Apolipoprotein M

Apolipoprotein M (apoM) is a 26-kDa protein that is mainly associated with high-density lipoprotein (HDL) in human plasma, with a small proportion present in triglyceride-rich lipoproteins (TGRLP) and low-density lipoproteins (LDL). Human apoM gene is located in p21.31 on chromosome 6 (chromosome 17, in mouse). Human apoM cDNA (734 base pairs) encodes 188-amino acid residue-long protein. It belongs to lipocalin protein superfamily. Human tissue expression array study indicates that apoM is only expressed in liver and in kidney and small amounts are found in fetal liver and kidney. In situ apoM mRNA hybridization demonstrates that apoM is exclusively expressed in the hepatocytes and in the tubule epithelial cells in kidney. Expression of apoM could be regulated by platelet activating factor (PAF), transforming growth factors (TGF), insulin-like growth factor (IGF) and leptin in vivo and/or in vitro. It has been demonstrated that apoM expression is dramatically decreased in apoA-I deficient mouse. Hepatocyte nuclear factor-1α (HNF-1α) is an activator of apoM gene promoter. Deficiency of HNF-1α mouse shows lack of apoM expression. Mutations in HNF-1α (MODY3) have reduced serum apoM levels. Expression of apoM is significantly decreased in leptin deficient (ob/ob) mouse or leptin receptor deficient (db/db) mouse. ApoM concentration in plasma is positively correlated to leptin level in obese subjects. These may suggest that apoM is related to the initiation and progression of MODY3 and/or obesity

A self-adaptive alarm method for tool condition monitoring based on Parzen window estimation

Tool condition monitoring (TCM) takes an important position in CNC manufacturing processes, especially in damages avoiding of working parts and CNC itself. This paper presents a self-adaptive alarm method using probability density functions estimated with the Parzen window based on current signals, which gives an adaptively and rapidly corresponding alarm when the cutting tool fracture occurs. A CNC with cutting tools was obtained by Guangzhou CNC Company for test purpose, and the relative experiments were done in the state key laboratory. Current signals of the spindle motor and the main feed motor were acquired during the tool life. A probability model estimated with the Parzen window is established for current data fusion to alarm adaptively. At the meantime, the acoustic emission (AE) signals were acquired for comparison purpose. Experimental results show that this technique is flexible and fast enough to be implemented in real time for online tool condition monitoring

Computed tomography Osteoabsorptiometry: Review of bone density, mechanical strength of material and clinical application

Computed Tomography (CT) imaging is an effective non-invasive examination. It is widely used in the diagnosis of fractures, arthritis, tumor, and some anatomical characteristics of patients. The density value (Hounsfield unit, HU) of a material in computed tomography can be the same for materials with varying elemental compositions. This value depends on the mass density of the material and the degree of X-ray attenuation. Computed Tomography Osteoabsorptiometry (CTOAM) imaging technology is developed on the basis of CT imaging technology. By applying pseudo-color image processing to the articular surface, it is used to analyze the distribution of bone mineralization under the articular cartilage, evaluate the position of prosthesis implantation, track the progression of osteoarthritis, and determine the joint injury prognosis. Furthermore, this technique was combined with indentation testing to discuss the relationship between the high bone density area of the articular surface, the mechanical strength of the bone, and the anchorage stability of the implant, in addition to the study of the relationship between mechanical strength and bone density. This narrative study discusses the pre- and postoperative evaluation of medical device implantation position, orthopedic surgery, and the clinical treatment of bone injury and degeneration. It also discusses the research status of CTOAM technology in image post-processing engineering and the relationship between bone material and mechanical strength

The Path and Enlightenment of Data-Driven Digital Transformation of Organizational Learning ——A Case Study of the Practice of China Telecom

This paper took China Telecom as a case. It has analyzed data-driven digital transformation in organizational learning, and summarized the methods and enlightenments of digital transformation

A new density estimation neural network to detect abnormal condition in streaming data

Along with the development of monitoring technologies, numerous measured data pour into monitoring system and form the high-volume and open-ended data stream. Usually, abnormal condition of monitored system can be characterized by the density variation of measured data stream. However, traditional density estimation methods can not dynamically track density variation of data stream due to the limitation of processing time and computation memory. In this paper, we propose a new density estimation neural network to continuously estimate the density of streaming data in a time-based sliding window. The network has a feedforward structure composed of discretization, input and summation layer. In the discretization layer, value range of data stream is discretized to network nodes with equal intervals. Measured data in the predefined time window are pushed into input layer and updated with the window sliding. In summation layer, the activation results between input neurons and discretization neurons are summed up and multiplied by a weight factor. The network outputs the kernel density estimators of sliding segment in data stream and achieves a one-pass estimation algorithm consuming constant computation memory. By subnet separation and local activation, computation load of the network is significantly reduced to catch up the pace of data stream. The nonlinear statistics, quantile and entropy, which can be consecutively figured out with the density estimators output by the density estimation neural network, are calculated as condition indictors to track the density variation of data stream. The proposed method is evaluated by a simulated data stream consisting of two mixing distribution data sets and a pressure data stream measured from a centrifugal compressor respectively. Results show that the underlying anomalies are successfully detected