Novel method for extraction of ship target with overlaps in SAR image via EM algorithm

The quality of synthetic aperture radar(SAR)image degrades in the case of multiple imaging projection planes(IPPs)and multiple overlapping ship targets,and then the performance of target classification and recognition can be influenced.For addressing this issue,a method for extracting ship targets with overlaps via the expectation maximization(EM)algorithm is pro-posed.First,the scatterers of ship targets are obtained via the target detection technique.Then,the EM algorithm is applied to extract the scatterers of a single ship target with a single IPP.Afterwards,a novel image amplitude estimation approach is pro-posed,with which the radar image of a single target with a sin-gle IPP can be generated.The proposed method can accom-plish IPP selection and targets separation in the image domain,which can improve the image quality and reserve the target information most possibly.Results of simulated and real mea-sured data demonstrate the effectiveness of the proposed method.

Signaling pathway/molecular targets and new targeted agents under development in hepatocellular carcinoma

Advances in molecular cell biology over the last de- cade have clarified the mechanisms involved in can- cer growth, invasion, and metastasis, and enabled the development of molecular-targeted agents. To date, sorafenib is the only molecular-targeted agent whose survival benefit has been demonstrated in two global phase 111 randomized controlled trials, and has been approved worldwide. Phase 111 clinical trials of other molecular targeted agents comparing them with sorafenib as first-line treatment agents are ongoing. Those agents target the vascular endothelial growth factor, platelet-derived growth factor receptors, as well as target the epidermal growth factor receptor, insulin- like growth factor receptor and mammalian target of rapamycin, in addition to other molecules targeting other components of the signal transduction pathways. In addition, the combination of sorafenib with standard treatment, such as resection, ablation, transarterial em- bolization, and hepatic arterial infusion chemotherapy are ongoing. This review outlines the main pathways involved in the development and progression of hepato- cellular carcinoma and the new agents that target these pathways. Finally, the current statuses of clinical trials of new agents or combination therapy with sorafenib and standard treatment will also be discussed.

Selective killing p53wT lung cancer cells harboring Arg72 homozygote by a small molecular CypA inhibitor

TP53, encoding a well-known tumor suppressor p53, plays essential roles in tumor initiation and pro- gression, and is frequently mutated in lung cancer. However, pharmacological stabilization and reactivation of p53 have not been actively explored for targeted cancer therapies. Here, we identified a novel Cyclophilin A （CypA） small molecule inhibitor （ HL001 ） that induces non-small cell lung cancer （NSCLC） cell cycle arrest and apoptosis via restoring p53 expression, and further stabilizes p53 through inhibiting the MDM2-mediated p53 ubiqutination. The down-regulation of G3BP1 by HL001 also contributes to p53 stabilization by inhibiting p53 redistribution from nucleus to cytoplasm. Furthermore, HE001 selectively suppresses tumor growth in p53 wild-type NSCLC harboring Arg72 homozygous alleles （p53-72R） through disrupting interaction between MDM2 and p53-72R in a CypA-de- pendent manner. Finally, administration of HE001 alone or co-treatment with cisplatin promotes significant tumor suppression in orthotopic NSCLC mouse model. Collectively, our preclinical study demonstrated that HE001, a small molecule inhibitor of CypA, selectively activated p53WT 72R homozygote and thus inhibits growth of human lung cancer cells. The results presented here demonstrate that the utility of CypA inhibitors serve as an approach to the targeted therapy for individual lung cancer patient.

Hepatitis C virus infection and insulin resistance

Approximately 170 million people worldwide are chronically infected with hepatitis C virus(HCV).Chronic HCV infection is the leading cause for the development of liver fibrosis,cirrhosis,hepatocellular carcinoma(HCC)and is the primary cause for liver transplantation in the western world.Insulin resistance is one of the pathological features in patients with HCV infection and often leads to development of typeⅡdiabetes.Insulin resistance plays an important role in the development of various complications associated with HCV infection.Recent evidence indicates that HCV associated insulin resistance may result in hepatic fibrosis,steatosis,HCC and resistance to anti-viral treatment.Thus,HCV associated insulin resistance is a therapeutic target at any stage of HCV infection.HCV modulates normal cellular gene expression and interferes with the insulin signaling pathway.Various mechanisms have been proposed in regard to HCV mediated insulin resistance,involving up regulation of inflammatory cytokines,like tumor necrosis factor-α,phosphorylation of insulin-receptor substrate-1,Akt,up-regulation of gluconeogenic genes like glucose 6 phosphatase,phosphoenolpyruvate carboxykinase 2,and accumulation of lipid droplets.In this review,we summarize the available information on how HCV infection interferes with insulin signaling pathways resulting in insulin resistance.

Half space object classification via incident angle based fusion of radar and infrared sensors

In this paper,we introduce an incident angle based fusion method for radar and infrared sensors to improve the recognition rate of complex targets under half space scenarios,e.g.,vehicles on the ground in this paper.For radar sensors,convolutional operation is introduced into the autoencoder,a“winner-take-all(WTA)”convolutional autoencoder(CAE)is used to improve the recognition rate of the radar high resolution range profile(HRRP).Moreover,different from the free space,the HRRP in half space is more complex.In order to get closer to the real situation,the half space HRRP is simulated as the dataset.The recognition rate has a growth more than 7%com-pared with the traditional CAE or denoised sparse autoencoder(DSAE).For infrared sensor,a convolutional neural network(CNN)is used for infrared image recognition.Finally,we com-bine the two results with the Dempster-Shafer(D-S)evidence theory,and the discounting operation is introduced in the fusion to improve the recognition rate.The recognition rate after fusion has a growth more than 7%compared with a single sensor.After the discounting operation,the accuracy rate has been improved by 1.5%,which validates the effectiveness of the proposed method.

Parameter estimation of maneuvering targets in OTHR based on sparse time-frequency representation

This paper proposes a new method for estimating the parameter of maneuvering targets based on sparse time-frequency transform in over-the-horizon radar（OTHR）. In this method, the sparse time-frequency distribution of the radar echo is obtained by solving a sparse optimization problem based on the short-time Fourier transform. Then Hough transform is employed to estimate the parameter of the targets. The proposed algorithm has the following advantages： Compared with the Wigner-Hough transform method, the computational complexity of the sparse optimization is low due to the application of fast Fourier transform（FFT）. And the computational cost of Hough transform is also greatly reduced because of the sparsity of the time-frequency distribution. Compared with the high order ambiguity function（HAF） method, the proposed method improves in terms of precision and robustness to noise. Simulation results show that compared with the HAF method, the required SNR and relative mean square error are 8 dB lower and 50 dB lower respectively in the proposed method. While processing the field experiment data, the execution time of Hough transform in the proposed method is only 4% of the Wigner-Hough transform method.

Rationale,design and baseline patient characteristics of the optimal type 2 diabetes management including benchmarking and standard treatment study in Greece

AIM:To describe baseline data of the optimal type2 diabetes management including benchmarking and standard treatment(OPTIMISE)study in Greece.METHODS:"Benchmarking"is the process of receiving feedback comparing one’s performance with that of others.The OPTIMISE(NCT00681850)study is a multinational,multicenter study assessing,at a primary care level,whether using"benchmarking"can help to improve the quality of patient care,compared with a set of guideline-based reference values("non-benchmarking").In the Greek region,797 outpatients(457men,mean age 63.8 years)with type 2 diabetes were enrolled by 84 office-based physicians.Baseline characteristics of this population are presented.RESULTS:Hypertension was the most prevalent concomitant disorder(77.3%)and coronary heart disease was the most frequent macrovascular complication of diabetes(23.8%).Most patients were overweight or obese(body mass index 29.6±5 kg/m2),exhibiting mostly abdominal obesity(waist circumference102.6±13.6 cm).Biguanides were the most prevalent prescribed drugs for the management of diabetes(70.1%of all prescriptions),whereas statins(93.5%of all prescriptions)and angiotensin receptor blockers(55.8%of all prescriptions)were the most prevalent prescribed drugs for hyperlipidemia and hypertension,respectively.Only 37.4%of patients were on aspirin.Despite treatment,pre-defined targets for fasting plasma glucose(<110 mg/dL),glycated hemoglobin(<7%),systolic blood pressure(<130 mmHg and<125mmHg for patients with proteinuria)and low density lipoprotein cholesterol levels(<100 mg/dL and<70mg/dL for patients with coronary heart disease)were reached in a relatively small proportion of patients(29%,53%,27%and 31%,respectively).In a Greek population with type 2 diabetes,the control of glycemia or concomitant disorders which increase cardiovascular risk remains poor.CONCLUSION:Despite relevant treatment,there is a poor control of diabetes,hypertension and hyperlipidemia in Greek outpatients with type 2 diabetes.

Hierarchical power control for OFDM based LTE system

3 GPP LTE has approved uplink intra-cell power control and defined overload indicator （OI） for uplink inter-cell power contrQ1 to mitigate the inter-cell interference （ICI）, respectively. In this pa- per, we propose a hierarchical power control （ HPC ） scheme where intra-eell and inter-cell power controls interact with each other. The inter-cell power control eommand is generated by radio re- source management （RRM） entity according to the ICI-load model together with the current ICI and served load information. This ICI-load model is proposed as a guideline for coordination among cells to enable the system to approach its system specific interference over thermal noise （IoT） work area. Simulation results show that for HPC scheme, the system＇ s IoT is well controlled to fit its pre-de- fined work area and the power efficiency is improved significantly. Our proposed scheme is also ro- bust to different settings of its inter-cell power control period.

Optimized placement of nodes for target detection in sensor networks

In order to improve the precision of the target detection in wireless sensor networks,a new approach based on genetic algorithm(GA)was proposed to optimize the placement of the sensor.The target location problem was transformed into locating a target at a grid point through modeling the sensor field as a grid of points.Moreover,the sensor placement problem was formulated as a combinatorial optimization problem,which is aimed at minimizing the maximum discrimination error under the restraints of limited cost and complete coverage.The GA approach uses binary coding to represent the location,and both single parent crossover operator and single parent mutation operator are used to improve its speed and efficiency.Experimental results have shown that a global optimal solution can be quickly obtained using the proposed method and the precision requirement for target location is satisfied.