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1. A Bag of Tricks for Scaling CPU-based Deep FFMs to more than 300m Predictions per Second

Author

Škrlj, Blaž, Ben-Shalom, Benjamin, Gašperšič, Grega, Schwartz, Adi, Hoseisi, Ramzi, Ziporin, Naama, Kopič, Davorin, and Tori, Andraž

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence, Computer Science - Information Retrieval
Abstract

Field-aware Factorization Machines (FFMs) have emerged as a powerful model for click-through rate prediction, particularly excelling in capturing complex feature interactions. In this work, we present an in-depth analysis of our in-house, Rust-based Deep FFM implementation, and detail its deployment on a CPU-only, multi-data-center scale. We overview key optimizations devised for both training and inference, demonstrated by previously unpublished benchmark results in efficient model search and online training. Further, we detail an in-house weight quantization that resulted in more than an order of magnitude reduction in bandwidth footprint related to weight transfers across data-centres. We disclose the engine and associated techniques under an open-source license to contribute to the broader machine learning community. This paper showcases one of the first successful CPU-only deployments of Deep FFMs at such scale, marking a significant stride in practical, low-footprint click-through rate prediction methodologies., Comment: 6p, KDD2024 - AdKDD workshop

Published
2024

2. Drifter: Efficient Online Feature Monitoring for Improved Data Integrity in Large-Scale Recommendation Systems

Author

Škrlj, Blaž, Ki-Tov, Nir, Edelist, Lee, Silberstein, Natalia, Weisman-Zohar, Hila, Mramor, Blaž, Kopič, Davorin, and Ziporin, Naama

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

Real-world production systems often grapple with maintaining data quality in large-scale, dynamic streams. We introduce Drifter, an efficient and lightweight system for online feature monitoring and verification in recommendation use cases. Drifter addresses limitations of existing methods by delivering agile, responsive, and adaptable data quality monitoring, enabling real-time root cause analysis, drift detection and insights into problematic production events. Integrating state-of-the-art online feature ranking for sparse data and anomaly detection ideas, Drifter is highly scalable and resource-efficient, requiring only two threads and less than a gigabyte of RAM per production deployments that handle millions of instances per minute. Evaluation on real-world data sets demonstrates Drifter's effectiveness in alerting and mitigating data quality issues, substantially improving reliability and performance of real-time live recommender systems., Comment: Accepted to ORSUM RecSys workshop

Published
2023

3. Unleash the Power of Context: Enhancing Large-Scale Recommender Systems with Context-Based Prediction Models

Author

Hartman, Jan, Klein, Assaf, Kopič, Davorin, and Silberstein, Natalia

Subjects
Computer Science - Information Retrieval, Computer Science - Machine Learning
Abstract

In this work, we introduce the notion of Context-Based Prediction Models. A Context-Based Prediction Model determines the probability of a user's action (such as a click or a conversion) solely by relying on user and contextual features, without considering any specific features of the item itself. We have identified numerous valuable applications for this modeling approach, including training an auxiliary context-based model to estimate click probability and incorporating its prediction as a feature in CTR prediction models. Our experiments indicate that this enhancement brings significant improvements in offline and online business metrics while having minimal impact on the cost of serving. Overall, our work offers a simple and scalable, yet powerful approach for enhancing the performance of large-scale commercial recommender systems, with broad implications for the field of personalized recommendations.

Published
2023
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6. Associations of intraoperative end–tidal CO2 levels with postoperative outcome–secondary analysis of a worldwide observational study

Author

Kroell, Wolfgang, Metzler, Helfried, Struber, Gerd, Wegscheider, Thomas, Gombotz, Hans, Hiesmayr, Michael, Schmid, Werner, Urbanek, Bernhard, Kahn, David, Momeni, Mona, Pospiech, Audrey, Lois, Fernande, Forget, Patrice, Grosu, Irina, Poelaert, Jan, van Mossevelde, Veerle, van Malderen, Marie-Claire, Dylst, Dimitri, van Melkebeek, Jeroen, Beran, Maud, de Hert, Stefan, De Baerdemaeker, Luc, Heyse, Bjorn, Van Limmen, Jurgen, Wyffels, Piet, Jacobs, Tom, Roels, Nathalie, De Bruyne, Ann, van de Velde, Stijn, Leva, Brigitte, Damster, Sandrine, Plichon, Benoit, JurosZovko, Marina, Djonovic-Omanoviċ, Dejana, Pernar, Selma, Zunic, Josip, Miskovic, Petar, Zilic, Antonio, Kvolik, Slavica, Ivic, Dubravka, Azenic-Venzera, Darija, Skiljic, Sonja, Vinkovic, Hrvoje, Oputric, Ivana, Juricic, Kazimir, Frkovic, Vedran, Kopic, Jasminka, Mirkovic, Ivan, Karanovic, Nenad, Carev, Mladen, Dropulic, Natasa, Saric, Jadranka Pavicic, Erceg, Gorjana, Dvorscak, Matea Bogdanovic, Mazul-Sunko, Branka, Pavicic, Anna Marija, Goranovic, Tanja, Maldini, Branka, Radocaj, Tomislav, Gavranovic, Zeljka, Mladic-Batinica, Inga, Sehovic, Mirna, Stourac, Petr, Harazim, Hana, Smekalova, Olga, Kosinova, Martina, Kolacek, Tomas, Hudacek, Kamil, Drab, Michal, Brujevic, Jan, Vitkova, Katerina, Jirmanova, Katerina, Volfova, Ivana, Dzurnakova, Paula, Liskova, Katarina, Dudas, Radovan, Filipsky, Radek, El Kafrawy, Samir, Abdelwahab, Hisham Hosny, Metwally, Tarek, Abdel-Razek, Ahmed, El-Shaarawy, Ahmed Mostafa, Hasan, Wael Fathy, Ahmed, Ahmed Gouda, Yassin, Hany, Magdy, Mohamed, Abdelhady, Mahdy, Mahran, Mohamed, Herodes, Eiko, Kivik, Peeter, Oganjan, Juri, Aun, Annika, Sormus, Alar, Sarapuu, Kaili, Mall, Merilin, Karjagin, Juri, Futier, Emmanuel, Petit, Antoine, Gerard, Adeline, Marret, Emmanuel, Solier, Marc, Jaber, Samir, Prades, Albert, Krassler, Jens, Merzky, Simone, de Abreu, Marcel Gama, Uhlig, Christopher, Kiss, Thomas, Bundy, Anette, Bluth, Thomas, Gueldner, Andreas, Spieth, Peter, Scharffenberg, Martin, Thiem, Denny Tran, Koch, Thea, Treschan, Tanja, Schaefer, Maximilian, Bastin, Bea, Geib, Johann, Weiss, Martin, Kienbaum, Peter, Pannen, Benedikt, Gottschalk, Andre, Konrad, Mirja, Westerheide, Diana, Schwerdtfeger, Ben, Wrigge, Hermann, Simon, Philipp, Reske, Andreas, Nestler, Christian, Valsamidis, Dimitrios, Stroumpoulis, Konstantinos, Antholopoulos, Georgios, Andreou, Antonis, Karapanos, Dimitris, Theodoraki, Kassiani, Gkiokas, Georgios, Tasoulis, Marios-Konstantinos, Sidiropoulou, Tatiana, Zafeiropoulou, Foteini, Florou, Panagiota, Pandazi, Aggeliki, Tsaousi, Georgia, Nouris, Christos, Pourzitaki, Chryssa, Bystritski, Dmitri, Pizov, Reuven, Eden, Arieh, Pesce, Caterina Valeria, Campanile, Annamaria, Marrella, Antonella, Grasso, Salvatore, De Michele, Michele, Bona, Francesco, Giacoletto, Gianmarco, Sardo, Elena, Giancarlo, Luigi, Sottosanti, Vicari, Solca, Maurizio, Volta, Carlo Alberto, Spadaro, Savino, Verri, Marco, Ragazzi, Riccardo, Zoppellari, Roberto, Cinnella, Gilda, Raimondo, Pasquale, La Bella, Daniela, Mirabella, Lucia, D’antini, Davide, Pelosi, Paolo, Molin, Alexandre, Brunetti, Iole, Gratarola, Angelo, Pellerano, Giulia, Sileo, Rosanna, Pezzatto, Stefano, Montagnani, Luca, Pasin, Laura, Landoni, Giovanni, Zangrillo, Alberto, Beretta, Luigi, Di Parma, Ambra Licia, Tarzia, Valentina, Dossi, Roberto, Sassone, Marta Eugenia, Sances, Daniele, Tredici, Stefano, Spano, Gianluca, Castellani, Gianluca, Delunas, Luigi, Peradze, Sopio, Venturino, Marco, Arpino, Ines, Sher, Sara, Tommasino, Concezione, Rapido, Francesca, Morelli, Paola, Vargas, Maria, Servillo, Giuseppe, Cortegiani, Andrea, Raineri, Santi Maurizio, Montalto, Francesca, Russotto, Vincenzo, Giarratano, Antonino, Baciarello, Marco, Generali, Michela, Cerati, Giorgia, Leykin, Yigal, Bressan, Filippo, Bartolini, Vittoria, Zamidei, Lucia, Brazzi, Luca, Liperi, Corrado, Sales, Gabriele, Pistidda, Laura, Severgnini, Paolo, Brugnoni, Elisa, Musella, Giuseppe, Bacuzzi, Alessandro, Muhardri, Dalip, Gecaj-Gashi, Agreta, Sada, Fatos, Bytyqi, Adem, Karbonskiene, Aurika, Aukstakalniene, Ruta, Teberaite, Zivile, Salciute, Erika, Tikuisis, Renatas, Miliauskas, Povilas, Jurate, Sipylaite, Kontrimaviciute, Egle, Tomkute, Gabija, Xuereb, John, Bezzina, Maureen, Borg, Francis Joseph, Hemmes, Sabrine, Schultz, Marcus, Hollmann, Markus, Wiersma, Irene, Binnekade, Jan, Bos, Lieuwe, Boer, Christa, Duvekot, Anne, In’t Veld, Bas, Werger, Alice, Dennesen, Paul, Severijns, Charlotte, De Jong, Jasper, Hering, Jens, van Beek, Rienk, Ivars, Stefan, Jammer, Ib, Breidablik, Alena, Hodt, Katharina Skirstad, Fjellanger, Frode, Avalos, Manuel Vico, MellinOlsen, Jannicke, Andersson, Elisabeth, Shafi-Kabiri, Amir, Molina, Ruby, Wutai, Stanley, Morais, Erick, Tareco, Gloria, Ferreira, Daniel, Amaral, Joana, de Lurdes Goncalves Castro, Maria, Cadilha, Susana, Appleton, Sofia, Parente, Suzana, Correia, Mariana, Martins, Diogo, Monteirosa, Angela, Ricardo, Ana, Rodrigues, Sara, Horhota, Lucian, Grintescu, Ioana Marina, Mirea, Liliana, Grintescu, Ioana Cristina, Corneci, Dan, Negoita, Silvius, Dutu, Madalina, Garotescu, Ioana Popescu, Filipescu, Daniela, Prodan, Alexandru Bogdan, Droc, Gabriela, Fota, Ruxandra, Popescu, Mihai, Tomescu, Dana, Petcu, Ana Maria, Tudoroiu, Marian Irinel, Moise, Alida, Guran, Catalin-Traian, Gherghina, Iorel, Costea, Dan, Cindea, Iulia, Copotoiu, Sanda-Maria, Copotoiu, Ruxandra, Barsan, Victoria, Tolcser, Zsolt, Riciu, Magda, Moldovan, Septimiu Gheorghe, Veres, Mihaly, Gritsan, Alexey, Kapkan, Tatyana, Gritsan, Galina, Korolkov, Oleg, Kulikov, Alexander, Lubnin, Andrey, Ovezov, Alexey, Prokoshev, Pavel, Lugovoy, Alexander, Anipchenko, Natalia, Babayants, Andrey, Komissarova, Irina, Zalina, Karginova, Likhvantsev, Valery, Fedorov, Sergei, Lazukic, Aleksandra, Pejakovic, Jasmina, Mihajlovic, Dunja, Kusnierikova, Zuzana, Zelinkova, Maria, Bruncakova, Katarina, Polakovicova, Lenka, Sobona, Villiam, Novak-Supe, Barbka, Pekle-Golez, Ana, Jovanov, Miroljub, Strazisar, Branka, Markovic-Bozic, Jasmina, Novak-Jankovic, Vesna, Voje, Minca, Grynyuk, Andriy, Kostadinov, Ivan, Spindler-Vesel, Alenka, Moral, Victoria, Unzueta, Mari Carmen, Puigbo, Carlos, Fava, Josep, Canet, Jaume, Moret, Enrique, Nunez, Monicá Rodriguez, Sendra, Mar, Brunelli, Andrea, Rodenas, Frederic, Monedero, Pablo, Martinez, Francisco Hidalgo, Temino, Maria Jose Yepes, Simon, Antonio Martínez, de Abajo Larriba, Ana, Lisi, Alberto, Perez, Gisela, Martinez, Raquel, Granell, Manuel, Vivo, Jose Tatay, Ruiz, Cristina Saiz, de Andrés Ibañez, Jose Antonio, Pastor, Ernesto, Soro, Marina, Ferrando, Carlos, Defez, Mario, Alvares-Santullano, Cesar Aldecoa, Perez, Rocio, Rico, Jesus, Jawad, Monir, Saeed, Yousif, Gillberg, Lars, Bengisun, Zuleyha Kazak, Kazbek, Baturay Kansu, Coskunfirat, Nesil, Boztug, Neval, Sanli, Suat, Yilmaz, Murat, Hadimioglu, Necmiye, Senturk, Nuzhet Mert, Camci, Emre, Kucukgoncu, Semra, Sungur, Zerrin, Sivrikoz, Nukhet, Ozgen, Serpil Ustalar, Toraman, Fevzi, Selvi, Onur, Senturk, Ozgur, Yildiz, Mine, Kuvaki, Bahar, Gunenc, Ferim, Kucukguclu, Semih, Ozbilgin, Şule, Maral, Jale, Canli, Seyda, Arun, Oguzhan, Saltali, Ali, Aydogan, Eyup, Akgun, Fatma Nur, Sanlikarip, Ceren, Karaman, Fatma Mine, Mazur, Andriy, Vorotyntsev, Sergiy, Rousseau, Guy, Barrett, Colin, Stancombe, Lucia, Shelley, Ben, Scholes, Helen, Limb, James, Rafi, Amir, Wayman, Lisa, Deane, Jill, Rogerson, David, Williams, John, Yates, Susan, Rogers, Elaine, Pulletz, Mark, Moreton, Sarah, Jones, Stephanie, Venkatesh, Suresh, Burton, Maudrian, Brown, Lucy, Goodall, Cait, Rucklidge, Matthew, Fuller, Debbie, Nadolski, Maria, Kusre, Sandeep, Lundberg, Michael, Everett, Lynn, Nutt, Helen, Zuleika, Maka, Carvalho, Peter, Clements, Deborah, Creagh-Brown, Ben, Watt, Philip, Raymode, Parizade, Pearse, Rupert, Mohr, Otto, Raj, Ashok, Creary, Thais, Chishti, Ahmed, Bell, Andrea, Higham, Charley, Cain, Alistair, Gibb, Sarah, Mowat, Stephen, Franklin, Danielle, West, Claire, Minto, Gary, Boyd, Nicholas, Mills, Gary, Calton, Emily, Walker, Rachel, Mackenzie, Felicity, Ellison, Branwen, Roberts, Helen, Chikungwa, Moses, Jackson, Clare, Donovan, Andrew, Foot, Jayne, Homan, Elizabeth, Montgomery, Jane, Portch, David, Mercer, Pauline, Palmer, Janet, Paddle, Jonathan, Fouracres, Anna, Datson, Amanda, Andrew, Alyson, Welch, Leanne, Rose, Alastair, Varma, Sandeep, Simeson, Karen, Rambhatla, Mrutyunjaya, Susarla, Jaysimha, Marri, Sudhakar, Kodaganallur, Krishnan, Das, Ashok, Algarsamy, Shivarajan, Colley, Julie, Davies, Simon, Szewczyk, Margaret, Smith, Thomas, Fernandez-Bustamante, Ana, Luzier, Elizabeth, Almagro, Angela, Melo, Marcos Vidal, Fernando, Luiz, Sulemanji, Demet, Sprung, Juraj, Weingarten, Toby, Kor, Daryl, Scavonetto, Federica, Tze, Yeo, Nasa, Prashant, van Meenen, David M.P., Paulus, Frederique, de Abreu, Marcelo Gama, Bossers, Sebastiaan M., Schober, Patrick, Schultz, Marcus J., Neto, Ary Serpa, and Hemmes, Sabrine N.T.

Published
2025
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7. Dynamic Surrogate Switching: Sample-Efficient Search for Factorization Machine Configurations in Online Recommendations

Author

Škrlj, Blaž, Schwartz, Adi, Ferlež, Jure, Kopič, Davorin, and Ziporin, Naama

Subjects
Computer Science - Machine Learning, Computer Science - Artificial Intelligence
Abstract

Hyperparameter optimization is the process of identifying the appropriate hyperparameter configuration of a given machine learning model with regard to a given learning task. For smaller data sets, an exhaustive search is possible; However, when the data size and model complexity increase, the number of configuration evaluations becomes the main computational bottleneck. A promising paradigm for tackling this type of problem is surrogate-based optimization. The main idea underlying this paradigm considers an incrementally updated model of the relation between the hyperparameter space and the output (target) space; the data for this model are obtained by evaluating the main learning engine, which is, for example, a factorization machine-based model. By learning to approximate the hyperparameter-target relation, the surrogate (machine learning) model can be used to score large amounts of hyperparameter configurations, exploring parts of the configuration space beyond the reach of direct machine learning engine evaluation. Commonly, a surrogate is selected prior to optimization initialization and remains the same during the search. We investigated whether dynamic switching of surrogates during the optimization itself is a sensible idea of practical relevance for selecting the most appropriate factorization machine-based models for large-scale online recommendation. We conducted benchmarks on data sets containing hundreds of millions of instances against established baselines such as Random Forest- and Gaussian process-based surrogates. The results indicate that surrogate switching can offer good performance while considering fewer learning engine evaluations., Comment: https://dl.acm.org/doi/abs/10.1145/3523227.3547384

Published
2022
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8. Feature embedding in click-through rate prediction

Author

Pahor, Samo, Kopič, Davorin, and Demšar, Jure

Subjects
Computer Science - Machine Learning
Abstract

We tackle the challenge of feature embedding for the purposes of improving the click-through rate prediction process. We select three models: logistic regression, factorization machines and deep factorization machines, as our baselines and propose five different feature embedding modules: embedding scaling, FM embedding, embedding encoding, NN embedding and the embedding reweighting module. The embedding modules act as a way to improve baseline model feature embeddings and are trained alongside the rest of the model parameters in an end-to-end manner. Each module is individually added to a baseline model to obtain a new augmented model. We test the predictive performance of our augmented models on a publicly accessible dataset used for benchmarking click-through rate prediction models. Our results show that several proposed embedding modules provide an important increase in predictive performance without a drastic increase in training time., Comment: 25 pages, 8 figures, 7 tables

Published
2022

9. Exploration with Model Uncertainty at Extreme Scale in Real-Time Bidding

Author

Hartman, Jan and Kopič, Davorin

Subjects
Computer Science - Machine Learning, Computer Science - Information Retrieval
Abstract

In this work, we present a scalable and efficient system for exploring the supply landscape in real-time bidding. The system directs exploration based on the predictive uncertainty of models used for click-through rate prediction and works in a high-throughput, low-latency environment. Through online A/B testing, we demonstrate that exploration with model uncertainty has a positive impact on model performance and business KPIs.

Published
2022
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10. Sex dependence of postoperative pulmonary complications – A post hoc unmatched and matched analysis of LAS VEGAS

Author

Kroell, Wolfgang, Metzler, Helfried, Struber, Gerd, Wegscheider, Thomas, Gombotz, Hans, Hiesmayr, Michael, Schmid, Werner, Urbanek, Bernhard, Kahn, David, Momeni, Mona, Pospiech, Audrey, Lois, Fernande, Forget, Patrice, Grosu, Irina, Poelaert, Jan, van Mossevelde, Veerle, van Malderen, Marie-Claire, Dylst, Dimitri, van Melkebeek, Jeroen, Beran, Maud, de Hert, Stefan, De Baerdemaeker, Luc, Heyse, Bjorn, Van Limmen, Jurgen, Wyffels, Piet, Jacobs, Tom, Roels, Nathalie, De Bruyne, Ann, van de Velde, Stijn, Leva, Brigitte, Damster, Sandrine, Plichon, Benoit, Juros-Zovko, Marina, Djonoviċ-Omanoviċ, Dejana, Pernar, Selma, Zunic, Josip, Miskovic, Petar, Zilic, Antonio, Kvolik, Slavica, Ivic, Dubravka, Azenic-Venzera, Darija, Skiljic, Sonja, Vinkovic, Hrvoje, Oputric, Ivana, Juricic, Kazimir, Frkovic, Vedran, Kopic, Jasminka, Mirkovic, Ivan, Karanovic, Nenad, Carev, Mladen, Dropulic, Natasa, Saric, Jadranka Pavicic, Erceg, Gorjana, Dvorscak, Matea Bogdanovic, Mazul-Sunko, Branka, Pavicic, Anna Marija, Goranovic, Tanja, Maldini, Branka, Radocaj, Tomislav, Gavranovic, Zeljka, Mladic-Batinica, Inga, Sehovic, Mirna, Stourac, Petr, Harazim, Hana, Smekalova, Olga, Kosinova, Martina, Kolacek, Tomas, Hudacek, Kamil, Drab, Michal, Brujevic, Jan, Vitkova, Katerina, Jirmanova, Katerina, Volfova, Ivana, Dzurnakova, Paula, Liskova, Katarina, Dudas, Radovan, Filipsky, Radek, el Kafrawy, Samir, Abdelwahab, Hisham Hosny, Metwally, Tarek, Abdel-Razek, Ahmed, El-Shaarawy, Ahmed Mostafa, Hasan, Wael Fathy, Ahmed, Ahmed Gouda, Yassin, Hany, Magdy, Mohamed, Abdelhady, Mahdy, Mahran, Mohamed, Herodes, Eiko, Kivik, Peeter, Oganjan, Juri, Aun, Annika, Sormus, Alar, Sarapuu, Kaili, Mall, Merilin, Karjagin, Juri, Futier, Emmanuel, Petit, Antoine, Gerard, Adeline, Marret, Emmanuel, Solier, Marc, Jaber, Samir, Prades, Albert, Krassler, Jens, Merzky, Simone, de Abreu, Marcel Gama, Uhlig, Christopher, Kiss, Thomas, Bundy, Anette, Bluth, Thomas, Gueldner, Andreas, Spieth, Peter, Scharffenberg, Martin, Thiem, Denny Tran, Koch, Thea, Treschan, Tanja, Schaefer, Maximilian, Bastin, Bea, Geib, Johann, Weiss, Martin, Kienbaum, Peter, Pannen, Benedikt, Gottschalk, Andre, Konrad, Mirja, Westerheide, Diana, Schwerdtfeger, Ben, Wrigge, Hermann, Simon, Philipp, Reske, Andreas, Nestler, Christian, Valsamidis, Dimitrios, Stroumpoulis, Konstantinos, Antholopoulos, Georgios, Andreou, Antonis, Karapanos, Dimitris, Theodoraki, Kassiani, Gkiokas, Georgios, Tasoulis, Marios-Konstantinos, Sidiropoulou, Tatiana, Zafeiropoulou, Foteini, Florou, Panagiota, Pandazi, Aggeliki, Tsaousi, Georgia, Nouris, Christos, Pourzitaki, Chryssa, Bystritski, Dmitri, Pizov, Reuven, Eden, Arieh, Pesce, Caterina Valeria, Campanile, Annamaria, Marrella, Antonella, Grasso, Salvatore, De Michele, Michele, Bona, Francesco, Giacoletto, Gianmarco, Sardo, Elena, Sottosanti, Luigi Giancarlo Vicari, Solca, Maurizio, Volta, Carlo Alberto, Spadaro, Savino, Verri, Marco, Ragazzi, Riccardo, Zoppellari, Roberto, Cinnella, Gilda, Raimondo, Pasquale, La Bella, Daniela, Mirabella, Lucia, D'antini, Davide, Pelosi, Paolo, Molin, Alexandre, Brunetti, Iole, Gratarola, Angelo, Pellerano, Giulia, Sileo, Rosanna, Pezzatto, Stefano, Montagnani, Luca, Pasin, Laura, Landoni, Giovanni, Zangrillo, Alberto, Beretta, Luigi, Di Parma, Ambra Licia, Tarzia, Valentina, Dossi, Roberto, Sassone, Marta Eugenia, Sances, Daniele, Tredici, Stefano, Spano, Gianluca, Castellani, Gianluca, Delunas, Luigi, Peradze, Sopio, Venturino, Marco, Arpino, Ines, Sher, Sara, Tommasino, Concezione, Rapido, Francesca, Morelli, Paola, Vargas, Maria, Servillo, Giuseppe, Cortegiani, Andrea, Raineri, Santi Maurizio, Montalto, Francesca, Russotto, Vincenzo, Giarratano, Antonino, Baciarello, Marco, Generali, Michela, Cerati, Giorgia, Leykin, Yigal, Bressan, Filippo, Bartolini, Vittoria, Zamidei, Lucia, Brazzi, Luca, Liperi, Corrado, Sales, Gabriele, Pistidda, Laura, Severgnini, Paolo, Brugnoni, Elisa, Musella, Giuseppe, Bacuzzi, Alessandro, Muhardri, Dalip, Gecaj-Gashi, Agreta, Sada, Fatos, Bytyqi, Adem, Karbonskiene, Aurika, Aukstakalniene, Ruta, Teberaite, Zivile, Salciute, Erika, Tikuisis, Renatas, Miliauskas, Povilas, Jurate, Sipylaite, Kontrimaviciute, Egle, Tomkute, Gabija, Xuereb, John, Bezzina, Maureen, Borg, Francis Joseph, Hemmes, Sabrine, Schultz, Marcus, Hollmann, Markus, Wiersma, Irene, Binnekade, Jan, Bos, Lieuwe, Nijbroek, Sunny, Boer, Christa, Duvekot, Anne, ’t Veld, Bas in, Werger, Alice, Dennesen, Paul, Severijns, Charlotte, De Jong, Jasper, Hering, Jens, van Beek, Rienk, Ivars, Stefan, Jammer, Ib, Breidablik, Alena, Hodt, Katharina Skirstad, Fjellanger, Frode, Avalos, Manuel Vico, Mellin-Olsen, Jannicke, Andersson, Elisabeth, Shafi-Kabiri, Amir, Molina, Ruby, Wutai, Stanley, Morais, Erick, Tareco, Glória, Ferreira, Daniel, Amaral, Joana, de Lurdes Goncalves Castro, Maria, Cadilha, Susana, Appleton, Sofia, Parente, Suzana, Correia, Mariana, Martins, Diogo, Monteirosa, Angela, Ricardo, Ana, Rodrigues, Sara, Horhota, Lucian, Grintescu, Ioana Marina, Mirea, Liliana, Grintescu, Ioana Cristina, Corneci, Dan, Negoita, Silvius, Dutu, Madalina, Garotescu, Ioana Popescu, Filipescu, Daniela, Prodan, Alexandru Bogdan, Droc, Gabriela, Fota, Ruxandra, Popescu, Mihai, Tomescu, Dana, Petcu, Ana Maria, Tudoroiu, Marian Irinel, Moise, Alida, Guran, Catalin-Traian, Gherghina, Iorel, Costea, Dan, Cindea, Iulia, Copotoiu, Sanda-Maria, Copotoiu, Ruxandra, Barsan, Victoria, Tolcser, Zsolt, Riciu, Magda, Moldovan, Septimiu Gheorghe, Veres, Mihaly, Gritsan, Alexey, Kapkan, Tatyana, Gritsan, Galina, Korolkov, Oleg, Kulikov, Alexander, Lubnin, Andrey, Ovezov, Alexey, Prokoshev, Pavel, Lugovoy, Alexander, Anipchenko, Natalia, Babayants, Andrey, Komissarova, Irina, Zalina, Karginova, Likhvantsev, Valery, Fedorov, Sergei, Lazukic, Aleksandra, Pejakovic, Jasmina, Mihajlovic, Dunja, Kusnierikova, Zuzana, Zelinkova, Maria, Bruncakova, Katarina, Polakovicova, Lenka, Sobona, Villiam, Novak-Supe, Barbka, Pekle-Golez, Ana, Jovanov, Miroljub, Strazisar, Branka, Markovic-Bozic, Jasmina, Novak-Jankovic, Vesna, Voje, Minca, Grynyuk, Andriy, Kostadinov, Ivan, Spindler-Vesel, Alenka, Moral, Victoria, Unzueta, Mari Carmen, Puigbo, Carlos, Fava, Josep, Canet, Jaume, Moret, Enrique, Nunez, Mónica Rodriguez, Sendra, Mar, Brunelli, Andrea, Rodenas, Frederic, Monedero, Pablo, Martinezv, Francisco Hidalgo, Temino, Maria Jose Yepes, Simon, Antonio Martínez, de Abajo Larriba, Ana, Lisi, Alberto, Perez, Gisela, Martinez, Raquel, Granell, Manuel, Vivo, Jose Tatay, Ruiz, Cristina Saiz, de Andrés Ibañez, Jose Antonio, Pastor, Ernesto, Soro, Marina, Ferrando, Carlos, Defez, Mario, Alvares-Santullano, Cesar Aldecoa, Perez, Rocio, Rico, Jesus, Jawad, Monir, Saeed, Yousif, Gillberg, Lars, Hedenstierma, Göran, Bengisun, Zuleyha Kazak, Kazbek, Baturay Kansu, Coskunfirat, Nesil, Boztug, Neval, Sanli, Suat, Yilmaz, Murat, Hadimioglu, Necmiye, Senturk, Nuzhet Mert, Camci, Emre, Kucukgoncu, Semra, Sungur, Zerrin, Sivrikoz, Nukhet, Ozgen, Serpil Ustalar, Toraman, Fevzi, Selvi, Onur, Senturk, Ozgur, Yildiz, Mine, Kuvaki, Bahar, Gunenc, Ferim, Kucukguclu, Semih, Ozbilgin, Şule, Maral, Jale, Canli, Seyda, Arun, Oguzhan, Saltali, Ali, Aydogan, Eyup, Akgun, Fatma Nur, Sanlikarip, Ceren, Karaman, Fatma Mine, Mazur, Andriy, Vorotyntsev, Sergiy, Rousseau, Guy, Barrett, Colin, Stancombe, Lucia, Shelley, Ben, Scholes, Helen, Limb, James, Rafi, Amir, Wayman, Lisa, Deane, Jill, Rogerson, David, Williams, John, Yates, Susan, Rogers, Elaine, Pulletz, Mark, Moreton, Sarah, Jones, Stephanie, Venkatesh, Suresh, Burton, Maudrian, Brown, Lucy, Goodall, Cait, Rucklidge, Matthew, Fuller, Debbie, Nadolski, Maria, Kusre, Sandeep, Lundberg, Michael, Everett, Lynn, Nutt, Helen, Zuleika, Maka, Carvalho, Peter, Clements, Deborah, Creagh-Brown, Ben, Watt, Philip, Raymode, Parizade, Pearse, Rupert, Mohr, Otto, Raj, Ashok, Creary, Thais, Chishti, Ahmed, Bell, Andrea, Higham, Charley, Cain, Alistair, Gibb, Sarah, Mowat, Stephen, Franklin, Danielle, West, Claire, Minto, Gary, Boyd, Nicholas, Mills, Gary, Calton, Emily, Walker, Rachel, Mackenzie, Felicity, Ellison, Branwen, Roberts, Helen, Chikungwa, Moses, Jackson, Clare, Donovan, Andrew, Foot, Jayne, Homan, Elizabeth, Montgomery, Jane, Portch, David, Mercer, Pauline, Palmer, Janet, Paddle, Jonathan, Fouracres, Anna, Datson, Amanda, Andrew, Alyson, Welch, Leanne, Rose, Alastair, Varma, Sandeep, Simeson, Karen, Rambhatla, Mrutyunjaya, Susarla, Jaysimha, Marri, Sudhakar, Kodaganallur, Krishnan, Das, Ashok, Algarsamy, Shivarajan, Colley, Julie, Davies, Simon, Szewczyk, Margaret, Smith, Thomas, Fernandez-Bustamante, Ana, Luzier, Elizabeth, Almagro, Angela, Melo, Marcos Vidal, Fernando, Luiz, Sulemanji, Demet, Sprung, Juraj, Weingarten, Toby, Kor, Daryl, Scavonetto, Federica, Tze, Yeo, Vermeulen, Tom D., Hol, Liselotte, Swart, Pien, Mills, Gary H., Putensen, Christian, Serpa Neto, Ary, Vidal Melo, Marcos F., Hollmann, Markus W., Gama de Abreu, Marcelo, Schultz, Marcus J., Hemmes, Sabrine N., and van Meenen, David M.

Published
2024
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12. Scaling TensorFlow to 300 million predictions per second

Author

Hartman, Jan and Kopič, Davorin

Subjects
Computer Science - Machine Learning, Computer Science - Performance
Abstract

We present the process of transitioning machine learning models to the TensorFlow framework at a large scale in an online advertising ecosystem. In this talk we address the key challenges we faced and describe how we successfully tackled them; notably, implementing the models in TF and serving them efficiently with low latency using various optimization techniques.

Published
2021
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16. High repetition rate Time-Resolved VUV ARPES at 10.8 eV photon energy

Author

Peli, Simone, Puntel, Denny, Kopic, Damir, Sockol, Benjamin, Parmigiani, Fulvio, and Cilento, Federico

Subjects
Physics - Optics, Condensed Matter - Materials Science
Abstract

The quest for mapping the femtosecond dynamics of the electronic band structure of complex materials via Time- and Angle-Resolved Photoelectron Spectroscopy (TR-ARPES) over their full First Brillouin Zone is pushing the development of schemes to efficiently generate ultrashort photon pulses in the VUV-range of photon energies. At present, the critical aspect is to combine a high photon energy with high photoemission count rates and a small pulse-bandwidth, necessary to achieve high energy resolution in ARPES, while preserving a good time resolution and mitigating space-charge effects. Here we describe a novel approach to produce light pulses at 10.8 eV, combining high repetition rate operation (1-4 MHz), high energy resolution ($\sim26$ meV) and space-charge free operation, with a time-resolution of $\sim$700 fs. These results have been achieved by generating the 9th harmonic of a Yb fiber laser, through a phase-matched process of third harmonic generation in Xenon of the laser third harmonic. The full up-conversion process is driven by a seed pulse energy as low as 10 $\mu$J, hence is easily scalable to multi-MHz operation. This source opens the way to TR-ARPES experiments for the investigation of the electron dynamics over the full first Brillouin zone of most complex materials, with unprecedented energy and momentum resolutions and high count rates. The performances of our setup are tested in a number of experiments on WTe$_2$ and Bi$_2$Se$_3$, of which we measure the electronic band structure in energy, two-dimensional momentum and time., Comment: 11 pages, 8 figures

Published
2019

17. Space Charge Free Ultrafast Photoelectron Spectroscopy on Solids by a Narrowband Tunable Extreme Ultraviolet Light Source

Author

Cucini, Riccardo, Pincelli, Tommaso, Panaccione, Giancarlo, Kopic, Damir, Frassetto, Fabio, Miotti, Paolo, Pierantozzi, Gian Marco, Peli, Simone, Fondacaro, Andrea, De Luisa, Aleksander, De Vita, Alessandro, Carrara, Pietro, Krizmancic, Damjan, Payne, Daniel T., Salvador, Federico, Sterzi, Andrea, Poletto, Luca, Parmigiani, Fulvio, Rossi, Giorgio, and Cilento, Federico

Subjects
Physics - Optics, Condensed Matter - Materials Science
Abstract

Here we report on a novel High Harmonic Generation (HHG) light source designed for space charge free ultrafast photoelectron spectroscopy (PES) on solids. The ultimate overall energy resolution achieved on a polycrystalline Au sample is ~22 meV at 40 K. These results have been obtained at a photon energy of 16.9 eV with a pulse bandwidth of ~19 meV, by varying, up to 200 kHz, the photon pulses repetition rate and the photon fluence on the sample. These features set a new benchmark for tunable narrowband HHG sources. By comparing the PES energy resolution and the photon pulse bandwidth with a pulse duration of ~105 fs, as retrieved from time-resolved (TR) angle resolved (AR) PES experiments on Bi$_2$Se$_3$, we validate a way for a space charge free photoelectric process close to Fourier transform limit conditions for ultrafast TR-PES experiments on solids., Comment: 10 pages, 7 figures, 46 references

Published
2019

18. Photoinduced nematic state in FeSe$_{0.4}$Te$_{0.6}$

Author

Fanfarillo, Laura, Kopić, Damir, Sterzi, Andrea, Manzoni, Giulia, Crepaldi, Alberto, Tsurkan, Vladimir, Croitori, Dorina, Deisenhofer, Joachim, Parmigiani, Fulvio, Capone, Massimo, and Cilento, Federico

Subjects
Condensed Matter - Strongly Correlated Electrons
Abstract

FeSe$_{x}$Te$_{1-x}$ compounds present a complex phase diagram, ranging from the nematicity of FeSe to the $(\pi, \pi)$ magnetism of FeTe. We focus on FeSe$_{0.4}$Te$_{0.6}$, where the nematic ordering is absent at equilibrium. We use a time-resolved approach based on femtosecond light pulses to study the dynamics following photoexcitation in this system. The use of polarization-dependent time- and angle-resolved photoelectron spectroscopy allows us to reveal a photoinduced nematic metastable state, whose stabilization cannot be interpreted in terms of an effective photodoping. We argue that the 1.55 eV photon-energy-pump-pulse perturbs the $C_4$ symmetry of the system triggering the realization of the nematic state. The possibility to induce nematicity using an ultra-short pulse sheds a new light on the driving force behind the nematic symmetry breaking in iron-based superconductors. Our results weaken the idea that a low-energy coupling with fluctuations is a necessary condition to stabilize the nematic order and ascribe the origin of the nematic order in iron-based superconductors to a clear tendency of those systems towards orbital differentiation due to strong electronic correlations induced by the Hund's coupling., Comment: 7 pages, 3 figures

Published
2019
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19. Origami‐Enabled Stretchable Electrodes Based on Parylene Deposition and 3D Printing

Author

Fulvia Del Duca, Lukas Hiendlmeier, Reem Al Fata, George Al Boustani, Inola Kopic, Hu Peng, Beatrice De Chiara, Marta Nikić, Francisco Zurita, Tetsuhiko Teshima, and Bernhard Wolfrum

Subjects
3D printing, microfabrication, origami, parylene, stretchable electronics, Electric apparatus and materials. Electric circuits. Electric networks, TK452-454.4, Physics, QC1-999
Abstract

Abstract Thin film electronic devices based on flexible biocompatible substrates are desired in various fields such as implants, soft robotics, and wearables, where stretchability is often necessary. Structure‐enabled stretchability in flexible thin films can be achieved by introducing origami‐inspired folds, thereby storing excess material in the out‐of‐plane direction to unfold upon stress. When using vapor‐deposited substrates such as parylene‐C, folds can be introduced prefabrication using molds patterned in repeated grooves and ridges. Here, this work reports the fabrication and parametrization of 10‐µm‐thick stretchable origami parylene‐C electrodes using 3D printed molds. The molds are printed with a sinusoidal pattern and tunable amplitude and slope, with accurate printing results up to 60° steepness. A 160‐nm‐thick gold layer on top of the folded parylene is patterned via laser ablation following the 3D mold shape. Depending on the design parameters, the resulting electrodes maintain functionality until 40%–100% strain. By 3D printing the molds, this technique can fabricate electrodes with complete control of the designed directions of stretchability in a rapid prototyping approach.

Published
2023
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20. Electronic properties of type-II Weyl semimetal WTe$_2$. A review perspective

Author

Das, P. K., Di Sante, D., Cilento, F., Bigi, C., Kopic, D., Soranzio, D., Sterzi, A., Krieger, J. A., Vobornik, I., Fujii, J., Okuda, T., Strocov, V. N., Breese, M. B. H., Parmigiani, F., Rossi, G., Picozzi, S., Thomale, R., Sangiovanni, G., Cava, R. J., and Panaccione, G.

Subjects
Condensed Matter - Materials Science
Abstract

Currently, there is a flurry of research interest on materials with an unconventional electronic structure, and we have already seen significant progress in their understanding and engineering towards real-life applications. The interest erupted with the discovery of graphene and topological insulators in the previous decade. The electrons in graphene simulate massless Dirac Fermions with a linearly dispersing Dirac cone in their band structure, while in topological insulators, the electronic bands wind non-trivially in momentum space giving rise to gapless surface states and bulk bandgap. Weyl semimetals in condensed matter systems are the latest addition to this growing family of topological materials. Weyl Fermions are known in the context of high energy physics since almost the beginning of quantum mechanics. They apparently violate charge conservation rules, displaying the "chiral anomaly", with such remarkable properties recently theoretically predicted and experimentally verified to exist as low energy quasiparticle states in certain condensed matter systems. Not only are these new materials extremely important for our fundamental understanding of quantum phenomena, but also they exhibit completely different transport phenomena. For example, massless Fermions are susceptible to scattering from non-magnetic impurities. Dirac semimetals exhibit non-saturating extremely large magnetoresistance as a consequence of their robust electronic bands being protected by time reversal symmetry. These open up whole new possibilities for materials engineering and applications including quantum computing. In this review, we recapitulate some of the outstanding properties of WTe$_2$, namely, its non-saturating titanic magnetoresistance due to perfect electron and hole carrier balance up to a very high magnetic field observed for the very first time. (Continued. Please see the main article)., Comment: 33 pages, 7 figures

Published
2018
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22. Neurodevelopmental patterns of early postnatal white matter maturation represent distinct underlying microstructure and histology

Author

Nazeri, Arash, Krsnik, Željka, Kostović, Ivica, Ha, Sung Min, Kopić, Janja, Alexopoulos, Dimitrios, Kaplan, Sydney, Meyer, Dominique, Luby, Joan L., Warner, Barbara B., Rogers, Cynthia E., Barch, Deanna M., Shimony, Joshua S., McKinstry, Robert C., Neil, Jeffrey J., Smyser, Christopher D., and Sotiras, Aristeidis

Published
2022
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23. Experimental comparison of aerosol transmission in displacement ventilation and mixing ventilation in a meeting scenario.

Author

Makris, Ruben, Kopic, Claudia, Tawackolian, Karsten, Schumann, Lukas, and Kriegel, Martin

Subjects
ATMOSPHERIC aerosols, VENTILATION, AIR pollutants, ENERGY conservation, ENERGY consumption
Abstract

The performance of displacement ventilation (DV) and mixing ventilation (MV) in aerosol contamination control was compared. The considered contamination was an aerosol emitted from a person in meeting scenarios of two and four persons. The experiments were carried out in a full-scale room measuring 4.4 m × 5.2 m × 2.9 m. Particle counting was carried out at 41 measurement points in the room to assess the concentration field of particles in the room and in the inhalation zone. The influence of the supply airflow rate and the number of activated thermal mannequins on the contaminant removal effectiveness were examined. The main conclusion is that the DV system was superior to the MV system in reducing contamination. An enhanced contaminant removal effectiveness of displacement ventilation was observed for all supply air flow rates and increased with higher airflow rates. At lower airflow rates, contamination in the inhalation zone was reduced by 40% compared to mixing ventilation, while at higher flow rates, the reduction surpassed 50%. This study highlights the advantages of displacement ventilation in terms of contamination control, infection prevention and energy efficiency, emphasising the importance of ventilation system selection for indoor environments, particularly those where airborne transmission risks are prevalent. Highlights: Displacement ventilation outperformed mixing ventilation in aerosol control in a meeting scenario. The contaminant removal effectiveness was superior in the inhalation zone and above exposed mannequins compared to the reference points at the breathing level. A possible flow rate reduction with displacement ventilation can significantly reduce energy costs. Ventilation system choice is crucial for infection control and energy saving. [ABSTRACT FROM AUTHOR]

Published
2025
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27. Secretory Diarrhea

Author

Ameen, Nadia, Kopic, Sascha, Ahsan, Kaimul, Figueroa-Hall, Leandra K., Hamilton, Kirk L., editor, and Devor, Daniel C., editor

Published
2020
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28. Inkjet‐Printed 3D Electrode Arrays for Recording Signals from Cortical Organoids.

Author

Kopic, Inola, Dedousi, Panagiota, Schmidt, Sebastian, Peng, Hu, Berezin, Oleksandr, Weiße, Annika, George, Richard M., Mayr, Christian, Westmeyer, Gil G., and Wolfrum, Bernhard

Subjects
*ELECTRODE performance, *MANUFACTURING processes, *RAPID prototyping, *POLYETHYLENE terephthalate, *CELL culture
Abstract

Monitoring electrical activity across multiple planes in 3D cell cultures and organoids is imperative to comprehensively understand their functional connections and behavior. However, traditional planar microelectrode arrays (MEAs) are intended for surface recordings and are inadequate in addressing this aspect. The limitations, such as longer production times and limited adaptability imposed by standard clean‐room techniques, constrain the design possibilities for 3D electrode arrays and potentially hinder effective cell‐electrode coupling. To tackle this challenge, a novel approach is presented that leverages rapid prototyping processes and additive manufacturing in combination with wet etching and electrodeposition to enhance electrode fabrication and performance. The laser‐patterned MEAs on glass, polyimide (PI) foil, or polyethylene terephthalate (PET) foil substrates incorporate high‐aspect ratio (up to 44:1) ink‐jet printed 3D electrode structures with heights up to 1 mm at a pitch of 200 µm, enabling precise recording within cell tissues. The specific shapes of the electrode tips and customizable 3D structures provide great flexibility in electrode placement. The versatility of the 3D MEAs is demonstrated by recording the electrophysiological activity of cortical organoids in situ, paving the way for investigating neural activity under regular or various pathologically altered conditions in vitro in a high throughput manner. [ABSTRACT FROM AUTHOR]

Published
2024
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29. Australian legislation concerning matters of international law 2018

Author

Malone, Trina, Lyas, Nicole, Cavenagh, Merryn, Gronke, Andrea, Karavolas, Kryssa, Keith, Angad, Kluckow, Tess, Konneh, Ameisa, Kopic, Maida, Lewis, Alicia, Malik, Fatima, Mandalawatta, Pranamie, Matley, Holly, Norris, Alex, Perera, Nish, Scott, Sephora, Subramanyan, Vaidehi, Uren, Ashlee, and Wahid, Kashpee

Published
2020

33. Aberrant DNA Methylation, Expression, and Occurrence of Transcript Variants of the ABC Transporter ABCA7 in Breast Cancer

Author

Katja Zappe, Antonio Kopic, Alexandra Scheichel, Ann-Katrin Schier, Lukas Emanuel Schmidt, Yasmin Borutzki, Heidi Miedl, Martin Schreiber, Theresa Mendrina, Christine Pirker, Georg Pfeiler, Stefan Hacker, Werner Haslik, Dietmar Pils, Andrea Bileck, Christopher Gerner, Samuel Meier-Menches, Petra Heffeter, and Margit Cichna-Markl

Subjects
ABCA7, ABC transporter, breast cancer, DNA methylation, gene expression, alternative splicing, Cytology, QH573-671
Abstract

The ABC transporter ABCA7 has been found to be aberrantly expressed in a variety of cancer types, including breast cancer. We searched for specific epigenetic and genetic alterations and alternative splicing variants of ABCA7 in breast cancer and investigated whether these alterations are associated with ABCA7 expression. By analyzing tumor tissues from breast cancer patients, we found CpGs at the exon 5–intron 5 boundary aberrantly methylated in a molecular subtype-specific manner. The detection of altered DNA methylation in tumor-adjacent tissues suggests epigenetic field cancerization. In breast cancer cell lines, DNA methylation levels of CpGs in promoter-exon 1, intron 1, and at the exon 5–intron 5 boundary were not correlated with ABCA7 mRNA levels. By qPCR involving intron-specific and intron-flanking primers, we identified intron-containing ABCA7 mRNA transcripts. The occurrence of intron-containing transcripts was neither molecular subtype-specific nor directly correlated with DNA methylation at the respective exon–intron boundaries. Treatment of breast cancer cell lines MCF-7, BT-474, SK-BR3, and MDA-MB-231 with doxorubicin or paclitaxel for 72 h resulted in altered ABCA7 intron levels. Shotgun proteomics revealed that an increase in intron-containing transcripts was associated with significant dysregulation of splicing factors linked to alternative splicing.

Published
2023
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35. Evaluation of Factor VIII Polysialylation: Identification of a Longer-Acting Experimental Therapy in Mice and Monkeys

Author

Glantschnig, Helmut, Bauer, Alexander, Benamara, Karima, Dockal, Michael, Ehrlich, Veronika, Gritsch, Herbert, Höbarth, Gerald, Horling, Frank M., Kopic, Alexandra, Leidenmühler, Peter, Reipert, Birgit M., Rottensteiner, Hanspeter, Ruthsatz, Tanja, Schrenk, Gerald, Schuster, Maria, Turecek, Peter L., Weber, Alfred, Wolfsegger, Martin, Scheiflinger, Friedrich, and Höllriegl, Werner

Published
2019
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37. Association between night-time surgery and occurrence of intraoperative adverse events and postoperative pulmonary complications

Author

Kroell, W., Metzler, H., Struber, G., Wegscheider, T., Gombotz, H., Hiesmayr, M., Schmid, W., Urbanek, B., Kahn, D., Momeni, M., Pospiech, A., Lois, F., Forget, P., Grosu, I., Poelaert, J., Mossevelde, V., van Malderen, M.C., Dylst, D., Melkebeek, J.V., Beran, M., Hert, S.D., Baerdemaeker, L.D., Heyse, B., Limmen, J.V., Wyffels, P., Jacobs, T., Roels, N., Bruyne, A.D., Velde, S.V.D., Marina, J.Z., Dejana, D.O., Pernar, S., Zunic, J., Miskovic, P., Zilic, A., Kvolik, S., Ivic, D., Darija, A.V., Skiljic, S., Vinkovic, H., Oputric, I., Juricic, K., Frkovic, V., Kopic, J., Mirkovic, I., Saric, J.P., Erceg, G., Dvorscak, M.B., Branka, M.S., Pavicic, A.M., Goranovic, T., Maldini, B., Radocaj, T., Gavranovic, Z., Inga, M.B., Sehovic, M., Stourac, P., Harazim, H., Smekalova, O., Kosinova, M., Kolacek, T., Hudacek, K., Drab, M., Brujevic, J., Vitkova, K., Jirmanova, K., Volfova, I., Dzurnakova, P., Liskova, K., Dudas, R., Filipsky, R., Kafrawy, S.E., Abdelwahab, H.H., Metwally, T., Ahmed, A.R., Ahmed Mostafa, E.S., Hasan, W.F., Ahmed, A.G., Yassin, H., Magdy, M., Abdelhady, M., Mahran, M., Herodes, E., Kivik, P., Oganjan, J., Aun, A., Sormus, A., Sarapuu, K., Mall, M., Karjagin, J., Futier, E., Petit, A., Gerard, A., Marret, E., Solier, M., Jaber, S., Prades, A., Krassler, J., Merzky, S., Abreu, M.G.D., Uhlig, C., Kiss, T., Bundy, A., Bluth, T., Gueldner, A., Spieth, P., Scharffenberg, M., Thiem, D.T., Koch, T., Treschan, T., Schaefer, M., Bastin, B., Geib, J., Weiss, M., Kienbaum, P., Pannen, B., Gottschalk, A., Konrad, M., Westerheide, D., Schwerdtfeger, B., Wrigge, H., Simon, P., Reske, A., Nestler, C., Valsamidis, D., Stroumpoulis, K., Antholopoulos, G., Andreou, A., Karapanos, D., Theodoraki, K., Gkiokas, G., Tasoulis, M.K., Sidiropoulou, T., Zafeiropoulou, F., Florou, P., Pandazi, A., Tsaousi, G., Nouris, C., Pourzitaki, C., Bystritski, D., Pizov, R., Eden, A., Pesce, C.V., Campanile, A., Marrella, A., Grasso, S., Michele, M.D., Bona, F., Giacoletto, G., Sardo, E., Sottosanti, L.G.V., Solca, M., Volta, C.A., Spadaro, S., Verri, M., Ragazzi, R., Zoppellari, R., Cinnella, G., Raimondo, P., Bella, D.L., Mirabella, L., D'antini, D., Pelosi, P., Molin, A., Brunetti, I., Gratarola, A., Pellerano, G., Sileo, R., Pezzatto, S., Montagnani, L., Pasin, L., Landoni, G., Zangrillo, A., Beretta, L., Parma, A.L.D., Tarzia, V., Dossi, R., Sassone, M.E., Sances, D., Tredici, S., Spano, G., Castellani, G., Delunas, L., Peradze, S., Venturino, M., Arpino, I., Sher, S., Tommasino, C., Rapido, F., Morelli, P., Vargas, M., Servillo, G., Cortegiani, A., Raineri, S.M., Montalto, F., Russotto, V., Giarratano, A., Baciarello, M., Generali, M., Cerati, G., Leykin, Y., Bressan, F., Bartolini, V., Zamidei, L., Brazzi, L., Liperi, C., Sales, G., Pistidda, L., Severgnini, P., Brugnoni, E., Musella, G., Bacuzzi, A., Muhardri, D., Agreta, G.G., Sada, F., Bytyqi, A., Karbonskiene, A., Aukstakalniene, R., Teberaite, Z., Salciute, E., Tikuisis, R., Miliauskas, P., Jurate, S., Kontrimaviciute, E., Tomkute, G., Xuereb, J., Bezzina, M., Borg, F. Joseph, Hemmes, S., Schultz, M., Hollmann, M., Wiersma, I., Binnekade, J., Bos, L., Boer, C., Duvekot, A., Veld, B.I ‘t, Werger, A., Dennesen, P., Severijns, C., Jong, J.D., Hering, J., Beek, R.V., Ivars, S., Jammer, I.B., Breidablik, A., Hodt, K.S., Fjellanger, F., Avalos, M.V., Jannicke, M.O., Andersson, E., Amir, S.K., Molina, R., Wutai, S., Morais, E., Tareco, G., Ferreira, D., Amaral, J., Castro, M.D.L.G., Cadilha, S., Appleton, S., Parente, S., Correia, M., Martins, D., Monteirosa, A., Ricardo, A., Rodrigues, S., Horhota, L., Grintescu, I.M., Mirea, L., Grintescu, I.C., Corneci, D., Negoita, S., Dutu, M., Popescu Garotescu, I., Filipescu, D., Prodan, A.B., Droc, G., Fota, R., Popescu, M., Tomescu, D., Petcu, A.M., Tudoroiu, M.I., Moise, A., Guran, C.T., Gherghina, I., Costea, D., Cindea, I., Copotoiu, S.M., Copotoiu, R., Barsan, V., Tolcser, Z., Riciu, M., Moldovan, S.G., Veres, M., Gritsan, A., Kapkan, T., Gritsan, G., Korolkov, O., Kulikov, A., Lubnin, A., Ovezov, A., Prokoshev, P., Lugovoy, A., Anipchenko, N., Babayants, A., Komissarova, I., Zalina, K., Likhvantsev, V., Fedorov, S., Lazukic, A., Pejakovic, J., Mihajlovic, D., Kusnierikova, Z., Zelinkova, M., Bruncakova, K., Polakovicova, L., Sobona, V., Barbka, N.S., Ana, P.G., Jovanov, M., Strazisar, B., Jasmina, M.B., Vesna, N.J., Voje, M., Grynyuk, A., Kostadinov, I., Alenka, S.V., Moral, V., Unzueta, M.C., Puigbo, C., Fava, J., Canet, J., Moret, E., Nunez, M.R., Sendra, M., Brunelli, A., Rodenas, F., Monedero, P., Hidalgo, F., Yepes, M.J., Martinez-Simon, A., Abajo, A., Lisi, A., Perez, G., Martinez, R., Granell, M., Vivo, J.T., Ruiz, C.S., Andrés Ibañez, J.A.D., Pastor, E., Soro, M., Ferrando, C., Defez, M., Cesar Aldecoa, A.S., Perez, R., Rico, J., Jawad, M., Saeed, Y., Gillberg, L., Bengisun, Z.K., Kazbek, B.K., Coskunfirat, N., Boztug, N., Sanli, S., Yilmaz, M., Hadimioglu, N., Senturk, N.M., Camci, E., Kucukgoncu, S., Sungur, Z., Sivrikoz, N., Ozgen, S.U., Toraman, F., Selvi, O., Senturk, O., Yildiz, M., Kuvaki, B., Gunenc, F., Kucukguclu, S., Ozbilgin, S., Maral, J., Canli, S., Arun, O., Saltali, A., Aydogan, E., Akgun, F.N., Sanlikarip, C., Karaman, F.M., Mazur, A., Vorotyntsev, S., Rousseau, G., Barrett, C., Stancombe, L., Shelley, B., Scholes, H., Limb, J., Rafi, A., Wayman, L., Deane, J., Rogerson, D., Williams, J., Yates, S., Rogers, E., Pulletz, M., Moreton, S., Jones, S., Venkatesh, S., Burton, M., Brown, L., Goodall, C., Rucklidge, M., Fuller, D., Nadolski, M., Kusre, S., Lundberg, M., Everett, L., Nutt, H., Zuleika, M., Carvalho, P., Clements, D., Ben, C.B., Watt, P., Raymode, P., Pearse, R., Mohr, O., Raj, A., Creary, T., Chishti, A., Bell, A., Higham, C., Cain, A., Gibb, S., Mowat, S., Franklin, D., West, C., Minto, G., Boyd, N., Mills, G., Calton, E., Walker, R., Mackenzie, F., Ellison, B., Roberts, H., Chikungwa, M., Jackson, C., Donovan, A., Foot, J., Homan, E., Montgomery, J., Portch, D., Mercer, P., Palmer, J., Paddle, J., Fouracres, A., Datson, A., Andrew, A., Welch, L., Rose, A., Varma, S., Simeson, K., Rambhatla, M., Susarla, J., Marri, S., Kodaganallur, K., Das, A., Algarsamy, S., Colley, J., Davies, S., Szewczyk, M., Smith, T., Ana, F.B., Luzier, E., Almagro, A., Melo, M.V., Fernando, L., Sulemanji, D., Sprung, J., Weingarten, T., Kor, D., Scavonetto, F., Tze, Y., Gregoretti, C., Neto, A.S., Hemmes, S.N.T., Ball, L., Hollmann, M.W., Mills, G.H., Melo, M.F.V., Putensen, C., Gama de Abreu, M., and Schultz, M.J.

Published
2019
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43. Importance of standardizing timing of hematocrit measurement when using cardiovascular magnetic resonance to calculate myocardial extracellular volume (ECV) based on pre- and post-contrast T1 mapping

Author

Henrik Engblom, Mikael Kanski, Sascha Kopic, David Nordlund, Christos G. Xanthis, Robert Jablonowski, Einar Heiberg, Anthony H. Aletras, Marcus Carlsson, and Håkan Arheden

Subjects
Extracellular volume, ECV, T1 mapping, Hematocrit, Diseases of the circulatory (Cardiovascular) system, RC666-701
Abstract

Abstract Background Cardiovascular magnetic resonance (CMR) can be used to calculate myocardial extracellular volume fraction (ECV) by relating the longitudinal relaxation rate in blood and myocardium before and after contrast-injection to hematocrit (Hct) in blood. Hematocrit is known to vary with body posture, which could affect the calculations of ECV. The aim of this study was to test the hypothesis that there is a significant increase in calculated ECV values if the Hct is sampled after the CMR examination in supine position compared to when the patient arrives at the MR department. Methods Forty-three consecutive patients including various pathologies as well as normal findings were included in the study. Venous blood samples were drawn upon arrival to the MR department and directly after the examination with the patient remaining in supine position. A Modified Look-Locker Inversion recovery (MOLLI) protocol was used to acquire mid-ventricular short-axis images before and after contrast injection from which motion-corrected T1 maps were derived and ECV was calculated. Results Hematocrit decreased from 44.0 ± 3.7% before to 40.6 ± 4.0% after the CMR examination (p

Published
2018
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45. Ausblick

Author

Schneider, Jutta, Kopic, Aida, Jasmund, Christina, Schneider, Jutta, Kopic, Aida, and Jasmund, Christina

Published
2015
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