Evolution of the pathogen content during co-composting of winery and distillery wastes

The aim of this study was to monitor some microbial indicators and pathogen contents (sulphite reducers clostridia, total enterobacteriaceae, total coliforms, faecal coliforms (Escherichia coli), enterococci, Staphylococcus aureus and Salmonella spp.) throughout the co-composting of wastes from the winery and distillery industry with other organic residues, as well as the effect of the composting system used. Seven different piles using mixtures of winery-distillery wastes with other organic materials were prepared. P1 and P2 were made using grape stalk (GS), grape marc (GM), exhausted grape marc (EGM) and sewage sludge (SS), whereas in P3 and P4 were also used exhausted grape marc with cow manure (CW) and poultry manure (PM), respectively, using the Rutgers system. Additionally, P2 was watered with vinasse (V). The rest of piles (P5, P6 and P7) were prepared with grape marc, exhausted grape marc, cow manure and poultry manure, using the turning system. The effectiveness of the composting process to reduce the pathogen content was higher in the static aerated piles than in those elaborated with the turning. The relatively high temperatures (50-60 degrees C) reached in some of the piles produced a notable decrease in some microbial groups, such as total and faecal coliforms (E. coli), but the characteristics of the raw materials used notably influenced the pathogen contents of the end-product.     

Assessing the agronomic and environmental effects of the application of cattle manure compost on soil by multivariate methods

Multivariate analysis was used for interpreting data from a pot experiment using samples of three Spanish soils. Samples of soil fertilized with compost were compared with untreated control samples. We also compared the effect of adding the compost to soil with a controlled moisture content of 50% of its water holding capacity (WHC), and to a near-saturated soil (95% WHC). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) were used; they perfectly differentiated sample groups both as a function of the treatment applied and by sampling date. The compost samples were characterized by higher pH, electrical conductivity (EC), organic matter (OM) content and cation exchange capacity (CEC), together with nutrient concentrations than the control pots. The pots with a soil–compost mixture at 95% WHC presented lower values of EC, CEC, inorganic N, K, Na and B than the mixtures at 50% WHC. Multivariate methods may therefore be useful for the analysis and interpretation of a large number of data in soil research.     

蔬菜-秸秆废物堆肥化中细菌群落变化研究

以蔬菜-秸秆废物为堆肥原料,在堆肥化过程中应用Biolog方法研究不同阶段细菌群落的动态变化。聚类分析与主成分分析表明,细菌群落结构在一次发酵期间发生着剧烈变化,二次发酵期间趋于稳定。能转化Biolog板上第一、二类碳源的细菌是蔬菜-秸秆废物堆肥化进程中的主要细菌种群,且与木质纤维素的转化有关;第四、六类碳源可表征堆肥中耐受高温的细菌,其中第四类碳源转化细菌与木质纤维素的降解有关,而第六类碳源转化细菌属于易降解有机物转化细菌。     

Spatiotemporal evaluation of trace elements in river water using multivariate methods

The multivariate statistical methods (cluster analysis and discriminant analysis [DA]) are used to evaluate the quality of water spatiotemporally in the Brahmani River basin. A total of 15 water quality indicators (pH, Chloramines, Sodium, Sulfate, Phosphate, Potassium, Boron, Fluoride, Cadmium, Copper, Lead, Nickel, Zinc, Iron, and Total Chromium) are collected for 13 years in two seasons (the dry and the wet season) from nine sampling stations. These stations are grouped into four clusters (Considerable [Co], Low Pollution [LP], Moderate Pollution [MP], and Extreme Pollution [(EP]) by hierarchical agglomerative cluster analysis based on the similarities of water quality characteristics. The nine important influencing discriminating water quality indicators are extracted by stepwise method of temporal DA,where as eight important influencing indicators for dry season and eight indicators (Potassium, Phosphate, Fluoride, Copper, Lead, Nickel, Iron, and Total Chromium) for wet season are found out by the spatial DA. The pollution status in the river basin was observed from overall trend in (Considerable) Co to (Extreme Pollution) EP regions. The study could be very useful in prioritizing the important sites for water resources and pollution management.     

Rapid discrimination of commercial strawberry cultivars using Fourier transform infrared spectroscopy data combined by multivariate analysis

To determine whether pattern recognition based on metabolite fingerprinting for whole cell extracts can be used to discriminate cultivars metabolically, leaves and fruits of five commercial strawberry cultivars were subjected to Fourier transform infrared (FT-IR) spectroscopy. FT-IR spectral data from leaves were analyzed by principal component analysis (PCA) and Fisher’s linear discriminant function analysis. The dendrogram based on hierarchical clustering analysis of these spectral data separated the five commercial cultivars into two major groups with originality. The first group consisted of Korean cultivars including ‘Maehyang’, ‘Seolhyang’, and ‘Gumhyang’, whereas in the second group, ‘Ryukbo’ clustered with ‘Janghee’, both Japanese cultivars. The results from analysis of fruits were the same as of leaves. We therefore conclude that the hierarchical dendrogram based on PCA of FT-IR data from leaves represents the most probable chemotaxonomical relationship between cultivars, enabling discrimination of cultivars in a rapid and simple manner. The authors Suk Weon Kim and Sung Ran Min contributed equally to this work.     

DGGE和T-RFLP在堆肥微生物群落结构研究中的应用

对堆肥微生物种群分布及其动态变化的研究进行了分析,论述了分子生物技术中的变性梯度凝胶电泳和末端标记限制性片段长度多态性的原理和特点,以及用于研究堆肥微生物的群落结构演变规律,为分析和筛选堆肥中的微生物提供更加有效、快速的信息,促进堆肥技术的发展。     

Relating the classical covariance adjustment techniques of multivariate growth curve models to modern univariate mixed effects models

The relationship between the modern univariate mixed model for analyzing longitudinal data, popularized by Laird and Ware (1982, Biometrics 38, 963-974), and its predecessor, the classical multivariate growth curve model, summarized by Grizzle and Allen (1969, Biometrics 25, 357-381), has never been clearly established. Here, the link between the two methodologies is derived, and balanced polynomial and cosinor examples cited in the literature are analyzed with both approaches. Relating the two models demonstrates that classical covariance adjustment for higher-order terms is analogous to including them as random effects in the mixed model. The polynomial example clearly illustrates the relationship between the methodologies and shows their equivalence when all matrices are properly defined. The cosinor example demonstrates how results from each method may differ when the total variance-covariance matrix is positive definite, but that the between-subjects component of that matrix is not so constrained by the growth curve approach. Additionally, advocates of each approach tend to consider different covariance structures. Modern mixed model analysts consider only those terms in a model's expectation (or linear combinations), and preferably the most parsimonious subset, as candidates for random effects. Classical growth curve analysts automatically consider all terms in a model's expectation as random effects and then investigate whether "covariance adjusting" for higher-order terms improves the model. We apply mixed model techniques to cosinor analyses of a large, unbalanced data set to demonstrate the relevance of classical covariance structures that were previously conceived for use only with completely balanced data.     

Application of multivariate resolution methods to the study of biochemical and biophysical processes

Multivariate resolution methods make up a set of mathematical tools that may be applied to the analysis and interpretation of spectroscopic data recorded when monitoring a physical or chemical process with multichannel detectors. The goal of resolution methods is the recovery of chemical and/or physical information from the experimental data. Such data include, for example, the number of intermediates present in a reaction, the rate or equilibrium constants, and the spectra for each one of those intermediates. Multivariate resolution methods have been shown to be useful for the study of biophysical and biochemical processes such as folding/unfolding of proteins or nucleic acids. The present article reviews the most frequently used resolution methods, the limitations on their use, and their latest applications in protein and nucleic acid research.     

Comparison of fatty acids of marine fungi using multivariate statistical analysis

Summary Ten obligate marine fungi have as their principal fatty acids 160, 180, 181n9 and 182n6. The fatty acids ranged from 14 to 22 carbons, completely dominated by those with even numbers of carbons. The amount of unsaturated fatty acids varied between 35% and 80%. Each isolate contained small amounts of the acids 183n3 and 204n6. Branched, hydroxy- or cyclic fatty acids were not detected. Multivariate statistical, i.e. principal component analysis, showed that all ten strains could be distinguished on the basis of their fatty acid composition. These results indicate that the marine fungi do not have an unusual fatty acid composition and suggest that chemometric, multivariate analysis might be employed to confirm taxonomic relationships among these organisms.     

Plant and animal wastes composting: Effects of the N source on process performance

The aim of this work was to evaluate the impact of different N-rich animal wastes on the composting of ligno-cellulosic wastes by a range of classical and novel methods, with particular emphasis on microbial community composition.     

Spatial structure of fish assemblages in a tropical estuarine lagoon: combining multivariate and geostatistical techniques

The spatial structure and seasonal changes of estuarine fish assemblages in the Ciénaga Grande de Santa Marta (CGSM) were analysed based on four seasonal comprehensive surveys conducted in 1993-1994 and 1997. Geostatistical and multivariate techniques were used to: (a) determine seasonal changes in spatial distribution of the species richness, and (b) identify assemblages of estuarine fish and their relation to abiotic factors. Potential biotic interactions affecting the assemblage structure were also explored. A total of 11075 individuals representing 39 species were collected, with Eugerres plumieri, Diapterus rhombeus, Micropogonias furnieri, Mugil incilis, Cathorops spixii, Elops saurus and Anchovia clupeoides as dominant species between seasons. Spatial distribution of fish richness differed between rainy and dry seasons in each year, whereas species mapping showed spatial patchiness in 1993-1994 and gradients during 1997. Strong evidence of species saturation was found in all seasons, suggesting biotic interactions limiting species richness at a threshold density of ca. 50 ind./5000 m². Marine, marine-estuarine and freshwater species were classified in each season according to their capability to cope with salinity fluctuations. Associations defined by functional feeding guilds were also identified. Empirical and statistic evidence showed that fish assemblages differed between seasons within each year, and each assemblage was always dominated by a small number of species, notably E. plumieri in both years 1993-1994 and 1997. Between-season differences in fish assemblage structure in the CGSM seem to be driven by abiotic factors; however, evidence of species saturation could suggest the existence of density-dependent factors operating together.     

Pattern analysis techniques to process fermentation curves: application to discrimination of enological alcoholic fermentations

In fermentation processes, kinetic curves are generally aimed at control purposes. However, these curves could also contain information about inherent features of the product (such as origin, quality, etc.). This article presents several pattern analysis techniques used to classify fermentation curves. An application to alcoholic fermentation is presented as an illustration: it aims at retrieving the origin of a must from its fermentation curve. The fermentation kinetics of five vineyard musts, harvested over 9 years on the same parcels, were recorded. From these curves two sets of variables were generated: The first (p(1)) gathers all the kinetic curve points. The second (p(2)) contains a restrained number of variables, generated by the expert knowledge of the enologist. The set p(2) was processed by two very different techniques: a linear one (factorial discriminant analysis) and a nonlinear one (artificial neural networks). The set p(1) was processed by a new chemometric technique, the discriminant partial least-squares regression. For all the sets and the techniques used the selection of variables was studied. The interest in the latter is largely demonstrated both by theoretical and practical discussions. The discrimination results (up to 94% of good predictions) enhance the interest of the on-line measurements and their use in such pattern analysis tools.     

Discrimination of Francisella tularensis subspecies using surface enhanced laser desorption ionization mass spectrometry and multivariate data analysis

Francisella tularensis causes the zoonotic disease tularemia, and is considered a potential bioterrorist agent due to its extremely low infection dose and potential for airborne transmission. Presently, F. tularensis is divided into four subspecies; tularensis, holarctica, mediasiatica and novicida. Phenotypic discrimination of the closely related subspecies with traditional methods is difficult and tedious. Furthermore, the results may be vague and they often need to be complemented with virulence tests in animals. Here, we have used surface enhanced laser desorption ionization time-of-flight mass spectrometry (SELDI-TOF-MS) to discriminate between the four subspecies of F. tularensis. The method is based on the differential binding of protein subsets to chemically modified surfaces. Bacterial thermolysates were added to anionic, cationic, and copper ion-loaded immobilized metal affinity SELDI chip surfaces. After binding, washing, and SELDI-TOF-MS different protein profiles were obtained. The spectra generated from the different surfaces were then used to characterize each bacterial strain. The results showed that the method was reproducible, with an average intensity variation of 21%, and that the mass precision was good (300-450 ppm). Moreover, in subsequent cluster analysis and principal component analysis (PCA) data for the analyzed Francisella strains grouped according to the recognized subspecies. Partial least squares-discriminant analysis (PLS-DA) of the protein profiles also identified proteins that differed between the strains. Thus, the protein profiling approach based on SELDI-TOF-MS holds great promise for rapid high-resolution phenotypic identification of bacteria.     

Characterization of the caryopsis of common wheat varieties and F2 generation hybrids by means of multivariate analysis

Summary Nineteen parameters of common wheat kernels were investigated by the multivariate procedure. A relationship between the DBC parameter and both specific gravity and kernel filling was found. In some populations an interdependence of the DBC parameter and the subaleurone endosperm thickness was found. In the analysis of principal components the DBC parameter was represented mostly by particular component. Significant dependences were found between the dimensions as well as the weight of the caryopsis and some dimensions of the crease and endosperm cavity. The dendrite constructed on the basis of the calculated Mahalanobis's generalized distances matrix represents the near affinity of reciprocal hybrids or pure lines originating from a single cultivar as well as hybrids and their parental forms.     

Structure-activity relationship study of cytotoxic neolignan derivatives using multivariate analysis and computation-aided drug design

Dehydrodieugenol B and five related natural neolignans were isolated from the Brazilian plant species Nectandra leucantha. Three of these compounds were shown to be active against murine (B16F10) and human (A2058) melanoma cells but non-toxic to human fibroblasts (T75). These results stimulated the preparation of a series of 23 semi-synthetic derivatives in order to explore structure-activity relationships and study the biological potential of these derivatives against B16F10 and A2058 cell lines. These structurally-related neolignan derivatives were analyzed by multivariate statistics and machine learning, which indicated that the most important characteristics were related to their three-dimensional structure and, mainly, to the substituents on the neolignan skeleton. The results suggested that the presence of hydroxyl or alkoxyl groups at positions 3, 4 and 5 (with appropriate sidechains) promoted an increase in electropological and charge density, which seem to be important for biological activity against murine (B16F10) and human (A2058) melanoma cells.     

Vertical distribution of bacterial community structure in the sediments of two eutrophic lakes revealed by denaturing gradient gel electrophoresis (DGGE) and multivariate analysis techniques

Vertical distribution of bacterial community structure was investigated in the sediments of two eutrophic lakes of China, Lake Taihu and Lake Xuanwu. Profiles of bacterial communities were generated using a molecular fingerprinting technique, denaturing gradient gel electrophoresis (DGGE) followed by DNA sequence analysis, and the results were interpreted with multivariate statistical analysis. To assess changes in the genetic diversity of bacterial communities with changing depth, DGGE banding patterns were analysed by cluster analysis. Distinct clusters were recognized in different sampling stations of Lake Taihu. Canonical correspondence analysis (CCA) was carried out to infer the relationship between environmental variables and bacterial community structure. DGGE samples collected at the same sampling site clustered together in both lakes. Total phosphorus, organic matter and pH were considered to be the key factors driving the changes in bacterial community composition.     

Sex determination of three raptor species using morphology and molecular techniques

ABSTRACT.   Accurate identification of sex is important for many raptor studies, but may be difficult to determine in the field for some species. Because of size differences between males and females, morphological measurements have often been used to sex raptors. However, few investigators have examined the accuracy of using measurements of individuals made at one location to sex individuals of the same species at another location. Our objective was to develop more accurate region-specific methods for determining the sex of Red-tailed Hawks ( Buteo jamaicensis ), Red-shouldered Hawks ( B. lineatus ), and Cooper's Hawks ( Accipiter cooperii ) migrating through and wintering in California. We determined sex using a polymerase chain reaction (PCR)-based genetic test and grouped individuals based on sex, age, and geographic area. We did not combine groups due to differences in measurements between age classes and geographic areas. We then compared a suite of morphological measurements between males and females of each combination, and developed both a discriminant function and a flowchart to determine the sex of Red-tailed and Red-shouldered hawks in the field. The flowcharts were more accurate than the functions for both species. We also confirmed the accuracy of the current flowchart used to determine the sex of Cooper's Hawks migrating along the California coast. These region-specific methods for Red-tailed and Cooper's hawks were generally more accurate than published methods, possibly indicating different populations of these species and highlighting the importance of validating sexing methods when using them in different locations.     

Effects of moisture content and initial pH in composting process on heavy metal removal characteristics of grass clipping compost used for stormwater filtration

Heavy metals are common contaminants in stormwater runoff. One of the devices that can be used to effectively and economically remove heavy metals from runoff is a yard waste compost stormwater filter. The primary goal of composting is to reduce waste volume rather than to produce stormwater filter media. Moisture content (MC) and initial pH, the two important parameters in composting, were studied for their effects on yard waste volume reduction and heavy metal adsorption performances of the compost. The main objective of this investigation was to examine whether the conditions that provided high yard waste volume reduction would also result in compost with good heavy metal removal performances. Manila grass was composted at different initial pHs (5–9) and MCs (30–70%) and the composts were used to adsorb cadmium, copper, lead and zinc from water. Results indicated that MC is more critical than initial pH for both volume reduction and production of compost with high metal adsorption performances. The most optimal conditions for the two attributes were not exactly the same but lower MCs of 30–40% and pH 7 or higher tended to satisfy both high volume reduction and effective metal adsorption.     

Spatial ordination of vegetation data using a generalization of Wartenberg's multivariate spatial correlation

Question: Are there spatial structures in the composition of plant communities? Methods: Identification and measurement of spatial structures is a topic of great interest in plant ecology. Univariate measurements of spatial autocorrelation such as Moran's I and Geary's c are widely used, but extensions to the multivariate case (i.e. multi‐species) are rare. Here, we propose a multivariate spatial analysis based on Moran's I (MULTISPATI) by introducing a row‐sum standardized spatial weight matrix in the statistical triplet notation. This analysis, which is a generalization of Wartenberg's approach to multivariate spatial correlation, would imply a compromise between the relations among many variables (multivariate analysis) and their spatial structure (autocorrelation). MULTISPATI approach is very flexible and can handle various kinds of data (quantitative and/or qualitative data, contingency tables). A study is presented to illustrate the method using a spatial version of Correspondence Analysis. Location: Territoire d'Etude et d'Expérimentation de Trois‐Fontaines (eastern France). Results: Ordination of vegetation plots by this spatial analysis is quite robust with reference to rare species and highlights spatial patterns related to soil properties.