Pairwise Distribution Research Articles

SnapFISH-IMPUTE: an imputation method for multiplexed DNA FISH data

Chromatin spatial organization plays a crucial role in gene regulation. Recently developed and prospering multiplexed DNA FISH technologies enable direct visualization of chromatin conformation in the nucleus. However, incomplete data caused by limited detection efficiency can substantially complicate and impair downstream analysis. Here, we present SnapFISH-IMPUTE that imputes missing values in multiplexed DNA FISH data. Analysis on multiple published datasets shows that the proposed method preserves the distribution of pairwise distances between imaging loci, and the imputed chromatin conformations are indistinguishable from the observed conformations. Additionally, imputation greatly improves downstream analyses such as identifying enhancer-promoter loops and clustering cells into distinct cell types. SnapFISH-IMPUTE is freely available at https://github.com/hyuyu104/SnapFISH-IMPUTE.

GALEON: a comprehensive bioinformatic tool to analyse and visualize gene clusters in complete genomes.

Gene clusters, defined as a set of genes encoding functionally related proteins, are abundant in eukaryotic genomes. Despite the increasing availability of chromosome-level genomes, the comprehensive analysis of gene family evolution remains largely unexplored, particularly for large and highly dynamic gene families or those including very recent family members. These challenges stem from limitations in genome assembly contiguity, particularly in repetitive regions such as large gene clusters. Recent advancements in sequencing technology, such as long reads and chromatin contact mapping, hold promise in addressing these challenges. To facilitate the identification, analysis, and visualization of physically clustered gene family members within chromosome-level genomes, we introduce GALEON, a user-friendly bioinformatic tool. GALEON identifies gene clusters by studying the spatial distribution of pairwise physical distances among gene family members along with the genome-wide gene density. The pipeline also enables the simultaneous analysis and comparison of two gene families and allows the exploration of the relationship between physical and evolutionary distances. This tool offers a novel approach for studying the origin and evolution of gene families. GALEON is freely available from https://www.ub.edu/softevol/galeon and https://github.com/molevol-ub/galeon.

Molecular dynamics simulation study of sodium ion structure & dynamics in water in ionic liquids electrolytes using 1-butyl-3-methyl imidazolium tetrafluoroborate and 1-butyl-3-methyl imidazolium hexafluorophosphate

In this paper, we have performed an all-atom molecular dynamics simulation to understand the structure and dynamics of Na+ ions in water mixed Ionic liquids (Water in Ionic liquid). Two ionic liquid (IL) systems consist of (1) 1-butyl-3-methylimidazolium [BMIM] tetrafluoroborate [BF4] and (2) 1-butyl-3-methylimidazolium [BMIM] hexafluorophosphate [PF6] were considered in this work. We understand various inter-molecular structures and dynamic and thermodynamic behaviours of Na+ ions in the water-mixed IL systems. The water (H2O) mole fractions (x) varied from 0.33 to 0.71. The neat ILs [BMIM][BF4] and [BMIM][PF6] pairwise radial distribution functions show a decrease with an increase in x. The [BMIM][PF6] exhibits a strong coordination structure with Na+ ions across the entire range of x values. The rdf between the pairs of Na+-[PF6] presents a significant interaction compared to Na+ and [BF4]. The Na + ions manifested greater coordination with H2O In H2O-[BMIM][PF6] compared to H2O-[BMIM][BF4]. The self-diffusion coefficient (D) values of Na + ions increase with the rise in x in both ILs. The D values of Na + ions are 10-fold higher in [BMIM][BF4] than [BMIM][PF6]. The ionic conductivity values are higher for [BMIM][BF4]. Overall, this paper unveils molecular-level insights for understanding the behavior of Na+ ions in the water in ionic liquid systems.

Adversarial pair-wise distribution matching for remote sensing image cross-scene classification

Remarkable achievements have been made in the field of remote sensing cross-scene classification in recent years. However, most methods directly align the entire image features for cross-scene knowledge transfer. They usually ignore the high background complexity and low category consistency of remote sensing images, which can significantly impair the performance of distribution alignment. Besides, shortcomings of the adversarial training paradigm and the inability to guarantee the prediction discriminability and diversity can also hinder cross-scene classification performance. To alleviate the above problems, we propose a novel cross-scene classification framework in a discriminator-free adversarial paradigm, called Adversarial Pair-wise Distribution Matching (APDM), to avoid irrelevant knowledge transfer and enable effective cross-domain modeling. Specifically, we propose the pair-wise cosine discrepancy for both inter-domain and intra-domain prediction measurements to fully leverage the prediction information, which can suppress negative semantic features and implicitly align the cross-scene distributions. Nuclear-norm maximization and minimization are introduced to enhance the target prediction quality and increase the applicability of the source knowledge, respectively. As a general cross-scene framework, APDM can be easily embedded with existing methods to boost the performance. Experimental results and analyses demonstrate that APDM can achieve competitive and effective performance on cross-scene classification tasks.

Quantifying the clusterness and trajectoriness of single-cell RNA-seq data.

Among existing computational algorithms for single-cell RNA-seq analysis, clustering and trajectory inference are two major types of analysis that are routinely applied. For a given dataset, clustering and trajectory inference can generate vastly different visualizations that lead to very different interpretations of the data. To address this issue, we propose multiple scores to quantify the "clusterness" and "trajectoriness" of single-cell RNA-seq data, in other words, whether the data looks like a collection of distinct clusters or a continuum of progression trajectory. The scores we introduce are based on pairwise distance distribution, persistent homology, vector magnitude, Ripley's K, and degrees of connectivity. Using simulated datasets, we demonstrate that the proposed scores are able to effectively differentiate between cluster-like data and trajectory-like data. Using real single-cell RNA-seq datasets, we demonstrate the scores can serve as indicators of whether clustering analysis or trajectory inference is a more appropriate choice for biological interpretation of the data.

A Measure of the DNA Barcode Gap for Applied and Basic Research.

DNA barcoding has largely established itself as a mainstay for rapid molecular taxonomic identification in both academic and applied research. The use of DNA barcoding as a molecular identification method depends on a "DNA barcode gap"-the separation between the maximum within-species difference and the minimum between-species difference. Previous work indicates the presence of a gap hinges on sampling effort for focal taxa and their close relatives. Furthermore, both theory and empirical work indicate a gap may not occur for related pairs of biological species. Here, we present a novel evaluation approach in the form of an easily calculated set of nonparametric metrics to quantify the extent of proportional overlap in inter- and intraspecific distributions of pairwise differences among target species and their conspecifics. The metrics are based on a simple count of the number of overlapping records for a species falling within the bounds of maximum intraspecific distance and minimum interspecific distance. Our approach takes advantage of the asymmetric directionality inherent in pairwise genetic distance distributions, which has not been previously done in the DNA barcoding literature. We apply the metrics to the predatory diving beetle genus Agabus as a case study because this group poses significant identification challenges due to its morphological uniformity despite both relative sampling ease and well-established taxonomy. Results herein show that target species and their nearest neighbor species were found to be tightly clustered and therefore difficult to distinguish. Such findings demonstrate that DNA barcoding can fail to fully resolve species in certain cases. Moving forward, we suggest the implementation of the proposed metrics be integrated into a common framework to be reported in any study that uses DNA barcoding for identification. In so doing, the importance of the DNA barcode gap and its components for the success of DNA-based identification using DNA barcodes can be better appreciated.

Spatial Transcriptomics Analysis of Vulnerable Cell Populations in Middle Temporal Gyrus

AbstractBackgroundAlzheimer’s disease (AD) is characterized by cognitive decline due to histopathological changes of neurodegeneration in a stereotypical neuroanatomical progression. Applying single‐nucleus ‘omics methods has extended our understanding of how AD impacts the brain and allowed the Seattle AD Brain Cell Atlas Center (SEA‐AD) to provide a detailed cellular‐molecular view of middle temporal gyrus (MTG) in AD. Previously, we identified transcriptomically‐defined cell types that are specifically affected by AD in MTG, however, cell types identified in ‘omics data lack spatial context. Identifying the location of these cell types in situ is necessary to pinpoint the location of affected cells and connect cellular‐molecular views of disease progression to histopathology. To accomplish this, spatial transcriptomics methods measure individual mRNA molecule locations with subcellular resolution and provide transcriptomic profiles of individual cells in situ. MethodUsing a panel of genes designed to identify transcriptomic cell types, we performed spatial transcriptomics experiments using the Vizgen MERSCOPE platform across MTG tissue from 25 donors spanning AD pathology, yielding transcriptional profiles of over 1M cells across the cortical depth and white matter of 68 sections. These cells were assigned to transcriptomically‐defined cell types by integrating the spatial transcriptomics data with our existing single nucleus dataset. From the resulting maps of cell type locations, we measured cortical layer thickness, cell proportions, densities and cell type‐dependent pairwise distance distributions for each donor.ResultWe have successfully applied the MERSCOPE platform to generate cellular‐resolution spatial transcriptomic data in MTG across a wide range of AD disease progression. We applied novel deep generative models to assign cell type identity. These measurements revealed cell type‐specific spatial distributions and changes in cell type proportions due to disease, validating observations from single‐nucleus studies. Enhanced loss of Layer 2/3 excitatory neuron types and somatostatin‐expressing inhibitory neurons is calculating cell density within layers. Additional analyses uniquely available to spatial transcriptomics studies will be useful for quantitative description of cytoarchitectural changes in AD.ConclusionSpatial transcriptomics experiments across disease progression in MTG confirm cell type abundance changes measured in single‐nucleus RNASeq. The cellular spatial distributions provide detailed knowledge of the location of vulnerable cells within cortex.

Dual-Stream Transformer With Distribution Alignment for Visible-Infrared Person Re-Identification

Visible-infrared person re-identification(VI-ReID) aims to match the person images captured by visible and infrared cameras and suffers from severe cross-modality discrepancy and intra-modality variations. Existing approaches mainly use convolution neural network (CNN)-based architectures to extract pedestrian features, which fail to capture the long-range dependencies within an image. In addition, previous works usually attempt to bridge the modality gap by using adversarial learning to generate style-consistent images or designing different feature-level metric learning constraints. However, few works consider the cross-modality disparity from the perspective of assessing overall distance distribution discrepancy. To address these problems, we design a pure Transformer-based Visible-Infrared (TransVI) network with a conventional two-stream structure, which can explicitly capture modality-specific representations and learn multi-modality sharable knowledge. TransVI can efficiently address the lack of global dependency in CNN-based architectures due to the multi-head self-attention modules in the transformer, which allows us to capture the long-range dependencies of pedestrian images. Furthermore, we introduce the Cross-Modality Dissimilarity-based Maximum Mean Discrepancy (CMD-MMD) constraint to handle the cross-modality discrepancy at the distance distribution level. Specifically, CMD-MMD leverages intra-modality distribution separability to guide inter-modality distribution separability learning, aligning pair-wise distance distributions of intra- and inter-modality for within-class and between-class, respectively. In this way, the distance distributions of intra- and inter-modality become more similar, significantly mitigating the cross-modality discrepancy and learning more modality invariant representations. Extensive experimental results on two public VI-ReID datasets confirm that our proposed framework can achieve state-of-the-art performance.

Ideal dynamic threshold Multi-secret data sharing in smart environments for sustainable cities

Nowadays, with continuous integration of big data, artificial intelligence and cloud computing technologies, there are increasing demands and specific requirements for data sharing in sustainable smart cities: (1) practical data sharing should be implemented in the non-interactive fashion without a trusted third party to be involved; (2) dynamic thresholds are preferred since the participants may join or leave at any time; (3) multi-secret sharing is desirable to increase the packing capacity. To fulfil these requirements, we propose a general construction of ideal threshold changeable multi-secret sharing scheme (TCMSS) with information-theoretic security, in which polynomials are employed to achieve dealer-free and non-interactive in the secret reconstruction phase. The TCMSS scheme can be built on any existing linear secret sharing scheme, and it is simpler and more efficient than the existing TCSS schemes in the literature. The main difference between TCMSS and Shamir’s SS is that univariate polynomial is used in Shamir’s SS to generate the shares for all shareholders; while in TCMSS, each shareholder can recover her own univariate polynomial using her share. This article demonstrates that with this novel modification, the classic polynomial-based SS can be transformed into an ideal TCMSS. Moreover, the TCMSS scheme is lightweight and it can resist both internal and external attacks. It does not require pairwise key distribution and its secret reconstruction phase is improved with enhanced properties. Therefore, the designed proposal is fairly suitable and attractive to be deployed in sustainable cities.

Simple and efficient threshold changeable secret sharing

Shamir's (t, n) threshold secret sharing (SS) is one of the most important cryptographic primitives in various security applications designed to protect highly sensitive information, such as intellectual property and network security. A dealer in a (t, n) threshold SS divides the secret into n shares such that with t or more than t of these shares, the secret can be retrieved; but with less than t of these shares, no information of the secret can be revealed. In order to adapt the dynamic environment in various applications, the threshold changeable SS (TCSS) allows the threshold of the original secret to be dynamically adjusted. Note that Shamir's original SS is unable to achieve this property. In the literature, most existing TCSS schemes are suffering from limitations that either it requires a trusted dealer to generate and distribute new shares, or requires interactions among shareholders to generate new shares. In this paper, we propose a novel idea to construct a dealer-free and non-interactive TCSS based on pairwise keys between users. Research papers related to pairwise key distribution schemes have been published over last 40 years and it has become one of fundamental tools used in design of cryptographic solutions. First, we show that our idea can be used to construct a scheme for anonymously releasing data and a secure multiparty computation scheme. Both schemes are very simple and security analysis can be understood easily. Second, we demonstrate that a TCSS can also be designed based on same approach. Our design of TCSS is novel and it can be applied to convert any existing SS into a TCSS.

Temporal and coevolutionary analyses reveal the events driving the emergence and circulation of human mamastroviruses

ABSTRACT Characterized by high genetic diversity, broad host range, and resistance to adverse conditions, coupled with recent reports of neurotropic astroviruses circulating in humans, mamastroviruses pose a threat to public health. The current astrovirus classification system based on host source prevents determining whether strains with distinct tropism or virulence are emerging. By using integrated phylogeny, we propose a standardized demarcation of species and genotypes, with reproducible cut-off values that reconcile the pairwise sequence distribution, genetic distances between lineages, and the topological reconstruction of the Mamastrovirus genus. We further define the various links established by co-evolution and resolve the dynamics of transmission chains to identify host-jump events and the sources from which different mamastrovirus species circulating in humans have emerged. We observed that recombination is relatively infrequent and restricted to within genotypes. The well-known “human” astrovirus, defined here as mamastrovirus species 7, has co-speciated with humans, while there have been two additional host-jumps into humans from distinct hosts. Newly defined species 6 genotype 2, linked to severe gastroenteritis in children, resulted from a marmot to human jump taking place ∼200 years ago while species 6 genotype 7 (MastV-Sp6Gt7), linked to neurological disease in immunocompromised patients, jumped from bovines only ∼50 years ago. Through demographic reconstruction, we determined that the latter reached coalescent viral population growth only 20 years ago and is evolving at a much higher evolutionary rate than other genotypes infecting humans. This study constitutes mounting evidence of MastV-Sp6Gt7 active circulation and highlights the need for diagnostics capable of detecting it.

Dispersal of Common Shrews (Sorex Araneus L.): The Dream and “An Accident”

Understanding the processes that affect the dispersal distance is essential from perspective of ecology and evolution. It is essential to understand processes that affect dispersal distances. Dispersal distances can may depend on environmental and demographic factors and on the motivation of an individual. Effective dispersal results in the distribution of related genotypes in space. The distribution of pairwise distances between related common shrews (sibs and half-sibs) is characterized by a nonrandom increase in the number of relatives at distances up to 200 m. Aggregations of relatives are formed in a part of individuals dispersed in a random direction to the nearest available home rang (“stright-line search”). The distribution of all distances between relatives (up to 1200 m) is satisfactorily approximated by the straight-line search model and is not consistent with the “spiral search” model as it is; however, the best match can be achieved by combining these two search types. The latter model variant (“mixed search”) assumes that the population includes animals with different personal traits: “superficial” and “thorough” explorers. Thorough explorers search for a vacant territory employing the spiral search strategy and correspond to “dreamers” in the model describing the movement and habitat selection strategy (MHSS). If vacant territories are in deficit and the environment is favorable, dreamers move over long distances and become randomly distributed in space: a random dispersion of related genotypes was recorded at distances from 200 to 1200 m. Therefore, searches for a dream territory in combination with a shortage of vacant territories (an accident) result in a random dispersal of related genotypes within a radius of at least 1200 m. The combination of temporal aggregations of relatives and the dispersal of related genotypes over a vast area explain well the previously discovered combination of an excess of homozygous alleles and a high allelic diversity.

Velocity-dependent J-factors for Milky Way dwarf spheroidal analogues in cosmological simulations

We study the impact of the dark matter velocity distribution modelling on signals from velocity-dependent dark matter annihilation in Milky Way dwarf spheroidal galaxies. Using the high resolution APOSTLE simulations, we identify analogues corresponding to Milky Way dwarf spheroidal galaxies, and from these directly determine the dark matter pair-wise relative velocity distribution, and compare to best-fitting Maxwell-Boltzmann distribution models. For three velocity-dependent annihilation models, p-wave, d-wave, and the Sommerfeld model, we quantify the errors introduced when using the Maxwell-Boltzmann parameterization. We extract a simple power-law relation between the maximum circular velocity of the dwarf spheroidal analogue and the peak speed of the Maxwell-Boltzmann distribution. We show that this relation can be used to accurately calculate the dark matter relative velocity distribution, and find that it allows us to estimate the dark matter annihilation signal without the need to directly calculate the relative velocity distribution for each galaxy. The scatter in the J-factors calculated from the analogues dominates the uncertainty obtained when compared to the J-factor as determined from the observational data for each dwarf spheroidal, with the largest scatter from d-wave models and the smallest from Sommerfeld models.

A maximum-entropy model to predict 3D structural ensembles of chromatin from pairwise distances with applications to interphase chromosomes and structural variants

The principles that govern the organization of genomes, which are needed for an understanding of how chromosomes are packaged and function in eukaryotic cells, could be deciphered if the three-dimensional (3D) structures are known. Recently, single-cell imaging techniques have been developed to determine the 3D coordinates of genomic loci in vivo. Here, we introduce a computational method (Distance Matrix to Ensemble of Structures, DIMES), based on the maximum entropy principle, with experimental pairwise distances between loci as constraints, to generate a unique ensemble of 3D chromatin structures. Using the ensemble of structures, we quantitatively account for the distribution of pairwise distances, three-body co-localization, and higher-order interactions. The DIMES method can be applied to both small and chromosome-scale imaging data to quantify the extent of heterogeneity and fluctuations in the shapes across various length scales. We develop a perturbation method in conjunction with DIMES to predict the changes in 3D structures from structural variations. Our method also reveals quantitative differences between the 3D structures inferred from Hi-C and those measured in imaging experiments. Finally, the physical interpretation of the parameters extracted from DIMES provides insights into the origin of phase separation between euchromatin and heterochromatin domains.

Lightweight Pairwise Key Distribution Scheme for IoTs

Embedding a pairwise key distribution approach in IoT systems is challenging as IoT devices have limited resources, such as memory, processing power, and battery life. This paper presents a secure and lightweight approach that is applied to IoT devices that are divided into Voronoi clusters. This proposed algorithm comprises XOR and concatenation operations for interactive authentication between the server and the IoT devices. Predominantly, the authentication is carried out by the server. It is observed that the algorithm is resilient against man-in-the-middle attacks, forward secrecy, Denial of Service (DoS) attacks, and offers mutual authentication. It is also observed that the given scheme has low communication and computing overheads compared to some existing methods.

Genetic analyses and detection of point mutations in the acetylcholinesterase-1 gene associated with organophosphate insecticide resistance in fall armyworm (Spodoptera frugiperda) populations from Uganda

BackgroundThe fall armyworm (FAW), Spodoptera frugiperda; J.E. Smith (Lepidoptera: Noctuidae), is now an economically important pest that causes huge losses to maize productivity in sub-Saharan Africa. Variations in sub-population genetics and the processes of rapid adaptation underpinning the invasion remain unclear. For this, the genetic identity and diversity of FAW populations in Uganda were revealed by sequencing 87 samples (collected across the country). Based on the partial mitochondrial cytochrome oxidase I (COI) gene polymorphisms, we further examined the mitochondrial haplotype configuration and compared the FAW in Uganda with sequences from other parts of the world. The molecular target for organophosphate and carbamate resistance, acetylcholinesterase, was also investigated.ResultsAnalysis of the partial COI gene sequences showed the presence of both rice (predominant) and corn strain haplotypes, with a haplotype diversity of 0.382. Based on the COI marker, pairwise difference distribution analyses, and neutrality tests, showed that the FAW populations in Uganda and the rest of Africa are evolving neutrally, but those in America and Asia are undergoing expansion. Our findings support observations that invasive FAW populations throughout the rest of Africa and Asia share a common origin. Sequencing of the S. frugiperda ace-1 gene revealed four amino acid substitutions, two of which (A201S and F290V) were previously shown to confer organophosphate resistance in both S. frugiperda and several other insect species. The other two previously reported new variations in positions g-396 and g-768, are presumed to be related to the development of insecticide resistance.ConclusionsThis research has increased our knowledge of the genetics of FAW in Uganda, which is critical for pest surveillance and the detection of resistance. However, due to the low gene polymorphism of COI, more evolutionary studies incorporating the Spodoptera frugiperda whole-genome sequence are required to precisely understand the FAW population dynamics, introduction paths, origin, and subsequent spread.

Weakly Supervised Distribution Discrepancy Minimization Learning With State Information for Person Re-Identification

Weakly supervised person re-identification (Re-ID) is appealing to handle real-world tasks by using state information that is available without manual annotation. At present, most methods perform unsupervised cross domain (UCD) learning by transferring the knowledge from the labeled source domain to the unlabeled target domain, which results in poor performance due to the severe shift. To address this problem, in this paper, we utilize the tracklet and camera information as weak supervision to propose a distribution discrepancy minimization learning (DDML) model for UCD person Re-ID. In addition to aligning data distributions from the perspective of domain adaptation learning, two losses are developed from the view of neighborhood invariance exploration to optimize matching results. Specifically, to bridge the gap between domains, we propose a camera-distribution-based (CDB) loss to align pair-wise distance distributions. Furthermore, to alleviate the biased search within the target domain, we propose a ranking-confidence-based (RCB) loss to perform the mined neighborhood for intra-camera and inter-camera separately to explore a high degree of confidence neighbor relations. Extensive experiments on three challenging datasets demonstrate that applying our method to unlabeled target domain outperforms current weakly supervised methods for person Re-ID.

Quantitative analyses of the geospatial characteristics of haunted sites using open data

This study attempts to bridge geography and ghost research by using the geolocation of haunted sites online as well as open data. The spatial relationship of the geolocation of haunted sites with government open data is examined. Open geodata and geolocations of haunted sites were deployed in QGIS. The pairwise distribution patterns of the horizontal axis (XY axis) were investigated using the nearest-neighbour spatial association measure. The forest inclusion rates were also analysed using forested area polygons, while the slope angles and altitudes (Z axis) for each feature were analysed using a digital elevation model. As a result, haunted sites showed significant adjoint trends with medical and penal facilities and showed a tendency to approximate water facilities in terms of altitude analyses. This finding was consistent with the results of our previous study, which showed that haunted sites do not show an associated trend with any of the other public facilities other than medical facilities. Our findings provide empirical support for the presented insights regarding the sociology of death.

Geographic ranges and the hypothesis of the dispersal of the fishes of superfamily Congiopodoidea (Pisces: Scorpaenoidei) with the Antarctic Circumpolar Current

The most probable habitat of the ancestor of the superfamily Congiopodoidea Gill, 1889 is the area of the modern Malay Archipelago and Australia; the paths of further distribution as well as places of origin of taxa of different ranks within the superfamily are shown here based on morphological data. The dispersal of Congiopodoidea most likely occurred via the cold Antarctic Circumpolar Current, while members of the sister clade, the family Synanceiidae Swainson, 1839, occupied new habitats primarily in a northerly direction. Fishes of both families of Congiopodoidea (Congiopodidae Gill, 1889 and Zanclorhynchidae Andriashev, 1993) occupied new habitats only eastward of Australia, thus Zanclorhynchus spinifer heracleus Zhukov et Balushkin, 2018 appeared at the modern distribution area in the Ridge of Hercules in the Pacific Sector of the Southern Ocean after the ancestral forms had overcome more than a round-the-world migration. Six species of the genus Congiopodus Perry, 1811 have pairwise sympatric distribution ranges. They apparently originated on the shelves of Australia – C. leucopaecilus (Richardson, 1846), C. coriaceus Paulin et Moreland, 1979, and southern South America – C. peruvianus (Cuvier, 1829), C. kieneri (Sauvage, 1878), C. torvus (Gronow, 1772) and C. spinifer (Smith, 1839); moreover, the last two migrated further – to the southern point of the African continent. In the family Zanclorhynchidae, the monotypic genus Alertichthys Moreland, 1960 remained close to the place of radiation of the ancestral forms of the superfamily; its modern range covers seamounts and shelfs of islands south of New Zealand. The path of the ancestral forms of the second genus of this family, Zanclorhynchus Günther, 1880 was much longer. The most probable place of its origin is the waters of southern part of South America, from here the ancestral form migrated to the Prince Edward Islands in the Indian Ocean sector of the Southern Ocean, where it differentiated into two species Z. spinifer Günther, 1880 and Z. chereshnevi Balushkin et Zhukov, 2016. Zanclorhynchus spinifer further continued to occupy more eastern shelfs of islands and seamounts within the Antarctic Circumpolar Current, dividing into four subspecies: Z. spinifer armatus Zhukov, 2019, Z. spinifer spinifer Günther, 1880, Z. spinifer macquariensis Zhukov, 2019, and Z. spinifer heracleus. The hypothesis about the possibility of the distribution with the Antarctic Circumpolar Current as a dispersive agent is confirmed by morphological data, when the sequence of occupation of modern habitats is considered through the degree of relatedness of the fish inhabiting them.

Multi-Source geometric metric transfer learning for EEG classification

Background and ObjectiveIn the brain computer interfaces (BCIs), transfer learning (TL) has proven its effectiveness and attracted more attention in recent research. However, traditional TL algorithms mainly use Euclidean metric to calculate distance between features, not fully exploiting the potential relationship between feature representations, which makes the improvement of performance limited. MethodsThis paper proposes a multi-source geometric metric transfer learning (MSGMTL) algorithm. Firstly, multiple sources are aggregated together through Euclidean alignment (EA) to minimize the marginal distribution. Secondly, the tangent space features are extracted from a manifold to obtain the covariance matrices of EEG samples. Thirdly, three optimization components are introduced into a unified function under Mahalanobis distance metric. Namely, MSGMTL integrates pairwise constraints balanced distribution adaption based metric and structure consistency, aiming to preserve discriminative information and geometric structure to improve the performance of motor imagery (MI) classification. ResultsExperiments conducted on three datasets show that, compared with other advanced methods, MSGMTL achieves better performance in classification accuracy and computational cost. Conclusion: It comes to the conclusion that the combination of metric learning and transfer learning has achieved superior performance for EEG classification and can be beneficial to advancing the application of MI-based BCIs.Index Terms— Brain computer interface (BCI), metric learning, multi-source geometric metric transfer learning (MSGMTL), Mahalanobis distance.