40 research outputs found

    Evaluating the effects of high-throughput structural neuroimaging predictors on whole-brain functional connectome outcomes via network-based vector-on-matrix regression

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    The joint analysis of multimodal neuroimaging data is critical in the field of brain research because it reveals complex interactive relationships between neurobiological structures and functions. In this study, we focus on investigating the effects of structural imaging (SI) features, including white matter micro-structure integrity (WMMI) and cortical thickness, on the whole brain functional connectome (FC) network. To achieve this goal, we propose a network-based vector-on-matrix regression model to characterize the FC-SI association patterns. We have developed a novel multi-level dense bipartite and clique subgraph extraction method to identify which subsets of spatially specific SI features intensively influence organized FC sub-networks. The proposed method can simultaneously identify highly correlated structural-connectomic association patterns and suppress false positive findings while handling millions of potential interactions. We apply our method to a multimodal neuroimaging dataset of 4,242 participants from the UK Biobank to evaluate the effects of whole-brain WMMI and cortical thickness on the resting-state FC. The results reveal that the WMMI on corticospinal tracts and inferior cerebellar peduncle significantly affect functional connections of sensorimotor, salience, and executive sub-networks with an average correlation of 0.81 (p<0.001).Comment: 20 pages, 5 figures, 2 table

    Utilizing Mass Spectrometry Imaging to Correlate N-Glycosylation of Hepatocellular Carcinoma with Tumor Subtypes for Biomarker Discovery

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    Hepatocellular carcinoma (HCC) is a leading cause of cancer deaths globally and is a growing clinical problem with poor survival outcomes beyond early-stage disease. Surveillance for HCC has primarily relied on ultrasound and serum α-fetoprotein (AFP), but combined they only have a sensitivity of 63% for early-stage HCC tumors, suggesting a need for improved diagnostic strategies. Alterations to N-glycan expression are relevant to the progression of cancer, and there a multitude of N-glycan-based cancer biomarkers that have been identified with sensitivity for various cancer types including HCC. Spatial HCC tissue profiling of N-linked glycosylation by matrix-assisted laser desorption ionization imaging mass spectrometry (MALDI-IMS) serves as a new method to evaluate tumor-correlated N-glycosylation and thereby identify potential HCC biomarkers. Previous work has identified significant changes in the N-linked glycosylation of HCC tumors, but has not accounted for the heterogeneous genetic and molecular nature of HCC, which has led to inadequate sensitivity of N-glycan biomarkers. Therefore, we hypothesized that the incorporation of genetic/molecular information into N-glycan-based biomarker development would result in improved sensitivity for HCC. To determine the correlation between HCC-specific N-glycosylation and genetic/molecular tumor features, we profiled HCC tissue samples with MALDI-IMS and correlated the spatial N-glycosylation with a widely used HCC molecular classification that utilizes histological, genetic, and clinical tumor features (Hoshida subtypes). MALDI-IMS data displayed trends that could approximately distinguish between subtypes, with Subtype 1 demonstrating significantly dysregulated N-glycosylation compared to Subtypes 2 and 3, particularly in regard to fucosylation. In order to further the clinical relevance of subtype-dependent N-glycosylation, we analyzed patient-matching HCC tumor tissue, background liver tissue and serum samples through MALDI-IMS. Results showed a N-glycan based model capable of differentiating tumor tissue from background liver tissue with an AUC of 0.9842. When analyzing the associated serum, 24.7% of detected N-glycans were significantly positively correlated between tumor tissue and serum, suggesting that N-glycosylation trends translate from tissue to serum. Additionally, a serum N-glycan-based model was capable of distinguishing Subtype 1/Subtype 2 tumors from Subtype 3 tumors with an AUC of 0.881. Through the utilization of MALDI-IMS, subtype-dependent N-glycosylation trends were identified in both tissue and serum, which can significantly further the development of HCC biomarkers for clinical application

    Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries

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    This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually. This is an open access book

    Any-way and Sparse Analyses for Multimodal Fusion and Imaging Genomics

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    This dissertation aims to develop new algorithms that leverage sparsity and mutual information across data modalities built upon the independent component analysis (ICA) framework to improve the performance of current ICA-based multimodal fusion approaches. These algorithms are further applied to both simulated data and real neuroimaging and genomic data to examine their performance. The identified neuroimaging and genomic patterns can help better delineate the pathology of mental disorders or brain development. To alleviate the signal-background separation difficulties in infomax-decomposed sources for genomic data, we propose a sparse infomax by enhancing a robust sparsity measure, the Hoyer index. Hoyer index is scale-invariant and well suited for ICA frameworks since the scale of decomposed sources is arbitrary. Simulation results demonstrate that sparse infomax increases the component detection accuracy for situations where the source signal-to-background (SBR) ratio is low, particularly for single nucleotide polymorphism (SNP) data. The proposed sparse infomax is further extended into two data modalities as a sparse parallel ICA for applications to imaging genomics in order to investigate the associations between brain imaging and genomics. Simulation results show that sparse parallel ICA outperforms parallel ICA with improved accuracy for structural magnetic resonance imaging (sMRI)-SNP association detection and component spatial map recovery, as well as with enhanced sparsity for sMRI and SNP components under noisy cases. Applying the proposed sparse parallel ICA to fuse the whole-brain sMRI and whole-genome SNP data of 24985 participants in the UK biobank, we identify three stable and replicable sMRI-SNP pairs. The identified sMRI components highlight frontal, parietal, and temporal regions and associate with multiple cognitive measures (with different association strengths in different age groups for the temporal component). Top SNPs in the identified SNP factor are enriched in inflammatory disease and inflammatory response pathways, which also regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region, and the regulation effects are significantly enriched. Applying the proposed sparse parallel ICA to imaging genomics in attention-deficit/hyperactivity disorder (ADHD), we identify and replicate one SNP component related to gray matter volume (GMV) alterations in superior and middle frontal gyri underlying working memory deficit in adults and adolescents with ADHD. The association is more significant in ADHD families than controls and stronger in adults and older adolescents than younger ones. The identified SNP component highlights SNPs in long non-coding RNAs (lncRNAs) in chromosome 5 and in several protein-coding genes that are involved in ADHD, such as MEF2C, CADM2, and CADPS2. Top SNPs are enriched in human brain neuron cells and regulate gene expression, isoform percentage, transcription expression, or methylation level in the frontal region. Moreover, to increase the flexibility and robustness in mining multimodal data, we propose aNy-way ICA, which optimizes the entire correlation structure of linked components across any number of modalities via the Gaussian independent vector analysis and simultaneously optimizes independence via separate (parallel) ICAs. Simulation results demonstrate that aNy-way ICA recover sources and loadings, as well as the true covariance patterns with improved accuracy compared to existing multimodal fusion approaches, especially under noisy conditions. Applying the proposed aNy-way ICA to integrate structural MRI, fractal n-back, and emotion identification task functional MRIs collected in the Philadelphia Neurodevelopmental Cohort (PNC), we identify and replicate one linked GMV-threat-2-back component, and the threat and 2-back components are related to intelligence quotient (IQ) score in both discovery and replication samples. Lastly, we extend the proposed aNy-way ICA with a reference constraint to enable prior-guided multimodal fusion. Simulation results show that aNy-way ICA with reference recovers the designed linkages between reference and modalities, cross-modality correlations, as well as loading and component matrices with improved accuracy compared to multi-site canonical correlation analysis with reference (MCCAR)+joint ICA under noisy conditions. Applying aNy-way ICA with reference to supervise structural MRI, fractal n-back, and emotion identification task functional MRIs fusion in PNC with IQ as the reference, we identify and replicate one IQ-related GMV-threat-2-back component, and this component is significantly correlated across modalities in both discovery and replication samples.Ph.D

    Regulación de modificaciones post-traduccionales con intervención farmacológica: enfermedades autoinmunes y cáncer

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    Post-Translational Modifications are key elements within the cell signalling pathways. These key events promptly reprogram protein's behaviour in presence of environmental alterations in a highly dynamic manner acting as regulators or even on/off switches. The DNA Damage Response (DDR) is triggered after the genomic material is compromised to either induce cell death and prevent tumorigenesis or cell cycle arrest to fix the damaged DNA. To induce cell cycle arrest, an extensive network of enzymes acts coordinately to phosphorylate the CDKs. It is widely believed that the DDR and the cell cycle control pathways are highly interconnected. Therefore, any alteration in the enzymes in charge of this process might trigger carcinogenesis by any of both pathways. Besides, the hypoxic response is another distinctive characteristic in many diseases and its pharmacological modulation is of great relevance in Huntington's treatment. In the present work, first, we show that DYRK2 is found in the interface between the DDR and the cell cycle control. We demonstrate that DYRK2 can phosphorylate and induce the proteasomal degradation of the proteins NOTCH1 and CDC25A. On the one hand, we proved that DNA damage triggers DYRK2 ability to reduce the effects of NOTCH1 in cell migration. On the other hand, DYRK2 was described as a key kinase controlling CDC25A stability throughout the cell cycle, thus determining the cell cycle phase transitions. Besides, we showed the ability of Betulinic Acid Hidroxymate (BAH) to activate the HIF pathway by reducing the PHD2 phosphorylation on Ser125 with clear implications for the treatment of Huntington´s disease. The description of NOTCH1 and CDC25A as new DYRK2 substrates established this kinase as a master modulator linking the DDR pathway and the cell cycle control. Again, this relation is tightly controlled by PTMs reinforcing their role as modulators of the cell signalling pathways. Finally, the understanding of the BAH action mechanism might drive further pharmacologic efforts into the development of more potent drugs to target PHD2 increasing HIF-1α stabilization.Las modificaciones postraduccionales son elementos clave dentro de las vías de señalización celular para reprogramar rápidamente el comportamiento de las proteínas en presencia de alteraciones ambientales de forma muy dinámica, actuando como reguladores o incluso como interruptores de encendido/apagado. La respuesta al daño en el ADN (DDR) se desencadena después de que el material genético se vea comprometido, ya sea para inducir la muerte celular y evitar la tumorigénesis o para detener el ciclo celular y arreglar el ADN dañado. Para inducir la detención del ciclo celular, una amplia red de enzimas actúa de forma coordinada para fosforilar las proteínas CDK. La vía de señalización DDR y las vías de control del ciclo celular están muy interconectadas. Por lo tanto, cualquier alteración en las enzimas encargadas de este proceso podría desencadenar la carcinogénesis por cualquiera de ambas vías. Además, la respuesta hipóxica es otra característica distintiva en muchas enfermedades y su modulación farmacológica es de gran relevancia en el tratamiento de Huntington. En el presente trabajo, en primer lugar, mostramos que DYRK2 se encuentra en la interfaz entre el DDR y el control del ciclo celular. Demostramos que DYRK2 puede fosforilar e inducir la degradación proteasomal de las proteínas Notch1-IC y CDC25A. Por un lado, demostramos que el daño al ADN desencadena la capacidad de DYRK2 para reducir los efectos de NOTCH1 en la migración celular. Por otro lado, DYRK2 se describió como una quinasa clave que controla la estabilidad de CDC25A a lo largo del ciclo celular, determinando así las transiciones de fase del ciclo celular. Además, demostramos la capacidad del Ácido Betulínico Hidroxamato (BAH) para activar la vía HIF reduciendo la fosforilación de PHD2 en Ser125, con claras implicaciones para el tratamiento de la enfermedad de Huntington. La descripción de NOTCH1 y CDC25A como nuevos sustratos de DYRK2 sitúa a esta quinasa como un modulador esencial que vincula la vía DDR y el control del ciclo celular. De nuevo, esta relación está estrechamente controlada por PTMs reforzando su papel como moduladores de las vías de señalización celular. Por último, la comprensión del mecanismo de acción del BAH podría impulsar nuevos esfuerzos farmacológicos para el desarrollo de fármacos más potentes dirigidos a que la proteína PHD2 aumente la estabilización de HIF-1α

    Data integration in inflammatory bowel disease

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    [eng] INTRODUCTION: Inflammatory bowel disease is a complex intestinal disease with several genetic and environmental factors that can influence its course. The ethiology and pathophysiology of the disease is not fully understood. There is some evidence that microbiome can play a role. Finding relationships between microbiome and host’s mucosa could help advance prevention, diagnosis or treatment. METHODS: We based our analysis on intestinal bacterial 16S rRNA and human transcriptome data from biopsies from multiple timepoints and intestine segments. We extended regularized generalized canonical correlation analysis to find models that are coherent with previous knowledge on the disease taking into account the samples’ information. Multiple inflammatory bowel disease datasets on different treatments and conditions were analysed and the models defining those dataset were compared. The results were compared with multiple co-inertia analysis. RESULTS: Splitting sample variables into different blocks results in models of these relationships that show differences on the genes and microorganisms selected. The models generated using our new method inteRmodel outperformed multiple coinertia analysis to classify the samples according to their location. Despite being used on datasets of different sources the resulting models show similar relationships between variables. DISCUSSION: Comparing multiple models helps find out the relationships within datasets. Our method finds how strong are the relationships between the microbiome, transcriptome and environmental variables. On different datasets genes selected were common. This approach is robust and flexible to different datasets and settings. CONCLUSION: With inteRmodel we found that the microbiome relates more closely to the sample location than to disease, but the transcriptome is highly related to the location of the sample on the intestine. There is a common transcriptome between datasets while microorganisms depend of the dataset. We can improve sample classification by taking into account both bacterial 16S rRNA and host transcriptome.[cat] INTRODUCCIÓ: La malaltia inflamatòria intestinal és una malaltia intestinal complexa amb diversos factors genètics i ambientals que poden influir en el seu curs. L'etiologia i fisiopatologia de la malaltia no es con eix del tot. Hi ha evidències que el microbioma pot tenir un paper rellevant. Trobar relacions entre el microbioma i la mucosa de l'hoste podria ajudar a avançar en la prevenció, el diagnòstic o el tractament. MÈTODES: Vam basar la nostra anàlisi en dades d'ARNr 16S bacteriana intestinal i de transcriptoma humà de biòpsies de múltiples punts de temps i segments intestinals. Hem ampliat l'anàlisi de correlació canònica generalitzada regularitzada per trobar models coherents amb el coneixement previ sobre la malaltia tenint en compte la informació de les mostres. Es van analitzar diversos conjunts de dades de malaltia inflamatòria intestinal sobre diferents tractaments i condicions i es van comparar els models que defineixen aquest conjunt de dades. Els resultats es van comparar amb l'anàlisi de coinèrcia múltiple. RESULTATS: Dividir les variables de la mostra en diferents blocs dona com a resultat models d'aquestes relacions que mostren diferències en els gens i els microorganismes seleccionats. Els models generats mitjançant el nostre nou mètode intermodel van superar l'anàlisi de coinèrcia múltiple per classificar les mostres segons la seva ubicació. Tot i utilitzar-se en conjunts de dades de diferents fonts, els models resultants mostren relacions similars entre variables. DISCUSSIÓ: La comparació de diversos models ajuda a esbrinar les relacions dins dels conjunts de dades. El nostre mètode troba com de fortes són les relacions entre el microbioma, el transcriptoma i les variables ambientals. En diferents conjunts de dades, els gens seleccionats eren comuns. Aquest enfocament és robust i flexible per a diferents conjunts de dades i configuracions. CONCLUSIÓ: Amb inteRmodel vam trobar que el microbioma es relaciona més estretament amb la ubicació de la mostra que amb la malaltia, però el transcriptoma està molt relacionat amb la ubicació de la mostra a l'intestí. Hi ha un transcriptoma comú entre conjunts de dades, mentre que els microorganismes depenen del conjunt de dades. Podem millorar la classificació de les mostres tenint en compte tant l'ARNr 16S bacterià com el transcriptoma hoste.[spa] INTRODUCCIÓN: La enfermedad inflamatoria intestinal es una enfermedad intestinal compleja con factores genéticos y ambientales que pueden influir en su curso. La etiología y la fisiopatología de la enfermedad no se conocen por completo. Existen evidencias que el microbioma puede desempeijar un papel relevante. Encontrar relaciones entre el microbioma y la mucosa del huésped podría ayudar a avanzar en la prevención, el diagnóstico o el tratamiento. MÉTODOS: Basamos nuestro análisis en el ARNr 16S bacteriano intestinal y en datos de transcriptomas humanos de biopsias de múltiples puntos temporales y segmentos intestinales. Extendimos el análisis de correlación canónica generalizada regularizado para encontrar modelos coherentes con el conocimiento previo sobre la enfermedad teniendo en cuenta la información de las muestras. Se analizaron múltiples conjuntos de datos de enfermedad inflamatoria intestinal en diferentes tratamientos y condiciones y se compararon los modelos que definen esos conjuntos de datos. Los resultados se compararon con análisis de coinercia múltiple. RESULTADOS: Dividir las variables de la muestra en diferentes bloques resulta en modelos de estas relaciones que muestran diferencias en los genes y microorganismos seleccionados. Los modelos generados con nuestro nuevo método, inter-Rmodel, superaron el análisis de múltiples coinercias para clasificar las muestras según su ubicación. A pesar de ser utilizados en conjuntos de datos de diferentes fuentes, los modelos resultantes muestran unas relaciones similares entre las variables. DISCUSIÓN: La comparación de varios modelos ayuda a descubrir las relaciones dentro de los conjuntos de datos. Nuestro método encuentra cuán fuertes son las relaciones entre el microbioma, el transcriptoma y las variables ambientales. En diferentes conjuntos de datos, los genes seleccionados eran comunes. Este enfoque es robusto y flexible para diferentes conjuntos de datos y configuraciones. CONCLUSIÓN: Con inteRmodel descubrimos que el microbioma se relaciona más estrechamente con la ubicación de la muestra que con la enfermedad, pero el transcriptoma está muy relacionado con la ubicación de la muestra en el intestino. Hay un transcriptoma común entre los conjuntos de datos, mientras que los microorganismos dependen del conjunto de datos. Podemos mejorar la clasificación de las muestras teniendo en cuenta tanto el ARNr 16S bacteriano como el transcriptoma del huésped

    Bayesian methodologies for constrained spaces.

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    Due to advances in technology, there is a presence of directional data in a wide variety of fields. Often distributions to model directional data are defined on manifold or constrained spaces. Regular statistical methods applied to data defined on special geometries can give misleading results, and this demands new statistical theory. This dissertation addresses two such problems and develops Bayesian methodologies to improve inference in these arenas. It consists of two projects: 1. A Bayesian Methodology for Estimation for Sparse Canonical Correlation, and 2. Bayesian Analysis of Finite Mixture Model for Spherical Data. In principle, it can be challenging to integrate data measured on the same individuals occurring from different experiments and model it together to gain a larger understanding of the problem. Canonical Correlation Analysis (CCA) provides a useful tool for establishing relationships between such data sets. When dealing with high dimensional data sets, Structured Sparse CCA (ScSCCA) is a rapidly developing methodological area which seeks to represent the interrelations using sparse direction vectors for CCA. There is less development in Bayesian methodology in this area. We propose a novel Bayesian ScSCCA method with the use of a Bayesian infinite factor model. Using a multiplicative half Cauchy prior process, we bring in sparsity at the level of the projection matrix. Additionally, we promote further sparsity in the covariance matrix by using graphical horseshoe prior or diagonal structure. We compare the results for our proposed model with competing frequentist and Bayesian methods and apply the developed method to omics data arising from a breast cancer study. In the second project, we perform Bayesian Analysis for the von Mises Fisher (vMF) distribution on the sphere which is a common and important distribution used for directional data. In the first part of this project, we propose a new conjugate prior for the mean vector and concentration parameter of the vMF distribution. Further we prove its properties like finiteness, unimodality, and provide interpretations of its hyperparameters. In the second part, we utilize a popular prior structure for a mixture of vMF distributions. In this case, the posterior of the concentration parameter consists of an intractable Bessel function of the first kind. We propose a novel Data Augmentation Strategy (DAS) using a Negative Binomial Distribution that removes this intractable Bessel function. Furthermore, we apply the developed methodology to Diffusion Tensor Imaging (DTI) data for clustering to explore voxel connectivity in human brain

    Role of Natural Bioactive Compounds in the Rise and Fall of Cancers

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    Recent years have seen the idea of a close association between nutrition and the modulation of cancer development/progression reinforced. An increasing amount of experimental and epidemiological evidence has been produced supporting the concept that many different bioactive components of food (e.g. polyphenols, mono- and polyunsaturated fatty acids, methyl-group donors, etc.) may be implicated in either the promotion of or the protection against carcinogenesis. At the cellular level, such compounds can have an impact on different but sometimes intertwined processes, such as growth and differentiation, DNA repair, programmed cell death, and oxidative stress. In addition, compelling evidence is starting to build up of the existence of primary epigenetic targets of dietary compounds, such as oncogenic/oncosuppressor miRNAs or DNA-modifying enzymes, which in turn impair gene expression and function. Since there is a growing interest in the study of the biochemical and molecular role played by food components and its impact on cellular processes and/or gene expressions directed towards the fine-tuning of cancer phenotypes, in this Special Issue researchers contributed with either research or review articles presenting the latest findings on the intracellular pathways and mechanisms affected by natural bioactive dietary molecules

    2021 Program and Abstracts for the Celebration of Student Scholarship

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    Abstracts from the Celebration of Student Scholarship held in the Spring of 2021

    Community Detection in Multimodal Networks

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    Community detection on networks is a basic, yet powerful and ever-expanding set of methodologies that is useful in a variety of settings. This dissertation discusses a range of different community detection on networks with multiple and non-standard modalities. A major focus of analysis is on the study of networks spanning several layers, which represent relationships such as interactions over time, different facets of high-dimensional data. These networks may be represented by several different ways; namely the few-layer (i.e. longitudinal) case as well as the many-layer (time-series cases). In the first case, we develop a novel application of variational expectation maximization as an example of the top-down mode of simultaneous community detection and parameter estimation. In the second case, we use a bottom-up strategy of iterative nodal discovery for these longer time-series, abetted with the assumption of their structural properties. In addition, we explore significantly self-looping networks, whose features are inseparable from the inherent construction of spatial networks whose weights are reflective of distance information. These types of networks are used to model and demarcate geographical regions. We also describe some theoretical properties and applications of a method for finding communities in bipartite networks that are weighted by correlations between samples. We discuss different strategies for community detection in each of these different types of networks, as well as their implications for the broader contributions to the literature. In addition to the methodologies, we also highlight the types of data wherein these ``non-standard" network structures arise and how they are fitting for the applications of the proposed methodologies: particularly spatial networks and multilayer networks. We apply the top-down and bottom-up community detection algorithms to data in the domains of demography, human mobility, genomics, climate science, psychiatry, politics, and neuroimaging. The expansiveness and diversity of these data speak to the flexibility and ubiquity of our proposed methods to all forms of relational data.Doctor of Philosoph
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