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Book ; Online: Animazione socioculturale dell'infanzia e della gioventù : Risultati del primo sondaggio nazionale svizzero

Gerodetti, Julia / Fuchs, Manuel / Fellmann, Lukas / Gerngross, Martina / Steiner, Olivier

2021

Keywords	Sociology ; sociocultural animation, open child and youth work, survey
Size	1 electronic resource (192 pages)
Publisher	Seismo
Document type	Book ; Online
Note	Italian ; Open Access
HBZ-ID	HT021046990
ISBN	9782883510975 ; 2883510970
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article: Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation.

Ho, David Joon / Chui, M Herman / Vanderbilt, Chad M / Jung, Jiwon / Robson, Mark E / Park, Chan-Sik / Roh, Jin / Fuchs, Thomas J

Journal of pathology informatics

2022 Volume 14, Page(s) 100160

Abstract: Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained ...

Abstract	Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time. Especially, employing a pretrained segmentation model can further reduce the time than starting annotation from scratch. We trained an accurate ovarian cancer segmentation model with a pretrained breast segmentation model by 3.5 hours of manual annotation which achieved intersection-over-union of 0.74, recall of 0.86, and precision of 0.84. With automatically extracted high-grade serous ovarian cancer patches, we attempted to train an additional classification deep learning model to predict
Language	English
Publishing date	2022-11-26
Publishing country	United States
Document type	Journal Article
ZDB-ID	2579241-6
ISSN	2153-3539 ; 2229-5089
ISSN (online)	2153-3539
ISSN	2229-5089
DOI	10.1016/j.jpi.2022.100160
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: e-Tourism Beyond COVID-19

Gretzel, Ulrike / Fuchs, Matthias / Baggio, Rodolfo / Hoepken, Wolfram / Law, Rob / Neidhardt, Julia / Pesonen, Juho / Zanker, Markus / Xiang, Zheng

A Call for Transformative Research

2020

Abstract: ... for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was ... before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e ... technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism ...

Abstract	This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing paradigms and critically evaluate its ontological and epistemological foundations. In light of the paramount importance to rethink contemporary science, growth, and technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism through their research, including historicity, reflexivity, equity, transparency, plurality, and creativity. We conclude the paper with a call to the e-Tourism research community to embrace transformative research. final draft peerReviewed
Keywords	transformative research ; COVID-19 ; e-Tourism ; research paradigm ; technology paradigm ; growth paradigm ; covid19
Language	englanti
Publishing date	2020-06-05T10:01:34Z
Publisher	Springer Science and Business Media LLC
Publishing country	fi
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: e-Tourism beyond COVID-19

Gretzel, Ulrike / Fuchs, Matthias / Baggio, Rodolfo / Hoepken, Wolfram / Law, Rob / Neidhardt, Julia / Pesonen, Juho / Zanker, Markus / Xiang, Zheng

a call for transformative research

2020

Abstract	This viewpoint article argues that the impacts of the novel coronavirus COVID-19 call for transformative e-Tourism research. We are at a crossroads where one road takes us to e-Tourism as it was before the crisis, whereas the other holds the potential to transform e-Tourism. To realize this potential, e-Tourism research needs to challenge existing paradigms and critically evaluate its ontological and epistemological foundations. In light of the paramount importance to rethink contemporary science, growth, and technology paradigms, we present six pillars to guide scholars in their efforts to transform e-Tourism through their research, including historicity, reflexivity, equity, transparency, plurality, and creativity. We conclude the paper with a call to the e-Tourism research community to embrace transformative research.
Keywords	Economics and Business ; Ekonomi och näringsliv ; covid19
Language	English
Publisher	Mittuniversitetet, Institutionen för ekonomi, geografi, juridik och turism
Publishing country	se
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: e-Tourism beyond COVID-19

Gretzel, Ulrike / Fuchs, Matthias / Baggio, Rodolfo / Hoepken, Wolfram / Law, Rob / Neidhardt, Julia / Pesonen, Juho / Zanker, Markus / Xiang, Zheng

Information Technology & Tourism

a call for transformative research

2020 Volume 22, Issue 2, Page(s) 187–203

Keywords	Social Sciences (miscellaneous) ; General Computer Science ; covid19
Language	English
Publisher	Springer Science and Business Media LLC
Publishing country	us
Document type	Article ; Online
ZDB-ID	2099258-0
ISSN	1943-4294 ; 1098-3058
ISSN (online)	1943-4294
ISSN	1098-3058
DOI	10.1007/s40558-020-00181-3
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Viel Rauch und sonst nichts? – Verbrennungsfolgen durch E-Zigaretten-Gebrauch.

Daniels, Marc / Fuchs, Paul / Oberländer, Henrik / Schiefer, Jennifer / Seyhan, Harun / Jan-Philipp, Stromps

Handchirurgie, Mikrochirurgie, plastische Chirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Handchirurgie : Organ der Deutschsprachigen Arbeitsgemeinschaft fur Mikrochirurgie der Peripheren Nerven und Gefasse : Organ der V...

2020 Volume 52, Issue 6, Page(s) 483–489

Abstract: Introduction: The popularity of E-Cigarettes is increasing. Besides addiction and pulmonary ... health damage, reports of burn injuries from e-cigarette explosions are also increasing. Mostly, explosions of e ... by e-cigarette explosions.: Patients/material and methods: Three cases of e-cigarette explosions ...

Title translation	Where there´s smoke - there´s no fire? - Burns from E-Cigarette explosions.
Abstract	Introduction: The popularity of E-Cigarettes is increasing. Besides addiction and pulmonary health damage, reports of burn injuries from e-cigarette explosions are also increasing. Mostly, explosions of e-cigarettes are attributed to its lithium-ion battery. Due to increasing cases and missing guidelines we want to present three cases of our hospital and publish recommendations for the management of burn injuries caused by e-cigarette explosions. Patients/material and methods: Three cases of e-cigarette explosions which occurred between 2016 and 2019, are presented. Results: All three e-cigarette explosions occurred in the trouser pockets. Two patients were male one patient was female. The age ranged from 24 to 64 years, the burned total body surface area (TBSA) from 3 % to 12.5 %. All three patients required skin grafting and the length of stay in hospital ranged from five to eleven days. Conclusion: In the synopsis of recent literature, we recommend the following management of burns due to e-cigarette explosions. The guidelines of the Advanced Trauma Life Support should be followed, signs of an inhalation trauma should be checked and litmus test should be performed prior to irrigation with aqueous solutions to prevent exothermic reactions with remaining metals. If litmus test shows alkali pH wounds should be irrigated by mineral oil.
MeSH term(s)	Adult ; Body Surface Area ; Burns/etiology ; Electronic Nicotine Delivery Systems ; Explosions ; Female ; Humans ; Male ; Middle Aged ; Retrospective Studies ; Smoke ; Young Adult
Chemical Substances	Smoke
Language	German
Publishing date	2020-12-08
Publishing country	Germany
Document type	Journal Article
ZDB-ID	392414-2
ISSN	1439-3980 ; 0722-1819
ISSN (online)	1439-3980
ISSN	0722-1819
DOI	10.1055/a-1237-4223
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Article ; Online: Automated diagnosis of 7 canine skin tumors using machine learning on H&E-stained whole slide images.

Fragoso-Garcia, Marco / Wilm, Frauke / Bertram, Christof A / Merz, Sophie / Schmidt, Anja / Donovan, Taryn / Fuchs-Baumgartinger, Andrea / Bartel, Alexander / Marzahl, Christian / Diehl, Laura / Puget, Chloe / Maier, Andreas / Aubreville, Marc / Breininger, Katharina / Klopfleisch, Robert

Veterinary pathology

2023 Volume 60, Issue 6, Page(s) 865–875

Abstract: Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer ... ...

Abstract	Microscopic evaluation of hematoxylin and eosin-stained slides is still the diagnostic gold standard for a variety of diseases, including neoplasms. Nevertheless, intra- and interrater variability are well documented among pathologists. So far, computer assistance via automated image analysis has shown potential to support pathologists in improving accuracy and reproducibility of quantitative tasks. In this proof of principle study, we describe a machine-learning-based algorithm for the automated diagnosis of 7 of the most common canine skin tumors: trichoblastoma, squamous cell carcinoma, peripheral nerve sheath tumor, melanoma, histiocytoma, mast cell tumor, and plasmacytoma. We selected, digitized, and annotated 350 hematoxylin and eosin-stained slides (50 per tumor type) to create a database divided into training,
MeSH term(s)	Animals ; Dogs ; Artificial Intelligence ; Deep Learning ; Eosine Yellowish-(YS) ; Hematoxylin ; Reproducibility of Results ; Skin Neoplasms/diagnosis ; Skin Neoplasms/veterinary ; Machine Learning ; Dog Diseases/diagnosis
Chemical Substances	Eosine Yellowish-(YS) (TDQ283MPCW) ; Hematoxylin (YKM8PY2Z55)
Language	English
Publishing date	2023-07-29
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	188012-3
ISSN	1544-2217 ; 0300-9858
ISSN (online)	1544-2217
ISSN	0300-9858
DOI	10.1177/03009858231189205
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Article ; Online: Measurement of the Cross Sections of Ξ_{c}^{0} and Ξ_{c}^{+} Baryons and of the Branching-Fraction Ratio BR(Ξ_{c}^{0}→Ξ^{-}e^{+}ν_{e})/BR(Ξ_{c}^{0}→Ξ^{-}π^{+}) in pp Collisions at sqrt[s]=13 TeV.

Acharya, S / Adamová, D / Adler, A / Adolfsson, J / Aglieri Rinella, G / Agnello, M / Agrawal, N / Ahammed, Z / Ahmad, S / Ahn, S U / Ahuja, I / Akbar, Z / Akindinov, A / Al-Turany, M / Alam, S N / Aleksandrov, D / Alessandro, B / Alfanda, H M / Alfaro Molina, R /

Ali, B / Ali, Y / Alici, A / Alizadehvandchali, N / Alkin, A / Alme, J / Alt, T / Altenkamper, L / Altsybeev, I / Anaam, M N / Andrei, C / Andreou, D / Andronic, A / Angeletti, M / Anguelov, V / Antinori, F / Antonioli, P / Anuj, C / Apadula, N / Aphecetche, L / Appelshäuser, H / Arcelli, S / Arnaldi, R / Arsene, I C / Arslandok, M / Augustinus, A / Averbeck, R / Aziz, S / Azmi, M D / Badalà, A / Baek, Y W / Bai, X / Bailhache, R / Bailung, Y / Bala, R / Balbino, A / Baldisseri, A / Balis, B / Ball, M / Banerjee, D / Barbera, R / Barioglio, L / Barlou, M / Barnaföldi, G G / Barnby, L S / Barret, V / Bartels, C / Barth, K / Bartsch, E / Baruffaldi, F / Bastid, N / Basu, S / Batigne, G / Batyunya, B / Bauri, D / Bazo Alba, J L / Bearden, I G / Beattie, C / Belikov, I / Bell Hechavarria, A D C / Bellini, F / Bellwied, R / Belokurova, S / Belyaev, V / Bencedi, G / Beole, S / Bercuci, A / Berdnikov, Y / Berdnikova, A / Berenyi, D / Bergmann, L / Besoiu, M G / Betev, L / Bhaduri, P P / Bhasin, A / Bhat, I R / Bhat, M A / Bhattacharjee, B / Bhattacharya, P / Bianchi, L / Bianchi, N / Bielčík, J / Bielčíková, J / Biernat, J / Bilandzic, A / Biro, G / Biswas, S / Blair, J T / Blau, D / Blidaru, M B / Blume, C / Boca, G / Bock, F / Bogdanov, A / Boi, S / Bok, J / Boldizsár, L / Bolozdynya, A / Bombara, M / Bond, P M / Bonomi, G / Borel, H / Borissov, A / Bossi, H / Botta, E / Bratrud, L / Braun-Munzinger, P / Bregant, M / Broz, M / Bruno, G E / Buckland, M D / Budnikov, D / Buesching, H / Bufalino, S / Bugnon, O / Buhler, P / Buthelezi, Z / Butt, J B / Bysiak, S A / Caffarri, D / Cai, M / Caines, H / Caliva, A / Calvo Villar, E / Camacho, J M M / Camacho, R S / Camerini, P / Canedo, F D M / Carnesecchi, F / Caron, R / Castillo Castellanos, J / Casula, E A R / Catalano, F / Ceballos Sanchez, C / Chakraborty, P / Chandra, S / Chapeland, S / Chartier, M / Chattopadhyay, S / Chauvin, A / Chavez, T G / Cheshkov, C / Cheynis, B / Chibante Barroso, V / Chinellato, D D / Cho, S / Chochula, P / Christakoglou, P / Christensen, C H / Christiansen, P / Chujo, T / Cicalo, C / Cifarelli, L / Cindolo, F / Ciupek, M R / Clai, G / Cleymans, J / Colamaria, F / Colburn, J S / Colella, D / Collu, A / Colocci, M / Concas, M / Conesa Balbastre, G / Conesa Del Valle, Z / Contin, G / Contreras, J G / Coquet, M L / Cormier, T M / Cortese, P / Cosentino, M R / Costa, F / Costanza, S / Crochet, P / Cruz-Torres, R / Cuautle, E / Cui, P / Cunqueiro, L / Dainese, A / Damas, F P A / Danisch, M C / Danu, A / Das, I / Das, P / Das, S / Dash, S / De, S / De Caro, A / de Cataldo, G / De Cilladi, L / de Cuveland, J / De Falco, A / De Gruttola, D / De Marco, N / De Martin, C / De Pasquale, S / Deb, S / Degenhardt, H F / Deja, K R / Dello Stritto, L / Delsanto, S / Deng, W / Dhankher, P / Di Bari, D / Di Mauro, A / Diaz, R A / Dietel, T / Ding, Y / Divià, R / Dixit, D U / Djuvsland, Ø / Dmitrieva, U / Do, J / Dobrin, A / Dönigus, B / Dordic, O / Dubey, A K / Dubla, A / Dudi, S / Dukhishyam, M / Dupieux, P / Dzalaiova, N / Eder, T M / Ehlers, R J / Eikeland, V N / Elia, D / Erazmus, B / Ercolessi, F / Erhardt, F / Erokhin, A / Ersdal, M R / Espagnon, B / Eulisse, G / Evans, D / Evdokimov, S / Fabbietti, L / Faggin, M / Faivre, J / Fan, F / Fantoni, A / Fasel, M / Fecchio, P / Feliciello, A / Feofilov, G / Fernández Téllez, A / Ferrero, A / Ferretti, A / Feuillard, V J G / Figiel, J / Filchagin, S / Finogeev, D / Fionda, F M / Fiorenza, G / Flor, F / Flores, A N / Foertsch, S / Foka, P / Fokin, S / Fragiacomo, E / Frajna, E / Fuchs, U / Funicello, N / Furget, C / Furs, A / Gaardhøje, J J / Gagliardi, M / Gago, A M / Gal, A / Galvan, C D / Ganoti, P / Garabatos, C / Garcia, J R A / Garcia-Solis, E / Garg, K / Gargiulo, C / Garibli, A / Garner, K / Gasik, P / Gauger, E F / Gautam, A / Gay Ducati, M B / Germain, M / Ghosh, J / Ghosh, P / Ghosh, S K / Giacalone, M / Gianotti, P / Giubellino, P / Giubilato, P / Glaenzer, A M C / Glässel, P / Goh, D J Q / Gonzalez, V / González-Trueba, L H / Gorbunov, S / Gorgon, M / Görlich, L / Gotovac, S / Grabski, V / Graczykowski, L K / Greiner, L / Grelli, A / Grigoras, C / Grigoriev, V / Grigoryan, A / Grigoryan, S / Groettvik, O S / Grosa, F / Grosse-Oetringhaus, J F / Grosso, R / Guardiano, G G / Guernane, R / Guilbaud, M / Gulbrandsen, K / Gunji, T / Gupta, A / Gupta, R / Guzman, I B / Guzman, S P / Gyulai, L / Habib, M K / Hadjidakis, C / Halimoglu, G / Hamagaki, H / Hamar, G / Hamid, M / Hannigan, R / Haque, M R / Harlenderova, A / Harris, J W / Harton, A / Hasenbichler, J A / Hassan, H / Hatzifotiadou, D / Hauer, P / Havener, L B / Hayashi, S / Heckel, S T / Hellbär, E / Helstrup, H / Herman, T / Hernandez, E G / Herrera Corral, G / Herrmann, F / Hetland, K F / Hillemanns, H / Hills, C / Hippolyte, B / Hofman, B / Hohlweger, B / Honermann, J / Hong, G H / Horak, D / Hornung, S / Horzyk, A / Hosokawa, R / Hristov, P / Huang, C / Hughes, C / Huhn, P / Humanic, T J / Hushnud, H / Husova, L A / Hutson, A / Hutter, D / Iddon, J P / Ilkaev, R / Ilyas, H / Inaba, M / Innocenti, G M / Ippolitov, M / Isakov, A / Islam, M S / Ivanov, M / Ivanov, V / Izucheev, V / Jablonski, M / Jacak, B / Jacazio, N / Jacobs, P M / Jadlovska, S / Jadlovsky, J / Jaelani, S / Jahnke, C / Jakubowska, M J / Janik, M A / Janson, T / Jercic, M / Jevons, O / Jonas, F / Jones, P G / Jowett, J M / Jung, J / Jung, M / Junique, A / Jusko, A / Kaewjai, J / Kalinak, P / Kalweit, A / Kaplin, V / Kar, S / Karasu Uysal, A / Karatovic, D / Karavichev, O / Karavicheva, T / Karczmarczyk, P / Karpechev, E / Kazantsev, A / Kebschull, U / Keidel, R / Keijdener, D L D / Keil, M / Ketzer, B / Khabanova, Z / Khan, A M / Khan, S / Khanzadeev, A / Kharlov, Y / Khatun, A / Khuntia, A / Kileng, B / Kim, B / Kim, D / Kim, D J / Kim, E J / Kim, J / Kim, J S / Kim, M / Kim, S / Kim, T / Kirsch, S / Kisel, I / Kiselev, S / Kisiel, A / Kitowski, J P / Klay, J L / Klein, J / Klein, S / Klein-Bösing, C / Kleiner, M / Klemenz, T / Kluge, A / Knospe, A G / Kobdaj, C / Köhler, M K / Kollegger, T / Kondratyev, A / Kondratyeva, N / Kondratyuk, E / Konig, J / Konigstorfer, S A / Konopka, P J / Kornakov, G / Koryciak, S D / Koska, L / Kotliarov, A / Kovalenko, O / Kovalenko, V / Kowalski, M / Králik, I / Kravčáková, A / Kreis, L / Krivda, M / Krizek, F / Krizkova Gajdosova, K / Kroesen, M / Krüger, M / Kryshen, E / Krzewicki, M / Kučera, V / Kuhn, C / Kuijer, P G / Kumaoka, T / Kumar, D / Kumar, L / Kumar, N / Kundu, S / Kurashvili, P / Kurepin, A / Kurepin, A B / Kuryakin, A / Kushpil, S / Kvapil, J / Kweon, M J / Kwon, J Y / Kwon, Y / La Pointe, S L / La Rocca, P / Lai, Y S / Lakrathok, A / Lamanna, M / Langoy, R / Lapidus, K / Larionov, P / Laudi, E / Lautner, L / Lavicka, R / Lazareva, T / Lea, R / Lee, J / Lehrbach, J / Lemmon, R C / León Monzón, I / Lesser, E D / Lettrich, M / Lévai, P / Li, X / Li, X L / Lien, J / Lietava, R / Lim, B / Lim, S H / Lindenstruth, V / Lindner, A / Lippmann, C / Liu, A / Liu, J / Lofnes, I M / Loginov, V / Loizides, C / Loncar, P / Lopez, J A / Lopez, X / López Torres, E / Luhder, J R / Lunardon, M / Luparello, G / Ma, Y G / Maevskaya, A / Mager, M / Mahmoud, T / Maire, A / Malaev, M / Malik, Q W / Malinina, L / Mal'Kevich, D / Mallick, N / Malzacher, P / Mandaglio, G / Manko, V / Manso, F / Manzari, V / Mao, Y / Mareš, J / Margagliotti, G V / Margotti, A / Marín, A / Markert, C / Marquard, M / Martin, N A / Martinengo, P / Martinez, J L / Martínez, M I / Martínez García, G / Masciocchi, S / Masera, M / Masoni, A / Massacrier, L / Mastroserio, A / Mathis, A M / Matonoha, O / Matuoka, P F T / Matyja, A / Mayer, C / Mazuecos, A L / Mazzaschi, F / Mazzilli, M / Mazzoni, M A / Mdhluli, J E / Mechler, A F / Meddi, F / Melikyan, Y / Menchaca-Rocha, A / Meninno, E / Menon, A S / Meres, M / Mhlanga, S / Miake, Y / Micheletti, L / Migliorin, L C / Mihaylov, D L / Mikhaylov, K / Mishra, A N / Miśkowiec, D / Modak, A / Mohanty, A P / Mohanty, B / Mohisin Khan, M / Moravcova, Z / Mordasini, C / Moreira De Godoy, D A / Moreno, L A P / Morozov, I / Morsch, A / Mrnjavac, T / Muccifora, V / Mudnic, E / Mühlheim, D / Muhuri, S / Mulligan, J D / Mulliri, A / Munhoz, M G / Munzer, R H / Murakami, H / Murray, S / Musa, L / Musinsky, J / Myers, C J / Myrcha, J W / Naik, B / Nair, R / Nandi, B K / Nania, R / Nappi, E / Naru, M U / Nassirpour, A F / Nath, A / Nattrass, C / Neagu, A / Nellen, L / Nesbo, S V / Neskovic, G / Nesterov, D / Nielsen, B S / Nikolaev, S / Nikulin, S / Nikulin, V / Noferini, F / Noh, S / Nomokonov, P / Norman, J / Novitzky, N / Nowakowski, P / Nyanin, A / Nystrand, J / Ogino, M / Ohlson, A / Okorokov, V A / Oleniacz, J / Oliveira Da Silva, A C / Oliver, M H / Onnerstad, A / Oppedisano, C / Ortiz Velasquez, A / Osako, T / Oskarsson, A / Otwinowski, J / Oyama, K / Pachmayer, Y / Padhan, S / Pagano, D / Paić, G / Palasciano, A / Pan, J / Panebianco, S / Pareek, P / Park, J / Parkkila, J E / Pathak, S P / Patra, R N / Paul, B / Pazzini, J / Pei, H / Peitzmann, T / Peng, X / Pereira, L G / Pereira Da Costa, H / Peresunko, D / Perez, G M / Perrin, S / Pestov, Y / Petráček, V / Petrovici, M / Pezzi, R P / Piano, S / Pikna, M / Pillot, P / Pinazza, O / Pinsky, L / Pinto, C / Pisano, S / Płoskoń, M / Planinic, M / Pliquett, F / Poghosyan, M G / Polichtchouk, B / Politano, S / Poljak, N / Pop, A / Porteboeuf-Houssais, S / Porter, J / Pozdniakov, V / Prasad, S K / Preghenella, R / Prino, F / Pruneau, C A / Pshenichnov, I / Puccio, M / Qiu, S / Quaglia, L / Quishpe, R E / Ragoni, S / Rakotozafindrabe, A / Ramello, L / Rami, F / Ramirez, S A R / Ramos, A G T / Rancien, T A / Raniwala, R / Raniwala, S / Räsänen, S S / Rath, R / Ravasenga, I / Read, K F / Redelbach, A R / Redlich, K / Rehman, A / Reichelt, P / Reidt, F / Reme-Ness, H A / Renfordt, R / Rescakova, Z / Reygers, K / Riabov, A / Riabov, V / Richert, T / Richter, M / Riegler, W / Riggi, F / Ristea, C / Rode, S P / Rodríguez Cahuantzi, M / Røed, K / Rogalev, R / Rogochaya, E / 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Physical review letters

2022 Volume 127, Issue 27, Page(s) 272001

Abstract: ... both the semileptonic decay (Ξ^{-}e^{+}ν_{e}) and the hadronic decay (Ξ^{-}π^{+}) channels. The Ξ_{c}^{+} baryon was ... e^{+}ν_{e})/BR(Ξ_{c}^{0}→Ξ^{-}π^{+})=1.38±0.14(stat)±0.22(syst) was measured with a total uncertainty ... processes are involved in charm hadronization in electron-positron (e^{+}e^{-}) and hadronic collisions. ...

Abstract	The p_{T}-differential cross sections of prompt charm-strange baryons Ξ_{c}^{0} and Ξ_{c}^{+} were measured at midrapidity (\|y\|<0.5) in proton-proton (pp) collisions at a center-of-mass energy sqrt[s]=13 TeV with the ALICE detector at the LHC. The Ξ_{c}^{0} baryon was reconstructed via both the semileptonic decay (Ξ^{-}e^{+}ν_{e}) and the hadronic decay (Ξ^{-}π^{+}) channels. The Ξ_{c}^{+} baryon was reconstructed via the hadronic decay (Ξ^{-}π^{+}π^{+}) channel. The branching-fraction ratio BR(Ξ_{c}^{0}→Ξ^{-}e^{+}ν_{e})/BR(Ξ_{c}^{0}→Ξ^{-}π^{+})=1.38±0.14(stat)±0.22(syst) was measured with a total uncertainty reduced by a factor of about 3 with respect to the current world average reported by the Particle Data Group. The transverse momentum (p_{T}) dependence of the Ξ_{c}^{0}- and Ξ_{c}^{+}-baryon production relative to the D^{0} meson and to the Σ_{c}^{0,+,++}- and Λ_{c}^{+}-baryon production are reported. The baryon-to-meson ratio increases toward low p_{T} up to a value of approximately 0.3. The measurements are compared with various models that take different hadronization mechanisms into consideration. The results provide stringent constraints to these theoretical calculations and additional evidence that different processes are involved in charm hadronization in electron-positron (e^{+}e^{-}) and hadronic collisions.
Language	English
Publishing date	2022-01-21
Publishing country	United States
Document type	Journal Article
ZDB-ID	208853-8
ISSN	1079-7114 ; 0031-9007
ISSN (online)	1079-7114
ISSN	0031-9007
DOI	10.1103/PhysRevLett.127.272001
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Deep Interactive Learning-based ovarian cancer segmentation of H&E-stained whole slide images to study morphological patterns of BRCA mutation

David Joon Ho / M. Herman Chui / Chad M. Vanderbilt / Jiwon Jung / Mark E. Robson / Chan-Sik Park / Jin Roh / Thomas J. Fuchs

Journal of Pathology Informatics, Vol 14, Iss , Pp 100160- (2023)

1480

Abstract: Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained ...

Abstract	Deep learning has been widely used to analyze digitized hematoxylin and eosin (H&E)-stained histopathology whole slide images. Automated cancer segmentation using deep learning can be used to diagnose malignancy and to find novel morphological patterns to predict molecular subtypes. To train pixel-wise cancer segmentation models, manual annotation from pathologists is generally a bottleneck due to its time-consuming nature. In this paper, we propose Deep Interactive Learning with a pretrained segmentation model from a different cancer type to reduce manual annotation time. Instead of annotating all pixels from cancer and non-cancer regions on giga-pixel whole slide images, an iterative process of annotating mislabeled regions from a segmentation model and training/finetuning the model with the additional annotation can reduce the time. Especially, employing a pretrained segmentation model can further reduce the time than starting annotation from scratch. We trained an accurate ovarian cancer segmentation model with a pretrained breast segmentation model by 3.5 hours of manual annotation which achieved intersection-over-union of 0.74, recall of 0.86, and precision of 0.84. With automatically extracted high-grade serous ovarian cancer patches, we attempted to train an additional classification deep learning model to predict BRCA mutation. The segmentation model and code have been released at https://github.com/MSKCC-Computational-Pathology/DMMN-ovary.
Keywords	Computational pathology ; Deep learning ; Ovarian cancer ; Segmentation ; Annotation ; Computer applications to medicine. Medical informatics ; R858-859.7 ; Pathology ; RB1-214
Subject code	004 ; 006
Language	English
Publishing date	2023-01-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Conference proceedings: Viel Rauch um nichts? – Wie gefährlich sind E-Zigaretten?

Stromps, J. / Perbix, W. / Schulz, A. / Schiefer, J. L. / Demir, E. / Fuchs, P. C.

2017 , Page(s) 17dav5.5

Event/congress	35. Jahrestagung der Deutschsprachigen Arbeitsgemeinschaft für Verbrennungsbehandlung (DAV 2017); Chur, Schweiz; Deutschsprachige Arbeitsgemeinschaft für Verbrennungsbehandlung; 2017
Keywords	Medizin, Gesundheit
Publishing date	2017-01-18
Publisher	German Medical Science GMS Publishing House; Düsseldorf
Document type	Conference proceedings
DOI	10.3205/17dav37
Database	German Medical Science

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