of ECS in healthy and pathological brains, as well as advanced tools to investigate properties of brain ECS. This review emphasizes modulation methods to manipulate ECS with implications to restore their function in brain diseases. Highlights: • Up-to-date overview of the regulation of brain ECS in health and disease. • Recent advances in detecting the biophysical properties, function, and dynamic changes of brain ECS are reviewed. • Advanced pharmacological and physical methods to manipulate brain ECS, which provides therapeutic potentials for treating neurological disorders. [ABSTRACT FROM AUTHOR]

Sentence Boundary Disambiguation (SBD) is crucial for building datasets for tasks such as machine translation, syntactic analysis, and semantic analysis. Currently, most automatic sentence segmentation in Tibetan adopts the methods of rule-based and statistical learning, as well as the combination of the two, which have high requirements on the corpus and the linguistic foundation of the researchers and are more costly to annotate manually. In this study, we explore Tibetan SBD using deep learning technology. Initially, we analyze Tibetan characteristics and various subword techniques, selecting Byte Pair Encoding (BPE) and Sentencepiece (SP) for text segmentation and training the Bidirectional Encoder Representations from Transformers (BERT) pre-trained language model. Secondly, we studied the Tibetan SBD based on different BERT pre-trained language models, which mainly learns the ambiguity of the shad ("།") in different positions in modern Tibetan texts and determines through the model whether the shad ("།") in the texts has the function of segmenting sentences. Meanwhile, this study introduces four models, BERT-CNN, BERT-RNN, BERT-RCNN, and BERT-DPCNN, based on the BERT model for performance comparison. Finally, to verify the performance of the pre-trained language models on the SBD task, this study conducts SBD experiments on both the publicly available Tibetan pre-trained language model TiBERT and the multilingual pre-trained language model (Multi-BERT). The experimental results show that the F1 score of the BERT (BPE) model trained in this study reaches 95.32% on 465,669 Tibetan sentences, nearly five percentage points higher than BERT (SP) and Multi-BERT. The SBD method based on pre-trained language models in this study lays the foundation for establishing datasets for the later tasks of Tibetan pre-training, summary extraction, and machine translation. [ABSTRACT FROM AUTHOR]