Bibliographic record
Abstract
抑制性差减杂交技术（SSH）是基于抑制PCR作用的cDNA差减杂交，是在转录水平上研究差异基因表达的技术。该技术在一个循环过程中完成差异表达基因丰度的均等化及目标群体和对照群体中相同基因的去除，从而实现差异表达基因的高度富集。一个典型的SSH过程，可在一个差减杂交过程中将稀有基因序列富集上千倍。本文通过详细分析该技术在植物差异表达基因分离中的应用发现：SSH技术主要用于分离组织特异性表达和诱导型表达的基因。首先，SSH技术广泛应用在分离植物发育过程中不同发育阶段和不同组织器官中的组织特异性表达的基因，为揭示植物生长发育过程的分子机理提供了有效手段；另外，在不同植物中，该技术被大量应用于分离各种生物及非生物胁迫条件下诱导表达的抗性相关基因，可以揭示植物抗逆的分子机理。目前，SSH技术己开始应用于分离与次生代谢产物合成相关的基因。SSH技术是分离植物差异表达基因，揭示植物复杂生命现象分子机理的有效方法，将在植物差异表达基因研究中得到更广泛的应用。
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".