Добавил:
Sekretar
kiopkiopkiop18@yandex.ru
t.me/Prokururor I Вовсе не секретарь, но почту проверяю
Опубликованный материал нарушает ваши авторские права? Сообщите нам.
Вуз:
Предмет:
Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5553_Библиотеки_им_академика_М_И_Перельмана
.pdf
Pasquinelli, Matteo (2023) The Eye of the Master: A Social History of
Artificial Intelligence. New York: Verso Books.
Penuel, H., F. Corbin, and Reginald G. Bickford (1955) “Studies of the
Electroencephalogram of Normal Children: Comparison of Visual and
Automatic Frequency Analyses.” Electroencephalography and Clinical
Neurophysiology 7, no. 1: 15– 28. https://doi.org/10.1016/0013-4694(55)9005
6-0.
R
Rosenblith, W. A. (1954) “Discussion of Correlation Analysis: In 3rd
International Congress.” Electroencephalography and Clinical
Neurophysiology Supplement 4: 46.
Roy, Yannick, Hubert Banville, Isabela Albuquerque, Alexandre Gramfort,
Tiago H. Falk, and Jocelyn Faubert (2019) “Deep Learning-Based
Electroencephalography Analysis: A Systematic Review.” Journal of Neural
Engineering 16, no. 5: 051001. https://doi.org/10.1088/1741-2552/ab260c.
S
Schabacher, Gabriele (2023) “AI and the Work of Patterns: Recognition
Technologies, Classification, and Security.” In -Kritik / Critique, edited by
Andreas Sudmann, Anna Echterhölter, Markus Ramsauer, Fabian Retkowski,
Jens Schröter, and Alexander Waibel, 1st edition, 6: 123 – 54. Bielefeld:
transcript. https://doi.org/10.14361/9783839467664-008.
Schirmann, Felix (2014) “‘The Wondrous Eyes of a New Technology’: A
History of the Early Electroencephalography () of Psychopathy,
Delinquency, and Immorality.” Frontiers in Human Neuroscience 8 (April):
232. https://doi.org/10.3389/fnhum.2014.00232.
Selfridge, Oliver G. (1955) “Pattern Recognition and Modern Computers.”
In Proceedings of the March 1–3, 1955, Western Joint Computer
Conference, 91– 93. AFIPS ’55 (Western). New York, NY: Association for
Computing Machinery. https://doi.org/10.1145/1455292.1455310.
Selfridge, Oliver G., and Ulric Neisser (1960) “Pattern Recognition by
Machine.” Scientific American 203, no. 2: 60– 68. https://doi.org/10.1038/sci
entificamerican0860-60.
Squires, Matthew, Xiaohui Tao, Soman Elangovan, Raj Gururajan, Xujuan
Zhou, U Rajendra Acharya, and Yuefeng Li (2023) “Deep Learning and
Machine Learning in Psychiatry: A Survey of Current Progress in Depression
Detection, Diagnosis and Treatment.” Brain Informatics 10, no. 1: 10. https://d
oi.org/10.1186/s40708-023-00188-6.
Stevens, Hallam (2017) “A Feeling for the Algorithm: Working Knowledge
and Big Data in Biology.” Osiris 32, no. 1: 151–74. https://doi.org/10.1086/69
3516.
U
Uhr, Leonard (1963) “‘Pattern Recognition’ Computers as Models for Form
Perception.” Psychological Bulletin 60, no. 1: 40–73. https://doi.org/10.1037/
h0048029.
Üstün, Berkay (2024) “Patterns before Recognition: The Historical
Ascendance of an Extractive Empiricism of Forms.” Humanities and Social
Sciences Communications 11 (January): 1– 9. https://doi.org/10.1057/s4159
9-023-02574-1.
V
Vidal, F. (2009) “Brainhood, Anthropological Figure of Modernity.” History of
the Human Sciences 22, no. 1 (February 1): 5–36. https://doi.org/10.1177/09
52695108099133.
W
Walter, William Grey (1942) “Electro-Encephalography in Cases of Mental
Disorder.” Journal of Mental Science 88 (370): 110– 21, https://doi.org/10.11
92/bjp.88.370.110.
Walter, William Grey (1943a) “An Automatic Low Frequency Analyser”
Electronic Engineering 16 (June): 9 –13.
Walter, William Grey (1943b) “An Improved Automatic Low Frequency
Analyser” Electronic Engineering 16 (November): 236 – 40.
Walter, Grey William (1950) “Technique – Interpretation.” In
Electroencephalography: A Symposium on Its Various Aspects, edited by Sir
Denis Hill, Geoffrey Parr, and William A. Cobb. Paulton and London:
MacDonald & Co.
Wan, Zhijiang, Jiajin Huang, Hao Zhang, Haiyan Zhou, Jie Yang, and Ning
Zhong (2020) “HybridNet: A Convolutional Neural Network for
Feature Learning and Depression Discrimination.” Access 8 (February):
30332– 42. https://doi.org/10.1109/ACCESS.2020.2971656.
Widge, Alik S., M. Taha Bilge, Rebecca Montana, Weilynn Chang, Carolyn
I. Rodriguez, Thilo Deckersbach, Linda L. Carpenter, Ned H. Kalin, and
Charles B. Nemeroff (2019) “Electroencephalographic Biomarkers for
Treatment Response Prediction in Major Depressive Illness: A Meta-Analysis.”
The American Journal of Psychiatry 176, no. 1: 44– 56. https://doi.org/10.11
76/appi.ajp.2018.17121358.
Wiener, Norbert (1956) I Am a Mathematician: The Later Life of a
Prodigy; an Autobiographical Account of the Mature Years and Career of
Norbert Wiener and a Continuation of the Account of His Childhood in Ex-
Prodigy. New York: Doubleday.
Wiener, Norbert, and Mary A. Brazier (1954) “Discussion of Correlation
Analysis: 3rd International Congress.” Electroencephalography and
Clinical Neurophysiology Supplement 4: 41– 44.
EBSCOhost: eBook Collection (EBSCOhost) printed on 12/28/2025 12:59:26 PM UTC via
HERZEN STATE PEDAGOGICAL UNIVERSITY OF RUSSIA. All use subject to
https://www.ebsco.com/terms-of-use.
Imaginary Imaging: Representing
the Normal and the Pathological in
a Vision of Cell-Based Interceptive
Biomedicine
Cornelius Borck and Robert Meunier
1
Introduction: Visualizing a Medicine of the Future and Visual
Studies of Science
Images are important for imagination. They help people to create a
shared vision of a future toward which they can agree to work. One
such vision was recently expressed by the LifeTime Initiative, a
multicenter, international research consortium (Rajewsky et al.
2020). The idea is as straightforward as it sounds futuristic: a
perfect medicine that does not wait for a disease to develop fully,
once it has affected the body, perhaps even destroyed an organ, and
made the patient suffer. In “interceptive medicine,” visionary medicine
would anticipate the course of events and intervene as early as
possible to prevent disease manifestation. This perfected medicine
would not have to act by drastic means, such as extirpating an
affected organ or shutting off a function, but it would guide the
system back on course by gently nudging some processes in
appropriate directions.
In this chapter, we will analyze in detail one image that forms the
core of the LifeTime Initiative’s imaginary vision of interceptive
medicine as part of a broader “sociotechnical imaginary” (Jasanoff
2015, 4) of precision medicine. We show how a careful
arrangement of visual metaphors and references helps to naturalize
this vision. We support our analysis by contextualizing the image in
the history of medical visualization and infographics more generally.
We thereby aim to contribute to the visual studies of science by
exemplifying how the interrogation of this kind of rhetorical image
facilitates a critical assessment of the desirability and feasibility of
1
FIG . 1
biomedical projects such as LifeTime and the accompanying changes
in the understanding of pathological phenomena.
FIG. 1 The image in question, as it appears in Rajewsky et al. (2020, 379, fig. 1).
The image was created by the designer Johannes Richers in collaboration with the
artist Karin Kimel.
LifeTime can be seen as part of a larger development in
biomedicine built on advances in genetic and epigenetic medicine,
multi-omics, and Big Data technologies, which fuel the expectation
that prediction and treatment regimens can be individualized (Topol
2014). This larger vision today is commonly referred to as “precision
medicine” and seen as replacing standard, disease-specific care by
treatment tailored to individual disease trajectories. Advertised by the
National Institutes of Health and announced by President Barack
Obama in 2015, precision medicine has been described as naming a
promissory agenda (Blasimme and Vayena 2016; Meunier and
Herzog 2023), though meanwhile it has been implemented, at least
partially, in cancer care as precision oncology (Tsimberidou et al.
2020). Precision medicine creates shared imaginations of a
biomedical future, and imaginary images are central in these
attempts, precisely because photographs or visual illustrations are
not yet available.
Focusing on the image in question, we further the discussion
on functions of visualizations in scientific practice by exploring one of
the multiple roles of scientific images in contemporary knowledge
FIG . 1
society. The Western tradition has always prioritized words over
images and linked theory with language (Baigrie 1996). The
philosophy of science especially initially neglected and distrusted
images, against the proverbial “a picture is worth a thousand words,”
as mere illustrations of ideas and concepts. History of science and
science and technology studies (), by contrast, noticed the pivotal
role of images and visualization in modern and contemporary scientific
practice. Bruno Latour praised visual representations as “immutable
mobiles” (Latour 1986, 7), and Karin Knorr-Cetina has suggested to
speak of “viscourses” in analogy to discourses (Knorr-Cetina 1999),
to name just two voices from the field of visual studies of science that
flourished at the turn of the century (Lynch and Woolgar 1990; Kemp
1997; Jones and Galison 1998; Heintz and Huber 2001; Pauwels
2006; Coopmans et al. 2014). Compared to discourse analysis or the
toolbox of approaches in , the study of visualizations has not (yet)
coalesced into a branch of its own with properly established methods.
Our chapter builds on and extends this earlier scholarship by
investigating a case of visionary science.
The shaping of expectations and their dynamics have become a
topic in (Konrad et al. 2017). In promissory science (Hedgecoe
2004), visualization relates less to scientific results but is part and
parcel of the attempt to promote the project and thereby to make it
happen. Describing the image from the LifeTime Initiative as a kind of
infographic designed not to visualize data but to convey an idea of
what data makes possible, we will argue that this image not only
illustrates or explains the project but presents it as realistically
achievable. Section will introduce the image, the LifeTime Initiative,
and its scientific background. Section will zoom in on the different
parts and panels of the image in the form of a close reading, focusing
on visual metaphors and references to other types of images implicit
in the design of the image. Section complements this account by
zooming out, offering a historicizing reading of the visual rhetoric, and
contextualizing it in larger traditions of medical visualization.
We conclude by positioning the image in question in the context of
infographics and highlight again the significance of such types of
images for the visual studies of science. Close reading and historical
contextualization together reveal how the image naturalizes the vision
of interceptive medicine, which, by monitoring the normal, aims to
make the pathological stand out as its deviation. Our two types of
analysis enable a critical assessment of the image and the imaginary
to which it belongs. Infographics visualize abstract information and
operate by a careful blend of attention to detail and conventions. In
our case, the image introduces and stipulates single cell lines as the
new locus of the pathological. But when does an individual response
turn from being a sign of active resistance into a biomarker of pre-
disease? In light of Georges Canguilhem’s (1991) analysis of the
normal and the pathological, it remains doubtful that an organism’s
path can be determined as steering toward the pathological before
the manifestation of a disease, because life and the living are
characterized by varying forms of setting norms rather than passively
obeying them or failing to do so. The question of how this argument
would apply to single cell trajectories transgresses the horizon of our
paper but highlights how a critical assessment of the desirability and
feasibility of a project like the one proposed by the LifeTime Initiative
can be achieved through the analysis of imaginary images in science.
2
The Image in Question and Its Imaginary
The image on which we focus in this chapter appeared in a
Perspective article in the journal Nature titled “LifeTime and Improving
European Healthcare through Cell-Based Interceptive Medicine”
(Rajewsky et al. 2020). The LifeTime Initiative is a large European
research community, involving scientists from almost 100 institutions,
and coordinated by Nikolaus Rajewsky (Delbrück Center, Berlin) and
Geneviève Almouzni (Institut Curie, Paris). It was set up by
researchers in 2018 to articulate a set of shared goals in biomedical
research and to acquire large-scale funding. The Initiative garnered
support from eighty companies and won a European Commission
Coordination and Support Action award. As a result, a Strategic
Research Agenda () document was developed and published on
the Initiative’s website. The Nature article can be seen as a
summary of the for the scientific community. The image was
created by the professional freelance designer Johannes Richers,
who specializes in visual science communication, in collaboration with
the artist Karin Kimel.
The LifeTime Initiative was a preparatory action with the aim of
establishing a Future and Emerging Technologies Flagship project
within the Horizon 2020 funding agenda. While this attempt failed,
FIG . 1
2
3
the vision it promoted is further pursued by its proponents, in
particular Nikolaus Rajewsky, through activities such as the “LifeTime
Conference 2.0” in 2021, the initiation of the “Berlin Cell Hospital,”
an Einstein Center for Early Disease Interception, the “Virchow 2.0”
proposal for funding by the German Federal Ministry of Education
and Research, and a project called “ImmunoPreCept,” which has
been selected for the German Cluster of Excellence program.
In the context of the recent history of the biomedical sciences, the
notion of cell-based interceptive medicine promoted by the LifeTime
Initiative and the follow-up enterprises can be seen as part of the
precision medicine imaginary (Erikainen and Chan 2019; Meunier and
Herzog 2023). Sheila Jasanoff characterizes sociotechnical
imaginaries as “collectively held, institutionally stabilized, and publicly
performed visions of desirable futures, animated by shared
understandings of forms of social life and social order attainable
through, and supportive of, advances in science and technology”
(Jasanoff 2015, 4). The imaginary of personalized medicine emerged
in the context of the Human Genome Project () in the 1990s and
gained currency when nucleic-acid sequencing became faster and
cheaper through next-generation sequencing technologies around
2005 (Tabery 2023). Under the assumption that most diseases have
at least some genetic components, the possibility to sequence
individual genomes suggested an assessment of individual risk, which
would in turn enable preventive measures. It was further assumed
that the identification of genes associated with disease would lead to
the discovery of pathogenic mechanisms and hence new drug
targets. In addition, and building on the insight that individuals react
differently to drugs, pharmacogenomics was promoted based on the
promise to determine which drug will work best for which patient, thus
avoiding unnecessary side effects (Hedgecoe 2004).
The vision of LifeTime, however, significantly differs from this earlier
version of genomic medicine as well as from classical biomedicine.
First, instead of ascribing a certain disease risk to a person based on
their genomic markers, multi-omics cellular biomarkers are envisioned
to capture a disease that has already begun. Second, unlike classical
screening regimes that can identify diseases shortly before they
become symptomatic, the envisioned monitoring seeks to catch
diseases closer to their origins. Third, the data-intensive approach is
no longer fixated on the genome and inherited risk alleles but
4 5
6
7
8
acknowledges environmental factors that can push cells on a
pathological path, mediated by epigenetic mechanisms. Monitoring
cells to detect pathogenesis at the earliest stage is agnostic about
the genetic or epigenetic nature of the causes.
How does figure 1 promote this vision? Presumably, even a
reader/viewer well-versed in the visual language of contemporary
biomedicine will be left perplexed by the image. At first sight, it is
reminiscent of many common types of images, from data plots to
mechanistic cartoons, to anatomical drawings and high-resolution
microscopic images; yet, at the same time, it cannot be directly
identified as belonging to any one of these. Additionally, the three
panels of the image, , , and , appear to stand in a
straightforward relation of magnification, yet they exhibit very different
visual styles. The puzzling character of the image results from the fact
that it is not a functional image in the sense of the above-mentioned
image types that are typically used to present concrete data, models,
or other information. The image in question is not related to any
specific experiment or data set, nor does it summarize a well-defined
area of knowledge in a synoptic manner. Instead, it expresses a
scientific vision, and for this purpose it invokes a visual language
associated with the fields in which this vision is rooted. The image
alludes to various types of images that are associated with biological
concepts underpinning the vision and would be used to present the
findings once the envisioned research is established. It is through this
heterogeneous visual language that figure 1 communicates the idea
that underlies the biomedical vision. In terms of its communicative
aims, it might be said to be a rhetorical image.
The envisioned research and results would not constitute an
entirely new way of doing biomedicine, hence the reference to well-
established types of graphs and models. However, the vision
suggests a decisive threefold shift: a shift in the focus of research
activities that is motivated by a shift in the understanding of disease
and entails a shift in the way healthcare would be organized and
delivered. At the core of both investigation and intervention is the
move from the clinical manifestation of disease, or even the pre-
clinical signs captured by classical screening regimes, to the very
origin of disease in the body. The question then becomes: How does
figure 1 communicate this shift in perspective on disease? To address
this question, the following section dissects the structure and
elements of the image.
3
Zooming In: Representing Concepts through Visual Metaphors
and References
Many images in biomedical research in contemporary life sciences
are artificial in the sense that they are not direct inscriptions but
render data according to the technical and conventional specifications
of a particular visualization style or diagrammatic code. The object of
investigation is not the specimen (animal, plant, organ, etc.) as it
simply appears to the human eye but a particular aspect that
escapes easy recognition—because of minuteness, distance, lack of
an appropriate sensory organ, or invisibility for fundamental reasons
—but is grasped through data. Scientific images in the life sciences
then have to “naturalize” their artificiality by the standardization of
visual formats and predefined frames of reference. “A central
paradox of biomedical illustration is that a well-crafted image is one
that appears not to have been crafted at all,” as scholar and
media theorist Drew Danielle Belsky stipulated the problem. “The
expert labor of medical illustrations lies in naturalizing their own
artefacts” (Belsky 2022, 219, 223). From microscopes to graphic
recording, X-rays, crystallography, immunostaining, optical recording,
and color coding, the development of research technologies went
hand in hand with the construction, implementation, and
standardization of visualization technologies and strategies (Hentschel
2014; Borck 2016; Borck 2017). While figure 1 does not visualize a
specific data set, it makes reference to many types of data
visualizations and associated data-generating technologies to indicate
the scientific basis of the envisioned research. Furthermore, its
design relies on visual metaphors familiar from the context of clinical
training and the popularization of (patho)physiological knowledge, to
express the conceptual frame in which these data-intensive research
strategies would play out. Together, these metaphors and references
work to facilitate the imaginative performance of the image. They
serve as strategic ingredients for making figure 1, despite its
promissory nature, scientific and not fantastic. This section first turns
to the visual metaphors in the overall design and then to the visual
components that constitute references to representations of cellular
processes.
Visual Metaphors
Following Rudolf Virchow’s (1821–1902) idea that diseases follow a
natural path but in the form of a deviation from the healthy track at
some specific bifurcation (see below), figure 1 visualizes the
theoretical construct of disease trajectories by tracing such
bifurcations. Obviously, a point of bifurcation cannot be found as a
physical intersection in the body, not even with some future
technology’s analytic superpowers, because it is the metaphorical
representation of the theoretical concept of disease as a deviating
path. Furthermore, the image imports a context of corporeal, bodily
reality by embedding a repertoire of visual strategies with long-
standing traditions for focusing on clinical and therapeutical relevance,
as we show in Section .
In the image, this corporeal, bodily reality is invoked by the three-
dimensional layout of panel and further stabilized by the zooming-in
on panels and , alluding to a navigation through the three-
dimensional body via different steps of magnification. However, at
close inspection, it becomes apparent that each panel exhibits
different representational modes rather than an increased
magnification, with panel culminating in the “magnification” of the
concept of disease trajectory. Without the textual labels
accompanying the graphical elements, panel would be read as a
blood vessel branching off into two smaller ones with small
corpuscles moving in them like erythrocytes. This reading comes so
“naturally” that it paradoxically stabilizes the alternative meaning that
is intended here and only decipherable by the labels “disease
trajectory” versus “health trajectory.” Zooming in conflates as
purposefully as skillfully space with time, cell with organism, anatomy
and histology with physiological processes. In the pictorial rhetoric the
panels appear as snippets from some organic tissues, but the logic of
the representational plane is not one of spatial relations but
conceptual distances between “healthy” and “diseased.” The tube-like
vessels do not connect organs or body parts but run from earlier
stages of life on the left to later ones on the right and ultimately—
though beyond the frames of the figure—to death.
Соседние файлы в папке Библиотека им академика М.И. Перельмана