Bibliographic record
Abstract
During neuronal development, neuron cells become asymmetric and form molecularly and functionally distinct areas. These areas are the cell body, a single axon and multiple dendrites. Initiation and maintenance of neuronal asymmetry strongly relies on cytoskeleton dynamics and rearrangements, as well as the polarized distribution of cellular components to the right neuron compartments. The role of the cytoskeleton in neuronal polarity has captured the attention of scientist in the past decade, resulting in notable discoveries of molecular regulatory pathways controlling establishment and maintenance of neuronal polarity. The aim of this thesis is to provide an overview of the current knowledge on neuronal polarity. In detail I will focus on the interaction of motor-proteins, kinases and cargo receptors and their role in achieving and maintaining neuronal polarity. In terms of neuronal polarity I will distinguish between cytoskeletal dynamics, organization and polarized trafficking with a focus on C. elegans. The main regulatory proteins involved in these processes are the kinases CDK-5 and UNC-51/ULK1, the motor proteins UNC-116/KHC and UNC-104/Kif1A, the “cargo” receptor proteins UNC-33/CRMP-2, UNC-16/JIP3 and UNC-14, as well as the akyrin-like protein UNC-44. The main problem I identified while summarizing the current knowledge, are ambiguous results of different labs. Especially the comparability of experiments conducted in different neurons and/or various stages of development is not straight forward.
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".