Biodrying for mechanical–biological treatment of wastes: A review of process science and engineering

Biodrying is a variation of aerobic decomposition, used within mechanical–biological treatment (MBT) plants to dry and partially stabilise residual municipal waste. Biodrying MBT plants can produce a high quality solid recovered fuel (SRF), high in biomass content. Here, process objectives, operating principles, reactor designs, parameters for process monitoring and control, and their effect on biodried output quality are critically examined. Within the biodrying reactors, waste is dried by air convection, the necessary heat provided by exothermic decomposition of the readily decomposable waste fraction. Biodrying is distinct from composting in attempting to dry and preserve most of biomass content of the waste matrix, rather than fully stabilise it. Commercial process cycles are completed within 7–15 days, with mostly H₂O_(g) and CO₂ loses of ca. 25–30% w/w, leading to moisture contents of <20% w/w. High airflow rate and dehumidifying of re-circulated process air provides for effective drying. We anticipate this review will be of value to MBT process operators, regulators and end-users of SRF.     

Food-chain length and adaptive foraging

Food-chain length, the number of feeding links from the basal species to the top predator, is a key characteristic of biological communities. However, the determinants of food-chain length still remain controversial. While classical theory predicts that food-chain length should increase with increasing resource availability, empirical supports of this prediction are limited to those from simple, artificial microcosms. A positive resource availability–chain length relationship has seldom been observed in natural ecosystems. Here, using a theoretical model, we show that those correlations, or no relationships, may be explained by considering the dynamic food-web reconstruction induced by predator''s adaptive foraging. More specifically, with foraging adaptation, the food-chain length becomes relatively invariant, or even decreases with increasing resource availability, in contrast to a non-adaptive counterpart where chain length increases with increasing resource availability; and that maximum chain length more sharply decreases with resource availability either when species richness is higher or potential link number is larger. The interactive effects of resource availability, adaptability and community complexity may explain the contradictory effects of resource availability in simple microcosms and larger ecosystems. The model also explains the recently reported positive effect of habitat size on food-chain length as a result of increased species richness and/or decreased connectance owing to interspecific spatial segregation.     

云南紫金牛属植物资源调查研究

通过对云南东南部、南部、西南部至西北部分布的紫金牛属植物资源的野外调查和资料整理,以及资源分布地过去的资源量、分布状况、用途、利用方法及其利用历史等走访调查。归纳概述了19种云南紫金牛属植物的地理分布、形态特征、药用价值、观赏性状及其资源状况,为紫金牛属植物的种质资源保护和持续合理开发利用提出切实可行的建议和措施。     

Using linear algebra for protein structural comparison and classification

In this article, we describe a novel methodology to extract semantic characteristics from protein structures using linear algebra in order to compose structural signature vectors which may be used efficiently to compare and classify protein structures into fold families. These signatures are built from the pattern of hydrophobic intrachain interactions using Singular Value Decomposition (SVD) and Latent Semantic Indexing (LSI) techniques. Considering proteins as documents and contacts as terms, we have built a retrieval system which is able to find conserved contacts in samples of myoglobin fold family and to retrieve these proteins among proteins of varied folds with precision of up to 80%. The classifier is a web tool available at our laboratory website. Users can search for similar chains from a specific PDB, view and compare their contact maps and browse their structures using a JMol plug-in.     

The interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands

It is widely considered that it is not appropriate to treat β-pairs in isolation, since other secondary structural models (such as helices, coils), protein topology and protein tertiary structures would limit β-strand pairing. However, to understand the underlying mechanisms of β-sheet formation, studies ought to be performed separately on more concrete aspects. In this study, we focus on the parallel or antiparallel orientation of β-strands. First, statistical analysis was performed on the relative frequencies of the interstrand amino acid pairs within parallel and antiparallel β-strands. Consequently, features were extracted by singular value decomposition from the statistical results. By using the support vector machine to distinguish the features extracted from the two types of β-strands, high accuracy was achieved (up to 99.4%). This suggests that the interstrand amino acid pairs play a significant role in determining the parallel or antiparallel orientation of β-strands. These results may provide useful information for developing other useful algorithms to examine to the β-strand folding pathways, and could eventually lead to protein structure predictions.     

The effect of genome size on detailed species traits within closely related species of the same habitat

In spite of the large number of studies on genome size, studies comparing genome size and growth‐related traits across a wider range of species from the same habitat, taking into account species phylogeny, are largely missing. I estimated the relationship between genome size and different seed and seedling traits in perennial herbs occurring in dry calcareous grasslands in northern Bohemia, Czech Republic. There was no relationship between genome size and plant traits in simple regression analyses, but several strong relationships emerged in analyses based on pairwise phylogenetically independent contrasts. There was a significant relationship between monoploid genome size and production of above‐ground biomass, seedling establishment success and seed weight and between holoploid genome size and seed dormancy. Because the results are based on phylogenetically independent contrasts over a range of species from the same type of habitat, they allow me to conclude that these patterns were not because of species group or habitat type, but really show a correlation with genome size. In contrast to previous studies, I found a higher number of relationships with monoploid than with holoploid genome size. This may be because the traits observed in this study are directly related to plant growth and thus to life‐cycle time, which is determined by monoploid genome size. © 2009 The Linnean Society of London, Botanical Journal of the Linnean Society, 2009, 160 , 290–298.     

Use of direct and iterative solvers for estimation of SNP effects in genome-wide selection

The aim of this study was to compare iterative and direct solvers for estimation of marker effects in genomic selection. One iterative and two direct methods were used: Gauss-Seidel with Residual Update, Cholesky Decomposition and Gentleman-Givens rotations. For resembling different scenarios with respect to number of markers and of genotyped animals, a simulated data set divided into 25 subsets was used. Number of markers ranged from 1,200 to 5,925 and number of animals ranged from 1,200 to 5,865. Methods were also applied to real data comprising 3081 individuals genotyped for 45181 SNPs. Results from simulated data showed that the iterative solver was substantially faster than direct methods for larger numbers of markers. Use of a direct solver may allow for computing (co)variances of SNP effects. When applied to real data, performance of the iterative method varied substantially, depending on the level of ill-conditioning of the coefficient matrix. From results with real data, Gentleman-Givens rotations would be the method of choice in this particular application as it provided an exact solution within a fairly reasonable time frame (less than two hours). It would indeed be the preferred method whenever computer resources allow its use.     

Studying phenotypic evolution using multivariate quantitative genetics

Quantitative genetics provides a powerful framework for studying phenotypic evolution and the evolution of adaptive genetic variation. Central to the approach is G, the matrix of additive genetic variances and covariances. G summarizes the genetic basis of the traits and can be used to predict the phenotypic response to multivariate selection or to drift. Recent analytical and computational advances have improved both the power and the accessibility of the necessary multivariate statistics. It is now possible to study the relationships between G and other evolutionary parameters, such as those describing the mutational input, the shape and orientation of the adaptive landscape, and the phenotypic divergence among populations. At the same time, we are moving towards a greater understanding of how the genetic variation summarized by G evolves. Computer simulations of the evolution of G, innovations in matrix comparison methods, and rapid development of powerful molecular genetic tools have all opened the way for dissecting the interaction between allelic variation and evolutionary process. Here I discuss some current uses of G, problems with the application of these approaches, and identify avenues for future research.     

Identification of conserved modes of expression profiles during hippocampal development and neuronal differentiation in vitro

Gene expression profiles can be regarded as sums of simpler modes, analogous to the modes of a vibrating violin string. Decomposition of temporal gene expression profiles into modes by singular value decomposition (SVD) was reported before, but the question as to what degree the SVD modes can be interpreted in terms of biology remains open. We report and compare the results of SVD of published datasets from hippocampal development, neuronal differentiation in vitro, and a control time-series hippocampal dataset. We demonstrate that the first SVD mode reflects the magnitude of expression, interpretable on the Affymetrix platform. In the datasets from gene profiling of hippocampal development and neuronal differentiation, the second mode reflects a monotonous change in expression, either up- or down-regulation, in the time course of experiment. We demonstrate that the top two SVD modes are conserved between datasets and therefore, likely reflect properties of the underlying system (gene expression in hippocampus) rather than of a particular experiment or dataset. Our results also indicate that the magnitude of expression, and the direction of change in expression during hippocampal development, are uncorrelated, suggesting that they are regulated by largely independent mechanisms.