Emissions modeling of fluidised bed co-combustion of poultry litter and peat

Gaseous emissions from the fluidised bed co-combustion of 50% w/w chicken litter and peat were monitored and recorded. Emission data were used to create a dispersion model for a proposed site on a poultry farm in Ireland. Variables within the combustion unit influenced both combustion and emission levels of pollutants such as SO(2) and NO(x), CO. Concentrations of atmospheric pollutants decreased with use of the correct ratio between fluidising and secondary air. Dispersion modelling of combustion at a proposed poultry unit predicted that ground level concentrations for the set of emissions data would be below the limits and guidelines set by air quality standards.     

The genome composition of hexaploid Psammopyrum athericum and octoploid Psammopyrum pungens (Poaceae: Triticeae).

The genomic constitution of two species in the genus Psammopyrum, i.e., Ps. athericum (2n = 6x = 42) and Ps. pungens (2n = 8x = 56), was studied by genomic in situ hybridization (GISH). In Ps. athericum, one diploid chromosome set hybridized to a genomic probe from Pseudoroegneria ferganensis (St genome), one diploid set to a probe from Agropyron cristatum (P genome), and one diploid set to a probe from Thinopyrum junceiforme (EbEe genomes) or Th. bessarabicum (Eb genome). Substituting the St-genome probe with an L-genome probe from Festucopsis serpentinii resulted in exactly the same hybridization pattern, suggesting a genomic constitution of EStP or ELP for Ps. athericum. The same probes used on Ps. pungens showed two diploid sets of chromosomes hybridizing to the St-genome probe, one diploid set hybridizing to the P-genome probe, and one diploid set hybridizing to the EbEe-genome probe. The L-genome probe hybridized to approximately 14 of the chromosomes that were labeled by the St-genome probe. Hence the genomic constitution for Ps. pungens is proposed to be EStStP or EStLP.     

Nonlinear biological batch process monitoring and fault identification based on kernel fisher discriminant analysis

A novel nonlinear biological batch process monitoring and fault identification approach based on kernel Fisher discriminant analysis (kernel FDA) is proposed. This method has a powerful ability to deal with nonlinear data and does not need to predict the future observations of variables. So it is more sensitive to fault detection. In order to improve the monitoring performance, variable trajectories of the batch processes are separated into several blocks. Then data in the original space is mapped into high-dimensional feature space via nonlinear kernel function and the optimal kernel Fisher feature vector and discriminant vector are extracted to perform process monitoring and fault identification. The key to the proposed approach is to calculate the distance of block data which are projected to the optimal kernel Fisher discriminant vector between new batch and reference batch. Through comparing distance with the predefined threshold, it can be considered whether the batch is normal or abnormal. Similar degree between the present discriminant vector and the optimal discriminant vector of fault in historical data set is used to perform fault diagnosis. The proposed method is applied to the process of fed-batch penicillin fermentation simulator benchmark and shows that it can effectively capture nonlinear relationships among process variables and is more efficient than MPCA approach.     

The emergence of Lyme disease in a changing environment in North America and Central Europe

Lyme disease has recently begun to emerge as a significant threat to human health, both in Europe and the United States. Late sequellae, resembling those of neurosyphilis and multiple sclerosis, may occur many years after initial infection. Spontaneous abortion accompanies arthritis, carditis and neuritis as burdensome short-term sequellae. Thousands of new infections are recognized each year on each side of the Atlantic, although reporting may be incomplete. The disease was described in Europe nearly a century ago and named erythema chronicum migrans, but its etiology has only recently been defined. The name Lyme disease was coined to describe a particularly intense American focus of disease, but the term has gained wide acceptance on both continents. The identity of the American and European etiological agents involved has yet to be determined.In America, a deer-associated, often bird-transported tick transmits this mouse-reservoired spirochete. The European situation seems more complex because the vector tick feeds on a greater variety of vertebrates. The reservoir hosts of the spirochete have yet to be determined. The role ofIxodes ricinus and possible other vectors in perpetuating transmission of the European infection remains to be defined. WhetherI. ricinus as well asI. dammini merely serve as a bridge to the human population or are important for the maintenance of the feral cycle remains to be seen.The capacity of a tick to maintain transmission of Lyme disease spirochetes depends upon a complex set of properties, including competence as a host for the spirochete, a pattern of feeding that focuses on a particular reservoir favored by a pattern of tick activity, during each transmission season, in which nymphs feed before larvae. Transmission would be favored by an environment, such as that of islands, in which the variety of potential reservoir hosts is restricted. Hosts, for example reptiles, that might fail to support growth of the spirochete would serve to dilute effective transmission in nature.Similarly, the capacity of a vertebrate to maintain the infection requires long-term support of the spirochete in a tissue site accessible to vector ticks, tolerance of repeated feeding by vector ticks and a pattern of host activity that exposes the host to numerous bites.The intensity of infection depends upon a continuous pattern of transmission in which each generation is infected anew. The rate event in which the vector inherits infection would serve mainly to transport the spirochete to a new site, most effectively by migrating birds.Due to the dispersed nature of Lyme disease and its recent emergence as an important hazard to health, measures for prophylaxis have only recently been devised. Lyme disease can be treated with antibiotics. But the effectiveness of such therapy depends upon correct and prompt diagnosis; delayed treatment is less effective, presumably because the spirochete becomes sequestered in immune-privileged sites.The present review lays stress on selected papers focusing on the epidemiological aspects and the experimental approach to the newly emerged disease rather than reviewing the complex literature concerning Lyme disease.     

Support vector regression applied to the determination of the developmental age of a Drosophila embryo from its segmentation gene expression patterns

MOTIVATION: In this paper we address the problem of the determination of developmental age of an embryo from its segmentation gene expression patterns in Drosophila. RESULTS: By applying support vector regression we have developed a fast method for automated staging of an embryo on the basis of its gene expression pattern. Support vector regression is a statistical method for creating regression functions of arbitrary type from a set of training data. The training set is composed of embryos for which the precise developmental age was determined by measuring the degree of membrane invagination. Testing the quality of regression on the training set showed good prediction accuracy. The optimal regression function was then used for the prediction of the gene expression based age of embryos in which the precise age has not been measured by membrane morphology. Moreover, we show that the same accuracy of prediction can be achieved when the dimensionality of the feature vector was reduced by applying factor analysis. The data reduction allowed us to avoid over-fitting and to increase the efficiency of the algorithm.     

Using a binaural biomimetic array to identify bottom objects ensonified by echolocating dolphins

The development of a unique dolphin biomimetic sonar produced data that were used to study signal processing methods for object identification. Echoes from four metallic objects proud on the bottom, and a substrate-only condition, were generated by bottlenose dolphins trained to ensonify the targets in very shallow water. Using the two-element ('binaural') receive array, object echo spectra were collected and submitted for identification to four neural network architectures. Identification accuracy was evaluated over two receive array configurations, and five signal processing schemes. The four neural networks included backpropagation, learning vector quantization, genetic learning and probabilistic network architectures. The processing schemes included four methods that capitalized on the binaural data, plus a monaural benchmark process. All the schemes resulted in above-chance identification accuracy when applied to learning vector quantization and backpropagation. Beam-forming or concatenation of spectra from both receive elements outperformed the monaural benchmark, with higher sensitivity and lower bias. Ultimately, best object identification performance was achieved by the learning vector quantization network supplied with beam-formed data. The advantages of multi-element signal processing for object identification are clearly demonstrated in this development of a first-ever dolphin biomimetic sonar.     

Representative transcript sets for evaluating a translational initiation sites predictor

Background  

Translational initiation site (TIS) prediction is a very important and actively studied topic in bioinformatics. In order to complete a comparative analysis, it is desirable to have several benchmark data sets which can be used to test the effectiveness of different algorithms. An ideal benchmark data set should be reliable, representative and readily available. Preferably, proteins encoded by members of the data set should also be representative of the protein population actually expressed in cellular specimens.     

A Multi-Label Learning Based Kernel Automatic Recommendation Method for Support Vector Machine

Choosing an appropriate kernel is very important and critical when classifying a new problem with Support Vector Machine. So far, more attention has been paid on constructing new kernels and choosing suitable parameter values for a specific kernel function, but less on kernel selection. Furthermore, most of current kernel selection methods focus on seeking a best kernel with the highest classification accuracy via cross-validation, they are time consuming and ignore the differences among the number of support vectors and the CPU time of SVM with different kernels. Considering the tradeoff between classification success ratio and CPU time, there may be multiple kernel functions performing equally well on the same classification problem. Aiming to automatically select those appropriate kernel functions for a given data set, we propose a multi-label learning based kernel recommendation method built on the data characteristics. For each data set, the meta-knowledge data base is first created by extracting the feature vector of data characteristics and identifying the corresponding applicable kernel set. Then the kernel recommendation model is constructed on the generated meta-knowledge data base with the multi-label classification method. Finally, the appropriate kernel functions are recommended to a new data set by the recommendation model according to the characteristics of the new data set. Extensive experiments over 132 UCI benchmark data sets, with five different types of data set characteristics, eleven typical kernels (Linear, Polynomial, Radial Basis Function, Sigmoidal function, Laplace, Multiquadric, Rational Quadratic, Spherical, Spline, Wave and Circular), and five multi-label classification methods demonstrate that, compared with the existing kernel selection methods and the most widely used RBF kernel function, SVM with the kernel function recommended by our proposed method achieved the highest classification performance.     

Detecting thermophilic proteins through selecting amino acid and dipeptide composition features

Detecting thermophilic proteins is an important task for designing stable protein engineering in interested temperatures. In this work, we develop a simple but efficient method to classify thermophilic proteins from mesophilic ones using the amino acid and dipeptide compositions. Since most of the amino acid and dipeptide compositions are redundant, we propose a new forward floating selection technique to select only a useful subset of these compositions as features for support vector machine-based classification. We test the proposed method on a benchmark data set of 915 thermophilic and 793 mesophilic proteins. The results show that our method using 28 amino acid and dipeptide compositions achieves an accuracy rate of 93.3% evaluated by the jackknife cross-validation test, which is higher not only than the existing methods but also than using all amino acid and dipeptide compositions.     

Patchiness in the dispersion of nectar resources: Evidence for hot and cold spots

Summary The dispersion pattern of resources can have a significant effect on foraging behavior. We examined the dispersion pattern of standing crop of nectar in a population of Delphinium nelsonii. The nectar content of flowers was measured for subject inflorescences and their two nearest neighbors. Inflorescences were divided into those which had some nectar (hot plants) and those which had none (cold plants). Tests for independence showed that subject plants and their neighbors were likely to have the same temperature, indicating that resources were patchy. The implications of this pattern for the foraging movements of bumblebees are discussed.The order of authorship was determined by the flip of a coin     

Multiple-vector self-adaptation in evolutionary algorithms.

Self-adaptation is a common method for learning online control parameters in an evolutionary algorithm. In one common implementation, each individual in the population is represented as a pair of vectors (x, sigma), where x is the candidate solution to an optimization problem scored in terms of f(x), and sigma is the so-called strategy parameter vector that influences how offspring will be created from the individual. Experimental evidence suggests that the elements of sigma can sometimes become too small to explore the given response surface adequately. The evolutionary search then stagnates, until the elements of sigma grow sufficiently large as a result of random variation. A potential solution to this deficiency associates multiple strategy parameter vectors with a single individual. A single strategy vector is active at any time and dictates how offspring will be generated. Experiments are conducted on four 10-dimensional benchmark functions where the number of strategy parameter vectors is varied over 1, 2, 3, 4, 5, 10, and 20. The results indicate advantages for using multiple strategy parameter vectors. Furthermore, the relationship between the mean best result after a fixed number of generations and the number of strategy parameter vectors can be determined reliably in each case.     

Cutting edge: functional characterization of the effect of the C3H/HeJ defect in mice that lack an Lpsn gene: in vivo evidence for a dominant negative mutation

A point mutation in the Tlr4 gene, which encodes Toll-like receptor 4, has recently been proposed to underlie LPS hyporesponsiveness in C3H/HeJ mice (Lpsd). The data presented herein demonstrate that F1 progeny from crosses between mice that carry a approximately 9-cM deletion of chromosome 4 (including deletion of LpsTlr4) and C3H/HeJ mice (i.e., Lps0 x Lpsd F1 mice) exhibit a pattern of LPS sensitivity, measured by TNF activity, that is indistinguishable from that exhibited by Lpsn x Lpsd F1 progeny and whose average response is "intermediate" to parental responses. Thus, these data provide clear functional support for the hypothesis that the C3H/HeJ defect exerts a dominant negative effect on LPS sensitivity; however, expression of a normal Toll-like receptor 4 molecule is apparently not required.     

The use of rotifer communities to provide a preliminary national classification of small water bodies in England

The rotifer species present in samples from 180 small water bodies, or ponds, in England were identified and listed for each pond. These communities were used to provide a system of classification of ponds applicable on a national basis, using the program TWINSPAN. Sites were also ordinated using DECORANA to identify major trends within the data set. The environmental factors most highly correlated with rotifer community were: (a) conductivity (P<0.001), (b) pH (P<0.001) and (c) permanence of water body (P<0.001). Out of 167 species, 32 were acting as indicators. It is suggested that a pond classification could be based on these species alone.     

CyClus: A fast,comprehensive cylindrical interface approximation clustering/reranking method for rigid‐body protein–protein docking decoys

We propose a fast clustering and reranking method, CyClus, for protein–protein docking decoys. This method enables comprehensive clustering of whole decoys generated by rigid‐body docking using cylindrical approximation of the protein–proteininterface and hierarchical clustering procedures. We demonstrate the clustering and reranking of 54,000 decoy structures generated by ZDOCK for each complex within a few minutes. After parameter tuning for the test set in ZDOCK benchmark 2.0 with the ZDOCK and ZRANK scoring functions, blind tests for the incremental data in ZDOCK benchmark 3.0 and 4.0 were conducted. CyClus successfully generated smaller subsets of decoys containing near‐native decoys. For example, the number of decoys required to create subsets containing near‐native decoys with 80% probability was reduced from 22% to 50% of the number required in the original ZDOCK. Although specific ZDOCK and ZRANK results were demonstrated, the CyClus algorithm was designed to be more general and can be applied to a wide range of decoys and scoring functions by adjusting just two parameters, p and T. CyClus results were also compared to those from ClusPro. Proteins 2013; © 2012 Wiley Periodicals, Inc.     

Score_set: A CAPRI benchmark for scoring protein complexes

Critical Assessment of PRedicted Interactions (CAPRI) has proven to be a catalyst for the development of docking algorithms. An essential step in docking is the scoring of predicted binding modes in order to identify stable complexes. In 2005, CAPRI introduced the scoring experiment, where upon completion of a prediction round, a larger set of models predicted by different groups and comprising both correct and incorrect binding modes, is made available to all participants for testing new scoring functions independently from docking calculations. Here we present an expanded benchmark data set for testing scoring functions, which comprises the consolidated ensemble of predicted complexes made available in the CAPRI scoring experiment since its inception. This consolidated scoring benchmark contains predicted complexes for 15 published CAPRI targets. These targets were subjected to 23 CAPRI assessments, due to existence of multiple binding modes for some targets. The benchmark contains more than 19,000 protein complexes. About 10% of the complexes represent docking predictions of acceptable quality or better, the remainder represent incorrect solutions (decoys). The benchmark set contains models predicted by 47 different predictor groups including web servers, which use different docking and scoring procedures, and is arguably as diverse as one may expect, representing the state of the art in protein docking. The data set is publicly available at the following URL: http://cb.iri.univ‐lille1.fr/Users/lensink/Score_set . Proteins 2014; 82:3163–3169. © 2014 Wiley Periodicals, Inc.     

Comparison of two unsupervised learning algorithms

The binary decision element described by the decision rule depending upon weight vector w is a model of neuron examined in this paper. The environment of the element is described by some unknown, stationary distribution p(x). The input signals x[n] of the element appear in each step n independently in accordance with the distribution p(x). During an unsupervised learning process the weight vector w[n] is changed on the base of the input vector x[n]. In the paper there are regarded two self-learning algorithms which are stochastic approximation type. For both algorithms the same rule of past experiences neglecting or the rule of weight decrease has been introduced. The first algorithm differs from the other one by a rule of weight increase. It has been proved that only one of these algorithms always leads to the same decision rule in a given environment p(x).This work was done during stay of Dr. L. Bobrowski at the University of Salerno in the frame of Polish-Italian Agreement on Scientific Cooperation     

Colon cancer prediction with genetic profiles using intelligent techniques

Micro array data provides information of expression levels of thousands of genes in a cell in a single experiment. Numerous efforts have been made to use gene expression profiles to improve precision of tumor classification. In our present study we have used the benchmark colon cancer data set for analysis. Feature selection is done using t‐statistic. Comparative study of class prediction accuracy of 3 different classifiers viz., support vector machine (SVM), neural nets and logistic regression was performed using the top 10 genes ranked by the t‐statistic. SVM turned out to be the best classifier for this dataset based on area under the receiver operating characteristic curve (AUC) and total accuracy. Logistic Regression ranks as the next best classifier followed by Multi Layer Perceptron (MLP). The top 10 genes selected by us for classification are all well documented for their variable expression in colon cancer. We conclude that SVM together with t-statistic based feature selection is an efficient and viable alternative to popular techniques.     

Variability of Human Systemic Humoral Immune Responses to Adenovirus Gene Transfer Vectors Administered to Different Organs

Administration of adenovirus (Ad) vectors to immunologically naive experimental animals almost invariably results in the induction of systemic anti-Ad neutralizing antibodies. To determine if the human systemic humoral host responses to Ad vectors follow a similar pattern, we evaluated the systemic (serum) anti-Ad serotype 5 (Ad5) neutralizing antibodies in humans after administration of first generation (E1(-) E3(-)) Ad5-based gene transfer vectors to different hosts. AdGVCFTR.10 (carrying the normal human cystic fibrosis [CF] transmembrane regulator cDNA) was sprayed (8 x 10(7) to 2 x 10(10) particle units [PU]) repetitively (every 3 months or every 2 weeks) to the airway epithelium of 15 individuals with CF. AdGVCD.10 (carrying the Escherichia coli cytosine deaminase gene) was administered (8 x 10(8) to 8 x 10(9) PU; once a week, twice) directly to liver metastasis of five individuals with colon cancer and by the intradermal route (8 x 10(7) to 8 x 10(9) PU, single administration) to six healthy individuals. AdGVVEGF121.10 (carrying the human vascular endothelial growth factor 121 cDNA) was administered (4 x 10(8) to 4 x 10(9.5) PU, single administration) directly to the myocardium of 11 individuals with ischemic heart disease. Ad vector administration to the airways of individuals with CF evoked no or minimal serum neutralizing antibodies, even with repetitive administration. In contrast, intratumor administration of an Ad vector to individuals with metastatic colon cancer resulted in a robust antibody response, with anti-Ad neutralizing antibody titers of 10(2) to >10(4). Healthy individuals responded to single intradermal Ad vector variably, from induction of no neutralizing anti-Ad antibodies to titers of 5 x 10(3). Likewise, individuals with ischemic heart disease had a variable response to single intramyocardial vector administration, ranging from minimal neutralizing antibody levels to titers of 10(4). Evaluation of the data from all trials showed no correlation between the peak serum neutralizing anti-Ad response and the dose of Ad vector administered (P > 0.1, all comparisons). In contrast, there was a striking correlation between the peak anti-Ad5 neutralizing antibody levels evoked by vector administration and the level of preexisting anti-Ad5 antibodies (P = 0.0001). Thus, unlike the case for experimental animals, administration of Ad vectors to humans does not invariably evoke a systemic anti-Ad neutralizing antibody response. In humans, the extent of the response is dictated by preexisting antibody titers and modified by route of administration but is not dose dependent. Since the extent of anti-Ad neutralizing antibodies will likely modify the efficacy of administration of Ad vectors, these observations are of fundamental importance in designing human gene therapy trials and in interpreting the efficacy of Ad vector-mediated gene transfer.     

利用伪氨基酸组分和支持向量机预测抗冻蛋白

抗冻蛋白是一类具有提高生物抗冻能力的蛋白质。抗冻蛋白能够特异性的与冰晶相结合,进而阻止体液内冰核的形成与生长。因此,对抗冻蛋白的生物信息学研究对生物工程发展。提高作物抗冻性有重要的推动作用。本文采用由400条抗冻蛋白序列和400条非抗冻蛋白序列构成数据集,以伪氨基酸组分为特征,利用支持向量机分类算法预测抗冻蛋白,对训练集预测精度达到91．3％,对测试集预测精度达到78．8％。该结果证明伪氨基酸组分能够很好的反映抗冻蛋白特性,并能够用于预测抗冻蛋白。     

Species-area curve,life history strategies,and succession: a field test of relationships

Changes of species richness along temporal and environmental gradients were investigated. Two data sets were used: a successional sere of old-field plant communities in the Bohemian Karst, and a set of plant communities under various intensities of disturbance in the Krkonoe (Giant) Mts, both in Czechoslovakia. The species richness of a plant community is a spatial phenomenon, and should be described by the species-area relationship (using e.g. the power function S=c\A ^z) rather than by a single number. In the old-field succession, the number of species in very small plots (0.1×0.1 m) tends to increase with successional age while the number of species in larger plots (4×4 m) decreases from the third year of succession. The plant community under the highest rate of disturbance of the Krkonoe Mts data set shows the lowest number of species on small plots and the highest number of species on large plots. The results may be explained using the distinction between founder-and dominance-controlled communities (Yodzis 1978, 1984). In accordance with this theory, the species-area relationship within a community is shaped mainly by the type of competitive interaction and may be predicted on the basis of life-history strategies of constituent species. Disturbance causes a shift from dominance to founder control. On the landscape scale, the species-area relationship is shaped by other factors, and so it is unjustified to extrapolate the relationship outside the range in which it was originally assessed.