Identification of Potential Prognostic Genes for Neuroblastoma

Background and Objective: Neuroblastoma (NB), the most common pediatric solid tumor apart from brain tumor, is associated with dismal long-term survival. The aim of this study was to identify a gene signature to predict the prognosis of NB patients.Materials and Methods: GSE49710 dataset from the Gene Expression Omnibus (GEO) database was downloaded and differentially expressed genes (DEGs) were analyzed using R package “limma” and SPSS software. The gene ontology (GO) and pathway enrichment analysis were established via DAVID database. Random forest (RF) and risk score model were used to pick out the gene signature in predicting the prognosis of NB patients. Simultaneously, the receiving operating characteristic (ROC) and Kaplan-Meier curve were plotted. GSE45480 and GSE16476 datasets were employed to validate the robustness of the gene signature.Results: A total of 131 DEGs were identified, which were mainly enriched in cancer-related pathways. Four genes (ERCC6L, AHCY, STK33, and NCAN) were selected as a gene signature, which was included in the top six important features in RF model, to predict the prognosis in NB patients, its area under the curve (AUC) could reach 0.86, and Cox regression analysis revealed that the 4-gene signature was an independent prognostic factor of overall survival and event-free survival. As well as in GSE16476. Additionally, the robustness of discriminating different groups of the 4-gene signature was verified to have a commendable performance in GSE45480 and GSE49710.Conclusion: The present study identified a gene-signature in predicting the prognosis in NB, which may provide novel prognostic markers, and some of the genes may be as treatment targets according to biological experiments in the future

Efficient Iris Recognition Based on Optimal Subfeature Selection and Weighted Subregion Fusion

In this paper, we propose three discriminative feature selection strategies and weighted subregion matching method to improve the performance of iris recognition system. Firstly, we introduce the process of feature extraction and representation based on scale invariant feature transformation (SIFT) in detail. Secondly, three strategies are described, which are orientation probability distribution function (OPDF) based strategy to delete some redundant feature keypoints, magnitude probability distribution function (MPDF) based strategy to reduce dimensionality of feature element, and compounded strategy combined OPDF and MPDF to further select optimal subfeature. Thirdly, to make matching more effective, this paper proposes a novel matching method based on weighted sub-region matching fusion. Particle swarm optimization is utilized to accelerate achieve different sub-region’s weights and then weighted different subregions’ matching scores to generate the final decision. The experimental results, on three public and renowned iris databases (CASIA-V3 Interval, Lamp, andMMU-V1), demonstrate that our proposed methods outperform some of the existing methods in terms of correct recognition rate, equal error rate, and computation complexity

Novel Approaches to Improve Iris Recognition System Performance Based on Local Quality Evaluation and Feature Fusion

For building a new iris template, this paper proposes a strategy to fuse different portions of iris based on machine learning method to evaluate local quality of iris. There are three novelties compared to previous work. Firstly, the normalized segmented iris is divided into multitracks and then each track is estimated individually to analyze the recognition accuracy rate (RAR). Secondly, six local quality evaluation parameters are adopted to analyze texture information of each track. Besides, particle swarm optimization (PSO) is employed to get the weights of these evaluation parameters and corresponding weighted coefficients of different tracks. Finally, all tracks’ information is fused according to the weights of different tracks. The experimental results based on subsets of three public and one private iris image databases demonstrate three contributions of this paper. (1) Our experimental results prove that partial iris image cannot completely replace the entire iris image for iris recognition system in several ways. (2) The proposed quality evaluation algorithm is a self-adaptive algorithm, and it can automatically optimize the parameters according to iris image samples’ own characteristics. (3) Our feature information fusion strategy can effectively improve the performance of iris recognition system

The Impact of Ecological Construction Programs on Grassland Conservation in Inner Mongolia, China

A series of Ecological Construction Programs have been initiated to protect the condition of grasslands in China during recent decades. However, grassland degradation is still severe, and conditions have not been restored as intended. This paper aims to empirically examine the effectiveness of these programs for protecting the grassland condition in the extensive pastoral areas of China. We focus on one major program that has been implemented widely on the grasslands, the Subsidy and Incentive System for Grassland Conservation (SISGC). The normalized difference vegetation index, measured with remote sensing technology, is used to quantify the grassland condition between 2001 and 2014. With data from 54 counties in the pastoral areas of Inner Mongolia, we estimate the impact of SISGC on the grassland condition. A fixed effects model is employed to control for livestock production, climate, time trends, and time-invariant heterogeneity between counties. The model results provide quantitative evidence that the condition of the grasslands has improved significantly because of SISGC; but that the effectiveness of SISGC was offset to some extent by other socio-economic and climate factors, such as increased producer prices and high temperature. This may explain why the actual grassland degradation has not been prevented as effectively as was expected. In addition, the impact of SISGC was stronger in counties with worse initial grassland condition. Furthermore, the effects of producer prices and climate changes were also more pronounced in these counties

A New Method of RNA Secondary Structure Prediction Based on Convolutional Neural Network and Dynamic Programming

In recent years, obtaining RNA secondary structure information has played an important role in RNA and gene function research. Although some RNA secondary structures can be gained experimentally, in most cases, efficient, and accurate computational methods are still needed to predict RNA secondary structure. Current RNA secondary structure prediction methods are mainly based on the minimum free energy algorithm, which finds the optimal folding state of RNA in vivo using an iterative method to meet the minimum energy or other constraints. However, due to the complexity of biotic environment, a true RNA structure always keeps the balance of biological potential energy status, rather than the optimal folding status that meets the minimum energy. For short sequence RNA its equilibrium energy status for the RNA folding organism is close to the minimum free energy status; therefore, the minimum free energy algorithm for predicting RNA secondary structure has higher accuracy. Nevertheless, in a longer sequence RNA, constant folding causes its biopotential energy balance to deviate far from the minimum free energy status. This deviation is because of its complex structure and results in a serious decline in the prediction accuracy of its secondary structure. In this paper, we propose a novel RNA secondary structure prediction algorithm using a convolutional neural network model combined with a dynamic programming method to improve the accuracy with large-scale RNA sequence and structure data. We analyze current experimental RNA sequences and structure data to construct a deep convolutional network model, and then we extract implicit features of an effective classification from large-scale data to predict the pairing probability of each base in an RNA sequence. For the obtained probabilities of RNA sequence base pairing, an enhanced dynamic programming method is applied to obtain the optimal RNA secondary structure. Results indicate that our proposed method is superior to the common RNA secondary structure prediction algorithms in predicting three benchmark RNA families. Based on the characteristics of deep learning algorithm, it can be inferred that the method proposed in this paper has a 30% higher prediction success rate when compared with other algorithms, which will be needed as the amount of real RNA structure data increases in the future

Long Non-coding RNA LINC00941 as a Potential Biomarker Promotes the Proliferation and Metastasis of Gastric Cancer

Gastric cancer (GC) is a considerable global health burden. Accumulating evidence suggests that long non-coding RNAs (lncRNAs) are aberrantly expressed in many cancers and play important roles in GC. However, only a few lncRNAs have been functionally characterized. In this study, we identified that long intergenic non-protein coding RNA 941 (LINC00941) is a potential biomarker for diagnosis and prognosis from the cancer genome atlas (TCGA), and we found that the expression of LINC00941 is associated with tumor depth and distant metastasis in GC. Furthermore, functional enrichment analysis of LINC00941 co-expression network demonstrated that LINC00941 might be an essential regulator of tumor metastasis and cancer cell proliferation. To validate our findings, we utilized the loss-of-function analysis to reveal the biological function of LINC00941 in GC cells. Loss-of-function analysis revealed that silence of LINC00941 inhibits GC cells proliferation, migration, and invasion in vitro and modulates tumor growth in vivo. Our findings confirmed that LINC00941 plays an important oncogenic function in GC and may serve as a potential biomarker for diagnosis and prognosis of GC

Conceptual design and progress of transmitting ∼\sim MV DC HV into 4 K LHe detectors

A dual-phase TPC (Time Projection Chamber) is more advanced in characterizing an event than a single-phase one because it can, in principle, reconstruct the 3D (X-Y-Z) image of the event, while a single-phase detector can only show a 2D (X-Y) picture. As a result, more enriched physics is expected for a dual-phase detector than a single-phase one. However, to build such a detector, DC HV (High Voltage) must be delivered into the chamber (to have a static electric field), which is a challenging task, especially for an LHe detector due to the extremely low temperature, $\sim$ 4 K, and the very high voltage, $\sim$ MV (Million Volts). This article introduces a convincing design for transmitting $\sim$ MV DC into a 4 K LHe detector. We also report the progress of manufacturing a 100 kV DC feedthrough capable of working at 4 K. Surprisingly, we realized that the technology we developed here might be a valuable reference to the scientists and engineers aiming to build residential bases on the Moon or Mars