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Relevant bibliographies by topics / Frederick Matthias Alexander

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Academic literature on the topic 'Frederick Matthias Alexander'

Author: Grafiati

Published: 4 June 2025

Last updated: 15 July 2025

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Journal articles on the topic "Frederick Matthias Alexander"

1

Jeroen, Staring. "F. Matthias Alexander and Edwardian Actresses/Actors." International Journal of Case Studies 7, no. 2 (2018): 09–26. https://doi.org/10.5281/zenodo.3538835.

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This case study investigates anecdotes and claims about Alexander Technique founding father Frederick Matthias Alexander who allegedly &bdquo;treated" numerous eminent Edwardian London actresses and actors. Research shows it is advisable to question those stories and claims.

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Dr., Jeroen Staring. "Aldous Huxley, the Bates Method, The Art of Seeing, and Frederick Matthias Alexander: An Unexpected, Overhauled History." International Journal of Case Studies 7, no. 11 (2018): 93–101. https://doi.org/10.5281/zenodo.3545099.

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Jeroen, Staring. "Frederick Matthias Alexander and the Bureau of Educational Experiments." International Journal of Case Studies 4, no. 9 (2015): 01–18. https://doi.org/10.5281/zenodo.3529898.

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In 1916, three educational reformers founded the Bureau of Educational Experiments. Between 1916 and 1919, the Bureau had no clear direction. On the one hand, they offered a clearinghouse gathering and distributing educational information, issuing bulletins, preparing exhibitions, and maintaining a specialized library. On the other hand, they subsidized, initiated, and conducted a range of educational experiments. Before Bureau members eventually found their direction in 1919, they scored a few successes and had their share of failures. One outstanding failure concerns Lucy Sprague Mitchell’s plan to investigate F. Matthias Alexander’s breathing and habit changing procedures.

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4

Jeroen, Staring. "F. M. Alexander, the Use of the Self, and a 1932 Book Review + Discussion in the Yorkshire Post: A Failure to Impact Medical Science." International Journal of Case Studies 4, no. 10 (2015): 26–43. https://doi.org/10.5281/zenodo.3529919.

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In 1931, Frederick Matthias Alexander, founding father of the Alexander Technique, who in 1894 began a career as a full-time stage artist and elocution and breathing teacher launched his book The Use of the Self explaining the history of his discovery of what he called a ―primary control of the psycho-physical mechanisms‖ (Alexander, 1932c). This case study critically describes the chronicle of Alexander‘s claim, notably citing the full text of a yet unreferenced letter to the editor of the Yorkshire Post written by Alexander in reaction to a book review by the young journalist Charles Davy. The case study shows how Alexander and his followers developed a strategy of self-sought isolation

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5

ÖNAL, M. Ayça. "A Research on the Use of the Alexander Technique in Flute Education Given in the Music Department and Music Teaching Departments of Faculties of Fine Arts in Turkey." International Education Studies 15, no. 4 (2022): 67. http://dx.doi.org/10.5539/ies.v15n4p67.

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The Alexander Technique is a method that emerged as a result of Actor Frederick Matthias Alexander&rsquo;s work on himself to solve the problems he had with his voice during his performance. This technique, which was effective for himself, rapidly became widespread because it was tried by different people over time and positive results were obtained. Today, it is effective not only for music but also for people from different disciplines to use their bodies more comfortably and thus have a natural posture. In this sense, in the study, ten different instructors working in various locations were selected as the study group to reach the findings related to the use of the Alexander Technique and the results of the technique in flute lessons given in the music departments of higher education institutions in Turkey. Framed by the researcher, interview questions with expert opinions were asked, and the data obtained were tabulated and presented with the content analysis method, one of the qualitative research methods.

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Jeroen, Staring. "Frederick Matthias Alexander, Born 150 Years Ago, on January 20, 1869. A Fierce Comment Regarding Interpretations of Alexander's Texts by Alexander Technique Teachers." International Journal of Case Studies 7, no. 12 (2018): 107–15. https://doi.org/10.5281/zenodo.3545139.

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This case study investigates F. M. Alexander’s newly coined phrase “the true primary movement in each and every act,” as well as the interpretations of this neologism by Alexander Technique teachers and a wellknown Alexander follower who wrote the first biography of Alexander. The case study further discusses the fact that Alexander was of the opinion that his method(s) constitute applied “race culture,” that is, applied eugenics

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Jeroen, Staring. "Stories about New Avenues to Do Research, Part III." International Journal of Case Studies (ISSN Online 2305-509X) 09, no. 04 (2020): 19–25. https://doi.org/10.5281/zenodo.4905772.

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This (third and final part of a three-part) case study loosely examines the results of several new ways of doing research — through sites such as Amazon.com, Ebay.com, Google.com, PriceMinister.com, Trove.nla.gov.au/newspaper, Used.addall.com, and Worldcat.org.

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8

Anders, Philipp, Philipp Erwin Seegerer, Katja Lingelbach, et al. "Abstract 3351: From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC." Cancer Research 85, no. 8_Supplement_1 (2025): 3351. https://doi.org/10.1158/1538-7445.am2025-3351.

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Abstract Background: Antibody-drug conjugates (ADCs) targeting trophoblast cell-surface antigen 2 (TROP-2) and hepatocyte growth factor receptor (cMET) are promising therapies for non-small cell lung cancer (NSCLC). However, their clinical application requires robust and rapid biomarker evaluation that addresses expression heterogeneity and avoid interobserver variability. Current approaches based on pathologist assessments are limited by subjectivity and scalability. This study aimed to develop a generalizable AI model for ADC biomarker evaluation, trained on TROP-2 and inferred on cMET, to validate its adaptability across markers. Additionally, the model’s performance was compared with expert pathologists to assess its clinical utility. Finally, biomarker prevalence in the two main NSCLC subtypes, namely adenocarcinoma (LUAD) and squamous cell carcinoma (LUSC), was evaluated. Methods: We collected a bicentric real-world sample group of 1142 patients with resected NSCLC from the Charité, Berlin and the University Hospital Cologne. For tissue microarray construction, two 1.5-mm tissue cores were punched from formalin-fixed and paraffin-embedded tumor blocks. Immunohistochemical staining for TROP-2 and cMET was performed and sections were scanned for AI-based analysis. The AI pipeline comprised three models: a cell detection model for the identification of cells, a cell classification model for the differentiation of tumor and other cells, and an expression scoring model for membranous biomarker quantification. The model was trained on TROP-2, and subsequently inferred on cMET, thus enabling an evaluation of cross-marker generalization. Five pathologists with varying levels of expertise manually evaluated a representative subset, using H-scoring. Finally, the results were compared with those yielded by the AI model. Results: The expression scoring model achieved a macro-averaged F1 score of 94% for TROP-2 and 91% for cMET. Moreover, the model demonstrated excellent concordance with expert pathologists (TROP-2: 93%; cMET: 95% average pair-wise Pearson correlation). TROP-2 overexpression was significantly higher in LUSC (mean H-score: 154.67) than LUAD (mean H-score: 86.57), while cMET showed the opposite trend (mean H-score LUAD: 59.52; LUSC: 25.68). Conclusion: This study highlights the potential of AI models to address key challenges in ADC biomarker evaluation, including expression heterogeneity, interobserver variability, and reduction in time expenditure. By successfully generalizing between TROP-2 and cMET, the model demonstrates adaptability and scalability for broader clinical applications. These findings pave the way for integrating AI into clinical workflows, improving patient stratification, and optimizing ADC therapy selection. Future efforts will focus on expanding this approach to additional biomarkers and validating its utility in prospective clinical trials. Citation Format: Philipp Anders, Philipp Erwin Seegerer, Katja Lingelbach, Suhas Pandhe, Sandip Ghosh, Cornelius Böhm, Stephan Tietz, Rosemarie Krupar, Lars Tharun, Marie-Lisa Eich, Julika Ribbat-Idel, Verena Aumiller, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens-Carsten Rückert, Reinhard Büttner, David Horst, Maximilian Alber, Lukas Ruff, Frederick Klauschen, Gabriel Dernbach, Simon Schallenberg. From bench to bedside: generalizable AI model for ADC biomarker evaluation in NSCLC [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 3351.

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9

Schallenberg, Simon, Gabriel Dernbach, Sharon Ruane, et al. "Abstract 5222: AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients." Cancer Research 84, no. 6_Supplement (2024): 5222. http://dx.doi.org/10.1158/1538-7445.am2024-5222.

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Abstract Background: Despite the rapidly increasing number of new targeted and immunotherapeutic options over the past two decades, the prognosis of patients with NSCLC, even with early-stage tumors, is still poor and novel biomarkers are needed to better stratify patients in terms of survival and treatment response. A novel approach is to gain a holistic understanding of the cellular composition and formation of the tumor microenvironment (TME). Therefore, we developed a miF-based, AI-driven approach for spatially resolved TME characterization at the cellular level and used this to successfully predict clinical outcome. Methods: We assembled a large bicentric real-world sample group of 1168 patients with resected NSCLC from the Charite and the University Hospital Cologne. For tissue microarray construction, four 1.5 mm tissue cores were punched from each formalin-fixed and paraffin-embedded tumor block. Sections were stained with a 12-plex IF panel followed by H&E staining. All stains were scanned and co-registered with single cell accuracy. Next, we trained a H&E-based tissue segmentation model to detect the different tumor regions: carcinoma, stroma, and necrosis. In addition, we developed a nucleus-based cell detection model, and 12 cell classification models to categorize each detected cell by single-miF channels. Different cell phenotypes were derived from the marker-specific cell classifications. Finally, we trained a model on the Charité cohort using the spatially resolved cell readouts, spot-wise phenotype log-density, co-clustering of marker expression, and frequency of co-occurrence of marker expression through Delaunay triangulation, to predict patient survival on the Cologne cohort. Results: The tissue segmentation model achieved a macro averaged F1 score of 92%. The cell detection model identified a total of 53 million cells that were classified marker-wise with an F1 score of at least 95% on hold-out data. Our final prediction model identified a stable spatially resolved cell signature, consisting of 10 different characteristic cell neighborhood niches, which could be used to predict overall patient survival. The model trained on the Charite cohort was validated with the Cologne cohort and achieved a high performance (C-score of 71). In comparison, the UICC8 stage and the immunoscore (CD20+CD3+/carcinoma cell ratio), which were used as a baseline, achieved C-scores of 63 and 54, respectively. Conclusions: The combination of our large real-world clinical cohort, multiplex panel, and automated AI approach enabled a broad spatially resolved exploration of the TME in NSCLC at single cell resolution. Our model identified a specific cell neighborhood signature predictive of patient survival outperforming the commonly used prognostic scores, UICC8 stage and immunoscore. This allows for an improved patient stratification with potential implication for therapy selection. Citation Format: Simon Schallenberg, Gabriel Dernbach, Sharon Ruane, Cornelius Böhm, Lukas Ruff, Kai Standvoss, Sandip Ghosh, Marco Frentsch, Mihnea Dragomir, Rebecca Fritz, Ines Koch, Corinna Friedrich, Il-Kang Na, Sabine Merkelbach-Bruse, Alexander Quaas, Nikolaj Frost, Kyrill Boschung, Winfried Randerath, Georg Schlachtenberger, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens Rückert, Reinhard Büttner, Angela Vasaturo, David Horst, Maximilian Alber, Frederick Klauschen. AI-driven, mIF-based cell-omics reveals spatially resolved cell signature for outcome prediction in NSCLC patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5222.

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Schallenberg, Simon, Gabriel Dernbach, Mihnea Dragomir, et al. "Abstract 5221: TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading." Cancer Research 84, no. 6_Supplement (2024): 5221. http://dx.doi.org/10.1158/1538-7445.am2024-5221.

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Abstract Background: Thyroid transcription factor 1 (TTF-1) is expressed in 70% to 80% of lung adenocarcinomas (LUAD). Several papers revealed that TTF-1 expression is associated with better patient outcomes independent of the tumor stage. However, it is unknown whether the prognostic impact of TTF-1 only results from a different growth pattern (tumor grading) or is independently associated with a biologically more aggressive phenotype. Thus, we analyzed a large bi-centric cohort of LUAD to assume the true prognostic value of TTF-1 in relation to the tumor grade. Methods: We collected a large, real-life cohort of 447 patients with completely resected LUAD from two large-volume German lung cancer centers. TTF-1 status, evaluated by IHC, and tumor grading were correlated with clinical, pathologic, and molecular data, as well as patient outcomes. Kaplan-Meier curves were used for comparison of TTF-1 status and different tumor grades in terms of the DFS. The impact of TTF-1 was measured by univariate and multivariate Cox regression. Finally, a causal graph analysis was performed to identify and account for potential confounders to improve the statistical estimation of the predictive power of TTF-1 expression for DFS in comparison to the tumor grade. Results: Kaplan-Meier curves revealed that TTF-1 positivity is associated with longer DFS independent of tumor grade, whereas a strong association of DFS with the tumor grade is observed only in TTF-1-positive patients. In univariate analysis, TTF-1 positivity was associated with significantly longer DFS (median log HR -0.83 [-1.43; -0.20]; p=0.018), whereas higher tumor grade showed a non-significant association with shorter DFS (median log HR 0.30 [-0.58; 1.60]; p=0,62 for G1 to G2 and 0.68 [-0.24; 1.89]; p=0,34 for G2 to G3). In multivariate analysis, TTF-1 positivity resulted in a significantly longer DFS (median log HR -0.65 [-1.13; -0.09]; p=0.05) independent of all other parameters, including tumor grade. Applying the adjustment sets suggested by the causal graph analysis, the superiority of TTF-1 (median log HR -0.86 [-1.25; -0.41]) over tumor grade (median log HR 0.31 [-0.32; 1.30]/0.61[-0.07; 1.65]) in terms of prognostic power was confirmed. Conclusion: This study draws three important conclusions: Firstly, it indicates that the prognostic power of tumor grade is limited to TTF-1-positive patients. Secondly, it underlines the independent prognostic value of TTF-1 expression for DFS regardless of tumor grade. Finally, our analyses reveal that the effect size of TTF-1 surpasses that of tumor grade. To transfer the results directly into the clinical area, we recommend distinguishing between TTF-1-positive and TTF-1-negative LUADs in the pathological report. Tumor grading should only be applied to TTF-1-positive LUADs (TTF-1+/G1-3). TTF-1-negative LUADs should either not be graded or always be classified as high-grade (TTF-1-/G3). Citation Format: Simon Schallenberg, Gabriel Dernbach, Mihnea Dragomir, Georg Schlachtenberger, Kyrill Boschung, Corinna Friedrich, Kai Standvoss, Lukas Ruff, Philipp Anders, Christian Grohe, Winfried Randerath, Sabine Merkelbach-Bruse, Alexander Quaas, Matthias Heldwein, Ulrich Keilholz, Khosro Hekmat, Jens Rückert, Reinhard Büttner, David Horst, Frederick Klauschen, Nikolaj Frost. TTF-1 status in early-stage lung adenocarcinoma is an independent predictor of relapse and survival superior to tumor grading [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 5221.

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Books on the topic "Frederick Matthias Alexander"

1

Evans, J. A. Frederick Matthias Alexander: A family history. Phillimore, 2001.

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Staring, Jeroen. Frederick Matthias Alexander (1869-1955): Een beknopte biografie. Integraal (Werkgroep Integrerende Wetenschapsbeoefening), 1988.

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Caro, Ness, ed. Secrets of Alexander technique. Dorling Kindersley, 2000.

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Pierce, Jones Frank, ed. Freedom to change: The development and science of the Alexander technique. 3rd ed. Mouritz, 1997.

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Barlow, Wilfred. The Alexander technique: How to use your body without stress. Healing Arts Press, 1990.

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Barlow, Wilfred. The Alexander technique: How to use your body without stress. Healing Arts Press, 1990.

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Nijmegen, Radboud Universiteit, ed. Frederick Matthias Alexander 1869-1955: The origins and history of the Alexander Technique : a medical historical analysis of F. M. Alexander's life, work, technique and writings : een wetenschappelijke proeve op het gebied van de medische wetenschappen. Radboud Universiteit, 2005.

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Evans, T. V. Frederick Matthias Alexander: A Family History. Phillimore, 2001.

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F.M.: The life of Frederick Matthias Alexander : founder of the Alexander technique. Little, Brown, 2004.

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Bloch, Michael. F.M.: The Life of Frederick Matthias - Founder of the Alexander Technique. Little, Brown Book Group Limited, 2011.

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Book chapters on the topic "Frederick Matthias Alexander"

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Ernst, Edzard. "Frederick Matthias Alexander—Inventor of the Alexander Technique." In Bizarre Medical Ideas. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-55102-4_7.

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