1,029 research outputs found
Regulation of gonadotropin receptor gene expression
The receptors for the gonadotropins differ from the other G protein-coupled receptors by having a large extracellular hormone-binding domain, encoded by nine or ten exons. Alternative splicing of the large pre-mRNA of approximately 100 kb can result in mRNA species that encode truncated receptor proteins. In this review we discuss the regulation of gonadotropin receptor mRNA expression and the possible roles of alternative splicing in gonadotropin receptor function
Expression and Activation of Gonadotropin Receptors
Among the many hormones that are produced by the anterior pituitary gland, luteinizing
hormone (LH, lutropin), follicle-stimulating hormone (FSH, follitropin), and thyroidstimulating
hormone (TSH, thyrotropin) form the separate family of so-called glycoprotein
hormones (reviewed by Oharib el al., 1990). These hormones consist of two glycosylated
subunits, a and p, which are associated through non-covalent interactions. The a-subunit is
identical for all glycoprotein hormones, whereas the p-subunit is hormone specific. The
gonadotropins, LH and FSH, are the key regulators of testis and ovary function, and are
synthesized in cells called the gonadotrophs of the pituitary gland. TSH, which regulates
thyroid function, is produced in the thyrotrophs. In primates and horses, a fourth
glycoprotein hormone exists, chorionic gonadotropin (CO), which is synthesized in the
placenta during pregnancy, and is structurally and functionally related to LH (Oharib el al.,
1990)
Alternative splicing of follicle-stimulating hormone receptor pre-mRNA: cloning and characterization of two alternatively spliced mRNA transcripts
Glycoprotein hormone receptors contain a large extracellular domain that
is encoded by multiple exons, facilitating the possibility of expressing
alternatively spliced transcripts. We have cloned two new splice variants
of the rat follicle-stimulating hormone (FSH) receptor gene: FSH-R1 and
FSH-R2. The splice variant FSH-R1 differs from the full-length FSH
receptor mRNA by the inclusion of a small extra exon between exons 9 and
10. FSH-R2 lacks the first three base pairs o
EPO and hepcidin plasma concentrations in blood donors and β-thalassemia intermedia are not related to commercially tested plasma ERFE concentrations
High-level feature detection from video in TRECVid: a 5-year retrospective of achievements
Successful and effective content-based access to digital
video requires fast, accurate and scalable methods to determine the video content automatically. A variety of contemporary approaches to this rely on text taken from speech within the video, or on matching one video frame against others using low-level characteristics like
colour, texture, or shapes, or on determining and matching objects appearing within the video. Possibly the most important technique, however, is one which determines the presence or absence of a high-level or semantic feature, within a video clip or shot. By utilizing dozens, hundreds or even thousands of such semantic features we can support many kinds of content-based video navigation. Critically however, this depends on being able to determine whether each feature is or is not present in a video clip.
The last 5 years have seen much progress in the development of techniques to determine the presence of semantic features within video. This progress can be tracked in the annual TRECVid benchmarking activity where dozens of research groups measure the effectiveness of their techniques on common data and using an open, metrics-based approach. In this chapter we summarise the work
done on the TRECVid high-level feature task, showing the
progress made year-on-year. This provides a fairly comprehensive statement on where the state-of-the-art is regarding this important task, not just for one research group or for one approach, but across the spectrum. We then use this past and on-going work as a basis for highlighting the trends that are emerging in this area, and the questions which remain to be addressed before we can
achieve large-scale, fast and reliable high-level feature detection on video
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