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The anatomic determinants of conductive hearing loss secondary to tympanic membrane perforation

Journal of Otology 2017年第3期12卷 125-131页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery & Communication Sciences Department of Surgery Duke University Medical Center Durham NC USA Computational Biology & Bioinformatics PhD Program Duke University Durham NC USA Department of Mechanical Engineering and Materials Science Duke University Durham NC USA

Objectives: Recent studies have introduced middle ear volume(MEV) as a novel determinant of perforation-induced conductive hearing loss(CHL) in a mechanism driven by trans-tympanic membrane pressure differences. The primary aims of this preliminary report are to: 1) correlate CHL with perforation size; 2) describe the relationship between CHL and MEV; and 3) compare CHL across a range of cholesteatoma ***: A retrospective pilot study was performed in 31 subjects with audiometry indicative of conductive hearing loss, temporal bone CT scans,and no prior middle ear surgery. Perforation size and MEV were analyzed with respect to CHL in a cohort of 10 perforated ears with no cholesteatoma. CHLs were compared in 3 groups defined by extent of cholesteatoma ***: Ears with large and small perforations showed mean ABG values of 32.0 ± 15.7 dB and 16.0 ± 16.4 dB, respectively. A direct relationship was observed between MEV and CHL for ears with large perforations across all frequencies, whereas this relationship for small perforations was frequency-dependent. Finally, a statistically significant increase in CHL was found across ears with increasing cholesteatoma involvement at 1000 Hz(X^2(2) = 9.786, p = 0.008),2000 Hz(x^2(2) = 8.455, p = 0.015),and 4000 Hz(x^2(2)= 8.253, p = 0.016).Conclusions: These pilot data suggest that greater perforation-induced conductive hearing losses may be associated with larger perforation sizes and cholesteatoma. The correlation between MEV and CHL may require additional study.

关键词： Perforation-induced hearing loss Conductive hearing loss Middle ear volume Three-dimensional volume reconstruction

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Unsupervised extraction of phenotypes from cancer clinical notes for association studies

arXiv

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arXiv 2019年

作者： Stark, Stefan G. Hyland, Stephanie L. Pradier, Melanie F. Lehmann, Kjong-Van Wicki, Andreas Perez-Cruz, Fernando Vogt, Julia E. Rätsch, Gunnar Computational Biology Program Memorial Sloan Kettering Cancer Center New York United States Tri-Institutional Ph.D. Program in Computational Biology and Medicine Weill Cornell Medicine New York United States Department of Computer Science ETH Zürich Zürich Switzerland Medical Informatics Group University Hospital Zürich Zürich Switzerland Swiss Institute for Bioinformatics Zurich Switzerland Department of Biology ETH Zürich Zürich Switzerland Department of Signal Processing and Information Theory University Carlos III in Madrid Leganés Spain School of Engineering and Applied Sciences Harvard University CambridgeMA United States Department of Biomedicine University of Basel Basel Switzerland Tumorzentrum University Hospital Basel Basel Switzerland Swiss Data Science Center ETH Zürich and EPFL Lausanne Switzerland Department of Mathematics and Computer Science University of Basel Basel Switzerland

The recent adoption of Electronic Health Records (EHRs) by healthcare providers has introduced an important source of data that provides detailed and highly specific insights into patient phenotypes over large cohorts. These datasets, in combination with machine learning and statistical approaches, generate new opportunities for research and clinical care. However, many methods require the patient representations to be in structured formats, while the information in the EHR is often locked in unstructured text designed for human readability. In this work, we develop the methodology to automatically extract clinical features from clinical narratives from large EHR corpora without the need for prior knowledge. We consider medical terms and sentences appearing in clinical narratives as atomic information units. We propose an efficient clustering strategy suitable for the analysis of large text corpora and utilize the clusters to represent information about the patient compactly. Additionally, we define the sentences on ontologic and natural language vocabularies to automatically detect pertinent combinations of concepts present in the corpus, even when an ontology is not available. To demonstrate the utility of our approach, we perform an association study of clinical features with somatic mutation profiles from 4,007 cancer patients and their tumors. We apply the proposed algorithm to a dataset consisting of .65 thousand documents with a total of .3.2 million sentences. After correcting for cancer type and other confounding factors, we identify a total of 340 significant statistical associations between the presence of somatic mutations and clinical features. We annotated these associations according to their novelty and we report several known associations. We also propose 37 plausible, testable hypothesis for associations where the underlying biological mechanism does not appear to be known. These results illustrate that the automated discovery of clinical features

关键词： Diseases

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Linking Symptom Inventories using Semantic Textual Similarity

arXiv

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arXiv 2023年

作者： Kennedy, Eamonn Vadlamani, Shashank Lindsey, Hannah M. Peterson, Kelly S. O'Connor, Kristen Dams Murray, Kenton Agarwal, Ronak Amiri, Houshang H. Andersen, Raeda K. Babikian, Talin Baron, David A. Bigler, Erin D. Caeyenberghs, Karen Delano-Wood, Lisa Disner, Seth G. Dobryakova, Ekaterina Eapen, Blessen C. Edelstein, Rachel M. Esopenko, Carrie Genova, Helen M. Geuze, Elbert Goodrich-Hunsaker, Naomi J. Grafman, Jordan Håberg, Asta K. Hodges, Cooper B. Hoskinson, Kristen R. Hovenden, Elizabeth S. Irimia, Andrei Jahanshad, Neda Jha, Ruchira M. Keleher, Finian Kenney, Kimbra Koerte, Inga K. Liebel, Spencer W. Livny, Abigail Løvstad, Marianne Martindale, Sarah L. Max, Jeffrey E. Mayer, Andrew R. Meier, Timothy B. Menefee, Deleene S. Mohamed, Abdalla Z. Mondello, Stefania Monti, Martin M. Morey, Rajendra A. Newcombe, Virginia Newsome, Mary R. Olsen, Alexander Pastorek, Nicholas J. Pugh, Mary Jo Razi, Adeel Resch, Jacob E. Rowland, Jared A. Russell, Kelly Ryan, Nicholas P. Scheibel, Randall S. Schmidt, Adam T. Spitz, Gershon Stephens, Jaclyn A. Tal, Assaf Talbert, Leah D. Tartaglia, Maria Carmela Taylor, Brian A. Thomopoulos, Sophia I. Troyanskaya, Maya Valera, Eve M. van der Horn, Harm Jan Van Horn, John D. Verma, Ragini Wade, Benjamin S.C. Walker, Willian S.C. Ware, Ashley L. Werner, J. Kent Yeates, Keith Owen Zafonte, Ross D. Zeineh, Michael M. Zielinski, Brandon Thompson, Paul M. Hillary, Frank G. Tate, David F. Wilde, Elisabeth A. Dennis, Emily L. Department of Neurology University of Utah School of Medicine Salt Lake CityUT United States George E. Wahlen Veterans Affairs Medical Center Salt Lake CityUT United States Division of Epidemiology University of Utah Salt Lake CityUT United States Office of Analytics and Performance Integration [VHA United States Department of Rehabilitation and Human Performance Icahn School of Medicine at Mount Sinai New YorkNY United States Department of Neurology Icahn School of Medicine at Mount Sinai New YorkNY United States Human Language Technologies Center of Excellence Johns Hopkins University BaltimoreMD United States Neuroscience Research Center Institute of Neuropharmacology Kerman University of Medical Sciences Kerman Iran Department of Radiology and Nuclear Medicine Vrije Universiteit Amsterdam Amsterdam UMC Location VUmc Amsterdam Netherlands Crawford Research Institute Shepherd Center AtlantaGA United States Department of Sociology Georgia State University AtlantaGA United States Department of Psychiatry and Biobehavioral Sciences Semel Institute for Neuroscience and Human Behavior UCLA Los AngelesCA United States UCLA BrainSPORT Program Los AngelesCA United States Center for Behavioral Health and Sport Dept. of Psychiatry Western University of Health Sciences Lebanon OR PomonaCA United States Department of Psychology Neuroscience Center Brigham Young University ProvoUT United States Department of Psychiatry University of Utah Salt Lake CityUT United States Cognitive Neuroscience Unit School of Psychology Deakin University Geelong Australia VA San Diego Healthcare System Center of Stress and Mental Health Department of Psychiatry UC San Diego School of Medicine San DiegoCA United States Department of Psychiatry UC San Diego School of Medicine San DiegoCA United States Minneapolis VA Health Care System MinneapolisMN United States Department of Psychiatry University of Minnesota MinneapolisMN United States Center for Traumatic Brain

An extensive library of symptom inventories has been developed over time to measure clinical symptoms, but this variety has led to several long standing issues. Most notably, results drawn from different settings and studies are not comparable, which limits reproducibility. Here, we present an artificial intelligence (AI) approach using semantic textual similarity (STS) to link symptoms and scores across previously incongruous symptom inventories. We tested the ability of four pre-trained STS models to screen thousands of symptom description pairs for related content - a challenging task typically requiring expert panels. Models were tasked to predict symptom severity across four different inventories for 6,607 participants drawn from 16 international data sources. The STS approach achieved 74.8% accuracy across five tasks, outperforming other models tested. This work suggests that incorporating contextual, semantic information can assist expert decision-making processes, yielding gains for both general and disease-specific clinical assessment. © 2023, CC BY-NC-SA.

关键词： Decision making

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Author Correction: lentiMPRA and MPRAflow for high-throughput functional characterization of gene regulatory elements

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Nature protocols 2021年第7期16卷 3736页

作者： M Grace Gordon Fumitaka Inoue Beth Martin Max Schubach Vikram Agarwal Sean Whalen Shiyun Feng Jingjing Zhao Tal Ashuach Ryan Ziffra Anat Kreimer Ilias Georgakopoulos-Soares Nir Yosef Chun Jimmie Ye Katherine S Pollard Jay Shendure Martin Kircher Nadav Ahituv Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. Institute for Human Genetics University of California San Francisco San Francisco CA USA. Biological and Medical Informatics Graduate Program University of California San Francisco San Francisco CA USA. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Institute for Human Genetics University of California San Francisco San Francisco CA USA. fumitaka.inoue@ucsf.edu. Berlin Institute of Health (BIH) Berlin Germany. Department of Genome Sciences University of Washington Seattle WA USA. Calico Life Sciences LLC South San Francisco CA USA. Gladstone Institutes San Francisco CA USA. Department of Electrical Engineering and Computer Sciences and Center for Computational Biology University of California Berkeley Berkeley CA USA. Chan-Zuckerberg Biohub San Francisco CA USA. Division of Rheumatology Department of Medicine University of California San Francisco San Francisco CA USA. Institute for Computational Health Sciences University of California San Francisco San Francisco California USA. Department of Epidemiology and Biostatistics and Institute of Computational Health Sciences University of California San Francisco San Francisco CA USA. Department of Genome Sciences University of Washington Seattle WA USA. shendure@uw.edu. Howard Hughes Medical Institute Seattle WA USA. shendure@uw.edu. Brotman Baty Institute for Precision Medicine University of Washington Seattle WA USA. shendure@uw.edu. Berlin Institute of Health (BIH) Berlin Germany. martin.kircher@bihealth.de. Charité-Universitätsmedizin Berlin Berlin Germany. martin.kircher@bihealth.de. Department of Bioengineering and Therapeutic Sciences University of California San Francisco San Francisco CA USA. nadav.ahituv@ucsf.edu. Institute for Human Genetics University of California San

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Disagreement in middle ear volume estimation between tympanometry and three-dimensional volume reconstruction in the context of tympanic membrane perforation

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Journal of Otology 2017年第2期12卷 74-79页

作者： David J.Carpenter Debara L.Tucci David M.Kaylie Dennis O.Frank-Ito Division of Head and Neck Surgery&Communication Sciences Department of SurgeryDuke University Medical CenterDurhamNCUSA Computational Biology&Bioinformatics PhD Program Duke UniversityDurhamNCUSA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNCUSA

Introduction: Middle ear volume(MEV) is a clinically relevant parameter across middle ear diseases. MEV values between these techniques have never before been tested for agreement in ears with perforated tympanic membranes(TMs).Methods: Middle ears were identified from 36 patients ranging 18-89 years of age with TM perforations who underwent tympanometry and temporal bone computed tomography(CT) between 2005 and 2015. MEVs calculated by both tympanometry and three-dimensional volume reconstruction(3DVR) were analyzed for agreement using Bland Altman plots. The differences between tympanometric and 3DVR MEV values for each given middle ear were characterized across MEV quartiles(1= smallest; 4= largest) and across increasing states of middle ear disease using Kruskale Wallis and Wilcoxon testing with Bonferroni ***: Bland Altman plots demonstrated significant disagreement between MEV measurement techniques. Differences between tympanometric(T) and 3DVR MEV values were significantly greater with increasing average(i.e.(Tt3DVR)/2)) MEV per linear regression(p < 0.0001). Significance was demonstrated between fourth and first average MEV quartiles(p= 0.0024), fourth and second quartiles(p= 0.0024), third and first quartiles(p= 0.0048), and third and second quartiles(p= 0.048). Absolute MEV difference was not significantly different across varying states of middle ear disease(p= 0.44).Conclusion: Statistically and clinically significant disagreement was demonstrated between tympanometric and 3DVR MEV values. Studies that vary in MEV estimation techniques may be expected to demonstrate significantly different results. These preliminary results suggest that clinicians should endeavor to seek further confirmation when interpreting high tympanometric MEV values.

关键词： Middle ear volume Tympanometry Three-dimensional volume reconstruction Tympanic membrane perforation

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The role of deformability in determining the structural and mechanical properties of bubbles and emulsions

arXiv

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arXiv 2019年

作者： Boromand, Arman Signoriello, Alexandra Lowensohn, Janna Orellana, Carlos S. Weeks, Eric R. Ye, Fangfu Shattuck, Mark D. O’Hern, Corey S. Department of Mechanical Engineering and Materials Science Yale University New HavenCT06520 United States Beijing National Laboratory for Condensed Matter Physics CAS Key Laboratory of Soft Matter Physics Institute of Physics Chinese Academy of Sciences Beijing China Program in Computational Biology and Bioinformatics Yale University New HavenCT06520 United States Department of Physics Emory University AtlantaGA30322 United States School of Physical Sciences University of Chinese Academy of Sciences Beijing China Benjamin Levich Institute and Physics Department City College of New York New YorkNY10031 United States Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

We perform computational studies of jammed particle packings in two dimensions undergoing isotropic compression using the well-characterized soft particle (SP) model and deformable particle (DP) model that we developed for bubbles and emulsions. In the SP model, circular particles are allowed to overlap, generating purely repulsive forces. In the DP model, particles minimize their perimeter, while deforming at fixed area to avoid overlap during compression. We compare the structural and mechanical properties of jammed packings generated using the SP and DP models as a function of the packing fraction ρ, instead of the reduced number density φ. We show that near jamming onset the excess contact number ∆z = z−zJ and shear modulus G scale as ∆ρ0.5 in the large system limit for both models, where ∆ρ = ρ−ρJ and zJ ≈4 and ρJ ≈0.842 are the values at jamming onset. ∆z and G for the SP and DP models begin to differ for ρ & 0.88. In this regime, ∆z∼G can be described by a sum of two power-laws in ∆ρ, i.e. ∆z∼G ∼C0∆ρ0.5+C1∆ρ1.0 to lowest order. We show that the ratio C1/C0 is much larger for the DP model compared to to that for the SP model. We also characterize the void space in jammed packings as a function of ρ. We find that the DP model can describe the formation of Plateau borders as ρ → 1. We further show that the results for z and the shape factor A versus ρ for the DP model agree with recent experimental studies of foams and emulsions. Copyright © 2019, The Authors. All rights reserved.

关键词： Jamming

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Author Correction: Genomic footprints of activated telomere maintenance mechanisms in cancer (Dec, 10.1038/s41467-019-13824-9, 2022)

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NATURE COMMUNICATIONS 2022年第1期13卷 1-1页

作者： Sieverling, Lina Hong, Chen Koser, Sandra D. Ginsbach, Philip Kleinheinz, Kortine Hutter, Barbara Braun, Delia M. Cortes-Ciriano, Isidro Xi, Ruibin Kabbe, Rolf Park, Peter J. Eils, Roland Schlesner, Matthias Brors, Benedikt Rippe, Karsten Jones, David T. W. Feuerbach, Lars Division of Applied Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Faculty of Biosciences Heidelberg University 69120 Heidelberg Germany Division of Applied Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Faculty of Biosciences Heidelberg University Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) 69120 Heidelberg Germany Department for Bioinformatics and Functional Genomics Institute for Pharmacy and Molecular Biotechnology (IPMB) and BioQuant 69120 Heidelberg Germany Division of Theoretical Bioinformatics German Cancer Research Center (DKFZ) Heidelberg Germany Institute of Pharmacy and Molecular Biotechnology and BioQuant Heidelberg University Heidelberg Germany German Cancer Consortium (DKTK) Heidelberg Germany National Center for Tumor Diseases (NCT) Heidelberg Heidelberg Germany Heidelberg Center for Personalized Oncology (DKFZ-HIPO) German Cancer Research Center (DKFZ) Heidelberg Germany German Cancer Consortium (DKTK) German Cancer Research Center (DKFZ) Heidelberg Germany Division of Chromatin Networks German Cancer Research Center (DKFZ) and BioQuant 69120 Heidelberg Germany Department of Biomedical Informatics Harvard Medical School Boston Massachusetts 02115 USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge CB2 1EW UK Ludwig Center at Harvard Medical School Boston Massachusetts 02115 USA Department of Biomedical Informatics Harvard Medical School Boston MA USA Department of Chemistry Centre for Molecular Science Informatics University of Cambridge Cambridge UK Ludwig Center at Harvard Medical School Boston MA USA School of Mathematical Sciences and Center for Statistical Science Peking University Beijing 100871 China Heidelberg University Heidelberg Germany New BIH Digital Health Center Berlin Institute of Health (BIH) and Charité - Universitätsmedizin Berlin Berlin Germany Bioi

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Analyses of protein cores reveal fundamental differences between solution and crystal structures

arXiv

引用

arXiv 2019年

作者： Mei, Zhe Treado, John D. Grigas, Alex T. Levine, Zachary A. Regan, Lynne O'Hern, Corey S. Integrated Graduate Program in Physical & Engineering Biology Yale University New HavenCT06520 United States Department of Chemistry Yale University New HavenCT06520 United States Department of Mechanical Engineering & Materials Science Yale University New HavenCT06520 United States Graduate Program in Computational Biology & Bioinformatics Yale University New HavenCT06520 United States Department of Pathology Yale University New HavenCT06520 United States Department of Molecular Biophysics and Biochemistry Yale University New HavenCT06520 United States Institute of Quantitative Biology Biochemistry and Biotechnology Center for Synthetic and Systems Biology School of Biological Sciences University of Edinburgh Department of Physics Yale University New HavenCT06520 United States Department of Applied Physics Yale University New HavenCT06520 United States

There have been several studies suggesting that protein structures solved by NMR spectroscopy and x-ray crystallography show significant differences. To understand the origin of these differences, we assembled a database of high-quality protein structures solved by both methods. We also find significant differences between NMR and crystal structures|in the root-mean-square deviations of the Cα atomic positions, identities of core amino acids, backbone and sidechain dihedral angles, and packing fraction of core residues. In contrast to prior studies, we identify the physical basis for these differences by modelling protein cores as jammed packings of amino-acid-shaped particles. We find that we can tune the jammed packing fraction by varying the degree of thermalization used to generate the packings. For an athermal protocol, we find that the average jammed packing fraction is identical to that observed in the cores of protein structures solved by x-ray crystallography. In contrast, highly thermalized packing-generation protocols yield jammed packing fractions that are even higher than those observed in NMR structures. These results indicate that thermalized systems can pack more densely than athermal systems, which suggests a physical basis for the structural differences between protein structures solved by NMR and x-ray crystallography. Copyright © 2019, The Authors. All rights reserved.

关键词： X ray crystallography

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Predictable and precise template-free CRISPR editing of pathogenic variants (vol 563, pg 646, 2018)

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NATURE 2019年第7746期567卷 E1-E2页

作者： Shen, Max W. Arbab, Mandana Hsu, Jonathan Y. Worstell, Daniel Culbertson, Sannie J. Krabbe, Olga Cassa, Christopher A. Liu, David R. Gifford, David K. Sherwood, Richard I. Computational and Systems Biology Program Massachusetts Institute of Technology Cambridge USA Computer Science and Artificial Intelligence Laboratory Massachusetts Institute of Technology Cambridge USA Merkin Institute of Transformative Technologies in Healthcare Broad Institute of Harvard and MIT Cambridge USA Department of Chemistry and Chemical Biology Harvard University Cambridge USA Howard Hughes Medical Institute Harvard University Cambridge USA Department of Biological Engineering Massachusetts Institute of Technology Cambridge USA Molecular Pathology Unit Center for Cancer Research and Center for Computational and Integrative Biology Massachusetts General Hospital Charlestown USA Division of Genetics Department of Medicine Brigham and Women’s Hospital and Harvard Medical School Boston USA Hubrecht Institute for Developmental Biology and Stem Cell Research Royal Netherlands Academy of Arts and Sciences (KNAW) Utrecht The Netherlands Broad Institute of MIT and Harvard Cambridge USA Department of Electrical Engineering and Computer Science Massachusetts Institute of Technology Cambridge USA

In this Article, a data processing error affected Fig. 3e and Extended Data Table 2; these errors have been corrected online.

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Corrigendum to "Multiband rabi antenna using nest microstrip add-drop filter (NMADF) for relativistic sensing applications" [Heliyon 9(2) (February 2023) e13611]

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Heliyon 2023年第7期9卷 e16846页

作者： Somchat Sonasang M Jamsai M A Jalil N T Pham K Ray Niwat Angkawisittpan Preecha Yupapin Sarawoot Boonkirdram Martha Alicia Palomino-Ovando Miller Toledo-Solano Khashayar Misaghian J E Lugo Electronic Technology Faculty of Industrial Technology Nakon Phanom University Nakon Phanom 48000 Thailand. Department of Electrical Engineering Faculty of Industry and Technology Rajamagala University of Technology Isan Sakon Nakhon Campus Sakon Nakhon 47160 Thailand. Department of Physics Universiti Teknologi Malaysia 81310 Skuda Johor Malaysia. Institute for Computational Science Ton Duc Thang University Ho Chi Minh City Viet Nam. Amity School of Applied Sciences Amity University Rajasthan Jaipur India. Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Puebla Av. San Claudio y AV. 18 sur Col. San Manuel Ciudad Universitaria Pueble Pue 72570 Mexico. Faubert Lab Ecole d'optométrie Université de Montréal Montréal QC H3T1P1 Canada. Research Unit for Computational Electromagnetics and Optical Systems Faculty of Engineering Mahasarakham University Maha Sarakham 44150 Thailand. Department of Electrical Technology School of Industrial Technology Sakonnakhon Technical College Institute of Vocational Education Northeastern 2 Sakonnakhon 47000 Thailand. Program of Electrical and Electronics Faculty of Industrial Technology Sakon Nakhon Rajabhat University 680 Nittayo Mueang Sakon Nakhon 47000 Thailand. CONACYT-Facultad de CienciasFisico-Matematicas Benemérita Universidad Autónoma de Pueble Av. San Claudio y Av. 18 sur Col. San Manuel Ciudad Universitaria Puebla Pue 72570 Mexico. Faubert Lab School of Optometry Université de Montréal Montréal QC H3T1P1 Canada. Sage-Sentinel Smart Solutions 1919-1 Tancha Onna-son Kunigamigun Okinawa 904-0495 Japan.

[This corrects the article DOI: 10.1016/***.2023.e13611.].

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