Dissimilarity Distance Research Articles

Spatial correlation of macroinvertebrate assemblages in streams and the implications for bioassessment programs.

Stream bioassessment using benthic macroinvertebrate assemblages is widely implemented by regulatory agencies, yet a critical question regarding spatial autocorrelation and sample independence remains: How much of a stream network does a point sample represent? Macroinvertebrate assemblages vary along a longitudinal gradient, likely due to a combination of natural and anthropogenic factors that alter water physiochemistry and habitat. A better understanding of how these gradients affect macroinvertebrate assemblage variance could prevent spatial over- and under-sampling within bioassessment efforts. This project investigated longitudinal patterns (10s of km) of macroinvertebrate assemblages in 14 Wisconsin streams. Spatial autocorrelation was assessed using Moran's I and other multivariate methods with an emphasis on estimating the distance at which assemblages no longer display spatial correlation. Within most streams, there was a linear, direct relationship between assemblage dissimilarity and longitudinal distance, with distance to independence (DTI) ranging from 1.7-13.5km. DTI was most strongly affected by conductivity, which is often a surrogate for a suite of anthropogenic effects. With increasing conductivity, DTI increases, suggesting more homogenous assemblages in disturbed streams. Natural factors like watershed size, channel gradient, and riparian slope also affected DTI. Considering spatial correlation in monitoring designs could improve the efficiency and application of regulatory bioassessment programs.

Disentangling spatial, environmental and historical effects on tropical forest tree species turnover

Abstract Aims We aimed at disentangling the effects of spatial distance, current and past environmental dissimilarity, and their combinations on tree community taxonomic and phylogenetic turnover by addressing the following questions: (i) Is tree community taxonomic and phylogenetic turnover related to the indirect effects of spatial distance via environmental dissimilarity? (ii) Does tree community taxonomic and phylogenetic turnover respond to paleoclimate (Last Glacial Maximum and Mid-Holocene)? Methods The study was carried out in 14 Atlantic rainforest sites in Brazil (20.4 ha sampled) containing 615 tree species from 83 plant families. We obtained plot-level geographic coordinates and soil variables and site-level bioclimatic variables in the current, Mid-Holocene and Last Glacial Maximum. We used structural equation models with a distance-based approach to (i) test the direct effects of spatial distance and environmental dissimilarity and (ii) test the indirect effects of spatial distance via environmental dissimilarity on taxonomic (Bray–Curtis distance) and phylogenetic turnover (Comdist and Comdistnt distances). Important Findings Our results suggest a weak indirect effect of spatial distance via environmental dissimilarity on taxonomic and phylogenetic turnover. Tree community turnover was driven by the direct effects of neutral, niche-based and historical processes. Thus, we inferred that the paleoclimate (historical processes) promoted the selection of the clades that gave rise to the current flora, while spatial distances (neutral processes) limited the dispersal range of species from the regional pool and environmental conditions (niche-based processes) locally selected the taxa that are able to persist.

16S rRNA gene and 18S rRNA gene diversity in microbial mat communities in meltwater ponds on the McMurdo Ice Shelf, Antarctica

The undulating ice of the McMurdo Ice Shelf, Southern Victoria Land, supports one of the largest networks of ice-based, multiyear meltwater pond habitats in Antarctica, where microbial mats are abundant and contribute most of the biomass and biodiversity. We used 16S rRNA and 18S rRNA gene high-throughput sequencing to compare variance of the community structure in microbial mats within and between ponds with different salinities and pH. Proteobacteria and Cyanobacteria were the most abundant phyla, and composition at OTU level was highly specific for the meltwater ponds with strong community sorting along the salinity gradient. Our study provides the first detailed evaluation of eukaryote communities for the McMurdo Ice Shelf using the 18S rRNA gene. They were dominated by Ochrophyta, Chlorophyta and Ciliophora, consistent with previous microscopic analyses, but many OTUs belonging to less well-described heterotrophic protists from Antarctic ice shelves were also identified including Amoebozoa, Rhizaria and Labyrinthulea. Comparison of 16S and 18S rRNA gene communities showed that the Eukaryotes had lower richness and greater similarity between ponds in comparison with Bacteria and Archaea communities on the McMurdo Ice shelf. While there was a weak correlation between community dissimilarity and geographic distance, the congruity of microbial assemblages within ponds, especially for Bacteria and Archaea, implies strong habitat filtering in ice shelf meltwater pond ecosystems, especially due to salinity. These findings help to understand processes that are important in sustaining biodiversity and the impact of climate change on ice-based aquatic habitats in Antarctica.

Community dissimilarity of angiosperm trees reveals deep‐time diversification across tropical and temperate forests

AbstractQuestionTo better understand the influence of deep‐time diversification on extant plant communities, we assessed how community dissimilarity increases with spatial and climatic distances at multiple taxonomic ranks (species, genus, family, and order) in angiosperm trees. We tested the prediction that the dissimilarity–distance relationship should change across taxonomic ranks depending on the deep‐time diversification in different biogeographical regions reflecting geohistories and geographical settings.LocationGlobal.MethodsUsing a data set of plot‐based surveys across the globe (861 plots), we compiled a community composition matrix comprising 21,455 species, 2,741 genera, 240 families, and 57 orders. We then calculated Sørensen's pairwise dissimilarity (βsor), and its turnover (βsim) and nestedness (βsne) components, among plots within seven biogeographical regions. Finally, we modeled the relationships between the biotic dissimilarities and the spatial/climatic distances at each taxonomic rank, and compared them among regions.Resultsβsor and βsim increased with increasing spatial and climatic distance in all biogeographical regions: βsim was dominant in all biogeographical regions in general, while βsne showed relatively high contributions to total dissimilarity in the temperate regions with historically unstable climatic conditions. The βsim‐distance curve was more saturated at smaller spatial scales in the tropics than in the temperate regions. In general, the curves became flatter at higher taxonomic ranks (order or family), with the exception of Africa, North America, and Australia, pointing to region‐specific geographical constraints.ConclusionsCompositional dissimilarity was generally shaped through the abrupt turnover of species along spatial/climatic gradients. The relatively high importance of the nestedness component in the temperate regions suggests that historical dispersal filters related to extinction/colonization may play important roles. Region‐specific changes in the turnover and nestedness components across taxonomic ranks suggest differential imprints of historical diversification over deep evolutionary time in shaping extant diversity patterns in each biogeographical region.

Head-to-head comparison of clustering methods for heterogeneous data: a simulation-driven benchmark

The choice of the most appropriate unsupervised machine-learning method for “heterogeneous” or “mixed” data, i.e. with both continuous and categorical variables, can be challenging. Our aim was to examine the performance of various clustering strategies for mixed data using both simulated and real-life data. We conducted a benchmark analysis of “ready-to-use” tools in R comparing 4 model-based (Kamila algorithm, Latent Class Analysis, Latent Class Model [LCM] and Clustering by Mixture Modeling) and 5 distance/dissimilarity-based (Gower distance or Unsupervised Extra Trees dissimilarity followed by hierarchical clustering or Partitioning Around Medoids, K-prototypes) clustering methods. Clustering performances were assessed by Adjusted Rand Index (ARI) on 1000 generated virtual populations consisting of mixed variables using 7 scenarios with varying population sizes, number of clusters, number of continuous and categorical variables, proportions of relevant (non-noisy) variables and degree of variable relevance (low, mild, high). Clustering methods were then applied on the EPHESUS randomized clinical trial data (a heart failure trial evaluating the effect of eplerenone) allowing to illustrate the differences between different clustering techniques. The simulations revealed the dominance of K-prototypes, Kamila and LCM models over all other methods. Overall, methods using dissimilarity matrices in classical algorithms such as Partitioning Around Medoids and Hierarchical Clustering had a lower ARI compared to model-based methods in all scenarios. When applying clustering methods to a real-life clinical dataset, LCM showed promising results with regard to differences in (1) clinical profiles across clusters, (2) prognostic performance (highest C-index) and (3) identification of patient subgroups with substantial treatment benefit. The present findings suggest key differences in clustering performance between the tested algorithms (limited to tools readily available in R). In most of the tested scenarios, model-based methods (in particular the Kamila and LCM packages) and K-prototypes typically performed best in the setting of heterogeneous data.

Fungal and bacterial communities of 'Pinot noir' must: effects of vintage, growing region, climate, and basic must chemistry.

BackgroundThe geographic and temporal distributions of bacterial and fungal populations are poorly understood within the same wine grape cultivar. In this work, we describe the microbial composition from ‘Pinot noir’ must with respect to vintage, growing region, climate, and must chemistry across the states of California and Oregon, USA.Materials and MethodsWe sampled ‘Pinot noir’ clone 667 clusters from 15 vineyards existing in a latitudinal gradient spanning nearly 1,200 km in California and Oregon for two vintages (2016 and 2017). Regions included five American Viticultural Areas (AVA). In order from southern California to Oregon, these AVAs were Santa Barbara, Monterey, Sonoma, Mendocino, and Willamette Valley. Uninoculated grape musts were subjected to 16S rRNA gene and ITS-1 amplicon sequencing to assess composition of microbial communities. We also measured grape maturity metrics. Finally, to describe regions by precipitation and growing degree days, we queried the Parameter-elevation Regressions on Independent Slopes Model (PRISM) spatial climate dataset.ResultsMost of the dominant bacterial taxa in must samples were in the family Enterobacteriaceae, notably the lactic acid bacteria or the acetic acid bacteria groups, but some, like the betaproteobacterial genus Massilia, belonged to groups not commonly found in grape musts. Fungal communities were dominated by Hanseniaspora uvarum (Saccharomycetaceae). We detected relationships between covariates (e.g., vintage, precipitation during the growing season, pH, titratable acidity, and total soluble solids) and bacterial genera Gluconobacter and Tatumella in the family Enterobacteraceae, Sphingomonas (Sphingomonodaceae), Lactobacillus (Lactobacillaceae), and Massilia (Oxalobacteraceae), as well as fungal genera in Hanseniaspora, Kazachstania, Lachancea, Torulaspora in the family Saccharomycetaceae, as well as Alternaria (Pleosporaceae), Erysiphe (Erysiphaceae), and Udeniomyces (Cystofilobasidiaceae). Fungal community distances were significantly correlated with geographic distances, but this was not observed for bacterial communities. Climate varied across regions and vintages, with growing season precipitation ranging from 11 mm to 285 mm and growing degree days ranging from 1,245 to 1,846.DiscussionWe determined that (1) bacterial beta diversity is structured by growing season precipitation, (2) fungal beta diversity reflects growing season precipitation and growing degree days, and (3) microbial differential abundances of specific genera vary with vintage, growing season precipitation, and fruit maturity metrics. Further, the correlation between fungal community dissimilarities and geographic distance suggests dispersal limitation and the vineyard as a source for abundant fungal taxa. Contrasting this observation, the lack of correlation between bacterial community dissimilarity and geographic distance suggests that environmental filtering is shaping these communities.

Developing a method for exploiting soil bacterial communities as evidence in environmental forensic investigations

In environmental forensic investigations, soil is often considered as the evidence associated with a crime scene in court. A major challenge is minimizing the uncertainty in linking sets of matched soil to a suspected offender. This article examined the potential methods of analyzing soil bacterial communities via high throughput sequencing, which could generate information of great importance to environmental forensic investigations. Here, we simulated local, regional, and large-scale scenarios, in addition to unknown samples, to investigate the potential of using soil as physical evidence in environmental forensics. At the broad-scale, using heatmap cluster and principal coordinate analyses, we observed that the microbial communities in samples were significantly different across different spatial distances based on Bray-Curtis dissimilarity distances. In soil samples obtained within 20 m of each other, the microbial communities revealed the detailed information based on the numbers of specific operational taxonomic units (OTUs) and biomarker species. In the criminal contexts, soils can be obtained and used to decode the origin information. To trace the soil samples to their origins, we analyzed the dissimilarity distances and matched sources of suspected soil samples to simulate crime investigation activities. In our study, source tracker was used to locate the sources of samples obtained from of a shoe or a tool according to soil microbial communities, and the method could be an effective auxiliary approach in forensic soil source tracking based on microbial data. The results demonstrated that high-throughput sequencing combined with the source tracker approach could make soil a powerful source of evidence to facilitate environmental forensic investigations.

Dynamic bending and rotation sensing based on high coherence interferometry in multicore fiber

Bending and rotation sensing based on high coherence interferometry in seven-core fiber (SCF) is proposed here for the first time. In the suggested scheme, distinct optical paths are formed from the selected cores, thus creating a very compact in-fiber Michelson interferometer. The interferometer consists of an in-line taper structure fabricated on the SCF for a light splitting/coupling element, and a Faraday rotating mirror used as a reflective element. Different strains are exerted on the reference and sensing cores because of the dissimilar distance from the neutral axis. The phase difference between these two interferometer arms resulting from the variations in strain is resolved using a fringe counting technique. The proposed sensor is revealed to be extremely sensitive for curvature and rotation measurement, with sensitivities of 629fringe/m−1 and 6.2fringe/°, respectively. The sensor is capable of detecting the smallest curvature of 0.0032 m−1 or radius of 312 m. The sensor also responds linearly to rotation, with a sensitivity of 6.2fringe/°. This response is highly repeatable; linear; insensitive to slow-varying parameters, such as temperature; and promising for the detection of direction. The proposed sensor is particularly attractive for robotics, wearable medical devices, and structural health-monitoring applications.

Oral microbiota and dental caries data from monozygotic and dizygotic twin children

There are recent studies which aimed to detect the inheritance on the etiology of dental caries exploring oral composition. We present data on the oral microbiota and its relation with dental caries and other factors in monozygotic (MZ) and dizygotic (DZ) twin children. Following clinical investigation, DNA samples were collected and isolated from saliva of 198 patients (49 MZ and 50 DZ twins) with an average age of 9.7 ± 2.7 years. Salivary bacterial microbiota analysis was performed using high throughput amplicon sequencing method targeting V3-V4 region of the 16S rRNA gene. A total of 8,297,859 raw reads corresponding to 41,908 reads per sample were obtained on average. The QIIME2-deblur workflow was used for 16S rRNA amplicon analysis. Microbiome similarity analyses between twins (based on Bray-Curtis dissimilarity, weighted and unweighted Unifrac distances) showed that monozygotic twins share more bacterial microbial content compared to dizygotic twins. This is a large microbial community dataset of MZ and DZ twins with or without dental findings which can be further used for children oral microbiome profile explorations.

Cropland connectivity affects genetic divergence of Colorado potato beetle along an invasion front.

The population genetic structure of invasive species can be strongly affected by environmental and landscape barriers to dispersal. Disentangling the relative contributions of these factors to genetic divergence among invading populations is a fundamental goal of landscape genetics with important implications for invasion management. Here, we relate patterns of genetic divergence in a global invasive agricultural pest, Colorado potato beetle (CPB; Leptinotarsa decemlineata), to environmental and landscape factors along an invasion front in Northwestern China. We first used microsatellite markers and spatial‐temporal samples to assess broad patterns of genetic diversity as well as fine‐scale changes in patterns of genetic divergence. We then distinguished the relative contributions of five factors to genetic divergence among front populations: geographic distance (isolation by distance), climate dissimilarity (isolation by environment), and least‐cost distances (isolation by resistance) modeled with three factors: climate suitability, cropland cover, and road networks. Genetic diversity broadly decreased from West to East, with the exception being Eastern China. Low levels of genetic diversity and varying degrees of divergence were observed in Northwestern China, reflecting the potential effect of landscape heterogeneity. Least‐cost distance across cropland cover was most positively correlated with genetic divergence, suggesting a role of croplands in facilitating gene flow. The contribution of climate to genetic divergence was secondary, whether modeled in terms of local adaptability or connectivity of the climatic landscape, suggesting that constraints to CPB gene flow imposed by a harsh climate may be ameliorated in agricultural landscapes. No evidence was found for an obvious effect of road networks on genetic divergence and population structuring. Our study provides an example of how agricultural landscape connectivity can facilitate the spread of invasive pests, even across a broad climatic gradient. More broadly, our findings can guide decisions about future land management for mitigating further spread.

Perhalophenyl Three-Coordinate Gold(I) Complexes as TADF Emitters: A Photophysical Study from Experimental and Computational Viewpoints.

We report the synthesis of novel perhalophenyl three-coordinated gold(I) complexes using 1,2-bis(diphenylphosphino)benzene (dppBz) as the chelating ligand and [AuR(tht)] (R = C6F5, C6Cl2F3, C6Cl5) as the perhalophenyl-gold(I) source, leading to [AuR(dppBz)] (R = C6F5 (1), C6Cl2F3 (2), C6Cl5 (3)) complexes. The solid-state structures of compounds 2 and 3 consist of discrete three-coordinated Au(I) complexes, which show a distorted trigonal planar geometry for the gold center with dissimilar Au-P distances. The distorted structural arrangement is closely related to its photophysical properties. The studied complexes display very intense emissions at room temperature (RT) and at 77 K in the solid state. Studies of the emissive properties of the complexes at different temperatures suggest that the emissions are phosphorescent at 77 K and exhibit thermally activated delayed fluorescence (TADF) at RT. First-principle calculations of the photophysical processes yielded rate constants for intersystem crossing and reverse intersystem crossing that are in excellent agreement with experimental data.

Non-ischemic endocardial scar geometric remodeling toward topological machine learning.

Scar tissues have been important factors in determining the progression of myocardial diseases and the development of adverse cardiac failure outcomes. Accurate segmentation of the scar tissues can be helpful to the clinicians for risk prediction and better evaluation of cardiovascular diseases. Our goal is to apply topology data analysis toward machine learning algorithms to confirm the geometry of scar tissue, in addition to gaining better visualization and quantification of the scar tissue present. We have introduced architecture for integrating geometry in the form of topology toward machine learning. Morphological image processing was carried out to define the regions of the endocardial wall. We implemented convolutional neural networks on delayed enhancement cardiac computed tomography images for the recognition of scar tissue. Segmented two-dimensional images were stacked up to build the geometry of the scar area for visualization purposes. Mathematical calculations were executed for the validation of the scar tissue in addition to performing morphological image processing and marking the scar tissue present on the endocardial wall of the left ventricular. We applied convolutional neural network over convolution and pooling the layers with small sizes; we achieved 89.23% accuracy, 91.11% sensitivity, and 87.75% specificity, and found the dissimilarity distance between the normal endocardial tissue distances to be 9.37. This new concept in this study contributes toward a better understanding of scar structure and transmural variation of the endocardial wall of the left ventricular.

Determining the Levels of Urbanization in Iran Using Hierar-chical Clustering

In this study, we used a variety of factors that affect urbanization in Iran to evaluate different provinces in Iran in terms of the level of urbanization. Using information from census 2011, we collected data on 33 indicators related to urbanization in 31 provinces in Iran. To rank the provinces we used density-based hierarchical clustering scheme. To determine similarities or differences between the provinces, the square of the Euclidean distance dissimilarity coefficient; Ward's algorithm was used to merge the provinces to minimize intra-cluster variance. One-way analysis of variance (ANOVA) was used to determine the variance between the variables used to rank the provinces in terms of different levels of urbanization. Statistical analysis was performed using SPSS. The provinces in Iran were combined with each other in 30 stages and classified into four levels. Taking into account the variables used to rank the level of urbanization, Tehran, and Alborz provinces were at the highest level of urbanization. On the other hand, the provinces of Sistan and Baluchistan, Kerman, North Khorasan, South Khorasan, Hormozgan, and Bushehr were at the lowest level of urbanization. Identification of provinces at the same level of urbanization can help us to discover the strengths and weaknesses in the infrastructures of each of them. Given the differences between various levels of urbanization, the identification of factors that are effective in the process of urbanization can help to access more information required for designing plans for the years to come.

Second Language Acquisition of Complex Structures: The Case of English Oblique Relative Clauses

Previous works on English relative clauses (RCs) have tested subject and direct object RCs to find an asymmetrical pattern in L2 syntactic development. Studies on English native speakers and L2 learners of English have shown that both groups were better at subject RCs than any other types of RCs. However, these test items potentially involve compounding factors such as animacy effect and word order canonicity. To resolve this issue, the present study used an elicited production task to see how Korean-speaking adult learners of English perform two types of English oblique RCs. Forty Korean college students (18 high proficiency group; 22 low proficiency group) showed better performance at oblique RCs with a short filler-gap dependency (FGD) than oblique RCs with a long FGD. Both proficiency groups showed better accuracy rates when oblique RCs with a short FGD were elicited. In addition, patterns of non-targeted responses can explain how L2 learners differently process two oblique RCs with dissimilar distance between the filler and the gap. Overall, the test results demonstrated a clear linear distance effect between the filler and the gap in producing oblique RCs.

Exploration and identification of Anredera cordifolia morphological characters in the highlands and lowlands

Abstract. Hasanah Y, Mawarni L. 2020. Exploration and identification of Anredera cordifolia morphological characters in the highlands and lowlands. Biodiversitas 21: 2759-2766. Information about genetic diversity and relationships between accessions is very important in plant breeding programs, because with the availability of this information, it is easier to determine relationship between accessions that can be used as a basis for plant selection. The objective of the research was to evaluate the morphological characteristics and relationship of binahong or Anredera cordifolia (Ten.) Steenis) accessions in the lowlands and highlands. The study was conducted in Karo (highlands) and Medan (lowlands), North Sumatra, Indonesia, from May to August 2019. This research used a descriptive method. Before the survey and exploration are conducted, first a pre-survey was conducted by gathering information from key informants and other informants. The characteristic of morphological diversity A. cordifolia can be distinguished based on leaf color, stem shape, axillary tuber, rhizome, and the presence of flowers. The dendrogram relationship between accessions is based on a genetic similarity matrix using cluster analysis. Analysis of qualitative morphological characters revealed the existence of variability among A. cordifolia accessions. There is a far relationship relation between A. cordifolia plants in Karo and Medan with Eucludian range of 0.446 to 57.725, hence resulting in two clusters that show differences in the variation of A. cordifolia plants. There is a distant relationship relation with M4 and M7 accessions with a dissimilar distance of 57,725 and the closest relationship with accession K1 and K2 with a dissimilar distance of 0.446. Therefore, morphological characterization in the research is valuable to understand the variability of genetic of A. cordifolia accessions in the lowland and highland in North Sumatra.

Impact of DNA Extraction Method on Variation in Human and Built Environment Microbial Community and Functional Profiles Assessed by Shotgun Metagenomics Sequencing.

Both the host microbiome and the microbiome of the built environment can have profound impacts on human health. While prior studies have suggested that the variability introduced by DNA extraction method is less than typical biologic variation, most studies have focused on 16S rRNA amplicon sequencing or on high biomass fecal samples. Shotgun metagenomic sequencing provides advantages over amplicon sequencing for surveying the microbiome, but is a challenge to perform in lower microbial biomass samples with high human DNA content such as sputum or vacuumed dust. Here we systematically evaluate the impact of four different extraction methods (phenol:choloroform, and three high-throughput kit-based approaches, the Promega Maxwell gDNA, Qiagen MagAttract PowerSoil DNA, and ZymoBIOMICS 96 MagBead). We report the variation in microbial community structure and predicted microbial function assessed by shotgun metagenomics sequencing in human stool, sputum, and vacuumed dust obtained from ongoing cohort studies or clinical trials. The same beadbeating protocol was used for all samples to focus our evaluation on the impact of kit chemistries on sequencing results. DNA yield was overall highest in the phenol:choloroform and Promega approaches. Only the phenol:choloroform approach showed evidence of contamination in negative controls. Bias was evaluated using mock community controls, and was noted across all extraction methods, although Promega exhibited the least amount of bias. The extraction method did not impact the proportion of human reads, although stool had the lowest proportion of human reads (0.1%) as compared to dust (44.1%) and sputum (80%). We calculated Bray-Curtis dissimilarity and Aitchison distances to evaluate the impact of extraction method on microbial community structure by sample type. Extraction method had the lowest impact in stool (extraction method responsible for 3.0–3.9% of the variability), the most impact in vacuumed dust (12–16% of the variability) and intermediate values for sputum (9.2–12% variability). Similar differences were noted when evaluating microbial community function. Our results will inform investigators planning microbiome studies using diverse sample types in large clinical studies. A consistent DNA extraction approach across all sample types is recommended, particularly with lower microbial biomass samples that are more heavily influenced by extraction method.

Gastric microbiota and Helicobacter pylori in Indonesian population.

The profile of gastric mucosal microbiota has not yet been described in the Indonesian population where the prevalence of Helicobacter pylori is low. This is a cross-sectional study analyzing 16S rRNA of 137 gastric biopsy specimens. We analyzed the association between gastric microbiota, H. pylori infection, and gastric mucosal damage. Among 137 analyzed samples, 27 were H. pylori-positive and 110 were H. pylori -negative based on culture, histology, and 16S rRNA gene analysis. Significantly lower α-diversity parameters, including Pielou's index, was observed in H. pylori-infected individuals compared with noninfected individuals (all P<.001). Among H. pylori-negative individuals, the permutational analysis of variance of Bray-Curtis dissimilarity distances showed a significant association with different ethnicities, suggesting some ethnic groups had specific microbiota profiles based on the presence of different operational taxonomic units. The linear discriminant analysis effect size (LEfSe) of the H. pylori-negative group showed significant associations between the presence of Micrococcus luteus and Sphingomonas yabuuchiae with Timor and Papuan ethnicities, respectively. The presence of Bulledia sp and Atopobium sp was associated with the Javanese ethnicity. We observed lower α-diversity scores in individuals with gastric mucosal damage and profiles with high abundances of Paludibacter sp and Dialister sp based on LEfSe analysis. Our findings suggest the presence of H. pylori is more correlated with a distinct microbiome profile than ethnic precedence.

Heterogeneity Analysis of Esophageal Squamous Cell Carcinoma for Precision Oncology Research

Background: Esophageal Squamous Cell Carcinoma (ESCC), which is the most common pathohistological type of esophageal cancer that accounts for roughly 90% of the 456,000 incidents of esophageal cancer cases each year, is a serious malignant disease, with a 15-20% 5-year survival rate worldwide. The outcome of the current established therapeutics for ESCC are not satisfactory. Heterogeneity, including not only the intratumor heterogeneity and complex micro-environment, but also the diversity of heterogeneity across different ESCC samples, is a major biological feature of cancer that is responsible for the establishment of drug resistance and morbidity. Therefore, the diversity of distinct genotypes and phenotypes contributing to this phenomenon should be analyzed for the purpose of precision research and better understanding of ESCC. Methods: Bioinformatic analysis of multi-omics data from The Cancer Cell Line Encyclopedia (CCLE), The Cancer Genome Atlas (TCGA), and Protein Atlas were conducted to investigate heterogeneity across different ESCC samples. Venn Diagrams were utilized to overlap shared and disparate regions of interest between data platforms. DAVID was used for joint functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) signal pathways enrichment. The Cytoscape open source software platform and the STRING database were used for integrating and visualizing complex networks with attribute data. Mutation and expression heterogeneity profiles of CCLE-ESCC and TCGA-ESCC samples were analyzed according to matching dissimilarity and correlation distance, respectively. Findings: Heterogeneity at the level of both gene expression and genomic mutations are widespread in the ESCC tumor tissues and established cell lines. In the analysis of animal model data, we observed that there is ambiguity with regards to the source of nearly 50% of identified proteins, which may indicate the existence of trans-species heterogeneity. Our research also indicate that substantial variability exists between different platforms after overlapping sequencing data from cell lines, tumor tissues, normal tissues and PDX tissues of ESCC . Furthermore, Olfactory transduction and Neuroactive ligand-receptor interaction signaling pathways, enriched from the TCGA-ESCC unique proteins, are originally reported in ESCC. Interpretation: Our research found that extensive mutation heterogeneity exists across the samples of ESCC patients and cell lines. The uniquely expressed proteins and relevant signaling pathways provide potential targets for the investigation of carcinogenesis and progression of ESCC. The identification and characterization of genotype and phenotype heterogeneity across different ESCC samples and platforms is essential for developing precise therapeutics for ESCC patients. Funding Statement: This work was supported by National Natural Science Foundation China (NSFC) [No.81872335], Henan Joint Fund [No.U1804196] and Henan Key Science and Technology Program [161100510300]. Declaration of Interests: The authors have no conflict of interest to declare.

Patterns of local, intercontinental and interseasonal variation of soil bacterial and eukaryotic microbial communities.

Although ongoing research has revealed some of the main drivers behind global spatial patterns of microbial communities, spatio-temporal dynamics of these communities still remain largely unexplored. Here, we investigate spatio-temporal variability of both bacterial and eukaryotic soil microbial communities at local and intercontinental scales. We compare how temporal variation in community composition scales with spatial variation in community composition, and explore the extent to which bacteria, protists, fungi and metazoa have similar patterns of temporal community dynamics. All soil microbial groups displayed a strong correlation between spatial distance and community dissimilarity, which was related to the ratio of organism to sample size. Temporal changes were variable, ranging from equal to local between-sample variation, to as large as that between communities several thousand kilometers apart. Moreover, significant correlations were found between bacterial and protist communities, as well as between protist and fungal communities, indicating that these microbial groups change in tandem, potentially driven by interactions between them. We conclude that temporal variation can be considerable in soil microbial communities, and that future studies need to consider temporal variation in order to reliably capture all drivers of soil microbiome changes.

Mathematical Analysis of Induced Antibiotic Resistance Among Uropathogenic Escherichia coli Strains.

The aim of the study was a profound insight into the antibiotic resistance development in uropathogenic Escherichia coli strains with the use of some mathematical and statistical methods. During the previous study some antibiotics (amoxicillin, ciprofloxacin, gentamycin, and tobramycin) were applied to induce the resistance of E. coli strains, which led to the generation of ∼120 derivative strains with changed antibiotic susceptibility profiles. In this work, quantitative analysis was performed based on the strains defined as values of vectors of susceptibility for all the antibiotics' use. The Pearson correlation coefficient was used to define the correlation dissimilarity (distance) of the strains, which was further applied to hierarchical clustering. Analogously, the antibiotics were presented as vectors of susceptibility values of all the investigated strains. Correlation and cluster analysis were performed for antibiotics. The hclust method from the R system with the Ward method was used as a class agglomeration method. Mathematical analysis revealed two types of statistically relevant interactions-between antibiotics and derivative strains, as well as between the effect of individual antibiotics on the bacterial strains. These observed correlations can play a potential role for modeling uropathogenic E. coli (UPEC) resistant changes, based on the particular antibiotic used to initiate resistance development, or a model helping to predict drug resistance interactions in various UPEC strains. The obtained results can lead to development of much more sophisticated mathematical models, which, in turn, can be a potentially useful tool as a drug resistance trend predictor, both for clinicians and epidemiologists.