Pairwise Similarity Research Articles

Esterase Profile in Distinguishing Inbreeds F1 And Backcross Hybrids in Indian Major Carps

Esterase was surveyed in four different tissues namely skeletal muscle, heart, liver and kidney obtained from two Indian major carps viz.., rohu (Labeo rohita) and catla (Catla catla) pair crossed F1 hybrids of catla (y) x rohu (6), and back crossed hybrid progenies of (catla x rohu) (?) x catla (J) (BC1C) and of (catla x rohu) (?) x rohu (6) (BC1R). The enzyme was found to be highly polymorphic in different tissues of the inbred as well as in hybrids. Detailed scanning of different activity zones in the zymograms revealed that amidst 15 bands, band numbers 4 and 12 of liver and kidney tissues were found to be common in both the inbreeds and hybrids were probably characteristics bands to these two species and their hybrid progenies. Band 4 of rohu and 6/7 of catla were diagnostic bands of pure rohu and catla, respectively, regardless of the nature of the tissue. However, both the bands were present in F 1 hybrid and BC1C backcross progenies only. Another backcross i.e. BC1R could not manifest shared bandings of the parents perhaps because of cleavage into or fusion with other sister esterase isomorphs through homo- or hetero-polymerization. Only band 4 was found to be present across all the tissues in BC 1R renders its demarcation difficult from rohu inbreed. However, band 9 and 13 of liver in BC1R can distinguishit from F1and inbreeds. They are likely to be instrumental in distinguishing inbreeds from hybrids reliably. The mean pair wise similarity (S) based on esterase profile, between the parents (catla and rohu) and the developed progenies (F, hybrids, BC1C and BC1R) showed BC1C was genetically more diverse than the F 1hybrids and BC1R for esterase which may be used in carp breeding and improvement programme if it is found to be tightly linked with any characters of economic importance.

Partner similarity andsocial cognitive traits predict social interaction success among strangers.

Social interactions are a ubiquitous part of engaging in the world around us, and determining what makes an interaction successful is necessary for social well-being. This study examined the separate contributions of individual social cognitive ability and partner similarity to social interaction success among strangers, measured by a cooperative communication task and self-reported interaction quality. Sixty participants engaged in a 1-h virtual social interaction with an unfamiliar partner (a laboratory confederate) including a 30-min cooperative 'mind-reading' game and then completed several individual tasks and surveys. They then underwent a separate functional MRI session in which they passively viewed video clips that varied in content. The neural responses to these videos were correlated with those of their confederate interaction partners to yield a measure of pairwise neural similarity. We found that trait empathy (assessed by the interpersonal reactivity index) and neural similarity to partner both predicted communication success in the mind-reading game. In contrast, perceived similarity to partner and (to a much lesser extent) trait mind-reading motivation predicted self-reported interaction quality. These results highlight the importance of sharing perspectives in successful communication as well as differences between neurobiological similarity and perceived similarity in supporting different types of interaction success.

Improving AlphaFold2-based protein tertiary structure prediction with MULTICOM in CASP15

Since the 14th Critical Assessment of Techniques for Protein Structure Prediction (CASP14), AlphaFold2 has become the standard method for protein tertiary structure prediction. One remaining challenge is to further improve its prediction. We developed a new version of the MULTICOM system to sample diverse multiple sequence alignments (MSAs) and structural templates to improve the input for AlphaFold2 to generate structural models. The models are then ranked by both the pairwise model similarity and AlphaFold2 self-reported model quality score. The top ranked models are refined by a novel structure alignment-based refinement method powered by Foldseek. Moreover, for a monomer target that is a subunit of a protein assembly (complex), MULTICOM integrates tertiary and quaternary structure predictions to account for tertiary structural changes induced by protein-protein interaction. The system participated in the tertiary structure prediction in 2022 CASP15 experiment. Our server predictor MULTICOM_refine ranked 3rd among 47 CASP15 server predictors and our human predictor MULTICOM ranked 7th among all 132 human and server predictors. The average GDT-TS score and TM-score of the first structural models that MULTICOM_refine predicted for 94 CASP15 domains are ~0.80 and ~0.92, 9.6% and 8.2% higher than ~0.73 and 0.85 of the standard AlphaFold2 predictor respectively.

Quantifying Pairwise Similarity for Complex Polymers

Quantifying Pairwise Similarity for Complex Polymers

Pre-Trained Semantic Embeddings for POI Categories Based on Multiple Contexts

The past decade has witnessed the increasingly created point-of-interest (POI) data, which are utilized to express the semantics of places. To understand the POI semantics, current methods usually embed POI categories into a latent space via certain trajectory sequential models, while neglecting the underlining spatial information. It is noteworthy that the complex spatial relationships among POI categories contain substantial information that benefits meaningful semantic embeddings for various POI categories. Inspired by this, we present a unified POI Category Embedding Method (CatEM for short), which jointly encodes the sequential transitions and spatial relations of POI categories as well as the adaptive semantic neighbors of each POI category. The merits of CatEM lie in that: (1) it considers the pairwise spatial similarities between categories and represents categories with larger similarity values with adjacent embeddings in the latent space, and (2) it adaptively locates neighbor categories with similar semantics in the embedding space to improve the adaptivity of POI category embedding. The proposed pre-trained POI category embeddings are justified by three downstream tasks. Extensive experiments demonstrate the superiority of our proposed model, as compared to several cutting-edge baselines.

Self-supervised deep learning for highly efficient spatial immunophenotyping

Efficient biomarker discovery and clinical translation depend on the fast and accurate analytical output from crucial technologies such as multiplex imaging. However, reliable cell classification often requires extensive annotations. Label-efficient strategies are urgently needed to reveal diverse cell distribution and spatial interactions in large-scale multiplex datasets. This study proposed Self-supervised Learning for Antigen Detection (SANDI) for accurate cell phenotyping while mitigating the annotation burden. The model first learns intrinsic pairwise similarities in unlabelled cell images, followed by a classification step to map learnt features to cell labels using a small set of annotated references. We acquired four multiplex immunohistochemistry datasets and one imaging mass cytometry dataset, comprising 2825 to 15,258 single-cell images to train and test the model. With 1% annotations (18-114cells), SANDI achieved weighted F1-scores ranging from 0.82 to 0.98 across the five datasets, which was comparable to the fully supervised classifier trained on 1828-11,459 annotated cells (-0.002 to-0.053 of averaged weighted F1-score, Wilcoxon rank-sum test, P=0.31). Leveraging the immune checkpoint markers stained in ovarian cancer slides, SANDI-based cell identification reveals spatial expulsion between PD1-expressing T helper cells and T regulatory cells, suggesting an interplay between PD1 expression and T regulatory cell-mediated immunosuppression. By striking a fine balance between minimal expert guidance and the power of deep learning to learn similarity within abundant data, SANDI presents new opportunities for efficient, large-scale learning for histology multiplex imaging data. This study was funded by the Royal Marsden/ICR National Institute of Health Research Biomedical Research Centre.

Semisupervised Defect Segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels

Semisupervised Defect Segmentation With Pairwise Similarity Map Consistency and Ensemble-Based Cross Pseudolabels

Large-scale weighted sequence alignment for the study of intertextuality in Finnic oral folk poetry

The digitization of large archival collections of oral folk poetry in Finland and Estonia has opened possibilities for large-scale quantitative studies of intertextuality. As an initial methodological step in this direction, I present a method for pairwise line-by-line comparison of poems using the weighted sequence alignment algorithm (a.k.a. ‘weighted edit distance’). The main contribution of the paper is a novel description of the algorithm in terms of matrix operations, which allows for much faster alignment of a poem against the entire corpus by utilizing modern numeric libraries and GPU capabilities. This way we are able to compute pairwise alignment scores between all pairs from among a corpus of over 280,000 poems. The resulting table of over 40 million pairwise poem similarities can be used in various ways to study the oral tradition. Some starting points for such research are sketched in the latter part of the article.

Local labor market and the cross section of stock returns

Local labor market pooling is a crucial benefit in the economics of agglomeration (Marshall, 1890). To capture the pooling function, we employ segment information and occupation statistics to capture average pairwise labor force similarities for each focal firm. We find that firms in thicker local labor markets are underpriced and display significantly positive stock returns afterwards. The return predictability of local labor market measure is concentrated in non-mega cap firms; high-tech firms; younger firms; and firms with more high-skilled labor, less routine tasks, and lower labor intensity. Our results are robust to a battery of control variables and alternative measure specifications.

Cascade Graph Neural Networks for Few-Shot Learning on Point Clouds

Point cloud data, a flexible 3D object representation, is critical for various applications such as autonomous driving, robotics and remote sensing. Despite the recent success of deep neural networks (DNNs) on supervised point cloud analysis tasks, they still rely on tedious manual annotation of point clouds and cannot make predictions for new classes. Unlike few-shot learning for 2D images with the advantages of large-scale datasets and high-quality deep pre-trained models like ResNet, for 3D few-shot learning, obtaining discriminative representations of unseen classes with high intra-class similarity and inter-class difference is very challenging. To address this issue, this work proposes a novel cascade graph neural network for few-shot learning on point clouds, termed as CGNN, in which two cascade GNNs are adopted to extract the intra-object topological information and learn the inter-object relations respectively. To further increase the discriminability of point cloud features, we first design a novel discriminative edge label to model the intra-class similarity and inter-class dissimilarity based on channel-wise feature variance and class consistency. Second, we propose a novel few-shot circle loss which classifies the nodes into two subsets, i.e., support to support pairs and support to query pairs, and optimizes the pair-wise similarity on two subsets independently. Extensive experiments on benchmark CAD and real LiDAR point cloud datasets have demonstrated that CGNN improves accuracy by <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$5.98\%$</tex-math> </inline-formula> over the state-of-the-art GNN-based few-shot classification methods.

Boosting Few-Shot Fine-Grained Recognition With Background Suppression and Foreground Alignment

Few-shot fine-grained recognition (FS-FGR) aims to recognize novel fine-grained categories with the help of limited available samples. Undoubtedly, this task inherits the main challenges from both few-shot learning and fine-grained recognition. First, the lack of labeled samples makes the learned model easy to overfit. Second, it also suffers from high intra-class variance and low inter-class differences in the datasets. To address this challenging task, we propose a two-stage background suppression and foreground alignment framework, which is composed of a background activation suppression (BAS) module, a foreground object alignment (FOA) module, and a local-to-local (L2L) similarity metric. Specifically, the BAS is introduced to generate a foreground mask for localization to weaken background disturbance and enhance dominative foreground objects. The FOA then reconstructs the feature map of each support sample according to its correction to the query ones, which addresses the problem of misalignment between support-query image pairs. To enable the proposed method to have the ability to capture subtle differences in confused samples, we present a novel L2L similarity metric to further measure the local similarity between a pair of aligned spatial features in the embedding space. What's more, considering that background interference brings poor robustness, we infer the pairwise similarity of feature maps using both the raw image and the refined image. Extensive experiments conducted on multiple popular fine-grained benchmarks demonstrate that our method outperforms the existing state of the art by a large margin. The source codes are available at: https://github.com/CSer-Tang-hao/BSFA-FSFG.

New protein families with hendecad coiled coils in the proteome of life

Coiled coils are a widespread and well understood protein fold. Their short and simple repeats underpin considerable structural and functional diversity. The vast majority of coiled coils consist of 7-residue (heptad) sequence repeats, but in essence most combinations of 3- and 4-residue segments, each starting with a residue of the hydrophobic core, are compatible with coiled-coil structure. The most frequent among these other repeat patterns are 11-residue (hendecad, 3 + 4 + 4) repeats. Hendecads are frequently found in low copy number, interspersed between heptads, but some proteins consist largely or entirely of hendecad repeats. Here we describe the first large-scale survey of these proteins in the proteome of life. For this, we scanned the protein sequence database for sequences with 11-residue periodicity that lacked β-strand prediction. We then clustered these by pairwise similarity to construct a map of potential hendecad coiled-coil families. Here we discuss these according to their structural properties, their potential cellular roles, and the evolutionary mechanisms shaping their diversity. We note in particular the continuous amplification of hendecads, both within existing proteins and de novo from previously non-coding sequence, as a powerful mechanism in the genesis of new coiled-coil forms.

The effect of chronic disturbance on the woody plant diversity in a tropical dry forest of Central Mexico

After deforestation, forest fragments can be impacted by chronic disturbances (e.g., small-scale constant wood extraction), which can alter the forest's richness and composition, favoring a few generalist species while forest specialist species decline. We tested the effect of chronic disturbance along a disturbance gradient in a tropical dry forest (TDF) of central Mexico, including mature, disturbed, and secondary forests. In the Río Apatlaco sub-basin, central Mexico, we randomly chose 73 fragments (23 mature, 25 disturbed, and 25 secondaries). Along a 0.1-ha transect within each fragment, we identified and measured the diameter at breast height of all plants (DBH ≥ 1 cm, height >1.30 m) and the number of dead trees (snags), stumps, trees with scars of machete/axe, and trees with carbonized trunks. We also quantified the number of cowpats within the transect. We calculated true diversity metrics and pairwise Morisita-Horn similarities. Disturbance diminished from the mature to the secondary forest, while diversity followed the opposite pattern. Along this gradient, distance to the nearest town directly increases plant diversities. An ordination based on floristic composition showed a mixture of forest types, mainly because the disturbed forest became split between secondary and mature forests. This divergence split can be explained because, in some disturbed forests, disturbance-prone pioneer species were favored, and these species were also common in secondary forests. In contrast, the most abundant species in some other disturbed forests are late-successional species. The data suggest that the main drivers of forest change are wood extraction and cattle ranching. Wood extraction generates gaps in the forest, and the cattle can then disperse seeds of pioneer species within these gaps.

DNA barcoding of recently diverging legume genera: Assessing the temperate Asian Caragana (Fabaceae: Papilionoideae)

AbstractObtaining reliable species identification of the legume genus Caragana has been challenging. Until now, species identification was mostly carried out utilizing diagnostic morphological characteristics, in addition to some successful applications of secondary chemical compounds. This study was designed to establish a DNA barcoding protocol enabling unambiguous identification of 238 accessions belonging to 67 species of Caragana. The performance of four DNA barcoding regions nrITS, trnH‐psbA, matK, and rbcL was explored using three analytical approaches, Pairwise Genetic Distance, Sequence Similarity and Phylogenetic Tree method. The chloroplast regions rbcL and matK showed lower discriminatory power compared with the nuclear region internal transcribed spacer (ITS) and the chloroplast region trnH‐psbA. The nrITS outperformed the other regions in the resolution rate. The present study brings forth an efficient barcode locus for Caragana. A barcode based either on a single‐locus nrITS or the combination of nrITS and trnH‐psbA was found to be most suitable for species discrimination with distinctive barcoding gaps. An attempt has also been made to resolve taxonomic issues in the Caragana opulens complex. DNA barcoding tools when complemented with alpha taxonomic evidence can aid in solving complex systematic problems, especially when taxa are characterized by overlapping traits, such as species belonging to the Fabaceae family.

A multi-view subspace representation learning approach powered by subspace transformation relationship

Traditional multi-view subspace representation learning typically captures the pairwise similarity among data objects but neglects the underlying relationship among subspace representations of different views, resulting in the essential structural information of multi-view data not being fully exploited. In this study, a multi-view representation learning approach is proposed using subspace transformation relationships. First, a subspace transformation relationship is defined to extract the underlying subspace relationship information. The transformation relationship is constrained by a low-rank term to enhance the consistency of the relationships and is used to construct a multi-view subspace representation model. Second, an optimization method is proposed for the multi-view subspace representation model using the ALM-ADM strategy. Third, a novel multi-view subspace representation learning algorithm is presented using the model and optimization method. Consequently, experimental findings on benchmark datasets indicated that the algorithm outperformed the state-of-the-art algorithm, benefitting from extracting subspace transformation relationships to guarantee high-order correlation information from multi-view data.

Comparison of k-mer-based de novo comparative metagenomic tools and approaches.

Aim: Comparative metagenomic analysis requires measuring a pairwise similarity between metagenomes in the dataset. Reference-based methods that compute a beta-diversity distance between two metagenomes are highly dependent on the quality and completeness of the reference database, and their application on less studied microbiota can be challenging. On the other hand, de-novo comparative metagenomic methods only rely on the sequence composition of metagenomes to compare datasets. While each one of these approaches has its strengths and limitations, their comparison is currently limited. Methods: We developed sets of simulated short-reads metagenomes to (1) compare k-mer-based and taxonomy-based distances and evaluate the impact of technical and biological variables on these metrics and (2) evaluate the effect of k-mer sketching and filtering. We used a real-world metagenomic dataset to provide an overview of the currently available tools for de novo metagenomic comparative analysis. Results: Using simulated metagenomes of known composition and controlled error rate, we showed that k-mer-based distance metrics were well correlated to the taxonomic distance metric for quantitative Beta-diversity metrics, but the correlation was low for presence/absence distances. The community complexity in terms of taxa richness and the sequencing depth significantly affected the quality of the k-mer-based distances, while the impact of low amounts of sequence contamination and sequencing error was limited. Finally, we benchmarked currently available de-novo comparative metagenomic tools and compared their output on two datasets of fecal metagenomes and showed that most k-mer-based tools were able to recapitulate the data structure observed using taxonomic approaches. Conclusion: This study expands our understanding of the strength and limitations of k-mer-based de novo comparative metagenomic approaches and aims to provide concrete guidelines for researchers interested in applying these approaches to their metagenomic datasets.

Interpreting phylogenetic placements for taxonomic assignment of environmental DNA.

Taxonomic assignment of operational taxonomic units (OTUs) is an important bioinformatics step in analyzing environmental sequencing data. Pairwise alignment and phylogenetic-placement methods represent two alternative approaches to taxonomic assignments, but their results can differ. Here we used available colpodean ciliate OTUs from forest soils to compare the taxonomic assignments of VSEARCH (which performs pairwise alignments) and EPA-ng (which performs phylogenetic placements). We showed that when there are differences in taxonomic assignments between pairwise alignments and phylogenetic placements at the subtaxon level, there is a low pairwise similarity of the OTUs to the reference database. We then showcase how the output of EPA-ng can be further evaluated using GAPPA to assess the taxonomic assignments when there exist multiple equally likely placements of an OTU, by taking into account the sum over the likelihood weights of the OTU placements within a subtaxon, and the branch distances between equally likely placement locations. We also inferred the evolutionary and ecological characteristics of the colpodean OTUs using their placements within subtaxa. This study demonstrates how to fully analyze the output of EPA-ng, by using GAPPA in conjunction with knowledge of the taxonomic diversity of the clade of interest.

Tensorized topological graph learning for generalized incomplete multi-view clustering

The success of the current multi-view clustering lies in the default assumption of completeness on each view, while it is hardly satisfied for real-world applications. Incomplete multi-view clustering (IMC) studies clustering multi-view instances that are partially observed due to data corruption or sensor failure. Although making some progress, the current research suffers from the following obstacles: (1) Existing works are always built on pairwise similarity measurement, which cannot fully capture the topological structure of incomplete multi-view samples for precise clustering; (2) Due to the absence of partially-observed samples across multiple views, how could we recover and enhance the high-order similarities across different views becomes one of the main bottlenecks of IMC; (3) Current research mainly aims to handle specific dual-view IMC, which greatly limits the deployment in real-world applications. In this paper, we propose a novel Tensorized Topological Graph Learning (TTGL) for the generalized incomplete multi-view clustering, which jointly considers the topological graph construction, missing feature completion, and high-order uncertain correlation enhancement. Specifically, instead of using pairwise similarity measurement, we formulate a consensus topological graph construction module, which can coalesce affinity matrices to analyze the topological structure of incomplete multi-view data. Moreover, we build an iterative missing similarity imputation scheme for feature completion, which ensures the intrinsic similarity preservation on observed instances as well as an imputation on unobserved ones. Furthermore, a low-rank tensor structure is imposed to capture the high-order similarities via both feature and similarity enhancement and completion across different views. Our method can cluster incomplete data with an arbitrary number of views and any missing statuses. Extensive experiments on several benchmark datasets validate the effectiveness of our method when compared with a series of state-of-the-art IMC clustering algorithms. The source code of our TTGL is available at: https://github.com/DarrenZZhang/TTGL.

Rhizophagus irregularis, the model fungus in arbuscular mycorrhiza research, forms dimorphic spores.

Rhizophagus irregularis is the model species for arbuscular mycorrhizal fungi (AMF) research and the most widely propagated species for commercial plant biostimulants. Using asymbiotic and symbiotic cultivation systems initiated from single spores, advanced microscopy, Sanger sequencing of the glomalin gene, and PacBio sequencing of the partial 45S rRNA gene, we show that four strains of R. irregularis produce spores of two distinct morphotypes, one corresponding to the morphotype described in the R. irregularis protologue and the other having the phenotype of R. fasciculatus. The two spore morphs are easily distinguished by spore colour, thickness of the subtending hypha, thickness of the second wall layer, lamination of the innermost layer, and the dextrinoid reaction of the two outer spore wall layers to Melzer's reagent. The glomalin gene of the two spore morphs is identical and that of the PacBio sequences of the partial SSU-ITS-LSU region (2780 bp) obtained from single spores of the R. cf fasciculatus morphotype has a median pairwise similarity of 99.8% (SD = 0.005%) to the rDNA ribotypes of R. irregularis DAOM 197198. Based on these results, we conclude that the model AMF species R. irregularis is dimorphic, which has caused taxonomic confusion in culture collections and possibly in AMF research.

English

Knowledge of the genetic variability of plantain cultivars in the Democratic Republic of the Congo (DRC) is limited. This study assessed genetic diversity and relationships in three plantain accessions differing in pseudostem color at seven microsatellite loci using SSR markers. DNA was extracted from banana leaves, and PCR was performed with unlabeled and fluorescent-labeled SSR primers and eventual capillary sequencing. Different population genetic analysis software was used to interpret the fragment analysis data. There was a high allelic power with a mean polymorphic information content of 0.83. A high average genetic diversity was indicated by Nei&#39;s Gene Diversity Index (h: 1.38) and the Shannon Information Index (I: 2.22). Pairwise genetic similarity between plantain genotypes averaged 0.82. A dendrogram grouped the 60 genotypes into three clusters. AMOVA showed high genetic variation within the population (98%) compared to the variation between populations, with no significant difference between the three cultivars regarding the color of the pseudostem. In contrast, these cultivars were closely related to each other. No correlation between the morphological and molecular characterization of cultivars was observed. We recommend a follow-up study with other plantain cultivars from other regions and large sample size for more representative data. &nbsp; Key words: Characterization, microsatellites, genetic diversity, plantain cultivars.