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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]