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28,099 results on '"Hornung, A."'

51. Field-induced compensation of magnetic exchange as the possible origin of reentrant superconductivity in UTe2

Author: Helm, Toni, Kimata, Motoi, Sudo, Kenta, Miyata, Atsuhiko, Stirnat, Julia, Förster, Tobias, Hornung, Jacob, König, Markus, Sheikin, Ilya, Pourret, Alexandre, Lapertot, Gerard, Aoki, Dai, Knebel, Georg, Wosnitza, Joachim, and Brison, Jean-Pascal
Published: 2024
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52. Taskforces: a cure for all ills? Policy advisory systems in times of polycrises

Author: Céline Mavrot, Caroline Schlaufer, Johanna Hornung, Fritz Sager, Caspar Hirschi, and Dylan Jaton
Subjects: Crisis governance, scientific expertise, taskforce, policy advisory system, COVID-19, polycrisis, Political science, Social Sciences
Abstract: This article explores the flourishing claims to create crisis taskforces in the wake of the COVID-19 pandemic. Are taskforces a remedy for all crises? Taskforces are ad-hoc created groups of highly specialized experts who provide advice to policymakers. They have the advantages of flexibility and allow to recruit high-level competences for short-term mandates. However, taskforces are not a panacea, as different types of crises (e.g. creeping crises, disruptive events, cyclical crises) require different types of policy advisory systems. A taskforce is also a short-term, reactive model, which runs the risk of politicization. This article analyzes the suitability of the taskforce model in various situations and compares expert advice provided to decision-makers during the financial crisis and during the COVID-19 pandemic in Switzerland. It answers the question of what types of advisory systems suit what type of crisis. The contribution shows that in the era of polycrises, one size does not fit all and the distinctive characteristics of crises must be considered to find a fit-for-purpose approach.
Published: 2024
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53. Age-related STING suppression in macrophages contributes to increased viral load during influenza a virus infection

Author: Thurid Lauf, Antje Häder, Franziska Hornung, Yasmina Reisser, Sandor Nietzsche, Fabian Schanz, Verena Trümper, Aldona Jeznach, Sascha Brunke, Torsten Doenst, Tomasz Skirecki, Bettina Löffler, and Stefanie Deinhardt-Emmer
Subjects: Influenza A virus, Mitochondria, cGAS-STING pathway, Ageing, Macrophages, Immunologic diseases. Allergy, RC581-607, Geriatrics, RC952-954.6
Abstract: Abstract Ageing is a major risk factor that contributes to increased mortality and morbidity rates during influenza A virus (IAV) infections. Macrophages are crucial players in the defense against viral infections and display impaired function during ageing. However, the impact of ageing on macrophage function in response to an IAV infection remains unclear and offers potential insight for underlying mechanisms. In this study, we investigated the immune response of young and aged human monocyte-derived macrophages to two different H1N1 IAV strains. Interestingly, macrophages of aged individuals showed a lower interferon response to IAV infection, resulting in increased viral load. Transcriptomic data revealed a reduced expression of stimulator of interferon genes (STING) in aged macrophages albeit the cGAS-STING pathway was upregulated. Our data clearly indicate the importance of STING signaling for interferon production by applying a THP-1 STING knockout model. Evaluation of mitochondrial function during IAV infection revealed the release of mitochondrial DNA to be the activator of cGAS-STING pathway. The subsequent induction of apoptosis was attenuated in aged macrophages due to decreased STING signaling. Our study provides new insights into molecular mechanisms underlying age-related immune impairment. To our best knowledge, we are the first to discover an age-dependent difference in gene expression of STING on a transcriptional level in human monocyte-derived macrophages possibly leading to a diminished interferon production.
Published: 2024
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54. Coping difficulties after inpatient hospital treatment: validity and reliability of the German version of the post-discharge coping difficulty scale

Author: Matthias Marsall, Thorsten Hornung, Alexander Bäuerle, Marianne E. Weiss, Martin Teufel, and Matthias Weigl
Subjects: Patient safety, Care transition, Psychometric properties, Patient survey, Measurement invariance, Patient-reported outcome measures, Public aspects of medicine, RA1-1270
Abstract: Abstract Background Patients transitioning between different care contexts are at increased risk of experiencing adverse events. In particular, being discharged to home after inpatient treatment involves significant risks. However, there is a lack of valid and internationally comparable assessment tools on patients’ experiences of difficulties following hospital discharge. Therefore, this study aimed to adapt and validate the German version of the post-discharge coping difficulty scale (PDCDS-G). Methods Patients were recruited at a German university hospital. 815 adult patients participated in a self-report survey following an inpatient stay of at least three days. Factorial validity of the PDCDS-G was evaluated via factor analyses. Further, examination of measurement invariance was performed. To establish criterion validity, associations with patients’ self-reported health status and occurrence of patient safety were determined. Further, group differences regarding patient characteristics, hospitalization factors, and survey-related variables were examined. Results Factorial validity of the PDCDS-G was confirmed by a two-factorial model with good model fit. Both factors showed good to excellent reliability. The two-factor model achieved measurement invariance across all patient characteristics, hospitalization factors, and survey-related variables. Significant relationships with patients’ health status and the occurrence of patient safety incidents corroborate criterion validity of the PDCDS-G. Differential associations of the two PDCDS-G factors regarding patient characteristics, hospitalization, and survey-related variables were found. Discussion Construct and criterion validity, as well as the reliability of the PDCDS-G, were verified. Further, instrument’s measurement invariance was confirmed allowing use of the scale for the interpretation of group differences and comparisons between studies. Conclusions The PDCDS-G provides a validated and comparable patient-reported outcomes measure for patient experiences after hospital discharge to home. The PDCDS-G can be used for patient surveys in quality or patient safety improvement in care transition processes.
Published: 2024
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55. Digital 3D Smocking Design

Author: Ren, Jing, Segall, Aviv, and Sorkine-Hornung, Olga
Subjects: Computer Science - Graphics
Abstract: We develop an optimization-based method to model smocking, a surface embroidery technique that provides decorative geometric texturing while maintaining stretch properties of the fabric. During smocking, multiple pairs of points on the fabric are stitched together, creating non-manifold geometric features and visually pleasing textures. Designing smocking patterns is challenging, because the outcome of stitching is unpredictable: the final texture is often revealed only when the whole smocking process is completed, necessitating painstaking physical fabrication and time consuming trial-and-error experimentation. This motivates us to seek a digital smocking design method. Straightforward attempts to compute smocked fabric geometry using surface deformation or cloth simulation methods fail to produce realistic results, likely due to the intricate structure of the designs, the large number of contacts and high-curvature folds. We instead formulate smocking as a graph embedding and shape deformation problem. We extract a coarse graph representing the fabric and the stitching constraints, and then derive the graph structure of the smocked result. We solve for the 3D embedding of this graph, which in turn reliably guides the deformation of the high-resolution fabric mesh. Our optimization based method is simple, efficient, and flexible, which allows us to build an interactive system for smocking pattern exploration. To demonstrate the accuracy of our method, we compare our results to real fabrications on a large set of smocking patterns, Comment: 17 pages, 35 figures
Published: 2023

56. Optical Probing of Ultrafast Laser-Induced Solid-to-Overdense-Plasma Transitions

Author: Azamoum, Yasmina, Becker, Georg Alexander, Keppler, Sebastian, Duchateau, Guillaume, Skupin, Stefan, Grech, Mickael, Catoire, Fabrice, Hell, Sebastian, Tamer, Issa, Hornung, Marco, Hellwing, Marco, Kessler, Alexander, Schorcht, Franck, and Kaluza, Malte Christoph
Subjects: Physics - Plasma Physics
Abstract: Understanding the target dynamics during its interaction with a relativistic ultrashort laser pulse is a challenging fundamental multi-physics problem involving at least atomic and solid-state physics, plasma physics, and laser physics. Already, the properties of the so-called pre-plasma formed as the laser pulse's rising edge ionizes the target are complicated to access in experiments and modeling, and many aspects of this laser-induced transition from solid to overdense plasma over picosecond time scales are still open questions. At the same time, applications like laser-driven ion acceleration require precise knowledge and control of the pre-plasma because the efficiency of the acceleration process itself crucially depends on the target properties at the arrival of the relativistic intensity peak of the pulse. By capturing the dynamics of the initial stage of the interaction, we report on a detailed visualization of the pre-plasma formation and evolution. Nanometer-thin diamond-like carbon foils are shown to transition from solid to plasma during the laser rising edge with intensities < 10^16 W/cm^2. Single-shot near-infrared probe transmission measurements evidence sub-picosecond dynamics of an expanding plasma with densities above 10^23 cm^-3 (about 100 times the critical plasma density). The complementarity of a solid-state interaction model and a kinetic plasma description provides deep insight into the interplay of ionization, collisions, and expansion.
Published: 2023
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57. Search for Eccentric Black Hole Coalescences during the Third Observing Run of LIGO and Virgo

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abac, A. G., Abbott, R., Abe, H., Acernese, F., Ackley, K., Adamcewicz, C., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aguilar, I., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Álvarez-López, S., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Aritomi, N., Armato, F., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Baxi, P., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Binetti, A., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, M. S., Bonnand, R., Booker, P., Boschi, V., Bose, S., Bossilkov, V., Boudart, V., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brown, D. D., Brozzetti, M. L., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Cagnoli, G., Cahillane, C., Cain III, H. W., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chalathadka-Subrahmanya, S., Chan, C., Chan, J. C. L., Chan, K. H. M., Chan, M., Chan, W. L., Chandra, K., Chang, I. P., Chang, R. -J., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, A., Chen, A. H. -Y., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Chia, H. Y., Chiadini, F., Chiang, C., Chiarini, G., Chiba, A., Chiba, R., Chierici, R., Chincarini, A., Chiofalo, M. L., Chiummo, A., Chou, C., Choudhary, S., Christensen, N., Chua, S. S. Y., Chung, K. W., Ciani, G., Ciecielag, P., Cieślar, M., Cifaldi, M., Ciobanu, A. A., Ciolfi, R., Clara, F., Clark, J. A., Clarke, T. A., Clearwater, P., Clesse, S., Cleva, F., Coccia, E., Codazzo, E., Cohadon, P. -F., Colleoni, M., Collette, C. G., Collins, J., Colombo, A., Colpi, M., Compton, C. M., Conti, L., Cooper, S. J., Corbitt, T. R., Cordero-Carrión, I., Corezzi, S., Cornish, N. J., Corsi, A., Cortese, S., Costa, C. A., Cottingham, R., Coughlin, M. W., Couineaux, A., Coulon, J. -P., Countryman, S. T., Coupechoux, J. -F., Cousins, B., Couvares, P., Coward, D. M., Cowart, M. J., Cowburn, B. D., Coyne, D. C., Coyne, R., Craig, K., Creighton, J. D. E., Creighton, T. D., Criswell, A. W., Crockett-Gray, J. C. G., Croquette, M., Crouch, R., Crowder, S. G., Cudell, J. R., Cullen, T. J., Cumming, A., Cuoco, E., Curyło, M., Cusinato, M., Dabadie, P., Canton, T. Dal, Dall'Osso, S., Dálya, G., D'Angelo, B., Danilishin, S., D'Antonio, S., Danzmann, K., Darroch, K. E., Darsow-Fromm, C., Dartez, L. P., Dasgupta, A., Datta, S., Dattilo, V., Daumas, A., Dave, I., Davenport, A., Davier, M., Davis, D., Davis, M. C., Daw, E. J., Dax, M., Deenadayalan, M., Degallaix, J., De Laurentis, M., Deléglise, S., Del Favero, V., De Lillo, F., Dell'Aquila, D., Del Pozzo, W., De Marco, F., De Matteis, F., D'Emilio, V., Demos, N., Dent, T., Depasse, A., De Pietri, R., De Rosa, R., De Rossi, C., De Simone, R., Dhurandhar, S., Diab, R., Diamond, P. Z., Díaz, M. C., Didio, N. A., Dietrich, T., Di Fiore, L., Di Fronzo, C., Di Giovanni, F., Di Giovanni, M., Di Girolamo, T., Diksha, D., Di Lieto, A., Di Michele, A., Ding, J., Di Pace, S., Di Palma, I., Di Renzo, F., Divyajyoti, Dmitriev, A., Doctor, Z., Dohmen, E., Doleva, P. P., Donahue, L., D'Onofrio, L., Donovan, F., Dooley, K. L., Dooney, T., Doravari, S., Dorosh, O., Drago, M., Driggers, J. C., Drori, Y., Du, H., Ducoin, J. -G., Dunn, L., Dupletsa, U., D'Urso, D., Duval, H., Duverne, P. -A., Dwyer, S. E., Eassa, C., Ebersold, M., Eckhardt, T., Eddolls, G., Edelman, B., Edo, T. B., Edy, O., Effler, A., Eichholz, J., Einsle, H., Eisenmann, M., Eisenstein, R. A., Ejlli, A., Engelby, E., Engl, A. J., Errico, L., Essick, R. C., Estellés, H., Estevez, D., Etzel, T., Evans, C. R., Evans, M., Evans, T. M., Evstafyeva, T., Ewing, B. E., Ezquiaga, J. M., Fabrizi, F., Faedi, F., Fafone, V., Fair, H., Fairhurst, S., Fan, P. C., Farah, A. M., Farr, B., Farr, W. M., Fauchon-Jones, E. J., Favaro, G., Favata, M., Fays, M., Feicht, J., Fejer, M. M., Fenyvesi, E., Ferguson, D. L., Ferrante, I., Ferreira, T. A., Fidecaro, F., Fiori, A., Fiori, I., Fishbach, M., Fisher, R. P., Fittipaldi, R., Fiumara, V., Flaminio, R., Fleischer, S. M., Fleming, L. S., Floden, E., Fong, H., Font, J. A., Fornal, B., Forsyth, P. W. F., Franceschetti, K., Franke, A., Frasca, S., Frasconi, F., Mascioli, A. Frattale, Frei, Z., Freise, A., Freitas, O., Frey, R., Frischhertz, W., Fritschel, P., Frolov, V. V., Fronzé, G. G., Fujii, S., Fukunaga, I., Fulda, P., Fyffe, M., Gabella, W. E., Gadre, B., Gair, J. R., Gais, J., Galaudage, S., Gallardo, S., Gamba, R., Ganapathy, D., Ganguly, A., Gaonkar, S. G., Garaventa, B., Garcia-Bellido, J., García-Núñez, C., García-Quirós, C., Gardner, J. W., Gardner, K. A., Gargiulo, J., Garufi, F., Gasbarra, C., Gateley, B., Gayathri, V., Gemme, G., Gennai, A., George, J., Gerberding, O., Gergely, L., Ghadiri, N., Ghosh, Abhirup, Ghosh, Archisman, Ghosh, Shaon, Ghosh, Shrobana, Ghosh, Suprovo, Ghosh, Tathagata, Giacoppo, L., Giaime, J. A., Giardina, K. D., Gibson, D. R., Gier, C., Giri, P., Gissi, F., Gkaitatzis, S., Glanzer, J., Gleckl, A. E., Glotin, F., Godfrey, J., Godwin, P., Goetz, E., Goetz, R., Golomb, J., Lopez, S. Gomez, Goncharov, B., González, G., Goodwin-Jones, A. W., Gosselin, M., Gouaty, R., Gould, D. W., Goyal, S., Grace, B., Grado, A., Graham, V., Granados, A. E., Granata, M., Granata, V., Gras, S., Grassia, P., Gray, C., Gray, R., Greco, G., Green, A. C., Green, S. M., Green, S. R., Gretarsson, A. M., Gretarsson, E. M., Griffith, D., Griffiths, W. L., Griggs, H. L., Grignani, G., Grimaldi, A., Grimaud, C., Grote, H., Gruson, A. S., Guerra, D., Guetta, D., Guidi, G. M., Guimaraes, A. R., Gulati, H. K., Gulminelli, F., Gunny, A. M., Guo, H., Guo, Y., Gupta, Anchal, Gupta, Anuradha, Gupta, Ish, Gupta, N. C., Gupta, P., Gupta, S. K., Gupte, N., Gurav, R., Gurs, J., Gustafson, E. K., Gutierrez, N., Guzman, F., Haba, D., Haegel, L., Hain, G., Haino, S., Halim, O., Hall, E. D., Hamilton, E. Z., Hammond, G., Han, W. -B., Haney, M., Hanks, J., Hanna, C., Hannam, M. D., Hannuksela, O. A., Hanselman, A. G., Hansen, H., Hanson, J., Harada, R., Harder, T., Haris, K., Harmark, T., Harms, J., Harry, G. M., Harry, I. W., Hartwig, D., Haskell, B., Haster, C. -J., Hathaway, J. S., Haughian, K., Hayakawa, H., Hayama, K., Hayes, F. J., Healy, J., Heffernan, A., Heidmann, A., Heintze, M. C., Heinze, J., Heinzel, J., Heitmann, H., Hellman, F., Hello, P., Helmling-Cornell, A. F., Hemming, G., Hendry, M., Heng, I. S., Hennes, E., Hennig, J. -S., Hennig, M., Henshaw, C., Hernandez, A., Hertog, T., Heurs, M., Hewitt, A. L., Higginbotham, S., Hild, S., Hill, P., Himemoto, Y., Hines, A. S., Hirata, N., Hirose, C., Ho, J., Hoang, S., Hochheim, S., Hofman, D., Hohmann, J. N., Holland, N. A., Holley-Bockelmann, K., Hollows, I. J., Holmes, Z. J., Holz, D. E., Hong, C., Hong, Q., Hornung, J., Hoshino, S., Hough, J., Hourihane, S., Howell, E. J., Hoy, C. G., Hoyland, D., Hsieh, H. -F., Hsiung, C., Hsu, H. C., Hsu, S. -C., Hsu, W. -F., Hu, P., Hu, Q., Huang, H. Y., Huang, Y. -J., Huang, Y., Huang, Y. T., Hübner, M. T., Huddart, A. D., Hughey, B., Hui, D. C. Y., Hui, V., Hur, R., Husa, S., Huxford, R., Huynh-Dinh, T., Hyland, J., Iakovlev, A., Iandolo, G. A., Iess, A., Inayoshi, K., Inoue, Y., Iorio, G., Iosif, P., Irwin, J., Isi, M., Ismail, M. A., Itoh, Y., Iwaya, M., Iyer, B. R., JaberianHamedan, V., Jacqmin, T., Jacquet, P. -E., Jadhav, S. J., Jadhav, S. P., Jain, D., Jain, T., James, A. L., James, P. A., Jamshidi, R., Jan, A. Z., Jani, K., Janiurek, L., Janquart, J., Janssens, K., Janthalur, N. N., Jaraba, S., Jaranowski, P., Jarov, S., Jasal, P., Jaume, R., Javed, W., Jenner, K., Jennings, A., Jia, W., Jiang, J., Jin, H. -B., Johansmeyer, K., Johns, G. R., Johnson, N. A., Johnston, R., Johny, N., Jones, D. H., Jones, D. I., Jones, R., Joshi, P., Ju, L., Jung, K., Junker, J., Juste, V., Kajita, T., Kalaghatgi, C., Kalogera, V., Kamiizumi, M., Kanda, N., Kandhasamy, S., Kang, G., Kanner, J. B., Kapadia, S. J., Kapasi, D. P., Karat, S., Karathanasis, C., Karki, S., Karydas, T., Kas-danouche, Y. A., Kashyap, R., Kasprzack, M., Kastaun, W., Kato, J., Kato, T., Katsanevas, S., Katsavounidis, E., Katsuren, J. K., Katzman, W., Kaur, T., Kawabe, K., Kéfélian, F., Keitel, D., Kelley-Derzon, J., Kemper, S. A., Kennington, J., Kesharwani, R., Key, J. S., Khadka, S., Khalili, F. Y., Khanam, T., Khazanov, E. A., Khursheed, M., Kijbunchoo, N., Kim, C., Kim, J. C., Kim, K., Kim, M. H., Kim, P., Kim, S., Kim, W. S., Kim, Y. -M., Kimball, C., Kimura, N., Kinley-Hanlon, M., Kirchhoff, R., Kissel, J. S., Kiyota, T., Klimenko, S., Klinger, T., Knee, A. M., Knust, N., Koch, P., Koehlenbeck, S. M., Koekoek, G., Kohri, K., Kokeyama, K., Koley, S., Koliadko, N. D., Kolitsidou, P., Kolstein, M., Komori, K., Kondrashov, V., Kong, A. K. H., Kontos, A., Korobko, M., Kossak, R. V., Kouvatsos, N., Kovalam, M., Koyama, N., Kozak, D. B., Kranzhoff, S. L., Kringel, V., Krishnendu, N. V., Królak, A., Kuehn, G., Kuijer, P., Kulkarni, S., Ramamohan, A. Kulur, Kumar, A., Kumar, Praveen, Kumar, Prayush, Kumar, Rahul, Kumar, Rakesh, Kume, J., Kuns, K., Kuroyanagi, S., Kuwahara, S., Kwak, K., Kwan, K., Lacaille, G., Lagabbe, P., Laghi, D., Lai, S., Lakkis, M. H., Lalande, E., Lalleman, M., Lamberts, A., Landry, M., Lane, B. B., Lang, R. N., Lange, J., Lantz, B., La Rana, A., La Rosa, I., Lartaux-Vollard, A., Lasky, P. D., Lawrence, J., Laxen, M., Lazzarini, A., Lazzaro, C., Leaci, P., Leavey, S., LeBohec, S., Lecoeuche, Y. K., Lee, H. M., Lee, H. W., Lee, K., Lee, R. -K., Lee, R., Lee, S., Lee, Y., Legred, I. N., Lehmann, J., Lehner, L., Lemaître, A., Lenti, M., Leonardi, M., Leonova, E., Leroy, N., Lesovsky, M., Letendre, N., Lethuillier, M., Levesque, C., Levin, Y., Leyde, K., Li, A. K. Y., Li, K. L., Li, T. G. F., Li, X., Lin, Chien-Yu, Lin, Chun-Yu, Lin, E. T., Lin, F., Lin, H., Lin, L. C. -C., Lin, Y., Linde, F., Linker, S. D., Littenberg, T. B., Liu, A., Liu, G. C., Liu, Jian, Llamas, F., Lo, R. K. L., Lo, T., Locquet, J. -P., London, L., Longo, A., Lopez, D., Portilla, M. Lopez, Lorenzini, M., Loriette, V., Lormand, M., Losurdo, G., Lott, T. P., Lough, J. D., Loughlin, H. A., Lousto, C. O., Lovelace, G., Lowry, M. J., Lück, H., Lumaca, D., Lundgren, A. P., Lussier, A. W., Lynam, J. E., Ma, L. -T., Ma, S., Ma'arif, M., Macas, R., MacInnis, M., Macleod, D. M., MacMillan, I. A. O., Macquet, A., Maeda, K., Maenaut, S., Hernandez, I. Magaña, Magazzù, C., Magee, R. M., Maggiore, R., Magnozzi, M., Mahesh, M., Mahesh, S., Maini, M., Majhi, S., Majorana, E., Makarem, C. N., Maliakal, S., Malik, A., Man, N., Mandic, V., Mangano, V., Mannix, B., Mansell, G. L., Mansingh, G., Manske, M., Mantovani, M., Mapelli, M., Marchesoni, F., Pina, D. Marín, Marion, F., Márka, S., Márka, Z., Markakis, C., Markosyan, A. S., Markowitz, A., Maros, E., Marquina, A., Marsat, S., Martelli, F., Martin, I. W., Martin, R. M., Martinez, B. B., Martinez, M., Martinez, V. A., Martinez, V., Martini, A., Martinovic, K., Martynov, D. V., Marx, E. J., Masalehdan, H., Masserot, A., Masso-Reid, M., Mastrodicasa, M., Mastrogiovanni, S., Mateu-Lucena, M., Matiushechkina, M., Matsuyama, M., Mavalvala, N., Maxwell, N., McCarrol, G., McCarthy, R., McClelland, D. E., McCormick, S., McCuller, L., McGhee, G. I., McGinn, J., Mchedlidze, M., McIsaac, C., McIver, J., McKinney, K., McLeod, A., McRae, T., McWilliams, S. T., Meacher, D., Mehmet, M., Mehta, A. K., Meijer, Q., Melatos, A., Mellaerts, S., Menendez-Vazquez, A., Menoni, C. S., Mercer, R. A., Mereni, L., Merfeld, K., Merilh, E. L., Merritt, J. D., Merzougui, M., Messenger, C., Messick, C., Meyer-Conde, M., Meylahn, F., Mhaske, A., Miani, A., Miao, H., Michaloliakos, I., Michel, C., Michimura, Y., Middleton, H., Mihaylov, D. P., Miller, A. L., Miller, A., Miller, B., Miller, S., Millhouse, M., Milotti, E., Minenkov, Y., Mio, N., Mir, Ll. M., Mirasola, L., Miravet-Tenés, M., Miritescu, C. ., Mishkin, A., Mishra, A., Mishra, C., Mishra, T., Mistry, T., Mitchell, A. L., Mitra, S., Mitrofanov, V. P., Mitselmakher, G., Mittleman, R., Miyakawa, O., Miyamoto, S., Miyoki, S., Mo, G., Mobilia, L., Modafferi, L. M., Mohapatra, S. R. P., Mohite, S. R., Molina-Ruiz, M., Mondal, C., Mondin, M., Montani, M., Moore, C. J., Morales, M., Moraru, D., Morawski, F., More, A., More, S., Moreno, C., Moreno, G., Morisaki, S., Moriwaki, Y., Morras, G., Moscatello, A., Mours, B., Mow-Lowry, C. M., Mozzon, S., Muciaccia, F., Mukherjee, Arunava, Mukherjee, D., Mukherjee, Soma, Mukherjee, Subroto, Mukherjee, Suvodip, Mukund, N., Mullavey, A., Munch, J., Muñiz, E. A., Murakoshi, M., Murray, P. G., Muusse, S., Nadji, S. L., Nagar, A., Nagar, T., Nagarajan, N., Nakamura, K., Nakano, H., Nakano, M., Napolano, V., Nardecchia, I., Narikawa, T., Narola, H., Naticchioni, L., Nayak, R. K., Neil, B. F., Neilson, J., Nelson, A., Nelson, T. J. N., Nery, M., Nesseris, S., Neunzert, A., Ng, K. Y., Ng, S. W. S., Nguyen, C., Nguyen, P., Quynh, L. Nguyen, Nichols, S. A., Nieradka, G., Niko, A., Nishino, Y., Nishizawa, A., Nissanke, S., Nitoglia, E., Niu, W., Nocera, F., Norman, M., North, C., Novak, J., Siles, J. F. Nuño, Nurbek, G., Nuttall, L. K., Obayashi, K., Oberling, J., O'Dell, J., Oelker, E., Oertel, M., Offermans, A., Oganesyan, G., Oh, J. J., Oh, K., Oh, S. H., O'Hanlon, T., Ohashi, M., Ohkawa, M., Ohme, F., Ohta, H., Oliveira, A. S., Oliveri, R., Oloworaran, V., O'Neal, B., Oohara, K., O'Reilly, B., Ormiston, R. G., Ormsby, N. D., Orselli, M., O'Shaughnessy, R., Oshima, Y., Oshino, S., Ossokine, S., Osthelder, C., Ottaway, D. J., Ouzriat, A., Overmier, H., Pace, A. E., Pagano, R., Page, M. A., Pai, A., Pai, S. A., Pal, A., Pal, S., Palashov, O., Pálfi, M., Palomba, C., Pan, K. -C., Panda, P. K., Panebianco, L., Pang, P. T. H., Pannarale, F., Pant, B. C., Panther, F. H., Panzer, C. D., Paoletti, F., Paoli, A., Paolone, A., Papalexakis, E. E., Papalini, L., Pappas, G., Parisi, A., Park, J., Parker, W., Pascucci, D., Pasqualetti, A., Passaquieti, R., Passuello, D., Patel, M., Pathak, D., Pathak, M., Patra, A., Patricelli, B., Patron, A. S., Paul, S., Payne, E., Pearce, T., Pedraza, M., Pegna, R., Pegoraro, M., Pele, A., Arellano, F. E. Peña, Penn, S., Perego, A., Pereira, A., Perez, C. J., Perez, J. J., Perez, L. H., Périgois, C., Perkins, C. C., Perreca, A., Perret, J., Perriès, S., Perry, J. W., Pesios, D., Petermann, J., Petrillo, C., Pfeiffer, H. P., Pham, H., Pham, K. A., Phukon, K. S., Phurailatpam, H., Piccinni, O. J., Pichot, M., Piendibene, M., Piergiovanni, F., Pierini, L., Pierra, G., Pierro, V., Pietrzak, M. ., Pillant, G., Pillas, M., Pilo, F., Pinard, L., Pineda-Bosque, C., Pinto, I. M., Pinto, M., Piotrzkowski, B. J., Pirello, M., Pitkin, M. D., Placidi, A., Placidi, E., Planas, M. L., Plastino, W., Poggiani, R., Polini, E., Pompili, L., Ponrathnam, S., Poon, J., Porcelli, E., Portell, J., Porter, E. K., Posnansky, C., Poulton, R., Powell, J., Pracchia, M., Pradhan, B. K., Pradier, T., Prajapati, A. K., Prasai, K., Prasanna, R., Prasia, P., Pratten, G., Principe, M., Prodi, G. A., Prokhorov, L., Prosposito, P., Prudenzi, L., Puecher, A., Pullin, J., Punturo, M., Puosi, F., Puppo, P., Pürrer, M., Qi, H., Qin, J., Quetschke, V., Quinonez, P. J., Quitzow-James, R., Raab, F. J., Raaijmakers, G., Radulesco, N., Raffai, P., Rail, S. X., Raja, S., Rajan, C., Ramirez, K. E., Ramos-Buades, A., Rana, D., Randel, E., Rangnekar, P. R., Rapagnani, P., Ray, A., Raymond, V., Razzano, M., Read, J., Payo, M. Recaman, Regimbau, T., Rei, L., Reid, S., Reid, S. W., Reitze, D. H., Relton, P., Renzini, A., Rettegno, P., Revenu, B., Reza, A., Rezac, M., Rezaei, A. S., Ricci, F., Ricci, M., Richards, D., Richardson, J. W., Rijal, A., Riles, K., Riley, H. K., Rinaldi, S., Robertson, C., Robertson, N. A., Robinet, F., Robinson, M., Rocchi, A., Rolland, L., Rollins, J. G., Romanelli, M., Romano, A. E., Romano, R., Romero, A., Romero-Shaw, I. M., Romie, J. H., Ronchini, S., Roocke, T. J., Rosa, L., Rosauer, T. J., Rose, C. A., Rosińska, D., Ross, M. P., Rossello, M., Rowan, S., Roy, S., Royzman, A., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Saha, S. S., Sainrat, T., Menon, S. Sajith, Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Salvador, S., Sanchez, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sänger, E. M., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sato, R., Sato, S., Sato, Y., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scherf, S. J., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schmitz, S. J., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schouteden, K., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Seo, J. W., Sequino, V., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, P., Sharma-Chaudhary, S., Shawhan, P., Shcheblanov, N. S., Sheela, A., Shen, B., Shepard, K. G., Shikano, Y., Shikauchi, M., Shimode, K., Shinkai, H., Shiota, J., Shoemaker, D. H., Shoemaker, D. M., Short, R. W., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Simmonds, M., Singer, L. P., Singh, A., Singh, D., Singh, M. K., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Soulard, R., Souradeep, T., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., Stevens, P., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takatani, K., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, S. J., Tanaka, T., Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tissino, J., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tonelli, M., Toriyama, A., Torres-Forné, A., Torrie, C. I., Toscani, M., Melo, I. Tosta e, Tournefier, E., Trani, A. A., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallejo-Peña, S. A., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Veutro, A., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wildberger, J. B., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wils, M., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, H. T., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Yadav, N., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T. S., Yamamoto, T., Yamamura, S., Yamazaki, R., Yan, S., Yang, F. W., Yang, K. Z., Yang, L. -C., Yang, Y. -C., Yang, Yang, Yang, Yi, Yap, M. J., Yarbrough, Z., Yeh, S. -W., Yelikar, A. B., Yeung, S. M. C., Yeung, T. Y., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, H., Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Yanqi, Zhang, Ya, Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Despite the growing number of confident binary black hole coalescences observed through gravitational waves so far, the astrophysical origin of these binaries remains uncertain. Orbital eccentricity is one of the clearest tracers of binary formation channels. Identifying binary eccentricity, however, remains challenging due to the limited availability of gravitational waveforms that include effects of eccentricity. Here, we present observational results for a waveform-independent search sensitive to eccentric black hole coalescences, covering the third observing run (O3) of the LIGO and Virgo detectors. We identified no new high-significance candidates beyond those that were already identified with searches focusing on quasi-circular binaries. We determine the sensitivity of our search to high-mass (total mass $M>70$ $M_\odot$) binaries covering eccentricities up to 0.3 at 15 Hz orbital frequency, and use this to compare model predictions to search results. Assuming all detections are indeed quasi-circular, for our fiducial population model, we place an upper limit for the merger rate density of high-mass binaries with eccentricities $0 < e \leq 0.3$ at $0.33$ Gpc$^{-3}$ yr$^{-1}$ at 90\% confidence level., Comment: 24 pages, 5 figures
Published: 2023

58. Increasing the rate capability for the cryogenic stopping cell of the FRS Ion Catcher

Author: Zhao, J. W., Amanbayev, D., Dickel, T., Miskun, I., Plass, W. R., Tortorelli, N., Andres, S. Ayet San, Beck, Soenke, Bergmann, J., Brencic, Z., Constantin, P., Geissel, H., Greiner, F., Groef, L., Hornung, C., Kuzminzuk, N., Kripko-Koncz, G., Mardor, I., Pohjalainen, I., Scheidenberger, C., Thirolf, P. G., Bagchi, S., Haettner, E., Kazantseva, E., Kostyleva, D., Oberstedt, A., Pietri, S., Reiter, M. P., Tanaka, Y. K., Wada, M., Balabanski, D. L., Benyaming, D., Harakeh, M. N., Hubbard, N., Kalantar-Nayestanaki, N., Mollaebrahimi, A., Mukhab, I., Narang, M., Niwasel, T., Patyk, Z., Purushothaman, S., Rotaru, A., Spataruf, A., Stanico, G., Vencelje, M., Weickb, H., Yu, J., and Collaboration, the Super-FRS Experiment
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: At the FRS Ion Catcher (FRS-IC), projectile and fission fragments are produced at relativistic energies, separated in-flight, energy-bunched, slowed down, and thermalized in the ultra-pure helium gas-filled cryogenic stopping cell (CSC). Thermalized nuclei are extracted from the CSC using a combination of DC and RF electric fields and gas flow. This CSC also serves as the prototype CSC for the Super-FRS, where exotic nuclei will be produced at unprecedented rates making it possible to go towards the extremes of the nuclear chart. Therefore, it is essential to efficiently extract thermalized exotic nuclei from the CSC under high beam rate conditions, in order to use the rare exotic nuclei which come as cocktail beams. The extraction efficiency dependence on the intensity of the impinging beam into the CSC was studied with a primary beam of 238U and its fragments. Tests were done with two different versions of the DC electrode structure inside the cryogenic chamber, the standard 1 m long and a short 0.5 m long DC electrode. In contrast to the rate capability of 10^4 ions/s with the long DC electrode, results show no extraction efficiency loss up to the rate of 2x10^5 ions/s with the new short DC electrode. This order of magnitude increase of the rate capability paves the way for new experiments at the FRS-IC, including exotic nuclei studies with in-cell multi-nucleon transfer reactions. The results further validate the design concept of the CSC for the Super-FRS, which was developed to effectively manage beams of even higher intensities.
Published: 2023

59. Recent Upgrades of the Gas Handling System for the Cryogenic Stopping Cell of the FRS Ion Catcher

Author: Mollaebrahimi, A., Amanbayev, D., Andrés, S. Ayet San, Beck, S., Bergmann, J., Dickel, T., Geissel, H., Hornung, C., Kalantar-Nayestanaki, N., Kripko-Koncz, G., Miskun, I., Nichita, D., Plaß, W. R., Pohjalainen, I., Scheidenberger, C., Stanic, G., State, A., and Zhao, J.
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: In this paper, the major upgrades and technical improvements of the buffer gas handling system for the cryogenic stopping cell of the FRS Ion Catcher at GSI/FAIR (in Darmstadt, Germany) are described. The upgrades include implementation of new gas lines and gas purifiers to achieve a higher buffer gas cleanliness for a more efficient extraction of reactive ions as well as suppression of the molecular background ionized in the stopping cell. Furthermore, additional techniques have been implemented for improved monitoring and quantification of the purity of the helium buffer gas.
Published: 2023
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60. The KiTS21 Challenge: Automatic segmentation of kidneys, renal tumors, and renal cysts in corticomedullary-phase CT

Author: Heller, Nicholas, Isensee, Fabian, Trofimova, Dasha, Tejpaul, Resha, Zhao, Zhongchen, Chen, Huai, Wang, Lisheng, Golts, Alex, Khapun, Daniel, Shats, Daniel, Shoshan, Yoel, Gilboa-Solomon, Flora, George, Yasmeen, Yang, Xi, Zhang, Jianpeng, Zhang, Jing, Xia, Yong, Wu, Mengran, Liu, Zhiyang, Walczak, Ed, McSweeney, Sean, Vasdev, Ranveer, Hornung, Chris, Solaiman, Rafat, Schoephoerster, Jamee, Abernathy, Bailey, Wu, David, Abdulkadir, Safa, Byun, Ben, Spriggs, Justice, Struyk, Griffin, Austin, Alexandra, Simpson, Ben, Hagstrom, Michael, Virnig, Sierra, French, John, Venkatesh, Nitin, Chan, Sarah, Moore, Keenan, Jacobsen, Anna, Austin, Susan, Austin, Mark, Regmi, Subodh, Papanikolopoulos, Nikolaos, and Weight, Christopher
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning
Abstract: This paper presents the challenge report for the 2021 Kidney and Kidney Tumor Segmentation Challenge (KiTS21) held in conjunction with the 2021 international conference on Medical Image Computing and Computer Assisted Interventions (MICCAI). KiTS21 is a sequel to its first edition in 2019, and it features a variety of innovations in how the challenge was designed, in addition to a larger dataset. A novel annotation method was used to collect three separate annotations for each region of interest, and these annotations were performed in a fully transparent setting using a web-based annotation tool. Further, the KiTS21 test set was collected from an outside institution, challenging participants to develop methods that generalize well to new populations. Nonetheless, the top-performing teams achieved a significant improvement over the state of the art set in 2019, and this performance is shown to inch ever closer to human-level performance. An in-depth meta-analysis is presented describing which methods were used and how they faired on the leaderboard, as well as the characteristics of which cases generally saw good performance, and which did not. Overall KiTS21 facilitated a significant advancement in the state of the art in kidney tumor segmentation, and provides useful insights that are applicable to the field of semantic segmentation as a whole., Comment: 34 pages, 12 figures
Published: 2023

61. Surface roughness optimization of hybrid PBF-LB/M-built Inconel 718 using in situ high-speed milling

Author: Sommer, David, Hornung, Simon, Esen, Cemal, and Hellmann, Ralf
Published: 2024
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62. Short-term safety and effectiveness of the mCLIP partial prosthesis

Author: Rasse, Thomas, Niederwanger, Lisa, Hornung, Joachim, Taha, Lava, Arndt, Susan, Offergeld, Christian, Beutner, Dirk, Bevis, Nicholas, Lenarz, Thomas, Teschner, Magnus, Schimanski, Esther, Georg Sprinzl, Magele, Astrid, Skarżyński, Piotr H., Plichta, Łukasz, Arnoldner, Christoph, Riss, Dominik, Loader, Benjamin, Windisch, Franz, Rubicz, Nina, and Zwittag, Paul Martin
Published: 2024
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63. SENS: Part-Aware Sketch-based Implicit Neural Shape Modeling

Author: Binninger, Alexandre, Hertz, Amir, Sorkine-Hornung, Olga, Cohen-Or, Daniel, and Giryes, Raja
Subjects: Computer Science - Graphics, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning
Abstract: We present SENS, a novel method for generating and editing 3D models from hand-drawn sketches, including those of abstract nature. Our method allows users to quickly and easily sketch a shape, and then maps the sketch into the latent space of a part-aware neural implicit shape architecture. SENS analyzes the sketch and encodes its parts into ViT patch encoding, subsequently feeding them into a transformer decoder that converts them to shape embeddings suitable for editing 3D neural implicit shapes. SENS provides intuitive sketch-based generation and editing, and also succeeds in capturing the intent of the user's sketch to generate a variety of novel and expressive 3D shapes, even from abstract and imprecise sketches. Additionally, SENS supports refinement via part reconstruction, allowing for nuanced adjustments and artifact removal. It also offers part-based modeling capabilities, enabling the combination of features from multiple sketches to create more complex and customized 3D shapes. We demonstrate the effectiveness of our model compared to the state-of-the-art using objective metric evaluation criteria and a user study, both indicating strong performance on sketches with a medium level of abstraction. Furthermore, we showcase our method's intuitive sketch-based shape editing capabilities, and validate it through a usability study., Comment: 25 pages, 24 figures
Published: 2023

64. GarmentCode: Programming Parametric Sewing Patterns

Author: Korosteleva, Maria and Sorkine-Hornung, Olga
Subjects: Computer Science - Graphics
Abstract: Garment modeling is an essential task of the global apparel industry and a core part of digital human modeling. Realistic representation of garments with valid sewing patterns is key to their accurate digital simulation and eventual fabrication. However, little-to-no computational tools provide support for bridging the gap between high-level construction goals and low-level editing of pattern geometry, e.g., combining or switching garment elements, semantic editing, or design exploration that maintains the validity of a sewing pattern. We suggest the first DSL for garment modeling -- GarmentCode -- that applies principles of object-oriented programming to garment construction and allows designing sewing patterns in a hierarchical, component-oriented manner. The programming-based paradigm naturally provides unique advantages of component abstraction, algorithmic manipulation, and free-form design parametrization. We additionally support the construction process by automating typical low-level tasks like placing a dart at a desired location. In our prototype garment configurator, users can manipulate meaningful design parameters and body measurements, while the construction of pattern geometry is handled by garment programs implemented with GarmentCode. Our configurator enables the free exploration of rich design spaces and the creation of garments using interchangeable, parameterized components. We showcase our approach by producing a variety of garment designs and retargeting them to different body shapes using our configurator. Project page: https://igl.ethz.ch/projects/garmentcode/, Comment: Presented at SIGGRAPH Asia 2023
Published: 2023
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65. Example-based Motion Synthesis via Generative Motion Matching

Author: Li, Weiyu, Chen, Xuelin, Li, Peizhuo, Sorkine-Hornung, Olga, and Chen, Baoquan
Subjects: Computer Science - Graphics, Computer Science - Computer Vision and Pattern Recognition
Abstract: We present GenMM, a generative model that "mines" as many diverse motions as possible from a single or few example sequences. In stark contrast to existing data-driven methods, which typically require long offline training time, are prone to visual artifacts, and tend to fail on large and complex skeletons, GenMM inherits the training-free nature and the superior quality of the well-known Motion Matching method. GenMM can synthesize a high-quality motion within a fraction of a second, even with highly complex and large skeletal structures. At the heart of our generative framework lies the generative motion matching module, which utilizes the bidirectional visual similarity as a generative cost function to motion matching, and operates in a multi-stage framework to progressively refine a random guess using exemplar motion matches. In addition to diverse motion generation, we show the versatility of our generative framework by extending it to a number of scenarios that are not possible with motion matching alone, including motion completion, key frame-guided generation, infinite looping, and motion reassembly. Code and data for this paper are at https://wyysf-98.github.io/GenMM/, Comment: SIGGRAPH 2023. Project page: https://wyysf-98.github.io/GenMM/, Video: https://www.youtube.com/watch?v=lehnxcade4I
Published: 2023

66. Mean range bunching of exotic nuclei produced by in-flight fragmentation and fission -- Stopped-beam experiments with increased efficiency

Author: Dickel, Timo, Hornung, Christine, Amanbayev, Daler, Andres, Samuel Ayet San, Beck, Soenke, Bergmann, Julian, Geissel, Hans, Gerl, Juergen, Gorska, Magdalena, Groef, Lizzy, Haettner, Emma, Hucka, Jan-Paul, Kostyleva, Daria A., Kripko-Koncz, Gabriella, Mollaebrahimi, Ali, Mukha, Ivan, Pietri, Stephane, Plaß, Wolfgang R., Podolyak, Zsolt, Purushothaman, Sivaji, Reiter, Moritz Pascal, Roesch, Heidi, Scheidenberger, Christoph, Tanaka, Yoshiki K., Weick, Helmut, Zhao, Jianwei, and Collaboration, Super-FRS Experiment
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: The novel technique of mean range bunching has been developed and applied at the projectile fragment separator FRS at GSI in four experiments of the FAIR phase-0 experimental program. Using a variable degrader system at the final focal plane of the FRS, the ranges of the different nuclides can be aligned, allowing to efficiently implant a large number of different nuclides simultaneously in a gas-filled stopping cell or an implantation detector. Stopping and studying a cocktail beam overcomes the present limitations of stopped-beam experiments. The conceptual idea of mean range bunching is described and illustrated using simulations. In a single setting of the FRS, 37 different nuclides were stopped in the cryogenic stopping cell and were measured in a single setting broadband mass measurement with the multiple-reflection time-of-flight mass spectrometer of the FRS Ion Catcher.
Published: 2023
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67. Storage, Accumulation and Deceleration of Secondary Beams for Nuclear Astrophysics

Author: Glorius, J., Litvinov, Yu. A., Aliotta, M., Amjad, F., Brückner, B., Bruno, C. G., Chen, R., Davinson, T., Dellmann, S. F., Dickel, T., Dillmann, I., Erbacher, P., Forstner, O., Geissel, H., Griffin, C. J., Grisenti, R., Gumberidze, A., Haettner, E., Hess, R., Hillenbrand, P. -M., Hornung, C., Joseph, R., Jurado, B., Kazanseva, E., Knöbel, R., Kostyleva, D., Kozhuharov, C., Kuzminchuk, N., Langer, C., Leckenby, G., Lederer-Woods, C., Lestinsky, M., Litvinov, S., Löher, B., Lorenz, B., Lorenz, E., Marsh, J., Menz, E., Morgenroth, T., Mukha, I., Petridis, N., Popp, U., Psaltis, A., Purushothaman, S., Reifarth, R., Rocco, E., Roy, P., Sanjari, M. S., Scheidenberger, C., Sguazzin, M., Sidhu, R. S., Spillmann, U., Steck, M., Stöhlker, T., Swartz, J. A., Tanaka, Y. K., Törnqvist, H., Varga, L., Vescovi, D., Weick, H., Weigand, M., Woods, P. J., Yamaguchi, T., and Zhao, J.
Subjects: Physics - Instrumentation and Detectors, Nuclear Experiment
Abstract: Low-energy investigations on rare ion beams are often limited by the available intensity and purity of the ion species in focus. Here, we present the first application of a technique that combines in-flight production at relativistic energies with subsequent secondary beam storage, accumulation and finally deceleration to the energy of interest. Using the FRS and ESR facilities at GSI, this scheme was pioneered to provide a secondary beam of $^{118}$Te$^{52+}$ for the measurement of nuclear proton-capture at energies of 6 and 7 MeV/u. The technique provided stored beam intensities of about $10^6$ ions at high purity and brilliance, representing a major step towards low-energy nuclear physics studies using rare ion beams.
Published: 2023
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68. Alternating Gradient Descent and Mixture-of-Experts for Integrated Multimodal Perception

Author: Akbari, Hassan, Kondratyuk, Dan, Cui, Yin, Hornung, Rachel, Wang, Huisheng, and Adam, Hartwig
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Computer Science - Multimedia, Electrical Engineering and Systems Science - Image and Video Processing
Abstract: We present Integrated Multimodal Perception (IMP), a simple and scalable multimodal multi-task training and modeling approach. IMP integrates multimodal inputs including image, video, text, and audio into a single Transformer encoder with minimal modality-specific components. IMP makes use of a novel design that combines Alternating Gradient Descent (AGD) and Mixture-of-Experts (MoE) for efficient model and task scaling. We conduct extensive empirical studies and reveal the following key insights: 1) Performing gradient descent updates by alternating on diverse modalities, loss functions, and tasks, with varying input resolutions, efficiently improves the model. 2) Sparsification with MoE on a single modality-agnostic encoder substantially improves the performance, outperforming dense models that use modality-specific encoders or additional fusion layers and greatly mitigates the conflicts between modalities. IMP achieves competitive performance on a wide range of downstream tasks including video classification, image classification, image-text, and video-text retrieval. Most notably, we train a sparse IMP-MoE-L variant focusing on video tasks that achieves new state-of-the-art in zero-shot video classification: 77.0% on Kinetics-400, 76.8% on Kinetics-600, and 68.3% on Kinetics-700, improving the previous state-of-the-art by +5%, +6.7%, and +5.8%, respectively, while using only 15% of their total training computational cost.
Published: 2023

69. Does combining numerous data types in multi-omics data improve or hinder performance in survival prediction? Insights from a large-scale benchmark study

Author: Yingxia Li, Tobias Herold, Ulrich Mansmann, and Roman Hornung
Subjects: Multi-omics data, Prediction, TCGA, Benchmark, Cancer, Survival analysis, Computer applications to medicine. Medical informatics, R858-859.7
Abstract: Abstract Background Predictive modeling based on multi-omics data, which incorporates several types of omics data for the same patients, has shown potential to outperform single-omics predictive modeling. Most research in this domain focuses on incorporating numerous data types, despite the complexity and cost of acquiring them. The prevailing assumption is that increasing the number of data types necessarily improves predictive performance. However, the integration of less informative or redundant data types could potentially hinder this performance. Therefore, identifying the most effective combinations of omics data types that enhance predictive performance is critical for cost-effective and accurate predictions. Methods In this study, we systematically evaluated the predictive performance of all 31 possible combinations including at least one of five genomic data types (mRNA, miRNA, methylation, DNAseq, and copy number variation) using 14 cancer datasets with right-censored survival outcomes, publicly available from the TCGA database. We employed various prediction methods and up-weighted clinical data in every model to leverage their predictive importance. Harrell’s C-index and the integrated Brier Score were used as performance measures. To assess the robustness of our findings, we performed a bootstrap analysis at the level of the included datasets. Statistical testing was conducted for key results, limiting the number of tests to ensure a low risk of false positives. Results Contrary to expectations, we found that using only mRNA data or a combination of mRNA and miRNA data was sufficient for most cancer types. For some cancer types, the additional inclusion of methylation data led to improved prediction results. Far from enhancing performance, the introduction of more data types most often resulted in a decline in performance, which varied between the two performance measures. Conclusions Our findings challenge the prevailing notion that combining multiple omics data types in multi-omics survival prediction improves predictive performance. Thus, the widespread approach in multi-omics prediction of incorporating as many data types as possible should be reconsidered to avoid suboptimal prediction results and unnecessary expenditure.
Published: 2024
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70. Metatranscriptomic analysis indicates prebiotic effect of isomalto/malto-polysaccharides on human colonic microbiota in-vitro

Author: Klaudyna Borewicz, Bastian Hornung, Fangjie Gu, Pieter H. van der Zaal, Henk A. Schols, Peter J. Schaap, and Hauke Smidt
Subjects: Medicine, Science
Abstract: Abstract Isomalto/malto-polysaccharides (IMMPs) are a novel type of soluble dietary fibres with a prebiotic potential promoting growth of beneficial microbes in the gut. However, the mode of action of IMMPs remains unknown. Previous studies on IMMPs showed an increase in total bacteria, especially lactobacilli, and higher production of short chain fatty acids (SCFA) when IMMPs were fed to rats or used during in vitro fermentation. Here we used metatranscriptomics to investigate how IMMPs with different amounts of α − (1 → 6) glycosidic linkages affected microbial function during incubation with human fecal inoculum. We showed that active microbial community dynamics during fermentation varied depending on the type of IMMP used and that the observed changes were reflected in the community gene expression profiles. Based on metatranscriptome analysis, members of Bacteroides, Lactobacillus and Bifidobacterium were the predominant degraders of IMMPs, and the increased gene expression in these bacteria correlated with high amounts of α − (1 → 6) glycosidic linkages. We also noted an increase in relative abundance of these bacteria and an activation of pathways involved in SCFA synthesis. Our findings could provide a baseline for more targeted approaches in designing prebiotics for specific bacteria and to achieve more controlled modulation of microbial activity towards desired health outcomes.
Published: 2024
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71. Phosphorylation of PFKL regulates metabolic reprogramming in macrophages following pattern recognition receptor activation

Author: Meiyue Wang, Heinrich Flaswinkel, Abhinav Joshi, Matteo Napoli, Sergi Masgrau-Alsina, Julia M. Kamper, Antonia Henne, Alexander Heinz, Marleen Berouti, Niklas A. Schmacke, Karsten Hiller, Elisabeth Kremmer, Benedikt Wefers, Wolfgang Wurst, Markus Sperandio, Jürgen Ruland, Thomas Fröhlich, and Veit Hornung
Subjects: Science
Abstract: Abstract Innate immune responses are linked to key metabolic pathways, yet the proximal signaling events that connect these systems remain poorly understood. Here we show that phosphofructokinase 1, liver type (PFKL), a rate-limiting enzyme of glycolysis, is phosphorylated at Ser775 in macrophages following several innate stimuli. This phosphorylation increases the catalytic activity of PFKL, as shown by biochemical assays and glycolysis monitoring in cells expressing phosphorylation-defective PFKL variants. Using a genetic mouse model in which PFKL Ser775 phosphorylation cannot take place, we observe that upon activation, glycolysis in macrophages is lower than in the same cell population of wild-type animals. Consistent with their higher glycolytic activity, wild-type cells have higher levels of HIF1α and IL-1β than Pfkl S775A/S775A after LPS treatment. In an in vivo inflammation model, Pfkl S775A/S775A mice show reduced levels of MCP-1 and IL-1β. Our study thus identifies a molecular link between innate immune activation and early induction of glycolysis.
Published: 2024
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72. Neurexin 3 Regulates Synaptic Connections Between Central Amygdala Neurons and Excitable Cells of the Lateral Parabrachial Nucleus in Rats with Varicella Zoster Induced Orofacial Pain

Author: Kramer PR, Hornung RS, Umorin M, Benson MD, and Kinchington PR
Subjects: orofacial pain, zoster, shingles, varicella zoster, post-herpetic neuralgia, synaptic plasticity, Medicine (General), R5-920
Abstract: Phillip R Kramer,1 Rebecca S Hornung,1 Mikhail Umorin,1 M Douglas Benson,1 Paul R Kinchington2 1Department of Biomedical Sciences, Texas A&M University School of Dentistry, Dallas, TX, USA; 2Department of Ophthalmology and of Molecular Microbiology and Genetics, University of Pittsburgh, Pittsburgh, PA, USACorrespondence: Phillip R Kramer, Email pkramer@tamu.eduIntroduction: Herpes Zoster in humans is the result of varicella zoster virus (VZV) infection. Injecting rats with varicella zoster virus produces pain similar to herpes zoster “shingles” pain in humans. . In a previous study, orofacial pain was induced by injecting the whisker pad of male rats with VZV and the pain response increased after attenuating neurexin 3 (Nrxn3) expression in the central amygdala. Neurons descend from the central amygdala to the lateral parabrachial nucleus and orofacial pain signals ascend to the lateral parabrachial nucleus. GABAergic neurons within the central amygdala regulate pain by inhibiting activity within the lateral parabrachial nucleus. Attenuating Nrxn3 expression in the central amygdala increased GABA release in the lateral parabrachial nucleus suggesting Nrxn3 controls pain by regulating GABA release. Nrxn3 can also control synaptic connections between neurons, and we hypothesized that Nrxn3 knockdown in the central amygdala would reduce the number of GABAergic synaptic connections in the lateral parabrachial nucleus and increase VZV associated pain.Methods: To test this idea, the number of synaptic connections between GABAergic cells of the central amygdala and excitatory or dynorphin positive neurons within the lateral parabrachial nucleus were quantitated after infusion of a virus expressing synaptophysin. Synaptophysin is a synaptic vesicle protein that labels neuronal synaptic connections. These connections were measured in rats with and without whisker pad injection of VZV and knockdown of Nrxn3 within the central amygdala. Orofacial pain was measured using a place escape avoidance paradigm.Results: GABAergic synaptic connections were reduced in the lateral parabrachial nucleus after Nrxn3 knockdown. Rats with a reduction in the number of connections had an increase in VZV associated orofacial pain. Immunostaining with the pain marker prodynorphin indicated that the reduction in GABAergic connections was primarily associated with prodynorphin positive neurons.Discussion: The results suggest Nrxn3 reduces VZV associated orofacial pain, in part, by enhancing synaptic connections between GABA cells of the central amygdala and pain neurons within the lateral parabrachial nucleus.Keywords: orofacial pain, zoster, shingles, varicella zoster, post-herpetic neuralgia, synaptic plasticity
Published: 2024

73. Preoperative factors predict prolonged length of stay, serious adverse complications, and readmission following operative intervention of proximal humerus fractures: a machine learning analysis of a national database

Author: Alexander L. Hornung, MD, Samuel S. Rudisill, MD, Johnathon R. McCormick, MD, John T. Streepy, MSc, William E. Harkin, MD, Noah Bryson, MSc, Xavier Simcock, MD, and Grant E. Garrigues, MD
Subjects: Proximal humerus fracture, Arthroplasty, ORIF, Machine learning, Outcomes, Complications, Orthopedic surgery, RD701-811, Diseases of the musculoskeletal system, RC925-935
Abstract: Background: Proximal humerus fractures are a common injury, predominantly affecting older adults. This study aimed to develop risk-prediction models for prolonged length of hospital stay (LOS), serious adverse complications, and readmission within 30 days of surgically treated proximal humerus fractures using machine learning (ML) techniques. Methods: Adult patients (age >18) who underwent open reduction internal fixation (ORIF), hemiarthroplasty, or total shoulder arthroplasty for proximal humerus fracture between 2016 and 2021 were included. Preoperative demographic and clinical variables were collected for all patients and used to establish ML-based algorithms. The model with optimal performance was selected according to area under the curve (AUC) on the receiver operating curve (ROC) curve and overall accuracy, and the specific predictive features most important to model derivation were identified. Results: A total of 7473 patients were included (72.1% male, mean age 66.2 ± 13.7 years). Models produced via gradient boosting performed best for predicting prolonged LOS and complications. The model predicting prolonged LOS demonstrated good discrimination and performance, as indicated by (Mean: 0.700, SE: 0.017), recall (Mean: 0.551, SE: 0.017), accuracy (Mean: 0.717, SE: 0.010), F1-score (Mean: 0.616, SE: 0.014), AUC (Mean: 0.779, SE: 0.010), and Brier score (Mean: 0.283, SE: 0.010) Preoperative hematocrit, preoperative platelet count, and patient age were considered the strongest predictive features. The model predicting serious adverse complications exhibited comparable discrimination [precision (Mean: 0.226, SE: 0.024), recall (Mean: 0.697, SE: 0.048), accuracy (Mean: 0.811, SE: 0.010), F1-score (Mean: 0.341, SE: 0.031)] and superior performance relative to the LOS model [AUC (Mean: 0.806, SE: 0.024), Brier score (Mean: 0.189, SE: 0.010), noting preoperative hematocrit, operative time, and patient age to be most influential. However, the 30-day readmission model achieved the weakest relative performance, displaying low measures of precision (Mean: 0.070, SE: 0.012) and recall (Mean: 0.389, SE: 0.053), despite good accuracy (Mean: 0.791, SE: 0.009). Conclusion: Predictive models constructed using ML techniques demonstrated favorable discrimination and satisfactory-to-excellent performance in forecasting prolonged LOS and serious adverse complications occurring within 30 days of surgical intervention for proximal humerus fracture. Modifiable preoperative factors such as hematocrit and platelet count were identified as significant predictive features, suggesting that clinicians could address these factors during preoperative patient optimization to enhance outcomes. Overall, these findings highlight the potential for ML techniques to enhance preoperative management, facilitate shared decision-making, and enable more effective and personalized orthopedic care by exploring alternative approaches to risk stratification.
Published: 2024
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74. Doing Phenomenography: A Practical Guide

Author: Taylor-Beswick, Amanda M.L. and Hornung, Eva
Published: 2024
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75. Summer Bridge Program: Helping Underserved Students Develop Social and Cultural Capital

Author: Simon, Nicolas P., Hornung, Rick, and Dugan, Kimberly B.
Abstract: Social and cultural capital are critical components of success among college students. In this paper, we examine the creation, accumulation, and distribution of social and cultural capital by and among underserved college students who participated in a summer bridge program at one regional public state university. Using qualitative data from interviews with participants, the analysis highlights the importance of trust, sense of belonging and use of noncognitive strategies. We argue that in addition to traditional achievement measures such as GPA and test scores, social and cultural capital need to be considered. While quantitative measures often label these students as below benchmarks, this study highlights how social and cultural capital are assets to be nurtured and facilitated. [Note: The publication year (2021) shown on the PDF is incorrect. The correct publication year for this article is 2022.]
Published: 2022

76. Prediction approaches for partly missing multi-omics covariate data: A literature review and an empirical comparison study

Author: Hornung, Roman, Ludwigs, Frederik, Hagenberg, Jonas, and Boulesteix, Anne-Laure
Subjects: Quantitative Biology - Genomics, Computer Science - Artificial Intelligence, Computer Science - Machine Learning, Statistics - Applications, Statistics - Computation
Abstract: As the availability of omics data has increased in the last few years, more multi-omics data have been generated, that is, high-dimensional molecular data consisting of several types such as genomic, transcriptomic, or proteomic data, all obtained from the same patients. Such data lend themselves to being used as covariates in automatic outcome prediction because each omics type may contribute unique information, possibly improving predictions compared to using only one omics data type. Frequently, however, in the training data and the data to which automatic prediction rules should be applied, the test data, the different omics data types are not available for all patients. We refer to this type of data as block-wise missing multi-omics data. First, we provide a literature review on existing prediction methods applicable to such data. Subsequently, using a collection of 13 publicly available multi-omics data sets, we compare the predictive performances of several of these approaches for different block-wise missingness patterns. Finally, we discuss the results of this empirical comparison study and draw some tentative conclusions.
Published: 2023

77. Open data from the third observing run of LIGO, Virgo, KAGRA and GEO

Author: The LIGO Scientific Collaboration, the Virgo Collaboration, the KAGRA Collaboration, Abbott, R., Abe, H., Acernese, F., Ackley, K., Adhicary, S., Adhikari, N., Adhikari, R. X., Adkins, V. K., Adya, V. B., Affeldt, C., Agarwal, D., Agathos, M., Aguiar, O. D., Aiello, L., Ain, A., Ajith, P., Akutsu, T., Albanesi, S., Alfaidi, R. A., Al-Jodah, A., Alléné, C., Allocca, A., Almualla, M., Altin, P. A., Amato, A., Amez-Droz, L., Amorosi, A., Anand, S., Ananyeva, A., Andersen, R., Anderson, S. B., Anderson, W. G., Andia, M., Ando, M., Andrade, T., Andres, N., Andrés-Carcasona, M., Andrić, T., Ansoldi, S., Antelis, J. M., Antier, S., Aoumi, M., Apostolatos, T., Appavuravther, E. Z., Appert, S., Apple, S. K., Arai, K., Araya, A., Araya, M. C., Areeda, J. S., Arène, M., Aritomi, N., Arnaud, N., Arogeti, M., Aronson, S. M., Arun, K. G., Asada, H., Ashton, G., Aso, Y., Assiduo, M., Melo, S. Assis de Souza, Aston, S. M., Astone, P., Aubin, F., AultONeal, K., Babak, S., Badalyan, A., Badaracco, F., Badger, C., Bae, S., Bagnasco, S., Bai, Y., Baier, J. G., Baiotti, L., Baird, J., Bajpai, R., Baka, T., Ball, M., Ballardin, G., Ballmer, S. W., Baltus, G., Banagiri, S., Banerjee, B., Bankar, D., Baral, P., Barayoga, J. C., Barber, J., Barish, B. C., Barker, D., Barneo, P., Barone, F., Barr, B., Barsotti, L., Barsuglia, M., Barta, D., Barthelmy, S. D., Barton, M. A., Bartos, I., Basak, S., Basalaev, A., Bassiri, R., Basti, A., Bawaj, M., Bayley, J. C., Baylor, A. C., Bazzan, M., Bécsy, B., Bedakihale, V. M., Beirnaert, F., Bejger, M., Bell, A. S., Benedetto, V., Beniwal, D., Benoit, W., Bentley, J. D., Yaala, M. Ben, Bera, S., Berbel, M., Bergamin, F., Berger, B. K., Bernuzzi, S., Beroiz, M., Berry, C. P. L., Bersanetti, D., Bertolini, A., Betzwieser, J., Beveridge, D., Bevins, N., Bhandare, R., Bhandari, A. V., Bhardwaj, U., Bhatt, R., Bhattacharjee, D., Bhaumik, S., Bianchi, A., Bilenko, I. A., Bilicki, M., Billingsley, G., Bini, S., Birnholtz, O., Biscans, S., Bischi, M., Biscoveanu, S., Bisht, A., Biswas, B., Bitossi, M., Bizouard, M. -A., Blackburn, J. K., Blair, C. D., Blair, D. G., Blair, R. M., Bobba, F., Bode, N., Boër, M., Bogaert, G., Boileau, G., Boldrini, M., Bolingbroke, G. N., Bonavena, L. D., Bondarescu, R., Bondu, F., Bonilla, E., Bonilla, G. S., Bonnand, R., Booker, P., Bork, R., Boschi, V., Bose, N., Bose, S., Bossilkov, V., Boudart, V., Bouffanais, Y., Bozzi, A., Bradaschia, C., Brady, P. R., Braglia, M., Branch, A., Branchesi, M., Brau, J. E., Breschi, M., Briant, T., Brillet, A., Brinkmann, M., Brockill, P., Brooks, A. F., Brooks, J., Brown, D. D., Brunett, S., Bruno, G., Bruntz, R., Bryant, J., Bucci, F., Buchanan, J., Bulashenko, O., Bulik, T., Bulten, H. J., Buonanno, A., Burtnyk, K., Buscicchio, R., Buskulic, D., Buy, C., Byer, R. L., Davies, G. S. Cabourn, Cabras, G., Cabrita, R., Cadonati, L., Caesar, S., Cagnoli, G., Cahillane, C., Bustillo, J. Calderón, Callaghan, J. D., Callister, T. A., Calloni, E., Camp, J. B., Canepa, M., Santoro, G. Caneva, Cannavacciuolo, M., Cannon, K. C., Cao, H., Cao, Z., Capistran, L. A., Capocasa, E., Capote, E., Carapella, G., Carbognani, F., Carlassara, M., Carlin, J. B., Carpinelli, M., Carter, J. J., Carullo, G., Diaz, J. Casanueva, Casentini, C., Castaldi, G., Castro-Lucas, S. Y., Caudill, S., Cavaglià, M., Cavalieri, R., Cella, G., Cerdá-Durán, P., Cesarini, E., Chaibi, W., Chakalis, W., Subrahmanya, S. Chalathadka, Champion, E., Chan, C., Chan, C. L., Chandra, K., Chang, I. P., Chang, W., Chanial, P., Chao, S., Chapman-Bird, C., Charlton, E. L., Charlton, P., Chassande-Mottin, E., Chastain, L., Chatterjee, C., Chatterjee, Debarati, Chatterjee, Deep, Chaturvedi, M., Chaty, S., Chatziioannou, K., Chen, D., Chen, H., Chen, H. Y., Chen, J., Chen, K. H., Chen, X., Chen, Y. -R., Chen, Y., Cheng, H., Chessa, P., Cheung, H. Y., Chia, H. 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P., Rossello, M., Roussel, A., Rowan, S., Rowlinson, S. J., Roy, S., Royzman, A., Rozza, D., Ruggi, P., Morales, E. Ruiz, Ruiz-Rocha, K., Ryan, K., Sachdev, S., Sadecki, T., Sadiq, J., Saffarieh, P., Saha, S. S., Saha, S., Saito, Y., Sakai, K., Sakellariadou, M., Sako, T., Sakon, S., Salafia, O. S., Salces-Carcoba, F., Salconi, L., Saleem, M., Salemi, F., Sallé, M., Samajdar, A., Sanchez, E. J., Sanchez, J. H., Sanchez, L. E., Sanchis-Gual, N., Sanders, J. R., Sanuy, A., Saravanan, T. R., Sarin, N., Sasli, A., Sassi, P., Sassolas, B., Satari, H., Sauter, O., Savage, R. L., Savant, V., Sawada, T., Sawant, H. L., Sayah, S., Schaetzl, D., Scheel, M., Scherf, S. J., Scheuer, J., Schiworski, M. G., Schmidt, P., Schmidt, S., Schmitz, S. J., Schnabel, R., Schneewind, M., Schofield, R. M. S., Schönbeck, A., Schuler, H., Schulte, B. W., Schutz, B. F., Schwartz, E., Scott, J., Scott, S. M., Seetharamu, T. C., Seglar-Arroyo, M., Sekiguchi, Y., Sellers, D., Sengupta, A. S., Sentenac, D., Seo, E. G., Sequino, V., Sergeev, A., Servignat, G., Setyawati, Y., Shaffer, T., Shahriar, M. S., Shaikh, M. A., Shams, B., Shao, L., Sharma, P., Chaudhary, S. Sharma, Shawhan, P., Shcheblanov, N. S., Sheela, A., Shen, B., Shepard, K. G., Sheridan, E., Shikano, Y., Shikauchi, M., Shimizu, H., Shimode, K., Shinkai, H., Shoemaker, D. H., Shoemaker, D. M., ShyamSundar, S., Sider, A., Siegel, H., Sieniawska, M., Sigg, D., Silenzi, L., Singer, L. P., Singh, D., Singh, M. K., Singh, N., Singha, A., Sintes, A. M., Sipala, V., Skliris, V., Slagmolen, B. J. J., Slaven-Blair, T. J., Smetana, J., Smith, J. R., Smith, L., Smith, R. J. E., Soldateschi, J., Somala, S. N., Somiya, K., Soni, K., Soni, S., Sordini, V., Sorrentino, F., Sorrentino, N., Sotani, H., Soulard, R., Souradeep, T., Sowell, E., Spagnuolo, V., Spencer, A. P., Spera, M., Spinicelli, P., Srivastava, A. K., Srivastava, V., Stachie, C., Stachurski, F., Steer, D. A., Steinlechner, J., Steinlechner, S., Stergioulas, N., StPierre, M., Strang, L. C., Stratta, G., Strong, M. D., Strunk, A., Sturani, R., Stuver, A. L., Suchenek, M., Sudhagar, S., Sueltmann, N., Sugiyama, T., Suh, H. G., Sullivan, A. G., Summerscales, T. Z., Sun, L., Sunil, S., Sur, A., Suresh, J., Sutton, P. J., Suzuki, Takamasa, Suzuki, Takanori, Swinkels, B. L., Syx, A., Szczepańczyk, M. J., Szewczyk, P., Tacca, M., Tagoshi, H., Tait, S. C., Takahashi, H., Takahashi, R., Takamori, A., Takano, S., Takeda, H., Takeda, M., Talbot, C. J., Talbot, C., Tamaki, M., Tamanini, N., Tanabe, D., Tanaka, K., Tanaka, T., Tanasijczuk, A. J., Tanioka, S., Tanner, D. B., Tao, D., Tao, L., Tapia, R. D., Martín, E. N. Tapia San, Tarafder, R., Taranto, C., Taruya, A., Tasson, J. D., Teloi, M., Tenorio, R., Terhune, J. E. S., Terkowski, L., Themann, H., Thirugnanasambandam, M. P., Thomas, L. M., Thomas, M., Thomas, P., Thomas, S., Thompson, J. E., Thondapu, S. R., Thorne, K. A., Thrane, E., Tiwari, Shubhanshu, Tiwari, Srishti, Tiwari, V., Toivonen, A. M., Tolley, A. E., Tomaru, T., Tomita, K., Tomura, T., Tonelli, M., Torres-Forné, A., Torrie, C. I., Melo, I. Tosta e, Tournefier, E., Trapananti, A., Travasso, F., Traylor, G., Trenado, J., Trevor, M., Tringali, M. C., Tripathee, A., Troiano, L., Trovato, A., Trozzo, L., Trudeau, R. J., Tsang, K. W., Tsang, T., Tse, M., Tso, R., Tsuchida, S., Tsukada, L., Tsutsui, T., Turbang, K., Turconi, M., Turski, C., Tuyenbayev, D., Ubach, H., Ubhi, A. S., Uchikata, N., Uchiyama, T., Udall, R. P., Uehara, T., Ueno, K., Unnikrishnan, C. S., Ushiba, T., Utina, A., Vahlbruch, H., Vaidya, N., Vajente, G., Vajpeyi, A., Valdes, G., Valentini, M., Vallero, S., Valsan, V., van Bakel, N., van Beuzekom, M., van Dael, M., Brand, J. F. J. van den, Broeck, C. Van Den, Vander-Hyde, D. C., van der Sluys, M., Van de Walle, A., van Dongen, J., van Haevermaet, H., van Heijningen, J. V., Vanosky, J., van Putten, M. H. P. M., van Ranst, Z., van Remortel, N., Vardaro, M., Vargas, A. F., Varma, V., Vasúth, M., Vecchio, A., Vedovato, G., Veitch, J., Veitch, P. J., Venneberg, J., Venugopalan, G., Verdier, P., Verkindt, D., Verma, P., Verma, Y., Vermeulen, S. M., Veske, D., Vetrano, F., Viceré, A., Vidyant, S., Viets, A. D., Vijaykumar, A., Villa-Ortega, V., Vina, M., Vincent, E. T., Vinet, J. -Y., Viret, S., Virtuoso, A., Vitale, S., Vocca, H., Voigt, D., von Reis, E. R. G., von Wrangel, J. S. A., Vorvick, C., Vyatchanin, S. P., Wade, L. E., Wade, M., Wagner, K. J., Walet, R. C., Walker, M., Wallace, G. S., Wallace, L., Wang, H., Wang, J. Z., Wang, W. H., Ward, R. L., Warner, J., Was, M., Washimi, T., Washington, N. Y., Watada, K., Watarai, D., Watchi, J., Wayt, K. E., Weaver, B., Weaving, C. R., Webster, S. A., Weinert, M., Weinstein, A. J., Weiss, R., Weller, C. M., Weller, R. A., Wellmann, F., Wen, L., Weßels, P., Wette, K., Whelan, J. T., White, D. D., Whiting, B. F., Whittle, C., Wilk, O. S., Wilken, D., Willetts, K., Williams, D., Williams, M. J., Williamson, A. R., Willis, J. L., Willke, B., Wipf, C. C., Woan, G., Woehler, J., Wofford, J. K., Wong, D., Wong, H. T., Wong, I. C. F., Wright, M., Wu, C., Wu, D. S., Wu, H., Wysocki, D. M., Xiao, L., Xu, V. A., Yadav, N., Yamada, T., Yamamoto, H., Yamamoto, K., Yamamoto, M., Yamamoto, T., Yamamoto, T. S., Yamashita, K., Yamazaki, R., Yang, F. W., Yang, K. Z., Yang, Y. -C., Yap, M. J., Yeeles, D. W., Yelikar, A. B., Yeung, T. Y., Yokoyama, J., Yokozawa, T., Yoo, J., Yu, Hang, Yu, Haocun, Yuzurihara, H., Zadrożny, A., Zannelli, A. J., Zanolin, M., Zeeshan, M., Zeidler, S., Zelenova, T., Zendri, J. -P., Zevin, M., Zhang, J., Zhang, L., Zhang, R., Zhang, T., Zhang, Y., Zhao, C., Zhao, Yue, Zhao, Yuhang, Zheng, Y., Zhong, H., Zhou, R., Zhu, X. J., Zhu, Z. -H., Zimmerman, A. B., Zucker, M. E., and Zweizig, J.
Subjects: General Relativity and Quantum Cosmology
Abstract: The global network of gravitational-wave observatories now includes five detectors, namely LIGO Hanford, LIGO Livingston, Virgo, KAGRA, and GEO 600. These detectors collected data during their third observing run, O3, composed of three phases: O3a starting in April of 2019 and lasting six months, O3b starting in November of 2019 and lasting five months, and O3GK starting in April of 2020 and lasting 2 weeks. In this paper we describe these data and various other science products that can be freely accessed through the Gravitational Wave Open Science Center at https://gwosc.org. The main dataset, consisting of the gravitational-wave strain time series that contains the astrophysical signals, is released together with supporting data useful for their analysis and documentation, tutorials, as well as analysis software packages., Comment: 27 pages, 3 figures
Published: 2023
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78. UVDoc: Neural Grid-based Document Unwarping

Author: Verhoeven, Floor, Magne, Tanguy, and Sorkine-Hornung, Olga
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics, I.4, I.7.5
Abstract: Restoring the original, flat appearance of a printed document from casual photographs of bent and wrinkled pages is a common everyday problem. In this paper we propose a novel method for grid-based single-image document unwarping. Our method performs geometric distortion correction via a fully convolutional deep neural network that learns to predict the 3D grid mesh of the document and the corresponding 2D unwarping grid in a dual-task fashion, implicitly encoding the coupling between the shape of a 3D piece of paper and its 2D image. In order to allow unwarping models to train on data that is more realistic in appearance than the commonly used synthetic Doc3D dataset, we create and publish our own dataset, called UVDoc, which combines pseudo-photorealistic document images with physically accurate 3D shape and unwarping function annotations. Our dataset is labeled with all the information necessary to train our unwarping network, without having to engineer separate loss functions that can deal with the lack of ground-truth typically found in document in the wild datasets. We perform an in-depth evaluation that demonstrates that with the inclusion of our novel pseudo-photorealistic dataset, our relatively small network architecture achieves state-of-the-art results on the DocUNet benchmark. We show that the pseudo-photorealistic nature of our UVDoc dataset allows for new and better evaluation methods, such as lighting-corrected MS-SSIM. We provide a novel benchmark dataset that facilitates such evaluations, and propose a metric that quantifies line straightness after unwarping. Our code, results and UVDoc dataset are available at https://github.com/tanguymagne/UVDoc., Comment: 14 pages, published in SIGGRAPH Asia 2023 Conference Papers
Published: 2023
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79. Early-diverging plesiosaurs from the Pliensbachian (Lower Jurassic) of northwestern Germany

Author: Sven Sachs, Jahn J. Hornung, and Daniel Madzia
Subjects: Pliosauridae, Plesiosauria, Pliensbachian, Early Jurassic, Europe, Medicine, Biology (General), QH301-705.5
Abstract: The knowledge of Pliensbachian (Early Jurassic, ∼192.9–184.2 Ma) plesiosaurs is notoriously insufficient. Although there have been specimens described from different parts of the world, only three of them have been established as diagnosable taxa. Here, we describe two previously unreported lower Pliensbachian plesiosaur occurrences that originate from two sites located in North Rhine-Westphalia, Germany. One of the new occurrences is represented by three cervical and three indeterminable vertebrae from Werther, the other includes two associated pectoral or anterior dorsal vertebrae from Bielefeld. Although highly incomplete, the Werther individual, which derived from the Uptonia jamesoni Zone, is found to represent the only reliably identified early Pliensbachian pliosaurid known to date. Its material is geographically and stratigraphically proximate to the late Pliensbachian pliosaurid Arminisaurus schuberti, found in a clay-pit located in the Bielefeld district of Jöllenbeck. However, even though the Werther plesiosaur and A. schuberti show a broadly similar morphology of the preserved cervical section, a precise identification of the Werther taxon is currently impossible.
Published: 2024
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80. Telomerase RNA component knockout exacerbates Staphylococcus aureus pneumonia by extensive inflammation and dysfunction of T cells

Author: Yasmina Reisser, Franziska Hornung, Antje Häder, Thurid Lauf, Sandor Nietzsche, Bettina Löffler, and Stefanie Deinhardt-Emmer
Subjects: Terc, Staphylococcus aureus, T cells, pneumonia, inflammation, aging, Medicine, Science, Biology (General), QH301-705.5
Abstract: The telomerase RNA component (Terc) constitutes a non-coding RNA critical for telomerase function, commonly associated with aging and pivotal in immunomodulation during inflammation. Our study unveils heightened susceptibility to pneumonia caused by Staphylococcus aureus (S. aureus) in Terc knockout (Tercko/ko) mice compared to both young and old infected counterparts. The exacerbated infection in Tercko/ko mice correlates with heightened inflammation, manifested by elevated interleukin-1β (IL-1β) levels and activation of the NLR family pyrin domain containing 3 (NLRP3) inflammasome within the lung. Employing mRNA sequencing methods alongside in vitro analysis of alveolar macrophages (AMs) and T cells, our study elucidates a compelling correlation between Tercko/ko, inflammation, and impaired T cell functionality. Terc deletion results in compromised T cell function, characterized by dysregulation of the T cell receptor and absence of CD247, potentially compromising the host’s capacity to mount an effective immune response against S. aureus. This investigation provides insights into the intricate mechanisms governing increased vulnerability to severe pneumonia in the context of Terc deficiency, which might also contribute to aging-related pathologies, while also highlighting the influence of Terc on T cell function.
Published: 2024
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81. Collectivity at the prolate-oblate transition: The 21+ lifetime of 190W

Author: E. Şahin, V. Werner, A.K. Mistry, M. Rudigier, K. Nomura, J. Jolie, N. Pietralla, P.H. Regan, G. Ağgez, H.M. Albers, U. Ahmed, Ö. Aktaş, A. Algora, S. Alhomaidhi, C. Appleton, T. Arıcı, M. Armstrong, A. Banerjee, J. Benito, G. Benzoni, A. Blazhev, P. Boutachkov, A.M. Bruce, B. Cederwall, M.M.R. Chishti, M.L. Cortés, F. Crespi, B. Das, T. Davinson, T. Dickel, M. Doncel, A. Ertoprak, A. Esmaylzadeh, L.M. Fraile, E.R. Gamba, J. Gerl, M. Górska, J. Ha, E. Haettner, O. Hall, H. Heggen, C. Hornung, N. Hubbard, S. Jazrawi, P.R. John, C.E. Jones, V. Karayonchev, E. Kazantseva, R. Kern, L. Knafla, I. Kojouharov, P. Koseoglou, G. Kosir, D. Kostyleva, N. Kurz, N. Kuzminchuk, M. Llanos-Expósito, R. Lozeva, D. Mengoni, T.J. Mertzimekis, M. Mikolajczuk, A.I. Morales, I. Mukha, J.R. Murias, B.S. Nara-Singh, S.E.A. Orrigo, J. Pellumaj, S. Pelonis, S. Pietri, S. Pigliapoco, Zs. Podolyák, M. Polettini, K. Rezynkina, H.A. Rösch, H. Schaffner, Ch. Scheidenberger, L. Sexton, P.-A. Söderström, Y.K. Tanaka, J.J. Valiente-Dobón, P. Vasileiou, J. Vasiljević, J. Vesic, H. Weick, J. Wiederhold, A. Yaneva, G. Zhang, J. Zhao, and A. Zyriliou
Subjects: Nuclear structure, γ-ray spectroscopy, Isomer spectroscopy, Shape phase transition, DESPEC, NUSTAR, Physics, QC1-999
Abstract: The neutron-rich rare isotope 190W is discussed as a candidate for a prolate-oblate transitional nucleus with maximum γ-softness. The collectivity of this isotope is assessed for the first time by the measurement of the reduced E2 transition probability of its first 2+ state to the ground state. The experiment employed the FAst TIming Array (FATIMA), comprised of 36 LaBr3(Ce) scintillators, which was part of the DESPEC setup at GSI, Darmstadt. The 41+ and 21+ states of 190W were populated subsequently to the decay of its 127(12) μs isomeric Jπ=10− state. The mean lifetime of the 21+ state was determined to be τ=274(28) ps, which corresponds to a B(E2;21+→01+) value of 95(10) W.u. The results motivated a revision of previous calculations within an energy-density functional-based interacting boson model-2 approach, yielding E2 transition properties and spectroscopic quadrupole moments for tungsten isotopes. From comparison to theory, the new data suggest that 190W is at the transition from prolate to oblate structure along the W isotopic chain, which had previously been discussed as a nuclear shape-phase transition.
Published: 2024
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82. Geometry Transfer for Stylizing Radiance Fields.

Author: Hyunyoung Jung 0001, Seonghyeon Nam, Nikolaos Sarafianos, Sungjoo Yoo, Alexander Sorkine-Hornung, and Rakesh Ranjan
Published: 2024
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83. Multi-Party Computation als Instrument zur Umsetzung datenschutzkonformer behördlicher Datenabgleiche: Eine interdisziplinäre Analyse am Beispiel der Diskussionen um das Gesetz zur Selbstbestimmung über den Geschlechtseintrag.

Author: Linda Seyda, Andreas Brüggemann, Gerrit Hornung, and Thomas Schneider 0003
Published: 2024
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84. Higher-Order SQL Lambda Functions.

Author: Maximilian E. Schüle and Jakob Hornung
Published: 2024
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85. Enabling RAJA on Intel GPUs with SYCL.

Author: Brian Homerding, Arturo Vargas, Tom Scogland, Robert Chen, Mike Davis, and Rich Hornung
Published: 2024
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86. Postmoderne

Author: Hornung, Alfred, Zapf, Hubert, editor, and Müller, Timo, editor
Published: 2024
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87. A Step Towards a Finite Element Model for an Impact Situation in Human-Robot Interaction

Author: Hornung, Luisa, Sóti, Gergely, Wurll, Christian, Kacprzyk, Janusz, Series Editor, Gomide, Fernando, Advisory Editor, Kaynak, Okyay, Advisory Editor, Liu, Derong, Advisory Editor, Pedrycz, Witold, Advisory Editor, Polycarpou, Marios M., Advisory Editor, Rudas, Imre J., Advisory Editor, Wang, Jun, Advisory Editor, Lee, Soon-Geul, editor, An, Jinung, editor, Chong, Nak Young, editor, Strand, Marcus, editor, and Kim, Joo H., editor
Published: 2024
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88. Social identities, emotions and policy preferences

Author: Hornung, Johanna and Bandelow, Nils C.
Published: 2024
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89. Biomarkers for parkinsonian disorders in CNS-originating EVs: promise and challenges

Author: Dutta, Suman, Hornung, Simon, Taha, Hash Brown, and Bitan, Gal
Subjects: Prevention, Neurodegenerative, Biotechnology, Brain Disorders, Genetics, Neurosciences, Parkinson's Disease, Neurological, Humans, Extracellular Vesicles, Central Nervous System, Exosomes, Neurodegenerative Diseases, Parkinson Disease, Biomarkers, Parkinson's disease, Multiple system atrophy, Lewy bodies, L1CAM, Neurodegenerative diseases, Neurons, Astrocytes, Microglia, Oligodendrocytes, Extracellular vesicles, Parkinson’s disease, Clinical Sciences, Neurology & Neurosurgery
Abstract: Extracellular vesicles (EVs), including exosomes, microvesicles, and oncosomes, are nano-sized particles enclosed by a lipid bilayer. EVs are released by virtually all eukaryotic cells and have been shown to contribute to intercellular communication by transporting proteins, lipids, and nucleic acids. In the context of neurodegenerative diseases, EVs may carry toxic, misfolded forms of amyloidogenic proteins and facilitate their spread to recipient cells in the central nervous system (CNS). CNS-originating EVs can cross the blood-brain barrier into the bloodstream and may be found in other body fluids, including saliva, tears, and urine. EVs originating in the CNS represent an attractive source of biomarkers for neurodegenerative diseases, because they contain cell- and cell state-specific biological materials. In recent years, multiple papers have reported the use of this strategy for identification and quantitation of biomarkers for neurodegenerative diseases, including Parkinson's disease and atypical parkinsonian disorders. However, certain technical issues have yet to be standardized, such as the best surface markers for isolation of cell type-specific EVs and validating the cellular origin of the EVs. Here, we review recent research using CNS-originating EVs for biomarker studies, primarily in parkinsonian disorders, highlight technical challenges, and propose strategies for overcoming them.
Published: 2023

90. Development of a Novel Electrochemiluminescence ELISA for Quantification of α‑Synuclein Phosphorylated at Ser129 in Biological Samples

Author: Dutta, Suman, Hornung, Simon, Taha, Hash Brown, Biggs, Karl, Siddique, Ibrar, Chamoun, Lea M, Shahpasand-Kroner, Hedieh, Lantz, Carter, Herrera-Vaquero, Marcos, Stefanova, Nadia, Loo, Joseph A, and Bitan, Gal
Subjects: Analytical Chemistry, Chemical Sciences, Dementia, Biotechnology, Acquired Cognitive Impairment, Parkinson's Disease, Prevention, Neurodegenerative, Rare Diseases, Neurosciences, Brain Disorders, Aging, Neurological, Mice, Animals, Humans, alpha-Synuclein, Synucleinopathies, Reproducibility of Results, Parkinson Disease, Multiple System Atrophy, Antibodies, Enzyme-Linked Immunosorbent Assay, Electrochemiluminescence ELISA, ?-synuclein, phosphorylation, pS129, biomarker, α-synuclein, Medicinal and Biomolecular Chemistry, Biochemistry and cell biology, Analytical chemistry, Medicinal and biomolecular chemistry
Abstract: Synucleinopathies are a group of neurodegenerative diseases including Parkinson's disease (PD), dementia with Lewy bodies (DLB), and multiple system atrophy (MSA). These diseases are characterized by the aggregation and deposition of α-synuclein (α-syn) in Lewy bodies (LBs) in PD and DLB or as glial cytoplasmic inclusions in MSA. In healthy brains, only ∼4% of α-syn is phosphorylated at Ser129 (pS129-α-syn), whereas >90% pS129-α-syn may be found in LBs, suggesting that pS129-α-syn could be a useful biomarker for synucleinopathies. However, a widely available, robust, sensitive, and reproducible method for measuring pS129-α-syn in biological fluids is currently missing. We used Meso Scale Discovery (MSD)'s electrochemiluminescence platform to create a new assay for sensitive detection of pS129-α-syn. We evaluated several combinations of capture and detection antibodies and used semisynthetic pS129-α-syn as a standard for the assay at a concentration range from 0.5 to 6.6 × 104 pg/mL. Using the antibody EP1536Y for capture and an anti-human α-syn antibody (MSD) for detection was the best combination in terms of assay sensitivity, specificity, and reproducibility. We tested the utility of the assay for the detection and quantification of pS129-α-syn in human cerebrospinal fluid, serum, plasma, saliva, and CNS-originating small extracellular vesicles, as well as in mouse brain lysates. Our data suggest that the assay can become a widely used method for detecting pS129-α-syn in biomedical studies including when only a limited volume of sample is available and high sensitivity is required, offering new opportunities for diagnostic biomarkers, monitoring disease progression, and quantifying outcome measures in clinical trials.
Published: 2023

91. Smooth Non-Rigid Shape Matching via Effective Dirichlet Energy Optimization

Author: Magnet, Robin, Ren, Jing, Sorkine-Hornung, Olga, and Ovsjanikov, Maks
Subjects: Computer Science - Computer Vision and Pattern Recognition, Computer Science - Graphics
Abstract: We introduce pointwise map smoothness via the Dirichlet energy into the functional map pipeline, and propose an algorithm for optimizing it efficiently, which leads to high-quality results in challenging settings. Specifically, we first formulate the Dirichlet energy of the pulled-back shape coordinates, as a way to evaluate smoothness of a pointwise map across discrete surfaces. We then extend the recently proposed discrete solver and show how a strategy based on auxiliary variable reformulation allows us to optimize pointwise map smoothness alongside desirable functional map properties such as bijectivity. This leads to an efficient map refinement strategy that simultaneously improves functional and point-to-point correspondences, obtaining smooth maps even on non-isometric shape pairs. Moreover, we demonstrate that several previously proposed methods for computing smooth maps can be reformulated as variants of our approach, which allows us to compare different formulations in a consistent framework. Finally, we compare these methods both on existing benchmarks and on a new rich dataset that we introduce, which contains non-rigid, non-isometric shape pairs with inter-category and cross-category correspondences. Our work leads to a general framework for optimizing and analyzing map smoothness both conceptually and in challenging practical settings., Comment: Main Manuscript: 10 pages, 5 Figures, 3 Tables // Supplementary: 4 pages, 3 Figures, 5 Tables
Published: 2022
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92. The impact of novel inflammation-preserving treatment towards lumbar disc herniation resorption in symptomatic patients: a prospective, multi-imaging and clinical outcomes study

Author: Albert, Hanne B., Sayari, Arash J., Barajas, J. Nicolas, Hornung, Alexander L., Harada, Garrett, Nolte, Michael T., Chee, Ana V., Samartzis, Dino, and Tkachev, Alexander
Published: 2024
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93. Biochar—just a black matter is not enough

Author: Hornung, Andreas, Stenzel, Fabian, and Grunwald, Jan
Published: 2024
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94. Freeform generative design of complex functional structures

Author: Gerald G. Pereira, David Howard, Paulus Lahur, Michael Breedon, Phil Kilby, and Christian H. Hornung
Subjects: Medicine, Science
Abstract: Abstract Generative machine learning is poised to revolutionise a range of domains where rational design has long been the de facto approach: where design is practically a time consuming and frustrating process guided by heuristics and intuition. In this article we focus on the domain of flow chemistry, which is an ideal candidate for generative design approaches. We demonstrate a generative machine learning framework that optimises diverse, bespoke reactor elements for flow chemistry applications, combining evolutionary algorithms and a scalable fluid dynamics solver for in silico performance assessment. Experimental verification confirms the discovery of never-before-seen bespoke mixers whose performance exceeds the state of the art by 45%. These findings highlight the power of autonomous generative design to improve the operational performance of complex functional structures, with potential wide-ranging industrial applications.
Published: 2024
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95. Optical probing of ultrafast laser-induced solid-to-overdense-plasma transitions

Author: Yasmina Azamoum, Georg Alexander Becker, Sebastian Keppler, Guillaume Duchateau, Stefan Skupin, Mickael Grech, Fabrice Catoire, Sebastian Hell, Issa Tamer, Marco Hornung, Marco Hellwing, Alexander Kessler, Franck Schorcht, and Malte Christoph Kaluza
Subjects: Applied optics. Photonics, TA1501-1820, Optics. Light, QC350-467
Abstract: Abstract Understanding the solid target dynamics resulting from the interaction with an ultrashort laser pulse is a challenging fundamental multi-physics problem involving atomic and solid-state physics, plasma physics, and laser physics. Knowledge of the initial interplay of the underlying processes is essential to many applications ranging from low-power laser regimes like laser-induced ablation to high-power laser regimes like laser-driven ion acceleration. Accessing the properties of the so-called pre-plasma formed as the laser pulse’s rising edge ionizes the target is complicated from the theoretical and experimental point of view, and many aspects of this laser-induced transition from solid to overdense plasma over picosecond timescales are still open questions. On the one hand, laser-driven ion acceleration requires precise control of the pre-plasma because the efficiency of the acceleration process crucially depends on the target properties at the arrival of the relativistic intensity peak of the pulse. On the other hand, efficient laser ablation requires, for example, preventing the so-called “plasma shielding”. By capturing the dynamics of the initial stage of the interaction, we report on a detailed visualization of the pre-plasma formation and evolution. Nanometer-thin diamond-like carbon foils are shown to transition from solid to plasma during the laser rising edge with intensities < 1016 W/cm². Single-shot near-infrared probe transmission measurements evidence sub-picosecond dynamics of an expanding plasma with densities above 1023 cm−3 (about 100 times the critical plasma density). The complementarity of a solid-state interaction model and kinetic plasma description provides deep insight into the interplay of initial ionization, collisions, and expansion.
Published: 2024
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96. Quality of care transition, patient safety incidents, and patients’ health status: a structural equation model on the complexity of the discharge process

Author: Matthias Marsall, Thorsten Hornung, Alexander Bäuerle, and Matthias Weigl
Subjects: Transitional safety, Care transitions measure, Structural equation model, Readmission, Medication safety, Discharge, Public aspects of medicine, RA1-1270
Abstract: Abstract Background The transition of patients between care contexts poses patient safety risks. Discharges to home from inpatient care can be associated with adverse patient outcomes. Quality in discharge processes is essential in ensuring safe transitions for patients. Current evidence relies on bivariate analyses and neglects contextual factors such as treatment and patient characteristics and the interactions of potential outcomes. This study aimed to investigate the associations between the quality and safety of the discharge process, patient safety incidents, and health-related outcomes after discharge, considering the treatments’ and patients’ contextual factors in one comprehensive model. Methods Patients at least 18 years old and discharged home after at least three days of inpatient treatment received a self-report questionnaire. A total of N = 825 patients participated. The assessment contained items to assess the quality and safety of the discharge process from the patient’s perspective with the care transitions measure (CTM), a self-report on the incidence of unplanned readmissions and medication complications, health status, and sociodemographic and treatment-related characteristics. Statistical analyses included structural equation modeling (SEM) and additional analyses using logistic regressions. Results Higher quality of care transition was related to a lower incidence of medication complications (B = -0.35, p
Published: 2024
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97. SARS-CoV-2 and its ORF3a, E and M viroporins activate inflammasome in human macrophages and induce of IL-1α in pulmonary epithelial and endothelial cells

Author: Magdalena Ambrożek-Latecka, Piotr Kozlowski, Grażyna Hoser, Magdalena Bandyszewska, Karolina Hanusek, Dominika Nowis, Jakub Gołąb, Małgorzata Grzanka, Agnieszka Piekiełko-Witkowska, Luise Schulz, Franziska Hornung, Stefanie Deinhardt-Emmer, Ewa Kozlowska, and Tomasz Skirecki
Subjects: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282, Cytology, QH573-671
Abstract: Abstract Inflammasome assembly is a potent mechanism responsible for the host protection against pathogens, including viruses. When compromised, it can allow viral replication, while when disrupted, it can perpetuate pathological responses by IL-1 signaling and pyroptotic cell death. SARS-CoV-2 infection was shown to activate inflammasome in the lungs of COVID-19 patients, however, potential mechanisms responsible for this response are not fully elucidated. In this study, we investigated the effects of ORF3a, E and M SARS-CoV-2 viroporins in the inflammasome activation in major populations of alveolar sentinel cells: macrophages, epithelial and endothelial cells. We demonstrated that each viroporin is capable of activation of the inflammasome in macrophages to trigger pyroptosis-like cell death and IL-1α release from epithelial and endothelial cells. Small molecule NLRP3 inflammasome inhibitors reduced IL-1 release but weakly affected the pyroptosis. Importantly, we discovered that while SARS-CoV-2 could not infect the pulmonary microvascular endothelial cells it induced IL-1α and IL-33 release. Together, these findings highlight the essential role of macrophages as the major inflammasome-activating cell population in the lungs and point to endothelial cell expressed IL-1α as a potential novel component driving the pulmonary immunothromobosis in COVID-19.
Published: 2024
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98. Split Pool Ligation-based Single-cell Transcriptome sequencing (SPLiT-seq) data processing pipeline comparison

Author: Lucas Kuijpers, Bastian Hornung, Mirjam C. G. N. van den Hout - van Vroonhoven, Wilfred F. J. van IJcken, Frank Grosveld, and Eskeatnaf Mulugeta
Subjects: SPLiT-seq, Split-pool barcoding, Combinatorial barcoding, Data-preprocessing, Single cell RNA sequencing, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract: Abstract Background Single-cell sequencing techniques are revolutionizing every field of biology by providing the ability to measure the abundance of biological molecules at a single-cell resolution. Although single-cell sequencing approaches have been developed for several molecular modalities, single-cell transcriptome sequencing is the most prevalent and widely applied technique. SPLiT-seq (split-pool ligation-based transcriptome sequencing) is one of these single-cell transcriptome techniques that applies a unique combinatorial-barcoding approach by splitting and pooling cells into multi-well plates containing barcodes. This unique approach required the development of dedicated computational tools to preprocess the data and extract the count matrices. Here we compare eight bioinformatic pipelines (alevin-fry splitp, LR-splitpipe, SCSit, splitpipe, splitpipeline, SPLiTseq-demultiplex, STARsolo and zUMI) that have been developed to process SPLiT-seq data. We provide an overview of the tools, their computational performance, functionality and impact on downstream processing of the single-cell data, which vary greatly depending on the tool used. Results We show that STARsolo, splitpipe and alevin-fry splitp can all handle large amount of data within reasonable time. In contrast, the other five pipelines are slow when handling large datasets. When using smaller dataset, cell barcode results are similar with the exception of SPLiTseq-demultiplex and splitpipeline. LR-splitpipe that is originally designed for processing long-read sequencing data is the slowest of all pipelines. Alevin-fry produced different down-stream results that are difficult to interpret. STARsolo functions nearly identical to splitpipe and produce results that are highly similar to each other. However, STARsolo lacks the function to collapse random hexamer reads for which some additional coding is required. Conclusion Our comprehensive comparative analysis aids users in selecting the most suitable analysis tool for efficient SPLiT-seq data processing, while also detailing the specific prerequisites for each of these pipelines. From the available pipelines, we recommend splitpipe or STARSolo for SPLiT-seq data analysis.
Published: 2024
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99. Studying Gamow-Teller transitions and the assignment of isomeric and ground states at $N=50$

Author: Mollaebrahimi, Ali, Hornung, Christine, Dickel, Timo, Amanbayev, Daler, Kripko-Koncz, Gabriella, Plaß, Wolfgang R., Andrés, Samuel Ayet San, Beck, Sönke, Blazhev, Andrey, Bergmann, Julian, Geissel, Hans, Górska, Magdalena, Grawe, Hubert, Greiner, Florian, Haettner, Emma, Kalantar-Nayestanaki, Nasser, Miskun, Ivan, Nowacki, Frédéric, Scheidenberger, Christoph, Bagchi, Soumya, Balabanski, Dimiter L., Brencic, Ziga, Charviakova, Olga, Constantin, Paul, Dehghan, Masoumeh, Ebert, Jens, Gröf, Lizzy, Hall, Oscar, Harakeh, Muhsin N., Kaur, Satbir, Kankainen, Anu, Knöbel, Ronja, Kostyleva, Daria A., Kurkova, Natalia, Kuzminchuk, Natalia, Mardor, Israel, Nichita, Dragos, Otto, Jan-Hendrik, Patyk, Zygmunt, Pietri, Stephane, Purushothaman, Sivaji, Reiter, Moritz Pascal, Rink, Ann-Kathrin, Roesch, Heidi, Spătaru, Anamaria, Stanic, Goran, State, Alexandru, Tanaka, Yoshiki K., Vencelj, Matjaz, Weick, Helmut, Winfield, John S., Yavor, Michael I., and Zhao, Jianwei
Subjects: Nuclear Experiment
Abstract: Direct mass measurements of neutron-deficient nuclides around the $N=50$ shell closure below $^{100}$Sn were performed at the FRS Ion Catcher (FRS-IC) at GSI, Germany. The nuclei were produced by projectile fragmentation of $^{124}$Xe, separated in the fragment separator FRS and delivered to the FRS-IC. The masses of 14 ground states and two isomers were measured with relative mass uncertainties down to $1\times 10^{-7}$ using the multiple-reflection time-of-flight mass spectrometer of the FRS-IC, including the first direct mass measurements of $^{98}$Cd and $^{97}$Rh. A new $Q_\mathrm{EC} = 5437\pm67$ keV was obtained for $^{98}$Cd, resulting in a summed Gamow-Teller (GT) strength for the five observed transitions ($0^+\longrightarrow1^+$) as $B(\text{GT})=2.94^{+0.32}_{-0.28}$. Investigation of this result in state-of-the-art shell model approaches sheds light into a better understanding of the GT transitions in even-even isotones at $N=50$. The excitation energy of the long-lived isomeric state in $^{94}$Rh was determined for the first time to be $293\pm 21$ keV. This, together with the shell model calculations, allows the level ordering in $^{94}$Rh to be understood.
Published: 2022
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100. Observation of anisotropy-independent magnetization dynamics in spatially disordered Heisenberg spin systems

Author: Franz, Titus, Geier, Sebastian, Hainaut, Clément, Braemer, Adrian, Thaicharoen, Nithiwadee, Hornung, Moritz, Braun, Eduard, Gärttner, Martin, Zürn, Gerhard, and Weidemüller, Matthias
Subjects: Quantum Physics, Physics - Atomic and Molecular Clusters
Abstract: An important step towards a comprehensive understanding of far-from-equilibrium dynamics of quantum many-body systems is the identification of unifying features that are independent of microscopic details of the system. We experimentally observe such robust features in the magnetization relaxation dynamics of disordered Heisenberg XX-, XXZ- and Ising Hamiltonians. We realize these Heisenberg spin models with tunable anisotropy parameter and power-law interactions in an ensemble of Rydberg atoms by encoding the spin in suitable Rydberg state combinations. We consistently observe stretched-exponential relaxation of magnetization for all considered spin models, collapsing onto a single curve after appropriate rescaling of time. This robust short-time relaxation behavior is explained by a perturbative treatment that exploits the strong disorder in pairwise couplings, which leads to a description in terms of approximately independent pairs of spins. In numerical simulations of small systems, we show that these pairs of spins constitute approximate local integrals of motion, which remain at least partially conserved on a timescale exceeding the duration of the relaxation dynamics of the magnetization.
Published: 2022
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