Polymorphisms of SP110 are associated with both pulmonary and extra-pulmonary tuberculosis among the Vietnamese

Background: Tuberculosis (TB) is an infectious disease that remains a major cause of morbidity and mortality worldwide, yet the reasons why only 10% of people infected with Mycobacterium tuberculosis go on to develop clinical disease are poorly understood. Genetically determined variation in the host immune response is one factor influencing the response to M. tuberculosis. SP110 is an interferon-responsive nuclear body protein with critical roles in cell cycling, apoptosis and immunity to infection. However association studies of the gene with clinical TB in different populations have produced conflicting results. Methods: To examine the importance of the SP110 gene in immunity to TB in the Vietnamese we conducted a case-control genetic association study of 24 SP110 variants, in 663 patients with microbiologically proven TB and 566 unaffected control subjects from three tertiary hospitals in northern Vietnam. Results: Five SNPs within SP110 were associated with all forms of TB, including four SNPs at the C terminus (rs10208770, rs10498244, rs16826860, rs11678451) under a dominant model and one SNP under a recessive model, rs7601176. Two of these SNPs were associated with pulmonary TB (rs10208770 and rs16826860) and one with extra-pulmonary TB (rs10498244). Conclusion: SP110 variants were associated with increased susceptibility to both pulmonary and extra-pulmonary TB in the Vietnamese. Genetic variants in SP110 may influence macrophage signaling responses and apoptosis during M. tuberculosis infection, however further research is required to establish the mechanism by which SP110 influences immunity to tuberculosis infection. © 2014 Fox et al

High levels of contamination and antimicrobial-resistant non-typhoidal Salmonella serovars on pig and poultry farms in the Mekong Delta of Vietnam.

We investigated the prevalence, diversity, and antimicrobial resistance (AMR) profiles of non-typhoidal Salmonella (NTS) and associated risk factors on 341 pig, chicken, and duck farms in Dong Thap province (Mekong Delta, Vietnam). Sampling was stratified by species, district (four categories), and farm size (three categories). Pooled faeces, collected using boot swabs, were tested using ISO 6575: 2002 (Annex D). Isolates were serogrouped; group B isolates were tested by polymerase chain reaction to detect S. Typhimurium and (monophasic) serovar 4,[5],12:i:- variants. The farm-level adjusted NTS prevalence was 64·7%, 94·3% and 91·3% for chicken, duck and pig farms, respectively. Factors independently associated with NTS were duck farms [odds ratio (OR) 21·2], farm with >50 pigs (OR 11·9), pig farm with 5-50 pigs (OR 4·88) (vs. chickens), and frequent rodent sightings (OR 2·3). Both S. Typhimurium and monophasic S. Typhimurium were more common in duck farms. Isolates had a high prevalence of resistance (77·6%) against tetracycline, moderate resistance (20-30%) against chloramphenicol, sulfamethoxazole-trimethoprim, ampicillin and nalidixic acid, and low resistance (<5%) against ciprofloxacin and third-generation cephalosporins. Multidrug resistance (resistance against ⩾3 classes of antimicrobial) was independently associated with monophasic S. Typhimurium and other group B isolates (excluding S. Typhimurium) and pig farms. The unusually high prevalence of NTS on Mekong Delta farms poses formidable challenges for control

Adaptive maximization of pointwise submodular functions with budget constraint

We study the worst-case adaptive optimization problem with budget constraint that is useful for modeling various practical applications in artificial intelligence and machine learning. We investigate the near-optimality of greedy algorithms for this problem with both modular and non-modular cost functions. In both cases, we prove that two simple greedy algorithms are not near-optimal but the best between them is near-optimal if the utility function satisfies pointwise submodularity and pointwise cost-sensitive submodularity respectively. This implies a combined algorithm that is near-optimal with respect to the optimal algorithm that uses half of the budget. We discuss applications of our theoretical results and also report experiments comparing the greedy algorithms on the active learning problem

An epidemiological investigation of Campylobacter in pig and poultry farms in the Mekong delta of Vietnam.

Campylobacter are zoonotic pathogens commonly associated with gastroenteritis. To assess the relevance of Campylobacter in Vietnam, an economically transitioning country in SE Asia, we conducted a survey of 343 pig and poultry farms in the Mekong delta, a region characterized by mixed species farming with limited biosecurity. The animal-level prevalence of Campylobacter was 31·9%, 23·9% and 53·7% for chickens, ducks and pigs, respectively. C. jejuni was predominant in all three host species, with the highest prevalence in pigs in high-density production areas. Campylobacter isolates demonstrated high levels of antimicrobial resistance (21% and 100% resistance against ciprofloxacin and erythromycin, respectively). Multilocus sequence type genotyping showed a high level of genetic diversity within C. jejuni, and predicted C. coli inter-species transmission. We suggest that on-going intensification of animal production systems, limited biosecurity, and increased urbanization in Vietnam is likely to result in Campylobacter becoming an increasingly significant cause of human diarrhoeal infections in coming years

Robustness of Bayesian pool-based active learning against prior misspecification

We study the robustness of active learning (AL) algorithms against prior misspecification: whether an algorithm achieves similar performance using a perturbed prior as compared to using the true prior. In both the average and worst cases of the maximum coverage setting, we prove that all α-approximate algorithms are robust (i.e., near α-approximate) if the utility is Lipschitz continuous in the prior. We further show that robustness may not be achieved if the utility is non-Lipschitz. This suggests we should use a Lipschitz utility for AL if robustness is required. For the minimum cost setting, we can also obtain a robustness result for approximate AL algorithms. Our results imply that many commonly used AL algorithms are robust against perturbed priors. We then propose the use of a mixture prior to alleviate the problem of prior misspecification. We analyze the robustness of the uniform mixture prior and show experimentally that it performs reasonably well in practice

Near-optimal adaptive pool-based active learning with general loss

We consider adaptive pool-based active learning in a Bayesian setting. We first analyze two commonly used greedy active learning criteria: the maximum entropy criterion, which selects the example with the highest entropy, and the least confidence criterion, which selects the example whose most probable label has the least probability value. We show that unlike the non-adaptive case, the maximum entropy criterion is not able to achieve an approximation that is within a constant factor of optimal policy entropy. For the least confidence criterion, we show that it is able to achieve a constant factor approximation to the optimal version space reduction in a worst-case setting, where the probability of labelings that have not been eliminated is considered as the version space. We consider a third greedy active learning criterion, the Gibbs error criterion, and generalize it to handle arbitrary loss functions between labelings. We analyze the properties of the generalization and its variants, and show that they perform well in practice

Generalization and robustness of batched weighted average algorithm with V-geometrically ergodic Markov data

We analyze the generalization and robustness of the batched weighted average algorithm for V-geometrically ergodic Markov data. This algorithm is a good alternative to the empirical risk minimization algorithm when the latter suffers from overfitting or when optimizing the empirical risk is hard. For the generalization of the algorithm, we prove a PAC-style bound on the training sample size for the expected L1-loss to converge to the optimal loss when training data are V-geometrically ergodic Markov chains. For the robustness, we show that if the training target variable's values contain bounded noise, then the generalization bound of the algorithm deviates at most by the range of the noise. Our results can be applied to the regression problem, the classification problem, and the case where there exists an unknown deterministic target hypothesis. © 2013 Springer-Verlag

Mel-frequency cepstral coefficients for eye movement identification

Human identification is an important task for various activities in society. In this paper, we consider the problem of human identification using eye movement information. This problem, which is usually called the eye movement identification problem, can be solved by training a multiclass classification model to predict a person's identity from his or her eye movements. In this work, we propose using Mel-frequency cepstral coefficients (MFCCs) to encode various features for the classification model. Our experiments show that using MFCCs to represent useful features such as eye position, eye difference, and eye velocity would result in a much better accuracy than using Fourier transform, cepstrum, or raw representations. We also compare various classification models for the task. From our experiments, linear-kernel SVMs achieve the best accuracy with 93.56% and 91.08% accuracy on the small and large datasets respectively. Besides, we conduct experiments to study how the movements of each eye contribute to the final classification accuracy. © 2012 IEEE

Scholarly Document Information Extraction using Extensible Features for Efficient Higher Order Semi-CRFs

We address the tasks of recovering bibliographic and document structure metadata from scholarly documents. We leverage higher order semi-Markov conditional random fields to model long-distance label sequences, improving upon the performance of the linear-chain conditional random field model. We introduce the notion of extensible features, which allows the expensive inference process to be simplified through memoization, resulting in lower computational complexity. Our method significantly betters the state-of-the-art on three related scholarly document extraction tasks

Conditional random field with high-order dependencies for sequence labeling and segmentation

Dependencies among neighboring labels in a sequence are important sources of information for sequence labeling and segmentation. However, only first-order dependencies, which are dependencies between adjacent labels or segments, are commonly exploited in practice because of the high computational complexity of typical inference algorithms when longer distance dependencies are taken into account. In this paper, we give efficient inference algorithms to handle high-order dependencies between labels or segments in conditional random fields, under the assumption that the number of distinct label patterns used in the features is small. This leads to efficient learning algorithms for these conditional random fields. We show experimentally that exploiting high-order dependencies can lead to substantial performance improvements for some problems, and we discuss conditions under which high-order features can be effective. © 2014 Nguyen Viet Cuong, Nan Ye, Wee Sun Lee and Hai Leong Chieu