Transcriptional programs: modelling higher order structure in transcriptional control.

BACKGROUND: Transcriptional regulation is an important part of regulatory control in eukaryotes. Even if binding motifs for transcription factors are known, the task of finding binding sites by scanning sequences is plagued by false positives. One way to improve the detection of binding sites from motifs is by taking cooperativity of transcription factor binding into account. We propose a non-parametric probabilistic model, similar to a document topic model, for detecting transcriptional programs, groups of cooperative transcription factors and co-regulated genes. The analysis results in transcriptional programs which generalise both transcriptional modules and TF-target gene incidence matrices and provide a higher-level summary of these structures. The method is independent of prior specification of training sets of genes, for example, via gene expression data. The analysis is based on known binding motifs. RESULTS: We applied our method to putative regulatory regions of 18,445 Mus musculus genes. We discovered just 68 transcriptional programs that effectively summarised the action of 149 transcription factors on these genes. Several of these programs were significantly enriched for known biological processes and signalling pathways. One transcriptional program has a significant overlap with a reference set of cell cycle specific transcription factors. CONCLUSION: Our method is able to pick out higher order structure from noisy sequence analyses. The transcriptional programs it identifies potentially represent common mechanisms of regulatory control across the genome. It simultaneously predicts which genes are co-regulated and which sets of transcription factors cooperate to achieve this co-regulation. The programs we discovered enable biologists to choose new genes and transcription factors to study in specific transcriptional regulatory systems.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Impact of strong disorder on the static magnetic properties of the spin-chain compound BaCu2SiGeO7

The disordered quasi-1D magnet BaCu2SiGeO7 is considered as one of the best physical realizations of the random Heisenberg chain model, which features an irregular distribution of the exchange parameters and whose ground state is predicted to be the scarcely investigated random-singlet state (RSS). Based on extensive 29Si NMR and magnetization studies of BaCu2SiGeO7, combined with numerical Quantum Monte Carlo simulations, we obtain remarkable quantitative agreement with theoretical predictions of the random Heisenberg chain model and strong indications for the formation of a random-singlet state at low temperatures in this compound. As a local probe, NMR is a well-adapted technique for studying the magnetism of disordered systems. In this case it also reveals an additional local transverse staggered field (LTSF), which affects the low-temperature properties of the RSS. The proposed model Hamiltonian satisfactorily accounts for the temperature dependence of the NMR line shapes.Comment: 10 pages, 7 figure

Requirements for Sensor Integrating Machine Elements : A Review of Wear and Vibration Characteristics of Gears

For condition monitoring of machines sensor integrating standard machine elements provide advantage in acquiring high-quality, robust data from individual machine elements and reducing effort in signal processing. However, research covering small and inexpensive consumer-grade MEMS sensors with respect to integration and measurement requirements for wear detection is limited. In order to define such requirements, the state of the art of vibration-based condition monitoring of gears is reviewed and summarised. The focus is on the characteristics of progressive wear and how it might show in the vibration signal. The review finds that correlation between wear and vibration characteristics of gears exist, but the interpretation of the vibration signals is challenging and requires purpose-built signal processing methods. The review also concludes that integrated MEMS acceleration sensors are theoretically able to measure the vibration characteristics of gears to detect wear. Important characteristics are the gear mesh acceleration with its frequencies and harmonic multiples (GMFi). Frequency range requirements for the sensors depend on the operating conditions of gears, the upper frequency limit needs to be greater or equal to 1.3 GMF$_{i,max}$. For the measuring range requirements, upper limits of 20 g RMS can be extracted within certain conditions. Data analysis requires a minimum frequency resolution which affects the size of memory needed for an integrated sensor system. However, there is a lack of research whether the sensitivity and internal noise behaviour of available MEMS sensors is good enough to measure relative changes in the vibration signals caused by wear

Prediction of tool forces in manual grinding using consumer-grade sensors and machine learning

Tool forces are a decisive parameter for manual grinding with hand-held power tools, which can be used to determine the productivity, quality of the work result, vibration exposition, and tool lifetime. One approach to tool force determination is the prediction of tool forces via measured operating parameters of a hand-held power tool. The problem is that the accuracy of tool force prediction with consumer-grade sensors remains unclear in manual grinding. Therefore, the accuracy of tool force prediction using Gaussian process regression is examined in a study for two hand-held angle grinders in four different applications in three directions using measurement data from an inertial measurement unit, a current sensor, and a voltage sensor. The prediction of the grinding normal force (rMAE = 11.44% and r = 0.84) and the grinding tangential force (rMAE = 18.21% and r = 0.82) for three tested applications, as well as the radial force for the application cutting with a cut-off wheel (rMAE = 19.67% and r = 0.80) is shown to be feasible. The prediction of the guiding force (rMAE = 87.02% and r = 0.37) for three tested applications is only possible to a limited extent. This study supports data acquisition and evaluation of hand-held power tools using consumer-grade sensors, such as an inertial measurement unit, in real-world applications, resulting in new potentials for product use and product development

A comparative study of S/MAR prediction tools

BACKGROUND: S/MARs are regions of the DNA that are attached to the nuclear matrix. These regions are known to affect substantially the expression of genes. The computer prediction of S/MARs is a highly significant task which could contribute to our understanding of chromatin organisation in eukaryotic cells, the number and distribution of boundary elements, and the understanding of gene regulation in eukaryotic cells. However, while a number of S/MAR predictors have been proposed, their accuracy has so far not come under scrutiny. RESULTS: We have selected S/MARs with sufficient experimental evidence and used these to evaluate existing methods of S/MAR prediction. Our main results are: 1.) all existing methods have little predictive power, 2.) a simple rule based on AT-percentage is generally competitive with other methods, 3.) in practice, the different methods will usually identify different sub-sequences as S/MARs, 4.) more research on the H-Rule would be valuable. CONCLUSION: A new insight is needed to design a method which will predict S/MARs well. Our data, including the control data, has been deposited as additional material and this may help later researchers test new predictors

On the propagation of information and the use of localization in ensemble Kalman filtering

Several localized versions of the ensemble Kalman filter have been proposed. Although tests applying such schemes have proven them to be extremely promising, a full basic understanding of the rationale and limitations of localization is currently lacking. It is one of the goals of this paper to contribute toward addressing this issue. The second goal is to elucidate the role played by chaotic wave dynamics in the propagation of information and the resulting impact on forecasts. To accomplish these goals, the principal tool used here will be analysis and interpretation of numerical experiments on a toy atmospheric model introduced by Lorenz in 2005. Propagation of the wave packets of this model is shown. It is found that, when an ensemble Kalman filter scheme is employed, the spatial correlation function obtained at each forecast cycle by averaging over the background ensemble members is short ranged, and this is in strong contrast to the much longer range correlation function obtained by averaging over states from free evolution of the model. Propagation of the effects of observations made in one region on forecasts in other regions is studied. The error covariance matrices from the analyses with localization and without localization are compared. From this study, major characteristics of the localization process and information propagation are extracted and summarized.Comment: 13 pages, 18 figures, uses ametsoc.bst and ametsoc2col.st

Design of sensor integrating gears: methodical development, integration and verification of an in-Situ MEMS sensor system

State of the art vibration-based condition monitoring at gearbox housings faces uncertainties in the interpretation of measurement data due to signal transformations and noise. The state of research shows that direct measurements at the source of vibrations with integrated sensors provide higher quality data. Capacitive MEMS sensors seem predestined for integration, but there is limited research covering compactly integrated MEMS sensor systems for condition monitoring by vibration measurement. In this contribution an integrated MEMS sensor system is designed methodically based on VDI 2206. A sensor system is selected based on requirements extracted of previous contributions and verified on a rotational shaker test rig. Afterwards it is integrated on a gear wheel in a gear test bench. Several verification measurements using different principles and locations are performed to verify the measurands. Results show that the gear mesh vibrations including the sidebands can be measured with the integrated sensors which provide superior signal-noise-ratios compared to other locations. This proofs that the sensor integrating gear system is principally able to perform high quality condition monitoring

Sensor-integrating gears: wear detection by in-situ MEMS acceleration sensors [Sensorintegrierende Zahnräder: Verschleißdetektion durch In-situ MEMS Beschleunigungssensoren]

Gear tooth wear is a common phenomenon leading to malfunctions in machines. To detect wear and faults, gear condition monitoring by vibration is established. The problem is that the measurement data quality for detection of wear by vibration is not good enough with currently established measurement methods, caused by long signal paths of the commonly used housing mounted sensors. In-situ sensors directly at the gear achieve better data quality, but are not yet proved in wear detection. Further it is unknown what analysis methods are suited for in-situ sensor data. Existing gear condition metrics are mainly focused on localized gear tooth faults, and do not estimate wear related values. This contribution aims to improve wear detection by investigating in-situ sensors and advance gear condition metrics. Using a gear test rig to conduct an end of life test, the wear detection ability of an in-situ sensor system and reference sensors on the bearing block are compared through standard gear condition metrics. Furthermore, a machine-learned regression model is developed that maps multiple features related to gear dynamics to the gear mass loss. The standard gear metrics used on the in-situ sensor data are able to detect wear, but not significantly better compared to the other sensors. The regression model is able to estimate the actual wear with a high accuracy. Providing a wear related output improves the wear detection by better interpretability

Variable structure motifs for transcription factor binding sites.

BACKGROUND: Classically, models of DNA-transcription factor binding sites (TFBSs) have been based on relatively few known instances and have treated them as sites of fixed length using position weight matrices (PWMs). Various extensions to this model have been proposed, most of which take account of dependencies between the bases in the binding sites. However, some transcription factors are known to exhibit some flexibility and bind to DNA in more than one possible physical configuration. In some cases this variation is known to affect the function of binding sites. With the increasing volume of ChIP-seq data available it is now possible to investigate models that incorporate this flexibility. Previous work on variable length models has been constrained by: a focus on specific zinc finger proteins in yeast using restrictive models; a reliance on hand-crafted models for just one transcription factor at a time; and a lack of evaluation on realistically sized data sets. RESULTS: We re-analysed binding sites from the TRANSFAC database and found motivating examples where our new variable length model provides a better fit. We analysed several ChIP-seq data sets with a novel motif search algorithm and compared the results to one of the best standard PWM finders and a recently developed alternative method for finding motifs of variable structure. All the methods performed comparably in held-out cross validation tests. Known motifs of variable structure were recovered for p53, Stat5a and Stat5b. In addition our method recovered a novel generalised version of an existing PWM for Sp1 that allows for variable length binding. This motif improved classification performance. CONCLUSIONS: We have presented a new gapped PWM model for variable length DNA binding sites that is not too restrictive nor over-parameterised. Our comparison with existing tools shows that on average it does not have better predictive accuracy than existing methods. However, it does provide more interpretable models of motifs of variable structure that are suitable for follow-up structural studies. To our knowledge, we are the first to apply variable length motif models to eukaryotic ChIP-seq data sets and consequently the first to show their value in this domain. The results include a novel motif for the ubiquitous transcription factor Sp1.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

Aggressive shadowing of a low-dimensional model of atmospheric dynamics

Predictions of the future state of the Earth's atmosphere suffer from the consequences of chaos: numerical weather forecast models quickly diverge from observations as uncertainty in the initial state is amplified by nonlinearity. One measure of the utility of a forecast is its shadowing time, informally given by the period of time for which the forecast is a reasonable description of reality. The present work uses the Lorenz 096 coupled system, a simplified nonlinear model of atmospheric dynamics, to extend a recently developed technique for lengthening the shadowing time of a dynamical system. Ensemble forecasting is used to make forecasts with and without inflation, a method whereby the ensemble is regularly expanded artificially along dimensions whose uncertainty is contracting. The first goal of this work is to compare model forecasts, with and without inflation, to a true trajectory created by integrating a modified version of the same model. The second goal is to establish whether inflation can increase the maximum shadowing time for a single optimal member of the ensemble. In the second experiment the true trajectory is known a priori, and only the closest ensemble members are retained at each time step, a technique known as stalking. Finally, a targeted inflation is introduced to both techniques to reduce the number of instances in which inflation occurs in directions likely to be incommensurate with the true trajectory. Results varied for inflation, with success dependent upon the experimental design parameters (e.g. size of state space, inflation amount). However, a more targeted inflation successfully reduced the number of forecast degradations without significantly reducing the number of forecast improvements. Utilized appropriately, inflation has the potential to improve predictions of the future state of atmospheric phenomena, as well as other physical systems.Comment: 14 pages, 16 figure