Artificial Image Tampering Distorts Spatial Distribution of Texture Landmarks and Quality Characteristics

Advances in AI based computer vision has led to a significant growth in synthetic image generation and artificial image tampering with serious implications for unethical exploitations that undermine person identification and could make render AI predictions less explainable.Morphing, Deepfake and other artificial generation of face photographs undermine the reliability of face biometrics authentication using different electronic ID documents.Morphed face photographs on e-passports can fool automated border control systems and human guards.This paper extends our previous work on using the persistent homology (PH) of texture landmarks to detect morphing attacks.We demonstrate that artificial image tampering distorts the spatial distribution of texture landmarks (i.e. their PH) as well as that of a set of image quality characteristics.We shall demonstrate that the tamper caused distortion of these two slim feature vectors provide significant potentials for building explainable (Handcrafted) tamper detectors with low error rates and suitable for implementation on constrained devices.Comment: 6 pages, 7 figures, 3 table

Stable topological summaries for analyzing the organization of cells in a packed tissue

We use topological data analysis tools for studying the inner organization of cells in segmented images of epithelial tissues. More specifically, for each segmented image, we compute different persistence barcodes, which codify the lifetime of homology classes (persistent homology) along different filtrations (increasing nested sequences of simplicial complexes) that are built from the regions representing the cells in the tissue. We use a complete and well-grounded set of numerical variables over those persistence barcodes, also known as topological summaries. A novel combination of normalization methods for both the set of input segmented images and the produced barcodes allows for the proven stability results for those variables with respect to small changes in the input, as well as invariance to image scale. Our study provides new insights to this problem, such as a possible novel indicator for the development of the drosophila wing disc tissue or the importance of centroids’ distribution to differentiate some tissues from their CVT-path counterpart (a mathematical model of epithelia based on Voronoi diagrams). We also show how the use of topological summaries may improve the classification accuracy of epithelial images using a Random Forest algorithm.Ministerio de Ciencia e Innovación PID2019-107339GB-I0

Persistent Homology Tools for Image Analysis

Topological Data Analysis (TDA) is a new field of mathematics emerged rapidly since the first decade of the century from various works of algebraic topology and geometry. The goal of TDA and its main tool of persistent homology (PH) is to provide topological insight into complex and high dimensional datasets. We take this premise onboard to get more topological insight from digital image analysis and quantify tiny low-level distortion that are undetectable except possibly by highly trained persons. Such image distortion could be caused intentionally (e.g. by morphing and steganography) or naturally in abnormal human tissue/organ scan images as a result of onset of cancer or other diseases. The main objective of this thesis is to design new image analysis tools based on persistent homological invariants representing simplicial complexes on sets of pixel landmarks over a sequence of distance resolutions. We first start by proposing innovative automatic techniques to select image pixel landmarks to build a variety of simplicial topologies from a single image. Effectiveness of each image landmark selection demonstrated by testing on different image tampering problems such as morphed face detection, steganalysis and breast tumour detection. Vietoris-Rips simplicial complexes constructed based on the image landmarks at an increasing distance threshold and topological (homological) features computed at each threshold and summarized in a form known as persistent barcodes. We vectorise the space of persistent barcodes using a technique known as persistent binning where we demonstrated the strength of it for various image analysis purposes. Different machine learning approaches are adopted to develop automatic detection of tiny texture distortion in many image analysis applications. Homological invariants used in this thesis are the 0 and 1 dimensional Betti numbers. We developed an innovative approach to design persistent homology (PH) based algorithms for automatic detection of the above described types of image distortion. In particular, we developed the first PH-detector of morphing attacks on passport face biometric images. We shall demonstrate significant accuracy of 2 such morph detection algorithms with 4 types of automatically extracted image landmarks: Local Binary patterns (LBP), 8-neighbour super-pixels (8NSP), Radial-LBP (R-LBP) and centre-symmetric LBP (CS-LBP). Using any of these techniques yields several persistent barcodes that summarise persistent topological features that help gaining insights into complex hidden structures not amenable by other image analysis methods. We shall also demonstrate significant success of a similarly developed PH-based universal steganalysis tool capable for the detection of secret messages hidden inside digital images. We also argue through a pilot study that building PH records from digital images can differentiate breast malignant tumours from benign tumours using digital mammographic images. The research presented in this thesis creates new opportunities to build real applications based on TDA and demonstrate many research challenges in a variety of image processing/analysis tasks. For example, we describe a TDA-based exemplar image inpainting technique (TEBI), superior to existing exemplar algorithm, for the reconstruction of missing image regions

Hooghännaliste (Collembola) ja nendega seotud seeneliikide molekulaarne määramine

Väitekirja elektrooniline versioon ei sisalda publikatsiooneMuld on mitmekesine elupaik, mis hõlmab suurt mikroobide ja loomade liigirikkust. Rikkalik mullaelustik on olulisel kohal paljudes looduslikes protsessides alates mulla kujundamisest ja lagundamisprotsessidest kuni mikrokliima reguleerimiseni. Molekulaarsete identifitseerimismeetodite areng on kaasa aidanud mullaorganismide tuvastamisele, mis võimaldavad määrata liike nii indiviidi kui ka koosluste tasemel. Oma doktoritöös uurisin ITS2 rakendatavust hooghännaliste (Collembola) määramisel, kuna vastava DNA lõiguga on potentsiaalselt võimalik mullaproovidest määrata samaaegselt mitmesugused eukarüootide rühmad liigi tasemele. Kuna hooghännalised on tihedalt seotud seenekooslustega (seened moodustavad olulise osa nende toidust), siis uurisin oma doktoritöös ka hooghännalistega seotud seenekoosluste ruumilist ja ajalist struktuuri kasutades nii seente kultuurides kasvatamise kui ka mass-sekveneerimise (HTS) meetodit. Vastavate HTS andmete lihtsaks ja kiireks bioinformaatiliste analüüside teostamiseks oli vaja välja töötada mass-sekveneerimisandmete töötlemise töölaud. Doktoritöö peamised tulemused ja järeldused on järgmised: 1) ITS2 lõik omab piisavat liikidevahelist erinevust, et eristada hooghännaliste liike; 2) hooghännalistega seotud seeneliikide tuvastamine on tõhusam mass-sekveneerimise meetodiga, mis tõi esile, et hooghännalised on seotud palju rohkemate seeneliikidega kui seni traditsiooniliste meetoditega kindlaks määratud; 3) tulenevalt seenekoosluste suktsessioonist on hooghännalistega seotud seenekoosluste struktuur ja liigirikkus mõjutatud nii sesoonist kui aastast; 4) töös kasutatud hooghännaliste liikide vahel ei tuvastatud toitumiseelistusi seente osas; 5) koostatud HTS andmete töötlemise programm võimaldas kiiret ja tõhusat DNA järjestuste töötlust.Microbial and faunal communities are highly diverse in soils where they play fundamental roles in several ecosystem processes ranging from soil formation to microclimate regulation. The identification of small soil organisms has benefited from the development of molecular methods that enable identification of single species to whole communities. In this thesis, I examined the usefulness of the rDNA ITS2 subregion for identification purposes of Collembola, because of its potential for simultaneous use in metabarcoding surveys of multiple taxa. Moreover, this thesis addresses the spatial and temporal structure of Collembola-associated fungal communities as based on culturing and high-throughput sequencing (HTS). To simplify the HTS data analyses, one of the objectives of this thesis was the compilation of a user-friendly and flexible platform for bioinformatics analysis of custom high-throughput amplicon sequencing data. The main results and conclusions are the following: 1) the ITS2 barcoding marker provides sufficient resolution for discriminating among Collembola species; 2) HTS outperformed the culturing method in terms of recovering Collembola-associated fungal species, and it revealed that collembolans are associated with much higher diversity of fungi than previously anticipated; 3) the Collembola-associated fungal richness and community structure exhibited significant variation in different temporal scales, which probably reflects the succession of the litter fungal community; 4) diet specialization among the studied Collembola species was not evident, suggesting that these arthropods possess relatively opportunistic feeding behavior; 5) the compiled high-throughput amplicon sequencing data analysis platform enabled efficient bioinformatics workflow for the analysis of fungal ITS2 amplicons in soil and Collembola-associated samples

Development of a DNA Barcoding Reference Library for Identification of Medicinal Plant Materials Used in the Rio Grande Valley of Texas: a Representative Case Study Using Arnica (Asteraceae)

DNA barcoding is a technique that uses a short DNA fragment to identify a specimen to the species level. This technique is essential in situations where a lack of distinguishing morphological characteristics makes identification impossible. In the Río Grande Valley a variety of herbal supplements are cheap, readily available and sold as “Arnica” with no information to identify the contents. The appearance of dried and shredded material suggests that a variety of plant species are involved, belonging to the family Asteraceae. Arnica montana, also part of Asteraceae, is found in Europe and has anti-inflammatory properties used to externally treat bruises and contusions. Many species in Asteraceae contain secondary metabolites that may be hepatotoxic. From a health perspective, it is important that these products are identified to rule out safety concerns of toxicity of potentially mixed-up or misidentified materials. In this study a DNA barcoding reference library of Río Grande Valley Asteraceae was developed and subsequently a Bayesian phylogenetic approach was used to identify these unknown plant samples. The approach consisted of using matK and rbcL sequence data to identify the samples. The Bayesian phylogenetic tree confirmed the samples were not A. montana, but instead identified one species to be Trixis inula, and the remaining species were narrowed down to the subtribal level. Having obtained this information, additional analyses were conducted with highly variable nuclear ribosomal spacer sequences within those subtribes to further narrow down the possibilities. As a result the other samples were identified as Heterotheca subaxillaris, Grindelia spp. and Pseudogynoxys spp. A literature search revealed that species within each of the genera identified possess antioxidant, anti-inflammatory, anti-bacterial, anti-fungal and anti-parasitic properties some of which are highly similar to those of A. montana. The evidence obtained in this study suggests that these “Arnica” plants are not random replacements or misidentifications, as has been found in similar studies in other parts of the country, but are so far unrecognized members of medicinal plants widely used in the Río Grande Valley. This finding is warranting a much more detailed and molecular data driven ethnobotanical study of medicinal plant use in the RGV

Phylogenetics and Phylogeography in the Planktonic Diatom Genus <i>Chaetoceros</i>

The initial aims of this thesis were to assess the systematics of the planktonic diatom genus Chaetoceros and the phylogeographic patterns of selected species in this genus across spatial and temporal scales. As expected in every experiment, some initial ideas have been pursued as they were; others have taken a different route and led to different questions. Consequently, the systematics of Chaetoceros has become a multigene phylogeny and a revision of the classical taxonomic scheme for the family Chaetocerotaceae (Chapter II). Then, the phylogeographic approach, initially meant as a Sanger sequencing of a few genes from specimens collected around the world, turned into the analysis of the C. curvisetus cryptic species complex by using an approach which combines haplotype networks and metabarcoding data (Chapter IV). The mapping of this complex against a temporal metabarcoding dataset (MareChiara, Gulf of Naples, IT) has become a story of concerted evolution and has been extended to different Chaetoceros species and supported by a single strain 18S-V4 high throughput sequencing (Chapter V). Amid these experiments, the potential of metabarcoding data for biological recording was explored and tested in the whole genus Chaetoceros to assess diversity and distribution (Chapter III). Such data were integrated with classical ones from public repositories and literature and used to produce, among the other results, distribution maps of Chaetoceros species

Contributions to the taxonomy of South African hermit crabs (Crustacea: Decapoda: Paguroidea) – integrating microCT scanning and barcoding

Hermit crabs form an important component of the marine benthos and globally more than 1,200 species have been described. In the unique bioregion of South Africa, hermit crabs are poorly known, and the last taxonomic revision of the group was that of K. H. Barnard in 1950, who recorded only 32 species. This study combines morphological taxonomy, threedimensional (3D) micro-computed tomography (µCT) visualisations, and molecular barcoding to add to, revise, and provide an updated listing of, the regional fauna. The first section of the thesis comprises four chapters, each giving a detailed account of a species either new to science, or to the region. The pagurid hermit crab Goreopagurus poorei, a new species and genus record to the country, is reported and described from deep sea habitats along the Agulhas Shelf, extending the distribution by >10,000 km across the Indian Ocean. Furthermore, three species are described as new to science, one each from the three most common families. The first of these, a deep-water species from a genus of the family Parapaguridae that was previously unknown to South Africa, Paragiopagurus atkinsonae n. sp., is fully described and illustrated, and compared with two other parapagurids that each play a dominant role in the regional benthic offshore invertebrate community. The other two species new to science, Diogenes n. sp. from the family Diogenidae, and Pagurus n. sp. from the family Paguridae, inhabit coastal reefs in subtidal waters off southern KwaZulu-Natal. For the first time in crustacean taxonomy, species descriptions, particularly the one of Pagurus n. sp., are informed by, or based on, µCT imagery of calcified body parts. Following on this technique, Chapter 6 is a short presentation of the 3D raw dataset of seven µCT scans of types and rare museum specimens used in this thesis, which is made publicly available for download. The taxonomic use of the scanning method, with disseminating volumetric data of hermit crabs, is discussed briefly. The final section investigates the fauna as a whole. In Chapter 7, 194 cytochrome c oxidase subunit I gene segments (COI ‘barcodes’) of 43 nominal species plus 12 additional putative species (n = 55 species-like units) were used to validate morphological identifications. Testing this dataset revealed high barcoding efficacy, with nearly 99% identification success rates, and with the best Kimura 2-parameter distance to safely delimit species of hermit crabs of about 3.5%. Chapter 8 updates the regional fauna and provides taxonomic accounts for 62 nominal species which have either been added subsequent to the previous monographic review, or which have undergone taxonomic revision since that time. Of these, 12 are added for the first time here, increasing the number of known South African hermit crab species to 72, an expansion of 56% since Barnard, and about 20% since a recent species list published by W. Emmerson in 2016. Because colour images are provided for 51 out of 72 species, Chapter 8 can also be used as a preliminary guide. However, this study has shown that the hermit crabs of South Africa are by far more diverse than originally thought, and the summary, which includes only the 72 nominal taxa and none of the additional 10 putative species included in the barcoding dataset, is speculated to be only 60–70% complete. Future taxonomic work, especially in the genera Diogenes and Paguristes, will most likely result in many more species descriptions. Therefore, this current study is to be seen as important step towards a fully illustrated taxonomic catalogue on the South African hermit crabs to be produced in the near future

Phylogeography of recent <i>Plesiastrea</i> (Scleractinia::Plesiastreidae) based on an integrated taxonomic approach

Scleractinian corals are a diverse group of ecologically important yet highly threatened marine invertebrates, which can be challenging to identify to the species level. An influx of molecular studies has transformed scleractinian systematics, highlighting that cryptic species may be more common than previously understood. In this study, we test the hypothesis that Plesiastrea versipora (Lamarck, 1816), a species currently considered to occur throughout the Indo-Pacific in tropical, sub-tropical and temperate waters, is a single species. Molecular and morphological analyses were conducted on 80 samples collected from 31 sites spanning the majority of the species putative range and twelve mitogenomes were assembled to identify informative regions for phylogenetic reconstruction. Congruent genetic data across three gene regions supports the existence of two monophyletic clades aligning with distinct tropical and temperate provenances. Multivariate macromorphological analyses based on 13 corallite characters provided additional support for the phylogeographic split, with the number of septa and corallite density varying across this biogeographic divide. Furthermore, micromorphological and microstructural analyses identified that the temperate representatives typically develop sub-cerioid corallites with sparse or absent coenosteal features and smooth septal faces. In contrast, tropical representatives typically develop plocoid corallites separated by a porous dissepimental coenosteum and have granulated septal faces. These data suggest that at least two species exist within the genus PlesiastreaMilne Edwards & Haime, 1848. Based on examination of type material, we retain the name Plesiastrea versipora (Lamarck, 1816) for the temperate representatives of the genus and resurrect the name Plesiastrea peroniMilne Edwards & Haime, 1857 for the tropical members. This study highlights how broadly distributed hard coral taxa still need careful re-examination through an integrated systematics approach to better understand their phylogeographic patterns. Furthermore, it demonstrates the utility of integrating micro-, macro-morphological and genetic datasets, and the importance of type specimens when dealing with taxonomic revisions of scleractinian taxa

Abstracts 2013: Highlights of Student Research and Creative Endeavors

https://csuepress.columbusstate.edu/abstracts/1005/thumbnail.jp

Approaches to integrating genetic data into ecological networks

As molecular tools for assessing trophic interactions become common, research is increasingly focused on the construction of interaction networks. Here, we demonstrate three key methods for incorporating DNA data into network ecology and discuss analytical considerations using a model consisting of plants, insects, bats and their parasites from the Costa Rica dry forest. The simplest method involves the use of Sanger sequencing to acquire long sequences to validate or refine field identifications, for example of bats and their parasites, where one specimen yields one sequence and one identification. This method can be fully quantified and resolved and these data resemble traditional ecological networks. For more complex taxonomic identifications, we target multiple DNA loci, for example from a seed or fruit pulp sample in faeces. These networks are also well resolved but gene targets vary in resolution and quantification is difficult. Finally, for mixed templates such as faecal contents of insectivorous bats, we use DNA metabarcoding targeting two sequence lengths (157 and 407 bp) of one gene region and a MOTU, BLAST and BIN association approach to resolve nodes. This network type is complex to generate and analyse, and we discuss the implications of this type of resolution on network analysis. Using these data, we construct the first molecular-based network of networks containing 3,304 interactions between 762 nodes of eight trophic functions and involving parasitic, mutualistic and predatory interactions. We provide a comparison of the relative strengths and weaknesses of these data types in network ecology