Hypophysiotrophic thyrotropin releasing hormone (TRH) synthesizing neurons. Ultrastructure, adrenergic innervation and putative transmitter action

The neuropeptide thyrotropin releasing hormone (TRH) is capable of influencing both neuronal mechanisms in the brain and the activity of the pituitary-thyroid endocrine axis. By the use of immunocytochemical techniques, first the ultrastructural features of TRH-immunoreactive (IR) perikarya and neuronal processes were studied, and then the relationship between TRH-IR neuronal elements and dopamine-beta-hydroxylase (DBH) or phenylethanolamine-N-methyltransferase (PNMT)-IR catecholaminergic axons was analyzed in the parvocellular subnuclei of the hypothalamic paraventricular nucleus (PVN). In control animals, only TRH-IR axons were detected and some of them seemed to follow the contour of immunonegative neurons. Colchicine treatment resulted in the appearance of TRH-IR material in parvocellular neurons of the PVN. At the ultrastructural level, immunolabel was associated with rough endoplasmic reticulum, free ribosomes and neurosecretory granules. Non-labelled axons formed synaptic specializations with both dendrites and perikarya of the TRH-synthesizing neurons. TRH-IR axons located in the parvocellular units of the PVN exhibited numerous intensely labelled dense-core and fewer small electron lucent vesicles. These axons were frequently observed to terminate on parvocellular neurons, forming both bouton- and en passant-type connections. The simultaneous light microscopic localization of DBH or PNMT-IR axons and TRH-synthesizing neurons demonstrated that catecholaminergic fibers established contacts with the dendrites and cell bodies of TRH-IR neurons. Ultrastructural analysis revealed the formation of asymmetric axo-somatic and axo-dendritic synaptic specializations between PNMT-immunopositive, adrenergic axons and TRH-IR neurons in the periventricular and medial parvocellular subnuclei of the PVN. These morphological data indicate that the hypophysiotrophic, thyrotropin releasing hormone synthesizing neurons of the PVN are directly influenced by the central epinephrine system and that TRH may act as a neurotransmitter or neuromodulator upon other paraventricular neurons.     

Phytochrome controlled acid RNase: An “attached” protein of ribosomes

The light-dependent increment in RNase activity (which is ribosome bound in cell extracts) is distributed as a gradient increasing from base to hook of lupin hypocotyls. No evidence was found of non-specific or of specific activation of pre-formed enzyme molecules following isolation, either before or after (latent activity) destruction of particles. The autodegradation capacity of ribosomes isolated from irradiated cells was almost double that of ribosomes from etiolated tissue. It is proposed that association between the bulk of the light-controlled RNase fraction and lupin ribosomes results from binding of soluble protein. It is not clear whether binding is specific or an artifact of isolation.     

Functional rarefaction: estimating functional diversity from field data

Studies in biodiversity-ecosystem function and conservation biology have led to the development of diversity indices that take species' functional differences into account. We identify two broad classes of indices: those that monotonically increase with species richness (MSR indices) and those that weight the contribution of each species by abundance or occurrence (weighted indices). We argue that weighted indices are easier to estimate without bias but tend to ignore information provided by rare species. Conversely, MSR indices fully incorporate information provided by rare species but are nearly always underestimated when communities are not exhaustively surveyed. This is because of the well-studied fact that additional sampling of a community may reveal previously undiscovered species. We use the rarefaction technique from species richness studies to address sample-size-induced bias when estimating functional diversity indices. Rarefaction transforms any given MSR index into a family of unbiased weighted indices, each with a different level of sensitivity to rare species. Thus rarefaction simultaneously solves the problem of bias and the problem of sensitivity to rare species. We present formulae and algorithms for conducting a functional rarefaction analysis of the two most widely cited MSR indices: functional attribute diversity (FAD) and Petchey and Gaston's functional diversity (FD). These formulae also demonstrate a relationship between three seemingly unrelated functional diversity indices: FAD, FD and Rao's quadratic entropy. Statistical theory is also provided in order to prove that all desirable statistical properties of species richness rarefaction are preserved for functional rarefaction.     

The unpredictability of favourability: condition assessment and protected areas in England

The probability that protected areas will deliver their potential for maintaining or enhancing biodiversity is likely to be maximised if they are appropriately and effectively managed. As a result, governments and conservation agencies are devoting much attention to the management of protected areas. In the U.K., the demand for performance accountability has resulted in Public Service Agreements (PSA) that set out targets for government departments to deliver results in return for investments being made. One such target for England is to ensure that all nationally important wildlife sites are in favourable condition by 2010. Here, we tested the hypothesis, of potential strategic importance, that the ecological condition of these sites is predictable from relationships with a range of physical, environmental and demographic variables. We used binary logistic regression to investigate these relationships, using the results of English Nature’s 1997–2003 condition assessment exercise. Generally, sites in unfavourable condition tend to be larger in area, located at higher elevations, but with higher human population density and are more spatially isolated from units of the same habitat. However, despite the range of different parameters included in our models, the extent to which the condition of any given site could be predicted was low. Our results have implications for the delivery of PSA targets, funding allocation, and the location of new protected areas.