Researches on Key Algorithms in Analogue Seismogram Records Vectorization

Abstract: History paper seismograms are very important information for earthquake monitoring and prediction, and the vectorization of paper seismograms is a very import problem to be resolved. In our study, a new tracing algorithm for simulated seismogram curves based on visual filed feature is presented. We also give out the technological process to vectorizing simulated seismograms, and an analog seismic record vectorization system has been accomplished independently. Using it, we can precisely and speedy vectorize analog seismic records (need professionals to participate interactively)

Phylogenetics of the skyhoppers (Kosciuscola) of the Australian Alps : evolutionary and conservation implications

The true biodiversity of Australia's alpine and subalpine endemics is unknown. Genetic studies to date have focused on sub-regions and restricted taxa, but even so, indicate deep divergences across small geographic scales and therefore that the bulk of biodiversity remains to be discovered. We aimed to study the phylogeography of the Australian Alps by focusing on the skyhoppers (Kosciuscola), a genus of five species of flightless grasshoppers whose combined distributions both span the region and are almost exclusively contained within it. Our sampling covered 650 km on the mainland and several sites in Tasmania with total of 260 specimens used to reconstruct a robust phylogeny of Koscisucola. Phylogenies were based on single nucleotide polymorphism data generated from double-digested restriction-associated DNA sequencing. Skyhoppers diverged around 2 million years ago and have since undergone complex diversification seemingly driven by climatic oscillations throughout the Pleistocene. We recovered not 5 but 14 clades indicating the presence of many unknown species. Our results support conspicuous geographic features as genetic breaks; e.g. the Murray Valley, and inconspicuous ones; e.g. between the Bogong High Plains and Mt Hotham. Climate change is progressing quickly in the region and its impact, particularly on snow, could have severe consequences for the skyhoppers' overwinter survival. The true diversity of skyhoppers highlights that biodiversity loss in the Alps as a result of climate change is likely to be far greater than what can be estimated based on current species numbers and that management including small geographical scales is key

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal Carcinoma Tumor Segmentation across Multiple Hospitals

Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant malignancy that predominantly impacts the head and neck area. Precise delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring effective radiotherapy for NPC. Despite recent methods that have achieved promising results on GTV segmentation, they are still limited by lacking carefully-annotated data and hard-to-access data from multiple hospitals in clinical practice. Although some unsupervised domain adaptation (UDA) has been proposed to alleviate this problem, unconditionally mapping the distribution distorts the underlying structural information, leading to inferior performance. To address this challenge, we devise a novel Sourece-Free Active Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV segmentation task. Specifically, we design a dual reference strategy to select domain-invariant and domain-specific representative samples from a specific target domain for annotation and model fine-tuning without relying on source-domain data. Our approach not only ensures data privacy but also reduces the workload for oncologists as it just requires annotating a few representative samples from the target domain and does not need to access the source data. We collect a large-scale clinical dataset comprising 1057 NPC patients from five hospitals to validate our approach. Experimental results show that our method outperforms the UDA methods and achieves comparable results to the fully supervised upper bound, even with few annotations, highlighting the significant medical utility of our approach. In addition, there is no public dataset about multi-center NPC segmentation, we will release code and dataset for future research

Divergent and convergent modes of interaction between wheat and Puccinia graminis f. sp tritici isolates revealed by the comparative gene co-expression network and genome analyses

Citation: Rutter, W. B., Salcedo, A., Akhunova, A., He, F., Wang, S. C., Liang, H. Q., . . . Akhunov, E. (2017). Divergent and convergent modes of interaction between wheat and Puccinia graminis f. sp tritici isolates revealed by the comparative gene co-expression network and genome analyses. Bmc Genomics, 18, 20. doi:10.1186/s12864-017-3678-6Background: Two opposing evolutionary constraints exert pressure on plant pathogens: one to diversify virulence factors in order to evade plant defenses, and the other to retain virulence factors critical for maintaining a compatible interaction with the plant host. To better understand how the diversified arsenals of fungal genes promote interaction with the same compatible wheat line, we performed a comparative genomic analysis of two North American isolates of Puccinia graminis f. sp. tritici (Pgt). Results: The patterns of inter-isolate divergence in the secreted candidate effector genes were compared with the levels of conservation and divergence of plant-pathogen gene co-expression networks (GCN) developed for each isolate. Comprative genomic analyses revealed substantial level of interisolate divergence in effector gene complement and sequence divergence. Gene Ontology (GO) analyses of the conserved and unique parts of the isolate-specific GCNs identified a number of conserved host pathways targeted by both isolates. Interestingly, the degree of inter-isolate sub-network conservation varied widely for the different host pathways and was positively associated with the proportion of conserved effector candidates associated with each sub- network. While different Pgt isolates tended to exploit similar wheat pathways for infection, the mode of plant-pathogen interaction varied for different pathways with some pathways being associated with the conserved set of effectors and others being linked with the diverged or isolate-specific effectors. Conclusions: Our data suggest that at the intra-species level pathogen populations likely maintain divergent sets of effectors capable of targeting the same plant host pathways. This functional redundancy may play an important role in the dynamic of the "arms-race" between host and pathogen serving as the basis for diverse virulence strategies and creating conditions where mutations in certain effector groups will not have a major effect on the pathogen's ability to infect the host

Genotype imputation in winter wheat using first-generation haplotype map SNPs improves genome-wide association mapping and genomic prediction of traits

Genome-wide single nucleotide polymorphism (SNP) variation allows for the capture of haplotype structure in populations and prediction of unobserved genotypes based on inferred regions of identity-by-descent (IBD). Here we have used a first-generation wheat haplotype map created by targeted re-sequencing of low-copy genomic regions in the reference panel of 62 lines to impute marker genotypes in a diverse panel of winter wheat cultivars from the U.S. Great Plains. The IBD segments between the reference population and winter wheat cultivars were identified based on SNP genotyped using the 90K iSelect wheat array and genotyping by sequencing (GBS). A genome-wide association study and genomic prediction of resistance to stripe rust in winter wheat cultivars showed that an increase in marker density achieved by imputation improved both the power and precision of trait mapping and prediction. The majority of the most significant marker-trait associations belonged to imputed genotypes. With the vast amount of SNP variation data accumulated for wheat in recent years, the presented imputation framework will greatly improve prediction accuracy in breeding populations and increase resolution of trait mapping hence, facilitate cross-referencing of genotype datasets available across different wheat populations

Factor Structure and Measurement Invariance for the Cognitive Emotion Regulation Questionnaire (CERQ) Among Women Newly Diagnosed With Breast Cancer

BackgroundGiven that emotion regulation counts for much in breast cancer, it is important to fully understand its construct. The cognitive emotion regulation questionnaire (CERQ) is a widely applied instrument for measuring conscious cognitive coping strategies in both general and clinical samples; however, there are no data on its factor structure in women with breast cancer, not to mention evidence of measurement invariance (MI) across sociodemographic variables. Thus, the purpose of the present study was to examine the latent factor structure and MI between different sociodemographic groups for CERQ in specific patients.MethodsThe sample consisted of 1032 women newly diagnosed with breast cancer, with a mean age of 47.54 years (SD = 8.51). The latent factor structure for CERQ was tested using confirmatory factor analysis (CFA). Further, MI various sociodemographic variables was evaluated by a series of multiple-group CFA process.ResultsThe nine-factor CFA model was an adequate fit for the data collected in women with breast cancer. Also, this nine-factor structure had strong factorial invariance across age, place of residence, educational levels, and employment status.ConclusionThis study firstly examined the latent factor structure for CERQ among Chinese women with malignancy and MI across various sociodemographic variables, which deepens the understanding of the construct for CERQ as a useful tool for assessing patients’ conscious cognitive component of emotion regulation based on self-report