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1. As hens age, egg weight increases but the eggs contain proportionally more yolk and less albumen and shell. However, at a given age, larger eggs contain proportionally more albumen. When modelling the nutrient requirements of the hen over a production cycle, based on the daily outputs of each nutrient, egg weight needs to be predicted as the sum of the three components, since each has a unique chemical composition, and these proportional changes will therefore influence the nutrient requirements of the hen. 2. Yolk weight is related to hen age and may be calculated using a logistic or Gompertz function. Allometric functions are used to predict albumen weight from yolk weight and shell weight from the weight of the egg contents. 3. A mechanistic, stochastic population model for layers may be used to verify that these functions correctly reflect the proportional changes in the egg components with advancing hen age and at a given age, over a range of egg weights. 4. The various parameters used in the equations need to be defined for the available genotypes.

Studies have evaluated the electroencephalography (EEG) of humans and laboratory animals during and after Radiofrequency (RF) exposures. Effects of RF exposure on the blood-brain barrier (BBB) have been generally accepted for exposures that are thermalizing. Low level exposures that report alterations of the BBB remain controversial. Exposure to high levels of RF energy can damage the structure and function of the nervous system. Much research has focused on the neurochemistry of the brain and the reported effects of RF exposure. Research with isolated brain tissue has provided new results that do not seem to rely on thermal mechanisms. Studies of individuals who are reported to be sensitive to electric and magnetic fields are discussed. In this review of the literature, it is difficult to draw conclusions concerning hazards to human health. The many exposure parameters such as frequency, orientation, modulation, power density, and duration of exposure make direct comparison of many experiments difficult. At high exposure power densities, thermal effects are prevalent and can lead to adverse consequences. At lower levels of exposure biological effects may still occur but thermal mechanisms are not ruled out. It is concluded that the diverse methods and experimental designs as well as lack of replication of many seemingly important studies prevents formation of definite conclusions concerning hazardous nervous system health effects from RF exposure. The only firm conclusion that may be drawn is the potential for hazardous thermal consequences of high power RF exposure.

We present critiques of epidemiologic studies and experimental investigations, published mostly in peer‐reviewed journals, on cancer and related effects from exposure to nonionizing electromagnetic fields in the nominal frequency range of 3 kHz to 300 GHz of interest to Subcommittee 4 (SC4) of the International Committee on Electromagnetic Safety (ICES). The major topics discussed are presented under the headings Epidemiologic and Other Findings on Human Exposure, Mammals Exposed In Vivo, Mammalian Live Tissues and Cell Preparations Exposed In Vitro, and Mutagenesis and Genotoxicity in Microorganisms and Fruit Flies. Under each major topic, we present minireviews of papers on various specific endpoints investigated. The section on Epidemiologic and Other Findings on Human Exposure is divided into two subsections, the first on possible carcinogenic effects of exposure from emitters not in physical contact with the populations studied, for example, transmitting antennas and other devices. Discussed in the second subsection are studies of postulated carcinogenic effects from use of mobile phones, with prominence given to brain tumors from use of cellular and cordless telephones in direct physical contact with an ear of each subject. In both subsections, some investigations yielded positive findings, others had negative findings, including papers directed toward experimentally verifying positive findings, and both were reported in a few instances. Further research on various important aspects may resolve such differences. Overall, however, the preponderance of published epidemiologic and experimental findings do not support the supposition that in vivo or in vitro exposures to such fields are carcinogenic. Bioelectromagnetics Supplement 6:S74–S100, 2003. Published 2003 Wiley‐Liss, Inc.

The organization of afferents to the pituitary was investigated by applying DiI crystals to the pituitary or pituitary stalk of the gymnotiform electric fish, Apteronotus leptorhynchus. Most hypophysiotrophic cells were found in the hypothalamus and were distributed throughout its rostrocaudal extent: nucleus preopticus periventricularis, pars anterior and posterior; suprachiasmatic nucleus; anterior, dorsal, ventral, lateral, and caudal hypothalamic nuclei; and nucleus tuberis lateralis, pars anterior and posterior. In addition a small number of retrogradely labeled cells were found in the ventral telencephalon (area ventralis, pars ventralis) and, most surprisingly, in a thalamic nucleus (nucleus centralis posterioris). The nucleus preopticus periventricularis pars posterior and the anterior hypothalamic nucleus appear to correspond to the parvicellular and magnocellular divisions of the nucleus preopticus of other teleosts. Integration of these results with immunohistochemical localization of monoamines and neuropeptides in the apteronotid brain suggests many homologies between the hypophysiotrophic nuclei of teleosts and other vertebrates, including mammals. Apteronotus communicates electrically during agonistic and sexual interactions. There are numerous anatomical links between the hypophysiotrophic systems and the brain areas related to electrocommunication.

1. The mathematical model of the hen's ovulatory cycle proposed by Etches and Schoch (British Poultry Science, 25: 65-76, 1984) predicts ovulation times for sequences of 2 to 9 ovulations only. 2. Continuous functions have been produced, representing the changes required to the parameters lambda1, lambda2, S1, S2, b1, b2 and b3, such that the prediction of any sequence length is now possible. 3. This improved ovulation model is capable of predicting ovulation times and intra-sequence ovulation intervals for any ovulation rate between 0.5 and 1.0. 4. The improved ovulatory model lends itself to stochasticity. The rate of lay of a population of hens at a time may be modelled with the use of means and standard errors for each of the parameters in the model. 5. Age-related changes in the ovulation rate of the population may be predicted using a combination of three methods, which are consistent with published theories that account for the decline in performance with time.

1. A mechanistic, stochastic egg production model is presented. Mean age at first egg may be predicted from the lighting programme applied during rearing, using the Bristol-Reading model (Lewis et al., 2002). 2. Rate of ovulation is determined by an amended version of the mathematical model of the ovulatory cycle, originally proposed by Etches and Schoch (1984). 3. Oviposition times are estimated from ovulation times. 4. Yolk, albumen and shell weights are calculated using allometric functions. 5. The model predicts egg production of a theoretical flock of laying hens for a full laying year, including random occurrences of double-yolked and soft-shelled eggs and internal ovulations.