Strain hardening behaviour and the Taylor factor of pure magnesium

Taylor orientation factors for strain hardening in textured and random polycrystals of magnesium were derived from the ratio of the strain hardening rates of polycrystals to that of single crystals deforming by equivalent polyslip. For polycrystals with textures that inhibit basal and prismatic slip while favouring pyramidal polyslip, the Taylor factor is estimated to be between 2.1 and 2.5, increasing to about 4.5 for randomly textured polycrystals. The micromechanics of strain hardening in polycrystals are discussed.     

Raman and IR reflection micro‐spectroscopic study of Er:YAG laser treated permanent and deciduous human teeth

Permanent and deciduous human teeth treated by a dental Er‐doped yttrium‐aluminium‐garnet pulse laser (λ = 2940 nm) as well as by classical drilling tools under conditions typical of the clinical practice were studied by ultraviolet Raman and Fourier transform infrared (FTIR) reflection microspectroscopy. Enamel was analyzed by both spectroscopic methods, whereas dentine was studied only by FTIR reflection because of the high level of photoluminescence continuum background even when a wavelength of 325 nm was used in inelastic light scattering experiments. The applied energy and pulse frequency of the dental laser varied between 200 and 500 mJ and between 10 and 30 Hz, respectively. The most important result is that after the laser treatment, the hydroxyapatite structure in both permanent and deciduous enamel is preserved: the apatite Ca‐P‐O framework remains intact, and the content of channel OH^‐ groups is not changed within experimental uncertainties. The calcium‐phosphate framework of dentine also exhibits negligible laser‐induced changes. The only alterations in enamel induced by laser as well as by mechanical drilling are reduction of the amount of CO₃^2‐ in apatite and changes in the protein conformation. The laser impact on the organic material and carbonate groups is strongest for laser power of 8 W; for powers of 4 or 5 W, the combination of higher pulse energy and lower pulse frequency has less impact than the combination of lower energy and higher frequency. No differences between deciduous and permanent teeth in their resistivity to laser irradiation with λ = 2940 nm were detected. Copyright © 2013 John Wiley & Sons, Ltd.     

A random forest of combined features in the classification of cut tobacco based on gas chromatography fingerprinting

We applied the random forest method to discriminate among different kinds of cut tobacco. To overcome the influence of the descending resolution caused by column pollution and the subsequent deterioration of column efficacy at different testing times, we constructed combined peaks by summing the peaks over a specific elution time interval Δt. On constructing tree classifiers, both the original peaks and the combined peaks were considered. A data set of 75 samples from three grades of the same tobacco brand was used to evaluate our method. Two parameters of the random forest were optimized using out-of-bag error, and the relationship between Δt and classification rate was investigated. Experiments show that partial least squares discriminant analysis was not suitable because of the overfitting, and the random forest with the combined features performed more accurately than Naïve Bayes, support vector machines, bootstrap aggregating and the random forest using only its original features.     

HIVCoR: A sequence-based tool for predicting HIV-1 CRF01_AE coreceptor usage

Determination of HIV-1 coreceptor usage is strongly recommended before starting the coreceptor-specific inhibitors for HIV treatment. Currently, the genotypic assays are the most interesting tools due to they are more feasible than phenotypic assays. However, most of prediction models were developed and validated by data set of HIV-1 subtype B and C. The present study aims to develop a powerful and reliable model to accurately predict HIV-1 coreceptor usage for CRF01_AE subtype called HIVCoR. HIVCoR utilized random forest and support vector machine as the prediction model, together with amino acid compositions, pseudo amino acid compositions and relative synonymous codon usage frequencies as the input feature. The overall success rate of 93.79% was achieved from the external validation test on the objective benchmark dataset. Comparison results indicated that HIVCoR was superior to other bioinformatics tools and genotypic predictors. For the convenience of experimental scientists, a user-friendly webserver has been established at http://codes.bio/hivcor/.     

Forest waste as a potential alternative energy source

Following a method based on the procedure given by Hubbardet al. [1] the calorific values of combustion in oxygen at 298.15 K were measured by static-bomb calorimetry for forest residues. This waste mainly coming from mount reforestation, construction of firebreaks, etc., constitutes an important risk to originate forest fire. From combustion experiments the mean calorific value of all species studied was found close to 19 000 kJ·kg^?1, similar to calorific values of Municipal Waste. It can be concluded that this forest residues can be used as an additional fuel to be added to Municipal Solid Waste in energy recovery plants. By doing this, energy, in this moment lost as abandoned residues, can be recovered. At the same time the elimination of this waste minimizes the risk of forest fires. Our results reasonably agree with literature values.     

Estimating the domain of applicability for machine learning QSAR models: a study on aqueous solubility of drug discovery molecules

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investigate error bars from a Bayesian model (Gaussian Process (GP)), an ensemble based approach (Random Forest), and approaches based on the Mahalanobis distance to training data (for Support Vector Machine and Ridge Regression models). We evaluate all approaches in terms of their prediction accuracy (in cross-validation, and on an external validation set of 536 molecules) and in how far the individual error bars can faithfully represent the actual prediction error.     

贡嘎山森林水资源水质状况分析

从地表水来源的整个水流过程对森林净化水质功能和影响贡嘎山自然保护区表水水质的因素进行分析,评价了贡嘎山森林流域水质状况,研究了贡嘎山森林生态系统对水域pH和Ca等营养元素的影响,为森林生态系统水质演化研究提供了例证。     

Multivariate feature selection and hierarchical classification for infrared spectroscopy: serum-based detection of bovine spongiform encephalopathy

A hierarchical scheme has been developed for detection of bovine spongiform encephalopathy (BSE) in serum on the basis of its infrared spectral signature. In the first stage, binary subsets between samples originating from diseased and non-diseased cattle are defined along known covariates within the data set. Random forests are then used to select spectral channels on each subset, on the basis of a multivariate measure of variable importance, the Gini importance. The selected features are then used to establish binary discriminations within each subset by means of ridge regression. In the second stage of the hierarchical procedure the predictions from all linear classifiers are used as input to another random forest that provides the final classification. When applied to an independent, blinded validation set of 160 further spectra (84 BSE-positives, 76 BSE-negatives), the hierarchical classifier achieves a sensitivity of 92% and a specificity of 95%. Compared with results from an earlier study based on the same data, the hierarchical scheme performs better than linear discriminant analysis with features selected by genetic optimization and robust linear discriminant analysis, and performs as well as a neural network and a support vector machine.     

基于分子描述符和机器学习方法预测和虚拟筛选MMP-13对MMP-1的选择性抑制剂

基质金属蛋白酶-13 (MMP-13)为预防和治疗骨关节炎(OA)提供了充满希望的靶标. 通过抑制剂来阻断MMP-13的活性将会对治疗OA疾病产生潜在的作用. 然而,宽谱抑制剂同样抑制MMP家族的其它成员,特别是MMP-1,这将会导致肌与骨的综合症. 因此,设计和发现潜在的MMP-13 相对于MMP-1 的高效选择性抑制剂,在对治疗OA新型药物的研发中具有相当重要的现实意义. 本研究通过两种机器学习方法(ML)：支持向量机(SVM)和随机森林(RF)来建立分类模型,用于预测不同结构的MMP-13 对MMP-1 的选择性抑制剂. 所建这些模型的预测效果都已经达到了令人满意的精度. 在这两种ML模型中,RF对于MMP-13选择性抑制剂和非抑制剂的精度分别达到97.58%和100%. 同时,与MMP-13对MMP-1的选择性抑制最相关的分子描述符也基于不同的特征选择方法被两种模型挑选出来. 最后,用预测效果最好的RF模型虚拟筛选了ZINC数据库的“fragment-like”子集,从而得到了一系列潜在的候选药物. 研究表明,机器学习方法,特别是RF方法,对于发现潜在的MMP-13选择性抑制剂十分有效. 同时还得到了一些与MMP-13的选择性抑制相关的分子描述符.     

Average distance and maximum induced forest

With the help of the Graffiti system, Fajtlowicz conjectured around 1992 that the average distance between two vertices of a connected graph G is at most half the maximum order of an induced bipartite subgraph of G, denoted α₂(G). We prove a strengthening of this conjecture by showing that the average distance between two vertices of a connected graph G is at most half the maximum order of an induced forest, denoted F(G). Moreover, we characterize the graphs maximizing the average distance among all graphs G having a fixed number of vertices and a fixed value of F(G) or α₂(G). Finally, we conjecture that the average distance between two vertices of a connected graph is at most half the maximum order of an induced linear forest (where a linear forest is a union of paths). © 2008 Wiley Periodicals, Inc. J Graph Theory 60: 31–54, 2009