1,704 research outputs found

    Comparative genomics of recent adaptation in Candida pathogens

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    [eng] Fungal infections pose a serious health threat, affecting >1,000 million people and causing ~1.5 million deaths each year. The problem is growing due to insufficient diagnostic and therapeutic options, increased number of susceptible patients, expansion of pathogens partly linked to climate change and the rise of antifungal drug resistance. Among other fungal pathogens, Candida species are a major cause of severe hospital-acquired infections, with high mortality in immunocompromised patients. Various Candida pathogens constitute a public health issue, which require further efforts to develop new drugs, optimize currently available treatments and improve diagnostics. Given the high dynamism of Candida genomes, a promising strategy to improve current therapies and diagnostics is to understand the evolutionary mechanisms of adaptation to antifungal drugs and to the human host. Previous work using in vitro evolution, population genomics, selection inferences and Genome Wide Association Studies (GWAS) have partially clarified such recent adaptation, but various open questions remain. In the three research articles that conform this PhD thesis we addressed some of these gaps from the perspective of comparative genomics. First, we addressed methodological issues regarding the analysis of Candida genomes. Studying recent adaptation in these pathogens requires adequate bioinformatic tools for variant calling, filtering and functional annotation. Among other reasons, current methods are suboptimal due to limited accuracy to identify structural variants from short read sequencing data. In addition, there is a need for easy-to-use, reproducible variant calling pipelines. To address these gaps we developed the “personalized Structural Variation detection” pipeline (perSVade), a framework to call, filter and annotate several variant types, including structural variants, directly from reads. PerSVade enables accurate identification of structural variants in any species of interest, such as Candida pathogens. In addition, our tool automatically predicts the structural variant calling accuracy on simulated genomes, which informs about the reliability of the calling process. Furthermore, perSVade can be used to analyze single nucleotide polymorphisms and copy number-variants, so that it facilitates multi-variant, reproducible genomic studies. This tool will likely boost variant analyses in Candida pathogens and beyond. Second, we addressed open questions about recent adaptation in Candida, using perSVade for variant identification. On the one hand, we investigated the evolutionary mechanisms of drug resistance in Candida glabrata. For this, we used a large-scale in vitro evolution experiment to study adaptation to two commonly-used antifungals: fluconazole and anidulafungin. Our results show rapid adaptation to one or both drugs, with moderate fitness costs and through few mutations in a narrow set of genes. In addition, we characterize a novel role of ERG3 mutations in cross-resistance towards fluconazole in anidulafungin-adapted strains. These findings illuminate the mutational paths leading to drug resistance and cross-resistance in Candida pathogens. On the other hand, we reanalyzed ~2,000 public genomes and phenotypes to understand the signs of recent selection and drug resistance in six major Candida species: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis and C. orthopsilosis. We found hundreds of genes under recent selection, suggesting that clinical adaptation is diverse and complex. These involve species-specific but also convergently affected processes, such as cell adhesion, which could underlie conserved adaptive mechanisms. In addition, using GWAS we predicted known drivers of antifungal resistance alongside potentially novel players. Furthermore, our analyses reveal an important role of generally-overlooked structural variants, and suggest an unexpected involvement of (para)sexual recombination in the spread of resistance. Taken together, our findings provide novel insights on how Candida pathogens adapt to human-related environments and suggest candidate genes that deserve future attention. In summary, the results of this thesis improve our knowledge about the mechanisms of recent adaptation in Candida pathogens, which may enable improved therapeutic and diagnostic applications.[cat] Les infeccions fúngiques representen una greu amenaça per a la salut, afectant a més de 1.000 milions de persones i causant aproximadament 1,5 milions de morts cada any. El problema està augmentant a causa d’unes opcions terapèutiques i diagnòstiques insuficients, l'increment del nombre de pacients susceptibles, l'expansió dels patògens parcialment vinculada al canvi climàtic i l'augment de la resistència als fàrmacs antifúngics. D’entre diversos fongs patògens, els llevats del gènere Candida són una causa important d'infeccions nosocomials, amb una alta mortalitat en pacients immunodeprimits. Diverses espècies de Candida constitueixen un problema de salut pública, cosa que requereix més esforços per a desenvolupar nous medicaments, optimitzar els tractaments disponibles i millorar els diagnòstics. Tenint en compte el dinamisme genòmic d’aquests patògens, una estratègia prometedora per millorar les teràpies i diagnòstics actuals és comprendre els mecanismes evolutius d'adaptació als fàrmacs antifúngics i a l’hoste humà. Treballs anteriors utilitzant l'evolució in vitro, la genòmica de poblacions, les inferències de selecció i els estudis d'associació de genoma complet (GWAS, per les sigles en anglès) han aclarit parcialment aquesta adaptació recent, però encara hi ha diverses preguntes obertes. En els tres articles que conformen aquesta tesi doctoral, hem abordat algunes d'aquestes preguntes des de la perspectiva de la genòmica comparativa. En primer lloc, hem abordat qüestions metodològiques relatives a l'anàlisi dels genomes de les espècies Candida. L'estudi de l'adaptació recent en aquests patògens requereix eines bioinformàtiques adequades per a la detecció, filtratge i anotació funcional de variants genètiques. Entre altres raons, els mètodes actuals són subòptims a causa de la limitada precisió per identificar variants estructurals a partir de dades de seqüenciació amb lectures curtes. A més, hi ha una necessitat d’eines computacionals per a la detecció de variants que siguin senzilles d'utilitzar i reproduibles. Per abordar aquestes mancances, hem desenvolupat el mètode bioinformàtic "personalized Structural Variation detection" (perSVade), una eina que permet la detecció, filtratge i anotació de diversos tipus de variants, incloent-hi les variants estructurals, directament des de les lectures. PerSVade permet la identificació precisa de les variants estructurals en qualsevol espècie d'interès, com ara els patògens Candida. A més, la nostra eina prediu automàticament la precisió de la detecció d’aquestes variants en genomes simulats, la qual cosa informa sobre la fiabilitat del procés. Finalment, perSVade es pot utilitzar per analitzar altres tipus de variants, com els polimorfismes de nucleòtid únic o els canvis en el nombre de còpies, facilitant així estudis genòmics integrals i reproduibles. Aquesta eina probablement impulsarà les anàlisis genòmiques en els patògens Candida i també en altres espècies. En segon lloc, hem abordat algunes de les preguntes obertes sobre l'adaptació recent en els llevats Candida, utilitzant perSVade per a la identificació de variants. D'una banda, hem investigat els mecanismes evolutius de resistència als fàrmacs antifúngics en Candida glabrata. Per a això, hem utilitzat un experiment d'evolució in vitro a gran escala per estudiar l'adaptació a dos antifúngics comuns: el fluconazol i l’anidulafungina. Els nostres resultats mostren una adaptació ràpida a un o ambdós fàrmacs, amb un cost per al creixement moderat i a través de poques mutacions en un nombre reduït de gens. A més, hem caracteritzat un paper nou de les mutacions en ERG3 en la resistència creuada al fluconazol en soques adaptades a anidulafungina. Aquests descobriments aclareixen els processos mutacionals que condueixen a la resistència als fàrmacs i a la resistència creuada en els patògens Candida. D'altra banda, hem re-analitzat aproximadament 2.000 genomes i fenotips disponibles en repositoris públics per a comprendre els senyals genòmics de selecció recent i de resistència a fàrmacs antifúngics, en sis espècies rellevants de Candida: C. auris, C. glabrata, C. albicans, C. tropicalis, C. parapsilosis i C. orthopsilosis. Hem trobat centenars de gens sota selecció recent, suggerint que l'adaptació clínica és diversa i complexa. Aquests gens estan relacionats amb funcions específiques de cada espècie, però també trobem processos alterats de manera similar en diferents patògens, com per exemple l’adhesió cel·lular, cosa que indica fenòmens d’adaptació conservats. A part, utilitzant GWAS hem predit mecanismes esperats de resistència a antifúngics i també possibles nous factors. A més, les nostres anàlisis revelen un paper important de les variants estructurals, generalment poc estudiades, i suggereixen una implicació inesperada de la recombinació (para)sexual en la propagació de la resistència. En conjunt, els nostres descobriments proporcionen noves perspectives sobre com els patògens Candida s'adapten als entorns humans, i suggereixen gens candidats que mereixen investigacions futures. En resum, els resultats d’aquesta tesi milloren el nostre coneixement sobre els mecanismes d'adaptació recent en els patògens Candida, cosa que pot permetre el disseny de noves teràpies i diagnòstics

    Exploring space situational awareness using neuromorphic event-based cameras

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    The orbits around earth are a limited natural resource and one that hosts a vast range of vital space-based systems that support international systems use by both commercial industries, civil organisations, and national defence. The availability of this space resource is rapidly depleting due to the ever-growing presence of space debris and rampant overcrowding, especially in the limited and highly desirable slots in geosynchronous orbit. The field of Space Situational Awareness encompasses tasks aimed at mitigating these hazards to on-orbit systems through the monitoring of satellite traffic. Essential to this task is the collection of accurate and timely observation data. This thesis explores the use of a novel sensor paradigm to optically collect and process sensor data to enhance and improve space situational awareness tasks. Solving this issue is critical to ensure that we can continue to utilise the space environment in a sustainable way. However, these tasks pose significant engineering challenges that involve the detection and characterisation of faint, highly distant, and high-speed targets. Recent advances in neuromorphic engineering have led to the availability of high-quality neuromorphic event-based cameras that provide a promising alternative to the conventional cameras used in space imaging. These cameras offer the potential to improve the capabilities of existing space tracking systems and have been shown to detect and track satellites or ‘Resident Space Objects’ at low data rates, high temporal resolutions, and in conditions typically unsuitable for conventional optical cameras. This thesis presents a thorough exploration of neuromorphic event-based cameras for space situational awareness tasks and establishes a rigorous foundation for event-based space imaging. The work conducted in this project demonstrates how to enable event-based space imaging systems that serve the goals of space situational awareness by providing accurate and timely information on the space domain. By developing and implementing event-based processing techniques, the asynchronous operation, high temporal resolution, and dynamic range of these novel sensors are leveraged to provide low latency target acquisition and rapid reaction to challenging satellite tracking scenarios. The algorithms and experiments developed in this thesis successfully study the properties and trade-offs of event-based space imaging and provide comparisons with traditional observing methods and conventional frame-based sensors. The outcomes of this thesis demonstrate the viability of event-based cameras for use in tracking and space imaging tasks and therefore contribute to the growing efforts of the international space situational awareness community and the development of the event-based technology in astronomy and space science applications

    Cre-loxP-mediated genetic lineage tracing: Unraveling cell fate and origin in the developing heart

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    The Cre-loxP-mediated genetic lineage tracing system is essential for constructing the fate mapping of single-cell progeny or cell populations. Understanding the structural hierarchy of cardiac progenitor cells facilitates unraveling cell fate and origin issues in cardiac development. Several prospective Cre-loxP-based lineage-tracing systems have been used to analyze precisely the fate determination and developmental characteristics of endocardial cells (ECs), epicardial cells, and cardiomyocytes. Therefore, emerging lineage-tracing techniques advance the study of cardiovascular-related cellular plasticity. In this review, we illustrate the principles and methods of the emerging Cre-loxP-based genetic lineage tracing technology for trajectory monitoring of distinct cell lineages in the heart. The comprehensive demonstration of the differentiation process of single-cell progeny using genetic lineage tracing technology has made outstanding contributions to cardiac development and homeostasis, providing new therapeutic strategies for tissue regeneration in congenital and cardiovascular diseases (CVDs)

    Behavior quantification as the missing link between fields: Tools for digital psychiatry and their role in the future of neurobiology

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    The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable, such as patient surveys or clinical interviews, can now be collected at a greater capacity and analyzed to produce new quantitative measures. Furthermore, recent capabilities for continuous collection of passive sensor streams, such as phone GPS or smartwatch accelerometer, open avenues of novel questioning that were previously entirely unrealistic. Their temporally dense nature enables a cohesive study of real-time neural and behavioral signals. To develop comprehensive neurobiological models of psychiatric disease, it will be critical to first develop strong methods for behavioral quantification. There is huge potential in what can theoretically be captured by current technologies, but this in itself presents a large computational challenge -- one that will necessitate new data processing tools, new machine learning techniques, and ultimately a shift in how interdisciplinary work is conducted. In my thesis, I detail research projects that take different perspectives on digital psychiatry, subsequently tying ideas together with a concluding discussion on the future of the field. I also provide software infrastructure where relevant, with extensive documentation. Major contributions include scientific arguments and proof of concept results for daily free-form audio journals as an underappreciated psychiatry research datatype, as well as novel stability theorems and pilot empirical success for a proposed multi-area recurrent neural network architecture.Comment: PhD thesis cop

    Methods and Metrics for Evaluating Explainable Artificial Intelligence in Healthcare Domain

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    Sometimes hard-to-interpret black-box artificial intelligence models might not induce trust in their users, particularly in the healthcare domain. Explainable artificial intelligence has been developed as a solution to this mistrust so that the specialists of different fields could better understand the processes that have led to the solutions offered them by the artificial intelligence model. Explainability is thought to give the specialists chance to evaluate feasibility of the algorithms and to encourage them to use AI in the decision-making process. Importance of explainability is particularly evident in the healthcare domain where the applications of artificial intelligence are used for example for treatment decisions of patients and for large-scale decisions about healthcare infrastructure. These decisions impact not only the health and welfare of a single patient but even larger communities. Decisions in healthcare domain require preciseness from the tools and responsibility from the decision makers. When the European regulations about the patients’ rights for explanation about the decisions considering them, it becomes clear that explainability is needed from artificial intelligence. To promote trust in specialists by using explanations we must be able to evaluate and validate the explanations with accurate metrics. At the moment, there are no standardised metrics or methods for evaluating explainable artificial intelligence and the field consensus is that rigorous study is needed to construct some. This thesis aims to find the state-of-art methods and metrics used for evaluating explainable artificial intelligence models, discuss their feasibility for healthcare and give basis for further studies to build unified set of metrics that can be used for validating new models. Overall, 54 metrics and methods were found and summarised in tables.Perinteiset vaikeasti tulkittavat tekoälymallit eivät aina herätä luottamusta käyttäjissään, varsinkaan terveydenhuollon alalla. Ratkaisuksi tähän on kehitetty selitettävä tekoäly, jotta eri alojen ammattilaiset voisivat paremmin ymmärtää prosesseja, jotka ovat johtaneet tekoälyn heille tarjoamiin ratkaisuihin. Selitettävyyden tarkoitus on antaa tekoälymallien käyttäjille mahdollisuus algoritmien ja niiden tuottamien ratkaisujen arviointiin ja niiden oikeellisuuden varmistamiseen rohkaisten heitä käyttämään tekoälyä päätöksenteossa. Selitettävyyden tärkeys korostuu terveydenhuollossa, sillä tekoälyä hyödyntäviä työkaluja käytetään esimerkiksi potilaiden hoitoa tai yleisesti sosiaali- ja terveydenhuoltoa koskeviin kysymyksiin, jolloin tarjotulla ratkaisulla voi olla suuri merkitys niin yksittäisen potilaan kuin kokonaisten ihmisryhmien terveyteen ja hyvinvointiin. Terveydenhuollossa päätökset vaativat tarkkuutta työkaluilta sekä vastuuta päättäjiltä, kuten lääkäreiltä. Kun huomioidaan myös Euroopan unionin asettama potilaan oikeus selitykseen häntä koskevaan päätökseen johtaneista tekijöistä, on ilmeistä, että terveydenhuollossa käytettävän tekoälyn on oltava selitettävää. Jotta luottamusta tekoälyyn voitaisiin tehokkaasti parantaa selitettävyyden avulla, on oltava keinoja arvioida selitysten paikkansapitävyyttä ja ymmärrettävyyttä objektiivisesti. Tällä hetkellä erilaisia mittareita ja metodeja on paljon, mutta yhtenäisiä käytänteitä tai standardeja selitettävyyden arviointiin ei ole. Tässä työssä kerätään yhteen ja esitellään nämä mittarit ja metodit. Arviointimenetelmät myös kerätään tiivistävin taulukoihin jaoteltuna sen mukaan, mitä mittaavat ja niiden soveltuvuutta terveydenhuollon työkalujen arviointiin arvioidaan. Tämän työn tarkoituksena on koota ajantasainen tieto selitettävän tekoälyn arviointimenetelmistä, jotta myöhemmät tutkimukset voisivat kehittää päteviä selitettävän tekoälyn arviointiprosesseja. Jatkotutkimusten pitäisi olla mahdollisia, sillä erilaisia mittareita ja metodeja esitellään 54 kappaletta ja useimmat niistä ovat hyvin käyttökelpoisia

    LIPIcs, Volume 261, ICALP 2023, Complete Volume

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    LIPIcs, Volume 261, ICALP 2023, Complete Volum

    Data analysis with merge trees

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    Today’s data are increasingly complex and classical statistical techniques need growingly more refined mathematical tools to be able to model and investigate them. Paradigmatic situations are represented by data which need to be considered up to some kind of trans- formation and all those circumstances in which the analyst finds himself in the need of defining a general concept of shape. Topological Data Analysis (TDA) is a field which is fundamentally contributing to such challenges by extracting topological information from data with a plethora of interpretable and computationally accessible pipelines. We con- tribute to this field by developing a series of novel tools, techniques and applications to work with a particular topological summary called merge tree. To analyze sets of merge trees we introduce a novel metric structure along with an algorithm to compute it, define a framework to compare different functions defined on merge trees and investigate the metric space obtained with the aforementioned metric. Different geometric and topolog- ical properties of the space of merge trees are established, with the aim of obtaining a deeper understanding of such trees. To showcase the effectiveness of the proposed metric, we develop an application in the field of Functional Data Analysis, working with functions up to homeomorphic reparametrization, and in the field of radiomics, where each patient is represented via a clustering dendrogram

    2019 GREAT Day Program

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    SUNY Geneseo’s Thirteenth Annual GREAT Day.https://knightscholar.geneseo.edu/program-2007/1013/thumbnail.jp
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