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Human adipose-derived stem cells are used in regenerative medicine for treating various diseases including osteoarthritis, degenerative arthritis, cartilage or tendon injury, etc. However, their use in neurological disorders is limited, probably due to the lack of a quick and efficient induction method of transforming these cells into neural stem or progenitor cells. In this study, we reported a highly efficient and simple method to induce adipose-derived stem cells into neural progenitor cells within 12 hours, using serum-free culture combined with a well-defined induction medium (epidermal growth factor 20 ng/ml and basic fibroblast growth factor, both at 20 ng/ml, with N2 and B27 supplements). These adipose-derived stem cell-derived neural progenitor cells grow as neurospheres, can self-renew to form secondary neurospheres, and can be induced to become neurons and glial cells. Real-time polymerase chain reaction showed significantly upregulated expression of neurogenic genes Sox2 and Nestin with a moderate increase in stemness gene expression. Raybio human growth factor analysis showed a significantly upregulated expression of multiple neurogenic and angiogenic cytokines such as brain-derived neurotrophic factor, glial cell line-derived neurotrophic growth factor, nerve growth factor, basic fibroblast growth factor and vascular endothelial growth factor etc. Therefore, adipose-derived stem cell-derived neurospheres can be a new source of neural progenitor cells and hold great potential for future cell replacement therapy for treatment of various refractory neurological diseases.

Tool wear state recognition is a challenging problem because the various types of signals corresponding to different wear states have similar features, making classification difficult. Convolutional neural networks are widely used in the field of tool wear state recognition. To address this problem, this study proposes a milling cutter wear state monitoring model that combines KANS and deep residual networks (ResNet). The traditional ResNet-34’s top linear classifier was replaced with a nonlinear convolutional classifier including _top_kan, and the data were preprocessed using continuous wavelet transform (CWT) to enhance the model’s immunity to interference and feature characterization. The experimental results based on the PHM dataset show that the improved KANS-ResNet-34 model improves accuracy by 1.07% compared to ResNet-34, making it comparable to ResNet-50, while its computation time is only 1/33.68 of the latter. This significantly improves computational efficiency, reduces the pressure on hardware resources, and provides an effective tool wear state recognition solution.

Cell therapy has emerged as a promising strategy for treating neurological diseases such as stroke, spinal cord injury, and various neurodegenerative diseases, but both embryonic neural stem cells and human induced Pluripotent Stem Cell- (iPSC-) derived neural stem cells have major limitations which restrict their broad use in these diseases. We want to find a one-step induction method to transdifferentiate the more easily accessible Umbilical Cord-Derived Mesenchymal Stem Cells (UC-MSCs) into neural stem/progenitor cells suitable for cell therapy purposes. In this study, UC-MSCs were induced to form neurospheres under a serum-free suspension culture with Epidermal Growth Factor- (EGF-) and basic Fibroblast Growth Factor- (bFGF-) containing medium within 12 hours. These MSC-derived neurospheres can self-renew to form secondary neurospheres and can be readily induced to become neurons and glial cells. Real-time PCR showed significantly upregulated expression of multiple stemness and neurogenic genes after induction. RNA transcriptional profiling study showed that UC-MSC-derived neurospheres had a unique transcriptional profile of their own, with features of both UC-MSCs and neural stem cells. RayBio human growth factor cytokine array analysis showed significantly upregulated expression levels of multiple neurogenic and angiogenic growth factors, skewing toward a neural stem cell phenotype. Thus, we believe that these UC-MSC-derived neurospheres have amenable features of both MSCs and neural stem/progenitor cells and have great potential in future stem cell transplantation clinical trials targeting neurological disorders.

Cell therapy has emerged as a promising strategy for treating neurological diseases such as stroke, spinal cord injury, and various neurodegenerative diseases, but both embryonic neural stem cells and human induced Pluripotent Stem Cell- (iPSC-) derived neural stem cells have major limitations which restrict their broad use in these diseases. We want to find a one-step induction method to transdifferentiate the more easily accessible Umbilical Cord-Derived Mesenchymal Stem Cells (UC-MSCs) into neural stem/progenitor cells suitable for cell therapy purposes. In this study, UC-MSCs were induced to form neurospheres under a serum-free suspension culture with Epidermal Growth Factor- (EGF-) and basic Fibroblast Growth Factor- (bFGF-) containing medium within 12 hours. These MSC-derived neurospheres can self-renew to form secondary neurospheres and can be readily induced to become neurons and glial cells. Real-time PCR showed significantly upregulated expression of multiple stemness and neurogenic genes after induction. RNA transcriptional profiling study showed that UC-MSC-derived neurospheres had a unique transcriptional profile of their own, with features of both UC-MSCs and neural stem cells. RayBio human growth factor cytokine array analysis showed significantly upregulated expression levels of multiple neurogenic and angiogenic growth factors, skewing toward a neural stem cell phenotype. Thus, we believe that these UC-MSC-derived neurospheres have amenable features of both MSCs and neural stem/progenitor cells and have great potential in future stem cell transplantation clinical trials targeting neurological disorders.

Cell therapy has emerged as a promising strategy for treating neurological diseases such as stroke, spinal cord injury, and various neurodegenerative diseases, but both embryonic neural stem cells and human induced Pluripotent Stem Cell- (iPSC-) derived neural stem cells have major limitations which restrict their broad use in these diseases. We want to find a one-step induction method to transdifferentiate the more easily accessible Umbilical Cord-Derived Mesenchymal Stem Cells (UC-MSCs) into neural stem/progenitor cells suitable for cell therapy purposes. In this study, UC-MSCs were induced to form neurospheres under a serum-free suspension culture with Epidermal Growth Factor- (EGF-) and basic Fibroblast Growth Factor- (bFGF-) containing medium within 12 hours. These MSC-derived neurospheres can self-renew to form secondary neurospheres and can be readily induced to become neurons and glial cells. Real-time PCR showed significantly upregulated expression of multiple stemness and neurogenic genes after induction. RNA transcriptional profiling study showed that UC-MSC-derived neurospheres had a unique transcriptional profile of their own, with features of both UC-MSCs and neural stem cells. RayBio human growth factor cytokine array analysis showed significantly upregulated expression levels of multiple neurogenic and angiogenic growth factors, skewing toward a neural stem cell phenotype. Thus, we believe that these UC-MSC-derived neurospheres have amenable features of both MSCs and neural stem/progenitor cells and have great potential in future stem cell transplantation clinical trials targeting neurological disorders.

A Local Superdirect SCF method has been developed which reduces the computational cost of a typical Hartree-Fock procedure from N $\\sp4$to N $\\sp2$ . The method is based on the author's modifications of McWeeny's (1960) density matrix formalism, Kirtman's (1981) LSA method and Almlof's (1982) Direct SCF method. Special features include a more efficient two-electron integral prescreening technique and the superdirect evaluation of the density matrix around the local region.

Indium doped Zn O films were grown on quartz glass substrates by radio frequency magnetron sputtering from powder targets. Indium content in the targets varied from 1at% to 9at%. In doping on the structure, optical and electrical properties of Zn O thin films were studied. X-ray diffraction shows that all the films are hexagonal wurtzite with c-axis perpendicular to the substrates. There is a positive strain in the films and it increases with indium content. All the films show a high transmittance of 86% in the visible light region. Undoped Zn O thin film exhibits a high transmittance in the near infrared region. The transmittance of indium doped Zn O thin films decreases sharply in the near infrared region, and a cut-off wavelength can be found. The lowest resistivity of 4.3×10~（-4） Ω·cm and the highest carrier concentration of 1.86×10~（21） cm~（-3） can be obtained from Zn O thin films with an indium content of 5at% in the target.

Dear Editor, Cell patterning is gaining more and more attention regarding a wide range of applications including cell biology, tissue engineering, and biosensor technology, to name a few. Magnetic force provides a promising tool to pattern cells because it is of excellent biocompatibility, tunable and reversible, and can be employed flexibly in or out of the cell culture system [1]. While extensive studies [2-5] contribute valuable approaches for positioning cells with high resolution, costly and time-consuming fabrication procedures, such as electrodeposition and electrical discharge, still limit the application of these techniques.

Urban boundaries, an essential property of cities, are widely used in many urban studies. However, extracting urban boundaries from satellite images is still a great challenge, especially at a global scale and a fine resolution. In this study, we developed an automatic delineation framework to generate a multi-temporal dataset of global urban boundaries (GUB) using 30 m global artificial impervious area (GAIA) data. First, we delineated an initial urban boundary by filling inner non-urban areas of each city. A kernel density estimation approach and cellular-automata based urban growth modeling were jointly used in this step. Second, we improved the initial urban boundaries around urban fringe areas, using a morphological approach by dilating and eroding the derived urban extent. We implemented this delineation on the Google Earth Engine platform and generated a 30 m resolution global urban boundary dataset in seven representative years (i.e. 1990, 1995, 2000, 2005, 2010, 2015, and 2018). Our extracted urban boundaries show a good agreement with results derived from nighttime light data and human interpretation, and they can well delineate the urban extent of cities when compared with high-resolution Google Earth images. The total area of 65 582 GUBs, each of which exceeds 1 km2, is 809 664 km2 in 2018. The impervious surface areas account for approximately 60% of the total. From 1990 to 2018, the proportion of impervious areas in delineated boundaries increased from 53% to 60%, suggesting a compact urban growth over the past decades. We found that the United States has the highest per capita urban area (i.e. more than 900 m2) among the top 10 most urbanized nations in 2018. This dataset provides a physical boundary of urban areas that can be used to study the impact of urbanization on food security, biodiversity, climate change, and urban health. The GUB dataset can be accessed from http://data.ess.tsinghua.edu.cn.