A bacterial artificial chromosome library of Lotus japonicus constructed in an Agrobacterium tumefaciens-transformable vector

We constructed a BAC library of the model legume Lotus japonicus with a 6-to 7-fold genome coverage. We used vector PCLD04541, which allows direct plant transformation by BACs. The average insert size is 94 kb. Clones were stable in Escherichia coli and Agrobacterium tumefaciens.

Exploring the learning potential of an artificial life simulation

CULTURE is an Artificial Life simulation that aims to provide primary school children with opportunities to become actively engaged in the high-order thinking processes of problem solving and critical thinking. A preliminary evaluation of CULTURE has found that it offers the freedom for children to take part in process-oriented learning experiences. Through providing children with opportunities to make inferences, validate results, explain discoveries and analyse situations, CULTURE encourages the development of high-order thinking skills. The evaluation found that CULTURE allows users to autonomously explore the important scientific concepts of life and living, and energy and change within a software environment that children find enjoyable and easy to use.

Segmenting the market of West Australian senior tourists using an artificial neural network

Measuring perceptions of customers can be a major problem for marketers of tourism and travel services. Much of the problem is to determine which attributes carry most weight in the purchasing decision. Older travellers weigh many travel features before making their travel decisions. This paper presents a descriptive analysis of neural network methodology and provides a research technique that assesses the weighting of different attributes and uses an unsupervised neural network model to describe a consumer-product relationship. The development of this rich class of models was inspired by the neural architecture of the human brain. These models mathematically emulate the neurophysical structure and decision making of the human brain, and, from a statistical perspective, are closely related to generalised linear models. Artificial neural networks or neural networks are, however, nonlinear and do not require the same restrictive assumptions about the relationship between the independent variables and dependent variables. Using neural networks is one way to determine what trade-offs older travellers make as they decide their travel plans. The sample of this study is from a syndicated data source of 200 valid cases from Western Australia. From senior groups, active learner, relaxed family body, careful participants and elementary vacation were identified and discussed. (C) 2003 Published by Elsevier Science Ltd.

Birth of Koalas Phascolarctos cinereus at Lone Pine Koala Sanctuary following artificial insemination

This paper documents the successful development of an artificial insemination (AI) programme for the Koala Phascolurctos cinereus. The protocols for trials involving two methods to induce ovulation and two insemination techniques are described. In Trial 1, interrupted coitus using a 'teaser'♂ successfully induced ovulation in nine Koalas. Five ♀♀ were inseminated while conscious using a modified 'foley catheter' (Cook insemination catheter) resulting in the births of two offspring. The other four ♀♀ were anaesthetized and inseminated using a technique which allowed visualization of the most cranial portion of the urogenital sinus, where semen was deposited using a 3.5 Fr. 'Tom-cat catheter' (urogen-itoscopic insemination). Three of the four ♀♀ inseminated by this technique produced pouch young. Microsatellite analysis of DNA from the pouch young excluded the teaser ♀♀ as possible sires, confirming that all offspring were sired by donor sperm. In Trial 2, eight ♀♀ were induced to ovulate by injecting them with 250 International Units of human chorionic gonadotrophin (hCG). A luteal phase was confirmed in all eight ♀♀ but only one gave birth following urogenitoscopic insemination. The Koala pouch young in this study are the first of any marsupial to be conceived and born following A1 procedures. Details of the A1 procedures used are presented and the significance of A1 to the conservation biology of P. cinereus discussed.

Use of confocal microscopy to follow the development of penetrative hyphae during growth of Rhizopus oligosporus in an artificial solid-state fermentation system

Two methods were compared for determining the concentration of penetrative biomass during growth of Rhizopus oligosporus on an artificial solid substrate consisting of an inert gel and starch as the sole source of carbon and energy. The first method was based on the use of a hand microtome to make sections of approximately 0.2- to 0.4-mm thickness parallel to the substrate surface and the determination of the glucosamine content in each slice. Use of glucosamine measurements to estimate biomass concentrations was shown to be problematic due to the large variations in glucosamine content with mycelial age. The second method was a novel method based on the use of confocal scanning laser microscopy to estimate the fractional volume occupied by the biomass. Although it is not simple to translate fractional volumes into dry weights of hyphae due to the lack of experimentally determined conversion factors, measurement of the fractional volumes in themselves is useful for characterizing fungal penetration into the substrate. Growth of penetrative biomass in the artificial model substrate showed two forms of growth with an indistinct mass in the region close to the substrate surface and a few hyphae penetrating perpendicularly to the surface in regions further away from the substrate surface. The biomass profiles against depth obtained from the confocal microscopy showed two linear regions on log-linear plots, which are possibly related to different oxygen availability at different depths within the substrate. Confocal microscopy has the potential to be a powerful tool in the investigation of fungal growth mechanisms in solid-state fermentation. (C) 2003 Wiley Periodicals, Inc.

Desenvolvimento e análise de uma rede neural artificial para estimativa da erosividade da chuva para o Estado de São Paulo

O conhecimento do valor da erosividade da chuva (R) de determinada localidade é fundamental para a estimativa das perdas de solo feitas a partir da Equação Universal de Perdas de Solo, sendo, portanto, de grande importância no planejamento conservacionista. A fim de obter estimativas do valor de R para localidades onde este é desconhecido, desenvolveu-se uma rede neural artificial (RNA) e analisou-se a acurácia desta com o método de interpolação "Inverso de uma Potência da Distância" (ID). Comparando a RNA desenvolvida com o método de interpolação ID, verificou-se que a primeira apresentou menor erro relativo médio na estimativa de R e melhor índice de confiança, classificado como "Ótimo", podendo, portanto, ser utilizada no planejamento de uso, manejo e conservação do solo no Estado de São Paulo.

Desfolha artificial simulando danos de pragas na cultura do girassol (Helianthus annuus L., Asteraceae)

O girassol está sujeito às perdas de área foliar por diferentes fatores, dentre eles os insetos desfolhadores, contra aos quais geralmente são dirigidas aplicações de inseticidas na cultura. A desfolha artificial em plantas de importância econômica é uma metodologia útil na simulação de ataques dessas pragas em lavouras na determinação dos níveis de dano econômico. O objetivo deste estudo foi avaliar componentes de produção das plantas de girassol submetidas a níveis crescentes de desfolha de 0, 10, 25, 50, 75 e 100%, realizada em três distintos estádios fenológicos da cultura, a saber: V6 (seis folhas com no mínimo 4,0 cm de comprimento), R1 (quando a inflorescência circundada pela bráctea imatura torna-se visível) e R5.5 (50% das flores do disco estão fertilizadas ou em antese), perfazendo um total de 18 tratamentos, os quais foram dispostos em blocos ao acaso, com quatro repetições. Para todos os componentes de produção avaliados (diâmetro do capítulo, biomassa total de sementes da planta e biomassa de 100 aquênios) houve efeito significativo da interação dos tratamentos, evidenciando que o efeito da desfolha será dependente do estágio fenológico da planta. O estádio R5.5 foi mais sensível à desfolha, ocasionando perdas em todos os componentes de produção avaliados.

Dose inseminante utilizada na fertilização artificial de ovócito de piracanjuba (Brycon orbignyanus)

A piracanjuba (Brycon orbignyanus Valenciennes, 1849) é uma espécie de peixe migratória, ameaçada de extinção. O objetivo do presente estudo foi determinar a dose inseminante na fertilização artificial de ovócitos de piracanjuba. Para isso, utilizou-se delineamento em blocos casualizados, com quatro tratamentos e três repetições. Três casais de piracanjuba, selecionados dos tanques de reprodutores da Estação Ambiental de Itutinga (EAI - CEMIG), no período de piracema 2006/2007, receberam aplicação de hormônio extrato bruto de hipófise de carpa (EBHC) para obtenção dos gametas. Adotaram-se quatro tratamentos diferentes para a fertilização de 0,1 grama de ovócitos: 10µL, 20µL, 30µL e 40µL de sêmen. As amostras foram ativadas com 5 mL de água do próprio tanque e, em seguida, levadas para incubadoras, dotadas de renovação constante de água, à temperatura de 28ºC. Após 8 e 16 horas, analisaram-se as taxas de fertilização (ovos viáveis) e de eclosão dos ovos, respectivamente. Os resultados obtidos foram comparados pelo teste de Tukey a 5%. As relações sêmen-ovócitos testadas não alteraram as taxas de fertilização e eclosão (P>0,05). O número de espermatozoides-ovócitos, variando de 10,4 x10(5) a 41,6 x10(5), foi eficiente para obtenção de boas taxas de fertilidade.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Automatic modeling of pectus excavatum corrective prosthesis using artificial neural networks

Pectus excavatum is the most common deformity of the thorax. Pre-operative diagnosis usually includes Computed Tomography (CT) to successfully employ a thoracic prosthesis for anterior chest wall remodeling. Aiming at the elimination of radiation exposure, this paper presents a novel methodology for the replacement of CT by a 3D laser scanner (radiation-free) for prosthesis modeling. The complete elimination of CT is based on an accurate determination of ribs position and prosthesis placement region through skin surface points. The developed solution resorts to a normalized and combined outcome of an artificial neural network (ANN) set. Each ANN model was trained with data vectors from 165 male patients and using soft tissue thicknesses (STT) comprising information from the skin and rib cage (automatically determined by image processing algorithms). Tests revealed that ribs position for prosthesis placement and modeling can be estimated with an average error of 5.0 ± 3.6 mm. One also showed that the ANN performance can be improved by introducing a manually determined initial STT value in the ANN normalization procedure (average error of 2.82 ± 0.76 mm). Such error range is well below current prosthesis manual modeling (approximately 11 mm), which can provide a valuable and radiation-free procedure for prosthesis personalization.

Artificial neural networks for automatic modelling of the pectus excavatum corrective prosthesis

Pectus excavatum is the most common deformity of the thorax and usually comprises Computed Tomography (CT) examination for pre-operative diagnosis. Aiming at the elimination of the high amounts of CT radiation exposure, this work presents a new methodology for the replacement of CT by a laser scanner (radiation-free) in the treatment of pectus excavatum using personally modeled prosthesis. The complete elimination of CT involves the determination of ribs external outline, at the maximum sternum depression point for prosthesis placement, based on chest wall skin surface information, acquired by a laser scanner. The developed solution resorts to artificial neural networks trained with data vectors from 165 patients. Scaled Conjugate Gradient, Levenberg-Marquardt, Resilient Back propagation and One Step Secant gradient learning algorithms were used. The training procedure was performed using the soft tissue thicknesses, determined using image processing techniques that automatically segment the skin and rib cage. The developed solution was then used to determine the ribs outline in data from 20 patient scanners. Tests revealed that ribs position can be estimated with an average error of about 6.82±5.7 mm for the left and right side of the patient. Such an error range is well below current prosthesis manual modeling (11.7±4.01 mm) even without CT imagiology, indicating a considerable step forward towards CT replacement by a 3D scanner for prosthesis personalization.

Comparative analysis between statistical and artificial intelligence models in business failure prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.

Comparative Analysis between Statistical and Artificial Intelligence Models in Business Failure Prediction

A growing number of predicting corporate failure models has emerged since 60s. Economic and social consequences of business failure can be dramatic, thus it is not surprise that the issue has been of growing interest in academic research as well as in business context. The main purpose of this study is to compare the predictive ability of five developed models based on three statistical techniques (Discriminant Analysis, Logit and Probit) and two models based on Artificial Intelligence (Neural Networks and Rough Sets). The five models were employed to a dataset of 420 non-bankrupt firms and 125 bankrupt firms belonging to the textile and clothing industry, over the period 2003–09. Results show that all the models performed well, with an overall correct classification level higher than 90%, and a type II error always less than 2%. The type I error increases as we move away from the year prior to failure. Our models contribute to the discussion of corporate financial distress causes. Moreover it can be used to assist decisions of creditors, investors and auditors. Additionally, this research can be of great contribution to devisers of national economic policies that aim to reduce industrial unemployment.