Individual Partitions Research Articles

Model-Based Classification for Digital PCR: Your Umbrella for Rain

Standard data analysis pipelines for digital PCR estimate the concentration of a target nucleic acid by digitizing the end-point fluorescence of the parallel micro-PCR reactions, using an automated hard threshold. While it is known that misclassification has a major impact on the concentration estimate and substantially reduces accuracy, the uncertainty of this classification is typically ignored. We introduce a model-based clustering method to estimate the probability that the target is present (absent) in a partition conditional on its observed fluorescence and the distributional shape in no-template control samples. This methodology acknowledges the inherent uncertainty of the classification and provides a natural measure of precision, both at individual partition level and at the level of the global concentration. We illustrate our method on genetically modified organism, inhibition, dynamic range, and mutation detection experiments. We show that our method provides concentration estimates of similar accuracy or better than the current standard, along with a more realistic measure of precision. The individual partition probabilities and diagnostic density plots further allow for some quality control. An R implementation of our method, called Umbrella, is available, providing a more objective and automated data analysis procedure for absolute dPCR quantification.

Semiparametric Bayesian Regression via Potts Model

ABSTRACTWe consider Bayesian nonparametric regression through random partition models. Our approach involves the construction of a covariate-dependent prior distribution on partitions of individuals. Our goal is to use covariate information to improve predictive inference. To do so, we propose a prior on partitions based on the Potts clustering model associated with the observed covariates. This drives by covariate proximity both the formation of clusters, and the prior predictive distribution. The resulting prior model is flexible enough to support many different types of likelihood models. We focus the discussion on nonparametric regression. Implementation details are discussed for the specific case of multivariate multiple linear regression. The proposed model performs well in terms of model fitting and prediction when compared to other alternative nonparametric regression approaches. We illustrate the methodology with an application to the health status of nations at the turn of the 21st century. Supplementary materials are available online.

Actin dynamics and competition for myosin monomer govern the sequential amplification of myosin filaments.

Cellular mechanisms governing non-muscle myosin 2 (NM2) filament assembly are largely unknown. Using EGFP-NM2A knock-in fibroblasts and multiple super-resolution imaging modalities, we characterized and quantified the sequential amplification of NM2 filaments within lamella, wherein filaments emanating from single nucleation events continuously partition, forming filament clusters that populate large-scale actomyosin structures deeper in the cell. Individual partitioning events coincide spatially and temporally with the movements of diverging actin fibers, suppression of which inhibits partitioning. These and other data indicate that NM2A filaments are partitioned by the dynamic movements of actin fibers to which they are bound. Finally, we showed that partition frequency and filament growth rate in the lamella depend on MLCK, and that MLCK is competing with centrally-active ROCK for a limiting pool of monomer with which to drive lamellar filament assembly. Together, our results provide new insights into the mechanism and spatio-temporal regulation of NM2 filament assembly in cells.

Droplet-Based Digital PCR: Application in Cancer Research.

The efficient characterization of genetic and epigenetic alterations in oncology, virology, or prenatal diagnostics requires highly sensitive and specific high-throughput approaches. Nevertheless, with the use of conventional methods, sensitivity and specificity were largely limited. By partitioning individual target molecules within distinct compartments, digital PCR (dPCR) could overcome these limitations and detect very rare sequences with unprecedented precision and sensitivity. In dPCR, the sample is diluted such that each individual partition will contain no more than one target sequence. Following the assay reaction, the dPCR process provides an absolute value and analyzable quantitative data. The recent coupling of dPCR with microfluidic systems in commercial platforms should lead to an essential tool for the management of patients with cancer, especially adapted to the analysis of precious samples. Applications in cancer research range from the analysis of tumor heterogeneity to that of a range of body fluids. Droplet-based dPCR is indeed particularly appropriate for the emerging field of liquid biopsy analysis. In this review, following an overview of the development in dPCR technology and different strategies based on the use of microcompartments, we will focus particularly on the applications and latest development of microfluidic droplet-based dPCR in oncology.

Mixed-Dimensional Coupling for Parallel Partitioned Nonlinear Finite-Element Analysis

AbstractThis paper presents a novel mixed-dimensional coupling method for parallel partitioned analysis, allowing efficient nonlinear structural analysis with selective deployment of differently dimensioned elements. With this method, a structural domain is decomposed into partitions that are processed in parallel using a recently developed dual super-element partitioning approach. Individual partitions consist exclusively of reduced dimensioned (1D) or higher dimensioned (3D continuum) elements, and coupling between 1D and 3D partitions is introduced at the boundary of 3D partitions, enabling a reduction in the communication overhead. Several examples are provided to verify the proposed approach and demonstrate its benefits for nonlinear structural analysis.

Agglomeration, Segregation, and Social Welfare in Group Formation Games

We consider a non cooperative game in which a continuum of heterogeneous individuals partition themselves into groups. A player's payoff depends on the group she chooses and the set of players who choose the same group as her.In the case of anonymous group externalities, we show that free mobility equilibria are socially optimal when group externalities vary logarithmically with community size. When they increase less (more) than logarithmically, the equilibrium partition exhibits excessive agglomeration (excessive fragmentation). In the case of nonanonymous group externalities, free mobility affects the size, but also the composition of groups. We characterize conditions under which free mobility equilibria are excessively or insufficiently segregated.When applied to local public goods economies, these results provide insights on the social welfare effect of free mobility as a function of the local public good technology, the income distribution, and the tax scheme.

Stability of Network Communities as a Function of Task Complexity.

The analysis of the community architecture in functional brain networks has revealed important relations between specific behavioral patterns and characteristic features of the associated functional organization. Numerous studies have assessed changes in functional communities during different states of awareness, learning, information processing, and various behavioral patterns. The robustness of detected communities within a network has been an often-discussed topic in complex systems research. However, our knowledge regarding the intersubject stability of functional communities in the human brain while performing different tasks is still lacking. In this study, we examined the variability of functional communities in weighted undirected graphs based on fMRI recordings of healthy participants across three conditions: the resting state, syllable production as a simple vocal motor task, and meaningful speech production representing a complex behavioral pattern with cognitive involvement. On the basis of the constructed empirical networks, we simulated a large cohort of artificial graphs and performed a leave-one-out stability analysis to assess the sensitivity of communities in the group-averaged networks with respect to perturbations in the averaging cohort. We found that the stability of partitions derived from group-averaged networks depended on task complexity. The determined community architecture in mean networks reflected within-behavior network stability and between-behavior flexibility of the human functional connectome. The sensitivity of functional communities increased from rest to syllable production to speaking, which suggests that the approximation quality of the community structure in the average network to reflect individual per-participant partitions depends on task complexity.

Time to be smart: Uncovering a complex interplay between intelligence and time perspectives

In the present studies we examine complex relationships between time perspective (the characteristic way in which an individual partitions the flow of personal experiences into time-bound categories; TP) and cognitive ability. Additionally, we consider cognitive, emotional and motivational mediators of these associations. In study 1 (n=238) we measured TP, fluid and verbal intelligences as well as subjectively assessed intelligence. Past Negative and Present Fatalistic TPs correlated negatively with fluid and verbal intelligences. Present Hedonism was negatively, and Future TP positively, associated with verbal intelligence. Subjectively assessed intelligence mediated the relationship between Present Fatalism and intelligence. Finally, Balanced TP positively correlated with fluid intelligence. Study 2 (n=306) revealed that Present Fatalism and Past Negative were associated with higher stress related to intelligence-test performance, while Balanced TP reduced this stress. The obtained results suggest that TP may play a significant role in acquiring abilities (crystallized intelligence), but also that it probably influences test performance.

Spectrometer-Driven Spectral Partitioning for Hyperspectral Image Classification

Classification is an important and widely used technique for remotely sensed hyperspectral data interpretation. Although most techniques developed for hyperspectral image classification assume that the spectral signatures provided by an imaging spectrometer can be interpreted as a unique and continuous signal, in practice, this signal may be obtained after the combination of several individual responses obtained from different spectrometers. In this work, we propose a new spectral partitioning strategy prior to classification which takes into account the physical design of the imaging spectrometer system for partitioning the spectral bands collected by each spectrometer, and resampling them into different groups or partitions. The final classification result is obtained as a combination of the results obtained from each individual partition by means of a multiple classifier system (MCS). The proposed strategy not only incorporates the design of the imaging spectrometer into the classification process but also circumvents problems such as the curse of dimensionality given by the unbalance between the high number of spectral bands and the generally limited number of training samples available for classification purposes. This concept is illustrated in this work using two different imaging spectrometers: the airborne visible infra-red imaging spectrometer, operated by NASA, and the digital airborne imaging system (DAIS), operated by the German Aerospace Center. Our experiments indicate that the proposed spectral partitioning strategy can lead to classification improvements on the order of 5% overall accuracy when using state-of-the-art spatial-spectral classifiers with very limited training samples.

Individual species affect plant traits structure in their surroundings: evidence of functional mechanisms of assembly.

Evaluating community assembly through the use of functional traits is a promising tool for testing predictions arising from Niche and Coexistence theories. Although interactions among neighboring species and their inter-specific differences are known drivers of coexistence with a strong spatial signal, assessing the role of individual species on the functional structure of the community at different spatial scales remains a challenge. Here, we ask whether individual species exert a measurable effect on the spatial organization of different functional traits in local assemblages. We first propose and compute two functions that describe different aspects of functional trait organization around individual species at multiple scales: individual weighted mean area relationship and individual functional diversity area relationship. Secondly, we develop a conceptual model on the relationship and simultaneous variation of these two metrics, providing five alternative scenarios in response to the ability of some target species to modify its neighbor environment and the possible assembly mechanisms involved. Our results show that some species influence the spatial structure of specific functional traits, but their effects were always restricted to the finest spatial scales. In the basis of our conceptual model, the observed patterns point to two main mechanisms driving the functional structure of the community at the fine scale, "biotic" filtering meditated by individual species and resource partitioning driven by indirect facilitation rather than by competitive mechanisms.

Network analyses support the role of prey preferences in shaping resource use patterns within five animal populations

Individual variation is an inherent aspect of animal populations and understanding the mechanisms shaping resource use patterns within populations is crucial to comprehend how individuals partition resources. Theory predicts that differences in prey preferences among consumers and/or differences in the likelihood of adding new resources to their diets are key mechanisms underlying intrapopulation variation in resource use. We developed network models based on optimal diet theory that simulate how individuals consume resources under varying scenarios of individual variation in prey preferences and in the willingness of consuming alternate resources. We then investigated how the structure of individual–resource networks generated under each model compared to the structure of observed networks representing five classical examples of individual diet variation. Our results support the notion that, for the studied populations, individual variation in prey preferences is the major factor explaining patterns in individual–resource networks. In contrast, variation in the willingness of adding prey does not seem to play an important role in shaping patterns of resource use. Individual differences in prey preferences in the studied populations may be generated by complex behavioral rules related to cognitive constraints and experience. Our approach provides a pathway for mapping foraging models into network patterns, which may allow determining the possible mechanisms leading to variation in resource use within populations.

3D T1-weighted self-gated cardiac MRI for assessing myocardial infarction in mouse models

Background Mouse models with mycardial infarction (MI) have been intensively used to investigate the cardiac remodeling and functional change [1,2]. MRI with ECG triggering and respiratory gating (or breath-holding) is commonly used to assess MI due to its noninvasive nature. However, the manipulations of external ECG triggering and respiratory gating are cumbersome due to the small size of mouse. To address this issue, a 3D self-gating (SG) MR technique with stack-of-stars sampling trajectories was proposed for retrospectively cardiac and respiratory-gated MI imaging in mouse models [3]. Methods: Technical Design: MR data was acquired by a T1-weighted GRE sequence with stack-of-stars sampling trajectories and a partition-first golden-angle reordering (Figure 1a). The centers of k-space lines acquired at the same angle from individual partitions were aggregated and used as an SG time point. The periodic respiratory and cardiac motions were then detected through an iterative filtering process on the SG time series according to the cardiac rate of 300~500 per minute and respiration rate of 70~110 per minute. After resorting the imaging data into appropriate cardiac and respiratory phases, motion-artifact-free were finally reconstructed. Experiments: The SG technique was preliminarily validated on 5 mice with MI induction and all MR scans were performed on a 3T scanner (Siemens Tim Trio, Germany) with a customized 4-channal mouse coil. Typical imaging parameters for the ungated GRE sequence included: flip angle = 18°, TR = 4.2 ms, TE = 2.4 ms, spatial resolution = 0.6 × 0.6 × 1.5 mm, bandwidth = 620 Hz/Pixel, partition number = 12, and a total number of 3200 projections were continuously collected, corresponding to a fixed scan time of 3 min. An amount of 0.5 ml gadolinium contrast agent with concentration of 0.5 mmol/ml (Consun Pharmaceutical Group Limited, GuangZhou, China) was injected to enhance the MI. The mice were sacrificed immediately after MRI for histological analysis and comparison to the MR results.

Predictions Based on the Clustering of Heterogeneous Functions via Shape and Subject-Specific Covariates

We consider a study of players employed by teams who are members of the National Basketball Association where units of observation are functional curves that are realizations of production measurements taken through the course of one’s career. The observed functional output displays large amounts of between player heterogeneity in the sense that some individuals produce curves that are fairly smooth while others are (much) more erratic. We argue that this variability in curve shape is a feature that can be exploited to guide decision making, learn about processes under study and improve prediction. In this paper we develop a methodology that takes advantage of this feature when clustering functional curves. Individual curves are flexibly modeled using Bayesian penalized B-splines while a hierarchical structure allows the clustering to be guided by the smoothness of individual curves. In a sense, the hierarchical structure balances the desire to fit individual curves well while still producing meaningful clusters that are used to guide prediction. We seamlessly incorporate available covariate information to guide the clustering of curves non-parametrically through the use of a product partition model prior for a random partition of individuals. Clustering based on curve smoothness and subject-specific covariate information is particularly important in carrying out the two types of predictions that are of interest, those that complete a partially observed curve from an active player, and those that predict the entire career curve for a player yet to play in the National Basketball Association.

Ethical consumption in Russia: Profiles, Factors, and Perspectives

The article presents the results of the first representative study of ethical consumption in Russia. The level and profiles of ethical consumption, as well as the main types of ethical consumers have been identified. Factors affecting the partition of individuals into the different types of consumers have been defined using the multinomial logit regression. On this basis, the paper draws implications on the potential and the conditions of expansion of ethical consumption in Russia.

Phase ratio variation approach for the study of partitioning behavior of volatile organic compounds in polymer sample bags: Nalophan case study.

Sorption of volatile organic compounds on the inner surface of polymer sampling bags leads to important underestimations of the real headspace concentration. Introducing a wide range of volatiles in a two-phase system containing Nalophan revealed that recoveries decreased down to 57 % in a period of 22 h. In this work, a phase ratio variation approach is investigated to quantify the degree of scalping, and thus enabling to compensate for sorption phenomena. This method requires limited measurements, without the need for time-consuming calibrations. By spiking identical amounts of volatiles in three two-phase systems, each having unique polymer volume/mass ratios β (β 1 = 0, β 2 = 80.9, β 3 = 161.9), individual partitioning coefficients could be experimentally determined for a wide range of compounds. Additionally, a correlation was found between these partitioning coefficients and the liquid molar volume for a number of aliphatic, aromatic, and oxygenated compounds.

Modeling water and hydrogen networks with partitioning regeneration units

Strict environment regulations in chemical and refinery industries lead to minimize resource consumption by designing utility networks within industrial process plants. The present study proposed a superstructure based optimization model for the synthesis of water and hydrogen networks with partitioning regenerators without mixing the regenerated sources. This method determines the number of partitioning regenerators needed for the regeneration of the sources. The number of the regenerators is based on the number of sources required to be treated for recovery. Each source is regenerated in an individual partitioning regenerator. Multiple regeneration systems can be employed to achieve minimum flowrate and costs. The formulation is linear in the regenerator balance equations. The optimized model is applied for two systems, partitioning regeneration systems of the fixed outlet impurity concentration and partitioning regeneration systems of the fixed impurity load removal ratio (RR) for water and hydrogen networks. Several case studies from the literature are solved to illustrate the ease and applicability of the proposed method.

Correlated Equilibrium, Conformity and Stereotyping in Social Groups

We argue that a social norm and the coordination of behavior within social groups can be expressed by a correlated equilibrium. Given a social group structure (a partition of individuals into social groups), we propose four conditions that one may expect of a correlated equilibrium consistent with social norms. These are: (a) within-group anonymity (conformity within groups), (b) group independence (no conformity between groups), (c) homophily (individuals in the same group have similar attributes), and (d) predictable group behavior (ex-post stability). We demonstrate that correlated equilibrium satisfying (a)-(c) exist very generally and equilibrium satisfying (a)-(d) exist in games with many players. We also consider stereotyped beliefs - beliefs that all individuals in a social group can be expected to behave in the same way - and show that stereotyping is not costly to the person who stereotypes but may or may not be beneficial to society.

Correlated Equilibrium, Conformity, and Stereotyping in Social Groups

AbstractWe argue that a social norm and the coordination of behavior within social groups can be expressed by a correlated equilibrium. Given a social group structure (a partition of individuals into social groups), we propose four conditions that one may expect of a correlated equilibrium consistent with social norms. These are: (1) within‐group anonymity (conformity within groups), (2) group independence (no conformity between groups), (3) homophily (individuals in the same group have similar attributes), and (4) predictable group behavior (ex post stability). We demonstrate that correlated equilibrium satisfying (1)–(3) exist very generally and equilibrium satisfying (1)–(4) exist in games with many players. We also consider stereotyped beliefs—beliefs that all individuals in a social group can be expected to behave in the same way—and show that stereotyping is not costly to the person who stereotypes but may or may not be beneficial to society.

ESTELA: a method for evaluating the source and travel time of the wave energy reaching a local area

The description of wave climate at a local scale is of paramount importance for offshore and coastal engineering applications. Conditions influencing wave characteristics at a specific location cannot, however, be fully understood by studying only local information. It is necessary to take into account the dynamics of the ocean surface over a large ‘upstream’ wave generation area. The goal of this work is to provide a methodology to easily characterize the area of influence of any particular ocean location worldwide. Moreover, the developed method is able to characterize the wave energy and travel time in that area. The method is based on a global scale analysis using both geographically and physically based criteria. The geographic criteria rely on the assumption that deep water waves travel along great circle paths. This limits the area of influence by neglecting energy that cannot reach a target point, as its path is blocked by land. The individual spectral partitions from a global wave reanalysis are used to reconstruct the spectral information and apply the physically based criteria. The criteria are based on the selection of the fraction of energy that travels towards the target point for each analysed grid point. The method has been tested on several locations worldwide. Results provide maps that inform about the relative importance of different oceanic areas to the local wave climate at any target point. This information cannot be inferred from local parameters and agrees with information from other approaches. The methodology may be useful in a number of applications, such as statistical downscaling, storm tracking and grid definition in numerical modelling.

Mapping environmental partitioning properties of nonpolar complex mixtures by use of GC × GC.

Comprehensive two-dimensional gas chromatography (GC × GC) is effective for separating and quantifying nonpolar organic chemicals in complex mixtures. Here we present a model to estimate 11 environmental partitioning properties for nonpolar analytes based on GC × GC chromatogram retention time information. The considered partitioning properties span several phases including pure liquid, air, water, octanol, hexadecane, particle natural organic matter, dissolved organic matter, and organism lipids. The model training set and test sets are based on a literature compilation of 648 individual experimental partitioning property data. For a test set of 50 nonpolar environmental contaminants, predicted partition coefficients exhibit root-mean-squared errors ranging from 0.19 to 0.48 log unit, outperforming Abraham-type solvation models for the same chemical set. The approach is applicable to nonpolar organic chemicals containing C, H, F, Cl, Br, and I, having boiling points ≤402 °C. The presented model is calibrated, easy to apply, and requires the user only to identify a small set of known analytes that adapt the model to the GC × GC instrument program. The analyst can thus map partitioning property estimates onto GC × GC chromatograms of complex mixtures. For example, analyzed nonpolar chemicals can be screened for long-range transport potential, aquatic bioaccumulation potential, arctic contamination potential, and other characteristic partitioning behaviors.