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Record W29617352 · doi:10.3390/ijms19041082

DECOUPLED LOUDNESS AND RANGE CONTROL FOR A SOURCE LOCATED WITHIN A SMALL VIRTUAL ACOUSTIC ENVIRONMENT

2004· article· en· W29617352 on OpenAlexaff
William L. Martens

Bibliographic record

VenueInternational Conference on Auditory Display · 2004
Typearticle
Languageen
FieldNeuroscience
TopicHearing Loss and Rehabilitation
Canadian institutionsMcGill University
Fundersnot available
KeywordsBinaural recordingLoudnessComputer scienceRange (aeronautics)Directional soundVirtual realityAcousticsSound localizationSound (geography)Speech recognitionArtificial intelligenceComputer visionEngineeringPhysics

Abstract

fetched live from OpenAlex

Over the past decade, evidence has identified a link between protein aggregation, RNA biology, and a subset of degenerative diseases. An important feature of these disorders is the cytoplasmic or nuclear aggregation of RNA-binding proteins (RBPs). Redistribution of RBPs, such as the human TAR DNA-binding 43 protein (TDP-43) from the nucleus to cytoplasmic inclusions is a pathological feature of several diseases. Indeed, sporadic and familial forms of amyotrophic lateral sclerosis (ALS) and fronto-temporal lobar degeneration share as hallmarks ubiquitin-positive inclusions. Recently, the wide spectrum of neurodegenerative diseases characterized by RBPs functions' alteration and loss was collectively named proteinopathies. Here, we show that TBPH (TAR DNA-binding protein-43 homolog), the <i>Drosophila</i> ortholog of human TDP-43 TAR DNA-binding protein-43, interacts with the arcRNA hsrω and with hsrω-associated hnRNPs. Additionally, we found that the loss of the omega speckles remodeler ISWI (Imitation SWI) changes the TBPH sub-cellular localization to drive a TBPH cytoplasmic accumulation. Our results, hence, identify TBPH as a new component of omega speckles and highlight a role of chromatin remodelers in hnRNPs nuclear compartmentalization.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.839
Threshold uncertainty score0.568

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.033
GPT teacher head0.272
Teacher spread0.239 · 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 designBench or experimental
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

Citations3
Published2004
Admission routes1
Has abstractyes

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