Comparison of large networks with sub-sampling strategies.
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ABSTRACT:
SUBMITTER: Ali W
PROVIDER: S-EPMC4933923 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BACKGROUND:Influenza A virus (IAV) is an important pathogen in pigs that affects productivity and has important public health implications because of its zoonotic nature. Surveillance is central to the control of influenza, however, detection of IAV infections can be challenging in endemically infected herds with low prevalence of infection. METHODS:In groups of suckling (18-21?days of age) and growing (35-45?days of age) pigs, we compared various sampling approaches to detect, isolate and sequence IAV using individual (nasal swabs, nasal wipes and oropharyngeal swabs), group (oral fluids, surface wipes and sow udder skin wipes) and environmental (airborne particles deposited on surfaces and air samples) sampling approaches. All samples were tested by IAV rRT-PCR and a subset was used for virus isolation and direct sequencing. RESULTS:In general, environmental and group samples resulted in higher odd ratios (range?=?3.87-16.5, p-value?<?0.05) of detecting a positive sample by rRT-PCR compared to individual pooled samples, except for oropharyngeal swabs (OR?=?8.07, p-value?<?0.05). In contrast, individual samples were most likely to yield a viral isolate by cell culture. Oropharyngeal swabs in suckling pigs (78.4%), and nasal swabs (47.6%) or nasal wipes (45%) in growing pigs, and udder wipes in lactating sows (75%) were the preferred samples to obtain an isolate. CONCLUSIONS:Our findings indicate that group and environmental sampling strategies should be considered in influenza surveillance programs in particular if the goal is just to detect infection. This study provides new information on sampling approaches to conduct effective influenza surveillance in pigs and identifies udder wipes from lactating sows as a novel sample type that offers a convenient, cheap and sensitive manner to monitor IAV in litters prior to weaning.
| S-EPMC6376652 | biostudies-literature
Project description:Epidemiologic data sets continue to grow larger. Probabilistic-bias analyses, which simulate hundreds of thousands of replications of the original data set, may challenge desktop computational resources.We implemented a probabilistic-bias analysis to evaluate the direction, magnitude, and uncertainty of the bias arising from misclassification of prepregnancy body mass index when studying its association with early preterm birth in a cohort of 773,625 singleton births. We compared 3 bias analysis strategies: (1) using the full cohort, (2) using a case-cohort design, and (3) weighting records by their frequency in the full cohort.Underweight and overweight mothers were more likely to deliver early preterm. A validation substudy demonstrated misclassification of prepregnancy body mass index derived from birth certificates. Probabilistic-bias analyses suggested that the association between underweight and early preterm birth was overestimated by the conventional approach, whereas the associations between overweight categories and early preterm birth were underestimated. The 3 bias analyses yielded equivalent results and challenged our typical desktop computing environment. Analyses applied to the full cohort, case cohort, and weighted full cohort required 7.75 days and 4 terabytes, 15.8 hours and 287 gigabytes, and 8.5 hours and 202 gigabytes, respectively.Large epidemiologic data sets often include variables that are imperfectly measured, often because data were collected for other purposes. Probabilistic-bias analysis allows quantification of errors but may be difficult in a desktop computing environment. Solutions that allow these analyses in this environment can be achieved without new hardware and within reasonable computational time frames.
| S-EPMC4306386 | biostudies-literature
Project description:Sampling moiré effects are well known in signal processing. They occur when a continuous periodic signal g(x) is sampled using a sampling frequency f s that does not respect the Nyquist condition, and the signal-frequency f folds over and gives a new, false low frequency in the sampled signal. However, some visible beating artefacts may also occur in the sampled signal when g(x) is sampled using a sampling frequency f s which fully respects the Nyquist condition. We call these phenomena sub-Nyquist artefacts. Although these beating effects have already been reported in the literature, their detailed mathematical behaviour is not widely known. In this paper, we study the behaviour of these phenomena and compare it with analogous results from the moiré theory. We show that both sampling moirés and sub-Nyquist artefacts obey the same basic mathematical rules, in spite of the differences between them. This leads us to a unified approach that explains all of these phenomena and puts them under the same roof. In particular, it turns out that all of these phenomena occur when the signal-frequency f and the sampling frequency f s satisfy f?(m/n)f s with integer m, n, where m/n is a reduced integer ratio; cases with n=1 correspond to true sampling moiré effects.
| S-EPMC4448827 | biostudies-other
Project description:BackgroundBiomolecular networks dynamically respond to stimuli and implement cellular function. Understanding these dynamic changes is the key challenge for cell biologists. As biomolecular networks grow in size and complexity, the model of a biomolecular network must become more rigorous to keep track of all the components and their interactions. In general this presents the need for computer simulation to manipulate and understand the biomolecular network model.ResultsIn this paper, we present a novel method to model the regulatory system which executes a cellular function and can be represented as a biomolecular network. Our method consists of two steps. First, a novel scale-free network clustering approach is applied to the large-scale biomolecular network to obtain various sub-networks. Second, a state-space model is generated for the sub-networks and simulated to predict their behavior in the cellular context. The modeling results represent hypotheses that are tested against high-throughput data sets (microarrays and/or genetic screens) for both the natural system and perturbations. Notably, the dynamic modeling component of this method depends on the automated network structure generation of the first component and the sub-network clustering, which are both essential to make the solution tractable.ConclusionExperimental results on time series gene expression data for the human cell cycle indicate our approach is promising for sub-network mining and simulation from large-scale biomolecular network.
| S-EPMC2213691 | biostudies-literature
Project description:BackgroundProtracted bacterial bronchitis (PBB) is an endobronchial infection and a the most common cause of chronic wet cough in young children. It is treated with antibiotics, which can only be targeted if the causative organism is known. As most affected children do not expectorate sputum, lower airway samples can only be obtained by bronchoalveolar lavage (BAL) samples taken during flexible bronchoscopy (FB-BAL). This is invasive and is therefore reserved for children with severe or relapsing cases. Most children with PBB are treated empirically with broad spectrum antibiotics. CLASSIC PBB will compare the pathogen yield from two less invasive strategies with that from FB-BAL to see if they are comparable.Methods131 children with PBB from four UK centres referred FB-BAL will be recruited. When attending for FB-BAL, they will have a cough swab and an induced sputum sample obtained. The primary outcome will be the discordance of the pathogen yield from the cough swab and the induced sputum when compared with FB-BAL. Secondary outcomes will be the sensitivity of each sampling strategy, the success rate of the induced sputum in producing a usable sample and the tolerability of each of the three sampling strategies.DiscussionIf either or both of the two less invasive airway sampling strategies are shown to be a useful alternative to FB-BAL, this will lead to more children with PBB having lower airway samples enabling targeted antibiotic prescribing. It would also reduce the need for FB, which is known to be burdensome for children and their families.Trial registration numberISRCTN79883982.
| S-EPMC9680323 | biostudies-literature
Project description:IntroductionIn the United States, tens of thousands of inspections of tobacco retailers are conducted each year. Various sampling choices can reduce travel costs, emphasize enforcement in areas with greater noncompliance, and allow for comparability between states and over time. We sought to develop a model sampling strategy for state tobacco retailer inspections.MethodsUsing a 2014 list of 10,161 North Carolina tobacco retailers, we compared results from simple random sampling; stratified, clustered at the ZIP code sampling; and, stratified, clustered at the census tract sampling. We conducted a simulation of repeated sampling and compared approaches for their comparative level of precision, coverage, and retailer dispersion.ResultsWhile maintaining an adequate design effect and statistical precision appropriate for a public health enforcement program, both stratified, clustered ZIP- and tract-based approaches were feasible. Both ZIP and tract strategies yielded improvements over simple random sampling, with relative improvements, respectively, of average distance between retailers (reduced 5.0% and 1.9%), percent Black residents in sampled neighborhoods (increased 17.2% and 32.6%), percent Hispanic residents in sampled neighborhoods (reduced 2.2% and increased 18.3%), percentage of sampled retailers located near schools (increased 61.3% and 37.5%), and poverty rate in sampled neighborhoods (increased 14.0% and 38.2%).ConclusionsStates can make retailer inspections more efficient and targeted with stratified, clustered sampling. Use of statistically appropriate sampling strategies like these should be considered by states, researchers, and the Food and Drug Administration to improve program impact and allow for comparisons over time and across states.ImplicationsThe authors present a model tobacco retailer sampling strategy for promoting compliance and reducing costs that could be used by US states and the Food and Drug Administration (FDA). The design is feasible to implement in North Carolina. Use of the sampling design would help document the impact of FDA's compliance and enforcement program, save money, and emphasize inspections in areas where they are needed most. FDA should consider requiring probability-based sampling in their inspections contracts with states and private contractors.
| S-EPMC6154981 | biostudies-literature
Project description:BACKGROUND:Network component analysis (NCA) became a popular tool to understand complex regulatory networks. The method uses high-throughput gene expression data and a priori topology to reconstruct transcription factor activity profiles. Current NCA algorithms are constrained by several conditions posed on the network topology, to guarantee unique reconstruction (termed compliancy). However, the restrictions these conditions pose are not necessarily true from biological perspective and they force network size reduction, pruning potentially important components. RESULTS:To address this, we developed a novel, Iterative Sub-Network Component Analysis (ISNCA) for reconstructing networks at any size. By dividing the initial network into smaller, compliant subnetworks, the algorithm first predicts the reconstruction of each subnetwork using standard NCA algorithms. It then subtracts from the reconstruction the contribution of the shared components from the other subnetwork. We tested the ISNCA on real, large datasets using various NCA algorithms. The size of the networks we tested and the accuracy of the reconstruction increased significantly. Importantly, FOXA1, ATF2, ATF3 and many other known key regulators in breast cancer could not be incorporated by any NCA algorithm because of the necessary conditions. However, their temporal activities could be reconstructed by our algorithm, and therefore their involvement in breast cancer could be analyzed. CONCLUSIONS:Our framework enables reconstruction of large gene expression data networks, without reducing their size or pruning potentially important components, and at the same time rendering the results more biological plausible. Our ISNCA method is not only suitable for prediction of key regulators in cancer studies, but it can be applied to any high-throughput gene expression data.
| S-EPMC4634733 | biostudies-literature
Project description:OBJECTIVE: In literature, a great diversity of limited sampling strategies (LSS) have been recommended for tacrolimus monitoring, however proper validation of these strategies to accurately predict the area under the time concentration curve (AUC0-12) is limited. The aim of this study was to determine whether these LSS might be useful for AUC prediction of other patient populations. METHODS: The LSS from literature studied were based on regression equations or on Bayesian fitting using MWPHARM 3.50 (Mediware, Groningen, the Netherlands). The performance was evaluated on 24 of these LSS in our population of 37 renal transplant patients with known AUCs. The results were also compared with the predictability of the regression equation based on the trough concentrations C0 and C12 of these 37 patients. Criterion was an absolute prediction error (APE) that differed less than 15% from the complete AUC0-12 calculated by the trapezoidal rule. RESULTS: Thirteen of the 18 (72%) LSS based on regression analysis were capable of predicting at least 90% of the 37 individual AUC0-12 within an APE of 15%. Additionally, all but three LSS examined gave a better prediction of the complete AUC0-12 in comparison with the trough concentrations C0 or C12 (mean 62%). All six LSS based on Bayesian fitting predicted <90% of the 37 complete AUC0-12 correctly (mean 67%). CONCLUSIONS: The present study indicated that implementation of LSS based on regression analysis could produce satisfactory predictions although careful evaluation is necessary.
| S-EPMC2039832 | biostudies-other
Project description:Sampling complex free-energy surfaces is one of the main challenges of modern atomistic simulation methods. The presence of kinetic bottlenecks in such surfaces often renders a direct approach useless. A popular strategy is to identify a small number of key collective variables and to introduce a bias potential that is able to favor their fluctuations in order to accelerate sampling. Here, we propose to use machine-learning techniques in conjunction with the recent variationally enhanced sampling method [O. Valsson, M. Parrinello, Phys. Rev. Lett. 113, 090601 (2014)] in order to determine such potential. This is achieved by expressing the bias as a neural network. The parameters are determined in a variational learning scheme aimed at minimizing an appropriate functional. This required the development of a more efficient minimization technique. The expressivity of neural networks allows representing rapidly varying free-energy surfaces, removes boundary effects artifacts, and allows several collective variables to be handled.
| S-EPMC6731643 | biostudies-literature
Project description:Advances in single-cell technologies have highlighted the prevalence and biological significance of cellular heterogeneity. A critical question researchers face is how to design experiments that faithfully capture the true range of heterogeneity from samples of cellular populations. Here we develop a data-driven approach, illustrated in the context of image data, that estimates the sampling depth required for prospective investigations of single-cell heterogeneity from an existing collection of samples.
| S-EPMC5658002 | biostudies-literature