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Record W2466219033 · doi:10.1242/jeb.132555

Well-fed larvae switch ion transport in rectal complex

2015· article· en· W2466219033 on OpenAlexaboutno aff
Kathryn Knight

Bibliographic record

VenueJournal of Experimental Biology · 2015
Typearticle
Languageen
FieldNeuroscience
TopicNeurobiology and Insect Physiology Research
Canadian institutionsnot available
Fundersnot available
KeywordsMalpighian tubule systemHemolymphTubuleLarvaExcretory systemBiologyInsectInstarIon transporterZoologyBotanyMidgutAnatomyKidneyBiochemistryEndocrinology

Abstract

fetched live from OpenAlex

The key to a good larval life is to eat as much as possible, regardless of the havoc wreaked on crops. So, gorging larvae have to balance their fluid intake and losses depending on how moist or dry their diet; which is why some species have evolved a specialised organ for reclaiming fluid from the gut, known as the rectal complex. Michael O'Donnell and Esau Ruiz-Sanchez from McMaster University, Canada, explain that the rectal complex also regulates the levels of Na+ and K+ ions in the insect's haemolymph, which would otherwise fluctuate wildly when the larvae are gorging and growing rapidly. However, little was known about the fine details of fluid transport between the rectal complex and the larva's other excretory organ, the Malpighian tubule. So, O'Donnell and Ruiz-Sanchez teamed up to build a more detailed understanding of the delicate interplay of fluid and ion transport between the two structures in well-fed and hungry larvae.Collecting fourth instar larvae from a lab colony of cabbage looper moths (Trichoplusia ni), the duo first investigated the relative architecture of the Malpighian tubule and rectal complex. They identified the distributions of two different cell types – known as principal (type I) cells and secondary (type II) cells – in the two structures. Then the duo began the incredibly intricate task of measuring ion transport at different locations across the structures using thin ion-selective microelectrodes positioned with computer-controlled micron precision at different locations on both structures.Having discovered that the rectal complex unexpectedly reabsorbs Na+ and K+ ions from the gut and returns them to the haemolymph, O'Donnell and Ruiz-Sanchez also realised that the larvae's ability to transport Na+ and K+ ions varies dramatically – depending on whether or not they are well fed and the location in the rectal complex and Malpighian tubules – despite the apparently identical appearance of many sections of the tubule. In one example, the duo describes how ion transport differs along the section of the gut known as the rectal lead when the larvae are hungry and well fed. They explain that larvae with full guts reabsorb K+ in the distal rectal lead, but when the larvae's guts are empty K+ is secreted in the proximal rectal lead. In contrast, well-fed larvae secrete Na+ ions across the distal rectal lead while reabsorbing the ions across the proximal rectal lead. And when the duo analysed which cell types were involved in ion transport, they found that the type I cells in the section of tubule linking the Malpighian tubule to the rectal complex – known as the ileac plexus – secreted K+ ions. However, they were surprised that the type II cells reabsorbed K+ and Na+ ions, with the type II cells transporting Na+ at twice the rate of the type I cells.O'Donnell and Ruiz-Sanchez admit that they are surprised by the complex pattern of secretion and ion reabsorption and they are also intrigued about the change in direction of ion secretion in different regions of the Malpighian tubule when the larvae are fed. However, they are optimistic that they will be able to learn more about these puzzles using the techniques that they have developed in this study and O'Donnell says, ‘I see 5–10 years of exciting work ahead’.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.093
GPT teacher head0.365
Teacher spread0.272 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2015
Admission routes1
Has abstractyes

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