NOSEP: Nonoverlapping Sequence Pattern Mining With Gap Constraints.

Sequence pattern mining aims to discover frequent subsequences as patterns in a single sequence or a sequence database. By combining gap constraints (or flexible wildcards), users can specify special characteristics of the patterns and discover meaningful subsequences suitable for their own application domains, such as finding gene transcription sites from DNA sequences or discovering patterns for time series data classification. Due to the inherent complexity of sequence patterns, including the exponential candidate space with respect to pattern letters and gap constraints, to date, existing sequence pattern mining methods are either incomplete or do not support the Apriori property because the support ratio of a pattern may be greater than that of its subpatterns. Most importantly, patterns discovered by these methods are either too restrictive or too general and cannot represent underlying meaningful knowledge in the sequences. In this paper, we focus on a nonoverlapping sequence pattern mining task with gap constraints, where a nonoverlapping sequence pattern allows sequence letters to be flexibly and maximally utilized for pattern discovery. A new Apriori-based nonoverlapping sequence pattern mining algorithm, NOSEP, is proposed. NOSEP is a complete pattern mining algorithm, which uses a specially designed data structure, Nettree, to calculate the exact occurrence of a pattern in the sequence. Experimental results and comparisons on biology DNA sequences, time series data, and Gazelle datasets demonstrate the efficiency of the proposed algorithm and the uniqueness of nonoverlapping sequence patterns compared to other methods.

Positive and Unlabeled Multi-Graph Learning.

In this paper, we advance graph classification to handle multi-graph learning for complicated objects, where each object is represented as a bag of graphs and the label is only available to each bag but not individual graphs. In addition, when training classifiers, users are only given a handful of positive bags and many unlabeled bags, and the learning objective is to train models to classify previously unseen graph bags with maximum accuracy. To achieve the goal, we propose a positive and unlabeled multi-graph learning (puMGL) framework to first select informative subgraphs to convert graphs into a feature space. To utilize unlabeled bags for learning, puMGL assigns a confidence weight to each bag and dynamically adjusts its weight value to select "reliable negative bags." A number of representative graphs, selected from positive bags and identified reliable negative graph bags, form a "margin graph pool" which serves as the base for deriving subgraph patterns, training graph classifiers, and further updating the bag weight values. A closed-loop iterative process helps discover optimal subgraphs from positive and unlabeled graph bags for learning. Experimental comparisons demonstrate the performance of puMGL for classifying real-world complicated objects.

Task Sensitive Feature Exploration and Learning for Multitask Graph Classification.

Multitask learning (MTL) is commonly used for jointly optimizing multiple learning tasks. To date, all existing MTL methods have been designed for tasks with feature-vector represented instances, but cannot be applied to structure data, such as graphs. More importantly, when carrying out MTL, existing methods mainly focus on exploring overall commonality or disparity between tasks for learning, but cannot explicitly capture task relationships in the feature space, so they are unable to answer important questions, such as what exactly is shared between tasks and what is the uniqueness of one task differing from others? In this paper, we formulate a new multitask graph learning problem, and propose a task sensitive feature exploration and learning algorithm for multitask graph classification. Because graphs do not have features available, we advocate a task sensitive feature exploration and learning paradigm to jointly discover discriminative subgraph features across different tasks. In addition, a feature learning process is carried out to categorize each subgraph feature into one of three categories: (1) common feature; (2) task auxiliary feature; and (3) task specific feature, indicating whether the feature is shared by all tasks, by a subset of tasks, or by only one specific task, respectively. The feature learning and the multiple task learning are iteratively optimized to form a multitask graph classification model with a global optimization goal. Experiments on real-world functional brain analysis and chemical compound categorization demonstrate the algorithm's performance. Results confirm that our method can be used to explicitly capture task correlations and uniqueness in the feature space, and explicitly answer what are shared between tasks and what is the uniqueness of a specific task.

Prevalence and genotypes of Chlamydia psittaci in pigeons in Jilin Province, Northeastern China / 中国人兽共患病学报

Chlamydia psittaci is a causative agent of psittacosis,which can infect a wide range of hosts including birds and humans.However,information regarding C.psittaci infection in pigeons is scarce.In the present study,a total of 399 fecal samples from pigeons were collected from Jilin Province,northeastern China,between March and May 2015,and examined by nested PCR amplification of outer membrane protein A (ompA) gene.The overall Chlamydiosis prevalence was 5.01％ (21/399),with 3.19％ in Changchun City and 9.40％ in Jilin City.Furthermore,breed was the major risk factor associated with Chlamydia infection in pigeon,boiler pigeons had a prevalence of 7.49％,whereas no C.psittaci was detected in racing pigeons.Sequence analysis of the ompA gene revealed that all the identified isolates represented C.psittaci genotype B.Our results firstly indicated the presence of zoonotic C.psittaci in boiler pigeons in Jilin Province,northeastern China,and effective measures should be implemented to reduce the risk of C.psittaci transmission from pigeons to humans.

Prevalence of intestinal parasitic infections in rats and mice in different provinces and their control strategies / 中国比较医学杂志

Objective This review was aimed to provide reference for production, management and use of laboratory animals by analyzing the test results on intestinal parasitic infections of mice and rats in different provinces from 1989 to 2013 in China.The results showed that the infection rates in clean and SPF mice and rats were reduced to 10%, being better than that in the past years, but the situation was still not optimistic for the control of flagellate parasites infections.

Cross-Domain Semi-Supervised Learning Using Feature Formulation.

Semi-Supervised Learning (SSL) traditionally makes use of unlabeled samples by including them into the training set through an automated labeling process. Such a primitive Semi-Supervised Learning (pSSL) approach suffers from a number of disadvantages including false labeling and incapable of utilizing out-of-domain samples. In this paper, we propose a formative Semi-Supervised Learning (fSSL) framework which explores hidden features between labeled and unlabeled samples to achieve semi-supervised learning. fSSL regards that both labeled and unlabeled samples are generated from some hidden concepts with labeling information partially observable for some samples. The key of the fSSL is to recover the hidden concepts, and take them as new features to link labeled and unlabeled samples for semi-supervised learning. Because unlabeled samples are only used to generate new features, but not to be explicitly included in the training set like pSSL does, fSSL overcomes the inherent disadvantages of the traditional pSSL methods, especially for samples not within the same domain as the labeled instances. Experimental results and comparisons demonstrate that fSSL significantly outperforms pSSL-based methods for both within-domain and cross-domain semi-supervised learning.

Study on distribution of cohere antigen of 12D5 and 21B7 monoclonal antibodies of Angiostrongylus cantonensis and its application of immnodiagnosis / 中华微生物学和免疫学杂志

Objective To detect infection of Angiostrongylus cantonensis and examine effection of treatment to prepare monoclonal antibodies(McAbs). Methods Six-week-old BALB/c mice were imrnunized by the intraperitoneal injection of e/s antigens of Angiostrongylus cantonensis. Fusion of splecn cells from immunized mice with prepared SP2/0-Ag14 myeloma cells was performed in RPMI 1640. Fused cells were suspended in RPMI 1640 containing 1% HAT and 20% fetal calf serum and dispensed into 96-well cell culture plates. The supernatants of clones were screened by ELISA with sera of patients of angiostrongyliasis.Distribution of cohere antigen of 12D5 and 21B7 monoclonal antibodies was analyzed with immunohistochemistry. Two McAbs ( 12D5 and 21B7) were applied to detect the circulating antigen (CAg) in the sera of rats infected with A. cantonensis and angiostrongyliasis patients respectively by double antibody sandwich ELISA.Results 12D5 McAb was identified as IgG1 and 21 B7 McAb was IgM. Western blot result showed two McAbs could used to identified 55 × 103 protein of adult worms of A. cantonensis. Cohere antigen of 12D5 and 21B7 monoclonal antibodies were distributed on intestine surface of A. cantonensis. The detection rates of CAg in the sera of infected rats 100% (48/48), the detection rates of CAg in the sera of angiostrongyliasis patients was 100% (32/32). No cross-reaction to sera of patients with other infection of parasites, such as clonochiasis, fasiolopsiasis, ancylostomiasis, trichinosis, anisakiasis as well as schsitosomiasis, and health srea did not reacted with 12D5 and 21B7 McAbs,and detaction rate of antibody of angiostrongyliasis patients only reached 75% (24/32) with antigen of A. cantonensis. Conclusion Cohere antigen of 12D5 and 21B7monoclonal antibodies were antigens of enteric epithelium. Sandwich ELISA with 12D5 and 21B7 McAbs showed high specificity act as detecting CAg of A. cantonensis in sera of infection animal and patients. It is apparent that Sandwich ELISA with 12D5 and 21 B7 is not only rapid and simple without requirement of special instrument, but also rather sensitive and specific for the detection of current infection with A. cantonensis.

Identification of the gene differentially expressed in female Anopheles anthropophagus and cloning of its full-length cDNA / 中国人兽共患病学报

To identify the gene differentially expressed in female Anopheles anthropophagus and to analyze its gene sequence, this gene amplified by PCR was identified by real-time PCR and its cDNA was then amplified with rapid amplification of cDNA ends (RACE) technology. It was found that the expression ratio of the female differentially expressed gene in female and male mosquitoes was 267.49 according to the formula F=2~(-⊿⊿CT).The size of mRNA of the gene was 364 bp, and the amino acid sequence deduced from the open reading frame (ORF) was found to be similar to the sequence of tectin protein of Culex quinquefasciatus and proteins of other species. The mRNA sequence of this gene was submitted to NCBI with a accession number of FJ907236. This gene may provide a foundation for further studies on the biological functions of mosquitoes.