scholarly journals Longitudinal profiling of the lung microbiome in the AERIS study demonstrates repeatability of bacterial and eosinophilic COPD exacerbations

Thorax ◽  
2018 ◽  
Vol 73 (5) ◽  
pp. 422-430 ◽  
Author(s):  
David Mayhew ◽  
Nathalie Devos ◽  
Christophe Lambert ◽  
James R Brown ◽  
Stuart C Clarke ◽  
...  
Keyword(s):  
Disease Severity ◽  
High Throughput Sequencing ◽  
Respiratory Infections ◽  
Markov Chain Model ◽  
Chain Model ◽  
Copd Exacerbations ◽  
Lung Microbiome ◽  
Registration Number ◽  
The Stability ◽  
Over Time

BackgroundAlterations in the composition of the lung microbiome associated with adverse clinical outcomes, known as dysbiosis, have been implicated with disease severity and exacerbations in COPD.ObjectiveTo characterise longitudinal changes in the lung microbiome in the AERIS study (Acute Exacerbation and Respiratory InfectionS in COPD) and their relationship with associated COPD outcomes.MethodsWe surveyed 584 sputum samples from 101 patients with COPD to analyse the lung microbiome at both stable and exacerbation time points over 1 year using high-throughput sequencing of the 16S ribosomal RNA gene. We incorporated additional lung microbiology, blood markers and in-depth clinical assessments to classify COPD phenotypes.ResultsThe stability of the lung microbiome over time was more likely to be decreased in exacerbations and within individuals with higher exacerbation frequencies. Analysis of exacerbation phenotypes using a Markov chain model revealed that bacterial and eosinophilic exacerbations were more likely to be repeated in subsequent exacerbations within a subject, whereas viral exacerbations were not more likely to be repeated. We also confirmed the association of bacterial genera, including Haemophilus and Moraxella, with disease severity, exacerbation events and bronchiectasis.ConclusionsSubtypes of COPD have distinct bacterial compositions and stabilities over time. Some exacerbation subtypes have non-random probabilities of repeating those subtypes in the future. This study provides insights pertaining to the identification of bacterial targets in the lung and biomarkers to classify COPD subtypes and to determine appropriate treatments for the patient.Trial registration numberResults, NCT01360398.

Stochastic recruitment control of shape memory alloy cellular actuators based on multi-state Markov chain model

2021 ◽  
pp. 1045389X2110026
Author(s):  
Chen Zhang ◽  
Jianjiang Cui
Keyword(s):  
Markov Chain ◽  
Shape Memory Alloy ◽  
Shape Memory ◽  
Markov Chain Model ◽  
Open Loop ◽  
Chain Model ◽  
Control Laws ◽  
Loop Control ◽  
Stochastic Recruitment ◽  
The Stability

A new broadcast stochastic recruitment approach to the control of shape memory alloy (SMA) cellular actuators is proposed. The control design is based on a Markov chain model of multi-state cells, which is able to better characterize the inherent hysteresis of SMA in phase transition. The closed-loop and open-loop control laws are derived from random Lyapunov stability analysis and the stability conditions are analyzed. Simulation experiments demonstrate the effectiveness of the proposed method.

On Perturbation Bounds for the Joint Stationary Distribution of Multivariate Markov Chain Models

2013 ◽  
Vol 3 (1) ◽  
pp. 1-17 ◽  
Author(s):  
Wen Li ◽  
Lin Jiang ◽  
Wai-Ki Ching ◽  
Lu-Bin Cui
Keyword(s):  
Markov Chain ◽  
Stationary Distribution ◽  
Categorical Data ◽  
Markov Chain Model ◽  
Chain Model ◽  
Perturbation Bounds ◽  
Markov Chain Models ◽  
Joint Stationary Distribution ◽  
Distribution Vector ◽  
The Stability

AbstractMultivariate Markov chain models have previously been proposed in for studying dependent multiple categorical data sequences. For a given multivariate Markov chain model, an important problem is to study its joint stationary distribution. In this paper, we use two techniques to present some perturbation bounds for the joint stationary distribution vector of a multivariate Markov chain with s categorical sequences. Numerical examples demonstrate the stability of the model and the effectiveness of our perturbation bounds.

On the Robustness of Solution Strategy Classifications

2010 ◽  
Vol 31 (2) ◽  
pp. 68-73 ◽  
Author(s):  
María José Contreras ◽  
Víctor J. Rubio ◽  
Daniel Peña ◽  
José Santacreu
Keyword(s):  
Individual Differences ◽  
Study Strategies ◽  
Solution Strategy ◽  
Specific Test ◽  
Spatial Tasks ◽  
The Stability ◽  
Strategy Changes ◽  
Over Time

Individual differences in performance when solving spatial tasks can be partly explained by differences in the strategies used. Two main difficulties arise when studying such strategies: the identification of the strategy itself and the stability of the strategy over time. In the present study strategies were separated into three categories: segmented (analytic), holistic-feedback dependent, and holistic-planned, according to the procedure described by Peña, Contreras, Shih, and Santacreu (2008) . A group of individuals were evaluated twice on a 1-year test-retest basis. During the 1-year interval between tests, the participants were not able to prepare for the specific test used in this study or similar ones. It was found that 60% of the individuals kept the same strategy throughout the tests. When strategy changes did occur, they were usually due to a better strategy. These results prove the robustness of using strategy-based procedures for studying individual differences in spatial tasks.

On the Stability of Evaluative Conditioning Effects

2013 ◽  
Vol 44 (6) ◽  
pp. 380-389 ◽  
Author(s):  
Sabine Förderer ◽  
Christian Unkelbach
Keyword(s):  
Evaluative Conditioning ◽  
Indirect Measures ◽  
The Stability ◽  
Over Time

Evaluative conditioning (EC) refers to valence changes in neutral stimuli (CSs) through repeated pairing with liked or disliked stimuli (USs). The present study examined the stability of EC effects in the course of 1 week. We investigated how this stability depends on memory for US valence and US identity. We also investigated whether CSs evaluations occurring immediately after conditioning (i.e., evaluative consolidation) are necessary for stable EC effects. Participants showed stable EC effects on direct and indirect measures, independent of evaluations immediately after conditioning. EC effects depended on memory for US valence but not for US identity. And although memory decreased significantly over time, EC effects remained stable. These data suggest that evaluative consolidation is not necessary, and that conditioned preferences and attitudes might persist even when people do not remember the concrete source anymore.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

scholarly journals Steady-state $$\dot{V}{\text{O}}_{2}$$ above MLSS: evidence that critical speed better represents maximal metabolic steady state in well-trained runners

2021 ◽  
Author(s):  
Rebekah J. Nixon ◽  
Sascha H. Kranen ◽  
Anni Vanhatalo ◽  
Andrew M. Jones
Keyword(s):  
Steady State ◽  
Critical Speed ◽  
Lactate Concentration ◽  
Practical Interest ◽  
Blood Lactate Concentration ◽  
Distance Runners ◽  
Severe Intensity ◽  
The Stability ◽  
Trained Runners ◽  
Over Time

AbstractThe metabolic boundary separating the heavy-intensity and severe-intensity exercise domains is of scientific and practical interest but there is controversy concerning whether the maximal lactate steady state (MLSS) or critical power (synonymous with critical speed, CS) better represents this boundary. We measured the running speeds at MLSS and CS and investigated their ability to discriminate speeds at which $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 was stable over time from speeds at which a steady-state $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 could not be established. Ten well-trained male distance runners completed 9–12 constant-speed treadmill tests, including 3–5 runs of up to 30-min duration for the assessment of MLSS and at least 4 runs performed to the limit of tolerance for assessment of CS. The running speeds at CS and MLSS were significantly different (16.4 ± 1.3 vs. 15.2 ± 0.9 km/h, respectively; P < 0.001). Blood lactate concentration was higher and increased with time at a speed 0.5 km/h higher than MLSS compared to MLSS (P < 0.01); however, pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 did not change significantly between 10 and 30 min at either MLSS or MLSS + 0.5 km/h. In contrast, $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 increased significantly over time and reached $$\dot{V}{\text{O}}_{2\,\,\max }$$ V ˙ O 2 max at end-exercise at a speed ~ 0.4 km/h above CS (P < 0.05) but remained stable at a speed ~ 0.5 km/h below CS. The stability of $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 at a speed exceeding MLSS suggests that MLSS underestimates the maximal metabolic steady state. These results indicate that CS more closely represents the maximal metabolic steady state when the latter is appropriately defined according to the ability to stabilise pulmonary $$\dot{V}{\text{O}}_{2}$$ V ˙ O 2 .

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