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Preliminary results indicate that membrane-type 1 matrix metalloproteinase levels correlate with increasing tumor stage.

BackgroundFree fatty acids (FFAs) play importance roles in the development of diabetes and cardiovascular diseases. We measured serum FFA levels from type 2 diabetes mellitus (T2DM) and acute myocardial infarction (AMI) patients and assay the correlation between serum FFA levels and related factors. The present study was undertaken to investigate a possible relation between the changes in serum free fatty acid concentration with acute myocardial infarction and type 2 diabetes mellitus.MethodsThe study population consisted of 540 healthy individuals and 103 patients with T2DM, 59 patients with AMI and 21 volunteers. Serum FFAs were measured with high pressure liquid chromatography. Blood urea nitrogen and uric acid were measured in clinical laboratory, as were glycemic, lipid and blood routine parameters. We selected 242 individuals with age over 60 years, 143 healthy individuals and 52 patients with T2DM, 47 patients with AMI were incorporated into three groups as control group, T2DM group and AMI group. Associations were analyzed with stepwise regression analysis with adjusted for age, sex, body mass index.ResultsSerum FFA levels were significantly higher in the age over 60 years individuals compared to 20 ~ 50 years (logFFA μmmol/L:2.60 ± 0.16 vs. 2.73 ± 0.18, P < .001) in the healthy group. We found lower FFA levels in the AMI compared to the T2DM and control group (2.64 ± 0.22 vs. 2.72 ± 0.13&2.72 ± 0.16, respectively, P < .05&P < 0.01) in the age over 60, fasting blood glucose level higher in the AMI and T2DM (5.78 ± 1.32&7.75 ± 2.93 mmol/L vs. 4.90 ± 0.47 mmol/L, P < .01&P < .001) compared with the normal group, HDL level (1.01 ± 0.22&0.98 ± 0.18 mmol/L vs.1.30 ± 0.22 mmol/L, P < .001&P < .001). With stepwise regression analysis, the serum FFA levels was positively associated with the HDL in the control group (YlogFFA = 2.32 + 0.33XHDL, R = 0.26, P < .01) and T2MD (YlogFFA = 2.46 + 0.27XHDL, R = 0.36, P < .05), AST in AMI (YlogFFA =2.24 + 0. 015XAST, R = 0.49, P < .01).ConclusionsCompared to control group, serum FFA levels were decreased only in AMI group, while HDL level was increased in both AMI and T2DM group. The serum FFA levels were positive association with the HDL level in both T2DM and control group, FFA levels were positive association with AST in AMI.

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It is difficult to recognize Automatic Link Establishment (ALE) behaviors of a shortwave radio station, if we do not acquire the radio station's communication protocol standard. A method is proposed to recognize different ALE behaviors by using an improved unidimensional DenseNet. In this work, we directly recognize ALE signals in physical layer without the radio station's communication protocol standard. Hence, we can avoid difficulties in demodulation, decryption and so on. Actually, the original DenseNet is used extensively in the field of computer vision, so the original DenseNet is firstly adapted for the unidimensional input. And then, two parallel dense blocks are used in our improved unidimensional DenseNet, which could improve the capability of network to extract ALE signals' deep features. The experimental results show that the proposed method is able to recognize different ALE behaviors of a shortwave radio station. And improved DenseNet has better recognition performance than simple DenseNet. The simple DenseNet only contains one dense block. Finally, the results of comparison experiments also show that some classic networks have worse performance in ALE behaviors recognition, such as LeNet-5, ResNet-34, and DenseNet-121. INDEX TERMS Recognition, unidimensional DenseNet, automatic link establishment, shortwave radio station, electronic countermeasure. I. INTRODUCTION The ALE behaviors of a shortwave radio station include Call, Handshake, Notification, Time Offset, Group Time Broadcast, Broadcast and Scanning Call. The Link Establishment (LE) behaviors recognition of a shortwave radio station is important and difficult in the field of electronic countermeasures. Mastering the ALE behaviors of enemy's radio station can effectively infer the communication intention and working status of the radio station in the wireless communication network. It provides a reference for further inferring the topological structure and tactical position of the shortwave radio station. As a reconnoiter in the field of electronic countermeasures, our goal is to recognize a radio station's ALE behaviors without the radio station's communication protocol (MIL-STD-188-141B) [1]. In this work, the purpose of ALE behaviors recognition is achieved by directly analyzing the signals in physical layer, avoiding The associate editor coordinating the review of this manuscript and approving it for publication was Miguel López-Benítez .

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