Metabolic induction and early responses of mouse blastocyst developmental programming following maternal low protein diet affecting life-long health

Previously, we have shown that a maternal low protein diet, fed exclusively during the preimplantation period of mouse development (Emb-LPD), is sufficient to induce by the blastocyst stage a compensatory growth phenotype in late gestation and postnatally, correlating with increased risk of adult onset cardiovascular disease and behavioural dysfunction. Here, we examine mechanisms of induction of maternal Emb-LPD programming and early compensatory responses by the embryo. Emb-LPD induced changes in maternal serum metabolites at the time of blastocyst formation (E3.5), notably reduced insulin and increased glucose, together with reduced levels of free amino acids (AAs) including branched chain AAs leucine, isoleucine and valine. Emb-LPD also caused reduction in the branched chain AAs within uterine fluid at the blastocyst stage. These maternal changes coincided with an altered content of blastocyst AAs and reduced mTORC1 signalling within blastocysts evident in reduced phosphorylation of effector S6 ribosomal protein and its ratio to total S6 protein but no change in effector 4E-BP1 phosphorylated and total pools. These changes were accompanied by increased proliferation of blastocyst trophectoderm and total cells and subsequent increased spreading of trophoblast cells in blastocyst outgrowths. We propose that induction of metabolic programming following Emb-LPD is achieved through mTORC1signalling which acts as a sensor for preimplantation embryos to detect maternal nutrient levels via branched chain AAs and/or insulin availability. Moreover, this induction step associates with changes in extra-embryonic trophectoderm behaviour occurring as early compensatory responses leading to later nutrient recovery. © 2012 Fleming et al.

Postgraduate training offered to primary care pharmacists by schools of pharmacy - changes over the last five years

Poster session - A review of the postgraduate training courses offered by the 15 schools of pharmacy within Britain was undertaken - The training currently on offer was compared with that offered by the same schools of pharmacy five years ago - It was found that although the numbers of training courses offered by the 15 schools had significantly increased, the orientation of the courses remained more or less as it was five years ago - The increases in the number of courses had not reflected the increased roles that pharmacists currently have and are hoping to undertake in the future - Any new training offered will need to reflect a wider “modernising and managing NHS organisations” agenda

Description and training of neural network dynamics

Attractor properties of a popular discrete-time neural network model are illustrated through numerical simulations. The most complex dynamics is found to occur within particular ranges of parameters controlling the symmetry and magnitude of the weight matrix. A small network model is observed to produce fixed points, limit cycles, mode-locking, the Ruelle-Takens route to chaos, and the period-doubling route to chaos. Training algorithms for tuning this dynamical behaviour are discussed. Training can be an easy or difficult task, depending whether the problem requires the use of temporal information distributed over long time intervals. Such problems require training algorithms which can handle hidden nodes. The most prominent of these algorithms, back propagation through time, solves the temporal credit assignment problem in a way which can work only if the relevant information is distributed locally in time. The Moving Targets algorithm works for the more general case, but is computationally intensive, and prone to local minima.

Learning for life:educational provision at Cadbury, 1831-1981

Cadbury showed concern for the welfare of its labour force in a variety of ways and not least in the provision of educational and educative-recreational facilities. The firm regarded the education of employees as having a positive effect on the efficiency of the business at the same time as being of benefit to the individual, the local community and the nation. The life-long education of people was seen as essential for personal fulfilament, social improvement, economic competitiveness and the proper functioning of democratic procedures. The educational system built up at Cadbury, and the philosophy on which it was founded, acquired both a domestic and international reputation. Its main components were the day continuation education of juniors; the Bournville Works Evening Institute; vocational and non-vocational scholarships; emphasis on the primary importance of general education as a basis for life, work and technical training; stress on equality of educational opportunity for females; and leisure and sporting amenities which the firm felt to be educative in the sense that they contributed to personal psychological and physical development and social skills. The system was primarily shaped and constructed in the first three decades of the twentieth century and went into decline and eventual demise in the 1960's and 1970's as a result of economic pressures, social changes, enhanced state arrangements for education, shifts in Cadbury management thinking and the merger with Schweppes in 1969.

Results of the IEA round Robin on viscosity and aging of fast pyrolysis bio-oils:long-term tests and repeatability

An international round robin study of the viscosity measurements and aging of fast pyrolysis bio-oil has been undertaken recently, and this work is an outgrowth from that effort. Two bio-oil samples were distributed to two laboratories for accelerated aging tests and to three laboratories of long-term aging studies. The accelerated aging test was defined as the change in viscosity of a sealed sample of bio-oil held for 24 h at 80 °C. The test was repeated 10 times over consecutive days to determine the intra-laboratory repeatability of the method. Other bio-oil samples were placed in storage at three temperatures, 21, 5, and -17 °C, for a period of up to 1 year to evaluate the change in viscosity. The variation in the results of the accelerated aging test was shown to be low within a given laboratory. The long-term aging studies showed that storage of a filtered bio-oil under refrigeration can minimize the amount of change in viscosity. The accelerated aging test gave a measure of change similar to that of 6-12 months of storage at room temperature for a filtered bio-oil. Filtration of solids was identified as a key contributor to improving the stability of the bio-oil as expressed by the viscosity based on results of the accelerated aging tests as well as long-term aging studies. Only the filtered bio-oil consistently gave useful results in the accelerated aging and long-term aging studies. The inconsistency suggests that better protocols need to be developed for sampling bio-oils. These results can be helpful in setting standards for use of bio-oil, which is just coming into the marketplace. © 2012 American Chemical Society.

Efficient training of RBF networks for classification.

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. We show how RBFs with logistic and softmax outputs can be trained efficiently using the Fisher scoring algorithm. This approach can be used with any model which consists of a generalised linear output function applied to a model which is linear in its parameters. We compare this approach with standard non-linear optimisation algorithms on a number of datasets.

Bayesian training of mixture density networks

Mixture Density Networks (MDNs) are a well-established method for modelling the conditional probability density which is useful for complex multi-valued functions where regression methods (such as MLPs) fail. In this paper we extend earlier research of a regularisation method for a special case of MDNs to the general case using evidence based regularisation and we show how the Hessian of the MDN error function can be evaluated using R-propagation. The method is tested on two data sets and compared with early stopping.

Efficient training of RBF networks for classification

Radial Basis Function networks with linear outputs are often used in regression problems because they can be substantially faster to train than Multi-layer Perceptrons. For classification problems, the use of linear outputs is less appropriate as the outputs are not guaranteed to represent probabilities. In this paper we show how RBFs with logistic and softmax outputs can be trained efficiently using algorithms derived from Generalised Linear Models. This approach is compared with standard non-linear optimisation algorithms on a number of datasets.

Sensing characteristics of a novel two-section long-period grating

The behavior of a temperature self-compensating, fiber, long-period grating (LPG) device is studied. This device consists of a single 325-µm-period LPG recorded across two sections of a single-mode B-Ge-codoped fiber—one section bare and the other coated with a 1-µm thickness of Ag. This structure generates two attenuation bands associated with the eighth and ninth cladding modes, which are spectrally close together (~60 nm). The attenuation band associated with the Ag-coated section is unaffected by changes in the refractive index of the surrounding medium and can be used to compensate for the temperature of the bare-fiber section. The sensor has a resolution of ±1.0 × 10-3 for the refractive index and ±0.3 °C for the temperature. The effect of bending on the spectral characteristics of the two attenuation bands was found to be nonlinear, with the Ag-coated LPG having the greater sensitivity.

Implementation of vectorial bend sensors using long-period gratings UV-inscribed in special shape fibres

We report the implementation of vector bending sensors using long-period gratings (LPGs) UV-inscribed in flat-clad, four-core and D-shaped fibres. Our experiments reveal a strong fibre-orientation dependence of the spectral response when such LPGs are subjected to dynamic bending, which provided an opportunity to realize curvature measurement with direction recognition.

Discriminative training of the hidden vector state model for semantic parsing

In this paper, we discuss how discriminative training can be applied to the hidden vector state (HVS) model in different task domains. The HVS model is a discrete hidden Markov model (HMM) in which each HMM state represents the state of a push-down automaton with a finite stack size. In previous applications, maximum-likelihood estimation (MLE) is used to derive the parameters of the HVS model. However, MLE makes a number of assumptions and unfortunately some of these assumptions do not hold. Discriminative training, without making such assumptions, can improve the performance of the HVS model by discriminating the correct hypothesis from the competing hypotheses. Experiments have been conducted in two domains: the travel domain for the semantic parsing task using the DARPA Communicator data and the Air Travel Information Services (ATIS) data and the bioinformatics domain for the information extraction task using the GENIA corpus. The results demonstrate modest improvements of the performance of the HVS model using discriminative training. In the travel domain, discriminative training of the HVS model gives a relative error reduction rate of 31 percent in F-measure when compared with MLE on the DARPA Communicator data and 9 percent on the ATIS data. In the bioinformatics domain, a relative error reduction rate of 4 percent in F-measure is achieved on the GENIA corpus.