COEFFICIENTS OF ASSOCIATION AND SIMILARITY, BASED ON BINARY (PRESENCE-ABSENCE) DATA: AN EVALUATION

The Evolution of Local Co-occurrence in Birds in Relation to Latitude, Degree of Sympatry, and Range Symmetry

AbstractRecent speciation rates and the degree of range-wide sympatry are usually higher farther from the equator. Is there also a higher degree of secondary syntopy (coexistence in local assemblages in sympatry) at higher latitudes and, subsequently, an increase in local species richness? We studied the evolution of syntopy in passerine birds using worldwide species distribution data. We chose recently diverged species pairs from subclades not older than 5 or 7 million years, range-wide degree of sympatry not lower than 5% or 25%, and three definitions of the breeding season. We related their syntopy to latitude, the degree of sympatry (breeding range overlap), range symmetry, and the age of split. Syntopy was positively related to latitude, but it did not differ between tropical and temperate regions, instead increasing from the Southern to the Northern Hemisphere. Syntopy was also higher in species pairs with a higher degree of sympatry and more symmetric ranges, but it did not predict local species richness. Following speciation, species in the Northern Hemisphere presumably achieve positive local co-occurrence faster than elsewhere, which could facilitate their higher speciation rates. However, this does not seem to be linked to local species richness, which is probably governed by other processes.

Phage display sequencing reveals that genetic, environmental, and intrinsic factors influence variation of human antibody epitope repertoire

Phage-displayed immunoprecipitation sequencing (PhIP-seq) has enabled high-throughput profiling of human antibody repertoires. However, a comprehensive overview of environmental and genetic determinants shaping human adaptive immunity is lacking. In this study, we investigated the effects of genetic, environmental, and intrinsic factors on the variation in human antibody repertoires. We characterized serological antibody repertoires against 344,000 peptides using PhIP-seq libraries from a wide range of microbial and environmental antigens in 1,443 participants from a population cohort. We detected individual-specificity, temporal consistency, and co-housing similarities in antibody repertoires. Genetic analyses showed the involvement of the HLA, IGHV, and FUT2 gene regions in antibody-bound peptide reactivity. Furthermore, we uncovered associations between phenotypic factors (including age, cell counts, sex, smoking behavior, and allergies, among others) and particular antibody-bound peptides. Our results indicate that human antibody epitope repertoires are shaped by both genetics and environmental exposures and highlight specific signatures of distinct phenotypes and genotypes.

The elusive metric of lesion load

One of the widely used metrics in lesion-symptom mapping is lesion load that codes the amount of damage to a given brain region of interest. Lesion load aims to reduce the complex 3D lesion information into a feature that can reflect both site of damage, defined by the location of the region of interest, and size of damage within that region of interest. Basically, the process of estimation of lesion load converts a voxel-based lesion map into a region-based lesion map, with regions defined as atlas-based or data-driven spatial patterns. Here, after examining current definitions of lesion load, four methodological issues are discussed: (1) lesion load is agnostic to the location of damage within the region of interest, and it disregards damage outside the region of interest, (2) lesion load estimates are prone to errors introduced by the uncertainty in lesion delineation, spatial warping of the lesion/region, and binarization of the lesion/region, (3) lesion load calculation depends on brain parcellation selection, and (4) lesion load does not necessarily reflect a white matter disconnection. Overall, lesion load, when calculated in a robust way, can serve as a clinically-useful feature for explaining and predicting post-stroke outcome and recovery.

Analysis and identification of drug similarity through drug side effects and indications data

BACKGROUND

The measurement of drug similarity has many potential applications for assessing drug therapy similarity, patient similarity, and the success of treatment modalities. To date, a family of computational methods has been employed to predict drug-drug similarity. Here, we announce a computational method for measuring drug-drug similarity based on drug indications and side effects.

METHODS

The model was applied for 2997 drugs in the side effects category and 1437 drugs in the indications category. The corresponding binary vectors were built to determine the Drug-drug similarity for each drug. Various similarity measures were conducted to discover drug-drug similarity.

RESULTS

Among the examined similarity methods, the Jaccard similarity measure was the best in overall performance results. In total, 5,521,272 potential drug pair's similarities were studied in this research. The offered model was able to predict 3,948,378 potential similarities.

CONCLUSION

Based on these results, we propose the current method as a robust, simple, and quick approach to identifying drug similarity.

Methods of Brain Extraction from Magnetic Resonance Images of Human Head: A Review

Medical images are providing vital information to aid physicians in diagnosing a disease afflicting the organ of a human body. Magnetic resonance imaging is an important imaging modality in capturing the soft tissues of the brain. Segmenting and extracting the brain is essential in studying the structure and pathological condition of brain. There are several methods that are developed for this purpose. Researchers in brain extraction or segmentation need to know the current status of the work that have been done. Such an information is also important for improving the existing method to get more accurate results or to reduce the complexity of the algorithm. In this paper we review the classical methods and convolutional neural network-based deep learning brain extraction methods.

Synergistic and Competitive Lipid Interactions in the Serotonin1A Receptor Microenvironment

The interaction of lipids with G-protein-coupled receptors (GPCRs) has been shown to modulate and dictate several aspects of GPCR organization and function. Diverse lipid interaction sites have been identified from structural biology, bioinformatics, and molecular dynamics studies. For example, multiple cholesterol interaction sites have been identified in the serotonin_1A receptor, along with distinct and overlapping sphingolipid interaction sites. How these lipids interact with each other and what is the resultant effect on the receptor is still not clear. In this work, we have analyzed lipid-lipid crosstalk at the receptor of the serotonin_1A receptor embedded in a membrane bilayer that mimics the neuronal membrane composition by long coarse-grain simulations. Using a set of similarity coefficients, we classified lipids that bind at the receptor together as synergistic cobinding, and those that bind individually as competitive. Our results show that certain lipids interact with the serotonin_1A receptor in synergy with each other. Not surprisingly, the ganglioside GM1 and cholesterol show a synergistic cobinding, along with the relatively uncommon GM1-phosphatidylethanolamine (PE) and cholesterol-PE synergy. In contrast, certain lipid pairs such as cholesterol and sphingomyelin appear to be in competition at several sites, despite their coexistence in lipid nanodomains. In addition, we observed intralipid competition between two lipid tails, with the receptor exhibiting increased interactions with the unsaturated lipid tails. We believe our work represents an important step in understanding the diversity of GPCR-lipid interactions and exploring synergistic cobinding and competition in natural membranes.

Response of riparian vegetation to short- and long-term hydrologic variation

Increasing demand for river water now conflicts with an increasing desire to maintain riparian ecosystems. Efficiently managing river flows for riparian vegetation requires an understanding of the time scale of flow effects, but this information is limited by the absence of long-term studies of vegetation change in response to flow variation. To investigate the influence of short- and long-term flow variability and dam operation on riparian vegetation, we determined the occurrence of 107 plant species in 133 permanent plots of known inundating discharge along the Gunnison River in Colorado on five different occasions between 1990 and 2013. Individual species moved up and down the gradient of inundating discharge coincident with increases and decreases in mean annual flow, and the correlations between flow and species occurrence were strongest when flows were weighted by time before vegetation sampling with a median half-life of 1.5 years. Some tall, rhizomatous, perennial species, however, responded to flows on a longer time scale. Logistic regression of species occurrence showed a significant relation with inundation duration for 70 out of 107 species. Plot species richness and total vegetative cover decreased in association with desiccation at low inundation durations and with fluvial disturbance at high inundation durations. Within-plot similarity in species occurrence between years decreased strongly with increasing inundation duration. Moderate inundation durations were dominated by tall, rhizomatous, perennial herbs, including invasive Phalaris arundinacea (reed canary grass). Over the 23-year study period, species richness declined, and the proportion of rhizomatous perennials increased, consistent with the hypothesis that decreases in flow peaks and increases in low flows caused by flow regulation have decreased establishment opportunities for disturbance-dependent species. In summary, annual-scale changes in vegetation were strongly influenced by flow variation, and decadal-scale changes were influenced by decreases in fluvial disturbance from upstream flow regulation beginning decades prior to the onset of this study.

Estimating ancient biogeographic patterns with statistical model discrimination

The geographic ranges in which species live is a function of many factors underlying ecological and evolutionary contingencies. Observing the geographic range of an individual species provides valuable information about these historical contingencies for a lineage, determining the distribution of many distantly related species in tandem provides information about large-scale constraints on evolutionary and ecological processes generally. We present a linear regression method that allows for the discrimination of various hypothetical biogeographical models for determining which landscape distributional pattern best matches data from the fossil record. The linear regression models used in the discrimination rely on geodesic distances between sampling sites (typically geologic formations) as the independent variable and three possible dependent variables: Dice/Sorensen similarity; Euclidean distance; and phylogenetic community dissimilarity. Both the similarity and distance measures are useful for full-community analyses without evolutionary information, whereas the phylogenetic community dissimilarity requires phylogenetic data. Importantly, the discrimination method uses linear regression residual error to provide relative measures of support for each biogeographical model tested, not absolute answers or p-values. When applied to a recently published dataset of Campanian pollen, we find evidence that supports two plant communities separated by a transitional zone of unknown size. A similar case study of ceratopsid dinosaurs using phylogenetic community dissimilarity provided no evidence of a biogeographical pattern, but this case study suffers from a lack of data to accurately discriminate and/or too much temporal mixing. Future research aiming to reconstruct the distribution of organisms across a landscape has a statistical-based method for determining what biogeographic distributional model best matches the available data.

Comparative Analysis of Binary Similarity Measures for Compound Identification in MassSpectrometry-Based Metabolomics

Compound identification is a critical step in untargeted metabolomics. Its most important procedure is to calculate the similarity between experimental mass spectra and either predicted mass spectra or mass spectra in a mass spectral library. Unlike the continuous similarity measures, there is no study to assess the performance of binary similarity measures in compound identification, even though the well-known Jaccard similarity measure has been widely used without proper evaluation. The objective of this study is thus to evaluate the performance of binary similarity measures for compound identification in untargeted metabolomics. Fifteen binary similarity measures, including the well-known Jaccard, Dice, Sokal–Sneath, Cosine, and Simpson measures, were selected to assess their performance in compound identification. using both electron ionization (EI) and electrospray ionization (ESI) mass spectra. Our theoretical evaluations show that the accuracy of the compound identification was exactly the same between the Jaccard, Dice, 3W-Jaccard, Sokal–Sneath, and Kulczynski measures, between the Cosine and Hellinger measures, and between the McConnaughey and Driver–Kroeber measures, which were practically confirmed using mass spectra libraries. From the mass spectrum-based evaluation, we observed that the best performing similarity measures were the McConnaughey and Driver–Kroeber measures for EI mass spectra and the Cosine and Hellinger measures for ESI mass spectra. The most robust similarity measure was the Fager–McGowan measure, the second-best performing similarity measure in both EI and ESI mass spectra.

PIF - A Java library for finding atomic interactions and extracting geometric features supporting the analysis of protein structures

Proteins play an essential role in the functioning of living organisms. The enormity of the atomic interactions in proteins is essential in controlling their spatial structures and dynamics. It can also provide scientists with valuable information that help to determine the native structures of proteins. This paper presents the PIF (Protein Interaction Finder) library for the Java language, enabling the identification of selected atomic interactions (hydrogen and disulfide bonds, ionic, hydrophobic, aromatic-aromatic, sulfur-aromatic, and amino-aromatic interactions) based on the three-dimensional structure of proteins. The interaction calculation rules applied in PIF rely on documented theoretical foundations gathered from experimental studies of interactions in native protein structures. The library has a universal purpose, supporting drug discovery and development processes and protein structure modeling. Finding the atomic interactions can also deliver numerical features for various Artificial Intelligence (AI) models built for protein analysis. The conducted research comparing the results obtained with the use of the PIF library and competing tools has shown that our solution can effectively determine the interactions occurring in protein structures for entire collections of proteins. Moreover, as a solution that provides a programming interface, the PIF library can be used in any Java project, making it a universal tool.

Interspecific Association and Community Stability of Tree Species in Natural Secondary Forests at Different Altitude Gradients in the Southern Taihang Mountains

An interspecific association represents an inter-relatedness of different species in spatial distribution and combined with the altitude factor, is key for revealing the formation and evolution of an ecological community. Therefore, we analyzed the changes in interspecific association and community stability at different altitudes in the southern Taihang Mountains using the variance ratio (VR), χ2 test, association coefficient (AC), percentage of co-occurrence (PC) and Godron stability method. In total, 27 sample plots measuring 20 × 20 m were set up and were divided into lower altitude (700~1100 m), medium altitude (1100~1500 m) and higher altitude areas (1500~1900 m) into. The results showed that the overall interspecies association of communities exhibited an insignificant negative association in both the lower (VR = 0.79, W = 7.15) and higher (VR = 0.81, W = 7.36) altitude areas, while an insignificant positive association was observed in the medium (VR = 1.48, W = 13.34) altitude area. Besides, the χ2 test showed the ratio of positively and negatively correlated species pairs decreased as altitude increased with values of 1.39, 1.22 and 0.95 in the lower, medium and higher altitude areas, respectively. Moreover, the AC and PC indices stated that most species pairs had a weaker association in the three altitude areas, but the AC indices also suggested the number of positive association species pairs was more than that of negative association only in medium altitude area. Meanwhile, the Godron stability method showed the distances from the intersection point to the stable point (20 and 80) were still far away, with values of 22.53, 11.92 and 21.34 in the lower, medium and higher altitude areas, respectively, which indicated an unstable succession stage, though the community appeared steadier in the medium altitude area. This study can provide some guidance for effective afforestation and vegetation restoration.

A better index for analysis of co-occurrence and similarity

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability. We propose an index, α, that is insensitive to prevalences. Published datasets reanalyzed with both α and Jaccard's index (J) yield profoundly different biological inferences. For example, a published analysis using J contradicted predictions of the island biogeography theory finding that community stability increased with increasing physical isolation. Reanalysis of the same dataset with the estimator [Formula: see text] reversed that result and supported theoretical predictions. We found similarly marked effects in reanalyses of antibiotic cross-resistance and human disease biomarkers. Our index α is not merely an improvement; its use changes data interpretation in fundamental ways.

Band-based similarity indices for gene expression classification and clustering

The concept of depth induces an ordering from centre outwards in multivariate data. Most depth definitions are unfeasible for dimensions larger than three or four, but the Modified Band Depth (MBD) is a notable exception that has proven to be a valuable tool in the analysis of high-dimensional gene expression data. This depth definition relates the centrality of each individual to its (partial) inclusion in all possible bands formed by elements of the data set. We assess (dis)similarity between pairs of observations by accounting for such bands and constructing binary matrices associated to each pair. From these, contingency tables are calculated and used to derive standard similarity indices. Our approach is computationally efficient and can be applied to bands formed by any number of observations from the data set. We have evaluated the performance of several band-based similarity indices with respect to that of other classical distances in standard classification and clustering tasks in a variety of simulated and real data sets. However, the use of the method is not restricted to these, the extension to other similarity coefficients being straightforward. Our experiments show the benefits of our technique, with some of the selected indices outperforming, among others, the Euclidean distance.

Repeatability and heritability of social reaction norms in a wild agamid lizard

In the evolutionary transition from solitary to group living, it should be adaptive for animals to respond to the environment and choose when to socialize to reduce conflict and maximize access to resources. Due to the associated proximate mechanisms (e.g. neural network, endocrine system), it is likely that this behavior varies between individuals according to genetic and non-genetic factors. We used long-term behavioral and genetic data from a population of eastern water dragons (Intellagama lesueurii) to explore variation in plasticity of social behavior, in response to sex ratio and density. To do so, we modeled individual variation in social reaction norms, which describe individuals' mean behavior and behavioral responses to changes in their environment, and partitioned variance into genetic and non-genetic components. We found that reaction norms were repeatable over multiple years, suggesting that individuals consistently differed in their behavioral responses to changes in the social environment. Despite high repeatability of reaction norm components, trait heritability was below our limit of detection based on power analyses (h² < 0.12), leading to very little power to detect heritability of plasticity. This was in contrast to a relatively greater amount of variance associated with environmental effects. This could suggest that mechanisms such as social learning and frequency-dependence may shape variance in reaction norms, which will be testable as the dataset grows.

A comparison of 71 binary similarity coefficients: The effect of base rates

There are many psychological applications that require collapsing the information in a two-mode (e.g., respondents-by-attributes) binary matrix into a one-mode (e.g., attributes-by-attributes) similarity matrix. This process requires the selection of a measure of similarity between binary attributes. A vast number of binary similarity coefficients have been proposed in fields such as biology, geology, and ecology. Although previous studies have reported cluster analyses of binary similarity coefficients, there has been little exploration of how cluster memberships are affected by the base rates (percentage of ones) for the binary attributes. We conducted a simulation experiment that compared two-cluster K-median partitions of 71 binary similarity coefficients based on their pairwise correlations obtained under 15 different base-rate configurations. The results reveal that some subsets of coefficients consistently group together regardless of the base rates. However, there are other subsets of coefficients that group together for some base rates, but not for others.

The Barts Explanatory Model Inventory for Dementia: An item reduction approach based on responses from South Asian communities

BACKGROUND

Cultural differences in how the symptoms, causes, consequences, and treatments of dementia are understood and interpreted by South Asian people are a commonly expressed reason for late- or nonuse of mental health and care services. However, systematic collection of information on South Asian perceptions of dementia is hindered by a lack of appropriate instrumentation.

OBJECTIVES

To produce a shortened version of the Barts Explanatory Model Inventory for Dementia (BEMI-D) schedule.

METHODS

A two stage item reduction approach was employed first using multidimensional scaling categorizing items as core, intermediate, or outlier. Then, item review was undertaken using three criteria: literature importance, clinical face validity, and sub-group prevalence. The analysis followed a nonmetric multidimensional scaling method based on a two-way proximity matrix.

RESULTS

The original BEMI-D had 197 items allocated to four checklists: symptoms, causes, consequences, and treatments. The two stage item reduction approach resulted in the removal of 75 items. These reductions were achieved across all four checklists in relatively equal proportions. There was no evidence of substantive content loss in the revised schedule. The reduced version of the schedule comprises 122 items.

CONCLUSIONS

A condensed version of the BEMI-D is more efficient as an assessment schedule that captures the culturally diverse perceptions of memory problems for South Asians offering a balanced trade-off between feasibility of use and content validity.

Sharks, rays and skates (Chondrichthyes, Elasmobranchii) from the Upper Marine Molasse (middle Burdigalian, early Miocene) of the Simssee area (Bavaria, Germany), with comments on palaeogeographic and ecological patterns

Elasmobranch remains are quite common in Miocene deposits and were the subject of numerous studies since the middle of the nineteenth century. Nevertheless, the taxonomic diversity of the Marine Molasse sharks, rays and skates is still largely unknown. Here, we describe 37 taxa from the lower Miocene of the Molasse Basin: 21 taxa could be identified at species level, whereas 15 taxa could only be assigned to genus and one taxon is left as order incertae sedis. The material was collected from deposits of the Auwiesholz Member of the Achen Formation (middle Burdigalian, middle Ottnangian age, ca. 17.8 Ma) exposed near Simssee, Upper Bavaria. This faunal assemblage is a mixture of shallow marine, near-coastal, pelagic and deep-water taxa. The fauna from Simssee displays different biogeographic dynamics at local and regional scales, possibly related to the intense climatic, oceanographic and tectonic events that occurred during the Eggenburgian-Ottnangian stages. The faunal relationships of the early Miocene chondrichthyan faunas from the Mediterranean Sea and Paratethys with others regions are established on the basis of qualitative (presence/absence) data. The beta diversity (Sørensen-Dice coefficient) of the Miocene Molasse elasmobranchs was used to characterize the taxonomic differentiation between localities and regions. According to our results, the fauna from Simssee shows close similarities with those from Switzerland, Austria, France and northern Germany. Faunal similarities and differences are mainly related to tectonic events and oceanographic variables (i.e. migration through seaway passages) or might represent collecting biases.

ASAP-SML: An antibody sequence analysis pipeline using statistical testing and machine learning

Antibodies are capable of potently and specifically binding individual antigens and, in some cases, disrupting their functions. The key challenge in generating antibody-based inhibitors is the lack of fundamental information relating sequences of antibodies to their unique properties as inhibitors. We develop a pipeline, Antibody Sequence Analysis Pipeline using Statistical testing and Machine Learning (ASAP-SML), to identify features that distinguish one set of antibody sequences from antibody sequences in a reference set. The pipeline extracts feature fingerprints from sequences. The fingerprints represent germline, CDR canonical structure, isoelectric point and frequent positional motifs. Machine learning and statistical significance testing techniques are applied to antibody sequences and extracted feature fingerprints to identify distinguishing feature values and combinations thereof. To demonstrate how it works, we applied the pipeline on sets of antibody sequences known to bind or inhibit the activities of matrix metalloproteinases (MMPs), a family of zinc-dependent enzymes that promote cancer progression and undesired inflammation under pathological conditions, against reference datasets that do not bind or inhibit MMPs. ASAP-SML identifies features and combinations of feature values found in the MMP-targeting sets that are distinct from those in the reference sets.

Spatial Reliability Assessment of Social Media Mining Techniques with Regard to Disaster Domain-Based Filtering

The data generated by social media such as Twitter are classified as big data and the usability of those data can provide a wide range of resources to various study areas including disaster management, tourism, political science, and health. However, apart from the acquisition of the data, the reliability and accuracy when it comes to using it concern scientists in terms of whether or not the use of social media data (SMD) can lead to incorrect and unreliable inferences. There have been many studies on the analyses of SMD in order to investigate their reliability, accuracy, or credibility, but that have not dealt with the filtering techniques applied to with the data before creating the results or after their acquisition. This study provides a methodology for detecting the accuracy and reliability of the filtering techniques for SMD and then a spatial similarity index that analyzes spatial intersections, proximity, and size, and compares them. Finally, we offer a comparison that shows the best combination of filtering techniques and similarity indices to create event maps of SMD by using the Getis-Ord Gi* technique. The steps of this study can be summarized as follows: an investigation of domain-based text filtering techniques for dealing with sentiment lexicons, machine learning-based sentiment analyses on reliability, and developing intermediate codes specific to domain-based studies; then, by using various similarity indices, the determination of the spatial reliability and accuracy of maps of the filtered social media data. The study offers the best combination of filtering, mapping, and spatial accuracy investigation methods for social media data, especially in the case of emergencies, where urgent spatial information is required. As a result, a new similarity index based on the spatial intersection, spatial size, and proximity relationships is introduced to determine the spatial accuracy of the fine-filtered SMD. The motivation for this research is to develop the ability to create an incidence map shortly after a disaster event such as a bombing. However, the proposed methodology can also be used for various domains such as concerts, elections, natural disasters, marketing, etc.

Sensitivity of comorbidity network analysis

OBJECTIVES

Comorbidity network analysis (CNA) is a graph-theoretic approach to systems medicine based on associations revealed from disease co-occurrence data. Researchers have used CNA to explore epidemiological patterns, differentiate populations, characterize disorders, and more; but these techniques have not been comprehensively evaluated. Our objectives were to assess the stability of common CNA techniques.

MATERIALS AND METHODS

We obtained seven co-occurrence data sets, most from previous CNAs, coded using several ontologies. We constructed comorbidity networks under various modeling procedures and calculated summary statistics and centrality rankings. We used regression, ordination, and rank correlation to assess these properties' sensitivity to the source of data and construction parameters.

RESULTS

Most summary statistics were robust to variation in link determination but somewhere sensitive to the association measure. Some more effectively than others discriminated among networks constructed from different data sets. Centrality rankings, especially among hubs, were somewhat sensitive to link determination and highly sensitive to ontology. As multivariate models incorporated additional effects, comorbid associations among low-prevalence disorders weakened while those between high-prevalence disorders shifted negative.

DISCUSSION

Pairwise CNA techniques are generally robust, but some analyses are highly sensitive to certain parameters. Multivariate approaches expose additional conceptual and technical limitations to the usual pairwise approach.

CONCLUSION

We conclude with a set of recommendations we believe will help CNA researchers improve the robustness of results and the potential of follow-up research.

Statistical Modeling of Distribution Patterns: A Markov Random Field Implementation and Its Application on Areas of Endemism

A statistical framework to infer areas of endemism from geographic distributions is proposed. This novel method is based on hidden Markov random fields (HMRFs), a type of undirected graph model commonly used in computer vision. This framework assumes areas of endemism are the states of the hidden layer of the model, whereas taxon distributions are emitted values in the observed layer. Taxon distributions are associated to the observed layer through a clustering procedure based on the extent of overlap. Observations are emitted by the hidden layer according to a Gaussian distribution, whereas the joint distribution of the hidden layer follows a Potts model. State and parameter inference of the maximum a posteriori configuration is performed through a modified version of the expectation-maximization algorithm. The optimal number of areas of endemism in the data set is estimated through the pseudolikelihood information criterion, a model selection procedure that uses an approximation to likelihood. The performance of the new algorithm was assessed on simulated data, and compared with the most popular methods for delimitation of areas of endemism: biotic element analysis, parsimony analysis of endemism, and endemicity analysis. HMRFs efficiently recovered the true pattern across a wide range of uncertainty values. The performance was also examined on empirical data: South African weevils (Sciobius) and Central American ground beetles and funnel-web tarantulas (Carabidae and Dipluridae, respectively). HMRFs uncovered six areas of endemism from the weevil data set, whereas eight were estimated for the Central American arthropods (compared with 3-5 and 3-14 from the other methods, respectively).

Factoring a 2 x 2 contingency table

We show that a two-component proportional representation provides the necessary framework to account for the properties of a 2 × 2 contingency table. This corresponds to the factorization of the table as a product of proportion and diagonal row or column sum matrices. The row and column sum invariant measures for proportional variation are obtained. Geometrically, these correspond to displacements of two point vectors in the standard one-simplex, which are reduced to a center-of-mass coordinate representation, [Formula: see text]. Then, effect size measures, such as the odds ratio and relative risk, correspond to different perspective functions for the mapping of (δ, μ) to [Formula: see text]. Furthermore, variations in δ and μ will be associated with different cost-benefit trade-offs for a given application. Therefore, pure mathematics alone does not provide the specification of a general form for the perspective function. This implies that the question of the merits of the odds ratio versus relative risk cannot be resolved in a general way. Expressions are obtained for the marginal sum dependence and the relations between various effect size measures, including the simple matching coefficient, odds ratio, relative risk, Yule's Q, ϕ, and Goodman and Kruskal's τc|r. We also show that Gini information gain (IGG) is equivalent to ϕ2 in the classification and regression tree (CART) algorithm. Then, IGG can yield misleading results due to the dependence on marginal sums. Monte Carlo methods facilitate the detailed specification of stochastic effects in the data acquisition process and provide a practical way to estimate the confidence interval for an effect size.

Presence of kin-biased social associations in a lizard with no parental care: the eastern water dragon (Intellagama lesueurii)

Numerous studies have observed kin-biased social associations in a variety of species. Many of these studies have focused on species exhibiting parental care, which may facilitate the transmission of the social environment from parents to offspring. This becomes problematic when disentangling whether kin-biased associations are driven by kin recognition, or are a product of transmission of the social environment during ontogeny, or a combination of both. Studying kin-biased associations in systems that lack parental care may aid in addressing this issue. Furthermore, when studying kin-biased social associations, it is important to differentiate whether these originate from preferential choice or occur randomly as a result of habitat use or limited dispersal. Here, we combined high-resolution single-nucleotide polymorphism data with a long-term behavioral data set of a reptile with no parental care to demonstrate that eastern water dragons (Intellagama lesueurii) bias their nonrandom social associations toward their kin. In particular, we found that although the overall social network was not linked to genetic relatedness, individuals associated with kin more than expected given availability in space and also biased social preferences toward kin. This result opens important opportunities for the study of kinship-driven associations without the confounding effect of vertical transmission of social environments. Furthermore, we present a robust multiple-step approach for determining whether kin-biased social associations are a result of active social decisions or random encounters resulting from habitat use and dispersal patterns.

Statistical methods for classification of 5hmC levels based on the Illumina Inifinium HumanMethylation450 (450k) array data, under the paired bisulfite (BS) and oxidative bisulfite (oxBS) treatment

Hydroxymethylcytosine (5hmC) methylation is a well-known epigenetic mark that is involved in gene regulation and may impact genome stability. To investigate a possible role of 5hmC in cancer development and progression, one must be able to detect and quantify its level first. In this paper, we address the issue of 5hmC detection at a single base resolution, starting with consideration of the well-established 5hmC measure Δβ and, in particular, with an analysis of its properties, both analytically and empirically. Then we propose several alternative hydroxymethylation measures and compare their properties with those of Δβ. In the absence of a gold standard, the (pairwise) resemblance of those 5hmC measures to Δβ is characterized by means of a similarity analysis and relative accuracy analysis. All results are illustrated on matched healthy and cancer tissue data sets as derived by means of bisulfite (BS) and oxidative bisulfite converting (oxBS) procedures.

Species richness and taxonomic composition of trawl macrofauna of the North Pacific and its adjacent seas

A checklist is presented of animal species obtained in 68,903 trawl tows during 459 research surveys performed by the Pacific Research Fisheries Center (TINRO-Center) over an area measuring nearly 25 million km² in the Chukchi and Bering seas, Sea of Okhotsk, Sea of Japan and North Pacific Ocean in 1977–2014 at depths of 5 to 2,200 m. The checklist comprises 949 fish species, 588 invertebrate species, and four cyclostome species (some specimens were identified only to genus or family level). For each species details are given on the type of trawl (benthic and/or pelagic) and basins where the species was found. Comprehensiveness of data, taxonomic composition of catches, dependence of species richness on the survey area, sample size, and habitat, are considered. Ratios of various taxonomic groups of trawl macrofauna in pelagic and benthic zones and in different basins are analysed. Basins are compared based on species composition.

Statistical analysis of co-occurrence patterns in microbial presence-absence datasets

Drawing on a long history in macroecology, correlation analysis of microbiome datasets is becoming a common practice for identifying relationships or shared ecological niches among bacterial taxa. However, many of the statistical issues that plague such analyses in macroscale communities remain unresolved for microbial communities. Here, we discuss problems in the analysis of microbial species correlations based on presence-absence data. We focus on presence-absence data because this information is more readily obtainable from sequencing studies, especially for whole-genome sequencing, where abundance estimation is still in its infancy. First, we show how Pearson's correlation coefficient (r) and Jaccard's index (J)-two of the most common metrics for correlation analysis of presence-absence data-can contradict each other when applied to a typical microbiome dataset. In our dataset, for example, 14% of species-pairs predicted to be significantly correlated by r were not predicted to be significantly correlated using J, while 37.4% of species-pairs predicted to be significantly correlated by J were not predicted to be significantly correlated using r. Mismatch was particularly common among species-pairs with at least one rare species (<10% prevalence), explaining why r and J might differ more strongly in microbiome datasets, where there are large numbers of rare taxa. Indeed 74% of all species-pairs in our study had at least one rare species. Next, we show how Pearson's correlation coefficient can result in artificial inflation of positive taxon relationships and how this is a particular problem for microbiome studies. We then illustrate how Jaccard's index of similarity (J) can yield improvements over Pearson's correlation coefficient. However, the standard null model for Jaccard's index is flawed, and thus introduces its own set of spurious conclusions. We thus identify a better null model based on a hypergeometric distribution, which appropriately corrects for species prevalence. This model is available from recent statistics literature, and can be used for evaluating the significance of any value of an empirically observed Jaccard's index. The resulting simple, yet effective method for handling correlation analysis of microbial presence-absence datasets provides a robust means of testing and finding relationships and/or shared environmental responses among microbial taxa.

A novel and robust Bayesian approach for segmentation of psoriasis lesions and its risk stratification

BACKGROUND AND OBJECTIVE

The need for characterization of psoriasis lesion severity is clinically valuable and vital for dermatologists since it provides a reliable and precise decision on risk assessment. The automated delineation of lesion is a prerequisite prior to characterization, which is challenging itself. Thus, this paper has two major objectives: (a) design of a segmentation system which can model by learning the lesion characteristics and this is posed as a Bayesian model; (b) develop a psoriasis risk assessment system (pRAS) by crisscrossing the blocks which drives the fundamental machine learning paradigm.

METHODS

The segmentation system uses the knowledge derived by the experts along with the features reflected by the lesions to build a Bayesian framework that helps to classify each pixel of the image into lesion vs.

BACKGROUND

Since this lesion has several stages and grades, hence the system undergoes the risk assessment to classify into five levels of severity: healthy, mild, moderate, severe and very severe. We build nine kinds of pRAS utilizing different combinations of the key blocks. These nine pRAS systems use three classifiers (Support Vector Machine (SVM), Decision Tree (DT) and Neural Network (NN)) and three feature selection techniques (Principal Component Analysis (PCA), Fisher Discriminant Ratio (FDR) and Mutual Information (MI)). The two major experiments conducted using these nine systems were: (i) selection of best system combination based on classification accuracy and (ii) understanding the reliability of the system. This leads us to computation of key system performance parameters such as: feature retaining power, aggregated feature effect and reliability index besides conventional attributes like accuracy, sensitivity, specificity.

RESULTS

Using the database used in this study consisted of 670 psoriasis images, the combination of SVM and FDR was revealed as the optimal pRAS system and yielded a classification accuracy of 99.84% using cross-validation protocol. Further, SVM-FDR system provides the reliability of 99.99% using cross-validation protocol.

CONCLUSIONS

The study demonstrates a fully novel model of segmentation embedded with risk assessment. Among all nine systems, SVM-FDR produced best results. Further, we validated our pRAS system with automatic segmented lesions against manually segmented lesions showing comparable performance.

A framework for the identification of long-term social avoidance in longitudinal datasets

Animal sociality is of significant interest to evolutionary and behavioural ecologists, with efforts focused on the patterns, causes and fitness outcomes of social preference. However, individual social patterns are the consequence of both attraction to (preference for) and avoidance of conspecifics. Despite this, social avoidance has received far less attention than social preference. Here, we detail the necessary steps to generate a spatially explicit, iterative null model which can be used to identify non-random social avoidance in longitudinal studies of social animals. We specifically identify and detail parameters which will influence the validity of the model. To test the usability of this model, we applied it to two longitudinal studies of social animals (Eastern water dragons (Intellegama lesueurii) and bottlenose dolphins (Tursiops aduncus)) to identify the presence of social avoidances. Using this model allowed us to identify the presence of social avoidances in both species. We hope that the framework presented here inspires interest in addressing this critical gap in our understanding of animal sociality, in turn allowing for a more holistic understanding of social interactions, relationships and structure.

The identification of Oligo-Miocene mammalian palaeocommunities from the Riversleigh World Heritage Area, Australia and an appraisal of palaeoecological techniques

Fourteen of the best sampled Oligo-Miocene local faunas from the Riversleigh World Heritage Area, north-western Queensland, Australia are analysed using classification and ordination techniques to identify potential mammalian palaeocommunities and palaeocommunity types. Abundance data for these faunas are used, for the first time, in conjunction with presence/absence data. An early Miocene Faunal Zone B and two middle Miocene Faunal Zone C palaeocommunities are recognised, as well as one palaeocommunity type. Change in palaeocommunity structure, between the early Miocene and middle Miocene, may be the result of significant climate change during the Miocene Carbon Isotope Excursion. The complexes of local faunas identified will allow researchers to use novel palaeocommunities in future analyses of Riversleigh’s fossil faunas. The utility of some palaeoecological multivariate indices and techniques is examined. The Dice index is found to outperform other binary similarity/distance coefficients, while the UPGMA algorithm is more useful than neighbour joining. Evidence is equivocal for the usefulness of presence/absence data compared to abundance.

Community Ecology of Metazoan Parasites in Two Species of <I>Mystus</I> from River Godavari, Andhra Pradesh, India

BACKGROUND AND OBJECTIVE

Fishes of the genus Mystus are the members of Bagridae family which occupy an important place in Godavari fishery. Two commonly available species, Mystus vittatus Bloch, 1800 and Mystus cavasius Hamilton, 1822 of River Godavari, Rajahmundry Andhra Pradesh serve as significant hosts for metazoa\n parasites. The present study was aimed to ascertain the population dynamics, community characteristics and the faunal similarity of the two bagridae fishes, Mystus vittatus (n = 116) and Mystus cavasius (n = 94) at both infra and component community level during the 2008-2009.

MATERIALS AND METHODS

Standard statistical analyses were conducted to study the parasitic communities of both the fishes. Jaccard's similarity coefficient was used to observe the faunal similarity of both the fishes. Various parameters such as Shannon-wiener index (H'), evenness (E) and Simpson's diversity indices were applied to the fully sampled metazoan infracommunities of both fishes. Mean-variance ratio described the distribution patterns of the parasites within the host. The correlation coefficient (R) explained the correlation between the standard length of host and parasitic abundance for all parasites. The Mann-Whitney U-test was applied to both the fishes to observe the influence of host sex on the overall parasitic abundance. Jaccard's interspecific association was used to find out the interspecific association between each pair of parasite species within a same host.

RESULTS

A total of nine metazoan parasites were obtained from both the fishes during the research study. The present investigation includes five species, i.e., Haplorchoides macrones, Bifurcohaptor indicus, Thaparocleidus tengra, Raosentis podderi and Raosentis thapari that are common to both the species. On the other hand, Metacercaria Isoparorchis hypselobagri, Raosentis godavarensis and Argulus striatus occurred specifically in Mystus vittatus and Lamproglena hospetensis occur exclusively from Mystus cavasius. There were no core and secondary species in the parasitic communities of both the fishes. Host length and rate of parasitisation showed very less correlation. There was no influence of sex on the parasitisation. Over-dispersed distribution is the generalized pattern of distribution of macroparasites and all the parasites showed over-dispersed distribution patterns except Argulus striatus, which displayed a random distribution pattern. The higher JI values indicate that there is very less competition among species as they occupy different niches within the same host.

CONCLUSION

Though, the faunal similarity of both the fishes was high but the parasitic communities of these fishes are less diverse, depauperate and non-interactive.

Cuttlefish see shape from shading, fine-tuning coloration in response to pictorial depth cues and directional illumination

Humans use shading as a cue to three-dimensional form by combining low-level information about light intensity with high-level knowledge about objects and the environment. Here, we examine how cuttlefish Sepia officinalis respond to light and shadow to shade the white square (WS) feature in their body pattern. Cuttlefish display the WS in the presence of pebble-like objects, and they can shade it to render the appearance of surface curvature to a human observer, which might benefit camouflage. Here we test how they colour the WS on visual backgrounds containing two-dimensional circular stimuli, some of which were shaded to suggest surface curvature, whereas others were uniformly coloured or divided into dark and light semicircles. WS shading, measured by lateral asymmetry, was greatest when the animal rested on a background of shaded circles and three-dimensional hemispheres, and less on plain white circles or black/white semicircles. In addition, shading was enhanced when light fell from the lighter side of the shaded stimulus, as expected for real convex surfaces. Thus, the cuttlefish acts as if it perceives surface curvature from shading, and takes account of the direction of illumination. However, the direction of WS shading is insensitive to the directions of background shading and illumination; instead the cuttlefish tend to turn to face the light source.

Finding an appropriate equation to measure similarity between binary vectors: case studies on Indonesian and Japanese herbal medicines

Background

The binary similarity and dissimilarity measures have critical roles in the processing of data consisting of binary vectors in various fields including bioinformatics and chemometrics. These metrics express the similarity and dissimilarity values between two binary vectors in terms of the positive matches, absence mismatches or negative matches. To our knowledge, there is no published work presenting a systematic way of finding an appropriate equation to measure binary similarity that performs well for certain data type or application. A proper method to select a suitable binary similarity or dissimilarity measure is needed to obtain better classification results.

Results

In this study, we proposed a novel approach to select binary similarity and dissimilarity measures. We collected 79 binary similarity and dissimilarity equations by extensive literature search and implemented those equations as an R package called bmeasures. We applied these metrics to quantify the similarity and dissimilarity between herbal medicine formulas belonging to the Indonesian Jamu and Japanese Kampo separately. We assessed the capability of binary equations to classify herbal medicine pairs into match and mismatch efficacies based on their similarity or dissimilarity coefficients using the Receiver Operating Characteristic (ROC) curve analysis. According to the area under the ROC curve results, we found Indonesian Jamu and Japanese Kampo datasets obtained different ranking of binary similarity and dissimilarity measures. Out of all the equations, the Forbes-2 similarity and the Variant of Correlation similarity measures are recommended for studying the relationship between Jamu formulas and Kampo formulas, respectively.

Conclusions

The selection of binary similarity and dissimilarity measures for multivariate analysis is data dependent. The proposed method can be used to find the most suitable binary similarity and dissimilarity equation wisely for a particular data. Our finding suggests that all four types of matching quantities in the Operational Taxonomic Unit (OTU) table are important to calculate the similarity and dissimilarity coefficients between herbal medicine formulas. Also, the binary similarity and dissimilarity measures that include the negative match quantity d achieve better capability to separate herbal medicine pairs compared to equations that exclude d.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-016-1392-z) contains supplementary material, which is available to authorized users.

Similarity of Scenic Bilevel Images

This paper presents a study of bilevel image similarity, including new objective metrics intended to quantify similarity consistent with human perception, and a subjective experiment to obtain ground truth for judging the performance of the objective similarity metrics. The focus is on scenic bilevel images, which are complex, natural or hand-drawn images, such as landscapes or portraits. The ground truth was obtained from ratings by 77 subjects of 44 distorted versions of seven scenic images, using a modified version of the SDSCE testing methodology. Based on hypotheses about human perception of bilevel images, several new metrics are proposed that outperform existing ones in the sense of attaining significantly higher Pearson and Spearman-rank correlation coefficients with respect to the ground truth from the subjective experiment. The new metrics include adjusted percentage error, bilevel local direction, and connected components comparison. Combinations of these metrics are also proposed, which exploit their complementarity to attain even better performance. These metrics and the ground truth are then used to assess the relative severity of various kinds of distortion and the performance of several lossy bilevel compression methods.

A New Distance Metric for Unsupervised Learning of Categorical Data

Distance metric is the basis of many learning algorithms, and its effectiveness usually has a significant influence on the learning results. In general, measuring distance for numerical data is a tractable task, but it could be a nontrivial problem for categorical data sets. This paper, therefore, presents a new distance metric for categorical data based on the characteristics of categorical values. In particular, the distance between two values from one attribute measured by this metric is determined by both the frequency probabilities of these two values and the values of other attributes that have high interdependence with the calculated one. Dynamic attribute weight is further designed to adjust the contribution of each attribute-distance to the distance between the whole data objects. Promising experimental results on different real data sets have shown the effectiveness of the proposed distance metric.