The importance of culling in Johne's disease control

Johne's disease is caused by Mycobacterium avium subsp. paratuberculosis (MAP) infection and results in economic losses in the dairy industry. To control MAP transmission in herds, test-based culling has been recommended and immediate culling of high shedding animals is typically implemented. In this study, we quantified the effects of MAP control in US dairy herds, using the basic reproduction ratio R₀. The effectiveness of culling strategies was evaluated for good and poor herd management (low- and high-transmission rates, respectively) by a phase diagram approach. To establish a quantitative relationship between culling rates and test properties, we defined the average detection times for low and high shedding animals. The effects of various culling strategies and test characteristics, such as test sensitivity, test turnaround time, and testing interval, were analyzed. To understand the overall effect of model parameters on R₀, we performed global uncertainty and sensitivity analyses. We also evaluated the effectiveness of culling only high shedding animals by comparing three test methods (fecal culture, fecal polymerase chain reaction, PCR, and enzyme-linked immunosorbent assay, ELISA). Our study shows that, in the case of good herd management, culling of only high shedding animals may be effective in controlling MAP transmission. However, in the case of poor management, in addition to immediate culling of high shedding animals, culling of low shedding animals (based on the fecal culture test) will be necessary. Culling of low shedding animals may be delayed 6-12 months, however, if a shorter testing interval is applied. This study suggests that if farmers prefer culling only high shedding animals, faster MAP detection tests (such as the fecal PCR and ELISA) of higher sensitivity should be applied with high testing frequency, particularly on farms with poor management. Culling of infectious animals with a longer testing interval is generally not effective to control MAP.     

Genome survey on invasive veined rapa whelk (<Emphasis Type="Italic">Rapana venosa</Emphasis>) and development of microsatellite loci on large scale

Increasing evidence shows that one variant can affect multiple traits, which is a widespread phenomenon in complex diseases. Joint analysis of multiple traits can increase statistical power of association analysis and uncover the underlying genetic mechanism. Although there are many statistical methods to analyse multiple traits, most of these methods are usually suitable for detecting common variants associated with multiple traits. However, because of low minor allele frequency of rare variant, these methods are not optimal for rare variant association analysis. In this paper, we extend an adaptive combination of P values method (termed ADA) for single trait to test association between multiple traits and rare variants in the given region. For a given region, we use reverse regression model to test each rare variant associated with multiple traits and obtain the P value of single-variant test. Further, we take the weighted combination of these P values as the test statistic. Extensive simulation studies show that our approach is more powerful than several other comparison methods in most cases and is robust to the inclusion of a high proportion of neutral variants and the different directions of effects of causal variants.     

Multi-scale parametric spectral analysis for exon detection in DNA sequences based on forward-backward linear prediction and singular value decomposition of the double-base curves

This paper presents a new method for exon detection in DNA sequences based on multi-scale parametric spectral analysis. A forward-backward linear prediction (FBLP) with the singular value decomposition (SVD) algorithm FBLP-SVD is applied to the double-base curves (DB-curves) of a DNA sequence using a variable moving window sizes to estimate the signal spectrum at multiple scales. Simulations are done on short human genes in the range of 11bp to 2032bp and the results show that our proposed method out-performs the classical Fourier transform method. The multi-scale approach is shown to be more effective than using a single scale with a fixed window size. In addition, our method is flexible as it requires no training data.     

HMM-based Supervised Machine Learning Framework for the Detection of fECG R-R Peak Locations

ObjectiveFetal Electro Cardiogram (fECG) provides critical information on the wellbeing of a foetus heart in its developing stages in the mother's womb. The objective of this work is to extract fECG which is buried in a composite signal consisting of itself, maternal ECG (mECG) and noises contributed from various unavoidable sources. In the past, the challenge of extracting fECG from the composite signal was dealt with by Stochastic Weiner filter, model-based Kalman filter and other adaptive filtering techniques. Blind Source Separation (BSS) based Independent Component Analysis (ICA) has shown an edge over the adaptive filtering techniques as the former does not require a reference signal. Recently, data-driven machine learning techniques e.g., adaptive neural networks, adaptive neuro-fuzzy inference system, support vector machine (SVM) are also applied.MethodThis work pursues hidden Markov model (HMM)-based supervised machine learning frame-work for the determination of the location of fECG QRS complex from the composite abdominal signal. HMM is used to model the underlying hidden states of the observable time series of the extracted and separated fECG data with its QRS peak location as one of the hidden states. The state transition probabilities are estimated in the training phase using the annotated data sets. Afterwards, using the estimated HMM networks, fQRS locations are detected in the testing phase. To evaluate the proposed technique, the accuracy of the correct detection of QRS complex with respect to the correct annotation of QRS complex location is considered and quantified by the sensitivity, probability of false alarm, and accuracy.ResultsThe best results that have been achieved using the proposed method are: accuracy – 97.1%, correct detection rate (translated to sensitivity) – 100%, and false alarm rate – 2.89%.ConclusionTwo primary challenges in these methods are finding the right reference threshold for the normalization of the extracted fECG signal during the initial trials and limitation of discrete frame work of HMM signal (converted from continuous time) which only offers a countable number of levels in observations. By feeding the posterior probabilities, obtained from SVM, into HMM, as emission probabilities, can further improve the accuracy of fQRS location detection.     

Recent Selective Sweeps in North American Drosophila melanogaster Show Signatures of Soft Sweeps

Adaptation from standing genetic variation or recurrent de novo mutation in large populations should commonly generate soft rather than hard selective sweeps. In contrast to a hard selective sweep, in which a single adaptive haplotype rises to high population frequency, in a soft selective sweep multiple adaptive haplotypes sweep through the population simultaneously, producing distinct patterns of genetic variation in the vicinity of the adaptive site. Current statistical methods were expressly designed to detect hard sweeps and most lack power to detect soft sweeps. This is particularly unfortunate for the study of adaptation in species such as Drosophila melanogaster, where all three confirmed cases of recent adaptation resulted in soft selective sweeps and where there is evidence that the effective population size relevant for recent and strong adaptation is large enough to generate soft sweeps even when adaptation requires mutation at a specific single site at a locus. Here, we develop a statistical test based on a measure of haplotype homozygosity (H12) that is capable of detecting both hard and soft sweeps with similar power. We use H12 to identify multiple genomic regions that have undergone recent and strong adaptation in a large population sample of fully sequenced Drosophila melanogaster strains from the Drosophila Genetic Reference Panel (DGRP). Visual inspection of the top 50 candidates reveals that in all cases multiple haplotypes are present at high frequencies, consistent with signatures of soft sweeps. We further develop a second haplotype homozygosity statistic (H2/H1) that, in combination with H12, is capable of differentiating hard from soft sweeps. Surprisingly, we find that the H12 and H2/H1 values for all top 50 peaks are much more easily generated by soft rather than hard sweeps. We discuss the implications of these results for the study of adaptation in Drosophila and in species with large census population sizes.     

Simple and Robust Realtime QRS Detection Algorithm Based on Spatiotemporal Characteristic of the QRS Complex

The purpose of this research is to develop an intuitive and robust realtime QRS detection algorithm based on the physiological characteristics of the electrocardiogram waveform. The proposed algorithm finds the QRS complex based on the dual criteria of the amplitude and duration of QRS complex. It consists of simple operations, such as a finite impulse response filter, differentiation or thresholding without complex and computational operations like a wavelet transformation. The QRS detection performance is evaluated by using both an MIT-BIH arrhythmia database and an AHA ECG database (a total of 435,700 beats). The sensitivity (SE) and positive predictivity value (PPV) were 99.85% and 99.86%, respectively. According to the database, the SE and PPV were 99.90% and 99.91% in the MIT-BIH database and 99.84% and 99.84% in the AHA database, respectively. The result of the noisy environment test using record 119 from the MIT-BIH database indicated that the proposed method was scarcely affected by noise above 5 dB SNR (SE = 100%, PPV > 98%) without the need for an additional de-noising or back searching process.     

Parallel adaptive evolution of Atlantic cod on both sides of the Atlantic Ocean in response to temperature

Despite the enormous economic and ecological importance of marine organisms, the spatial scales of adaptation and biocomplexity remain largely unknown. Yet, the preservation of local stocks that possess adaptive diversity is critical to the long-term maintenance of productive stable fisheries and ecosystems. Here, we document genomic evidence of range-wide adaptive differentiation in a broadcast spawning marine fish, Atlantic cod (Gadus morhua), using a genome survey of single nucleotide polymorphisms. Of 1641 gene-associated polymorphisms examined, 70 (4.2%) tested positive for signatures of selection using a Bayesian approach. We identify a subset of these loci (n = 40) for which allele frequencies show parallel temperature-associated clines (p < 0.001, r² = 0.89) in the eastern and western north Atlantic. Temperature associations were robust to the statistical removal of geographic distance or latitude effects, and contrasted ‘neutral’ loci, which displayed no temperature association. Allele frequencies at temperature-associated loci were significantly correlated, spanned three linkage groups and several were successfully annotated supporting the involvement of multiple independent genes. Our results are consistent with the evolution and/or selective sweep of multiple genes in response to ocean temperature, and support the possibility of a new conservation paradigm for non-model marine organisms based on genomic approaches to resolving functional and adaptive diversity.     

Evaluation of a rapid and inexpensive liquid culture system for the detection of Mycobacterium avium subsp. paratuberculosis in bovine faeces

Mycobacterium avium subsp. paratuberculosis (MAP) is the etiologic agent of paratuberculosis, a chronic granulomatous enteric disease of ruminants. MAP detection by faecal culture provides the definitive diagnosis of the infection. Automated liquid systems for MAP culture are more sensitive and rapid than culture on solid media, but they are expensive and require specialised equipment. In this study, a non-automated culture method using a modified Middlebrook 7H9 liquid medium (7H9+) was compared with Herrold's solid medium (HEYM) and direct real-time PCR on dairy cattle faeces. MAP growth in 7H9+ was monitored weekly by real-time PCR until the 12th week post-inoculation. The analytical sensitivity of the three methods was evaluated using faecal samples from a healthy cow spiked with ten-fold dilutions of MAP organisms (10⁴-10^− 1) and naturally MAP-infected faeces serially diluted 1 to 10 in negative faecal samples. The limits of detection of the solid culture and direct real-time PCR were 10² and 10³ MAP/g, respectively. In comparison, the 7H9+ culture revealed as few as 1 MAP/g. A marked reduction in time to detection of the pathogen, compared with HEYM culture, was obtained. In addition, the three methods were applied to environmental faecal samples collected from a high- and a low-prevalence herd. The culture in 7H9+ showed to be the most sensitive test in the low-prevalence herd and provided faster results than HEYM. In the high-prevalence herd the three methods showed the same sensitivity and the real-time PCR had the shortest turnaround time. In conclusion, the use of 7H9+ for MAP-detection from cattle faeces maximizes diagnostic sensitivity and reduces turnaround time and, therefore, could replace culture in solid medium. Hence, we propose a two-step protocol for the assessment of MAP faecal excretion based on: 1) direct real-time PCR on all samples; and 2) inoculation of negative samples into 7H9+ and analysis after 3 and, if necessary, 6 weeks by real-time PCR.     

Searching for thresholds in climate–radial growth relationships of Engelmann spruce and subalpine fir,Jasper National Park,Alberta, Canada

The relationship between monthly climate predictors and radial growth of Engelmann spruce (Picea engelmanni Parry) and subalpine fir (Abies lasiocarpa (Hook.) Nutt) were explored using both a standard dendroclimatological approach and a multiple adaptive regressions splines (MARS) framework. Consistent with previous research, the radial growth of fir and spruce was related to temperature variables over the time period of the instrumental record. We identify important temporal instability in the statistical relationships between climate variables and the radial growth of both subalpine fir and Engelmann spruce. Using a 30-year running window, only four of the climate variables related to the radial growth of either spruce or fir did not show a switch in the sign of the correlation. A multiple adaptive regressions spline method was then used to gain insight into thresholds that may relate to radial growth–climate instabilities. Using MARS, we were able to identify knots and non-monotonic relationships between radial growth and climate predictors that may be indicators of ecological thresholds. This combination of dendroclimatic methods provides valuable insight into the complex nonlinear responses that both subalpine fir and Engelmann spruce have been growing under in the past centuries.     

Centronuclear (myotubular) myopathy

In recent years, the use of adaptive design methods in clinical research and development based on accrued data has become very popular due to its flexibility and efficiency. Based on adaptations applied, adaptive designs can be classified into three categories: prospective, concurrent (ad hoc), and retrospective adaptive designs. An adaptive design allows modifications made to trial and/or statistical procedures of ongoing clinical trials. However, it is a concern that the actual patient population after the adaptations could deviate from the originally target patient population and consequently the overall type I error (to erroneously claim efficacy for an infective drug) rate may not be controlled. In addition, major adaptations of trial and/or statistical procedures of on-going trials may result in a totally different trial that is unable to address the scientific/medical questions the trial intends to answer. In this article, several commonly considered adaptive designs in clinical trials are reviewed. Impacts of ad hoc adaptations (protocol amendments), challenges in by design (prospective) adaptations, and obstacles of retrospective adaptations are described. Strategies for the use of adaptive design in clinical development of rare diseases are discussed. Some examples concerning the development of Velcade intended for multiple myeloma and non-Hodgkin's lymphoma are given. Practical issues that are commonly encountered when implementing adaptive design methods in clinical trials are also discussed.     

MAPK cascades and major abiotic stresses

Plants have evolved with complex signaling circuits that operate under multiple conditions and govern numerous cellular functions. Stress signaling in plant cells is a sophisticated network composed of interacting proteins organized into tiered cascades where the function of a molecule is dependent on the interaction and the activation of another. In a linear scheme, the receptors of cell surface sense the stimuli and convey stress signals through specific pathways and downstream phosphorylation events controlled by mitogen-activated protein (MAP) kinases and second messengers, leading to appropriate adaptive responses. The specificity of the pathway is guided by scaffolding proteins and docking domains inside the interacting partners with distinctive structures and functions. The flexibility and the fine-tuned organization of the signaling molecules drive the activated MAP kinases into the appropriate location and connection to control and integrate the information flow. Here, we overview recent findings of the involvement of MAP kinases in major abiotic stresses (drought, cold and temperature fluctuations) and we shed light on the complexity and the specificity of MAP kinase signaling modules.     

Adaptive tests for detecting gene-gene and gene-environment interactions

There has been an increasing interest in detecting gene-gene and gene-environment interactions in genetic association studies. A major statistical challenge is how to deal with a large number of parameters measuring possible interaction effects, which leads to reduced power of any statistical test due to a large number of degrees of freedom or high cost of adjustment for multiple testing. Hence, a popular idea is to first apply some dimension reduction techniques before testing, while another is to apply only statistical tests that are developed for and robust to high-dimensional data. To combine both ideas, we propose applying an adaptive sum of squared score (SSU) test and several other adaptive tests. These adaptive tests are extensions of the adaptive Neyman test [Fan, 1996], which was originally proposed for high-dimensional data, providing a simple and effective way for dimension reduction. On the other hand, the original SSU test coincides with a version of a test specifically developed for high-dimensional data. We apply these adaptive tests and their original nonadaptive versions to simulated data to detect interactions between two groups of SNPs (e.g. multiple SNPs in two candidate regions). We found that for sparse models (i.e. with only few non-zero interaction parameters), the adaptive SSU test and its close variant, an adaptive version of the weighted sum of squared score (SSUw) test, improved the power over their non-adaptive versions, and performed consistently well across various scenarios. The proposed adaptive tests are built in the general framework of regression analysis, and can thus be applied to various types of traits in the presence of covariates.     

基于小波变换的QRS综合波的检测

本文描述了基于二进制小波变换（DyWT),ECG信号中QRS综合波的检测。设计－小波它适合于QRS检测,将基于心电信号的特殊的特征的特征为小波的尺度。DyWT较之其它方法最基本的优点为强有力的抑制噪声检测以及在分析随时间变化ECG波形时的灵活性。     

Dynamics of Specific Anti-Mycobacterium avium Subsp. paratuberculosis Antibody Response through Age

Mycobacterium avium subsp. paratuberculosis (MAP) causes a chronic infection in cattle. MAP infected cattle with humoral immune (HI) reactions with IgG antibodies are usually those where latency of infection has ceased and their infection is progressing towards reduced milk yield, weight loss and significant bacterial excretion in feces. The proportion of detectable infections among all infected animals that will develop disease is often referred to as ‘the tip of the iceberg’. The purpose of this study was to estimate this proportion. Test-records from 18,972 Danish dairy cows with MAP specific IgG antibodies on their final test-record were used to estimate age-specific sensitivities (Se). These cows were the infected ones considered to develop disease in a population with a representative age-distribution and were defined as cases. The specificity (Sp) of the test was estimated based on test-results from 166,905 cows, which had no MAP IgG antibodies in their final four test-records. The Sp, age-specific Se and maximum Se were used to estimate the probability of having HI at a given age resulting in the proportion of infected cows with HI at a given age. For cows 2 years of age, the proportion of detectable cases was 0.33, while it was 0.94 for cows 5 years of age. Thus, there was a significant shift in the tip of the iceberg with aging. This study provided a model for estimating the proportion of latent chronic infections that would progress to disease, and the results can be used to model infection dynamics.     

Model-based analysis on the relationship of signal quality to real-time extraction of information in bioprocesses

Quality by design (QbD) is a current structured approach to design processes yielding a quality product. Knowledge and process understanding cannot be achieved without proper experimental data; hence requirements for measurement error and frequency of measurement of bioprocess variables have to be defined. In this contribution, a model-based approach is used to investigate impact factors on calculated rates to predict the obtainable information from real-time measurements (= signal quality). Measurement error, biological activity, and averaging window (= period of observation) were identified as biggest impact factors on signal quality. Moreover, signal quality has been set in context with a quantifiable measure using statistical error testing, which can be used as a benchmark for process analytics and exploitation of data. Results have been validated with data from an E. coli batch process. This approach is useful to get an idea which process dynamics can be observed with a given bioprocess setup and sampling strategy beforehand.     

A Sliding Window-Based Method to Detect Selective Constraints in Protein-Coding Genes and Its Application to RNA Viruses

Here we present a new sliding window-based method specially designed to detect selective constraints in specific regions of a multiple protein-coding sequence alignment. In contrast to previous window-based procedures, our method is based on a nonarbitrary statistical approach to find the appropriate codon-window size to test deviations of synonymous (dS) and nonsynonymous (dN) nucleotide substitutions from the expectation. The probabilities of dN and dS are obtained from simulated data and used to detect significant deviations of dN and dS in a specific window region of the real sequence alignment. The nonsynonymous-to-synonymous rate ratio (w = dN/dS) was used to highlight selective constraints in any window wherein dS or dN was significantly different from the expectation. In these significant windows, w and its variance [V(w)] were calculated and used to test the neutral hypothesis. Computer simulations showed that the method is accurate even for highly divergent sequences. The main advantages of the new method are that it (i) uses a statistically appropriate window size to detect different selective patterns, (ii) is computationally less intensive than maximum likelihood methods, and (iii) detects saturation of synonymous sites, which can give deviations from neutrality. Hence, it allows the analysis of highly divergent sequences and the test of different alternative hypothesis as well. The application of the method to different human immunodeficiency virus type 1 and to foot-and-mouth disease virus genes confirms the action of positive selection on previously described regions as well as on new regions.     

Multifactor dimensionality reduction-phenomics: a novel method to capture genetic heterogeneity with use of phenotypic variables

Complex human diseases do not have a clear inheritance pattern, and it is expected that risk involves multiple genes with modest effects acting independently or interacting. Major challenges for the identification of genetic effects are genetic heterogeneity and difficulty in analyzing high-order interactions. To address these challenges, we present MDR-Phenomics, a novel approach based on the multifactor dimensionality reduction (MDR) method, to detect genetic effects in pedigree data by integration of phenotypic covariates (PCs) that may reflect genetic heterogeneity. The P value of the test is calculated using a permutation test adjusted for multiple tests. To validate MDR-Phenomics, we compared it with two MDR-based methods: (1) traditional MDR pedigree disequilibrium test (PDT) without consideration of PCs (MDR-PDT) and (2) stratified phenotype (SP) analysis based on PCs, with use of MDR-PDT with a Bonferroni adjustment (SP-MDR). Using computer simulations, we examined the statistical power and type I error of the different approaches under several genetic models and sampling scenarios. We conclude that MDR-Phenomics is more powerful than MDR-PDT and SP-MDR when there is genetic heterogeneity, and the statistical power is affected by sample size and the number of PC levels. We further compared MDR-Phenomics with conditional logistic regression (CLR) for testing interactions across single or multiple loci with consideration of PC. The results show that CLR with PC has only slightly smaller power than does MDR-Phenomics for single-locus analysis but has considerably smaller power for multiple loci. Finally, by applying MDR-Phenomics to autism, a complex disease in which multiple genes are believed to confer risk, we attempted to identify multiple gene effects in two candidate genes of interest—the serotonin transporter gene (SLC6A4) and the integrin beta 3 gene (ITGB3) on chromosome 17. Analyzing four markers in SLC6A4 and four markers in ITGB3 in 117 white family triads with autism and using sex of the proband as a PC, we found significant interaction between two markers—rs1042173 in SLC6A4 and rs3809865 in ITGB3.     

Bovine IFNGR2, IL12RB1, IL12RB2, and IL23R polymorphisms and MAP infection status

Mycobacterium avium ssp. paratuberculosis (MAP) infection causes a chronic granulomatous inflammatory condition of the bovine gut that is characterized by diarrhea, progressive weight loss, and emaciation, and ultimately leads to loss in productivity and profitability of dairy operations. The host cytokine machinery is known to play an important role in protecting against MAP infection. Therefore, the goal of the present study was to assess whether polymorphisms in candidate genes encoding important cytokines and cytokine receptors are associated with MAP infection status of dairy cattle. MAP infection status was evaluated based on serum and milk enzyme-linked immunosorbent assays (ELISAs) for MAP-specific antibodies. Twenty previously reported polymorphisms in genes encoding bovine interferon gamma (IFNG), IFNGR1, IFNGR2, IL22, IL22RA1, IL12RB1, IL12RB2, and IL23R were genotyped in a resource population of 446 dairy Holsteins with known MAP infection status, and logistic regression was used to assess the statistical association with a binomial MAP infection status phenotype. Four SNPs in IFNGR2, IL12RB1, IL12RB2, and IL23R were found to be associated with the MAP infection status of the resource population. These results underscore the importance of cytokines and their receptors in conferring protection against MAP infection and warrant further functional characterization of these associations.