Probabilistic Profiles Research Articles

Creating Probabilistic Idiographic Performance Profiles from Discrete Feelings: Combining the IZOF and IAPZ models

Abstract The purpose of the study was to determine if probabilistic profiling could be used to develop probabilistic individual zones of optimal functioning (pIZOF) profiles comprised from discrete feelings aggregated into valence-function categories (Hanin, 2000a). Ten male college tennis players developed individualized profiles, and then assessed their personal profile’s items during changeovers of intrasquad matches. Support for the viability of using ordinal logistic regression (OLR; a foundation of the IAPZ method) to develop idiographic discrete performance-feeling profiles (common to the IZOF framework) is provided. Evidence of the idiosyncratic nature of the profiles and for the effectiveness of the IZOF-IAPZ integration is provided through visual assessments of figures, qualitative comparisons of feeling-items selected by each player, and statistical analyses of differences in the size (variance) and location (means) of zones - which revealed that 62 to 76 percent of zones were individually unique. The results of this study augment the IZOF model by (1) establishing that OLR can be used to develop discrete probabilistic (pIZOF) profiles, and (2) providing a variety of qualitative and quantitative methods of assessing idiosyncrasy.

Subcellular localization for Gram positive and Gram negative bacterial proteins using linear interpolation smoothing model

Protein subcellular localization is an important topic in proteomics since it is related to a protein׳s overall function, helps in the understanding of metabolic pathways, and in drug design and discovery. In this paper, a basic approximation technique from natural language processing called the linear interpolation smoothing model is applied for predicting protein subcellular localizations. The proposed approach extracts features from syntactical information in protein sequences to build probabilistic profiles using dependency models, which are used in linear interpolation to determine how likely is a sequence to belong to a particular subcellular location. This technique builds a statistical model based on maximum likelihood. It is able to deal effectively with high dimensionality that hinders other traditional classifiers such as Support Vector Machines or k-Nearest Neighbours without sacrificing performance. This approach has been evaluated by predicting subcellular localizations of Gram positive and Gram negative bacterial proteins.

Variability between domestic buildings: the impact on energy use

Variations in operational use (in the time domain) and in design and use (between buildings) are critical for district systems. The effects on energy use of behavioural (stochastic profiles of occupancy and end uses) and physical variations (size, orientation, insulation and air tightness) amongst many buildings is examined. Rather than investigating just the variability of these factors, the aim is to identify subsequent impacts on building energy use. To achieve this, dynamic building energy simulations in EnergyPlus are performed. Results include total demands and their distributions, and temporal and probabilistic profiles. Very large variations in total heating demand are noted. Temporal profiles show changes in peak loads, load durations and periods of zero load. Probabilistic profiles and cumulative distributions show that a few buildings are responsible for the majority of total loads. Full detailed simulations are identified as critical when assessing temporal effects such as peak loads and storage sizing.

Assessment of fibre content and 3D profile in cylindrical SFRC specimens

The inductive method from Torrents et al. (Mater Struct 45(10):1577–1592, 2012, 1) is used to assess the fibre content and orientation in steel fibre reinforced concrete. Despite several advantages, the method presents limitations. On one hand, it was conceived for cubic specimens, which complicates its application in existing structures given the difficulty to obtain cubic cores. On the other hand, it only shows the fibre orientation in the three axes perpendicular to the faces of the specimen, being impossible to derive the orientation in other directions with these results. Moreover, it only gives average values without providing any information on the scatter or probabilistic distribution of the fibre orientation—a parameter that may be essential to for design and to explain differences in the behaviour of concretes apparently with the same average fibre distribution. The objective of this paper is to propose an assessment of the fibre content and orientation profile using the inductive method and cylindrical specimens. First, a modification of the method is proposed. Then, new equations are deducted to generalize the test to samples with different shapes and to assess the anisotropy level as well as the directions with the maximum and the minimum fibre contribution. An extensive experimental program and FEM simulations are performed to validate and determine the accuracy of the formulation developed. The results show that the execution of only one additional measurement per specimen is enough to determine the fibre probabilistic profile in all in-plane directions with a high accuracy.

Probabilistic optimal sizing of PV units in a distribution network

The aim of this paper is to reduce losses and improve voltage profile of a distribution feeder taking into account probabilistic nature of PV sources and loads, using particle swarm optimization algorithm. The proposed method uses normal distribution and beta distribution for probabilistic modeling of the loads and irradiation respectively. By applying probabilistic load flow, voltage profile and losses for the network are calculated 24 hours a day and four seasons a year. Based on overall results the optimal size of installed PV resources in each buses on the feeder are obtained and compared to the results obtained from a conventional method.

Infernal 1.1: 100-fold faster RNA homology searches

Summary: Infernal builds probabilistic profiles of the sequence and secondary structure of an RNA family called covariance models (CMs) from structurally annotated multiple sequence alignments given as input. Infernal uses CMs to search for new family members in sequence databases and to create potentially large multiple sequence alignments. Version 1.1 of Infernal introduces a new filter pipeline for RNA homology search based on accelerated profile hidden Markov model (HMM) methods and HMM-banded CM alignment methods. This enables ∼100-fold acceleration over the previous version and ∼10 000-fold acceleration over exhaustive non-filtered CM searches.Availability: Source code, documentation and the benchmark are downloadable from http://infernal.janelia.org. Infernal is freely licensed under the GNU GPLv3 and should be portable to any POSIX-compliant operating system, including Linux and Mac OS/X. Documentation includes a user’s guide with a tutorial, a discussion of file formats and user options and additional details on methods implemented in the software.Contact: nawrockie@janelia.hhmi.org

New insights into the diversity dynamics of Triassic conodonts

In this paper, we examine the diversity trends and the evolutionary patterns of Triassic conodonts through a newly powered large-scale data-set compiled directly from the primary literature. Paleodiversity dynamics analyses have been undertaken by working at the species level and using a system of time units based on biozone subdivisions for a fine temporal level resolution. The role of heterogeneous duration of taxa in diversity estimates has been evaluated through the probabilistic profiles. Results reveal three different stages in the diversity behaviour of Triassic conodonts from standing metrics delimited by two inflections at the mid-Anisian and mid-Carnian. Survivorship analysis supports this pattern. Origination–extinction metrics report a diversification pattern characterised by important fluctuations during the Lopingian–Induan (earliest Triassic), the early-middle Olenekian (Early Triassic) and the Anisian–Ladinian transitions (Middle Triassic), as well as in the early Late Triassic. In addition, two clear diversification peaks are observed in the late Carnian and in the end-Norian. Reported patterns are interpreted in the context of deep extinction and environmental instability by documenting the biological signal of the main diversification and turnover patterns observed from such records. This study emphasises the singularity behaviour of diversity trends derived from the conodont record.

Impact of Flow Dependence, Column Covariance, and Forecast Model Type on Surface-Observation Assimilation for Probabilistic PBL Profile Nowcasts

Abstract A probabilistic verification and factor-separation analysis (FSA) elucidate skillful nowcasts of planetary boundary layer (PBL) temperature, moisture, and wind profiles with a single-column model (SCM) and ensemble filter (EF) assimilation of surface observations. Recently, an FSA showed the importance of surface assimilation versus advection and radiation on ensemble-mean skill. That work addressed the necessary complexity of the model and assimilation scheme for improving PBL nowcasts, relative to deterministic-mesoscale predictions. Here, probabilistic ensemble-based SCM forecasts are compared to a simple probabilistic postprocessing scheme termed climatological dressing (CD). CD adjusts a deterministic mesoscale forecast using surface-atmosphere 3D-climatological covariances, a 30-min persistence model, and surface-forecast errors. It also dresses the adjusted profile with an in-sample uncertainty distribution (obtained from archives) scaled by the 30-min forecast error. Superior deterministic skill from SCM/EF results during night when flow-dependent covariances are more accurate than climatological covariances. CD is deterministically more skillful for temperature and moisture profiles during daytime because SCM/PBL parameterization yields biased covariances. SCM/EF is most probabilistically skillful because (a) the EF covariances accommodate large seasonal variability, (b) the 30-min error persistence assumption fails during nighttime, and (c) vertical error covariance estimates from archived forecasts are generally poor estimates of actual error covariances. A probabilistic FSA of the SCM/EF shows the relative importance of surface assimilation, radiation parameterization, and advection during night. Results confirm surface assimilation as the most important factor. A factor can be deterministically beneficial and probabilistically detrimental, or vice versa, depending on its role in reducing mean error or improving sharpness. Assimilation results in notable probabilistic improvement for nowcasts of low-level jet structures.

In silico identification and characterization of the ion transport specificity for P-type ATPases in the Mycobacterium tuberculosis complex

BackgroundP-type ATPases hydrolyze ATP and release energy that is used in the transport of ions against electrochemical gradients across plasma membranes, making these proteins essential for cell viability. Currently, the distribution and function of these ion transporters in mycobacteria are poorly understood.ResultsIn this study, probabilistic profiles were constructed based on hidden Markov models to identify and classify P-type ATPases in the Mycobacterium tuberculosis complex (MTBC) according to the type of ion transported across the plasma membrane. Topology, hydrophobicity profiles and conserved motifs were analyzed to correlate amino acid sequences of P-type ATPases and ion transport specificity. Twelve candidate P-type ATPases annotated in the M. tuberculosis H37Rv proteome were identified in all members of the MTBC, and probabilistic profiles classified them into one of the following three groups: heavy metal cation transporters, alkaline and alkaline earth metal cation transporters, and the beta subunit of a prokaryotic potassium pump. Interestingly, counterparts of the non-catalytic beta subunits of Hydrogen/Potassium and Sodium/Potassium P-type ATPases were not found.ConclusionsThe high content of heavy metal transporters found in the MTBC suggests that they could play an important role in the ability of M. tuberculosis to survive inside macrophages, where tubercle bacilli face high levels of toxic metals. Finally, the results obtained in this work provide a starting point for experimental studies that may elucidate the ion specificity of the MTBC P-type ATPases and their role in mycobacterial infections.

Initial-state anisotropies and their uncertainties in ultrarelativistic heavy-ion collisions from the Monte Carlo Glauber model

In hydrodynamical modeling of heavy-ion collisions, the initial-state spatial anisotropies are translated into momentum anisotropies of the final-state particle distributions. Thus, understanding the origin of the initial-state anisotropies and their uncertainties is important before extracting specific QCD matter properties, such as viscosity, from the experimental data. In this work we review the wounded nucleon approach based on the Monte Carlo Glauber model, charting in particular the uncertainties arising from modeling of the nucleon-nucleon interactions between the colliding nucleon pairs and nucleon-nucleon correlations inside the colliding nuclei. We discuss the differences between the black disk model and a probabilistic profile function approach for the inelastic nucleon-nucleon interactions, and investigate the influence of initial-state correlations using state-of-the-art modeling of these.

Identification of Plasmodium vivax Proteins with Potential Role in Invasion Using Sequence Redundancy Reduction and Profile Hidden Markov Models

BackgroundThis study describes a bioinformatics approach designed to identify Plasmodium vivax proteins potentially involved in reticulocyte invasion. Specifically, different protein training sets were built and tuned based on different biological parameters, such as experimental evidence of secretion and/or involvement in invasion-related processes. A profile-based sequence method supported by hidden Markov models (HMMs) was then used to build classifiers to search for biologically-related proteins. The transcriptional profile of the P. vivax intra-erythrocyte developmental cycle was then screened using these classifiers.ResultsA bioinformatics methodology for identifying potentially secreted P. vivax proteins was designed using sequence redundancy reduction and probabilistic profiles. This methodology led to identifying a set of 45 proteins that are potentially secreted during the P. vivax intra-erythrocyte development cycle and could be involved in cell invasion. Thirteen of the 45 proteins have already been described as vaccine candidates; there is experimental evidence of protein expression for 7 of the 32 remaining ones, while no previous studies of expression, function or immunology have been carried out for the additional 25.ConclusionsThe results support the idea that probabilistic techniques like profile HMMs improve similarity searches. Also, different adjustments such as sequence redundancy reduction using Pisces or Cd-Hit allowed data clustering based on rational reproducible measurements. This kind of approach for selecting proteins with specific functions is highly important for supporting large-scale analyses that could aid in the identification of genes encoding potential new target antigens for vaccine development and drug design. The present study has led to targeting 32 proteins for further testing regarding their ability to induce protective immune responses against P. vivax malaria.

Quantifying the onset, rate and duration of sugar accumulation in berries from commercial vineyards in contrasting climates of Australia

Background and Aim: To predict the trajectory of sugar accumulation in berries for planning of harvest and post-harvest operations, we derived a simple model that is consistent with the known biological and viticultural drivers of berry ripening and requires inputs that are readily available to industry. Methods and Results: We used data from three vintages in climatically contrasting regions of Australia to derive a relationship between total soluble solids (TSS) and thermal time. A linear-plateau function was fitted to the 90th percentile data, hence providing a boundary function representing the potential for each variety-environment combination. Biologically relevant parameters were derived including the rate of change in TSS, maximum TSS (TSSmax), the threshold thermal time when berries reached TSSmax, the onset and the duration of the period of linear increase in TSS. Gaps, calculated as the difference between actual TSS and the boundary function, correlated positively with vapour pressure. A model with the observed onset of ripening as an input and a variety and site specific rate as parameter accounted for 81–92% of the variation in TSS for independent data sets. Conclusion: The trajectory of TSS in berries can be modelled ‘as if’ the main environmental and management sources of variation affect the onset rather than the rate of sugar accumulation. Significance of the Study: Our onset-rate model can be coupled with: (i) short-term temperature forecasts to predict the trajectory of TSS for management purposes; and (ii) long-term records of temperature to produce probabilistic profiles of maturity date.

An application of comonotonicity theory in a stochastic life annuity framework

A life annuity contract is an insurance instrument which pays pre-scheduled living benefits conditional on the survival of the annuitant. In order to manage the risk borne by annuity providers, one needs to take into account all sources of uncertainty that affect the value of future obligations under the contract. In this paper, we define the concept of annuity rate as the conditional expected present value random variable of future payments of the annuity, given the future dynamics of its risk factors. The annuity rate deals with the non-diversifiable systematic risk contained in the life annuity contract, and it involves mortality risk as well as investment risk. While it is plausible to assume that there is no correlation between the two risks, each affects the annuity rate through a combination of dependent random variables. In order to understand the probabilistic profile of the annuity rate, we apply comonotonicity theory to approximate its quantile function. We also derive accurate upper and lower bounds for prediction intervals for annuity rates. We use the Lee–Carter model for mortality risk and the Vasicek model for the term structure of interest rates with an annually renewable fixed-income investment policy. Different investment strategies can be handled using this framework.

SpliceIT: A hybrid method for splice signal identification based on probabilistic and biological inference

Splice sites define the boundaries of exonic regions and dictate protein synthesis and function. The splicing mechanism involves complex interactions among positional and compositional features of different lengths. Computational modeling of the underlying constructive information is especially challenging, in order to decipher splicing-inducing elements and alternative splicing factors. SpliceIT (Splice Identification Technique) introduces a hybrid method for splice site prediction that couples probabilistic modeling with discriminative computational or experimental features inferred from published studies in two subsequent classification steps. The first step is undertaken by a Gaussian support vector machine (SVM) trained on the probabilistic profile that is extracted using two alternative position-dependent feature selection methods. In the second step, the extracted predictions are combined with known species-specific regulatory elements, in order to induce a tree-based modeling. The performance evaluation on human and Arabidopsis thaliana splice site datasets shows that SpliceIT is highly accurate compared to current state-of-the-art predictors in terms of the maximum sensitivity, specificity tradeoff without compromising space complexity and in a time-effective way. The source code and supplementary material are available at: http://www.med.auth.gr/research/spliceit/.

Using Experimental Designs, Assisted History-Matching Tools, and Bayesian Framework To Get Probabilistic Gas-Storage Pressure Forecasts

Summary One of the main concerns in the oil and gas business is generating reliable reservoir hydrodynamics forecasts. Such profiles are the cornerstones of optimal technico-economical management decisions. A workflow combining different methods to integrate and reduce most of the subsurface uncertainties using multiple history matched models (explaining the past) to infer reasonably reliable forecasts is proposed. A sensitivity study is first performed using experimental design to scan the whole range of static and dynamic uncertainty parameters using a proxy model of the fluid-flow simulator. Only the most sensitive ones with respect to an objective function (OF) (quantifying the mismatch between the simulation results and the observations) are retained for subsequent steps. Assisted history-matching tools are then used to obtain multiple history-matched models. To obtain probabilistic pressure profiles, multiple history-matched models are combined with the uncertain parameters not retained in the sensitivity study, using the joint modeling method. Another way to constrain uncertain parameters with observation data is to use Bayesian framework where a posteriori distributions of the input parameters are derived from the a priori distributions and the likelihood function. The latter is computed through a nonlinear proxy model using experimental design, kriging, and dynamic training techniques. These two workflows have been applied to a real gas storage case submitted to significant seasonal pressure variations. The obtained probabilistic operational pressure profiles for a given period are then compared to the actual gas storage dynamic behavior so that we can compare the two approaches and assess the added value of both proposed workflows.

Fast and accurate search for non-coding RNA pseudoknot structures in genomes

Searching genomes for non-coding RNAs (ncRNAs) by their secondary structure has become an important goal for bioinformatics. For pseudoknot-free structures, ncRNA search can be effective based on the covariance model and CYK-type dynamic programming. However, the computational difficulty in aligning an RNA sequence to a pseudoknot has prohibited fast and accurate search of arbitrary RNA structures. Our previous work introduced a graph model for RNA pseudoknots and proposed to solve the structure-sequence alignment by graph optimization. Given k candidate regions in the target sequence for each of the n stems in the structure, we could compute a best alignment in time O(k(t)n) based upon a tree width t decomposition of the structure graph. However, to implement this method to programs that can routinely perform fast yet accurate RNA pseudoknot searches, we need novel heuristics to ensure that, without degrading the accuracy, only a small number of stem candidates need to be examined and a tree decomposition of a small tree width can always be found for the structure graph. The current work builds on the previous one with newly developed preprocessing algorithms to reduce the values for parameters k and t and to implement the search method into a practical program, called RNATOPS, for RNA pseudoknot search. In particular, we introduce techniques, based on probabilistic profiling and distance penalty functions, which can identify for every stem just a small number k (e.g. k <or= 10) of plausible regions in the target sequence to which the stem needs to align. We also devised a specialized tree decomposition algorithm that can yield tree decomposition of small tree width t (e.g. t <or= 4) for almost all RNA structure graphs. Our experiments show that with RNATOPS it is possible to routinely search prokaryotic and eukaryotic genomes for specific RNA structures of medium to large sizes, including pseudoknots, with high sensitivity and high specificity, and in a reasonable amount of time.

Adaptive parameter control of evolutionary algorithms to improve quality-time trade-off

Parameter control of evolutionary algorithms (EAs) poses special challenges as EA uses a population and requires many parameters to be controlled for an effective search. Quality improvement is dependent on several factors, such as, fitness estimation, population diversity and convergence rate. A widely practiced approach to identify a good set of parameters for a particular class of problem is through experimentation. Ideally, the parameter selection should depend on the resource availability, and thus, a rigid choice may not be suitable. In this work, we propose an automated framework for parameter selection, which can adapt according to the constraints specified. To condition the parameter choice through resource constraint/utilization, we consider two typical scenarios, one where maximum available run-time is pre-specified and the other in which a utility function modeling the quality-time trade-off is used instead of a rigid deadline. We present static and dynamic parameter selection strategies based on a probabilistic profiling method. Experiments performed with traveling salesman problem (TSP) and standard cell placement problem show that an informed adaptive parameter control mechanism can yield better results than a static selection.

Construction of characteristic sets for individually adapted pilot support in typical flight regimes

An approach to solution of problems of individually adapted support of actions of the pilot in typical flight regimes is developed. This approach can be recommended, in particular, in the course of preparation and re-preparation of air crew and for implementation of onboard expert systems of advanced aircrafts. The basis of the proposed approach is based on integral evaluation of control actions of the pilot formed based on indirect data and thus not requiring online registration of factors reflecting the current psychophysiological state of the pilot. In this case, data of the regular onboard measurement system of the aircraft are used for obtaining the mentioned integral evaluation. The method of implementation of this approach to evaluating control actions of the pilot based on the formation and parametric representation in the state space of the “aircraft-pilot” system of individual reachable set used further for creation of “Electronic passport of the pilot” is proposed. Algorithms for construction of such sets in the class of ellipsoids and cubes of confidence and their compact representation in the form of the individual probabilistic profile of the pilot are described. Results of simulation of the process of obtaining such sets, which prove the efficiency of the developed concept, are presented

Probabilistic Lifetime-Oriented Multiobjective Optimization of Bridge Maintenance: Combination of Maintenance Types

In this paper, the performance of deteriorating bridges is analyzed considering the combination of essential and preventive maintenance actions. Multiobjective optimization under uncertainty is used to find the best combinations of condition, safety, and cost; satisfying all performance constraints of deteriorating bridges during a specified time horizon under multiple maintenance types. The thresholds at which essential maintenance actions are applied and the times of application of preventive maintenance actions are considered as design variables. The evolution in time of nondeterministic performance is modeled as probabilistic condition, safety, and cost profiles, and genetic algorithms are combined with Latin hypercube sampling to optimize the maintenance strategies in a full probabilistic context. Owing to the scarce information on the structural performance effects, due to the interaction among different types of maintenance actions, special attention is paid to the assumptions with respect to the combination of effects of different maintenance types. Examples of application, based on data collected on reinforced concrete bridge crossheads in the United Kingdom, are presented. The benefits of the combination of preventive and essential maintenance actions over single maintenance type strategies are clearly indicated; namely, the reduction in cost for similar condition and safety indices. The use of alternative probabilistic indicators in bridge maintenance, such as the lower percentiles of performance, is also analyzed.

Membrane profile-based probabilistic method for predicting transmembrane segments via multiple protein sequence alignment

Prediction of transmembrane (TM) segments of amino acid sequences of membrane proteins is a well-known and very important problem. The accuracy of its solution can be improved for approaches that do not use a homology search in an additional data bank. There is a lack of tested data in this area of research, because information on the structure of membrane proteins is scarce. In this work we created a test sample of structural alignments for membrane proteins. The TM segments of these proteins were mapped according to aligned 3D structures resolved for these proteins. A method for predicting TM segments in an alignment was developed on the basis of the forward-backward algorithm from the HMM theory. This method allows a user not only to predict TM segments, but also to create a probabilistic membrane profile, which can be employed in multiple alignment procedures taking the secondary structure of proteins into account. The method was implemented in a computer program available at http://bioinf.fbb.msu.ru/fwdbck/. It provides better results than the MEMSAT method, which is nearly the only tool predicting TM segments in multiple alignments, without a homology search. DOI: 10.1134/S0026893306030150