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Tracking eye movements to uncover the nature of visual-linguistic interaction in static and dynamic scenes

2008· dissertation· en· W30224377 on OpenAlexfundno aff
Caroline Van de Velde

Bibliographic record

VenueCancer Research · 2008
Typedissertation
Languageen
FieldComputer Science
TopicGaze Tracking and Assistive Technology
Canadian institutionsnot available
FundersNational Center for Advancing Translational SciencesConcordia UniversityGeorgia Clinical and Translational Science Alliance
KeywordsVerbNounLinguisticsPsychologySentenceTransitive relationReferentArtificial intelligenceNatural language processingCognitive psychologyComputer scienceMathematics

Abstract

fetched live from OpenAlex

Cancer immunotherapies that remove checkpoint restraints on adaptive immunity are gaining clinical momentum but have not achieved widespread success in breast cancers, a tumor type considered poorly immunogenic and which harbors a decreased presence of tumor-infiltrating lymphocytes. Approaches that activate innate immunity in breast cancer cells and the tumor microenvironment are of increasing interest, based on their ability to induce immunogenic tumor cell death, type I IFNs, and lymphocyte-recruiting chemokines. In agreement with reports in other cancers, we observe loss, downregulation, or mutation of the innate viral nucleotide sensor retinoic acid-inducible gene I (RIG-I/<i>DDX58</i>) in only 1% of clinical breast cancers, suggesting potentially widespread applicability for therapeutic RIG-I agonists that activate innate immunity. This was tested using an engineered RIG-I agonist in a breast cancer cell panel representing each of three major clinical breast cancer subtypes. Treatment with RIG-I agonist resulted in upregulation and mitochondrial localization of RIG-I and activation of proinflammatory transcription factors STAT1 and NF-κB. RIG-I agonist triggered the extrinsic apoptosis pathway and pyroptosis, a highly immunogenic form of cell death in breast cancer cells. RIG-I agonist also induced expression of lymphocyte-recruiting chemokines and type I IFN, confirming that cell death and cytokine modulation occur in a tumor cell-intrinsic manner. Importantly, RIG-I activation in breast tumors increased tumor lymphocytes and decreased tumor growth and metastasis. Overall, these findings demonstrate successful therapeutic delivery of a synthetic RIG-I agonist to induce tumor cell killing and to modulate the tumor microenvironment <i>in vivo</i> <b>Significance:</b> These findings describe the first in vivo delivery of RIG-I mimetics to tumors, demonstrating a potent immunogenic and therapeutic effect in the context of otherwise poorly immunogenic breast cancers. <i>Cancer Res; 78(21); 6183-95. ©2018 AACR</i>.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.856
Threshold uncertainty score0.556

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.038
GPT teacher head0.448
Teacher spread0.410 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2008
Admission routes1
Has abstractyes

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