ISBDD model for classification of hyperspectral remote sensing imagery

The diverse density (DD) algorithm was proposed to handle the problem of low classification accuracy when training samples contain interference such as mixed pixels. The DD algorithm can learn a feature vector from training bags, which comprise instances (pixels). However, the feature vector learned by the DD algorithm cannot always effectively represent one type of ground cover. To handle this problem, an instance space-based diverse density (ISBDD) model that employs a novel training strategy is proposed in this paper. In the ISBDD model, DD values of each pixel are computed instead of learning a feature vector, and as a result, the pixel can be classified according to its DD values. Airborne hyperspectral data collected by the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) sensor and the Push-broom Hyperspectral Imager (PHI) are applied to evaluate the performance of the proposed model. Results show that the overall classification accuracy of ISBDD model on the AVIRIS and PHI images is up to 97.65% and 89.02%, respectively, while the kappa coefficient is up to 0.97 and 0.88, respectively

Variants of the low oxygen sensors EGLN1 and HIF-1AN associated with acute mountain sickness.

Two low oxygen sensors, Egl nine homolog 1 (EGLN1) and hypoxia-inducible factor 1-α inhibitor (HIF-1AN), play pivotal roles in the regulation of HIF-1α, and high altitude adaption may be involved in the pathology of acute mountain sickness (AMS). Here, we aimed to analyze single nucleotide polymorphisms (SNPs) in the untranslated regions of the EGLN1 and HIF-1AN genes and SNPs chosen from a genome-wide adaptation study of the Han Chinese population. To assess the association between EGLN1 and HIF-1AN SNPs and AMS in a Han Chinese population, a case-control study was performed including 190 patients and 190 controls. In total, thirteen SNPs were genotyped using the MassARRAY® MALDI-TOF system. Multiple genetic models were tested; The Akaike's information criterion (AIC) and Bayesian information criterion (BIC) values indicated that the dominant model may serve as the best-fit model for rs12406290 and rs2153364 of significant difference. However, these data were not significant after Bonferroni correction. No significant association was noted between AMS and rs12757362, rs1339894, rs1361384, rs2009873, rs2739513 or rs2486729 before and after Bonferroni correction. Further haplotype analyses indicated the presence of two blocks in EGLN1; one block consists of rs12406290-rs2153364, located upstream of the EGLN1 gene. Carriers of the "GG" haplotype of rs12406290-rs2153364 exhibited an increased risk of AMS after adjustments for age and smoking status. However, no significant association was observed among HIF-1AN 3'-untranslated region (3'-UTR) polymorphisms, haplotype and AMS. Our study indicates that variants in the EGLN1 5'-UTR influence the susceptibility to AMS in a Han Chinese population

Optimization of extraction condition for phytic acid from peanut meal by response surface methodology

Phytic acid (PA), a molecule with high commercial value, is one of the important component in peanutmeal. However, PA has not yet been isolated from peanut meal and played its role. This paper reportedthe extraction conditions of PA from peanut meal after removed protein. The independent variables werehydrochloric acid (HCl) concentration, solid to liquid ratio, extraction time and extraction temperature.Response surface methodology (RSM) was used to optimize the extraction conditions based on the extractionyield of PA. The results show that the second-order polynomial models derived from responseswell with the experimental (R2 = 0.9783). The optimal extraction condition was obtained with solid toliquid ratio of 1:16 (g:mL), HCl concentration of 0.02 mol/L, extraction time of 105 min, and extractiontemperature of 30 °C. At this condition, PA with higher purity were obtained. the extraction ratio was6.12%, and the content of PA was 182.7 mg/g dry PA extract. The experimental values under optimal conditionwere in good consistent with the predicted values. The PA extracted from peanut meal was verifiedqualitatively by IR spectra. The extraction technology of PA from peanut meal has a strong potential forrealized high-value utilization of peanut meal

miR-1908 as a novel prognosis marker of glioma via promoting malignant phenotype and modulating SPRY4/RAF1 axis

MicroRNAs (miRNAs) are reported to be involved in the development of glioma. However, study on miRNAs in glioma is limited. The present study aimed to identify miRNAs which can act as potential novel prognostic markers for glioma and analyze its possible mechanism. We show that miR-1908 correlates with shorter survival time of glioma patients via promoting cell proliferation, invasion, anti-apoptosis and regulating SPRY4/RAF1 axis. Analysis of GEO and TCGA database found that miR-1908 was significantly upregulated in glioma tissues, and strongly associated with shorter survival time of glioma patients. Further Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis revealed that miR-1908 is mainly involved in regulating cell proliferation, invasion and apoptosis. To further confirm the above results, in vitro, glioma U251 cells were transfected with miR-1908 mimics or inhibitor, and upregulated miR-1908 promoted U251 cell proliferation, and enhanced the ability of invasion by transwell assay. In addition, upregulated miR-1908 also enhanced anti-apoptosis ability of U251 cells through decreasing pro-apoptosis protein Bax expression. Since miRNAs regulate numerous biological processes by targeting broad set of messenger RNAs, validated target genes of miR-1908 in glioma were analyzed by Targetscan and miRTarBase databases. Among them SPRY4 was significantly decreased in glioma tissues and associated with short survival time, which was selected as the key target gene of miR-1908. Moreover, protein-protein interaction (PPI) showed that SPRY4 could interacted with pro-oncogene RAF1 and negatively correlated with RAF1 expression. Consistent with above analysis, in vitro, western blot analysis identified that miR-1908 upregulated significantly decreased SPRY4 expression and increased RAF1 expression. Hence, miR-1908 was correlated with poor prognosis of glioma via promoting cell proliferation, invasion, anti-apoptosis and regulating SPRF4/RAF1 axis. Our results elucidated the tumor promoting role of miR-1908 and established miR-1908 as a potential novel prognostic marker for glioma

Collective Chaos Induced by Structures of Complex Networks

Mapping a complex network of $N$coupled identical oscillators to a quantum system, the nearest neighbor level spacing (NNLS) distribution is used to identify collective chaos in the corresponding classical dynamics on the complex network. The classical dynamics on an Erdos-Renyi network with the wiring probability $p_{ER} \le \frac{1}{N}$ is in the state of collective order, while that on an Erdos-Renyi network with $p_{ER} > \frac{1}{N}$ in the state of collective chaos. The dynamics on a WS Small-world complex network evolves from collective order to collective chaos rapidly in the region of the rewiring probability $p_r \in [0.0,0.1]$, and then keeps chaotic up to $p_r = 1.0$. The dynamics on a Growing Random Network (GRN) is in a special state deviates from order significantly in a way opposite to that on WS small-world networks. Each network can be measured by a couple values of two parameters $(\beta ,\eta)$.Comment: 15 pages, 12 figures, To appear in Physica

A Dynamic Hashing Algorithm Suitable for Embedded System

With the increasing of the data numbers, the linear hashing will be a lot of overflow blocks result from Data skew and the index size of extendible hash will surge so as to waste too much memory. This lead to the above two Typical Dynamic hashing algorithm don’t suitable for embedded system that need certain real-time requirements and memory resources are very scarce. To solve this problem, this paper was proposed a dynamic hashing algorithm suitable for embedded system combining with the characteristic of extendible hashing and linear hashing.it is no overflow buckets and the index size is proportional to the adjustment number. DOI: http://dx.doi.org/10.11591/telkomnika.v11i6.267