High Similarity Values Research Articles

Complex network structure extraction based on community relevance

One way to understand the network function and analyze the network structure is to find the communities of the network accurately. Now, there are many works about designing algorithms for community detection. Most community detection algorithms are based on modularity optimization. However, these methods not only have disadvantages in computational complexity, but also have the problem of resolution restriction. Designing a community detection algorithm that is fast and effective remains a challenge in the field. We attempt to solve the community detection problem in a new perspective in this paper, believing that the assumption used to solve the link prediction problem is useful for the problem of community detection. By using the similarity between modules of the network, we propose a new method to extract the community structure in this paper. Our algorithm consists of three steps. First, we initialize a community partition based on the distribution of the node degree; second, we calculate the similarity between different communities, where the similarity is the index to describe the closeness of the different communities. We assume that the much closer the two different communities are, the greater the likelihood of being divided together; finally, merge the pairs of communities which has the highest similarity value as possible as we can and stop when the condition is not satisfied. Because the convergence of our algorithm is very fast in the process of merging, we find that our method has advantages both in the computational complexity and in the accuracy when compared with other six classical algorithms. Moreover, we design a new measure to describe how difficulty the network division is.

Comparing euclidean distance and nearest neighbor algorithm in an expert system for diagnosis of diabetes mellitus

Increasing sugar levels in the body which exceeds the normal limit is a metabolic disease commonly called diabetes mellitus. Long-term diabetes mellitus is one of the causes of other diseases such as liver, heart and other body organs. Early diagnosis of diabetes mellitus in a person is very important to know earlier. Early diagnosis is made to prevent other diseases to reduce the occurrence of complications in the body. The use of existing cases can be compared to new cases to diagnose whether the patient has diabetes. One method that can be used is a case-based expert system which is a reasoning system that uses old knowledge to be compared with new knowledge to overcome new problems. This case-based expert system provides a solution based on the similarity of new cases to existing cases. Some methods that can be used to do the similarity process are Euclidean distance and Nearest neighbor. Old cases taken are cases that have the highest similarity value. Result of the similarity value of a case is considered unsuccessful if it is diagnosed or the target case is <80, then the new case will be revised by the expert. The test results show that the system is able to recognize diabetes mellitus using the nearest neighbor similarity method, and Euclidean distance similarity with the calculation of accuracy using the euclidean distance similarity method is 93.33% and the nearest neighbor similarity method of 86.67%, So that the euclidean distance method is more effective because it has a higher accuracy value than the nearest neighbor method.

Bayesian correlation is a robust gene similarity measure for single-cell RNA-seq data.

Assessing similarity is highly important for bioinformatics algorithms to determine correlations between biological information. A common problem is that similarity can appear by chance, particularly for low expressed entities. This is especially relevant in single-cell RNA-seq (scRNA-seq) data because read counts are much lower compared to bulk RNA-seq. Recently, a Bayesian correlation scheme that assigns low similarity to genes that have low confidence expression estimates has been proposed to assess similarity for bulk RNA-seq. Our goal is to extend the properties of the Bayesian correlation in scRNA-seq data by considering three ways to compute similarity. First, we compute the similarity of pairs of genes over all cells. Second, we identify specific cell populations and compute the correlation in those populations. Third, we compute the similarity of pairs of genes over all clusters, by considering the total mRNA expression. We demonstrate that Bayesian correlations are more reproducible than Pearson correlations. Compared to Pearson correlations, Bayesian correlations have a smaller dependence on the number of input cells. We show that the Bayesian correlation algorithm assigns high similarity values to genes with a biological relevance in a specific population. We conclude that Bayesian correlation is a robust similarity measure in scRNA-seq data.

Clustering Sparse Data With Feature Correlation With Application to Discover Subtypes in Cancer.

In this paper, given data with high-dimensional features, we study this problem of how to calculate the similarity between two samples by considering feature interaction network, where a feature interaction network represents the relationship between features. This is different from some traditional methods, those of which learn similarities based on a sample network that represents the relationship between samples. Therefore, we propose a novel network-based similarity metric for computing the similarity between samples, which incorporates the knowledge of feature interaction network, in order to overcome the data sparseness problem. Our similarity metric uses a new Feature Alignment Similarity measure, which does not directly compute the similarities among samples, but projects each sample into a feature interaction network and measures the similarities between two samples using the similarities between the vertices of the samples in the network. As such, when two samples do not share any common features, they are likely to have higher similarity values when their features share the similar network regions. For ensuring that the metric is useful in a real-world application, we apply our metric to discover subtypes in tumor mutational data by incorporating the information of the gene interaction network. Our experimental results from using synthetic data and real-world tumor mutational data show that our approach outperforms the top competitors in cancer subtype discovery. Furthermore, our approach can identify cancer subtypes that cannot be detected by other clustering algorithms in real cancer data.

The importance of Serra do Mar State Park for liverworts conservation in the Atlantic Rainforest

Abstract Serra do Mar State Park (PESM) is located in southeastern São Paulo state, Brazil, and is the largest Atlantic Rainforest conservation area in the country. The park is divided into 10 nuclei, and the Itutinga-Pilões nucleus (NIP) was selected for study since no survey of liverworts species richness had yet been conducted there. The floristic similarities between the NIP and different areas of Atlantic Rainforest in São Paulo state were also assessed. One hundred and eighty liverworts species were identified in the three vegetation types (lowland, submontane, and montane ombrophilous forests) encountered in the NIP, distributed among 62 genera and 21 families. Lejeuneaceae showed the highest species richness with 80 species. The high number of epiphyllous species found in the study area (26% of all species) was notable. In terms of their worldwide distributions, 57% of the species are Neotropical. Clustering analysis showed that the areas of the PESM and Jureia-Itatins Ecological Station were grouped together with the highest similarity values. The liverworts flora of the NIP demonstrates the importance of that nucleus for the conservation of liverworts diversity in the Atlantic Rainforest of the São Paulo state, as well as in Brazil.

Bioinformatics Analysis Identifies Potential Key Genes of Peripheral Blood Mononuclear Cell in Idiopathic Pulmonary Fibrosis

Idiopathic pulmonary fibrosis (IPF) is a chronic progressive fibrotic interstitial pneumonia with progressive worsening of dyspnea and lung function. The etiology of IPF is unknown, and the pathogenesis remains unclear. Our study aimed to investigate the key genes of the peripheral blood mononuclear cell in IPF by bioinformatics analysis. Our study used the online Gene Expression Omnibus (GEO) microarray expression profiling dataset GSE28042 to identify differentially expressed genes (DEGs) between IPF patients and healthy controls. We performed the Gene Ontology (GO) and pathway enrichment analyses of genes for annotation, visualization, and integrated discovery. The STRING database constructed Protein-protein interaction (PPI) network analysis, and hub genes were identified by the CytoHubba plugin. Moreover, we used the receiver operating characteristic (ROC) curve to assess the diagnostic value of the hub genes. In total, 28 upregulated and 44 downregulated genes were identified in the differential expression analysis. The protein-protein interaction network (PPI) was established with 69 nodes and 68 edges. The top 10 hub genes were JUN, FOS, STAT3, SOCS3, JUNB, DUSP1, IL4, FCER1A, MS4A2, and CPA3. Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways enriched for the important module containing hub genes contained Fc epsilon RI signaling pathway, TNF signaling pathway, Jak-STAT signaling pathway, and MAPK signaling pathway. Additionally, the identified hub genes show high functional similarity and diagnostic value in IPF. Our study used bioinformatics analysis to provide new insight into the mechanisms underlying IPF. However, more experiments are needed to explore the relationships between the top 10 hub genes and IPF in the future.

Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering for Genomic Predictive Pattern Analytics

Gene expression data clustering is a significant problem to be resolved as it provides functional relationships of genes in a biological process. Finding co-expressed groups of genes is a challenging problem. To identify interesting patterns from the given gene expression data set, a Tanimoto Coefficient Similarity based Mean Shift Gentle Adaptive Boosted Clustering (TCS-MSGABC) Model is proposed. TCS-MSGABC model comprises two processes namely feature selection and clustering. In first process, Tanimoto Coefficient Similarity Measurement based Feature selection (TCSM-FS) is introduced to identify relevant gene features based on the similarity value for performing the genomic expression clustering. Tanimoto Coefficient Similarity Value ranges from ‘ ’ to ‘ ’ where ‘ ’ is highest similarity. The gene feature with higher similarity value is taken to perform clustering process. After feature selection, Mean Shift Gentle Adaptive Boosted Clustering (MSGABC) algorithm is carried out in TCS-MSGABC model to cluster the similar gene expression data based on the selected features. The MSGABC algorithm is a boosting method for combining the many weak clustering results into one strong learner. By this way, the similar gene expression data are clustered with higher accuracy with minimal time. Experimental evaluation of TCS-MSGABC model is carried out on factors such as clustering accuracy, clustering time and error rate with respect to number of gene data. The experimental results show that the TCS-MSGABC model is able to increases the clustering accuracy and also minimizes clustering time of genomic predictive pattern analytics as compared to state-of-the-art works.

Ammonite biostratigraphy and bioevents in the Jurassic – Cretaceous boundary of central Chile

Ammonites are abundant and well preserved in the Tithonian-Valanginian (Upper Jurassic – Lower Cretaceous) sedimentary successions of the central Chilean Andes, known as the Lo Valdés and Baños del Flaco Formations. A total of 1429 ammonites were collected from these stratigraphic units and the following index zones based on ammonites are here proposed, from base to top: Virgatosphinctes mexicanus/Pseudolissoceras cf. zitteli, Windhauseniceras internispinosum, Micracanthoceras microcanthum/Corongoceras alternans, Substeueroceras koeneni (Berriasella fraudans and Groebericeras rocardi sub-zones) and Thurmanniceras thurmanni/Argentiniceras fasciculatum.Our analyses indicate that “Ammonite Richness” is high in the middle and upper Tithonian and into the lower Berriasian part of the succession, but decreases towards the lower Valanginian. Highest similarity values are established for the upper Tithonian, coincident with a high diversity; similarity declines in the lower Berriasian. The main faunal turnover occurred between the early and late Berriasian followed by a secondary bioevent between the late Berriasian and early Valanginian.

Constructing a Virtual Space for Enhancing the Classification Performance of Fuzzy Clustering

Clustering offers a general methodology and comes with a remarkably rich conceptual and algorithmic framework for data analysis and data interpretation. As one of the most representative algorithms of fuzzy clustering, fuzzy C-means (FCM) is a widely used objective function-based clustering method exploited in various applications. In this study, a virtual-based fuzzy clustering algorithm is proposed to improve the classification performance coming as a result of using fuzzy clustering. This improvement is achieved by forming a virtual space based on the original data space. First, we construct a piecewise linear transformation function to modify the similarity matrix of the original data and build the so-called virtual similarity matrix (VSM). Considering the VSM, the effect of closeness becomes amplified; in other words, high similarity values (say, larger than α which is a cutoff value of the large and small similarity in this paper) present in the original similarity matrix are made higher, whereas lower similarity levels (say, smaller than α ) are further reduced. In addition, data with high similarity (say, larger than a certain threshold value) observed in the original space will overlap (the attributes of the samples are exactly the same) significantly in the virtual space; the overlapping samples can be treated as one sample. This modification makes possible easier to identify clusters. Second, we build a relationship matrix between the original dataset and the determined similarity values and present two closed-form solutions to the problem of building the relationship matrix. Subsequently, a virtual space of the original data space is derived through the modified similarity matrix and the introduced relationship matrix. We offer a thorough analysis behind the developed clustering algorithm. The experimental results are in agreement with the underlying conceptual basis. Furthermore, the resulting classification performance is significantly improved compared with the results produced by the FCM and the kernel-based fuzzy C-means.

Weissella cryptocerci sp. nov., isolated from gut of the insect Cryptocercus kyebangensis.

A taxonomic study of a Gram-stain-positive, rod-shaped, non-motile, non-spore-forming, catalase-negative bacterium, isolated from the gut of an insect, Cryptocercus kyebangensis collected from the mountainous area of Seoraksan, Yangyang-gun, Republic of Korea, was conducted. Its 16S rRNA gene sequence showed high similarity values to Weissella ghanensis LMG 24286T (95.9 %), Weissella beninensis 2L24P13T (95.9 %), Weissella fabalis M75T (95.7 %) and Weissella fabaria 257T (95.7 %). The phylogenetic tree indicated that the novel organism formed a cluster with W. ghanensis LMG 24286T, W. beninensis 2L24P13T, W. fabalis M75T and W. fabaria 257T. The G+C content was 41.1 mol% on the basis of the whole-genome sequence. Polar lipids consisted of diphosphatidylglycerol, phosphatidylglycerol, two unidentified aminophospholipids, one unidentified phospholipid and four unidentified lipids. The major cellular fatty acids were C18 : 1 ω9c, C16 : 0, C14 : 0, summed feature 8 (C18 : 1ω7c and/or C18 : 1ω6c) and summed feature 8 (C16 : 1 ω7c and/or C16 : 1 ω6c). The cell-wall peptidoglycan was of A4α type with the interpeptide bridge of Gly-d-Glu. Based on these results, strain 26KH-42T could be classified as a novel species of the genus Weissella, for which the name Weissellacryptocerci sp. nov. is proposed. The type strain is 26KH-42T (=KACC 18423T=NBRC 113066T).

Streptomyces dangxiongensis sp. nov., isolated from soil of Qinghai-Tibet Plateau.

A novel actinobacterial strain, designated Z022T, isolated from a soil sample collected from Dangxiong in Tibet Autonomous Region (PR China), was determined by polyphasic taxonomic approach. The organism had chemotaxonomic and morphological properties consistent with its classification in the genus Streptomyces. Strain Z022T showed high similarity value to Streptomyces lucensis NBRC 13056T (98.87 %) and S. achromogenes subsp. achromogenes NBRC 12735T (98.68 %) based on the 16S rRNA gene phylogenetic tree. The genomic DNA G+C content of strain Z022T based on the genome sequence was 72.16 mol%. DNA-DNA relatedness values between strain Z022T and strain Streptomyces lucensis NBRC 13056T was 23.7±1.3 % and significantly lower than 70 %. Chemotaxonomic data revealed that strain Z022T possessed MK-9(H8) and MK-9(H6) as the predominant menaquinone, ll-diaminopimelic acid as the diagnostic diamino acid, and galactose, glucose, xylose and ribose as whole cell sugars. Diphosphatidylglycerol (DPG) and phosphatidylethanolamine (PE) were the predominant polar lipids; anteiso-C15 : 0, iso-C16 : 0, and anteiso-C17 : 0 were the major fatty acids. On the basis of these genotypic and phenotypic data, it is proposed that isolate Z022T (=JCM 31053T=CGMCC 4.7273T) should be classified in the genus Streptomyces as Streptomyces dangxiongensis sp. nov.

Sürgü Çayı (Malatya, Türkiye)’nda Makrobentik Omurgasızların Tür Kompozisyonu ve Çeşitliliği

The study was carried out on Sürgü Stream, located in Malatya Province (Turkey), and aimed to determine the diversity and species composition of macrobenthic invertebrates. Six stations were determined on the stream and macrobenthic invertebrates were taken seasonally. As a result, 34 taxa belonging to five classes were detected; one of which belongs to Hirudinea, one of which belong to Arachnida, three to Gastropoda, five to Malacostraca and 24 to Insecta classes. Insecta was the most dominant group among the determined taxa. The Shannon Weaver Diversity Index and the Simpson Index showed that the lowest species diversity was observed at the third station while the highest species diversity was found at the second station. Also, UPGMA algorithm was used to show possible clustering relationships among the seven stations based on organisms. According to UPGMA analysis, the highest similarity value was observed between the first and second stations (87%), while the lowest similarity value was found between the second and fourth stations (13%). To date, there is no study on the determination of macrobenthic invertebrate fauna of Sürgü Stream. Therefore, all the identified taxa have been recorded for the first time for the study area.

Identification of diagnostic markers for major depressive disorder by cross-validation of data from whole blood samples

BackgroundMajor depressive disorder (MDD) is a severe disease characterized by multiple pathological changes. However, there are no reliable diagnostic biomarkers for MDD. The aim of the current study was to investigate the gene network and biomarkers underlying the pathophysiology of MDD.MethodsIn this study, we conducted a comprehensive analysis of the mRNA expression profile of MDD using data from Gene Expression Omnibus (GEO). The MDD dataset (GSE98793) with 128 MDD and 64 control whole blood samples was divided randomly into two non-overlapping groups for cross-validated differential gene expression analysis. The gene ontology (GO) enrichment and gene set enrichment analysis (GSEA) were performed for annotation, visualization, and integrated discovery. Protein–protein interaction (PPI) network was constructed by STRING database and hub genes were identified by the CytoHubba plugin. The gene expression difference and the functional similarity of hub genes were investigated for further gene expression and function exploration. Moreover, the receiver operating characteristic curve was performed to verify the diagnostic value of the hub genes.ResultsWe identified 761 differentially expressed genes closely related to MDD. The Venn diagram and GO analyses indicated that changes in MDD are mainly enriched in ribonucleoprotein complex biogenesis, antigen receptor-mediated signaling pathway, catalytic activity (acting on RNA), structural constituent of ribosome, mitochondrial matrix, and mitochondrial protein complex. The GSEA suggested that tumor necrosis factor signaling pathway, Toll-like receptor signaling pathway, apoptosis pathway, and NF-kappa B signaling pathway are all crucial in the development of MDD. A total of 20 hub genes were selected via the PPI network. Additionally, the identified hub genes were downregulated and show high functional similarity and diagnostic value in MDD.ConclusionsOur findings may provide novel insight into the functional characteristics of MDD through integrative analysis of GEO data, and suggest potential biomarkers and therapeutic targets for MDD.

Cased Based Reasoning to Identify Cause Conflicts in Marriage

The function of KUA in the activities surrounding the religion of Islam, including providing service and guidance in the area of present services in terms of marriage and reconcilement for Muslims, provide services and guidance in the field of development of Sakina, family consultation conflict or household problems, and so on. Integration between the computer and artificial intelligence into the post-wedding consulting services is one approach in overcoming the limitations of the expert (religious instructor).This research aims to identify conflict in marriage by applying Naive Bayes algorithm at the stage of determining the groups of test data (retrieve), then entered the stage of the search process of the highest similarity value by using the Nearest Neighbor algorithm (reuse). The data source and the test data used are divided into two groups, namely marriage, and history data consultation, While the group conflicts are identified will be divided into five classes, namely an employment factor, the factor of age, educational factors, factors the number of weddings, and social status.Testing is performed by the use of 12 data, consisting of 11 data cases and 1 test data. At the stage of determination of group conflict acquired test data included in group one i.e. F001 (factor of the job), so at the stage of looking for value similarities used only the base case of the class F001 i.e. KK001, KK003, and KK008. The KK001 similarity has a value of 0476, KK003 of 0882, and KK008 of 0142. The case with most high similarity value will be stored as a base case. If the value similarity obtained less than the threshold value that is 0.8, then the solution of the case will be revised by experts. The results of the calculation accuracy, using 35 new test data that gets the value of 82.86%.

Improving Arabic Text to Image Mapping Using a Robust Machine Learning Technique

In this paper, we introduce an approach to automatically convert simple modern standard Arabic children’s stories to the best representative images that can efficiently illustrate the meaning of words. It is a kind of imitating the imaginative process when children read a story, yet a great challenge for a machine to achieve it. For simplification issues, we apply several techniques to find the images and we associate them with related words dynamically. First, we apply natural language processing techniques to analyze the text in stories and we extract keywords of all characters and events in each sentence. Second, we apply an image captioning process through a pre-trained deep learning model for all retrieved images from our multimedia database as well as the Google search engine. Third, using sentence similarities, most significant images are retrieved back by selecting top- $k$ highest similarity values. It is worth mentioning that using the captioning process, to rank top- $k$ images, has shown reasonable precision values as per our preliminary results. The option to refine or validate the ranked images to compose the final visualization for each story is also provided to ensure a flexible and safe learning environment.

Refining the Measurement of Topic Similarities Through Bibliographic Coupling and LDA

Generally, two topics with vastly different terminology probably indicate different implied concepts. However, these topics themselves might share common references (bibliographic coupling), which suggest the underlying joint concept. Therefore, searching for these joint concepts in different topics would be of scientific interest. Previous studies have measured the similarity between topics based on comparison of the topics' word probability distributions. In contrast, this paper presents an approach for measuring the similarity between topics based on the bibliographic coupling. Besides, the similarity is independent of the topic's word probability distributions generated by a Latent Dirichlet Allocation (LDA) model. The proposed approach was evaluated using its counterpart (intra-topic similarity), baseline topic similarity matrices, and cosine measure. The method was exampled on brain cancer patents. A cross-topic similarity network of eight topics showcases 28 cross-topic pairs to profile which topics were associated with particular topics. Interestingly, some of the 28 combinations may be of scientific interest. For instance, the findings of the top five cross-topic pairs suggest that “growth of cancer cells” and “imbalances in the hormones” have common knowledge sources with the highest similarity value. These two entirely different concepts may suggest some common causative factors within the field. We believe that finding such an association between unrelated innovative inventions across various industries may help public and private research units in planning research direction and serve as a reference for future research.

Polychaete diversity patterns along an environmental gradient in Términos Lagoon, a tropical lagoon-estuarine system in the southern Gulf of Mexico

In this study we analyze the faunal changes along 2 transects (5 stations each) across Terminos Lagoon. We also examined the role of the environmental gradients in controlling the number and composition of the polychaete species, and evaluated the spatial changes of beta-diversity. In total, 299 individuals from 51 species and 24 families were identified. Salinity was the most important factor to define the environmental gradients ( r2 > 0.83), but the variations in the number of species did not follow this spatial trend. The highest species richness was found in the eastern transect (mean: 12 spp./station, 78.0 ind./0.1 m2), while the western transect harbored a less diverse (mean: 6 spp./station) and less abundant fauna (mean: 21.67 ind./0.1 m2). A high similarity value (0.70) was found between the transects, but the distribution of each species was very irregular, which in turn caused a decrease in the similarity among stations at each transect (< 0.54). Except among the middle stations of the western transect, where the nestedness component represented all the beta-diversity value (βnes: 0.80 to 0.88), the species turnover (βsim: 0.38-1.0) was the dominant factor of the total dissimilarity among the polychaetes inhabiting Terminos Lagoon.

Distribution and β-diversity of tree species in igapó forests (Negro River basin, Brazilian Amazon)

QUESTIONS: How are the tree species of the periodically flooded black water forest distributed along the Negro River basin (igapo)? Can we classify the tree communities into similarity groups? Are oligarchic species present? METHODS: We analysed available data on tree species composition in vegetation plots located in the Negro River basin (igapo forests) in Brazil and built a similarity matrix based on Sorensen's similarity index. We applied principal coordinates analysis, established community groups (a posteriori) and applied both multi‐response permutation procedures and indicator species analysis. RESULTS: Among 19 surveys conducted in igapo forests, at least 604 woody taxa were recognized, and 384 taxa were identified to species level. Igapo tree species were not as frequent or abundant as were other tree species of the Amazon upland forest, although they were widely distributed in other forest types. The Sorensen similarity index was low (Sₛ = 0.16). Generally, the highest similarity values were recorded between surveys that were spatially close. The multivariate analyses suggested the existence of a strong vegetation gradient along the Negro River. The two groups established a posteriori showed 25 species with an indicator value (IV) >50%, and 68 species presented IndValues in both groups. CONCLUSIONS: Our results show a strong turnover of tree species replacement along the course of the Negro River, with 57.5% of species restricted to one survey of occurrence. This trend is particularly notable from the point at which the Branco River strongly discharges into the Negro River. No species show high values for range distribution or local abundance, although four were considered moderately frequent. The upper parts of the main rivers have indicator species that are less frequent downstream.

How much should one sample to accurately predict the distribution of species assemblages? A virtual community approach

Correlative species distribution models (SDMs) are widely used to predict species distributions and assemblages, with many fundamental and applied uses. Different factors were shown to affect SDM prediction accuracy. However, real data cannot give unambiguous answers on these issues, and for this reason, artificial data have been increasingly used in recent years. Here, we move one step further by assessing how different factors can affect the prediction accuracy of virtual assemblages obtained by stacking individual SDM predictions (stacked SDMs, S-SDM). We modelled 100 virtual species in a real study area, testing five different factors: sample size (200-800-3200), sampling method (nested, non-nested), sampling prevalence (25%, 50%, 75% and species true prevalence), modelling technique (GAM, GLM, BRT and RF) and thresholding method (ROC, MaxTSS, and MaxKappa). We showed that the accuracy of S-SDM predictions is mostly affected by modelling technique followed by sample size. Models fitted by GAM/GLM had a higher accuracy and lower variance than BRT/RF. Model accuracy increased with sample size and a sampling strategy reflecting the true prevalence of the species was most successful. However, even with sample sizes as high as >3000 sites, residual uncertainty remained in the predictions, potentially reflecting a bias introduced by creating and/or resampling the virtual species. Therefore, when evaluating the accuracy of predictions from S-SDMs fitted with real field data, one can hardly expect reaching perfect accuracy, and reasonably high values of similarity or predictive success can already be seen as valuable predictions. We recommend the use of a ‘plot-like’ sampling method (best approximation of the species' true prevalence) and not simply increasing the number of presences-absences of species. As presented here, virtual simulations might be used more systematically in future studies to inform about the best accuracy level that one could expect given the characteristics of the data and the methods used to fit and stack SDMs.

Eco-geographic variation of common wild rice - Oryza ru?pogon Griff. in Sri Lanka

Wild species of rice (Oryza) have superior agronomic characteristics to be incorporated in rice breeding programs worldwide. Population studies of wild relatives of rice in Sri Lanka has not being well documented despite a few of attempts. In the present study, phenotypic diversity of Oryza rufipogon populations exist in Sri Lanka were characterized based on nine quantitative morphological traits. Populations (P1, P2, P3, P4 and P5) were established in a common-garden and were characterized. The results revealed moderate phenotypic diversity among O. rufipogon populations studied. However, flag leaf length and awn length were the most variable traits while plant height, flag leaf angle, flag leaf panicle neck length and spikelet angle were the least variable traits. O. rufipogon can be simply distinguished using flag leaf length and width, panicle branching type and distance from panicle base to lowest spikelet insertion. The dendrogram results indicated that four main clusters are at a similarity level of 98.73, showing the diversely related populations with a high identity based on higher similarity values. P1 and P2 populations grouped together by forming the first cluster. The second, third and fourth clusters consisted of P3, P5 and P4 populations, respectively. One population from first cluster and P3, P5 and P4 populations can be used for conservation. This study highlights the phenotypic diversity of O. rufipogon populations existing in Sri Lanka across the geographic locations and Knowledge on such morphological diversity provides opportunities to design conservation strategies and the potentials of using particular population based on breeding objectives.&#x0D;