Effects of maternal bisphenol A diglycidyl ether exposure during gestation and lactation on behavior and brain development of the offspring

Bisphenol A diglycidyl ether (BADGE) is an epoxy resin used for the inner coating of canned food and beverages. BADGE can easily migrate from the containers and become a contaminant. In this study, we examined the effects of BADGE exposure to the dams on the behavioral, structural, and developmental abnormalities in the offspring. Female pregnant mice were fed with a diet containing BADGE (0.15 or 1.5 mg/kg/day) during gestation and lactation periods. In an open field test, the time spent in the corner area significantly increases in male mice of high-dose BADGE group at 5 weeks old. The histological analysis using offspring brain at postnatal day 1 delivered from BADGE (1.5 mg/kg/day)-treated dams demonstrates that positive signals of Forkhead box P2- and COUP-TF interacting protein 2 are restricted in each cortical layer, but not in the control brain. In addition, the maternal BADGE exposure reduces nestin-positive fibers of the radial glia and T-box transcription factor 2-positive intermediate progenitors in the inner subventricular zone. Furthermore, a direct BADGE exposure promotes neurite outgrowth and neuronal connection in the primary cultured cortical neurons. These data suggest that maternal BADGE exposure can accelerate neuronal differentiation in fetuses and induce anxiety-like behavior in juvenile mice

Detection of ambrosia beetles using a pan-sharpened image generated from ALOS/AVNIR-2 and ALOS/PRISM imagery

Aim of study: The ambrosia beetle, Platypus quercivorus, is a vector of Japanese oak wilt, which causes massive mortality of oak trees in Japan. ALOS/AVNIR-2 true color images can be used to help detect areas of oak wilt, although such detection by inventory surveys is not realistic. Applying pan-sharpening techniques, a higher spatial resolution multispectral image can be generated from lower-resolution multispectral images and higher-resolution panchromatic images. In this study, some pan-sharpening algorithms were considered and evaluated for the detection of damage points. Area of study: The oak forests in Kanazawa prefecture, Japan. Materials and methods: The ALOS/AVNIR-2 and ALOS/PRISM sensors were used. The pan-sharpening algorithms adopted were: Brovey transformation, Modified IHS transformation, Wavelet transformation, Ehlers fusion and High Pass Filter Resolution Merge. Four types of quantitative spectral analyses and visual detection were conducted to evaluate these algorithms. Main results: The Brovey transformation was the most useful algorithm to detect damage points, although it had an issue with the preservation of spectral characteristics.Research highlights: The detection rate of damage points was improved in 50% by applying the Brovey algorithm to a 10 m panchromatic image and 62.5 m multispectral image

Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

Tea is second only to water as the world’s most popular drink and it is consumed in various forms, such as black and green teas. A range of cultivars has therefore been developed in response to customer preferences. In Japan, farmers may grow several cultivars to produce different types of tea. Leaf chlorophyll content is affected by disease, nutrition, and environmental factors. It also affects the color of the dried tea leaves: a higher chlorophyll content improves their appearance. The ability to quantify chlorophyll content would therefore facilitate improved tea tree management. Here, we measured the hyperspectral reflectance of 38 cultivars using a compact spectrometer. We also compared various combinations of preprocessing techniques and 14 variable selection methods. According to the ratio of performance to deviation (RPD), detrending was effective at reducing the influence of additive interference of scattered light from particles and then regression coefficients was the best variable selection method for estimating the chlorophyll content of tea leaves, achieving an RPD of 2.60 and a root mean square error of 3.21 μg cm−2

Hyperspectral indices for quantifying leaf chlorophyll concentrations performed differently with different leaf types in deciduous forests

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Nondestructive assessments of carotenoids content of broadleaved plant species using hyperspectral indices

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Applying Variable Selection Methods and Preprocessing Techniques to Hyperspectral Reflectance Data to Estimate Tea Cultivar Chlorophyll Content

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Assessing hyperspectral indices for tracing chlorophyll fluorescence parameters in deciduous forests

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Random Forest classification of crop type using multi-temporal TerraSAR-X dual-polarimetric data

The classification maps are required for the management and the estimation of agricultural disaster compensation; however, those techniques have yet to be established. Some supervised learning models may allow accurate classification. In this study, the Random Forest (RF) classifier and the classification and regression tree (CART) were applied to evaluate the potential of multi-temporal TerraSAR-X dualpolarimetric data, on the StripMap mode, for the classification of crop type. Furthermore, comparisons of the two algorithms and polarizations were carried out. In the study area, beans, beet, grasslands, maize, potato and winter wheat were cultivated, and these crop types were classified using the data set acquired in 2009. The classification results of RF were superior to those of CART, and the overall accuracies were 0.91–0.93

Discrimination of crop types with TerraSAR-X-derived information

Although classification maps are required for management and for the estimation of agricultural disaster compensation, those techniques have yet to be established. This paper describes the comparison of three different classification algorithms for mapping crops in Hokkaido, Japan, using TerraSAR-X (including TanDEM-X) dual-polarimetric data. In the study area, beans, beets, grasslands, maize, potatoes and winter wheat were cultivated. In this study, classification using TerraSAR-X-derived information was performed. Coherence values, polarimetric parameters and gamma nought values were also obtained and evaluated regarding their usefulness in crop classification. Accurate classification may be possible with currently existing supervised learning models. A comparison between the classification and regression tree (CART), support vector machine (SVM) and random forests (RF) algorithms was performed. Even though J–M distances were lower than 1.0 on all TerraSAR-X acquisition days, good results were achieved (e.g., separability between winter wheat and grass) due to the characteristics of the machine learning algorithm. It was found that SVM performed best, achieving an overall accuracy of 95.0% based on the polarimetric parameters and gamma nought values for HH and VV polarizations. The misclassified fields were less than 100 a in area and 79.5–96.3% were less than 200 a with the exception of grassland. When some feature such as a road or windbreak forest is present in the TerraSAR-X data, the ratio of its extent to that of the field is relatively higher for the smaller fields, which leads to misclassifications