A novel approach for predicting the probability of ignition of palaeofires using fossil leaf assemblages

Many metrics are used to predict and manage wildfires today but it is difficult to directly apply these to past wildfires histories. Developing an approach that enables estimation of these fire metrics in the past would provide significant power in our ability to compare past fire risk with that of the modern day. We use the Climate Leaf Analysis Multivariate Program (CLAMP) to estimate warm month mean temperature and annual mean relative humidity for a modern day leaf dataset and establish a novel methodology by which we can generate ‘pseudo‐daily’ meteorological parameters to calculate the probability of ignition (p(I)), an aspect of the North American Fire Danger Rating System. We test this methodology on Tortonian aged fossil leaves (11.62–7.25 Ma) from five sites across California, which reveal that the Tortonian was cooler and considerably more humid and that p(I) was lower during this time than in the region today.     

Comparison of plant uptake and plant toxicity of various ions in wheat

The effects of varying solution concentrations of manganese (Mn), zinc (Zn), copper (Cu), boron (B), iron (Fe), gallium (Ga) and lanthanum (La) on plant chemical concentrations, plant uptake and plant toxicity were determined in wheat (Triticum aestivum L.) grown in a low ionic strength (2.7×10^–3 M solution culture). Increasing the solution concentration of Mn, Zn, Cu, B, Fe, Ga and La increased plant concentrations of that ion. Asymptotic maximum plant concentrations were reached for Zn (10 mg kg DM^–1 in the roots), Ga (2 mg kg DM^–1 in the tops and 18 mg kg DM^–1 in the roots) and La (0.4 mg kg DM^–1 in the tops and 4 mg kg DM^–1 in the roots). Plant ion concentrations were, on average, 3 times higher in the roots than the tops for Mn and Zn, 7 times for Cu, 9 times for Fe, 12 times for Ga and 15 times for La. In contrast, B concentrations were higher in the tops than the roots by, on average, 2 times. The estimated toxicity threshold (plant concentration at which a rapid decrease in yield occurred) in the tops was 0.4 mg g DM^–1 for B, 2 for Zn, 0.075 for Cu and 0.09 for La and in the roots 0.2 mg g DM^–1 for B, 5 for Zn, 0.3 for Cu and 3 for La. Plant uptake rates of the ions (as estimated by the slope of the relationship between solution ion concentrations and plant ion concentrations) was in the order B 250 mg kg DM^–1 M ^–1). Plant toxicity was estimated as the reciprocal of the plant concentration that reduced yield by 50% (change in relative yield per mg ion kg DM^–1). The plant toxicity of the ions tested was in the order Mn相似文献   

Tolerance ofAzotobacter for metallic and non-metallic ions

The effect of 51 different inorganic salts on the growth of threeAzotobacter strains was studied by means of a diffusion technique in which 10^–4 mole of each salt was deposited in the centre of a solid medium in a petri dish previously streaked with anAzotobacter suspension.After 1 or 2 days, sterile zones appeared around the point of deposition of the salt, ranging from 5 to 100 mm in diameter. Besides inhibitory effects on growth, effects on slime formation and pigmentation were noted.High toxicity was found for the cations of Cs, Ba, Co, Ni, Cu, Au, Zn, Hg and Tl, and for KIO₃, Na₂CrO₄, Sb₂O₃SbOCl, Na₂SeO₃, Na₂TeO₃ and Na₂B₄O₇. Chlorides or sulfates of Na, K, Mg, Ca and Sr were well tolerated. The cations of Li, Rb, Be, Cr, Cd, Al, In, Sn, Pb, and UO₂(NO₃)₂, VOSO₄, NaVO₃, Na₂WO₄, NaBiO₃ and Na₂HAsO₄ had an intermediate effect.The results are discussed against the background of the limitations inherent to the method used. They are further compared with those of a previous study on the tolerance ofChlorella for inorganic ions (den Dooren de Jong, 1965).The author is indebted to the Director of the Academic Hospital Dijkzigt, Rotterdam, and to Prof. Dr. H. Esseveld, Head of the Central Bacteriological Laboratory, Rotterdam for providing facilities for the performance of this study.     

A novel biclustering approach to association rule mining for predicting HIV-1-human protein interactions

Identification of potential viral-host protein interactions is a vital and useful approach towards development of new drugs targeting those interactions. In recent days, computational tools are being utilized for predicting viral-host interactions. Recently a database containing records of experimentally validated interactions between a set of HIV-1 proteins and a set of human proteins has been published. The problem of predicting new interactions based on this database is usually posed as a classification problem. However, posing the problem as a classification one suffers from the lack of biologically validated negative interactions. Therefore it will be beneficial to use the existing database for predicting new viral-host interactions without the need of negative samples. Motivated by this, in this article, the HIV-1-human protein interaction database has been analyzed using association rule mining. The main objective is to identify a set of association rules both among the HIV-1 proteins and among the human proteins, and use these rules for predicting new interactions. In this regard, a novel association rule mining technique based on biclustering has been proposed for discovering frequent closed itemsets followed by the association rules from the adjacency matrix of the HIV-1-human interaction network. Novel HIV-1-human interactions have been predicted based on the discovered association rules and tested for biological significance. For validation of the predicted new interactions, gene ontology-based and pathway-based studies have been performed. These studies show that the human proteins which are predicted to interact with a particular viral protein share many common biological activities. Moreover, literature survey has been used for validation purpose to identify some predicted interactions that are already validated experimentally but not present in the database. Comparison with other prediction methods is also discussed.     

A mathematical model for the uptake of ions from the soil solution

Summary A model is developed in which the uptake of ions which exist wholly in the soil solution is described in terms of their net movement towards the surfaces of roots. The ions are assumed to move either by diffusion, or in the mass flow of water towards the roots, and, given these two ways of movement, the model is based on five main assumptions. The validity of these assumptions is discussed, together with some of the model's implications, and a few experiments are suggested by which it could be tested.     

Tolerance ofChlorella vulgaris for metallic and non-metallic ions

The well-known, extreme sensitivity of algae towards Cu⁺⁺ ions prompted a systematic investigation of the tolerance ofChlorella vulgaris for both metallic (49) and non-metallic (7) ions. With thirty metals forming weak bases, pH effects were to some extent super-imposed on the toxic effects of the metal ions themselves. With the elements U, Zr, V and Sb, oxy-compounds had to be used. The elements Mo, W and Bi were tested as components of anions.From the metals that form strong bases, the salts of Na, K, Rb, Ca, Sr and Mg were tolerated in high concentrations; the maximum values of these ranged from 0.11 – 0.98 g at/liter. Notwithstanding some unavoidable simplifications of the experimental technique, it could be concluded from the results that in four intermetal groups, arranged according to I.U.P.A.C., toxicity has a definite tendency to increase with increasing atomic number. This held for the series: Na, K, Rb, Cs; Mg, Ca, Sr, Ba; Zn, Cd, Hg; Al, In, Tl. In like manner, the rare earths were found to be more toxic than the alkaline earth metals.Co, Ni and Cu completely inhibit growth at very low concentrations ranging from 4.2×10^–6–2×10^–5 g at/liter; in view of the relatively low atomic numbers of these metals, the toxicity must be regarded as specific (algotoxicity).Among the non-metals, Sb and As proved highly toxic. Fluoride ions were considerably more toxic than chloride and bromide ions.     

A novel approach to predicting human ingress motion using an artificial neural network

Due to the increased availability of digital human models, the need for knowing human movement is important in product design process. If the human motion is derived rapidly as design parameters change, a developer could determine the optimal parameters. For example, the optimal design of the door panel of an automobile can be obtained for a human operator to conduct the easiest ingress and egress motion. However, acquiring motion data from existing methods provides only unrealistic motion or requires a great amount of time. This not only leads to an increased time consumption for a product development, but also causes inefficiency of the overall design process. To solve such problems, this research proposes an algorithm to rapidly and accurately predict full-body human motion using an artificial neural network (ANN) and a motion database, as the design parameters are varied. To achieve this goal, this study refers to the processes behind human motor learning procedures. According to the previous research, human generate new motion based on past motion experience when they encounter new environments. Based on this principle, we constructed a motion capture database. To construct the database, motion capture experiments were performed in various environments using an optical motion capture system. To generate full-body human motion using this data, a generalized regression neural network (GRNN) was used. The proposed algorithm not only guarantees rapid and accurate results but also overcomes the ambiguity of the human motion objective function, which has been pointed out as a limitation of optimization-based research. Statistical criteria were utilized to confirm the similarity between the generated motion and actual human motion. Our research provides the basis for a rapid motion prediction algorithm that can include a variety of environmental variables. This research contributes to an increase in the usability of digital human models, and it can be applied to various research fields.     

A traveling salesman approach for predicting protein functions

Background  

Protein-protein interaction information can be used to predict unknown protein functions and to help study biological pathways.     

Assessing a novel approach for predicting local 3D protein structures from sequence

We developed a novel approach for predicting local protein structure from sequence. It relies on the Hybrid Protein Model (HPM), an unsupervised clustering method we previously developed. This model learns three-dimensional protein fragments encoded into a structural alphabet of 16 protein blocks (PBs). Here, we focused on 11-residue fragments encoded as a series of seven PBs and used HPM to cluster them according to their local similarities. We thus built a library of 120 overlapping prototypes (mean fragments from each cluster), with good three-dimensional local approximation, i.e., a mean accuracy of 1.61 A Calpha root-mean-square distance. Our prediction method is intended to optimize the exploitation of the sequence-structure relations deduced from this library of long protein fragments. This was achieved by setting up a system of 120 experts, each defined by logistic regression to optimize the discrimination from sequence of a given prototype relative to the others. For a target sequence window, the experts computed probabilities of sequence-structure compatibility for the prototypes and ranked them, proposing the top scorers as structural candidates. Predictions were defined as successful when a prototype <2.5 A from the true local structure was found among those proposed. Our strategy yielded a prediction rate of 51.2% for an average of 4.2 candidates per sequence window. We also proposed a confidence index to estimate prediction quality. Our approach predicts from sequence alone and will thus provide valuable information for proteins without structural homologs. Candidates will also contribute to global structure prediction by fragment assembly.     

A metabolomics-based approach for predicting stages of chronic kidney disease

Chronic kidney disease (CKD) is a major epidemiologic problem and a risk factor for cardiovascular events and cerebrovascular accidents. Because CKD shows irreversible progression, early diagnosis is desirable. Renal function can be evaluated by measuring creatinine-based estimated glomerular filtration rate (eGFR). This method, however, has low sensitivity during early phases of CKD. Cystatin C (CysC) may be a more sensitive predictor. Using a metabolomic method, we previously identified metabolites in CKD and hemodialysis patients. To develop a new index of renal hypofunction, plasma samples were collected from volunteers with and without CKD and metabolite concentrations were assayed by quantitative liquid chromatography/mass spectrometry. These results were used to construct a multivariate regression equation for an inverse of CysC-based eGFR, with eGFR and CKD stage calculated from concentrations of blood metabolites. This equation was able to predict CKD stages with 81.3% accuracy (range, 73.9–87.0% during 20 repeats). This procedure may become a novel method of identifying patients with early-stage CKD.     

A novel approach for predicting the evolutionary time of acquisition of foreign genes by bacteria: Application of codon usage analyses

Lysogenic bacteriophages are considered as a major player for the introduction of foreign genes into bacterial strains. At the time of introduction foreign genes do not fit well into the translation system of the recipient host bacterium as they tend to retain the characteristics of the donor bacterium from which they have been transferred. Consequently foreign genes are poorly transcribed at the early phase of their evolution within the host bacterium. This is largely due to the difference in the codon usage pattern between the horizontally transferred genes and the host bacterium. In this study we present detailed analyses of various parameters of the codon usages such as codon adaptation index (CAI), mean difference (MD) of the relative adaptiveness, synonymous substitution rate (SSR) of six different phage encoded toxin genes (cholera toxin, shiga toxin, diphtheria toxin, neurotoxin C1, enterotoxin type A and cytotoxin), and proposed conceptual relationship between the evolutionary time of acquisition of the foreign genes and the selected set of parameters of the codon usage. On the basis of the observed data we hypothesize that CAI, MD and SSR of the phage encoded toxin genes are correlated with the evolutionary time of their acquisition, and have developed a novel approach based on the analyses of these parameters, which can be used to predict the evolutionary time of their acquisition by the corresponding host bacterium.     

A novel approach for the identification of pointer years

In the context of extreme event ecology, identification of pointer years has become a central aspect of tree-ring research. However, the variety of methods employed for pointer year detection since the introduction of the concept in 1979 impedes a direct comparison among studies. Moreover, most commonly used methods partly rely on arbitrarily selected thresholds, resulting in a potentially inconsistent application of those means. To overcome these discrepancies, we here introduce the “standardized growth change” method SGC, which relies on probability density functions of standardized year-to-year ring width differences and internationally accepted significance levels. To evaluate the performance of SGC, it is applied to 1000 pseudo-populations with known properties as well as to an existing Scots pine tree ring data set and compare the results derived from SGC to the four most frequently applied pointer year detection methods. Our comparative evaluation indicates SGC to supersede the other considered methods. In particular, it identified all artificially introduced pointer years in the pseudo-populations, whereas the other methods missed between 3 and 96 percent of known events. A detailed evaluation of misclassifications by the other approaches points out method-specific weaknesses. Finally, we provide technical aspects and recommendations for the application of SGC in a broader context.     

A novel mechanism of silicon uptake

Summary.  Crystal-like structures in vacuoles, precipitates in the cytoplasm and on the tonoplast membrane have been found to store remarkable amounts of Si in a number of higher plants. In most of the cases the final storage product is a SiO₂ gel. Accumulation inside the cells presumes a membrane and cytoplasm passage, driven by unknown transporters. Beside this uptake into the cytoplasm, Si-accumulating species possess a mechanism that does not involve a membrane and cytoplasm passage. Unusual small invaginations comprising the two membranes, plasmalemma and tonoplast, which enclose a small border of cytoplasm, were observed. The same cells contained vacuolar vesicles surrounded by two membranes, obviously derived from the invaginations. By energy-dispersive X-ray analysis and electron spectroscopic imaging, Si was shown in the invaginations and vacuolar vesicles. This novel endocytotic process allows the uptake of condensed, higher-molecular-weight Si compounds. In Zn hyperaccumulators, frequently SiO₂ precipitates were found in different cell compartments. Such plants showed the same invaginations and vacuolar vesicles, but Zn, colocalized with Si, was detected in these structures. Electron energy loss spectra confirmed the assumption that Zn-silicate is present in the vesicles. In the vacuoles the unstable Zn-silicate is degraded, forming SiO₂ precipitates, while the released Zn is bound to an unknown partner. Received January 22, 2002; accepted July 2, 2002; published online October 31, 2002 RID="*" ID="*" Correspondence and reprints: Institute of Plant Biochemistry, Weinberg 3, 06120 Halle, Federal Republic of Germany. Abbreviations: EELS electron energy loss spectroscopy; EDX energy-dispersive analysis of X-rays; ESI electron spectroscopic imaging.     

A novel nuclear relaxation approach for estimating distance between enzyme- and nucleotide-bound metal ions at the catalytic site of pyruvate kinase.

This communication introduces a nuclear relaxation approach for an estimation of the distance between two paramagnetic metal ion sites on a metal-activated enzyme. The method is based on the existence of an exchange of unpaired electron spin magnetizations between the two metals via energy-conserving concerted mutual spin flips which arise from time-dependent dipolar interactions of the electronic magnetizations. This cross-relaxation of electronic magnetizations depends on the inverse sixth power of the intermetal distance and may, under suitable conditions, affect the longitudinal relaxation rate of inner sphere water protons by altering the electron-proton dipolar correlation time when the latter is dominated by electron spin relaxation. The technique is applied to estimate the distance of 5.2 +/- 0.9 A between Mn2+ and Cr3+ in the pyruvate kinase-Mn2+-ATPCr3+ complex and indicates the existence of a van der Waals contact between the hydration spheres of the enzyme- and nucleotide-bound metal ions.     

A rational approach for predicting the dissolved oxygen profile in receiving waters

A practical method for predicting the dissolved oxygen (DO) profile in a stream receiving biodegradable waste is presented. In this method the BOD (accumulated O₂ uptake) curve is obtained using an open jug technique. The accumulated O₂ uptake curve thus developed is employed in numerical integration with the physical reaeration data for the receiving stream to predict the DO profile in the stream. In the present study, the method was examined using 10-liter open jar reactors to obtain the O₂ uptake curves, and the receiving stream was a 670-liter simulated stream apparatus which has been employed in previous studies on stream reaeration. The method was found to provide a fairly good prediction of the actual dissolved oxygen profile observed in the receiving stream. The effect of the reaeration constant, K₂ )agitation effect(, on the kinetics of O₂ uptake was also investigated and it was found that increased agitation (higher K₂ value) caused some increase in the accumulated oxygen uptake (BOD) curve with most of the increase coming after the so-called “plateau” area in the O₂ uptake curve, i.e., in the general case, after the low point along the DO sag curve.     

A holistic molecular docking approach for predicting protein-protein complex structure

A holistic protein-protein molecular docking approach, HoDock, was established, composed of such steps as binding site prediction, initial complex structure sampling, refined complex structure sampling, structure clustering, scoring and final structure selection. This article explains the detailed steps and applications for CAPRI Target 39. The CAPRI result showed that three predicted binding site residues, A191HIS, B512ARG and B531ARG, were correct, and there were five submitted structures with a high fraction of correct receptor-ligand interface residues, indicating that this docking approach may improve prediction accuracy for protein-protein complex structures.     

A p-Median approach for predicting drug response in tumour cells

Background

The complexity of biological data related to the genetic origins of tumour cells, originates significant challenges to glean valuable knowledge that can be used to predict therapeutic responses. In order to discover a link between gene expression profiles and drug responses, a computational framework based on Consensus p-Median clustering is proposed. The main goal is to simultaneously predict (in silico) anticancer responses by extracting common patterns among tumour cell lines, selecting genes that could potentially explain the therapy outcome and finally learning a probabilistic model able to predict the therapeutic responses.

Results

The experimental investigation performed on the NCI60 dataset highlights three main findings: (1) Consensus p-Median is able to create groups of cell lines that are highly correlated both in terms of gene expression and drug response; (2) from a biological point of view, the proposed approach enables the selection of genes that are strongly involved in several cancer processes; (3) the final prediction of drug responses, built upon Consensus p-Median and the selected genes, represents a promising step for predicting potential useful drugs.

Conclusion

The proposed learning framework represents a promising approach predicting drug response in tumour cells.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0353-7) contains supplementary material, which is available to authorized users.