Graph Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering

The article introduces a new method for applying Quantum Clustering to graph structures. Quantum Clustering (QC) is a novel density-based unsupervised learning method that determines cluster centers by constructing a potential function. In this method, we use the Graph Gradient Descent algorithm to find the centers of clusters. GPU parallelization is utilized for computing potential values. We also conducted experiments on five widely used datasets and evaluated using four indicators. The results show superior performance of the method. Finally, we discuss the influence of $\sigma$ on the experimental results

Identification of Novel Genes for the Development of a Rapid Diagnostic Test for Theileria uilenbergi Infection by Screening of a Merozoite cDNA Library

The major aim of this thesis was to identify novel genes of T. uilenbergi through establishment and screening of a merozoite cDNA library with the eventual goal to develop diagnostic tools using identified genes for detection of Theileria infections. The experiments were initiated by infection of sheep using T. uilenbergi stock. When parasiteamia rose, blood was collected and the merozoites were purified. Messenger RNA was isolated from purified merozoites was then utilized to establish a cDNA library. The library was titrated to be 6 x 108 pfu/ml and the recombinant clones were estimated to be 70%. Random PCR identification of the library indicated all of the inserts were of parasite origin, indicating the usefulness of the library for the identification of new genes. Random PCR amplification of inserts of the cDNA library led to the discovery of 12 single clones, among which Clone 2, 9 and 26 exhibited a high degree of identity, especially at the 3' terminus and 3' untranslated region, indicating that they belong to the same gene family. Furthermore, PCR designed to target Clone 2 amplified again four variant genes from genomic DNA of T. uilenbergi and one from genomic DNA of T. luwenshuni, suggesting this gene family is likely isolate-specific since the DNA samples for PCR were not derived from the same parasite isolate used for library construction. Sequence analysis of another genomic fragment generated with primers targeting the 3' untranslated region of the Clone 26 sequence showed that both 5' and 3' termini were highly identical to the Clone 2 gene family and these homologous termini were separated by a 136 bp sequence fragment highly identical to the 3' untranslated region of the Clone 2 gene family, indicating Clone 2 gene family members are tandemly arranged. Bioinformatic analysis of cDNA sequences of the Clone 2 gene family indicated these genes contain signal peptides and encode potential immunogenic proteins. Analysis of recombinantly expressed Clone 2 revealed immunoreactivity with sera from Theileria-infected animals from China. No cross reaction with sera of T. lestoquardi-, Babesia motasi- or Anaplasma ovis- infected animals was observed, indicating a potential specificity of this gene family. The features of the Clone 2 gene family are similar to the Tpr gene family of T. parva, which is believed to play a role in concerted evolution. Based on the highly conserved region of the Clone 2 gene family, a set of six primers were designed for the development of a loop mediated isothermal amplification (LAMP). The established assay allowed the detection of T. uilenbergi and T. luwenshuni infections simultaneously and the reaction could be simply accomplished by incubation at 63ºC for 15 min. The specificity of the reaction was confirmed through EcoRI restriction enzyme digestion analysis and sequencing. The assay was sensitive as it detected 0.1 pg DNA of T. luwenshuni or T. uilenbergi. Moreover, the assay was evaluated by testing 86 field samples in comparison to the reverse line blot method, showing a calculated sensitivity and specificity of 66.0% and 97.4%, respectively. These results indicate that the LAMP assay has a potential usefulness for application in diagnostic and pidemiological studies on T. luwenshuni and T. uilenbergi infection of small ruminants. In addition, serological screening of the library led to discovery of a positive clone called TuIP, which has been deposited in Genbank under accession number FJ467922. Partially recombinantly cloned and expressed TuIP showed strong reactivity with serum from T. uilenbergi infected animals, indicating its potential usefulness for development of novel serological diagnostic tests or serving as a candidate for vaccine development in the future

Discovering Predictable Latent Factors for Time Series Forecasting

Modern time series forecasting methods, such as Transformer and its variants, have shown strong ability in sequential data modeling. To achieve high performance, they usually rely on redundant or unexplainable structures to model complex relations between variables and tune the parameters with large-scale data. Many real-world data mining tasks, however, lack sufficient variables for relation reasoning, and therefore these methods may not properly handle such forecasting problems. With insufficient data, time series appear to be affected by many exogenous variables, and thus, the modeling becomes unstable and unpredictable. To tackle this critical issue, in this paper, we develop a novel algorithmic framework for inferring the intrinsic latent factors implied by the observable time series. The inferred factors are used to form multiple independent and predictable signal components that enable not only sparse relation reasoning for long-term efficiency but also reconstructing the future temporal data for accurate prediction. To achieve this, we introduce three characteristics, i.e., predictability, sufficiency, and identifiability, and model these characteristics via the powerful deep latent dynamics models to infer the predictable signal components. Empirical results on multiple real datasets show the efficiency of our method for different kinds of time series forecasting. The statistical analysis validates the predictability of the learned latent factors

Room-Temperature High-Performance H2S Sensor Based on Porous CuO Nanosheets Prepared by Hydrothermal Method

Porous CuO nanosheets were prepared on alumina tubes using a facile hydrothermal method, and their morphology, microstructure, and gas-sensing properties were investigated. The monoclinic CuO nanosheets had an average thickness of 62.5 nm and were embedded with numerous holes with diameters ranging from 5 to 17 nm. The porous CuO nanosheets were used to fabricate gas sensors to detect hydrogen sulfide (H2S) operating at room temperature. The sensor showed a good response sensitivity of 1.25 with respond/recovery times of 234 and 76 s, respectively, when tested with the H2S concentrations as low as 10 ppb. It also showed a remarkably high selectivity to the H2S, but only minor responses to other gases such as SO2, NO, NO2, H2, CO, and C2H5OH. The working principle of the porous CuO nanosheet based sensor to detect the H2S was identified to be the phase transition from semiconducting CuO to a metallic conducting CuS