Neural networks in chemosystematic studies of asteraceae: A classification based on a dichotomic approach

This paper describes the application of artificial neural nets as an alternative and efficient method for the classification of botanical taxa based on chemical data (chemosystematics). A total of 28,000 botanical occurrences of chemical compounds isolated from the Asteraceae family were chosen from the literature, and grouped by chemical class for each species. Four tests were carried out to differentiate and classify different botanical taxa. The qualifying capacity of the artificial neural nets was dichotomically tested at different hierarchical levels of the family, such as subfamilies and groups of Heliantheae subtribes. Furthermore, two specific subtribes of the Heliantheae and two genera of one of these subtribes were also tested. In general, the artificial neural net gave rise to good results, with multiple-correlation values R > 0.90. Hence, it was possible to differentiate the dichotomic character of the botanical taxa studied.

Chemical composition of flavonoids and styrylpyrones and the genetic variability of isozymes in natural populations of Cryptocarya mandioccana Meisner (Lauraceae)

The chemical composition of leaves of 57 trees of Cryptocarya mandioccana from three populations of southeastern Brazil was investigated through HPLC, assaying six flavonoids, seven styrylpyrones, and seven unidentified compounds. From 51 of the former trees, genotypes were obtained from 40 polymorphic loci of 19 isozymes. Cluster analyses of the phytochemical and genetical variation revealed that trees exhibited four chemotypes and five clusters from isozymes, respectively. Discriminant analyses from selected variables of the isozymic and chemical data sets were performed, respectively, in relation to the four chemotypes and the five isozyme clusters. The classification of individuals presented respective error estimates of 9.16% and 13.57%, indicating that the genetic data could explain the clusters from chernotypes and vice versa at acceptable error levels. Linear regressions with Dummy variable showed significant association of locus Skdh-2 with quercetin-3-O-beta-D-glucopyranoside and cryptofolione, indicating that its alleles would be responsible for the chemotype variation between individuals. (c) 2006 Elsevier Ltd. All rights reserved.

Delineation of specific management areas for coffee cultivation based on the soil-relief relationship and numerical classification

Predicting and mapping productivity areas allows crop producers to improve their planning of agricultural activities. The primary aims of this work were the identification and mapping of specific management areas allowing coffee bean quality to be predicted from soil attributes and their relationships to relief. The study area was located in the Southeast of the Minas Gerais state, Brazil. A grid containing a total of 145 uniformly spaced nodes 50 m apart was established over an area of 31. 7 ha from which samples were collected at depths of 0. 00-0. 20 m in order to determine physical and chemical attributes of the soil. These data were analysed in conjunction with plant attributes including production, proportion of beans retained by different sieves and drink quality. The results of principal component analysis (PCA) in combination with geostatistical data showed the attributes clay content and available iron to be the best choices for identifying four crop production environments. Environment A, which exhibited high clay and available iron contents, and low pH and base saturation, was that providing the highest yield (30. 4l ha-1) and best coffee beverage quality (61 sacks ha-1). Based on the results, we believe that multivariate analysis, geostatistics and the soil-relief relationships contained in the digital elevation model (DEM) can be effectively used in combination for the hybrid mapping of areas of varying suitability for coffee production. © 2012 Springer Science+Business Media New York.

A study of neolignan compounds with biological activity against Paracoccidioides brasiliensis by using quantum chemical and chemometric methods

Métodos quimiométricos (estatísticos) são empregados para classificar um conjunto de compostos derivados de neolignanas com atividade biológica contra a Paracoccidioides brasiliensis. O método AM1 (Austin Model 1) foi utilizado para calcular um conjunto de descritores moleculares (propriedades) para os compostos em estudo. A seguir, os descritores foram analisados utilizando os seguintes métodos de reconhecimento de padrões: Análise de Componentes Principais (PCA), Análise Hierárquica de Agrupamentos (HCA) e o método de K-vizinhos mais próximos (KNN). Os métodos PCA e HCA mostraram-se bastante eficientes para classificação dos compostos estudados em dois grupos (ativos e inativos). Três descritores moleculares foram responsáveis pela separação entre os compostos ativos e inativos: energia do orbital molecular mais alto ocupado (EHOMO), ordem de ligação entre os átomos C1'-R7 (L14) e ordem de ligação entre os átomos C5'-R6 (L22). Como as variáveis responsáveis pela separação entre compostos ativos e inativos são descritores eletrônicos, conclui-se que efeitos eletrônicos podem desempenhar um importante papel na interação entre receptor biológico e compostos derivados de neolignanas com atividade contra a Paracoccidioides brasiliensis.

Classification of honeys from Pará State (Amazon region, Brazil) produced by three different species of bees

Vinte e sete amostras de mel, produzidas em dez cidades do Estado do Pará (Região Amazônica, norte do Brasil) por três espécies diferentes de abelhas (Apis mellifera, Melipona fasciculata e Melipona flavoneata), foram analisadas em seus teores de elementos minerais (Al, As, Ba, Be, Bi, Ca, Cd, Co, Cr, Cu, Fe, K, Li, Mg, Mn, Na, Ni, Sr e Zn) e alguns parâmetros fisicoquímicos (cor, umidade, densidade, pH, sólidos insolúveis e solúveis totais, cinzas, condutividade elétrica, índice de formol, acidez livre, hidroximetilfurfural, açúcares redutores e totais e sacarose). Os teores minerais foram determinados via espectrometria de emissão atômica por plasma acoplado indutivamente (ICP OES) e as análises dos parâmetros físico-químicos seguiram metodologias oficiais. Os resultados das análises físico-químicas apresentaram-se de acordo com a legislação nacional e internacional, bem como com outros trabalhos similares ao redor do mundo. A análise estatística multivariada (análise por agrupamento hierárquico (HCA) e por componentes principais (PCA)) foi aplicada aos resultados dos teores metálicos e aos parâmetros físico-químicos, sendo possível a separação das amostras de mel conforme a espécie produtora.

Quantitative Chemical Profile and Multivariate Statistical Analysis of Alembic Distilled Sugarcane Spirit Fractions

Concentrations of 39 organic compounds were determined in three fractions (head, heart and tail) obtained from the pot still distillation of fermented sugarcane juice. The results were evaluated using analysis of variance (ANOVA), Tukey's test, principal component analysis (PCA), hierarchical cluster analysis (HCA) and linear discriminant analysis (LDA). According to PCA and HCA, the experimental data lead to the formation of three clusters. The head fractions give rise to a more defined group. The heart and tail fractions showed some overlap consistent with its acid composition. The predictive ability of calibration and validation of the model generated by LDA for the three fractions classification were 90.5 and 100%, respectively. This model recognized as the heart twelve of the thirteen commercial cachacas (92.3%) with good sensory characteristics, thus showing potential for guiding the process of cuts.

Provisional Biopharmaceutical Classification of Some Common Herbs Used in Western Medicine

The aim of this study was to classify some markers of common herbs used in Western medicine according to the Biopharmaceutical Classification System (BCS). The BCS is a scientific approach to classify drug substances based upon their intestinal permeability and their solubility, at the highest single dose used, within the physiologically relevant pH ranges. Known marker components of twelve herbs were chosen from the USP Dietary Supplement Compendium Monographs. Different BCS parameters such as intestinal permeability (P-eff) and solubility (C-s) were predicted using the ADMET Predictor, which is a software program to estimate biopharmaceutical relevant molecular descriptors. The dose number (D-0) was calculated when information from the literature was available to identify an upper dose for individual markers. In these cases the herbs were classified according to the traditional BCS parameters using Peff and Do. When no upper dose could be determined, then the amount of a marker that is just soluble in 250 mL of water was calculated. This value, M-x, defines when a marker is changing from highly soluble to poorly soluble according to BCS criteria. This biopharmaceutically relevant value can be a useful tool for marker selection. The present study showed that a provisional BCS classification of herbs is possible but some special considerations need to be included into the classification strategy. The BCS classification can be used to choose appropriate quality control tests for products containing these markers. A provisional BCS classification of twelve common herbs and their 35 marker compounds is presented.

Elemental abundances and classification of carbon-enhanced metal-poor stars

We present a detailed study of carbon-enhanced metal-poor (CEMP) stars, based on high-resolution spectroscopic observations of a sample of 18 stars. The stellar spectra for this sample were obtained at the 4.2 m William Herschel Telescope in 2001 and 2002, using the Utrecht Echelle Spectrograph, at a resolving power R similar to 52 000 and S/N similar to 40, covering the wavelength range lambda lambda 3700-5700 angstrom. The atmospheric parameters determined for this sample indicate temperatures ranging from 4750 K to 7100 K, log g from 1.5 to 4.3, and metallicities -3.0 <= [Fe/H]<=-1.7. Elemental abundances for C, Na, Mg, Sc, Ti, Cr, Cu, Zn, Sr, Y, Zr, Ba, La, Ce, Nd, Sm, Eu, Gd, Dy are determined. Abundances for an additional 109 stars were taken from the literature and combined with the data of our sample. The literature sample reveals a lack of reliable abundance estimates for species that might be associated with the r-process elements for about 67% of CEMP stars, preventing a complete understanding of this class of stars, since [Ba/Eu] ratios are used to classify them. Although eight stars in our observed sample are also found in the literature sample, Eu abundances or limits are determined for four of these stars for the first time. From the observed correlations between C, Ba, and Eu, we argue that the CEMP-r/s class has the same astronomical origin as CEMP-s stars, highlighting the need for a more complete understanding of Eu production.

Tumor classification of six common cancer types based on proteomic profiling by MALDI imaging

In clinical diagnostics, it is of outmost importance to correctly identify the source of a metastatic tumor, especially if no apparent primary tumor is present. Tissue-based proteomics might allow correct tumor classification. As a result, we performed MALDI imaging to generate proteomic signatures for different tumors. These signatures were used to classify common cancer types. At first, a cohort comprised of tissue samples from six adenocarcinoma entities located at different organ sites (esophagus, breast, colon, liver, stomach, thyroid gland, n = 171) was classified using two algorithms for a training and test set. For the test set, Support Vector Machine and Random Forest yielded overall accuracies of 82.74 and 81.18%, respectively. Then, colon cancer liver metastasis samples (n = 19) were introduced into the classification. The liver metastasis samples could be discriminated with high accuracy from primary tumors of colon cancer and hepatocellular carcinoma. Additionally, colon cancer liver metastasis samples could be successfully classified by using colon cancer primary tumor samples for the training of the classifier. These findings demonstrate that MALDI imaging-derived proteomic classifiers can discriminate between different tumor types at different organ sites and in the same site.

INVESTIGATION OF AIR JIGGING AND AIR CLASSIFICATION TO RECOVER METALLIC PARTICLES FROM ANALYTICAL SAMPLES

Analyzing “nuggety” gold samples commonly produces erratic fire assay results, due to random inclusion or exclusion of coarse gold in analytical samples. Preconcentrating gold samples might allow the nuggets to be concentrated and fire assayed separately. In this investigation synthetic gold samples were made using similar density tungsten powder and silica, and were preconcentrated using two approaches: an air jig and an air classifier. Current analytical gold sampling method is time and labor intensive and our aim is to design a set-up for rapid testing. It was observed that the preliminary air classifier design showed more promise than the air jig in terms of control over mineral recovery and preconcentrating bulk ore sub-samples. Hence the air classifier was modified with the goal of producing 10-30 grams samples aiming to capture all of the high density metallic particles, tungsten in this case. Effects of air velocity and feed rate on the recovery of tungsten from synthetic tungsten-silica mixtures were studied. The air classifier achieved optimal high density metal recovery of 97.7% at an air velocity of 0.72 m/s and feed rate of 160 g/min. Effects of density on classification were investigated by using iron as the dense metal instead of tungsten and the recovery was seen to drop from 96.13% to 20.82%. Preliminary investigations suggest that preconcentration of gold samples is feasible using the laboratory designed air classifier.

Incidence and causative agents of chemical eye injuries in Switzerland

CONTEXT Chemical eye injuries are ophthalmological emergencies with a high risk of secondary complications and severe visual loss. Only limited epidemiological data for such injuries are available for many countries. PATIENTS AND METHODS We performed two independent studies. The cause of chemical eye injuries was assessed with a prospective questionnaire study. Questionnaires were sent to all ophthalmologists in Switzerland. A total of 163 patients (205 eyes) were included, between December 2012 and October 2014. Independent of the questionnaire study, the incidence of chemical eye injuries was assessed with a retrospective cohort study design using the database of the mandatory accident insurance. RESULTS Ophthalmological questionnaires revealed that plaster/cement (20.5%), alkaline (12.2%) and acid (10.2%) solutions caused the highest number of chemical injuries. Only 2% of all injuries were classified as grade III and none as grade IV (Roper-Hall classification). The official toxicological information phone-hotline was contacted in 4.3% of cases. Using data from the accident insurance, an incidence of chemical eye injuries of about 50/100 000/year was found in the working population. CONCLUSION Here, we present data on the involved agents of chemical eye injuries in Switzerland, and also the incidence of such injuries in the working population. This may also help to assess the need for further education programs and to improve and direct preventive measures.

Chemical composition of surficial bottom sediments from the Chukchi Sea and adjacent part of the Arctic Ocean

This paper presents data on chemical composition of bottom sediments from the Chukchi Sea and the adjacent Arctic Ocean. Multivariate statistical techniques were used for analysis of the data set and revealed that grain size fractionation of the original terrigenous component during sedimentation was the major factor of clustering of the samples in study. Secondary factors include accumulation of biogenic siliceous and carbonate material and chemogenic or biochemical accumulation of iron, manganese, and some trace elements. The latter factor was significant in areas of tectonic activity within the graben-rift system of the Chukchi Sea.

Morphological, chemical and physical characteristics of cryosolic soils from Arctic Canada

Cryosols are permafrost-affected soils whose genesis is dominated by cryogenic processes, resulting in unique macromorphologies, micromorphologies, thermal characteristics, and physical and chemical properties. In addition, these soils are carbon sinks, storing high amounts of organic carbon collected for thousands of years. In the Canadian soil classification, the Cryosolic Order includes mineral and organic soils that have both cryogenic properties and permafrost within 1 or 2 m of the soil surface. This soil order is divided into Turbic, Static and Organic great groups on the basis of the soil materials (mineral or organic), cryogenic properties and depth to permafrost. The great groups are subdivided into subgroups on the basis of soil development and the resulting diagnostic soil horizons. Cryosols are commonly associated with the presence of ground ice in the subsoil. This causes serious problems when areas containing these soils are used for agriculture and construction projects (such as roads, town sites and airstrips). Therefore, where Cryosols have high ice content, it is especially important either to avoid these activities or to use farming and construction methods that maintain the negative thermal balance.