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.

Feeding non-preference by Spodoptera frugiperda and Spodoptera eridania on tomato genotypes

Larvae of the genus Spodoptera spp. are highly polyphagous and can cause economical losses in several agricultural crops. Given their growing importance in the tomato crop, especially for industry, this work aimed to evaluate the feeding non-preference by larvae of Spodoptera frugiperda (J. E. Smith, 1797) and Spodoptera eridania (Cramer, 1782) on tomato genotypes and classify them by the levels of resistance. The commercial cultivar Santa Clara was set as the susceptible standard and line PI 134417 as the resistant standard to evaluate the lines PI 134418, PI 126931, LA 462 and LA 716. Feeding non-preference tests were performed under non-choice and free-choice conditions to evaluate the genotype attractiveness to larvae at predetermined times after their release, as well as the leaf area consumed. Overall, the genotypes LA 716 and PI 126931 were the least attractive to S. frugiperda, whereas Santa Clara was the most attractive and consumed. For S. eridania, the genotypes PI 126931, LA 462, LA 716 and PI 134418 were the least preferred for feeding, and Santa Clara and PI 134417 were the most attractive and consumed. The genotypes LA 716 and PI 126931 are moderately resistant to S. frugiperda and S. eridania; PI 134418 and LA 462 are moderately resistant to S. eridania; PI 134417 is susceptible to S. frugiperda and S. eridania; and Santa Clara is highly susceptible to both S. frugiperda and S. eridania.

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.

Applications of artificial intelligence to condition-based maintenance

In this paper we discuss interesting developments of expert systems for machine diagnosis and condition-based maintenance. We review some elements of condition-based maintenance and its applications, expert systems for machine diagnosis, and an example of machine diagnosis. In the last section we note some problems to be resolved so that expert systems for machine diagnosis may gain wider acceptance in the future.

Aspectos ecológicos da leishmaniose tegumentar americana: 2. ecótopo artificial como abrigo de Psychodopygus intermedius e observações sobre alimentação e reprodução sob influência de fatores físicos naturais

As observações levadas a cabo em um galinheiro experimental mostraram que Psychodopygus intermedius tem a capacidade de nele abrigar-se. Para se chegar a este resultado, a metodologia utilizada consistiu na coleta total diurna e exame do estado de alimentação e digestão sangüínea dos Ps. intermedius como parâmetro da sua maior ou menor permanência no ecótopo estudado. Além disso, observou-se paralelamente, os tempos para o repasto sangüíneo, digestão completa, oviposição, sobrevivência e cópula sob a influência direta dos fatores físicos naturais. A importância epidemiológica dos resultados reside em novas elucidações experimentais sobre a viabilidade da transmissão da leshmaniose tegumentar ocorrer em ambiente domiciliar.

Supplementary feeding programs: a critical analysis

A critical analysis of the Supplementary Feeding Programs which have been undertaken around the world is presented. Although only a few programs have been rigorously evaluated it seems that in relation to their main objectives, most of them have not proved to be successful. Some suggestions for improvement are put forward and the particular case of the Brazilian programs is assessed in detail.

Opinião de médicos brasileiros sobre fecundação artificial do ponto de vista ético

Foi realizado levantamento da opinião dos médicos que compareceram ao "XIV Congresso Brasileiro de Reprodução Humana", sob o ponto de vista ético, a respeito da fecundação artificial. Foram analisados os resultados, chegando-se a selecionar alguns itens comportamentais como preliminarmente aceitos. Propõe-se a realização de estudos mais profundos que objetivam abordar os vários aspectos aceitos ou não pela sociedade.