482 research outputs found
Mining genetic, transcriptomic, and imaging data in Parkinson’s disease
No full textParkinson’s disease (PD) is a brain disorder that leads to shaking, stiffness and difficulties with walking, balance, and coordination. Affected people may also have mental and behavioral changes, sleep problems, depression, memory difficulties and fatigue. PD is an age-related disease, with an increased prevalence in populations of subjects over the age of 60. About 5 to 10% of PD patients have an "early-onset" variant and it is often, but not always, inherited. PD is characterized by the loss of groups of neurons involved in the control of voluntary movements. Here we present a novel imaging-genetics workflow on Parkinson’s disease aimed to discover some new potential candidate biomarkers for Parkinson’s disease onset, by interpolating genotyping, transcriptomic, functional (Dopamine Transporter Scan) and morphological (Magnetic Resonance Imaging) imaging data. The proposed tutorial has the aim to encourage and stimulate the attendees on the biomedical research with the advantage of integration of heterogenous data. In the last decade the use of images together with genetics data has become widespread among the bioinformatics researchers. This has allowed to inspect and investigate in detail different specific diseases, to better understand their origin and cause. While in recent years many imaging genetics analyses have been developed and successfully applied to characterize brain functioning and neurodegenerative diseases such as Alzheimer’s disease, to our knowledge, no standard imaging genetics workflow has been proposed for PD. The novelty of our workflow can be summarized as follows: • We propose a domain free and easy-to-use workflow, integrating heterogenous data, such as genotyping, transcriptomic, and imaging data. • The workflow addresses the complexity of integrating real multi-source data when a limited number of data are available by proposing three step-based method, where the first step integrates genotyping and imaging features considering each feature individually, the second step summarizes imaging features in a single measure, and the last step focuses on linking potential functional effects caused by the biomarkers found during the two previous phases. • We propose a validation of the method on genetic and imaging data related to PD, showing our new results. The data used for this tutorial were obtained from the Parkinson’s Progression Marker Initiative (PPMI) data portal. Currently, PPMI is the most complete and comprehensive collection of PD-related data. The dataset that will be used in the tutorial consists in a set of polymorphisms, more specifically insertions and deletions (indels) or Single Nucleotide Polymorphisms (SNPs), and transcriptomic data retrieved by RNA sequencing. In addition, DaTSCAN and MRI data are used, which have been shown to be effective in providing potential biomarkers for PD onset and progression. The attendees will acquire an experience on how to conduct a complete imaging-genetics workflow, in a specific case study of Parkinsonian subjects. After the tutorial session the attendees will be able to conduct themselves an imaging-genetics pipeline, which could also be applied to study other neurological diseases. The tutorial will introduce the partecipants to the biological background, especially with the notion of DNA, RNA, Single-nucleotide polymorphism (SNP) and Genome-Wide Association Study (GWAS). The participants will have the opportunity to get familiar with PLINK, a free, open-source whole genome association analysis toolset, designed to perform a range of basic, large-scale analyzes in a computationally efficient manner. It provides a large range of functionalities designed for data management, summary statistics, quality control, population stratification detection, association analysis, etc. for genotyping data analysis. The audience will also learn how to run code on the widely used R programming environment for statistical computing and graphics. They will also learn some notions about Python, especially how to deal efficiently, with genotyping data using Pandas library, which was designed for data manipulation and analysis. The tutorial code is wrapped in different Jupyter notebooks (formerly IPython Notebooks), that is a web-based and system-independent interactive computational environment for easy analysis reproducibility
Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities
No full textState-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes
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No full textAltmann tells the story of the Jewish community in Nikolsburg starting in 1370. He focuses specifically on the history of the Altmann family, especially Siegfried Altmann's grandparents. The second part of the manuscript deals with stories of Rabbi Mordechai Benet (1753-1829) as told to the author by his grand-aunt.See also archival collection.Altmann was born in Nikolsburg (Maehren) in 1887 and died in 1963 in New York. He was the director of the Institute for the Blind "Hohe Warte" in Vienna.see archival collection AR 1788Benet, MordechaiWalter, BrunoNikolsburg (Moravia)digitize
Fashion Culture: Fashion Metropolis Berlin
No full textBerlin was a fashion capital in the 1920s, with hundreds of thriving clothing manufacturers, most of them Jewish, before it was decimated by the Nazis. Author Uwe Westphal shares this history in a discussion with FIT historian Keren Ben-Horin and journalist Jennifer Altmann, whose grandfather ran one of Berlin’s fashion houses.Organized in partnership with the Museum at Eldridge Street
Multi view based imaging genetics analysis on Parkinson disease
No full textLongitudinal studies integrating imaging and genetic data have recently become widespread among bioinformatics researchers. Combining such heterogeneous data allows a better understanding of complex diseases origins and causes. Through a multi-view based workflow proposal, we show the common steps and tools used in imaging genetics analysis, interpolating genotyping, neuroimaging and transcriptomic data. We describe the advantages of existing methods to analyze heterogeneous datasets, using Parkinson’s Disease (PD) as a case study. Parkinson's disease is associated with both genetic and neuroimaging factors, however such imaging genetics associations are at an early investigation stage. Therefore it is desirable to have a free and open source workflow that integrates different analysis flows in order to recover potential genetic biomarkers in PD, as in other complex diseases
Economics in Persian-period biblical texts : their interactions with economic developments in the Persian period and earlier biblical traditions
No full textLarge-scale economic change such as the rise of coinage occurred during the Persian-dominated centuries (6th –4th centuries BCE) in the Eastern Mediterranean and ancient Near East. How do the biblical texts of the time respond to such developments?
In this study, Peter Altmann lays out foundational economic conceptions from the ancient Near East and earlier biblical traditions in order to show how Persian-period biblical texts build on these traditions to address the challenges of their day. Economic issues are central to the way that Ezra and Nehemiah approach the topics of temple building and of Judean self-understanding. Economic terminology and considerations also appear in Second Isaiah and the “Holiness Code.” Following significant interaction with the material culture and extra-biblical texts, the author devotes special attention to the ascendancy of economics and its theological and identity implications as structuring metaphors for divine action and human community in the Persian period.
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Bone mineral density and cardiovascular diseases: a two-sample Mendelian randomization study
No full textThe link between BMD and cardiovascular disease (CVD) remains a topic of extensive debate in observational studies, with inconsistent reports regarding the causality of this relationship. This study implements robust methodologies to evaluate the causal relationship between BMD and various CVDs. Two sample Mendelian randomization (MR) method was used to estimate the relationship between genetically predicted BMD and seven key CVDs: atrial fibrillation and flutter, angina, ischemic heart disease, heart failure, hypertension, myocardial infarction, and non-ischemic cardiomyopathy. Data were obtained from independent publicly available genome-wide association studies (GWAS) for BMD and CVDs, using two separate datasets for the cardiovascular outcomes: the UK Biobank cohort (primary analysis) and the FinnGen cohort (validation analysis). The MR Pleiotropy RESidual Sum and Outlier test assessed the heterogeneity and pleiotropy of selected instrumental variables (IVs). We applied the inverse variance weighted model (IVW), weighted median, weighted mode method, and MR-Egger regression model to estimate causal effects. MR results indicate no relationship between BMD and atrial fibrillation and flutter (IVW, beta-estimate: 0.011, SE: 0.03, p =. 73), angina (IVW, beta-estimate: 0.04, SE: 0.03, p =. 17), chronic ischemic heart disease (IVW, beta-estimate: 0.009, SE: 0.03, p =. 74), heart failure (IVW, beta-estimate: 0.004, SE: 0.04, p =. 91), hypertension (IVW, beta-estimate: -0.01, SE: 0.01, p =. 44), myocardial infarction (IVW, beta-estimate: 0.02, SE: 0.03, p =. 36), or non-ischemic cardiomyopathy (IVW, beta-estimate: 0.1, SE: 0.08, p =. 20). These findings remained consistent across all complementary analyses (MR-Egger, weighted median and weighted mode) and were validated using the FinnGen cohort GWAS dataset. This comprehensive analysis identified no evidence for a causal link between genetically predicted BMD and a range of key CVDs. Previously reported observational associations between bone and cardiovascular health likely represent shared risk factors rather than direct causal mechanisms.</p
Unsupervised and multi-modal representation learning for studying heterogeneous neurological diseases
No full textOne of the challenges of studying common neurological disorders is disease heterogeneity including differences in causes, neuroimaging characteristics, comorbidities, or genetic variation. Because of this, disease labels are often poorly defined, if available at all, making supervised learning methods unsuitable for studying heterogeneous diseases. Normative modelling is one approach to studying heterogeneous brain disorders by quantifying how brain imaging-based measures of individuals deviate from a healthy population. The normative model provides a statistical description of the ‘normal’ range that can be used at subject level to detect deviations, which relate to pathological effects. Traditional normative models use a mass-univariate approach which is computationally costly and ignores the interactions and dependencies among multiple brain regions. This thesis introduces deep learning-based normative models, using autoencoders, and accompanying deviation metrics in the latent and feature space.
For many neurological diseases, we expect to observe abnormalities across a range of neuroimaging and biological variables. However, existing normative models have predominantly focused on studying a single imaging modality. In this thesis, we develop a multi-modal normative modelling framework using multi-modal Variational Autoencoders. Aggregating abnormality across variables of multiple modalities, this framework proves more effective in detecting deviations compared to uni-modal baselines. The multi-modal normative models developed in this work are then applied to complex, real-world datasets for the study of epilepsy and early-stage Alzheimer's disease.
Multi-modal autoencoders are increasingly being applied to biomedical data. However, comparing different approaches remains a challenge as existing implementations, if available, use different deep learning frameworks and programming styles. To address this issue, we develop a Python library of multi-modal autoencoder implementations, accompanied by educational materials. This library forms the foundation for all multi-modal normative modelling analyses conducted in this thesis
Reinhard Köhler's Scientific Production: Words, Numbers and Pictures, di Arjuna Tuzzi
No full textThis study draws upon statistical analysis techniques of textual data to examine a corpus composed of 22 research articles published between 1997 and 2010 by Reinhard Köhler as a single author or in collaboration with other scholars. The aim of this article is to draw a representation of the main areas of interest of his research. After having drawn an overall representation of the corpus, Köhler’s latest work – an unpublished volume on Quantitative Syntax Analysis – was analysed to understand its role within the context of his research
Disentangling the association between genetics and functional connectivity in Mild Cognitive Impairment
No full textDespite the increasing effort being devoted to the investigation of the link between imaging endophenotypes (IDPs) and genetic determinants (GDs) in Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD), many issues remain open and deserve investigation. Among these, the role of functional connectivity (FC) is still blurred. This paper aims at shading some light on the topic relying on the ADNI repository (177 patients, out of which 82 MCI and 95 controls). The within/between-network connectivities were derived from individual FC matrices and used as IDPs. Conversely, the GDs consisted of two Polygenic Risk Scores (PRS) that have recently been proven to play a role in AD. A Partial Least Squares (PLS) model equipped with LASSO regularization was finally fitted to the data for associating IDPs and GDs. In the first component, all FC coefficients had the same sign, and were correlated with PRS2. Connectivities involving the dorsal attention (DAN) and frontoparietal control (CON) networks reached the highest weights, while within/between-network FC for the limbic (LIM) were less represented. Overall, the within-network FC values were less pronounced compared to the between-network ones. In the second component, most of the FC features had zero weights. Visual (VIS) and somatomotory (SMN) showed a correlated trend, while being anti-correlated with LIM, CON and default mode network as well as with PRS1. Our findings suggest that the two PRSs correlated with a possible pattern of aberrant within/between-network FC changes occurring in RSNs devoted to higher cognitive functions and more vulnerable in this pathology
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