1,276 research outputs found

    Analysis and monitoring of single HaCaT cells using volumetric Raman mapping and machine learning

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    No explorer reached a pole without a map, no chef served a meal without tasting, and no surgeon implants untested devices. Higher accuracy maps, more sensitive taste buds, and more rigorous tests increase confidence in positive outcomes. Biomedical manufacturing necessitates rigour, whether developing drugs or creating bioengineered tissues [1]–[4]. By designing a dynamic environment that supports mammalian cells during experiments within a Raman spectroscope, this project provides a platform that more closely replicates in vivo conditions. The platform also adds the opportunity to automate the adaptation of the cell culture environment, alongside spectral monitoring of cells with machine learning and three-dimensional Raman mapping, called volumetric Raman mapping (VRM). Previous research highlighted key areas for refinement, like a structured approach for shading Raman maps [5], [6], and the collection of VRM [7]. Refining VRM shading and collection was the initial focus, k-means directed shading for vibrational spectroscopy map shading was developed in Chapter 3 and exploration of depth distortion and VRM calibration (Chapter 4). “Cage” scaffolds, designed using the findings from Chapter 4 were then utilised to influence cell behaviour by varying the number of cage beams to change the scaffold porosity. Altering the porosity facilitated spectroscopy investigation into previously observed changes in cell biology alteration in response to porous scaffolds [8]. VRM visualised changed single human keratinocyte (HaCaT) cell morphology, providing a complementary technique for machine learning classification. Increased technical rigour justified progression onto in-situ flow chamber for Raman spectroscopy development in Chapter 6, using a Psoriasis (dithranol-HaCaT) model on unfixed cells. K-means-directed shading and principal component analysis (PCA) revealed HaCaT cell adaptations aligning with previous publications [5] and earlier thesis sections. The k-means-directed Raman maps and PCA score plots verified the drug-supplying capacity of the flow chamber, justifying future investigation into VRM and machine learning for monitoring single cells within the flow chamber

    Defining the immune phenotype of extremely early-onset type 1 diabetes

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    Type 1 diabetes is a lifelong autoimmune disease that is very rarely diagnosed in the first two years of life. We recently described a novel T1D phenotype, termed extremely early-onset T1D (here referred to as EXE-T1D) characterised by the development of T1D very early in life, which is associated with rapid and complete b-cell destruction. I therefore hypothesise that EXE-T1D may be the consequence of a more aggressive form of autoimmunity than seen in individuals who develop T1D at a later age. In this thesis this hypothesis was tested by comparing the immune phenotype of individuals with EXE-T1D and individuals with childhood or adulthood-onset T1D. Specifically, the frequency and phenotype of islet-specific T cells was assessed using sensitive FluoroSpot assays, and unbiased immune phenotyping was undertaken using five broad flow cytometry panels. Assessment of b-cell-specific T cell responses showed lower autoreactivity in the blood of EXE-T1D, and a decreased proportion of IL-10 responses close to diagnosis, suggestive of reduced immune regulation. Phenotypic characterisation of the circulating immune system identified increased frequencies of activated circulating follicular helper (aTfh) and peripheral helper (aTph) T cells co-expressing PD-1 and ICOS in EXE-T1D. Detailed examination of the phenotypic characteristics of these cells by single-cell transcriptional profiling revealed minor phenotypic differences between clinical cohorts, including a reduction in cell populations expressing heat shock proteins (HSP) and genes associated with an interferon (IFN) response. This study has revealed novel immune phenotypes associated with EXE-T1D which are currently being investigated as biomarkers of rapid b-cell destruction in larger cohorts of individuals with T1D. This work may therefore guide the selection of patients suitable for specific immune-based therapies to halt disease progression

    Understanding Agreement and Disagreement in Listeners’ Perceived Emotion in Live Music Performance

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    Emotion perception of music is subjective and time dependent. Most computational music emotion recognition (MER) systems overlook time- and listener-dependent factors by averaging emotion judgments across listeners. In this work, we investigate the influence of music, setting (live vs lab vs online), and individual factors on music emotion perception over time. In an initial study, we explore changes in perceived music emotions among audience members during live classical music performances. Fifteen audience members used a mobile application to annotate time-varying emotion judgments based on the valence-arousal model. Inter-rater reliability analyses indicate that consistency in emotion judgments varies significantly across rehearsal segments, with systematic disagreements in certain segments. In a follow-up study, we examine listeners' reasons for their ratings in segments with high and low agreement. We relate these reasons to acoustic features and individual differences. Twenty-one listeners annotated perceived emotions while watching a recorded video of the live performance. They then reflected on their judgments and provided explanations retrospectively. Disagreements were attributed to listeners attending to different musical features or being uncertain about the expressed emotions. Emotion judgments were significantly associated with personality traits, gender, cultural background, and music preference. Thematic analysis of explanations revealed cognitive processes underlying music emotion perception, highlighting attributes less frequently discussed in MER studies, such as instrumentation, arrangement, musical structure, and multimodal factors related to performer expression. Exploratory models incorporating these semantic features and individual factors were developed to predict perceived music emotion over time. Regression analyses confirmed the significance of listener-informed semantic features as independent variables, with individual factors acting as moderators between loudness, pitch range, and arousal. In our final study, we analyzed the effects of individual differences on music emotion perception among 128 participants with diverse backgrounds. Participants annotated perceived emotions for 51 piano performances of different compositions from the Western canon, spanning various era. Linear mixed effects models revealed significant variations in valence and arousal ratings, as well as the frequency of emotion ratings, with regard to several individual factors: music sophistication, music preferences, personality traits, and mood states. Additionally, participants' ratings of arousal, valence, and emotional agreement were significantly associated to the historical time periods of the examined clips. This research highlights the complexity of music emotion perception, revealing it to be a dynamic, individual and context-dependent process. It paves the way for the development of more individually nuanced, time-based models in music psychology, opening up new avenues for personalised music emotion recognition and recommendation, music emotion-driven generation and therapeutic applications

    Advances and Applications of DSmT for Information Fusion. Collected Works, Volume 5

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    This fifth volume on Advances and Applications of DSmT for Information Fusion collects theoretical and applied contributions of researchers working in different fields of applications and in mathematics, and is available in open-access. The collected contributions of this volume have either been published or presented after disseminating the fourth volume in 2015 in international conferences, seminars, workshops and journals, or they are new. The contributions of each part of this volume are chronologically ordered. First Part of this book presents some theoretical advances on DSmT, dealing mainly with modified Proportional Conflict Redistribution Rules (PCR) of combination with degree of intersection, coarsening techniques, interval calculus for PCR thanks to set inversion via interval analysis (SIVIA), rough set classifiers, canonical decomposition of dichotomous belief functions, fast PCR fusion, fast inter-criteria analysis with PCR, and improved PCR5 and PCR6 rules preserving the (quasi-)neutrality of (quasi-)vacuous belief assignment in the fusion of sources of evidence with their Matlab codes. Because more applications of DSmT have emerged in the past years since the apparition of the fourth book of DSmT in 2015, the second part of this volume is about selected applications of DSmT mainly in building change detection, object recognition, quality of data association in tracking, perception in robotics, risk assessment for torrent protection and multi-criteria decision-making, multi-modal image fusion, coarsening techniques, recommender system, levee characterization and assessment, human heading perception, trust assessment, robotics, biometrics, failure detection, GPS systems, inter-criteria analysis, group decision, human activity recognition, storm prediction, data association for autonomous vehicles, identification of maritime vessels, fusion of support vector machines (SVM), Silx-Furtif RUST code library for information fusion including PCR rules, and network for ship classification. Finally, the third part presents interesting contributions related to belief functions in general published or presented along the years since 2015. These contributions are related with decision-making under uncertainty, belief approximations, probability transformations, new distances between belief functions, non-classical multi-criteria decision-making problems with belief functions, generalization of Bayes theorem, image processing, data association, entropy and cross-entropy measures, fuzzy evidence numbers, negator of belief mass, human activity recognition, information fusion for breast cancer therapy, imbalanced data classification, and hybrid techniques mixing deep learning with belief functions as well

    Chatbots for Modelling, Modelling of Chatbots

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    Tesis Doctoral inédita leída en la Universidad Autónoma de Madrid, Escuela Politécnica Superior, Departamento de Ingeniería Informática. Fecha de Lectura: 28-03-202

    Discovering circulating protein biomarkers through in-depth plasma proteomics

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    Plasma, i.e., the liquid component of blood, is one of the most clinically used samples for biomarker measurement. Despite that plasma proteins and metabolites are the most frequently analysed biomarkers in practice, identifying and implementing new circulating protein biomarkers for diagnosis, treatment prediction, prognosis, and disease monitoring has been limited. This PhD thesis compiles the discovery of systemic alterations in the blood plasma proteome and potential biomarkers related to disease status, prognosis, or treatment through plasma proteomics. We analysed plasma and serum samples with global proteomics by high-resolution isoelectric focusing (HiRIEF) and liquid chromatography coupled with mass-spectrometry (LC-MS/MS), and targeted proteomics by antibody-based proximity extension assays (PEA) in three diseases that would benefit from blood biomarkers: stage IV metastatic cutaneous melanoma (mCM), glioblastoma (GBM), and coronavirus disease 2019 (COVID-19). Specifically: a.) New treatment options for mCM substantially prolong overall survival (OS), but multiple patients do not respond to treatment or develop treatment resistance, thus having shorter progression free survival (PFS). Corroborated by the presence of multiple metastases, which makes biomarker sampling difficult, circulating proteins derived from the tumour and in response to treatment could serve as predictive and prognostic biomarkers in mCM. b.) GBM is the most malignant primary brain tumour with limited treatment options and notoriously short OS. Sampling biomarkers for GBM requires an invasive surgical intervention on the skull, which makes GBM a good candidate for circulating protein biomarkers for prognosis and monitoring. c.) COVID-19 is an inflammation-driven infectious disease that affects multiple organs and systems, thus making the plasma proteome a good source to explore systemic biological processes occurring in COVID-19. In papers I and II, using HiRIEF LC-MS/MS and PEA, we explored the treatment-driven plasma proteome alterations in mCM patients treated with anti-PD-1 immune checkpoint inhibitors (ICI) and MAPK-inhibitors (MAPKi), respectively, and identified potential treatment predictive and monitoring biomarkers. mCM patients treated with anti-PD-1 ICI had a strong increase in soluble PD-1 levels during treatment, and upregulation of proteins involved in T-cell response. BRAF[V600]-mutated mCM patients treated with MAPKi had deregulation in proteins involved in immune response and proteolysis. CPB1 had the highest increase in patients treated with BRAF- and MEK-inhibitors and was associated with longer PFS. Higher levels of several proteins involved in inflammation before treatment were associated with shorter PFS regardless of ICI or MAPKi treatment. In paper III, using HiRIEF LC-MS/MS and PEA, we longitudinally analysed the plasma proteome dynamics of GBM patients, collecting plasma samples before surgery and at three timepoints after surgery. Through consensus clustering, based on treatment-naïve plasma protein levels, we identified two patient clusters that differed in median OS. The association between the cluster membership and OS remained consistent after adjustment for age, sex, and treatment. Through machine learning, we identified protein panels that separated the patient clusters and may serve as prognostic biomarkers. The largest alterations in the plasma proteome of GBM patients occurred within two months after surgery, whereas the plasma protein levels at later timepoints had no difference compared to pre- surgery levels. We observed a decrease in glioma-elevated proteins in the blood after surgery, identifying potential monitoring biomarkers. In paper IV, using HiRIEF LC-MS/MS, we analysed serum proteome alterations in hospitalised COVID-19 patients in comparison to healthy controls, and identified a strong upregulation in inflammatory, interferon-induced, and proteasomal proteins. Several protein groups showed association with clinical parameters of COVID-19 severity, including proteasomal proteins. Serum proteome alterations were traceable to proteome alterations induced in a lung adenocarcinoma cell line (Calu-3) by infection with SARS-CoV-2. Finally, we performed the first meta-analysis of global proteomics studies of the soluble blood proteome in COVID-19, providing estimates of standardised mean differences and summary receiver operating characteristics curves. We demonstrate the high accuracy and precision of HiRIEF LC-MS/MS when compared to the meta-analysis estimates and pinpoint proteins that may serve as biomarkers of COVID-19. In summary, this thesis postulates that new circulating protein biomarkers would be clinically useful. By combining mass-spectrometry- and antibody-based-proteomics, we demonstrate the potential of in-depth analyses of the plasma proteome in capturing systemic alterations related to treatment, survival, and disease status, pinpointing potentially novel biomarkers that require validation in larger cohorts

    Ανάλυση προωτεομικών δεδομένων απο φασματομετρία μάζας και ενσωμάτωσή τους με άλλα κλινικά και μοριακά δεδομένα σε κλινικά δείγματα και καρκινικές σειρές

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    Οι μοριακοί υπότυποι μιας ασθένειας συχνά συσχετίζονται με διαφορές ως προς την επιβίωση ή πρόοδο της νόσου και άλλοτε ως προς την απόκριση σε συγκεκριμένη θεραπεία. Την τελευταία δεκαετία, μελέτες μοριακής ταξινόμησης του ουροθηλιακού καρκίνου εστιάζουν κυρίως στον διηθητικό τύπο της ασθένειας (~20% των ασθένων στην αρχική διάγνωση) ο οποίος χαρακτηρίζεται από υψηλό κίνδυνο για μετάσταση και χαμηλά ποσοστά πενταετούς επιβίωσης. Οι παραπάνω μελέτες επέτρεψαν την ταυτοποιήση πολλαπλών γενομικών και μεταγραφικών υποτύπων οι οποίοι διαφέρουν ριζικά ως προς το μοριακό τους προφίλ, σχηματίζοντας δύο μεγάλες κατηγορίες: τους basal και τους luminal όγκους. Οι πρώτοι φαίνεται να σχετίζονται με πιο επιθετικούς καρκίνους εμπερικλείοντας όμως ένα σημαντικό ποσοστό ασθενών που ανταποκρίνονται στο βασικό χημειοθεραπευτικό σχήμα. Οι δέυτεροι (luminal) αρχικά προσδιορίστηκαν ως λιγότερο επιθετικοί, επόμενες μελέτες όμως αποκάλυψαν την σημαντική μοριακή ετερογένεια που τους χαρακτηρίζει και που αντανακλάται σε κλινικές παραμέτρους. Σήμερα, πιστέυεται ότι ο διηθητικός καρκίνος της ουροδόχου κύστης ταξινομείται σε 6 βασικούς υποτύπους, αλλά τα δεδομένα που υπάρχουν για να υποστηρίξουν την ένταξη των υποτύπων στην κλινική πράξη είναι ατελή και δεν συμφωνούν μεταξύ τους. Από την άλλη, ο μη διηθητικός τύπος της ασθενεις (~80% των περιπτώσεων στην αρχική διάγνωση) χαρακτηρίζεται από υψηλά ποσοστά υποτροπής και προόδου σε ανώτερο στάδιο καθώς και από σημαντικό δημόσιο οικονομικό κόστος εξαιτίας της αυξημένης συχνότητας παρακολούθησης που απαιτεί. Το μοριακό προφίλ του μη-διηθητικού καρκίνου έχει μελετηθεί σημαντικά λιγότερο από αυτό του διηθητικού, και μέχρι σήμερα υπάρχουν δύο μελέτες που επιχειρούν την ταξινόμησή του σε μοριακούς υποτύπους: η πρώτη στη βάση του μεταγραφώματος, η δέυτερη στη βάση της διακύμνασης αριθμού αντιγράφων. Το πρωτεομικό προφίλ όμως, τόσο του διηθητικού όσο και του μη-διηθητικού καρκίνου της ουροδόχου κύστης, μέχρι και σήμερα έχει μελετηθεί υποτυπωδώς. Σκοπός της παρούσας μελέτης είναι η διερεύνηση της ύπαρξης πρωτεομικών υποτύπων του μη διηθητικού ουροθηλιακού καρκίνου, ο μοριακός χαρακτηρισμός τους, η σχέση τους με προηγούμενα συστήματα ταξινόμησης, καθώς και η ταυτοποίηση απορυθμισμένων πρωτεϊνών και μονοπατιών με δυνητική προγνωστική αξία. Για την εξυπηρέτηση του παραπάνω σκοπού, 117 δείγματα καρκινικού ιστού από ασθενείς που πρωτοδιαγνώσθηκαν με ουροθηλιακό καρκίνο (98 μη-διηθητικό, 19 διηθητικό) συλλέχθησαν και το ολικό πρωτέομά τους απομονώθηκε και αρχικά ποσοτικοποιήθηκε με τη μέθοδο Bradford. Κατόπιν διάσπασης με θρυψίνη, τα πεπτίδια διαχωρίστηκαν σε χρωματογραφική στήλη συνδεδεμένη με φασματογράφο μάζας τύπου Orbitrap. Οι φασματικές πληροφορίες για τα πεπτίδια αναλύθηκαν με το πρόγραμμα Proteome Discoverer θέτοντας FDR (False Discovery Rate) <0.01 και αντιστοιχήθηκαν σε πρωτεινικές ταυτότητες. Η πρωτεϊνική ποσοτικοποίηση έγινε με τη χρήση των τριών πιο άφθονων και μοναδικών πεπτιδίων ανά πρωτεΐνη, ενώ κατόπιν επεξεργασίας τα πρωτεομικά δεδομένα υποβλήθηκαν σε μια σειρά από υπολογιστικές αναλύσεις: μη επιτηρούμενη k-means συσταδοποίηση, ανάλυση κύριων συνιστωσών, ανάλυση για στατιστική σημαντικόντητα πρωτεϊνών, πρωτεϊνικών μονοπατιών, βιολογικών λειτουργιών και γονιδιακής έκφρασης καθώς και στην μοντελοιποίηση ενός μοριακού ταξινομητή Radnom Forest. Μέγιστη σταθερότητα συσταδοποίησης επιτεύχηκε για κ = 3 ομάδες, υποδηλώνοντας την ύπαρξη τριών πρωτεομικών υποτύπων στα δεδομένα. Η ομάδα 1 ήταν η μικρότερη σε μέγεθος (17/98), περιείχε κυρίως καρκίνους υψηλού σταδίου, αλλοίωσης και ρίσκου και παρουσίασε ένα μοριακό φαινότυπο ανοσοδιήθησης με υψηλά επιπέδα των μεταγραφικών παραγόντων STAT1, STAT3 και SND1, καθώς και πρωτεϊνων της αντιγονοπαρουσίασης, υποδηλώνοντας ενεργή ανταλλαγή πληροφοριών μεταξύ του ανοσοποιητικού και των καρκινικών κυττάρων. Παράλληλα, χαρακτηρίζονταν απο υψηλότερες ποσότητες πρωτεϊνών που συμμετέχουν στο κυτταρικό κύκλο, και στη μετάδοση στρεσογόνων σημάτων (αντίδραση μη αναδιπλωμένης πρωτεϊνης και επιδιόρθωση βλαβών του DNA). Η όμαδα 2 συγκέντρωσε ασθενείς με ποικίλα κλινικά χαρακτηριστικά που όμως έφεραν κοινώς, αυξημένες ποσότητες εξωκυττάριων πρωτεϊνών (στρώματος), και χαμηλά επιθηλιακά σήματα. Οι ασθενείς στην ομάδα 3 παρουσίασαν έναν πιο διαφοροποιημένο μοριακό φαινότυπο με υψηλότερα επίπεδα (UPKs και KRT20 κάθως και CDH1) που συμβαδίζει με τα κλινικά χαρακτηριστικά τους αφού οι περισσότεροι διαγιγνώσθηκαν με καρκίνους χαμηλού σταδίου και κινδύνου. Η ανάλυση για ενεργοποιημένα πρωτεϊνικά μονοπάτια έδειξε ότι οι ασθενείς της ομάδας 1 έιχαν ενεργή σηματοδότηση για βιοσυνθετικές διεργασίες, για ιντερφερόνη-γ, και αυξημένη δραστηριότητα των μεταγραφικών παραγόντων MYC και E2F, που ελέγχουν θετικά τον κυτταρικό κύκλο. Από την άλλη οι ασθνενείς της ομάδας 3 σχετίστηκαν με ενεργοποίηση μεταβολικών μονοπατιών όπως αυτό της αποτοξίνωσης μεσολαβούμενο από γλουταθειόνη καθώς και της γλυκογονόλυσης – γλυκόλυσης, αλλά και της απόπτωσης. Συγκρίνοντας το πρωτεομικό προφιλ των ασθένων με μη-διηθητικό καρκίνο με ασθενέις που είχαν διηθητικό καρκίνο χρησιμοποιώντας ανάλυση κύριων συνιστωσών, αποκαλύφθηκε κοντινή σχέση της ομάδας 1 με ασθενείς που έφεραν διηθητικό ουροθηλιακό καρκίνο και αντίστροφα, μακρινή σχέση της ομάδας 3 με τους τελευταίους. Η ομάδα 2 εμφάνισε μεγάλη διασπορά επικαλύπτοντας περιοχές των προηγούμενων δύο ομάδων. Για την επικύρωση των πρωτεομικών αποτελεσμάτων, δεδομένα από μεταγραφικές έρευνες (UROMOL και LUND) αναλύθηκαν αναδρομικά. Στην UROMOL έρευνα επίσης ταυτοποιήθηκαν 3 υπότυποι ο ένας εκ των οποίων συγκέντρωσε τους περισσότερους ασθενείς με πρόδοο σε ανώτερο στάδιο (κακής πρόγνωσης υπότυπος). Συγκριτική ανάλυση μεταξύ των τριών πρωτεομικών ομάδων και των τριών υποτύπων της UROMOL έρευνας με το στατιστικό εργαλείο GSEA, έδειξε στατιστικώς σημαντικές φαινοτυπικές ομοιότητες μεταξύ της πρωτεομικής ομάδας 1 και του υποτύπου «κακής» πρόγνωσης της UROMOL καθώς και μεταξύ της πρωτεομικής ομάδας 3 και του υποτύπου «καλής πρόγνωσης». Χρησιμοποιώντας έναν μη επιτηρούμενο μοριακό ταξινομητή Random Forest, οι υψηλού κινδύνου και χαμηλού κινδύνου φαινότυποι των πρωτεομικών ομάδων 1 και 3, επιβεβαιώθηκαν ύστερα από την ταξινόμηση των ασθενών στους υποτύπους «κακής» και «καλής» πρόγνωσης αντίστοιχα, της UROMOL έρευνας. Στατιστικώς σημαντικες πρωτεΐνες που ξεχωρίζουν αυτές τις δυο ακραίες πρωτεομικές ομάδες αλλά και ταυτόχρονα τον διηθητικό από τον μη διηθητικό καρκίνο βρέθηκαν να διαφέρουν σημαντικά και στο επίπεδο του μεταγραφώματος μεταξύ των ομάδων «κακής» και «καλής» πρόγνωσης σε δύο ανεξάρτητες έρευνες (UROMOL και LUND). Τα παραπάνω μόρια συμμετέχουν σε βιολογικές λειτουργίες-κλειδιά για την ανάπτυξη του μη-διηθητικού καρκίνου, όπως στην επαγωγή αποκρίσεων πρωτεϊνικής σταθερότητας, στη σηματοδότηση κυτοκινών και ιντερφερονών, στην αντιγονοπαρουσίαση, στην επεξεργασία πρώιμων mRNAs, σε μετα-μεταφραστικές τροποποιήσεις αλλά και σε μονοπάτια κυτταρικής αύξησης. Συνολικά, η παρούσα μελέτη ταυτοποιεί τρεις πρωτεομικούς υποτύπους του μη διηθητικού καρκίνου και ακολουθώντας μια σύγκριτική ανάλυση με δύο ανεξάρτητες μεταγραφικές έρευνες, παρέχει ομάδες μορίων που μπορεί να οδηγούν τη πρόοδο του καρκίνου και που χρειάζονται επιπλέον επικύρωση στη κλινική πράξη.DNA/RNA-based classification of Bladder Cancer (BC) supports the existence of multiple molecular subtypes, while investigations at the protein level are scarce. The purpose of this study was to investigate if Non-Muscle Invasive Bladder Cancer (NMIBC) can be stratified to biologically meaningful proteomic groups, to establish associations between the proteomics subtypes and previous transcriptomics classification systems and to characterize the continuum of transcriptomics alterations observed in the different stages of the disease. Subsequently, tissue specimens from 117 patients at primary diagnosis (98 with NMIBC and 19 with MIBC), were processed for high resolution LC-MS/MS analysis. Protein quantification was conducted by utilizing the mean abundance of the top three most abundant unique peptides per protein. The proteomics output was subjected to unsupervised consensus clustering, principal component analysis (PCA), and investigation of subtype-specific features, pathways, and genesets, as well as for the construction and validation of a Random Forest based classifier. NMIBC patients were optimally stratified to 3 proteomic subtypes (classes), differing at size, clinico-pathological and molecular backgrounds: Class 1 (mostly high stage/grade/risk samples) was the smallest in size (17/98) and expressed an immune/inflammatory phenotype, along with features involved in cell proliferation, unfolded protein response and DNA damage response, whereas class 2 (mixed stage/grade/risk composition) presented with an infiltrated/mesenchymal profile. Class 3 was rich in luminal/differentiation markers, in line with its pathological composition (mostly low stage/grade/risk samples). PCA revealed a close proximity of class 1 and conversely, remoteness of class 3 to the proteome of MIBC. Samples from class 2 were distributed in a wider fashion at the rotated space. Comparative analysis with GSEA between the three proteomic classes and the three UROMOL subtypes indicated statistically significant associations between the proteomics class 1 and UROMOL subtype 2 (subtype with a bad prognosis) and also between the proteomics class 3 and UROMOL subtype 1 (subtype with the best prognosis). Utilizing a Random Forest based classifier, the predicted high- and low-risk phenotypes for the proteomic class 1 and class 3, were further supported by their classification into the “progressed” and “non-progressed” subtypes of the UROMOL study, respectively. Statistically significant proteins distinguishing these two extreme classes (1 and 3) and also MIBC from NMIBC samples were found to consistently differ at the mRNA levels between NMIBC “Progressors” and “Non-Progressors” groups of the UROMOL and LUND cohorts. Functional assessment of the observed molecular de-regulations suggested severe pathway alterations at unfolded protein response, cytokine and inferferone-γ signaling, antigen presentation, mRNA processing, post translational modifications and in cell growth/division. Collectively, this study identifies three proteomic NMIBC subtypes and following a cross-omics analysis using transcriptomic data from two independent cohorts, shortlists molecular features potentially driving non-invasive carcinogenesis, meriting further validation in clinical trials

    Digital agriculture: research, development and innovation in production chains.

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    Digital transformation in the field towards sustainable and smart agriculture. Digital agriculture: definitions and technologies. Agroenvironmental modeling and the digital transformation of agriculture. Geotechnologies in digital agriculture. Scientific computing in agriculture. Computer vision applied to agriculture. Technologies developed in precision agriculture. Information engineering: contributions to digital agriculture. DIPN: a dictionary of the internal proteins nanoenvironments and their potential for transformation into agricultural assets. Applications of bioinformatics in agriculture. Genomics applied to climate change: biotechnology for digital agriculture. Innovation ecosystem in agriculture: Embrapa?s evolution and contributions. The law related to the digitization of agriculture. Innovating communication in the age of digital agriculture. Driving forces for Brazilian agriculture in the next decade: implications for digital agriculture. Challenges, trends and opportunities in digital agriculture in Brazil

    A Classification of Money Laundering Incidents

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    Money laundering is typically described as a three-stage process, including the placement, layering, and integration of criminal assets. However, the precision of the so-called three-stage model is doubtful, with significant shortcomings in the theoretical foundation, empirical support, and research practice. Previous research has failed to trigger a scientific debate about the numerous manifestations of money laundering going beyond the three-stage model. This thesis introduces a new conceptual framework in which money laundering incidents comprise properties from crime events and their immediate environment. The conceptual framework was developed in an iterative process between data and crime science theory. Original data was gathered using a quantitative approach to content analysis applied to 180 full judgment transcripts from the Court of Appeal and Administrative Court of England and Wales (1997- 2017). In a series of studies, the money laundering properties outlined in court transcripts were identified, conceptualised, and refined from the crime science perspective. In the last step, money laundering incidents were classified based on the hierarchical clustering of information from court records. The classification of money laundering incidents shows little resemblance with the standard three-stage model and offers a new viewpoint on how money laundering works. The novel approach enables researchers and practitioners to consider a broader range of properties, improving the examination and prevention of money laundering
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