Through-Wall Detection with LS-SVM under Unknown Wall Characteristics

One of the main challenges in through-wall imaging (TWI) is the presence of the walls, whose returns tend to obscure the target behind the walls and must be considered and computed in the imaging procedure. In this paper, a two-step procedure for the through-wall detection is proposed. Firstly, an effective clutter mitigation method based on singular value decomposition (SVD) is used. It does not require knowledge of the background scene or rely on accurate modeling and estimation of wall parameters. Then, TWI problem is cast as a regression one and solved by means of least-squares support vector machine (LS-SVM). The complex scattering process due to the presence of the walls is automatically included in the nonlinear relationship between the feature vector extracted from the target scattered fields and the position of the target. The relationship is obtained through a training phase using LS-SVM. Simulated results show that the proposed approach is effective. We also analyze the impacts of training samples and signalto-noise ratio (SNR) on test detection accuracy. Simulated results reveal that the proposed LS-SVM based approach can provide comparative performances in terms of accuracy, convergence, robustness, and generalization in comparison with the support vector machine (SVM) based approach

A Drosophila protein-interaction map centered on cell-cycle regulators

BACKGROUND: Maps depicting binary interactions between proteins can be powerful starting points for understanding biological systems. A proven technology for generating such maps is high-throughput yeast two-hybrid screening. In the most extensive screen to date, a Gal4-based two-hybrid system was used recently to detect over 20,000 interactions among Drosophila proteins. Although these data are a valuable resource for insights into protein networks, they cover only a fraction of the expected number of interactions. RESULTS: To complement the Gal4-based interaction data, we used the same set of Drosophila open reading frames to construct arrays for a LexA-based two-hybrid system. We screened the arrays using a novel pooled mating approach, initially focusing on proteins related to cell-cycle regulators. We detected 1,814 reproducible interactions among 488 proteins. The map includes a large number of novel interactions with potential biological significance. Informative regions of the map could be highlighted by searching for paralogous interactions and by clustering proteins on the basis of their interaction profiles. Surprisingly, only 28 interactions were found in common between the LexA- and Gal4-based screens, even though they had similar rates of true positives. CONCLUSIONS: The substantial number of new interactions discovered here supports the conclusion that previous interaction mapping studies were far from complete and that many more interactions remain to be found. Our results indicate that different two-hybrid systems and screening approaches applied to the same proteome can generate more comprehensive datasets with more cross-validated interactions. The cell-cycle map provides a guide for further defining important regulatory networks in Drosophila and other organisms

Fosinopril improves liver fibrosis by upregulating ACE2/Angiotensin-(1-7) axis activation in rats with nonalcoholic steatohepatitis

The aim of this research was to evaluate the effect of the angiotensin converting enzyme (ACE) inhibitor fosinopril on liver fibrosis in rats with high fat diet (HFD) induced nonalcoholic steatohepatitis (NASH). We found that treatment with fosinopril improved liver fibrosis. Moreover, treatment with fosinopril decreased serum Angiotensin (Ang) II, leptin, transforming growth factor β1 and hyaluronic acid concentrations, increased serum ACE2, Ang-(1-7), and adiponectin concentrations in rats fed with HFD. In the liver, fosinopril led to decreased leptin, α-smooth muscle actin, and collagen I expression, increased ACE2 and adiponectin expression. In conclusion, Fosinopril improves liver fibrosis by upregulating ACE2/Ang-(1-7) axis activation in rats with HFD-induced NASH. Furthermore, fosinopril might regulate the progression of liver fibrosis through the downregulation of leptin and the upregulation of adiponectin.Colegio de Farmacéuticos de la Provincia de Buenos Aire

Optimization of Extraction Process of Elaeagnus angustifolia Flower Polysaccharide and Its Proliferation on Probiotic

The study aimed to explore the optimal conditions for the extraction of polysaccharide from Elaeagnus angustifolia flower and its effect on probiotic proliferation in vitro. Polysaccharide was extracted from Elaeagnus angustifolia flower using hot water and ultrasonication. The effects of the material-liquid ratio, duration of ultrasonication, extraction time and extraction temperature on the polysaccharide yield were analyzed. The extraction conditions were optimized by response surface methodology, and the effects of different polysaccharide concentrations (0, 0.5%, 1.0%, 1.5%, 2.0%, and 3.0%) on the proliferation and acid production of three probiotics were compared. The results showed that the optimal extraction conditions of Elaeagnus angustifolia flower polysaccharide were as follows: Material-liquid ratio, 1:25 g/mL, duration of ultrasonication, 21 min, extraction temperature, 72℃, extraction time, 62 min. The polysaccharide yield was 12.45%±0.15%, which was close to the theoretical predicted yield (12.587%). The highest OD values of Lactobacillus acidophilus, Bifidobacterium bifidum, and Bifidobacterium adolescentis were obtained at a polysaccharide mass concentration of 2%, being 1.23±0.01, 1.06±0.02, and 1.22±0.02, respectively, and the lowest pH values (5.17±0.04, 5.95±0.04, and 5.52±0.02, respectively). The growth of the three probiotics stabilized after the incubation time reached to 40 h. It indicated that Elaeagnus angustifolia flower polysaccharide promoted the proliferation and acid production of three probiotics. These findings indicate the potential of the polysaccharide from Elaeagnus angustifolia flower as a prebiotic and provide a theoretical basis for further research and the utilization of Elaeagnus angustifolia flower resources

Effect of solution-focused approach on anxiety and depression in patients with rheumatoid arthritis: A quasi-experimental study

IntroductionAnxiety and depression are common psychological problems in rheumatoid arthritis (RA) patients. However, few effective nursing intervention models have been designed specifically to improve anxiety and depression in RA patients. Solution-focused approach (SFA) is an effective intervention method for psychosocial issues. There have been no studies involving SFA yet in RA patients. This study investigated the effects of SFA-based nursing intervention on anxiety and depression in RA patients.MethodsA quasi-experimental study using a convenience sampling of RA patients was conducted. The 48 RA patients were divided into the control group (n = 24) and the experimental group (n = 24). The control group received routine nursing intervention, while the experimental group received SFA-based nursing intervention. The scores on the self-rating anxiety scale (SAS), self-rating depression scale (SDS), arthritis self-efficacy scale-8 (ASES-8), and questionnaire on patient satisfaction with nursing care were collected before and after nursing interventions.ResultsBetween-Group Comparison: Before the nursing intervention, there was no statistically significant difference in the SDS, SAS, and ASES-8 scores between the two groups (p > 0.05). However, after the nursing intervention, the SDS and SAS scores of the experimental group were statistically significantly lower than those of the control group (p < 0.05). In contrast, the ASES-8 score of the experimental group was statistically significantly higher than that of the control group (p < 0.05). In addition, patient satisfaction with nursing care of the experimental group was better than that of the control group (p > 0.05). Within-Group Comparison: There was no statistically significant difference in the SDS, SAS, and ASES-8 scores in the control group before and after routine nursing intervention (p > 0.05). However, in the experimental group, the SDS and SAS scores before SFA-based nursing intervention were statistically significantly higher than those after SFA nursing intervention (p < 0.05), and the ASES-8 score before SFA-based nursing intervention was considerably lower than that after SFA nursing intervention (p < 0.05).DiscussionSFA-based nursing intervention can effectively improve anxiety, depression, and arthritis self-efficacy of RA patients. This study broadens clinical psychological nursing intervention models for RA patients. SFA may be an effective nursing model for various psychosocial problems in the current medical context

Radiogenomics analysis reveals the associations of dynamic contrast-enhanced–MRI features with gene expression characteristics, PAM50 subtypes, and prognosis of breast cancer

BackgroundTo investigate reliable associations between dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) features and gene expression characteristics in breast cancer (BC) and to develop and validate classifiers for predicting PAM50 subtypes and prognosis from DCE-MRI non-invasively.MethodsTwo radiogenomics cohorts with paired DCE-MRI and RNA-sequencing (RNA-seq) data were collected from local and public databases and divided into discovery (n = 174) and validation cohorts (n = 72). Six external datasets (n = 1,443) were used for prognostic validation. Spatial–temporal features of DCE-MRI were extracted, normalized properly, and associated with gene expression to identify the imaging features that can indicate subtypes and prognosis.ResultsExpression of genes including RBP4, MYBL2, and LINC00993 correlated significantly with DCE-MRI features (q-value < 0.05). Importantly, genes in the cell cycle pathway exhibited a significant association with imaging features (p-value < 0.001). With eight imaging-associated genes (CHEK1, TTK, CDC45, BUB1B, PLK1, E2F1, CDC20, and CDC25A), we developed a radiogenomics prognostic signature that can distinguish BC outcomes in multiple datasets well. High expression of the signature indicated a poor prognosis (p-values < 0.01). Based on DCE-MRI features, we established classifiers to predict BC clinical receptors, PAM50 subtypes, and prognostic gene sets. The imaging-based machine learning classifiers performed well in the independent dataset (areas under the receiver operating characteristic curve (AUCs) of 0.8361, 0.809, 0.7742, and 0.7277 for estrogen receptor (ER), human epidermal growth factor receptor 2 (HER2)-enriched, basal-like, and obtained radiogenomics signature). Furthermore, we developed a prognostic model directly using DCE-MRI features (p-value < 0.0001).ConclusionsOur results identified the DCE-MRI features that are robust and associated with the gene expression in BC and displayed the possibility of using the features to predict clinical receptors and PAM50 subtypes and to indicate BC prognosis