Identification of CITES-Listed Euphorbia royleana through DNA Barcoding Technology: A New Facet in Wildlife Forensics

Disorganized and chaotic collection of the Euphorbia plant species from the wild is one of the major reasons for its endangered status. According to CITES, the trade in Euphorbia royleana species is prohibited under Appendix II. However, the trade continues unabated as current identification methods do not discriminate between closely related species. In the present study, a DNA barcoding method has been used to establish inter- and intra-specific divergences of both matK and rbcL regions by using pairwise genetic distance measurement methods for evaluating the maximum barcoding gap. The matk and rbcL yielded a 100% amplification and sequencing success rate to distinguish closely related species of Euphorbia royleana unambiguously. The matk and rbcL showed average interspecific genetic distance divergence values of 0.031and 0.015, respectively. The maximum number of species-specific SNPs was observed in matK sequences at seven consecutive sites, which could distinguish Euphorbia royleana from closely related species. The best candidate barcoding region to identify Euphorbia royleana was found to be matK with a single-locus barcoding approach. Furthermore, the species discrimination method was developed with the help of species-specific SNPs derived from the matK barcoding region to accurately authenticate Euphorbia royleana, and it provided 100% species resolution

A Discrimination Method Between Transformer Inrush and Fault Current Based on Chromatic Monitoring

or successful transformer differential protection employment, correct discrimination between inrush current and fault current is essential. It is one of the main focuses of research and one of the main challenges for transformer protection. In this paper, a discrimination method based on utilizing chromatic monitoring of the box dimension algorithm outcome curve for transformer differential current in time-domain analysis is proposed. The x-L chromatic mapping is employed for general detection of fault cases, while the x-y chromatic mapping result is used for distinguishing inrush current from the fault current cycles. The preliminary results show that the proposed method can effectively provide correct discrimination of the current type within quite a short time and thus help in providing efficient decision-making supportive protection tool.

Decreased Risk of Ovarian Cancer Associated With rs9898876 Sex Hormone-binding Globulin Gene Variant

Background. Ovarian cancer (OC) is one of the most common gynecologic cancers,with significant morbidity and mortality. The risk of OCis influenced by hormone status, of which sex hormone-binding globulin (SHBG), whichinfluences the serum availability of steroid sex hormones, is implicated in the pathogenesis and evolution of OC. The aim of this study is to evaluate the involvement of common SHBG gene variants in OC susceptibility and evolution. Materials. A case control study including 71 OC patients and 74 cancer-free controls, who were genotyped for rs9898876, rs13894, rs1799941 and rs6257 SHBG SNP. Genotyping was done by the allelic discrimination method, using VIC- and FAM-labeled primers.Results. The minor allele frequencies of rs9898876, rs13894, rs1799941 and rs6257 SHBG SNP was comparable between OC cases and control women, implying no significant associations of the tested variants and overall OC risk. Taking homozygous wild-type genotype as reference (OR=1.00), heterozygous rs9898876 (G/T), and minor allele-carrying genotypes [G/T+T/T] were associated with reduced risk of OC. Whilers9898876 heterozygosity (G/T) was predictive of OC occurrence, no significant association of the remaining three tested SNPs was noted with altered risk of OC. Irrespective of FIGO staging, the four tested SHBG SNPs were not associated with the clinical progression of OC.Conclusion. In conclusion, SHBG rs9898876 is associated with a decreased risk of OC, and thus constitutes a potential diagnostic biomarker of OC.

Computer-Aided Diagnosis for Pneumoconiosis Staging Based on Multi-scale Feature Mapping

In this research, we explored a method of multi-scale feature mapping to pre-screen radiographs quickly and accurately in the aided diagnosis of pneumoconiosis staging. We utilized an open dataset and a self-collected dataset as research datasets. We proposed a multi-scale feature mapping model based on deep learning feature extraction technology for detecting pulmonary fibrosis and a discrimination method for pneumoconiosis staging. The diagnostic accuracy was evaluated using under the curve (AUC) of the receiver operating characteristic (ROC) curve. The AUC value of our model was 0.84, which showed the best performance compared with previous work on datasets. The diagnosis results indicated that our method was highly consistent with that of clinical experts on real patient. Furthermore, the AUC value obtained through categories I–IV on the testing dataset demonstrated that categories I (AUC = 0.86) and IV (AUC = 0.82) obtained the best performance and achieved the level of clinician categorization. Our research could be applied to the pre-screening and diagnosis of pneumoconiosis in the clinic.

A novel method for discriminating multi-sensor fusion based on coherent feature

In order to solve the problem of multi-sensor fusion discrimination of the same radar emitter, a multi-sensor fusion discrimination method based on coherent feature is proposed in this paper. First, the simultaneous interpreting of the pulse sequences received by different sensors is carried out. Then the peak value of the autocorrelation function between pulses is used to determine whether the same source is used. The simulation results show the effectiveness of the proposed method.