Decreased Photosynthetic Efficiency in Nicotiana tabacum L. under Transient Heat Stress.

Heat stress is an abiotic factor that affects the photosynthetic parameters of plants. In this study, we examined the photosynthetic mechanisms underlying the rapid response of tobacco plants to heat stress in a controlled environment. To evaluate transient heat stress conditions, changes in photochemical, carboxylative, and fluorescence efficiencies were measured using an infrared gas analyser (IRGA Licor 6800) coupled with chlorophyll a fluorescence measurements. Our findings indicated that significant disruptions in the photosynthetic machinery occurred at 45 °C for 6 h following transient heat treatment, as explained by 76.2% in the principal component analysis. The photosynthetic mechanism analysis revealed that the dark respiration rate (Rd and Rd*CO2) increased, indicating a reduced potential for carbon fixation during plant growth and development. When the light compensation point (LCP) increased as the light saturation point (LSP) decreased, this indicated potential damage to the photosystem membrane of the thylakoids. Other photosynthetic parameters, such as AMAX, VCMAX, JMAX, and ΦCO2, also decreased, compromising both photochemical and carboxylative efficiencies in the Calvin-Benson cycle. The energy dissipation mechanism, as indicated by the NPQ, qN, and thermal values, suggested that a photoprotective strategy may have been employed. However, the observed transitory damage was a result of disruption of the electron transport rate (ETR) between the PSII and PSI photosystems, which was initially caused by high temperatures. Our study highlights the impact of rapid temperature changes on plant physiology and the potential acclimatisation mechanisms under rapid heat stress. Future research should focus on exploring the adaptive mechanisms involved in distinguishing mutants to improve crop resilience against environmental stressors.

Chemometric Analysis for the Prediction of Biochemical Compounds in Leaves Using UV-VIS-NIR-SWIR Hyperspectroscopy.

Reflectance hyperspectroscopy is recognised for its potential to elucidate biochemical changes, thereby enhancing the understanding of plant biochemistry. This study used the UV-VIS-NIR-SWIR spectral range to identify the different biochemical constituents in Hibiscus and Geranium plants. Hyperspectral vegetation indices (HVIs), principal component analysis (PCA), and correlation matrices provided in-depth insights into spectral differences. Through the application of advanced algorithms-such as PLS, VIP, iPLS-VIP, GA, RF, and CARS-the most responsive wavelengths were discerned. PLSR models consistently achieved R2 values above 0.75, presenting noteworthy predictions of 0.86 for DPPH and 0.89 for lignin. The red-edge and SWIR bands displayed strong associations with pivotal plant pigments and structural molecules, thus expanding the perspectives on leaf spectral dynamics. These findings highlight the efficacy of spectroscopy coupled with multivariate analysis in evaluating the management of biochemical compounds. A technique was introduced to measure the photosynthetic pigments and structural compounds via hyperspectroscopy across UV-VIS-NIR-SWIR, underpinned by rapid multivariate PLSR. Collectively, our results underscore the burgeoning potential of hyperspectroscopy in precision agriculture. This indicates a promising paradigm shift in plant phenotyping and biochemical evaluation.

Non-Invasive Assessment, Classification, and Prediction of Biophysical Parameters Using Reflectance Hyperspectroscopy.

Hyperspectral technology offers significant potential for non-invasive monitoring and prediction of morphological parameters in plants. In this study, UV-VIS-NIR-SWIR reflectance hyperspectral data were collected from Nicotiana tabacum L. plants using a spectroradiometer. These plants were grown under different light and gibberellic acid (GA3) concentrations. Through spectroscopy and multivariate analyses, key growth parameters, such as height, leaf area, energy yield, and biomass, were effectively evaluated based on the interaction of light with leaf structures. The shortwave infrared (SWIR) bands, specifically SWIR1 and SWIR2, showed the strongest correlations with these growth parameters. When classifying tobacco plants grown under different GA3 concentrations in greenhouses, artificial intelligence (AI) and machine learning (ML) algorithms were employed, achieving an average accuracy of over 99.1% using neural network (NN) and gradient boosting (GB) algorithms. Among the 34 tested vegetation indices, the photochemical reflectance index (PRI) demonstrated the strongest correlations with all evaluated plant phenotypes. Partial least squares regression (PLSR) models effectively predicted morphological attributes, with R2CV values ranging from 0.81 to 0.87 and RPDP values exceeding 2.09 for all parameters. Based on Pearson's coefficient XYZ interpolations and HVI algorithms, the NIR-SWIR band combination proved the most effective for predicting height and leaf area, while VIS-NIR was optimal for optimal energy yield, and VIS-VIS was best for predicting biomass. To further corroborate these findings, the SWIR bands for certain morphological characteristic wavelengths selected with s-PLS were most significant for SWIR1 and SWIR2, while i-PLS showed a more uniform distribution in VIS-NIR-SWIR bands. Therefore, SWIR hyperspectral bands provide valuable insights into developing alternative bands for remote sensing measurements to estimate plant morphological parameters. These findings underscore the potential of remote sensing technology for rapid, accurate, and non-invasive monitoring within stationary high-throughput phenotyping systems in greenhouses. These insights align with advancements in digital and precision technology, indicating a promising future for research and innovation in this field.

Reflectance Spectroscopy for the Classification and Prediction of Pigments in Agronomic Crops.

Reflectance spectroscopy, in combination with machine learning and artificial intelligence algorithms, is an effective method for classifying and predicting pigments and phenotyping in agronomic crops. This study aims to use hyperspectral data to develop a robust and precise method for the simultaneous evaluation of pigments, such as chlorophylls, carotenoids, anthocyanins, and flavonoids, in six agronomic crops: corn, sugarcane, coffee, canola, wheat, and tobacco. Our results demonstrate high classification accuracy and precision, with principal component analyses (PCAs)-linked clustering and a kappa coefficient analysis yielding results ranging from 92 to 100% in the ultraviolet-visible (UV-VIS) to near-infrared (NIR) to shortwave infrared (SWIR) bands. Predictive models based on partial least squares regression (PLSR) achieved R2 values ranging from 0.77 to 0.89 and ratio of performance to deviation (RPD) values over 2.1 for each pigment in C3 and C4 plants. The integration of pigment phenotyping methods with fifteen vegetation indices further improved accuracy, achieving values ranging from 60 to 100% across different full or range wavelength bands. The most responsive wavelengths were selected based on a cluster heatmap, ß-loadings, weighted coefficients, and hyperspectral vegetation index (HVI) algorithms, thereby reinforcing the effectiveness of the generated models. Consequently, hyperspectral reflectance can serve as a rapid, precise, and accurate tool for evaluating agronomic crops, offering a promising alternative for monitoring and classification in integrated farming systems and traditional field production. It provides a non-destructive technique for the simultaneous evaluation of pigments in the most important agronomic plants.

Biophysical, Biochemical, and Photochemical Analyses Using Reflectance Hyperspectroscopy and Chlorophyll a Fluorescence Kinetics in Variegated Leaves.

The adjustments that occur during photosynthesis are correlated with morphological, biochemical, and photochemical changes during leaf development. Therefore, monitoring leaves, especially when pigment accumulation occurs, is crucial for monitoring organelles, cells, tissue, and whole-plant levels. However, accurately measuring these changes can be challenging. Thus, this study tests three hypotheses, whereby reflectance hyperspectroscopy and chlorophyll a fluorescence kinetics analyses can improve our understanding of the photosynthetic process in Codiaeum variegatum (L.) A. Juss, a plant with variegated leaves and different pigments. The analyses include morphological and pigment profiling, hyperspectral data, chlorophyll a fluorescence curves, and multivariate analyses using 23 JIP test parameters and 34 different vegetation indexes. The results show that photochemical reflectance index (PRI) is a useful vegetation index (VI) for monitoring biochemical and photochemical changes in leaves, as it strongly correlates with chlorophyll and nonphotochemical dissipation (Kn) parameters in chloroplasts. In addition, some vegetation indexes, such as the pigment-specific simple ratio (PSSRc), anthocyanin reflectance index (ARI1), ratio analysis of reflectance spectra (RARS), and structurally insensitive pigment index (SIPI), are highly correlated with morphological parameters and pigment levels, while PRI, moisture stress index (MSI), normalized difference photosynthetic (PVR), fluorescence ratio (FR), and normalized difference vegetation index (NDVI) are associated with photochemical components of photosynthesis. Combined with the JIP test analysis, our results showed that decreased damage to energy transfer in the electron transport chain is correlated with the accumulation of carotenoids, anthocyanins, flavonoids, and phenolic compounds in the leaves. Phenomenological energy flux modelling shows the highest changes in the photosynthetic apparatus based on PRI and SIPI when analyzed with Pearson's correlation, the hyperspectral vegetation index (HVI) algorithm, and the partial least squares (PLS) to select the most responsive wavelengths. These findings are significant for monitoring nonuniform leaves, particularly when leaves display high variation in pigment profiling in variegated and colorful leaves. This is the first study on the rapid and precise detection of morphological, biochemical, and photochemical changes combined with vegetation indexes for different optical spectroscopy techniques.

A Novel Method for Estimating Chlorophyll and Carotenoid Concentrations in Leaves: A Two Hyperspectral Sensor Approach.

Leaf optical properties can be used to identify environmental conditions, the effect of light intensities, plant hormone levels, pigment concentrations, and cellular structures. However, the reflectance factors can affect the accuracy of predictions for chlorophyll and carotenoid concentrations. In this study, we tested the hypothesis that technology using two hyperspectral sensors for both reflectance and absorbance data would result in more accurate predictions of absorbance spectra. Our findings indicated that the green/yellow regions (500-600 nm) had a greater impact on photosynthetic pigment predictions, while the blue (440-485 nm) and red (626-700 nm) regions had a minor impact. Strong correlations were found between absorbance (R2 = 0.87 and 0.91) and reflectance (R2 = 0.80 and 0.78) for chlorophyll and carotenoids, respectively. Carotenoids showed particularly high and significant correlation coefficients using the partial least squares regression (PLSR) method (R2C = 0.91, R2cv = 0.85, and R2P = 0.90) when associated with hyperspectral absorbance data. Our hypothesis was supported, and these results demonstrate the effectiveness of using two hyperspectral sensors for optical leaf profile analysis and predicting the concentration of photosynthetic pigments using multivariate statistical methods. This method for two sensors is more efficient and shows better results compared to traditional single sensor techniques for measuring chloroplast changes and pigment phenotyping in plants.

Enhancing Pigment Phenotyping and Classification in Lettuce through the Integration of Reflectance Spectroscopy and AI Algorithms.

In this study, we investigated the use of artificial intelligence algorithms (AIAs) in combination with VIS-NIR-SWIR hyperspectroscopy for the classification of eleven lettuce plant varieties. For this purpose, a spectroradiometer was utilized to collect hyperspectral data in the VIS-NIR-SWIR range, and 17 AIAs were applied to classify lettuce plants. The results showed that the highest accuracy and precision were achieved using the full hyperspectral curves or the specific spectral ranges of 400-700 nm, 700-1300 nm, and 1300-2400 nm. Four models, AdB, CN2, G-Boo, and NN, demonstrated exceptional R2 and ROC values, exceeding 0.99, when compared between all models and confirming the hypothesis and highlighting the potential of AIAs and hyperspectral fingerprints for efficient, precise classification and pigment phenotyping in agriculture. The findings of this study have important implications for the development of efficient methods for phenotyping and classification in agriculture and the potential of AIAs in combination with hyperspectral technology. To advance our understanding of the capabilities of hyperspectroscopy and AIs in precision agriculture and contribute to the development of more effective and sustainable agriculture practices, further research is needed to explore the full potential of these technologies in different crop species and environments.

Classification and Prediction by Pigment Content in Lettuce (Lactuca sativa L.) Varieties Using Machine Learning and ATR-FTIR Spectroscopy.

Green or purple lettuce varieties produce many secondary metabolites, such as chlorophylls, carotenoids, anthocyanins, flavonoids, and phenolic compounds, which is an emergent search in the field of biomolecule research. The main objective of this study was to use multivariate and machine learning algorithms on Attenuated Total Reflectance Fourier Transform Infrared Spectroscopy (ATR-FTIR)-based spectra to classify, predict, and categorize chemometric attributes. The cluster heatmap showed the highest efficiency in grouping similar lettuce varieties based on pigment profiles. The relationship among pigments was more significant than the absolute contents. Other results allow classification based on ATR-FTIR fingerprints of inflections associated with structural and chemical components present in lettuce, obtaining high accuracy and precision (>97%) by using principal component analysis and discriminant analysis (PCA-LDA)-associated linear LDA and SVM machine learning algorithms. In addition, PLSR models were capable of predicting Chla, Chlb, Chla+b, Car, AnC, Flv, and Phe contents, with R2P and RPDP values considered very good (0.81−0.88) for Car, Anc, and Flv and excellent (0.91−0.93) for Phe. According to the RPDP metric, the models were considered excellent (>2.10) for all variables estimated. Thus, this research shows the potential of machine learning solutions for ATR-FTIR spectroscopy analysis to classify, estimate, and characterize the biomolecules associated with secondary metabolites in lettuce.

Rapid Quantification Method for Yield, Calorimetric Energy and Chlorophyll a Fluorescence Parameters in Nicotiana tabacum L. Using Vis-NIR-SWIR Hyperspectroscopy.

High-throughput and large-scale data are part of a new era of plant remote sensing science. Quantification of the yield, energetic content, and chlorophyll a fluorescence (ChlF) remains laborious and is of great interest to physiologists and photobiologists. We propose a new method that is efficient and applicable for estimating photosynthetic performance and photosystem status using remote sensing hyperspectroscopy with visible, near-infrared and shortwave spectroscopy (Vis-NIR-SWIR) based on rapid multivariate partial least squares regression (PLSR) as a tool to estimate biomass production, calorimetric energy content and chlorophyll a fluorescence parameters. The results showed the presence of typical inflections associated with chemical and structural components present in plants, enabling us to obtain PLSR models with R2P and RPDP values greater than >0.82 and 3.33, respectively. The most important wavelengths were well distributed into 400 (violet), 440 (blue), 550 (green), 670 (red), 700−750 (red edge), 1330 (NIR), 1450 (SWIR), 1940 (SWIR) and 2200 (SWIR) nm operating ranges of the spectrum. Thus, we report a methodology to simultaneously determine fifteen attributes (i.e., yield (biomass), ΔH°area, ΔH°mass, Fv/Fm, Fv'/Fm', ETR, NPQ, qP, qN, ΦPSII, P, D, SFI, PI(abs), D.F.) with high accuracy and precision and with excellent predictive capacity for most of them. These results are promising for plant physiology studies and will provide a better understanding of photosystem dynamics in tobacco plants when a large number of samples must be evaluated within a short period and with remote acquisition data.

Simple, Fast and Efficient Methods for Analysing the Structural, Ultrastructural and Cellular Components of the Cell Wall.

Plant cell walls are a fundamental component of plant biology and play an essential role in plant growth and development. The metabolic components of the cell wall can be investigated in a fast, simple, and highly efficient manner using various and distinct microscopy techniques. Here, we report implementing a flowchart to analyse tobacco plants' structural, ultrastructural, and metabolic components supplemented with far-red light. In addition, biochemical components, such as lignin, cellulose, phenolic compounds, and reducing sugars, present in the plant cell walls were quantified using light, fluorescence, and electron microscopy. Our data were generated from samples prepared via tissue fixation, incorporation in resins, and slicing using microtomes. Moreover, we have used routine staining and contrast techniques to characterise plant cell walls. Here, we describe several protocols that use classic and modern techniques as well as qualitative and quantitative analytical methods to study cell walls, enabling the plant research community to understand and select the most suitable methods for the microscopic analysis of metabolic components. Finally, we discuss specific ideas aimed at new students of plant anatomy and microscopy. This research not only described the structural, ultrastructural, and metabolic components of the plant cell wall, but also explained the strategies for understanding cellular development.

High resolution leaf spectral signature as a tool for foliar pigment estimation displaying potential for species differentiation.

Optical leaf profiles depend on foliar pigment type and content, as well as anatomical aspects and cellular ultrastructure, whose effects are shown in several species. Monocotyledon and Dicotyledon plants presenting natural pigment content variations and anatomical alterations were analyzed. Each plant species displays its own spectral signatures, which are, in turn, influenced by foliar pigment class (composition) and concentration, as well as anatomical and ultrastructural plant cell characteristics. Plants with no anthocyanin displayed increased reflectance and transmittance in the green spectral region (501-565 nm), while values decreased in the presence of anthocyanin. At wavelengths below 500 nm (350-500 nm), strong overlapping signatures of phenolics, carotenoids, chlorophylls, flavonoids and anthocyanins were observed. Using a partial least squares regression applied to 350-700 nm spectral data allowed for accurate estimations of different foliar pigment levels. In addition, a PCA and discriminant analysis were able to efficiently discriminate different species displaying spectra overlapping. The use of absorbance spectra only was able to discriminate species with 100 % confidence. Finally, a discussion on how different wavelengths are absorbed and on anatomical interference of light interaction in leaf profiles is presented.

Nitrogen-improved photosynthesis quantum yield is driven by increased thylakoid density, enhancing green light absorption.

A nitrogen supply is necessary for all plants. The multifaceted reasons why this nutrient stimulates plant dry weight accumulation are assessed herein. We compared tomato plants grown in full sunlight and in low light environments under four N doses and evaluated plant growth, photosynthetic and calorimetric parameters, leaf anatomy, chloroplast transmission electron microscopy (TEM) and a high resolution profile of optical leaf properties. Increases in N supplies allow tomato plants to grow faster in low light environments (91.5% shading), displaying a robust light harvesting machinery and, consequently, improved light harvesting efficiency. Ultrastructurally, high N doses were associated to a high number of grana per chloroplast and greater thylakoid stacking, as well as high electrodensity by TEM. Robust photosynthetic machinery improves green light absorption, but not blue or red. In addition, low construction and dark respiration costs were related to improved total dry weight accumulation in shade conditions. By applying multivariate analyses, we conclude that improved green light absorbance, improved quantum yield and greater palisade parenchyma cell area are the primary components that drive increased plant growth under natural light-limited photosynthesis.

Discriminação entre solos formados em região transicional por meio de sua resposta espectral / Dicrimination between soils formed in transitional region through spectral response

Os levantamentos pedológicos são amplamente utilizados nos mapeamentos de solo por serem métodos confiáveis, no entanto, apesar de tal vantagem são demorados e trabalhosos. Dentro deste contexto, surge o sensoriamento remoto como uma técnica rápida e promissora capaz de auxiliar nos levantamentos, de forma a tornar o processo mais dinâmico. O objetivo deste trabalho foi avaliar a possibilidade de discriminação de cinco classes de solos localizadas no planalto de Apucarana por meio de suas respostas espectrais. Foi estabelecido um grid de 500 m x 500 m em uma área com dimensão de 2500 ha, a partir do qual foram coletadas amostras a 0-0,2 m e 0,8-1,0 m de profundidade. As reflectâncias foram obtidas com o FiedSpec 3 JR, na faixa de 350 a 2500 nm. Equações discriminantes e simulações foram geradas a partir das respostas espectrais das amostras de solo. Das 88 variáveis avaliadas, apenas 8 foram selecionadas pelo procedimento STEPDISC para fazerem parte dos modelos. As equações discriminantes geradas foram testadas, obtendo-se matrizes de confusão, as quais apresentaram acerto acima de 70% para cada classe de solo. Da mesma forma, equações discriminantes simuladas foram geradas, obtendo-se resultados mais significativos para reclassificação quando utilizados dados que fizeram parte da geração do modelo (60%) em comparação com os dados independentes do modelo (40%). As respostas espectrais das amostras de solo empregadas na análise discriminante foram capazes de dar subsídio para separação das cinco classes de solo da área de estudo, comprovando ser uma ferramenta valiosa mesmo em condições de elevada variabilidade pedológica e de atributos como em regiões transicionais.

The pedological surveys are widely used in soil mapping because they are reliable methods, however, although this advantage are time consuming and laborious. Within this context, remote sensing appears as a quickly and promising technique able to assist in the surveys in order to make the process more dynamic. The objective this work was to evaluate the possibility of discrimination of five classes of soils located in the plateau of Apucarana through their spectral responses. Was established a grid of 500 m x 500 m in an area with dimensions of 2500 ha, from which samples were collected at 0 to 0.2 and of 0.8 to 1.0 m deep. The reflectances were obtained with the FiedSpec 3 JR, in the range 350 to 2500 nm. Discriminant equations and simulations were generated from the spectral responses of soil samples. Of the 88 variables evaluated, only 8 were selected by the procedure STEPDISC to be part of the models. The discriminant equations generated were tested, resulting in confusion matrices, which showed accuracy above 70% for each class of soil. Likewise, simulated discriminant equations were generated, obtaining most significant results for reclassification when used data that were part of the model generation (60%) in comparison with the model independent data (40%). The spectral responses of soil samples used in discriminant analysis were able to give support for separation of five classes of soil in the study area, proving to be a valuable tool even in conditions of high pedological and attribute variability as in transitional regions.

Leishmaniose tegumentar americana em municípios da região noroeste do estado do Paraná: utilização de sensoriamento remoto para análise do tipo de vegetação e os locais de ocorrência da doença / American cutaneous leishmaniasis in municipalities in the northwestern region of Paraná State: use of remote sensing for analysis of vegetation types and places with disease occurrence

A leishmaniose tegumentar americana, doença endêmica da região noroeste do Paraná, é transmitida por flebotomíneos ao homem e a animais como cães, tatu, gambá e roedores silvestres. A doença vem ocorrendo em locais de derrubadas de matas e às margens de rios e lagoas com vegetação arbórea, onde o homem entra em contato com o inseto infectado. Esta doença constitui-se num problema de saúde pública devido à ocorrência de surtos epidêmicos em todo o Paraná. Em decorrência da importância de se conhecer áreas endêmicas, este trabalho utilizou dados de fichas epidemiológicas de pacientes atendidos no período de 1999 a 2004 associado às áreas de desmatamento identificadas por técnicas e produtos de sensoriamento remoto por satélite. Os resultados mostraram que a ocorrência de casos em municípios da região coincidem com as prováveis áreas supostas de infecção dos pacientes.

American cutaneous leishmaniasis, an endemic disease in the northwestern region of Paraná, Brazil, is transmitted by phlebotomines to man and animals like dogs, armadillos, opossums and wild rodents. This disease has been occurring in places where forests have been felled and on the banks of rivers and lakes with arboreal vegetation, where man comes into contact with infected insects. This disease is a public health problem because of the occurrence of epidemic outbreaks throughout Paraná. Because of the importance of finding out about endemic areas, this study used epidemiological file data on patients attended between 1999 and 2004. These data were correlated with areas of forest felling that were identified by means of satellite remote sensing techniques and products. The results showed that the occurrences of cases in the municipalities of this region coincided with the presumed likely areas for patient infection.

[Serological investigation of subclinical cutaneous leishmaniasis cases following an outbreak in an endemic area]. / Investigação sorológica de casos subclínicos de leishmaniose tegumentar após um surto em uma localidade endêmica.

Subclinical cases of cutaneous leishmaniasis were investigated in a district of Maringá, State of Paraná, by means of the indirect immunofluorescence reaction and enzyme immunoassay. Among the 130 samples analyzed, one presented titer 40 and three presented titer 20 in the indirect immunofluorescence reaction, while 11 samples were positive in the enzyme immunoassay reaction. These data suggest that subclinical cases exist at this locality.

Investigação sorológica de casos subclínicos de leishmaniose tegumentar após um surto em uma localidade endêmica / Serological investigation of subclinical cutaneous leishmaniasis cases following an outbreak in an endemic area

Investigou-se casos subclínicos de leishmaniose tegumentar em um bairro de Maringá-Paraná, pelas reações de imunofluorescência indireta e enzimaimunoensaio. Das 130 amostras analisadas, uma amostra apresentou título 40 e três apresentaram título 20 pela imunofluorescência indireta, enquanto 11 amostras apresentaram-se positivas pela reação de enzimaimunoensaio. Estes dados sugerem a existência de casos subclínicos nesta localidade.

Subclinical cases of cutaneous leishmaniasis were investigated in a district of Maringá, State of Paraná, by means of the indirect immunofluorescence reaction and enzyme immunoassay. Among the 130 samples analyzed, one presented titer 40 and three presented titer 20 in the indirect immunofluorescence reaction, while 11 samples were positive in the enzyme immunoassay reaction. These data suggest that subclinical cases exist at this locality.

[American cutaneous leishmaniasis in municipalities in the northwestern region of Paraná State: use of remote sensing for analysis of vegetation types and places with disease occurrence]. / Leishmaniose tegumentar americana em municípios da região noroeste do estado do Paraná: utilização de sensoriamento remoto para análise do tipo de vegetação e os locais de ocorrência da doença.

American cutaneous leishmaniasis, an endemic disease in the northwestern region of Paraná, Brazil, is transmitted by phlebotomines to man and animals like dogs, armadillos, opossums and wild rodents. This disease has been occurring in places where forests have been felled and on the banks of rivers and lakes with arboreal vegetation, where man comes into contact with infected insects. This disease is a public health problem because of the occurrence of epidemic outbreaks throughout Paraná. Because of the importance of finding out about endemic areas, this study used epidemiological file data on patients attended between 1999 and 2004. These data were correlated with areas of forest felling that were identified by means of satellite remote sensing techniques and products. The results showed that the occurrences of cases in the municipalities of this region coincided with the presumed likely areas for patient infection.

Intervenções pedagógicas em educação para a saúde realizadas junto aos grupos indígenas kaingang de Ivaí e Faxinal no Paraná / Pedagogic intervention in health education, developed with the Kaingang indegenous group of Ivai and Faxinal (State of Paraná)

O presente trabalho expõe experiências de intervenções pedagógicas, em Educação para a Saúde, desenvolvidas junto aos grupos indígenas Kaingang de Ivaí e Faxinal, no Paraná, em decorrência dos resultados de pesquisas na área de saúde e meio ambiente, realizadas nos anos de 2004 a 2007, por pesquisadores do Programa Interdisciplinar de Estudos de Populações, Laboratório de Arqueologia, Etnologia e Etno-História da Universidade Estadual de Maringá. As pesquisas e as intervenções pedagógicas que possibilitaram a elaboração e publicação de material pedagógico bilíngüe, Educação para a Saúde - Eg hárh keto venhkarhrán e Terra Limpa – Ga Jãnhkri, tiveram apoio do Fundo Nacional do Meio Ambiente e da Fundação Nacional de Saúde. A metodologia utilizada pautou-se na elaboração de Projetos de Trabalho, possibilitando a ampla participação de professores e estudantes das instituições educativas existentes nas Terras Indígenas. Após a publicação e divulgação do material pedagógico, organizou-se um curso de formação de professores e agentes de saúde. Estas intervenções tiveram como objetivo a divulgação de informações provenientes dos diagnósticos e formulação de ações coletivas para o controle de doenças parasitárias existentes entre estas populações, visando a melhoria das condições ambientais e de saúde das áreas. Tanto as pesquisas como as intervenções foram desenvolvidas com a ampla participação das comunidades (grupos familiares, lideranças, professores índios e não-índios, auxiliares de pesquisa e estudantes) visando ao favorecimento da aprendizagem dos estudantes indígenas e ao desenvolvimento de práticas de saúde acessíveis à comunidade.

The present work exposes experiences of pedagogic interventions in Health Education, developed with the Kaingang indigenous group of Ivaí and Faxinal in Paraná, from the results of studies in the area of health and environment, accomplished between 2004 and 2007 by researchers of the Interdisciplinary Program of Population Studies, Laboratory of Archeology, Ethnology and Ethno-history of the State University of Maringá. The research and the pedagogic interventions made the elaboration and publication of bilingual pedagogic material possible: Eg hárh ke to venhkarhrán (Health Education) and Ga Jãnhkri (Clean Earth) had support the from National Fund for the Environment and the National Health Foundation. The methodology used was basedon the elaboration of work projects that enabled wide participation from teachers and students of the existent educational institutions in the Indigenous Lands. After the publication and popularization of the pedagogic material, a course was organized for the formation of teachers and health agents. These interventions had as objective the popularization of information disclosure of the diagnoses and formulation of collective actions forcontrol of existing parasitic diseases among these populations, seeking the improvement of the environmentaland health conditions and the areas. The studies and interventions were developed with the communities (family groups, leaderships, Indian teachers and non-Indians, research assistants and students) seeking the learning improvement of the indigenous students and the development of health practices accessible to the community.

El trabajo trata las experiencias hacia intervenciones pedagógicas en Educación y Salud echas con los Kaingang del Ivaí y Faxinal en la província del Paraná despues de las investigaciones en salud y medio ambiente hechas en los anos de 2004 a 2007 por el Programa Interdisciplinar de Estudios de Populaciones enla Universidad Estadual de Maringá. Los trabajos hicieran publicar libros escolares bilíngües: Eg hárh ke tovenhkarhrán – Educación hacia la Salud y Tiera Limpia - Ga Jãnhkri, con el apoio del Fondo Nacional del Medio Ambiente y de la Fundación Nacional hacia la Salud. La metodologia utilizada fue bazada en Proyetos deTrabajo con la participación de ninos e sus profesores en las escuelas indígenas. Con la publicación de los libros se organizo un curso de formación hacia los profesores y agentes de salud. Las intervenciones mostraran las informaciones de las investigaciones hechas y formulación de acciones colectivas hacia elcontrol de enfermedades parasitologicas en estas populaciones hacia la mejoria de las condiciones ambientales y de salud en las areas. Los trabajos fueran desarrollados con la participación de las comunidades (familias,lideranzas, profesores, indígenas y non-indigenas y estudiantes) hacia el mejoramienteo de la aprendizage de los estudiantes indígenas y el desarrollo de practicas de salud acecibles a la comunidad.

Floristic and phytosociological description of a riparian forest and the relationship with the edaphic environment in Caiuá Ecological Station - Paraná - Brazil

Relationship in soil physical and chemical properties of soil, floristic and phytosociological association of semi-deciduous riparian forest of the Paranapanema River (R) and Rosana Reservoir (Re) in the Caiuá Ecological Station were evaluated. Aerial photography and satellite image were used to determine the forest cover and to locate 15 transects 50 x 30m (1500m²),which were used to sample trees with diameters at breast height (DBH)>15 cm. R contained 1487 individuals from 33 families, 64 genders and 73 species. The Shannon-weaver index (H') was 3.318. Re contained 1146 individuals from 35 families, 72 genders and 85 species and the H' was 3.755. There was a statistically significant difference (P<0.05) between R and Re environments and statistically significant correlations (p<0.05) were detected between soil physical attributes and Importance Values (IV) using the Detrended Correspondence Analysis (DCA).

Das análises das propriedades física e química dos solos, da florística e da fítossociológica da vegetação arbórea de fragmentos da floresta estacional semidecidual localizados às margens do Rio Paranapanema (R) e do Reservatório de Rosana (Re) na Estação Ecológica do Caiuá (22°41'S e 52°55'W), verificou-se as correlações vegetação x solo. Utilizou-se fotografias aéreas e imagens de satélite para determinar a similaridade da cobertura florestal e alocar 15 transectos de 50m x 30m (1500m²) cada, amostrou-se os indivíduos com perímetro a altura do peito (PAP) 15 cm. No R amostrou-se 1487 indivíduos, de 33 famílias, 64 gêneros e 73 espécies e índice de Shannon-weaver (H') de 3,318. No Re registrou-se 1146 indivíduos de 35 famílias, 72 gêneros e 85 espécies e H' de 3,755. Os resultados mostraram ambientes estatisticamente diferentes e efetiva correlação entre os atributos físicos do solo e os Valores de Importância (VI) ordenados pela Detrended Correspondence Analysis (DCA).