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  1. Article ; Online: R

    Huang, ChaoWen / Zhang, Hong / Cheng, XinLu

    The journal of physical chemistry. A

    2022  Volume 126, Issue 13, Page(s) 2061–2074

    Abstract: Low-energy electron collisions with the ... ...

    Abstract Low-energy electron collisions with the X
    Language English
    Publishing date 2022-03-24
    Publishing country United States
    Document type Journal Article
    ISSN 1520-5215
    ISSN (online) 1520-5215
    DOI 10.1021/acs.jpca.1c09153
    Database MEDical Literature Analysis and Retrieval System OnLINE

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  2. Book ; Online: R-Judge

    Yuan, Tongxin / He, Zhiwei / Dong, Lingzhong / Wang, Yiming / Zhao, Ruijie / Xia, Tian / Xu, Lizhen / Zhou, Binglin / Li, Fangqi / Zhang, Zhuosheng / Wang, Rui / Liu, Gongshen

    Benchmarking Safety Risk Awareness for LLM Agents

    2024  

    Abstract: ... within diverse environments. We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs ... in judging safety risks given agent interaction records. R-Judge comprises 162 agent interaction records ... consensus on safety with annotated safety risk labels and high-quality risk descriptions. Utilizing R-Judge ...

    Abstract Large language models (LLMs) have exhibited great potential in autonomously completing tasks across real-world applications. Despite this, these LLM agents introduce unexpected safety risks when operating in interactive environments. Instead of centering on LLM-generated content safety in most prior studies, this work addresses the imperative need for benchmarking the behavioral safety of LLM agents within diverse environments. We introduce R-Judge, a benchmark crafted to evaluate the proficiency of LLMs in judging safety risks given agent interaction records. R-Judge comprises 162 agent interaction records, encompassing 27 key risk scenarios among 7 application categories and 10 risk types. It incorporates human consensus on safety with annotated safety risk labels and high-quality risk descriptions. Utilizing R-Judge, we conduct a comprehensive evaluation of 8 prominent LLMs commonly employed as the backbone for agents. The best-performing model, GPT-4, achieves 72.29% in contrast to the human score of 89.38%, showing considerable room for enhancing the risk awareness of LLMs. Notably, leveraging risk descriptions as environment feedback significantly improves model performance, revealing the importance of salient safety risk feedback. Furthermore, we design an effective chain of safety analysis technique to help the judgment of safety risks and conduct an in-depth case study to facilitate future research. R-Judge is publicly available at https://github.com/Lordog/R-Judge.
    Keywords Computer Science - Computation and Language ; Computer Science - Artificial Intelligence
    Publishing date 2024-01-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  3. Article ; Online: Can R

    Zhang, Jinggang / Chen, Jie / Chen, Qin / Chen, Jing / Luo, Kai / Pan, Liang / Zhang, Yongcheng / Dou, Weiqiang / Xing, Wei

    Magnetic resonance in medicine

    2021  Volume 86, Issue 2, Page(s) 974–983

    Abstract: Purpose: To explore if R: Methods: Forty rabbits were randomly divided into 4 groups according ... All rabbits were performed 5 times (IRI: Results: Compared to the baseline, the medullary R: Conclusion ... R ...

    Abstract Purpose: To explore if R
    Methods: Forty rabbits were randomly divided into 4 groups according to the clipping time: the sham group and 45 min, 60 min, and 75 min for the mild, moderate, and severe groups (with n = 10 each group), respectively. Intravenous furosemide (FU) was administered 24 h after IRI. All rabbits were performed 5 times (IRI
    Results: Compared to the baseline, the medullary R
    Conclusion: R
    MeSH term(s) Animals ; Furosemide ; Hypoxia/diagnostic imaging ; Ischemia ; Kidney/diagnostic imaging ; Rabbits ; Reperfusion Injury/diagnostic imaging
    Chemical Substances Furosemide (7LXU5N7ZO5)
    Language English
    Publishing date 2021-03-16
    Publishing country United States
    Document type Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 605774-3
    ISSN 1522-2594 ; 0740-3194
    ISSN (online) 1522-2594
    ISSN 0740-3194
    DOI 10.1002/mrm.28696
    Database MEDical Literature Analysis and Retrieval System OnLINE

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    In stock of ZB MED Cologne/Königswinter

    Zs.A 1959: Show issues Location:
    Je nach Verfügbarkeit (siehe Angabe bei Bestand)
    bis Jg. 1994: Bestellungen von Artikeln über das Online-Bestellformular
    Jg. 1995 - 2021: Lesesall (1.OG)
    ab Jg. 2022: Lesesaal (EG)

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  4. Book ; Online: ESCOXLM-R

    Zhang, Mike / van der Goot, Rob / Plank, Barbara

    Multilingual Taxonomy-driven Pre-training for the Job Market Domain

    2023  

    Abstract: ... R, based on XLM-R, which uses domain-adaptive pre-training on the European Skills, Competences ... Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R ... taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling ...

    Abstract The increasing number of benchmarks for Natural Language Processing (NLP) tasks in the computational job market domain highlights the demand for methods that can handle job-related tasks such as skill extraction, skill classification, job title classification, and de-identification. While some approaches have been developed that are specific to the job market domain, there is a lack of generalized, multilingual models and benchmarks for these tasks. In this study, we introduce a language model called ESCOXLM-R, based on XLM-R, which uses domain-adaptive pre-training on the European Skills, Competences, Qualifications and Occupations (ESCO) taxonomy, covering 27 languages. The pre-training objectives for ESCOXLM-R include dynamic masked language modeling and a novel additional objective for inducing multilingual taxonomical ESCO relations. We comprehensively evaluate the performance of ESCOXLM-R on 6 sequence labeling and 3 classification tasks in 4 languages and find that it achieves state-of-the-art results on 6 out of 9 datasets. Our analysis reveals that ESCOXLM-R performs better on short spans and outperforms XLM-R on entity-level and surface-level span-F1, likely due to ESCO containing short skill and occupation titles, and encoding information on the entity-level.

    Comment: Accepted at ACL2023 (Main)
    Keywords Computer Science - Computation and Language
    Subject code 004
    Publishing date 2023-05-20
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  5. Book ; Online: R-Tuning

    Zhang, Hanning / Diao, Shizhe / Lin, Yong / Fung, Yi R. / Lian, Qing / Wang, Xingyao / Chen, Yangyi / Ji, Heng / Zhang, Tong

    Teaching Large Language Models to Refuse Unknown Questions

    2023  

    Abstract: ... Instruction Tuning (R-Tuning). This approach is formalized by first identifying the knowledge gap between ... github.com/shizhediao/R-Tuning. ... Comment: 20 pages, 8 figures ...

    Abstract Large language models (LLMs) have revolutionized numerous domains with their impressive performance but still face their challenges. A predominant issue is the propensity for these models to generate non-existent facts, a concern termed hallucination. Our research is motivated by the observation that previous instruction tuning methods force the model to complete a sentence no matter whether the model knows the knowledge or not. When the question is out of the parametric knowledge, it will try to make up something and fail to indicate when it lacks knowledge. In this paper, we present a new approach called Refusal-Aware Instruction Tuning (R-Tuning). This approach is formalized by first identifying the knowledge gap between parametric knowledge and the instruction tuning data. Then, we construct the refusal-aware data based on the knowledge intersection, to tune LLMs to refrain from responding to questions beyond its parametric knowledge. Experimental results demonstrate this new instruction tuning approach effectively improves a model's ability to answer known questions and refrain from answering unknown questions. Furthermore, when tested on out-of-domain datasets, the refusal ability was found to be a meta-skill that could be generalized to other tasks. Further analysis surprisingly finds that learning the uncertainty during training displays a better ability to estimate uncertainty than uncertainty-based testing. Our code will be released at https://github.com/shizhediao/R-Tuning.

    Comment: 20 pages, 8 figures
    Keywords Computer Science - Computation and Language
    Subject code 004
    Publishing date 2023-11-16
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: R-Cut

    Niu, Yingjie / Ding, Ming / Ge, Maoning / Karlsson, Robin / Zhang, Yuxiao / Takeda, Kazuya

    Enhancing Explainability in Vision Transformers with Relationship Weighted Out and Cut

    2023  

    Abstract: Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability ... ...

    Abstract Transformer-based models have gained popularity in the field of natural language processing (NLP) and are extensively utilized in computer vision tasks and multi-modal models such as GPT4. This paper presents a novel method to enhance the explainability of Transformer-based image classification models. Our method aims to improve trust in classification results and empower users to gain a deeper understanding of the model for downstream tasks by providing visualizations of class-specific maps. We introduce two modules: the ``Relationship Weighted Out" and the ``Cut" modules. The ``Relationship Weighted Out" module focuses on extracting class-specific information from intermediate layers, enabling us to highlight relevant features. Additionally, the ``Cut" module performs fine-grained feature decomposition, taking into account factors such as position, texture, and color. By integrating these modules, we generate dense class-specific visual explainability maps. We validate our method with extensive qualitative and quantitative experiments on the ImageNet dataset. Furthermore, we conduct a large number of experiments on the LRN dataset, specifically designed for automatic driving danger alerts, to evaluate the explainability of our method in complex backgrounds. The results demonstrate a significant improvement over previous methods. Moreover, we conduct ablation experiments to validate the effectiveness of each module. Through these experiments, we are able to confirm the respective contributions of each module, thus solidifying the overall effectiveness of our proposed approach.
    Keywords Computer Science - Computer Vision and Pattern Recognition ; Computer Science - Artificial Intelligence
    Subject code 006
    Publishing date 2023-07-18
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: R-Mixup

    Kan, Xuan / Li, Zimu / Cui, Hejie / Yu, Yue / Xu, Ran / Yu, Shaojun / Zhang, Zilong / Guo, Ying / Yang, Carl

    Riemannian Mixup for Biological Networks

    2023  

    Abstract: ... networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data ... from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages ... effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R ...

    Abstract Biological networks are commonly used in biomedical and healthcare domains to effectively model the structure of complex biological systems with interactions linking biological entities. However, due to their characteristics of high dimensionality and low sample size, directly applying deep learning models on biological networks usually faces severe overfitting. In this work, we propose R-MIXUP, a Mixup-based data augmentation technique that suits the symmetric positive definite (SPD) property of adjacency matrices from biological networks with optimized training efficiency. The interpolation process in R-MIXUP leverages the log-Euclidean distance metrics from the Riemannian manifold, effectively addressing the swelling effect and arbitrarily incorrect label issues of vanilla Mixup. We demonstrate the effectiveness of R-MIXUP with five real-world biological network datasets on both regression and classification tasks. Besides, we derive a commonly ignored necessary condition for identifying the SPD matrices of biological networks and empirically study its influence on the model performance. The code implementation can be found in Appendix E.

    Comment: Accepted to KDD 2023
    Keywords Computer Science - Machine Learning ; Computer Science - Artificial Intelligence ; Quantitative Biology - Quantitative Methods ; 68T07 ; 68T05 ; I.2.6 ; J.3
    Subject code 612 ; 006
    Publishing date 2023-06-04
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Article ; Online: Measurement of the Ratios of Branching Fractions R(D^{*}) and R(D^{0}).

    Aaij, R / Abdelmotteleb, A S W / Abellan Beteta, C / Abudinén, F / Ackernley, T / Adeva, B / Adinolfi, M / Adlarson, P / Afsharnia, H / Agapopoulou, C / Aidala, C A / Ajaltouni, Z / Akar, S / Akiba, K / Albicocco, P / Albrecht, J / Alessio, F / Alexander, M / Alfonso Albero, A /
    Aliouche, Z / Alvarez Cartelle, P / Amalric, R / Amato, S / Amey, J L / Amhis, Y / An, L / Anderlini, L / Andersson, M / Andreianov, A / Andreotti, M / Andreou, D / Ao, D / Archilli, F / Artamonov, A / Artuso, M / Aslanides, E / Atzeni, M / Audurier, B / Bachiller Perea, I B / Bachmann, S / Bachmayer, M / Back, J J / Bailly-Reyre, A / Baladron Rodriguez, P / Balagura, V / Baldini, W / Baptista de Souza Leite, J / Barbetti, M / Barlow, R J / Barsuk, S / Barter, W / Bartolini, M / Baryshnikov, F / Basels, J M / Bassi, G / Batsukh, B / Battig, A / Bay, A / Beck, A / Becker, M / Bedeschi, F / Bediaga, I B / Beiter, A / Belin, S / Bellee, V / Belous, K / Belov, I / Belyaev, I / Benane, G / Bencivenni, G / Ben-Haim, E / Berezhnoy, A / Bernet, R / Bernet Andres, S / Berninghoff, D / Bernstein, H C / Bertella, C / Bertolin, A / Betancourt, C / Betti, F / Bezshyiko, Ia / Bhasin, S / Bhom, J / Bian, L / Bieker, M S / Biesuz, N V / Billoir, P / Biolchini, A / Birch, M / Bishop, F C R / Bitadze, A / Bizzeti, A / Blago, M P / Blake, T / Blanc, F / Blank, J E / Blusk, S / Bobulska, D / Boelhauve, J A / Boente Garcia, O / Boettcher, T / Boldyrev, A / Bolognani, C S / Bolzonella, R / Bondar, N / Borgato, F / Borghi, S / Borsato, M / Borsuk, J T / Bouchiba, S A / Bowcock, T J V / Boyer, A / Bozzi, C / Bradley, M J / Braun, S / Brea Rodriguez, A / Brodzicka, J / Brossa Gonzalo, A / Brown, J / Brundu, D / Buonaura, A / Buonincontri, L / Burke, A T / Burr, C / Bursche, A / Butkevich, A / Butter, J S / Buytaert, J / Byczynski, W / Cadeddu, S / Cai, H / Calabrese, R / Calefice, L / Cali, S / Calvi, M / Calvo Gomez, M / Campana, P / Campora Perez, D H / Campoverde Quezada, A F / Capelli, S / Capriotti, L / Carbone, A / Cardinale, R / Cardini, A / Carniti, P / Carus, L / Casais Vidal, A / Caspary, R / Casse, G / Cattaneo, M / Cavallero, G / Cavallini, V / Celani, S / Cerasoli, J / Cervenkov, D / Chadwick, A J / Chahrour, I / Chapman, M G / Charles, M / Charpentier, Ph / Chavez Barajas, C A / Chefdeville, M / Chen, C / Chen, S / Chernov, A / Chernyshenko, S / Chobanova, V / Cholak, S / Chrzaszcz, M / Chubykin, A / Chulikov, V / Ciambrone, P / Cicala, M F / Cid Vidal, X / Ciezarek, G / Cifra, P / Ciullo, G / Clarke, P E L / Clemencic, M / Cliff, H V / Closier, J / Cobbledick, J L / Coco, V / Coelho, J A B / Cogan, J / Cogneras, E / Cojocariu, L / Collins, P / Colombo, T / Congedo, L / Contu, A / Cooke, N / Corredoira, I / Corti, G / Couturier, B / Craik, D C / Cruz Torres, M / Currie, R / Da Silva, C L / Dadabaev, S / Dai, L / Dai, X / Dall'Occo, E / Dalseno, J / D'Ambrosio, C / Daniel, J / Danilina, A / d'Argent, P / Davies, J E / Davis, A / De Aguiar Francisco, O / de Boer, J / De Bruyn, K / De Capua, S / De Cian, M / De Freitas Carneiro Da Graca, U / De Lucia, E / De Miranda, J M / De Paula, L / De Serio, M / De Simone, D / De Simone, P / De Vellis, F / de Vries, J A / Dean, C T / Debernardis, F / Decamp, D / Dedu, V / Del Buono, L / Delaney, B / Dembinski, H-P / Denysenko, V / Deschamps, O / Dettori, F / Dey, B / Di Nezza, P / Diachkov, I / Didenko, S / Dieste Maronas, L / Ding, S / Dobishuk, V / Dolmatov, A / Dong, C / Donohoe, A M / Dordei, F / Dos Reis, A C / Douglas, L / Downes, A G / Duda, P / Dudek, M W / Dufour, L / Duk, V / Durante, P / Duras, M M / Durham, J M / Dutta, D / Dziurda, A / Dzyuba, A / Easo, S / Egede, U / Egorychev, V / Eirea Orro, C / Eisenhardt, S / Ejopu, E / Ek-In, S / Eklund, L / Elashri, M E / Ellbracht, J / Ely, S / Ene, A / Epple, E / Escher, S / Eschle, J / Esen, S / Evans, T / Fabiano, F / Falcao, L N / Fan, Y / Fang, B / Fantini, L / Faria, M / Farry, S / Fazzini, D / Felkowski, L F / Feo, M / Fernandez Gomez, M / Fernez, A D / Ferrari, F / Ferreira Lopes, L / Ferreira Rodrigues, F / Ferreres Sole, S / Ferrillo, M / Ferro-Luzzi, M / Filippov, S / Fini, R A / Fiorini, M / Firlej, M / Fischer, K M / Fitzgerald, D S / Fitzpatrick, C / Fiutowski, T / Fleuret, F / Fontana, M / Fontanelli, F / Forty, R / Foulds-Holt, D / Franco Lima, V / Franco Sevilla, M / Frank, M / Franzoso, E / Frau, G / Frei, C / Friday, D A / Frontini, L / Fu, J / Fuehring, Q / Fulghesu, T / Gabriel, E / Galati, G / Galati, M D / Gallas Torreira, A / Galli, D / Gambetta, S / Gandelman, M / Gandini, P / Gao, Y / Garau, M / Garcia Martin, L M / Garcia Moreno, P / García Pardiñas, J / Garcia Plana, B / Garcia Rosales, F A / Garrido, L / Gaspar, C / Geertsema, R E / Gerick, D / Gerken, L L / Gersabeck, E / Gersabeck, M / Gershon, T / Giambastiani, L / Gibson, V / Giemza, H K / Gilman, A L / Giovannetti, M / Gioventù, A / Gironella Gironell, P / Giugliano, C / Giza, M A / Gizdov, K / Gkougkousis, E L / Gligorov, V V / Göbel, C / Golobardes, E / Golubkov, D / Golutvin, A / Gomes, A / Gomez Fernandez, S / Goncalves Abrantes, F / Goncerz, M / Gong, G / Gorelov, I V / Gotti, C / Grabowski, J P / Grammatico, T / Granado Cardoso, L A / Graugés, E / Graverini, E / Graziani, G / Grecu, A T / Greeven, L M / Grieser, N A / Grillo, L / Gromov, S / Gruberg Cazon, B R / Gu, C / Guarise, M / Guittiere, M / Günther, P A / Gushchin, E / Guth, A / Guz, Y / Gys, T / Hadavizadeh, T / Hadjivasiliou, C / Haefeli, G / Haen, C / Haimberger, J / Haines, S C / Halewood-Leagas, T / Halvorsen, M M / Hamilton, P M / Hammerich, J / Han, Q / Han, X / Hansen, E B / Hansmann-Menzemer, S / Hao, L / Harnew, N / Harrison, T / Hasse, C / Hatch, M / He, J / Heijhoff, K / Hemmer, F H / Henderson, C / Henderson, R D L / Hennequin, A M / Hennessy, K / Henry, L / Herd, J / Heuel, J / Hicheur, A / Hill, D / Hilton, M / Hollitt, S E / Horswill, J / Hou, R / Hou, Y / Hu, J / Hu, W / Hu, X / Huang, W / Huang, X / Hulsbergen, W / Hunter, R J / Hushchyn, M / Hutchcroft, D / Ibis, P / Idzik, M / Ilin, D / Ilten, P / Inglessi, A / Iniukhin, A / Ishteev, A / Ivshin, K / Jacobsson, R / Jage, H / Jaimes Elles, S J / Jakobsen, S / Jans, E / Jashal, B K / Jawahery, A / Jevtic, V / Jiang, E / Jiang, X / Jiang, Y / John, M / Johnson, D / Jones, C R / Jones, T P / Jost, B / Jurik, N / Juszczak, I / Kandybei, S / Kang, Y / Karacson, M / Karpenkov, D / Karpov, M / Kautz, J W / Keizer, F / Keller, D M / Kenzie, M / Ketel, T / Khanji, B / Kharisova, A / Kholodenko, S / Khreich, G / Kirn, T / Kirsebom, V S / Kitouni, O / Klaver, S / Kleijne, N / Klimaszewski, K / Kmiec, M R / Koliiev, S / Kolk, L / Kondybayeva, A / Konoplyannikov, A / Kopciewicz, P / Kopecna, R / Koppenburg, P / Korolev, M / Kostiuk, I / Kot, O / Kotriakhova, S / Kozachuk, A / Kravchenko, P / Kravchuk, L / Krawczyk, R D / Kreps, M / Kretzschmar, S / Krokovny, P / Krupa, W / Krzemien, W / Kubat, J / Kubis, S / Kucewicz, W / Kucharczyk, M / Kudryavtsev, V / Kulikova, E K / Kupsc, A / Lacarrere, D / Lafferty, G / Lai, A / Lampis, A / Lancierini, D / Landesa Gomez, C / Lane, J J / Lane, R / Langenbruch, C / Langer, J / Lantwin, O / Latham, T / Lazzari, F / Lazzaroni, M / Le Gac, R / Lee, S H / Lefèvre, R / Leflat, A / Legotin, S / Lenisa, P / Leroy, O / Lesiak, T / 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Vidal, F / Massafferri, A / Materok, M / Matev, R / Mathad, A / Matiunin, V / Matteuzzi, C / Mattioli, K R / Mauri, A / Maurice, E / Mauricio, J / Mazurek, M / McCann, M / Mcconnell, L / McGrath, T H / McHugh, N T / McNab, A / McNulty, R / Mead, J V / Meadows, B / Meier, G / Melnychuk, D / Meloni, S / Merk, M / Merli, A / Meyer Garcia, L / Miao, D / Mikhasenko, M / Milanes, D A / Millard, E / Milovanovic, M / Minard, M-N / Minotti, A / Miralles, T / Mitchell, S E / Mitreska, B / Mitzel, D S / Mödden, A / Mohammed, R A / Moise, R D / Mokhnenko, S / Mombächer, T / Monk, M / Monroy, I A / Monteil, S / Morello, G / Morello, M J / Morgenthaler, M P / Moron, J / Morris, A B / Morris, A G / Mountain, R / Mu, H / Muhammad, E / Muheim, F / Mulder, M / Müller, K / Murphy, C H / Murray, D / Murta, R / Muzzetto, P / Naik, P / Nakada, T / Nandakumar, R / Nanut, T / Nasteva, I / Needham, M / Neri, N / Neubert, S / Neufeld, N / Neustroev, P / Newcombe, R / Nicolini, J / Nicotra, D / Niel, E M / 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I / Polycarpo, E / Ponce, S / Popov, D / Poslavskii, S / Prasanth, K / Promberger, L / Prouve, C / Pugatch, V / Puill, V / Punzi, G / Qi, H R / Qian, W / Qin, N / Qu, S / Quagliani, R / Raab, N V / Rachwal, B / Rademacker, J H / Rajagopalan, R / Rama, M / Ramos Pernas, M / Rangel, M S / Ratnikov, F / Raven, G / Rebollo De Miguel, M / Redi, F / Reich, J / Reiss, F / Remon Alepuz, C / Ren, Z / Resmi, P K / Ribatti, R / Ricci, A M / Ricciardi, S / Richardson, K / Richardson-Slipper, M / Rinnert, K / Robbe, P / Robertson, G / Rodrigues, A B / Rodrigues, E / Rodriguez Fernandez, E / Rodriguez Lopez, J A / Rodriguez Rodriguez, E / Rolf, D L / Rollings, A / Roloff, P / Romanovskiy, V / Romero Lamas, M / Romero Vidal, A / Roth, J D / Rotondo, M / Rudolph, M S / Ruf, T / Ruiz Fernandez, R A / Ruiz Vidal, J / Ryzhikov, A / Ryzka, J / Saborido Silva, J J / Sagidova, N / Sahoo, N / Saitta, B / Salomoni, M / Sanchez Gras, C / Sanderswood, I / Santacesaria, R / Santamarina Rios, C / Santimaria, M / 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/ Zheng, Y / Zhou, T / Zhou, X / Zhou, Y / Zhovkovska, V / Zhu, X / Zhu, Z / Zhukov, V / Zou, Q / Zucchelli, S / Zuliani, D / Zunica, G

    Physical review letters

    2023  Volume 131, Issue 11, Page(s) 111802

    Abstract: The ratios of branching fractions R(D^{*})≡B(B[over ¯]→D^{*}τ^{-}ν[over ¯]_{τ})/B(B[over ¯]→D^{*}μ^ ... ν[over ¯]_{μ}) and R(D^{0})≡B(B^{-}→D^{0}τ^{-}ν[over ¯]_{τ})/B(B^{-}→D^{0}μ^{-}ν[over ¯]_{μ}) are ... is identified in the decay mode τ^{-}→μ^{-}ν_{τ}ν[over ¯]_{μ}. The measured values are R(D^{*})=0 ...

    Abstract The ratios of branching fractions R(D^{*})≡B(B[over ¯]→D^{*}τ^{-}ν[over ¯]_{τ})/B(B[over ¯]→D^{*}μ^{-}ν[over ¯]_{μ}) and R(D^{0})≡B(B^{-}→D^{0}τ^{-}ν[over ¯]_{τ})/B(B^{-}→D^{0}μ^{-}ν[over ¯]_{μ}) are measured, assuming isospin symmetry, using a sample of proton-proton collision data corresponding to 3.0  fb^{-1} of integrated luminosity recorded by the LHCb experiment during 2011 and 2012. The tau lepton is identified in the decay mode τ^{-}→μ^{-}ν_{τ}ν[over ¯]_{μ}. The measured values are R(D^{*})=0.281±0.018±0.024 and R(D^{0})=0.441±0.060±0.066, where the first uncertainty is statistical and the second is systematic. The correlation between these measurements is ρ=-0.43. The results are consistent with the current average of these quantities and are at a combined 1.9 standard deviations from the predictions based on lepton flavor universality in the standard model.
    Language English
    Publishing date 2023-09-29
    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.131.111802
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  9. Article ; Online: A CAR-T response prediction model for r/r B-NHL patients based on a T cell subset nomogram.

    Zhang, Xiaomei / Sun, Rui / Zhang, Meng / Zhao, Yifan / Cao, Xinping / Guo, Ruiting / Zhang, Yi / Liu, Xingzhong / Lyu, Cuicui / Zhao, Mingfeng

    Cancer immunology, immunotherapy : CII

    2024  Volume 73, Issue 2, Page(s) 33

    Abstract: Background: Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no ... influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish ... an early prediction model.: Methods: A total of 43 r/r B-NHL patients were enrolled ...

    Abstract Background: Chimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B cell no-Hodgkin lymphoma (NHL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here investigate the independent influencing factors of the efficacy of CD19 CAR-T cell infusion in the treatment of r/r B-NHL and to establish an early prediction model.
    Methods: A total of 43 r/r B-NHL patients were enrolled in this retrospective study. The patients' general data were recorded, and the primary endpoint is the patients' treatment response. The independent factors of complete remission (CR) and partial remission (PR) were investigated by univariate and binary logistic regression analysis, and the prediction model of the probability of CR was constructed according to the determined independent factors. Receiver operating characteristic (ROC) and calibration plot were used to assess the discrimination and calibration of the established model. Furthermore, we collected 15 participators to validate the model.
    Results: Univariate analysis and binary logistic regression analysis of 43 patients showed that the ratio of central memory T cell (Tcm) and naïve T cell (Tn) in cytotoxic T cells (Tc) was an independent risk factor for response to CD19 CAR-T cell therapy in r/r B-NHL. On this basis, the area under the curve (AUC) of Tcm in the Tc and Tn in the Tc nomogram model was 0.914 (95%CI 0.832-0.996), the sensitivity was 83%, and the specificity was 74.2%, which had excellent predictive value. We did not found the difference of the progression-free survival (PFS).
    Conclusions: The ratio of Tcm and Tn in Tc was found to be able to predict the treatment response of CD19 CAR-T cells in r/r B-NHL. We have established a nomogram model for the assessment of the CD19 CAR-T therapy response presented high specificity and sensitivity.
    MeSH term(s) Humans ; Receptors, Chimeric Antigen ; Nomograms ; Retrospective Studies ; Immunotherapy, Adoptive ; T-Lymphocyte Subsets ; Antigens, CD19
    Chemical Substances Receptors, Chimeric Antigen ; Antigens, CD19
    Language English
    Publishing date 2024-01-27
    Publishing country Germany
    Document type Journal Article
    ZDB-ID 195342-4
    ISSN 1432-0851 ; 0340-7004
    ISSN (online) 1432-0851
    ISSN 0340-7004
    DOI 10.1007/s00262-023-03618-w
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  10. Article ; Online: Comparative efficacy and safety of tisagenlecleucel and axicabtagene ciloleucel among adults with r/r follicular lymphoma.

    Dickinson, Michael / Martinez-Lopez, Joaquin / Jousseaume, Etienne / Yang, Hongbo / Chai, Xinglei / Xiang, Cheryl / Wang, Travis / Zhang, Jie / Ramos, Roberto / Schuster, Stephen J / Fowler, Nathan

    Leukemia & lymphoma

    2024  Volume 65, Issue 3, Page(s) 323–332

    Abstract: ... with relapsed or refractory follicular lymphoma (r/r FL). This study used individual patient data from ELARA ... outcomes in r/r FL using matching-adjusted indirect comparison methods. After adjustment for baseline ...

    Abstract Regulatory approvals of tisagenlecleucel (tisa-cel) and axicabtagene ciloleucel (axi-cel) have established the feasibility of chimeric antigen receptor T-cell therapies for the treatment of adults with relapsed or refractory follicular lymphoma (r/r FL). This study used individual patient data from ELARA (tisa-cel) and aggregate published patient data from ZUMA-5 (axi-cel) to compare efficacy and safety outcomes in r/r FL using matching-adjusted indirect comparison methods. After adjustment for baseline differences in the trial populations, the results suggested that tisa-cel (
    MeSH term(s) Adult ; Humans ; Lymphoma, Follicular/therapy ; Immunotherapy, Adoptive/adverse effects ; Biological Products/adverse effects ; Cytokine Release Syndrome ; Lymphoma, Large B-Cell, Diffuse ; Antigens, CD19/adverse effects ; Receptors, Antigen, T-Cell
    Chemical Substances tisagenlecleucel (Q6C9WHR03O) ; axicabtagene ciloleucel (U2I8T43Y7R) ; Biological Products ; Antigens, CD19 ; Receptors, Antigen, T-Cell
    Language English
    Publishing date 2024-01-05
    Publishing country United States
    Document type Journal Article
    ZDB-ID 1042374-6
    ISSN 1029-2403 ; 1042-8194
    ISSN (online) 1029-2403
    ISSN 1042-8194
    DOI 10.1080/10428194.2023.2289854
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