Matriks Jordan Dan Aplikasinya Pada Sistem Linier Waktu Diskrit

Matrix is diagonalizable (similar with matrix diagonal) if and only if the sum of geometric multiplicities of its eigenvalues is n.If we search for an upper triangular form that is nearly diagonal as possible but is still attainable by similarity for every matrix, especially the sum of geometric multiplicities of its eigenvalues is less than n, the result is the Jordan canonical form, which is denoted by , and . In this paper, will be described how to get matrix S(in order to get matrix ) by using generalized eigenvector. In addition, it will also describe the Jordan canonical form and its properties, and some observation and application on discrete time linear system

Elevated Serum Levels of Retinol-Binding Protein 4 Are Associated with Breast Cancer Risk: A Case-Control Study

<div><p>Background</p><p>Retinol binding protein 4 (RBP4) is a recently identified adipokine that is elevated in patients with obesity or type 2 diabetes. A growing body of research has shown that RBP4 is associated with several types of cancer. However, no studies have investigated the relationship between serum RBP4 levels and breast cancer risk. We performed a case-control study to evaluate the association between serum RBP4 levels and the risk of breast cancer.</p><p>Methods</p><p>From August 2012 to December 2013, four-hundred subjects including 200 patients diagnosed with primary breast cancer and 200 matched healthy women were consecutively enrolled from Affiliated Hospital of Qingdao University Medical College. Blood samples were collected from healthy controls and breast cancer patients before commencement of treatment. Enzyme-linked immunosorbent assay was used to evaluate the serum RBP4 levels in separated serum samples. Meanwhile, the characteristics of breast cancer cases and controls were collected from medical records and pathological data.</p><p>Results</p><p>The serum levels of RBP4 were significantly higher in patients with breast cancer than that in the healthy control group (33.77±9.92 vs. 28.77±6.47μg/ml, P < 0.05). Compared to the subjects in the lowest quartile of serum RBP4 level, the adjusted ORs (95% CIs) is 2.16(1.01–4.61) and 2.07 (1.07–4.00) for women in the second and highest RBP4 tertile, respectively. For breast cancer patients, patients with PR or ER negative displayed significantly higher serum RBP4 levels than those with PR or ER positive.</p><p>Conclusion</p><p>Our results for the first time suggested serum RBP4 levels could be associated with the risk of breast cancer. However, further prospective studies are essential to confirm these observed results.</p></div

Stratified analyses of Odds ratios and 95% confidence intervals of breast cancer with RBP4.

<p>Stratified analyses of Odds ratios and 95% confidence intervals of breast cancer with RBP4.</p

Logistic Regression Analysis of Risk of Breast cancer for Serum Levels of RBP4.

<p>Logistic Regression Analysis of Risk of Breast cancer for Serum Levels of RBP4.</p

Partial correlation coefficient (β) for RBP4 and metabolism indexes.

<p>Partial correlation coefficient (β) for RBP4 and metabolism indexes.</p

Correlation between Serum RBP4 and clinical characteristics in breast cancer patients.

<p>Correlation between Serum RBP4 and clinical characteristics in breast cancer patients.</p

Additional file 2: Figure S2. of Label-Free 3D Ag Nanoflower-Based Electrochemical Immunosensor for the Detection of Escherichia coli O157:H7 Pathogens

SEM images of Ag@BSA microspheres prepared without BSA addition. (TIF 398 kb

Additional file 1: Figure S1. of Label-Free 3D Ag Nanoflower-Based Electrochemical Immunosensor for the Detection of Escherichia coli O157:H7 Pathogens

EDX spectrum of Ag@BSA nanoflowers on Si wafer. (TIF 123 kb

A Highly Efficient Gene Expression Programming (GEP) Model for Auxiliary Diagnosis of Small Cell Lung Cancer

<div><p>Background</p><p>Lung cancer is an important and common cancer that constitutes a major public health problem, but early detection of small cell lung cancer can significantly improve the survival rate of cancer patients. A number of serum biomarkers have been used in the diagnosis of lung cancers; however, they exhibit low sensitivity and specificity.</p><p>Methods</p><p>We used biochemical methods to measure blood levels of lactate dehydrogenase (LDH), C-reactive protein (CRP), Na⁺, Cl^-, carcino-embryonic antigen (CEA), and neuron specific enolase (NSE) in 145 small cell lung cancer (SCLC) patients and 155 non-small cell lung cancer and 155 normal controls. A gene expression programming (GEP) model and Receiver Operating Characteristic (ROC) curves incorporating these biomarkers was developed for the auxiliary diagnosis of SCLC.</p><p>Results</p><p>After appropriate modification of the parameters, the GEP model was initially set up based on a training set of 115 SCLC patients and 125 normal controls for GEP model generation. Then the GEP was applied to the remaining 60 subjects (the test set) for model validation. GEP successfully discriminated 281 out of 300 cases, showing a correct classification rate for lung cancer patients of 93.75% (225/240) and 93.33% (56/60) for the training and test sets, respectively. Another GEP model incorporating four biomarkers, including CEA, NSE, LDH, and CRP, exhibited slightly lower detection sensitivity than the GEP model, including six biomarkers. We repeat the models on artificial neural network (ANN), and our results showed that the accuracy of GEP models were higher than that in ANN. GEP model incorporating six serum biomarkers performed by NSCLC patients and normal controls showed low accuracy than SCLC patients and was enough to prove that the GEP model is suitable for the SCLC patients.</p><p>Conclusion</p><p>We have developed a GEP model with high sensitivity and specificity for the auxiliary diagnosis of SCLC. This GEP model has the potential for the wide use for detection of SCLC in less developed regions.</p></div

The detection capability of ANN models in SCLC patients and normal controls.

<p>The detection capability of ANN models in SCLC patients and normal controls.</p