Bandwidth Tunable Optical Filter Based on the Quad-Mode Resonator

An innovative bandwidth tunable optical filter with controllable bandwidth, center frequency, and transmission zero is proposed in this paper. The proposed filter utilizes a quad-mode resonator to achieve a wideband filter centered at 2.42 GHz. By incorporating varactor diodes into the open branches of the resonator, the proposed filter's center frequency and bandwidth can be dynamically adjusted via the voltage applied to the diodes. This tunable filter exhibits low insertion loss of less than 2 dB, return loss exceeding 10 dB, and a relative bandwidth of up to 40%

Research on The Offensive Characteristics of La Liga Team Based on Social Network Analysis

To explore the difference of social network parameters between the network of passing before scoring and the network of passing before missing the goal, and to explore the correlation between social network parameters and team performance, this paper establishes the offensive pass network of 20 teams in the La Liga from 2017 to 2018, and 11 social network parameters are calculated. The Pearson correlation test is used to explore the linear correlation between 11 social network parameters and team performance. The results show that the linear correlation between the network parameters of passing before scoring and team performance is stronger than the network parameters of passing before missing the goal. According to the results, we can provide reliable and effective information to the football coaches to help improve the performance of football matches

Occurrence and Aquatic Ecological Risk Assessment of Typical Organic Pollutants in Water of Yangtze River Estuary

AbstractThe occurrence and distribution of organic pollutants were investigated and their initial aquatic ecological risks were assessed in water of the Yangtze River Estuary (YRE). A total of 18 samples were collected from South Branch of YRE during the flood season in August 2012. Out of 956 organic compounds, 23 organic pollutants were detected by GC-MS and NAGINATATM software which were dominated by phthalate esters (PAEs), petroleum hydrocarbons (PHCs) and substituted benzenes. The total concentration of detected 23 organic pollutants varied from 0.585 to 53.7μg/L in the studied sites. Moreover, the total amounts of PAEs (∑PAEs), PHCs (∑PHCs) and substituted benzenes (∑substituted benzenes) were in the range of 0.184-53.344μg/L, 0-0.164μg/L, and 0.196-1.559μg/L, respectively. The study revealed that PEC/PNEC ratios of 8 organic pollutants were higher than 1 (PEC: Predicted environmental concentration; PNEC: Predicted no effect concentration), while 3 of them Bis(2-ethylhexyl)phthalate, octadecane and nonadecane were found to be >100 and the remaining organic pollutants including diisobutyl phthalate, tridecane, dihexyl phthalate, methyl palmitate and methyl stearate ranged from 1 to 100. These results indicated significant ecological risks of the specific organic pollutants to the aquatic environment of YRE

Structured sparse CCA for brain imaging genetics via graph OSCAR

Recently, structured sparse canonical correlation analysis (SCCA) has received increased attention in brain imaging genetics studies. It can identify bi-multivariate imaging genetic associations as well as select relevant features with desired structure information. These SCCA methods either use the fused lasso regularizer to induce the smoothness between ordered features, or use the signed pairwise difference which is dependent on the estimated sign of sample correlation. Besides, several other structured SCCA models use the group lasso or graph fused lasso to encourage group structure, but they require the structure/group information provided in advance which sometimes is not available

Sparse Canonical Correlation Analysis via Truncated ℓ1-norm with Application to Brain Imaging Genetics

Discovering bi-multivariate associations between genetic markers and neuroimaging quantitative traits is a major task in brain imaging genetics. Sparse Canonical Correlation Analysis (SCCA) is a popular technique in this area for its powerful capability in identifying bi-multivariate relationships coupled with feature selection. The existing SCCA methods impose either the ℓ1-norm or its variants. The ℓ0-norm is more desirable, which however remains unexplored since the ℓ0-norm minimization is NP-hard. In this paper, we impose the truncated ℓ1-norm to improve the performance of the ℓ1-norm based SCCA methods. Besides, we propose two efficient optimization algorithms and prove their convergence. The experimental results, compared with two benchmark methods, show that our method identifies better and meaningful canonical loading patterns in both simulated and real imaging genetic analyse

Dihydroartemisinin Increases the Sensitivity of Photodynamic Therapy Via NF-κB/HIF-1α/VEGF Pathway in Esophageal Cancer Cell in vitro and in vivo

Background/Aims: Although photodynamic therapy (PDT) can relieve esophageal obstruction and prolong survival time of patients with esophageal cancer, it can induce nuclear factor-kappa B (NF-κB) activation in many cancers, which plays a negative role in PDT. Dihydroartemisinin (DHA), the most potent artemisinin derivative, can enhance the effect of PDT on esophageal cancer cells. However, the mechanism is still unclear. Methods: We generated stable cell lines expressing the super-repressor form of the NF-κB inhibitor IκBα and cell lines with lentivirus vector-mediated silencing of the HIF-1α gene. Esophageal xenograft tumors were created by subcutaneous injection of Eca109 cells into BALB/c nude mice. Four treatment groups were analyzed: a control group, photosensitizer alone group, light alone group, and PDT group. NF-κB expression was detected by an electrophoretic mobility shift assay, hypoxia-inducible factor α (HIF-1α) and vascular endothelial growth factor (VEGF) by real-time PCR, NF-κB, HIF-1α, and VEGF protein by western blot, and Ki-67, HIF-1α, VEGF, and NF-κB protein by immunohistochemistry. Results: PDT increased NF-κB activity and the gene expression of HIF-1α and VEGF in vitro and in vivo. In contrast, the DHA groups, particularly the combined DHA and PDT treatment group, abolished the effect. The combined treatment significantly inhibited tumor growth in vitro and in vivo. NF-κB activity and HIF-1α expression were also reduced in the stable IκBα expression group, whereas the former showed no change in HIF-1α-silenced cells. Conclusion: DHA might increase the sensitivity of esophageal cancer cells to PDT by inhibiting the NF-κB/HIF-1α/VEGF pathway

Statistical Optimization of Operational Parameters for Enhanced Naphthalene Degradation by Photocatalyst

The optimization of operational parameters for enhanced naphthalene degradation by TiO2/Fe3O4-SiO2 (TFS) photocatalyst was conducted using statistical experimental design and analysis. Central composite design method of response surface methodology (RSM) was adopted to investigate the optimum value of the selected factors for achieving maximum naphthalene degradation. Experimental results showed that irradiation time, pH, and TFS photocatalyst loading had significant influence on naphthalene degradation and the maximum degradation rate of 97.39% was predicted when the operational parameters were irradiation time 97.1 min, pH 2.1, and catalyst loading 0.962 g/L, respectively. The results were further verified by repeated experiments under optimal conditions. The excellent correlation between predicted and measured values further confirmed the validity and practicability of this statistical optimum strategy

Mining Outcome-relevant Brain Imaging Genetic Associations via Three-way Sparse Canonical Correlation Analysis in Alzheimer’s Disease

Neuroimaging genetics is an emerging field that aims to identify the associations between genetic variants (e.g., single nucleotide polymorphisms (SNPs)) and quantitative traits (QTs) such as brain imaging phenotypes. In recent studies, in order to detect complex multi-SNP-multi-QT associations, bi-multivariate techniques such as various structured sparse canonical correlation analysis (SCCA) algorithms have been proposed and used in imaging genetics studies. However, associations between genetic markers and imaging QTs identified by existing bi-multivariate methods may not be all disease specific. To bridge this gap, we propose an analytical framework, based on three-way sparse canonical correlation analysis (T-SCCA), to explore the intrinsic associations among genetic markers, imaging QTs, and clinical scores of interest. We perform an empirical study using the Alzheimer’s Disease Neuroimaging Initiative (ADNI) cohort to discover the relationships among SNPs from AD risk gene APOE, imaging QTs extracted from structural magnetic resonance imaging scans, and cognitive and diagnostic outcomes. The proposed T-SCCA model not only outperforms the traditional SCCA method in terms of identifying strong associations, but also discovers robust outcome-relevant imaging genetic patterns, demonstrating its promise for improving disease-related mechanistic understanding

Identifying Associations Between Brain Imaging Phenotypes and Genetic Factors via A Novel Structured SCCA Approach

Brain imaging genetics attracts more and more attention since it can reveal associations between genetic factors and the structures or functions of human brain. Sparse canonical correlation analysis (SCCA) is a powerful bi-multivariate association identification technique in imaging genetics. There have been many SCCA methods which could capture different types of structured imaging genetic relationships. These methods either use the group lasso to recover the group structure, or employ the graph/network guided fused lasso to find out the network structure. However, the group lasso methods have limitation in generalization because of the incomplete or unavailable prior knowledge in real world. The graph/network guided methods are sensitive to the sign of the sample correlation which may be incorrectly estimated. We introduce a new SCCA model using a novel graph guided pairwise group lasso penalty, and propose an efficient optimization algorithm. The proposed method has a strong upper bound for the grouping effect for both positively and negatively correlated variables. We show that our method performs better than or equally to two state-of-the-art SCCA methods on both synthetic and real neuroimaging genetics data. In particular, our method identifies stronger canonical correlations and captures better canonical loading profiles, showing its promise for revealing biologically meaningful imaging genetic associations