Determining “safe” levels of exposure: The validity of the use of 10X safety factors

The current guideline for risk assessment of chemicals having a toxic end point routinely uses the reference dose (RfD) approach based on uncertainty factors of 10. With this method the quality of individual risk assessment varies among chemicals, often resulting in either an over‐ or under‐estimation of adverse health risk. The purpose of this investigation is to evaluate whether the magnitude of the 10X uncertainty factors have scientific merit against data from published experimental studies. A compilation and comparison of ratios between LOAEL/NOAEL (Lowest Observed Adverse Effect Level/No Observed Adverse Effect Level), subchronic/chronic, and animal/human values were made. The results of the present investigation revealed that the use of default factors could be over‐conservative or unprotective. More reasonable estimates of the risk to human health would result in a reduction of unnecessary, and expensive over‐regulation. In addition to the LOAEL to NOAEL, and subchronic to chronic ratios, the adequacy of uncertainty factors for animal to human extrapolations were examined. Although a 10‐fold uncertainty factor (UF) is most commonly used in the risk assessment process, an examination of the literature for the compounds presented here suggests that the use of different values is scientifically justifiable.     

On theoretical principles of constitution typology

Constitution typology is thought of as a classification that is based on a few fundamental properties of the organism. These properties steadily distinguish individuals and permit the prediction of manifold secondary (external) features of types. At the transition from one constitutional type to another, concordant changes of trait values are determined by the respective changes of general causal factors. Theoretically, external constitution typology should be constructed on the basis of trait concordant "dynamics" under the change of fundamental factors at the individual level. Therefore, it does not depend on statistical relations characterizing population level. Causal factors and their influences on traits are impossible to be correctly and uniquely determined on the basis of population statistics relations. Causal factors do not coincide with the statistical ones of multivariate biometric analysis; equally, trait regression dependencies on statistical factors do not coincide with the causal influences.     

Keeping Small Mammals in Schools A Note on Maintenance Costs

The brine shrimp, Artemia salina, is a small marine invertebrate. Although not generally used in schools, it has many advantages as an animal for experimental investigation. It is widely available, cheap, and easily cultured. A large number of experiments can be devised to investigate such factors as the importance of salinity on egg hatching success rale, and the effects of temperature and density-dependent competition for food on growth rates. Observations can also be made on development and reproductive behaviour. This paper includes a general account of the biology of Artemia, instructions on how the organism may be cultured, the results of some experiments on the ecology and population biology of the species, and some suggestions for further studies.     

Dynamic co‐culture metabolic models reveal the fermentation dynamics,metabolic capacities and interplays of cheese starter cultures

In this study, we have investigated the cheese starter culture as a microbial community through a question: can the metabolic behaviour of a co‐culture be explained by the characterized individual organism that constituted the co‐culture? To address this question, the dairy‐origin lactic acid bacteria Lactococcus lactis subsp. cremoris, Lactococcus lactis subsp. lactis, Streptococcus thermophilus and Leuconostoc mesenteroides, commonly used in cheese starter cultures, were grown in pure and four different co‐cultures. We used a dynamic metabolic modelling approach based on the integration of the genome‐scale metabolic networks of the involved organisms to simulate the co‐cultures. The strain‐specific kinetic parameters of dynamic models were estimated using the pure culture experiments and they were subsequently applied to co‐culture models. Biomass, carbon source, lactic acid and most of the amino acid concentration profiles simulated by the co‐culture models fit closely to the experimental results and the co‐culture models explained the mechanisms behind the dynamic microbial abundance. We then applied the co‐culture models to estimate further information on the co‐cultures that could not be obtained by the experimental method used. This includes estimation of the profile of various metabolites in the co‐culture medium such as flavour compounds produced and the individual organism level metabolic exchange flux profiles, which revealed the potential metabolic interactions between organisms in the co‐cultures.     

Improving Collective Estimations Using Resistance to Social Influence

Groups can make precise collective estimations in cases like the weight of an object or the number of items in a volume. However, in others tasks, for example those requiring memory or mental calculation, subjects often give estimations with large deviations from factual values. Allowing members of the group to communicate their estimations has the additional perverse effect of shifting individual estimations even closer to the biased collective estimation. Here we show that this negative effect of social interactions can be turned into a method to improve collective estimations. We first obtained a statistical model of how humans change their estimation when receiving the estimates made by other individuals. We confirmed using existing experimental data its prediction that individuals use the weighted geometric mean of private and social estimations. We then used this result and the fact that each individual uses a different value of the social weight to devise a method that extracts the subgroups resisting social influence. We found that these subgroups of individuals resisting social influence can make very large improvements in group estimations. This is in contrast to methods using the confidence that each individual declares, for which we find no improvement in group estimations. Also, our proposed method does not need to use historical data to weight individuals by performance. These results show the benefits of using the individual characteristics of the members in a group to better extract collective wisdom.     

基于不同可加性方法的黑龙江省红松人工林林分生物量模型

大尺度估算森林生物量一直是人们关注的焦点,而构建林分水平的生物量模型是一种估算森林乔木层生物量的方法。本研究基于聚合法1、聚合法2、平差法、分解法构建红松人工林林分生物量模型,并对比分析4种可加性方法的预测精度,为黑龙江省红松人工林的生物量预测提供科学依据。各模型均使用权函数来消除各模型的异方差,并以留一交叉验证法(LOOCV)作为各模型的检验方法。结果表明: 平差法的整体预测能力略优于聚合法1、聚合法2和分解法,预测精度排序为平差法>聚合法1>聚合法2>分解法;分别对比不同林分断面积的预测能力时,4种可加性方法的预测精度不一致。当红松人工林的林分断面积分布于0～10或50～60 m²·hm^-2区间时,建议采用分解法的参数估计值,而林分断面积分布于其他区间时,建议采用平差法的参数估计值。     

PHYLOGENETIC VARIATION IN THE TOLERANCE AND UPTAKE OF ORGANIC CONTAMINANTS

An investigation into the phylogenetic variation of plant tolerance and the root and shoot uptake of organic contaminants was undertaken. The aim was to determine if particular families or genera were tolerant of, or accumulated organic pollutants. Data were collected from sixty-nine studies. The variation between experiments was accounted for using a residual maximum likelihood analysis to approximate means for individual taxa. A nested ANOVA was subsequently used to determine differences at a number of differing phylogenetic levels. Significant differences were observed at a number of phylogenetic levels for the tolerance to TPH, the root concentration factor and the shoot concentration factor. There was no correlation between the uptake of organic pollutants and that of heavy metals. The data indicate that plant phylogeny is an important influence on both the plant tolerance and uptake of organic pollutants. If this study can be expanded, such information can be used when designing plantings for phytoremediation or risk reduction during the restoration of contaminated sites.     

A quantitative gas-liquid chromatographic method for the estimation of hecogenin and tigogenin in the leaves, juice and sapogenin concentrates of Agave sisalana

A gas-liquid chromatographic method has been devised for the routine estimation of the hecogenin [3beta-hydroxy-(25R)-5beta-spirostan-12-one] and tigogenin [ (25R)-5beta-spirostan-3beta-ol] contents of Agave sisalana leaf and juice samples and of the crude sapogenin concentrates known as "coffee grounds". Because of partial degradation of the sapogenins in the GLC system it was found necessary to acetylate the compounds prior to their estimation. In East African samples the tigogenin proportion of the total sapogenin content is usually about 10%. At this level, the 95% inverse tolerance limits on predicted tigogenin weights are approximately +/- 7%.     

Functional Knowledge Transfer for High-accuracy Prediction of Under-studied Biological Processes

A key challenge in genetics is identifying the functional roles of genes in pathways. Numerous functional genomics techniques (e.g. machine learning) that predict protein function have been developed to address this question. These methods generally build from existing annotations of genes to pathways and thus are often unable to identify additional genes participating in processes that are not already well studied. Many of these processes are well studied in some organism, but not necessarily in an investigator''s organism of interest. Sequence-based search methods (e.g. BLAST) have been used to transfer such annotation information between organisms. We demonstrate that functional genomics can complement traditional sequence similarity to improve the transfer of gene annotations between organisms. Our method transfers annotations only when functionally appropriate as determined by genomic data and can be used with any prediction algorithm to combine transferred gene function knowledge with organism-specific high-throughput data to enable accurate function prediction.We show that diverse state-of-art machine learning algorithms leveraging functional knowledge transfer (FKT) dramatically improve their accuracy in predicting gene-pathway membership, particularly for processes with little experimental knowledge in an organism. We also show that our method compares favorably to annotation transfer by sequence similarity. Next, we deploy FKT with state-of-the-art SVM classifier to predict novel genes to 11,000 biological processes across six diverse organisms and expand the coverage of accurate function predictions to processes that are often ignored because of a dearth of annotated genes in an organism. Finally, we perform in vivo experimental investigation in Danio rerio and confirm the regulatory role of our top predicted novel gene, wnt5b, in leftward cell migration during heart development. FKT is immediately applicable to many bioinformatics techniques and will help biologists systematically integrate prior knowledge from diverse systems to direct targeted experiments in their organism of study.     

Prediction of enteric methane production,yield, and intensity in dairy cattle using an intercontinental database

Enteric methane (CH₄) production from cattle contributes to global greenhouse gas emissions. Measurement of enteric CH₄ is complex, expensive, and impractical at large scales; therefore, models are commonly used to predict CH₄ production. However, building robust prediction models requires extensive data from animals under different management systems worldwide. The objectives of this study were to (1) collate a global database of enteric CH₄ production from individual lactating dairy cattle; (2) determine the availability of key variables for predicting enteric CH₄ production (g/day per cow), yield [g/kg dry matter intake (DMI)], and intensity (g/kg energy corrected milk) and their respective relationships; (3) develop intercontinental and regional models and cross‐validate their performance; and (4) assess the trade‐off between availability of on‐farm inputs and CH₄ prediction accuracy. The intercontinental database covered Europe (EU), the United States (US), and Australia (AU). A sequential approach was taken by incrementally adding key variables to develop models with increasing complexity. Methane emissions were predicted by fitting linear mixed models. Within model categories, an intercontinental model with the most available independent variables performed best with root mean square prediction error (RMSPE) as a percentage of mean observed value of 16.6%, 14.7%, and 19.8% for intercontinental, EU, and United States regions, respectively. Less complex models requiring only DMI had predictive ability comparable to complex models. Enteric CH₄ production, yield, and intensity prediction models developed on an intercontinental basis had similar performance across regions, however, intercepts and slopes were different with implications for prediction. Revised CH₄ emission conversion factors for specific regions are required to improve CH₄ production estimates in national inventories. In conclusion, information on DMI is required for good prediction, and other factors such as dietary neutral detergent fiber (NDF) concentration, improve the prediction. For enteric CH₄ yield and intensity prediction, information on milk yield and composition is required for better estimation.     

Idiosyncratic relationships between pond invertebrates and environmental, temporal and patch-specific predictors of incidence

The incidence of eight species of microcrustacea and seven species of Dytiscidae (adults) was recorded from 30 small, temporary pools, in January and early summer, over six years. Incidence of each species, both in January and in summer, was modelled using logistic regression. Three categories of predictor variables were included in the models. Firstly systematic environmental variables; length of previous summer's dry phase, inter-pond links caused by flooding, distance between ponds and macrophyte structural density. Secondly temporal factors; year and previous incidence. Thirdly individual ponds, representing patch-specific effects. Logistic regression provided effective models of incidence for most species. However individual species varied in the factors that provided significant prediction of presence and absence. Systematic, temporal and patch-specific predictors were all significant predictors for different species of both microcrustacea and Dytiscidae. No single factor dominated most models. Individual factors showed positive or negative relationships with incidence with different species. Length of previous summer dry-phase was the most common predictor for microcrustacea and beetles in January. Summer models were more varied; length of previous dry phase and flood linkage between ponds and macrophyte density were significant predictors in many models, year and distance less important. Patch-specific predictors were significant for five of the microcrustacea and four of the beetles. The results suggest that individual species show idiosyncratic responses to systematic, temporal and patch-specific factors, rather than most species responding in a similar way to one or two dominant influences.     

No Longer Confidential: Estimating the Confidence of Individual Regression Predictions

Quantitative predictions in computational life sciences are often based on regression models. The advent of machine learning has led to highly accurate regression models that have gained widespread acceptance. While there are statistical methods available to estimate the global performance of regression models on a test or training dataset, it is often not clear how well this performance transfers to other datasets or how reliable an individual prediction is–a fact that often reduces a user’s trust into a computational method. In analogy to the concept of an experimental error, we sketch how estimators for individual prediction errors can be used to provide confidence intervals for individual predictions. Two novel statistical methods, named CONFINE and CONFIVE, can estimate the reliability of an individual prediction based on the local properties of nearby training data. The methods can be applied equally to linear and non-linear regression methods with very little computational overhead. We compare our confidence estimators with other existing confidence and applicability domain estimators on two biologically relevant problems (MHC–peptide binding prediction and quantitative structure-activity relationship (QSAR)). Our results suggest that the proposed confidence estimators perform comparable to or better than previously proposed estimation methods. Given a sufficient amount of training data, the estimators exhibit error estimates of high quality. In addition, we observed that the quality of estimated confidence intervals is predictable. We discuss how confidence estimation is influenced by noise, the number of features, and the dataset size. Estimating the confidence in individual prediction in terms of error intervals represents an important step from plain, non-informative predictions towards transparent and interpretable predictions that will help to improve the acceptance of computational methods in the biological community.     

Co-evolving residues in membrane proteins

MOTIVATION: The analysis of co-evolving residues has been exhaustively evaluated for the prediction of intramolecular amino acid contacts in soluble proteins. Although a variety of different methods for the detection of these co-evolving residues have been developed, the fraction of correctly predicted contacts remained insufficient for their reliable application in the construction of structural models. Membrane proteins, which constitute between one-fourth and one-third of all proteins in an organism, were only considered in few individual case studies. RESULTS: We present the first general study of correlated mutations in alpha-helical membrane proteins. Using seven different prediction algorithms, we extracted co-evolving residues for 14 membrane proteins having a solved 3D structure. On average, distances between correlated pairs of residues lying on different transmembrane segments were found to be significantly smaller compared to a random prediction. Covariation of residues was frequently found in direct sequence neighborhood to helix-helix contacts. Based on the results obtained from individual prediction methods, we constructed a consensus prediction for every protein in the dataset that combines obtained correlations from different prediction algorithms and simultaneously removes likely false positives. Using this consensus prediction, 53% of all predicted residue pairs were found within one helix turn of an observed helix-helix contact. Based on the combination of co-evolving residues detected with the four best prediction algorithms, interacting helices could be predicted with a specificity of 83% and sensitivity of 42%. AVAILABILITY: http://webclu.bio.wzw.tum.de/helixcorr/     

Data fusion-based assessment of raw materials in mammalian cell culture

In mammalian cell culture producing therapeutic proteins, one of the important challenges is the use of several complex raw materials whose compositional variability is relatively high and their influences on cell culture is poorly understood. Under these circumstances, application of spectroscopic techniques combined with chemometrics can provide fast, simple, and non‐destructive ways to evaluate raw material quality, leading to more consistent cell culture performance. In this study, a comprehensive data fusion strategy of combining multiple spectroscopic techniques is investigated for the prediction of raw material quality in mammalian cell culture. To achieve this purpose, four different spectroscopic techniques of near‐infrared, Raman, 2D fluorescence, and X‐ray fluorescence spectra were employed for comprehensive characterization of soy hydrolysates which are commonly used as supplements in culture media. First, the different spectra were compared separately in terms of their prediction capability. Then, ensemble partial least squares (EPLS) was further employed by combining all of these spectral datasets in order to produce a more accurate estimation of raw material properties, and compared with other data fusion techniques. The results showed that data fusion models based on EPLS always exhibit best prediction accuracy among all the models including individual spectroscopic methods, demonstrating the synergetic effects of data fusion in characterizing the raw material quality. Biotechnol. Bioeng. 2012; 109: 2819–2828. © 2012 Wiley Periodicals, Inc.     

Estimation of the leaf chlorophyll content using multiangular spectral reflectance factor

Chlorophyll is one of the primary pigments of plant leaves, and changes in its content can be used to characterize the physiological status of plants. Spectral indices have been devised and validated for estimating leaf chlorophyll content (LCC). However, most of the existing spectral indices do not consider the influence of angular reflection on the accuracy of the LCC estimation. In this study, the spectral reflectance factors of leaves from three plant species were measured from several observations in the principal plane. The relationship between the existing spectral indices and the LCC from different directions suggests that the directional reflection of a leaf surface impacts the accuracy of its LCC estimation. Subsequently, the ratio of reflectance differences, that is, the modified Datt index, was tested to reduce the directional reflection effect when predicting LCC. Our results indicated that the modified Datt index not only estimated LCC with high accuracy for all observation directions and plant species but also consistently predicted the LCC of each species in individual observation directions. Our method opens the possibility for optical detection of LCC using multiangular spectral reflection, which is convenient for plant science studies focused on the variation in LCC.     

Microbiological aspects of soil structure

Summary A simple experimental model has been devised to study the effects of organic amendments on aggregate stability and microbial activity in the soil. In the two soils investigated the different organic materials used all produced increases in aggregate stability but significant differences were observed both in the magnitude and time of the increase attributable to individual treatments. Microbial activity, as assessed by visual techniques, was broadly correlated with the changes in aggregate stability though the different treatments tended to produce their own characteristic patterns of colonization. It was generally difficult to relate changes in aggregate stability with the activity of specific micro-organisms but in the case of samples treated with glucose and cellulose, yeasts were the dominant organisms. These have subsequently been shown to produce substantial quantities of an extracellular polysaccharide which is effective in bringing about aggregation.     

Spatial distribution and abundance of nonindigenous coral genus Tubastraea (Cnidaria, Scleractinia) around Ilha Grande, Brazil.

The distribution and abundance of azooxanthellate coral Tubastraea Lesson, 1829 were examined at different depths and their slope preference was measured on rocky shores on Ilha Grande, Brazil. Tubastraea is an ahermatypic scleractinian nonindigenous to Brazil, which probably arrived on a ship's hull or oil platform in the late 1980's. The exotic coral was found along a great geographic range of the Canal Central of Ilha Grande, extending over a distance of 25 km. The abundance of Tubastraea was quantified by depth, using three different sampling methods: colony density, visual estimation and intercept points (100) for percentage of cover. Tubastraea showed ample tolerance to temperature and desiccation since it was found more abundantly in very shallow waters (0.1-0.5 m), despite the fact that hard substratum is available at greater depths at all the stations sampled. At most sites, 1 to 5 colonies per 0.25 m2 were found most frequently, but occasionally more than 50 colonies were found per 0.25 m2, indicating a somewhat gregarious spatial distribution for this coral. The coral Tubastraea was found to occupy slopes of every possible angle in the Canal Central of Ilha Grande, but more colonies were found occupying slopes of 80 to 100 degrees. Therefore, its insensitivity to angles of recruitment and its tolerance for different depths makes it an organism with great ecological tolerance, with a potential to colonize new areas and increase its current range in Brazil's coastal waters.     

Starvation,Together with the SOS Response,Mediates High Biofilm-Specific Tolerance to the Fluoroquinolone Ofloxacin

High levels of antibiotic tolerance are a hallmark of bacterial biofilms. In contrast to well-characterized inherited antibiotic resistance, molecular mechanisms leading to reversible and transient antibiotic tolerance displayed by biofilm bacteria are still poorly understood. The physiological heterogeneity of biofilms influences the formation of transient specialized subpopulations that may be more tolerant to antibiotics. In this study, we used random transposon mutagenesis to identify biofilm-specific tolerant mutants normally exhibited by subpopulations located in specialized niches of heterogeneous biofilms. Using Escherichia coli as a model organism, we demonstrated, through identification of amino acid auxotroph mutants, that starved biofilms exhibited significantly greater tolerance towards fluoroquinolone ofloxacin than their planktonic counterparts. We demonstrated that the biofilm-associated tolerance to ofloxacin was fully dependent on a functional SOS response upon starvation to both amino acids and carbon source and partially dependent on the stringent response upon leucine starvation. However, the biofilm-specific ofloxacin increased tolerance did not involve any of the SOS-induced toxin–antitoxin systems previously associated with formation of highly tolerant persisters. We further demonstrated that ofloxacin tolerance was induced as a function of biofilm age, which was dependent on the SOS response. Our results therefore show that the SOS stress response induced in heterogeneous and nutrient-deprived biofilm microenvironments is a molecular mechanism leading to biofilm-specific high tolerance to the fluoroquinolone ofloxacin.