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417 results on '"Wild, P.-J."'

1. Somatic CAG repeat expansion in blood associates with biomarkers of neurodegeneration in Huntington’s disease decades before clinical motor diagnosis

Author

Scahill, Rachael I., Farag, Mena, Murphy, Michael J., Hobbs, Nicola Z., Leocadi, Michela, Langley, Christelle, Knights, Harry, Ciosi, Marc, Fayer, Kate, Nakajima, Mitsuko, Thackeray, Olivia, Gobom, Johan, Rönnholm, John, Weiner, Sophia, Hassan, Yara R., Ponraj, Nehaa K. P., Estevez-Fraga, Carlos, Parker, Christopher S., Malone, Ian B., Hyare, Harpreet, Long, Jeffrey D., Heslegrave, Amanda, Sampaio, Cristina, Zhang, Hui, Robbins, Trevor W., Zetterberg, Henrik, Wild, Edward J., Rees, Geraint, Rowe, James B., Sahakian, Barbara J., Monckton, Darren G., Langbehn, Douglas R., and Tabrizi, Sarah J.

Published
2025
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17. A proteomics analysis of 5xFAD mouse brain regions reveals the lysosome-associated protein Arl8b as a candidate biomarker for Alzheimer’s disease

Author

Boeddrich, Annett, Haenig, Christian, Neuendorf, Nancy, Blanc, Eric, Ivanov, Andranik, Kirchner, Marieluise, Schleumann, Philipp, Bayraktaroğlu, Irem, Richter, Matthias, Molenda, Christine Mirjam, Sporbert, Anje, Zenkner, Martina, Schnoegl, Sigrid, Suenkel, Christin, Schneider, Luisa-Sophie, Rybak-Wolf, Agnieszka, Kochnowsky, Bianca, Byrne, Lauren M., Wild, Edward J., Nielsen, Jørgen E., Dittmar, Gunnar, Peters, Oliver, Beule, Dieter, and Wanker, Erich E.

Published
2023
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20. CT-radiomics and clinical risk scores for response and overall survival prognostication in TACE HCC patients

Author

Bernatz, Simon, Elenberger, Oleg, Ackermann, Jörg, Lenga, Lukas, Martin, Simon S., Scholtz, Jan-Erik, Koch, Vitali, Grünewald, Leon D., Herrmann, Yannis, Kinzler, Maximilian N., Stehle, Angelika, Koch, Ina, Zeuzem, Stefan, Bankov, Katrin, Doering, Claudia, Reis, Henning, Flinner, Nadine, Schulze, Falko, Wild, Peter J., Hammerstingl, Renate, Eichler, Katrin, Gruber-Rouh, Tatjana, Vogl, Thomas J., dos Santos, Daniel Pinto, and Mahmoudi, Scherwin

Published
2023
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21. Huntington’s disease mice and human brain tissue exhibit increased G3BP1 granules and TDP43 mislocalization

Author

Sanchez, Isabella I, Nguyen, Thai B, England, Whitney E, Lim, Ryan G, Vu, Anthony Q, Miramontes, Ricardo, Byrne, Lauren M, Markmiller, Sebastian, Lau, Alice L, Orellana, Iliana, Curtis, Maurice A, Faull, Richard Lewis Maxwell, Yeo, Gene W, Fowler, Christie D, Reidling, Jack C, Wild, Edward J, Spitale, Robert C, and Thompson, Leslie M

Subjects
Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Huntington's Disease, Brain Disorders, Neurodegenerative, Rare Diseases, Genetics, Neurosciences, 2.1 Biological and endogenous factors, Neurological, Animals, Cytoplasmic Granules, DNA Helicases, DNA-Binding Proteins, Female, Hippocampus, Humans, Huntington Disease, Male, Mice, MicroRNAs, Neurons, Poly-ADP-Ribose Binding Proteins, Prefrontal Cortex, Protein Transport, RNA Helicases, RNA Recognition Motif Proteins, Neurodegeneration, Neuroscience, Medical and Health Sciences, Immunology, Biological sciences, Biomedical and clinical sciences, Health sciences
Abstract

Chronic cellular stress associated with neurodegenerative disease can result in the persistence of stress granule (SG) structures, membraneless organelles that form in response to cellular stress. In Huntington's disease (HD), chronic expression of mutant huntingtin generates various forms of cellular stress, including activation of the unfolded protein response and oxidative stress. However, it has yet to be determined whether SGs are a feature of HD neuropathology. We examined the miRNA composition of extracellular vesicles (EVs) present in the cerebrospinal fluid (CSF) of patients with HD and show that a subset of their target mRNAs were differentially expressed in the prefrontal cortex. Of these targets, SG components were enriched, including the SG-nucleating Ras GTPase-activating protein-binding protein 1 (G3BP1). We investigated localization and levels of G3BP1 and found a significant increase in the density of G3BP1-positive granules in the cortex and hippocampus of R6/2 transgenic mice and in the superior frontal cortex of the brains of patients with HD. Intriguingly, we also observed that the SG-associated TAR DNA-binding protein 43 (TDP43), a nuclear RNA/DNA binding protein, was mislocalized to the cytoplasm of G3BP1 granule-positive HD cortical neurons. These findings suggest that G3BP1 SG dynamics may play a role in the pathophysiology of HD.

Published
2021

26. On Traversing the Data Landscape: Introducing APIs to Data-Science Students

Author

Fergusson, Anna and Wild, Chris J.

Abstract

The explosion in availability and variety of data requires learning experiences that reveal more of the data world faster and develop practical skills with digital technologies. Key high-level goals of the International Data Science in Schools Project (IDSSP) include having students continually immersed in the cycle of learning from data, and data science being fun to teach and fun to learn. We advocate curiosity-driven, exploratory learning for pursuing these goals. Our illustrations use tasks embedded in contexts that teenagers can relate to, provide visual rewards for computational actions, use rich data-contexts, and integrate statistical and computational thinking. They provide engaging introductions to modern data sourced from databases via Application Programming Interfaces (APIs) that are accessible to a broad range of students and facilitate student personalization for investigation. We provide in-depth discussion of teaching strategies that heavily involve questioning and student tinkering supported by graphical-user interfaces that enable students to interact with the data sources rapidly in multiple ways.

Published
2021
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27. Computational Skills by Stealth in Introductory Data Science Teaching

Author

Burr, Wesley, Chevalier, Fanny, Collins, Christopher, Gibbs, Alison L, Ng, Raymond, and Wild, Chris J

Abstract

In 2010, Nolan and Temple Lang proposed "integration of computing concepts into statistics curricula at all levels." The unprecedented growth in data and emphasis on data science has provided an impetus to finally realizing full implementations of this in new statistics and data science programs and courses. We discuss a proposal for the stealth development of computational skills in students' exposure to introductory data science through careful, scaffolded exposure to computation and its power. Our intent is to support students, regardless of interest and self-efficacy in coding, in becoming data-driven learners, who are capable of asking complex questions about the world around them, and then answering those questions through the use of data-driven inquiry. Reference is made to the computer science and statistics consensus curriculum frameworks the International Data Science in Schools Project (IDSSP) recently published for secondary school data science or introductory tertiary programs, designed to optimize data-science accessibility.

Published
2021
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34. Inferring clonal composition from multiple tumor biopsies

Author

Manica, Matteo, Kim, Hyunjae Ryan, Mathis, Roland, Chouvarine, Philippe, Rutishauser, Dorothea, Roditi, Laura De Vargas, Szalai, Bence, Wagner, Ulrich, Oehl, Kathrin, Saba, Karim, Pati, Arati, Saez-Rodriguez, Julio, Roy, Angshumoy, Parsons, Donald W., Wild, Peter J., Martínez, María Rodríguez, and Sumazin, Pavel

Subjects
Quantitative Biology - Genomics
Abstract

Explicit accounting for copy number alterations can dramatically improve mutation frequency estimates, leading to more accurate phylogeny reconstructions and subclone characterizations.

Published
2017

35. On locating statistics in the world of finding out

Author

Wild, Chris J.

Subjects
Statistics - Other Statistics
Abstract

This paper attempts to situate statistics in relation to qualitative research methods and other means of "finding out". It compares and contrasts aspects of qualitative research methods and statistical inquiry and attempts to answer the question of whether and how elements of qualitative research methods should be included in statistics teaching., Comment: 9 pages. The paper is an extended discussion of "Enhancing statistics education by including qualitative research" by Irena Ograjen\v{s}ek and Iddo Gal that will appear, in some form, in the International Statistical Review together with the parent paper. It can however be read as a stand-alone paper

Published
2015

36. Highly functionalized organic nitrates in the southeast United States: Contribution to secondary organic aerosol and reactive nitrogen budgets

Author

Lee, Ben H, Mohr, Claudia, Lopez-Hilfiker, Felipe D, Lutz, Anna, Hallquist, Mattias, Lee, Lance, Romer, Paul, Cohen, Ronald C, Iyer, Siddharth, Kurtén, Theo, Hu, Weiwei, Day, Douglas A, Campuzano-Jost, Pedro, Jimenez, Jose L, Xu, Lu, Ng, Nga Lee, Guo, Hongyu, Weber, Rodney J, Wild, Robert J, Brown, Steven S, Koss, Abigail, de Gouw, Joost, Olson, Kevin, Goldstein, Allen H, Seco, Roger, Kim, Saewung, McAvey, Kevin, Shepson, Paul B, Starn, Tim, Baumann, Karsten, Edgerton, Eric S, Liu, Jiumeng, Shilling, John E, Miller, David O, Brune, William, Schobesberger, Siegfried, D'Ambro, Emma L, and Thornton, Joel A

Subjects
particulate organic nitrates, online measurement, lifetime calculation, high-resolution time-of-flight chemical ionization mass spectrometer, biogenic volatile organic compound oxidation
Abstract

Speciated particle-phase organic nitrates (pONs) were quantified using online chemical ionization MS during June and July of 2013 in rural Alabama as part of the Southern Oxidant and Aerosol Study. A large fraction of pONs is highly functionalized, possessing between six and eight oxygen atoms within each carbon number group, and is not the common first generation alkyl nitrates previously reported. Using calibrations for isoprene hydroxynitrates and the measured molecular compositions, we estimate that pONs account for 3% and 8% of total submicrometer organic aerosol mass, on average, during the day and night, respectively. Each of the isoprene- and monoterpenes-derived groups exhibited a strong diel trend consistent with the emission patterns of likely biogenic hydrocarbon precursors. An observationally constrained diel box model can replicate the observed pON assuming that pONs (i) are produced in the gas phase and rapidly establish gas-particle equilibrium and (ii) have a short particle-phase lifetime (∼2-4 h). Such dynamic behavior has significant implications for the production and phase partitioning of pONs, organic aerosol mass, and reactive nitrogen speciation in a forested environment.

Published
2016

37. A possible gut microbiota basis for weight gain side effects of antipsychotic drugs

Author

Joshi, Harshad, Parihar, Ankita, Jiao, Dazhi, Murali, Shwetha, and Wild, David J.

Subjects
Quantitative Biology - Genomics
Abstract

Weight gain is a well-established side effect of both conventional and newer anti-psychotic drugs, but the cause is not well understood. Recent studies correlate obesity with the presence or absence of particular genetic sequences in the gut microbiota. We identified strong associations between protein targets of antipsychotics and microbiota sequences directly related to weight regulation in human body, leading to a potential metagenomic mechanism of action. Further experimental study is recommended., Comment: 8 pages, 1 Table

Published
2014

38. Comparison of machine learning algorithms to predict clinically significant prostate cancer of the peripheral zone with multiparametric MRI using clinical assessment categories and radiomic features

Author

Bernatz, Simon, Ackermann, Jörg, Mandel, Philipp, Kaltenbach, Benjamin, Zhdanovich, Yauheniya, Harter, Patrick N., Döring, Claudia, Hammerstingl, Renate, Bodelle, Boris, Smith, Kevin, Bucher, Andreas, Albrecht, Moritz, Rosbach, Nicolas, Basten, Lajos, Yel, Ibrahim, Wenzel, Mike, Bankov, Katrin, Koch, Ina, Chun, Felix K.-H., Köllermann, Jens, Wild, Peter J., and Vogl, Thomas J.

Published
2020
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39. Predictors for a dementia gene mutation based on gene-panel next-generation sequencing of a large dementia referral series

Author

Koriath, C., Kenny, J., Adamson, G., Druyeh, R., Taylor, W., Beck, J., Quinn, L., Mok, T. H., Dimitriadis, A., Norsworthy, P., Bass, N., Carter, J., Walker, Z., Kipps, C., Coulthard, E., Polke, J. M., Bernal-Quiros, M., Denning, N., Thomas, R., Raybould, R., Williams, J., Mummery, C. J., Wild, E. J., Houlden, H., Tabrizi, S. J., Rossor, M. N., Hummerich, H., Warren, J. D., Rowe, J. B., Rohrer, J. D., Schott, J. M., Fox, N. C., Collinge, J., and Mead, S.

Published
2020
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42. Meta Path-Based Collective Classification in Heterogeneous Information Networks

Author

Kong, Xiangnan, Cao, Bokai, Yu, Philip S., Ding, Ying, and Wild, David J.

Subjects
Computer Science - Learning, Statistics - Machine Learning
Abstract

Collective classification has been intensively studied due to its impact in many important applications, such as web mining, bioinformatics and citation analysis. Collective classification approaches exploit the dependencies of a group of linked objects whose class labels are correlated and need to be predicted simultaneously. In this paper, we focus on studying the collective classification problem in heterogeneous networks, which involves multiple types of data objects interconnected by multiple types of links. Intuitively, two objects are correlated if they are linked by many paths in the network. However, most existing approaches measure the dependencies among objects through directly links or indirect links without considering the different semantic meanings behind different paths. In this paper, we study the collective classification problem taht is defined among the same type of objects in heterogenous networks. Moreover, by considering different linkage paths in the network, one can capture the subtlety of different types of dependencies among objects. We introduce the concept of meta-path based dependencies among objects, where a meta path is a path consisting a certain sequence of linke types. We show that the quality of collective classification results strongly depends upon the meta paths used. To accommodate the large network size, a novel solution, called HCC (meta-path based Heterogenous Collective Classification), is developed to effectively assign labels to a group of instances that are interconnected through different meta-paths. The proposed HCC model can capture different types of dependencies among objects with respect to different meta paths. Empirical studies on real-world networks demonstrate that effectiveness of the proposed meta path-based collective classification approach.

Published
2013

46. Huntington’s Disease Clinical Trials Update: September 2024

Author

Farag, Mena, Tabrizi, Sarah J, and Wild, Edward J

Abstract

In this edition of the Huntington's Disease Clinical Trials Update, we expand on the ongoing extension study of PTC518 from PTC Therapeutics, including 12-month interim results from the parent study. We also discuss 24-month interim results from uniQure's AMT-130 program and 28-week follow-up results from Wave Life Sciences’ SELECT-HD clinical trial of WVE-003. Additionally, we provide a comprehensive listing of all currently registered and ongoing clinical trials in Huntington's disease.

Published
2024
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47. Contact Measurements on Atomic BEC

Author

Wild, R. J., Makotyn, P., Pino, J. M., Cornell, E. A., and Jin, D. S.

Subjects
Condensed Matter - Quantum Gases
Abstract

A powerful set of universal relations, centered on a quantity called the contact, connects the strength of short-range two-body correlations to the thermodynamics of a many-body system with delta-function interactions. We report on measurements of the contact, using RF spectroscopy, for an $^{85}$Rb atomic Bose-Einstein condensate (BEC). For bosons, the fact that contact spectroscopy can be used to probe the gas on short timescales is useful given the decreasing stability of BECs with increasing interactions. A complication is the added possibility, for bosons, of three-body interactions. In investigating this issue, we have located an Efimov resonance for $^{85}$Rb atoms with loss measurements and thus determined the three-body interaction parameter. In our contact spectroscopy, in a region of observable beyond-mean-field effects, we find no measurable contribution from three-body physics., Comment: 5 pages, 4 figures

Published
2011
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48. Semantic Inference using Chemogenomics Data for Drug Discovery

Author

Zhu, Qian, Sun, Yuyin, Challa, Sashikiran, Ding, Ying, Lajiness, Michael S., and Wild, David J.

Subjects
Quantitative Biology - Quantitative Methods, Computer Science - Digital Libraries, Computer Science - Information Retrieval
Abstract

Background Semantic Web Technology (SWT) makes it possible to integrate and search the large volume of life science datasets in the public domain, as demonstrated by well-known linked data projects such as LODD, Bio2RDF, and Chem2Bio2RDF. Integration of these sets creates large networks of information. We have previously described a tool called WENDI for aggregating information pertaining to new chemical compounds, effectively creating evidence paths relating the compounds to genes, diseases and so on. In this paper we examine the utility of automatically inferring new compound-disease associations (and thus new links in the network) based on semantically marked-up versions of these evidence paths, rule-sets and inference engines. Results Through the implementation of a semantic inference algorithm, rule set, Semantic Web methods (RDF, OWL and SPARQL) and new interfaces, we have created a new tool called Chemogenomic Explorer that uses networks of ontologically annotated RDF statements along with deductive reasoning tools to infer new associations between the query structure and genes and diseases from WENDI results. The tool then permits interactive clustering and filtering of these evidence paths. Conclusions We present a new aggregate approach to inferring links between chemical compounds and diseases using semantic inference. This approach allows multiple evidence paths between compounds and diseases to be identified using a rule-set and semantically annotated data, and for these evidence paths to be clustered to show overall evidence linking the compound to a disease. We believe this is a powerful approach, because it allows compound-disease relationships to be ranked by the amount of evidence supporting them., Comment: 23 pages, 9 figures, 4 tables

Published
2011
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49. Finding Complex Biological Relationships in Recent PubMed Articles Using Bio-LDA

Author

Wang, Huijun, Ding, Ying, Tang, Jie, Dong, Xiao, He, Bing, Qiu, Judy, and Wild, David J.

Subjects
Quantitative Biology - Quantitative Methods
Abstract

The overwhelming amount of available scholarly literature in the life sciences poses significant challenges to scientists wishing to keep up with important developments related to their research, but also provides a useful resource for the discovery of recent information concerning genes, diseases, compounds and the interactions between them. In this paper, we describe an algorithm called Bio-LDA that uses extracted biological terminology to automatically identify latent topics, and provides a variety of measures to uncover putative relations among topics and bio-terms. Relationships identified using those approaches are combined with existing data in life science datasets to provide additional insight. Three case studies demonstrate the utility of the Bio-LDA model, including association predication, association search and connectivity map generation. This combined approach offers new opportunities for knowledge discovery in many areas of biology including target identification, lead hopping and drug repurposing., Comment: 14 pages, 8 figures, 10 tables

Published
2011
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50. Chem2Bio2RDF: A Linked Open Data Portal for Chemical Biology

Author

Chen, Bin, Wild, David J, Zhu, Qian, Ding, Ying, Dong, Xiao, Sankaranarayanan, Madhuvanthi, Wang, Huijun, and Sun, Yuyin

Subjects
Computer Science - Information Retrieval, Quantitative Biology - Other Quantitative Biology, D.2.12
Abstract

The Chem2Bio2RDF portal is a Linked Open Data (LOD) portal for systems chemical biology aiming for facilitating drug discovery. It converts around 25 different datasets on genes, compounds, drugs, pathways, side effects, diseases, and MEDLINE/PubMed documents into RDF triples and links them to other LOD bubbles, such as Bio2RDF, LODD and DBPedia. The portal is based on D2R server and provides a SPARQL endpoint, but adds on few unique features like RDF faceted browser, user-friendly SPARQL query generator, MEDLINE/PubMed cross validation service, and Cytoscape visualization plugin. Three use cases demonstrate the functionality and usability of this portal., Comment: 8 pages, 10 figures

Published
2010

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