Advanced monitoring of high-rate anaerobic reactors through quantitative image analysis of granular sludge and multivariate statistical analysis

Four organic loading disturbances were performed in lab-scale EGSB reactors fed with ethanol. In load disturbance 1 (LD1) and 2 (LD2), the organic loading rate (OLR) was increased between 5 and 18.5 kg COD m(-3) day(-1), through the influent ethanol concentration increase, and the hydraulic retention time decrease from 7.8 to 2.5 h, respectively. Load disturbances 3 (LD3) and 4 (LD4) were applied by increasing the OLR to 50 kg COD m(-3) day(-1) during 3 days and 16 days, respectively. The granular sludge morphology was quantified by image analysis and was related to the reactor performance, including effluent volatile suspended solids, indicator of washout events. In general, it was observed the selective washout of filamentous forms associated to granules erosion/fragmentation and to a decrease in the specific acetoclastic activity. These phenomena induced the transitory deterioration of reactor performance in LD2, LD3, and LD4, but not in LD1. Extending the exposure time in LD4 promoted acetogenesis inhibition after 144 h. The application of Principal Components Analysis determined a latent variable that encompasses a weighted sum of performance, physiological and morphological information. This new variable was highly sensitive to reactor efficiency deterioration, enclosing variations between 27% and 268% in the first hours of disturbances. The high loadings raised by image analysis parameters, especially filaments length per aggregates area (LfA), revealed that morphological changes of granular sludge, should be considered to monitor and control load disturbances in high rate anaerobic (granular) sludge bed digesters.     

Hybrid neural network modeling of a full-scale industrial wastewater treatment process

In recent years, hybrid neural network approaches, which combine mechanistic and neural network models, have received considerable attention. These approaches are potentially very efficient for obtaining more accurate predictions of process dynamics by combining mechanistic and neural network models in such a way that the neural network model properly accounts for unknown and nonlinear parts of the mechanistic model. In this work, a full-scale coke-plant wastewater treatment process was chosen as a model system. Initially, a process data analysis was performed on the actual operational data by using principal component analysis. Next, a simplified mechanistic model and a neural network model were developed based on the specific process knowledge and the operational data of the coke-plant wastewater treatment process, respectively. Finally, the neural network was incorporated into the mechanistic model in both parallel and serial configurations. Simulation results showed that the parallel hybrid modeling approach achieved much more accurate predictions with good extrapolation properties as compared with the other modeling approaches even in the case of process upset caused by, for example, shock loading of toxic compounds. These results indicate that the parallel hybrid neural modeling approach is a useful tool for accurate and cost-effective modeling of biochemical processes, in the absence of other reasonably accurate process models.     

Comment on: "Energy landscape of a small peptide revealed by dihedral angle principal component analysis"

The dihedral angle principal component analysis method published recently by Mu, Nguyen, and Stock, is shown to produce distortions of the free energy landscape due to the neglect of constraints in the coordinates. It is further shown that these distortions can create artificial minima and energy barriers. The rugged energy landscape that the authors find for a small peptide chain might thus be an artifact of their method.     

主成分分析在麦蚜发生量预报中的应用

介绍了主成分分析的计算方法。对利用主成分建立回归方程的一些问题进行了探讨。建立了山东菏泽地区麦蚜发生量预报模式,预报效果理想。     

Predation of bacteria by the protozoa Tetrahymena pyriformis in toluene-degrading cultures

The influence of the toluene concentration on predation of toluene-degrading bacteria by the protozoa Tetrahymena pyriformis was investigated in suspended batch cultures continuously aerated with toluene-contaminated air. At gas phase concentrations of 0.035 to 0.74 g m–3, toluene did not significantly affected protozoan activity and the final bacteria concentration was reduced by growing protozoa by 98 to 99.9% compared to protozoa-free controls. As the toluene concentration was increased to 1.16–1.33 g m–3, the reduction of the bacteria cell concentration was 80%. At 3.35 g toluene m–3, growth of T. pyriformis was completely inhibited. Overall, the results presented herein demonstrate that protozoa grazing on bacteria play a major role in controlling bacterial cell concentration, but that the toxicity of the treated pollutants to the protozoa is an important factor that needs to be taken into account in biological treatment processes.     

基于图像识别技术的金针菇表型高通量采集与分析

金针菇Flammulina filiformis作为国内产销量大、工厂化生产程度高的食用菌种类之一,其育种工作因表型信息采集效率低下而受到限制.本研究以金针菇子实体可见光图像为信息来源,基于育种工作对表型信息的需求,利用图像识别技术和深度学习模型,提出了金针菇高通量表型信息采集分析方法,并开发了相应分析系统软件.利用该...     

Simultaneous confidence corridors for mean functions in functional data analysis of imaging data

Motivated by recent work involving the analysis of biomedical imaging data, we present a novel procedure for constructing simultaneous confidence corridors for the mean of imaging data. We propose to use flexible bivariate splines over triangulations to handle an irregular domain of the images that is common in brain imaging studies and in other biomedical imaging applications. The proposed spline estimators of the mean functions are shown to be consistent and asymptotically normal under some regularity conditions. We also provide a computationally efficient estimator of the covariance function and derive its uniform consistency. The procedure is also extended to the two-sample case in which we focus on comparing the mean functions from two populations of imaging data. Through Monte Carlo simulation studies, we examine the finite sample performance of the proposed method. Finally, the proposed method is applied to analyze brain positron emission tomography data in two different studies. One data set used in preparation of this article was obtained from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database.     

The use of principal component analysis (PCA) for pattern recognition in Eucalyptus grandis wood biodegradation experiments

Eucalyptus grandis wood was biodegraded by eight basidiomycetes and two ascomycetes. Four groups of decayed wood samples were recognized based on the principal component analysis (PCA) of weight and component loss data. Among the 10 fungal species studied, no selective lignin biodegradation was achieved. PCA was very efficient in recognizing wood decay patterns and seems to be a useful tool to analyse large group of weight- and component-loss data.     

Relative diversity and community structure analysis of rumen protozoa according to T-RFLP and microscopic methods

Protozoa are common inhabitants of the rumen where they play roles in host nutrition and methanogenesis. Knowledge of how changes in the composition of protozoa communities affect these processes is limited in part due to a lack of efficient methods for protozoa community analysis. In this study, a terminal-restriction fragment length polymorphism (T-RFLP) assay targeting the 18S rRNA gene was developed for comparative analysis of rumen protozoa communities. Comparison of diversity and structure of protozoa communities from hay-fed versus silage/grain-fed cattle via T-RFLP analysis yielded similar overall results to microscopy analysis. According to both methods, Entodinium spp. were more abundant in the silage/grain-fed cattle and protozoa diversity (as calculated using the Shannon index) was higher for the hay-fed cattle due to greater species evenness. Type B protozoa were more prevalent in the hay-fed cattle, whereas Type A protozoa were more prevalent in the silage/grain-fed cattle. Analysis of similarity (ANOSIM) indicated that the protozoa communities from hay-fed and silage/grain-fed cattle were different, and multivariate analysis indicated that pen mates (i.e., cattle fed the same diet and housed together) tended to have similar protozoa communities types. In summary, we present a T-RFLP method for analyzing rumen protozoa communities which complements traditional microscopy approaches but has the advantage of being amenable to high-throughput.     

Accurate root length measurement by image analysis

Algorithms for estimating root length by image analysis should lead to results that have no systematic error (bias), be insensitive to preferential root orientation, valid across a wide range of sample sizes and adjust for overlap between roots in samples, to reduce the effort needed in spreading out root systems. We propose a new algorithm that forms a compromise between small bias and robustness (insensitivity to variation in sample size and preferential root orientation), and provide a simple way of dealing with root overlap. Image analysis was performed on a Macintosh computer using the public domain NIH Image program. The digital image of the root was processed to get the thinned image (skeleton). The numbers of orthogonally and diagonally connected pairs of pixels (N _o and N _d, respectively) in the skeleton were counted separately and used for length (L) calculation. A new length calculation equation was introduced so that the effect of orientation on length calculation was minimized; L=[N d ²+(N _d+N _o/2)²]^1/2+N _o/2. The maximum error due to orientation of a single line was evaluated for an ideal line, and the analysis revealed that the new equation was less affected by orientation than previous equations. Copper wire and rice (Oryza sativa L.) roots containing both primary and fine secondary root were measured manually and with image analysis. The two methods showed good agreement within 1.5%. The proposed image analysis method yielded length estimates with CV from 0.23 to 0.88%, which was lower than the CVs of the line-intersect method. Moreover, the lengths of overlapping samples were calculated correctly because the image analysis method distinguished an overlapping pixel from a thinned image, while the calculation with the line-intersect method showed underestimation because overlaps were not considered in that method. This revised version was published online in June 2006 with corrections to the Cover Date.     

Multivariate analysis of the Vicia sativa L. aggregate

GIL, J. & CUBERO, J. I., 1993. Multivariate analysis of the Vicia sativa L. aggregate . The sativa group of species was studied by using multivariate analysis and karyological analysis. Thirty biometrical characters were studied by using 92 Vicia sativa L. accessions from a variety of geographical sources. The four main taxa analysed were: sativa, macrocarpa, cordata and angustifolia. Principal component analysis provided clear separation among sativa, cordata and angustifolia in terms of the first two components. Karyotype analysis confirmed the PCA results, and by combining both methods, misclassed specimens can be correctly determined. Cluster analyses using the characters which displayed the greatest loads over the first components were performed in order to identify the most valuable characters from the taxonomic point of view. These were flower length, calyx length, pod width, constriction of the pod between seeds, leaf length, leaflet width, seed length and seed width. Our results suggest an early separation of angustifolia from the ancestral stem. The taxon cordata may be useful in future plant breeding programmes not only for crossing with V. saliva to increase the leaflet size of this taxon, but also as a bridge species between sativa and angustifolia.     

Cystatin-like cysteine proteinase inhibitors of parasitic protozoa

A new method has been developed for detecting cystatins and other cysteine proteinase inhibitors. The method, which involves protein separation by SDS-PAGE followed by a cysteine proteinase overlay step, is more sensitive than previously reported techniques: as little as 1 ng of recombinant human cystatin C can be detected and cysteine proteinase inhibitors could also be detected in complex protein mixtures such as bovine foetal serum. The method has been used to show, for the first time, cysteine proteinase inhibitors in lysates of a range of parasitic protozoa (Trypanosoma brucei, Leishmania mexicana mexicana, Toxoplasma gondii and Tritrichomonas foetus) and to confirm that one occurs in the free-living ciliate Tetrahymena pyriformis. Cystatin-like inhibitory activity was also demonstrated in boiled lysates of L. mexicana mexicana using conventional assays methods.     

A comparative study of ciliated protozoa communities in activated-sludge plants

Abstract: Ciliated protozoa present in ten activated sludge plants at Madrid (Spain) were identified. The abundance of key groups of ciliates was determined at each plant; attached ciliates made up the most abundant and representative group. Multivariate statistical analysis was employed to study relationships between ciliates and both the physico-chemical and operational parameters of the plants. Partial correlation analysis revealed: (1) The indicator value of attached ciliates in assessing management and performance of the activated sludge process, (2) the relationship of swimming ciliates with short-aged sludges and lower quality effluents and (3) the direct association between swimming-crawling ciliates and bad settlement conditions of the sludge. Factor analysis showed the associations of the most frequent species of ciliates with the operational parameters of the plants, suggesting the indicator value of some of the species: Vorticella striata was related with poor quality of effluent; Aspidisca cicada with stable plant conditions, and Litonotus lamella with a deficiently settling sludge.     

Reconstructing the population history of Puerto Rico by means of mtDNA phylogeographic analysis

The haplogroup identities of 800 mtDNAs randomly and systematically selected to be representative of the population of Puerto Rico were determined by restriction fragment length polymorphism (RFLP), revealing maternal ancestries in this highly mixed population of 61.3% Amerindian, 27.2% sub‐Saharan African, and 11.5% West Eurasian. West Eurasian frequencies were low in all 28 municipalities sampled, and displayed no geographic patterns. Thus, a statistically significant negative correlation was observed between the Amerindian and African frequencies of the municipalities. In addition, a statistically highly significant geographic pattern was observed for Amerindian and African mtDNAs. In a scenario in which Amerindian mtDNAs prevailed on either side of longitude 66°16′ West, Amerindian mtDNAs were more frequent west of longitude 66°16′ West than east of it, and the opposite was true for African mtDNAs. Haplogroup A had the highest frequency among Amerindian samples (52.4%), suggesting its predominance among the native Taínos. Principal component analysis showed that the sub‐Saharan African fraction had a strong affinity to West Africans. In addition, the magnitudes of the Senegambian and Gulf of Guinea components in Puerto Rico were between those of Cape Verde and São Tomé. Furthermore, the West Eurasian component did not conform to European haplogroup frequencies. HVR‐I sequences of haplogroup U samples revealed a strong North African influence among West Eurasian mtDNAs and a new sub‐Saharan African clade. Am J Phys Anthropol 128:131‐155, 2005. © 2005 Wiley‐Liss, Inc.     

基于高光谱成像和主成分分析的水稻茎叶分割

在单株水稻表型测量研究中,为了实现绿叶面积和茎叶相关表型参数的准确计算提供技术保障,茎叶的分割是非常重要的一步。传统的人工测量方法费时费力,且主观性较强,而基于普通相机拍摄的彩色图像进行分割效果很差。本研究介绍了一种使用可见光-近红外高光谱成像系统自动区分单株盆栽水稻茎叶的方法。首先将各波长下的图像从原始二进制数据中提取出来,接着使用主成分分析所有波长下的图像,并提取出主要的主成分图像,再基于数字图像处理技术将茎叶区分开。实验结果表明,本系统以及文中所用方法对分蘖盛期的水稻茎叶有很好的分割效果,这为后续水稻茎叶表型性状高通量、数字化、无损准确提取提供了重要的技术保障,并进一步促进植物表型组学的发展。     

Morphological characterization of yeast by image analysis

A semiautomatic image analysis method, with minimal operator intervention, has been developed to characterize the morphology of yeast cells under the assumption that they have an ellipsoidic shape. The cells are observed by optical microscopy and the surface and the minor and major half-axes of the projection of the ellipsoid on the image plane are determined. Using this method, yeast size distributions and population kinetics (single and budding cells, cell clusters) are determined during alcoholic fermentations. Combination of image analysis with a methylene blue viability test is examined but the staining procedure induces a change in the size of the cells. .     

Supervised two-dimensional functional principal component analysis with time-to-event outcomes and mammogram imaging data

Screening mammography aims to identify breast cancer early and secondarily measures breast density to classify women at higher or lower than average risk for future breast cancer in the general population. Despite the strong association of individual mammography features to breast cancer risk, the statistical literature on mammogram imaging data is limited. While functional principal component analysis (FPCA) has been studied in the literature for extracting image-based features, it is conducted independently of the time-to-event response variable. With the consideration of building a prognostic model for precision prevention, we present a set of flexible methods, supervised FPCA (sFPCA) and functional partial least squares (FPLS), to extract image-based features associated with the failure time while accommodating the added complication from right censoring. Throughout the article, we hope to demonstrate that one method is favored over the other under different clinical setups. The proposed methods are applied to the motivating data set from the Joanne Knight Breast Health cohort at Siteman Cancer Center. Our approaches not only obtain the best prediction performance compared to the benchmark model, but also reveal different risk patterns within the mammograms.