Individual Partitions Research Articles

Network partitioning on time-dependent origin-destination electronic trace data

In this study, we identify spatial regions based on an empirical data set consisting of time-dependent origin-destination (OD) pairs. These OD pairs consist of electronic traces collected from smartphone data by Google in the Amsterdam metropolitan region and is aggregated by the volume of trips per hour at neighbourhood level. By means of community detection, we examine the structure of this empirical data set in terms of connectedness. We show that we can distinguish spatially connected regions when we use a performance metric called modularity and the trip directionality is incorporated. From this, we proceed to analyse variations in the partitions that arise due to the non-optimal greedy optimisation method. We use a method known as ensemble learning to combine these variations by means of the overlap in community partitions. Ultimately, the combined partition leads to a more consistent result when evaluated again, compared to the individual partitions. Analysis of the partitions gives insights with respect to connectivity and spatial travel patterns, thereby supporting policy makers in their decisions for future infra structural adjustments.

Assessing the diversity of Western North American Juga (Semisulcospiridae, Gastropoda)

Juga is a genus of freshwater gastropods distributed in Pacific and Interior drainages of the Pacific Northwest from central California to northern Washington. The current classification has relied heavily on features of the shell, which vary within and across drainages, and often intergrade without sharp distinctions between species. The only previous molecular analysis included limited population sampling, which did not allow robust assessment of intra- versus interspecific levels of genetic diversity, and concluded almost every sampled population to be a distinct OTU. We assembled a multilocus mitochondrial (COI, 16S) and nuclear gene (ITS1) dataset for ∼100 populations collected across the range of the genus. We generated primary species hypotheses using ABGD with best-fit model-corrected distances and further explored our data, both individual gene partitions and concatenated datasets, using a diversity of phylogenetic and species delimitation methods (Bayesian inference, maximum likelihood estimation, StarBEAST2, bGMYC, bPTP, BP&P). Our secondary species delimitation hypotheses, based primarily on the criterion of reciprocal monophyly, and informed by a combination of geography and morphology, support the interpretation that Juga comprises a mixture of geographically widespread species and narrow range endemics. As might be expected in taxa with low vagility and poor dispersal capacities, analysis of molecular variance (AMOVA) revealed highly structured populations with up to 80% of the observed genetic variance explained by variation between populations. Analyses with bGMYC, bPTP, and BP&P appeared sensitive to this genetic structure and returned highly dissected species hypotheses that are likely oversplit. The species diversity of Juga is concluded to be lower than presently recognized, and the systematics to require extensive revision. Features of the teleoconch considered significant in species-level and subgeneric classification were found to be variable within some species, sometimes at a single site. Of a number of potentially new species identified in non-peer reviewed reports and field guides, only one was supported as a distinct OTU.

A Generic Framework for Combining Multiple Segmentations in Geographic Object-Based Image Analysis

The Geographic Object-Based Image Analysis (GEOBIA) paradigm relies strongly on the segmentation concept, i.e., partitioning of an image into regions or objects that are then further analyzed. Segmentation is a critical step, for which a wide range of methods, parameters and input data are available. To reduce the sensitivity of the GEOBIA process to the segmentation step, here we consider that a set of segmentation maps can be derived from remote sensing data. Inspired by the ensemble paradigm that combines multiple weak classifiers to build a strong one, we propose a novel framework for combining multiple segmentation maps. The combination leads to a fine-grained partition of segments (super-pixels) that is built by intersecting individual input partitions, and each segment is assigned a segmentation confidence score that relates directly to the local consensus between the different segmentation maps. Furthermore, each input segmentation can be assigned some local or global quality score based on expert assessment or automatic analysis. These scores are then taken into account when computing the confidence map that results from the combination of the segmentation processes. This means the process is less affected by incorrect segmentation inputs either at the local scale of a region, or at the global scale of a map. In contrast to related works, the proposed framework is fully generic and does not rely on specific input data to drive the combination process. We assess its relevance through experiments conducted on ISPRS 2D Semantic Labeling. Results show that the confidence map provides valuable information that can be produced when combining segmentations, and fusion at the object level is competitive w.r.t. fusion at the pixel or decision level.

Differences in food consumption in regions of Poland that had been previously under Austrian, Prussian and Russian annexations

It seems that one of the factors affecting the way of nutrition in Poland was the dietary habits of countries that in the late 18th century made partitions, ie. Russia, Prussia and Austria. Therefore, nutrition models in the areas of individual partitions have undergone significant changes. The aim of the study was to check whether after 85 years after regaining independence there were still differences in the way of feeding indigenous inhabitants of areas previously under Austrian, Prussian and Russian annexation. The data collected during the WOBASZ survey carried out in the years 2003-2005 were used for the analysis. The greatest differences in the way of feeding were found in the area that was previously under the rule of Prussia. The nutrition model was still largely reminiscent of typical German cuisine. There was a large consumption of sausages and spreads, and small vegetables and fruit. Nutrition models in other areas were less characteristic. The way of feeding indigenous people living in areas that were once partitions of Poland is still diverse and similar to the cuisine of the occupying country.

Hansen solubility parameters and thermodynamic modeling for LLE description during glycerol-settling in ester production from coconut oil

The liquid–liquid equilibrium behavior is important when describing the separation of components involved in alkyl ester production during the glycerol-settling step. Therefore, this study presents original data on a liquid–liquid equilibrium system composed of glycerol + ethanol + ethyl esters from coconut oil at 298 K using an untapped approach based on the Hansen solubility parameters describing the individual ethyl ester distribution between the glycerol and ester rich phases. The difference in average molar masses of the ester mixtures in both phases when compared to fatty acid alkyl ester in overall composition reaches more than 10% depending on the ethanol content in the overall composition. The individual partition of ethyl esters in the glycerol-rich phase increases as result of the decrease in molecular size of this class of components. This behavior was shown based on the smaller difference in Hansen solubility parameters among esters with less carbon and the glycerol-rich phases as compared to esters with longer carbon chain atoms. Deviations between the experimental and calculated mass fractions were 0.83%, 4.07%, and 0.87% when using NRTL, UNIFAC-LLE, and UNIFAC-OHgly.

The Species Problem from the Modeler's Point of View.

How to define a partition of individuals into species is a long-standing question called the species problem in systematics. Here, we focus on this problem in the thought experiment where individuals reproduce clonally and both the differentiation process and the population genealogies are explicitly known. We specify three desirable properties of species partitions: (A) Heterotypy between species, (B) Homotypy within species and (M) Genealogical monophyly of each species. We then ask: How and when is it possible to delineate species in a way satisfying these properties? We point out that the three desirable properties cannot in general be satisfied simultaneously, but that any two of them can. We mathematically prove the existence of the finest partition satisfying (A) and (M) and the coarsest partition satisfying (B) and (M). For each of them, we propose a simple algorithm to build the associated phylogeny out of the genealogy. The ways we propose to phrase the species problem shed new light on the interaction between the genealogical and phylogenetic scales in modeling work. The two definitions centered on the monophyly property can readily be used at a higher taxonomic level as well, e.g., to cluster species into monophyletic genera.

Efficient leaf solute partioning in Salicornia fruticosa allows growth under salinity

The halophyte Salicornia fruticosa is able to tolerate a wide range of salinity. Since the cellular distribution of ions and solutes within leaves can be linked to the mechanisms to cope with salt stress, the aim of this work was to stablish the relation between the solute gradient along S. fruticosa leaves, distinguishing two parts: leaf base and apex, and the physiological parameters involved in response to distinct salt levels. For that, plants were grown at 0, 100, 200 and 300 mM NaCl, and plant growth, leaf gas exchange parameters and water status were measured as well as leaf ion and metabolite content. Also, their relation with the transcript levels of the NHX1 antiporter and PIP1 and TIP aquaporins was determined. Plant growth was optimal at 200 mM NaCl and was decreased at 300 mM. At 200 mM NaCl, these proteins play an important role in leaf growth through a differential Na and water partitioning between the leaf base and apex that regulates a progressive leaf solute gradient. At 300 mM NaCl, no differential expression of NHX and aquaporins in both parts of the leaf was found. However, the transcript levels were elevated according to an increased vacuolar osmotic pressure and a high organic solute content in the cytosol. At the individual level distinct malic acid and glucose partitioning was observed. All the data indicated that NHX and the aquaporins PIP and TIP exerted a dual function according to the salt intensity and are involved in the regulation of leaf solute partitioning; this could be useful in order to estimate plant availability for Na+ accumulation in a further bioremediation study. All the data indicated that leaf solute partitioning depended on salt intensity where NHX and the aquaporins PIP and TIP exerted an important and dual function favouring a different Na and water distribution in the leaf cells. This could be useful in order to estimate plant availability for Na+ accumulation in a further bioremediation study.

Data-driven damage diagnosis under environmental and operational variability by novel statistical pattern recognition methods

Feature extraction by time-series analysis and decision making through distance-based methods are powerful and efficient statistical pattern recognition techniques for data-driven structural health monitoring. The motivation of this article is to propose an innovative residual-based feature extraction approach based on AutoRegressive modeling and a novel statistical distance method named as Partition-based Kullback–Leibler Divergence for damage detection and localization by using randomly high-dimensional damage-sensitive features under environmental and operational variability. The key novel element of the proposed feature extraction approach is to establish a two-stage offline and online learning algorithms for extracting the residuals of AutoRegressive model as the main damage-sensitive features. This technique brings the great benefit of reducing the computational time and storage space for feature extraction in long-term monitoring conditions. The major contribution of Partition-based Kullback–Leibler Divergence method is to exploit a partitioning strategy for dividing random features into individual partitions and utilize numerical information of partitioning in distance calculation rather than directly applying random samples. Dealing with the major challenging issue of using the high-dimensional features in decision making and applicability to both correlated and uncorrelated random datasets are the main advantages of Partition-based Kullback–Leibler Divergence method. The accuracy and reliability of the proposed approaches are experimentally validated by two well-known benchmark structures. The stationarity and linearity of measured vibration responses for using in AutoRegressive modeling are evaluated by two hypothesis tests. Comparative studies are also conducted to demonstrate the superiority of the proposed methods over some exciting state-of-the-art techniques. Results show that the methods presented here succeed in detecting and locating damage and make time-saving and efficient tools for feature extraction and damage diagnosis.

Exploring Multiobjective Optimization for Multiview Clustering

We present a new multiview clustering approach based on multiobjective optimization. In contrast to existing clustering algorithms based on multiobjective optimization, it is generally applicable to data represented by two or more views and does not require specifying the number of clusters a priori . The approach builds upon the search capability of a multiobjective simulated annealing based technique, AMOSA, as the underlying optimization technique. In the first version of the proposed approach, an internal cluster validity index is used to assess the quality of different partitionings obtained using different views. A new way of checking the compatibility of these different partitionings is also proposed and this is used as another objective function. A new encoding strategy and some new mutation operators are introduced. Finally, a new way of computing a consensus partitioning from multiple individual partitions obtained on multiple views is proposed. As a baseline and for comparison, two multiobjective based ensemble clustering techniques are proposed to combine the outputs of different simple clustering approaches. The efficacy of the proposed clustering methods is shown for partitioning several real-world datasets having multiple views. To show the practical usefulness of the method, we present results on web-search result clustering, where the task is to find a suitable partitioning of web snippets.

Reducing Bias in Digital PCR Quantification Experiments: The Importance of Appropriately Modeling Volume Variability.

Multiple simulation studies have shown that volume variability of partition sizes in digital PCR (dPCR) causes bias in the resulting concentration estimates and their associated standard errors. These biases are especially apparent when the volume variability is large, and the targeted nucleic acid concentration is high. Currently, only a single method for the elimination or reduction of these biases is available, and it assumes a fixed class of models for the volume variability. We show that the form in which volumetric variability occurs in empirical data is variable and cannot be modeled by a single distribution. We propose a new volume-modeling method, NPVolMod, which takes volume variability of an arbitrary form into account and is applicable to both absolute and relative quantification. The method is nonparametric in the sense that no distributional assumption is needed. Moreover, the volumes of each of the individual partitions are not needed. We empirically demonstrate by simulation that NPVolMod nearly eliminates the biases caused by volumetric variability and that it often outperforms the existing method. The possibility of the proper modeling of volume variability may have implications for platform design and may increase the performance of existing dPCR platforms in terms of, for example, their trueness and linear dynamic range.

Soil textures of nest partitions made by the mason bees Osmia lignaria and O. cornifrons (Hymenoptera: Megachilidae)

Osmia lignaria and O. cornifrons require mineral soil to build partitions between cells in their nests. We examined the textures of soil collected by these bees, using laser particle size analysis. For both species, soil in the nest was generally loam or sandy loam in texture, although individual partitions had wide variation in particle size. Partitions in O. cornifrons nests had 5.8% higher clay content than those in O. lignaria nests. Textural trends were similar when data from individual partitions were analyzed. For both species, we found no significant effect of nearby soil texture or the position of the partition in the nest, on the texture of individual partitions. Observations of partitions indicate that some females collect soil material from different locations to build one single partition. These results shed new light on the ecology of soil used by cavity nesting mason bees, with implications for their management as alternative pollinators.

Consensus Guided Multi-View Clustering

In recent decades, tremendous emerging techniques thrive the artificial intelligence field due to the increasing collected data captured from multiple sensors. These multi-view data provide more rich information than traditional single-view data. Fusing heterogeneous information for certain tasks is a core part of multi-view learning, especially for multi-view clustering. Although numerous multi-view clustering algorithms have been proposed, most scholars focus on finding the common space of different views, but unfortunately ignore the benefits from partition level by ensemble clustering. For ensemble clustering, however, there is no interaction between individual partitions from each view and the final consensus one. To fill the gap, we propose a Consensus Guided Multi-View Clustering (CMVC) framework, which incorporates the generation of basic partitions from each view and fusion of consensus clustering in an interactive way, i.e., the consensus clustering guides the generation of basic partitions, and high quality basic partitions positively contribute to the consensus clustering as well. We design a non-trivial optimization solution to formulate CMVC into two iterative k -means clusterings by an approximate calculation. In addition, the generalization of CMVC provides a rich feasibility for different scenarios, and the extension of CMVC with incomplete multi-view clustering further validates the effectiveness for real-world applications. Extensive experiments demonstrate the advantages of CMVC over other widely used multi-view clustering methods in terms of cluster validity, and the robustness of CMVC to some important parameters and incomplete multi-view data.

DPCR: A Technology Review.

Digital Polymerase Chain Reaction (dPCR) is a novel method for the absolute quantification of target nucleic acids. Quantification by dPCR hinges on the fact that the random distribution of molecules in many partitions follows a Poisson distribution. Each partition acts as an individual PCR microreactor and partitions containing amplified target sequences are detected by fluorescence. The proportion of PCR-positive partitions suffices to determine the concentration of the target sequence without a need for calibration. Advances in microfluidics enabled the current revolution of digital quantification by providing efficient partitioning methods. In this review, we compare the fundamental concepts behind the quantification of nucleic acids by dPCR and quantitative real-time PCR (qPCR). We detail the underlying statistics of dPCR and explain how it defines its precision and performance metrics. We review the different microfluidic digital PCR formats, present their underlying physical principles, and analyze the technological evolution of dPCR platforms. We present the novel multiplexing strategies enabled by dPCR and examine how isothermal amplification could be an alternative to PCR in digital assays. Finally, we determine whether the theoretical advantages of dPCR over qPCR hold true by perusing studies that directly compare assays implemented with both methods.

Life history trade-offs and the partitioning of maternal investment: Implications for health of mothers and offspring.

ABSTRACTLay Summary: This review sets out the hypothesis that life history trade-offs in the maternal generation favour the emergence of similar trade-offs in the offspring generation, mediated by the partitioning of maternal investment between pregnancy and lactation, and that these trade-offs help explain widely reported associations between growth trajectories and NCD risk.Growth patterns in early life predict the risk of non-communicable diseases (NCDs), but adaptive explanations remain controversial. It is widely assumed that NCDs occur either because of physiological adjustments to early constraints, or because early ecological cues fail to predict adult environmental conditions (mismatch). I present an inter-generational perspective on developmental plasticity, based on the over-arching hypothesis that a key axis of variability in maternal metabolism derives from life history trade-offs, which influence how individual mothers partition nutritional investment in their offspring between pregnancy and lactation. I review evidence for three resulting predictions: (i) Allocating relatively more energy to growth during development promotes the capacity to invest in offspring during pregnancy. Relevant mechanisms include greater fat-free mass and metabolic turnover, and a larger physical space for fetal growth. (ii) Allocating less energy to growth during development constrains fetal growth of the offspring, but mothers may compensate by a tendency to attain higher adiposity around puberty, ecological conditions permitting, which promotes nutritional investment during lactation. (iii) Since the partitioning of maternal investment between pregnancy and lactation impacts the allocation of energy to ‘maintenance’ as well as growth, it is expected to shape offspring NCD risk as well as adult size and body composition. Overall, this framework predicts that life history trade-offs in the maternal generation favour the emergence of similar trade-offs in the offspring generation, mediated by the partitioning of maternal investment between pregnancy and lactation, and that these trade-offs help explain widely reported associations between growth trajectories and NCD risk.

Robust Multiplexed Clustering and Denoising of Digital PCR Assays by Data Gridding.

Digital PCR (dPCR) relies on the analysis of individual partitions to accurately quantify nucleic acid species. The most widely used analysis method requires manual clustering through individual visual inspection. Some automated analysis methods have emerged but do not robustly account for multiplexed targets, low target concentration, and assay noise. In this study, we describe an open source analysis software called Calico that uses "data gridding" to increase the sensitivity of clustering toward small clusters. Our workflow also generates quality score metrics in order to gauge and filter individual assay partitions by how well they were classified. We applied our analysis algorithm to multiplexed droplet-based digital PCR data sets in both EvaGreen and probes-based schemes, and targeted the oncogenic BRAF V600E and KRAS G12D mutations. We demonstrate an automated clustering sensitivity of down to 0.1% mutant fraction and filtering of artifactual assay partitions from low quality DNA samples. Overall, we demonstrate a vastly improved approach to analyzing ddPCR data that can be applied to clinical use, where automation and reproducibility are critical.

Optimal Bayesian estimators for latent variable cluster models

In cluster analysis interest lies in probabilistically capturing partitions of individuals, items or observations into groups, such that those belonging to the same group share similar attributes or relational profiles. Bayesian posterior samples for the latent allocation variables can be effectively obtained in a wide range of clustering models, including finite mixtures, infinite mixtures, hidden Markov models and block models for networks. However, due to the categorical nature of the clustering variables and the lack of scalable algorithms, summary tools that can interpret such samples are not available. We adopt a Bayesian decision theoretical approach to define an optimality criterion for clusterings and propose a fast and context-independent greedy algorithm to find the best allocations. One important facet of our approach is that the optimal number of groups is automatically selected, thereby solving the clustering and the model-choice problems at the same time. We consider several loss functions to compare partitions and show that our approach can accommodate a wide range of cases. Finally, we illustrate our approach on both artificial and real datasets for three different clustering models: Gaussian mixtures, stochastic block models and latent block models for networks.

A modeling enterprise for chemistry of indoor environments (CIE).

A modeling enterprise for chemistry of indoor environments (CIE).

Cluster Ensemble with Link-Based Approach for Botnet Detection

Botnet detection is one of the most imminent tasks for cyber security. Among popular botnet countermeasures, an intrusion detection system is the prominent mechanism. In the past, packet-based intrusion detection systems were popular. However, flow-based intrusion detection systems have been preferred in recent years due to their ability to adapt to modern high-speed networks. A collection of flows from an enterprise network usually contains both botnet traffic and normal traffic. To classify this traffic, supervised machine learning algorithms, i.e., classifications, have been applied and achieved a high accuracy. In an effort to improve the ability of intrusion detection systems against botnets, some studies have suggested partitioning flows into clusters before applying the classifications and this step could significantly reduce the complexity of a flow set. However, the instability of individual clustering algorithms is still a constraint for botnet detection.To overcome this bottleneck, we propose a novel method that combines individual partitions to become a strong learner through the use of a link-based algorithm. Our experiments show that our cluster ensemble model outperforms existing botnet detection mechanisms with a high reliability. We also determine the balance between accuracy and computer resources for botnet detection, and thereby propose a range for the maximum duration time of flows in botnet research.

Małżeństwo świeckie i wyznaniowe w nauczaniu prawa polskiego w latach 1945–1993

The institution of marriage is important for the both systems of law: state and church, as both of these two bodies must respect the truth about the institution of marriage. As the analysis of the problem shows, i.e. the possibility of contracting marriage in the religious form and in presence of other religious elements, the mentioned rule was not always respected by the Polish State. Religious elements were in fact significantly present in different jurisdictions of the partitioning states, which were in force on Poland’s territory under the rule of individual partitioning states. The possibility of the presence of religious elements in executing institution of marriage was taken into account in discussion that took place in Poland after the year 1918. All projects of acts of law were unfortunately rejected. The first act of law that was promulgated in 1945 presented a completely new, strange and even inimical to the Polish tradition, lay model of executing the institution of marriage. Its tightening followed rapidly after the promulgation of the next act of law, i.e. the Code of Family in the year of 1950. A change came about in the year 1998 through the Polish Concordat which came into force beginning with the year 1993. The possibility of contracting a civil marriage in the religious form was opened first for the Catholic Church. At the next stage, the same possibility was recognized in the case of ten other churches. As the analysis of the problem shows, contracting of a civil marriage in the religious form has always stayed under the control of the registry office. A clergyman is only an executor of the procedure.

Missing data, clade support and "reticulation": the molecular systematics of Heliconius and related genera (Lepidoptera: Nymphalidae) re-examined.

Kozak etal. (2015, Syst. Biol., 64: 505) portrayed the inference of evolutionary history among Heliconius and allied butterfly genera as a particularly difficult problem for systematics due to prevalent gene conflict caused by interspecific reticulation. To control for this, Kozak etal. conducted a series of multispecies coalescent phylogenetic analyses that they claimed revealed pervasive conflict among markers, but ultimately chose as their preferred hypothesis a phylogenetic tree generated by the traditional supermatrix approach. Intrigued by this seemingly contradictory set of conclusions, we conducted further analyses focusing on two prevalent aspects of the data set: missing data and the uneven contribution of phylogenetic signal among markers. Here, we demonstrate that Kozak etal. overstated their findings of reticulation and that evidence of gene-tree conflict is largely lacking. The distribution of intrinsic homoplasy and incongruence homoplasy in their data set does not follow the pattern expected if phylogenetic history had been obscured by pervasive horizontal gene flow; in fact, noise within individual gene partitions is ten times higher than the incongruence among gene partitions. We show that the patterns explained by Kozak etal. as a result of reticulation can be accounted for by missing data and homoplasy. We also find that although the preferred topology is resilient to missing data, measures of support are sensitive to, and strongly eroded by too many empty cells in the data matrix. Perhaps more importantly, we show that when some taxa are missing almost all characters, adding more genes to the data set provides little or no increase in support for the tree.