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  1. Article ; Online: Guselkumab for treating immune checkpoint inhibitor-induced psoriatic arthritis.

    Takeda, Koichi / Yanagitani, Noriko

    Annals of the rheumatic diseases

    2022  

    Language English
    Publishing date 2022-05-19
    Publishing country England
    Document type Journal Article
    ZDB-ID 7090-7
    ISSN 1468-2060 ; 0003-4967
    ISSN (online) 1468-2060
    ISSN 0003-4967
    DOI 10.1136/annrheumdis-2022-222628
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  2. Article ; Online: Acute effect of short-term immobilization on lower leg muscle tissue hardness in healthy adults.

    Ikeda, Takuro / Takeda, Koichi / Ikeda, Masashi

    Journal of back and musculoskeletal rehabilitation

    2023  Volume 36, Issue 4, Page(s) 941–946

    Abstract: Background: Previous studies have reported altered neural activity in the motor cortex after short-term cast immobilization, even in healthy participants. However, the effects of short-term movement restriction on tissue structure are not well ... ...

    Abstract Background: Previous studies have reported altered neural activity in the motor cortex after short-term cast immobilization, even in healthy participants. However, the effects of short-term movement restriction on tissue structure are not well understood.
    Objective: To investigate the effects of short-term lower limb immobilization on muscle tissue hardness.
    Methods: Seventeen healthy participants were enrolled in the study. Each participant's non-dominant lower limb was fixed with a soft bandage and medical splint for 10 h. Gastrocnemius muscle tissue hardness was measured using a tissue hardness meter before cast application and immediately after cast removal. Measurements were performed five times for each lower limb, and the three values with the lowest coefficient of variance were adopted as the value of muscle tissue hardness.
    Results: Gastrocnemius muscle tissue hardness in the immobilized limb was lower after cast removal than that before cast application (from 53.6 to 51.8; p< 0.01), whereas the non-fixed limb showed an increase in muscle tissue hardness at the end of the experiment (from 52.9 to 54.3; p= 0.03).
    Conclusion: The findings indicate that 10 h movement restriction induced a reduction in muscle tissue hardness, suggesting acute adverse effects of cast immobilization for orthopedic treatment.
    MeSH term(s) Humans ; Adult ; Leg ; Hardness ; Muscle, Skeletal/physiology ; Lower Extremity
    Language English
    Publishing date 2023-05-29
    Publishing country Netherlands
    Document type Journal Article
    ZDB-ID 1184721-9
    ISSN 1878-6324 ; 1053-8127
    ISSN (online) 1878-6324
    ISSN 1053-8127
    DOI 10.3233/BMR-220339
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    Zs.A 4580: Show issues Location:
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  3. Article ; Online: Unique morphological architecture of the hamstring muscles and its functional relevance revealed by analysis of isolated muscle specimens and quantification of structural parameters.

    Takeda, Koichi / Kato, Kota / Ichimura, Koichiro / Sakai, Tatsuo

    Journal of anatomy

    2023  Volume 243, Issue 2, Page(s) 284–296

    Abstract: The structural and functional differences of individual hamstrings have not been sufficiently evaluated. This study aimed to clarify the morphological architecture of the hamstrings including the superficial tendons in detail using isolated muscle ... ...

    Abstract The structural and functional differences of individual hamstrings have not been sufficiently evaluated. This study aimed to clarify the morphological architecture of the hamstrings including the superficial tendons in detail using isolated muscle specimens, together with quantification of structural parameters of the muscle. Sixteen lower limbs of human cadavers were used in this study. The semimembranosus (SM), semitendinosus (ST), biceps femoris long head (BFlh), and biceps femoris short head (BFsh) were dissected from cadavers to prepare isolated muscle specimens. Structural parameters, including muscle volume, muscle length, fiber length, sarcomere length, pennation angle, and physiological cross-sectional area (PCSA) were measured. In addition, the proximal and distal attachment areas of the muscle fibers were measured, and the proximal/distal area ratio was calculated. The SM, ST, and BFlh were spindle-shaped with the superficial origin and insertion tendons on the muscle surface, and the BFsh was quadrate with direct attachment to the skeleton and BFlh tendon. The muscle architecture was pennate in the four muscles. The four hamstrings possessed either of two types of structural parameters, one with shorter fiber length and larger PCSA, as in the SM and BFlh, and the other with longer fiber length and smaller PCSA, as in the ST and BFsh. Sarcomere length was unique in each of the four hamstrings, and thus the fiber length was suitably normalized using the average sarcomere length for each, instead of uniform length of 2.7 μm. The proximal/distal area ratio was even in the SM, large in the ST, and small in the BFsh and BFlh. This study clarified that the superficial origin and insertion tendons are critical determinants of the unique internal structure and structural parameters representing the functional properties of the hamstring muscles.
    MeSH term(s) Humans ; Hamstring Muscles ; Tendons/anatomy & histology ; Muscle Fibers, Skeletal ; Lower Extremity ; Cadaver ; Muscle, Skeletal/anatomy & histology
    Language English
    Publishing date 2023-03-13
    Publishing country England
    Document type Journal Article
    ZDB-ID 2955-5
    ISSN 1469-7580 ; 0021-8782
    ISSN (online) 1469-7580
    ISSN 0021-8782
    DOI 10.1111/joa.13860
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    Ub I Zs.38: Show issues Location:
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  4. Article ; Online: ASO Author Reflections: Are Fecal Microbes Associated With Outcomes After Esophageal Cancer Surgery?

    Maruyama, Suguru / Okamura, Akihiko / Takeda, Koichi / Watanabe, Masayuki

    Annals of surgical oncology

    2022  Volume 29, Issue 12, Page(s) 7458–7459

    MeSH term(s) Esophageal Neoplasms/surgery ; Esophagectomy/adverse effects ; Feces ; Humans
    Language English
    Publishing date 2022-07-11
    Publishing country United States
    Document type Journal Article ; Comment
    ZDB-ID 1200469-8
    ISSN 1534-4681 ; 1068-9265
    ISSN (online) 1534-4681
    ISSN 1068-9265
    DOI 10.1245/s10434-022-12187-8
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  5. Book ; Online: Japanese SimCSE Technical Report

    Tsukagoshi, Hayato / Sasano, Ryohei / Takeda, Koichi

    2023  

    Abstract: We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE. Since there is a lack of sentence embedding models for Japanese that can be used as a baseline in sentence embedding research, we conducted extensive ...

    Abstract We report the development of Japanese SimCSE, Japanese sentence embedding models fine-tuned with SimCSE. Since there is a lack of sentence embedding models for Japanese that can be used as a baseline in sentence embedding research, we conducted extensive experiments on Japanese sentence embeddings involving 24 pre-trained Japanese or multilingual language models, five supervised datasets, and four unsupervised datasets. In this report, we provide the detailed training setup for Japanese SimCSE and their evaluation results.
    Keywords Computer Science - Computation and Language
    Publishing date 2023-10-30
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  6. Book ; Online: Acquiring Frame Element Knowledge with Deep Metric Learning for Semantic Frame Induction

    Yamada, Kosuke / Sasano, Ryohei / Takeda, Koichi

    2023  

    Abstract: The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument ...

    Abstract The semantic frame induction tasks are defined as a clustering of words into the frames that they evoke, and a clustering of their arguments according to the frame element roles that they should fill. In this paper, we address the latter task of argument clustering, which aims to acquire frame element knowledge, and propose a method that applies deep metric learning. In this method, a pre-trained language model is fine-tuned to be suitable for distinguishing frame element roles through the use of frame-annotated data, and argument clustering is performed with embeddings obtained from the fine-tuned model. Experimental results on FrameNet demonstrate that our method achieves substantially better performance than existing methods.

    Comment: Findings of ACL 2023
    Keywords Computer Science - Computation and Language
    Publishing date 2023-05-23
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  7. Book ; Online: Semantic Frame Induction with Deep Metric Learning

    Yamada, Kosuke / Sasano, Ryohei / Takeda, Koichi

    2023  

    Abstract: Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction. However, they have also revealed that generic contextualized embeddings are not always consistent with human intuitions about ... ...

    Abstract Recent studies have demonstrated the usefulness of contextualized word embeddings in unsupervised semantic frame induction. However, they have also revealed that generic contextualized embeddings are not always consistent with human intuitions about semantic frames, which causes unsatisfactory performance for frame induction based on contextualized embeddings. In this paper, we address supervised semantic frame induction, which assumes the existence of frame-annotated data for a subset of predicates in a corpus and aims to build a frame induction model that leverages the annotated data. We propose a model that uses deep metric learning to fine-tune a contextualized embedding model, and we apply the fine-tuned contextualized embeddings to perform semantic frame induction. Our experiments on FrameNet show that fine-tuning with deep metric learning considerably improves the clustering evaluation scores, namely, the B-cubed F-score and Purity F-score, by about 8 points or more. We also demonstrate that our approach is effective even when the number of training instances is small.

    Comment: EACL 2023
    Keywords Computer Science - Computation and Language
    Subject code 006
    Publishing date 2023-04-27
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  8. Book ; Online: Sentence Representations via Gaussian Embedding

    Yoda, Shohei / Tsukagoshi, Hayato / Sasano, Ryohei / Takeda, Koichi

    2023  

    Abstract: Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a ... ...

    Abstract Recent progress in sentence embedding, which represents the meaning of a sentence as a point in a vector space, has achieved high performance on tasks such as a semantic textual similarity (STS) task. However, sentence representations as a point in a vector space can express only a part of the diverse information that sentences have, such as asymmetrical relationships between sentences. This paper proposes GaussCSE, a Gaussian distribution-based contrastive learning framework for sentence embedding that can handle asymmetric relationships between sentences, along with a similarity measure for identifying inclusion relations. Our experiments show that GaussCSE achieves the same performance as previous methods in natural language inference tasks, and is able to estimate the direction of entailment relations, which is difficult with point representations.
    Keywords Computer Science - Computation and Language
    Publishing date 2023-05-22
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  9. Book ; Online: Comparison and Combination of Sentence Embeddings Derived from Different Supervision Signals

    Tsukagoshi, Hayato / Sasano, Ryohei / Takeda, Koichi

    2022  

    Abstract: There have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddings depending on the supervision signals. In this paper, we focus on two ... ...

    Abstract There have been many successful applications of sentence embedding methods. However, it has not been well understood what properties are captured in the resulting sentence embeddings depending on the supervision signals. In this paper, we focus on two types of sentence embedding methods with similar architectures and tasks: one fine-tunes pre-trained language models on the natural language inference task, and the other fine-tunes pre-trained language models on word prediction task from its definition sentence, and investigate their properties. Specifically, we compare their performances on semantic textual similarity (STS) tasks using STS datasets partitioned from two perspectives: 1) sentence source and 2) superficial similarity of the sentence pairs, and compare their performances on the downstream and probing tasks. Furthermore, we attempt to combine the two methods and demonstrate that combining the two methods yields substantially better performance than the respective methods on unsupervised STS tasks and downstream tasks.

    Comment: Accepted at *SEM 2022
    Keywords Computer Science - Computation and Language
    Subject code 430
    Publishing date 2022-02-07
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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  10. Book ; Online: DefSent

    Tsukagoshi, Hayato / Sasano, Ryohei / Takeda, Koichi

    Sentence Embeddings using Definition Sentences

    2021  

    Abstract: Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, ... ...

    Abstract Sentence embedding methods using natural language inference (NLI) datasets have been successfully applied to various tasks. However, these methods are only available for limited languages due to relying heavily on the large NLI datasets. In this paper, we propose DefSent, a sentence embedding method that uses definition sentences from a word dictionary. Since dictionaries are available for many languages, DefSent is more broadly applicable than methods using NLI datasets without constructing additional datasets. We demonstrate that DefSent performs comparably on unsupervised semantics textual similarity (STS) tasks and slightly better on SentEval tasks to the methods using large NLI datasets.

    Comment: Accepted at ACL-IJCNLP 2021 main conference, camera-ready version coming soon
    Keywords Computer Science - Computation and Language
    Publishing date 2021-05-10
    Publishing country us
    Document type Book ; Online
    Database BASE - Bielefeld Academic Search Engine (life sciences selection)

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