Single-celled cotton fiber (Gossypium hirsutum) provides a unique experimental system to study cell elongation. To investigate the role of the actin cytoskeleton during fiber development, 15 G. hirsutum ACTIN (GhACT) cDNA clones were characterized. RNA gel blot and real-time RT-PCR analysis revealed that GhACT genes are differentially expressed in different tissues and can be classified into four groups. One group, represented by GhACT1, is expressed predominantly in fiber cells and was studied in detail. A 0.8-kb GhACT1 promoter sufficient to confirm its fiber-specific expression was identified. RNA interference of GhACT1 caused significant reduction of its mRNA and protein levels and disrupted the actin cytoskeleton network in fibers. No defined actin network was observed in these fibers and, consequently, fiber elongation was inhibited. Our results suggested that GhACT1 plays an important role in fiber elongation but not fiber initiation
The off-treatment responses to NAs differ in CHB patients with different pretreatment HBeAg status. NA withdrawal is generally safe and feasible in young patients with CHB. Long consolidation periods should be preferred in HBeAg-positive patients to achieve better durability. Benefits of cessation of NAs do not last long in HBeAg-negative CHB patients.
Document recommendation systems for locating relevant literature have mostly relied on methods developed a decade ago. This is largely due to the lack of a large offline gold-standard benchmark of relevant documents that cover a variety of research fields such that newly developed literature search techniques can be compared, improved and translated into practice. To overcome this bottleneck, we have established the RElevant LIterature SearcH consortium consisting of more than 1500 scientists from 84 countries, who have collectively annotated the relevance of over 180 000 PubMed-listed articles with regard to their respective seed (input) article/s. The majority of annotations were contributed by highly experienced, original authors of the seed articles. The collected data cover 76% of all unique PubMed Medical Subject Headings descriptors. No systematic biases were observed across different experience levels, research fields or time spent on annotations. More importantly, annotations of the same document pairs contributed by different scientists were highly concordant. We further show that the three representative baseline methods used to generate recommended articles for evaluation (Okapi Best Matching 25, Term Frequency–Inverse Document Frequency and PubMed Related Articles) had similar overall performances. Additionally, we found that these methods each tend to produce distinct collections of recommended articles, suggesting that a hybrid method may be required to completely capture all relevant articles. The established database server located at https://relishdb.ict.griffith.edu.au is freely available for the downloading of annotation data and the blind testing of new methods. We expect that this benchmark will be useful for stimulating the development of new powerful techniques for title and title/abstract-based search engines for relevant articles in biomedical research.
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