Serum αFP Level in Cord Blood of Full Term Neonates Born in Babol City

BACKGROUNDANDOBJECTIVE: Serum alpha-fetoprotein (αFP) level is considered as a diagnostic marker is higher than normal in many congenital tumors such as germ cell, hepatoblastoma, as well as liver and metabolic diseases in neonates. Normal neonates also have a higher level of alpha-fetoprotein than others, so it is important to diagnose this interference. In valid sources, the normal serum alpha-fetoprotein level in infants is related to advanced countries, which may vary in our country. Therefore, this study was conducted to determine the serum levels of alpha-fetoprotein in the umbilical cord blood of term neonates born in Babol and to compare them in two genders. METHODS: This cross-sectional study was performed on 500 neonates (37-42 weeks) born in hospitals in Babol city where physical examination was normal. At birth, 5 ml of umbilical cord blood was taken and samples were sent to the lab for measurement of alpha-fetoprotein. Serum alpha-fetoprotein level was measured by ELISA method and was compared in two genders. FINDINGS: Mean serum a FP levels was 76.57±35.25 ng/ml (range 2.3-160) and it was significantly higher in males (80.54±36.95 vs. 73.69±33.73 ng/ml) which was statistically significant (p=0.002). CONCLUSION: The results of this study showed that the level of alpha-fetoprotein in neonates born in Babol is relatively high and also in males is more than females

Inductive learning spatial attention

This paper investigates the automatic induction of spatial attention from the visual observation of objects manipulated on a table top. In this work, space is represented in terms of a novel observer-object relative reference system, named Local Cardinal System, defined upon the local neighbourhood of objects on the table. We present results of applying the proposed methodology on five distinct scenarios involving the construction of spatial patterns of coloured blocks

Nutritional requirements to increase the survival rate of black-lip pearl oyster, Pinctada margaritifera from D-shape to spate stage

Black lip pearl oyster (pinctada magaritifera) is one of the most important species of Pearl maker in the world. Its reserves in the Persian Gulf waters is facing to a variety of causes, especially oil pollution from Persian Gulf war that cause genetic changes in pearl oyster. 65 broodstocks black lip pearl oyster mature with average length of the dorsal ventral (15 ± 2cm DVM) were collected from its typical natural Lavan Island in the months of June and July 2007 and were transferred to the research station and then were washed in cold rooms (20 ° C). The gonad indices were determined by microscope based on the gametogenez steps. After 2 days from collection time, the broodstocks were exposed to thermal shock in order of spawning stimulation. For the analysis of biochemical compounds, microalgae were sampled at logarithmic phase stage with three replications. Firstly, the samples were concentrated by centrifuges (3500g, 20 min). After washing with ammonium formate solution 0.5 molar, the samples were again centrifuged at 3500 g for 15 min. After spawning, D shape larvae form and then the larva ambo feeded from different micro-algae and lipid and the amount of growth and survival rates were studied. D shape larvae with 3 larvae per ml density and dorsal- abdominal length of 78.9 ± 0.7 μm were cultured in 5 liter Erlenmeyer flask containing 2 liters water. Whereas, samples at umbo stage with average dorsal-ventral length of 133 ± 3.1 μm were cultured in 15 liters plastic containers at 27- 29 ° C and salinity of 34 to 35 parts per thousand. The studied microalgae were Isochrysis aff galbana, Chaetoceros calcitrans and c.muelleri. The results of biochemical compounds showed that protein is the most component of all species with a maximum amount of 527.5 ± 2.1 mg g^-1 in T. Iso microalgae. Also, T. Iso has a maximum amount of lipids, following by seek c. muelleri and c. calcitrans, respectively. Test results showed that feeding of larvae black lip pearl oyster in stage D shape with microalgae T. Iso alone has more growth rate than double or triple mixtures. However, diatoms have a high nutritional value for larval mussels in umbo state, and they are accounted as important components in a double and triple mixture of microalgae diet. The size of the larval D shape after 10 days showed a difference among treatments. The cultured larves that feeded from T Iso reached to maximum length of 111.4 ± 10 μm and the maximum survival rate of 57.7% was related to larves that feeded by fresh microalgae T. Iso. D.V.M for microalgae with microalgae or lipid nutrition showed no significant difference

Ultra-Strong Machine Learning: comprehensibility of programs learned with ILP

During the 1980s Michie defined Machine Learning in terms of two orthogonal axes of performance: predictive accuracy and comprehensibility of generated hypotheses. Since predictive accuracy was readily measurable and comprehensibility not so, later definitions in the 1990s, such as Mitchell’s, tended to use a one-dimensional approach to Machine Learning based solely on predictive accuracy, ultimately favouring statistical over symbolic Machine Learning approaches. In this paper we provide a definition of comprehensibility of hypotheses which can be estimated using human participant trials. We present two sets of experiments testing human comprehensibility of logic programs. In the first experiment we test human comprehensibility with and without predicate invention. Results indicate comprehensibility is affected not only by the complexity of the presented program but also by the existence of anonymous predicate symbols. In the second experiment we directly test whether any state-of-the-art ILP systems are ultra-strong learners in Michie’s sense, and select the Metagol system for use in humans trials. Results show participants were not able to learn the relational concept on their own from a set of examples but they were able to apply the relational definition provided by the ILP system correctly. This implies the existence of a class of relational concepts which are hard to acquire for humans, though easy to understand given an abstract explanation. We believe improved understanding of this class could have potential relevance to contexts involving human learning, teaching and verbal interaction

Automated Discovery of Food Webs from Ecological Data Using Logic-Based Machine Learning

Networks of trophic links (food webs) are used to describe and understand mechanistic routes for translocation of energy (biomass) between species. However, a relatively low proportion of ecosystems have been studied using food web approaches due to difficulties in making observations on large numbers of species. In this paper we demonstrate that Machine Learning of food webs, using a logic-based approach called A/ILP, can generate plausible and testable food webs from field sample data. Our example data come from a national-scale Vortis suction sampling of invertebrates from arable fields in Great Britain. We found that 45 invertebrate species or taxa, representing approximately 25% of the sample and about 74% of the invertebrate individuals included in the learning, were hypothesized to be linked. As might be expected, detritivore Collembola were consistently the most important prey. Generalist and omnivorous carabid beetles were hypothesized to be the dominant predators of the system. We were, however, surprised by the importance of carabid larvae suggested by the machine learning as predators of a wide variety of prey. High probability links were hypothesized for widespread, potentially destabilizing, intra-guild predation; predictions that could be experimentally tested. Many of the high probability links in the model have already been observed or suggested for this system, supporting our contention that A/ILP learning can produce plausible food webs from sample data, independent of our preconceptions about “who eats whom.” Well-characterised links in the literature correspond with links ascribed with high probability through A/ILP. We believe that this very general Machine Learning approach has great power and could be used to extend and test our current theories of agricultural ecosystem dynamics and function. In particular, we believe it could be used to support the development of a wider theory of ecosystem responses to environmental change

On deducing causality in metabolic networks

© 2008 Bodei et al.; licensee BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution Licens

Fast relational learning using bottom clause propositionalization with artificial neural networks

Relational learning can be described as the task of learning first-order logic rules from examples. It has enabled a number of new machine learning applications, e.g. graph mining and link analysis. Inductive Logic Programming (ILP) performs relational learning either directly by manipulating first-order rules or through propositionalization, which translates the relational task into an attribute-value learning task by representing subsets of relations as features. In this paper, we introduce a fast method and system for relational learning based on a novel propositionalization called Bottom Clause Propositionalization (BCP). Bottom clauses are boundaries in the hypothesis search space used by ILP systems Progol and Aleph. Bottom clauses carry semantic meaning and can be mapped directly onto numerical vectors, simplifying the feature extraction process. We have integrated BCP with a well-known neural-symbolic system, C-IL2P, to perform learning from numerical vectors. C-IL2P uses background knowledge in the form of propositional logic programs to build a neural network. The integrated system, which we call CILP++, handles first-order logic knowledge and is available for download from Sourceforge. We have evaluated CILP++ on seven ILP datasets, comparing results with Aleph and a well-known propositionalization method, RSD. The results show that CILP++ can achieve accuracy comparable to Aleph, while being generally faster, BCP achieved statistically significant improvement in accuracy in comparison with RSD when running with a neural network, but BCP and RSD perform similarly when running with C4.5. We have also extended CILP++ to include a statistical feature selection method, mRMR, with preliminary results indicating that a reduction of more than 90 % of features can be achieved with a small loss of accuracy