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Background: Transitivity assumption is the cornerstone of network meta-analysis (NMA). Investigating the plausibility of transitivity can unveil the credibility of NMA results. The commonness of transitivity was examined based on study dissimilarities regarding several study-level aggregate clinical and methodological characteristics reported in the systematic reviews. The present study also demonstrated the disadvantages of using multiple statistical tests to assess transitivity and compared the conclusions drawn from multiple statistical tests with those from the approach of study dissimilarities for transitivity assessment. Methods: An empirical study was conducted using 209 published systematic reviews with NMA to create a database of study-level aggregate clinical and methodological characteristics found in the tracenma R package. For each systematic review, the network of the primary outcome was considered to create a dataset with extracted study-level aggregate clinical and methodological characteristics reported in the systematic review that may act as effect modifiers. Transitivity was evaluated by calculating study dissimilarities based on the extracted characteristics to provide a measure of overall dissimilarity within and between the observed treatment comparisons. Empirically driven thresholds of low dissimilarity were employed to determine the proportion of datasets with evidence of likely intransitivity. One-way ANOVA and chi-squared test were employed for each characteristic to investigate comparison dissimilarity at a significance level of 5%. Results: Study dissimilarities covered a wide range of possible values across the datasets. A 'likely concerning' extent of study dissimilarities, both intra-comparison and inter-comparison, dominated the analysed datasets. Using a higher dissimilarity threshold, a 'likely concerning' extent of study dissimilarities persisted for objective outcomes but decreased substantially for subjective outcomes. A likely intransitivity prevailed in all datasets; however, using a higher dissimilarity threshold resulted in few networks with transitivity for semi-objective and subjective outcomes. Statistical tests were feasible in 127 (61%) datasets, yielding conflicting conclusions with the approach of study dissimilarities in many datasets. Conclusions: Study dissimilarity, manifested from variations in the effect modifiers' distribution across the studies, should be expected and properly quantified. Measuring the overall study dissimilarity between observed comparisons and comparing it with a proper threshold can aid in determining whether concerns of likely intransitivity are warranted. [ABSTRACT FROM AUTHOR]

Mosses play a key role in Antarctic ecosystems. Understanding of moss diversity and its likely drivers across Antarctica is, however, limited, as is the extent to which Antarctic Specially Protected Areas (ASPAs) represent this diversity. Both are important given changing climates and direct human impacts in the region. Here we investigate variation in moss diversity, the frequency distribution of their range sizes, and their continent‐wide conservation. Richness is positively related to temperature, but negatively related to latitude, distance from bird colonies and geothermal sites; terrain roughness showed weak, yet positive, effects. Beta‐diversity is similar to that found for assemblages separated by long distances, dominated by species turnover. Multi‐site turnover (zeta diversity) suggests that niche‐related mechanisms are likely more responsible for diversity patterns than neutral mechanisms, despite the significant role wind‐driven dispersal is thought to play in structuring Antarctic biodiversity patterns. The frequency distribution of range sizes of mosses was right skewed, indicating that several moss species have very small range sizes, while a few species have larger ranges. Where ASPAs include mosses, richness varies between 1 and 41 species, with 65.1% (71 species) of the 109 species known from the continent included in the ASPA network. Twenty‐four species lie within 25 km2 radius of an ASPA, and 14 species beyond this distance could be considered relatively more difficult to protect. These findings lend support to the proposal that changing temperatures and expanding ice‐free areas will substantially increase Antarctica's diversity. Nonetheless, the mosses are reasonably well represented by the ASPA network, contrasting with other Antarctic taxa. [ABSTRACT FROM AUTHOR]

Negation provides a novel perspective for the representation of information. However, current research seldom addresses the issue of negation within the random permutation set theory. Based on the concept of belief reassignment, this paper proposes a method for obtaining the negation of permutation mass function in the of random set theory. The convergence of proposed negation is verified, the trends of uncertainty and dissimilarity after each negation operation are investigated. Furthermore, this paper introduces a negation-based uncertainty measure, and designs a multi-source information fusion approach based on the proposed measure. Numerical examples are used to verify the rationality of proposed method. [ABSTRACT FROM AUTHOR]

A primary challenge in pattern recognition is imbalanced datasets, resulting in skewed and biased predictions. This problem is exacerbated by limited data availability, increasing the reliance on expensive expert data labeling. The study introduces a novel method called contrastive dissimilarity, which combines dissimilarity-based representation with contrastive learning to improve classification performance in imbalance and data scarcity scenarios. Based on pairwise sample differences, dissimilarity representation excels in situations with numerous overlapping classes and limited samples per class. Unlike traditional methods that use fixed distance functions like Euclidean or cosine, our proposal employs metric learning with contrastive loss to estimate a custom dissimilarity function. We conducted extensive evaluations in 13 databases across multiple training–test splits. The results showed that this approach outperforms traditional models like SVM, random forest, and Naive Bayes, particularly in settings with limited training data. [ABSTRACT FROM AUTHOR]

The distance-based method is effective for hydrological prediction in ungauged basins, but challenges emerge when predicting flow regimes, as flow magnitude varies with time non-monotonically. Besides, substantial prediction errors often arise in basins with varying local characteristics and dynamics compared to their neighbour basins. This paper proposed a novel two-stage prediction approach that integrates the distance-based method also addressing high-flow timing with model swapping to improve predictions of monthly flow regimes in ungauged basins. In the distance-based method, dissimilarity is assessed based on lateral (timing) and vertical (magnitude) differences of high flows besides point-to-point differences between flow regimes. Model swapping improves regional model predictions by incorporating localized information at selected sites through the classical nearest neighbour principle in the descriptors' space. This approach was applied in a case study in northwestern Italy, where model swapping was conducted at 77 out of 124 stations. The results indicate that incorporating model swapping enhances prediction accuracy by reducing the dissimilarity between observed and predicted flow regimes by 15% compared to the distance-based method. Moreover, this approach demonstrated improved predictions of high flow (i.e., ≥ 80% of the maximum monthly flow) and its timing. These results support the potential of this integrated approach for predicting complex flow regimes and their like. [ABSTRACT FROM AUTHOR]

When a spiky object is occluded, we expect its spiky features to continue behind the occluder. Although many real-world objects contain complex features, it is unclear how more complex features are amodally completed and whether this process is automatic. To investigate this issue, we created pairs of displays with identical contour edges up to the point of occlusion, but with occluded portions exchanged. We then asked participants to search for oddball targets among distractors and asked whether relations between searches involving occluded displays would match better with relations between searches involving completions that are either globally consistent or inconsistent with the visible portions of these displays. Across two experiments involving simple and complex shapes, search times involving occluded displays matched better with those involving globally consistent compared with inconsistent displays. Analogous analyses on deep networks pretrained for object categorization revealed a similar pattern of results for simple but not complex shapes. Thus, deep networks seem to extrapolate simple occluded contours but not more complex contours. Taken together, our results show that amodal completion in humans is sophisticated and can be based on extrapolating global statistical properties. [ABSTRACT FROM AUTHOR]

Aim: We map global patterns of taxonomic and functional change between past (pre‐human impacts) and present (after anthropogenic extinctions and introductions) in large oceanic island bird assemblages and investigate if these patterns can be explained by island characteristics and anthropogenic factors. Location: Sixty‐four oceanic islands (>100 km2). Time Period: Late Holocene. Major Taxa Studied: Terrestrial and freshwater bird species. Methods: We compiled information on extinct, extant native and introduced bird species for all islands and used a probabilistic hypervolume approach to build a multi‐dimensional trait space and calculate several functional diversity metrics before and after extinctions and introductions. We identified which islands are global hotspots of human‐induced transformation by mapping multiple facets of biotic change and investigated intrinsic island characteristics and anthropogenic factors as drivers for these observed patterns. Results: The Hawaiian and Mascarene islands stand out as hotspots of taxonomic and functional change, but all islands changed taxonomically and functionally, mostly gaining species but losing functional richness. Taxonomic and functional changes vary across islands but are often consistent within the same archipelago. Island isolation and surface can explain some of the observed variations, but anthropogenic factors, namely human occupation, also shaped both taxonomic and functional changes. Islands with higher human pressure, as well as larger islands with high elevation ranges, tended to have greater losses in functional richness. Main Conclusions: Most biodiversity change assessments are still largely based exclusively on taxonomic diversity, which is particularly worrying in the case of oceanic islands given that the magnitude of functional diversity change is often considerably larger. We call for comprehensive assessments of changes in both taxonomic and functional diversity across oceanic islands in order to better understand the drivers of these changes and, in turn, predict future trends. [ABSTRACT FROM AUTHOR]

The Jaccard dissimilarity metric identifies the distance between two finite sets by the number of their unique elements as a proportion of their joint cardinality. This note gives an elementary characterization of the Jaccard metric by means of three simple axioms. Relaxing the most substantial one allows for a general family of new dissimilarity quasi-metrics to emerge that encompasses Jaccard's metric. [ABSTRACT FROM AUTHOR]

Increased consumption of edamame soybean (Glycine max L.) needs supplementation by production boosts through crop area and productivity expansion utilizing high-yielding varieties. This research aimed to identify the genetic diversity of germplasm soybeans. This research transpired in Leuwikopo Experimental Farm, IPB University, using a single-factor randomized complete block design with three replications. This research evaluated 28 soybean genotypes consisting of edamame and grain soybean. The results showed a diversity in morphological characters at a value of 84.62% dissimilarity level with two main groups. The edamame soybean genotype, G.AGS 439 x L Tegal-1, and grain soybean Dega-1 gave the highest weight of pods (79.677 g and 75.596 g, respectively) and the best agronomic characters, making them potential parents in crosses for yield improvement. BioMax-1 and Grobogan genotypes could serve for quality improvement with high sugar, protein, and low-fat contents. [ABSTRACT FROM AUTHOR]

Biotic homogenization is a process whereby species assemblages become more similar through time. The standard way of identifying the process of biotic homogenization is to look for decreases in spatial beta–diversity. However, using a single assemblage‐level metric to assess homogenization can mask important changes in the occupancy patterns of individual species.Here, we analysed changes in the spatial beta–diversity patterns (i.e. biotic heterogenization or homogenization) of British bird assemblages within 30 km × 30 km regions between two periods (1988–1991 and 2008–2011). We partitioned the change in spatial beta–diversity into extirpation and colonization‐resultant change (i.e. change in spatial beta–diversity within each region resulting from both extirpation and colonization). We used measures of abiotic change in combination with Bayesian modelling to disentangle the drivers of biotic heterogenization and homogenization.We detected both heterogenization and homogenization across the two time periods and three measures of diversity (taxonomic, phylogenetic, and functional). In addition, both extirpation and colonization contributed to the observed changes, with heterogenization mainly driven by extirpation and homogenization by colonization. These assemblage‐level changes were primarily due to shifting occupancy patterns of generalist species.Compared to habitat generalists, habitat specialists had significantly (i) higher average contributions to colonization‐resultant change (indicating heterogenization within a region due to colonization) and (ii) lower average contributions to extirpation‐resultant change (indicating homogenization from extirpation). Generalists showed the opposite pattern.Increased extirpation‐resultant homogenization within regions was associated with increased urban land cover and decreased habitat diversity, precipitation, and temperature. Changes in extirpation‐resultant heterogenization and colonization‐resultant heterogenization were associated with differences in elevation between regions and changes in temperature and land cover.Many of the 'winners' (i.e. species that increased in occupancy) were species that had benefitted from conservation action (e.g. buzzard (Buteo buteo)). The 'losers' (i.e. those that decreased in occupancy) consisted primarily of previously common species, such as cuckoo (Cuculus canorus).Our results show that focusing purely on changes in spatial beta–diversity over time may obscure important information about how changes in the occupancy patterns of individual species contribute to homogenization and heterogenization. [ABSTRACT FROM AUTHOR]

Abstract Negation provides a novel perspective for the representation of information. However, current research seldom addresses the issue of negation within the random permutation set theory. Based on the concept of belief reassignment, this paper proposes a method for obtaining the negation of permutation mass function in the of random set theory. The convergence of proposed negation is verified, the trends of uncertainty and dissimilarity after each negation operation are investigated. Furthermore, this paper introduces a negation-based uncertainty measure, and designs a multi-source information fusion approach based on the proposed measure. Numerical examples are used to verify the rationality of proposed method.

Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity. [ABSTRACT FROM AUTHOR]

Demi-plié is a coordinated dance movement involving knees bent while keeping hips turned out and heels grounded. It consists of descending and ascending phases, the latter often preparing for the next move. Task goals may influence its trajectory. Ten classical dancers (eight females, two males), aged 20–45 years old performed demi-plié itself and prior to pirouette en dehors, relevé, sauté, and sissonne fermée de côté. Lower limb and hip trajectory data were collected through a motion capture system. Time-series data were aligned across all conditions using a dynamic time warping algorithm. The Euclidean distance measured the hip trajectory between each ballet movement, providing a continuous dissimilarity function over time. Descriptive analysis focused on each participant’s maximum dissimilarity values across conditions. Results showed maximum hip trajectory differences of 40% for demi-plié before sauté, 30% before sissonne, 20% before relevé, and 10% before pirouette. Dissimilarities began in the descending phase of the demi-plié. ANOVAs for repeated measures and Bonferroni’s post-hoc showed that the standard deviation of dissimilarity had the largest effect size for the demi-plié – demi-plié before sauté, and the smallest for the demi-plié – demi-plié before pirouette. Demi-plié prior to jumps demands plyometric forces and has open-loop motor planning, requiring acceleration, whereas demi-plié is closed-loop, relying on proprioceptive updating. [ABSTRACT FROM AUTHOR]

Ongoing habitat loss and fragmentation caused by human activities represent one of the greatest causes of biodiversity loss. However, the effects of habitat loss and fragmentation are not felt equally among species. Here, we examined how habitat loss influenced the diversity and abundance of species from different trophic levels, with different traits, by taking advantage of an inadvertent experiment that created habitat islands from a once continuous forest via the creation of the Thousand Island Lake, a large reservoir in China. On 28 of these islands with more than a 9000‐fold difference in their area (0.12–1154 ha), we sampled plants, herbivorous insects, and predatory insects using effort‐controlled sampling and analyses. This allowed us to discern whether any observed differences in species diversity were due to passive sampling alone or to demographic effects that disproportionately influenced some species relative to others. We found that while most metrics of sampling effort‐controlled diversity increased with island area, the strength of the effect was exacerbated for species in higher trophic levels. When we more explicitly examined differences in species composition among islands, we found that the pairwise difference in species composition among islands was dominated by species turnover but that nestedness increased with differences in island area, indicating that some species are more likely to be absent from smaller islands. Furthermore, by examining trends of several dispersal‐related traits of species, we found that species with lower dispersal propensity tended to be those that were lost from smaller islands, which was observed for herbivorous and predatory insects. Our results emphasize the importance of incorporating within‐patch demographic effects, as well as the taxa and traits of species when understanding the influence of habitat loss on biodiversity. [ABSTRACT FROM AUTHOR]

Stochastic textured surfaces (STSs) do not have well-defined features, and their quality characteristics are reflected through the stochastic nature of their surface textures. Monitoring general global changes in the stochastic nature of STSs is a relatively new, yet important problem. The limited literature for solving this problem has not considered the common situation in which the normal, in-control STS data are subject to structured surface-to-surface variation in their stochastic nature, due to the challenging nature of this problem. In this paper, we propose a dissimilarity-based multivariate control charting approach for monitoring general global changes in STSs in the presence of such structured in-control variation. Our approach is novel in that it quantifies the level of abnormality from multiple 'spanning points', instead of a single reference as in prior work. The spanning points are selected via dissimilarity-based manifold learning and space filling sampling methods. We test our approach with simulated and real textile examples and demonstrate its superior robustness to the structured in-control variation. Our approach has potential to provide a general control charting framework for any applications involving complex data structures other than STS data. [ABSTRACT FROM AUTHOR]

Background Attempts at assessing heterogeneity in countries' mortality profiles often rely on measures of cause of death (CoD) diversity. Unfortunately, such indicators fail to take into consideration the degree of (dis)similarity among pairs of causes (e.g. 'transport injuries' and 'unintentional injuries' are implicitly assumed to be as dissimilar as 'transport injuries' and 'Alzheimer's disease')－an unrealistic and unduly restrictive assumption. Development We extend diversity indicators proposing a broader class of heterogeneity measures that are sensitive to the similarity between the causes of death one works with. The so-called 'CoD inequality' measures are defined as the average expected 'dissimilarity between any two causes of death'. A strength of the approach is that such measures are decomposable, so that users can assess the contribution of each cause to overall CoD heterogeneity levels—a useful property for the evaluation of public health policies. Application We have applied the method to 15 low-mortality countries between 1990 and 2019, using data from the Global Burden of Disease project. CoD inequality and CoD diversity generally increase over time across countries and sex, but with some exceptions. In several cases (notably, Finland), both indicators run in opposite directions. Conclusions CoD inequality and diversity indicators capture complementary information about the heterogeneity of mortality profiles, so they should be analysed alongside other population health metrics, such as life expectancy and lifespan inequality. [ABSTRACT FROM AUTHOR]

The Mexican Republic ranks fifth in the world in terms of amphibian diversity, and within Mexico, the state of Oaxaca has the greatest amphibian richness and endemism. Unfortunately, various factors, such as land use change and global warming, have caused a global crisis that threatens the conservation of this class. In the face of these threats, an analysis of beta diversity provides information that can be applied to conservation strategies, since its study reveals the spatial scaling of diversity loss and clarifies the mechanisms of regional diversity maintenance. In this work, we analyzed the beta diversity at the species and higher taxa level (order, family, subfamily, genus and species) for the amphibians of Oaxaca and their replacement components and the differences in richness for anurans and caudates separately between physiographic subprovinces. Very high beta diversity was recorded, with higher diversity occurring among caudates (0.92) than among anurans (0.84). Species replacement was the component that most contributed to this result, and the subprovinces with substantial environmental differences had the most dissimilar amphibian communities. The results of this study show the need to implement conservation strategies in subprovinces with high amphibian richness and endemism levels, following the example of Sierra Madre de Oaxaca (SMO), where local communities have developed conservation actions in most of the territory. [ABSTRACT FROM AUTHOR]

Agglomerative hierarchical clustering methods are the most popular type of hierarchical clustering used to group objects in clusters based on their similarity. The methods uses a bottom-up approach and it starts clustering by treating the individual data points as a single cluster, then it is merged continuously based on similarity until it forms one big cluster containing all objects. In this paper, we reviewed eight agglomerative hierarchical clustering methods namely: single linkage method, complete linkage method, average linkage method, weighted group average method, centroid method, median method, Ward's method and the flexible beta method; we also discussed measures of similarity and dissimilarity using quantitative data as our reference point. [ABSTRACT FROM AUTHOR]

Fisheries of the Mekong Basin are crucial to regional food security. They rely on seasonal monsoon rains that inundate rice fields and reconnect water bodies. Species assemblages vary over time and space, but infrastructure developments and climate change are negatively impacting the timing and magnitude of the flood pulse as well as fish migration routes and populations. In lowland rice field landscapes of Cambodia, community fish refuges (CFRs) are used to maintain fish abundance and biodiversity, with varying success. There is little knowledge of the drivers of this variation to guide management interventions. We used beta diversity to explore the effectiveness of 40 CFRs in maintaining species abundance and diversity during the dry seasons from 2012 to 2015. Results show CFR connectivity to the floodplain is important and suggest the type of inlet/outlet influences fish retention. CFRs connected to a large area of rice fields during the wet season had lower losses in species abundance through the dry season. However, large CFRs, or those part of a larger body of water, had higher losses in species abundance. Key design features of CFRs must be coupled with strong management capacity to guide the conservation and fisheries strategy in the Mekong Basin. [ABSTRACT FROM AUTHOR]

Question: Remote sensing is a fundamental tool to monitor biodiversity and spectral diversity may represent a proxy for different biodiversity facets such as taxonomic (TD) and functional diversity (FD). We used fine‐resolution multispectral imagery to explore spectral diversity (SD) patterns across spatial scales (i.e., plot, transect, area), and assess SD relationships with TD and FD along an environmental gradient. Location: Coastal sand dune, Viareggio, Italy ("Migliarino–San Rossore–Massaciuccoli" Regional Park, 43°83′ N, 10°25′ E). Methods: We measured TD as species richness, while SD and FD were computed using probability density functions based on pixel and species position in multivariate spaces. We compared SD and FD patterns in space occupation, and we tested whether these patterns are coordinated along the sea–inland gradient. We also assessed univariate relationships between SD and biodiversity facets, and we tested how these facets were partitioned across scales. Results: We found a strong correspondence between functional and spectral patterns in space occupation and along the environmental gradient, with a lack of significance when considering TD. However, TD and SD showed higher variation at broader scale while most FD variation occurred at plot level. Conclusion: By measuring FD and SD with a common methodological framework, we demonstrate that SD approximates functional patterns in plant communities. Moreover, we show that SD retrieved using high‐resolution images can capture different aspects of FD, and that the occupation of the spectral space is analogous to the occupation of the functional space. [ABSTRACT FROM AUTHOR]

A functional approach to understand natural plant communities helps elucidate the dynamics of environmental filters and community assembly. Species classification into functional groups allows the translation of ecological complexity into simplified and applicable knowledge. Here, we developed a plant functional type (PFT) system to investigate whether functional structure (PFT composition and abundance) of plant communities varies between substrate types and plant physiognomies in an old climatically buffered infertile landscape (OCBIL), namely Brazilian campo rupestre. We sampled the herbaceous and shrubby vegetation and collected soil variables in four different plant physiognomies growing in quartzitic and ferruginous substrates. We classified the sampled species according to life‐history traits related to species persistence, reproduction, and biological interactions. Functional dissimilarity among plant physiognomies and the two substrate types and the role of soil factors affecting the dissimilarities were assessed. Pollination and seed dispersal syndromes, mycorrhizal association, and reproductive phenology were the main traits defining the PFTs. We found distinct functional structure between plant communities of different substrates and physiognomies, with a marked influence of chemical and physical soil factors. Also, we defined main PFTs profiles for each physiognomy, identifying core PFTs with basic combinations of traits, common to all or almost all physiognomies. The functional perspective proposed by this study provides applicable knowledge to the restoration and conservation of campo rupestre ecosystem, reinforcing the importance of habitat heterogeneity for maintaining its functioning. [ABSTRACT FROM AUTHOR]

Abstract Classifications of forest vegetation types and characterization of related species assemblages are important analytical tools for mapping and diversity monitoring of forest communities. The discrimination of forest communities is often based on β‐diversity, which can be quantified via numerous indices to derive compositional dissimilarity between samples. This study aims to evaluate the applicability of unsupervised classification for National Forest Inventory data from Georgia by comparing two cluster hierarchies. We calculated the mean basal area per hectare for each woody species across 1059 plot observations and quantified interspecies distances for all 87 species. Following an unspuervised cluster analysis, we compared the results derived from the species‐neutral dissimilarity (Bray‐Curtis) with those based on the Discriminating Avalanche dissimilarity, which incorporates interspecies phylogenetic variation. Incorporating genetic variation in the dissimilarity quantification resulted in a more nuanced discrimination of woody species assemblages and increased cluster coherence. Favorable statistics include the total number of clusters (23 vs. 20), mean distance within clusters (0.773 vs. 0.343), and within sum of squares (344.13 vs. 112.92). Clusters derived from dissimilarities that account for genetic variation showed a more robust alignment with biogeographical units, such as elevation and known habitats. We demonstrate that the applicability of unsupervised classification of species assemblages to large‐scale forest inventory data strongly depends on the underlying quantification of dissimilarity. Our results indicate that by incorporating phylogenetic variation, a more precise classification aligned with biogeographic units is attained. This supports the concept that the genetic signal of species assemblages reflects biogeographical patterns and facilitates more precise analyses for mapping, monitoring, and management of forest diversity.

Abstract Automakers spend billions of dollars annually towards warranty costs, and warranty reduction is typically high on their priorities. An accurate understanding of warranty performance plays a critical role in controlling and steering the business, and it is of crucial importance to fully understand the actual situation as well as be able to predict future performance, for example, to set up adequate financial reserves or to prioritize improvement actions based on expected forthcoming claims. Data maturation, a major nuisance causing changes in performance metrics with observation time, is one of the factors complicating warranty data analysis and typically leads to over‐optimistic conclusions. In this paper, we propose a sequence of steps, decomposing and addressing the main reasons causing data maturation. We first compensate for reporting delay effects using a Cox regression model. For the compensation of heterogeneous build quality, sales delay, and warranty expiration rushes, a constrained quadratic optimization approach is presented, and finally, a sales pattern forecast is provided to properly weigh adjusted individual warranty key performance indicators. The results are shown to dramatically improve prior modeling approaches.

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance.

The studies carried out in extreme environments are of vital importance to know the variability of plankton in low temperature conditions, during the Southern summer, during the XVIII Ecuadorian Antarctic Expedition, between January and February 2014, the study area was located in the Guayaquil Cove and Chile Bay (Greenwich Island), plankton samples were collected at surface and vertical level using simple cylindro-conical-conical nets of 335µ and 50µ respectively, in order to determine the distribution of phytoplankton and zooplankton. Ten sampling stations were carried out in Guayaquil Inlet and 8 stations in Chile Bay in both strata. Nineteen phytoplankton species were identified at surface level and Thalassiosira sp. 60.3%, Cyclotella sp. 12.4% and Corethron criophylum with 12.7% were observed as dominant and 9 species in the water column, being dominant Thalassiosira sp. 52%, Cyclotella sp. 25% and C. criophylum with 11%, observing a 68.12% of dissimilarity between both zones, registering as dissimilar species Thalassiosira sp, Cyclotella sp., Limnophora abreviata, Synedra ocus and Diploneis coffeif.ormis. In Chile Bay, 19 species were recorded at surface level, with two dominant species, Cyclotella sp. 36% and C. criophylum with 28%, while 10 species were recorded in the water column, with Cyclotella sp. 45% and C. criophylum 25%. Zooplankton was represented in Guayaquil Cove by 11 groups, with copepods and amphipods as dominant, in the vertical there were 7 groups represented mostly by copepods, in Chile Bay in both strata an average of 6 groups were recorded and copepods were the dominant ones. Within the Ecological indexes in the phytoplankton recorded a Shannon value of 1.3 bits/cel at the surface level and 1.2 bits/cel in the vertical, Simpson of 0.4 bits/cel in both strata and Pielou recorded an average of 0.75 bits/cel, in Guayaquil Inlet, in Chile Bay at surface level a H value of 1.5 bits/cel and 1.2 bits/cel in the vertical was observed, Simpson with an average of 0.35 bits/cel in both strata and Pielou with 0.70 bits/cel at surface level and 0.9 bits/cel in the vertical. [ABSTRACT FROM AUTHOR]

A standard unsupervised analysis is to cluster observations into discrete groups using a dissimilarity measure, such as Euclidean distance. If there does not exist a ground-truth label for each observation necessary for external validity metrics, then internal validity metrics, such as the tightness or separation of the clusters, are often used. However, the interpretation of these internal metrics can be problematic when using different dissimilarity measures as they have different magnitudes and ranges of values that they span. To address this problem, previous work introduced the "scale-agnostic" $G_{+}$ discordance metric; however, this internal metric is slow to calculate for large data. Furthermore, in the setting of unsupervised clustering with $k$ groups, we show that $G_{+}$ varies as a function of the proportion of observations assigned to each of the groups (or clusters), referred to as the group balance, which is an undesirable property. To address this problem, we propose a modification of $G_{+}$, referred to as $H_{+}$, and demonstrate that $H_{+}$ does not vary as a function of group balance using a simulation study and with public single-cell RNA-sequencing data. Finally, we provide scalable approaches to estimate $H_{+}$, which are available in the $\mathtt{fasthplus}$ R package. [ABSTRACT FROM AUTHOR]

The variability in the propagation pathway in epilepsy is a main factor contributing to surgical treatment failure. Ways to accurately capture the brain propagation network and quantitatively assess its evolution remain poorly described. This work aims to develop a dynamic step effective network (dSTE) to obtain the propagation path network of multiple seizures in the same patient and explore the degree of dissimilarity. Multichannel stereo-electroencephalography (sEEG) signals were acquired with ictal processes involving continuous changes in information propagation. We utilized high-order dynamic brain networks to obtain propagation networks through different levels of linking steps. We proposed a dissimilarity index based on singular value decomposition to quantitatively compare seizure pathways. Simulated data were generated through The Virtual Brain, and the reliability of this method was verified through ablation experiments. By applying the proposed method to two datasets consisting of 29 patients total, the evolution processes of each patient’s seizure networks was obtained, and the within-patient dissimilarities were quantitatively compared. Finally, three types of brain network connectivity patterns were found. Type I patients have a good prognosis, while type III patients are prone to postoperative recurrence. This method captures the evolution of seizure propagation networks and assesses their dissimilarity more reliably than existing methods, demonstrating good robustness for studying the propagation path differences for multiple seizures in epilepsy patients. The three different patterns will be important considerations when planning epilepsy surgery under sEEG guidance. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the Internet, network attack methods are constantly evolving and adapting. To better identify the network attack behavior, a universal gravitation clustering algorithm was proposed by analyzing the dissimilarities and similarities of the clustering algorithms. First, the algorithm designated the cluster set as vacant, with the introduction of a new object. Subsequently, a new cluster based on the given object was constructed. The dissimilarities between it and each existing cluster were calculated using a defined difference measure. The minimum dissimilarity was selected. Through comparing the proposed algorithm with the traditional Back Propagation (BP) neural network and nearest neighbor detection algorithm, the application of the Defense Advanced Research Projects Agency (DARPA) 00 and Knowledge Discovery and Data Mining (KDD) Cup 99 datasets revealed that the performance of the proposed algorithm surpassed that of both algorithms in terms of the detection rate, speed, false positive rate, and false negative rate. [ABSTRACT FROM AUTHOR]

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In modern data analysis and machine learning, data are often represented in the form of pairwise comparisons of the elements of the data set. The pairwise comparisons immediately correspond to the similarity or dissimilarity of objects under investigation, and such a situation regularly arises in the domains of image and signal analysis, bioinformatics, expert evaluation, etc. The practical pairwise comparison functions may be incorrect in terms of potentially using them as scalar products or distances. In contrast to other approaches, we develop in this paper a technique based on the so-called metric approach, which proposes to modify the values of empirical functions so as to get scalar products or distances. The methods for obtaining the correct matrices of pairwise comparisons and for improving their conditionality are developed here. [ABSTRACT FROM AUTHOR]

Introduction: Bacterial communities are important for soil functions, but the effect of clomazone on network complexity, composition, and stability is not well studied. Method: In this study, two agricultural soils were used to test the impact of clomazone on bacterial communities, and the two soils were treated with three concentrations of clomazone (0, 0.8, 8, and 80 mg kg1) in an incubator. Results and discussion: Bacterial network nodes, links, and average degrees were all decreased by 9-384, 648-829, and 0.703-2.429, respectively. Based on keystone nodes, the topological roles of the nodes were also influenced by clomazone. Bacterial network composition was also impacted based on the analysis of similarity (ANOSIM) and network dissimilarity. Compared with control and clomazone treatments in both soils, the ANOSIM between control and all clomazone treatments was higher than 0.6, network dissimilarities were 0.97-0.98, shared nodes were 131-260, and shared links were 12-100. The bacterial network stability was decreased by clomazone, with decreased robustness by 0.01-0.016 and increased vulnerability by 0.00023-0.00147 in both soils. There were fewer bacterial network modules preserved after clomazone treatment, and the bacterial network community functions were also impacted in both soils. Based on these results, soil bacterial species connections, modularization, and network stability were significantly impacted by clomazone. [ABSTRACT FROM AUTHOR]

We studied patterns of changes in host–flea interactions measured as total turnover (TT) which can be partitioned into components, namely species turnover (ST), interaction rewiring (RW), and mixed turnover (MX) in networks from Europe, Asia, Africa, and South America, applying a multi‐site interaction turnover metric. We also searched for environmental drivers of TT and its components.We asked whether (a) different components contribute differently to TT in rare versus common interactions (in terms of frequency of interaction occurrence); (b) relative roles of turnover components for rare and common interactions differ between continents; and (c) the environmental drivers of interaction turnover differ between turnover components, rare and common interactions, and/or continental networks.Between‐network dissimilarity of interactions increased with an increase in the number of compared networks. Pure ST contributed the most to the turnover of rare interactions, whereas the turnover of common interactions was predominated by MX.The effects of environmental factors, interaction richness, and spatial distance on TT and its components differed between continental networks, turnover components, and rare versus common interactions. Climate and vegetation exerted the strongest effects on (a) ST for rare (except Asia) and, to a lesser degree, common (South America) interactions, (b) RW for both rare and common interactions in Europe/Asia, and (c) MX for both rare and common interactions (except Africa). Interaction richness and spatial distance mainly influenced ST.We conclude that the patterns of interaction turnover and its components were geographically invariant and did not depend on the identity of the interactors, whereas the drivers of the turnover differed between continental networks because of species‐specific responses to the environment. [ABSTRACT FROM AUTHOR]

Data clustering is a commonly used data processing technique in many fields, which divides objects into different clusters in terms of some similarity measure between data points. Comparing to partitioning clustering methods which give a flat partition of the data, hierarchical clustering methods can give multiple consistent partitions of the data at different levels for the same data without rerunning clustering, it can be used to better analyze the complex structure of the data. There are usually two kinds of hierarchical clustering methods: divisive and agglomerative. For the divisive clustering, the key issue is how to select a cluster for the next splitting procedure according to dissimilarity and how to divide the selected cluster. For agglomerative hierarchical clustering, the key issue is the similarity measure that is used to select the two most similar clusters for the next merge. Although both types of the methods produce the dendrogram of the data as output, the clustering results may be very different depending on the dissimilarity or similarity measure used in the clustering, and different types of methods should be selected according to different types of the data and different application scenarios. So, we have reviewed various hierarchical clustering methods comprehensively, especially the most recently developed methods, in this work. The similarity measure plays a crucial role during hierarchical clustering process, we have reviewed different types of the similarity measure along with the hierarchical clustering. More specifically, different types of hierarchical clustering methods are comprehensively reviewed from six aspects, and their advantages and drawbacks are analyzed. The application of some methods in real life is also discussed. Furthermore, we have also included some recent works in combining deep learning techniques and hierarchical clustering, which is worth serious attention and may improve the hierarchical clustering significantly in the future. [ABSTRACT FROM AUTHOR]

In recent years, researchers have grappled with the phenomenon that public demand for redistribution has not systematically increased in response to rising inequality. Meritocratic beliefs have been suggested as an explanation for this observation, because they can help legitimize inequalities. Past research has identified local-level inequality, segregation, or diversity as important factors for how these beliefs might be formed and maintained. Different theoretical approaches have been advanced and tested to determine the direction and extent of these effects, producing mixed results. We put these theories to the test by focusing on a country in which changes in the level of inequality have indeed been met with equal changes in justice perceptions: Germany. Furthermore, we broaden the scope by focusing on local segregation between different socioeconomic status groups, rather than income inequality. To do so, we utilize geocoded individual-level data from the German Socio-Economic Panel (SOEP), microm data, and relevant geospatial indicators from the INKAR database. We find some indication that residential segregation of status groups and isolation of high status groups is associated with less support of meritocratic beliefs, contradicting previous work. Additionally, we find evidence of urban–rural differences in the effects of segregation. [ABSTRACT FROM AUTHOR]

Routine evaluations of cooking time trait in common bean (Phaseolus vulgaris L.) can be performed in different ways resulting in different variables. At the same time, the univariate statistical analysis does not consider the interdependencies between the variables, and may omit important information regarding the genotypes. With this, the objective of this work was to present an alternative proposal for analysis of the cooking time in common bean, allowing the discrimination between genotypes. The experiment used for this approach was conducted under field conditions in the 2017/18 agricultural season in Lages, Santa Catarina, Brazil. The treatments consisted of twelve genotypes, (four parents, structured in two crossings BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru, with their generations F2, F3, F8 and F9). The design used was randomized blocks, with two blocks and two observations in each experimental unit. After the harvest, the response variable cooking time of the grains was measured with a Mattson cooker, considering the cooking time of the 13 initial stems. In the multivariate analysis, the variables cooking time of the second (TCH2), twelfth (TCH12) and thirteenth stem (TCH13) were used based on their significance by the stepwise variable selection method. Multivariate analysis of variance showed differences between genotypes (P<0.05). From the dissimilarity matrix with the Mahalanobis distances and the clustering dendrogram, it was possible to verify the distances of the genotypes derived from crosses BAF50 x BAF07 and BAF09 x IPR 88 Uirapuru. With that, the multivariate analysis enabled the genotypes, additionally the crossing BAF50 x BAF07 showed higher estimates of dissimilarity in the progenies. [ABSTRACT FROM AUTHOR]

Biological soil crusts (biocrusts) are communities of microorganisms, fungi, algae, lichens and mosses inhabiting on the soil surface and within the uppermost soil millimetres. They play an important ecological role in drylands, determining physical and chemical soil properties and reducing soil erosion. Studies on biocrust natural recovery establish highly variable recovery times. The different objectives and methodologies of experimentation and analysis, strongly influence these predictions. The main purpose of this research is to analyze the recovery dynamics of four biocrust communities and their relationship with microclimatic variables. In 2004, in Tabernas Desert, some of us removed the biocrust in central 30 cm 30 cm area of three 50 cm x 50 cm plots in each of four biocrust communities (Cyanobacteria, Squamarina, Diploschistes, and Lepraria), installing a microclimatic station in each one with sensors for temperature and humidity of the soil and air, dew point, PAR and rain. Yearly, the 50 cm < 50 cm plots were photographed, and the cover of every species was monitored in every 5 cm <5 cm cell of a 36-cells grid covering the removed central area. We analyzed different functions to fit the cover recovery, the differences in cover recovery speed between communities, the recovery dynamics from the spatial analysis of the plot, the changes in dissimilarity and biodiversity and the possible relationships with the climatic variables. The recovery of the biocrust cover fits to a sigmoidal function. The community dominated by Cyanobacteria developed faster than those dominated by lichens. The Squamarina and Diploschistes communities recovered faster than that of Lepraria and appears to be influenced by the surrounding undisturbed areas. Species-based dissimilarity between consecutive inventories fluctuated and decreased over time, while biodiversity increases in a similar way. The speed of recovery of the biocrust in each community, along with the order in which the species appeared, support the hypothesis about the succession, which would include three phases: firstly Cyanobacteria, then Diploschistes and/or Squamarina and finally Lepraria. The relationship between biocrust recovery and microclimate is complex and this work highlights the need to carry out further research on this topic and on biocrust dynamics in general. [ABSTRACT FROM AUTHOR]

Introduction: The composition and stability of soil fungal network are important for soil function, but the effect of trifluralin on network complexity and stability is not well understood Methods: In this study, two agricultural soils were used to test the impact of trifluralin on a fungal network. The two soils were treated with trifluralin (0, 0.84, 8.4, and 84mg kg-1) and kept in artificial weather boxes. Results and discussion: Under the impact of trifluralin, the fungal network nodes, edges, and average degrees were increased by 6-45, 134-392, and 0.169-1.468 in the two soils, respectively; however, the average path length was decreased by 0.304-0.70 in both soils. The keystone nodes were also changed in trifluralin treatments in the two soils. In the two soils, trifluralin treatments shared 219-285 nodes and 16-27 links with control treatments, and the network dissimilarity was 0.98-0.99. These results indicated that fungal network composition was significantly influenced. After trifluralin treatment, fungal network stability was increased. Specifically, the network robustness was increased by trifluralin with 0.002-0.009, and vulnerability was decreased by trifluralin with 0.0001-0.00032 in the two soils. Fungal network community functions were also impacted by trifluralin in both soils. Trifluralin significantly impacts the fungal network. [ABSTRACT FROM AUTHOR]