Analysis Of Co-occurrence Patterns Research Articles

Niche divergence mechanisms of closely related hybridising bird species. Modeling approach based on remote sensing data of forest habitats

Describing mechanisms that ensure stable co-occurrence of sympatric species is fundamental to understanding the complexity of ecological community dynamics. In this study, the Pied flycatcher and the Collared flycatcher were used as model species for the analysis of co-occurrence patterns of closely related hybridising species that inhabit overlapping forest habitats. I hypothesise that spatial niche partitioning between species emerges as a vital adaptive response to interspecific competition and manifests itself in significant shifts in habitat preferences in syntopic areas but not in the allotopic ones. The study leverages a comprehensive dataset that includes species density in 372 randomly selected 1 km² grid cells across the territory of Poland. The analysis of habitat preferences was performed with linear mixed-effect modeling, whereas a contrast analysis was used to investigate changes in habitat preferences resulting from the presence of a competitor. The findings indicate that both species modify their habitat preferences when transitioning between syntopic and allotopic sites. Remarkably, in regions without competitors, such shifts are significantly less pronounced, as demonstrated by the Pied flycatcher that prefers the same habitats both in allopatric regions and allotopic sites. When these results are compared with other studies on closely related hybridising and non-hybridising species, it turns out that what influences the degree and number of niches subjected to competition is the time that passes from species divergence. This study highlights the imperative need to incorporate co-occurrence parameters of closely related species into niche and species distribution models to enhance their ecological realism.

Promising membrane photobioreactor for continuous flow treatment of Low C/N wastewater: Treating process, community structure, and cooperation mechanism

Low carbon/nitrogen (C/N) wastewater treatment is an emerging issue for conventional treatment technologies. To achieve this goal, a novel membrane photobioreactor (MPBR), integrating microalgae consortium (MC) and activated sludge (AS) phases, was investigated. The separation of the MC and AS phases could improve the removal of pollutants in low C/N wastewater, which could achieve the rapid denitrogenation through NH4+-N assimilation in MC phase, and effectively reduce organic matter concentration in AS phase, resulting in better nutrient recovery, lower costs, and stable pH. The results in phase I showed that the pollutants, such as chemical oxygen demand (COD), ammonium (NH4+-N), and total phosphorus (TP), met the Environmental Quality Standards for Surface Water (GB3838–2002) by MPBR. The AS phase with low DO, which could enrich heterotrophic bacteria (e.g., Bacteroidetes vadinHA17, Spirochaeta and Ahniella) and nitrogen cycling bacteria (e.g., Mycobacterium, Bacillus and Ellin6067), could promote the removal of COD and NO3--N in the continuous effluent. Based on the co-occurrence pattern analysis, the main influencing factor of MC and AS phases was the reduction of COD, which also illustrated the synergistic removal of organic matter by the cooperative interaction of microalgae and heterotrophic bacteria. From a genetic perspective, the internal circulation mode also enhanced microbial nitrogen metabolism in the MPBRs, with higher abundances of nitrification- and denitrification-related functional genes in the MC phase, and higher abundances of nitrification, dissimilatory nitrate reduction, and denitrification genes in the AS phase. This study provided an experimental and theoretical basis for MPBR in low-C/N wastewater treatment.

Diatom and macroinvertebrate communities dynamic: A co-occurrence pattern analysis on plastic substrates

Wetlands are habitats that provide numerous ecosystem services, but are often understudied and threatened by anthropogenic pollution, particularly plastic pollution. Macroplastics are a significant component of plastic litter that have high biological impacts but are often understudied. Previous studies have highlighted negative impacts on biota, but there is a lack of information about the communities of micro and macro organisms that settle on macroplastic litter. In this context, we investigated the colonization patterns and community structures of diatoms and macroinvertebrates on virgin substrates composed of two different plastic polymers, polystyrene and polyethylene terephthalate, located at two different depths in a protected wetland in Central Italy over a period of 10 months. The results show that diatom community is not highly structured by competitive forces and aggregation patterns emerges. In contrast, macroinvertebrate community appears to be randomly structured, without the presence of patterns following specific assembly rules. Randomness in macroinvertebrates assemblages could highlight the presence of different niches available for settlement of different taxa. Combined matrix analyses show that diatoms and macroinvertebrates co-occur, and their community assemblages are sometimes structured, while they appeared to be randomly assembled at other times. Whenever non-randomness of diatoms and macroinvertebrates co-occurrences was detected, it suggested aggregation. Moreover, the possible predatory relationship between different macroinvertebrates taxa should be investigated, as it could reveal important scenarios in the establishment of macroinvertebrate structured communities on plastic litter, including taxa that exploit different ecological niches. This could lead to an enrichment of the biological community within areas impacted by plastics.

Diversity and Co-Occurrence Pattern Analysis of Cecal and Jejunal Microbiota in Two Rabbit Breeds.

This study aimed to evaluate the productive performance and microbiota variation in the jejunum and cecum of two rabbit breeds with different growth rates. This study was carried out on Native Middle-Egypt Breed (NMER) and Giant Flanders (GF) rabbits from 5 weeks to 12 weeks of age. Twenty NMER (NM) and GF male rabbits were slaughtered, and the jejunum and cecum tracts were collected to assay gut microbiota composition via 16S ribosomal RNA (rRNA) gene sequencing and histology examination. At 12 weeks of age, daily weight gain, villus height in the jejunum, total protein, and albumin were higher in GF rabbits than in NMER rabbits. Also, the jejunal villi of GF were well arranged in their dense borders. The microbiota between the jejunum and cecum was significantly different in terms of Beta-diversity. A significant correlation between Enterococcus (jejunum NM samples) and Lactobacillus (cecum GF samples) with body weight and weight gain was found (p < 0.05). Moreover, Escherichia-Shigella in the cecum of NM was significantly correlated with weight gain (p < 0.05). The most abundant genera identified in the jejunal and cecal contents of GF were generally beneficial microbiota. They may also play a role in reducing the pathogenic effects of Escherichia coli in these rabbits.

IPRESTO: Automated discovery of biosynthetic sub-clusters linked to specific natural product substructures.

Microbial specialised metabolism is full of valuable natural products that are applied clinically, agriculturally, and industrially. The genes that encode their biosynthesis are often physically clustered on the genome in biosynthetic gene clusters (BGCs). Many BGCs consist of multiple groups of co-evolving genes called sub-clusters that are responsible for the biosynthesis of a specific chemical moiety in a natural product. Sub-clusters therefore provide an important link between the structures of a natural product and its BGC, which can be leveraged for predicting natural product structures from sequence, as well as for linking chemical structures and metabolomics-derived mass features to BGCs. While some initial computational methodologies have been devised for sub-cluster detection, current approaches are not scalable, have only been run on small and outdated datasets, or produce an impractically large number of possible sub-clusters to mine through. Here, we constructed a scalable method for unsupervised sub-cluster detection, called iPRESTO, based on topic modelling and statistical analysis of co-occurrence patterns of enzyme-coding protein families. iPRESTO was used to mine sub-clusters across 150,000 prokaryotic BGCs from antiSMASH-DB. After annotating a fraction of the resulting sub-cluster families, we could predict a substructure for 16% of the antiSMASH-DB BGCs. Additionally, our method was able to confirm 83% of the experimentally characterised sub-clusters in MIBiG reference BGCs. Based on iPRESTO-detected sub-clusters, we could correctly identify the BGCs for xenorhabdin and salbostatin biosynthesis (which had not yet been annotated in BGC databases), as well as propose a candidate BGC for akashin biosynthesis. Additionally, we show for a collection of 145 actinobacteria how substructures can aid in linking BGCs to molecules by correlating iPRESTO-detected sub-clusters to MS/MS-derived Mass2Motifs substructure patterns. This work paves the way for deeper functional and structural annotation of microbial BGCs by improved linking of orphan molecules to their cognate gene clusters, thus facilitating accelerated natural product discovery.

Deciphering basic and key traits of antibiotic resistome in influent and effluent of hospital wastewater treatment systems

Hospital wastewater treatment system (HWTS) is an important source and environmental reservoir of clinically relevant antibiotic resistance genes (ARGs). However, how antibiotic resistome of clinical wastewater changed in HWTS is poorly understood. Herein, the basic quantitative traits (i.e., diversity and abundance) of ARGs in three HWTSs were profiled by metagenomics. In total, 709 ARG subtypes belonging to 20 ARG types were detected with relative abundance ranging from 1.12 × 10−5 to 7.33 × 10−1 copies/cell. Notably, most ARGs could not be significantly removed by chlorination treatment in the HWTS. These ARGs were identified to confer resistance to almost all major classes of antibiotics and include ARGs of last-resort antibiotics, such as blaNDM, mcr and tet(X) which were abundantly occurred in HWTS with 19, 5 and 7 variants, respectively. Moreover, qualitative analysis based on metagenome-assembled genome (MAG) analysis revealed that the putative hosts of the identified ARGs were broadly distributed into at least 8 dominant bacterial phyla. Of the 107 ARG-carrying MAGs recovered, 39 encoded multi-antibiotic resistance and 16 belonged to antibiotic resistant pathogens. Further analysis of co-occurrence patterns of ARGs with mobile genetic elements suggested their potential mobility. These key qualitative traits of ARGs provided further information about their phylogeny and genetic context. This study sheds light on the key traits of ARGs associated with resistance dissemination and pathogenicity and health risks of clinical wastewater.

Structural and functional characteristics of soil microbial communities in response to different ecological risk levels of heavy metals.

The potential ecological risk index (RI) is the most commonly used method to assess heavy metals (HMs) contamination in soils. However, studies have focused on the response of soil microorganisms to different concentrations, whereas little is known about the responses of the microbial community structures and functions to HMs at different RI levels. Here, we conducted soil microcosms with low (L), medium (M) and high (H) RI levels, depending on the Pb and Cd concentrations, were conducted. The original soil was used as the control (CK). High-throughput sequencing, qPCR, and Biolog plate approaches were applied to investigate the microbial community structures, abundance, diversity, metabolic capacity, functional genes, and community assembly processes. The abundance and alpha diversity indices for the bacteria at different RI levels were significantly lower than those of the CK. Meanwhile, the abundance and ACE index for the fungi increased significantly with RI levels. Acidobacteria, Basidiomycota and Planctomycetes were enriched as the RI level increased. Keystone taxa and co-occurrence pattern analysis showed that rare taxa play a vital role in the stability and function of the microbial community at different RI levels. Network analysis indicates that not only did the complexity and vulnerability of microbial community decrease as risk levels increased, but that the lowest number of keystone taxa was found at the H level. However, the microbial community showed enhanced intraspecific cooperation to adapt to the HMs stress. The Biolog plate data suggested that the average well color development (AWCD) reduced significantly with RI levels in bacteria, whereas the fungal AWCD was dramatically reduced only at the H level. The functional diversity indices and gene abundance for the microorganisms at the H level were significantly lower than those the CK. In addition, microbial community assembly tended to be more stochastic with an increase in RI levels. Our results provide new insight into the ecological impacts of HMs on the soil microbiome at different risk levels, and will aid in future risk assessments for Pb and Cd contamination.

Changes in Temporal Dynamics and Factors Influencing the Environment of the Bacterial Community in Mangrove Rhizosphere Sediments in Hainan

The structural characteristics of the rhizosphere soil’s microbial community is crucial to understanding the ecological function of mangroves. However, the mechanism influencing mangrove plants in soil microbial communities has yet to be determined. Here, the mangrove ecosystem of Xinying Mangrove National Wetland Park in Hainan Province was taken as the research object. The microbial communities, external regulatory factors, and the relationship between communities were analyzed using 16S rRNA high-throughput sequencing in the rhizosphere and non-rhizosphere sediments of mangrove forests under different spatiotemporal conditions. The results showed that there was no significant difference in the α-diversity of the bacterial community between the rhizosphere and non-rhizosphere sediments. However, β-diversity was significantly different. Redundancy analysis (RDA) showed that other environmental factors besides sulfide and Fe2+ affected the bacterial community structure in sediments. The co-occurrence pattern analysis of bacteria in the mangrove ecosystem indicates that the bacteria in rhizosphere sediments were more closely related than those in non-rhizosphere sediments. The results reveal significant differences between the rhizosphere and non-rhizosphere bacterial community diversity, structure, and their interaction in the mangrove ecosystem. Therefore, the ecological system of the mangrove wetland needs to be preserved and rehabilitated, which would have a tremendous impact on the sustainable development.

Deciphering the toxic effects of metals in gold mining area: Microbial community tolerance mechanism and change of antibiotic resistance genes

Mine tailing dumps represent significant threats to ecological environments due to the presence of toxic substances. The present work investigated the relationship among microbial activity, the community, antibiotic resistance genes (ARGs) and trace metals in soil surrounding gold mine tailings. Using microbial metabolic activity and high-throughput sequencing analysis, we found the trace metals Cd and Hg could be main factors influencing the microbial community. According to bacterial co-occurrence pattern analysis, the effects of total cadmium and total mercury on bacterial diversity are potentially mediated by influencing bacteria community in the keystone module II. Additionally, most of metal-resistant bacteria belong to Actinobacteria and Proteobacteria, and the metal tolerance suggested to be linked with various functions including replication, recombination and repair, as well as inorganic ion transport and metabolism based on PICRUSt2 analysis. We also found that metals generated by mining activity may trigger the co-selection of antibiotic resistance in the phyla Actinobacteria and Proteobacteria due to co-resistance or cross resistance. Additionally, PLS-PM analysis revealed that metals could indirectly affect ARGs by influencing bacterial diversity in gold mining areas.

Microbiome Dynamics Associated With the Atacama Flowering Desert.

In a desert, plants as holobionts quickly respond to resource pulses like precipitation. However, little is known on how environment and plants modulate the rhizosphere-associated microbiome. As a model species to represent the Atacama Desert bloom, Cistanthe longiscapa (Montiaceae family) was selected to study the influence of abiotic and biotic environment on the diversity and structure of the microbiota associated to its rhizosphere. We analyzed the rhizosphere and soil microbiome along a North-South precipitation gradient and between a dry and rainy year by using Illumina high−throughput sequencing of 16S rRNA gene fragments and ITS2 regions for prokaryotes and fungi, respectively. In the rhizosphere of C. longiscapa the microbiota clearly differs in composition and structure from the surrounding bulk soil. The fungal and bacterial communities respond differently to environmental conditions. The diversity and richness of fungal OTUs were negatively correlated with aridity, as predicted. The community structure was predominantly influenced by other soil characteristics (pH, organic matter content) but not by aridity. In contrast, diversity, composition, and structure of the bacterial community were not influenced by aridity or any other evaluated soil parameter. These findings coincide with the identification of mainly site-specific microbial communities, not shared along the sites. These local communities contain a group of OTUs, which are exclusive to the rhizosphere of each site and presumably vertically inherited as seed endophytes. Their ecological functions and dispersal mechanisms remain unclear. The analysis of co-occurrence patterns highlights the strong effect of the desert habitat over the soil- and rhizosphere-microbiome. The site-independent enrichment of only a small bacterial cluster consistently associated with the rhizosphere of C. longiscapa further supports this conclusion. In a rainy year, the rhizosphere microbiota significantly differed from bulk and bare soil, whereas in a dry year, the community structure of the former rhizosphere approximates to the one found in the bulk. In the context of plant–microbe interactions in desert environments, our study contributes new insights into the importance of aridity in microbial community structure and composition, discovering the influence of other soil parameters in this complex dynamic network, which needs further to be investigated.

The Potential Link between Gut Microbiota and Serum TRAb in Chinese Patients with Severe and Active Graves' Orbitopathy.

Background and Objective A previous study reported alterations in the intestinal microbiota in patients with Graves' orbitopathy (GO). Thyrotropin receptor autoantibody (TRAb) stimulates orbital and periorbital tissues and plays a pivotal role in the development of GO. However, the association between gut microbiota and TRAb in GO patients has still remained elusive. In this study, we explored the relationships between gut microbiota and GO-related traits, in which we applied a metabolic-network-driven analysis to identify GO trait-related modules and extracted significant operational taxonomic units (OTUs). Methods In the present study, we profiled gut microbiota using 16S rRNA gene sequencing in 31 GO patients. We performed metabolic-network-driven analysis to investigate the association between gut microbiota and GO-related traits (e.g., TRAb, TGAb, and TPOAb) in the combination of microbial effects. Results Applying microbiome network analysis of cooccurrence patterns and analysis of topological properties, we found that s_Prevotella_copri and f_Prevotellaceae showed a significant correlation with TRAb. In particular, we applied the latent class model to explore the association between gut microbiota and GO-related traits in the combination of microbial effects. It was revealed that the subjects involved in the latent class model with the higher abundance of s_Prevotella_copri and g_Bacteroides had a higher TRAb level. Conclusions Our results revealed the potential relationships between gut microbiota and GO-related traits in the combination of microbial effects. This study may provide a new insight into the interaction between the intestinal microbiota and TRAb-associated immune responses in GO patients.

Microbiome network analysis of co-occurrence patterns in anaerobic co-digestion of sewage sludge and food waste

Addition of food waste (FW) as a co-substrate in anaerobic digesters of wastewater treatment plants is a desirable strategy towards achievement of the potential of wastewater treatment plants to become energy-neutral, diverting at the same time organic waste from landfills. Because substrate type is a driver of variations in phylogenetic structure of digester microbiomes, it is critical to understand how microbial communities respond to changes in substrate composition and concentration. In this work, high throughput sequencing was used to monitor the dynamics of microbiome changes in four parallel laboratory-scale anaerobic digesters treating sewage sludge during acclimation to an increasing amount of food waste. A co-occurrence network was constructed using data from 49 metagenomes sampled over the 161 days of the digesters' operation. More than half of the nodes in the network were clustered in two major modules, i.e. groups of highly interconnected taxa that had much fewer connections with taxa outside the group. The dynamics of co-occurrence networks evidenced shifts that occurred within microbial communities due to the addition of food waste in the co-digestion process. A diverse and reproducible group of hydrolytic and fermentative bacteria, syntrophic bacteria and methanogenic archaea appeared to grow in a concerted fashion to allow stable performance of anaerobic co-digestion at high FW.

Diversity and Co-occurrence Pattern Analysis of Cecal Microbiota Establishment at the Onset of Solid Feeding in Young Rabbits

This study aimed to evaluate how the feeding strategy of rabbit kits at the onset of solid feed intake could affect ecological diversity and co-occurrence patterns of the cecal bacterial community. From birth to 18 days of age kits were exclusively milk-fed, and between 18 and 35 days the young rabbits also had access to solid feed. After weaning at (35 days), young rabbits were exclusively fed solid feed. Three experimental feeds were used: a high concentrate diet [H: 10.16 MJ digestible energy (DE)/kg and 15.3% crude protein (CP)], a low concentrate diet (L: 9.33 MJ DE/kg and 14.7% CP) and a reproductive female diet (R: 10.57 MJ DE/kg and 17.3% CP). The rabbit kits (n = 357) were divided into three groups, differing by the diet received during two periods: from 18 to 28 and from 28 to 49 days of age. In the groups LL and HH, rabbit kits were fed L or H diets, respectively, during both periods. Kits in the group RL received feeds R and L from 18 to 28 and 28 to 49 days of age, respectively. Cecal bacterial communities of 10 rabbits per group were carried out at 18, 28, 35, 43 and 49 days of age by MiSeq Illumina sequencing 16S rRNA encoding genes. Between 18 and 28 days of age, solid feed intake was higher in the group RL compared to the other two groups (+24%; P < 0.01). Overall, 13.4% of the OTUs detected were present in the cecal ecosystem from 18 to 49 days old, whereas 17.4% were acquired with the onset of solid feeding and kept from 28 days on. Exclusive milk consumption constrains the bacterial community toward a similar structure but high phylogenetic beta-diversity. Introduction of solid feed induced a sharp change of microbial community structure and decreased phylogenetic diversity. A strong relationship in bacterial community network occurred only from 43 days on. Our feeding strategy at the onset of solid feed ingestion exhibited only a moderate effect on the microbial community structure (P = 0.072), although the LL group seemed to reach faster maturity compared to the two other groups.

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

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

Preference Organization and Cross-Cultural Variation in Request Responses: A Corpus-Based Comparison of British and American English

This study compares request responses in two national varieties of English, American English (AmE) and British English. The responses are manually retrieved from two corpora of English, the British component to the International Corpus of English and the Santa Barbara Corpus of Spoken American English and analyzed both for their function and format. It is shown that request responses in both varieties pattern as preference structure would predict: compliant responses occur more frequently than non-compliant ones and can thus be understood as the preferred response type to requestive first pair parts. The analysis of co-occurrence patterns of first and second pair parts reveals that the directness level of the first pair part does not significantly influence the response type of the second pair part. It does, however, have an impact on the level of explicitness of the response. While cross-cultural differences in request responses are generally rare in the present database, they do surface in the higher numbers for implicit compliance in the AmE corpus. These results show the importance of a cross-cultural approach to the study of naturally occurring request responses retrieved from corpora of authentic speech.

Identification of Fraktur and Latin Scripts in German Historical Documents Using Image Texture Analysis

ABSTRACTThis article proposes an algorithm for script identification by textural analysis of the image corresponding to the script types. In the first phase, each letter is modeled by the equivalent script type, which is determined by its position in the baseline area. Then, feature extraction is carried out. It is based on the script type cooccurrence pattern analysis. The obtained features of the script are stored for further analysis. The difference in script characteristics contributes to the diversity of the extracted features, which simplify the feature classification obtained by an extension of a state-of-the-art classification tool called Genetic Algorithms Image Clustering for Document Analysis. Accordingly, it represents the key element in the decision-making process of script identification. The proposed method is tested on an example of German printed documents, which contain Latin and Fraktur scripts. The experiment shows correct results, which is promising.

BSPAT: a fast online tool for DNA methylation co-occurrence pattern analysis based on high-throughput bisulfite sequencing data.

BackgroundBisulfite sequencing is one of the most widely used technologies in analyzing DNA methylation patterns, which are important in understanding and characterizing the mechanism of DNA methylation and its functions in disease development. Efficient and user-friendly tools are critical in carrying out such analysis on high-throughput bisulfite sequencing data. However, existing tools are either not scalable well, or inadequate in providing visualization and other desirable functionalities.ResultsIn order to handle ultra large sequencing data and to provide additional functions and features, we have developed BSPAT, a fast online tool for bisulfite sequencing pattern analysis. With a user-friendly web interface, BSPAT seamlessly integrates read mapping/quality control/methylation calling with methylation pattern generation and visualization. BSPAT has the following important features: 1) instead of using multiple/pairwise sequence alignment methods, BSPAT adopts an efficient and widely used sequence mapping tool to provide fast alignment of sequence reads; 2) BSPAT summarizes and visualizes DNA methylation co-occurrence patterns at a single nucleotide level, which provide valuable information in understanding the mechanism and regulation of DNA methylation; 3) based on methylation co-occurrence patterns, BSPAT can automatically detect potential allele-specific methylation (ASM) patterns, which can greatly enhance the detection and analysis of ASM patterns; 4) by linking directly with other popular databases and tools, BSPAT allows users to perform integrative analysis of methylation patterns with other genomic features together within regions of interest.ConclusionBy utilizing a real bisulfite sequencing dataset generated from prostate cancer cell lines, we have shown that BSPAT is highly efficient. It has also reported some interesting methylation co-occurrence patterns and a potential allele-specific methylation case. In conclusion, BSPAT is an efficient and convenient tool for high-throughput bisulfite sequencing data analysis that can be broadly used.

Demonstrating microbial co-occurrence pattern analyses within and between ecosystems.

Co-occurrence patterns are used in ecology to explore interactions between organisms and environmental effects on coexistence within biological communities. Analysis of co-occurrence patterns among microbial communities has ranged from simple pairwise comparisons between all community members to direct hypothesis testing between focal species. However, co-occurrence patterns are rarely studied across multiple ecosystems or multiple scales of biological organization within the same study. Here we outline an approach to produce co-occurrence analyses that are focused at three different scales: co-occurrence patterns between ecosystems at the community scale, modules of co-occurring microorganisms within communities, and co-occurring pairs within modules that are nested within microbial communities. To demonstrate our co-occurrence analysis approach, we gathered publicly available 16S rRNA amplicon datasets to compare and contrast microbial co-occurrence at different taxonomic levels across different ecosystems. We found differences in community composition and co-occurrence that reflect environmental filtering at the community scale and consistent pairwise occurrences that may be used to infer ecological traits about poorly understood microbial taxa. However, we also found that conclusions derived from applying network statistics to microbial relationships can vary depending on the taxonomic level chosen and criteria used to build co-occurrence networks. We present our statistical analysis and code for public use in analysis of co-occurrence patterns across microbial communities.

Linguistic characteristics of AAC discourse in the workplace

This study examines linguistic co-occurrence patterns in the discourse of individuals with communication impairments who use augmentative and alternative communication (AAC) devices in the workplace by comparing them to those of non-AAC users in similar job settings. A typical workweek (≈ 40 hours) per focal participant (four AAC; four non-AAC) was recorded and transcribed to create a specialized corpus of workplace discourse of approximately 464,000 words at the time of this analysis. A multidimensional analysis of co-occurrence patterns along functional linguistic dimensions, following Biber (1988, 1995) [ Variation across Speech and Writing. Cambridge: Cambridge University Press; Dimensions of Register Variation: A Cross-Linguistic Perspective. Cambridge: Cambridge University Press], reveals differences in the macro discourse characteristics of AAC vis-a-vis non-AAC texts. Results indicate that AAC texts make use of more informational, non-narrative, and explicit textual features of discourse than their non-AAC counterparts. Implications to improve the capabilities of AAC devices to produce speech that matches baseline expectations of co-workers in the workplace are discussed.

Co-occurrence patterns and abiotic stress in sand-dune communities: Their relationship varies with spatial scale and the stress estimator

The analysis of co-occurrence patterns between plant species is being increasingly employed to study biotic interactions at the community level. We investigated changes in such patterns along two different abiotic stress gradients in eight sand-dune plant communities of the western coast of Portugal. We sampled a total of 72 transects consisting on 25 1 m 2 quadrats each, and we calculated the standardized effect size for each transect (SES), i.e. the differences between the observed co-occurrence patterns and simulated patterns for a random co-occurrence. The relationships between the average SES values for each site and the abiotic stress gradients, defined by the north–south geographic gradient (a surrogate of the climatic stress) and total plant cover (a surrogate of productivity), were analyzed. The factors driving co-occurrence patterns at smaller spatial scale were also investigated by a stepwise multiple regression analysis. Average SES values were significantly higher than zero in five of the studied sites, indicating lower species co-occurrence than expected by chance. These results suggest that the studied sand-dune communities studied are structured mainly by competitive interactions. At the site scale, SES values were not related to plant cover or the location along the north–south gradient. However, at the transect scale, SES values were inversely explained by different abiotic factors (the geographic position along the north–south gradient and the plant cover). Our results indicate that the magnitude and the shifts in co-occurrence patterns along environmental gradients varied with the spatial scale and the surrogate of abiotic stress considered.