Improved image decompression for reduced transform coding artifacts

NASA Technical Reports Server (NTRS)

Orourke, Thomas P.; Stevenson, Robert L.

1994-01-01

The perceived quality of images reconstructed from low bit rate compression is severely degraded by the appearance of transform coding artifacts. This paper proposes a method for producing higher quality reconstructed images based on a stochastic model for the image data. Quantization (scalar or vector) partitions the transform coefficient space and maps all points in a partition cell to a representative reconstruction point, usually taken as the centroid of the cell. The proposed image estimation technique selects the reconstruction point within the quantization partition cell which results in a reconstructed image which best fits a non-Gaussian Markov random field (MRF) image model. This approach results in a convex constrained optimization problem which can be solved iteratively. At each iteration, the gradient projection method is used to update the estimate based on the image model. In the transform domain, the resulting coefficient reconstruction points are projected to the particular quantization partition cells defined by the compressed image. Experimental results will be shown for images compressed using scalar quantization of block DCT and using vector quantization of subband wavelet transform. The proposed image decompression provides a reconstructed image with reduced visibility of transform coding artifacts and superior perceived quality.

Real-time optical laboratory solution of parabolic differential equations

NASA Technical Reports Server (NTRS)

Casasent, David; Jackson, James

1988-01-01

An optical laboratory matrix-vector processor is used to solve parabolic differential equations (the transient diffusion equation with two space variables and time) by an explicit algorithm. This includes optical matrix-vector nonbase-2 encoded laboratory data, the combination of nonbase-2 and frequency-multiplexed data on such processors, a high-accuracy optical laboratory solution of a partial differential equation, new data partitioning techniques, and a discussion of a multiprocessor optical matrix-vector architecture.

The prediction of blood-tissue partitions, water-skin partitions and skin permeation for agrochemicals.

PubMed

Abraham, Michael H; Gola, Joelle M R; Ibrahim, Adam; Acree, William E; Liu, Xiangli

2014-07-01

There is considerable interest in the blood-tissue distribution of agrochemicals, and a number of researchers have developed experimental methods for in vitro distribution. These methods involve the determination of saline-blood and saline-tissue partitions; not only are they indirect, but they do not yield the required in vivo distribution. The authors set out equations for gas-tissue and blood-tissue distribution, for partition from water into skin and for permeation from water through human skin. Together with Abraham descriptors for the agrochemicals, these equations can be used to predict values for all of these processes. The present predictions compare favourably with experimental in vivo blood-tissue distribution where available. The predictions require no more than simple arithmetic. The present method represents a much easier and much more economic way of estimating blood-tissue partitions than the method that uses saline-blood and saline-tissue partitions. It has the added advantages of yielding the required in vivo partitions and being easily extended to the prediction of partition of agrochemicals from water into skin and permeation from water through skin. © 2013 Society of Chemical Industry.

Task-specific image partitioning.

PubMed

Kim, Sungwoong; Nowozin, Sebastian; Kohli, Pushmeet; Yoo, Chang D

2013-02-01

Image partitioning is an important preprocessing step for many of the state-of-the-art algorithms used for performing high-level computer vision tasks. Typically, partitioning is conducted without regard to the task in hand. We propose a task-specific image partitioning framework to produce a region-based image representation that will lead to a higher task performance than that reached using any task-oblivious partitioning framework and existing supervised partitioning framework, albeit few in number. The proposed method partitions the image by means of correlation clustering, maximizing a linear discriminant function defined over a superpixel graph. The parameters of the discriminant function that define task-specific similarity/dissimilarity among superpixels are estimated based on structured support vector machine (S-SVM) using task-specific training data. The S-SVM learning leads to a better generalization ability while the construction of the superpixel graph used to define the discriminant function allows a rich set of features to be incorporated to improve discriminability and robustness. We evaluate the learned task-aware partitioning algorithms on three benchmark datasets. Results show that task-aware partitioning leads to better labeling performance than the partitioning computed by the state-of-the-art general-purpose and supervised partitioning algorithms. We believe that the task-specific image partitioning paradigm is widely applicable to improving performance in high-level image understanding tasks.

Use of strainrange partitioning to predict high temperature low-cycle fatigue life. [of metallic materials

NASA Technical Reports Server (NTRS)

Hirschberg, M. H.; Halford, G. R.

1976-01-01

The fundamental concepts of the strainrange partitioning approach to high temperature, low low-cycle fatigue are reviewed. Procedures are presented by which the partitioned strainrange versus life relationships for any material can be generated. Laboratory tests are suggested for further verifying the ability of the method of strainrange partitioning to predict life.

Empirical Constraints on Proton and Electron Heating in the Fast Solar Wind

NASA Technical Reports Server (NTRS)

Cranmer, Steven R.; Matthaeus, William H.; Breech, Benjamin A.; Kasper, Justin C.

2009-01-01

This paper presents analyses of measured proton and electron temperatures in the high-speed solar wind that are used to calculate the separate rates of heat deposition for protons and electrons. It was found that the protons receive about 60% of the total plasma heating in the inner heliosphere, and that this fraction increases to approximately 80% by the orbit of Jupiter. The empirically derived partitioning of heat between protons and electrons is in rough agreement with theoretical predictions from a model of linear Vlasov wave damping. For a modeled power spectrum consisting only of Alfvenic fluctuations, the best agreement was found for a distribution of wavenumber vectors that evolves toward isotropy as distance increases.

Strainrange partitioning life predictions of the long time metal properties council creep-fatigue tests

NASA Technical Reports Server (NTRS)

Saltsman, J. F.; Halford, G. R.

1979-01-01

The method of strainrange partitioning is used to predict the cyclic lives of the Metal Properties Council's long time creep-fatigue interspersion tests of several steel alloys. Comparisons are made with predictions based upon the time- and cycle-fraction approach. The method of strainrange partitioning is shown to give consistently more accurate predictions of cyclic life than is given by the time- and cycle-fraction approach.

Application of strainrange partitioning to the prediction of MPC creep-fatigue data for 2 1/4 Cr-1Mo steel

NASA Technical Reports Server (NTRS)

Saltsman, J. F.; Halford, G. R.

1976-01-01

Strainrange partitioning is used to predict the long time cyclic lives of the metal properties council (MPC) creep-fatigue interspersion and cyclic creep-rupture tests conducted with annealed 2 1/4 Cr-1Mo steel. Observed lives agree with predicted lives within factors of two. The strainrange partitioning life relations used for the long time predictions were established from short time creep-fatigue data generated at NASA-Lewis on the same heat of material.

Prediction of Partition Coefficients of Organic Compounds between SPME/PDMS and Aqueous Solution

PubMed Central

Chao, Keh-Ping; Lu, Yu-Ting; Yang, Hsiu-Wen

2014-01-01

Polydimethylsiloxane (PDMS) is commonly used as the coated polymer in the solid phase microextraction (SPME) technique. In this study, the partition coefficients of organic compounds between SPME/PDMS and the aqueous solution were compiled from the literature sources. The correlation analysis for partition coefficients was conducted to interpret the effect of their physicochemical properties and descriptors on the partitioning process. The PDMS-water partition coefficients were significantly correlated to the polarizability of organic compounds (r = 0.977, p < 0.05). An empirical model, consisting of the polarizability, the molecular connectivity index, and an indicator variable, was developed to appropriately predict the partition coefficients of 61 organic compounds for the training set. The predictive ability of the empirical model was demonstrated by using it on a test set of 26 chemicals not included in the training set. The empirical model, applying the straightforward calculated molecular descriptors, for estimating the PDMS-water partition coefficient will contribute to the practical applications of the SPME technique. PMID:24534804

Scalar products of Bethe vectors in models with {\\mathfrak{gl}}(2| 1) symmetry 1. Super-analog of Reshetikhin formula

NASA Astrophysics Data System (ADS)

Hutsalyuk, A.; Liashyk, A.; Pakuliak, S. Z.; Ragoucy, E.; Slavnov, N. A.

2016-11-01

We study the scalar products of Bethe vectors in integrable models solvable by the nested algebraic Bethe ansatz and possessing {gl}(2| 1) symmetry. Using explicit formulas of the monodromy matrix entries’ multiple actions onto Bethe vectors we obtain a representation for the scalar product in the most general case. This explicit representation appears to be a sum over partitions of the Bethe parameters. It can be used for the analysis of scalar products involving on-shell Bethe vectors. As a by-product, we obtain a determinant representation for the scalar products of generic Bethe vectors in integrable models with {gl}(1| 1) symmetry. Dedicated to the memory of Petr Petrovich Kulish.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN.

PubMed

Liu, Chang; Cheng, Gang; Chen, Xihui; Pang, Yusong

2018-05-11

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears.

Planetary Gears Feature Extraction and Fault Diagnosis Method Based on VMD and CNN

PubMed Central

Cheng, Gang; Chen, Xihui

2018-01-01

Given local weak feature information, a novel feature extraction and fault diagnosis method for planetary gears based on variational mode decomposition (VMD), singular value decomposition (SVD), and convolutional neural network (CNN) is proposed. VMD was used to decompose the original vibration signal to mode components. The mode matrix was partitioned into a number of submatrices and local feature information contained in each submatrix was extracted as a singular value vector using SVD. The singular value vector matrix corresponding to the current fault state was constructed according to the location of each submatrix. Finally, by training a CNN using singular value vector matrices as inputs, planetary gear fault state identification and classification was achieved. The experimental results confirm that the proposed method can successfully extract local weak feature information and accurately identify different faults. The singular value vector matrices of different fault states have a distinct difference in element size and waveform. The VMD-based partition extraction method is better than ensemble empirical mode decomposition (EEMD), resulting in a higher CNN total recognition rate of 100% with fewer training times (14 times). Further analysis demonstrated that the method can also be applied to the degradation recognition of planetary gears. Thus, the proposed method is an effective feature extraction and fault diagnosis technique for planetary gears. PMID:29751671

Strainrange partitioning behavior of the nickel-base superalloys, Rene' 80 and in 100

NASA Technical Reports Server (NTRS)

Halford, G. R.; Nachtigall, A. J.

1978-01-01

A study was made to assess the ability of the method of Strainrange Partitioning (SRP) to both correlate and predict high-temperature, low cycle fatigue lives of nickel base superalloys for gas turbine applications. The partitioned strainrange versus life relationships for uncoated Rene' 80 and cast IN 100 were also determined from the ductility normalized-Strainrange Partitioning equations. These were used to predict the cyclic lives of the baseline tests. The life predictability of the method was verified for cast IN 100 by applying the baseline results to the cyclic life prediction of a series of complex strain cycling tests with multiple hold periods at constant strain. It was concluded that the method of SRP can correlate and predict the cyclic lives of laboratory specimens of the nickel base superalloys evaluated in this program.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes.

PubMed

Srinivasulu, Yerukala Sathipati; Wang, Jyun-Rong; Hsu, Kai-Ti; Tsai, Ming-Ju; Charoenkwan, Phasit; Huang, Wen-Lin; Huang, Hui-Ling; Ho, Shinn-Ying

2015-01-01

Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes.

Characterizing informative sequence descriptors and predicting binding affinities of heterodimeric protein complexes

PubMed Central

2015-01-01

Background Protein-protein interactions (PPIs) are involved in various biological processes, and underlying mechanism of the interactions plays a crucial role in therapeutics and protein engineering. Most machine learning approaches have been developed for predicting the binding affinity of protein-protein complexes based on structure and functional information. This work aims to predict the binding affinity of heterodimeric protein complexes from sequences only. Results This work proposes a support vector machine (SVM) based binding affinity classifier, called SVM-BAC, to classify heterodimeric protein complexes based on the prediction of their binding affinity. SVM-BAC identified 14 of 580 sequence descriptors (physicochemical, energetic and conformational properties of the 20 amino acids) to classify 216 heterodimeric protein complexes into low and high binding affinity. SVM-BAC yielded the training accuracy, sensitivity, specificity, AUC and test accuracy of 85.80%, 0.89, 0.83, 0.86 and 83.33%, respectively, better than existing machine learning algorithms. The 14 features and support vector regression were further used to estimate the binding affinities (Pkd) of 200 heterodimeric protein complexes. Prediction performance of a Jackknife test was the correlation coefficient of 0.34 and mean absolute error of 1.4. We further analyze three informative physicochemical properties according to their contribution to prediction performance. Results reveal that the following properties are effective in predicting the binding affinity of heterodimeric protein complexes: apparent partition energy based on buried molar fractions, relations between chemical structure and biological activity in principal component analysis IV, and normalized frequency of beta turn. Conclusions The proposed sequence-based prediction method SVM-BAC uses an optimal feature selection method to identify 14 informative features to classify and predict binding affinity of heterodimeric protein complexes. The characterization analysis revealed that the average numbers of beta turns and hydrogen bonds at protein-protein interfaces in high binding affinity complexes are more than those in low binding affinity complexes. PMID:26681483

Uncertain Henry's law constants compromise equilibrium partitioning calculations of atmospheric oxidation products

NASA Astrophysics Data System (ADS)

Wang, Chen; Yuan, Tiange; Wood, Stephen A.; Goss, Kai-Uwe; Li, Jingyi; Ying, Qi; Wania, Frank

2017-06-01

Gas-particle partitioning governs the distribution, removal, and transport of organic compounds in the atmosphere and the formation of secondary organic aerosol (SOA). The large variety of atmospheric species and their wide range of properties make predicting this partitioning equilibrium challenging. Here we expand on earlier work and predict gas-organic and gas-aqueous phase partitioning coefficients for 3414 atmospherically relevant molecules using COSMOtherm, SPARC Performs Automated Reasoning in Chemistry (SPARC), and poly-parameter linear free-energy relationships. The Master Chemical Mechanism generated the structures by oxidizing primary emitted volatile organic compounds. Predictions for gas-organic phase partitioning coefficients (KWIOM/G) by different methods are on average within 1 order of magnitude of each other, irrespective of the numbers of functional groups, except for predictions by COSMOtherm and SPARC for compounds with more than three functional groups, which have a slightly higher discrepancy. Discrepancies between predictions of gas-aqueous partitioning (KW/G) are much larger and increase with the number of functional groups in the molecule. In particular, COSMOtherm often predicts much lower KW/G for highly functionalized compounds than the other methods. While the quantum-chemistry-based COSMOtherm accounts for the influence of intra-molecular interactions on conformation, highly functionalized molecules likely fall outside of the applicability domain of the other techniques, which at least in part rely on empirical data for calibration. Further analysis suggests that atmospheric phase distribution calculations are sensitive to the partitioning coefficient estimation method, in particular to the estimated value of KW/G. The large uncertainty in KW/G predictions for highly functionalized organic compounds needs to be resolved to improve the quantitative treatment of SOA formation.

On the star partition dimension of comb product of cycle and path

NASA Astrophysics Data System (ADS)

Alfarisi, Ridho; Darmaji

2017-08-01

Let G = (V, E) be a connected graphs with vertex set V(G), edge set E(G) and S ⊆ V(G). Given an ordered partition Π = {S1, S2, S3, …, Sk} of the vertex set V of G, the representation of a vertex v ∈ V with respect to Π is the vector r(v|Π) = (d(v, S1), d(v, S2), …, d(v, Sk)), where d(v, Sk) represents the distance between the vertex v and the set Sk and d(v, Sk) = min{d(v, x)|x ∈ Sk }. A partition Π of V(G) is a resolving partition if different vertices of G have distinct representations, i.e., for every pair of vertices u, v ∈ V(G), r(u|Π) ≠ r(v|Π). The minimum k of Π resolving partition is a partition dimension of G, denoted by pd(G). The resolving partition Π = {S1, S2, S3, …, Sk } is called a star resolving partition for G if it is a resolving partition and each subgraph induced by Si, 1 ≤ i ≤ k, is a star. The minimum k for which there exists a star resolving partition of V(G) is the star partition dimension of G, denoted by spd(G). Finding the star partition dimension of G is classified to be a NP-Hard problem. In this paper, we will show that the partition dimension of comb product of cycle and path namely Cm⊳Pn and Pn⊳Cm for n ≥ 2 and m ≥ 3.

The partition dimension of subdivision of a graph

NASA Astrophysics Data System (ADS)

Amrullah, Baskoro, Edy Tri; Uttunggadewa, Saladin; Simanjuntak, Rinovia

2016-02-01

Let G = (V,E) be a connected graph, u,v ∈ V (G), e = uv ∈ E(G) and k be a positive integer. A k-subdivision of an edge e is a replacement of e = uv with a path u, x1, x2, x ..., xk, v. A graph G with a k-subdivided edge is denoted with S(G(e; k)). Let p be a positive integer and Π = {L1, L2, L3, …, Lp} be a p-partition of V (G). The representation of a vertex v with respect to Π, r(v|Π), is the vector (d(v, L1), d(v, L2), d(v, L3),…, d(v, Lp)) where d(v, Li) for i ∈ [1, p] is the minimum distance between v and the vertices of Li. The partition Π is called a resolving partition of G if r(w|Π) ≠ r(v|Π) for all w ≠ v ∈ V (G). The partition dimension, pd(G), of G is the smallest integer p such that G has a resolving p-partition. In this paper, we present sharp upper and lower bounds of the partition dimension of S(G(e; k)) for any graph G.

Predicting appointment misses in hospitals using data analytics

PubMed Central

Karpagam, Sylvia; Ma, Nang Laik

2017-01-01

Background There is growing attention over the last few years about non-attendance in hospitals and its clinical and economic consequences. There have been several studies documenting the various aspects of non-attendance in hospitals. Project Predicting Appoint Misses (PAM) was started with the intention of being able to predict the type of patients that would not come for appointments after making bookings. Methods Historic hospital appointment data merged with “distance from hospital” variable was used to run Logistic Regression, Support Vector Machine and Recursive Partitioning to decide the contributing variables to missed appointments. Results Variables that are “class”, “time”, “demographics” related have an effect on the target variable, however, prediction models may not perform effectively due to very subtle influence on the target variable. Previously assumed major contributors like “age”, “distance” did not have a major effect on the target variable. Conclusions With the given data it will be very difficult to make any moderate/strong prediction of the Appointment misses. That being said with the help of the cut off we are able to capture all of the “appointment misses” in addition to also capturing the actualized appointments. PMID:28567409

Dissociative Recombination Chemistry and Plasma Dynamics

DTIC Science & Technology

2008-06-16

the fractional square of product momentum with product momentum vectors . Qx and Qy denote the degenerate bend two-body dissociation limits...with product momentum vectors . Qx and Qy denote the degenerate bend normal modes for C2v symmetry H2D and HD2 isomers of H3. symmetry for the Qx...heavy (D atom) products in general receive a greater partitioning of energy than the light product. This may have important implications for

Reducing Memory Cost of Exact Diagonalization using Singular Value Decomposition

NASA Astrophysics Data System (ADS)

Weinstein, Marvin; Chandra, Ravi; Auerbach, Assa

2012-02-01

We present a modified Lanczos algorithm to diagonalize lattice Hamiltonians with dramatically reduced memory requirements. In contrast to variational approaches and most implementations of DMRG, Lanczos rotations towards the ground state do not involve incremental minimizations, (e.g. sweeping procedures) which may get stuck in false local minima. The lattice of size N is partitioned into two subclusters. At each iteration the rotating Lanczos vector is compressed into two sets of nsvd small subcluster vectors using singular value decomposition. For low entanglement entropy See, (satisfied by short range Hamiltonians), the truncation error is bounded by (-nsvd^1/See). Convergence is tested for the Heisenberg model on Kagom'e clusters of 24, 30 and 36 sites, with no lattice symmetries exploited, using less than 15GB of dynamical memory. Generalization of the Lanczos-SVD algorithm to multiple partitioning is discussed, and comparisons to other techniques are given. Reference: arXiv:1105.0007

Determination of octanol-air partition coefficients and supercooled liquid vapor pressures of PAHs as a function of temperature: Application to gas-particle partitioning in an urban atmosphere

NASA Astrophysics Data System (ADS)

Odabasi, Mustafa; Cetin, Eylem; Sofuoglu, Aysun

Octanol-air partition coefficients ( KOA) for 14 polycyclic aromatic hydrocarbons (PAHs) were determined as a function of temperature using the gas chromatographic retention time method. log KOA values at 25° ranged over six orders of magnitude, between 6.34 (acenaphthylene) and 12.59 (dibenz[ a,h]anthracene). The determined KOA values were within factor of 0.7 (dibenz[ a,h]anthracene) to 15.1 (benz[ a]anthracene) of values calculated as the ratio of octanol-water partition coefficient to dimensionless Henry's law constant. Supercooled liquid vapor pressures ( PL) of 13 PAHs were also determined using the gas chromatographic retention time technique. Activity coefficients in octanol calculated using KOA and PL ranged between 3.2 and 6.2 indicating near-ideal solution behavior. Atmospheric concentrations measured in this study in Izmir, Turkey were used to investigate the partitioning of PAHs between particle and gas-phases. Experimental gas-particle partition coefficients ( Kp) were compared to the predictions of KOA absorption and KSA (soot-air partition coefficient) models. Octanol-based absorptive partitioning model predicted lower partition coefficients especially for relatively volatile PAHs. Ratios of measured/modeled partition coefficients ranged between 1.1 and 15.5 (4.5±6.0, average±SD) for KOA model. KSA model predictions were relatively better and measured to modeled ratios ranged between 0.6 and 5.6 (2.3±2.7, average±SD).

Investigation of migrant-polymer interaction in pharmaceutical packaging material using the linear interaction energy algorithm.

PubMed

Feenstra, Peter; Brunsteiner, Michael; Khinast, Johannes

2014-10-01

The interaction between drug products and polymeric packaging materials is an important topic in the pharmaceutical industry and often associated with high costs because of the required elaborative interaction studies. Therefore, a theoretical prediction of such interactions would be beneficial. Often, material parameters such as the octanol water partition coefficient are used to predict the partitioning of migrant molecules between a solvent and a polymeric packaging material. Here, we present the investigation of the partitioning of various migrant molecules between polymers and solvents using molecular dynamics simulations for the calculation of interaction energies. Our results show that the use of a model for the interaction between the migrant and the polymer at atomistic detail can yield significantly better results when predicting the polymer solvent partitioning than a model based on the octanol water partition coefficient. © 2014 Wiley Periodicals, Inc. and the American Pharmacists Association.

A Meinardus Theorem with Multiple Singularities

NASA Astrophysics Data System (ADS)

Granovsky, Boris L.; Stark, Dudley

2012-09-01

Meinardus proved a general theorem about the asymptotics of the number of weighted partitions, when the Dirichlet generating function for weights has a single pole on the positive real axis. Continuing (Granovsky et al., Adv. Appl. Math. 41:307-328, 2008), we derive asymptotics for the numbers of three basic types of decomposable combinatorial structures (or, equivalently, ideal gas models in statistical mechanics) of size n, when their Dirichlet generating functions have multiple simple poles on the positive real axis. Examples to which our theorem applies include ones related to vector partitions and quantum field theory. Our asymptotic formula for the number of weighted partitions disproves the belief accepted in the physics literature that the main term in the asymptotics is determined by the rightmost pole.

Life prediction of thermal-mechanical fatigue using strainrange partitioning

NASA Technical Reports Server (NTRS)

Halford, G. R.; Manson, S. S.

1975-01-01

This paper describes the applicability of the method of Strainrange Partitioning to the life prediction of thermal-mechanical strain-cycling fatigue. An in-phase test on 316 stainless steel is analyzed as an illustrative example. The observed life is in excellent agreement with the life predicted by the method using the recently proposed Step-Stress Method of experimental partitioning, the Interaction Damage Rule, and the life relationships determined at an isothermal temperature of 705 C. Implications of the present study are discussed relative to the general thermal fatigue problem.

Life prediction of thermal-mechanical fatigue using strain-range partitioning

NASA Technical Reports Server (NTRS)

Halford, G. R.; Manson, S. S.

1975-01-01

The applicability is described of the method of Strainrange Partitioning to the life prediction of thermal-mechanical strain-cycling fatigue. An in-phase test on 316 stainless steel is analyzed as an illustrative example. The observed life is in excellent agreement with the life predicted by the method using the recently proposed Step-Stress Method of experimental partitioning, the Interation Damage Rule, and the life relationships determined at an isothermal temperature of 705 C. Implications of the study are discussed relative to the general thermal fatigue problem.

Studies on image compression and image reconstruction

NASA Technical Reports Server (NTRS)

Sayood, Khalid; Nori, Sekhar; Araj, A.

1994-01-01

During this six month period our works concentrated on three, somewhat different areas. We looked at and developed a number of error concealment schemes for use in a variety of video coding environments. This work is described in an accompanying (draft) Masters thesis. In the thesis we describe application of this techniques to the MPEG video coding scheme. We felt that the unique frame ordering approach used in the MPEG scheme would be a challenge to any error concealment/error recovery technique. We continued with our work in the vector quantization area. We have also developed a new type of vector quantizer, which we call a scan predictive vector quantization. The scan predictive VQ was tested on data processed at Goddard to approximate Landsat 7 HRMSI resolution and compared favorably with existing VQ techniques. A paper describing this work is included. The third area is concerned more with reconstruction than compression. While there is a variety of efficient lossless image compression schemes, they all have a common property that they use past data to encode future data. This is done either via taking differences, context modeling, or by building dictionaries. When encoding large images, this common property becomes a common flaw. When the user wishes to decode just a portion of the image, the requirement that the past history be available forces the decoding of a significantly larger portion of the image than desired by the user. Even with intelligent partitioning of the image dataset, the number of pixels decoded may be four times the number of pixels requested. We have developed an adaptive scanning strategy which can be used with any lossless compression scheme and which lowers the additional number of pixels to be decoded to about 7 percent of the number of pixels requested! A paper describing these results is included.

On the partition dimension of comb product of path and complete graph

NASA Astrophysics Data System (ADS)

Darmaji, Alfarisi, Ridho

2017-08-01

For a vertex v of a connected graph G(V, E) with vertex set V(G), edge set E(G) and S ⊆ V(G). Given an ordered partition Π = {S1, S2, S3, …, Sk} of the vertex set V of G, the representation of a vertex v ∈ V with respect to Π is the vector r(v|Π) = (d(v, S1), d(v, S2), …, d(v, Sk)), where d(v, Sk) represents the distance between the vertex v and the set Sk and d(v, Sk) = min{d(v, x)|x ∈ Sk}. A partition Π of V(G) is a resolving partition if different vertices of G have distinct representations, i.e., for every pair of vertices u, v ∈ V(G), r(u|Π) ≠ r(v|Π). The minimum k of Π resolving partition is a partition dimension of G, denoted by pd(G). Finding the partition dimension of G is classified to be a NP-Hard problem. In this paper, we will show that the partition dimension of comb product of path and complete graph. The results show that comb product of complete grapph Km and path Pn namely p d (Km⊳Pn)=m where m ≥ 3 and n ≥ 2 and p d (Pn⊳Km)=m where m ≥ 3, n ≥ 2 and m ≥ n.

Analytical prediction of the interior noise for cylindrical models of aircraft fuselages for prescribed exterior noise fields. Phase 2: Models for sidewall trim, stiffened structures and cabin acoustics with floor partition

NASA Technical Reports Server (NTRS)

Pope, L. D.; Wilby, E. G.

1982-01-01

An airplane interior noise prediction model is developed to determine the important parameters associated with sound transmission into the interiors of airplanes, and to identify apropriate noise control methods. Models for stiffened structures, and cabin acoustics with floor partition are developed. Validation studies are undertaken using three test articles: a ring stringer stiffened cylinder, an unstiffened cylinder with floor partition, and ring stringer stiffened cylinder with floor partition and sidewall trim. The noise reductions of the three test articles are computed using the heoretical models and compared to measured values. A statistical analysis of the comparison data indicates that there is no bias in the predictions although a substantial random error exists so that a discrepancy of more than five or six dB can be expected for about one out of three predictions.

Gas-particle partitioning of semi-volatile organics on organic aerosols using a predictive activity coefficient model: analysis of the effects of parameter choices on model performance

NASA Astrophysics Data System (ADS)

Chandramouli, Bharadwaj; Jang, Myoseon; Kamens, Richard M.

The partitioning of a diverse set of semivolatile organic compounds (SOCs) on a variety of organic aerosols was studied using smog chamber experimental data. Existing data on the partitioning of SOCs on aerosols from wood combustion, diesel combustion, and the α-pinene-O 3 reaction was augmented by carrying out smog chamber partitioning experiments on aerosols from meat cooking, and catalyzed and uncatalyzed gasoline engine exhaust. Model compositions for aerosols from meat cooking and gasoline combustion emissions were used to calculate activity coefficients for the SOCs in the organic aerosols and the Pankow absorptive gas/particle partitioning model was used to calculate the partitioning coefficient Kp and quantitate the predictive improvements of using the activity coefficient. The slope of the log K p vs. log p L0 correlation for partitioning on aerosols from meat cooking improved from -0.81 to -0.94 after incorporation of activity coefficients iγ om. A stepwise regression analysis of the partitioning model revealed that for the data set used in this study, partitioning predictions on α-pinene-O 3 secondary aerosol and wood combustion aerosol showed statistically significant improvement after incorporation of iγ om, which can be attributed to their overall polarity. The partitioning model was sensitive to changes in aerosol composition when updated compositions for α-pinene-O 3 aerosol and wood combustion aerosol were used. The octanol-air partitioning coefficient's ( KOA) effectiveness as a partitioning correlator over a variety of aerosol types was evaluated. The slope of the log K p- log K OA correlation was not constant over the aerosol types and SOCs used in the study and the use of KOA for partitioning correlations can potentially lead to significant deviations, especially for polar aerosols.

Community detection in sequence similarity networks based on attribute clustering

DOE PAGES

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

2017-07-24

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

Community detection in sequence similarity networks based on attribute clustering

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chowdhary, Janamejaya; Loeffler, Frank E.; Smith, Jeremy C.

Networks are powerful tools for the presentation and analysis of interactions in multi-component systems. A commonly studied mesoscopic feature of networks is their community structure, which arises from grouping together similar nodes into one community and dissimilar nodes into separate communities. Here in this paper, the community structure of protein sequence similarity networks is determined with a new method: Attribute Clustering Dependent Communities (ACDC). Sequence similarity has hitherto typically been quantified by the alignment score or its expectation value. However, pair alignments with the same score or expectation value cannot thus be differentiated. To overcome this deficiency, the method constructs,more » for pair alignments, an extended alignment metric, the link attribute vector, which includes the score and other alignment characteristics. Rescaling components of the attribute vectors qualitatively identifies a systematic variation of sequence similarity within protein superfamilies. The problem of community detection is then mapped to clustering the link attribute vectors, selection of an optimal subset of links and community structure refinement based on the partition density of the network. ACDC-predicted communities are found to be in good agreement with gold standard sequence databases for which the "ground truth" community structures (or families) are known. ACDC is therefore a community detection method for sequence similarity networks based entirely on pair similarity information. A serial implementation of ACDC is available from https://cmb.ornl.gov/resources/developments« less

Bluetongue virus spread in Europe is a consequence of climatic, landscape and vertebrate host factors as revealed by phylogeographic inference

PubMed Central

Palmarini, Massimo; Mertens, Peter

2017-01-01

Spatio-temporal patterns of the spread of infectious diseases are commonly driven by environmental and ecological factors. This is particularly true for vector-borne diseases because vector populations can be strongly affected by host distribution as well as by climatic and landscape variables. Here, we aim to identify environmental drivers for bluetongue virus (BTV), the causative agent of a major vector-borne disease of ruminants that has emerged multiple times in Europe in recent decades. In order to determine the importance of climatic, landscape and host-related factors affecting BTV diffusion across Europe, we fitted different phylogeographic models to a dataset of 113 time-stamped and geo-referenced BTV genomes, representing multiple strains and serotypes. Diffusion models using continuous space revealed that terrestrial habitat below 300 m altitude, wind direction and higher livestock densities were associated with faster BTV movement. Results of discrete phylogeographic analysis involving generalized linear models broadly supported these findings, but varied considerably with the level of spatial partitioning. Contrary to common perception, we found no evidence for average temperature having a positive effect on BTV diffusion, though both methodological and biological reasons could be responsible for this result. Our study provides important insights into the drivers of BTV transmission at the landscape scale that could inform predictive models of viral spread and have implications for designing control strategies. PMID:29021180

Multi-color incomplete Cholesky conjugate gradient methods for vector computers. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Poole, E. L.

1986-01-01

In this research, we are concerned with the solution on vector computers of linear systems of equations, Ax = b, where A is a larger, sparse symmetric positive definite matrix. We solve the system using an iterative method, the incomplete Cholesky conjugate gradient method (ICCG). We apply a multi-color strategy to obtain p-color matrices for which a block-oriented ICCG method is implemented on the CYBER 205. (A p-colored matrix is a matrix which can be partitioned into a pXp block matrix where the diagonal blocks are diagonal matrices). This algorithm, which is based on a no-fill strategy, achieves O(N/p) length vector operations in both the decomposition of A and in the forward and back solves necessary at each iteration of the method. We discuss the natural ordering of the unknowns as an ordering that minimizes the number of diagonals in the matrix and define multi-color orderings in terms of disjoint sets of the unknowns. We give necessary and sufficient conditions to determine which multi-color orderings of the unknowns correpond to p-color matrices. A performance model is given which is used both to predict execution time for ICCG methods and also to compare an ICCG method to conjugate gradient without preconditioning or another ICCG method. Results are given from runs on the CYBER 205 at NASA's Langley Research Center for four model problems.

Scoring and staging systems using cox linear regression modeling and recursive partitioning.

PubMed

Lee, J W; Um, S H; Lee, J B; Mun, J; Cho, H

2006-01-01

Scoring and staging systems are used to determine the order and class of data according to predictors. Systems used for medical data, such as the Child-Turcotte-Pugh scoring and staging systems for ordering and classifying patients with liver disease, are often derived strictly from physicians' experience and intuition. We construct objective and data-based scoring/staging systems using statistical methods. We consider Cox linear regression modeling and recursive partitioning techniques for censored survival data. In particular, to obtain a target number of stages we propose cross-validation and amalgamation algorithms. We also propose an algorithm for constructing scoring and staging systems by integrating local Cox linear regression models into recursive partitioning, so that we can retain the merits of both methods such as superior predictive accuracy, ease of use, and detection of interactions between predictors. The staging system construction algorithms are compared by cross-validation evaluation of real data. The data-based cross-validation comparison shows that Cox linear regression modeling is somewhat better than recursive partitioning when there are only continuous predictors, while recursive partitioning is better when there are significant categorical predictors. The proposed local Cox linear recursive partitioning has better predictive accuracy than Cox linear modeling and simple recursive partitioning. This study indicates that integrating local linear modeling into recursive partitioning can significantly improve prediction accuracy in constructing scoring and staging systems.

Compositional and phase relations among rare earth element minerals

NASA Technical Reports Server (NTRS)

Burt, D. M.

1990-01-01

This paper discusses the compositional and phase relationships among minerals in which rare earth elements (REE) occur as essential constituents (e.g., bastnaesite, monazite, xenotime, aeschynite, allanite). Particular consideration is given to the vector representation of complex coupled substitutions in selected REE-bearing minerals and to the REE partitioning between minerals as related to the acid-base tendencies and mineral stabilities. It is shown that the treatment of coupled substitutions as vector quantities facilitates graphical representation of mineral composition spaces.

PREFERENTIAL PARTITIONING OF A PAH AND PCB TO A MARINE SEDIMENT AMENDED WITH SEVERAL SOURCES OF SOOT CARBON

EPA Science Inventory

Over the last decade, several studies reported that the partitioning of PAHs to sediments, in some cases, did not follow predictions based on equilibrium partitioning theory. One explanation for these differences is the presence of a second sedimentary phase with partitioning cha...

Predicting bioconcentration of chemicals into vegetation from soil or air using the molecular connectivity index

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowdy, D.L.; McKone, T.E.; Hsieh, D.P.H.

1995-12-31

Bioconcentration factors (BCFs) are the ratio of chemical concentration found in an exposed organism (in this case a plant) to the concentration in an air or soil exposure medium. The authors examine here the use of molecular connectivity indices (MCIs) as quantitative structure-activity relationships (QSARS) for predicting BCFs for organic chemicals between plants and air or soil. The authors compare the reliability of the octanol-air partition coefficient (K{sub oa}) to the MC based prediction method for predicting plant/air partition coefficients. The authors also compare the reliability of the octanol/water partition coefficient (K{sub ow}) to the MC based prediction method formore » predicting plant/soil partition coefficients. The results here indicate that, relative to the use of K{sub ow} or K{sub oa} as predictors of BCFs the MC can substantially increase the reliability with which BCFs can be estimated. The authors find that the MC provides a relatively precise and accurate method for predicting the potential biotransfer of a chemical from environmental media into plants. In addition, the MC is much faster and more cost effective than direct measurements.« less

Characterization of Phase Chemistry and Partitioning in a Family of High-Strength Nickel-Based Superalloys

NASA Astrophysics Data System (ADS)

Lapington, M. T.; Crudden, D. J.; Reed, R. C.; Moody, M. P.; Bagot, P. A. J.

2018-06-01

A family of novel polycrystalline Ni-based superalloys with varying Ti:Nb ratios has been created using computational alloy design techniques, and subsequently characterized using atom probe tomography and electron microscopy. Phase chemistry, elemental partitioning, and γ' character have been analyzed and compared with thermodynamic predictions created using Thermo-Calc. Phase compositions and γ' volume fraction were found to compare favorably with the thermodynamically predicted values, while predicted partitioning behavior for Ti, Nb, Cr, and Co tended to overestimate γ' preference over the γ matrix, often with opposing trends vs Nb concentration.

Implementation of a partitioned algorithm for simulation of large CSI problems

NASA Technical Reports Server (NTRS)

Alvin, Kenneth F.; Park, K. C.

1991-01-01

The implementation of a partitioned numerical algorithm for determining the dynamic response of coupled structure/controller/estimator finite-dimensional systems is reviewed. The partitioned approach leads to a set of coupled first and second-order linear differential equations which are numerically integrated with extrapolation and implicit step methods. The present software implementation, ACSIS, utilizes parallel processing techniques at various levels to optimize performance on a shared-memory concurrent/vector processing system. A general procedure for the design of controller and filter gains is also implemented, which utilizes the vibration characteristics of the structure to be solved. Also presented are: example problems; a user's guide to the software; the procedures and algorithm scripts; a stability analysis for the algorithm; and the source code for the parallel implementation.

Computational prediction of ionic liquid 1-octanol/water partition coefficients.

PubMed

Kamath, Ganesh; Bhatnagar, Navendu; Baker, Gary A; Baker, Sheila N; Potoff, Jeffrey J

2012-04-07

Wet 1-octanol/water partition coefficients (log K(ow)) predicted for imidazolium-based ionic liquids using adaptive bias force-molecular dynamics (ABF-MD) simulations lie in excellent agreement with experimental values. These encouraging results suggest prospects for this computational tool in the a priori prediction of log K(ow) values of ionic liquids broadly with possible screening implications as well (e.g., prediction of CO(2)-philic ionic liquids).

Diametrical clustering for identifying anti-correlated gene clusters.

PubMed

Dhillon, Inderjit S; Marcotte, Edward M; Roshan, Usman

2003-09-01

Clustering genes based upon their expression patterns allows us to predict gene function. Most existing clustering algorithms cluster genes together when their expression patterns show high positive correlation. However, it has been observed that genes whose expression patterns are strongly anti-correlated can also be functionally similar. Biologically, this is not unintuitive-genes responding to the same stimuli, regardless of the nature of the response, are more likely to operate in the same pathways. We present a new diametrical clustering algorithm that explicitly identifies anti-correlated clusters of genes. Our algorithm proceeds by iteratively (i). re-partitioning the genes and (ii). computing the dominant singular vector of each gene cluster; each singular vector serving as the prototype of a 'diametric' cluster. We empirically show the effectiveness of the algorithm in identifying diametrical or anti-correlated clusters. Testing the algorithm on yeast cell cycle data, fibroblast gene expression data, and DNA microarray data from yeast mutants reveals that opposed cellular pathways can be discovered with this method. We present systems whose mRNA expression patterns, and likely their functions, oppose the yeast ribosome and proteosome, along with evidence for the inverse transcriptional regulation of a number of cellular systems.

Field-scale Prediction of Enhanced DNAPL Dissolution Using Partitioning Tracers and Flow Pattern Effects

NASA Astrophysics Data System (ADS)

Wang, F.; Annable, M. D.; Jawitz, J. W.

2012-12-01

The equilibrium streamtube model (EST) has demonstrated the ability to accurately predict dense nonaqueous phase liquid (DNAPL) dissolution in laboratory experiments and numerical simulations. Here the model is applied to predict DNAPL dissolution at a PCE-contaminated dry cleaner site, located in Jacksonville, Florida. The EST is an analytical solution with field-measurable input parameters. Here, measured data from a field-scale partitioning tracer test were used to parameterize the EST model and the predicted PCE dissolution was compared to measured data from an in-situ alcohol (ethanol) flood. In addition, a simulated partitioning tracer test from a calibrated spatially explicit multiphase flow model (UTCHEM) was also used to parameterize the EST analytical solution. The ethanol prediction based on both the field partitioning tracer test and the UTCHEM tracer test simulation closely matched the field data. The PCE EST prediction showed a peak shift to an earlier arrival time that was concluded to be caused by well screen interval differences between the field tracer test and alcohol flood. This observation was based on a modeling assessment of potential factors that may influence predictions by using UTCHEM simulations. The imposed injection and pumping flow pattern at this site for both the partitioning tracer test and alcohol flood was more complex than the natural gradient flow pattern (NGFP). Both the EST model and UTCHEM were also used to predict PCE dissolution under natural gradient conditions, with much simpler flow patterns than the forced-gradient double five spot of the alcohol flood. The NGFP predictions based on parameters determined from tracer tests conducted with complex flow patterns underestimated PCE concentrations and total mass removal. This suggests that the flow patterns influence aqueous dissolution and that the aqueous dissolution under the NGFP is more efficient than dissolution under complex flow patterns.

Brain Network Regional Synchrony Analysis in Deafness

PubMed Central

Xu, Lei; Liang, Mao-Jin

2018-01-01

Deafness, the most common auditory disease, has greatly affected people for a long time. The major treatment for deafness is cochlear implantation (CI). However, till today, there is still a lack of objective and precise indicator serving as evaluation of the effectiveness of the cochlear implantation. The goal of this EEG-based study is to effectively distinguish CI children from those prelingual deafened children without cochlear implantation. The proposed method is based on the functional connectivity analysis, which focuses on the brain network regional synchrony. Specifically, we compute the functional connectivity between each channel pair first. Then, we quantify the brain network synchrony among regions of interests (ROIs), where both intraregional synchrony and interregional synchrony are computed. And finally the synchrony values are concatenated to form the feature vector for the SVM classifier. What is more, we develop a new ROI partition method of 128-channel EEG recording system. That is, both the existing ROI partition method and the proposed ROI partition method are used in the experiments. Compared with the existing EEG signal classification methods, our proposed method has achieved significant improvements as large as 87.20% and 86.30% when the existing ROI partition method and the proposed ROI partition method are used, respectively. It further demonstrates that the new ROI partition method is comparable to the existing ROI partition method. PMID:29854776

Color Image Classification Using Block Matching and Learning

NASA Astrophysics Data System (ADS)

Kondo, Kazuki; Hotta, Seiji

In this paper, we propose block matching and learning for color image classification. In our method, training images are partitioned into small blocks. Given a test image, it is also partitioned into small blocks, and mean-blocks corresponding to each test block are calculated with neighbor training blocks. Our method classifies a test image into the class that has the shortest total sum of distances between mean blocks and test ones. We also propose a learning method for reducing memory requirement. Experimental results show that our classification outperforms other classifiers such as support vector machine with bag of keypoints.

Applications of Some Artificial Intelligence Methods to Satellite Soundings

NASA Technical Reports Server (NTRS)

Munteanu, M. J.; Jakubowicz, O.

1985-01-01

Hard clustering of temperature profiles and regression temperature retrievals were used to refine the method using the probabilities of membership of each pattern vector in each of the clusters derived with discriminant analysis. In hard clustering the maximum probability is taken and the corresponding cluster as the correct cluster are considered discarding the rest of the probabilities. In fuzzy partitioned clustering these probabilities are kept and the final regression retrieval is a weighted regression retrieval of several clusters. This method was used in the clustering of brightness temperatures where the purpose was to predict tropopause height. A further refinement is the division of temperature profiles into three major regions for classification purposes. The results are summarized in the tables total r.m.s. errors are displayed. An approach based on fuzzy logic which is intimately related to artificial intelligence methods is recommended.

A hardware-oriented concurrent TZ search algorithm for High-Efficiency Video Coding

NASA Astrophysics Data System (ADS)

Doan, Nghia; Kim, Tae Sung; Rhee, Chae Eun; Lee, Hyuk-Jae

2017-12-01

High-Efficiency Video Coding (HEVC) is the latest video coding standard, in which the compression performance is double that of its predecessor, the H.264/AVC standard, while the video quality remains unchanged. In HEVC, the test zone (TZ) search algorithm is widely used for integer motion estimation because it effectively searches the good-quality motion vector with a relatively small amount of computation. However, the complex computation structure of the TZ search algorithm makes it difficult to implement it in the hardware. This paper proposes a new integer motion estimation algorithm which is designed for hardware execution by modifying the conventional TZ search to allow parallel motion estimations of all prediction unit (PU) partitions. The algorithm consists of the three phases of zonal, raster, and refinement searches. At the beginning of each phase, the algorithm obtains the search points required by the original TZ search for all PU partitions in a coding unit (CU). Then, all redundant search points are removed prior to the estimation of the motion costs, and the best search points are then selected for all PUs. Compared to the conventional TZ search algorithm, experimental results show that the proposed algorithm significantly decreases the Bjøntegaard Delta bitrate (BD-BR) by 0.84%, and it also reduces the computational complexity by 54.54%.

Partition coefficients of organic compounds in lipid-water systems and correlations with fish bioconcentration factors

USGS Publications Warehouse

Chiou, C.T.

1985-01-01

Triolein-water partition coefficients (KtW) have been determined for 38 slightly water-soluble organic compounds, and their magnitudes have been compared with the corresponding octanol-water partition coefficients (KOW). In the absence of major solvent-solute interaction effects in the organic solvent phase, the conventional treatment (based on Raoult's law) predicts sharply lower partition coefficients for most of the solutes in triolein because of its considerably higher molecular weight, whereas the Flory-Huggins treatment predicts higher partition coefficients with triolein. The data are in much better agreement with the Flory-Huggins model. As expected from the similarity in the partition coefficients, the water solubility (which was previously found to be the major determinant of the KOW) is also the major determinant for the Ktw. When the published BCF values (bioconcentration factors) of organic compounds in fish are based on the lipid content rather than on total mass, they are approximately equal to the Ktw, which suggests at least near equilibrium for solute partitioning between water and fish lipid. The close correlation between Ktw and Kow suggests that Kow is also a good predictor for lipid-water partition coefficients and bioconcentration factors.

A Multiple-Window Video Embedding Transcoder Based on H.264/AVC Standard

NASA Astrophysics Data System (ADS)

Li, Chih-Hung; Wang, Chung-Neng; Chiang, Tihao

2007-12-01

This paper proposes a low-complexity multiple-window video embedding transcoder (MW-VET) based on H.264/AVC standard for various applications that require video embedding services including picture-in-picture (PIP), multichannel mosaic, screen-split, pay-per-view, channel browsing, commercials and logo insertion, and other visual information embedding services. The MW-VET embeds multiple foreground pictures at macroblock-aligned positions. It improves the transcoding speed with three block level adaptive techniques including slice group based transcoding (SGT), reduced frame memory transcoder (RFMT), and syntax level bypassing (SLB). The SGT utilizes prediction from the slice-aligned data partitions in the original bitstreams such that the transcoder simply merges the bitstreams by parsing. When the prediction comes from the newly covered area without slice-group data partitions, the pixels at the affected macroblocks are transcoded with the RFMT based on the concept of partial reencoding to minimize the number of refined blocks. The RFMT employs motion vector remapping (MVR) and intra mode switching (IMS) to handle intercoded blocks and intracoded blocks, respectively. The pixels outside the macroblocks that are affected by newly covered reference frame are transcoded by the SLB. Experimental results show that, as compared to the cascaded pixel domain transcoder (CPDT) with the highest complexity, our MW-VET can significantly reduce the processing complexity by 25 times and retain the rate-distortion performance close to the CPDT. At certain bit rates, the MW-VET can achieve up to 1.5 dB quality improvement in peak signal-to-noise-ratio (PSNR).

Prediction of enzyme activity with neural network models based on electronic and geometrical features of substrates.

PubMed

Szaleniec, Maciej

2012-01-01

Artificial Neural Networks (ANNs) are introduced as robust and versatile tools in quantitative structure-activity relationship (QSAR) modeling. Their application to the modeling of enzyme reactivity is discussed, along with methodological issues. Methods of input variable selection, optimization of network internal structure, data set division and model validation are discussed. The application of ANNs in the modeling of enzyme activity over the last 20 years is briefly recounted. The discussed methodology is exemplified by the case of ethylbenzene dehydrogenase (EBDH). Intelligent Problem Solver and genetic algorithms are applied for input vector selection, whereas k-means clustering is used to partition the data into training and test cases. The obtained models exhibit high correlation between the predicted and experimental values (R(2) > 0.9). Sensitivity analyses and study of the response curves are used as tools for the physicochemical interpretation of the models in terms of the EBDH reaction mechanism. Neural networks are shown to be a versatile tool for the construction of robust QSAR models that can be applied to a range of aspects important in drug design and the prediction of biological activity.

Strainrange partitioning behavior of an automotive turbine alloy

NASA Technical Reports Server (NTRS)

Annis, C. G.; Vanwanderham, M. C.; Wallace, R. M.

1976-01-01

This report addresses Strainrange Partitioning, an advanced life prediction analysis procedure, as applied to CA-101 (cast IN 792 + Hf), an alloy proposed for turbine disks in automotive gas turbine engines. The methodology was successful in predicting specimen life under thermal-mechanical cycling, to within a factor of + or - 2.

A comparative study on the predictive ability of the decision tree, support vector machine and neuro-fuzzy models in landslide susceptibility mapping using GIS

NASA Astrophysics Data System (ADS)

Pradhan, Biswajeet

2013-02-01

The purpose of the present study is to compare the prediction performances of three different approaches such as decision tree (DT), support vector machine (SVM) and adaptive neuro-fuzzy inference system (ANFIS) for landslide susceptibility mapping at Penang Hill area, Malaysia. The necessary input parameters for the landslide susceptibility assessments were obtained from various sources. At first, landslide locations were identified by aerial photographs and field surveys and a total of 113 landslide locations were constructed. The study area contains 340,608 pixels while total 8403 pixels include landslides. The landslide inventory was randomly partitioned into two subsets: (1) part 1 that contains 50% (4000 landslide grid cells) was used in the training phase of the models; (2) part 2 is a validation dataset 50% (4000 landslide grid cells) for validation of three models and to confirm its accuracy. The digitally processed images of input parameters were combined in GIS. Finally, landslide susceptibility maps were produced, and the performances were assessed and discussed. Total fifteen landslide susceptibility maps were produced using DT, SVM and ANFIS based models, and the resultant maps were validated using the landslide locations. Prediction performances of these maps were checked by receiver operating characteristics (ROC) by using both success rate curve and prediction rate curve. The validation results showed that, area under the ROC curve for the fifteen models produced using DT, SVM and ANFIS varied from 0.8204 to 0.9421 for success rate curve and 0.7580 to 0.8307 for prediction rate curves, respectively. Moreover, the prediction curves revealed that model 5 of DT has slightly higher prediction performance (83.07), whereas the success rate showed that model 5 of ANFIS has better prediction (94.21) capability among all models. The results of this study showed that landslide susceptibility mapping in the Penang Hill area using the three approaches (e.g., DT, SVM and ANFIS) is viable. As far as the performance of the models are concerned, the results appeared to be quite satisfactory, i.e., the zones determined on the map being zones of relative susceptibility.

The influence of hydrogen bonding on partition coefficients

NASA Astrophysics Data System (ADS)

Borges, Nádia Melo; Kenny, Peter W.; Montanari, Carlos A.; Prokopczyk, Igor M.; Ribeiro, Jean F. R.; Rocha, Josmar R.; Sartori, Geraldo Rodrigues

2017-02-01

This Perspective explores how consideration of hydrogen bonding can be used to both predict and better understand partition coefficients. It is shown how polarity of both compounds and substructures can be estimated from measured alkane/water partition coefficients. When polarity is defined in this manner, hydrogen bond donors are typically less polar than hydrogen bond acceptors. Analysis of alkane/water partition coefficients in conjunction with molecular electrostatic potential calculations suggests that aromatic chloro substituents may be less lipophilic than is generally believed and that some of the effect of chloro-substitution stems from making the aromatic π-cloud less available to hydrogen bond donors. Relationships between polarity and calculated hydrogen bond basicity are derived for aromatic nitrogen and carbonyl oxygen. Aligned hydrogen bond acceptors appear to present special challenges for prediction of alkane/water partition coefficients and this may reflect `frustration' of solvation resulting from overlapping hydration spheres. It is also shown how calculated hydrogen bond basicity can be used to model the effect of aromatic aza-substitution on octanol/water partition coefficients.

CONTRIBUTION TO THE THEORY OF MATRICES PARTITIONED INTO BLOCKS.

DTIC Science & Technology

results were obtained on cones of matrices and vectors, and an extension of the well-known Perron - Frobenius theorem was proved. Also a necessary and...sufficient condition was derived, in order that to a given matrix corresponds a cone on which it is a positive operator. Easily computed upper and

Quantified degree of eccentricity of aortic valve calcification predicts risk of paravalvular regurgitation and response to balloon post-dilation after self-expandable transcatheter aortic valve replacement.

PubMed

Park, Jun-Bean; Hwang, In-Chang; Lee, Whal; Han, Jung-Kyu; Kim, Chi-Hoon; Lee, Seung-Pyo; Yang, Han-Mo; Park, Eun-Ah; Kim, Hyung-Kwan; Chiam, Paul T L; Kim, Yong-Jin; Koo, Bon-Kwon; Sohn, Dae-Won; Ahn, Hyuk; Kang, Joon-Won; Park, Seung-Jung; Kim, Hyo-Soo

2018-05-15

Limited data exist regarding the impact of aortic valve calcification (AVC) eccentricity on the risk of paravalvular regurgitation (PVR) and response to balloon post-dilation (BPD) after transcatheter aortic valve replacement (TAVR). We investigated the prognostic value of AVC eccentricity in predicting the risk of PVR and response to BPD in patients undergoing TAVR. We analyzed 85 patients with severe aortic stenosis who underwent self-expandable TAVR (43 women; 77.2±7.1years). AVC was quantified as the total amount of calcification (total AVC load) and as the eccentricity of calcium (EoC) using calcium volume scoring with contrast computed tomography angiography (CTA). The EoC was defined as the maximum absolute difference in calcium volume scores between 2 adjacent sectors (bi-partition method) or between sectors based on leaflets (leaflet-based method). Total AVC load and bi-partition EoC, but not leaflet-based EoC, were significant predictors for the occurrence of ≥moderate PVR, and bi-partition EoC had a better predictive value than total AVC load (area under the curve [AUC]=0.863 versus 0.760, p for difference=0.006). In multivariate analysis, bi-partition EoC was an independent predictor for the risk of ≥moderate PVR regardless of perimeter oversizing index. The greater bi-partition EoC was the only significant parameter to predict poor response to BPD (AUC=0.775, p=0.004). Pre-procedural assessment of AVC eccentricity using CTA as "bi-partition EoC" provides useful predictive information on the risk of significant PVR and response to BPD in patients undergoing TAVR with self-expandable valves. Copyright © 2017 Elsevier B.V. All rights reserved.

Aqueous solubility, effects of salts on aqueous solubility, and partitioning behavior of hexafluorobenzene: experimental results and COSMO-RS predictions.

PubMed

Schröder, Bernd; Freire, Mara G; Varanda, Fatima R; Marrucho, Isabel M; Santos, Luís M N B F; Coutinho, João A P

2011-07-01

The aqueous solubility of hexafluorobenzene has been determined, at 298.15K, using a shake-flask method with a spectrophotometric quantification technique. Furthermore, the solubility of hexafluorobenzene in saline aqueous solutions, at distinct salt concentrations, has been measured. Both salting-in and salting-out effects were observed and found to be dependent on the nature of the cationic/anionic composition of the salt. COSMO-RS, the Conductor-like Screening Model for Real Solvents, has been used to predict the corresponding aqueous solubilities at conditions similar to those used experimentally. The prediction results showed that the COSMO-RS approach is suitable for the prediction of salting-in/-out effects. The salting-in/-out phenomena have been rationalized with the support of COSMO-RS σ-profiles. The prediction potential of COSMO-RS regarding aqueous solubilities and octanol-water partition coefficients has been compared with typically used QSPR-based methods. Up to now, the absence of accurate solubility data for hexafluorobenzene hampered the calculation of the respective partition coefficients. Combining available accurate vapor pressure data with the experimentally determined water solubility, a novel air-water partition coefficient has been derived. Copyright © 2011 Elsevier Ltd. All rights reserved.

Improved prediction of octanol-water partition coefficients from liquid-solute water solubilities and molar volumes

USGS Publications Warehouse

Chiou, C.T.; Schmedding, D.W.; Manes, M.

2005-01-01

A volume-fraction-based solvent-water partition model for dilute solutes, in which the partition coefficient shows a dependence on solute molar volume (V??), is adapted to predict the octanol-water partition coefficient (K ow) from the liquid or supercooled-liquid solute water solubility (Sw), or vice versa. The established correlation is tested for a wide range of industrial compounds and pesticides (e.g., halogenated aliphatic hydrocarbons, alkylbenzenes, halogenated benzenes, ethers, esters, PAHs, PCBs, organochlorines, organophosphates, carbamates, and amidesureas-triazines), which comprise a total of 215 test compounds spanning about 10 orders of magnitude in Sw and 8.5 orders of magnitude in Kow. Except for phenols and alcohols, which require special considerations of the Kow data, the correlation predicts the Kow within 0.1 log units for most compounds, much independent of the compound type or the magnitude in K ow. With reliable Sw and V data for compounds of interest, the correlation provides an effective means for either predicting the unavailable log Kow values or verifying the reliability of the reported log Kow data. ?? 2005 American Chemical Society.

A combined vector potential-scalar potential method for FE computation of 3D magnetic fields in electrical devices with iron cores

NASA Technical Reports Server (NTRS)

Wang, R.; Demerdash, N. A.

1991-01-01

A method of combined use of magnetic vector potential based finite-element (FE) formulations and magnetic scalar potential (MSP) based formulations for computation of three-dimensional magnetostatic fields is introduced. In this method, the curl-component of the magnetic field intensity is computed by a reduced magnetic vector potential. This field intensity forms the basic of a forcing function for a global magnetic scalar potential solution over the entire volume of the region. This method allows one to include iron portions sandwiched in between conductors within partitioned current-carrying subregions. The method is most suited for large-scale global-type 3-D magnetostatic field computations in electrical devices, and in particular rotating electric machinery.

Spatial coding-based approach for partitioning big spatial data in Hadoop

NASA Astrophysics Data System (ADS)

Yao, Xiaochuang; Mokbel, Mohamed F.; Alarabi, Louai; Eldawy, Ahmed; Yang, Jianyu; Yun, Wenju; Li, Lin; Ye, Sijing; Zhu, Dehai

2017-09-01

Spatial data partitioning (SDP) plays a powerful role in distributed storage and parallel computing for spatial data. However, due to skew distribution of spatial data and varying volume of spatial vector objects, it leads to a significant challenge to ensure both optimal performance of spatial operation and data balance in the cluster. To tackle this problem, we proposed a spatial coding-based approach for partitioning big spatial data in Hadoop. This approach, firstly, compressed the whole big spatial data based on spatial coding matrix to create a sensing information set (SIS), including spatial code, size, count and other information. SIS was then employed to build spatial partitioning matrix, which was used to spilt all spatial objects into different partitions in the cluster finally. Based on our approach, the neighbouring spatial objects can be partitioned into the same block. At the same time, it also can minimize the data skew in Hadoop distributed file system (HDFS). The presented approach with a case study in this paper is compared against random sampling based partitioning, with three measurement standards, namely, the spatial index quality, data skew in HDFS, and range query performance. The experimental results show that our method based on spatial coding technique can improve the query performance of big spatial data, as well as the data balance in HDFS. We implemented and deployed this approach in Hadoop, and it is also able to support efficiently any other distributed big spatial data systems.

New Linear Partitioning Models Based on Experimental Water: Supercritical CO2 Partitioning Data of Selected Organic Compounds.

PubMed

Burant, Aniela; Thompson, Christopher; Lowry, Gregory V; Karamalidis, Athanasios K

2016-05-17

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch-reactor system with dual spectroscopic detectors: a near-infrared spectrometer for measuring the organic analyte in the CO2 phase and a UV detector for quantifying the analyte in the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly parameter linear free-energy relationship and to develop five new linear free-energy relationships for predicting water-sc-CO2 partitioning coefficients. A total of four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than does the model built for the entire data set.

New Linear Partitioning Models Based on Experimental Water: Supercritical CO 2 Partitioning Data of Selected Organic Compounds

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burant, Aniela; Thompson, Christopher; Lowry, Gregory V.

2016-05-17

Partitioning coefficients of organic compounds between water and supercritical CO2 (sc-CO2) are necessary to assess the risk of migration of these chemicals from subsurface CO2 storage sites. Despite the large number of potential organic contaminants, the current data set of published water-sc-CO2 partitioning coefficients is very limited. Here, the partitioning coefficients of thiophene, pyrrole, and anisole were measured in situ over a range of temperatures and pressures using a novel pressurized batch reactor system with dual spectroscopic detectors: a near infrared spectrometer for measuring the organic analyte in the CO2 phase, and a UV detector for quantifying the analyte inmore » the aqueous phase. Our measured partitioning coefficients followed expected trends based on volatility and aqueous solubility. The partitioning coefficients and literature data were then used to update a published poly-parameter linear free energy relationship and to develop five new linear free energy relationships for predicting water-sc-CO2 partitioning coefficients. Four of the models targeted a single class of organic compounds. Unlike models that utilize Abraham solvation parameters, the new relationships use vapor pressure and aqueous solubility of the organic compound at 25 °C and CO2 density to predict partitioning coefficients over a range of temperature and pressure conditions. The compound class models provide better estimates of partitioning behavior for compounds in that class than the model built for the entire dataset.« less

Determination of polyparameter linear free energy relationship (pp-LFER) substance descriptors for established and alternative flame retardants.

PubMed

Stenzel, Angelika; Goss, Kai-Uwe; Endo, Satoshi

2013-02-05

Polyparameter linear free energy relationships (pp-LFERs) can predict partition coefficients for a multitude of environmental and biological phases with high accuracy. In this work, the pp-LFER substance descriptors of 40 established and alternative flame retardants (e.g., polybrominated diphenyl ethers, hexabromocyclododecane, bromobenzenes, trialkyl phosphates) were determined experimentally. In total, 251 data for gas-chromatographic (GC) retention times and liquid/liquid partition coefficients (K) were measured and used to calibrate the pp-LFER substance descriptors. Substance descriptors were validated through a comparison between predicted and experimental log K for the systems octanol/water (K(ow)), water/air (K(wa)), organic carbon/water (K(oc)) and liposome/water (K(lipw)), revealing a high reliability of pp-LFER predictions based on our descriptors. For instance, the difference between predicted and experimental log K(ow) was <0.3 log units for 17 out of 21 compounds for which experimental values were available. Moreover, we found an indication that the H-bond acceptor value (B) depends on the solvent for some compounds. Thus, for predicting environmentally relevant partition coefficients it is important to determine B values using measurements in aqueous systems. The pp-LFER descriptors calibrated in this study can be used to predict partition coefficients for which experimental data are unavailable, and the predicted values can serve as references for further experimental measurements.

Prediction of gas/particle partitioning of polybrominated diphenyl ethers (PBDEs) in global air: a theoretical study

NASA Astrophysics Data System (ADS)

Li, Y.-F.; Ma, W.-L.; Yang, M.

2014-09-01

Gas/particle (G / P) partitioning for most semivolatile organic compounds (SVOCs) is an important process that primarily governs their atmospheric fate, long-range atmospheric transport potential, and their routs to enter human body. All previous studies on this issue have been hypothetically derived from equilibrium conditions, the results of which do not predict results from monitoring studies well in most cases. In this study, a steady-state model instead of an equilibrium-state model for the investigation of the G / P partitioning behavior for polybrominated diphenyl ethers (PBDEs) was established, and an equation for calculating the partition coefficients under steady state (KPS) for PBDE congeners (log KPS = log KPE + logα) was developed, in which an equilibrium term (log KPE = log KOA + logfOM -11.91, where fOM is organic matter content of the particles) and a nonequilibrium term (logα, mainly caused by dry and wet depositions of particles), both being functions of log KOA (octanol-air partition coefficient), are included, and the equilibrium is a special case of steady state when the nonequilibrium term equals to zero. A criterion to classify the equilibrium and nonequilibrium status for PBDEs was also established using two threshold values of log KOA, log KOA1 and log KOA2, which divide the range of log KOA into 3 domains: equilibrium, nonequilibrium, and maximum partition domains; and accordingly, two threshold values of temperature t, tTH1 when log KOA = log KOA1 and tTH2 when log KOA = log KOA2, were identified, which divide the range of temperature also into the same 3 domains for each BDE congener. We predicted the existence of the maximum partition domain (the values of log KPS reach a maximum constant of -1.53) that every PBDE congener can reach when log KOA ≥ log KOA2, or t ≤ tTH2. The novel equation developed in this study was applied to predict the G / P partition coefficients of PBDEs for the published monitoring data worldwide, including Asia, Europe, North America, and the Arctic, and the results matched well with all the monitoring data, except those obtained at e-waste sites due to the unpredictable PBDE emissions at these sites. This study provided evidence that, the new developed steady-state-based equation is superior to the equilibrium-state-based equation that has been used in describing the G / P partitioning behavior in decades. We suggest that, the investigation on G / P partitioning behavior for PBDEs should be based on steady state, not equilibrium state, and equilibrium is just a special case of steady state when nonequilibrium factors can be ignored. We also believe that our new equation provides a useful tool for environmental scientists in both monitoring and modeling research on G / P partitioning for PBDEs and can be extended to predict G / P partitioning behavior for other SVOCs as well.

Application of a Model for Quenching and Partitioning in Hot Stamping of High-Strength Steel

NASA Astrophysics Data System (ADS)

Zhu, Bin; Liu, Zhuang; Wang, Yanan; Rolfe, Bernard; Wang, Liang; Zhang, Yisheng

2018-04-01

Application of quenching and partitioning process in hot stamping has proven to be an effective method to improve the plasticity of advanced high-strength steels (AHSSs). In this study, the hot stamping and partitioning process of advanced high-strength steel 30CrMnSi2Nb is investigated with a hot stamping mold. Given the specific partitioning time and temperature, the influence of quenching temperature on the volume fraction of microstructure evolution and mechanical properties of the above steel are studied in detail. In addition, a model for quenching and partitioning process is applied to predict the carbon diffusion and interface migration during partitioning, which determines the retained austenite volume fraction and final properties of the part. The predicted trends of the retained austenite volume fraction agree with the experimental results. In both cases, the volume fraction of retained austenite increases first and then decreases with the increasing quenching temperature. The optimal quenching temperature is approximately 290 °C for 30CrMnSi2Nb with the partition conditions of 425 °C and 20 seconds. It is suggested that the model can be used to help determine the process parameters to obtain retained austenite as much as possible.

Seasonal variations in atmospheric concentrations and gas-particle partitioning of PCDD/Fs and dioxin-like PCBs around industrial sites in Shanghai, China

NASA Astrophysics Data System (ADS)

Die, Qingqi; Nie, Zhiqiang; Liu, Feng; Tian, Yajun; Fang, Yanyan; Gao, Hefeng; Tian, Shulei; He, Jie; Huang, Qifei

2015-10-01

Gas and particle phase air samples were collected in summer and winter around industrial sites in Shanghai, China, to allow the concentrations, profiles, and gas-particle partitioning of polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (dl-PCBs) to be determined. The total 2,3,7,8-substituted PCDD/F and dl-PCB toxic equivalent (TEQ) concentrations were 14.2-182 fg TEQ/m3 (mean 56.8 fg TEQ/m3) in summer and 21.9-479 fg TEQ/m3 (mean 145 fg TEQ/m3) in winter. The PCDD/Fs tended to be predominantly in the particulate phase, while the dl-PCBs were predominantly found in the gas phase, and the proportions of all of the PCDD/F and dl-PCB congeners in the particle phase increased as the temperature decreased. The logarithms of the gas-particle partition coefficients correlated well with the subcooled liquid vapor pressures of the PCDD/Fs and dl-PCBs for most of the samples. Gas-particle partitioning of the PCDD/Fs deviated from equilibrium either in summer or winter close to local sources, and the Junge-Pankow model and predictions made using a model based on the octanol-air partition coefficient fitted the measured particulate PCDD/F fractions well, indicating that absorption and adsorption mechanism both contributed to the partitioning process. However, gas-particle equilibrium of the dl-PCBs was reached more easily in winter than in summer. The Junge-Pankow model predictions fitted the dl-PCB data better than did the predictions made using the model based on the octanol-air partition coefficient, indicating that adsorption mechanism made dominated contribution to the partitioning process.

Luneburg lens and optical matrix algebra research

NASA Technical Reports Server (NTRS)

Wood, V. E.; Busch, J. R.; Verber, C. M.; Caulfield, H. J.

1984-01-01

Planar, as opposed to channelized, integrated optical circuits (IOCs) were stressed as the basis for computational devices. Both fully-parallel and systolic architectures are considered and the tradeoffs between the two device types are discussed. The Kalman filter approach is a most important computational method for many NASA problems. This approach to deriving a best-fit estimate for the state vector describing a large system leads to matrix sizes which are beyond the predicted capacities of planar IOCs. This problem is overcome by matrix partitioning, and several architectures for accomplishing this are described. The Luneburg lens work has involved development of lens design techniques, design of mask arrangements for producing lenses of desired shape, investigation of optical and chemical properties of arsenic trisulfide films, deposition of lenses both by thermal evaporation and by RF sputtering, optical testing of these lenses, modification of lens properties through ultraviolet irradiation, and comparison of measured lens properties with those expected from ray trace analyses.

Strike-slip deformation reflects complex partitioning of strain in the Nankai Accretionary Prism (SE Japan)

NASA Astrophysics Data System (ADS)

Azevedo, Marco C.; Alves, Tiago M.; Fonseca, Paulo E.; Moore, Gregory F.

2018-01-01

Previous studies have suggested predominant extensional tectonics acting, at present, on the Nankai Accretionary Prism (NAP), and following a parallel direction to the convergence vector between the Philippine Sea and Amur Plates. However, a complex set of thrusts, pop-up structures, thrust anticlines and strike-slip faults is observed on seismic data in the outer wedge of the NAP, hinting at a complex strain distribution across SE Japan. Three-dimensional (3D) seismic data reveal three main families of faults: (1) NE-trending thrusts and back-thrusts; (2) NNW- to N-trending left-lateral strike-slip faults; and (3) WNW-trending to E-W right-lateral strike-slip faults. Such a fault pattern suggests that lateral slip, together with thrusting, are the two major styles of deformation operating in the outer wedge of the NAP. Both styles of deformation reflect a transpressional tectonic regime in which the maximum horizontal stress is geometrically close to the convergence vector. This work is relevant because it shows a progressive change from faults trending perpendicularly to the convergence vector, to a broader partitioning of strain in the form of thrusts and conjugate strike-slip faults. We suggest that similar families of faults exist within the inner wedge of the NAP, below the Kumano Basin, and control stress accumulation and strain accommodation in this latter region.

An Improved Compressive Sensing and Received Signal Strength-Based Target Localization Algorithm with Unknown Target Population for Wireless Local Area Networks.

PubMed

Yan, Jun; Yu, Kegen; Chen, Ruizhi; Chen, Liang

2017-05-30

In this paper a two-phase compressive sensing (CS) and received signal strength (RSS)-based target localization approach is proposed to improve position accuracy by dealing with the unknown target population and the effect of grid dimensions on position error. In the coarse localization phase, by formulating target localization as a sparse signal recovery problem, grids with recovery vector components greater than a threshold are chosen as the candidate target grids. In the fine localization phase, by partitioning each candidate grid, the target position in a grid is iteratively refined by using the minimum residual error rule and the least-squares technique. When all the candidate target grids are iteratively partitioned and the measurement matrix is updated, the recovery vector is re-estimated. Threshold-based detection is employed again to determine the target grids and hence the target population. As a consequence, both the target population and the position estimation accuracy can be significantly improved. Simulation results demonstrate that the proposed approach achieves the best accuracy among all the algorithms compared.

Multiway spectral community detection in networks

NASA Astrophysics Data System (ADS)

Zhang, Xiao; Newman, M. E. J.

2015-11-01

One of the most widely used methods for community detection in networks is the maximization of the quality function known as modularity. Of the many maximization techniques that have been used in this context, some of the most conceptually attractive are the spectral methods, which are based on the eigenvectors of the modularity matrix. Spectral algorithms have, however, been limited, by and large, to the division of networks into only two or three communities, with divisions into more than three being achieved by repeated two-way division. Here we present a spectral algorithm that can directly divide a network into any number of communities. The algorithm makes use of a mapping from modularity maximization to a vector partitioning problem, combined with a fast heuristic for vector partitioning. We compare the performance of this spectral algorithm with previous approaches and find it to give superior results, particularly in cases where community sizes are unbalanced. We also give demonstrative applications of the algorithm to two real-world networks and find that it produces results in good agreement with expectations for the networks studied.

Efficient low-bit-rate adaptive mesh-based motion compensation technique

NASA Astrophysics Data System (ADS)

Mahmoud, Hanan A.; Bayoumi, Magdy A.

2001-08-01

This paper proposes a two-stage global motion estimation method using a novel quadtree block-based motion estimation technique and an active mesh model. In the first stage, motion parameters are estimated by fitting block-based motion vectors computed using a new efficient quadtree technique, that divides a frame into equilateral triangle blocks using the quad-tree structure. Arbitrary partition shapes are achieved by allowing 4-to-1, 3-to-1 and 2-1 merge/combine of sibling blocks having the same motion vector . In the second stage, the mesh is constructed using an adaptive triangulation procedure that places more triangles over areas with high motion content, these areas are estimated during the first stage. finally the motion compensation is achieved by using a novel algorithm that is carried by both the encoder and the decoder to determine the optimal triangulation of the resultant partitions followed by affine mapping at the encoder. Computer simulation results show that the proposed method gives better performance that the conventional ones in terms of the peak signal-to-noise ration (PSNR) and the compression ratio (CR).

On the star partition dimension of comb product of cycle and complete graph

NASA Astrophysics Data System (ADS)

Alfarisi, Ridho; Darmaji; Dafik

2017-06-01

Let G = (V, E) be a connected graphs with vertex set V (G), edge set E(G) and S ⊆ V (G). For an ordered partition Π = {S 1, S 2, S 3, …, Sk } of V (G), the representation of a vertex v ∈ V (G) with respect to Π is the k-vectors r(v|Π) = (d(v, S 1), d(v, S 2), …, d(v, Sk )), where d(v, Sk ) represents the distance between the vertex v and the set Sk , defined by d(v, Sk ) = min{d(v, x)|x ∈ Sk}. The partition Π of V (G) is a resolving partition if the k-vektors r(v|Π), v ∈ V (G) are distinct. The minimum resolving partition Π is a partition dimension of G, denoted by pd(G). The resolving partition Π = {S 1, S 2, S 3, …, Sk} is called a star resolving partition for G if it is a resolving partition and each subgraph induced by Si , 1 ≤ i ≤ k, is a star. The minimum k for which there exists a star resolving partition of V (G) is the star partition dimension of G, denoted by spd(G). Finding a star partition dimension of G is classified to be a NP-Hard problem. Furthermore, the comb product between G and H, denoted by G ⊲ H, is a graph obtained by taking one copy of G and |V (G)| copies of H and grafting the i-th copy of H at the vertex o to the i-th vertex of G. By definition of comb product, we can say that V (G ⊲ H) = {(a, u)|a ∈ V (G), u ∈ V (H)} and (a, u)(b, v) ∈ E(G ⊲ H) whenever a = b and uv ∈ E(H), or ab ∈ E(G) and u = v = o. In this paper, we will study the star partition dimension of comb product of cycle and complete graph, namely Cn ⊲ Km and Km ⊲ Cn for n ≥ 3 and m ≥ 3.

Toward prediction of alkane/water partition coefficients.

PubMed

Toulmin, Anita; Wood, J Matthew; Kenny, Peter W

2008-07-10

Partition coefficients were measured for 47 compounds in the hexadecane/water ( P hxd) and 1-octanol/water ( P oct) systems. Some types of hydrogen bond acceptor presented by these compounds to the partitioning systems are not well represented in the literature of alkane/water partitioning. The difference, DeltalogP, between logP oct and logP hxd is a measure of the hydrogen bonding potential of a molecule and is identified as a target for predictive modeling. Minimized molecular electrostatic potential ( V min) was shown to be an effective predictor of the contribution of hydrogen bond acceptors to DeltalogP. Carbonyl oxygen atoms were found to be stronger hydrogen bond acceptors for their electrostatic potential than heteroaromatic nitrogen or oxygen bound to hypervalent sulfur or nitrogen. Values of V min calculated for hydrogen-bonded complexes were used to explore polarization effects. Predicted logP hxd and DeltalogP were shown to be more effective than logP oct for modeling brain penetration for a data set of 18 compounds.

Partitioning of polar and non-polar neutral organic chemicals into human and cow milk.

PubMed

Geisler, Anett; Endo, Satoshi; Goss, Kai-Uwe

2011-10-01

The aim of this work was to develop a predictive model for milk/water partition coefficients of neutral organic compounds. Batch experiments were performed for 119 diverse organic chemicals in human milk and raw and processed cow milk at 37°C. No differences (<0.3 log units) in the partition coefficients of these types of milk were observed. The polyparameter linear free energy relationship model fit the calibration data well (SD=0.22 log units). An experimental validation data set including hormones and hormone active compounds was predicted satisfactorily by the model. An alternative modelling approach based on log K(ow) revealed a poorer performance. The model presented here provides a significant improvement in predicting enrichment of potentially hazardous chemicals in milk. In combination with physiologically based pharmacokinetic modelling this improvement in the estimation of milk/water partitioning coefficients may allow a better risk assessment for a wide range of neutral organic chemicals. Copyright © 2011 Elsevier Ltd. All rights reserved.

The assisted prediction modelling frame with hybridisation and ensemble for business risk forecasting and an implementation

NASA Astrophysics Data System (ADS)

Li, Hui; Hong, Lu-Yao; Zhou, Qing; Yu, Hai-Jie

2015-08-01

The business failure of numerous companies results in financial crises. The high social costs associated with such crises have made people to search for effective tools for business risk prediction, among which, support vector machine is very effective. Several modelling means, including single-technique modelling, hybrid modelling, and ensemble modelling, have been suggested in forecasting business risk with support vector machine. However, existing literature seldom focuses on the general modelling frame for business risk prediction, and seldom investigates performance differences among different modelling means. We reviewed researches on forecasting business risk with support vector machine, proposed the general assisted prediction modelling frame with hybridisation and ensemble (APMF-WHAE), and finally, investigated the use of principal components analysis, support vector machine, random sampling, and group decision, under the general frame in forecasting business risk. Under the APMF-WHAE frame with support vector machine as the base predictive model, four specific predictive models were produced, namely, pure support vector machine, a hybrid support vector machine involved with principal components analysis, a support vector machine ensemble involved with random sampling and group decision, and an ensemble of hybrid support vector machine using group decision to integrate various hybrid support vector machines on variables produced from principle components analysis and samples from random sampling. The experimental results indicate that hybrid support vector machine and ensemble of hybrid support vector machines were able to produce dominating performance than pure support vector machine and support vector machine ensemble.

Controls on continental strain partitioning above an oblique subduction zone, Northern Andes

NASA Astrophysics Data System (ADS)

Schütt, Jorina M.; Whipp, David M., Jr.

2016-04-01

Strain partitioning is a common process at obliquely convergent plate margins dividing oblique convergence into margin-normal slip on the plate-bounding fault and horizontal shearing on a strike-slip system parallel to the subduction margin. In subduction zones, strain partitioning in the upper continental plate is mainly controlled by the shear forces acting on the plate interface and the strength of the continental crust. The plate interface forces are influenced by the subducting plate dip angle and the obliquity angle between the normal to the plate margin and the convergence velocity vector, and the crustal strength of the continent is strongly affected by the presence or absence of a volcanic arc, with the presence of the volcanic arcs being common at steep subduction zones. Along the ˜7000 km western margin of South America the convergence obliquity, subduction dip angles and presence of a volcanic arc all vary, but strain partitioning is only observed along parts of it. This raises the questions, to what extent do subduction zone characteristics control strain partitioning in the overriding continental plate, and which factors have the largest influence? We address these questions using lithospheric-scale 3D numerical geodynamic experiments to investigate the influence of subduction dip angle, convergence obliquity, and weaknesses in the crust owing to the volcanic arc on strain partitioning behavior. We base the model design on the Northern Volcanic Zone of the Andes (5° N - 2° S), characterized by steep subduction (˜ 35°), a convergence obliquity between 31° -45° and extensive arc volcanism, and where strain partitioning is observed. The numerical modelling software (DOUAR) solves the Stokes flow and heat transfer equations for a viscous-plastic creeping flow to calculate velocity fields, thermal evolution, rock uplift and strain rates in a 1600 km x 1600 km box with depth 160 km. Subduction geometry and material properties are based on a simplified, generic subduction zone similar to the northern Andes. The upper surface is initially defined to resemble the Andes, but is free to deform during the experiments. We consider two main model designs, one with and one without a volcanic arc (weak continental zone). A relatively high angle of convergence obliquity is predicted to favor strain partitioning, but preliminary model results show no strain partitioning for a uniform continental crustal strength with a friction angle of Φ = 15° . However, strain partitioning does occur when including a weak zone in the continental crust resulting from arc volcanic activity with Φ = 5° . This results in margin-parallel northeastward translation of a continental sliver at 3.2 cm/year. The presence of the sliver agrees well with observations of a continental sliver identified by GPS measurements in the Northern Volcanic Zone with a translation velocity of about 1 cm/year, though the GPS-derived velocity may not be representative of the long-term rate of translation depending on whether the observation period includes one or more seismic cycles. Regardless, the observed behavior is consistent with the observed earthquake focal mechanisms and GPS measurements, suggesting significant northeastward transport of Andean crust along the margin of the northern Andes.

K-Partite RNA Secondary Structures

NASA Astrophysics Data System (ADS)

Jiang, Minghui; Tejada, Pedro J.; Lasisi, Ramoni O.; Cheng, Shanhong; Fechser, D. Scott

RNA secondary structure prediction is a fundamental problem in structural bioinformatics. The prediction problem is difficult because RNA secondary structures may contain pseudoknots formed by crossing base pairs. We introduce k-partite secondary structures as a simple classification of RNA secondary structures with pseudoknots. An RNA secondary structure is k-partite if it is the union of k pseudoknot-free sub-structures. Most known RNA secondary structures are either bipartite or tripartite. We show that there exists a constant number k such that any secondary structure can be modified into a k-partite secondary structure with approximately the same free energy. This offers a partial explanation of the prevalence of k-partite secondary structures with small k. We give a complete characterization of the computational complexities of recognizing k-partite secondary structures for all k ≥ 2, and show that this recognition problem is essentially the same as the k-colorability problem on circle graphs. We present two simple heuristics, iterated peeling and first-fit packing, for finding k-partite RNA secondary structures. For maximizing the number of base pair stackings, our iterated peeling heuristic achieves a constant approximation ratio of at most k for 2 ≤ k ≤ 5, and at most frac6{1-(1-6/k)^k} le frac6{1-e^{-6}} < 6.01491 for k ≥ 6. Experiment on sequences from PseudoBase shows that our first-fit packing heuristic outperforms the leading method HotKnots in predicting RNA secondary structures with pseudoknots. Source code, data set, and experimental results are available at http://www.cs.usu.edu/ mjiang/rna/kpartite/.

Field-scale prediction of enhanced DNAPL dissolution based on partitioning tracers.

PubMed

Wang, Fang; Annable, Michael D; Jawitz, James W

2013-09-01

The equilibrium streamtube model (EST) has demonstrated the ability to accurately predict dense nonaqueous phase liquid (DNAPL) dissolution in laboratory experiments and numerical simulations. Here the model is applied to predict DNAPL dissolution at a tetrachloroethylene (PCE)-contaminated dry cleaner site, located in Jacksonville, Florida. The EST model is an analytical solution with field-measurable input parameters. Measured data from a field-scale partitioning tracer test were used to parameterize the EST model and the predicted PCE dissolution was compared to measured data from an in-situ ethanol flood. In addition, a simulated partitioning tracer test from a calibrated, three-dimensional, spatially explicit multiphase flow model (UTCHEM) was also used to parameterize the EST analytical solution. The EST ethanol prediction based on both the field partitioning tracer test and the simulation closely matched the total recovery well field ethanol data with Nash-Sutcliffe efficiency E=0.96 and 0.90, respectively. The EST PCE predictions showed a peak shift to earlier arrival times for models based on either field-measured or simulated partitioning tracer tests, resulting in poorer matches to the field PCE data in both cases. The peak shifts were concluded to be caused by well screen interval differences between the field tracer test and ethanol flood. Both the EST model and UTCHEM were also used to predict PCE aqueous dissolution under natural gradient conditions, which has a much less complex flow pattern than the forced-gradient double five spot used for the ethanol flood. The natural gradient EST predictions based on parameters determined from tracer tests conducted with a complex flow pattern underestimated the UTCHEM-simulated natural gradient total mass removal by 12% after 170 pore volumes of water flushing indicating that some mass was not detected by the tracers likely due to stagnation zones in the flow field. These findings highlight the important influence of well configuration and the associated flow patterns on dissolution. © 2013.

Field-scale prediction of enhanced DNAPL dissolution based on partitioning tracers

NASA Astrophysics Data System (ADS)

Wang, Fang; Annable, Michael D.; Jawitz, James W.

2013-09-01

The equilibrium streamtube model (EST) has demonstrated the ability to accurately predict dense nonaqueous phase liquid (DNAPL) dissolution in laboratory experiments and numerical simulations. Here the model is applied to predict DNAPL dissolution at a tetrachloroethylene (PCE)-contaminated dry cleaner site, located in Jacksonville, Florida. The EST model is an analytical solution with field-measurable input parameters. Measured data from a field-scale partitioning tracer test were used to parameterize the EST model and the predicted PCE dissolution was compared to measured data from an in-situ ethanol flood. In addition, a simulated partitioning tracer test from a calibrated, three-dimensional, spatially explicit multiphase flow model (UTCHEM) was also used to parameterize the EST analytical solution. The EST ethanol prediction based on both the field partitioning tracer test and the simulation closely matched the total recovery well field ethanol data with Nash-Sutcliffe efficiency E = 0.96 and 0.90, respectively. The EST PCE predictions showed a peak shift to earlier arrival times for models based on either field-measured or simulated partitioning tracer tests, resulting in poorer matches to the field PCE data in both cases. The peak shifts were concluded to be caused by well screen interval differences between the field tracer test and ethanol flood. Both the EST model and UTCHEM were also used to predict PCE aqueous dissolution under natural gradient conditions, which has a much less complex flow pattern than the forced-gradient double five spot used for the ethanol flood. The natural gradient EST predictions based on parameters determined from tracer tests conducted with a complex flow pattern underestimated the UTCHEM-simulated natural gradient total mass removal by 12% after 170 pore volumes of water flushing indicating that some mass was not detected by the tracers likely due to stagnation zones in the flow field. These findings highlight the important influence of well configuration and the associated flow patterns on dissolution.

Handling of computational in vitro/in vivo correlation problems by Microsoft Excel: V. Predictive absorbability models.

PubMed

Langenbucher, Frieder

2007-08-01

This paper discusses Excel applications related to the prediction of drug absorbability from physicochemical constants. PHDISSOC provides a generalized model for pH profiles of electrolytic dissociation, water solubility, and partition coefficient. SKMODEL predicts drug absorbability, based on a log-log plot of water solubility and O/W partitioning; augmented by additional features such as electrolytic dissociation, melting point, and the dose administered. GIABS presents a mechanistic model of g.i. drug absorption. BIODATCO presents a database compiling relevant drug data to be used for quantitative predictions.

What are the structural features that drive partitioning of proteins in aqueous two-phase systems?

PubMed

Wu, Zhonghua; Hu, Gang; Wang, Kui; Zaslavsky, Boris Yu; Kurgan, Lukasz; Uversky, Vladimir N

2017-01-01

Protein partitioning in aqueous two-phase systems (ATPSs) represents a convenient, inexpensive, and easy to scale-up protein separation technique. Since partition behavior of a protein dramatically depends on an ATPS composition, it would be highly beneficial to have reliable means for (even qualitative) prediction of partitioning of a target protein under different conditions. Our aim was to understand which structural features of proteins contribute to partitioning of a query protein in a given ATPS. We undertook a systematic empirical analysis of relations between 57 numerical structural descriptors derived from the corresponding amino acid sequences and crystal structures of 10 well-characterized proteins and the partition behavior of these proteins in 29 different ATPSs. This analysis revealed that just a few structural characteristics of proteins can accurately determine behavior of these proteins in a given ATPS. However, partition behavior of proteins in different ATPSs relies on different structural features. In other words, we could not find a unique set of protein structural features derived from their crystal structures that could be used for the description of the protein partition behavior of all proteins in all ATPSs analyzed in this study. We likely need to gain better insight into relationships between protein-solvent interactions and protein structure peculiarities, in particular given limitations of the used here crystal structures, to be able to construct a model that accurately predicts protein partition behavior across all ATPSs. Copyright © 2016 Elsevier B.V. All rights reserved.

QSAR modeling of human serum protein binding with several modeling techniques utilizing structure-information representation.

PubMed

Votano, Joseph R; Parham, Marc; Hall, L Mark; Hall, Lowell H; Kier, Lemont B; Oloff, Scott; Tropsha, Alexander

2006-11-30

Four modeling techniques, using topological descriptors to represent molecular structure, were employed to produce models of human serum protein binding (% bound) on a data set of 1008 experimental values, carefully screened from publicly available sources. To our knowledge, this data is the largest set on human serum protein binding reported for QSAR modeling. The data was partitioned into a training set of 808 compounds and an external validation test set of 200 compounds. Partitioning was accomplished by clustering the compounds in a structure descriptor space so that random sampling of 20% of the whole data set produced an external test set that is a good representative of the training set with respect to both structure and protein binding values. The four modeling techniques include multiple linear regression (MLR), artificial neural networks (ANN), k-nearest neighbors (kNN), and support vector machines (SVM). With the exception of the MLR model, the ANN, kNN, and SVM QSARs were ensemble models. Training set correlation coefficients and mean absolute error ranged from r2=0.90 and MAE=7.6 for ANN to r2=0.61 and MAE=16.2 for MLR. Prediction results from the validation set yielded correlation coefficients and mean absolute errors which ranged from r2=0.70 and MAE=14.1 for ANN to a low of r2=0.59 and MAE=18.3 for the SVM model. Structure descriptors that contribute significantly to the models are discussed and compared with those found in other published models. For the ANN model, structure descriptor trends with respect to their affects on predicted protein binding can assist the chemist in structure modification during the drug design process.

[Analytic methods for seed models with genotype x environment interactions].

PubMed

Zhu, J

1996-01-01

Genetic models with genotype effect (G) and genotype x environment interaction effect (GE) are proposed for analyzing generation means of seed quantitative traits in crops. The total genetic effect (G) is partitioned into seed direct genetic effect (G0), cytoplasm genetic of effect (C), and maternal plant genetic effect (Gm). Seed direct genetic effect (G0) can be further partitioned into direct additive (A) and direct dominance (D) genetic components. Maternal genetic effect (Gm) can also be partitioned into maternal additive (Am) and maternal dominance (Dm) genetic components. The total genotype x environment interaction effect (GE) can also be partitioned into direct genetic by environment interaction effect (G0E), cytoplasm genetic by environment interaction effect (CE), and maternal genetic by environment interaction effect (GmE). G0E can be partitioned into direct additive by environment interaction (AE) and direct dominance by environment interaction (DE) genetic components. GmE can also be partitioned into maternal additive by environment interaction (AmE) and maternal dominance by environment interaction (DmE) genetic components. Partitions of genetic components are listed for parent, F1, F2 and backcrosses. A set of parents, their reciprocal F1 and F2 seeds is applicable for efficient analysis of seed quantitative traits. MINQUE(0/1) method can be used for estimating variance and covariance components. Unbiased estimation for covariance components between two traits can also be obtained by the MINQUE(0/1) method. Random genetic effects in seed models are predictable by the Adjusted Unbiased Prediction (AUP) approach with MINQUE(0/1) method. The jackknife procedure is suggested for estimation of sampling variances of estimated variance and covariance components and of predicted genetic effects, which can be further used in a t-test for parameter. Unbiasedness and efficiency for estimating variance components and predicting genetic effects are tested by Monte Carlo simulations.

The influence of precipitation kinetics on trace element partitioning between solid and liquid solutions: A coupled fluid dynamics/thermodynamics framework to predict distribution coefficients

NASA Astrophysics Data System (ADS)

Kavner, A.

2017-12-01

In a multicomponent multiphase geochemical system undergoing a chemical reaction such as precipitation and/or dissolution, the partitioning of species between phases is determined by a combination of thermodynamic properties and transport processes. The interpretation of the observed distribution of trace elements requires models integrating coupled chemistry and mechanical transport. Here, a framework is presented that predicts the kinetic effects on the distribution of species between two reacting phases. Based on a perturbation theory combining Navier-Stokes fluid flow and chemical reactivity, the framework predicts rate-dependent partition coefficients in a variety of different systems. We present the theoretical framework, with applications to two systems: 1. species- and isotope-dependent Soret diffusion of species in a multicomponent silicate melt subjected to a temperature gradient, and 2. Elemental partitioning and isotope fractionation during precipitation of a multicomponent solid from a multicomponent liquid phase. Predictions will be compared with results from experimental studies. The approach has applications for understanding chemical exchange in at boundary layers such as the Earth's surface magmatic systems and at the core/mantle boundary.

Skin injury model classification based on shape vector analysis

PubMed Central

2012-01-01

Background: Skin injuries can be crucial in judicial decision making. Forensic experts base their classification on subjective opinions. This study investigates whether known classes of simulated skin injuries are correctly classified statistically based on 3D surface models and derived numerical shape descriptors. Methods: Skin injury surface characteristics are simulated with plasticine. Six injury classes – abrasions, incised wounds, gunshot entry wounds, smooth and textured strangulation marks as well as patterned injuries - with 18 instances each are used for a k-fold cross validation with six partitions. Deformed plasticine models are captured with a 3D surface scanner. Mean curvature is estimated for each polygon surface vertex. Subsequently, distance distributions and derived aspect ratios, convex hulls, concentric spheres, hyperbolic points and Fourier transforms are used to generate 1284-dimensional shape vectors. Subsequent descriptor reduction maximizing SNR (signal-to-noise ratio) result in an average of 41 descriptors (varying across k-folds). With non-normal multivariate distribution of heteroskedastic data, requirements for LDA (linear discriminant analysis) are not met. Thus, shrinkage parameters of RDA (regularized discriminant analysis) are optimized yielding a best performance with λ = 0.99 and γ = 0.001. Results: Receiver Operating Characteristic of a descriptive RDA yields an ideal Area Under the Curve of 1.0for all six categories. Predictive RDA results in an average CRR (correct recognition rate) of 97,22% under a 6 partition k-fold. Adding uniform noise within the range of one standard deviation degrades the average CRR to 71,3%. Conclusions: Digitized 3D surface shape data can be used to automatically classify idealized shape models of simulated skin injuries. Deriving some well established descriptors such as histograms, saddle shape of hyperbolic points or convex hulls with subsequent reduction of dimensionality while maximizing SNR seem to work well for the data at hand, as predictive RDA results in CRR of 97,22%. Objective basis for discrimination of non-overlapping hypotheses or categories are a major issue in medicolegal skin injury analysis and that is where this method appears to be strong. Technical surface quality is important in that adding noise clearly degrades CRR. Trial registration: This study does not cover the results of a controlled health care intervention as only plasticine was used. Thus, there was no trial registration. PMID:23497357

Prediction of gas/particle partitioning of polybrominated diphenyl ethers (PBDEs) in global air: A theoretical study

NASA Astrophysics Data System (ADS)

Li, Y.-F.; Ma, W.-L.; Yang, M.

2015-02-01

Gas/particle (G/P) partitioning of semi-volatile organic compounds (SVOCs) is an important process that primarily governs their atmospheric fate, long-range atmospheric transport, and their routes of entering the human body. All previous studies on this issue are hypothetically based on equilibrium conditions, the results of which do not predict results from monitoring studies well in most cases. In this study, a steady-state model instead of an equilibrium-state model for the investigation of the G/P partitioning behavior of polybrominated diphenyl ethers (PBDEs) was established, and an equation for calculating the partition coefficients under steady state (KPS) of PBDEs (log KPS = log KPE + logα) was developed in which an equilibrium term (log KPE = log KOA + logfOM -11.91 where fOM is organic matter content of the particles) and a non-equilibrium term (log α, caused by dry and wet depositions of particles), both being functions of log KOA (octanol-air partition coefficient), are included. It was found that the equilibrium is a special case of steady state when the non-equilibrium term equals zero. A criterion to classify the equilibrium and non-equilibrium status of PBDEs was also established using two threshold values of log KOA, log KOA1, and log KOA2, which divide the range of log KOA into three domains: equilibrium, non-equilibrium, and maximum partition domain. Accordingly, two threshold values of temperature t, tTH1 when log KOA = log KOA1 and tTH2 when log KOA = log KOA2, were identified, which divide the range of temperature also into the same three domains for each PBDE congener. We predicted the existence of the maximum partition domain (the values of log KPS reach a maximum constant of -1.53) that every PBDE congener can reach when log KOA ≥ log KOA2, or t ≤ tTH2. The novel equation developed in this study was applied to predict the G/P partition coefficients of PBDEs for our Chinese persistent organic pollutants (POPs) Soil and Air Monitoring Program, Phase 2 (China-SAMP-II) program and other monitoring programs worldwide, including in Asia, Europe, North America, and the Arctic, and the results matched well with all the monitoring data, except those obtained at e-waste sites due to the unpredictable PBDE emissions at these sites. This study provided evidence that the newly developed steady-state-based equation is superior to the equilibrium-state-based equation that has been used in describing the G/P partitioning behavior over decades. We suggest that the investigation on G/P partitioning behavior for PBDEs should be based onsteady-state, not equilibrium state, and equilibrium is just a special case of steady-state when non-equilibrium factors can be ignored. We also believe that our new equation provides a useful tool for environmental scientists in both monitoring and modeling research on G/P partitioning of PBDEs and can be extended to predict G/P partitioning behavior for other SVOCs as well.

Reprint of "versatile and stable vectors for efficient gene expression in Ralstonia eutropha H16".

PubMed

Gruber, Steffen; Hagen, Jeremias; Schwab, Helmut; Koefinger, Petra

2014-12-20

The Gram-negative β-proteobacterium Ralstonia eutropha H16 is primarily known for polyhydroxybutyrate (PHB) production and its ability to grow chemolithoautotrophically by using CO2 and H2 as sole carbon and energy sources. The majority of metabolic engineering and heterologous expression studies conducted so far rely on a small number of suitable expression systems. Particularly the plasmid based expression systems already developed for the use in R. eutropha H16 suffer from high segregational instability and plasmid loss after a short time of fermentation. In order to develop efficient and highly stable plasmid expression vectors for the use in R. eutropha H16, a new plasmid design was created including the RP4 partitioning system, as well as various promoters and origins of replication. The application of minireplicons derived from broad-host-range plasmids RSF1010, pBBR1, RP4 and pSa for the construction of expression vectors and the use of numerous, versatile promoters extend the range of feasible expression levels considerably. In particular, the use of promoters derived from the bacteriophage T5 was described for the first time in this work, characterizing the j5 promoter as the strongest promoter yet to be applied in R. eutropha H16. Moreover, the implementation of the RP4 partition sequence in plasmid design increased plasmid stability significantly and enables fermentations with marginal plasmid loss of recombinant R. eutropha H16 for at least 96h. The utility of the new vector family in R. eutropha H16 is demonstrated by providing expression data with different model proteins and consequently further raises the value of this organism as cell factory for biotechnological applications including protein and metabolite production. Copyright © 2014 Elsevier B.V. All rights reserved.

Versatile and stable vectors for efficient gene expression in Ralstonia eutropha H16.

PubMed

Gruber, Steffen; Hagen, Jeremias; Schwab, Helmut; Koefinger, Petra

2014-09-30

The Gram-negative β-proteobacterium Ralstonia eutropha H16 is primarily known for polyhydroxybutyrate (PHB) production and its ability to grow chemolithoautotrophically by using CO2 and H2 as sole carbon and energy sources. The majority of metabolic engineering and heterologous expression studies conducted so far rely on a small number of suitable expression systems. Particularly the plasmid based expression systems already developed for the use in R. eutropha H16 suffer from high segregational instability and plasmid loss after a short time of fermentation. In order to develop efficient and highly stable plasmid expression vectors for the use in R. eutropha H16, a new plasmid design was created including the RP4 partitioning system, as well as various promoters and origins of replication. The application of minireplicons derived from broad-host-range plasmids RSF1010, pBBR1, RP4 and pSa for the construction of expression vectors and the use of numerous, versatile promoters extend the range of feasible expression levels considerably. In particular, the use of promoters derived from the bacteriophage T5 was described for the first time in this work, characterizing the j5 promoter as the strongest promoter yet to be applied in R. eutropha H16. Moreover, the implementation of the RP4 partition sequence in plasmid design increased plasmid stability significantly and enables fermentations with marginal plasmid loss of recombinant R. eutropha H16 for at least 96 h. The utility of the new vector family in R. eutropha H16 is demonstrated by providing expression data with different model proteins and consequently further raises the value of this organism as cell factory for biotechnological applications including protein and metabolite production. Copyright © 2014 Elsevier B.V. All rights reserved.

Octanol-Water Partition Coefficient from 3D-RISM-KH Molecular Theory of Solvation with Partial Molar Volume Correction.

PubMed

Huang, WenJuan; Blinov, Nikolay; Kovalenko, Andriy

2015-04-30

The octanol-water partition coefficient is an important physical-chemical characteristic widely used to describe hydrophobic/hydrophilic properties of chemical compounds. The partition coefficient is related to the transfer free energy of a compound from water to octanol. Here, we introduce a new protocol for prediction of the partition coefficient based on the statistical-mechanical, 3D-RISM-KH molecular theory of solvation. It was shown recently that with the compound-solvent correlation functions obtained from the 3D-RISM-KH molecular theory of solvation, the free energy functional supplemented with the correction linearly related to the partial molar volume obtained from the Kirkwood-Buff/3D-RISM theory, also called the "universal correction" (UC), provides accurate prediction of the hydration free energy of small compounds, compared to explicit solvent molecular dynamics [ Palmer , D. S. ; J. Phys.: Condens. Matter 2010 , 22 , 492101 ]. Here we report that with the UC reparametrized accordingly this theory also provides an excellent agreement with the experimental data for the solvation free energy in nonpolar solvent (1-octanol) and so accurately predicts the octanol-water partition coefficient. The performance of the Kovalenko-Hirata (KH) and Gaussian fluctuation (GF) functionals of the solvation free energy, with and without UC, is tested on a large library of small compounds with diverse functional groups. The best agreement with the experimental data for octanol-water partition coefficients is obtained with the KH-UC solvation free energy functional.

Modeling of adipose/blood partition coefficient for environmental chemicals.

PubMed

Papadaki, K C; Karakitsios, S P; Sarigiannis, D A

2017-12-01

A Quantitative Structure Activity Relationship (QSAR) model was developed in order to predict the adipose/blood partition coefficient of environmental chemical compounds. The first step of QSAR modeling was the collection of inputs. Input data included the experimental values of adipose/blood partition coefficient and two sets of molecular descriptors for 67 organic chemical compounds; a) the descriptors from Linear Free Energy Relationship (LFER) and b) the PaDEL descriptors. The datasets were split to training and prediction set and were analysed using two statistical methods; Genetic Algorithm based Multiple Linear Regression (GA-MLR) and Artificial Neural Networks (ANN). The models with LFER and PaDEL descriptors, coupled with ANN, produced satisfying performance results. The fitting performance (R 2 ) of the models, using LFER and PaDEL descriptors, was 0.94 and 0.96, respectively. The Applicability Domain (AD) of the models was assessed and then the models were applied to a large number of chemical compounds with unknown values of adipose/blood partition coefficient. In conclusion, the proposed models were checked for fitting, validity and applicability. It was demonstrated that they are stable, reliable and capable to predict the values of adipose/blood partition coefficient of "data poor" chemical compounds that fall within the applicability domain. Copyright © 2017. Published by Elsevier Ltd.

Fast bi-directional prediction selection in H.264/MPEG-4 AVC temporal scalable video coding.

PubMed

Lin, Hung-Chih; Hang, Hsueh-Ming; Peng, Wen-Hsiao

2011-12-01

In this paper, we propose a fast algorithm that efficiently selects the temporal prediction type for the dyadic hierarchical-B prediction structure in the H.264/MPEG-4 temporal scalable video coding (SVC). We make use of the strong correlations in prediction type inheritance to eliminate the superfluous computations for the bi-directional (BI) prediction in the finer partitions, 16×8/8×16/8×8 , by referring to the best temporal prediction type of 16 × 16. In addition, we carefully examine the relationship in motion bit-rate costs and distortions between the BI and the uni-directional temporal prediction types. As a result, we construct a set of adaptive thresholds to remove the unnecessary BI calculations. Moreover, for the block partitions smaller than 8 × 8, either the forward prediction (FW) or the backward prediction (BW) is skipped based upon the information of their 8 × 8 partitions. Hence, the proposed schemes can efficiently reduce the extensive computational burden in calculating the BI prediction. As compared to the JSVM 9.11 software, our method saves the encoding time from 48% to 67% for a large variety of test videos over a wide range of coding bit-rates and has only a minor coding performance loss. © 2011 IEEE

Regression modeling of gas-particle partitioning of atmospheric oxidized mercury from temperature data

NASA Astrophysics Data System (ADS)

Cheng, Irene; Zhang, Leiming; Blanchard, Pierrette

2014-10-01

Models describing the partitioning of atmospheric oxidized mercury (Hg(II)) between the gas and fine particulate phases were developed as a function of temperature. The models were derived from regression analysis of the gas-particle partitioning parameters, defined by a partition coefficient (Kp) and Hg(II) fraction in fine particles (fPBM) and temperature data from 10 North American sites. The generalized model, log(1/Kp) = 12.69-3485.30(1/T) (R2 = 0.55; root-mean-square error (RMSE) of 1.06 m3/µg for Kp), predicted the observed average Kp at 7 of the 10 sites. Discrepancies between the predicted and observed average Kp were found at the sites impacted by large Hg sources because the model had not accounted for the different mercury speciation profile and aerosol compositions of different sources. Site-specific equations were also generated from average Kp and fPBM corresponding to temperature interval data. The site-specific models were more accurate than the generalized Kp model at predicting the observations at 9 of the 10 sites as indicated by RMSE of 0.22-0.5 m3/µg for Kp and 0.03-0.08 for fPBM. Both models reproduced the observed monthly average values, except for a peak in Hg(II) partitioning observed during summer at two locations. Weak correlations between the site-specific model Kp or fPBM and observations suggest the role of aerosol composition, aerosol water content, and relative humidity factors on Hg(II) partitioning. The use of local temperature data to parameterize Hg(II) partitioning in the proposed models potentially improves the estimation of mercury cycling in chemical transport models and elsewhere.

Development of TLSER model and QSAR model for predicting partition coefficients of hydrophobic organic chemicals between low density polyethylene film and water.

PubMed

Liu, Huihui; Wei, Mengbi; Yang, Xianhai; Yin, Cen; He, Xiao

2017-01-01

Partition coefficients are vital parameters for measuring accurately the chemicals concentrations by passive sampling devices. Given the wide use of low density polyethylene (LDPE) film in passive sampling, we developed a theoretical linear solvation energy relationship (TLSER) model and a quantitative structure-activity relationship (QSAR) model for the prediction of the partition coefficient of chemicals between LDPE and water (K pew ). For chemicals with the octanol-water partition coefficient (log K ow ) <8, a TLSER model with V x (McGowan volume) and qA - (the most negative charge on O, N, S, X atoms) as descriptors was developed, but the model had relatively low determination coefficient (R 2 ) and cross-validated coefficient (Q 2 ). In order to further explore the theoretical mechanisms involved in the partition process, a QSAR model with four descriptors (MLOGP (Moriguchi octanol-water partition coeff.), P_VSA_s_3 (P_VSA-like on I-state, bin 3), Hy (hydrophilic factor) and NssO (number of atoms of type ssO)) was established, and statistical analysis indicated that the model had satisfactory goodness-of-fit, robustness and predictive ability. For chemicals with log K OW >8, a TLSER model with V x and a QSAR model with MLOGP as descriptor were developed. This is the first paper to explore the models for highly hydrophobic chemicals. The applicability domain of the models, characterized by the Euclidean distance-based method and Williams plot, covered a large number of structurally diverse chemicals, which included nearly all the common hydrophobic organic compounds. Additionally, through mechanism interpretation, we explored the structural features those governing the partition behavior of chemicals between LDPE and water. Copyright © 2016 Elsevier B.V. All rights reserved.

Rational design of polymer-based absorbents: application to the fermentation inhibitor furfural.

PubMed

Nwaneshiudu, Ikechukwu C; Schwartz, Daniel T

2015-01-01

Reducing the amount of water-soluble fermentation inhibitors like furfural is critical for downstream bio-processing steps to biofuels. A theoretical approach for tailoring absorption polymers to reduce these pretreatment contaminants would be useful for optimal bioprocess design. Experiments were performed to measure aqueous furfural partitioning into polymer resins of 5 bisphenol A diglycidyl ether (epoxy) and polydimethylsiloxane (PDMS). Experimentally measured partitioning of furfural between water and PDMS, the more hydrophobic polymer, showed poor performance, with the logarithm of PDMS-to-water partition coefficient falling between -0.62 and -0.24 (95% confidence). In contrast, the fast setting epoxy was found to effectively partition furfural with the logarithm of the epoxy-to-water partition coefficient falling between 0.41 and 0.81 (95% confidence). Flory-Huggins theory is used to predict the partitioning of furfural into diverse polymer absorbents and is useful for predicting these results. We show that Flory-Huggins theory can be adapted to guide the selection of polymer adsorbents for the separation of low molecular weight organic species from aqueous solutions. This work lays the groundwork for the general design of polymers for the separation of a wide range of inhibitory compounds in biomass pretreatment streams.

Study and modeling of the evolution of gas-liquid partitioning of hydrogen sulfide in model solutions simulating winemaking fermentations.

PubMed

Mouret, Jean-Roch; Sablayrolles, Jean-Marie; Farines, Vincent

2015-04-01

The knowledge of gas-liquid partitioning of aroma compounds during winemaking fermentation could allow optimization of fermentation management, maximizing concentrations of positive markers of aroma and minimizing formation of molecules, such as hydrogen sulfide (H2S), responsible for defects. In this study, the effect of the main fermentation parameters on the gas-liquid partition coefficients (Ki) of H2S was assessed. The Ki for this highly volatile sulfur compound was measured in water by an original semistatic method developed in this work for the determination of gas-liquid partitioning. This novel method was validated and then used to determine the Ki of H2S in synthetic media simulating must, fermenting musts at various steps of the fermentation process, and wine. Ki values were found to be mainly dependent on the temperature but also varied with the composition of the medium, especially with the glucose concentration. Finally, a model was developed to quantify the gas-liquid partitioning of H2S in synthetic media simulating must to wine. This model allowed a very accurate prediction of the partition coefficient of H2S: the difference between observed and predicted values never exceeded 4%.

Partitioning behavior of aromatic components in jet fuel into diverse membrane-coated fibers.

PubMed

Baynes, Ronald E; Xia, Xin-Rui; Barlow, Beth M; Riviere, Jim E

2007-11-01

Jet fuel components are known to partition into skin and produce occupational irritant contact dermatitis (OICD) and potentially adverse systemic effects. The purpose of this study was to determine how jet fuel components partition (1) from solvent mixtures into diverse membrane-coated fibers (MCFs) and (2) from biological media into MCFs to predict tissue distribution. Three diverse MCFs, polydimethylsiloxane (PDMS, lipophilic), polyacrylate (PA, polarizable), and carbowax (CAR, polar), were selected to simulate the physicochemical properties of skin in vivo. Following an appropriate equilibrium time between the MCF and dosing solutions, the MCF was injected directly into a gas chromatograph/mass spectrometer (GC-MS) to quantify the amount that partitioned into the membrane. Three vehicles (water, 50% ethanol-water, and albumin-containing media solution) were studied for selected jet fuel components. The more hydrophobic the component, the greater was the partitioning into the membranes across all MCF types, especially from water. The presence of ethanol as a surrogate solvent resulted in significantly reduced partitioning into the MCFs with discernible differences across the three fibers based on their chemistries. The presence of a plasma substitute (media) also reduced partitioning into the MCF, with the CAR MCF system being better correlated to the predicted partitioning of aromatic components into skin. This study demonstrated that a single or multiple set of MCF fibers may be used as a surrogate for octanol/water systems and skin to assess partitioning behavior of nine aromatic components frequently formulated with jet fuels. These diverse inert fibers were able to assess solute partitioning from a blood substitute such as media into a membrane possessing physicochemical properties similar to human skin. This information may be incorporated into physiologically based pharmacokinetic (PBPK) models to provide a more accurate assessment of tissue dosimetry of related toxicants.

Sensitivity of Aerosol Mass and Microphysics to varying treatments of Condensational Growth of Secondary Organic Compounds in a regional model

NASA Astrophysics Data System (ADS)

Lowe, Douglas; Topping, David; McFiggans, Gordon

2017-04-01

Gas to particle partitioning of atmospheric compounds occurs through disequilibrium mass transfer rather than through instantaneous equilibrium. However, it is common to treat only the inorganic compounds as partitioning dynamically whilst organic compounds, represented by the Volatility Basis Set (VBS), are partitioned instantaneously. In this study we implement a more realistic dynamic partitioning of organic compounds in a regional framework and assess impact on aerosol mass and microphysics. It is also common to assume condensed phase water is only associated with inorganic components. We thus also assess sensitivity to assuming all organics are hygroscopic according to their prescribed molecular weight. For this study we use WRF-Chem v3.4.1, focusing on anthropogenic dominated North-Western Europe. Gas-phase chemistry is represented using CBM-Z whilst aerosol dynamics are simulated using the 8-section MOSAIC scheme, including a 9-bin VBS treatment of organic aerosol. Results indicate that predicted mass loadings can vary significantly. Without gas phase ageing of higher volatility compounds, dynamic partitioning always results in lower mass loadings downwind of emission sources. The inclusion of condensed phase water in both partitioning models increases the predicted PM mass, resulting from a larger contribution from higher volatility organics, if present. If gas phase ageing of VBS compounds is allowed to occur in a dynamic model, this can often lead to higher predicted mass loadings, contrary to expected behaviour from a simple non-reactive gas phase box model. As descriptions of aerosol phase processes improve within regional models, the baseline descriptions of partitioning should retain the ability to treat dynamic partitioning of organics compounds. Using our simulations, we discuss whether derived sensitivities to aerosol processes in existing models may be inherently biased. This work was supported by the Natural Environment Research Council within the RONOCO (NE/F004656/1) and CCN-Vol (NE/L007827/1) projects.

Sensitivity of Aerosol Mass and Microphysics to Treatments of Condensational Growth of Secondary Organic Compounds in a Regional Model

NASA Astrophysics Data System (ADS)

Topping, D. O.; Lowe, D.; McFiggans, G.; Zaveri, R. A.

2016-12-01

Gas to particle partitioning of atmospheric compounds occurs through disequilibrium mass transfer rather than through instantaneous equilibrium. However, it is common to treat only the inorganic compounds as partitioning dynamically whilst organic compounds, represented by the Volatility Basis Set (VBS), are partitioned instantaneously. In this study we implement a more realistic dynamic partitioning of organic compounds in a regional framework and assess impact on aerosol mass and microphysics. It is also common to assume condensed phase water is only associated with inorganic components. We thus also assess sensitivity to assuming all organics are hygroscopic according to their prescribed molecular weight.For this study we use WRF-Chem v3.4.1, focusing on anthropogenic dominated North-Western Europe. Gas-phase chemistry is represented using CBM-Z whilst aerosol dynamics are simulated using the 8-section MOSAIC scheme, including a 9-bin volatility basis set (VBS) treatment of organic aerosol. Results indicate that predicted mass loadings can vary significantly. Without gas phase ageing of higher volatility compounds, dynamic partitioning always results in lower mass loadings downwind of emission sources. The inclusion of condensed phase water in both partitioning models increases the predicted PM mass, resulting from a larger contribution from higher volatility organics, if present. If gas phase ageing of VBS compounds is allowed to occur in a dynamic model, this can often lead to higher predicted mass loadings, contrary to expected behaviour from a simple non-reactive gas phase box model. As descriptions of aerosol phase processes improve within regional models, the baseline descriptions of partitioning should retain the ability to treat dynamic partitioning of organic compounds. Using our simulations, we discuss whether derived sensitivities to aerosol processes in existing models may be inherently biased.This work was supported by the Nature Environment Research Council within the RONOCO (NE/F004656/1) and CCN-Vol (NE/L007827/1) projects.

Lefschetz thimbles in fermionic effective models with repulsive vector-field

NASA Astrophysics Data System (ADS)

Mori, Yuto; Kashiwa, Kouji; Ohnishi, Akira

2018-06-01

We discuss two problems in complexified auxiliary fields in fermionic effective models, the auxiliary sign problem associated with the repulsive vector-field and the choice of the cut for the scalar field appearing from the logarithmic function. In the fermionic effective models with attractive scalar and repulsive vector-type interaction, the auxiliary scalar and vector fields appear in the path integral after the bosonization of fermion bilinears. When we make the path integral well-defined by the Wick rotation of the vector field, the oscillating Boltzmann weight appears in the partition function. This "auxiliary" sign problem can be solved by using the Lefschetz-thimble path-integral method, where the integration path is constructed in the complex plane. Another serious obstacle in the numerical construction of Lefschetz thimbles is caused by singular points and cuts induced by multivalued functions of the complexified scalar field in the momentum integration. We propose a new prescription which fixes gradient flow trajectories on the same Riemann sheet in the flow evolution by performing the momentum integration in the complex domain.

Efficient storage and management of radiographic images using a novel wavelet-based multiscale vector quantizer

NASA Astrophysics Data System (ADS)

Yang, Shuyu; Mitra, Sunanda

2002-05-01

Due to the huge volumes of radiographic images to be managed in hospitals, efficient compression techniques yielding no perceptual loss in the reconstructed images are becoming a requirement in the storage and management of such datasets. A wavelet-based multi-scale vector quantization scheme that generates a global codebook for efficient storage and transmission of medical images is presented in this paper. The results obtained show that even at low bit rates one is able to obtain reconstructed images with perceptual quality higher than that of the state-of-the-art scalar quantization method, the set partitioning in hierarchical trees.

Genetic algorithm and graph theory based matrix factorization method for online friend recommendation.

PubMed

Li, Qu; Yao, Min; Yang, Jianhua; Xu, Ning

2014-01-01

Online friend recommendation is a fast developing topic in web mining. In this paper, we used SVD matrix factorization to model user and item feature vector and used stochastic gradient descent to amend parameter and improve accuracy. To tackle cold start problem and data sparsity, we used KNN model to influence user feature vector. At the same time, we used graph theory to partition communities with fairly low time and space complexity. What is more, matrix factorization can combine online and offline recommendation. Experiments showed that the hybrid recommendation algorithm is able to recommend online friends with good accuracy.

Integral transformation solution of free-space cylindrical vector beams and prediction of modified Bessel-Gaussian vector beams.

PubMed

Li, Chun-Fang

2007-12-15

A unified description of free-space cylindrical vector beams is presented that is an integral transformation solution to the vector Helmholtz equation and the transversality condition. In the paraxial condition, this solution not only includes the known J(1) Bessel-Gaussian vector beam and the axisymmetric Laguerre-Gaussian vector beam that were obtained by solving the paraxial wave equations but also predicts two kinds of vector beam, called a modified Bessel-Gaussian vector beam.

Geometric Representations of Condition Queries on Three-Dimensional Vector Fields

NASA Technical Reports Server (NTRS)

Henze, Chris

1999-01-01

Condition queries on distributed data ask where particular conditions are satisfied. It is possible to represent condition queries as geometric objects by plotting field data in various spaces derived from the data, and by selecting loci within these derived spaces which signify the desired conditions. Rather simple geometric partitions of derived spaces can represent complex condition queries because much complexity can be encapsulated in the derived space mapping itself A geometric view of condition queries provides a useful conceptual unification, allowing one to intuitively understand many existing vector field feature detection algorithms -- and to design new ones -- as variations on a common theme. A geometric representation of condition queries also provides a simple and coherent basis for computer implementation, reducing a wide variety of existing and potential vector field feature detection techniques to a few simple geometric operations.

Computational Prediction of Kinetic Rate Constants

DTIC Science & Technology

2006-11-30

without requiring additional data. Zero-point energy ( ZPE ) anharmonicity has a large effect on the accuracy of approximate partition function estimates. If...the accurate ZPE is taken into account, separable approximation partition functions using the most accurate torsion treatment and harmonic treatments...for the remaining degrees of freedom agree with accurate QM partition functions to within a mean accuracy of 9%. If no ZPE anharmonicity correction

GAS-PARTICLE PARTITIONING OF SEMI-VOLATILE ORGANICS ON ORGANIC AEROSOLS USING A PREDICTIVE ACTIVITY COEFFICIENT MODEL: ANALYSIS OF THE EFFECTS OF PARAMETER CHOICES ON MODEL PERFORMANCE. (R826771)

EPA Science Inventory

The partitioning of a diverse set of semivolatile organic compounds (SOCs) on a variety of organic aerosols was studied using smog chamber experimental data. Existing data on the partitioning of SOCs on aerosols from wood combustion, diesel combustion, and the Information-theoretic indices usage for the prediction and calculation of octanol-water partition coefficient.

PubMed

Persona, Marek; Kutarov, Vladimir V; Kats, Boris M; Persona, Andrzej; Marczewska, Barbara

2007-01-01

The paper describes the new prediction method of octanol-water partition coefficient, which is based on molecular graph theory. The results obtained using the new method are well correlated with experimental values. These results were compared with the ones obtained by use of ten other structure correlated methods. The comparison shows that graph theory can be very useful in structure correlation research.

BPS/CFT Correspondence III: Gauge Origami Partition Function and qq-Characters

NASA Astrophysics Data System (ADS)

Nekrasov, Nikita

2018-03-01

We study generalized gauge theories engineered by taking the low energy limit of the Dp branes wrapping {X × {T}^{p-3}}, with X a possibly singular surface in a Calabi-Yau fourfold Z. For toric Z and X the partition function can be computed by localization, making it a statistical mechanical model, called the gauge origami. The random variables are the ensembles of Young diagrams. The building block of the gauge origami is associated with a tetrahedron, whose edges are colored by vector spaces. We show the properly normalized partition function is an entire function of the Coulomb moduli, for generic values of the {Ω} -background parameters. The orbifold version of the theory defines the qq-character operators, with and without the surface defects. The analytic properties are the consequence of a relative compactness of the moduli spaces M({ěc n}, k) of crossed and spiked instantons, demonstrated in "BPS/CFT correspondence II: instantons at crossroads, moduli and compactness theorem".

The nondeterministic divide

NASA Technical Reports Server (NTRS)

Charlesworth, Arthur

1990-01-01

The nondeterministic divide partitions a vector into two non-empty slices by allowing the point of division to be chosen nondeterministically. Support for high-level divide-and-conquer programming provided by the nondeterministic divide is investigated. A diva algorithm is a recursive divide-and-conquer sequential algorithm on one or more vectors of the same range, whose division point for a new pair of recursive calls is chosen nondeterministically before any computation is performed and whose recursive calls are made immediately after the choice of division point; also, access to vector components is only permitted during activations in which the vector parameters have unit length. The notion of diva algorithm is formulated precisely as a diva call, a restricted call on a sequential procedure. Diva calls are proven to be intimately related to associativity. Numerous applications of diva calls are given and strategies are described for translating a diva call into code for a variety of parallel computers. Thus diva algorithms separate logical correctness concerns from implementation concerns.

AGT, N-Burge partitions and {{W}}_N minimal models

NASA Astrophysics Data System (ADS)

Belavin, Vladimir; Foda, Omar; Santachiara, Raoul

2015-10-01

Let {B}_{N,n}^{p,p', H} be a conformal block, with n consecutive channels χ ι , ι = 1, ⋯ n, in the conformal field theory {M}_N^{p,p'× {M}^{H} , where {M}_N^{p,p' } is a {W}_N minimal model, generated by chiral spin-2, ⋯ spin- N currents, and labeled by two co-prime integers p and p', 1 < p < p', while {M}^{H} is a free boson conformal field theory. {B}_{N,n}^{p,p', H} is the expectation value of vertex operators between an initial and a final state. Each vertex operator is labelled by a charge vector that lives in the weight lattice of the Lie algebra A N - 1, spanned by weight vectors {overrightarrow{ω}}_1,\\cdots, {overrightarrow{ω}}_{N-1} . We restrict our attention to conformal blocks with vertex operators whose charge vectors point along {overrightarrow{ω}}_1 . The charge vectors that label the initial and final states can point in any direction.

Test aspects of the JPL Viterbi decoder

NASA Technical Reports Server (NTRS)

Breuer, M. A.

1989-01-01

The generation of test vectors and design-for-test aspects of the Jet Propulsion Laboratory (JPL) Very Large Scale Integration (VLSI) Viterbi decoder chip is discussed. Each processor integrated circuit (IC) contains over 20,000 gates. To achieve a high degree of testability, a scan architecture is employed. The logic has been partitioned so that very few test vectors are required to test the entire chip. In addition, since several blocks of logic are replicated numerous times on this chip, test vectors need only be generated for each block, rather than for the entire circuit. These unique blocks of logic have been identified and test sets generated for them. The approach employed for testing was to use pseudo-exhaustive test vectors whenever feasible. That is, each cone of logid is tested exhaustively. Using this approach, no detailed logic design or fault model is required. All faults which modify the function of a block of combinational logic are detected, such as all irredundant single and multiple stuck-at faults.

Identification of early-stage usual interstitial pneumonia from low-dose chest CT scans using fractional high-density lung distribution

NASA Astrophysics Data System (ADS)

Xie, Yiting; Salvatore, Mary; Liu, Shuang; Jirapatnakul, Artit; Yankelevitz, David F.; Henschke, Claudia I.; Reeves, Anthony P.

2017-03-01

A fully-automated computer algorithm has been developed to identify early-stage Usual Interstitial Pneumonia (UIP) using features computed from low-dose CT scans. In each scan, the pre-segmented lung region is divided into N subsections (N = 1, 8, 27, 64) by separating the lung from anterior/posterior, left/right and superior/inferior in 3D space. Each subsection has approximately the same volume. In each subsection, a classic density measurement (fractional high-density volume h) is evaluated to characterize the disease severity in that subsection, resulting in a feature vector of length N for each lung. Features are then combined in two different ways: concatenation (2*N features) and taking the maximum in each of the two corresponding subsections in the two lungs (N features). The algorithm was evaluated on a dataset consisting of 51 UIP and 56 normal cases, a combined feature vector was computed for each case and an SVM classifier (RBF kernel) was used to classify them into UIP or normal using ten-fold cross validation. A receiver operating characteristic (ROC) area under the curve (AUC) was used for evaluation. The highest AUC of 0.95 was achieved by using concatenated features and an N of 27. Using lung partition (N = 27, 64) with concatenated features had significantly better result over not using partitions (N = 1) (p-value < 0.05). Therefore this equal-volume partition fractional high-density volume method is useful in distinguishing early-stage UIP from normal cases.

A high temperature fatigue life prediction computer code based on the total strain version of StrainRange Partitioning (SRP)

NASA Technical Reports Server (NTRS)

Mcgaw, Michael A.; Saltsman, James F.

1993-01-01

A recently developed high-temperature fatigue life prediction computer code is presented and an example of its usage given. The code discussed is based on the Total Strain version of Strainrange Partitioning (TS-SRP). Included in this code are procedures for characterizing the creep-fatigue durability behavior of an alloy according to TS-SRP guidelines and predicting cyclic life for complex cycle types for both isothermal and thermomechanical conditions. A reasonably extensive materials properties database is included with the code.

Effectiveness of water-air and octanol-air partition coefficients to predict lipophilic flavor release behavior from O/W emulsions.

PubMed

Tamaru, Shunji; Igura, Noriyuki; Shimoda, Mitsuya

2018-01-15

Flavor release from food matrices depends on the partition of volatile flavor compounds between the food matrix and the vapor phase. Thus, we herein investigated the relationship between released flavor concentrations and three different partition coefficients, namely octanol-water, octanol-air, and water-air, which represented the oil, water, and air phases present in emulsions. Limonene, 2-methylpyrazine, nonanal, benzaldehyde, ethyl benzoate, α-terpineol, benzyl alcohol, and octanoic acid were employed. The released concentrations of these flavor compounds from oil-in-water (O/W) emulsions were measured under equilibrium using static headspace gas chromatography. The results indicated that water-air and octanol-air partition coefficients correlated with the logarithms of the released concentrations in the headspace for highly lipophilic flavor compounds. Moreover, the same tendency was observed over various oil volume ratios in the emulsions. Our findings therefore suggest that octanol-air and water-air partition coefficients can be used to predict the released concentration of lipophilic flavor compounds from O/W emulsions. Copyright © 2017 Elsevier Ltd. All rights reserved.

Development of polyparameter linear free energy relationship models for octanol-air partition coefficients of diverse chemicals.

PubMed

Jin, Xiaochen; Fu, Zhiqiang; Li, Xuehua; Chen, Jingwen

2017-03-22

The octanol-air partition coefficient (K OA ) is a key parameter describing the partition behavior of organic chemicals between air and environmental organic phases. As the experimental determination of K OA is costly, time-consuming and sometimes limited by the availability of authentic chemical standards for the compounds to be determined, it becomes necessary to develop credible predictive models for K OA . In this study, a polyparameter linear free energy relationship (pp-LFER) model for predicting K OA at 298.15 K and a novel model incorporating pp-LFERs with temperature (pp-LFER-T model) were developed from 795 log K OA values for 367 chemicals at different temperatures (263.15-323.15 K), and were evaluated with the OECD guidelines on QSAR model validation and applicability domain description. Statistical results show that both models are well-fitted, robust and have good predictive capabilities. Particularly, the pp-LFER model shows a strong predictive ability for polyfluoroalkyl substances and organosilicon compounds, and the pp-LFER-T model maintains a high predictive accuracy within a wide temperature range (263.15-323.15 K).

Vector adaptive predictive coder for speech and audio

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey (Inventor); Gersho, Allen (Inventor)

1990-01-01

A real-time vector adaptive predictive coder which approximates each vector of K speech samples by using each of M fixed vectors in a first codebook to excite a time-varying synthesis filter and picking the vector that minimizes distortion. Predictive analysis for each frame determines parameters used for computing from vectors in the first codebook zero-state response vectors that are stored at the same address (index) in a second codebook. Encoding of input speech vectors s.sub.n is then carried out using the second codebook. When the vector that minimizes distortion is found, its index is transmitted to a decoder which has a codebook identical to the first codebook of the decoder. There the index is used to read out a vector that is used to synthesize an output speech vector s.sub.n. The parameters used in the encoder are quantized, for example by using a table, and the indices are transmitted to the decoder where they are decoded to specify transfer characteristics of filters used in producing the vector s.sub.n from the receiver codebook vector selected by the vector index transmitted.

Prediction of distribution coefficient from structure. 1. Estimation method.

PubMed

Csizmadia, F; Tsantili-Kakoulidou, A; Panderi, I; Darvas, F

1997-07-01

A method has been developed for the estimation of the distribution coefficient (D), which considers the microspecies of a compound. D is calculated from the microscopic dissociation constants (microconstants), the partition coefficients of the microspecies, and the counterion concentration. A general equation for the calculation of D at a given pH is presented. The microconstants are calculated from the structure using Hammett and Taft equations. The partition coefficients of the ionic microspecies are predicted by empirical equations using the dissociation constants and the partition coefficient of the uncharged species, which are estimated from the structure by a Linear Free Energy Relationship method. The algorithm is implemented in a program module called PrologD.

Prediction of the sorption capacities and affinities of organic chemicals by XAD-7.

PubMed

Yang, Kun; Qi, Long; Wei, Wei; Wu, Wenhao; Lin, Daohui

2016-01-01

Macro-porous resins are widely used as adsorbents for the treatment of organic contaminants in wastewater and for the pre-concentration of organic solutes from water. However, the sorption mechanisms for organic contaminants on such adsorbents have not been systematically investigated so far. Therefore, in this study, the sorption capacities and affinities of 24 organic chemicals by XAD-7 were investigated and the experimentally obtained sorption isotherms were fitted to the Dubinin-Ashtakhov model. Linear positive correlations were observed between the sorption capacities and the solubilities (SW) of the chemicals in water or octanol and between the sorption affinities and the solvatochromic parameters of the chemicals, indicating that the sorption of various organic compounds by XAD-7 occurred by non-linear partitioning into XAD-7, rather than by adsorption on XAD-7 surfaces. Both specific interactions (i.e., hydrogen-bonding interactions) as well as nonspecific interactions were considered to be responsible for the non-linear partitioning. The correlation equations obtained in this study allow the prediction of non-linear partitioning using well-known chemical parameters, namely SW, octanol-water partition coefficients (KOW), and the hydrogen-bonding donor parameter (αm). The effect of pH on the sorption of ionizable organic compounds (IOCs) could also be predicted by combining the correlation equations with additional equations developed from the estimation of IOC dissociation rates. The prediction equations developed in this study and the proposed non-linear partition mechanism shed new light on the selective removal and pre-concentration of organic solutes from water and on the regeneration of exhausted XAD-7 using solvent extraction.

Evaluation of Pharmacokinetic Assumptions Using a 443 ...

EPA Pesticide Factsheets

With the increasing availability of high-throughput and in vitro data for untested chemicals, there is a need for pharmacokinetic (PK) models for in vitro to in vivo extrapolation (IVIVE). Though some PBPK models have been created for individual compounds using in vivo data, we are now able to rapidly parameterize generic PBPK models using in vitro data to allow IVIVE for chemicals tested for bioactivity via high-throughput screening. However, these new models are expected to have limited accuracy due to their simplicity and generalization of assumptions. We evaluated the assumptions and performance of a generic PBPK model (R package “httk”) parameterized by a library of in vitro PK data for 443 chemicals. We evaluate and calibrate Schmitt’s method by comparing the predicted volume of distribution (Vd) and tissue partition coefficients to in vivo measurements. The partition coefficients are initially over predicted, likely due to overestimation of partitioning into phospholipids in tissues and the lack of lipid partitioning in the in vitro measurements of the fraction unbound in plasma. Correcting for phospholipids and plasma binding improved the predictive ability (R2 to 0.52 for partition coefficients and 0.32 for Vd). We lacked enough data to evaluate the accuracy of changing the model structure to include tissue blood volumes and/or separate compartments for richly/poorly perfused tissues, therefore we evaluated the impact of these changes on model

Adiabatic partition effect on natural convection heat transfer inside a square cavity: experimental and numerical studies

NASA Astrophysics Data System (ADS)

Mahmoudinezhad, S.; Rezania, A.; Yousefi, T.; Shadloo, M. S.; Rosendahl, L. A.

2018-02-01

A steady state and two-dimensional laminar free convection heat transfer in a partitioned cavity with horizontal adiabatic and isothermal side walls is investigated using both experimental and numerical approaches. The experiments and numerical simulations are carried out using a Mach-Zehnder interferometer and a finite volume code, respectively. A horizontal and adiabatic partition, with angle of θ is adjusted such that it separates the cavity into two identical parts. Effects of this angel as well as Rayleigh number on the heat transfer from the side-heated walls are investigated in this study. The results are performed for the various Rayleigh numbers over the cavity side length, and partition angles ranging from 1.5 × 105 to 4.5 × 105, and 0° to 90°, respectively. The experimental verification of natural convective flow physics has been done by using FLUENT software. For a given adiabatic partition angle, the results show that the average Nusselt number and consequently the heat transfer enhance as the Rayleigh number increases. However, for a given Rayleigh number the maximum and the minimum heat transfer occurs at θ = 45°and θ = 90°, respectively. Two responsible mechanisms for this behavior, namely blockage ratio and partition orientation, are identified. These effects are explained by numerical velocity vectors and experimental temperatures contours. Based on the experimental data, a new correlation that fairly represents the average Nusselt number of the heated walls as functions of Rayleigh number and the angel of θ for the aforementioned ranges of data is proposed.

Predicting Salt Permeability Coefficients in Highly Swollen, Highly Charged Ion Exchange Membranes.

PubMed

Kamcev, Jovan; Paul, Donald R; Manning, Gerald S; Freeman, Benny D

2017-02-01

This study presents a framework for predicting salt permeability coefficients in ion exchange membranes in contact with an aqueous salt solution. The model, based on the solution-diffusion mechanism, was tested using experimental salt permeability data for a series of commercial ion exchange membranes. Equilibrium salt partition coefficients were calculated using a thermodynamic framework (i.e., Donnan theory), incorporating Manning's counterion condensation theory to calculate ion activity coefficients in the membrane phase and the Pitzer model to calculate ion activity coefficients in the solution phase. The model predicted NaCl partition coefficients in a cation exchange membrane and two anion exchange membranes, as well as MgCl 2 partition coefficients in a cation exchange membrane, remarkably well at higher external salt concentrations (>0.1 M) and reasonably well at lower external salt concentrations (<0.1 M) with no adjustable parameters. Membrane ion diffusion coefficients were calculated using a combination of the Mackie and Meares model, which assumes ion diffusion in water-swollen polymers is affected by a tortuosity factor, and a model developed by Manning to account for electrostatic effects. Agreement between experimental and predicted salt diffusion coefficients was good with no adjustable parameters. Calculated salt partition and diffusion coefficients were combined within the framework of the solution-diffusion model to predict salt permeability coefficients. Agreement between model and experimental data was remarkably good. Additionally, a simplified version of the model was used to elucidate connections between membrane structure (e.g., fixed charge group concentration) and salt transport properties.

Sound transmission through lightweight double-leaf partitions: theoretical modelling

NASA Astrophysics Data System (ADS)

Wang, J.; Lu, T. J.; Woodhouse, J.; Langley, R. S.; Evans, J.

2005-09-01

This paper presents theoretical modelling of the sound transmission loss through double-leaf lightweight partitions stiffened with periodically placed studs. First, by assuming that the effect of the studs can be replaced with elastic springs uniformly distributed between the sheathing panels, a simple smeared model is established. Second, periodic structure theory is used to develop a more accurate model taking account of the discrete placing of the studs. Both models treat incident sound waves in the horizontal plane only, for simplicity. The predictions of the two models are compared, to reveal the physical mechanisms determining sound transmission. The smeared model predicts relatively simple behaviour, in which the only conspicuous features are associated with coincidence effects with the two types of structural wave allowed by the partition model, and internal resonances of the air between the panels. In the periodic model, many more features are evident, associated with the structure of pass- and stop-bands for structural waves in the partition. The models are used to explain the effects of incidence angle and of the various system parameters. The predictions are compared with existing test data for steel plates with wooden stiffeners, and good agreement is obtained.

Direct calculation of 1-octanol-water partition coefficients from adaptive biasing force molecular dynamics simulations.

PubMed

Bhatnagar, Navendu; Kamath, Ganesh; Chelst, Issac; Potoff, Jeffrey J

2012-07-07

The 1-octanol-water partition coefficient log K(ow) of a solute is a key parameter used in the prediction of a wide variety of complex phenomena such as drug availability and bioaccumulation potential of trace contaminants. In this work, adaptive biasing force molecular dynamics simulations are used to determine absolute free energies of hydration, solvation, and 1-octanol-water partition coefficients for n-alkanes from methane to octane. Two approaches are evaluated; the direct transfer of the solute from 1-octanol to water phase, and separate transfers of the solute from the water or 1-octanol phase to vacuum, with both methods yielding statistically indistinguishable results. Calculations performed with the TIP4P and SPC∕E water models and the TraPPE united-atom force field for n-alkanes show that the choice of water model has a negligible effect on predicted free energies of transfer and partition coefficients for n-alkanes. A comparison of calculations using wet and dry octanol phases shows that the predictions for log K(ow) using wet octanol are 0.2-0.4 log units lower than for dry octanol, although this is within the statistical uncertainty of the calculation.

Classification of jet fuel properties by near-infrared spectroscopy using fuzzy rule-building expert systems and support vector machines.

PubMed

Xu, Zhanfeng; Bunker, Christopher E; Harrington, Peter de B

2010-11-01

Monitoring the changes of jet fuel physical properties is important because fuel used in high-performance aircraft must meet rigorous specifications. Near-infrared (NIR) spectroscopy is a fast method to characterize fuels. Because of the complexity of NIR spectral data, chemometric techniques are used to extract relevant information from spectral data to accurately classify physical properties of complex fuel samples. In this work, discrimination of fuel types and classification of flash point, freezing point, boiling point (10%, v/v), boiling point (50%, v/v), and boiling point (90%, v/v) of jet fuels (JP-5, JP-8, Jet A, and Jet A1) were investigated. Each physical property was divided into three classes, low, medium, and high ranges, using two evaluations with different class boundary definitions. The class boundaries function as the threshold to alarm when the fuel properties change. Optimal partial least squares discriminant analysis (oPLS-DA), fuzzy rule-building expert system (FuRES), and support vector machines (SVM) were used to build the calibration models between the NIR spectra and classes of physical property of jet fuels. OPLS-DA, FuRES, and SVM were compared with respect to prediction accuracy. The validation of the calibration model was conducted by applying bootstrap Latin partition (BLP), which gives a measure of precision. Prediction accuracy of 97 ± 2% of the flash point, 94 ± 2% of freezing point, 99 ± 1% of the boiling point (10%, v/v), 98 ± 2% of the boiling point (50%, v/v), and 96 ± 1% of the boiling point (90%, v/v) were obtained by FuRES in one boundaries definition. Both FuRES and SVM obtained statistically better prediction accuracy over those obtained by oPLS-DA. The results indicate that combined with chemometric classifiers NIR spectroscopy could be a fast method to monitor the changes of jet fuel physical properties.

Multi-allelic haplotype model based on genetic partition for genomic prediction and variance component estimation using SNP markers.

PubMed

Da, Yang

2015-12-18

The amount of functional genomic information has been growing rapidly but remains largely unused in genomic selection. Genomic prediction and estimation using haplotypes in genome regions with functional elements such as all genes of the genome can be an approach to integrate functional and structural genomic information for genomic selection. Towards this goal, this article develops a new haplotype approach for genomic prediction and estimation. A multi-allelic haplotype model treating each haplotype as an 'allele' was developed for genomic prediction and estimation based on the partition of a multi-allelic genotypic value into additive and dominance values. Each additive value is expressed as a function of h - 1 additive effects, where h = number of alleles or haplotypes, and each dominance value is expressed as a function of h(h - 1)/2 dominance effects. For a sample of q individuals, the limit number of effects is 2q - 1 for additive effects and is the number of heterozygous genotypes for dominance effects. Additive values are factorized as a product between the additive model matrix and the h - 1 additive effects, and dominance values are factorized as a product between the dominance model matrix and the h(h - 1)/2 dominance effects. Genomic additive relationship matrix is defined as a function of the haplotype model matrix for additive effects, and genomic dominance relationship matrix is defined as a function of the haplotype model matrix for dominance effects. Based on these results, a mixed model implementation for genomic prediction and variance component estimation that jointly use haplotypes and single markers is established, including two computing strategies for genomic prediction and variance component estimation with identical results. The multi-allelic genetic partition fills a theoretical gap in genetic partition by providing general formulations for partitioning multi-allelic genotypic values and provides a haplotype method based on the quantitative genetics model towards the utilization of functional and structural genomic information for genomic prediction and estimation.

Gas/particle partitioning of 2-methyltetrols and levoglucosan at an urban site in Denver.

PubMed

Xie, Mingjie; Hannigan, Michael P; Barsanti, Kelley C

2014-01-01

In this study, a medium volume sampler incorporating quartz fiber filters (QFFs) and a polyurethane foam (PUF)/XAD/PUF sandwich (PXP) was used to collect 2-methyltetrols (isoprene tracer) and levoglucosan (biomass burning tracer) in gaseous and particle (PM2.5) phases. The measured gas/particle (G/P) partitioning coefficients (Kp,OMm) of 2-methyltetrols and levoglucosan were calculated and compared to their predicted G/P partitioning coefficients (Kp,OMt) based on an absorptive partitioning theory. The breakthrough experiments showed that gas-phase 2-methyltetrols and levoglucosan could be collected using the PXP or PUF adsorbent alone, with low breakthrough; however, the recoveries of levoglucosan in PXP samples were lower than 70% (average of 51.9–63.3%). The concentration ratios of 2-methyltetrols and levoglucosan in the gas phase to those in the particle phase were often close to or higher than unity in summer, indicating that these polar species are semi-volatile and their G/P partitioning should be considered when applying particle-phase data for source apportionment. The Kp,OMm values of 2-methyltetrols had small variability in summer Denver, which was ascribed to large variations in concentrations of particulate organic matter (5.14 ± 3.29 μg m–3) and small changes in ambient temperature (21.8 ± 4.05 °C). The regression between log Kp,OMm and log Kp,OMt suggested that the absorptive G/P partitioning theory could reasonably predict the measured G/P partitioning of levoglucosan in ambient samples.

Equilibrium water and solute uptake in silicone hydrogels.

PubMed

Liu, D E; Dursch, T J; Oh, Y; Bregante, D T; Chan, S Y; Radke, C J

2015-05-01

Equilibrium water content of and solute partitioning in silicone hydrogels (SiHys) are investigated using gravimetric analysis, fluorescence confocal laser-scanning microscopy (FCLSM), and back extraction with UV/Vis-absorption spectrophotometry. Synthesized silicone hydrogels consist of silicone monomer, hydrophilic monomer, cross-linking agent, and triblock-copolymer macromer used as an amphiphilic compatibilizer to prevent macrophase separation. In all cases, immiscibility of the silicone and hydrophilic polymers results in microphase-separated morphologies. To investigate solute uptake in each of the SiHy microphases, equilibrium partition coefficients are obtained for two hydrophilic solutes (i.e., theophylline and caffeine dissolved in aqueous phosphate-buffered saline) and two oleophilic solutes (i.e., Nile Red and Bodipy Green dissolved in silicone oil), respectively. Measured water contents and aqueous-solute partition coefficients increase linearly with increasing solvent-free hydrophilic-polymer volume fraction. Conversely, oleophilic-solute partition coefficients decrease linearly with rising solvent-free hydrophilic-polymer volume fraction (i.e., decreasing hydrophobic silicone-polymer fraction). We quantitatively predict equilibrium SiHy water and solute uptake assuming that water and aqueous solutes reside only in hydrophilic microdomains, whereas oleophilic solutes partition predominately into silicone microdomains. Predicted water contents and solute partition coefficients are in excellent agreement with experiment. Our new procedure permits a priori estimation of SiHy water contents and solute partition coefficients based solely on properties of silicone and hydrophilic homopolymer hydrogels, eliminating the need for further mixed-polymer-hydrogel experiments. Copyright © 2015 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.

Constant-Time Pattern Matching For Real-Time Production Systems

NASA Astrophysics Data System (ADS)

Parson, Dale E.; Blank, Glenn D.

1989-03-01

Many intelligent systems must respond to sensory data or critical environmental conditions in fixed, predictable time. Rule-based systems, including those based on the efficient Rete matching algorithm, cannot guarantee this result. Improvement in execution-time efficiency is not all that is needed here; it is important to ensure constant, 0(1) time limits for portions of the matching process. Our approach is inspired by two observations about human performance. First, cognitive psychologists distinguish between automatic and controlled processing. Analogously, we partition the matching process across two networks. The first is the automatic partition; it is characterized by predictable 0(1) time and space complexity, lack of persistent memory, and is reactive in nature. The second is the controlled partition; it includes the search-based goal-driven and data-driven processing typical of most production system programming. The former is responsible for recognition and response to critical environmental conditions. The latter is responsible for the more flexible problem-solving behaviors consistent with the notion of intelligence. Support for learning and refining the automatic partition can be placed in the controlled partition. Our second observation is that people are able to attend to more critical stimuli or requirements selectively. Our match algorithm uses priorities to focus matching. It compares priority of information during matching, rather than deferring this comparison until conflict resolution. Messages from the automatic partition are able to interrupt the controlled partition, enhancing system responsiveness. Our algorithm has numerous applications for systems that must exhibit time-constrained behavior.

PARTITIONING OF THE REFRACTORY METALS, NICKEL AND CHROMIUM, IN COMBUSTION SYSTEMS

EPA Science Inventory

The partitioning of nickel (Ni) and Chromium (Cr) in combustion systems was investigated theoretically and experimentally. In comparison to other volatile and semi-volatile metals, both Ni and Cr are usually considered to be refractory (non-volatile). Theoretical predictions ba...

Convex Regression with Interpretable Sharp Partitions

PubMed Central

Petersen, Ashley; Simon, Noah; Witten, Daniela

2016-01-01

We consider the problem of predicting an outcome variable on the basis of a small number of covariates, using an interpretable yet non-additive model. We propose convex regression with interpretable sharp partitions (CRISP) for this task. CRISP partitions the covariate space into blocks in a data-adaptive way, and fits a mean model within each block. Unlike other partitioning methods, CRISP is fit using a non-greedy approach by solving a convex optimization problem, resulting in low-variance fits. We explore the properties of CRISP, and evaluate its performance in a simulation study and on a housing price data set. PMID:27635120

Performance analysis of clustering techniques over microarray data: A case study

NASA Astrophysics Data System (ADS)

Dash, Rasmita; Misra, Bijan Bihari

2018-03-01

Handling big data is one of the major issues in the field of statistical data analysis. In such investigation cluster analysis plays a vital role to deal with the large scale data. There are many clustering techniques with different cluster analysis approach. But which approach suits a particular dataset is difficult to predict. To deal with this problem a grading approach is introduced over many clustering techniques to identify a stable technique. But the grading approach depends on the characteristic of dataset as well as on the validity indices. So a two stage grading approach is implemented. In this study the grading approach is implemented over five clustering techniques like hybrid swarm based clustering (HSC), k-means, partitioning around medoids (PAM), vector quantization (VQ) and agglomerative nesting (AGNES). The experimentation is conducted over five microarray datasets with seven validity indices. The finding of grading approach that a cluster technique is significant is also established by Nemenyi post-hoc hypothetical test.

A Study of the Thermal Environment Developed by a Traveling Slipper at High Velocity

DTIC Science & Technology

2013-03-01

Power Partition Function The next partition function takes the same formulation as the powered function but now the exponent is squared. The...function and note the squared term in the exponent . 66 Equation 4.27 (4.36) Thus far the three partition functions each give a predicted...hypothesized that the function would fall somewhere between the first exponential decay function and the power function. However, by squaring the exponent

Technical evaluation report of the Specialists Meeting on Characterization of Low Cycle High Temperature Fatigue by the Strainrange Partitioning Method

NASA Technical Reports Server (NTRS)

Drapier, J. M.; Hirschberg, M. H.

1979-01-01

The ability of the Strainrange Partitioning Method SRP was evaluated to correlate the creep-fatigue behavior of gas turbine materials and to predict the creep fatigue life of laboratory specimens subjected to complex cycling conditions. A reference body of high temperature creep fatigue data which can be used in the evaluation of other SRP and low cycle high temperature fatigue predictive techniques was provided.

Partitioning of polychlorinated biphenyls into human cells and adipose tissues: evaluation of octanol, triolein, and liposomes as surrogates.

PubMed

Quinn, Cristina L; van der Heijden, Stephan A; Wania, Frank; Jonker, Michiel T O

2014-05-20

Whereas octanol, triacylglycerides, and liposomes have all been proposed as surrogates for measuring the affinity of hydrophobic organic contaminants to human lipids, no comparative evaluation of their suitability exists. Here we conducted batch sorption experiments with polyoxymethylene passive samplers to determine the partition coefficients at 37 °C of 18 polychlorinated biphenyls (PCBs) from water into (i) triolein (Ktriolein/water), (ii) eight types of liposomes (Kliposome/water), (iii) human abdominal fat tissues (KAFT/water) from seven individuals, and (iv) human MCF-7 cells cultured in vitro (Kcell/water). Differences between KAFT/water among individuals and between Kliposome/water among liposome types were very small and not correlated to structural attributes of the PCBs. Similarly, the length and degree of saturation of the phospholipid carbon chains, the headgroup, and the composition of the liposome did not affect the partitioning of PCBs into the studied liposomes. Whereas Kliposome/water values were similar to literature values of Koctanol/water adjusted to 37 °C, they both were lower than KAFT/water and Kcell/water by a factor of 3 on average. Partitioning of PCBs into triolein on the other hand closely mimicked that into human lipids, for which triolein is thus a better surrogate than either octanol or liposomes. Previously published polyparameter linear free energy relationships for partitioning from water into storage lipids and liposomes predicted the measured partition coefficients with a root-mean-square error of less than 0.15 log units, if the chosen equations and solute descriptors do not allow chlorine substitution in the ortho-position to influence the prediction. By guiding the selection of (i) a surrogate for the experimental determination and (ii) a method for the prediction of partitioning into human lipids, this study contributes to a better assessment of hydrophobic organic contaminant bioaccumulation in humans.

Partition coefficients of organic compounds between water and imidazolium-, pyridinium-, and phosphonium-based ionic liquids.

PubMed

Padró, Juan M; Pellegrino Vidal, Rocío B; Reta, Mario

2014-12-01

The partition coefficients, P IL/w, of several compounds, some of them of biological and pharmacological interest, between water and room-temperature ionic liquids based on the imidazolium, pyridinium, and phosphonium cations, namely 1-octyl-3-methylimidazolium hexafluorophosphate, N-octylpyridinium tetrafluorophosphate, trihexyl(tetradecyl)phosphonium chloride, trihexyl(tetradecyl)phosphonium bromide, trihexyl(tetradecyl)phosphonium bis(trifluoromethylsulfonyl)imide, and trihexyl(tetradecyl)phosphonium dicyanamide, were accurately measured. In this way, we extended our database of partition coefficients in room-temperature ionic liquids previously reported. We employed the solvation parameter model with different probe molecules (the training set) to elucidate the chemical interactions involved in the partition process and discussed the most relevant differences among the three types of ionic liquids. The multiparametric equations obtained with the aforementioned model were used to predict the partition coefficients for compounds (the test set) not present in the training set, most being of biological and pharmacological interest. An excellent agreement between calculated and experimental log P IL/w values was obtained. Thus, the obtained equations can be used to predict, a priori, the extraction efficiency for any compound using these ionic liquids as extraction solvents in liquid-liquid extractions.

Study of oil-water partitioning of a chemical dispersant using an acute bioassay with marine crustaceans

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wells, P.G.; Abernethy, S.; Mackay, D.

1982-01-01

The toxicity of seawater dispersions of a chemical dispersant to two marine crustaceans was investigated in the presence and absence of various quantities of a non-toxic mineral oil. From the results and a physical-chemical partitioning analysis, a limiting value of the oil-water partition coefficient of the toxic compounds is deduced suggesting that essentially all of the toxic compounds in the dispersant will partition into solution in water following dispersant application to an oil spill. This conclusion simplifies interpretation and prediction of the toxic effects of a dispersed oil spill. The combined bioassay-partitioning procedure may have applications to the study ofmore » the toxicity of other complex mixtures such as industrial effluents.« less

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease.

PubMed

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G; Tybjaerg-Hansen, Anne; Sing, Charles F

2009-05-01

This article extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (Genet Epidemiol 31:515-527) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2,258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors.

Modifications to the Patient Rule-Induction Method that utilize non-additive combinations of genetic and environmental effects to define partitions that predict ischemic heart disease

PubMed Central

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G.; Tybjærg-Hansen, Anne; Sing, Charles F.

2009-01-01

This paper extends the Patient Rule-Induction Method (PRIM) for modeling cumulative incidence of disease developed by Dyson et al. (2007) to include the simultaneous consideration of non-additive combinations of predictor variables, a significance test of each combination, an adjustment for multiple testing and a confidence interval for the estimate of the cumulative incidence of disease in each partition. We employ the partitioning algorithm component of the Combinatorial Partitioning Method (CPM) to construct combinations of predictors, permutation testing to assess the significance of each combination, theoretical arguments for incorporating a multiple testing adjustment and bootstrap resampling to produce the confidence intervals. An illustration of this revised PRIM utilizing a sample of 2258 European male participants from the Copenhagen City Heart Study is presented that assesses the utility of genetic variants in predicting the presence of ischemic heart disease beyond the established risk factors. PMID:19025787

In silico screening of drug-membrane thermodynamics reveals linear relations between bulk partitioning and the potential of mean force

NASA Astrophysics Data System (ADS)

Menichetti, Roberto; Kanekal, Kiran H.; Kremer, Kurt; Bereau, Tristan

2017-09-01

The partitioning of small molecules in cell membranes—a key parameter for pharmaceutical applications—typically relies on experimentally available bulk partitioning coefficients. Computer simulations provide a structural resolution of the insertion thermodynamics via the potential of mean force but require significant sampling at the atomistic level. Here, we introduce high-throughput coarse-grained molecular dynamics simulations to screen thermodynamic properties. This application of physics-based models in a large-scale study of small molecules establishes linear relationships between partitioning coefficients and key features of the potential of mean force. This allows us to predict the structure of the insertion from bulk experimental measurements for more than 400 000 compounds. The potential of mean force hereby becomes an easily accessible quantity—already recognized for its high predictability of certain properties, e.g., passive permeation. Further, we demonstrate how coarse graining helps reduce the size of chemical space, enabling a hierarchical approach to screening small molecules.

Medical Image Compression Based on Vector Quantization with Variable Block Sizes in Wavelet Domain

PubMed Central

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality. PMID:23049544

Medical image compression based on vector quantization with variable block sizes in wavelet domain.

PubMed

Jiang, Huiyan; Ma, Zhiyuan; Hu, Yang; Yang, Benqiang; Zhang, Libo

2012-01-01

An optimized medical image compression algorithm based on wavelet transform and improved vector quantization is introduced. The goal of the proposed method is to maintain the diagnostic-related information of the medical image at a high compression ratio. Wavelet transformation was first applied to the image. For the lowest-frequency subband of wavelet coefficients, a lossless compression method was exploited; for each of the high-frequency subbands, an optimized vector quantization with variable block size was implemented. In the novel vector quantization method, local fractal dimension (LFD) was used to analyze the local complexity of each wavelet coefficients, subband. Then an optimal quadtree method was employed to partition each wavelet coefficients, subband into several sizes of subblocks. After that, a modified K-means approach which is based on energy function was used in the codebook training phase. At last, vector quantization coding was implemented in different types of sub-blocks. In order to verify the effectiveness of the proposed algorithm, JPEG, JPEG2000, and fractal coding approach were chosen as contrast algorithms. Experimental results show that the proposed method can improve the compression performance and can achieve a balance between the compression ratio and the image visual quality.

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Rajnarayan, Dev (Inventor); Sturdza, Peter (Inventor)

2016-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For an instability mode in the plurality of instability modes, a covariance vector is determined. A predicted local instability growth rate at the point is determined using the covariance vector and the vector of regressor weights. Based on the predicted local instability growth rate, an n-factor envelope at the point is determined.

Partitioning of Nanoparticles into Organic Phases and Model Cells

DOE Office of Scientific and Technical Information (OSTI.GOV)

Posner, J.D.; Westerhoff, P.; Hou, W-C.

2011-08-25

There is a recognized need to understand and predict the fate, transport and bioavailability of engineered nanoparticles (ENPs) in aquatic and soil ecosystems. Recent research focuses on either collection of empirical data (e.g., removal of a specific NP through water or soil matrices under variable experimental conditions) or precise NP characterization (e.g. size, degree of aggregation, morphology, zeta potential, purity, surface chemistry, and stability). However, it is almost impossible to transition from these precise measurements to models suitable to assess the NP behavior in the environment with complex and heterogeneous matrices. For decades, the USEPA has developed and applies basicmore » partitioning parameters (e.g., octanol-water partition coefficients) and models (e.g., EPI Suite, ECOSAR) to predict the environmental fate, bioavailability, and toxicity of organic pollutants (e.g., pesticides, hydrocarbons, etc.). In this project we have investigated the hypothesis that NP partition coefficients between water and organic phases (octanol or lipid bilayer) is highly dependent on their physiochemical properties, aggregation, and presence of natural constituents in aquatic environments (salts, natural organic matter), which may impact their partitioning into biological matrices (bioaccumulation) and human exposure (bioavailability) as well as the eventual usage in modeling the fate and bioavailability of ENPs. In this report, we use the terminology "partitioning" to operationally define the fraction of ENPs distributed among different phases. The mechanisms leading to this partitioning probably involve both chemical force interactions (hydrophobic association, hydrogen bonding, ligand exchange, etc.) and physical forces that bring the ENPs in close contact with the phase interfaces (diffusion, electrostatic interactions, mixing turbulence, etc.). Our work focuses on partitioning, but also provides insight into the relative behavior of ENPs as either "more like dissolved substances" or "more like colloids" as the division between behaviors of macromolecules versus colloids remains ill-defined. Below we detail our work on two broadly defined objectives: (i) Partitioning of ENP into octanol, lipid bilayer, and water, and (ii) disruption of lipid bilayers by ENPs. We have found that the partitioning of NP reaches pseudo-equilibrium distributions between water and organic phases. The equilibrium partitioning most strongly depends on the particle surface charge, which leads us to the conclusion that electrostatic interactions are critical to understanding the fate of NP in the environment. We also show that the kinetic rate at which particle partition is a function of their size (small particles partition faster by number) as can be predicted from simple DLVO models. We have found that particle number density is the most effective dosimetry to present our results and provide quantitative comparison across experiments and experimental platforms. Cumulatively, our work shows that lipid bilayers are a more effective organic phase than octanol because of the definable surface area and ease of interpretation of the results. Our early comparison of NP partitioning between water and lipids suggest that this measurement can be predictive of bioaccumulation in aquatic organisms. We have shown that nanoparticle disrupt lipid bilayer membranes and detail how NP-bilayer interaction leads to the malfunction of lipid bilayers in regulating the fluxes of ionic charges and molecules. Our results show that the disruption of the lipid membranes is similar to that of toxin melittin, except single particles can disrupt a bilayer. We show that only a single particle is required to disrupt a 150 nm DOPC liposome. The equilibrium leakage of membranes is a function of the particle number density and particle surface charge, consistent with results from our partitioning experiments. Our disruption experiments with varying surface functionality show that positively charged particles (poly amine) are most disruptive, consistent with in in vitro toxicity panels using cell cultures. Overall, this project has resulted in 8 published or submitted archival papers and has been presented 12 times. We have trained five students and provided growth opportunities for a postdoc.« less

Improved partition equilibrium model for predicting analyte response in electrospray ionization mass spectrometry.

PubMed

Du, Lihong; White, Robert L

2009-02-01

A previously proposed partition equilibrium model for quantitative prediction of analyte response in electrospray ionization mass spectrometry is modified to yield an improved linear relationship. Analyte mass spectrometer response is modeled by a competition mechanism between analyte and background electrolytes that is based on partition equilibrium considerations. The correlation between analyte response and solution composition is described by the linear model over a wide concentration range and the improved model is shown to be valid for a wide range of experimental conditions. The behavior of an analyte in a salt solution, which could not be explained by the original model, is correctly predicted. The ion suppression effects of 16:0 lysophosphatidylcholine (LPC) on analyte signals are attributed to a combination of competition for excess charge and reduction of total charge due to surface tension effects. In contrast to the complicated mathematical forms that comprise the original model, the simplified model described here can more easily be employed to predict analyte mass spectrometer responses for solutions containing multiple components. Copyright (c) 2008 John Wiley & Sons, Ltd.

Partitioning and lipophilicity in quantitative structure-activity relationships.

PubMed Central

Dearden, J C

1985-01-01

The history of the relationship of biological activity to partition coefficient and related properties is briefly reviewed. The dominance of partition coefficient in quantitation of structure-activity relationships is emphasized, although the importance of other factors is also demonstrated. Various mathematical models of in vivo transport and binding are discussed; most of these involve partitioning as the primary mechanism of transport. The models describe observed quantitative structure-activity relationships (QSARs) well on the whole, confirming that partitioning is of key importance in in vivo behavior of a xenobiotic. The partition coefficient is shown to correlate with numerous other parameters representing bulk, such as molecular weight, volume and surface area, parachor and calculated indices such as molecular connectivity; this is especially so for apolar molecules, because for polar molecules lipophilicity factors into both bulk and polar or hydrogen bonding components. The relationship of partition coefficient to chromatographic parameters is discussed, and it is shown that such parameters, which are often readily obtainable experimentally, can successfully supplant partition coefficient in QSARs. The relationship of aqueous solubility with partition coefficient is examined in detail. Correlations are observed, even with solid compounds, and these can be used to predict solubility. The additive/constitutive nature of partition coefficient is discussed extensively, as are the available schemes for the calculation of partition coefficient. Finally the use of partition coefficient to provide structural information is considered. It is shown that partition coefficient can be a valuable structural tool, especially if the enthalpy and entropy of partitioning are available. PMID:3905374

Trace element partitioning between ionic crystal and liquid

NASA Technical Reports Server (NTRS)

Tsang, T.; Philpotts, J. A.; Yin, L.

1978-01-01

The partitioning of trace elements between ionic crystals and the melt has been correlated with lattice energy of the host. The solid-liquid partition coefficient has been expressed in terms of the difference in relative ionic radius of the trace element and the homogeneous and heterogeneous strain of the host lattice. Predictions based on this model appear to be in general agreement with data for alkali nitrates and for rare-earth elements in natural garnet phenocrysts.

Vector Adaptive/Predictive Encoding Of Speech

NASA Technical Reports Server (NTRS)

Chen, Juin-Hwey; Gersho, Allen

1989-01-01

Vector adaptive/predictive technique for digital encoding of speech signals yields decoded speech of very good quality after transmission at coding rate of 9.6 kb/s and of reasonably good quality at 4.8 kb/s. Requires 3 to 4 million multiplications and additions per second. Combines advantages of adaptive/predictive coding, and code-excited linear prediction, yielding speech of high quality but requires 600 million multiplications and additions per second at encoding rate of 4.8 kb/s. Vector adaptive/predictive coding technique bridges gaps in performance and complexity between adaptive/predictive coding and code-excited linear prediction.

High-order graph matching based feature selection for Alzheimer's disease identification.

PubMed

Liu, Feng; Suk, Heung-Il; Wee, Chong-Yaw; Chen, Huafu; Shen, Dinggang

2013-01-01

One of the main limitations of l1-norm feature selection is that it focuses on estimating the target vector for each sample individually without considering relations with other samples. However, it's believed that the geometrical relation among target vectors in the training set may provide useful information, and it would be natural to expect that the predicted vectors have similar geometric relations as the target vectors. To overcome these limitations, we formulate this as a graph-matching feature selection problem between a predicted graph and a target graph. In the predicted graph a node is represented by predicted vector that may describe regional gray matter volume or cortical thickness features, and in the target graph a node is represented by target vector that include class label and clinical scores. In particular, we devise new regularization terms in sparse representation to impose high-order graph matching between the target vectors and the predicted ones. Finally, the selected regional gray matter volume and cortical thickness features are fused in kernel space for classification. Using the ADNI dataset, we evaluate the effectiveness of the proposed method and obtain the accuracies of 92.17% and 81.57% in AD and MCI classification, respectively.

Critical Evaluation of Prediction Models for Phosphorus Partition between CaO-based Slags and Iron-based Melts during Dephosphorization Processes

NASA Astrophysics Data System (ADS)

Yang, Xue-Min; Li, Jin-Yan; Chai, Guo-Ming; Duan, Dong-Ping; Zhang, Jian

2016-08-01

According to the experimental results of hot metal dephosphorization by CaO-based slags at a commercial-scale hot metal pretreatment station, the collected 16 models of equilibrium quotient k_{{P}} or phosphorus partition L_{{P}} between CaO-based slags and iron-based melts from the literature have been evaluated. The collected 16 models for predicting equilibrium quotient k_{{P}} can be transferred to predict phosphorus partition L_{{P}} . The predicted results by the collected 16 models cannot be applied to be criteria for evaluating k_{{P}} or L_{{P}} due to various forms or definitions of k_{{P}} or L_{{P}} . Thus, the measured phosphorus content [pct P] in a hot metal bath at the end point of the dephosphorization pretreatment process is applied to be the fixed criteria for evaluating the collected 16 models. The collected 16 models can be described in the form of linear functions as y = c0 + c1 x , in which independent variable x represents the chemical composition of slags, intercept c0 including the constant term depicts the temperature effect and other unmentioned or acquiescent thermodynamic factors, and slope c1 is regressed by the experimental results of k_{{P}} or L_{{P}} . Thus, a general approach to developing the thermodynamic model for predicting equilibrium quotient k_{{P}} or phosphorus partition L P or [pct P] in iron-based melts during the dephosphorization process is proposed by revising the constant term in intercept c0 for the summarized 15 models except for Suito's model (M3). The better models with an ideal revising possibility or flexibility among the collected 16 models have been selected and recommended. Compared with the predicted result by the revised 15 models and Suito's model (M3), the developed IMCT- L_{{P}} model coupled with the proposed dephosphorization mechanism by the present authors can be applied to accurately predict phosphorus partition L_{{P}} with the lowest mean deviation δ_{{L_{{P}} }} of log L_{{P}} as 2.33, as well as to predict [pct P] in a hot metal bath with the smallest mean deviation δ_{{[% {{ P}}]}} of [pct P] as 12.31.

Application of Equilibrium Partitioning Theory to Soil PAH Contamination (External Review Draft)

EPA Science Inventory

In March 2004, ORD's Ecological Risk Assessment Support Center (ERASC) received a request from the Ecological Risk Assessment Forum (ERAF) to provide insight into the issue of whether equilibrium partitioning (EqP) techniques can be used to predict the toxicity of polycyclic arom...

ADMET Evaluation in Drug Discovery. 16. Predicting hERG Blockers by Combining Multiple Pharmacophores and Machine Learning Approaches.

PubMed

Wang, Shuangquan; Sun, Huiyong; Liu, Hui; Li, Dan; Li, Youyong; Hou, Tingjun

2016-08-01

Blockade of human ether-à-go-go related gene (hERG) channel by compounds may lead to drug-induced QT prolongation, arrhythmia, and Torsades de Pointes (TdP), and therefore reliable prediction of hERG liability in the early stages of drug design is quite important to reduce the risk of cardiotoxicity-related attritions in the later development stages. In this study, pharmacophore modeling and machine learning approaches were combined to construct classification models to distinguish hERG active from inactive compounds based on a diverse data set. First, an optimal ensemble of pharmacophore hypotheses that had good capability to differentiate hERG active from inactive compounds was identified by the recursive partitioning (RP) approach. Then, the naive Bayesian classification (NBC) and support vector machine (SVM) approaches were employed to construct classification models by integrating multiple important pharmacophore hypotheses. The integrated classification models showed improved predictive capability over any single pharmacophore hypothesis, suggesting that the broad binding polyspecificity of hERG can only be well characterized by multiple pharmacophores. The best SVM model achieved the prediction accuracies of 84.7% for the training set and 82.1% for the external test set. Notably, the accuracies for the hERG blockers and nonblockers in the test set reached 83.6% and 78.2%, respectively. Analysis of significant pharmacophores helps to understand the multimechanisms of action of hERG blockers. We believe that the combination of pharmacophore modeling and SVM is a powerful strategy to develop reliable theoretical models for the prediction of potential hERG liability.

Exact Path Integral for 3D Quantum Gravity.

PubMed

Iizuka, Norihiro; Tanaka, Akinori; Terashima, Seiji

2015-10-16

Three-dimensional Euclidean pure gravity with a negative cosmological constant can be formulated in terms of the Chern-Simons theory, classically. This theory can be written in a supersymmetric way by introducing auxiliary gauginos and scalars. We calculate the exact partition function of this Chern-Simons theory by using the localization technique. Thus, we obtain the quantum gravity partition function, assuming that it can be obtained nonperturbatively by summing over partition functions of the Chern-Simons theory on topologically different manifolds. The resultant partition function is modular invariant, and, in the case in which the central charge is expected to be 24, it is the J function, predicted by Witten.

An examination of the role of particles in oceanic mercury cycling

NASA Astrophysics Data System (ADS)

Lamborg, Carl H.; Hammerschmidt, Chad R.; Bowman, Katlin L.

2016-11-01

Recent models of global mercury (Hg) cycling have identified the downward flux of sinking particles in the ocean as a prominent Hg removal process from the ocean. At least one of these models estimates the amount of anthropogenic Hg in the ocean to be about 400 Mmol, with deep water formation and sinking fluxes representing the largest vectors by which pollutant Hg is able to penetrate the ocean interior. Using data from recent cruises to the Atlantic, we examined the dissolved and particulate partitioning of Hg in the oceanic water column as a cross-check on the hypothesis that sinking particle fluxes are important. Interestingly, these new data suggest particle-dissolved partitioning (Kd) that is approximately 20× greater than previous estimates, which thereby challenges certain assumptions about the scavenging and active partitioning of Hg in the ocean used in earlier models. For example, the new particle data suggest that regenerative scavenging is the most likely mechanism by which the association of Hg and particles occurs. This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'.

Prediction task guided representation learning of medical codes in EHR.

PubMed

Cui, Liwen; Xie, Xiaolei; Shen, Zuojun

2018-06-18

There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental and crucial step in developing such models is to convert medical codes in EHR to feature vectors. These medical codes are used to represent diagnoses or procedures. Their vector representations have a tremendous impact on the performance of machine learning models. Recently, some researchers have utilized representation learning methods from Natural Language Processing (NLP) to learn vector representations of medical codes. However, most previous approaches are unsupervised, i.e. the generation of medical code vectors is independent from prediction tasks. Thus, the obtained feature vectors may be inappropriate for a specific prediction task. Moreover, unsupervised methods often require a lot of samples to obtain reliable results, but most practical problems have very limited patient samples. In this paper, we develop a new method called Prediction Task Guided Health Record Aggregation (PTGHRA), which aggregates health records guided by prediction tasks, to construct training corpus for various representation learning models. Compared with unsupervised approaches, representation learning models integrated with PTGHRA yield a significant improvement in predictive capability of generated medical code vectors, especially for limited training samples. Copyright © 2018. Published by Elsevier Inc.

Deformation Partitioning: The Missing Link Between Outcrop-Scale Observations And Orogen-Scale Processes

NASA Astrophysics Data System (ADS)

Attia, S.; Paterson, S. R.; Jiang, D.; Miller, R. B.

2017-12-01

Structural studies of orogenic deformation fields are mostly based on small-scale structures ubiquitous in field exposures, hand samples, and under microscopes. Relating deformation histories derived from such structures to changing lithospheric-scale deformation and boundary conditions is not trivial due to vast scale separation (10-6 107 m) between characteristic lengths of small-scale structures and lithospheric plates. Rheological heterogeneity over the range of orogenic scales will lead to deformation partitioning throughout intervening scales of structural development. Spectacular examples of structures documenting deformation partitioning are widespread within hot (i.e., magma-rich) orogens such as the well-studied central Sierra Nevada and Cascades core of western North America: (1) deformation partitioned into localized, narrow, triclinic shear zones separated by broad domains of distributed pure shear at micro- to 10 km scales; (2) deformation partitioned between plutons and surrounding metamorphic host rocks as shown by pluton-wide magmatic fabrics consistently oriented differently than coeval host rock fabrics; (3) partitioning recorded by different fabric intensities, styles, and orientations established from meter-scale grid mapping to 100 km scale domainal analyses; and (4) variations in the causes of strain and kinematics within fold-dominated domains. These complex, partitioned histories require synthesized mapping, geochronology, and structural data at all scales to evaluate partitioning and in the absence of correct scaling can lead to incorrect interpretations of histories. Forward modeling capable of addressing deformation partitioning in materials containing multiple scales of rheologically heterogeneous elements of varying characteristic lengths provides the ability to upscale the large synthesized datasets described above to plate-scale tectonic processes and boundary conditions. By comparing modeling predictions from the recently developed self-consistent Multi-Order Power-Law Approach (MOPLA) to multi-scale field observations, we constrain likely paleo-tectonic controls of orogenic structural evolution rather than predicting a unique, but likely incorrect deformation history.

Prediction of soil organic carbon partition coefficients by soil column liquid chromatography.

PubMed

Guo, Rongbo; Liang, Xinmiao; Chen, Jiping; Wu, Wenzhong; Zhang, Qing; Martens, Dieter; Kettrup, Antonius

2004-04-30

To avoid the limitation of the widely used prediction methods of soil organic carbon partition coefficients (KOC) from hydrophobic parameters, e.g., the n-octanol/water partition coefficients (KOW) and the reversed phase high performance liquid chromatographic (RP-HPLC) retention factors, the soil column liquid chromatographic (SCLC) method was developed for KOC prediction. The real soils were used as the packing materials of RP-HPLC columns, and the correlations between the retention factors of organic compounds on soil columns (ksoil) and KOC measured by batch equilibrium method were studied. Good correlations were achieved between ksoil and KOC for three types of soils with different properties. All the square of the correlation coefficients (R2) of the linear regression between log ksoil and log KOC were higher than 0.89 with standard deviations of less than 0.21. In addition, the prediction of KOC from KOW and the RP-HPLC retention factors on cyanopropyl (CN) stationary phase (kCN) was comparatively evaluated for the three types of soils. The results show that the prediction of KOC from kCN and KOW is only applicable to some specific types of soils. The results obtained in the present study proved that the SCLC method is appropriate for the KOC prediction for different types of soils, however the applicability of using hydrophobic parameters to predict KOC largely depends on the properties of soil concerned.

Performance of chromatographic systems to model soil-water sorption.

PubMed

Hidalgo-Rodríguez, Marta; Fuguet, Elisabet; Ràfols, Clara; Rosés, Martí

2012-08-24

A systematic approach for evaluating the goodness of chromatographic systems to model the sorption of neutral organic compounds by soil from water is presented in this work. It is based on the examination of the three sources of error that determine the overall variance obtained when soil-water partition coefficients are correlated against chromatographic retention factors: the variance of the soil-water sorption data, the variance of the chromatographic data, and the variance attributed to the dissimilarity between the two systems. These contributions of variance are easily predicted through the characterization of the systems by the solvation parameter model. According to this method, several chromatographic systems besides the reference octanol-water partition system have been selected to test their performance in the emulation of soil-water sorption. The results from the experimental correlations agree with the predicted variances. The high-performance liquid chromatography system based on an immobilized artificial membrane and the micellar electrokinetic chromatography systems of sodium dodecylsulfate and sodium taurocholate provide the most precise correlation models. They have shown to predict well soil-water sorption coefficients of several tested herbicides. Octanol-water partitions and high-performance liquid chromatography measurements using C18 columns are less suited for the estimation of soil-water partition coefficients. Copyright © 2012 Elsevier B.V. All rights reserved.

Allometric biomass partitioning under nitrogen enrichment: Evidence from manipulative experiments around the world.

PubMed

Peng, Yunfeng; Yang, Yuanhe

2016-06-28

Allometric and optimal hypotheses have been widely used to explain biomass partitioning in response to resource changes for individual plants; however, little evidence has been reported from measurements at the community level across a broad geographic scale. This study assessed the nitrogen (N) effect on community-level root to shoot (R/S) ratios and biomass partitioning functions by synthesizing global manipulative experiments. Results showed that, in aggregate, N addition decreased the R/S ratios in various biomes. However, the scaling slopes of the allometric equations were not significantly altered by the N enrichment, possibly indicating that N-induced reduction of the R/S ratio is a consequence of allometric allocation as a function of increasing plant size rather than an optimal partitioning model. To further illustrate this point, we developed power function models to explore the relationships between aboveground and belowground biomass for various biomes; then, we generated the predicted root biomass from the observed shoot biomass and predicted R/S ratios. The comparison of predicted and observed N-induced changes of the R/S ratio revealed no significant differences between each other, supporting the allometric allocation hypothesis. These results suggest that allometry, rather than optimal allocation, explains the N-induced reduction in the R/S ratio across global biomes.

Solvation thermodynamics and the physical-chemical meaning of the constant in Abraham solvation equations.

PubMed

van Noort, Paul C M

2012-04-01

Abraham solvation equations find widespread use in environmental chemistry. Until now, the intercept in these equations was determined by fitting experimental data. To simplify the determination of the coefficients in Abraham solvation equations, this study derives theoretical expressions for the value of the intercept for various partition processes. To that end, a modification of the description of the Ben-Naim standard state into the van der Waals volume is proposed. Differences between predicted and fitted values of the Abraham solvation equation intercept for the enthalpy of solvation, the entropy of solvation, solvent-water partitioning, air-solvent partitioning, partitioning into micelles, partitioning into lipid membranes and lipids, and chromatographic retention indices are comparable to experimental uncertainties in these values. Copyright © 2011 Elsevier Ltd. All rights reserved.

Application of strainrange partitioning to the prediction of creep-fatigue lives of AISI types 304 and 316 stainless steel

NASA Technical Reports Server (NTRS)

Saltsman, J. F.; Halford, G. R.

1976-01-01

As a demonstration of the predictive capabilities of the method of Strainrange Partitioning, published high-temperature, low cycle, creep-fatigue test results on AISI Types 304 and 316 stainless steel were analyzed and calculated, cyclic lives compared with observed lives. Predicted lives agreed with observed lives within factors of two for 76 percent, factors of three for 93 percent, and factors of four for 98 percent of the laboratory tests analyzed. Agreement between observed and predicted lives is judged satisfactory considering that the data are associated with a number of variables (two alloys, several heats and heat treatments, a range of temperatures, different testing techniques, etc.) that are not directly accounted for in the calculations.

E-beam generated holographic masks for optical vector-matrix multiplication

NASA Technical Reports Server (NTRS)

Arnold, S. M.; Case, S. K.

1981-01-01

An optical vector matrix multiplication scheme that encodes the matrix elements as a holographic mask consisting of linear diffraction gratings is proposed. The binary, chrome on glass masks are fabricated by e-beam lithography. This approach results in a fairly simple optical system that promises both large numerical range and high accuracy. A partitioned computer generated hologram mask was fabricated and tested. This hologram was diagonally separated outputs, compact facets and symmetry about the axis. The resultant diffraction pattern at the output plane is shown. Since the grating fringes are written at 45 deg relative to the facet boundaries, the many on-axis sidelobes from each output are seen to be diagonally separated from the adjacent output signals.

LBP and SIFT based facial expression recognition

NASA Astrophysics Data System (ADS)

Sumer, Omer; Gunes, Ece O.

2015-02-01

This study compares the performance of local binary patterns (LBP) and scale invariant feature transform (SIFT) with support vector machines (SVM) in automatic classification of discrete facial expressions. Facial expression recognition is a multiclass classification problem and seven classes; happiness, anger, sadness, disgust, surprise, fear and comtempt are classified. Using SIFT feature vectors and linear SVM, 93.1% mean accuracy is acquired on CK+ database. On the other hand, the performance of LBP-based classifier with linear SVM is reported on SFEW using strictly person independent (SPI) protocol. Seven-class mean accuracy on SFEW is 59.76%. Experiments on both databases showed that LBP features can be used in a fairly descriptive way if a good localization of facial points and partitioning strategy are followed.

Supersymmetric dS/CFT

NASA Astrophysics Data System (ADS)

Hertog, Thomas; Tartaglino-Mazzucchelli, Gabriele; Van Riet, Thomas; Venken, Gerben

2018-02-01

We put forward new explicit realisations of dS/CFT that relate N = 2 supersymmetric Euclidean vector models with reversed spin-statistics in three dimensions to specific supersymmetric Vasiliev theories in four-dimensional de Sitter space. The partition function of the free supersymmetric vector model deformed by a range of low spin deformations that preserve supersymmetry appears to specify a well-defined wave function with asymptotic de Sitter boundary conditions in the bulk. In particular we find the wave function is globally peaked at undeformed de Sitter space, with a low amplitude for strong deformations. This suggests that supersymmetric de Sitter space is stable in higher-spin gravity and in particular free from ghosts. We speculate this is a limiting case of the de Sitter realizations in exotic string theories.

Calculation of site affinity constants and cooperativity coefficients for binding of ligands and/or protons to macromolecules. II. Relationships between chemical model and partition function algorithm.

PubMed

Fisicaro, E; Braibanti, A; Lamb, J D; Oscarson, J L

1990-05-01

The relationships between the chemical properties of a system and the partition function algorithm as applied to the description of multiple equilibria in solution are explained. The partition functions ZM, ZA, and ZH are obtained from powers of the binary generating functions Jj = (1 + kappa j gamma j,i[Y])i tau j, where i tau j = p tau j, q tau j, or r tau j represent the maximum number of sites in sites in class j, for Y = M, A, or H, respectively. Each term of the generating function can be considered an element (ij) of a vector Jj and each power of the cooperativity factor gamma ij,i can be considered an element of a diagonal cooperativity matrix gamma j. The vectors Jj are combined in tensor product matrices L tau = (J1) [J2]...[Jj]..., thus representing different receptor-ligand combinations. The partition functions are obtained by summing elements of the tensor matrices. The relationship of the partition functions with the total chemical amounts TM, TA, and TH has been found. The aim is to describe the total chemical amounts TM, TA, and TH as functions of the site affinity constants kappa j and cooperativity coefficients bj. The total amounts are calculated from the sum of elements of tensor matrices Ll. Each set of indices (pj..., qj..., rj...) represents one element of a tensor matrix L tau and defines each term of the summation. Each term corresponds to the concentration of a chemical microspecies. The distinction between microspecies MpjAqjHrj with ligands bound on specific sites and macrospecies MpAqHR corresponding to a chemical stoichiometric composition is shown. The translation of the properties of chemical model schemes into the algorithms for the generation of partition functions is illustrated with reference to a series of examples of gradually increasing complexity. The equilibria examined concern: (1) a unique class of sites; (2) the protonation of a base with two classes of sites; (3) the simultaneous binding of ligand A and proton H to a macromolecule or receptor M with four classes of sites; and (4) the binding to a macromolecule M of ligand A which is in turn a receptor for proton H. With reference to a specific example, it is shown how a computer program for least-squares refinement of variables kappa j and bj can be organized. The chemical model from the free components M, A, and H to the saturated macrospecies MpAQHR, with possible complex macrospecies MpAq and AHR, is defined first.(ABSTRACT TRUNCATED AT 250 WORDS)

Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers.

PubMed

Nmor, Jephtha C; Sunahara, Toshihiko; Goto, Kensuke; Futami, Kyoko; Sonye, George; Akweywa, Peter; Dida, Gabriel; Minakawa, Noboru

2013-01-16

Identification of malaria vector breeding sites can enhance control activities. Although associations between malaria vector breeding sites and topography are well recognized, practical models that predict breeding sites from topographic information are lacking. We used topographic variables derived from remotely sensed Digital Elevation Models (DEMs) to model the breeding sites of malaria vectors. We further compared the predictive strength of two different DEMs and evaluated the predictability of various habitat types inhabited by Anopheles larvae. Using GIS techniques, topographic variables were extracted from two DEMs: 1) Shuttle Radar Topography Mission 3 (SRTM3, 90-m resolution) and 2) the Advanced Spaceborne Thermal Emission Reflection Radiometer Global DEM (ASTER, 30-m resolution). We used data on breeding sites from an extensive field survey conducted on an island in western Kenya in 2006. Topographic variables were extracted for 826 breeding sites and for 4520 negative points that were randomly assigned. Logistic regression modelling was applied to characterize topographic features of the malaria vector breeding sites and predict their locations. Model accuracy was evaluated using the area under the receiver operating characteristics curve (AUC). All topographic variables derived from both DEMs were significantly correlated with breeding habitats except for the aspect of SRTM. The magnitude and direction of correlation for each variable were similar in the two DEMs. Multivariate models for SRTM and ASTER showed similar levels of fit indicated by Akaike information criterion (3959.3 and 3972.7, respectively), though the former was slightly better than the latter. The accuracy of prediction indicated by AUC was also similar in SRTM (0.758) and ASTER (0.755) in the training site. In the testing site, both SRTM and ASTER models showed higher AUC in the testing sites than in the training site (0.829 and 0.799, respectively). The predictability of habitat types varied. Drains, foot-prints, puddles and swamp habitat types were most predictable. Both SRTM and ASTER models had similar predictive potentials, which were sufficiently accurate to predict vector habitats. The free availability of these DEMs suggests that topographic predictive models could be widely used by vector control managers in Africa to complement malaria control strategies.

Predicting the partitioning of biological compounds between room-temperature ionic liquids and water by means of the solvation-parameter model.

PubMed

Padró, Juan M; Ponzinibbio, Agustín; Mesa, Leidy B Agudelo; Reta, Mario

2011-03-01

The partition coefficients, P(IL/w), for different probe molecules as well as for compounds of biological interest between the room-temperature ionic liquids (RTILs) 1-butyl-3-methylimidazolium hexafluorophosphate, [BMIM][PF(6)], 1-hexyl-3-methylimidazolium hexafluorophosphate, [HMIM][PF(6)], 1-octyl-3-methylimidazolium tetrafluoroborate, [OMIM][BF(4)] and water were accurately measured. [BMIM][PF(6)] and [OMIM][BF(4)] were synthesized by adapting a procedure from the literature to a simpler, single-vessel and faster methodology, with a much lesser consumption of organic solvent. We employed the solvation-parameter model to elucidate the general chemical interactions involved in RTIL/water partitioning. With this purpose, we have selected different solute descriptor parameters that measure polarity, polarizability, hydrogen-bond-donor and hydrogen-bond-acceptor interactions, and cavity formation for a set of specifically selected probe molecules (the training set). The obtained multiparametric equations were used to predict the partition coefficients for compounds not present in the training set (the test set), most being of biological interest. Partial solubility of the ionic liquid in water (and water into the ionic liquid) was taken into account to explain the obtained results. This fact has not been deeply considered up to date. Solute descriptors were obtained from the literature, when available, or else calculated through commercial software. An excellent agreement between calculated and experimental log P(IL/w) values was obtained, which demonstrated that the resulting multiparametric equations are robust and allow predicting partitioning for any organic molecule in the biphasic systems studied.

Predicting Implantation Outcome of In Vitro Fertilization and Intracytoplasmic Sperm Injection Using Data Mining Techniques.

PubMed

Hafiz, Pegah; Nematollahi, Mohtaram; Boostani, Reza; Namavar Jahromi, Bahia

2017-10-01

In vitro fertilization (IVF) and intracytoplasmic sperm injection (ICSI) are two important subsets of the assisted reproductive techniques, used for the treatment of infertility. Predicting implantation outcome of IVF/ICSI or the chance of pregnancy is essential for infertile couples, since these treatments are complex and expensive with a low probability of conception. In this cross-sectional study, the data of 486 patients were collected using census method. The IVF/ICSI dataset contains 29 variables along with an identifier for each patient that is either negative or positive. Mean accuracy and mean area under the receiver operating characteristic (ROC) curve are calculated for the classifiers. Sensitivity, specificity, positive and negative predictive values, and likelihood ratios of classifiers are employed as indicators of performance. The state-of-art classifiers which are candidates for this study include support vector machines, recursive partitioning (RPART), random forest (RF), adaptive boosting, and one-nearest neighbor. RF and RPART outperform the other comparable methods. The results revealed the areas under the ROC curve (AUC) as 84.23 and 82.05%, respectively. The importance of IVF/ICSI features was extracted from the output of RPART. Our findings demonstrate that the probability of pregnancy is low for women aged above 38. Classifiers RF and RPART are better at predicting IVF/ICSI cases compared to other decision makers that were tested in our study. Elicited decision rules of RPART determine useful predictive features of IVF/ICSI. Out of 20 factors, the age of woman, number of developed embryos, and serum estradiol level on the day of human chorionic gonadotropin administration are the three best features for such prediction. Copyright© by Royan Institute. All rights reserved.

Atmospheric occurrence, transport and gas-particle partitioning of polychlorinated biphenyls over the northwestern Pacific Ocean

NASA Astrophysics Data System (ADS)

Wu, Zilan; Lin, Tian; Li, Zhongxia; Li, Yuanyuan; Guo, Tianfeng; Guo, Zhigang

2017-10-01

Ship-board air samples were collected during March to May 2015 from the East China Sea (ECS) to the northwestern Pacific Ocean (NWP) to explore the atmospheric occurrence and gas-particle partitioning of polychlorinated biphenyls (PCBs) when the westerly East Asian Monsoon prevailed. Total PCB concentrations in the atmosphere ranged from 56.8 to 261 pg m-3. Higher PCB levels were observed off the coast and minor temperature-induced changes showed that continuous emissions from East Asia remain as an important source to the regional atmosphere. A significant relationship between Koa (octanol-air partition coefficient) and KP (gas-particle partition coefficient) for PCBs was observed under continental air masses, suggesting that land-derived organic aerosols affected the PCB gas-particle partitioning after long-range transport, while an absence of this correlation was identified in marine air masses. The PCB partitioning cannot be fully explained by the absorptive mechanism as the predicted KP were found to be 2-3 orders of magnitude lower than the measured Kp, while the prediction was closely matched when soot adsorption was considered. The results suggested the importance of soot carbon as a transport medium for PCBs during their long-range transport and considerable impacts of continental outflows on PCBs across the downwind area. The estimated transport mass of particulate PCBs into the ECS and NWP totals 2333 kg during the spring, constituting ca. 17% of annual emission inventories of unintentionally produced PCB in China.

Aircraft interior noise models - Sidewall trim, stiffened structures, and cabin acoustics with floor partition

NASA Technical Reports Server (NTRS)

Pope, L. D.; Wilby, E. G.; Willis, C. M.; Mayes, W. H.

1983-01-01

As part of the continuing development of an aircraft interior noise prediction model, in which a discrete modal representation and power flow analysis are used, theoretical results are considered for inclusion of sidewall trim, stiffened structures, and cabin acoustics with floor partition. For validation purposes, predictions of the noise reductions for three test articles (a bare ring-stringer stiffened cylinder, an unstiffened cylinder with floor and insulation, and a ring-stringer stiffened cylinder with floor and sidewall trim) are compared with measurements.

Total strain version of strainrange partitioning for thermomechanical fatigue at low strains

NASA Technical Reports Server (NTRS)

Halford, G. R.; Saltsman, J. F.

1987-01-01

A new method is proposed for characterizing and predicting the thermal fatigue behavior of materials. The method is based on three innovations in characterizing high temperature material behavior: (1) the bithermal concept of fatigue testing; (2) advanced, nonlinear, cyclic constitutive models; and (3) the total strain version of traditional strainrange partitioning.

Partition function of free conformal fields in 3-plet representation

NASA Astrophysics Data System (ADS)

Beccaria, Matteo; Tseytlin, Arkady A.

2017-05-01

Simplest examples of AdS/CFT duality correspond to free CFTs in d dimensions with fields in vector or adjoint representation of an internal symmetry group dual in the large N limit to a theory of massless or massless plus massive higher spins in AdS d+1. One may also study generalizations when conformal fields belong to higher dimensional representations, i.e. carry more than two internal symmetry indices. Here we consider the case of the 3-fundamental ("3-plet") representation. One motivation is a conjectured connection to multiple M5-brane theory: heuristic arguments suggest that it may be related to an (interacting) CFT of 6d (2,0) tensor multiplets in 3-plet representation of large N symmetry group that has an AdS7 dual. We compute the singlet partition function Z on S 1 × S d-1 for a free field in 3-plet representation of U( N) and analyse its novel large N behaviour. The large N limit of the low temperature expansion of Z which is convergent in the vector and adjoint cases here is only asymptotic, reflecting the much faster growth of the number of singlet operators with dimension, indicating a phase transition at very low temperature. Indeed, while the critical temperatures in the vector ( T c ˜ N γ , γ > 0) and adjoint ( T c ˜ 1) cases are finite, we find that in the 3-plet case T c ˜ (log N)-1, i.e. it approaches zero at large N. We discuss some details of large N solution for the eigenvalue distribution. Similar conclusions apply to higher p-plet representations of U( N) or O( N) and also to the free p-tensor theories invariant under [U( N)] p or [ O( N)] p with p ≥ 3.

Partitioning of residual D-limonene cleaner vapor among organic materials in weapons

DOE Office of Scientific and Technical Information (OSTI.GOV)

LeMay, J.D.

1993-03-01

D-limonene is a replacement solvent selected by Sandia and Allied-Signal to clean solder flux from electronics assemblies in firesets and programmers. D-limonene is much slower drying than the solvents it has replaced and this has raised concerns that residual quantities of the cleaner could be trapped in the electronics assemblies and eventually carried into warhead assemblies. This paper describes a study designed to evaluate how vapors from residual d-limonene cleaner would be partitioned among typical organic materials in a Livermore device. The goal was to identify possible compatibility problems arising from the use of d-limonene and, in particular, any interactionsmore » it may have with energetic materials. To predict the partitioning behavior of d-limonene, a simple model was developed and its predictions are compared to the experimental findings.« less

Lossless medical image compression using geometry-adaptive partitioning and least square-based prediction.

PubMed

Song, Xiaoying; Huang, Qijun; Chang, Sheng; He, Jin; Wang, Hao

2018-06-01

To improve the compression rates for lossless compression of medical images, an efficient algorithm, based on irregular segmentation and region-based prediction, is proposed in this paper. Considering that the first step of a region-based compression algorithm is segmentation, this paper proposes a hybrid method by combining geometry-adaptive partitioning and quadtree partitioning to achieve adaptive irregular segmentation for medical images. Then, least square (LS)-based predictors are adaptively designed for each region (regular subblock or irregular subregion). The proposed adaptive algorithm not only exploits spatial correlation between pixels but it utilizes local structure similarity, resulting in efficient compression performance. Experimental results show that the average compression performance of the proposed algorithm is 10.48, 4.86, 3.58, and 0.10% better than that of JPEG 2000, CALIC, EDP, and JPEG-LS, respectively. Graphical abstract ᅟ.

Parallel Conjugate Gradient: Effects of Ordering Strategies, Programming Paradigms, and Architectural Platforms

NASA Technical Reports Server (NTRS)

Oliker, Leonid; Heber, Gerd; Biswas, Rupak

2000-01-01

The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse linear systems that are symmetric and positive definite. A sparse matrix-vector multiply (SPMV) usually accounts for most of the floating-point operations within a CG iteration. In this paper, we investigate the effects of various ordering and partitioning strategies on the performance of parallel CG and SPMV using different programming paradigms and architectures. Results show that for this class of applications, ordering significantly improves overall performance, that cache reuse may be more important than reducing communication, and that it is possible to achieve message passing performance using shared memory constructs through careful data ordering and distribution. However, a multi-threaded implementation of CG on the Tera MTA does not require special ordering or partitioning to obtain high efficiency and scalability.

Comparison of prediction methods for octanol-air partition coefficients of diverse organic compounds.

PubMed

Fu, Zhiqiang; Chen, Jingwen; Li, Xuehua; Wang, Ya'nan; Yu, Haiying

2016-04-01

The octanol-air partition coefficient (KOA) is needed for assessing multimedia transport and bioaccumulability of organic chemicals in the environment. As experimental determination of KOA for various chemicals is costly and laborious, development of KOA estimation methods is necessary. We investigated three methods for KOA prediction, conventional quantitative structure-activity relationship (QSAR) models based on molecular structural descriptors, group contribution models based on atom-centered fragments, and a novel model that predicts KOA via solvation free energy from air to octanol phase (ΔGO(0)), with a collection of 939 experimental KOA values for 379 compounds at different temperatures (263.15-323.15 K) as validation or training sets. The developed models were evaluated with the OECD guidelines on QSAR models validation and applicability domain (AD) description. Results showed that although the ΔGO(0) model is theoretically sound and has a broad AD, the prediction accuracy of the model is the poorest. The QSAR models perform better than the group contribution models, and have similar predictability and accuracy with the conventional method that estimates KOA from the octanol-water partition coefficient and Henry's law constant. One QSAR model, which can predict KOA at different temperatures, was recommended for application as to assess the long-range transport potential of chemicals. Copyright © 2016 Elsevier Ltd. All rights reserved.

Predicting Transition from Laminar to Turbulent Flow over a Surface

NASA Technical Reports Server (NTRS)

Sturdza, Peter (Inventor); Rajnarayan, Dev (Inventor)

2013-01-01

A prediction of whether a point on a computer-generated surface is adjacent to laminar or turbulent flow is made using a transition prediction technique. A plurality of boundary-layer properties at the point are obtained from a steady-state solution of a fluid flow in a region adjacent to the point. A plurality of instability modes are obtained, each defined by one or more mode parameters. A vector of regressor weights is obtained for the known instability growth rates in a training dataset. For each instability mode in the plurality of instability modes, a covariance vector is determined, which is the covariance of a predicted local growth rate with the known instability growth rates. Each covariance vector is used with the vector of regressor weights to determine a predicted local growth rate at the point. Based on the predicted local growth rates, an n-factor envelope at the point is determined.

Dermal uptake of phthalates from clothing: Comparison of model to human participant results.

PubMed

Morrison, G C; Weschler, C J; Bekö, G

2017-05-01

In this research, we extend a model of transdermal uptake of phthalates to include a layer of clothing. When compared with experimental results, this model better estimates dermal uptake of diethylphthalate and di-n-butylphthalate (DnBP) than a previous model. The model predictions are consistent with the observation that previously exposed clothing can increase dermal uptake over that observed in bare-skin participants for the same exposure air concentrations. The model predicts that dermal uptake from clothing of DnBP is a substantial fraction of total uptake from all sources of exposure. For compounds that have high dermal permeability coefficients, dermal uptake is increased for (i) thinner clothing, (ii) a narrower gap between clothing and skin, and (iii) longer time intervals between laundering and wearing. Enhanced dermal uptake is most pronounced for compounds with clothing-air partition coefficients between 10 4 and 10 7 . In the absence of direct measurements of cotton cloth-air partition coefficients, dermal exposure may be predicted using equilibrium data for compounds in equilibrium with cellulose and water, in combination with computational methods of predicting partition coefficients. © 2016 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Partitioning Behavior of Organic Contaminants in Carbon Storage Environments: A Critical Review

DOE Office of Scientific and Technical Information (OSTI.GOV)

Burant, Aniela; Lowry, Gregory V; Karamalidis, Athanasios K

2012-12-04

Carbon capture and storage is a promising strategy for mitigating the CO{sub 2} contribution to global climate change. The large scale implementation of the technology mandates better understanding of the risks associated with CO{sub 2} injection into geologic formations and the subsequent interactions with groundwater resources. The injected supercritical CO{sub 2} (sc-CO{sub 2}) is a nonpolar solvent that can potentially mobilize organic compounds that exist at residual saturation in the formation. Here, we review the partitioning behavior of selected organic compounds typically found in depleted oil reservoirs in the residual oil–brine–sc-CO{sub 2} system under carbon storage conditions. The solubility ofmore » pure phase organic compounds in sc-CO{sub 2} and partitioning of organic compounds between water and sc-CO{sub 2} follow trends predicted based on thermodynamics. Compounds with high volatility and low aqueous solubility have the highest potential to partition to sc-CO{sub 2}. The partitioning of low volatility compounds to sc-CO{sub 2} can be enhanced by co-solvency due to the presence of higher volatility compounds in the sc-CO{sub 2}. The effect of temperature, pressure, salinity, pH, and dissolution of water molecules into sc-CO{sub 2} on the partitioning behavior of organic compounds in the residual oil-brine-sc-CO{sub 2} system is discussed. Data gaps and research needs for models to predict the partitioning of organic compounds in brines and from complex mixtures of oils are presented. Models need to be able to better incorporate the effect of salinity and co-solvency, which will require more experimental data from key classes of organic compounds.« less

Nonequilibrium partitioning during rapid solidification of SiAs alloys

NASA Astrophysics Data System (ADS)

Kittl, J. A.; Aziz, M. J.; Brunco, D. P.; Thompson, M. O.

1995-02-01

The velocity dependence of the partition coefficient was measured for rapid solidification of polycrystalline Si-4.5 at% As and Si-9 at% As alloys induced by pulsed laser melting. The results constitute the first test of partitioning models both for the high velocity regime and for non-dilute alloys. The continuous growth model (CGM) of Aziz and Kaplan fits the data well, but with an unusually low diffusive speed of 0.46 m/s. The data show negligible dependence of partitioning on concentration, also consistent with the CGM. The predictions of the Hillert-Sundman model are inconsistent with partitioning results. Using the aperiodic stepwise growth model (ASGM) of Goldman and Aziz, an average over crystallographic orientations with parameters from independent single-crystal experiments is shown to be reasonably consistent with these polycrystalline partitioning results. The results, combined with others, indicate that the CGM without solute drag and its extension to lateral ledge motion, the ASGM, are the only models that fit the data for both solute partioning and kinetic undercooling interface response functions. No current solute drag models can match both partitioning and undercooling measurements.

Predicting the velocity and azimuth of fragments generated by the range destruction or random failure of rocket casings and tankage

NASA Technical Reports Server (NTRS)

Eck, Marshall; Mukunda, Meera

1988-01-01

A calculational method is described which provides a powerful tool for predicting solid rocket motor (SRM) casing and liquid rocket tankage fragmentation response. The approach properly partitions the available impulse to each major system-mass component. It uses the Pisces code developed by Physics International to couple the forces generated by an Eulerian-modeled gas flow field to a Lagrangian-modeled fuel and casing system. The details of the predictive analytical modeling process and the development of normalized relations for momentum partition as a function of SRM burn time and initial geometry are discussed. Methods for applying similar modeling techniques to liquid-tankage-overpressure failures are also discussed. Good agreement between predictions and observations are obtained for five specific events.

On the estimation of the domain of attraction for discrete-time switched and hybrid nonlinear systems

NASA Astrophysics Data System (ADS)

Kit Luk, Chuen; Chesi, Graziano

2015-11-01

This paper addresses the estimation of the domain of attraction for discrete-time nonlinear systems where the vector field is subject to changes. First, the paper considers the case of switched systems, where the vector field is allowed to arbitrarily switch among the elements of a finite family. Second, the paper considers the case of hybrid systems, where the state space is partitioned into several regions described by polynomial inequalities, and the vector field is defined on each region independently from the other ones. In both cases, the problem consists of computing the largest sublevel set of a Lyapunov function included in the domain of attraction. An approach is proposed for solving this problem based on convex programming, which provides a guaranteed inner estimate of the sought sublevel set. The conservatism of the provided estimate can be decreased by increasing the size of the optimisation problem. Some numerical examples illustrate the proposed approach.

Coupling of organic and inorganic aerosol systems and the effect on gas–particle partitioning in the southeastern US

PubMed Central

Pye, Havala O. T.; Zuend, Andreas; Fry, Juliane L.; Isaacman-VanWertz, Gabriel; Capps, Shannon L.; Appel, K. Wyat; Foroutan, Hosein; Xu, Lu; Ng, Nga L.; Goldstein, Allen H.

2018-01-01

Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2× sulfate, RN/2S ≈ 0.8 to 0.9) with approximately 70% of total ammonia and ammonium (NHx) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+]air (H+ in μgm−3 air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid–liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic–organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C≥0.6) compounds including several isoprene-derived tracers as well as levoglu-cosan but decrease particle-phase partitioning for low O: C, monoterpene-derived species.

Coupling of organic and inorganic aerosol systems and the effect on gas-particle partitioning in the southeastern US

NASA Astrophysics Data System (ADS)

Pye, Havala O. T.; Zuend, Andreas; Fry, Juliane L.; Isaacman-VanWertz, Gabriel; Capps, Shannon L.; Wyat Appel, K.; Foroutan, Hosein; Xu, Lu; Ng, Nga L.; Goldstein, Allen H.

2018-01-01

Several models were used to describe the partitioning of ammonia, water, and organic compounds between the gas and particle phases for conditions in the southeastern US during summer 2013. Existing equilibrium models and frameworks were found to be sufficient, although additional improvements in terms of estimating pure-species vapor pressures are needed. Thermodynamic model predictions were consistent, to first order, with a molar ratio of ammonium to sulfate of approximately 1.6 to 1.8 (ratio of ammonium to 2 × sulfate, RN/2S ≈ 0.8 to 0.9) with approximately 70 % of total ammonia and ammonium (NHx) in the particle. Southeastern Aerosol Research and Characterization Network (SEARCH) gas and aerosol and Southern Oxidant and Aerosol Study (SOAS) Monitor for AeRosols and Gases in Ambient air (MARGA) aerosol measurements were consistent with these conditions. CMAQv5.2 regional chemical transport model predictions did not reflect these conditions due to a factor of 3 overestimate of the nonvolatile cations. In addition, gas-phase ammonia was overestimated in the CMAQ model leading to an even lower fraction of total ammonia in the particle. Chemical Speciation Network (CSN) and aerosol mass spectrometer (AMS) measurements indicated less ammonium per sulfate than SEARCH and MARGA measurements and were inconsistent with thermodynamic model predictions. Organic compounds were predicted to be present to some extent in the same phase as inorganic constituents, modifying their activity and resulting in a decrease in [H+]air (H+ in µg m-3 air), increase in ammonia partitioning to the gas phase, and increase in pH compared to complete organic vs. inorganic liquid-liquid phase separation. In addition, accounting for nonideal mixing modified the pH such that a fully interactive inorganic-organic system had a pH roughly 0.7 units higher than predicted using traditional methods (pH = 1.5 vs. 0.7). Particle-phase interactions of organic and inorganic compounds were found to increase partitioning towards the particle phase (vs. gas phase) for highly oxygenated (O : C ≥ 0.6) compounds including several isoprene-derived tracers as well as levoglucosan but decrease particle-phase partitioning for low O : C, monoterpene-derived species.

Selecting polymers for two-phase partitioning bioreactors (TPPBs): Consideration of thermodynamic affinity, crystallinity, and glass transition temperature.

PubMed

Bacon, Stuart L; Peterson, Eric C; Daugulis, Andrew J; Parent, J Scott

2015-01-01

Two-phase partitioning bioreactor technology involves the use of a secondary immiscible phase to lower the concentration of cytotoxic solutes in the fermentation broth to subinhibitory levels. Although polymeric absorbents have attracted recent interest due to their low cost and biocompatibility, material selection requires the consideration of properties beyond those of small molecule absorbents (i.e., immiscible organic solvents). These include a polymer's (1) thermodynamic affinity for the target compound, (2) degree of crystallinity (wc ), and (3) glass transition temperature (Tg ). We have examined the capability of three thermodynamic models to predict the partition coefficient (PC) for n-butyric acid, a fermentation product, in 15 polymers. Whereas PC predictions for amorphous materials had an average absolute deviation (AAD) of ≥16%, predictions for semicrystalline polymers were less accurate (AAD ≥ 30%). Prediction errors were associated with uncertainties in determining the degree of crystallinity within a polymer and the effect of absorbed water on n-butyric acid partitioning. Further complications were found to arise for semicrystalline polymers, wherein strongly interacting solutes increased the polymer's absorptive capacity by actually dissolving the crystalline fraction. Finally, we determined that diffusion limitations may occur for polymers operating near their Tg , and that the Tg can be reduced by plasticization by water and/or solute. This study has demonstrated the impact of basic material properties that affects the performance of polymers as sequestering phases in TPPBs, and reflects the additional complexity of polymers that must be taken into account in material selection. © 2015 American Institute of Chemical Engineers.

Multivariate regression model for partitioning tree volume of white oak into round-product classes

Treesearch

Daniel A. Yaussy; David L. Sonderman

1984-01-01

Describes the development of multivariate equations that predict the expected cubic volume of four round-product classes from independent variables composed of individual tree-quality characteristics. Although the model has limited application at this time, it does demonstrate the feasibility of partitioning total tree cubic volume into round-product classes based on...

Co-Clustering by Bipartite Spectral Graph Partitioning for Out-of-Tutor Prediction

ERIC Educational Resources Information Center

Trivedi, Shubhendu; Pardos, Zachary A.; Sarkozy, Gabor N.; Heffernan, Neil T.

2012-01-01

Learning a more distributed representation of the input feature space is a powerful method to boost the performance of a given predictor. Often this is accomplished by partitioning the data into homogeneous groups by clustering so that separate models could be trained on each cluster. Intuitively each such predictor is a better representative of…

Experimental determination of solvent-water partition coefficients and Abraham parameters for munition constituents.

PubMed

Liang, Yuzhen; Kuo, Dave T F; Allen, Herbert E; Di Toro, Dominic M

2016-10-01

There is concern about the environmental fate and effects of munition constituents (MCs). Polyparameter linear free energy relationships (pp-LFERs) that employ Abraham solute parameters can aid in evaluating the risk of MCs to the environment. However, poor predictions using pp-LFERs and ABSOLV estimated Abraham solute parameters are found for some key physico-chemical properties. In this work, the Abraham solute parameters are determined using experimental partition coefficients in various solvent-water systems. The compounds investigated include hexahydro-1,3,5-trinitro-1,3,5-triazacyclohexane (RDX), octahydro-1,3,5,7-tetranitro-1,3,5,7-tetraazacyclooctane (HMX), hexahydro-1-nitroso-3,5-dinitro-1,3,5-triazine (MNX), hexahydro-1,3,5-trinitroso-1,3,5-triazine (TNX), hexahydro-1,3-dinitroso-5- nitro-1,3,5-triazine (DNX), 2,4,6-trinitrotoluene (TNT), 1,3,5-trinitrobenzene (TNB), and 4-nitroanisole. The solvents in the solvent-water systems are hexane, dichloromethane, trichloromethane, octanol, and toluene. The only available reported solvent-water partition coefficients are for octanol-water for some of the investigated compounds and they are in good agreement with the experimental measurements from this study. Solvent-water partition coefficients fitted using experimentally derived solute parameters from this study have significantly smaller root mean square errors (RMSE = 0.38) than predictions using ABSOLV estimated solute parameters (RMSE = 3.56) for the investigated compounds. Additionally, the predictions for various physico-chemical properties using the experimentally derived solute parameters agree with available literature reported values with prediction errors within 0.79 log units except for water solubility of RDX and HMX with errors of 1.48 and 2.16 log units respectively. However, predictions using ABSOLV estimated solute parameters have larger prediction errors of up to 7.68 log units. This large discrepancy is probably due to the missing R2NNO2 and R2NNO2 functional groups in the ABSOLV fragment database. Copyright © 2016. Published by Elsevier Ltd.

Identifying risk profiles for childhood obesity using recursive partitioning based on individual, familial, and neighborhood environment factors.

PubMed

Van Hulst, Andraea; Roy-Gagnon, Marie-Hélène; Gauvin, Lise; Kestens, Yan; Henderson, Mélanie; Barnett, Tracie A

2015-02-15

Few studies consider how risk factors within multiple levels of influence operate synergistically to determine childhood obesity. We used recursive partitioning analysis to identify unique combinations of individual, familial, and neighborhood factors that best predict obesity in children, and tested whether these predict 2-year changes in body mass index (BMI). Data were collected in 2005-2008 and in 2008-2011 for 512 Quebec youth (8-10 years at baseline) with a history of parental obesity (QUALITY study). CDC age- and sex-specific BMI percentiles were computed and children were considered obese if their BMI was ≥95th percentile. Individual (physical activity and sugar-sweetened beverage intake), familial (household socioeconomic status and measures of parental obesity including both BMI and waist circumference), and neighborhood (disadvantage, prestige, and presence of parks, convenience stores, and fast food restaurants) factors were examined. Recursive partitioning, a method that generates a classification tree predicting obesity based on combined exposure to a series of variables, was used. Associations between resulting varying risk group membership and BMI percentile at baseline and 2-year follow up were examined using linear regression. Recursive partitioning yielded 7 subgroups with a prevalence of obesity equal to 8%, 11%, 26%, 28%, 41%, 60%, and 63%, respectively. The 2 highest risk subgroups comprised i) children not meeting physical activity guidelines, with at least one BMI-defined obese parent and 2 abdominally obese parents, living in disadvantaged neighborhoods without parks and, ii) children with these characteristics, except with access to ≥1 park and with access to ≥1 convenience store. Group membership was strongly associated with BMI at baseline, but did not systematically predict change in BMI. Findings support the notion that obesity is predicted by multiple factors in different settings and provide some indications of potentially obesogenic environments. Alternate group definitions as well as longer duration of follow up should be investigated to predict change in obesity.

Novel methods for predicting gas-particle partitioning during the formation of secondary organic aerosol

NASA Astrophysics Data System (ADS)

Wania, F.; Lei, Y. D.; Wang, C.; Abbatt, J. P. D.; Goss, K.-U.

2014-12-01

Several methods have been presented in the literature to predict an organic chemical's equilibrium partitioning between the water insoluble organic matter (WIOM) component of aerosol and the gas phase, Ki,WIOM, as a function of temperature. They include (i) polyparameter linear free energy relationships calibrated with empirical aerosol sorption data, as well as (ii) the solvation models implemented in SPARC and (iii) the quantum-chemical software COSMOtherm, which predict solvation equilibria from molecular structure alone. We demonstrate that these methods can be used to predict Ki,WIOM for large numbers of individual molecules implicated in secondary organic aerosol (SOA) formation, including those with multiple functional groups. Although very different in their theoretical foundations, these methods give remarkably consistent results for the products of the reaction of normal alkanes with OH, i.e. their partition coefficients Ki,WIOM generally agree within one order of magnitude over a range of more than ten orders of magnitude. This level of agreement is much better than that achieved by different vapour pressure estimation methods that are more commonly used in the SOA community. Also, in contrast to the agreement between vapour pressure estimates, the agreement between the Ki,WIOM estimates does not deteriorate with increasing number of functional groups. Furthermore, these partitioning coefficients Ki,WIOM predicted SOA mass yields in agreement with those measured in chamber experiments of the oxidation of normal alkanes. If a Ki,WIOM prediction method was based on one or more surrogate molecules representing the solvation properties of the mixed OM phase of SOA, the choice of those molecule(s) was found to have a relatively minor effect on the predicted Ki,WIOM, as long as the molecule(s) are not very polar. This suggests that a single surrogate molecule, such as 1-octanol or a hypothetical SOA structure proposed by Kalberer et al. (2004), may often be sufficient to represent the WIOM component of the SOA phase, greatly simplifying the prediction. The presented methods could substitute for vapour-pressure-based methods in studies such as the explicit modelling of SOA formation from single precursor molecules in chamber experiments.

An SOA model for toluene oxidation in the presence of inorganic aerosols.

PubMed

Cao, Gang; Jang, Myoseon

2010-01-15

A predictive model for secondary organic aerosol (SOA) formation including both partitioning and heterogeneous reactions is explored for the SOA produced from the oxidation of toluene in the presence of inorganic seed aerosols. The predictive SOA model comprises the explicit gas-phase chemistry of toluene, gas-particle partitioning, and heterogeneous chemistry. The resulting products from the explicit gas phase chemistry are lumped into several classes of chemical species based on their vapor pressure and reactivity for heterogeneous reactions. Both the gas-particle partitioning coefficient and the heterogeneous reaction rate constant of each lumped gas-phase product are theoretically determined using group contribution and molecular structure-reactivity. In the SOA model, the predictive SOA mass is decoupled into partitioning (OM(P)) and heterogeneous aerosol production (OM(H)). OM(P) is estimated from the SOA partitioning model developed by Schell et al. (J. Geophys. Res. 2001, 106, 28275-28293 ) that has been used in a regional air quality model (CMAQ 4.7). OM(H) is predicted from the heterogeneous SOA model developed by Jang et al. (Environ. Sci. Technol. 2006, 40, 3013-3022 ). The SOA model is evaluated using a number of the experimental SOA data that are generated in a 2 m(3) indoor Teflon film chamber under various experimental conditions (e.g., humidity, inorganic seed compositions, NO(x) concentrations). The SOA model reasonably predicts not only the gas-phase chemistry, such as the ozone formation, the conversion of NO to NO(2), and the toluene decay, but also the SOA production. The model predicted that the OM(H) fraction of the total toluene SOA mass increases as NO(x) concentrations decrease: 0.73-0.83 at low NO(x) levels and 0.17-0.47 at middle and high NO(x) levels for SOA experiments with high initial toluene concentrations. Our study also finds a significant increase in the OM(H) mass fraction in the SOA generated with low initial toluene concentrations, compared to those with high initial toluene concentrations. On average, more than a 1-fold increase in OM(H) fraction is observed when the comparison is made between SOA experiments with 40 ppb toluene to those with 630 ppb toluene. Such an observation implies that heterogeneous reactions of the second-generation products of toluene oxidation can contribute considerably to the total SOA mass under atmospheric relevant conditions.

Scoring Coreference Partitions of Predicted Mentions: A Reference Implementation.

PubMed

Pradhan, Sameer; Luo, Xiaoqiang; Recasens, Marta; Hovy, Eduard; Ng, Vincent; Strube, Michael

2014-06-01

The definitions of two coreference scoring metrics- B 3 and CEAF-are underspecified with respect to predicted , as opposed to key (or gold ) mentions. Several variations have been proposed that manipulate either, or both, the key and predicted mentions in order to get a one-to-one mapping. On the other hand, the metric BLANC was, until recently, limited to scoring partitions of key mentions. In this paper, we (i) argue that mention manipulation for scoring predicted mentions is unnecessary, and potentially harmful as it could produce unintuitive results; (ii) illustrate the application of all these measures to scoring predicted mentions; (iii) make available an open-source, thoroughly-tested reference implementation of the main coreference evaluation measures; and (iv) rescore the results of the CoNLL-2011/2012 shared task systems with this implementation. This will help the community accurately measure and compare new end-to-end coreference resolution algorithms.

Temperature and composition dependencies of trace element partitioning - Olivine/melt and low-Ca pyroxene/melt

NASA Technical Reports Server (NTRS)

Colson, R. O.; Mckay, G. A.; Taylor, L. A.

1988-01-01

This paper presents a systematic thermodynamic analysis of the effects of temperature and composition on olivine/melt and low-Ca pyroxene/melt partitioning. Experiments were conducted in several synthetic basalts with a wide range of Fe/Mg, determining partition coefficients for Eu, Ca, Mn, Fe, Ni, Sm, Cd, Y, Yb, Sc, Al, Zr, and Ti and modeling accurately the changes in free energy for trace element exchange between crystal and melt as functions of the trace element size and charge. On the basis of this model, partition coefficients for olivine/melt and low-Ca pyroxene/melt can be predicted for a wide range of elements over a variety of basaltic bulk compositions and temperatures. Moreover, variations in partition coeffeicients during crystallization or melting can be modeled on the basis of changes in temperature and major element chemistry.

Criteria for genuine N -partite continuous-variable entanglement and Einstein-Podolsky-Rosen steering

NASA Astrophysics Data System (ADS)

Teh, R. Y.; Reid, M. D.

2014-12-01

Following previous work, we distinguish between genuine N -partite entanglement and full N -partite inseparability. Accordingly, we derive criteria to detect genuine multipartite entanglement using continuous-variable (position and momentum) measurements. Our criteria are similar but different to those based on the van Loock-Furusawa inequalities, which detect full N -partite inseparability. We explain how the criteria can be used to detect the genuine N -partite entanglement of continuous variable states generated from squeezed and vacuum state inputs, including the continuous-variable Greenberger-Horne-Zeilinger state, with explicit predictions for up to N =9 . This makes our work accessible to experiment. For N =3 , we also present criteria for tripartite Einstein-Podolsky-Rosen (EPR) steering. These criteria provide a means to demonstrate a genuine three-party EPR paradox, in which any single party is steerable by the remaining two parties.

Prediction of Spirometric Forced Expiratory Volume (FEV1) Data Using Support Vector Regression

NASA Astrophysics Data System (ADS)

Kavitha, A.; Sujatha, C. M.; Ramakrishnan, S.

2010-01-01

In this work, prediction of forced expiratory volume in 1 second (FEV1) in pulmonary function test is carried out using the spirometer and support vector regression analysis. Pulmonary function data are measured with flow volume spirometer from volunteers (N=175) using a standard data acquisition protocol. The acquired data are then used to predict FEV1. Support vector machines with polynomial kernel function with four different orders were employed to predict the values of FEV1. The performance is evaluated by computing the average prediction accuracy for normal and abnormal cases. Results show that support vector machines are capable of predicting FEV1 in both normal and abnormal cases and the average prediction accuracy for normal subjects was higher than that of abnormal subjects. Accuracy in prediction was found to be high for a regularization constant of C=10. Since FEV1 is the most significant parameter in the analysis of spirometric data, it appears that this method of assessment is useful in diagnosing the pulmonary abnormalities with incomplete data and data with poor recording.

Predictive modeling of mosquito abundance and dengue transmission in Kenya

NASA Astrophysics Data System (ADS)

Caldwell, J.; Krystosik, A.; Mutuku, F.; Ndenga, B.; LaBeaud, D.; Mordecai, E.

2017-12-01

Approximately 390 million people are exposed to dengue virus every year, and with no widely available treatments or vaccines, predictive models of disease risk are valuable tools for vector control and disease prevention. The aim of this study was to modify and improve climate-driven predictive models of dengue vector abundance (Aedes spp. mosquitoes) and viral transmission to people in Kenya. We simulated disease transmission using a temperature-driven mechanistic model and compared model predictions with vector trap data for larvae, pupae, and adult mosquitoes collected between 2014 and 2017 at four sites across urban and rural villages in Kenya. We tested predictive capacity of our models using four temperature measurements (minimum, maximum, range, and anomalies) across daily, weekly, and monthly time scales. Our results indicate seasonal temperature variation is a key driving factor of Aedes mosquito abundance and disease transmission. These models can help vector control programs target specific locations and times when vectors are likely to be present, and can be modified for other Aedes-transmitted diseases and arboviral endemic regions around the world.

A robust H.264/AVC video watermarking scheme with drift compensation.

PubMed

Jiang, Xinghao; Sun, Tanfeng; Zhou, Yue; Wang, Wan; Shi, Yun-Qing

2014-01-01

A robust H.264/AVC video watermarking scheme for copyright protection with self-adaptive drift compensation is proposed. In our scheme, motion vector residuals of macroblocks with the smallest partition size are selected to hide copyright information in order to hold visual impact and distortion drift to a minimum. Drift compensation is also implemented to reduce the influence of watermark to the most extent. Besides, discrete cosine transform (DCT) with energy compact property is applied to the motion vector residual group, which can ensure robustness against intentional attacks. According to the experimental results, this scheme gains excellent imperceptibility and low bit-rate increase. Malicious attacks with different quantization parameters (QPs) or motion estimation algorithms can be resisted efficiently, with 80% accuracy on average after lossy compression.

A Robust H.264/AVC Video Watermarking Scheme with Drift Compensation

PubMed Central

Sun, Tanfeng; Zhou, Yue; Shi, Yun-Qing

2014-01-01

A robust H.264/AVC video watermarking scheme for copyright protection with self-adaptive drift compensation is proposed. In our scheme, motion vector residuals of macroblocks with the smallest partition size are selected to hide copyright information in order to hold visual impact and distortion drift to a minimum. Drift compensation is also implemented to reduce the influence of watermark to the most extent. Besides, discrete cosine transform (DCT) with energy compact property is applied to the motion vector residual group, which can ensure robustness against intentional attacks. According to the experimental results, this scheme gains excellent imperceptibility and low bit-rate increase. Malicious attacks with different quantization parameters (QPs) or motion estimation algorithms can be resisted efficiently, with 80% accuracy on average after lossy compression. PMID:24672376

How psychological framing affects economic market prices in the lab and field.

PubMed

Sonnemann, Ulrich; Camerer, Colin F; Fox, Craig R; Langer, Thomas

2013-07-16

A fundamental debate in social sciences concerns how individual judgments and choices, resulting from psychological mechanisms, are manifested in collective economic behavior. Economists emphasize the capacity of markets to aggregate information distributed among traders into rational equilibrium prices. However, psychologists have identified pervasive and systematic biases in individual judgment that they generally assume will affect collective behavior. In particular, recent studies have found that judged likelihoods of possible events vary systematically with the way the entire event space is partitioned, with probabilities of each of N partitioned events biased toward 1/N. Thus, combining events into a common partition lowers perceived probability, and unpacking events into separate partitions increases their perceived probability. We look for evidence of such bias in various prediction markets, in which prices can be interpreted as probabilities of upcoming events. In two highly controlled experimental studies, we find clear evidence of partition dependence in a 2-h laboratory experiment and a field experiment on National Basketball Association (NBA) and Federation Internationale de Football Association (FIFA World Cup) sports events spanning several weeks. We also find evidence consistent with partition dependence in nonexperimental field data from prediction markets for economic derivatives (guessing the values of important macroeconomic statistics) and horse races. Results in any one of the studies might be explained by a specialized alternative theory, but no alternative theories can explain the results of all four studies. We conclude that psychological biases in individual judgment can affect market prices, and understanding those effects requires combining a variety of methods from psychology and economics.

How psychological framing affects economic market prices in the lab and field

PubMed Central

Sonnemann, Ulrich; Camerer, Colin F.; Fox, Craig R.; Langer, Thomas

2013-01-01

A fundamental debate in social sciences concerns how individual judgments and choices, resulting from psychological mechanisms, are manifested in collective economic behavior. Economists emphasize the capacity of markets to aggregate information distributed among traders into rational equilibrium prices. However, psychologists have identified pervasive and systematic biases in individual judgment that they generally assume will affect collective behavior. In particular, recent studies have found that judged likelihoods of possible events vary systematically with the way the entire event space is partitioned, with probabilities of each of N partitioned events biased toward 1/N. Thus, combining events into a common partition lowers perceived probability, and unpacking events into separate partitions increases their perceived probability. We look for evidence of such bias in various prediction markets, in which prices can be interpreted as probabilities of upcoming events. In two highly controlled experimental studies, we find clear evidence of partition dependence in a 2-h laboratory experiment and a field experiment on National Basketball Association (NBA) and Federation Internationale de Football Association (FIFA World Cup) sports events spanning several weeks. We also find evidence consistent with partition dependence in nonexperimental field data from prediction markets for economic derivatives (guessing the values of important macroeconomic statistics) and horse races. Results in any one of the studies might be explained by a specialized alternative theory, but no alternative theories can explain the results of all four studies. We conclude that psychological biases in individual judgment can affect market prices, and understanding those effects requires combining a variety of methods from psychology and economics. PMID:23818628

Predicting solute partitioning in lipid bilayers: Free energies and partition coefficients from molecular dynamics simulations and COSMOmic

DOE Office of Scientific and Technical Information (OSTI.GOV)

Jakobtorweihen, S., E-mail: jakobtorweihen@tuhh.de; Ingram, T.; Gerlach, T.

2014-07-28

Quantitative predictions of biomembrane/water partition coefficients are important, as they are a key property in pharmaceutical applications and toxicological studies. Molecular dynamics (MD) simulations are used to calculate free energy profiles for different solutes in lipid bilayers. How to calculate partition coefficients from these profiles is discussed in detail and different definitions of partition coefficients are compared. Importantly, it is shown that the calculated coefficients are in quantitative agreement with experimental results. Furthermore, we compare free energy profiles from MD simulations to profiles obtained by the recent method COSMOmic, which is an extension of the conductor-like screening model for realisticmore » solvation to micelles and biomembranes. The free energy profiles from these molecular methods are in good agreement. Additionally, solute orientations calculated with MD and COSMOmic are compared and again a good agreement is found. Four different solutes are investigated in detail: 4-ethylphenol, propanol, 5-phenylvaleric acid, and dibenz[a,h]anthracene, whereby the latter belongs to the class of polycyclic aromatic hydrocarbons. The convergence of the free energy profiles from biased MD simulations is discussed and the results are shown to be comparable to equilibrium MD simulations. For 5-phenylvaleric acid the influence of the carboxyl group dihedral angle on free energy profiles is analyzed with MD simulations.« less

An examination of the role of particles in oceanic mercury cycling.

PubMed

Lamborg, Carl H; Hammerschmidt, Chad R; Bowman, Katlin L

2016-11-28

Recent models of global mercury (Hg) cycling have identified the downward flux of sinking particles in the ocean as a prominent Hg removal process from the ocean. At least one of these models estimates the amount of anthropogenic Hg in the ocean to be about 400 Mmol, with deep water formation and sinking fluxes representing the largest vectors by which pollutant Hg is able to penetrate the ocean interior. Using data from recent cruises to the Atlantic, we examined the dissolved and particulate partitioning of Hg in the oceanic water column as a cross-check on the hypothesis that sinking particle fluxes are important. Interestingly, these new data suggest particle-dissolved partitioning ( K d ) that is approximately 20× greater than previous estimates, which thereby challenges certain assumptions about the scavenging and active partitioning of Hg in the ocean used in earlier models. For example, the new particle data suggest that regenerative scavenging is the most likely mechanism by which the association of Hg and particles occurs.This article is part of the themed issue 'Biological and climatic impacts of ocean trace element chemistry'. © 2016 The Author(s).

On the Problem of Bandwidth Partitioning in FDD Block-Fading Single-User MISO/SIMO Systems

NASA Astrophysics Data System (ADS)

Ivrlač, Michel T.; Nossek, Josef A.

2008-12-01

We report on our research activity on the problem of how to optimally partition the available bandwidth of frequency division duplex, multi-input single-output communication systems, into subbands for the uplink, the downlink, and the feedback. In the downlink, the transmitter applies coherent beamforming based on quantized channel information which is obtained by feedback from the receiver. As feedback takes away resources from the uplink, which could otherwise be used to transfer payload data, it is highly desirable to reserve the "right" amount of uplink resources for the feedback. Under the assumption of random vector quantization, and a frequency flat, independent and identically distributed block-fading channel, we derive closed-form expressions for both the feedback quantization and bandwidth partitioning which jointly maximize the sum of the average payload data rates of the downlink and the uplink. While we do introduce some approximations to facilitate mathematical tractability, the analytical solution is asymptotically exact as the number of antennas approaches infinity, while for systems with few antennas, it turns out to be a fairly accurate approximation. In this way, the obtained results are meaningful for practical communication systems, which usually can only employ a few antennas.

Influence of Temperature, Relative Humidity, and Soil Properties on the Soil-Air Partitioning of Semivolatile Pesticides: Laboratory Measurements and Predictive Models.

PubMed

Davie-Martin, Cleo L; Hageman, Kimberly J; Chin, Yu-Ping; Rougé, Valentin; Fujita, Yuki

2015-09-01

Soil-air partition coefficient (Ksoil-air) values are often employed to investigate the fate of organic contaminants in soils; however, these values have not been measured for many compounds of interest, including semivolatile current-use pesticides. Moreover, predictive equations for estimating Ksoil-air values for pesticides (other than the organochlorine pesticides) have not been robustly developed, due to a lack of measured data. In this work, a solid-phase fugacity meter was used to measure the Ksoil-air values of 22 semivolatile current- and historic-use pesticides and their degradation products. Ksoil-air values were determined for two soils (semiarid and volcanic) under a range of environmentally relevant temperature (10-30 °C) and relative humidity (30-100%) conditions, such that 943 Ksoil-air measurements were made. Measured values were used to derive a predictive equation for pesticide Ksoil-air values based on temperature, relative humidity, soil organic carbon content, and pesticide-specific octanol-air partition coefficients. Pesticide volatilization losses from soil, calculated with the newly derived Ksoil-air predictive equation and a previously described pesticide volatilization model, were compared to previous results and showed that the choice of Ksoil-air predictive equation mainly affected the more-volatile pesticides and that the way in which relative humidity was accounted for was the most critical difference.

Robust model predictive control for satellite formation keeping with eccentricity/inclination vector separation

NASA Astrophysics Data System (ADS)

Lim, Yeerang; Jung, Youeyun; Bang, Hyochoong

2018-05-01

This study presents model predictive formation control based on an eccentricity/inclination vector separation strategy. Alternative collision avoidance can be accomplished by using eccentricity/inclination vectors and adding a simple goal function term for optimization process. Real-time control is also achievable with model predictive controller based on convex formulation. Constraint-tightening approach is address as well improve robustness of the controller, and simulation results are presented to verify performance enhancement for the proposed approach.

Measures of Residual Risk with Connections to Regression, Risk Tracking, Surrogate Models, and Ambiguity

DTIC Science & Technology

2015-01-07

vector that helps to manage , predict, and mitigate the risk in the original variable. Residual risk can be exemplified as a quantification of the improved... the random variable of interest is viewed in concert with a related random vector that helps to manage , predict, and mitigate the risk in the original...measures of risk. They view a random variable of interest in concert with an auxiliary random vector that helps to manage , predict and mitigate the risk

A vector-dyadic development of the equations of motion for N-coupled flexible bodies and point masses. [spacecraft trajectories

NASA Technical Reports Server (NTRS)

Frisch, H. P.

1975-01-01

The equations of motion for a system of coupled flexible bodies, rigid bodies, point masses, and symmetric wheels were derived. The equations were cast into a partitioned matrix form in which certain partitions became nontrivial when the effects of flexibility were treated. The equations are shown to contract to the coupled rigid body equations or expand to the coupled flexible body equations all within the same basic framework. Furthermore, the coefficient matrix always has the computationally desirable property of symmetry. Making use of the derived equations, a comparison was made between the equations which described a flexible body model and those which described a rigid body model of the same elastic appendage attached to an arbitrary coupled body system. From the comparison, equivalence relations were developed which defined how the two modeling approaches described identical dynamic effects.

Competitive learning with pairwise constraints.

PubMed

Covões, Thiago F; Hruschka, Eduardo R; Ghosh, Joydeep

2013-01-01

Constrained clustering has been an active research topic since the last decade. Most studies focus on batch-mode algorithms. This brief introduces two algorithms for on-line constrained learning, named on-line linear constrained vector quantization error (O-LCVQE) and constrained rival penalized competitive learning (C-RPCL). The former is a variant of the LCVQE algorithm for on-line settings, whereas the latter is an adaptation of the (on-line) RPCL algorithm to deal with constrained clustering. The accuracy results--in terms of the normalized mutual information (NMI)--from experiments with nine datasets show that the partitions induced by O-LCVQE are competitive with those found by the (batch-mode) LCVQE. Compared with this formidable baseline algorithm, it is surprising that C-RPCL can provide better partitions (in terms of the NMI) for most of the datasets. Also, experiments on a large dataset show that on-line algorithms for constrained clustering can significantly reduce the computational time.

Non-crystallographic nets: characterization and first steps towards a classification.

PubMed

Moreira de Oliveira, Montauban; Eon, Jean Guillaume

2014-05-01

Non-crystallographic (NC) nets are periodic nets characterized by the existence of non-trivial bounded automorphisms. Such automorphisms cannot be associated with any crystallographic symmetry in realizations of the net by crystal structures. It is shown that bounded automorphisms of finite order form a normal subgroup F(N) of the automorphism group of NC nets (N, T). As a consequence, NC nets are unstable nets (they display vertex collisions in any barycentric representation) and, conversely, stable nets are crystallographic nets. The labelled quotient graphs of NC nets are characterized by the existence of an equivoltage partition (a partition of the vertex set that preserves label vectors over edges between cells). A classification of NC nets is proposed on the basis of (i) their relationship to the crystallographic net with a homeomorphic barycentric representation and (ii) the structure of the subgroup F(N).

Influential role of black carbon in the soil-air partitioning of polychlorinated biphenyls (PCBs) in the Indus River Basin, Pakistan.

PubMed

Ali, Usman; Syed, Jabir Hussain; Mahmood, Adeel; Li, Jun; Zhang, Gan; Jones, Kevin C; Malik, Riffat Naseem

2015-09-01

Levels of polychlorinated biphenyls (PCBs) were assessed in surface soils and passive air samples from the Indus River Basin, and the influential role of black carbon (BC) in the soil-air partitioning process was examined. ∑26-PCBs ranged between 0.002-3.03 pg m(-3) and 0.26-1.89 ng g(-1) for passive air and soil samples, respectively. Lower chlorinated (tri- and tetra-) PCBs were abundant in both air (83.9%) and soil (92.1%) samples. Soil-air partitioning of PCBs was investigated through octanol-air partition coefficients (KOA) and black carbon-air partition coefficients (KBC-A). The results of the paired-t test revealed that both models showed statistically significant agreement between measured and predicted model values for the PCB congeners. Ratios of fBCKBC-AδOCT/fOMKOA>5 explicitly suggested the influential role of black carbon in the retention and soil-air partitioning of PCBs. Lower chlorinated PCBs were strongly adsorbed and retained by black carbon during soil-air partitioning because of their dominance at the sampling sites and planarity effect. Copyright © 2015 Elsevier Ltd. All rights reserved.

Anionic Pt in Silicate Melts at Low Oxygen Fugacity: Speciation, Partitioning and Implications for Core Formation Processes on Asteroids

NASA Technical Reports Server (NTRS)

Medard, E.; Martin, A. M.; Righter, K.; Malouta, A.; Lee, C.-T.

2017-01-01

Most siderophile element concentrations in planetary mantles can be explained by metal/ silicate equilibration at high temperature and pressure during core formation. Highly siderophile elements (HSE = Au, Re, and the Pt-group elements), however, usually have higher mantle abundances than predicted by partitioning models, suggesting that their concentrations have been set by late accretion of material that did not equilibrate with the core. The partitioning of HSE at the low oxygen fugacities relevant for core formation is however poorly constrained due to the lack of sufficient experimental constraints to describe the variations of partitioning with key variables like temperature, pressure, and oxygen fugacity. To better understand the relative roles of metal/silicate partitioning and late accretion, we performed a self-consistent set of experiments that parameterizes the influence of oxygen fugacity, temperature and melt composition on the partitioning of Pt, one of the HSE, between metal and silicate melts. The major outcome of this project is the fact that Pt dissolves in an anionic form in silicate melts, causing a dependence of partitioning on oxygen fugacity opposite to that reported in previous studies.

Transverse limited phase space model with Glauber geometry for high-energy nucleus-nucleus collisions

NASA Astrophysics Data System (ADS)

Huang, Ding Wei; Yen, Edward

1989-08-01

We propose a detailed model, combining the concepts from a partition temperature model and wounded nucleon model, to describe high-energy nucleus-nucleus collisions. One partition temperature is associated with collisions at a fixed wounded nucleon number. The (pseudo-) rapidity distributions are calculated and compared with experimental data. Predictions at higher energy are also presented.

Developing QSPR model of gas/particle partition coefficients of neutral poly-/perfluoroalkyl substances

NASA Astrophysics Data System (ADS)

Yuan, Quan; Ma, Guangcai; Xu, Ting; Serge, Bakire; Yu, Haiying; Chen, Jianrong; Lin, Hongjun

2016-10-01

Poly-/perfluoroalkyl substances (PFASs) are a class of synthetic fluorinated organic substances that raise increasing concern because of their environmental persistence, bioaccumulation and widespread presence in various environment media and organisms. PFASs can be released into the atmosphere through both direct and indirect sources, and the gas/particle partition coefficient (KP) is an important parameter that helps us to understand their atmospheric behavior. In this study, we developed a temperature-dependent predictive model for log KP of PFASs and analyzed the molecular mechanism that governs their partitioning equilibrium between gas phase and particle phase. All theoretical computation was carried out at B3LYP/6-31G (d, p) level based on neutral molecular structures by Gaussian 09 program package. The regression model has a good statistical performance and robustness. The application domain has also been defined according to OECD guidance. The mechanism analysis shows that electrostatic interaction and dispersion interaction play the most important role in the partitioning equilibrium. The developed model can be used to predict log KP values of neutral fluorotelomer alcohols and perfluor sulfonamides/sulfonamidoethanols with different substitutions at nitrogen atoms, providing basic data for their ecological risk assessment.

Copula-based prediction of economic movements

NASA Astrophysics Data System (ADS)

García, J. E.; González-López, V. A.; Hirsh, I. D.

2016-06-01

In this paper we model the discretized returns of two paired time series BM&FBOVESPA Dividend Index and BM&FBOVESPA Public Utilities Index using multivariate Markov models. The discretization corresponds to three categories, high losses, high profits and the complementary periods of the series. In technical terms, the maximal memory that can be considered for a Markov model, can be derived from the size of the alphabet and dataset. The number of parameters needed to specify a discrete multivariate Markov chain grows exponentially with the order and dimension of the chain. In this case the size of the database is not large enough for a consistent estimation of the model. We apply a strategy to estimate a multivariate process with an order greater than the order achieved using standard procedures. The new strategy consist on obtaining a partition of the state space which is constructed from a combination, of the partitions corresponding to the two marginal processes and the partition corresponding to the multivariate Markov chain. In order to estimate the transition probabilities, all the partitions are linked using a copula. In our application this strategy provides a significant improvement in the movement predictions.

Multi-color incomplete Cholesky conjugate gradient methods for vector computers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Poole, E.L.

1986-01-01

This research is concerned with the solution on vector computers of linear systems of equations. Ax = b, where A is a large, sparse symmetric positive definite matrix with non-zero elements lying only along a few diagonals of the matrix. The system is solved using the incomplete Cholesky conjugate gradient method (ICCG). Multi-color orderings are used of the unknowns in the linear system to obtain p-color matrices for which a no-fill block ICCG method is implemented on the CYBER 205 with O(N/p) length vector operations in both the decomposition of A and, more importantly, in the forward and back solvesmore » necessary at each iteration of the method. (N is the number of unknowns and p is a small constant). A p-colored matrix is a matrix that can be partitioned into a p x p block matrix where the diagonal blocks are diagonal matrices. The matrix is stored by diagonals and matrix multiplication by diagonals is used to carry out the decomposition of A and the forward and back solves. Additionally, if the vectors across adjacent blocks line up, then some of the overhead associated with vector startups can be eliminated in the matrix vector multiplication necessary at each conjugate gradient iteration. Necessary and sufficient conditions are given to determine which multi-color orderings of the unknowns correspond to p-color matrices, and a process is indicated for choosing multi-color orderings.« less

The RNA Newton polytope and learnability of energy parameters.

PubMed

Forouzmand, Elmirasadat; Chitsaz, Hamidreza

2013-07-01

Computational RNA structure prediction is a mature important problem that has received a new wave of attention with the discovery of regulatory non-coding RNAs and the advent of high-throughput transcriptome sequencing. Despite nearly two score years of research on RNA secondary structure and RNA-RNA interaction prediction, the accuracy of the state-of-the-art algorithms are still far from satisfactory. So far, researchers have proposed increasingly complex energy models and improved parameter estimation methods, experimental and/or computational, in anticipation of endowing their methods with enough power to solve the problem. The output has disappointingly been only modest improvements, not matching the expectations. Even recent massively featured machine learning approaches were not able to break the barrier. Why is that? The first step toward high-accuracy structure prediction is to pick an energy model that is inherently capable of predicting each and every one of known structures to date. In this article, we introduce the notion of learnability of the parameters of an energy model as a measure of such an inherent capability. We say that the parameters of an energy model are learnable iff there exists at least one set of such parameters that renders every known RNA structure to date the minimum free energy structure. We derive a necessary condition for the learnability and give a dynamic programming algorithm to assess it. Our algorithm computes the convex hull of the feature vectors of all feasible structures in the ensemble of a given input sequence. Interestingly, that convex hull coincides with the Newton polytope of the partition function as a polynomial in energy parameters. To the best of our knowledge, this is the first approach toward computing the RNA Newton polytope and a systematic assessment of the inherent capabilities of an energy model. The worst case complexity of our algorithm is exponential in the number of features. However, dimensionality reduction techniques can provide approximate solutions to avoid the curse of dimensionality. We demonstrated the application of our theory to a simple energy model consisting of a weighted count of A-U, C-G and G-U base pairs. Our results show that this simple energy model satisfies the necessary condition for more than half of the input unpseudoknotted sequence-structure pairs (55%) chosen from the RNA STRAND v2.0 database and severely violates the condition for ~ 13%, which provide a set of hard cases that require further investigation. From 1350 RNA strands, the observed 3D feature vector for 749 strands is on the surface of the computed polytope. For 289 RNA strands, the observed feature vector is not on the boundary of the polytope but its distance from the boundary is not more than one. A distance of one essentially means one base pair difference between the observed structure and the closest point on the boundary of the polytope, which need not be the feature vector of a structure. For 171 sequences, this distance is larger than two, and for only 11 sequences, this distance is larger than five. The source code is available on http://compbio.cs.wayne.edu/software/rna-newton-polytope.

STOVL Control Integration Program

NASA Technical Reports Server (NTRS)

Weiss, C.; Mcdowell, P.; Watts, S.

1994-01-01

An integrated flight/propulsion control for an advanced vector thrust supersonic STOVL aircraft, was developed by Pratt & Whitney and McDonnell Douglas Aerospace East. The IFPC design was based upon the partitioning of the global requirements into flight control and propulsion control requirements. To validate the design, aircraft and engine models were also developed for use on a NASA Ames piloted simulator. Different flight control implementations, evaluated for their handling qualities, are documented in the report along with the propulsion control, engine model, and aircraft model.

Predicted Distribution of Visceral Leishmaniasis Vectors (Diptera: Psychodidae; Phlebotominae) in Iran: A Niche Model Study.

PubMed

Hanafi-Bojd, A A; Rassi, Y; Yaghoobi-Ershadi, M R; Haghdoost, A A; Akhavan, A A; Charrahy, Z; Karimi, A

2015-12-01

Visceral leishmaniasis (VL) is an important vector-borne disease in Iran. Till now, Leishmania infantum has been detected from five species of sand flies in the country including Phlebotomus kandelakii, Phlebotomus major s.l., Phlebotomus perfiliewi, Phlebotomus alexandri and Phlebotomus tobbi. Also, Phlebotomus keshishiani was found to be infected with Leishmania parasites. This study aimed at predicting the probable niches and distribution of vectors of visceral leishmaniasis in Iran. Data on spatial distribution studies of sand flies were obtained from Iranian database on sand flies. Sample points were included in data from faunistic studies on sand flies conducted during 1995-2013. MaxEnt software was used to predict the appropriate ecological niches for given species, using climatic and topographical data. Distribution maps were prepared and classified in ArcGIS to find main ecological niches of the vectors and hot spots for VL transmission in Iran. Phlebotomus kandelakii, Ph. major s.l. and Ph. alexandri seem to have played a more important role in VL transmission in Iran, so this study focuses on them. Representations of MaxEnt model for probability of distribution of the studied sand flies showed high contribution of climatological and topographical variables to predict the potential distribution of three vector species. Isothermality was found to be an environmental variable with the highest gain when used in isolation for Ph. kandelakii and Ph. major s.l., while for Ph. alexandri, the most effective variable was precipitation of the coldest quarter. The results of this study present the first prediction on distribution of sand fly vectors of VL in Iran. The predicted distributions were matched with the disease-endemic areas in the country, while it was found that there were some unaffected areas with the potential transmission. More comprehensive studies are recommended on the ecology and vector competence of VL vectors in the country. © 2015 Blackwell Verlag GmbH.

Gauge-invariant expectation values of the energy of a molecule in an electromagnetic field

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mandal, Anirban; Hunt, Katharine L. C.

In this paper, we show that the full Hamiltonian for a molecule in an electromagnetic field can be separated into a molecular Hamiltonian and a field Hamiltonian, both with gauge-invariant expectation values. The expectation value of the molecular Hamiltonian gives physically meaningful results for the energy of a molecule in a time-dependent applied field. In contrast, the usual partitioning of the full Hamiltonian into molecular and field terms introduces an arbitrary gauge-dependent potential into the molecular Hamiltonian and leaves a gauge-dependent form of the Hamiltonian for the field. With the usual partitioning of the Hamiltonian, this same problem of gaugemore » dependence arises even in the absence of an applied field, as we show explicitly by considering a gauge transformation from zero applied field and zero external potentials to zero applied field, but non-zero external vector and scalar potentials. We resolve this problem and also remove the gauge dependence from the Hamiltonian for a molecule in a non-zero applied field and from the field Hamiltonian, by repartitioning the full Hamiltonian. It is possible to remove the gauge dependence because the interaction of the molecular charges with the gauge potential cancels identically with a gauge-dependent term in the usual form of the field Hamiltonian. We treat the electromagnetic field classically and treat the molecule quantum mechanically, but nonrelativistically. Our derivation starts from the Lagrangian for a set of charged particles and an electromagnetic field, with the particle coordinates, the vector potential, the scalar potential, and their time derivatives treated as the variables in the Lagrangian. We construct the full Hamiltonian using a Lagrange multiplier method originally suggested by Dirac, partition this Hamiltonian into a molecular term H{sub m} and a field term H{sub f}, and show that both H{sub m} and H{sub f} have gauge-independent expectation values. Any gauge may be chosen for the calculations; but following our partitioning, the expectation values of the molecular Hamiltonian are identical to those obtained directly in the Coulomb gauge. As a corollary of this result, the power absorbed by a molecule from a time-dependent, applied electromagnetic field is equal to the time derivative of the non-adiabatic term in the molecular energy, in any gauge.« less

Quantum chemical calculations to determine partitioning coefficients for HgCl2 on iron-oxide aerosols.

PubMed

Tacey, Sean A; Xu, Lang; Szilvási, Tibor; Schauer, James J; Mavrikakis, Manos

2018-04-30

Gas-to-particle phase partitioning controls the pathways for oxidized mercury deposition from the atmosphere to the Earth's surface. The propensity of oxidized mercury species to transition between these two phases is described by the partitioning coefficient (K p ). Experimental measurements of K p values for HgCl 2 in the presence of atmospheric aerosols are difficult and time-consuming. Quantum chemical calculations, therefore, offer a promising opportunity to efficiently estimate partitioning coefficients for HgCl 2 on relevant aerosols. In this study, density functional theory (DFT) calculations are used to predict K p values for HgCl 2 on relevant iron-oxide surfaces. The model is first verified using a NaCl(100) surface, showing good agreement between the calculated (2.8) and experimental (29-43) dimensionless partitioning coefficients at room temperature. Then, the methodology is applied to six atmospherically relevant terminations of α-Fe 2 O 3 (0001): OH-Fe-R, (OH) 3 -Fe-R, (OH) 3 -R, O-Fe-R, Fe-O 3 -R, and O 3 -R (where R denotes bulk ordering). The OH-Fe-R termination is predicted to be the most stable under typical atmospheric conditions, and on this surface termination, a dimensionless HgCl 2 K p value of 5.2 × 10 3 at 295 K indicates a strong preference for the particle phase. This work demonstrates DFT as a promising approach to obtain partitioning coefficients, which can lead to improved models for the transport of mercury, as well as for other atmospheric pollutant species, through and between the anthroposphere and troposphere. Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of plasma proteins on sieving of tracer macromolecules in glomerular basement membrane.

PubMed

Lazzara, M J; Deen, W M

2001-11-01

It was found previously that the sieving coefficients of Ficoll and Ficoll sulfate across isolated glomerular basement membrane (GBM) were greatly elevated when BSA was present at physiological levels, and it was suggested that most of this increase might have been the result of steric interactions between BSA and the tracers (5). To test this hypothesis, we extended the theory for the sieving of macromolecular tracers to account for the presence of a second, abundant solute. Increasing the concentration of an abundant solute is predicted to increase the equilibrium partition coefficient of a tracer in a porous or fibrous membrane, thereby increasing the sieving coefficient. The magnitude of this partitioning effect depends on solute size and membrane structure. The osmotic reduction in filtrate velocity caused by an abundant, mostly retained solute will also tend to elevate the tracer sieving coefficient. The osmotic effect alone explained only about one-third of the observed increase in the sieving coefficients of Ficoll and Ficoll sulfate, whereas the effect of BSA on tracer partitioning was sufficient to account for the remainder. At physiological concentrations, predictions for tracer sieving in the presence of BSA were found to be insensitive to the assumed shape of the protein (sphere or prolate spheroid). For protein mixtures, the theoretical effect of 6 g/dl BSA on the partitioning of spherical tracers was indistinguishable from that of 3 g/dl BSA and 3 g/dl IgG. This suggests that for partitioning and sieving studies in vitro, a good experimental model for plasma is a BSA solution with a mass concentration matching that of total plasma protein. The effect of plasma proteins on tracer partitioning is expected to influence sieving not only in isolated GBM but also in intact glomerular capillaries in vivo.

Shear Stress Partitioning in Large Patches of Roughness in the Atmospheric Inertial Sublayer

NASA Technical Reports Server (NTRS)

Gillies, John A.; Nickling, William G.; King, James

2007-01-01

Drag partition measurements were made in the atmospheric inertial sublayer for six roughness configurations made up of solid elements in staggered arrays of different roughness densities. The roughness was in the form of a patch within a large open area and in the shape of an equilateral triangle with 60 m long sides. Measurements were obtained of the total shear stress (tau) acting on the surfaces, the surface shear stress on the ground between the elements (tau(sub S)) and the drag force on the elements for each roughness array. The measurements indicated that tau(sub S) quickly reduced near the leading edge of the roughness compared with tau, and a tau(sub S) minimum occurs at a normalized distance (x/h, where h is element height) of approx. -42 (downwind of the roughness leading edge is negative), then recovers to a relatively stable value. The location of the minimum appears to scale with element height and not roughness density. The force on the elements decreases exponentially with normalized downwind distance and this rate of change scales with the roughness density, with the rate of change increasing as roughness density increases. Average tau(sub S): tau values for the six roughness surfaces scale predictably as a function of roughness density and in accordance with a shear stress partitioning model. The shear stress partitioning model performed very well in predicting the amount of surface shear stress, given knowledge of the stated input parameters for these patches of roughness. As the shear stress partitioning relationship within the roughness appears to come into equilibrium faster for smaller roughness element sizes it would also appear the shear stress partitioning model can be applied with confidence for smaller patches of smaller roughness elements than those used in this experiment.

Model-based recursive partitioning to identify risk clusters for metabolic syndrome and its components: findings from the International Mobility in Aging Study

PubMed Central

Pirkle, Catherine M; Wu, Yan Yan; Zunzunegui, Maria-Victoria; Gómez, José Fernando

2018-01-01

Objective Conceptual models underpinning much epidemiological research on ageing acknowledge that environmental, social and biological systems interact to influence health outcomes. Recursive partitioning is a data-driven approach that allows for concurrent exploration of distinct mixtures, or clusters, of individuals that have a particular outcome. Our aim is to use recursive partitioning to examine risk clusters for metabolic syndrome (MetS) and its components, in order to identify vulnerable populations. Study design Cross-sectional analysis of baseline data from a prospective longitudinal cohort called the International Mobility in Aging Study (IMIAS). Setting IMIAS includes sites from three middle-income countries—Tirana (Albania), Natal (Brazil) and Manizales (Colombia)—and two from Canada—Kingston (Ontario) and Saint-Hyacinthe (Quebec). Participants Community-dwelling male and female adults, aged 64–75 years (n=2002). Primary and secondary outcome measures We apply recursive partitioning to investigate social and behavioural risk factors for MetS and its components. Model-based recursive partitioning (MOB) was used to cluster participants into age-adjusted risk groups based on variabilities in: study site, sex, education, living arrangements, childhood adversities, adult occupation, current employment status, income, perceived income sufficiency, smoking status and weekly minutes of physical activity. Results 43% of participants had MetS. Using MOB, the primary partitioning variable was participant sex. Among women from middle-incomes sites, the predicted proportion with MetS ranged from 58% to 68%. Canadian women with limited physical activity had elevated predicted proportions of MetS (49%, 95% CI 39% to 58%). Among men, MetS ranged from 26% to 41% depending on childhood social adversity and education. Clustering for MetS components differed from the syndrome and across components. Study site was a primary partitioning variable for all components except HDL cholesterol. Sex was important for most components. Conclusion MOB is a promising technique for identifying disease risk clusters (eg, vulnerable populations) in modestly sized samples. PMID:29500203

A new method for the prediction of chatter stability lobes based on dynamic cutting force simulation model and support vector machine

NASA Astrophysics Data System (ADS)

Peng, Chong; Wang, Lun; Liao, T. Warren

2015-10-01

Currently, chatter has become the critical factor in hindering machining quality and productivity in machining processes. To avoid cutting chatter, a new method based on dynamic cutting force simulation model and support vector machine (SVM) is presented for the prediction of chatter stability lobes. The cutting force is selected as the monitoring signal, and the wavelet energy entropy theory is used to extract the feature vectors. A support vector machine is constructed using the MATLAB LIBSVM toolbox for pattern classification based on the feature vectors derived from the experimental cutting data. Then combining with the dynamic cutting force simulation model, the stability lobes diagram (SLD) can be estimated. Finally, the predicted results are compared with existing methods such as zero-order analytical (ZOA) and semi-discretization (SD) method as well as actual cutting experimental results to confirm the validity of this new method.

Predicting the host of influenza viruses based on the word vector.

PubMed

Xu, Beibei; Tan, Zhiying; Li, Kenli; Jiang, Taijiao; Peng, Yousong

2017-01-01

Newly emerging influenza viruses continue to threaten public health. A rapid determination of the host range of newly discovered influenza viruses would assist in early assessment of their risk. Here, we attempted to predict the host of influenza viruses using the Support Vector Machine (SVM) classifier based on the word vector, a new representation and feature extraction method for biological sequences. The results show that the length of the word within the word vector, the sequence type (DNA or protein) and the species from which the sequences were derived for generating the word vector all influence the performance of models in predicting the host of influenza viruses. In nearly all cases, the models built on the surface proteins hemagglutinin (HA) and neuraminidase (NA) (or their genes) produced better results than internal influenza proteins (or their genes). The best performance was achieved when the model was built on the HA gene based on word vectors (words of three-letters long) generated from DNA sequences of the influenza virus. This results in accuracies of 99.7% for avian, 96.9% for human and 90.6% for swine influenza viruses. Compared to the method of sequence homology best-hit searches using the Basic Local Alignment Search Tool (BLAST), the word vector-based models still need further improvements in predicting the host of influenza A viruses.

The photochemical formation and gas-particle partitioning of oxidation products of decamethyl cyclopentasiloxane and decamethyl tetrasiloxane in the atmosphere

NASA Astrophysics Data System (ADS)

Chandramouli, Bharadwaj; Kamens, Richard M.

Decamethyl cyclopentasiloxane (D 5) and decamethyl tetrasiloxane (MD 2M) were injected into a smog chamber containing fine Arizona road dust particles (95% surface area <2.6 μM) and an urban smog atmosphere in the daytime. A photochemical reaction - gas-particle partitioning reaction scheme, was implemented to simulate the formation and gas-particle partitioning of hydroxyl oxidation products of D 5 and MD 2M. This scheme incorporated the reactions of D 5 and MD 2M into an existing urban smog chemical mechanism carbon bond IV and partitioned the products between gas and particle phase by treating gas-particle partitioning as a kinetic process and specifying an uptake and off-gassing rate. A photochemical model PKSS was used to simulate this set of reactions. A Langmuirian partitioning model was used to convert the measured and estimated mass-based partitioning coefficients ( KP) to a molar or volume-based form. The model simulations indicated that >99% of all product silanol formed in the gas-phase partition immediately to particle phase and the experimental data agreed with model predictions. One product, D 4TOH was observed and confirmed for the D 5 reaction and this system was modeled successfully. Experimental data was inadequate for MD 2M reaction products and it is likely that more than one product formed. The model set up a framework into which more reaction and partitioning steps can be easily added.

Predicting the effect of climate change on African trypanosomiasis: integrating epidemiology with parasite and vector biology

PubMed Central

Moore, Sean; Shrestha, Sourya; Tomlinson, Kyle W.; Vuong, Holly

2012-01-01

Climate warming over the next century is expected to have a large impact on the interactions between pathogens and their animal and human hosts. Vector-borne diseases are particularly sensitive to warming because temperature changes can alter vector development rates, shift their geographical distribution and alter transmission dynamics. For this reason, African trypanosomiasis (sleeping sickness), a vector-borne disease of humans and animals, was recently identified as one of the 12 infectious diseases likely to spread owing to climate change. We combine a variety of direct effects of temperature on vector ecology, vector biology and vector–parasite interactions via a disease transmission model and extrapolate the potential compounding effects of projected warming on the epidemiology of African trypanosomiasis. The model predicts that epidemics can occur when mean temperatures are between 20.7°C and 26.1°C. Our model does not predict a large-range expansion, but rather a large shift of up to 60 per cent in the geographical extent of the range. The model also predicts that 46–77 million additional people may be at risk of exposure by 2090. Future research could expand our analysis to include other environmental factors that influence tsetse populations and disease transmission such as humidity, as well as changes to human, livestock and wildlife distributions. The modelling approach presented here provides a framework for using the climate-sensitive aspects of vector and pathogen biology to predict changes in disease prevalence and risk owing to climate change. PMID:22072451

Determining metal origins and availability in fluvial deposits by analysis of geochemical baselines and solid-solution partitioning measurements and modelling.

PubMed

Vijver, Martina G; Spijker, Job; Vink, Jos P M; Posthuma, Leo

2008-12-01

Metals in floodplain soils and sediments (deposits) can originate from lithogenic and anthropogenic sources, and their availability for uptake in biota is hypothesized to depend on both origin and local sediment conditions. In criteria-based environmental risk assessments, these issues are often neglected, implying local risks to be often over-estimated. Current problem definitions in river basin management tend to require a refined, site-specific focus, resulting in a need to address both aspects. This paper focuses on the determination of local environmental availabilities of metals in fluvial deposits by addressing both the origins of the metals and their partitioning over the solid and solution phases. The environmental availability of metals is assumed to be a key force influencing exposure levels in field soils and sediments. Anthropogenic enrichments of Cu, Zn and Pb in top layers could be distinguished from lithogenic background concentrations and described using an aluminium-proxy. Cd in top layers was attributed to anthropogenic enrichment almost fully. Anthropogenic enrichments for Cu and Zn appeared further to be also represented by cold 2M HNO3 extraction of site samples. For Pb the extractions over-estimated the enrichments. Metal partitioning was measured, and measurements were compared to predictions generated by an empirical regression model and by a mechanistic-kinetic model. The partitioning models predicted metal partitioning in floodplain deposits within about one order of magnitude, though a large inter-sample variability was found for Pb.

Response surface methodology optimization of partitioning of xylanase form Aspergillus Niger by metal affinity polymer-salt aqueous two-phase systems.

PubMed

Fakhari, Mohamad Ali; Rahimpour, Farshad; Taran, Mojtaba

2017-09-15

Aqueous two phase affinity partitioning system using metal ligands was applied for partitioning and purification of xylanase produced by Aspergillus Niger. To minimization the number of experiments for the design parameters and develop predictive models for optimization of the purification process, response surface methodology (RSM) with a face-centered central composite design (CCF) has been used. Polyethylene glycol (PEG) 6000 was activated using epichlorohydrin, covalently linked to iminodiacetic acid (IDA), and the specific metal ligand Cu was attached to the polyethylene glycol-iminodiacetic acid (PEG-IDA). The influence of some experimental variables such as PEG (10-18%w/w), sodium sulfate (8-12%), PEG-IDA-Cu 2+ concentration (0-50% w/w of total PEG), pH of system (4-8) and crude enzyme loading (6-18%w/w) on xylanase and total protein partitioning coefficient, enzyme yield and enzyme specific activity were systematically evaluated. Two optimal point with high enzyme partitioning factor 10.97 and yield 79.95 (including 10% PEG, 12% Na 2 SO 4 , 50% ligand, pH 8 and 6% crude enzyme loading) and high specific activity in top phase 42.21 (including 14.73% PEG, 8.02% Na 2 SO 4 , 28.43% ligand, pH 7.7 and 6.08% crude enzyme loading) were attained. The adequacy of the RSM models was verified by a good agreement between experimental and predicted results. Copyright © 2017 Elsevier B.V. All rights reserved.

Vector optical activity in the Weyl semimetal TaAs

DOE PAGES

Norman, M. R.

2015-12-15

Here, it is shown that the Weyl semimetal TaAs can have a significant polar vector contribution to its optical activity. This is quantified by ab initio calculations of the resonant x-ray diffraction at the Ta L1 edge. For the Bragg vector (400), this polar vector contribution to the circular intensity differential between left and right polarized x-rays is predicted to be comparable to that arising from linear dichroism. Implications this result has in regards to optical effects predicted for topological Weyl semimetals are discussed.

Photoproduction of vector mesons in proton-proton ultraperipheral collisions at the CERN Large Hadron Collider

NASA Astrophysics Data System (ADS)

Xie, Ya-Ping; Chen, Xurong

2018-05-01

Photoproduction of vector mesons is computed with dipole model in proton-proton ultraperipheral collisions (UPCs) at the CERN Large Hadron Collider (LHC). The dipole model framework is employed in the calculations of vector mesons production in diffractive processes. Parameters of the bCGC model are refitted with the latest inclusive deep inelastic scattering experimental data. Employing the bCGC model and boosted Gaussian light-cone wave function for vector mesons, we obtain the prediction of rapidity distributions of J/ψ and ψ(2s) mesons in proton-proton ultraperipheral collisions at the LHC. The predictions give a good description of the experimental data of LHCb. Predictions of ϕ and ω mesons are also evaluated in this paper.

HIGH TIME-RESOLVED COMPARISONS FOR IN-DEPTH PROBING OF CMAQ FINE-PARTICLE AND GAS PREDICTIONS

EPA Science Inventory

Input errors affect model predictions. The diurnal behavior of two inputs NHx, which partitions in the inorganic system between gas and particle, and EC, a nonreactive emitted specie, is compared for CMAQ predictions and observations. A monthly average diurnal profile based on ho...

PREDICTING SEDIMENT METAL TOXICITY USING A SEDIMENT BIOTIC LIGAND MODEL: METHODOLOGY AND INITIAL APPLICATION

EPA Science Inventory

An extension of the simultaneously extracted metals/acid-volatile sulfide (SEM/AVS) procedure is presented that predicts the acute and chronic sediment metals effects concentrations. A biotic ligand model (BLM) and a pore water–sediment partitioning model are used to predict the ...

Lysine acetylation sites prediction using an ensemble of support vector machine classifiers.

PubMed

Xu, Yan; Wang, Xiao-Bo; Ding, Jun; Wu, Ling-Yun; Deng, Nai-Yang

2010-05-07

Lysine acetylation is an essentially reversible and high regulated post-translational modification which regulates diverse protein properties. Experimental identification of acetylation sites is laborious and expensive. Hence, there is significant interest in the development of computational methods for reliable prediction of acetylation sites from amino acid sequences. In this paper we use an ensemble of support vector machine classifiers to perform this work. The experimentally determined acetylation lysine sites are extracted from Swiss-Prot database and scientific literatures. Experiment results show that an ensemble of support vector machine classifiers outperforms single support vector machine classifier and other computational methods such as PAIL and LysAcet on the problem of predicting acetylation lysine sites. The resulting method has been implemented in EnsemblePail, a web server for lysine acetylation sites prediction available at http://www.aporc.org/EnsemblePail/. Copyright (c) 2010 Elsevier Ltd. All rights reserved.

Method for enhanced accuracy in predicting peptides using liquid separations or chromatography

DOEpatents

Kangas, Lars J.; Auberry, Kenneth J.; Anderson, Gordon A.; Smith, Richard D.

2006-11-14

A method for predicting the elution time of a peptide in chromatographic and electrophoretic separations by first providing a data set of known elution times of known peptides, then creating a plurality of vectors, each vector having a plurality of dimensions, and each dimension representing the elution time of amino acids present in each of these known peptides from the data set. The elution time of any protein is then be predicted by first creating a vector by assigning dimensional values for the elution time of amino acids of at least one hypothetical peptide and then calculating a predicted elution time for the vector by performing a multivariate regression of the dimensional values of the hypothetical peptide using the dimensional values of the known peptides. Preferably, the multivariate regression is accomplished by the use of an artificial neural network and the elution times are first normalized using a transfer function.

Predictions of malaria vector distribution in Belize based on multispectral satellite data.

PubMed

Roberts, D R; Paris, J F; Manguin, S; Harbach, R E; Woodruff, R; Rejmankova, E; Polanco, J; Wullschleger, B; Legters, L J

1996-03-01

Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.

Predictions of malaria vector distribution in Belize based on multispectral satellite data

NASA Technical Reports Server (NTRS)

Roberts, D. R.; Paris, J. F.; Manguin, S.; Harbach, R. E.; Woodruff, R.; Rejmankova, E.; Polanco, J.; Wullschleger, B.; Legters, L. J.

1996-01-01

Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.

Load partitioning between the bcc-iron matrix and NiAl-type precipitates in a ferritic alloy on multiple length scales

PubMed Central

Sun, Zhiqian; Song, Gian; Sisneros, Thomas A.; Clausen, Bjørn; Pu, Chao; Li, Lin; Gao, Yanfei; Liaw, Peter K.

2016-01-01

An understanding of load sharing among constituent phases aids in designing mechanical properties of multiphase materials. Here we investigate load partitioning between the body-centered-cubic iron matrix and NiAl-type precipitates in a ferritic alloy during uniaxial tensile tests at 364 and 506 °C on multiple length scales by in situ neutron diffraction and crystal plasticity finite element modeling. Our findings show that the macroscopic load-transfer efficiency is not as high as that predicted by the Eshelby model; moreover, it depends on the matrix strain-hardening behavior. We explain the grain-level anisotropic load-partitioning behavior by considering the plastic anisotropy of the matrix and elastic anisotropy of precipitates. We further demonstrate that the partitioned load on NiAl-type precipitates relaxes at 506 °C, most likely through thermally-activated dislocation rearrangement on the microscopic scale. The study contributes to further understanding of load-partitioning characteristics in multiphase materials. PMID:26979660

Load partitioning between the bcc-iron matrix and NiAl-type precipitates in a ferritic alloy on multiple length scales

DOE PAGES

Sun, Zhiqian; Song, Gian; Sisneros, Thomas A.; ...

2016-03-16

An understanding of load sharing among constituent phases aids in designing mechanical properties of multiphase materials. Here we investigate load partitioning between the body-centered-cubic iron matrix and NiAl-type precipitates in a ferritic alloy during uniaxial tensile tests at 364 and 506 C on multiple length scales by in situ neutron diffraction and crystal plasticity finite element modeling. Our findings show that the macroscopic load-transfer efficiency is not as high as that predicted by the Eshelby model; moreover, it depends on the matrix strain-hardening behavior. We explain the grain-level anisotropic load-partitioning behavior by considering the plastic anisotropy of the matrix andmore » elastic anisotropy of precipitates. We further demonstrate that the partitioned load on NiAl-type precipitates relaxes at 506 C, most likely through thermally-activated dislocation rearrangement on the microscopic scale. Furthermore, the study contributes to further understanding of load-partitioning characteristics in multiphase materials.« less

Direct model-based predictive control scheme without cost function for voltage source inverters with reduced common-mode voltage

NASA Astrophysics Data System (ADS)

Kim, Jae-Chang; Moon, Sung-Ki; Kwak, Sangshin

2018-04-01

This paper presents a direct model-based predictive control scheme for voltage source inverters (VSIs) with reduced common-mode voltages (CMVs). The developed method directly finds optimal vectors without using repetitive calculation of a cost function. To adjust output currents with the CMVs in the range of -Vdc/6 to +Vdc/6, the developed method uses voltage vectors, as finite control resources, excluding zero voltage vectors which produce the CMVs in the VSI within ±Vdc/2. In a model-based predictive control (MPC), not using zero voltage vectors increases the output current ripples and the current errors. To alleviate these problems, the developed method uses two non-zero voltage vectors in one sampling step. In addition, the voltage vectors scheduled to be used are directly selected at every sampling step once the developed method calculates the future reference voltage vector, saving the efforts of repeatedly calculating the cost function. And the two non-zero voltage vectors are optimally allocated to make the output current approach the reference current as close as possible. Thus, low CMV, rapid current-following capability and sufficient output current ripple performance are attained by the developed method. The results of a simulation and an experiment verify the effectiveness of the developed method.

Predicting domain-domain interaction based on domain profiles with feature selection and support vector machines

PubMed Central

2010-01-01

Background Protein-protein interaction (PPI) plays essential roles in cellular functions. The cost, time and other limitations associated with the current experimental methods have motivated the development of computational methods for predicting PPIs. As protein interactions generally occur via domains instead of the whole molecules, predicting domain-domain interaction (DDI) is an important step toward PPI prediction. Computational methods developed so far have utilized information from various sources at different levels, from primary sequences, to molecular structures, to evolutionary profiles. Results In this paper, we propose a computational method to predict DDI using support vector machines (SVMs), based on domains represented as interaction profile hidden Markov models (ipHMM) where interacting residues in domains are explicitly modeled according to the three dimensional structural information available at the Protein Data Bank (PDB). Features about the domains are extracted first as the Fisher scores derived from the ipHMM and then selected using singular value decomposition (SVD). Domain pairs are represented by concatenating their selected feature vectors, and classified by a support vector machine trained on these feature vectors. The method is tested by leave-one-out cross validation experiments with a set of interacting protein pairs adopted from the 3DID database. The prediction accuracy has shown significant improvement as compared to InterPreTS (Interaction Prediction through Tertiary Structure), an existing method for PPI prediction that also uses the sequences and complexes of known 3D structure. Conclusions We show that domain-domain interaction prediction can be significantly enhanced by exploiting information inherent in the domain profiles via feature selection based on Fisher scores, singular value decomposition and supervised learning based on support vector machines. Datasets and source code are freely available on the web at http://liao.cis.udel.edu/pub/svdsvm. Implemented in Matlab and supported on Linux and MS Windows. PMID:21034480

ADME evaluation in drug discovery. 1. Applications of genetic algorithms to the prediction of blood-brain partitioning of a large set of drugs.

PubMed

Hou, Tingjun; Xu, Xiaojie

2002-12-01

In this study, the relationships between the brain-blood concentration ratio of 96 structurally diverse compounds with a large number of structurally derived descriptors were investigated. The linear models were based on molecular descriptors that can be calculated for any compound simply from a knowledge of its molecular structure. The linear correlation coefficients of the models were optimized by genetic algorithms (GAs), and the descriptors used in the linear models were automatically selected from 27 structurally derived descriptors. The GA optimizations resulted in a group of linear models with three or four molecular descriptors with good statistical significance. The change of descriptor use as the evolution proceeds demonstrates that the octane/water partition coefficient and the partial negative solvent-accessible surface area multiplied by the negative charge are crucial to brain-blood barrier permeability. Moreover, we found that the predictions using multiple QSPR models from GA optimization gave quite good results in spite of the diversity of structures, which was better than the predictions using the best single model. The predictions for the two external sets with 37 diverse compounds using multiple QSPR models indicate that the best linear models with four descriptors are sufficiently effective for predictive use. Considering the ease of computation of the descriptors, the linear models may be used as general utilities to screen the blood-brain barrier partitioning of drugs in a high-throughput fashion.

An Energy Balance Model to Predict Chemical Partitioning in a Photosynthetic Microbial Mat

NASA Technical Reports Server (NTRS)

Hoehler, Tori M.; Albert, Daniel B.; DesMarais, David J.

2006-01-01

Studies of biosignature formation in photosynthetic microbial mat communities offer potentially useful insights with regards to both solar and extrasolar astrobiology. Biosignature formation in such systems results from the chemical transformation of photosynthetically fixed carbon by accessory microorganisms. This fixed carbon represents a source not only of reducing power, but also energy, to these organisms, so that chemical and energy budgets should be coupled. We tested this hypothesis by applying an energy balance model to predict the fate of photosynthetic productivity under dark, anoxic conditions. Fermentation of photosynthetically fixed carbon is taken to be the only source of energy available to cyanobacteria in the absence of light and oxygen, and nitrogen fixation is the principal energy demand. The alternate fate for fixed carbon is to build cyanobacterial biomass with Redfield C:N ratio. The model predicts that, under completely nitrogen-limited conditions, growth is optimized when 78% of fixed carbon stores are directed into fermentative energy generation, with the remainder allocated to growth. These predictions were compared to measurements made on microbial mats that are known to be both nitrogen-limited and populated by actively nitrogen-fixing cyanobacteria. In these mats, under dark, anoxic conditions, 82% of fixed carbon stores were diverted into fermentation. The close agreement between these independent approaches suggests that energy balance models may provide a quantitative means of predicting chemical partitioning within such systems - an important step towards understanding how biological productivity is ultimately partitioned into biosignature compounds.

The impacts of land use change on malaria vector abundance in a water-limited, highland region of Ethiopia.

PubMed

Stryker, Jody J; Bomblies, Arne

2012-12-01

Changes in land use and climate are expected to alter the risk of malaria transmission in areas where rainfall limits vector abundance. We use a coupled hydrology-entomology model to investigate the effects of land use change on hydrological processes impacting mosquito abundance in a highland village of Ethiopia. Land use affects partitioning of rainfall into infiltration and runoff that reaches small-scale topographic depressions, which constitute the primary breeding habitat of Anopheles arabiensis mosquitoes. A physically based hydrology model isolates hydrological mechanisms by which land use impacts pool formation and persistence, and an agent-based entomology model evaluates the response of mosquito populations. This approach reproduced observed interannual variability in mosquito abundance between the 2009 and 2010 wet seasons. Several scenarios of land cover were then evaluated using the calibrated, field-validated model. Model results show variation in pool persistence and depth, as well as in mosquito abundance, due to land use changes alone. The model showed particular sensitivity to surface roughness, but also to root zone uptake. Scenarios in which land use was modified from agriculture to forest generally resulted in lowest mosquito abundance predictions; classification of the entire domain as rainforest produced a 34% decrease in abundance compared to 2010 results. This study also showed that in addition to vegetation type, spatial proximity of land use change to habitat locations has an impact on mosquito abundance. This modeling approach can be applied to assess impacts of climate and land use conditions that fall outside of the range of previously observed variability.

The Impacts of Land Use Change on Malaria Vector Abundance in a Water-Limited Highland Region of Ethiopia

NASA Astrophysics Data System (ADS)

Stryker, J.; Bomblies, A.

2012-12-01

Changes in land use and climate are expected to alter risk of malaria transmission in areas where rainfall limits vector abundance. We use a coupled hydrology-entomology model to investigate the effects of land use change on hydrological processes impacting mosquito abundance in a highland village of Ethiopia. Land use affects partitioning of rainfall into infiltration and runoff that reaches small-scale topographic depressions, which constitute the primary breeding habitat of Anopheles arabiensis mosquitoes. A physically-based hydrology model isolates hydrological mechanisms by which land use impacts pool formation and persistence, and an agent-based entomology model evaluates the response of mosquito populations. This approach reproduced observed interannual variability in mosquito abundance between the 2009 and 2010 wet seasons. Several scenarios of land cover were then evaluated using the calibrated, field-validated model. Model results show variation in pool persistence and depth, as well as in mosquito abundance, due to land use changes alone. The model showed particular sensitivity to surface roughness, but also to root zone uptake. Scenarios in which land use was modified from agriculture to forest generally resulted in lowest mosquito abundance predictions; classification of the entire domain as rainforest produced a 34% decrease in abundance compared to 2010 results. This study also showed that in addition to vegetation type, spatial proximity of land use change to habitat locations has an impact on mosquito abundance. This modeling approach can be applied to assess impacts of climate and land use conditions that fall outside of the range of previously observed variability.

The neural network classification of false killer whale (Pseudorca crassidens) vocalizations.

PubMed

Murray, S O; Mercado, E; Roitblat, H L

1998-12-01

This study reports the use of unsupervised, self-organizing neural network to categorize the repertoire of false killer whale vocalizations. Self-organizing networks are capable of detecting patterns in their input and partitioning those patterns into categories without requiring that the number or types of categories be predefined. The inputs for the neural networks were two-dimensional characterization of false killer whale vocalization, where each vocalization was characterized by a sequence of short-time measurements of duty cycle and peak frequency. The first neural network used competitive learning, where units in a competitive layer distributed themselves to recognize frequently presented input vectors. This network resulted in classes representing typical patterns in the vocalizations. The second network was a Kohonen feature map which organized the outputs topologically, providing a graphical organization of pattern relationships. The networks performed well as measured by (1) the average correlation between the input vectors and the weight vectors for each category, and (2) the ability of the networks to classify novel vocalizations. The techniques used in this study could easily be applied to other species and facilitate the development of objective, comprehensive repertoire models.

The gas/particle partitioning of nitro- and oxy-polycyclic aromatic hydrocarbons in the atmosphere of northern China

NASA Astrophysics Data System (ADS)

Li, Wei; Shen, Guofeng; Yuan, Chenyi; Wang, Chen; Shen, Huizhong; Jiang, Huai; Zhang, Yanyan; Chen, Yuanchen; Su, Shu; Lin, Nan; Tao, Shu

2016-05-01

The gas/particle partitioning of nitro-polycyclic aromatic hydrocarbons (nPAHs) and oxy-PAHs (oPAHs) is pivotal to estimate their environmental fate. Simultaneously measured atmospheric concentrations of nPAHs and oPAHs in both gaseous and particulate phases at 18 sites in northern China make it possible to investigate their partitioning process in a large region. The gas/particle partitioning coefficients (Kp) in this study were higher than those measured in the emission exhausts. The Kp for most individual nPAHs was higher than those for their corresponding parent PAHs. Generally higher Kp values were found at rural field sites compared to values in the rural villages and cities. Temperature, subcooled liquid-vapor pressure (Pl0) and octanol-air partition coefficient (Koa) were all significantly correlated with Kp. The slope values between log Kp and log Pl0, ranging from - 0.54 to - 0.34, indicate that the equilibrium of gas/particle partitioning might not be reached, which could be also revealed from a positive correlation between log Kp and particulate matter (PM) concentrations. Underestimation commonly exists in all three partitioning models, but the predicted values of Kp from the dual model are closer to the measured Kp for derivative PAHs in northern China.

Systems and methods for knowledge discovery in spatial data

DOEpatents

Obradovic, Zoran; Fiez, Timothy E.; Vucetic, Slobodan; Lazarevic, Aleksandar; Pokrajac, Dragoljub; Hoskinson, Reed L.

2005-03-08

Systems and methods are provided for knowledge discovery in spatial data as well as to systems and methods for optimizing recipes used in spatial environments such as may be found in precision agriculture. A spatial data analysis and modeling module is provided which allows users to interactively and flexibly analyze and mine spatial data. The spatial data analysis and modeling module applies spatial data mining algorithms through a number of steps. The data loading and generation module obtains or generates spatial data and allows for basic partitioning. The inspection module provides basic statistical analysis. The preprocessing module smoothes and cleans the data and allows for basic manipulation of the data. The partitioning module provides for more advanced data partitioning. The prediction module applies regression and classification algorithms on the spatial data. The integration module enhances prediction methods by combining and integrating models. The recommendation module provides the user with site-specific recommendations as to how to optimize a recipe for a spatial environment such as a fertilizer recipe for an agricultural field.

A GRAPH PARTITIONING APPROACH TO PREDICTING PATTERNS IN LATERAL INHIBITION SYSTEMS

PubMed Central

RUFINO FERREIRA, ANA S.; ARCAK, MURAT

2017-01-01

We analyze spatial patterns on networks of cells where adjacent cells inhibit each other through contact signaling. We represent the network as a graph where each vertex represents the dynamics of identical individual cells and where graph edges represent cell-to-cell signaling. To predict steady-state patterns we find equitable partitions of the graph vertices and assign them into disjoint classes. We then use results from monotone systems theory to prove the existence of patterns that are structured in such a way that all the cells in the same class have the same final fate. To study the stability properties of these patterns, we rely on the graph partition to perform a block decomposition of the system. Then, to guarantee stability, we provide a small-gain type criterion that depends on the input-output properties of each cell in the reduced system. Finally, we discuss pattern formation in stochastic models. With the help of a modal decomposition we show that noise can enhance the parameter region where patterning occurs. PMID:29225552

Comprehensive Study of the Evolution of the Gas-Liquid Partitioning of Acetaldehyde during Wine Alcoholic Fermentation.

PubMed

Aguera, Evelyne; Sire, Yannick; Mouret, Jean-Roch; Sablayrolles, Jean-Marie; Farines, Vincent

2018-06-20

Determining the gas-liquid partitioning ( K i ) of acetaldehyde during alcoholic fermentation is an important step in the optimization of fermentation control with the aim of minimizing the accumulation of this compound, which is responsible for the undesired attributes of green apples and fresh-cut grass in wines. In this work, the effects of the main fermentation parameters on the K i of acetaldehyde were assessed. K i values were found to be dependent on the temperature and composition of the medium. A nonlinear correlation between the evolution of the K i and fermentation progress was observed, attributable to the strong retention effect of ethanol at low concentrations, and it was demonstrated that the partitioning of this specific molecule was not influenced by the CO 2 production rate. A model was developed that quantifies the K i of acetaldehyde with a very accurate prediction, as the difference between the observed and predicted values did not exceed 9%.

Field determination and QSPR prediction of equilibrium-status soil/vegetation partition coefficient of PCDD/Fs.

PubMed

Li, Li; Wang, Qiang; Qiu, Xinghua; Dong, Yian; Jia, Shenglan; Hu, Jianxin

2014-07-15

Characterizing pseudo equilibrium-status soil/vegetation partition coefficient KSV, the quotient of respective concentrations in soil and vegetation of a certain substance at remote background areas, is essential in ecological risk assessment, however few previous attempts have been made for field determination and developing validated and reproducible structure-based estimates. In this study, KSV was calculated based on measurements of seventeen 2,3,7,8-substituted PCDD/F congeners in soil and moss (Dicranum angustum), and rouzi grass (Thylacospermum caespitosum) of two background sites, Ny-Ålesund of the Arctic and Zhangmu-Nyalam region of the Tibet Plateau, respectively. By both fugacity modeling and stepwise regression of field data, the air-water partition coefficient (KAW) and aqueous solubility (SW) were identified as the influential physicochemical properties. Furthermore, validated quantitative structure-property relationship (QSPR) model was developed to extrapolate the KSV prediction to all 210 PCDD/F congeners. Molecular polarizability, molecular size and molecular energy demonstrated leading effects on KSV. Copyright © 2014 Elsevier B.V. All rights reserved.

Magnetic BiMn-α phase synthesis prediction: First-principles calculation, thermodynamic modeling and nonequilibrium chemical partitioning

DOE PAGES

Zhou, S. H.; Liu, C.; Yao, Y. X.; ...

2016-04-29

BiMn-α is promising permanent magnet. Due to its peritectic formation feature, there is a synthetic challenge to produce single BiMn-α phase. The objective of this study is to assess driving force for crystalline phase pathways under far-from-equilibrium conditions. First-principles calculations with Hubbard U correction are performed to provide a robust description of the thermodynamic behavior. The energetics associated with various degrees of the chemical partitioning are quantified to predict temperature, magnetic field, and time dependence of the phase selection. By assessing the phase transformation under the influence of the chemical partitioning, temperatures, and cooling rate from our calculations, we suggestmore » that it is possible to synthesize the magnetic BiMn-α compound in a congruent manner by rapid solidification. The external magnetic field enhances the stability of the BiMn-α phase. In conclusion, the compositions of the initial compounds from these highly driven liquids can be far from equilibrium.« less

Estimating Grass-Soil Bioconcentration of Munitions Compounds from Molecular Structure.

PubMed

Torralba Sanchez, Tifany L; Liang, Yuzhen; Di Toro, Dominic M

2017-10-03

A partitioning-based model is presented to estimate the bioconcentration of five munitions compounds and two munition-like compounds in grasses. The model uses polyparameter linear free energy relationships (pp-LFERs) to estimate the partition coefficients between soil organic carbon and interstitial water and between interstitial water and the plant cuticle, a lipid-like plant component. Inputs for the pp-LFERs are a set of numerical descriptors computed from molecular structure only that characterize the molecular properties that determine the interaction with soil organic carbon, interstitial water, and plant cuticle. The model is validated by predicting concentrations measured in the whole plant during independent uptake experiments with a root-mean-square error (log predicted plant concentration-log observed plant concentration) of 0.429. This highlights the dominant role of partitioning between the exposure medium and the plant cuticle in the bioconcentration of these compounds. The pp-LFERs can be used to assess the environmental risk of munitions compounds and munition-like compounds using only their molecular structure as input.

Predicting volume of distribution with decision tree-based regression methods using predicted tissue:plasma partition coefficients.

PubMed

Freitas, Alex A; Limbu, Kriti; Ghafourian, Taravat

2015-01-01

Volume of distribution is an important pharmacokinetic property that indicates the extent of a drug's distribution in the body tissues. This paper addresses the problem of how to estimate the apparent volume of distribution at steady state (Vss) of chemical compounds in the human body using decision tree-based regression methods from the area of data mining (or machine learning). Hence, the pros and cons of several different types of decision tree-based regression methods have been discussed. The regression methods predict Vss using, as predictive features, both the compounds' molecular descriptors and the compounds' tissue:plasma partition coefficients (Kt:p) - often used in physiologically-based pharmacokinetics. Therefore, this work has assessed whether the data mining-based prediction of Vss can be made more accurate by using as input not only the compounds' molecular descriptors but also (a subset of) their predicted Kt:p values. Comparison of the models that used only molecular descriptors, in particular, the Bagging decision tree (mean fold error of 2.33), with those employing predicted Kt:p values in addition to the molecular descriptors, such as the Bagging decision tree using adipose Kt:p (mean fold error of 2.29), indicated that the use of predicted Kt:p values as descriptors may be beneficial for accurate prediction of Vss using decision trees if prior feature selection is applied. Decision tree based models presented in this work have an accuracy that is reasonable and similar to the accuracy of reported Vss inter-species extrapolations in the literature. The estimation of Vss for new compounds in drug discovery will benefit from methods that are able to integrate large and varied sources of data and flexible non-linear data mining methods such as decision trees, which can produce interpretable models. Graphical AbstractDecision trees for the prediction of tissue partition coefficient and volume of distribution of drugs.

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors

PubMed Central

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-01-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of log BB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (log P), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental log BB data had been determined in vivo. In particular, since molecules with log BB > 0.3 cross the blood-brain barrier (BBB) readily while molecules with log BB < −1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the log BB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. PMID:20427217

Prediction of passive blood-brain partitioning: straightforward and effective classification models based on in silico derived physicochemical descriptors.

PubMed

Vilar, Santiago; Chakrabarti, Mayukh; Costanzi, Stefano

2010-06-01

The distribution of compounds between blood and brain is a very important consideration for new candidate drug molecules. In this paper, we describe the derivation of two linear discriminant analysis (LDA) models for the prediction of passive blood-brain partitioning, expressed in terms of logBB values. The models are based on computationally derived physicochemical descriptors, namely the octanol/water partition coefficient (logP), the topological polar surface area (TPSA) and the total number of acidic and basic atoms, and were obtained using a homogeneous training set of 307 compounds, for all of which the published experimental logBB data had been determined in vivo. In particular, since molecules with logBB>0.3 cross the blood-brain barrier (BBB) readily while molecules with logBB<-1 are poorly distributed to the brain, on the basis of these thresholds we derived two distinct models, both of which show a percentage of good classification of about 80%. Notably, the predictive power of our models was confirmed by the analysis of a large external dataset of compounds with reported activity on the central nervous system (CNS) or lack thereof. The calculation of straightforward physicochemical descriptors is the only requirement for the prediction of the logBB of novel compounds through our models, which can be conveniently applied in conjunction with drug design and virtual screenings. Published by Elsevier Inc.

Sarment: Python modules for HMM analysis and partitioning of sequences.

PubMed

Guéguen, Laurent

2005-08-15

Sarment is a package of Python modules for easy building and manipulation of sequence segmentations. It provides efficient implementation of usual algorithms for hidden Markov Model computation, as well as for maximal predictive partitioning. Owing to its very large variety of criteria for computing segmentations, Sarment can handle many kinds of models. Because of object-oriented programming, the results of the segmentation are very easy tomanipulate.

Calculation of the octanol-water partition coefficient of armchair polyhex BN nanotubes

NASA Astrophysics Data System (ADS)

Mohammadinasab, E.; Pérez-Sánchez, H.; Goodarzi, M.

2017-12-01

A predictive model for determination partition coefficient (log P) of armchair polyhex BN nanotubes by using simple descriptors was built. The relationship between the octanol-water log P and quantum chemical descriptors, electric moments, and topological indices of some armchair polyhex BN nanotubes with various lengths and fixed circumference are represented. Based on density functional theory electric moments and physico-chemical properties of those nanotubes are calculated.

Design of a Dual Waveguide Normal Incidence Tube (DWNIT) Utilizing Energy and Modal Methods

NASA Technical Reports Server (NTRS)

Betts, Juan F.; Jones, Michael G. (Technical Monitor)

2002-01-01

This report investigates the partition design of the proposed Dual Waveguide Normal Incidence Tube (DWNIT). Some advantages provided by the DWNIT are (1) Assessment of coupling relationships between resonators in close proximity, (2) Evaluation of "smart liners", (3) Experimental validation for parallel element models, and (4) Investigation of effects of simulated angles of incidence of acoustic waves. Energy models of the two chambers were developed to determine the Sound Pressure Level (SPL) drop across the two chambers, through the use of an intensity transmission function for the chamber's partition. The models allowed the chamber's lengthwise end samples to vary. The initial partition design (2" high, 16" long, 0.25" thick) was predicted to provide at least 160 dB SPL drop across the partition with a compressive model, and at least 240 dB SPL drop with a bending model using a damping loss factor of 0.01. The end chamber sample transmissions coefficients were set to 0.1. Since these results predicted more SPL drop than required, a plate thickness optimization algorithm was developed. The results of the algorithm routine indicated that a plate with the same height and length, but with a thickness of 0.1" and 0.05 structural damping loss, would provide an adequate SPL isolation between the chambers.

Alternative measures of lipophilicity: from octanol-water partitioning to IAM retention.

PubMed

Giaginis, Costas; Tsantili-Kakoulidou, Anna

2008-08-01

This review describes lipophilicity parameters currently used in drug design and QSAR studies. After a short historical overview, the complex nature of lipophilicity as the outcome of polar/nonpolar inter- and intramolecular interactions is analysed and considered as the background for the discussion of the different lipophilicity descriptors. The first part focuses on octanol-water partitioning of neutral and ionisable compounds, evaluates the efficiency of predictions and provides a short description of the experimental methods for the determination of distribution coefficients. A next part is dedicated to reversed-phase chromatographic techniques, HPLC and TLC in lipophilicity assessment. The two methods are evaluated for their efficiency to simulate octanol-water and the progress achieved in the refinement of suitable chromatographic conditions, in particular in the field of HPLC, is outlined. Liposomes as direct models of biological membranes are examined and phospolipophilicity is compared to the traditional lipophilicity concept. Difficulties associated with liposome-water partitioning are discussed. The last part focuses on Immobilised Artificial Membrane (IAM) chromatography as an alternative which combines membrane simulation with rapid measurements. IAM chromatographic retention is compared to octanol-water and liposome-water partitioning as well as to reversed-phase retention and its potential to predict biopartitioning and biological activities is discussed.

Dengue: recent past and future threats

PubMed Central

Rogers, David J.

2015-01-01

This article explores four key questions about statistical models developed to describe the recent past and future of vector-borne diseases, with special emphasis on dengue: (1) How many variables should be used to make predictions about the future of vector-borne diseases?(2) Is the spatial resolution of a climate dataset an important determinant of model accuracy?(3) Does inclusion of the future distributions of vectors affect predictions of the futures of the diseases they transmit?(4) Which are the key predictor variables involved in determining the distributions of vector-borne diseases in the present and future?Examples are given of dengue models using one, five or 10 meteorological variables and at spatial resolutions of from one-sixth to two degrees. Model accuracy is improved with a greater number of descriptor variables, but is surprisingly unaffected by the spatial resolution of the data. Dengue models with a reduced set of climate variables derived from the HadCM3 global circulation model predictions for the 1980s are improved when risk maps for dengue's two main vectors (Aedes aegypti and Aedes albopictus) are also included as predictor variables; disease and vector models are projected into the future using the global circulation model predictions for the 2020s, 2040s and 2080s. The Garthwaite–Koch corr-max transformation is presented as a novel way of showing the relative contribution of each of the input predictor variables to the map predictions. PMID:25688021

Prediction of the partitioning behaviour of proteins in aqueous two-phase systems using only their amino acid composition.

PubMed

Salgado, J Cristian; Andrews, Barbara A; Ortuzar, Maria Fernanda; Asenjo, Juan A

2008-01-18

The prediction of the partition behaviour of proteins in aqueous two-phase systems (ATPS) using mathematical models based on their amino acid composition was investigated. The predictive models are based on the average surface hydrophobicity (ASH). The ASH was estimated by means of models that use the three-dimensional structure of proteins and by models that use only the amino acid composition of proteins. These models were evaluated for a set of 11 proteins with known experimental partition coefficient in four-phase systems: polyethylene glycol (PEG) 4000/phosphate, sulfate, citrate and dextran and considering three levels of NaCl concentration (0.0% w/w, 0.6% w/w and 8.8% w/w). The results indicate that such prediction is feasible even though the quality of the prediction depends strongly on the ATPS and its operational conditions such as the NaCl concentration. The ATPS 0 model which use the three-dimensional structure obtains similar results to those given by previous models based on variables measured in the laboratory. In addition it maintains the main characteristics of the hydrophobic resolution and intrinsic hydrophobicity reported before. Three mathematical models, ATPS I-III, based only on the amino acid composition were evaluated. The best results were obtained by the ATPS I model which assumes that all of the amino acids are completely exposed. The performance of the ATPS I model follows the behaviour reported previously, i.e. its correlation coefficients improve as the NaCl concentration increases in the system and, therefore, the effect of the protein hydrophobicity prevails over other effects such as charge or size. Its best predictive performance was obtained for the PEG/dextran system at high NaCl concentration. An increase in the predictive capacity of at least 54.4% with respect to the models which use the three-dimensional structure of the protein was obtained for that system. In addition, the ATPS I model exhibits high correlation coefficients in that system being higher than 0.88 on average. The ATPS I model exhibited correlation coefficients higher than 0.67 for the rest of the ATPS at high NaCl concentration. Finally, we tested our best model, the ATPS I model, on the prediction of the partition coefficient of the protein invertase. We found that the predictive capacities of the ATPS I model are better in PEG/dextran systems, where the relative error of the prediction with respect to the experimental value is 15.6%.

Grouped fuzzy SVM with EM-based partition of sample space for clustered microcalcification detection.

PubMed

Wang, Huiya; Feng, Jun; Wang, Hongyu

2017-07-20

Detection of clustered microcalcification (MC) from mammograms plays essential roles in computer-aided diagnosis for early stage breast cancer. To tackle problems associated with the diversity of data structures of MC lesions and the variability of normal breast tissues, multi-pattern sample space learning is required. In this paper, a novel grouped fuzzy Support Vector Machine (SVM) algorithm with sample space partition based on Expectation-Maximization (EM) (called G-FSVM) is proposed for clustered MC detection. The diversified pattern of training data is partitioned into several groups based on EM algorithm. Then a series of fuzzy SVM are integrated for classification with each group of samples from the MC lesions and normal breast tissues. From DDSM database, a total of 1,064 suspicious regions are selected from 239 mammography, and the measurement of Accuracy, True Positive Rate (TPR), False Positive Rate (FPR) and EVL = TPR* 1-FPR are 0.82, 0.78, 0.14 and 0.72, respectively. The proposed method incorporates the merits of fuzzy SVM and multi-pattern sample space learning, decomposing the MC detection problem into serial simple two-class classification. Experimental results from synthetic data and DDSM database demonstrate that our integrated classification framework reduces the false positive rate significantly while maintaining the true positive rate.

Estimated effects of temperature on secondary organic aerosol concentrations.

PubMed

Sheehan, P E; Bowman, F M

2001-06-01

The temperature-dependence of secondary organic aerosol (SOA) concentrations is explored using an absorptive-partitioning model under a variety of simplified atmospheric conditions. Experimentally determined partitioning parameters for high yield aromatics are used. Variation of vapor pressures with temperature is assumed to be the main source of temperature effects. Known semivolatile products are used to define a modeling range of vaporization enthalpy of 10-25 kcal/mol-1. The effect of diurnal temperature variations on model predictions for various assumed vaporization enthalpies, precursor emission rates, and primary organic concentrations is explored. Results show that temperature is likely to have a significant influence on SOA partitioning and resulting SOA concentrations. A 10 degrees C decrease in temperature is estimated to increase SOA yields by 20-150%, depending on the assumed vaporization enthalpy. In model simulations, high daytime temperatures tend to reduce SOA concentrations by 16-24%, while cooler nighttime temperatures lead to a 22-34% increase, compared to constant temperature conditions. Results suggest that currently available constant temperature partitioning coefficients do not adequately represent atmospheric SOA partitioning behavior. Air quality models neglecting the temperature dependence of partitioning are expected to underpredict peak SOA concentrations as well as mistime their occurrence.

Trophic diversity in the evolution and community assembly of loricariid catfishes

PubMed Central

2012-01-01

Background The Neotropical catfish family Loricariidae contains over 830 species that display extraordinary variation in jaw morphologies but nonetheless reveal little interspecific variation from a generalized diet of detritus and algae. To investigate this paradox, we collected δ13C and δ15N stable isotope signatures from 649 specimens representing 32 loricariid genera and 82 species from 19 local assemblages distributed across South America. We calculated vectors representing the distance and direction of each specimen relative to the δ15N/δ13C centroid for its local assemblage, and then examined the evolutionary diversification of loricariids across assemblage isotope niche space by regressing the mean vector for each genus in each assemblage onto a phylogeny reconstructed from osteological characters. Results Loricariids displayed a total range of δ15N assemblage centroid deviation spanning 4.9‰, which is within the tissue–diet discrimination range known for Loricariidae, indicating that they feed at a similar trophic level and that δ15N largely reflects differences in their dietary protein content. Total range of δ13C deviation spanned 7.4‰, which is less than the minimum range reported for neotropical river fish communities, suggesting that loricariids selectively assimilate a restricted subset of the full basal resource spectrum available to fishes. Phylogenetic regression of assemblage centroid-standardized vectors for δ15N and δ13C revealed that loricariid genera with allopatric distributions in disjunct river basins partition basal resources in an evolutionarily conserved manner concordant with patterns of jaw morphological specialization and with evolutionary diversification via ecological radiation. Conclusions Trophic partitioning along elemental/nutritional gradients may provide an important mechanism of dietary segregation and evolutionary diversification among loricariids and perhaps other taxonomic groups of apparently generalist detritivores and herbivores. Evolutionary patterns among the Loricariidae show a high degree of trophic niche conservatism, indicating that evolutionary lineage affiliation can be a strong predictor of how basal consumers segregate trophic niche space. PMID:22835218

BIG DATA ANALYTICS AND PRECISION ANIMAL AGRICULTURE SYMPOSIUM: Data to decisions.

PubMed

White, B J; Amrine, D E; Larson, R L

2018-04-14

Big data are frequently used in many facets of business and agronomy to enhance knowledge needed to improve operational decisions. Livestock operations collect data of sufficient quantity to perform predictive analytics. Predictive analytics can be defined as a methodology and suite of data evaluation techniques to generate a prediction for specific target outcomes. The objective of this manuscript is to describe the process of using big data and the predictive analytic framework to create tools to drive decisions in livestock production, health, and welfare. The predictive analytic process involves selecting a target variable, managing the data, partitioning the data, then creating algorithms, refining algorithms, and finally comparing accuracy of the created classifiers. The partitioning of the datasets allows model building and refining to occur prior to testing the predictive accuracy of the model with naive data to evaluate overall accuracy. Many different classification algorithms are available for predictive use and testing multiple algorithms can lead to optimal results. Application of a systematic process for predictive analytics using data that is currently collected or that could be collected on livestock operations will facilitate precision animal management through enhanced livestock operational decisions.

Deep Learning Accurately Predicts Estrogen Receptor Status in Breast Cancer Metabolomics Data.

PubMed

Alakwaa, Fadhl M; Chaudhary, Kumardeep; Garmire, Lana X

2018-01-05

Metabolomics holds the promise as a new technology to diagnose highly heterogeneous diseases. Conventionally, metabolomics data analysis for diagnosis is done using various statistical and machine learning based classification methods. However, it remains unknown if deep neural network, a class of increasingly popular machine learning methods, is suitable to classify metabolomics data. Here we use a cohort of 271 breast cancer tissues, 204 positive estrogen receptor (ER+), and 67 negative estrogen receptor (ER-) to test the accuracies of feed-forward networks, a deep learning (DL) framework, as well as six widely used machine learning models, namely random forest (RF), support vector machines (SVM), recursive partitioning and regression trees (RPART), linear discriminant analysis (LDA), prediction analysis for microarrays (PAM), and generalized boosted models (GBM). DL framework has the highest area under the curve (AUC) of 0.93 in classifying ER+/ER- patients, compared to the other six machine learning algorithms. Furthermore, the biological interpretation of the first hidden layer reveals eight commonly enriched significant metabolomics pathways (adjusted P-value <0.05) that cannot be discovered by other machine learning methods. Among them, protein digestion and absorption and ATP-binding cassette (ABC) transporters pathways are also confirmed in integrated analysis between metabolomics and gene expression data in these samples. In summary, deep learning method shows advantages for metabolomics based breast cancer ER status classification, with both the highest prediction accuracy (AUC = 0.93) and better revelation of disease biology. We encourage the adoption of feed-forward networks based deep learning method in the metabolomics research community for classification.

The R package "sperrorest" : Parallelized spatial error estimation and variable importance assessment for geospatial machine learning

NASA Astrophysics Data System (ADS)

Schratz, Patrick; Herrmann, Tobias; Brenning, Alexander

2017-04-01

Computational and statistical prediction methods such as the support vector machine have gained popularity in remote-sensing applications in recent years and are often compared to more traditional approaches like maximum-likelihood classification. However, the accuracy assessment of such predictive models in a spatial context needs to account for the presence of spatial autocorrelation in geospatial data by using spatial cross-validation and bootstrap strategies instead of their now more widely used non-spatial equivalent. The R package sperrorest by A. Brenning [IEEE International Geoscience and Remote Sensing Symposium, 1, 374 (2012)] provides a generic interface for performing (spatial) cross-validation of any statistical or machine-learning technique available in R. Since spatial statistical models as well as flexible machine-learning algorithms can be computationally expensive, parallel computing strategies are required to perform cross-validation efficiently. The most recent major release of sperrorest therefore comes with two new features (aside from improved documentation): The first one is the parallelized version of sperrorest(), parsperrorest(). This function features two parallel modes to greatly speed up cross-validation runs. Both parallel modes are platform independent and provide progress information. par.mode = 1 relies on the pbapply package and calls interactively (depending on the platform) parallel::mclapply() or parallel::parApply() in the background. While forking is used on Unix-Systems, Windows systems use a cluster approach for parallel execution. par.mode = 2 uses the foreach package to perform parallelization. This method uses a different way of cluster parallelization than the parallel package does. In summary, the robustness of parsperrorest() is increased with the implementation of two independent parallel modes. A new way of partitioning the data in sperrorest is provided by partition.factor.cv(). This function gives the user the possibility to perform cross-validation at the level of some grouping structure. As an example, in remote sensing of agricultural land uses, pixels from the same field contain nearly identical information and will thus be jointly placed in either the test set or the training set. Other spatial sampling resampling strategies are already available and can be extended by the user.

A study of methods of prediction and measurement of the transmission sound through the walls of light aircraft

NASA Technical Reports Server (NTRS)

Forssen, B.; Wang, Y. S.; Crocker, M. J.

1981-01-01

Several aspects were studied. The SEA theory was used to develop a theoretical model to predict the transmission loss through an aircraft window. This work mainly consisted of the writing of two computer programs. One program predicts the sound transmission through a plexiglass window (the case of a single partition). The other program applies to the case of a plexiglass window window with a window shade added (the case of a double partition with an air gap). The sound transmission through a structure was measured in experimental studies using several different methods in order that the accuracy and complexity of all the methods could be compared. Also, the measurements were conducted on the simple model of a fuselage (a cylindrical shell), on a real aircraft fuselage, and on stiffened panels.

A study of methods of prediction and measurement of the transmission sound through the walls of light aircraft

NASA Astrophysics Data System (ADS)

Forssen, B.; Wang, Y. S.; Crocker, M. J.

1981-12-01

Several aspects were studied. The SEA theory was used to develop a theoretical model to predict the transmission loss through an aircraft window. This work mainly consisted of the writing of two computer programs. One program predicts the sound transmission through a plexiglass window (the case of a single partition). The other program applies to the case of a plexiglass window window with a window shade added (the case of a double partition with an air gap). The sound transmission through a structure was measured in experimental studies using several different methods in order that the accuracy and complexity of all the methods could be compared. Also, the measurements were conducted on the simple model of a fuselage (a cylindrical shell), on a real aircraft fuselage, and on stiffened panels.

Hexons from adenovirus serotypes 5 and 48 differentially protect adenovirus vectors from neutralization by mouse and human serum

PubMed Central

Harmon, Andrew W.; Moitra, Rituparna; Xu, Zhili

2018-01-01

Adenovirus vectors are widely used in gene therapy clinical trials, and preclinical studies with these vectors are often conducted in mice. It is therefore critical to understand whether mouse studies adequately predict the behavior of adenovirus vectors in humans. The most commonly-used adenovirus vectors are derived from adenovirus serotype 5 (Ad5). The Ad5 hexon protein can bind coagulation factor X (FX), and binding of FX has a major impact on vector interactions with other blood proteins. In mouse serum, FX protects Ad5 vectors from neutralization by natural antibodies and complement. In the current study, we similarly find that human FX inhibits neutralization of Ad5 vectors by human serum, and this finding is consistent among individual human sera. We show that human IgM and human IgG can each induce complement-mediated neutralization when Ad5 vectors are not protected by FX. Although mouse and human serum had similar effects on Ad5 vectors, we found that this was not true for a chimeric Ad5 vector that incorporated hexon regions from adenovirus serotype 48. Interestingly, this hexon-chimeric vector was neutralized by human serum, but not by mouse serum. These findings indicate that studies in mouse serum accurately predict the behavior of Ad5 vectors in human serum, but mouse serum is not an accurate model system for all adenovirus vectors. PMID:29401488

Patterns and multi-scale drivers of phytoplankton species richness in temperate peri-urban lakes.

PubMed

Catherine, Arnaud; Selma, Maloufi; Mouillot, David; Troussellier, Marc; Bernard, Cécile

2016-07-15

Local species richness (SR) is a key characteristic affecting ecosystem functioning. Yet, the mechanisms regulating phytoplankton diversity in freshwater ecosystems are not fully understood, especially in peri-urban environments where anthropogenic pressures strongly impact the quality of aquatic ecosystems. To address this issue, we sampled the phytoplankton communities of 50 lakes in the Paris area (France) characterized by a large gradient of physico-chemical and catchment-scale characteristics. We used large phytoplankton datasets to describe phytoplankton diversity patterns and applied a machine-learning algorithm to test the degree to which species richness patterns are potentially controlled by environmental factors. Selected environmental factors were studied at two scales: the lake-scale (e.g. nutrients concentrations, water temperature, lake depth) and the catchment-scale (e.g. catchment, landscape and climate variables). Then, we used a variance partitioning approach to evaluate the interaction between lake-scale and catchment-scale variables in explaining local species richness. Finally, we analysed the residuals of predictive models to identify potential vectors of improvement of phytoplankton species richness predictive models. Lake-scale and catchment-scale drivers provided similar predictive accuracy of local species richness (R(2)=0.458 and 0.424, respectively). Both models suggested that seasonal temperature variations and nutrient supply strongly modulate local species richness. Integrating lake- and catchment-scale predictors in a single predictive model did not provide increased predictive accuracy; therefore suggesting that the catchment-scale model probably explains observed species richness variations through the impact of catchment-scale variables on in-lake water quality characteristics. Models based on catchment characteristics, which include simple and easy to obtain variables, provide a meaningful way of predicting phytoplankton species richness in temperate lakes. This approach may prove useful and cost-effective for the management and conservation of aquatic ecosystems. Copyright © 2016 Elsevier B.V. All rights reserved.

Virus Database and Online Inquiry System Based on Natural Vectors.

PubMed

Dong, Rui; Zheng, Hui; Tian, Kun; Yau, Shek-Chung; Mao, Weiguang; Yu, Wenping; Yin, Changchuan; Yu, Chenglong; He, Rong Lucy; Yang, Jie; Yau, Stephen St

2017-01-01

We construct a virus database called VirusDB (http://yaulab.math.tsinghua.edu.cn/VirusDB/) and an online inquiry system to serve people who are interested in viral classification and prediction. The database stores all viral genomes, their corresponding natural vectors, and the classification information of the single/multiple-segmented viral reference sequences downloaded from National Center for Biotechnology Information. The online inquiry system serves the purpose of computing natural vectors and their distances based on submitted genomes, providing an online interface for accessing and using the database for viral classification and prediction, and back-end processes for automatic and manual updating of database content to synchronize with GenBank. Submitted genomes data in FASTA format will be carried out and the prediction results with 5 closest neighbors and their classifications will be returned by email. Considering the one-to-one correspondence between sequence and natural vector, time efficiency, and high accuracy, natural vector is a significant advance compared with alignment methods, which makes VirusDB a useful database in further research.

Binary recursive partitioning: background, methods, and application to psychology.

PubMed

Merkle, Edgar C; Shaffer, Victoria A

2011-02-01

Binary recursive partitioning (BRP) is a computationally intensive statistical method that can be used in situations where linear models are often used. Instead of imposing many assumptions to arrive at a tractable statistical model, BRP simply seeks to accurately predict a response variable based on values of predictor variables. The method outputs a decision tree depicting the predictor variables that were related to the response variable, along with the nature of the variables' relationships. No significance tests are involved, and the tree's 'goodness' is judged based on its predictive accuracy. In this paper, we describe BRP methods in a detailed manner and illustrate their use in psychological research. We also provide R code for carrying out the methods.

Ductility normalized-strainrange partitioning life relations for creep-fatigue life predictions

NASA Technical Reports Server (NTRS)

Halford, G. R.; Saltsman, J. F.; Hirschberg, M. H.

1977-01-01

Procedures based on Strainrange Partitioning (SRP) are presented for estimating the effects of environment and other influences on the high temperature, low cycle, creep fatigue resistance of alloys. It is proposed that the plastic and creep, ductilities determined from conventional tensile and creep rupture tests conducted in the environment of interest be used in a set of ductility normalized equations for making a first order approximation of the four SRP inelastic strainrange life relations. Different levels of sophistication in the application of the procedures are presented by means of illustrative examples with several high temperature alloys. Predictions of cyclic lives generally agree with observed lives within factors of three.

High Throughput pharmacokinetic modeling using computationally predicted parameter values: dissociation constants (TDS)

EPA Science Inventory

Estimates of the ionization association and dissociation constant (pKa) are vital to modeling the pharmacokinetic behavior of chemicals in vivo. Methodologies for the prediction of compound sequestration in specific tissues using partition coefficients require a parameter that ch...

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes.

PubMed

Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

2017-10-03

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes.

Chromosome preference of disease genes and vectorization for the prediction of non-coding disease genes

PubMed Central

Peng, Hui; Lan, Chaowang; Liu, Yuansheng; Liu, Tao; Blumenstein, Michael; Li, Jinyan

2017-01-01

Disease-related protein-coding genes have been widely studied, but disease-related non-coding genes remain largely unknown. This work introduces a new vector to represent diseases, and applies the newly vectorized data for a positive-unlabeled learning algorithm to predict and rank disease-related long non-coding RNA (lncRNA) genes. This novel vector representation for diseases consists of two sub-vectors, one is composed of 45 elements, characterizing the information entropies of the disease genes distribution over 45 chromosome substructures. This idea is supported by our observation that some substructures (e.g., the chromosome 6 p-arm) are highly preferred by disease-related protein coding genes, while some (e.g., the 21 p-arm) are not favored at all. The second sub-vector is 30-dimensional, characterizing the distribution of disease gene enriched KEGG pathways in comparison with our manually created pathway groups. The second sub-vector complements with the first one to differentiate between various diseases. Our prediction method outperforms the state-of-the-art methods on benchmark datasets for prioritizing disease related lncRNA genes. The method also works well when only the sequence information of an lncRNA gene is known, or even when a given disease has no currently recognized long non-coding genes. PMID:29108274

Evaluation of the impacts of climate change on disease vectors through ecological niche modelling.

PubMed

Carvalho, B M; Rangel, E F; Vale, M M

2017-08-01

Vector-borne diseases are exceptionally sensitive to climate change. Predicting vector occurrence in specific regions is a challenge that disease control programs must meet in order to plan and execute control interventions and climate change adaptation measures. Recently, an increasing number of scientific articles have applied ecological niche modelling (ENM) to study medically important insects and ticks. With a myriad of available methods, it is challenging to interpret their results. Here we review the future projections of disease vectors produced by ENM, and assess their trends and limitations. Tropical regions are currently occupied by many vector species; but future projections indicate poleward expansions of suitable climates for their occurrence and, therefore, entomological surveillance must be continuously done in areas projected to become suitable. The most commonly applied methods were the maximum entropy algorithm, generalized linear models, the genetic algorithm for rule set prediction, and discriminant analysis. Lack of consideration of the full-known current distribution of the target species on models with future projections has led to questionable predictions. We conclude that there is no ideal 'gold standard' method to model vector distributions; researchers are encouraged to test different methods for the same data. Such practice is becoming common in the field of ENM, but still lags behind in studies of disease vectors.

Soft computing techniques toward modeling the water supplies of Cyprus.

PubMed

Iliadis, L; Maris, F; Tachos, S

2011-10-01

This research effort aims in the application of soft computing techniques toward water resources management. More specifically, the target is the development of reliable soft computing models capable of estimating the water supply for the case of "Germasogeia" mountainous watersheds in Cyprus. Initially, ε-Regression Support Vector Machines (ε-RSVM) and fuzzy weighted ε-RSVMR models have been developed that accept five input parameters. At the same time, reliable artificial neural networks have been developed to perform the same job. The 5-fold cross validation approach has been employed in order to eliminate bad local behaviors and to produce a more representative training data set. Thus, the fuzzy weighted Support Vector Regression (SVR) combined with the fuzzy partition has been employed in an effort to enhance the quality of the results. Several rational and reliable models have been produced that can enhance the efficiency of water policy designers. Copyright © 2011 Elsevier Ltd. All rights reserved.

Predictiveness of Disease Risk in a Global Outreach Tourist Setting in Thailand Using Meteorological Data and Vector-Borne Disease Incidences

PubMed Central

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-01-01

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate. PMID:25325356

Predictiveness of disease risk in a global outreach tourist setting in Thailand using meteorological data and vector-borne disease incidences.

PubMed

Ninphanomchai, Suwannapa; Chansang, Chitti; Hii, Yien Ling; Rocklöv, Joacim; Kittayapong, Pattamaporn

2014-10-16

Dengue and malaria are vector-borne diseases and major public health problems worldwide. Changes in climatic factors influence incidences of these diseases. The objective of this study was to investigate the relationship between vector-borne disease incidences and meteorological data, and hence to predict disease risk in a global outreach tourist setting. The retrospective data of dengue and malaria incidences together with local meteorological factors (temperature, rainfall, humidity) registered from 2001 to 2011 on Koh Chang, Thailand were used in this study. Seasonal distribution of disease incidences and its correlation with local climatic factors were analyzed. Seasonal patterns in disease transmission differed between dengue and malaria. Monthly meteorological data and reported disease incidences showed good predictive ability of disease transmission patterns. These findings provide a rational basis for identifying the predictive ability of local meteorological factors on disease incidence that may be useful for the implementation of disease prevention and vector control programs on the tourism island, where climatic factors fluctuate.

Pulse Vector-Excitation Speech Encoder

NASA Technical Reports Server (NTRS)

Davidson, Grant; Gersho, Allen

1989-01-01

Proposed pulse vector-excitation speech encoder (PVXC) encodes analog speech signals into digital representation for transmission or storage at rates below 5 kilobits per second. Produces high quality of reconstructed speech, but with less computation than required by comparable speech-encoding systems. Has some characteristics of multipulse linear predictive coding (MPLPC) and of code-excited linear prediction (CELP). System uses mathematical model of vocal tract in conjunction with set of excitation vectors and perceptually-based error criterion to synthesize natural-sounding speech.

Using remote sensing, ecological niche modeling, and Geographic Information Systems for Rift Valley fever risk assessment in the United States

NASA Astrophysics Data System (ADS)

Tedrow, Christine Atkins

The primary goal in this study was to explore remote sensing, ecological niche modeling, and Geographic Information Systems (GIS) as aids in predicting candidate Rift Valley fever (RVF) competent vector abundance and distribution in Virginia, and as means of estimating where risk of establishment in mosquitoes and risk of transmission to human populations would be greatest in Virginia. A second goal in this study was to determine whether the remotely-sensed Normalized Difference Vegetation Index (NDVI) can be used as a proxy variable of local conditions for the development of mosquitoes to predict mosquito species distribution and abundance in Virginia. As part of this study, a mosquito surveillance database was compiled to archive the historical patterns of mosquito species abundance in Virginia. In addition, linkages between mosquito density and local environmental and climatic patterns were spatially and temporally examined. The present study affirms the potential role of remote sensing imagery for species distribution prediction, and it demonstrates that ecological niche modeling is a valuable predictive tool to analyze the distributions of populations. The MaxEnt ecological niche modeling program was used to model predicted ranges for potential RVF competent vectors in Virginia. The MaxEnt model was shown to be robust, and the candidate RVF competent vector predicted distribution map is presented. The Normalized Difference Vegetation Index (NDVI) was found to be the most useful environmental-climatic variable to predict mosquito species distribution and abundance in Virginia. However, these results indicate that a more robust prediction is obtained by including other environmental-climatic factors correlated to mosquito densities (e.g., temperature, precipitation, elevation) with NDVI. The present study demonstrates that remote sensing and GIS can be used with ecological niche and risk modeling methods to estimate risk of virus establishment in mosquitoes and transmission to humans. Maps delineating the geographic areas in Virginia with highest risk for RVF establishment in mosquito populations and RVF disease transmission to human populations were generated in a GIS using human, domestic animal, and white-tailed deer population estimates and the MaxEnt potential RVF competent vector species distribution prediction. The candidate RVF competent vector predicted distribution and RVF risk maps presented in this study can help vector control agencies and public health officials focus Rift Valley fever surveillance efforts in geographic areas with large co-located populations of potential RVF competent vectors and human, domestic animal, and wildlife hosts. Keywords. Rift Valley fever, risk assessment, Ecological Niche Modeling, MaxEnt, Geographic Information System, remote sensing, Pearson's Product-Moment Correlation Coefficient, vectors, mosquito distribution, mosquito density, mosquito surveillance, United States, Virginia, domestic animals, white-tailed deer, ArcGIS

Prediction of shear critical behavior of high-strength reinforced concrete columns using finite element methods

NASA Astrophysics Data System (ADS)

Alrasyid, Harun; Safi, Fahrudin; Iranata, Data; Chen-Ou, Yu

2017-11-01

This research shows the prediction of shear behavior of High-Strength Reinforced Concrete Columns using Finite-Element Method. The experimental data of nine half scale high-strength reinforced concrete were selected. These columns using specified concrete compressive strength of 70 MPa, specified yield strength of longitudinal and transverse reinforcement of 685 and 785 MPa, respectively. The VecTor2 finite element software was used to simulate the shear critical behavior of these columns. The combination axial compression load and monotonic loading were applied at this prediction. It is demonstrated that VecTor2 finite element software provides accurate prediction of load-deflection up to peak at applied load, but provide similar behavior at post peak load. The shear strength prediction provide by VecTor 2 are slightly conservative compare to test result.

Orbifold Schur index and IR formula

NASA Astrophysics Data System (ADS)

Imamura, Yosuke

2018-04-01

We discuss an orbifold version of the Schur index defined as the supersymmetric partition function in S^3/{Z}_n×{S}^1. We first give a general formula for Lagrangian theories obtained by the localization technique, and then suggest a generalization of the Cordova and Shao IR formula. We confirm that the generalized IR formula gives the correct answer for systems with free hypermultiplets if we tune the background fields so that they are invariant under the orbifold action. Unfortunately, we find disagreement for theories with dynamical vector multiplets.

Predictions of the physicochemical properties of amino acid side chain analogs using molecular simulation.

PubMed

Ahmed, Alauddin; Sandler, Stanley I

2016-03-07

A candidate drug compound is released for clinical trails (in vivo activity) only if its physicochemical properties meet desirable bioavailability and partitioning criteria. Amino acid side chain analogs play vital role in the functionalities of protein and peptides and as such are important in drug discovery. We demonstrate here that the predictions of solvation free energies in water, in 1-octanol, and self-solvation free energies computed using force field-based expanded ensemble molecular dynamics simulation provide good accuracy compared to existing empirical and semi-empirical methods. These solvation free energies are then, as shown here, used for the prediction of a wide range of physicochemical properties important in the assessment of bioavailability and partitioning of compounds. In particular, we consider here the vapor pressure, the solubility in both water and 1-octanol, and the air-water, air-octanol, and octanol-water partition coefficients of amino acid side chain analogs computed from the solvation free energies. The calculated solvation free energies using different force fields are compared against each other and with available experimental data. The protocol here can also be used for a newly designed drug and other molecules where force field parameters and charges are obtained from density functional theory.

High Pressure/Temperature Metal Silicate Partitioning of Tungsten

NASA Technical Reports Server (NTRS)

Shofner, G. A.; Danielson, L.; Righter, K.; Campbell, A. J.

2010-01-01

The behavior of chemical elements during metal/silicate segregation and their resulting distribution in Earth's mantle and core provide insight into core formation processes. Experimental determination of partition coefficients allows calculations of element distributions that can be compared to accepted values of element abundances in the silicate (mantle) and metallic (core) portions of the Earth. Tungsten (W) is a moderately siderophile element and thus preferentially partitions into metal versus silicate under many planetary conditions. The partitioning behavior has been shown to vary with temperature, silicate composition, oxygen fugacity, and pressure. Most of the previous work on W partitioning has been conducted at 1-bar conditions or at relatively low pressures, i.e. <10 GPa, and in two cases at or near 20 GPa. According to those data, the stronger influences on the distribution coefficient of W are temperature, composition, and oxygen fugacity with a relatively slight influence in pressure. Predictions based on extrapolation of existing data and parameterizations suggest an increased pressured dependence on metal/ silicate partitioning of W at higher pressures 5. However, the dependence on pressure is not as well constrained as T, fO2, and silicate composition. This poses a problem because proposed equilibration pressures for core formation range from 27 to 50 GPa, falling well outside the experimental range, therefore requiring exptrapolation of a parametereized model. Higher pressure data are needed to improve our understanding of W partitioning at these more extreme conditions.

A possibilistic approach to clustering

NASA Technical Reports Server (NTRS)

Krishnapuram, Raghu; Keller, James M.

1993-01-01

Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering methods in that total commitment of a vector to a given class is not required at each image pattern recognition iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from the 'Fuzzy C-Means' (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Recently, we cast the clustering problem into the framework of possibility theory using an approach in which the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Incoherent vector mesons production in PbPb ultraperipheral collisions at the LHC

NASA Astrophysics Data System (ADS)

Xie, Ya-Ping; Chen, Xurong

2017-03-01

The incoherent rapidity distributions of vector mesons are computed in dipole model in PbPb ultraperipheral collisions at the CERN Large Hadron Collider (LHC). The IIM model fitted from newer data is employed in the dipole amplitude. The Boosted Gaussian and Gaus-LC wave functions for vector mesons are implemented in the calculations as well. Predictions for the J / ψ, ψ (2 s), ρ and ϕ incoherent rapidity distributions are evaluated and compared with experimental data and other theoretical predictions in this paper. We obtain closer predictions of the incoherent rapidity distributions for J / ψ than previous calculations in the IIM model.

More About Vector Adaptive/Predictive Coding Of Speech

NASA Technical Reports Server (NTRS)

Jedrey, Thomas C.; Gersho, Allen

1992-01-01

Report presents additional information about digital speech-encoding and -decoding system described in "Vector Adaptive/Predictive Encoding of Speech" (NPO-17230). Summarizes development of vector adaptive/predictive coding (VAPC) system and describes basic functions of algorithm. Describes refinements introduced enabling receiver to cope with errors. VAPC algorithm implemented in integrated-circuit coding/decoding processors (codecs). VAPC and other codecs tested under variety of operating conditions. Tests designed to reveal effects of various background quiet and noisy environments and of poor telephone equipment. VAPC found competitive with and, in some respects, superior to other 4.8-kb/s codecs and other codecs of similar complexity.

Evidence that implicit assumptions of ‘no evolution’ of disease vectors in changing environments can be violated on a rapid timescale

PubMed Central

Egizi, Andrea; Fefferman, Nina H.; Fonseca, Dina M.

2015-01-01

Projected impacts of climate change on vector-borne disease dynamics must consider many variables relevant to hosts, vectors and pathogens, including how altered environmental characteristics might affect the spatial distributions of vector species. However, many predictive models for vector distributions consider their habitat requirements to be fixed over relevant time-scales, when they may actually be capable of rapid evolutionary change and even adaptation. We examine the genetic signature of a spatial expansion by an invasive vector into locations with novel temperature conditions compared to its native range as a proxy for how existing vector populations may respond to temporally changing habitat. Specifically, we compare invasions into different climate ranges and characterize the importance of selection from the invaded habitat. We demonstrate that vector species can exhibit evolutionary responses (altered allelic frequencies) to a temperature gradient in as little as 7–10 years even in the presence of high gene flow, and further, that this response varies depending on the strength of selection. We interpret these findings in the context of climate change predictions for vector populations and emphasize the importance of incorporating vector evolution into models of future vector-borne disease dynamics. PMID:25688024

Density-dependent host choice by disease vectors: epidemiological implications of the ideal free distribution.

PubMed

Basáñez, María-Gloria; Razali, Karina; Renz, Alfons; Kelly, David

2007-03-01

The proportion of vector blood meals taken on humans (the human blood index, h) appears as a squared term in classical expressions of the basic reproduction ratio (R(0)) for vector-borne infections. Consequently, R(0) varies non-linearly with h. Estimates of h, however, constitute mere snapshots of a parameter that is predicted, from evolutionary theory, to vary with vector and host abundance. We test this prediction using a population dynamics model of river blindness assuming that, before initiation of vector control or chemotherapy, recorded measures of vector density and human infection accurately represent endemic equilibrium. We obtain values of h that satisfy the condition that the effective reproduction ratio (R(e)) must equal 1 at equilibrium. Values of h thus obtained decrease with vector density, decrease with the vector:human ratio and make R(0) respond non-linearly rather than increase linearly with vector density. We conclude that if vectors are less able to obtain human blood meals as their density increases, antivectorial measures may not lead to proportional reductions in R(0) until very low vector levels are achieved. Density dependence in the contact rate of infectious diseases transmitted by insects may be an important non-linear process with implications for their epidemiology and control.

Gas-particle partitioning of alcohol vapors on organic aerosols.

PubMed

Chan, Lap P; Lee, Alex K Y; Chan, Chak K

2010-01-01

Single particle levitation using an electrodynamic balance (EDB) has been found to give accurate and direct hygroscopic measurements (gas-particle partitioning of water) for a number of inorganic and organic aerosol systems. In this paper, we extend the use of an EDB to examine the gas-particle partitioning of volatile to semivolatile alcohols, including methanol, n-butanol, n-octanol, and n-decanol, on levitated oleic acid particles. The measured K(p) agreed with Pankow's absorptive partitioning model. At high n-butanol vapor concentrations (10(3) ppm), the uptake of n-butanol reduced the average molecular-weight of the oleic acid particle appreciably and hence increased the K(p) according to Pankow's equation. Moreover, the hygroscopicity of mixed oleic acid/n-butanol particles was higher than the predictions given by the UNIFAC model (molecular group contribution method) and the ZSR equation (additive rule), presumably due to molecular interactions between the chemical species in the mixed particles. Despite the high vapor concentrations used, these findings warrant further research on the partitioning of atmospheric organic vapors (K(p)) near sources and how collectively they affect the hygroscopic properties of organic aerosols.

Measuring the critical band for speech.

PubMed

Healy, Eric W; Bacon, Sid P

2006-02-01

The current experiments were designed to measure the frequency resolution employed by listeners during the perception of everyday sentences. Speech bands having nearly vertical filter slopes and narrow bandwidths were sharply partitioned into various numbers of equal log- or ERBN-width subbands. The temporal envelope from each partition was used to amplitude modulate a corresponding band of low-noise noise, and the modulated carriers were combined and presented to normal-hearing listeners. Intelligibility increased and reached asymptote as the number of partitions increased. In the mid- and high-frequency regions of the speech spectrum, the partition bandwidth corresponding to asymptotic performance matched current estimates of psychophysical tuning across a number of conditions. These results indicate that, in these regions, the critical band for speech matches the critical band measured using traditional psychoacoustic methods and nonspeech stimuli. However, in the low-frequency region, partition bandwidths at asymptote were somewhat narrower than would be predicted based upon psychophysical tuning. It is concluded that, overall, current estimates of psychophysical tuning represent reasonably well the ability of listeners to extract spectral detail from running speech.

Summer-winter concentrations and gas-particle partitioning of short chain chlorinated paraffins in the atmosphere of an urban setting.

PubMed

Wang, Thanh; Han, Shanlong; Yuan, Bo; Zeng, Lixi; Li, Yingming; Wang, Yawei; Jiang, Guibin

2012-12-01

Short chain chlorinated paraffins (SCCPs) are semi-volatile chemicals that are considered persistent in the environment, potential toxic and subject to long-range transport. This study investigates the concentrations and gas-particle partitioning of SCCPs at an urban site in Beijing during summer and wintertime. The total atmospheric SCCP levels ranged 1.9-33.0 ng/m(3) during wintertime. Significantly higher levels were found during the summer (range 112-332 ng/m(3)). The average fraction of total SCCPs in the particle phase (ϕ) was 0.67 during wintertime but decreased significantly during the summer (ϕ = 0.06). The ten and eleven carbon chain homologues with five to eight chlorine atoms were the predominant SCCP formula groups in air. Significant linear correlations were found between the gas-particle partition coefficients and the predicted subcooled vapor pressures and octanol-air partition coefficients. The gas-particle partitioning of SCCPs was further investigated and compared with both the Junge-Pankow adsorption and K(oa)-based absorption models. Copyright © 2012 Elsevier Ltd. All rights reserved.

Evaluation on subcellular partitioning and biodynamics of pulse copper toxicity in tilapia reveals impacts of a major environmental disturbance.

PubMed

Ju, Yun-Ru; Yang, Ying-Fei; Tsai, Jeng-Wei; Cheng, Yi-Hsien; Chen, Wei-Yu; Liao, Chung-Min

2017-07-01

Fluctuation exposure of trace metal copper (Cu) is ubiquitous in aquatic environments. The purpose of this study was to investigate the impacts of chronically pulsed exposure on biodynamics and subcellular partitioning of Cu in freshwater tilapia (Oreochromis mossambicus). Long-term 28-day pulsed Cu exposure experiments were performed to explore subcellular partitioning and toxicokinetics/toxicodynamics of Cu in tilapia. Subcellular partitioning linking with a metal influx scheme was used to estimate detoxification and elimination rates. A biotic ligand model-based damage assessment model was used to take into account environmental effects and biological mechanisms of Cu toxicity. We demonstrated that the probability causing 50% of susceptibility risk in response to pulse Cu exposure in generic Taiwan aquaculture ponds was ~33% of Cu in adverse physiologically associated, metabolically active pool, implicating no significant susceptibility risk for tilapia. We suggest that our integrated ecotoxicological models linking chronic exposure measurements with subcellular partitioning can facilitate a risk assessment framework that provides a predictive tool for preventive susceptibility reduction strategies for freshwater fish exposed to pulse metal stressors.

Prediction of B-cell linear epitopes with a combination of support vector machine classification and amino acid propensity identification.

PubMed

Wang, Hsin-Wei; Lin, Ya-Chi; Pai, Tun-Wen; Chang, Hao-Teng

2011-01-01

Epitopes are antigenic determinants that are useful because they induce B-cell antibody production and stimulate T-cell activation. Bioinformatics can enable rapid, efficient prediction of potential epitopes. Here, we designed a novel B-cell linear epitope prediction system called LEPS, Linear Epitope Prediction by Propensities and Support Vector Machine, that combined physico-chemical propensity identification and support vector machine (SVM) classification. We tested the LEPS on four datasets: AntiJen, HIV, a newly generated PC, and AHP, a combination of these three datasets. Peptides with globally or locally high physicochemical propensities were first identified as primitive linear epitope (LE) candidates. Then, candidates were classified with the SVM based on the unique features of amino acid segments. This reduced the number of predicted epitopes and enhanced the positive prediction value (PPV). Compared to four other well-known LE prediction systems, the LEPS achieved the highest accuracy (72.52%), specificity (84.22%), PPV (32.07%), and Matthews' correlation coefficient (10.36%).

The Interaction between Vector Life History and Short Vector Life in Vector-Borne Disease Transmission and Control.

PubMed

Brand, Samuel P C; Rock, Kat S; Keeling, Matt J

2016-04-01

Epidemiological modelling has a vital role to play in policy planning and prediction for the control of vectors, and hence the subsequent control of vector-borne diseases. To decide between competing policies requires models that can generate accurate predictions, which in turn requires accurate knowledge of vector natural histories. Here we highlight the importance of the distribution of times between life-history events, using short-lived midge species as an example. In particular we focus on the distribution of the extrinsic incubation period (EIP) which determines the time between infection and becoming infectious, and the distribution of the length of the gonotrophic cycle which determines the time between successful bites. We show how different assumptions for these periods can radically change the basic reproductive ratio (R0) of an infection and additionally the impact of vector control on the infection. These findings highlight the need for detailed entomological data, based on laboratory experiments and field data, to correctly construct the next-generation of policy-informing models.

Robust vector quantization for noisy channels

NASA Technical Reports Server (NTRS)

Demarca, J. R. B.; Farvardin, N.; Jayant, N. S.; Shoham, Y.

1988-01-01

The paper briefly discusses techniques for making vector quantizers more tolerant to tranmsission errors. Two algorithms are presented for obtaining an efficient binary word assignment to the vector quantizer codewords without increasing the transmission rate. It is shown that about 4.5 dB gain over random assignment can be achieved with these algorithms. It is also proposed to reduce the effects of error propagation in vector-predictive quantizers by appropriately constraining the response of the predictive loop. The constrained system is shown to have about 4 dB of SNR gain over an unconstrained system in a noisy channel, with a small loss of clean-channel performance.

QCD next-to-leading-order predictions matched to parton showers for vector-like quark models.

PubMed

Fuks, Benjamin; Shao, Hua-Sheng

2017-01-01

Vector-like quarks are featured by a wealth of beyond the Standard Model theories and are consequently an important goal of many LHC searches for new physics. Those searches, as well as most related phenomenological studies, however, rely on predictions evaluated at the leading-order accuracy in QCD and consider well-defined simplified benchmark scenarios. Adopting an effective bottom-up approach, we compute next-to-leading-order predictions for vector-like-quark pair production and single production in association with jets, with a weak or with a Higgs boson in a general new physics setup. We additionally compute vector-like-quark contributions to the production of a pair of Standard Model bosons at the same level of accuracy. For all processes under consideration, we focus both on total cross sections and on differential distributions, most these calculations being performed for the first time in our field. As a result, our work paves the way to precise extraction of experimental limits on vector-like quarks thanks to an accurate control of the shapes of the relevant observables and emphasise the extra handles that could be provided by novel vector-like-quark probes never envisaged so far.

Data-driven identification of potential Zika virus vectors

PubMed Central

Evans, Michelle V; Dallas, Tad A; Han, Barbara A; Murdock, Courtney C; Drake, John M

2017-01-01

Zika is an emerging virus whose rapid spread is of great public health concern. Knowledge about transmission remains incomplete, especially concerning potential transmission in geographic areas in which it has not yet been introduced. To identify unknown vectors of Zika, we developed a data-driven model linking vector species and the Zika virus via vector-virus trait combinations that confer a propensity toward associations in an ecological network connecting flaviviruses and their mosquito vectors. Our model predicts that thirty-five species may be able to transmit the virus, seven of which are found in the continental United States, including Culex quinquefasciatus and Cx. pipiens. We suggest that empirical studies prioritize these species to confirm predictions of vector competence, enabling the correct identification of populations at risk for transmission within the United States. DOI: http://dx.doi.org/10.7554/eLife.22053.001 PMID:28244371

Hydromorphone transfer into breast milk after intranasal administration.

PubMed

Edwards, Jeffrey E; Rudy, Anita C; Wermeling, Daniel P; Desai, Nirmala; McNamara, Patrick J

2003-02-01

To determine the distribution of hydromorphone into breast milk and the potential exposure of the suckling infant, and whether the distribution of hydromorphone into milk can be predicted accurately by a passive diffusion model. Single-dose, pharmacokinetic study. University clinical research unit. Eight lactating, nonsmoking, healthy women aged 24-32 years. Hydromorphone HCl 2 mg was given intranasally to the women to characterize its pharmacokinetics and extent of its transfer into breast milk. Plasma and milk samples were analyzed using liquid chromatography with tandem mass spectrometry detection. The milk:plasma ratio (M:P) was calculated as the total area under the concentration-time curve (AUC) of the milk divided by the total AUC of the plasma. Predicted in vitro M:P ratios were calculated using a diffusion model. Protein binding in milk and plasma, partitioning into milk fat (whole milk:skim milk ratios), as well as pH partitioning between plasma and milk were incorporated in the model. Protein binding was determined by equilibrium dialysis. Protein binding was minimal in both milk and plasma, with unbound fractions of 1 and 0.84, respectively There was little partitioning into milk fat, as demonstrated by the whole milk:skim milk ratio of 0.98. The observed and predicted M:P ratios +/- SD for hydromorphone were 2.57 +/- 0.47 and 1.11 +/- 0.28, respectively. The 95% confidence interval for the observed M:P ratio overlapped the confidence interval of the predicted M:P ratio, a finding that supports a role for both passive diffusion and active transport as mechanisms of hydromorphone transfer into milk. Hydromorphone distributes rapidly from plasma into breast milk; however, the drug does not partition into fat. The suckling infant would receive approximately 0.67% of the maternal dose of hydromorphone (adjusted for body weight). As this is a limited exposure, further studies are needed to determine any potential impact to an infant who is fed breast milk from a mother treated with hydromorphone.

Application of Recursive Partitioning to Derive and Validate a Claims-Based Algorithm for Identifying Keratinocyte Carcinoma (Nonmelanoma Skin Cancer).

PubMed

Chan, An-Wen; Fung, Kinwah; Tran, Jennifer M; Kitchen, Jessica; Austin, Peter C; Weinstock, Martin A; Rochon, Paula A

2016-10-01

Keratinocyte carcinoma (nonmelanoma skin cancer) accounts for substantial burden in terms of high incidence and health care costs but is excluded by most cancer registries in North America. Administrative health insurance claims databases offer an opportunity to identify these cancers using diagnosis and procedural codes submitted for reimbursement purposes. To apply recursive partitioning to derive and validate a claims-based algorithm for identifying keratinocyte carcinoma with high sensitivity and specificity. Retrospective study using population-based administrative databases linked to 602 371 pathology episodes from a community laboratory for adults residing in Ontario, Canada, from January 1, 1992, to December 31, 2009. The final analysis was completed in January 2016. We used recursive partitioning (classification trees) to derive an algorithm based on health insurance claims. The performance of the derived algorithm was compared with 5 prespecified algorithms and validated using an independent academic hospital clinic data set of 2082 patients seen in May and June 2011. Sensitivity, specificity, positive predictive value, and negative predictive value using the histopathological diagnosis as the criterion standard. We aimed to achieve maximal specificity, while maintaining greater than 80% sensitivity. Among 602 371 pathology episodes, 131 562 (21.8%) had a diagnosis of keratinocyte carcinoma. Our final derived algorithm outperformed the 5 simple prespecified algorithms and performed well in both community and hospital data sets in terms of sensitivity (82.6% and 84.9%, respectively), specificity (93.0% and 99.0%, respectively), positive predictive value (76.7% and 69.2%, respectively), and negative predictive value (95.0% and 99.6%, respectively). Algorithm performance did not vary substantially during the 18-year period. This algorithm offers a reliable mechanism for ascertaining keratinocyte carcinoma for epidemiological research in the absence of cancer registry data. Our findings also demonstrate the value of recursive partitioning in deriving valid claims-based algorithms.

Sharply curved turn around duct flow predictions using spectral partitioning of the turbulent kinetic energy and a pressure modified wall law

NASA Technical Reports Server (NTRS)

Santi, L. Michael

1986-01-01

Computational predictions of turbulent flow in sharply curved 180 degree turn around ducts are presented. The CNS2D computer code is used to solve the equations of motion for two-dimensional incompressible flows transformed to a nonorthogonal body-fitted coordinate system. This procedure incorporates the pressure velocity correction algorithm SIMPLE-C to iteratively solve a discretized form of the transformed equations. A multiple scale turbulence model based on simplified spectral partitioning is employed to obtain closure. Flow field predictions utilizing the multiple scale model are compared to features predicted by the traditional single scale k-epsilon model. Tuning parameter sensitivities of the multiple scale model applied to turn around duct flows are also determined. In addition, a wall function approach based on a wall law suitable for incompressible turbulent boundary layers under strong adverse pressure gradients is tested. Turn around duct flow characteristics utilizing this modified wall law are presented and compared to results based on a standard wall treatment.

QSAR models for predicting octanol/water and organic carbon/water partition coefficients of polychlorinated biphenyls.

PubMed

Yu, S; Gao, S; Gan, Y; Zhang, Y; Ruan, X; Wang, Y; Yang, L; Shi, J

2016-04-01

Quantitative structure-property relationship modelling can be a valuable alternative method to replace or reduce experimental testing. In particular, some endpoints such as octanol-water (KOW) and organic carbon-water (KOC) partition coefficients of polychlorinated biphenyls (PCBs) are easier to predict and various models have been already developed. In this paper, two different methods, which are multiple linear regression based on the descriptors generated using Dragon software and hologram quantitative structure-activity relationships, were employed to predict suspended particulate matter (SPM) derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of 209 PCBs. The predictive ability of the derived models was validated using a test set. The performances of all these models were compared with EPI Suite™ software. The results indicated that the proposed models were robust and satisfactory, and could provide feasible and promising tools for the rapid assessment of the SPM derived log KOC and generator column, shake flask and slow stirring method derived log KOW values of PCBs.

Comparing machine learning and logistic regression methods for predicting hypertension using a combination of gene expression and next-generation sequencing data.

PubMed

Held, Elizabeth; Cape, Joshua; Tintle, Nathan

2016-01-01

Machine learning methods continue to show promise in the analysis of data from genetic association studies because of the high number of variables relative to the number of observations. However, few best practices exist for the application of these methods. We extend a recently proposed supervised machine learning approach for predicting disease risk by genotypes to be able to incorporate gene expression data and rare variants. We then apply 2 different versions of the approach (radial and linear support vector machines) to simulated data from Genetic Analysis Workshop 19 and compare performance to logistic regression. Method performance was not radically different across the 3 methods, although the linear support vector machine tended to show small gains in predictive ability relative to a radial support vector machine and logistic regression. Importantly, as the number of genes in the models was increased, even when those genes contained causal rare variants, model predictive ability showed a statistically significant decrease in performance for both the radial support vector machine and logistic regression. The linear support vector machine showed more robust performance to the inclusion of additional genes. Further work is needed to evaluate machine learning approaches on larger samples and to evaluate the relative improvement in model prediction from the incorporation of gene expression data.

Tightly regulated, high-level expression from controlled copy number vectors based on the replicon of temperate phage N15.

PubMed

Mardanov, Andrey V; Strakhova, Taisia S; Smagin, Vladimir A; Ravin, Nikolai V

2007-06-15

A new Escherichia coli host/vector system has been developed to allow a dual regulation of both the plasmid copy number and gene expression. The new pN15E vectors are low copy number plasmids based on the replicon of temperate phage N15, comprising the repA replicase gene and cB repressor gene, controlling the plasmid copy number. Regulation of pN15E copy number is achieved through arabinose-inducible expression of phage N15 antirepressor protein, AntA, whose gene was integrated into the chromosome of the host strain under control of the PBAD promoter. The host strain also carried phage N15 partition operon, sop, allowing stable inheritance of pN15E vectors in the absence of selection pressure. In the first vector, pN15E4, the same PBAD promoter controls expression of a cloned gene. The second vector, pN15E6, carries the phage T5 promoter with a double lac operator repression module thus allowing independent regulation of promoter activity and copy number. Using the lacZ gene to monitor expression in these vectors, we show that the ratio of induction/repression can be about 7600-fold for pN15E4 and more than 15,000-fold for pN15E6. The low copy number of these vectors ensures very low basal level of expression allowing cloning genes encoding toxic products that was demonstrated by the stable maintenance of a gene encoding a restriction endonuclease in pN15E4. The tight control of transcription and the potential to regulate gene activities quantitatively over wide ranges will open up new approaches in the study of gene function in vivo and controlled expression of heterologous genes.

Can different quantum state vectors correspond to the same physical state? An experimental test

NASA Astrophysics Data System (ADS)

Nigg, Daniel; Monz, Thomas; Schindler, Philipp; Martinez, Esteban A.; Hennrich, Markus; Blatt, Rainer; Pusey, Matthew F.; Rudolph, Terry; Barrett, Jonathan

2016-01-01

A century after the development of quantum theory, the interpretation of a quantum state is still discussed. If a physicist claims to have produced a system with a particular quantum state vector, does this represent directly a physical property of the system, or is the state vector merely a summary of the physicist’s information about the system? Assume that a state vector corresponds to a probability distribution over possible values of an unknown physical or ‘ontic’ state. Then, a recent no-go theorem shows that distinct state vectors with overlapping distributions lead to predictions different from quantum theory. We report an experimental test of these predictions using trapped ions. Within experimental error, the results confirm quantum theory. We analyse which kinds of models are ruled out.

Determination of partition coefficients of biomolecules in a microfluidic aqueous two phase system platform using fluorescence microscopy.

PubMed

Silva, D F C; Azevedo, A M; Fernandes, P; Chu, V; Conde, J P; Aires-Barros, M R

2017-03-03

Aqueous two phase systems (ATPS) offer great potential for selective separation of a wide range of biomolecules by exploring differences in molecular solubility in each of the two immiscible phases. However, ATPS use has been limited due to the difficulty in predicting the behavior of a given biomolecule in the partition environment together with the empirical and time-consuming techniques that are used for the determination of partition and extraction parameters. In this work, a fast and novel technique based on a microfluidic platform and using fluorescence microscopy was developed to determine the partition coefficients of biomolecules in different ATPS. This method consists of using a microfluidic device with a single microchannel and three inlets. In two of the inlets, solutions containing the ATPS forming components were loaded while the third inlet was fed with the FITC tagged biomolecule of interest prepared in milli-Q water. Using fluorescence microscopy, it was possible to follow the location of the FITC-tagged biomolecule and, by simply varying the pumping rates of the solutions, to quickly test a wide variety of ATPS compositions. The ATPS system is allowed 4min for stabilization and fluorescence micrographs are used to determine the partition coefficient.The partition coefficients obtained were shown to be consistent with results from macroscale ATPS partition. This process allows for faster screening of partition coefficients using only a few microliters of material for each ATPS composition and is amenable to automation. The partitioning behavior of several biomolecules with molecular weights (MW) ranging from 5.8 to 150kDa, and isoelectric points (pI) ranging from 4.7 to 6.4 was investigated, as well as the effect of the molecular weight of the polymer ATPS component. Copyright © 2016 Elsevier B.V. All rights reserved.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study.

PubMed

Wu, Jia; Gensheimer, Michael F; Dong, Xinzhe; Rubin, Daniel L; Napel, Sandy; Diehn, Maximilian; Loo, Billy W; Li, Ruijiang

2016-08-01

To develop an intratumor partitioning framework for identifying high-risk subregions from (18)F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. In this institutional review board-approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05). We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types. Copyright © 2016 Elsevier Inc. All rights reserved.

Robust Intratumor Partitioning to Identify High-Risk Subregions in Lung Cancer: A Pilot Study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, Jia; Gensheimer, Michael F.; Dong, Xinzhe

2016-08-01

Purpose: To develop an intratumor partitioning framework for identifying high-risk subregions from {sup 18}F-fluorodeoxyglucose positron emission tomography (FDG-PET) and computed tomography (CT) imaging and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods and Materials: In this institutional review board–approved retrospective study, we analyzed the pretreatment FDG-PET and CT scans of 44 lung cancer patients treated with radiation therapy. A novel, intratumor partitioning method was developed, based on a 2-stage clustering process: first at the patient level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET andmore » CT images; next, tumor subregions were identified by merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor that were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI of 0.66-0.67. When restricting the analysis to patients with stage III disease (n=32), the same subregion achieved an even higher CI of 0.75 (hazard ratio 3.93, log-rank P=.002) for predicting OS, and a CI of 0.76 (hazard ratio 4.84, log-rank P=.002) for predicting OFP. In comparison, conventional imaging markers, including tumor volume, maximum standardized uptake value, and metabolic tumor volume using threshold of 50% standardized uptake value maximum, were not predictive of OS or OFP, with CI mostly below 0.60 (log-rank P>.05). Conclusion: We propose a robust intratumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.« less

Control of ice chromatographic retention mechanism by changing temperature and dopant concentration.

PubMed

Tasaki, Yuiko; Okada, Tetsuo

2011-12-15

A liquid phase coexists with solid water ice in a typical binary system, such as NaCl-water, in the temperature range between the freezing point and the eutectic point (t(eu)) of the system. In ice chromatography with salt-doped ice as the stationary phase, both solid and liquid phase can contribute to solute retention in different fashions; that is, the solid ice surface acts as an adsorbent, while a solute can be partitioned into the liquid phase. Thus, both adsorption and partition mechanisms can be utilized for ice chromatographic separation. An important feature in this approach is that the liquid phase volume can be varied by changing the temperature and the concentration of a salt incorporated into the ice stationary phase. Thus, we can control the relative contribution from the partition mechanism in the entire retention because the liquid phase volume can be estimated from the freezing depression curve. Separation selectivity can thereby be modified. The applicability of this concept has been confirmed for the solutes of different adsorption and partition abilities. The predicted retention based on thermodynamics basically agrees well with the corresponding experimental retention. However, one important inconsistency has been found. The calculation predicts a step-like discontinuity of the solute retention at t(eu) because the phase diagram suggests that the liquid phase abruptly appears at t(eu) when the temperature increases. In contrast, the corresponding experimental plots are continuous over the wider range including the subeutectic temperatures. This discrepancy is explained by the existence of the liquid phase below t(eu). A difference between predicted and measured retention factors allows the estimation of the volume of the subeutectic liquid phase.

A linear solvation energy relationship model of organic chemical partitioning to dissolved organic carbon.

PubMed

Kipka, Undine; Di Toro, Dominic M

2011-09-01

Predicting the association of contaminants with both particulate and dissolved organic matter is critical in determining the fate and bioavailability of chemicals in environmental risk assessment. To date, the association of a contaminant to particulate organic matter is considered in many multimedia transport models, but the effect of dissolved organic matter is typically ignored due to a lack of either reliable models or experimental data. The partition coefficient to dissolved organic carbon (K(DOC)) may be used to estimate the fraction of a contaminant that is associated with dissolved organic matter. Models relating K(DOC) to the octanol-water partition coefficient (K(OW)) have not been successful for many types of dissolved organic carbon in the environment. Instead, linear solvation energy relationships are proposed to model the association of chemicals with dissolved organic matter. However, more chemically diverse K(DOC) data are needed to produce a more robust model. For humic acid dissolved organic carbon, the linear solvation energy relationship predicts log K(DOC) with a root mean square error of 0.43. Copyright © 2011 SETAC.

Prediction of octanol-water partition coefficients of organic compounds by multiple linear regression, partial least squares, and artificial neural network.

PubMed

Golmohammadi, Hassan

2009-11-30

A quantitative structure-property relationship (QSPR) study was performed to develop models those relate the structure of 141 organic compounds to their octanol-water partition coefficients (log P(o/w)). A genetic algorithm was applied as a variable selection tool. Modeling of log P(o/w) of these compounds as a function of theoretically derived descriptors was established by multiple linear regression (MLR), partial least squares (PLS), and artificial neural network (ANN). The best selected descriptors that appear in the models are: atomic charge weighted partial positively charged surface area (PPSA-3), fractional atomic charge weighted partial positive surface area (FPSA-3), minimum atomic partial charge (Qmin), molecular volume (MV), total dipole moment of molecule (mu), maximum antibonding contribution of a molecule orbital in the molecule (MAC), and maximum free valency of a C atom in the molecule (MFV). The result obtained showed the ability of developed artificial neural network to prediction of partition coefficients of organic compounds. Also, the results revealed the superiority of ANN over the MLR and PLS models. Copyright 2009 Wiley Periodicals, Inc.

Partition of volatile organic compounds from air and from water into plant cuticular matrix: An LFER analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Platts, J.A.; Abraham, M.H.

The partitioning of organic compounds between air and foliage and between water and foliage is of considerable environmental interest. The purpose of this work is to show that partitioning into the cuticular matrix of one particular species can be satisfactorily modeled by general equations the authors have previously developed and, hence, that the same general equations could be used to model partitioning into other plant materials of the same or different species. The general equations are linear free energy relationships that employ descriptors for polarity/polarizability, hydrogen bond acidity and basicity, dispersive effects, and volume. They have been applied to themore » partition of 62 very varied organic compounds between cuticular matrix of the tomato fruit, Lycopersicon esculentum, and either air (MX{sub a}) or water (MX{sub w}). Values of log MX{sub a} covering a range of 12.4 log units are correlated with a standard deviation of 0.232 log unit, and values of log MX{sub w} covering a range of 7.6 log unit are correlated with an SD of 0.236 log unit. Possibilities are discussed for the prediction of new air-plant cuticular matrix and water-plant cuticular matrix partition values on the basis of the equations developed.« less

The Influence of Oxygen and Sulfur on Uranium Partitioning Into the Core

NASA Astrophysics Data System (ADS)

Moore, R. D., Jr.; Van Orman, J. A.; Hauck, S. A., II

2017-12-01

Uranium, along with K and Th, may provide substantial long-term heating in planetary cores, depending on the magnitude of their partitioning into the metal during differentiation. In general, non-metallic light elements are known to have a large influence on the partitioning of trace elements, and the presence of sulfur is known to enhance the partitioning of uranium into the metal. Data from the steelmaking literature indicate that oxygen also enhances the solubility of oxygen in liquid iron alloys. Here we present experimental data on the partitioning of U between immiscible liquids in the Fe-S-O system, and use these data along with published metal-silicate partitioning data to calibrate a quantitative activity model for U in the metal. We also determined partition coefficients for Th, K, Nb, Nd, Sm, and Yb, but were unable to fully constrain activity models for these elements with available data. A Monte Carlo fitting routine was used to calculate U-S, U-O, and U-S-O interaction coefficients, and their associated uncertainties. We find that the combined interaction of uranium with sulfur and oxygen is predominant, with S and O together enhancing the solubility of uranium to a far greater degree than either element in isolation. This suggests that uranium complexes with sulfite or sulfate species in the metal. For a model Mars core composition containing 14 at% S and 5 at% O, the metal/silicate partition coefficient for U is predicted to be an order of magnitude larger than for a pure Fe-Ni core.

Predictive control strategies for wind turbine system based on permanent magnet synchronous generator.

PubMed

Maaoui-Ben Hassine, Ikram; Naouar, Mohamed Wissem; Mrabet-Bellaaj, Najiba

2016-05-01

In this paper, Model Predictive Control and Dead-beat predictive control strategies are proposed for the control of a PMSG based wind energy system. The proposed MPC considers the model of the converter-based system to forecast the possible future behavior of the controlled variables. It allows selecting the voltage vector to be applied that leads to a minimum error by minimizing a predefined cost function. The main features of the MPC are low current THD and robustness against parameters variations. The Dead-beat predictive control is based on the system model to compute the optimum voltage vector that ensures zero-steady state error. The optimum voltage vector is then applied through Space Vector Modulation (SVM) technique. The main advantages of the Dead-beat predictive control are low current THD and constant switching frequency. The proposed control techniques are presented and detailed for the control of back-to-back converter in a wind turbine system based on PMSG. Simulation results (under Matlab-Simulink software environment tool) and experimental results (under developed prototyping platform) are presented in order to show the performances of the considered control strategies. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.

Fault Slip Partitioning in the Eastern California Shear Zone-Walker Lane Belt: Pliocene to Late Pleistocene Contraction Across the Mina Deflection

NASA Astrophysics Data System (ADS)

Lee, J.; Stockli, D.; Gosse, J.

2007-12-01

Two different mechanisms have been proposed for fault slip transfer between the subparallel NW-striking dextral- slip faults that dominant the Eastern California Shear Zone (ECSZ)-Walker Lane Belt (WLB). In the northern WLB, domains of sinistral-slip along NE-striking faults and clockwise block rotation within a zone of distributed deformation accommodated NW-dextral shear. A somewhat modified version of this mechanism was also proposed for the Mina deflection, southern WLB, whereby NE-striking sinistral faults formed as conjugate faults to the primary zone of NW-dextral shear; clockwise rotation of the blocks bounding the sinistral faults accommodated dextral slip. In contrast, in the northern ECSZ and Mina deflection, domains of NE-striking pure dip-slip normal faults, bounded by NW-striking dextral-slip faults, exhibited no rotation; the proposed mechanism of slip transfer was one of right-stepping, high angle normal faults in which the magnitude of extension was proportional to the amount of strike-slip motion transferred. New geologic mapping, tectonic geomorphologic, and geochronologic data from the Queen Valley area, southern Mina deflection constrain Pliocene to late Quaternary fault geometries, slip orientations, slip magnitudes, and slip rates that bear on the mechanism of fault slip transfer from the relatively narrow northern ECSZ to the broad deformation zone that defines the Mina deflection. Four different fault types and orientations cut across the Queen Valley area: (1) The NE-striking normal-slip Queen Valley fault; (2) NE-striking sinistral faults; (3) the NW-striking dextral Coyote Springs fault, which merges into (4) a set of EW-striking thrust faults. (U-Th)/He apatite and cosmogenic radionuclide data, combined with magnitude of fault offset measurements, indicate a Pliocene to late Pleistocene horizontal extension rate of 0.2-0.3 mm/yr across the Queen Valley fault. Our results, combined with published slip rates for the dextral White Mountain fault zone (0.3-0.8 mm/yr) and the eastern sinistral Coaldale fault (0.4 mm/yr) suggest that transfer of dextral slip from the narrow White Mountains fault zone is explained best by a simple shear couple whereby slip is partitioned into three different components: horizontal extension along the Queen Valley fault, dominantly dextral slip along the Coyote Springs fault, and dominantly sinistral slip along the Coaldale fault. A velocity vector diagram illustrating fault slip partitioning predicts contraction rates of <0.1 to 0.5 mm/yr across the Coyote Springs and western Coaldale faults. The predicted long-term contraction across the Mina deflection is consistent with present-day GPS data.

The role of remote sensing and GIS for spatial prediction of vector-borne diseases transmission: a systematic review.

PubMed

Palaniyandi, M

2012-12-01

There have been several attempts made to the appreciation of remote sensing and GIS for the study of vectors, biodiversity, vector presence, vector abundance and the vector-borne diseases with respect to space and time. This study was made for reviewing and appraising the potential use of remote sensing and GIS applications for spatial prediction of vector-borne diseases transmission. The nature of the presence and the abundance of vectors and vector-borne diseases, disease infection and the disease transmission are not ubiquitous and are confined with geographical, environmental and climatic factors, and are localized. The presence of vectors and vector-borne diseases is most complex in nature, however, it is confined and fueled by the geographical, climatic and environmental factors including man-made factors. The usefulness of the present day availability of the information derived from the satellite data including vegetation indices of canopy cover and its density, soil types, soil moisture, soil texture, soil depth, etc. is integrating the information in the expert GIS engine for the spatial analysis of other geoclimatic and geoenvironmental variables. The present study gives the detailed information on the classical studies of the past and present, and the future role of remote sensing and GIS for the vector-borne diseases control. The ecological modeling directly gives us the relevant information to understand the spatial variation of the vector biodiversity, vector presence, vector abundance and the vector-borne diseases in association with geoclimatic and the environmental variables. The probability map of the geographical distribution and seasonal variations of horizontal and vertical distribution of vector abundance and its association with vector -borne diseases can be obtained with low cost remote sensing and GIS tool with reliable data and speed.

Effects of phase vector and history extension on prediction power of adaptive-network based fuzzy inference system (ANFIS) model for a real scale anaerobic wastewater treatment plant operating under unsteady state.

PubMed

Perendeci, Altinay; Arslan, Sever; Tanyolaç, Abdurrahman; Celebi, Serdar S

2009-10-01

A conceptual neural fuzzy model based on adaptive-network based fuzzy inference system, ANFIS, was proposed using available input on-line and off-line operational variables for a sugar factory anaerobic wastewater treatment plant operating under unsteady state to estimate the effluent chemical oxygen demand, COD. The predictive power of the developed model was improved as a new approach by adding the phase vector and the recent values of COD up to 5-10 days, longer than overall retention time of wastewater in the system. History of last 10 days for COD effluent with two-valued phase vector in the input variable matrix including all parameters had more predictive power. History of 7 days with two-valued phase vector in the matrix comprised of only on-line variables yielded fairly well estimations. The developed ANFIS model with phase vector and history extension has been able to adequately represent the behavior of the treatment system.

Autonomous Environment-Monitoring Networks

NASA Technical Reports Server (NTRS)

Hand, Charles

2004-01-01

Autonomous environment-monitoring networks (AEMNs) are artificial neural networks that are specialized for recognizing familiarity and, conversely, novelty. Like a biological neural network, an AEMN receives a constant stream of inputs. For purposes of computational implementation, the inputs are vector representations of the information of interest. As long as the most recent input vector is similar to the previous input vectors, no action is taken. Action is taken only when a novel vector is encountered. Whether a given input vector is regarded as novel depends on the previous vectors; hence, the same input vector could be regarded as familiar or novel, depending on the context of previous input vectors. AEMNs have been proposed as means to enable exploratory robots on remote planets to recognize novel features that could merit closer scientific attention. AEMNs could also be useful for processing data from medical instrumentation for automated monitoring or diagnosis. The primary substructure of an AEMN is called a spindle. In its simplest form, a spindle consists of a central vector (C), a scalar (r), and algorithms for changing C and r. The vector C is constructed from all the vectors in a given continuous stream of inputs, such that it is minimally distant from those vectors. The scalar r is the distance between C and the most remote vector in the same set. The construction of a spindle involves four vital parameters: setup size, spindle-population size, and the radii of two novelty boundaries. The setup size is the number of vectors that are taken into account before computing C. The spindle-population size is the total number of input vectors used in constructing the spindle counting both those that arrive before and those that arrive after the computation of C. The novelty-boundary radii are distances from C that partition the neighborhood around C into three concentric regions (see Figure 1). During construction of the spindle, the changing spindle radius is denoted by h. It is the final value of h, reached before beginning construction on the next spindle, that is denoted by r. During construction of a spindle, if a new vector falls between C and the inner boundary, the vector is regarded as completely familiar and no action is taken. If the new vector falls into the region between the inner and outer boundaries, it is considered unusual enough to warrant the adjustment of C and r by use of the aforementioned algorithms, but not unusual enough to be considered novel. If a vector falls outside the outer boundary, it is considered novel, in which case one of several appropriate responses could be initiation of construction of a new spindle.

Predicting disulfide connectivity from protein sequence using multiple sequence feature vectors and secondary structure.

PubMed

Song, Jiangning; Yuan, Zheng; Tan, Hao; Huber, Thomas; Burrage, Kevin

2007-12-01

Disulfide bonds are primary covalent crosslinks between two cysteine residues in proteins that play critical roles in stabilizing the protein structures and are commonly found in extracy-toplasmatic or secreted proteins. In protein folding prediction, the localization of disulfide bonds can greatly reduce the search in conformational space. Therefore, there is a great need to develop computational methods capable of accurately predicting disulfide connectivity patterns in proteins that could have potentially important applications. We have developed a novel method to predict disulfide connectivity patterns from protein primary sequence, using a support vector regression (SVR) approach based on multiple sequence feature vectors and predicted secondary structure by the PSIPRED program. The results indicate that our method could achieve a prediction accuracy of 74.4% and 77.9%, respectively, when averaged on proteins with two to five disulfide bridges using 4-fold cross-validation, measured on the protein and cysteine pair on a well-defined non-homologous dataset. We assessed the effects of different sequence encoding schemes on the prediction performance of disulfide connectivity. It has been shown that the sequence encoding scheme based on multiple sequence feature vectors coupled with predicted secondary structure can significantly improve the prediction accuracy, thus enabling our method to outperform most of other currently available predictors. Our work provides a complementary approach to the current algorithms that should be useful in computationally assigning disulfide connectivity patterns and helps in the annotation of protein sequences generated by large-scale whole-genome projects. The prediction web server and Supplementary Material are accessible at http://foo.maths.uq.edu.au/~huber/disulfide

Partitioning the Uncertainty in Estimates of Mean Basal Area Obtained from 10-year Diameter Growth Model Predictions

Treesearch

Ronald E. McRoberts

2005-01-01

Uncertainty in model-based predictions of individual tree diameter growth is attributed to three sources: measurement error for predictor variables, residual variability around model predictions, and uncertainty in model parameter estimates. Monte Carlo simulations are used to propagate the uncertainty from the three sources through a set of diameter growth models to...

Partitioning of Ni, Co and V between Spinel-Structured Oxides and Silicate Melts: Importance of Spinel Composition

NASA Technical Reports Server (NTRS)

Righter, K.; Leeman, W. P.; Hervig, R. L.

2006-01-01

Partitioning of Ni, Co and V between Cr-rich spinels and basaltic melt has been studied experimentally between 1150 and 1325 C, and at controlled oxygen fugacity from the Co-CoO buffer to slightly above the hematite magnetite buffer. These new results, together with new Ni, Co and V analyses of experimental run products from Leeman [Leeman, W.P., 1974. Experimental determination of the partitioning of divalent cations between olivine and basaltic liquid, Pt. II. PhD thesis, Univ. Oregon, 231 - 337.], show that experimentally determined spinel melt partition coefficients (D) are dependent upon temperature (T), oxygen fugacity (fO2) and spinel composition. In particular, partition coefficients determined on doped systems are higher than those in natural (undoped) systems, perhaps due to changing activity coefficients over the composition range defined by the experimental data. Using our new results and published runs (n =85), we obtain a multilinear regression equation that predicts experimental D(V) values as a function of T, fO2, concentration of V in melt and spinel composition. This equation allows prediction of D(V) spinel/melt values for natural mafic liquids at relevant crystallization conditions. Similarly, D(Ni) and D(Co) values can be inferred from our experiments at redox conditions approaching the QFM buffer, temperatures of 1150 to 1250 C and spinel composition (early Cr-bearing and later Ti-magnetite) appropriate for basic magma differentiation. When coupled with major element modelling of liquid lines of descent, these values (D(Ni) sp/melt=10 and D(Co) sp/melt=5) closely reproduce the compositional variation observed in komatiite, mid-ocean ridge basalt (MORB), ocean island basalt (OIB) and basalt to rhyolite suites.

Quantitative structure-activity relationship for the partition coefficient of hydrophobic compounds between silicone oil and air.

PubMed

Qu, Yanfei; Ma, Yongwen; Wan, Jinquan; Wang, Yan

2018-06-01

The silicon oil-air partition coefficients (K SiO/A ) of hydrophobic compounds are vital parameters for applying silicone oil as non-aqueous-phase liquid in partitioning bioreactors. Due to the limited number of K SiO/A values determined by experiment for hydrophobic compounds, there is an urgent need to model the K SiO/A values for unknown chemicals. In the present study, we developed a universal quantitative structure-activity relationship (QSAR) model using a sequential approach with macro-constitutional and micromolecular descriptors for silicone oil-air partition coefficients (K SiO/A ) of hydrophobic compounds with large structural variance. The geometry optimization and vibrational frequencies of each chemical were calculated using the hybrid density functional theory at the B3LYP/6-311G** level. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates that a regression model derived from logK SiO/A , the number of non-hydrogen atoms (#nonHatoms) and energy gap of E LUMO and E HOMO (E LUMO -E HOMO ) could explain the partitioning mechanism of hydrophobic compounds between silicone oil and air. The correlation coefficient R 2 of the model is 0.922, and the internal and external validation coefficient, Q 2 LOO and Q 2 ext , are 0.91 and 0.89 respectively, implying that the model has satisfactory goodness-of-fit, robustness, and predictive ability and thus provides a robust predictive tool to estimate the logK SiO/A values for chemicals in application domain. The applicability domain of the model was visualized by the Williams plot.

Allometric growth and allocation in forests: a perspective from FLUXNET.

PubMed

Wolf, Adam; Field, Christopher B; Berry, Joseph A

2011-07-01

To develop a scheme for partitioning the products of photosynthesis toward different biomass components in land-surface models, a database on component mass and net primary productivity (NPP), collected from FLUXNET sites, was examined to determine allometric patterns of allocation. We found that NPP per individual of foliage (Gfol), stem and branches (Gstem), coarse roots (Gcroot) and fine roots (Gfroot) in individual trees is largely explained (r2 = 67-91%) by the magnitude of total NPP per individual (G). Gfol scales with G isometrically, meaning it is a fixed fraction of G ( 25%). Root-shoot trade-offs were manifest as a slow decline in Gfroot, as a fraction of G, from 50% to 25% as stands increased in biomass, with Gstem and Gcroot increasing as a consequence. These results indicate that a functional trade-off between aboveground and belowground allocation is essentially captured by variations in G, which itself is largely governed by stand biomass and only secondarily by site-specific resource availability. We argue that forests are characterized by strong competition for light, observed as a race for individual trees to ascend by increasing partitioning toward wood, rather than by growing more leaves, and that this competition stronglyconstrains the allocational plasticity that trees may be capable of. The residual variation in partitioning was not related to climatic or edaphic factors, nor did plots with nutrient or water additions show a pattern of partitioning distinct from that predicted by G alone. These findings leverage short-term process studies of the terrestrial carbon cycle to improve decade-scale predictions of biomass accumulation in forests. An algorithm for calculating partitioning in land-surface models is presented.

Subcellular partitioning of metals in Aporrectodea caliginosa along a gradient of metal exposure in 31 field-contaminated soils.

PubMed

Beaumelle, Léa; Gimbert, Frédéric; Hedde, Mickaël; Guérin, Annie; Lamy, Isabelle

2015-07-01

Subcellular fractionation of metals in organisms was proposed as a better way to characterize metal bioaccumulation. Here we report the impact of a laboratory exposure to a wide range of field-metal contaminated soils on the subcellular partitioning of metals in the earthworm Aporrectodea caliginosa. Soils moderately contaminated were chosen to create a gradient of soil metal availability; covering ranges of both soil metal contents and of several soil parameters. Following exposure, Cd, Pb and Zn concentrations were determined both in total earthworm body and in three subcellular compartments: cytosolic, granular and debris fractions. Three distinct proxies of soil metal availability were investigated: CaCl2-extractable content dissolved content predicted by a semi-mechanistic model and free ion concentration predicted by a geochemical speciation model. Subcellular partitionings of Cd and Pb were modified along the gradient of metal exposure, while stable Zn partitioning reflected regulation processes. Cd subcellular distribution responded more strongly to increasing soil Cd concentration than the total internal content, when Pb subcellular distribution and total internal content were similarly affected. Free ion concentrations were better descriptors of Cd and Pb subcellular distribution than CaCl2 extractable and dissolved metal concentrations. However, free ion concentrations and soil total metal contents were equivalent descriptors of the subcellular partitioning of Cd and Pb because they were highly correlated. Considering lowly contaminated soils, our results raise the question of the added value of three proxies of metal availability compared to soil total metal content in the assessment of metal bioavailability to earthworm. Copyright © 2015 Elsevier B.V. All rights reserved.

Microplastics as vector for heavy metal contamination from the marine environment

NASA Astrophysics Data System (ADS)

Brennecke, Dennis; Duarte, Bernardo; Paiva, Filipa; Caçador, Isabel; Canning-Clode, João

2016-09-01

The permanent presence of microplastics in the marine environment is considered a global threat to several marine animals. Heavy metals and microplastics are typically included in two different classes of pollutants but the interaction between these two stressors is poorly understood. During 14 days of experimental manipulation, we examined the adsorption of two heavy metals, copper (Cu) and zinc (Zn), leached from an antifouling paint to virgin polystyrene (PS) beads and aged polyvinyl chloride (PVC) fragments in seawater. We demonstrated that heavy metals were released from the antifouling paint to the water and both microplastic types adsorbed the two heavy metals. This adsorption kinetics was described using partition coefficients and mathematical models. Partition coefficients between pellets and water ranged between 650 and 850 for Cu on PS and PVC, respectively. The adsorption of Cu was significantly greater in PVC fragments than in PS, probably due to higher surface area and polarity of PVC. Concentrations of Cu and Zn increased significantly on PVC and PS over the course of the experiment with the exception of Zn on PS. As a result, we show a significant interaction between these types of microplastics and heavy metals, which can have implications for marine life and the environment. These results strongly support recent findings where plastics can play a key role as vectors for heavy metal ions in the marine system. Finally, our findings highlight the importance of monitoring marine litter and heavy metals, mainly associated with antifouling paints, particularly in the framework of the Marine Strategy Framework Directive (MSFD).

DIA-datasnooping and identifiability

NASA Astrophysics Data System (ADS)

Zaminpardaz, S.; Teunissen, P. J. G.

2018-04-01

In this contribution, we present and analyze datasnooping in the context of the DIA method. As the DIA method for the detection, identification and adaptation of mismodelling errors is concerned with estimation and testing, it is the combination of both that needs to be considered. This combination is rigorously captured by the DIA estimator. We discuss and analyze the DIA-datasnooping decision probabilities and the construction of the corresponding partitioning of misclosure space. We also investigate the circumstances under which two or more hypotheses are nonseparable in the identification step. By means of a theorem on the equivalence between the nonseparability of hypotheses and the inestimability of parameters, we demonstrate that one can forget about adapting the parameter vector for hypotheses that are nonseparable. However, as this concerns the complete vector and not necessarily functions of it, we also show that parameter functions may exist for which adaptation is still possible. It is shown how this adaptation looks like and how it changes the structure of the DIA estimator. To demonstrate the performance of the various elements of DIA-datasnooping, we apply the theory to some selected examples. We analyze how geometry changes in the measurement setup affect the testing procedure, by studying their partitioning of misclosure space, the decision probabilities and the minimal detectable and identifiable biases. The difference between these two minimal biases is highlighted by showing the difference between their corresponding contributing factors. We also show that if two alternative hypotheses, say Hi and Hj , are nonseparable, the testing procedure may have different levels of sensitivity to Hi -biases compared to the same Hj -biases.

Multi-Objective Community Detection Based on Memetic Algorithm

PubMed Central

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels. PMID:25932646

Multi-objective community detection based on memetic algorithm.

PubMed

Wu, Peng; Pan, Li

2015-01-01

Community detection has drawn a lot of attention as it can provide invaluable help in understanding the function and visualizing the structure of networks. Since single objective optimization methods have intrinsic drawbacks to identifying multiple significant community structures, some methods formulate the community detection as multi-objective problems and adopt population-based evolutionary algorithms to obtain multiple community structures. Evolutionary algorithms have strong global search ability, but have difficulty in locating local optima efficiently. In this study, in order to identify multiple significant community structures more effectively, a multi-objective memetic algorithm for community detection is proposed by combining multi-objective evolutionary algorithm with a local search procedure. The local search procedure is designed by addressing three issues. Firstly, nondominated solutions generated by evolutionary operations and solutions in dominant population are set as initial individuals for local search procedure. Then, a new direction vector named as pseudonormal vector is proposed to integrate two objective functions together to form a fitness function. Finally, a network specific local search strategy based on label propagation rule is expanded to search the local optimal solutions efficiently. The extensive experiments on both artificial and real-world networks evaluate the proposed method from three aspects. Firstly, experiments on influence of local search procedure demonstrate that the local search procedure can speed up the convergence to better partitions and make the algorithm more stable. Secondly, comparisons with a set of classic community detection methods illustrate the proposed method can find single partitions effectively. Finally, the method is applied to identify hierarchical structures of networks which are beneficial for analyzing networks in multi-resolution levels.

Hybrid state vector methods for structural dynamic and aeroelastic boundary value problems

NASA Technical Reports Server (NTRS)

Lehman, L. L.

1982-01-01

A computational technique is developed that is suitable for performing preliminary design aeroelastic and structural dynamic analyses of large aspect ratio lifting surfaces. The method proves to be quite general and can be adapted to solving various two point boundary value problems. The solution method, which is applicable to both fixed and rotating wing configurations, is based upon a formulation of the structural equilibrium equations in terms of a hybrid state vector containing generalized force and displacement variables. A mixed variational formulation is presented that conveniently yields a useful form for these state vector differential equations. Solutions to these equations are obtained by employing an integrating matrix method. The application of an integrating matrix provides a discretization of the differential equations that only requires solutions of standard linear matrix systems. It is demonstrated that matrix partitioning can be used to reduce the order of the required solutions. Results are presented for several example problems in structural dynamics and aeroelasticity to verify the technique and to demonstrate its use. These problems examine various types of loading and boundary conditions and include aeroelastic analyses of lifting surfaces constructed from anisotropic composite materials.

Optical Coherence Tomography Machine Learning Classifiers for Glaucoma Detection: A Preliminary Study

PubMed Central

Burgansky-Eliash, Zvia; Wollstein, Gadi; Chu, Tianjiao; Ramsey, Joseph D.; Glymour, Clark; Noecker, Robert J.; Ishikawa, Hiroshi; Schuman, Joel S.

2007-01-01

Purpose Machine-learning classifiers are trained computerized systems with the ability to detect the relationship between multiple input parameters and a diagnosis. The present study investigated whether the use of machine-learning classifiers improves optical coherence tomography (OCT) glaucoma detection. Methods Forty-seven patients with glaucoma (47 eyes) and 42 healthy subjects (42 eyes) were included in this cross-sectional study. Of the glaucoma patients, 27 had early disease (visual field mean deviation [MD] ≥ −6 dB) and 20 had advanced glaucoma (MD < −6 dB). Machine-learning classifiers were trained to discriminate between glaucomatous and healthy eyes using parameters derived from OCT output. The classifiers were trained with all 38 parameters as well as with only 8 parameters that correlated best with the visual field MD. Five classifiers were tested: linear discriminant analysis, support vector machine, recursive partitioning and regression tree, generalized linear model, and generalized additive model. For the last two classifiers, a backward feature selection was used to find the minimal number of parameters that resulted in the best and most simple prediction. The cross-validated receiver operating characteristic (ROC) curve and accuracies were calculated. Results The largest area under the ROC curve (AROC) for glaucoma detection was achieved with the support vector machine using eight parameters (0.981). The sensitivity at 80% and 95% specificity was 97.9% and 92.5%, respectively. This classifier also performed best when judged by cross-validated accuracy (0.966). The best classification between early glaucoma and advanced glaucoma was obtained with the generalized additive model using only three parameters (AROC = 0.854). Conclusions Automated machine classifiers of OCT data might be useful for enhancing the utility of this technology for detecting glaucomatous abnormality. PMID:16249492

PREDICTION OF THE SOLUBILITY, ACTIVITY COEFFICIENT AND LIQUID/LIQUID PARTITION COEFFICIENT OF ORGANIC COMPOUNDS

EPA Science Inventory

Solvation models, based on fundamental chemical structure theory, were developed in the SPARC mechanistic tool box to predict a large array of physical properties of organic compounds in water and in non-aqueous solvents strictly from molecular structure. The SPARC self-interact...

Assessing the impact of climate change on vector-borne viruses in the EU through the elicitation of expert opinion.

PubMed

Gale, P; Brouwer, A; Ramnial, V; Kelly, L; Kosmider, R; Fooks, A R; Snary, E L

2010-02-01

Expert opinion was elicited to undertake a qualitative risk assessment to estimate the current and future risks to the European Union (EU) from five vector-borne viruses listed by the World Organization for Animal Health. It was predicted that climate change will increase the risk of incursions of African horse sickness virus (AHSV), Crimean-Congo haemorrhagic fever virus (CCHFV) and Rift Valley fever virus (RVFV) into the EU from other parts of the world, with African swine fever virus (ASFV) and West Nile virus (WNV) being less affected. Currently the predicted risks of incursion were lowest for RVFV and highest for ASFV. Risks of incursion were considered for six routes of entry (namely vectors, livestock, meat products, wildlife, pets and people). Climate change was predicted to increase the risk of incursion from entry of vectors for all five viruses to some degree, the strongest effects being predicted for AHSV, CCHFV and WNV. This work will facilitate identification of appropriate risk management options in relation to adaptations to climate change.

Prediction of hourly PM2.5 using a space-time support vector regression model

NASA Astrophysics Data System (ADS)

Yang, Wentao; Deng, Min; Xu, Feng; Wang, Hang

2018-05-01

Real-time air quality prediction has been an active field of research in atmospheric environmental science. The existing methods of machine learning are widely used to predict pollutant concentrations because of their enhanced ability to handle complex non-linear relationships. However, because pollutant concentration data, as typical geospatial data, also exhibit spatial heterogeneity and spatial dependence, they may violate the assumptions of independent and identically distributed random variables in most of the machine learning methods. As a result, a space-time support vector regression model is proposed to predict hourly PM2.5 concentrations. First, to address spatial heterogeneity, spatial clustering is executed to divide the study area into several homogeneous or quasi-homogeneous subareas. To handle spatial dependence, a Gauss vector weight function is then developed to determine spatial autocorrelation variables as part of the input features. Finally, a local support vector regression model with spatial autocorrelation variables is established for each subarea. Experimental data on PM2.5 concentrations in Beijing are used to verify whether the results of the proposed model are superior to those of other methods.

Sine Rotation Vector Method for Attitude Estimation of an Underwater Robot

PubMed Central

Ko, Nak Yong; Jeong, Seokki; Bae, Youngchul

2016-01-01

This paper describes a method for estimating the attitude of an underwater robot. The method employs a new concept of sine rotation vector and uses both an attitude heading and reference system (AHRS) and a Doppler velocity log (DVL) for the purpose of measurement. First, the acceleration and magnetic-field measurements are transformed into sine rotation vectors and combined. The combined sine rotation vector is then transformed into the differences between the Euler angles of the measured attitude and the predicted attitude; the differences are used to correct the predicted attitude. The method was evaluated according to field-test data and simulation data and compared to existing methods that calculate angular differences directly without a preceding sine rotation vector transformation. The comparison verifies that the proposed method improves the attitude estimation performance. PMID:27490549

The partition behavior of perfluorooctanesulfonate (PFOS) and perfluorooctanesulfonamide (FOSA) on microplastics.

PubMed

Wang, Fei; Shih, Kai Min; Li, Xiao Yan

2015-01-01

Microplastics have been recognized as transport vectors for heavy metals and organic pollutants to marine animals. Thus, the sorption behavior of contaminant on microplastic is crucial to their transport in marine system. In this study, the sorption behavior of PFOS and FOSA (two perfluorochemicals) on three kinds of microplastics (PE, PS, and PVC) are reported. The isotherm study showed that the sorption of PFOS and FOSA on microplastics is highly linear, and it indicated that partition by hydrophobic interaction is the predominant sorption mechanism. The Kd values of FOSA on three kinds of microplastics are all higher than those of PFOS, and the reason is attributed to their different functional groups. The Kd value of FOSA on three types of microplastics followed the order as: PE>PVC>PS. Such finding may indicate that the molecule composition and structure of microplastics play important roles in their sorption processes of organic pollutants. The PFOS sorption levels on PE and PS particles were increased with the increase of NaCl and CaCl2 concentrations, while the ion concentrations have no effect on FOSA sorption. The study on the pH effects on PFOS and FOSA sorption indicated FOSA could partition under various pH conditions on three types of microplastics while PFOS sorption on PE and PS were favored with lower pH. Copyright © 2014 Elsevier Ltd. All rights reserved.

Ligand-promoted protein folding by biased kinetic partitioning.

PubMed

Hingorani, Karan S; Metcalf, Matthew C; Deming, Derrick T; Garman, Scott C; Powers, Evan T; Gierasch, Lila M

2017-04-01

Protein folding in cells occurs in the presence of high concentrations of endogenous binding partners, and exogenous binding partners have been exploited as pharmacological chaperones. A combined mathematical modeling and experimental approach shows that a ligand improves the folding of a destabilized protein by biasing the kinetic partitioning between folding and alternative fates (aggregation or degradation). Computationally predicted inhibition of test protein aggregation and degradation as a function of ligand concentration are validated by experiments in two disparate cellular systems.

Ligand-Promoted Protein Folding by Biased Kinetic Partitioning

PubMed Central

Hingorani, Karan S.; Metcalf, Matthew C.; Deming, Derrick T.; Garman, Scott C.; Powers, Evan T.; Gierasch, Lila M.

2017-01-01

Protein folding in cells occurs in the presence of high concentrations of endogenous binding partners, and exogenous binding partners have been exploited as pharmacological chaperones. A combined mathematical modeling and experimental approach shows that a ligand improves the folding of a destabilized protein by biasing the kinetic partitioning between folding and alternative fates (aggregation or degradation). Computationally predicted inhibition of test protein aggregation and degradation as a function of ligand concentration are validated by experiments in two disparate cellular systems. PMID:28218913

Fine particle pH and gas-particle phase partitioning of inorganic species in Pasadena, California, during the 2010 CalNex campaign

NASA Astrophysics Data System (ADS)

Guo, Hongyu; Liu, Jiumeng; Froyd, Karl D.; Roberts, James M.; Veres, Patrick R.; Hayes, Patrick L.; Jimenez, Jose L.; Nenes, Athanasios; Weber, Rodney J.

2017-05-01

pH is a fundamental aerosol property that affects ambient particle concentration and composition, linking pH to all aerosol environmental impacts. Here, PM1 and PM2. 5 pH are calculated based on data from measurements during the California Research at the Nexus of Air Quality and Climate Change (CalNex) study from 15 May to 15 June 2010 in Pasadena, CA. Particle pH and water were predicted with the ISORROPIA-II thermodynamic model and validated by comparing predicted to measured gas-particle partitioning of inorganic nitrate, ammonium, and chloride. The study mean ± standard deviation PM1 pH was 1.9 ± 0.5 for the SO42--NO3--NH4+-HNO3-NH3 system. For PM2. 5, internal mixing of sea salt components (SO42--NO3--NH4+-Na+-Cl--K+-HNO3-NH3-HCl system) raised the bulk pH to 2.7 ± 0.3 and improved predicted nitric acid partitioning with PM2. 5 components. The results show little effect of sea salt on PM1 pH, but significant effects on PM2. 5 pH. A mean PM1 pH of 1.9 at Pasadena was approximately one unit higher than what we have reported in the southeastern US, despite similar temperature, relative humidity, and sulfate ranges, and is due to higher total nitrate concentrations (nitric acid plus nitrate) relative to sulfate, a situation where particle water is affected by semi-volatile nitrate concentrations. Under these conditions nitric acid partitioning can further promote nitrate formation by increasing aerosol water, which raises pH by dilution, further increasing nitric acid partitioning and resulting in a significant increase in fine particle nitrate and pH. This study provides insights into the complex interactions between particle pH and nitrate in a summertime coastal environment and a contrast to recently reported pH in the eastern US in summer and winter and the eastern Mediterranean. All studies have consistently found highly acidic PM1 with pH generally below 3.

Analysis of Genetic Algorithm for Rule-Set Production (GARP) modeling approach for predicting distributions of fleas implicated as vectors of plague, Yersinia pestis, in California.

PubMed

Adjemian, Jennifer C Z; Girvetz, Evan H; Beckett, Laurel; Foley, Janet E

2006-01-01

More than 20 species of fleas in California are implicated as potential vectors of Yersinia pestis. Extremely limited spatial data exist for plague vectors-a key component to understanding where the greatest risks for human, domestic animal, and wildlife health exist. This study increases the spatial data available for 13 potential plague vectors by using the ecological niche modeling system Genetic Algorithm for Rule-Set Production (GARP) to predict their respective distributions. Because the available sample sizes in our data set varied greatly from one species to another, we also performed an analysis of the robustness of GARP by using the data available for flea Oropsylla montana (Baker) to quantify the effects that sample size and the chosen explanatory variables have on the final species distribution map. GARP effectively modeled the distributions of 13 vector species. Furthermore, our analyses show that all of these modeled ranges are robust, with a sample size of six fleas or greater not significantly impacting the percentage of the in-state area where the flea was predicted to be found, or the testing accuracy of the model. The results of this study will help guide the sampling efforts of future studies focusing on plague vectors.

Centrifuge models simulating magma emplacement during oblique rifting

NASA Astrophysics Data System (ADS)

Corti, Giacomo; Bonini, Marco; Innocenti, Fabrizio; Manetti, Piero; Mulugeta, Genene

2001-07-01

A series of centrifuge analogue experiments have been performed to model the mechanics of continental oblique extension (in the range of 0° to 60°) in the presence of underplated magma at the base of the continental crust. The experiments reproduced the main characteristics of oblique rifting, such as (1) en-echelon arrangement of structures, (2) mean fault trends oblique to the extension vector, (3) strain partitioning between different sets of faults and (4) fault dips higher than in purely normal faults (e.g. Tron, V., Brun, J.-P., 1991. Experiments on oblique rifting in brittle-ductile systems. Tectonophysics 188, 71-84). The model results show that the pattern of deformation is strongly controlled by the angle of obliquity ( α), which determines the ratio between the shearing and stretching components of movement. For α⩽35°, the deformation is partitioned between oblique-slip and normal faults, whereas for α⩾45° a strain partitioning arises between oblique-slip and strike-slip faults. The experimental results show that for α⩽35°, there is a strong coupling between deformation and the underplated magma: the presence of magma determines a strain localisation and a reduced strain partitioning; deformation, in turn, focuses magma emplacement. Magmatic chambers form in the core of lower crust domes with an oblique trend to the initial magma reservoir and, in some cases, an en-echelon arrangement. Typically, intrusions show an elongated shape with a high length/width ratio. In nature, this pattern is expected to result in magmatic and volcanic belts oblique to the rift axis and arranged en-echelon, in agreement with some selected natural examples of continental rifts (i.e. Main Ethiopian Rift) and oceanic ridges (i.e. Mohns and Reykjanes Ridges).

Predicting Jakarta composite index using hybrid of fuzzy time series and support vector regression models

NASA Astrophysics Data System (ADS)

Febrian Umbara, Rian; Tarwidi, Dede; Budi Setiawan, Erwin

2018-03-01

The paper discusses the prediction of Jakarta Composite Index (JCI) in Indonesia Stock Exchange. The study is based on JCI historical data for 1286 days to predict the value of JCI one day ahead. This paper proposes predictions done in two stages., The first stage using Fuzzy Time Series (FTS) to predict values of ten technical indicators, and the second stage using Support Vector Regression (SVR) to predict the value of JCI one day ahead, resulting in a hybrid prediction model FTS-SVR. The performance of this combined prediction model is compared with the performance of the single stage prediction model using SVR only. Ten technical indicators are used as input for each model.

Modelling the distributions and spatial coincidence of bluetongue vectors Culicoides imicola and the Culicoides obsoletus group throughout the Iberian peninsula.

PubMed

Calvete, C; Estrada, R; Miranda, M A; Borrás, D; Calvo, J H; Lucientes, J

2008-06-01

Data obtained by a Spanish national surveillance programme in 2005 were used to develop climatic models for predictions of the distribution of the bluetongue virus (BTV) vectors Culicoides imicola Kieffer (Diptera: Ceratopogonidae) and the Culicoides obsoletus group Meigen throughout the Iberian peninsula. Models were generated using logistic regression to predict the probability of species occurrence at an 8-km spatial resolution. Predictor variables included the annual mean values and seasonalities of a remotely sensed normalized difference vegetation index (NDVI), a sun index, interpolated precipitation and temperature. Using an information-theoretic paradigm based on Akaike's criterion, a set of best models accounting for 95% of model selection certainty were selected and used to generate an average predictive model for each vector. The predictive performances (i.e. the discrimination capacity and calibration) of the average models were evaluated by both internal and external validation. External validation was achieved by comparing average model predictions with surveillance programme data obtained in 2004 and 2006. The discriminatory capacity of both models was found to be reasonably high. The estimated areas under the receiver operating characteristic (ROC) curve (AUC) were 0.78 and 0.70 for the C. imicola and C. obsoletus group models, respectively, in external validation, and 0.81 and 0.75, respectively, in internal validation. The predictions of both models were in close agreement with the observed distribution patterns of both vectors. Both models, however, showed a systematic bias in their predicted probability of occurrence: observed occurrence was systematically overestimated for C. imicola and underestimated for the C. obsoletus group. Average models were used to determine the areas of spatial coincidence of the two vectors. Although their spatial distributions were highly complementary, areas of spatial coincidence were identified, mainly in Portugal and in the southwest of peninsular Spain. In a hypothetical scenario in which both Culicoides members had similar vectorial capacity for a BTV strain, these areas should be considered of special epidemiological concern because any epizootic event could be intensified by consecutive vector activity developed for both species during the year; consequently, the probability of BTV spreading to remaining areas occupied by both vectors might also be higher.

Ecological determinants of avian malaria infections: An integrative analysis at landscape, mosquito and vertebrate community levels.

PubMed

Ferraguti, Martina; Martínez-de la Puente, Josué; Bensch, Staffan; Roiz, David; Ruiz, Santigo; Viana, Duarte S; Soriguer, Ramón C; Figuerola, Jordi

2018-05-01

Vector and host communities, as well as habitat characteristics, may have important but different impacts on the prevalence, richness and evenness of vector-borne parasites. We investigated the relative importance of (1) the mosquito community composition, (2) the vertebrate community composition and (3) landscape characteristics on the prevalence, richness and evenness of avian Plasmodium. We hypothesized that parasite prevalence will be more affected by vector-related parameters, while host parameters should be also important to explain Plasmodium richness and evenness. We sampled 2,588 wild house sparrows (Passer domesticus) and 340,829 mosquitoes, and we performed vertebrate censuses at 45 localities in the Southwest of Spain. These localities included urban, rural and natural landscapes that were characterized by several habitat variables. Twelve Plasmodium lineages were identified in house sparrows corresponding to three major clades. Variation partitioning showed that landscape characteristics explained the highest fraction of variation in all response variables (21.0%-44.8%). Plasmodium prevalence was in addition explained by vector-related variables (5.4%) and its interaction with landscape (10.2%). Parasite richness and evenness were mostly explained by vertebrate community-related variables. The structuring role of landscape characteristics in vector and host communities was a key factor in determining parasite prevalence, richness and evenness, although the role of each factor differed according to the parasite parameters studied. These results show that the biotic and abiotic contexts are important to explain the transmission dynamics of mosquito-borne pathogens in the wild. © 2018 The Authors. Journal of Animal Ecology © 2018 British Ecological Society.

Fate of heavy metals during municipal solid waste incineration.

PubMed

Abanades, S; Flamant, G; Gagnepain, B; Gauthier, D

2002-02-01

A thermodynamic analysis was performed to determine whether it is suitable to predict the heavy metal (HM) speciation during the Municipal Solid Waste Incineration process. The fate of several selected metals (Cd, Pb, Zn, Cr, Hg, As, Cu, Co, Ni) during incineration was theoretically investigated. The equilibrium analysis predicted the metal partitioning during incineration and determined the impact of operating conditions (temperature and gas composition) on their speciation. The study of the gas composition influence was based on the effects of the contents of oxygen (reducing or oxidising conditions) and chlorine on the HM partitioning. The theoretical HM speciation which was calculated in a complex system representing a burning sample of Municipal Solid Waste can explain the real partitioning (obtained from literature results) of all metals among the various ashes except for Pb. Then, the results of the thermodynamic study were compared with those of characterisation of real incinerator residues, using complementary techniques (chemical extraction series and X-ray micro-analyses). These analysis were performed to determine experimentally the speciation of the three representative metals Cr, Pb, and Zn. The agreement is good for Cr and Zn but not for Pb again, which mainly shows unleachable chemical speciations in the residues. Pb tends to remain in the bottom ash whereas thermodynamics often predicts its complete volatilisation under chlorides, and thus its presence exclusively in fly ash.

Using soil properties to predict in vivo bioavailability of lead in soils.

PubMed

Wijayawardena, M A Ayanka; Naidu, Ravi; Megharaj, Mallavarapu; Lamb, Dane; Thavamani, Palanisami; Kuchel, Tim

2015-11-01

Soil plays a significant role in controlling the potential bioavailability of contaminants in the environment. In this study, eleven soils were used to investigate the relationship between soil properties and relative bioavailability (RB) of lead (Pb). To minimise the effect of source of Pb on in vivo bioavailability, uncontaminated study soils were spiked with 1500 mg Pb/kg soil and aged for 10-12 months prior to investigating the relationships between soil properties and in vivo RB of Pb using swine model. The biological responses to oral administration of Pb in aqueous phase or as spiked soils were compared by applying a two-compartment pharmacokinetic model to blood Pb concentration. The study revealed that RB of Pb from aged soils ranged from 30±9% to 83±7%. The very different RB of Pb in these soils was attributed to variations in the soils' physico-chemical properties. This was established using sorption studies showing: firstly, Freundlich partition coefficients that ranged from 21 to 234; and secondly, a strongly significant (R(2)=0.94, P<0.001) exponential relationship between RB and Freundlich partition coefficient (Kd). This simple exponential model can be used to predict relative bioavailability of Pb in contaminated soils. To the best of our knowledge, this is the first such model derived using sorption partition coefficient to predict the relative bioavailability of Pb. Copyright © 2015 Elsevier Ltd. All rights reserved.

Interseismic Coupling and Seismic Potential along the Indo-Burmese Arc and the Sagaing fault

NASA Astrophysics Data System (ADS)

Earnest, A.

2017-12-01

The Indo-burmese arc is formed by the oblique subduction of the Indian plate under the Eurasia. This region is a transition zone between the main Himalayan collision belt and the Andaman subduction zone. This obliquity causes strain partitioning which causes separation of a sliver plate, the Burma Plate. Considering the geomorphic, tectonic and geophysical signatures, IBR comprises all the structural features of an active subduction zone, whereas the present day tectonics of this region is perplexing. Ni et al. [1989] and Rao and Kalpana [2005] suggested that the subduction might have stopped in recent times or continues relatively in an aseismic fashion. This is implied by the NNE compressional stress orientations, instead of its downdip direction. The focal mechanism stress inversions show distinct stress fields above and below the 90 km depth. It is widely believed that the partitioning of Indian-Eurasia plate motion along the Indo-buremse arc and the Sagaing fault region the reason for earthquake occurrence in this region. The relative motion of 36mm/yr, between India and Eurasia, is partitioned across the Sagaing fault through a dextral movement of ˜20mm/yr and remaining velocity is accommodated at the Churachandapur-Mao fault (CMF) through dextral motion. The CMF and its surroundings are considered as seismically a low hazard region, an observation made from the absence of significant earthquakes and lack of field evidences. This made Kundu and Gahalaut [2013] to propose that the motion across the CMF happens in an aseismic manner. Recently, based on GPS studies Steckler et al. [2016] suggested that the region is still actively subducting and the presence of a locked megathrust plate boundary depicts the region as highly vulnerable for large magnitude seismic activities. Our study, based on various geodetic solutions and earthquake slip vectors, focus on interseisimic block models for the Indo-burmese arc and Sagaing fault region so as to model the crustal deformation of this area using an elastic block modelling approach. Results from our best fit model predicts the spatial distribution of interseismic coupling coefficient (φ) and the backslip component. These coefficients characterize the fault interface, which helps in estimating the seismic potential across Indo-burmese arc and the Sagaing fault region.

A double-panel active segmented partition module using decoupled analog feedback controllers: numerical model.

PubMed

Sagers, Jason D; Leishman, Timothy W; Blotter, Jonathan D

2009-06-01

Low-frequency sound transmission has long plagued the sound isolation performance of lightweight partitions. Over the past 2 decades, researchers have investigated actively controlled structures to prevent sound transmission from a source space into a receiving space. An approach using active segmented partitions (ASPs) seeks to improve low-frequency sound isolation capabilities. An ASP is a partition which has been mechanically and acoustically segmented into a number of small individually controlled modules. This paper provides a theoretical and numerical development of a single ASP module configuration, wherein each panel of the double-panel structure is independently actuated and controlled by an analog feedback controller. A numerical model is developed to estimate frequency response functions for the purpose of controller design, to understand the effects of acoustic coupling between the panels, to predict the transmission loss of the module in both passive and active states, and to demonstrate that the proposed ASP module will produce bidirectional sound isolation.

Accounting for water levels and black carbon-inclusive sediment-water partitioning of organochlorines in Lesser Himalaya, Pakistan using two-carbon model.

PubMed

Ali, Usman; Sweetman, Andrew James; Jones, Kevin C; Malik, Riffat Naseem

2018-06-18

This study was designed to monitor organochlorine pesticides (OCPs) and polychlorinated biphenyls (PCBs) in riverine water of Lesser Himalaya along the altitude. Further, the sediment-water partitioning employing organic carbon and black carbon models were assessed. Results revealed higher water levels of organochlorine pesticides (0.07-41.4 ng L -1 ) and polychlorinated biphenyls (0.671-84.5 ng L -1 ) in Lesser Himalayan Region (LHR) of Pakistan. Spatially, elevated levels were observed in the altitudinal zone (737-975 masl) which is influenced by anthropogenic and industrial activities. Sediment-water partitioning of OCPs and PCBs were deduced using field data by employing one-carbon (f OC K OC ) and two-carbon Freundlich models (f OC K OC + f BC K BC C W nF-1 ). Results suggested improved measured vs predicted model concentrations when black carbon was induced in the model and suggested adsorption to be the dominant mechanism in phase partitioning of organochlorines in LHR.

Performance metrics and variance partitioning reveal sources of uncertainty in species distribution models

USGS Publications Warehouse

Watling, James I.; Brandt, Laura A.; Bucklin, David N.; Fujisaki, Ikuko; Mazzotti, Frank J.; Romañach, Stephanie; Speroterra, Carolina

2015-01-01

Species distribution models (SDMs) are widely used in basic and applied ecology, making it important to understand sources and magnitudes of uncertainty in SDM performance and predictions. We analyzed SDM performance and partitioned variance among prediction maps for 15 rare vertebrate species in the southeastern USA using all possible combinations of seven potential sources of uncertainty in SDMs: algorithms, climate datasets, model domain, species presences, variable collinearity, CO2 emissions scenarios, and general circulation models. The choice of modeling algorithm was the greatest source of uncertainty in SDM performance and prediction maps, with some additional variation in performance associated with the comprehensiveness of the species presences used for modeling. Other sources of uncertainty that have received attention in the SDM literature such as variable collinearity and model domain contributed little to differences in SDM performance or predictions in this study. Predictions from different algorithms tended to be more variable at northern range margins for species with more northern distributions, which may complicate conservation planning at the leading edge of species' geographic ranges. The clear message emerging from this work is that researchers should use multiple algorithms for modeling rather than relying on predictions from a single algorithm, invest resources in compiling a comprehensive set of species presences, and explicitly evaluate uncertainty in SDM predictions at leading range margins.

FLAPS (Fatigue Life Analysis Programs): Computer Programs to Predict Cyclic Life Using the Total Strain Version of Strainrange Partitioning and Other Life Prediction Methods. Users' Manual and Example Problems, Version 1.0

NASA Technical Reports Server (NTRS)

Arya, Vinod K.; Halford, Gary R. (Technical Monitor)

2003-01-01

This manual presents computer programs FLAPS for characterizing and predicting fatigue and creep-fatigue resistance of metallic materials in the high-temperature, long-life regime for isothermal and nonisothermal fatigue. The programs use the Total Strain version of Strainrange Partitioning (TS-SRP), and several other life prediction methods described in this manual. The user should be thoroughly familiar with the TS-SRP and these life prediction methods before attempting to use any of these programs. Improper understanding can lead to incorrect use of the method and erroneous life predictions. An extensive database has also been developed in a parallel effort. The database is probably the largest source of high-temperature, creep-fatigue test data available in the public domain and can be used with other life-prediction methods as well. This users' manual, software, and database are all in the public domain and can be obtained by contacting the author. The Compact Disk (CD) accompanying this manual contains an executable file for the FLAPS program, two datasets required for the example problems in the manual, and the creep-fatigue data in a format compatible with these programs.

Pan evaporation modeling using six different heuristic computing methods in different climates of China

NASA Astrophysics Data System (ADS)

Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Li, Hui

2017-01-01

Pan evaporation (Ep) plays important roles in agricultural water resources management. One of the basic challenges is modeling Ep using limited climatic parameters because there are a number of factors affecting the evaporation rate. This study investigated the abilities of six different soft computing methods, multi-layer perceptron (MLP), generalized regression neural network (GRNN), fuzzy genetic (FG), least square support vector machine (LSSVM), multivariate adaptive regression spline (MARS), adaptive neuro-fuzzy inference systems with grid partition (ANFIS-GP), and two regression methods, multiple linear regression (MLR) and Stephens and Stewart model (SS) in predicting monthly Ep. Long-term climatic data at various sites crossing a wide range of climates during 1961-2000 are used for model development and validation. The results showed that the models have different accuracies in different climates and the MLP model performed superior to the other models in predicting monthly Ep at most stations using local input combinations (for example, the MAE (mean absolute errors), RMSE (root mean square errors), and determination coefficient (R2) are 0.314 mm/day, 0.405 mm/day and 0.988, respectively for HEB station), while GRNN model performed better in Tibetan Plateau (MAE, RMSE and R2 are 0.459 mm/day, 0.592 mm/day and 0.932, respectively). The accuracies of above models ranked as: MLP, GRNN, LSSVM, FG, ANFIS-GP, MARS and MLR. The overall results indicated that the soft computing techniques generally performed better than the regression methods, but MLR and SS models can be more preferred at some climatic zones instead of complex nonlinear models, for example, the BJ (Beijing), CQ (Chongqing) and HK (Haikou) stations. Therefore, it can be concluded that Ep could be successfully predicted using above models in hydrological modeling studies.

Sorption of vapors of some organic liquids on soil humic acid and its relation to partitioning of organic compounds in soil organic matter

USGS Publications Warehouse

Chlou, G.T.; Kile, D.E.; Malcolm, R.L.

1988-01-01

Vapor sorption of water, ethanol, benzene, hexane, carbon tetrachloride, 1,1,1-trichloroethane, trichloroethylene, tetrachloroethylene, and 1,2-dibromoethane on (Sanhedron) soil humic acid has been determined at room temperature. Isotherms for all organic liquids are highly linear over a wide range of relative pressure (P/P??), characteristic of the partitioning (dissolution) of the organic compounds in soil humic acid. Polar liquids exhibit markedly greater sorption capacities on soil humic acid than relatively nonpolar liquids, in keeping with the polar nature of the soil humic acid as a partition medium. The limiting sorption (partition) capacities of relatively non-polar liquids are remarkably similar when expressed in terms of volumes per unit weight of soil humic acid. The soil humic acid is found to be about half as effective as soil organic matter in sorption of relatively nonpolar organic compounds. The nearly constant limiting sorption capacity for nonpolar organic liquids with soil humic acid on a volume-to-weight basis and its efficiency in sorption relative to soil organic matter provide a basis for predicting the approximate sorption (partition) coefficients of similar compounds in uptake by soil in aqueous systems.

Multi-centre diagnostic classification of individual structural neuroimaging scans from patients with major depressive disorder.

PubMed

Mwangi, Benson; Ebmeier, Klaus P; Matthews, Keith; Steele, J Douglas

2012-05-01

Quantitative abnormalities of brain structure in patients with major depressive disorder have been reported at a group level for decades. However, these structural differences appear subtle in comparison with conventional radiologically defined abnormalities, with considerable inter-subject variability. Consequently, it has not been possible to readily identify scans from patients with major depressive disorder at an individual level. Recently, machine learning techniques such as relevance vector machines and support vector machines have been applied to predictive classification of individual scans with variable success. Here we describe a novel hybrid method, which combines machine learning with feature selection and characterization, with the latter aimed at maximizing the accuracy of machine learning prediction. The method was tested using a multi-centre dataset of T(1)-weighted 'structural' scans. A total of 62 patients with major depressive disorder and matched controls were recruited from referred secondary care clinical populations in Aberdeen and Edinburgh, UK. The generalization ability and predictive accuracy of the classifiers was tested using data left out of the training process. High prediction accuracy was achieved (~90%). While feature selection was important for maximizing high predictive accuracy with machine learning, feature characterization contributed only a modest improvement to relevance vector machine-based prediction (~5%). Notably, while the only information provided for training the classifiers was T(1)-weighted scans plus a categorical label (major depressive disorder versus controls), both relevance vector machine and support vector machine 'weighting factors' (used for making predictions) correlated strongly with subjective ratings of illness severity. These results indicate that machine learning techniques have the potential to inform clinical practice and research, as they can make accurate predictions about brain scan data from individual subjects. Furthermore, machine learning weighting factors may reflect an objective biomarker of major depressive disorder illness severity, based on abnormalities of brain structure.

Trans-dimensional inversion of microtremor array dispersion data with hierarchical autoregressive error models

NASA Astrophysics Data System (ADS)

Dettmer, Jan; Molnar, Sheri; Steininger, Gavin; Dosso, Stan E.; Cassidy, John F.

2012-02-01

This paper applies a general trans-dimensional Bayesian inference methodology and hierarchical autoregressive data-error models to the inversion of microtremor array dispersion data for shear wave velocity (vs) structure. This approach accounts for the limited knowledge of the optimal earth model parametrization (e.g. the number of layers in the vs profile) and of the data-error statistics in the resulting vs parameter uncertainty estimates. The assumed earth model parametrization influences estimates of parameter values and uncertainties due to different parametrizations leading to different ranges of data predictions. The support of the data for a particular model is often non-unique and several parametrizations may be supported. A trans-dimensional formulation accounts for this non-uniqueness by including a model-indexing parameter as an unknown so that groups of models (identified by the indexing parameter) are considered in the results. The earth model is parametrized in terms of a partition model with interfaces given over a depth-range of interest. In this work, the number of interfaces (layers) in the partition model represents the trans-dimensional model indexing. In addition, serial data-error correlations are addressed by augmenting the geophysical forward model with a hierarchical autoregressive error model that can account for a wide range of error processes with a small number of parameters. Hence, the limited knowledge about the true statistical distribution of data errors is also accounted for in the earth model parameter estimates, resulting in more realistic uncertainties and parameter values. Hierarchical autoregressive error models do not rely on point estimates of the model vector to estimate data-error statistics, and have no requirement for computing the inverse or determinant of a data-error covariance matrix. This approach is particularly useful for trans-dimensional inverse problems, as point estimates may not be representative of the state space that spans multiple subspaces of different dimensionalities. The order of the autoregressive process required to fit the data is determined here by posterior residual-sample examination and statistical tests. Inference for earth model parameters is carried out on the trans-dimensional posterior probability distribution by considering ensembles of parameter vectors. In particular, vs uncertainty estimates are obtained by marginalizing the trans-dimensional posterior distribution in terms of vs-profile marginal distributions. The methodology is applied to microtremor array dispersion data collected at two sites with significantly different geology in British Columbia, Canada. At both sites, results show excellent agreement with estimates from invasive measurements.

Predicting dense nonaqueous phase liquid dissolution using a simplified source depletion model parameterized with partitioning tracers

NASA Astrophysics Data System (ADS)

Basu, Nandita B.; Fure, Adrian D.; Jawitz, James W.

2008-07-01

Simulations of nonpartitioning and partitioning tracer tests were used to parameterize the equilibrium stream tube model (ESM) that predicts the dissolution dynamics of dense nonaqueous phase liquids (DNAPLs) as a function of the Lagrangian properties of DNAPL source zones. Lagrangian, or stream-tube-based, approaches characterize source zones with as few as two trajectory-integrated parameters, in contrast to the potentially thousands of parameters required to describe the point-by-point variability in permeability and DNAPL in traditional Eulerian modeling approaches. The spill and subsequent dissolution of DNAPLs were simulated in two-dimensional domains having different hydrologic characteristics (variance of the log conductivity field = 0.2, 1, and 3) using the multiphase flow and transport simulator UTCHEM. Nonpartitioning and partitioning tracers were used to characterize the Lagrangian properties (travel time and trajectory-integrated DNAPL content statistics) of DNAPL source zones, which were in turn shown to be sufficient for accurate prediction of source dissolution behavior using the ESM throughout the relatively broad range of hydraulic conductivity variances tested here. The results were found to be relatively insensitive to travel time variability, suggesting that dissolution could be accurately predicted even if the travel time variance was only coarsely estimated. Estimation of the ESM parameters was also demonstrated using an approximate technique based on Eulerian data in the absence of tracer data; however, determining the minimum amount of such data required remains for future work. Finally, the stream tube model was shown to be a more unique predictor of dissolution behavior than approaches based on the ganglia-to-pool model for source zone characterization.

Representation of Vegetation and Other Nonerodible Elements in Aeolian Shear Stress Partitioning Models for Predicting Transport Threshold

NASA Technical Reports Server (NTRS)

King, James; Nickling, William G.; Gillies, John A.

2005-01-01

The presence of nonerodible elements is well understood to be a reducing factor for soil erosion by wind, but the limits of its protection of the surface and erosion threshold prediction are complicated by the varying geometry, spatial organization, and density of the elements. The predictive capabilities of the most recent models for estimating wind driven particle fluxes are reduced because of the poor representation of the effectiveness of vegetation to reduce wind erosion. Two approaches have been taken to account for roughness effects on sediment transport thresholds. Marticorena and Bergametti (1995) in their dust emission model parameterize the effect of roughness on threshold with the assumption that there is a relationship between roughness density and the aerodynamic roughness length of a surface. Raupach et al. (1993) offer a different approach based on physical modeling of wake development behind individual roughness elements and the partition of the surface stress and the total stress over a roughened surface. A comparison between the models shows the partitioning approach to be a good framework to explain the effect of roughness on entrainment of sediment by wind. Both models provided very good agreement for wind tunnel experiments using solid objects on a nonerodible surface. However, the Marticorena and Bergametti (1995) approach displays a scaling dependency when the difference between the roughness length of the surface and the overall roughness length is too great, while the Raupach et al. (1993) model's predictions perform better owing to the incorporation of the roughness geometry and the alterations to the flow they can cause.

SOLAR FLARE PREDICTION USING SDO/HMI VECTOR MAGNETIC FIELD DATA WITH A MACHINE-LEARNING ALGORITHM

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bobra, M. G.; Couvidat, S., E-mail: couvidat@stanford.edu

2015-01-10

We attempt to forecast M- and X-class solar flares using a machine-learning algorithm, called support vector machine (SVM), and four years of data from the Solar Dynamics Observatory's Helioseismic and Magnetic Imager, the first instrument to continuously map the full-disk photospheric vector magnetic field from space. Most flare forecasting efforts described in the literature use either line-of-sight magnetograms or a relatively small number of ground-based vector magnetograms. This is the first time a large data set of vector magnetograms has been used to forecast solar flares. We build a catalog of flaring and non-flaring active regions sampled from a databasemore » of 2071 active regions, comprised of 1.5 million active region patches of vector magnetic field data, and characterize each active region by 25 parameters. We then train and test the machine-learning algorithm and we estimate its performances using forecast verification metrics with an emphasis on the true skill statistic (TSS). We obtain relatively high TSS scores and overall predictive abilities. We surmise that this is partly due to fine-tuning the SVM for this purpose and also to an advantageous set of features that can only be calculated from vector magnetic field data. We also apply a feature selection algorithm to determine which of our 25 features are useful for discriminating between flaring and non-flaring active regions and conclude that only a handful are needed for good predictive abilities.« less

Compositional and phase relations among rare earth element minerals

NASA Technical Reports Server (NTRS)

Burt, D. M.

1989-01-01

A review is presented that mainly treats minerals in which the rare-earth elements are essential constituents, e.g., bastnaesite, monazite, xenotime, aeschynite, allanite. The chemical mechanisms and limits of REE substitution in some rock-forming minerals (zircon, apatite, titanite, garnet) are also derived. Vector representation of complex coupled substitutions in selected REE-bearing minerals is examined and some comments on REE-partitioning between minerals as related to acid-based tendencies and mineral stabilities are presented. As the same or analogous coupled substitutions involving the REE occur in a wide variety of mineral structures, they are discussed together.

Predicting potential ranges of primary malaria vectors and malaria in northern South America based on projected changes in climate, land cover and human population.

PubMed

Alimi, Temitope O; Fuller, Douglas O; Qualls, Whitney A; Herrera, Socrates V; Arevalo-Herrera, Myriam; Quinones, Martha L; Lacerda, Marcus V G; Beier, John C

2015-08-20

Changes in land use and land cover (LULC) as well as climate are likely to affect the geographic distribution of malaria vectors and parasites in the coming decades. At present, malaria transmission is concentrated mainly in the Amazon basin where extensive agriculture, mining, and logging activities have resulted in changes to local and regional hydrology, massive loss of forest cover, and increased contact between malaria vectors and hosts. Employing presence-only records, bioclimatic, topographic, hydrologic, LULC and human population data, we modeled the distribution of malaria and two of its dominant vectors, Anopheles darlingi, and Anopheles nuneztovari s.l. in northern South America using the species distribution modeling platform Maxent. Results from our land change modeling indicate that about 70,000 km(2) of forest land would be lost by 2050 and 78,000 km(2) by 2070 compared to 2010. The Maxent model predicted zones of relatively high habitat suitability for malaria and the vectors mainly within the Amazon and along coastlines. While areas with malaria are expected to decrease in line with current downward trends, both vectors are predicted to experience range expansions in the future. Elevation, annual precipitation and temperature were influential in all models both current and future. Human population mostly affected An. darlingi distribution while LULC changes influenced An. nuneztovari s.l. distribution. As the region tackles the challenge of malaria elimination, investigations such as this could be useful for planning and management purposes and aid in predicting and addressing potential impediments to elimination.

Soft sensor modeling based on variable partition ensemble method for nonlinear batch processes

NASA Astrophysics Data System (ADS)

Wang, Li; Chen, Xiangguang; Yang, Kai; Jin, Huaiping

2017-01-01

Batch processes are always characterized by nonlinear and system uncertain properties, therefore, the conventional single model may be ill-suited. A local learning strategy soft sensor based on variable partition ensemble method is developed for the quality prediction of nonlinear and non-Gaussian batch processes. A set of input variable sets are obtained by bootstrapping and PMI criterion. Then, multiple local GPR models are developed based on each local input variable set. When a new test data is coming, the posterior probability of each best performance local model is estimated based on Bayesian inference and used to combine these local GPR models to get the final prediction result. The proposed soft sensor is demonstrated by applying to an industrial fed-batch chlortetracycline fermentation process.

Fuzzy time series forecasting model with natural partitioning length approach for predicting the unemployment rate under different degree of confidence

NASA Astrophysics Data System (ADS)

Ramli, Nazirah; Mutalib, Siti Musleha Ab; Mohamad, Daud

2017-08-01

Fuzzy time series forecasting model has been proposed since 1993 to cater for data in linguistic values. Many improvement and modification have been made to the model such as enhancement on the length of interval and types of fuzzy logical relation. However, most of the improvement models represent the linguistic term in the form of discrete fuzzy sets. In this paper, fuzzy time series model with data in the form of trapezoidal fuzzy numbers and natural partitioning length approach is introduced for predicting the unemployment rate. Two types of fuzzy relations are used in this study which are first order and second order fuzzy relation. This proposed model can produce the forecasted values under different degree of confidence.

Characterizing gas-particle interactions of phthalate plasticizer emitted from vinyl flooring.

PubMed

Benning, Jennifer L; Liu, Zhe; Tiwari, Andrea; Little, John C; Marr, Linsey C

2013-03-19

Phthalates are widely used as plasticizers, and improved ability to predict emissions of phthalates is of interest because of concern about their health effects. An experimental chamber was used to measure emissions of di-2-ethylhexyl-phthalate (DEHP) from vinyl flooring, with ammonium sulfate particles introduced to examine their influence on the emission rate and to measure the partitioning of DEHP onto airborne particles. When particles were introduced to the chamber at concentrations of 100 to 245 μg/m(3), the total (gas + particle) DEHP concentrations increased by a factor of 3 to 8; under these conditions, emissions were significantly enhanced compared to the condition without particles. The measured DEHP partition coefficient to ammonium sulfate particles with a median diameter of 45 ± 5 nm was 0.032 ± 0.003 m(3)/μg (95% confidence interval). The DEHP-particle sorption equilibration time was demonstrated to be less than 1 min. Both the partition coefficient and equilibration time agree well with predictions from the literature. This study represents the first known measurements of the particle-gas partition coefficient for DEHP. Furthermore, the results demonstrate that the emission rate of DEHP is substantially enhanced in the presence of particles. The particles rapidly sorb DEHP from the gas phase, allowing more to be emitted from the source, and also appear to enhance the convective mass-transfer coefficient itself. Airborne particles can influence SVOC fate and transport in the indoor environment, and these mechanisms must be considered in evaluating exposure and human health.

Predictive Value of Morphological Features in Patients with Autism versus Normal Controls

ERIC Educational Resources Information Center

Ozgen, H.; Hellemann, G. S.; de Jonge, M. V.; Beemer, F. A.; van Engeland, H.

2013-01-01

We investigated the predictive power of morphological features in 224 autistic patients and 224 matched-pairs controls. To assess the relationship between the morphological features and autism, we used the receiver operator curves (ROC). In addition, we used recursive partitioning (RP) to determine a specific pattern of abnormalities that is…

Tectonic studies of the Caribbean: Pure GPS Euler vectors to test for rigidity and for the existence of a Northern Lesser Antilles Forearc block; constraints for tsunami risk from reassessment of the April 1, 1946 Alaska-Aleutians and August 4, 1946 Hispaniola events

NASA Astrophysics Data System (ADS)

Lopez, Alberto M.

New Global Positioning System (GPS) data from nine sites within stable portions of the Caribbean (CA) plate have been used to constrain its motion in the International GPS Service reference frame (IGSb00) and with respect to North (NA) and South (SA) American plates. A comparison of GPS velocities between eastern and western Caribbean sites shows that there is no significant internal deformation occurring within the plate (0.9 mm/yr). The CA-NA Euler vector was used to predict azimuths at the northern Lesser Antilles arc, which slip vectors still misfit (˜ 5°--25°). This confirms slip partitioning in the forearc, where a forearc sliver, for the first time introduced here as the Northern Lesser Antilles Forearc block (NLAF) moves counter-clockwise with respect to Caribbean crust. Seismicity of the plate in conjunction with historical records of tsunamis in the Caribbean serves as evidence the region is prone to tsunamis. The Hispaniola August 4, 1946 earthquake generated a tsunami that 5 resulted in great loss of lives. A quantitative comparison of this event with that of the Aleutians April 1, 1946 show the tsunami originated from a submarine landslide and not as a "tsunami earthquake". The April 1, 1946 earthquake stands out as an exceptional case of slow rupture velocity. With a re-computed fault area of 181 x 115 km from aftershock relocations, and seismic moment of 5 x 1027 dyn-cm, the event features the smallest radiated energy to moment ratio (theta = -7.03) ever recorded to date. Tsunami earthquakes, such as the Hispaniola 1946, may have already occured in the past and could occur again in the Caribbean. Hence, potential rupture segments have been identified and used as loci of possible events in an effort to help assess the tsunami risk in the Caribbean and Atlantic region.

Automatic prediction of protein domains from sequence information using a hybrid learning system.

PubMed

Nagarajan, Niranjan; Yona, Golan

2004-06-12

We describe a novel method for detecting the domain structure of a protein from sequence information alone. The method is based on analyzing multiple sequence alignments that are derived from a database search. Multiple measures are defined to quantify the domain information content of each position along the sequence and are combined into a single predictor using a neural network. The output is further smoothed and post-processed using a probabilistic model to predict the most likely transition positions between domains. The method was assessed using the domain definitions in SCOP and CATH for proteins of known structure and was compared with several other existing methods. Our method performs well both in terms of accuracy and sensitivity. It improves significantly over the best methods available, even some of the semi-manual ones, while being fully automatic. Our method can also be used to suggest and verify domain partitions based on structural data. A few examples of predicted domain definitions and alternative partitions, as suggested by our method, are also discussed. An online domain-prediction server is available at http://biozon.org/tools/domains/

Review of the Agard S&M panel evaluation program of the NASA-Lewis 'SRP' approach to high-temperature LCF life prediction. [Strainrange Partitioning for Low Cycle Fatigue

NASA Technical Reports Server (NTRS)

Hirschberg, M. H.

1978-01-01

Twenty laboratories in six countries participated in this program, each testing its own materials of interest under its own laboratory conditions. In this way the results obtained provided validation of the Strainrange Partitioning (SRP) method for a wide range of materials and insured maximum usefulness to each of the participating laboratories. The first, very necessary step in the evaluation of any life prediction approach - assessing the ability of the method to predict life of simple laboratory specimens subjected to complex loading, was thereby taken. The culmination of this program was the Specialists Meeting that was held in Aalborg, Denmark in April of 1978. At that meeting the various investigators shared their findings, thus providing the basis for an in-depth evaluation of the SRP method. While the results were variable from laboratory to laboratory, most investigators agreed that the SRP method was a significant step toward life prediction in the presence of high temperature and cyclic stresses.

Investigation of the seismic resistance of interior building partitions, phase 1

NASA Astrophysics Data System (ADS)

Anderson, R. W.; Yee, Y. C.; Savulian, G.; Barclay, B.; Lee, G.

1981-02-01

The effective participation of wood-framed interior shear wall partitions when determining the ultimate resistance capacity of two- and three-story masonry apartment buildings to seismic loading was investigated. Load vs. deflection tests were performed on 8 ft by 8 ft wall panel specimens constructed of four different facing materials, including wood lath and plaster, gypsum lath and plaster, and gypsum wallboard with joints placed either horizontally or vertically. The wood lath and plaster construction is found to be significantly stronger and stiffer than the other three specimens. Analyses of the test panels using finite element methods to predict their static resistance characteristics indicates that the facing material acts as the primary shear-resisting structural element. Resistance of shear wall partitions to lateral loads was assessed.

Time-specific ecological niche modeling predicts spatial dynamics of vector insects and human dengue cases.

PubMed

Peterson, A Townsend; Martínez-Campos, Carmen; Nakazawa, Yoshinori; Martínez-Meyer, Enrique

2005-09-01

Numerous human diseases-malaria, dengue, yellow fever and leishmaniasis, to name a few-are transmitted by insect vectors with brief life cycles and biting activity that varies in both space and time. Although the general geographic distributions of these epidemiologically important species are known, the spatiotemporal variation in their emergence and activity remains poorly understood. We used ecological niche modeling via a genetic algorithm to produce time-specific predictive models of monthly distributions of Aedes aegypti in Mexico in 1995. Significant predictions of monthly mosquito activity and distributions indicate that predicting spatiotemporal dynamics of disease vector species is feasible; significant coincidence with human cases of dengue indicate that these dynamics probably translate directly into transmission of dengue virus to humans. This approach provides new potential for optimizing use of resources for disease prevention and remediation via automated forecasting of disease transmission risk.

A novel representation for apoptosis protein subcellular localization prediction using support vector machine.

PubMed

Zhang, Li; Liao, Bo; Li, Dachao; Zhu, Wen

2009-07-21

Apoptosis, or programmed cell death, plays an important role in development of an organism. Obtaining information on subcellular location of apoptosis proteins is very helpful to understand the apoptosis mechanism. In this paper, based on the concept that the position distribution information of amino acids is closely related with the structure and function of proteins, we introduce the concept of distance frequency [Matsuda, S., Vert, J.P., Ueda, N., Toh, H., Akutsu, T., 2005. A novel representation of protein sequences for prediction of subcellular location using support vector machines. Protein Sci. 14, 2804-2813] and propose a novel way to calculate distance frequencies. In order to calculate the local features, each protein sequence is separated into p parts with the same length in our paper. Then we use the novel representation of protein sequences and adopt support vector machine to predict subcellular location. The overall prediction accuracy is significantly improved by jackknife test.

A model for predicting field-directed particle transport in the magnetofection process.

PubMed

Furlani, Edward P; Xue, Xiaozheng

2012-05-01

To analyze the magnetofection process in which magnetic carrier particles with surface-bound gene vectors are attracted to target cells for transfection using an external magnetic field and to obtain a fundamental understanding of the impact of key factors such as particle size and field strength on the gene delivery process. A numerical model is used to study the field-directed transport of the carrier particle-gene vector complex to target cells in a conventional multiwell culture plate system. The model predicts the transport dynamics and the distribution of particle accumulation at the target cells. The impact of several factors that strongly influence gene vector delivery is assessed including the properties of the carrier particles, the strength of the field source, and its extent and proximity relative to the target cells. The study demonstrates that modeling can be used to predict and optimize gene vector delivery in the magnetofection process for novel and conventional in vitro systems.

IND - THE IND DECISION TREE PACKAGE

NASA Technical Reports Server (NTRS)

Buntine, W.

1994-01-01

A common approach to supervised classification and prediction in artificial intelligence and statistical pattern recognition is the use of decision trees. A tree is "grown" from data using a recursive partitioning algorithm to create a tree which has good prediction of classes on new data. Standard algorithms are CART (by Breiman Friedman, Olshen and Stone) and ID3 and its successor C4 (by Quinlan). As well as reimplementing parts of these algorithms and offering experimental control suites, IND also introduces Bayesian and MML methods and more sophisticated search in growing trees. These produce more accurate class probability estimates that are important in applications like diagnosis. IND is applicable to most data sets consisting of independent instances, each described by a fixed length vector of attribute values. An attribute value may be a number, one of a set of attribute specific symbols, or it may be omitted. One of the attributes is delegated the "target" and IND grows trees to predict the target. Prediction can then be done on new data or the decision tree printed out for inspection. IND provides a range of features and styles with convenience for the casual user as well as fine-tuning for the advanced user or those interested in research. IND can be operated in a CART-like mode (but without regression trees, surrogate splits or multivariate splits), and in a mode like the early version of C4. Advanced features allow more extensive search, interactive control and display of tree growing, and Bayesian and MML algorithms for tree pruning and smoothing. These often produce more accurate class probability estimates at the leaves. IND also comes with a comprehensive experimental control suite. IND consists of four basic kinds of routines: data manipulation routines, tree generation routines, tree testing routines, and tree display routines. The data manipulation routines are used to partition a single large data set into smaller training and test sets. The generation routines are used to build classifiers. The test routines are used to evaluate classifiers and to classify data using a classifier. And the display routines are used to display classifiers in various formats. IND is written in C-language for Sun4 series computers. It consists of several programs with controlling shell scripts. Extensive UNIX man entries are included. IND is designed to be used on any UNIX system, although it has only been thoroughly tested on SUN platforms. The standard distribution medium for IND is a .25 inch streaming magnetic tape cartridge in UNIX tar format. An electronic copy of the documentation in PostScript format is included on the distribution medium. IND was developed in 1992.

Experimental and computational prediction of glass transition temperature of drugs.

PubMed

Alzghoul, Ahmad; Alhalaweh, Amjad; Mahlin, Denny; Bergström, Christel A S

2014-12-22

Glass transition temperature (Tg) is an important inherent property of an amorphous solid material which is usually determined experimentally. In this study, the relation between Tg and melting temperature (Tm) was evaluated using a data set of 71 structurally diverse druglike compounds. Further, in silico models for prediction of Tg were developed based on calculated molecular descriptors and linear (multilinear regression, partial least-squares, principal component regression) and nonlinear (neural network, support vector regression) modeling techniques. The models based on Tm predicted Tg with an RMSE of 19.5 K for the test set. Among the five computational models developed herein the support vector regression gave the best result with RMSE of 18.7 K for the test set using only four chemical descriptors. Hence, two different models that predict Tg of drug-like molecules with high accuracy were developed. If Tm is available, a simple linear regression can be used to predict Tg. However, the results also suggest that support vector regression and calculated molecular descriptors can predict Tg with equal accuracy, already before compound synthesis.

Data-driven reduced order models for effective yield strength and partitioning of strain in multiphase materials

NASA Astrophysics Data System (ADS)

Latypov, Marat I.; Kalidindi, Surya R.

2017-10-01

There is a critical need for the development and verification of practically useful multiscale modeling strategies for simulating the mechanical response of multiphase metallic materials with heterogeneous microstructures. In this contribution, we present data-driven reduced order models for effective yield strength and strain partitioning in such microstructures. These models are built employing the recently developed framework of Materials Knowledge Systems that employ 2-point spatial correlations (or 2-point statistics) for the quantification of the heterostructures and principal component analyses for their low-dimensional representation. The models are calibrated to a large collection of finite element (FE) results obtained for a diverse range of microstructures with various sizes, shapes, and volume fractions of the phases. The performance of the models is evaluated by comparing the predictions of yield strength and strain partitioning in two-phase materials with the corresponding predictions from a classical self-consistent model as well as results of full-field FE simulations. The reduced-order models developed in this work show an excellent combination of accuracy and computational efficiency, and therefore present an important advance towards computationally efficient microstructure-sensitive multiscale modeling frameworks.

Regional variation in the hierarchical partitioning of diversity in coral-dwelling fishes.

PubMed

Belmaker, Jonathan; Ziv, Yaron; Shashar, Nadav; Connolly, Sean R

2008-10-01

The size of the regional species pool may influence local patterns of diversity. However, it is unclear whether certain spatial scales are less sensitive to regional influences than others. Additive partitioning was used to separate coral-dwelling fish diversity to its alpha and beta components, at multiple scales, in several regions across the Indo-Pacific. We then examined how the relative contribution of these components changes with increased regional diversity. By employing specific random-placement null models, we overcome methodological problems with local-regional regressions. We show that, although alpha and beta diversities within each region are consistently different from random-placement null models, the increase in beta diversities among regions was similar to that predicted once heterogeneity in coral habitat was accounted for. In contrast, alpha diversity within single coral heads was limited and increased less than predicted by the null models. This was correlated with increased intraspecific aggregation in more diverse regions and is consistent with ecological limitations on the number of coexisting species at the local scale. These results suggest that, apart from very small spatial scales, variation in the partitioning of fish diversity along regional species richness gradients is driven overwhelmingly by the corresponding gradients in coral assemblage structure.

Development and evaluation of predictive model for bovine serum albumin-water partition coefficients of neutral organic chemicals.

PubMed

Ma, Guangcai; Yuan, Quan; Yu, Haiying; Lin, Hongjun; Chen, Jianrong; Hong, Huachang

2017-04-01

The binding of organic chemicals to serum albumin can significantly reduce their unbound concentration in blood and affect their biological reactions. In this study, we developed a new QSAR model for bovine serum albumin (BSA) - water partition coefficients (K BSA/W ) of neutral organic chemicals with large structural variance, logK BSA/W values covering 3.5 orders of magnitude (1.19-4.76). All chemical geometries were optimized by semi-empirical PM6 algorithm. Several quantum chemical parameters that reflect various intermolecular interactions as well as hydrophobicity were selected to develop QSAR model. The result indicates the regression model derived from logK ow , the most positive net atomic charges on an atom, Connolly solvent excluded volume, polarizability, and Abraham acidity could explain the partitioning mechanism of organic chemicals between BSA and water. The simulated external validation and cross validation verifies the developed model has good statistical robustness and predictive ability, thus can be used to estimate the logK BSA/W values for chemicals in application domain, accordingly to provide basic data for the toxicity assessment of the chemicals. Copyright © 2016 Elsevier Inc. All rights reserved.

Assessment of atmospheric distribution of polycyclic aromatic hydrocarbons using a molecular structure model

NASA Astrophysics Data System (ADS)

Turk Sekulić, Maja; Okuka, Marija; Šenk, Nevena; Radonić, Jelena; Vojinović Miloradov, Mirjana; Vidicki, Branko

2013-07-01

In this paper, a comparison of experimentally obtained and SPARC software v4.6 modelled values of gas/particle partitioning coefficients was conducted to determine whether the evaluation of atmospheric distribution of PAH molecules can be performed using a molecular structure model. Partitioning coefficients were calculated for sixteen EPA PAHs, in thirty-nine samples of ambient air collected at nineteen urban, industrial, highly contaminated and background sites in the Republic of Serbia and Bosnia and Herzegovina. For obtaining samples of ambient air, the conventional high volume (Hi-Vol) methodology was applied, whereby gaseous and particulate phase data collection was conducted simultaneously by glass fibre filters (GFFs) and polyurethane foam filters (PUFs). The best prediction was for PAHs with 5 or more rings (benzo(b)fluoranthene, benzo(k)fluoranthene, benzo(a)pyrene, indeno(1,2,3-cd)perylene and benzo(ghi)perylene). For evaluating the applicability of SPARC software predictions of gas/particle partitioning coefficients for the existing conditions, the results were compared with those obtained by applying other frequently used and highly ranked theoretical models of phase distributions, namely Junge-Pankow adsorption model, KOA absorption model, Dachs-Eisenreich dual model and PP-LFER model.

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures

PubMed Central

Sloma, Michael F.; Mathews, David H.

2016-01-01

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures. PMID:27852924

High Throughput Analyses of Budding Yeast ARSs Reveal New DNA Elements Capable of Conferring Centromere-Independent Plasmid Propagation

PubMed Central

Hoggard, Timothy; Liachko, Ivan; Burt, Cassaundra; Meikle, Troy; Jiang, Katherine; Craciun, Gheorghe; Dunham, Maitreya J.; Fox, Catherine A.

2016-01-01

The ability of plasmids to propagate in Saccharomyces cerevisiae has been instrumental in defining eukaryotic chromosomal control elements. Stable propagation demands both plasmid replication, which requires a chromosomal replication origin (i.e., an ARS), and plasmid distribution to dividing cells, which requires either a chromosomal centromere for segregation or a plasmid-partitioning element. While our knowledge of yeast ARSs and centromeres is relatively advanced, we know less about chromosomal regions that can function as plasmid partitioning elements. The Rap1 protein-binding site (RAP1) present in transcriptional silencers and telomeres of budding yeast is a known plasmid-partitioning element that functions to anchor a plasmid to the inner nuclear membrane (INM), which in turn facilitates plasmid distribution to daughter cells. This Rap1-dependent INM-anchoring also has an important chromosomal role in higher-order chromosomal structures that enhance transcriptional silencing and telomere stability. Thus, plasmid partitioning can reflect fundamental features of chromosome structure and biology, yet a systematic screen for plasmid partitioning elements has not been reported. Here, we couple deep sequencing with competitive growth experiments of a plasmid library containing thousands of short ARS fragments to identify new plasmid partitioning elements. Competitive growth experiments were performed with libraries that differed only in terms of the presence or absence of a centromere. Comparisons of the behavior of ARS fragments in the two experiments allowed us to identify sequences that were likely to drive plasmid partitioning. In addition to the silencer RAP1 site, we identified 74 new putative plasmid-partitioning motifs predicted to act as binding sites for DNA binding proteins enriched for roles in negative regulation of gene expression and G2/M-phase associated biology. These data expand our knowledge of chromosomal elements that may function in plasmid partitioning and suggest underlying biological roles shared by such elements. PMID:26865697

Optimization of Control Strategies for Non-Domiciliated Triatoma dimidiata, Chagas Disease Vector in the Yucatán Peninsula, Mexico

PubMed Central

Barbu, Corentin; Dumonteil, Eric; Gourbière, Sébastien

2009-01-01

Background Chagas disease is the most important vector-borne disease in Latin America. Regional initiatives based on residual insecticide spraying have successfully controlled domiciliated vectors in many regions. Non-domiciliated vectors remain responsible for a significant transmission risk, and their control is now a key challenge for disease control. Methodology/Principal Findings A mathematical model was developed to predict the temporal variations in abundance of non-domiciliated vectors inside houses. Demographic parameters were estimated by fitting the model to two years of field data from the Yucatan peninsula, Mexico. The predictive value of the model was tested on an independent data set before simulations examined the efficacy of control strategies based on residual insecticide spraying, insect screens, and bednets. The model accurately fitted and predicted field data in the absence and presence of insecticide spraying. Pyrethroid spraying was found effective when 50 mg/m2 were applied yearly within a two-month period matching the immigration season. The >80% reduction in bug abundance was not improved by larger doses or more frequent interventions, and it decreased drastically for different timing and lower frequencies of intervention. Alternatively, the use of insect screens consistently reduced bug abundance proportionally to the reduction of the vector immigration rate. Conclusion/Significance Control of non-domiciliated vectors can hardly be achieved by insecticide spraying, because it would require yearly application and an accurate understanding of the temporal pattern of immigration. Insect screens appear to offer an effective and sustainable alternative, which may be part of multi-disease interventions for the integrated control of neglected vector-borne diseases. PMID:19365542

A Novel Coarsening Method for Scalable and Efficient Mesh Generation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yoo, A; Hysom, D; Gunney, B

2010-12-02

In this paper, we propose a novel mesh coarsening method called brick coarsening method. The proposed method can be used in conjunction with any graph partitioners and scales to very large meshes. This method reduces problem space by decomposing the original mesh into fixed-size blocks of nodes called bricks, layered in a similar way to conventional brick laying, and then assigning each node of the original mesh to appropriate brick. Our experiments indicate that the proposed method scales to very large meshes while allowing simple RCB partitioner to produce higher-quality partitions with significantly less edge cuts. Our results further indicatemore » that the proposed brick-coarsening method allows more complicated partitioners like PT-Scotch to scale to very large problem size while still maintaining good partitioning performance with relatively good edge-cut metric. Graph partitioning is an important problem that has many scientific and engineering applications in such areas as VLSI design, scientific computing, and resource management. Given a graph G = (V,E), where V is the set of vertices and E is the set of edges, (k-way) graph partitioning problem is to partition the vertices of the graph (V) into k disjoint groups such that each group contains roughly equal number of vertices and the number of edges connecting vertices in different groups is minimized. Graph partitioning plays a key role in large scientific computing, especially in mesh-based computations, as it is used as a tool to minimize the volume of communication and to ensure well-balanced load across computing nodes. The impact of graph partitioning on the reduction of communication can be easily seen, for example, in different iterative methods to solve a sparse system of linear equation. Here, a graph partitioning technique is applied to the matrix, which is basically a graph in which each edge is a non-zero entry in the matrix, to allocate groups of vertices to processors in such a way that many of matrix-vector multiplication can be performed locally on each processor and hence to minimize communication. Furthermore, a good graph partitioning scheme ensures the equal amount of computation performed on each processor. Graph partitioning is a well known NP-complete problem, and thus the most commonly used graph partitioning algorithms employ some forms of heuristics. These algorithms vary in terms of their complexity, partition generation time, and the quality of partitions, and they tend to trade off these factors. A significant challenge we are currently facing at the Lawrence Livermore National Laboratory is how to partition very large meshes on massive-size distributed memory machines like IBM BlueGene/P, where scalability becomes a big issue. For example, we have found that the ParMetis, a very popular graph partitioning tool, can only scale to 16K processors. An ideal graph partitioning method on such an environment should be fast and scale to very large meshes, while producing high quality partitions. This is an extremely challenging task, as to scale to that level, the partitioning algorithm should be simple and be able to produce partitions that minimize inter-processor communications and balance the load imposed on the processors. Our goals in this work are two-fold: (1) To develop a new scalable graph partitioning method with good load balancing and communication reduction capability. (2) To study the performance of the proposed partitioning method on very large parallel machines using actual data sets and compare the performance to that of existing methods. The proposed method achieves the desired scalability by reducing the mesh size. For this, it coarsens an input mesh into a smaller size mesh by coalescing the vertices and edges of the original mesh into a set of mega-vertices and mega-edges. A new coarsening method called brick algorithm is developed in this research. In the brick algorithm, the zones in a given mesh are first grouped into fixed size blocks called bricks. These brick are then laid in a way similar to conventional brick laying technique, which reduces the number of neighboring blocks each block needs to communicate. Contributions of this research are as follows: (1) We have developed a novel method that scales to a really large problem size while producing high quality mesh partitions; (2) We measured the performance and scalability of the proposed method on a machine of massive size using a set of actual large complex data sets, where we have scaled to a mesh with 110 million zones using our method. To the best of our knowledge, this is the largest complex mesh that a partitioning method is successfully applied to; and (3) We have shown that proposed method can reduce the number of edge cuts by as much as 65%.« less

Computer programs to characterize alloys and predict cyclic life using the total strain version of strainrange partitioning: Tutorial and users manual, version 1.0

NASA Technical Reports Server (NTRS)

Saltsman, James F.

1992-01-01

This manual presents computer programs for characterizing and predicting fatigue and creep-fatigue resistance of metallic materials in the high-temperature, long-life regime for isothermal and nonisothermal fatigue. The programs use the total strain version of Strainrange Partitioning (TS-SRP). An extensive database has also been developed in a parallel effort. This database is probably the largest source of high-temperature, creep-fatigue test data available in the public domain and can be used with other life prediction methods as well. This users manual, software, and database are all in the public domain and are available through COSMIC (382 East Broad Street, Athens, GA 30602; (404) 542-3265, FAX (404) 542-4807). Two disks accompany this manual. The first disk contains the source code, executable files, and sample output from these programs. The second disk contains the creep-fatigue data in a format compatible with these programs.

Polybrominated Diphenyl Ethers in Human Milk and Serum from the U.S. EPA MAMA Study: Modeled Predictions of Infant Exposure and Considerations for Risk Assessment

PubMed Central

Marchitti, Satori A.; Fenton, Suzanne E.; Mendola, Pauline; Kenneke, John F.; Hines, Erin P.

2016-01-01

Background: Serum concentrations of polybrominated diphenyl ethers (PBDEs) in U.S. women are believed to be among the world’s highest; however, little information exists on the partitioning of PBDEs between serum and breast milk and how this may affect infant exposure. Objectives: Paired milk and serum samples were measured for PBDE concentrations in 34 women who participated in the U.S. EPA MAMA Study. Computational models for predicting milk PBDE concentrations from serum were evaluated. Methods: Samples were analyzed using gas chromatography isotope-dilution high-resolution mass spectrometry. Observed milk PBDE concentrations were compared with model predictions, and models were applied to NHANES serum data to predict milk PBDE concentrations and infant intakes for the U.S. population. Results: Serum and milk samples had detectable concentrations of most PBDEs. BDE-47 was found in the highest concentrations (median serum: 18.6; milk: 31.5 ng/g lipid) and BDE-28 had the highest milk:serum partitioning ratio (2.1 ± 0.2). No evidence of depuration was found. Models demonstrated high reliability and, as of 2007–2008, predicted U.S. milk concentrations of BDE-47, BDE-99, and BDE-100 appear to be declining but BDE-153 may be rising. Predicted infant intakes (ng/kg/day) were below threshold reference doses (RfDs) for BDE-99 and BDE-153 but above the suggested RfD for BDE-47. Conclusions: Concentrations and partitioning ratios of PBDEs in milk and serum from women in the U.S. EPA MAMA Study are presented for the first time; modeled predictions of milk PBDE concentrations using serum concentrations appear to be a valid method for estimating PBDE exposure in U.S. infants. Citation: Marchitti SA, Fenton SE, Mendola P, Kenneke JF, Hines EP. 2017. Polybrominated diphenyl ethers in human milk and serum from the U.S. EPA MAMA Study: modeled predictions of infant exposure and considerations for risk assessment. Environ Health Perspect 125:706–713; http://dx.doi.org/10.1289/EHP332 PMID:27405099

Predicting plant uptake of cadmium: validated with long-term contaminated soils.

PubMed

Lamb, Dane T; Kader, Mohammed; Ming, Hui; Wang, Liang; Abbasi, Sedigheh; Megharaj, Mallavarapu; Naidu, Ravi

2016-10-01

Cadmium accumulates in plant tissues at low soil loadings and is a concern for human health. Yet at higher levels it is also of concern for ecological receptors. We determined Cd partitioning constants for 41 soils to examine the role of soil properties controlling Cd partitioning and plant uptake. From a series of sorption and dose response studies, transfer functions were developed for predicting Cd uptake in Cucumis sativa L. (cucumber). The parameter log K f was predicted with soil pH ca , logCEC and log OC. Transfer of soil pore-water Cd 2+ to shoots was described with a power function (R 2  = 0.73). The dataset was validated with 13 long-term contaminated soils (plus 2 control soils) ranging in Cd concentration from 0.2 to 300 mg kg -1 . The series of equations predicting Cd shoot from pore-water Cd 2+ were able to predict the measured data in the independent dataset (root mean square error = 2.2). The good relationship indicated that Cd uptake to cucumber shoots could be predicted with Cd pore and Cd 2+ without other pore-water parameters such as pH or Ca 2+ . The approach may be adapted to a range of plant species.

A new clustering algorithm applicable to multispectral and polarimetric SAR images

NASA Technical Reports Server (NTRS)

Wong, Yiu-Fai; Posner, Edward C.

1993-01-01

We describe an application of a scale-space clustering algorithm to the classification of a multispectral and polarimetric SAR image of an agricultural site. After the initial polarimetric and radiometric calibration and noise cancellation, we extracted a 12-dimensional feature vector for each pixel from the scattering matrix. The clustering algorithm was able to partition a set of unlabeled feature vectors from 13 selected sites, each site corresponding to a distinct crop, into 13 clusters without any supervision. The cluster parameters were then used to classify the whole image. The classification map is much less noisy and more accurate than those obtained by hierarchical rules. Starting with every point as a cluster, the algorithm works by melting the system to produce a tree of clusters in the scale space. It can cluster data in any multidimensional space and is insensitive to variability in cluster densities, sizes and ellipsoidal shapes. This algorithm, more powerful than existing ones, may be useful for remote sensing for land use.

DNA melting profiles from a matrix method.

PubMed

Poland, Douglas

2004-02-05

In this article we give a new method for the calculation of DNA melting profiles. Based on the matrix formulation of the DNA partition function, the method relies for its efficiency on the fact that the required matrices are very sparse, essentially reducing matrix multiplication to vector multiplication and thus making the computer time required to treat a DNA molecule containing N base pairs proportional to N(2). A key ingredient in the method is the result that multiplication by the inverse matrix can also be reduced to vector multiplication. The task of calculating the melting profile for the entire genome is further reduced by treating regions of the molecule between helix-plateaus, thus breaking the molecule up into independent parts that can each be treated individually. The method is easily modified to incorporate changes in the assignment of statistical weights to the different structural features of DNA. We illustrate the method using the genome of Haemophilus influenzae. Copyright 2003 Wiley Periodicals, Inc.

Breast Cancer Detection with Reduced Feature Set.

PubMed

Mert, Ahmet; Kılıç, Niyazi; Bilgili, Erdem; Akan, Aydin

2015-01-01

This paper explores feature reduction properties of independent component analysis (ICA) on breast cancer decision support system. Wisconsin diagnostic breast cancer (WDBC) dataset is reduced to one-dimensional feature vector computing an independent component (IC). The original data with 30 features and reduced one feature (IC) are used to evaluate diagnostic accuracy of the classifiers such as k-nearest neighbor (k-NN), artificial neural network (ANN), radial basis function neural network (RBFNN), and support vector machine (SVM). The comparison of the proposed classification using the IC with original feature set is also tested on different validation (5/10-fold cross-validations) and partitioning (20%-40%) methods. These classifiers are evaluated how to effectively categorize tumors as benign and malignant in terms of specificity, sensitivity, accuracy, F-score, Youden's index, discriminant power, and the receiver operating characteristic (ROC) curve with its criterion values including area under curve (AUC) and 95% confidential interval (CI). This represents an improvement in diagnostic decision support system, while reducing computational complexity.

Bayesian data assimilation provides rapid decision support for vector-borne diseases.

PubMed

Jewell, Chris P; Brown, Richard G

2015-07-06

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host-vector-pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Finite size effects in the thermodynamics of a free neutral scalar field

NASA Astrophysics Data System (ADS)

Parvan, A. S.

2018-04-01

The exact analytical lattice results for the partition function of the free neutral scalar field in one spatial dimension in both the configuration and the momentum space were obtained in the framework of the path integral method. The symmetric square matrices of the bilinear forms on the vector space of fields in both configuration space and momentum space were found explicitly. The exact lattice results for the partition function were generalized to the three-dimensional spatial momentum space and the main thermodynamic quantities were derived both on the lattice and in the continuum limit. The thermodynamic properties and the finite volume corrections to the thermodynamic quantities of the free real scalar field were studied. We found that on the finite lattice the exact lattice results for the free massive neutral scalar field agree with the continuum limit only in the region of small values of temperature and volume. However, at these temperatures and volumes the continuum physical quantities for both massive and massless scalar field deviate essentially from their thermodynamic limit values and recover them only at high temperatures or/and large volumes in the thermodynamic limit.

The holographic dual of the Penrose transform

NASA Astrophysics Data System (ADS)

Neiman, Yasha

2018-01-01

We consider the holographic duality between type-A higher-spin gravity in AdS4 and the free U( N) vector model. In the bulk, linearized solutions can be translated into twistor functions via the Penrose transform. We propose a holographic dual to this transform, which translates between twistor functions and CFT sources and operators. We present a twistorial expression for the partition function, which makes global higher-spin symmetry manifest, and appears to automatically include all necessary contact terms. In this picture, twistor space provides a fully nonlocal, gauge-invariant description underlying both bulk and boundary spacetime pictures. While the bulk theory is handled at the linear level, our formula for the partition function includes the effects of bulk interactions. Thus, the CFT is used to solve the bulk, with twistors as a language common to both. A key ingredient in our result is the study of ordinary spacetime symmetries within the fundamental representation of higher-spin algebra. The object that makes these "square root" spacetime symmetries manifest becomes the kernel of our boundary/twistor transform, while the original Penrose transform is identified as a "square root" of CPT.

Footprint Map Partitioning Using Airborne Laser Scanning Data

NASA Astrophysics Data System (ADS)

Xiong, B.; Oude Elberink, S.; Vosselman, G.

2016-06-01

Nowadays many cities and countries are creating their 3D building models for a better daily management and smarter decision making. The newly created 3D models are required to be consistent with existing 2D footprint maps. Thereby the 2D maps are usually combined with height data for the task of 3D reconstruction. Many buildings are often composed by parts that are discontinuous over height. Building parts can be reconstructed independently and combined into a complete building. Therefore, most of the state-of-the-art work on 3D building reconstruction first decomposes a footprint map into parts. However, those works usually change the footprint maps for easier partitioning and cannot detect building parts that are fully inside the footprint polygon. In order to solve those problems, we introduce two methodologies, one more dependent on height data, and the other one more dependent on footprints. We also experimentally evaluate the two methodologies and compare their advantages and disadvantages. The experiments use Airborne Laser Scanning (ALS) data and two vector maps, one with 1:10,000 scale and another one with 1:500 scale.

Bayesian data assimilation provides rapid decision support for vector-borne diseases

PubMed Central

Jewell, Chris P.; Brown, Richard G.

2015-01-01

Predicting the spread of vector-borne diseases in response to incursions requires knowledge of both host and vector demographics in advance of an outbreak. Although host population data are typically available, for novel disease introductions there is a high chance of the pathogen using a vector for which data are unavailable. This presents a barrier to estimating the parameters of dynamical models representing host–vector–pathogen interaction, and hence limits their ability to provide quantitative risk forecasts. The Theileria orientalis (Ikeda) outbreak in New Zealand cattle demonstrates this problem: even though the vector has received extensive laboratory study, a high degree of uncertainty persists over its national demographic distribution. Addressing this, we develop a Bayesian data assimilation approach whereby indirect observations of vector activity inform a seasonal spatio-temporal risk surface within a stochastic epidemic model. We provide quantitative predictions for the future spread of the epidemic, quantifying uncertainty in the model parameters, case infection times and the disease status of undetected infections. Importantly, we demonstrate how our model learns sequentially as the epidemic unfolds and provide evidence for changing epidemic dynamics through time. Our approach therefore provides a significant advance in rapid decision support for novel vector-borne disease outbreaks. PMID:26136225

Spatially-partitioned many-body vortices

NASA Astrophysics Data System (ADS)

Klaiman, S.; Alon, O. E.

2016-02-01

A vortex in Bose-Einstein condensates is a localized object which looks much like a tiny tornado storm. It is well described by mean-field theory. In the present work we go beyond the current paradigm and introduce many-body vortices. These are made of spatially- partitioned clouds, carry definite total angular momentum, and are fragmented rather than condensed objects which can only be described beyond mean-field theory. A phase diagram based on a mean-field model assists in predicting the parameters where many-body vortices occur. Implications are briefly discussed.

A Parallel Vector Machine for the PM Programming Language

NASA Astrophysics Data System (ADS)

Bellerby, Tim

2016-04-01

PM is a new programming language which aims to make the writing of computational geoscience models on parallel hardware accessible to scientists who are not themselves expert parallel programmers. It is based around the concept of communicating operators: language constructs that enable variables local to a single invocation of a parallelised loop to be viewed as if they were arrays spanning the entire loop domain. This mechanism enables different loop invocations (which may or may not be executing on different processors) to exchange information in a manner that extends the successful Communicating Sequential Processes idiom from single messages to collective communication. Communicating operators avoid the additional synchronisation mechanisms, such as atomic variables, required when programming using the Partitioned Global Address Space (PGAS) paradigm. Using a single loop invocation as the fundamental unit of concurrency enables PM to uniformly represent different levels of parallelism from vector operations through shared memory systems to distributed grids. This paper describes an implementation of PM based on a vectorised virtual machine. On a single processor node, concurrent operations are implemented using masked vector operations. Virtual machine instructions operate on vectors of values and may be unmasked, masked using a Boolean field, or masked using an array of active vector cell locations. Conditional structures (such as if-then-else or while statement implementations) calculate and apply masks to the operations they control. A shift in mask representation from Boolean to location-list occurs when active locations become sufficiently sparse. Parallel loops unfold data structures (or vectors of data structures for nested loops) into vectors of values that may additionally be distributed over multiple computational nodes and then split into micro-threads compatible with the size of the local cache. Inter-node communication is accomplished using standard OpenMP and MPI. Performance analyses of the PM vector machine, demonstrating its scaling properties with respect to domain size and the number of processor nodes will be presented for a range of hardware configurations. The PM software and language definition are being made available under unrestrictive MIT and Creative Commons Attribution licenses respectively: www.pm-lang.org.

Predicting forest attributes from climate data using a recursive partitioning and regression tree algorithm

Treesearch

Greg C. Liknes; Christopher W. Woodall; Charles H. Perry

2009-01-01

Climate information frequently is included in geospatial modeling efforts to improve the predictive capability of other data sources. The selection of an appropriate climate data source requires consideration given the number of choices available. With regard to climate data, there are a variety of parameters (e.g., temperature, humidity, precipitation), time intervals...

Ecological Niche Modelling Predicts Southward Expansion of Lutzomyia (Nyssomyia) flaviscutellata (Diptera: Psychodidae: Phlebotominae), Vector of Leishmania (Leishmania) amazonensis in South America, under Climate Change.

PubMed

Carvalho, Bruno M; Rangel, Elizabeth F; Ready, Paul D; Vale, Mariana M

2015-01-01

Vector borne diseases are susceptible to climate change because distributions and densities of many vectors are climate driven. The Amazon region is endemic for cutaneous leishmaniasis and is predicted to be severely impacted by climate change. Recent records suggest that the distributions of Lutzomyia (Nyssomyia) flaviscutellata and the parasite it transmits, Leishmania (Leishmania) amazonensis, are expanding southward, possibly due to climate change, and sometimes associated with new human infection cases. We define the vector's climatic niche and explore future projections under climate change scenarios. Vector occurrence records were compiled from the literature, museum collections and Brazilian Health Departments. Six bioclimatic variables were used as predictors in six ecological niche model algorithms (BIOCLIM, DOMAIN, MaxEnt, GARP, logistic regression and Random Forest). Projections for 2050 used 17 general circulation models in two greenhouse gas representative concentration pathways: "stabilization" and "high increase". Ensemble models and consensus maps were produced by overlapping binary predictions. Final model outputs showed good performance and significance. The use of species absence data substantially improved model performance. Currently, L. flaviscutellata is widely distributed in the Amazon region, with records in the Atlantic Forest and savannah regions of Central Brazil. Future projections indicate expansion of the climatically suitable area for the vector in both scenarios, towards higher latitudes and elevations. L. flaviscutellata is likely to find increasingly suitable conditions for its expansion into areas where human population size and density are much larger than they are in its current locations. If environmental conditions change as predicted, the range of the vector is likely to expand to southeastern and central-southern Brazil, eastern Paraguay and further into the Amazonian areas of Bolivia, Peru, Ecuador, Colombia and Venezuela. These areas will only become endemic for L. amazonensis, however, if they have competent reservoir hosts and transmission dynamics matching those in the Amazon region.

Dynamic slip of polydisperse linear polymers using partitioned plate

NASA Astrophysics Data System (ADS)

Ebrahimi, Marzieh; Konaganti, Vinod Kumar; Hatzikiriakos, Savvas G.

2018-03-01

The slip velocity of an industrial grade high molecular weight high-density polyethylene (HDPE) is studied in steady and dynamic shear experiments using a stress/strain controlled rotational rheometer equipped with a parallel partitioned plate geometry. Moreover, fluoroalkyl silane-based coating is used to understand the effect of surface energy on slip in steady and dynamic conditions. The multimode integral Kaye-Bernstein-Kearsley-Zapas constitutive model is applied to predict the transient shear response of the HDPE melt obtained from rotational rheometer. It is found that a dynamic slip model with a slip relaxation time is needed to adequately predict the experimental data at large shear deformations. Comparison of the results before and after coating shows that the slip velocity is largely affected by surface energy. Decreasing surface energy by coating increases slip velocity and decreases the slip relaxation time.

Quantum Chemically Estimated Abraham Solute Parameters Using Multiple Solvent-Water Partition Coefficients and Molecular Polarizability.

PubMed

Liang, Yuzhen; Xiong, Ruichang; Sandler, Stanley I; Di Toro, Dominic M

2017-09-05

Polyparameter Linear Free Energy Relationships (pp-LFERs), also called Linear Solvation Energy Relationships (LSERs), are used to predict many environmentally significant properties of chemicals. A method is presented for computing the necessary chemical parameters, the Abraham parameters (AP), used by many pp-LFERs. It employs quantum chemical calculations and uses only the chemical's molecular structure. The method computes the Abraham E parameter using density functional theory computed molecular polarizability and the Clausius-Mossotti equation relating the index refraction to the molecular polarizability, estimates the Abraham V as the COSMO calculated molecular volume, and computes the remaining AP S, A, and B jointly with a multiple linear regression using sixty-five solvent-water partition coefficients computed using the quantum mechanical COSMO-SAC solvation model. These solute parameters, referred to as Quantum Chemically estimated Abraham Parameters (QCAP), are further adjusted by fitting to experimentally based APs using QCAP parameters as the independent variables so that they are compatible with existing Abraham pp-LFERs. QCAP and adjusted QCAP for 1827 neutral chemicals are included. For 24 solvent-water systems including octanol-water, predicted log solvent-water partition coefficients using adjusted QCAP have the smallest root-mean-square errors (RMSEs, 0.314-0.602) compared to predictions made using APs estimated using the molecular fragment based method ABSOLV (0.45-0.716). For munition and munition-like compounds, adjusted QCAP has much lower RMSE (0.860) than does ABSOLV (4.45) which essentially fails for these compounds.

The Prediction of Broadband Shock-Associated Noise Including Propagation Effects

NASA Technical Reports Server (NTRS)

Miller, Steven; Morris, Philip J.

2011-01-01

An acoustic analogy is developed based on the Euler equations for broadband shock- associated noise (BBSAN) that directly incorporates the vector Green's function of the linearized Euler equations and a steady Reynolds-Averaged Navier-Stokes solution (SRANS) as the mean flow. The vector Green's function allows the BBSAN propagation through the jet shear layer to be determined. The large-scale coherent turbulence is modeled by two-point second order velocity cross-correlations. Turbulent length and time scales are related to the turbulent kinetic energy and dissipation. An adjoint vector Green's function solver is implemented to determine the vector Green's function based on a locally parallel mean flow at streamwise locations of the SRANS solution. However, the developed acoustic analogy could easily be based on any adjoint vector Green's function solver, such as one that makes no assumptions about the mean flow. The newly developed acoustic analogy can be simplified to one that uses the Green's function associated with the Helmholtz equation, which is consistent with the formulation of Morris and Miller (AIAAJ 2010). A large number of predictions are generated using three different nozzles over a wide range of fully expanded Mach numbers and jet stagnation temperatures. These predictions are compared with experimental data from multiple jet noise labs. In addition, two models for the so-called 'fine-scale' mixing noise are included in the comparisons. Improved BBSAN predictions are obtained relative to other models that do not include the propagation effects, especially in the upstream direction of the jet.

Modeling Free Energies of Solvation in Olive Oil

PubMed Central

Chamberlin, Adam C.; Levitt, David G.; Cramer, Christopher J.; Truhlar, Donald G.

2009-01-01

Olive oil partition coefficients are useful for modeling the bioavailability of drug-like compounds. We have recently developed an accurate solvation model called SM8 for aqueous and organic solvents (Marenich, A. V.; Olson, R. M.; Kelly, C. P.; Cramer, C. J.; Truhlar, D. G. J. Chem. Theory Comput. 2007, 3, 2011) and a temperature-dependent solvation model called SM8T for aqueous solution (Chamberlin, A. C.; Cramer, C. J.; Truhlar, D. G. J. Phys. Chem. B 2008, 112, 3024). Here we describe an extension of SM8T to predict air–olive oil and water–olive oil partitioning for drug-like solutes as functions of temperature. We also describe the database of experimental partition coefficients used to parameterize the model; this database includes 371 entries for 304 compounds spanning the 291–310 K temperature range. PMID:19434923

Cache-Oblivious parallel SIMD Viterbi decoding for sequence search in HMMER.

PubMed

Ferreira, Miguel; Roma, Nuno; Russo, Luis M S

2014-05-30

HMMER is a commonly used bioinformatics tool based on Hidden Markov Models (HMMs) to analyze and process biological sequences. One of its main homology engines is based on the Viterbi decoding algorithm, which was already highly parallelized and optimized using Farrar's striped processing pattern with Intel SSE2 instruction set extension. A new SIMD vectorization of the Viterbi decoding algorithm is proposed, based on an SSE2 inter-task parallelization approach similar to the DNA alignment algorithm proposed by Rognes. Besides this alternative vectorization scheme, the proposed implementation also introduces a new partitioning of the Markov model that allows a significantly more efficient exploitation of the cache locality. Such optimization, together with an improved loading of the emission scores, allows the achievement of a constant processing throughput, regardless of the innermost-cache size and of the dimension of the considered model. The proposed optimized vectorization of the Viterbi decoding algorithm was extensively evaluated and compared with the HMMER3 decoder to process DNA and protein datasets, proving to be a rather competitive alternative implementation. Being always faster than the already highly optimized ViterbiFilter implementation of HMMER3, the proposed Cache-Oblivious Parallel SIMD Viterbi (COPS) implementation provides a constant throughput and offers a processing speedup as high as two times faster, depending on the model's size.

Possibilistic clustering for shape recognition

NASA Technical Reports Server (NTRS)

Keller, James M.; Krishnapuram, Raghu

1993-01-01

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, the clustering problem was cast into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. An appropriate objective function whose minimum will characterize a good possibilistic partition of the data was constructed, and the membership and prototype update equations from necessary conditions for minimization of our criterion function were derived. The ability of this approach to detect linear and quartic curves in the presence of considerable noise is shown.

Possibilistic clustering for shape recognition

NASA Technical Reports Server (NTRS)

Keller, James M.; Krishnapuram, Raghu

1992-01-01

Clustering methods have been used extensively in computer vision and pattern recognition. Fuzzy clustering has been shown to be advantageous over crisp (or traditional) clustering in that total commitment of a vector to a given class is not required at each iteration. Recently fuzzy clustering methods have shown spectacular ability to detect not only hypervolume clusters, but also clusters which are actually 'thin shells', i.e., curves and surfaces. Most analytic fuzzy clustering approaches are derived from Bezdek's Fuzzy C-Means (FCM) algorithm. The FCM uses the probabilistic constraint that the memberships of a data point across classes sum to one. This constraint was used to generate the membership update equations for an iterative algorithm. Unfortunately, the memberships resulting from FCM and its derivatives do not correspond to the intuitive concept of degree of belonging, and moreover, the algorithms have considerable trouble in noisy environments. Recently, we cast the clustering problem into the framework of possibility theory. Our approach was radically different from the existing clustering methods in that the resulting partition of the data can be interpreted as a possibilistic partition, and the membership values may be interpreted as degrees of possibility of the points belonging to the classes. We constructed an appropriate objective function whose minimum will characterize a good possibilistic partition of the data, and we derived the membership and prototype update equations from necessary conditions for minimization of our criterion function. In this paper, we show the ability of this approach to detect linear and quartic curves in the presence of considerable noise.

Understanding of Cyclic Volatile Methyl Siloxane Fate in a High Latitude Lake Is Constrained by Uncertainty in Organic Carbon-Water Partitioning.

PubMed

Krogseth, Ingjerd Sunde; Whelan, Michael John; Christensen, Guttorm Normann; Breivik, Knut; Evenset, Anita; Warner, Nicholas Alexander

2017-01-03

Cyclic volatile methyl siloxanes (cVMS) are emitted to aquatic environments with wastewater effluents. Here, we evaluate the environmental behavior of three cVMS compounds (octamethylcyclotetrasiloxane (D4), decamethylcyclopentasiloxane (D5) and dodecamethylcyclohexasiloxane (D6)) in a high latitude lake (Storvannet, 70°N 23°E), experiencing intermittent wastewater emissions and high latitude environmental conditions (low temperatures and seasonal ice cover). Measured cVMS concentrations in lake water were below detection limits in both March and June 2014. However, mean concentrations in sediments were 207 ± 30, 3775 ± 973 and 848 ± 211 ng g -1 organic carbon for D4, D5 and D6, respectively. To rationalize measurements, a fugacity-based model for lakes (QWASI) was parametrized for Storvannet. The key removal process for cVMS from the lake was predicted to be advection due to the low hydraulic retention time of the lake, followed by volatilization. Predicted cVMS behavior was highly sensitive to the partition coefficient between organic carbon and water (K OC ) and its temperature dependence. Predictions indicated lower overall persistence with decreasing temperature due to enhanced partitioning from sediments to water. Inverse modeling to predict steady-state emissions from cVMS concentrations in sediment provided unrealistically high emissions, when evaluated against measured concentrations in sewage. However, high concentrations of cVMS in sediment and low concentrations in water could be explained via a hypothetical dynamic emission scenario consistent with combined sewer overflows. The study illustrates the importance of considering compound-specific behavior of emerging contaminants that may differ from legacy organic contaminants.

Methamphetamine absorption by skin lipids: accumulated mass, partition coefficients, and the influence of fatty acids.

PubMed

Parker, K; Morrison, G

2016-08-01

Occupants of former methamphetamine laboratories, often residences, may experience increased exposure through the accumulation of the methamphetamine in the organic films that coat skin and indoor surfaces. The objectives of this study were to determine equilibrium partition coefficients of vapor-phase methamphetamine with artificial sebum (AS-1), artificial sebum without fatty acids (AS-2), and real skin surface films, herein called skin oils. Sebum and skin oil-coated filters were exposed to vapor-phase methamphetamine at concentrations ranging from 8 to 159 ppb, and samples were analyzed for exposure time periods from 2 h to 60 days. For a low vapor-phase methamphetamine concentration range of ~8-22 ppb, the equilibrium partition coefficient for AS-1 was 1500 ± 195 μg/g/ppb. For a high concentration range of 98-112 ppb, the partition coefficient was lower, 459 ± 80 μg/g/ppb, suggesting saturation of the available absorption capacity. The low partition coefficient for AS-2 (33 ± 6 μg/g/ppb) suggests that the fatty acids in AS-1 and skin oil are responsible for much high partition coefficients. We predict that the methamphetamine concentration in skin lipids coating indoor surfaces can exceed recommended surface remediation standards even for air concentrations well below 1 ppb. © 2015 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

Atmospheric concentrations and gas-particle partitioning of PCDD/Fs and dioxin-like PCBs around Hochiminh city.

PubMed

Trinh, Minh Man; Tsai, Ching Lan; Hien, To Thi; Thuan, Ngo Thi; Chi, Kai Hsien; Lien, Chien Guo; Chang, Moo Been

2018-07-01

Atmospheric PCDD/Fs and dl-PCBs samples were collected in Hochiminh city, Vietnam to address the effect of meteorological parameters, especially rainfall, on the occurrence and gas/particle partitioning of these persistent organic pollutants. The results indicate that PCDD/Fs and dl-PCBs concentrations in industrial site are higher than those measured in commercial and rural sites during both rainy and dry seasons. In terms of mass concentration, ambient PCDD/F levels measured in dry season are significantly higher than those measured in rainy season while dl-PCB levels do not vary significantly between rainy and dry seasons. The difference could be attributed to different gas/particle partitioning characteristics between PCDD/Fs and dl-PCBs. PCDD/Fs are found to be mainly distributed in particle phase while dl- PCBs are predominantly distributed in gas phase in both rainy and dry seasons. Additionally, Junge-Pankow and Harner-Bidleman models are applied to better understand the gas/particle partitioning of these pollutants in atmosphere. As a results, both PCDD/Fs and dl-PCBs are under non-equilibrium gas/particle partitioning condition, and PCDD/Fs tend to reach equilibrium easier in rainy season while there are no clear trend for dl-PCBs. Harner-Bidleman model performs better in evaluating the gas/particle partitioning of PCDD/Fs while Junge-Pankow model results in better prediction for dl-PCBs. Copyright © 2018 Elsevier Ltd. All rights reserved.

Predicting human liver microsomal stability with machine learning techniques.

PubMed

Sakiyama, Yojiro; Yuki, Hitomi; Moriya, Takashi; Hattori, Kazunari; Suzuki, Misaki; Shimada, Kaoru; Honma, Teruki

2008-02-01

To ensure a continuing pipeline in pharmaceutical research, lead candidates must possess appropriate metabolic stability in the drug discovery process. In vitro ADMET (absorption, distribution, metabolism, elimination, and toxicity) screening provides us with useful information regarding the metabolic stability of compounds. However, before the synthesis stage, an efficient process is required in order to deal with the vast quantity of data from large compound libraries and high-throughput screening. Here we have derived a relationship between the chemical structure and its metabolic stability for a data set of in-house compounds by means of various in silico machine learning such as random forest, support vector machine (SVM), logistic regression, and recursive partitioning. For model building, 1952 proprietary compounds comprising two classes (stable/unstable) were used with 193 descriptors calculated by Molecular Operating Environment. The results using test compounds have demonstrated that all classifiers yielded satisfactory results (accuracy > 0.8, sensitivity > 0.9, specificity > 0.6, and precision > 0.8). Above all, classification by random forest as well as SVM yielded kappa values of approximately 0.7 in an independent validation set, slightly higher than other classification tools. These results suggest that nonlinear/ensemble-based classification methods might prove useful in the area of in silico ADME modeling.

An Application of the Patient Rule-Induction Method for Evaluating the Contribution of the Apolipoprotein E and Lipoprotein Lipase Genes to Predicting Ischemic Heart Disease

PubMed Central

Dyson, Greg; Frikke-Schmidt, Ruth; Nordestgaard, Børge G.; Tybjærg-Hansen, Anne; Sing, Charles F.

2007-01-01

Different combinations of genetic and environmental risk factors are known to contribute to the complex etiology of ischemic heart disease (IHD) in different subsets of individuals. We employed the Patient Rule-Induction Method (PRIM) to select the combination of risk factors and risk factor values that identified each of 16 mutually exclusive partitions of individuals having significantly different levels of risk of IHD. PRIM balances two competing objectives: (1) finding partitions where the risk of IHD is high and (2) maximizing the number of IHD cases explained by the partitions. A sequential PRIM analysis was applied to data on the incidence of IHD collected over 8 years for a sample of 5,455 unrelated individuals from the Copenhagen City Heart Study (CCHS) to assess the added value of variation in two candidate susceptibility genes beyond the traditional, lipid and body mass index risk factors for IHD. An independent sample of 362 unrelated individuals also from the city of Copenhagen was used to test the model obtained for each of the hypothesized partitions. PMID:17436307

An epidemiological model for externally sourced vector-borne viruses applied to Bean yellow mosaic virus in lupin crops in a Mediterranean-type environment.

PubMed

Maling, T; Diggle, A J; Thackray, D J; Siddique, K H M; Jones, R A C

2008-12-01

A hybrid mechanistic/statistical model was developed to predict vector activity and epidemics of vector-borne viruses spreading from external virus sources to an adjacent crop. The pathosystem tested was Bean yellow mosaic virus (BYMV) spreading from annually self-regenerating, legume-based pastures to adjacent crops of narrow-leafed lupin (Lupinus angustifolius) in the winter-spring growing season in a region with a Mediterranean-type environment where the virus persists over summer within dormant seed of annual clovers. The model uses a combination of daily rainfall and mean temperature during late summer and early fall to drive aphid population increase, migration of aphids from pasture to lupin crops, and the spread of BYMV. The model predicted time of arrival of aphid vectors and resulting BYMV spread successfully for seven of eight datasets from 2 years of field observations at four sites representing different rainfall and geographic zones of the southwestern Australian grainbelt. Sensitivity analysis was performed to determine the relative importance of the main parameters that describe the pathosystem. The hybrid mechanistic/statistical approach used created a flexible analytical tool for vector-mediated plant pathosystems that made useful predictions even when field data were not available for some components of the system.

Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics

DOE PAGES

Brooks, Paul D.; Chorover, Jon; Fan, Ying; ...

2015-08-07

Here, hydrology is an integrative discipline linking the broad array of water–related research with physical, ecological, and social sciences. The increasing breadth of hydrological research, often where subdisciplines of hydrology partner with related sciences, reflects the central importance of water to environmental science, while highlighting the fractured nature of the discipline itself. This lack of coordination among hydrologic subdisciplines has hindered the development of hydrologic theory and integrated models capable of predicting hydrologic partitioning across time and space. The recent development of the concept of the critical zone (CZ), an open system extending from the top of the canopy tomore » the base of groundwater, brings together multiple hydrological subdisciplines with related physical and ecological sciences. Observations obtained by CZ researchers provide a diverse range of complementary process and structural data to evaluate both conceptual and numerical models. Consequently, a cross–site focus on “critical zone hydrology” has potential to advance the discipline of hydrology and to facilitate the transition of CZ observatories into a research network with immediate societal relevance. Here we review recent work in catchment hydrology and hydrochemistry, hydrogeology, and ecohydrology that highlights a common knowledge gap in how precipitation is partitioned in the critical zone: “how is the amount, routing, and residence time of water in the subsurface related to the biogeophysical structure of the CZ?” Addressing this question will require coordination among hydrologic subdisciplines and interfacing sciences, and catalyze rapid progress in understanding current CZ structure and predicting how climate and land cover changes will affect hydrologic partitioning.« less

Naphthalene SOA: redox activity and naphthoquinone gas-particle partitioning

NASA Astrophysics Data System (ADS)

McWhinney, R. D.; Zhou, S.; Abbatt, J. P. D.

2013-10-01

Chamber secondary organic aerosol (SOA) from low-NOx photooxidation of naphthalene by hydroxyl radical was examined with respect to its redox cycling behaviour using the dithiothreitol (DTT) assay. Naphthalene SOA was highly redox-active, consuming DTT at an average rate of 118 ± 14 pmol per minute per μg of SOA material. Measured particle-phase masses of the major previously identified redox active products, 1,2- and 1,4-naphthoquinone, accounted for only 21 ± 3% of the observed redox cycling activity. The redox-active 5-hydroxy-1,4-naphthoquinone was identified as a new minor product of naphthalene oxidation, and including this species in redox activity predictions increased the predicted DTT reactivity to 30 ± 5% of observations. These results suggest that there are substantial unidentified redox-active SOA constituents beyond the small quinones that may be important toxic components of these particles. A gas-to-SOA particle partitioning coefficient was calculated to be (7.0 ± 2.5) × 10-4 m3 μg-1 for 1,4-naphthoquinone at 25 °C. This value suggests that under typical warm conditions, 1,4-naphthoquinone is unlikely to contribute strongly to redox behaviour of ambient particles, although further work is needed to determine the potential impact under conditions such as low temperatures where partitioning to the particle is more favourable. Also, higher order oxidation products that likely account for a substantial fraction of the redox cycling capability of the naphthalene SOA are likely to partition much more strongly to the particle phase.

An Analytical Solution for the Impact of Vegetation Changes on Hydrological Partitioning Within the Budyko Framework

NASA Astrophysics Data System (ADS)

Zhang, Shulei; Yang, Yuting; McVicar, Tim R.; Yang, Dawen

2018-01-01

Vegetation change is a critical factor that profoundly affects the terrestrial water cycle. Here we derive an analytical solution for the impact of vegetation changes on hydrological partitioning within the Budyko framework. This is achieved by deriving an analytical expression between leaf area index (LAI) change and the Budyko land surface parameter (n) change, through the combination of a steady state ecohydrological model with an analytical carbon cost-benefit model for plant rooting depth. Using China where vegetation coverage has experienced dramatic changes over the past two decades as a study case, we quantify the impact of LAI changes on the hydrological partitioning during 1982-2010 and predict the future influence of these changes for the 21st century using climate model projections. Results show that LAI change exhibits an increasing importance on altering hydrological partitioning as climate becomes drier. In semiarid and arid China, increased LAI has led to substantial streamflow reductions over the past three decades (on average -8.5% in 1990s and -11.7% in 2000s compared to the 1980s baseline), and this decreasing trend in streamflow is projected to continue toward the end of this century due to predicted LAI increases. Our result calls for caution regarding the large-scale revegetation activities currently being implemented in arid and semiarid China, which may result in serious future water scarcity issues here. The analytical model developed here is physically based and suitable for simultaneously assessing both vegetation changes and climate change induced changes to streamflow globally.

Hydrological partitioning in the critical zone: Recent advances and opportunities for developing transferable understanding of water cycle dynamics

NASA Astrophysics Data System (ADS)

Brooks, Paul D.; Chorover, Jon; Fan, Ying; Godsey, Sarah E.; Maxwell, Reed M.; McNamara, James P.; Tague, Christina

2015-09-01

Hydrology is an integrative discipline linking the broad array of water-related research with physical, ecological, and social sciences. The increasing breadth of hydrological research, often where subdisciplines of hydrology partner with related sciences, reflects the central importance of water to environmental science, while highlighting the fractured nature of the discipline itself. This lack of coordination among hydrologic subdisciplines has hindered the development of hydrologic theory and integrated models capable of predicting hydrologic partitioning across time and space. The recent development of the concept of the critical zone (CZ), an open system extending from the top of the canopy to the base of groundwater, brings together multiple hydrological subdisciplines with related physical and ecological sciences. Observations obtained by CZ researchers provide a diverse range of complementary process and structural data to evaluate both conceptual and numerical models. Consequently, a cross-site focus on "critical zone hydrology" has potential to advance the discipline of hydrology and to facilitate the transition of CZ observatories into a research network with immediate societal relevance. Here we review recent work in catchment hydrology and hydrochemistry, hydrogeology, and ecohydrology that highlights a common knowledge gap in how precipitation is partitioned in the critical zone: "how is the amount, routing, and residence time of water in the subsurface related to the biogeophysical structure of the CZ?" Addressing this question will require coordination among hydrologic subdisciplines and interfacing sciences, and catalyze rapid progress in understanding current CZ structure and predicting how climate and land cover changes will affect hydrologic partitioning.

Predictable oxygen isotope exchange of plant lipids improves our ability to understand hydrologic shifts and partition evapotranspiration across scales

NASA Astrophysics Data System (ADS)

Maxwell, T.; Silva, L. C. R.; Horwath, W. R.

2016-12-01

Understanding the partitioning of evapotranspiration is critical to assessing how changes in climate affect the terrestrial water cycle. N-alkyl lipids have been successfully used to integrate local to regional scale hydrologic change through the integration of δD measured in specific compounds found in sediments. However, such studies are limited compared to contemporary hydrologic studies which have the advantage of using dual isotope methods whereby δD and δ18O are used in conjunction to partition evapotranspiration. δD values in n-alkyl lipids have been established as resistant to exchange with environmental water and, this approach has allowed for routine measurement and reconstruction of plant water δD. In contrast, the use of δ18O in organic matter remains incipient because the low oxygen content of plant lipids makes it difficult to accurately measure δ18O. In the interest of addressing both fundamental and practical potential of a lipid δ18O proxy, we present the first evidence for predictable exchange of δ18O between environmental water and hydrophobic bulk organic matter, neutral saponified lipids, and specific plant derived compounds Our data suggests that these different pools may be used to reconstruct the original source water δD/δ18O relationship from soil or sedimentary organic matter, which will help elucidate hydrologic shifts in terrestrial systems. Our results bring new insight into methods by which organic compounds might be used to partition evapotranspiration across large spatial scales in both contemporary and reconstructed systems.

Alkyl nitrate formation from the reactions of C8-C14 n-alkanes with OH radicals in the presence of NO(x): measured yields with essential corrections for gas-wall partitioning.

PubMed

Yeh, Geoffrey K; Ziemann, Paul J

2014-09-18

In this study, C8-C14 n-alkanes were reacted with OH radicals in the presence of NO(x) in a Teflon film environmental chamber and isomer-specific yields of alkyl nitrates were determined using gas chromatography. Because results indicated significant losses of alkyl nitrates to chamber walls, gas-wall partitioning was investigated by monitoring the concentrations of a suite of synthesized alkyl nitrates added to the chamber. Gas-to-wall partitioning increased with increasing carbon number and with proximity of the nitrooxy group to the terminal carbon, with losses as high as 86%. The results were used to develop a structure-activity model to predict the effects of carbon number and isomer structure on gas-wall partitioning, which was used to correct the measured yields of alkyl nitrate isomers formed in chamber reactions. The resulting branching ratios for formation of secondary alkyl nitrates were similar for all isomers of a particular carbon number, and average values, which were almost identical to alkyl nitrate yields, were 0.219, 0.206, 0.254, 0.291, and 0.315 for reactions of n-octane, n-decane, n-dodecane, n-tridecane, and n-tetradecane, respectively. The increase in average branching ratios and alkyl nitrate yields with increasing carbon number to a plateau value of ∼0.30 at about C13-C14 is consistent with predictions of a previously developed model, indicating that the model is valid for alkane carbon numbers ≥C3.

Analysis of red blood cell partitioning at bifurcations in simulated microvascular networks

NASA Astrophysics Data System (ADS)

Balogh, Peter; Bagchi, Prosenjit

2018-05-01

Partitioning of red blood cells (RBCs) at vascular bifurcations has been studied over many decades using in vivo, in vitro, and theoretical models. These studies have shown that RBCs usually do not distribute to the daughter vessels with the same proportion as the blood flow. Such disproportionality occurs, whereby the cell distribution fractions are either higher or lower than the flow fractions and have been referred to as classical partitioning and reverse partitioning, respectively. The current work presents a study of RBC partitioning based on, for the first time, a direct numerical simulation (DNS) of a flowing cell suspension through modeled vascular networks that are comprised of multiple bifurcations and have topological similarity to microvasculature in vivo. The flow of deformable RBCs at physiological hematocrits is considered through the networks, and the 3D dynamics of each individual cell are accurately resolved. The focus is on the detailed analysis of the partitioning, based on the DNS data, as it develops naturally in successive bifurcations, and the underlying mechanisms. We find that while the time-averaged partitioning at a bifurcation manifests in one of two ways, namely, the classical or reverse partitioning, the time-dependent behavior can cycle between these two types. We identify and analyze four different cellular-scale mechanisms underlying the time-dependent partitioning. These mechanisms arise, in general, either due to an asymmetry in the RBC distribution in the feeding vessels caused by the events at an upstream bifurcation or due to a temporary increase in cell concentration near capillary bifurcations. Using the DNS results, we show that a positive skewness in the hematocrit profile in the feeding vessel is associated with the classical partitioning, while a negative skewness is associated with the reverse one. We then present a detailed analysis of the two components of disproportionate partitioning as identified in prior studies, namely, plasma skimming and cell screening. The plasma skimming component is shown to under-predict the disproportionality, leaving the cell screening component to make up for the difference. The crossing of the separation surface by the cells is observed to be a dominant mechanism underlying the cell screening, which is shown to mitigate extreme heterogeneity in RBC distribution across the networks.

Combined Inter- and Intrafractional Plan Adaptation Using Fraction Partitioning in Magnetic Resonance-guided Radiotherapy Delivery.

PubMed

Lagerwaard, Frank; Bohoudi, Omar; Tetar, Shyama; Admiraal, Marjan A; Rosario, Tezontl S; Bruynzeel, Anna

2018-04-05

Magnetic resonance-guided radiation therapy (MRgRT) not only allows for superior soft-tissue setup and online MR-guidance during delivery but also for inter-fractional plan re-optimization or adaptation. This plan adaptation involves repeat MR imaging, organs at risk (OARs) re-contouring, plan prediction (i.e., recalculating the baseline plan on the anatomy of that moment), plan re-optimization, and plan quality assurance. In contrast, intrafractional plan adaptation cannot be simply performed by pausing delivery at any given moment, adjusting contours, and re-optimization because of the complex and composite nature of deformable dose accumulation. To overcome this limitation, we applied a practical workaround by partitioning treatment fractions, each with half the original fraction dose. In between successive deliveries, the patient remained in the treatment position and all steps of the initial plan adaptation were repeated. Thus, this second re-optimization served as an intrafractional plan adaptation at 50% of the total delivery. The practical feasibility of this partitioning approach was evaluated in a patient treated with MRgRT for locally advanced pancreatic cancer (LAPC). MRgRT was delivered in 40Gy in 10 fractions, with two fractions scheduled successively on each treatment day. The contoured gross tumor volume (GTV) was expanded by 3 mm, excluding parts of the OARs within this expansion to derive the planning target volume for daily re-optimization (PTV OPT ). The baseline GTVV 95%  achieved in this patient was 80.0% to adhere to the high-dose constraints for the duodenum, stomach, and bowel (V 33 Gy <1 cc and V 36 Gy <0.1 cc). Treatment was performed on the MRIdian (ViewRay Inc, Mountain View, USA) using video-assisted breath-hold in shallow inspiration. The dual plan adaptation resulted, for each partitioned fraction, in the generation of Plan PREDICTED1 , Plan RE-OPTIMIZED1  (inter-fractional adaptation), Plan PREDICTED2 , and Plan RE-OPTIMIZED2  (intrafractional adaptation). An offline analysis was performed to evaluate the benefit of inter-fractional versus intrafractional plan adaptation with respect to GTV coverage and high-dose OARs sparing for all five partitioned fractions. Interfractional changes in adjacent OARs were substantially larger than intrafractional changes. Mean GTV V 95% was 76.8 ± 1.8% (Plan PREDICTED1 ), 83.4 ± 5.7% (Plan RE-OPTIMIZED1 ), 82.5 ± 4.3% (Plan PREDICTED2 ),and 84.4 ± 4.4% (Plan RE-OPTIMIZED2 ). Both plan re-optimizations appeared important for correcting the inappropriately high duodenal V 33 Gy values of 3.6 cc (Plan PREDICTED1 ) and 3.9 cc (Plan PREDICTED2 ) to 0.2 cc for both re-optimizations. To a smaller extent, this improvement was also observed for V 25 Gy values. For the stomach, bowel, and all other OARs, high and intermediate doses were well below preset constraints, even without re-optimization. The mean delivery time of each daily treatment was 90 minutes. This study presents the clinical application of combined inter-fractional and intrafractional plan adaptation during MRgRT for LAPC using fraction partitioning with successive re-optimization. Whereas, in this study, interfractional plan adaptation appeared to benefit both GTV coverage and OARs sparing, intrafractional adaptation was particularly useful for high-dose OARs sparing. Although all necessary steps lead to a prolonged treatment duration, this may be applied in selected cases where high doses to adjacent OARs are regarded as critical.

Combined Inter- and Intrafractional Plan Adaptation Using Fraction Partitioning in Magnetic Resonance-guided Radiotherapy Delivery

PubMed Central

Bohoudi, Omar; Tetar, Shyama; Admiraal, Marjan A; Rosario, Tezontl S; Bruynzeel, Anna

2018-01-01

Magnetic resonance-guided radiation therapy (MRgRT) not only allows for superior soft-tissue setup and online MR-guidance during delivery but also for inter-fractional plan re-optimization or adaptation. This plan adaptation involves repeat MR imaging, organs at risk (OARs) re-contouring, plan prediction (i.e., recalculating the baseline plan on the anatomy of that moment), plan re-optimization, and plan quality assurance. In contrast, intrafractional plan adaptation cannot be simply performed by pausing delivery at any given moment, adjusting contours, and re-optimization because of the complex and composite nature of deformable dose accumulation. To overcome this limitation, we applied a practical workaround by partitioning treatment fractions, each with half the original fraction dose. In between successive deliveries, the patient remained in the treatment position and all steps of the initial plan adaptation were repeated. Thus, this second re-optimization served as an intrafractional plan adaptation at 50% of the total delivery. The practical feasibility of this partitioning approach was evaluated in a patient treated with MRgRT for locally advanced pancreatic cancer (LAPC). MRgRT was delivered in 40Gy in 10 fractions, with two fractions scheduled successively on each treatment day. The contoured gross tumor volume (GTV) was expanded by 3 mm, excluding parts of the OARs within this expansion to derive the planning target volume for daily re-optimization (PTVOPT). The baseline GTVV95% achieved in this patient was 80.0% to adhere to the high-dose constraints for the duodenum, stomach, and bowel (V33 Gy <1 cc and V36 Gy <0.1 cc). Treatment was performed on the MRIdian (ViewRay Inc, Mountain View, USA) using video-assisted breath-hold in shallow inspiration. The dual plan adaptation resulted, for each partitioned fraction, in the generation of PlanPREDICTED1, PlanRE-OPTIMIZED1 (inter-fractional adaptation), PlanPREDICTED2, and PlanRE-OPTIMIZED2 (intrafractional adaptation). An offline analysis was performed to evaluate the benefit of inter-fractional versus intrafractional plan adaptation with respect to GTV coverage and high-dose OARs sparing for all five partitioned fractions. Interfractional changes in adjacent OARs were substantially larger than intrafractional changes. Mean GTV V95% was 76.8 ± 1.8% (PlanPREDICTED1), 83.4 ± 5.7% (PlanRE-OPTIMIZED1), 82.5 ± 4.3% (PlanPREDICTED2),and 84.4 ± 4.4% (PlanRE-OPTIMIZED2). Both plan re-optimizations appeared important for correcting the inappropriately high duodenal V33 Gy values of 3.6 cc (PlanPREDICTED1) and 3.9 cc (PlanPREDICTED2) to 0.2 cc for both re-optimizations. To a smaller extent, this improvement was also observed for V25 Gy values. For the stomach, bowel, and all other OARs, high and intermediate doses were well below preset constraints, even without re-optimization. The mean delivery time of each daily treatment was 90 minutes. This study presents the clinical application of combined inter-fractional and intrafractional plan adaptation during MRgRT for LAPC using fraction partitioning with successive re-optimization. Whereas, in this study, interfractional plan adaptation appeared to benefit both GTV coverage and OARs sparing, intrafractional adaptation was particularly useful for high-dose OARs sparing. Although all necessary steps lead to a prolonged treatment duration, this may be applied in selected cases where high doses to adjacent OARs are regarded as critical. PMID:29876156

Anticipatory Monitoring and Control of Complex Systems using a Fuzzy based Fusion of Support Vector Regressors

DOE Office of Scientific and Technical Information (OSTI.GOV)

Miltiadis Alamaniotis; Vivek Agarwal

This paper places itself in the realm of anticipatory systems and envisions monitoring and control methods being capable of making predictions over system critical parameters. Anticipatory systems allow intelligent control of complex systems by predicting their future state. In the current work, an intelligent model aimed at implementing anticipatory monitoring and control in energy industry is presented and tested. More particularly, a set of support vector regressors (SVRs) are trained using both historical and observed data. The trained SVRs are used to predict the future value of the system based on current operational system parameter. The predicted values are thenmore » inputted to a fuzzy logic based module where the values are fused to obtain a single value, i.e., final system output prediction. The methodology is tested on real turbine degradation datasets. The outcome of the approach presented in this paper highlights the superiority over single support vector regressors. In addition, it is shown that appropriate selection of fuzzy sets and fuzzy rules plays an important role in improving system performance.« less

Recent and past musical activity predicts cognitive aging variability: direct comparison with general lifestyle activities.

PubMed

Hanna-Pladdy, Brenda; Gajewski, Byron

2012-01-01

Studies evaluating the impact of modifiable lifestyle factors on cognition offer potential insights into sources of cognitive aging variability. Recently, we reported an association between extent of musical instrumental practice throughout the life span (greater than 10 years) on preserved cognitive functioning in advanced age. These findings raise the question of whether there are training-induced brain changes in musicians that can transfer to non-musical cognitive abilities to allow for compensation of age-related cognitive declines. However, because of the relationship between engagement in general lifestyle activities and preserved cognition, it remains unclear whether these findings are specifically driven by musical training or the types of individuals likely to engage in greater activities in general. The current study controlled for general activity level in evaluating cognition between musicians and nomusicians. Also, the timing of engagement (age of acquisition, past versus recent) was assessed in predictive models of successful cognitive aging. Seventy age and education matched older musicians (>10 years) and non-musicians (ages 59-80) were evaluated on neuropsychological tests and general lifestyle activities. Musicians scored higher on tests of phonemic fluency, verbal working memory, verbal immediate recall, visuospatial judgment, and motor dexterity, but did not differ in other general leisure activities. Partition analyses were conducted on significant cognitive measures to determine aspects of musical training predictive of enhanced cognition. The first partition analysis revealed education best predicted visuospatial functions in musicians, followed by recent musical engagement which offset low education. In the second partition analysis, early age of musical acquisition (<9 years) predicted enhanced verbal working memory in musicians, while analyses for other measures were not predictive. Recent and past musical activity, but not general lifestyle activities, predicted variability across both verbal and visuospatial domains in aging. These findings are suggestive of different use-dependent adaptation periods depending on cognitive domain. Furthermore, they imply that early age of musical acquisition, sustained and maintained during advanced age, may enhance cognitive functions and buffer age and education influences.

Embedding of multidimensional time-dependent observations.

PubMed

Barnard, J P; Aldrich, C; Gerber, M

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Embedding of multidimensional time-dependent observations

NASA Astrophysics Data System (ADS)

Barnard, Jakobus P.; Aldrich, Chris; Gerber, Marius

2001-10-01

A method is proposed to reconstruct dynamic attractors by embedding of multivariate observations of dynamic nonlinear processes. The Takens embedding theory is combined with independent component analysis to transform the embedding into a vector space of linearly independent vectors (phase variables). The method is successfully tested against prediction of the unembedded state vector in two case studies of simulated chaotic processes.

Predicting healthcare associated infections using patients' experiences

NASA Astrophysics Data System (ADS)

Pratt, Michael A.; Chu, Henry

2016-05-01

Healthcare associated infections (HAI) are a major threat to patient safety and are costly to health systems. Our goal is to predict the HAI performance of a hospital using the patients' experience responses as input. We use four classifiers, viz. random forest, naive Bayes, artificial feedforward neural networks, and the support vector machine, to perform the prediction of six types of HAI. The six types include blood stream, urinary tract, surgical site, and intestinal infections. Experiments show that the random forest and support vector machine perform well across the six types of HAI.

Study of VOCs transport and storage in porous media and assemblies

NASA Astrophysics Data System (ADS)

Xu, Jing

Indoor VOCs concentrations are influenced greatly by the transport and storage of VOCs in building and furnishing materials, majority of which belong to porous media. The transport and storage ability of a porous media for a given VOC can be characterized by its diffusion coefficient and partition coefficient, respectively, and such data are currently lacking. Besides, environmental conditions are another important factor that affects the VOCs emission. The main purposes of this dissertation are: (1) validate the similarity hypothesis between the transport of water vapor and VOCs in porous materials, and help build a database of VOC transport and storage properties with the assistance of the similarity hypothesis; (2) investigate the effect of relative humidity on the diffusion and partition coefficients; (3) develop a numerical multilayer model to simulate the VOCs' emission characteristics in both short and long term. To better understand the similarity and difference between moisture and volatile organic compounds (VOCs) diffusion through porous media, a dynamic dual-chamber experimental system was developed. The diffusion coefficients and partition coefficients of moisture and selected VOCs in materials were compared. Based on the developed similarity theory, the diffusion behavior of each particular VOC in porous media is predictable as long as the similarity coefficient of the VOC is known. Experimental results showed that relative humidity in the 80%RH led to a higher partition coefficient for formaldehyde compared to 50%RH. However, between 25% and 50% RH, there was no significant difference in partition coefficient. The partition coefficient of toluene decreased with the increase of humidity due to competition with water molecules for pore surface area and the non-soluble nature of toluene. The solubility of VOCs was found to correlate well with the partition coefficient of VOCs. The partition coefficient of VOCs was not simply inversely proportional to the vapor pressure of the compound, but also increased with the increase of the Henry's law constant. Experiment results also showed that a higher relative humidity led to a larger effective diffusion coefficient for both conventional wallboard and green wallboard. The partition coefficient (Kma) of formaldehyde in conventional wallboard was larger at 50% RH than at 20% RH, while the difference in partition coefficient between 50% RH and 70% RH was insignificant. For the green wallboard and green carpet, the partition coefficient increased slightly with the increase of relative humidity from 20% to 50% and 70%. Engineered wood products such as particleboard have widely been used with wood veneer and laminate to form multilayer assembly work surfaces or panels. The multilayer model study in this dissertation comprised both numerical and experimental investigation of the VOCs emission from such an assembly. A coupled 1D multilayer model based on CHAMPS (coupled heat, air, moisture and pollutant simulations) was first described. Later, the transport properties of each material layer were determined. Several emission cases from a three-layered heterogeneous work assembly were modeled using a developed simulation model. At last, the numerical model was verified by the experimental data of both hexanal and acetaldehyde emissions in a 50L standard small scale chamber. The model is promising in predicting VOCs' emissions for multilayered porous materials in emission tests.

The partitioning of heavy metals in incineration of sludges and waste in a bubbling fluidized bed 2. Interpretation of results with a conceptual model.

PubMed

Toledo, José M; Corella, José; Corella, Luis M

2005-11-11

This work addresses the behavior, fate and/or partitioning of six targeted (Cd, Pb, Cr, Cu, Zn and Ni) heavy metals (HMs) in the incineration of sludges and waste in a bubbling fluidized bed (BFB) of 15 cm i.d. and 5.2m high followed by a filter chamber operated at 750-760 degrees C with a commercial ceramic filter. This paper presents three different things: (1) an in depth review of the published work relating to the problem of partitioning of the HMs in BFBs, (2) some more experimental incineration tests regarding the influence of the temperature of the bed of the BFB and the effect of the chlorine content in the feedstock on the partitioning of the HMs, and (3) the modelling of the partitioning of the HMs in the exit flows: bottom ash, coarse fly ashes, fine fly ash and vapour phase. The partitioning of the HMs is governed by fluid dynamic principles together with the kinetics of the diffusion of the HMs inside the ash particles and the kinetics of the reactions between the HMs and the components of the matrix of the ash. Some thermodynamic predictions do not fit the results from the BFB incinerator well enough because equilibria are not reached in at least three exit ash flows: coarse fly ash, fine fly ash and submicron particles. The residence time of these ash particles in these type of incinerators is very short and most of the HMs have no time to diffuse out of the ash particle. Finally, an examination was made on how in the ceramic hot filter the partition coefficients for the HMs increased, mainly for Cd and Pb, when the Cl-content in the feedstock was increased.

A Turn-Projected State-Based Conflict Resolution Algorithm

NASA Technical Reports Server (NTRS)

Butler, Ricky W.; Lewis, Timothy A.

2013-01-01

State-based conflict detection and resolution (CD&R) algorithms detect conflicts and resolve them on the basis on current state information without the use of additional intent information from aircraft flight plans. Therefore, the prediction of the trajectory of aircraft is based solely upon the position and velocity vectors of the traffic aircraft. Most CD&R algorithms project the traffic state using only the current state vectors. However, the past state vectors can be used to make a better prediction of the future trajectory of the traffic aircraft. This paper explores the idea of using past state vectors to detect traffic turns and resolve conflicts caused by these turns using a non-linear projection of the traffic state. A new algorithm based on this idea is presented and validated using a fast-time simulator developed for this study.

Climate Change and Risk of Leishmaniasis in North America: Predictions from Ecological Niche Models of Vector and Reservoir Species

PubMed Central

González, Camila; Wang, Ophelia; Strutz, Stavana E.; González-Salazar, Constantino; Sánchez-Cordero, Víctor; Sarkar, Sahotra

2010-01-01

Background Climate change is increasingly being implicated in species' range shifts throughout the world, including those of important vector and reservoir species for infectious diseases. In North America (México, United States, and Canada), leishmaniasis is a vector-borne disease that is autochthonous in México and Texas and has begun to expand its range northward. Further expansion to the north may be facilitated by climate change as more habitat becomes suitable for vector and reservoir species for leishmaniasis. Methods and Findings The analysis began with the construction of ecological niche models using a maximum entropy algorithm for the distribution of two sand fly vector species (Lutzomyia anthophora and L. diabolica), three confirmed rodent reservoir species (Neotoma albigula, N. floridana, and N. micropus), and one potential rodent reservoir species (N. mexicana) for leishmaniasis in northern México and the United States. As input, these models used species' occurrence records with topographic and climatic parameters as explanatory variables. Models were tested for their ability to predict correctly both a specified fraction of occurrence points set aside for this purpose and occurrence points from an independently derived data set. These models were refined to obtain predicted species' geographical distributions under increasingly strict assumptions about the ability of a species to disperse to suitable habitat and to persist in it, as modulated by its ecological suitability. Models successful at predictions were fitted to the extreme A2 and relatively conservative B2 projected climate scenarios for 2020, 2050, and 2080 using publicly available interpolated climate data from the Third Intergovernmental Panel on Climate Change Assessment Report. Further analyses included estimation of the projected human population that could potentially be exposed to leishmaniasis in 2020, 2050, and 2080 under the A2 and B2 scenarios. All confirmed vector and reservoir species will see an expansion of their potential range towards the north. Thus, leishmaniasis has the potential to expand northwards from México and the southern United States. In the eastern United States its spread is predicted to be limited by the range of L. diabolica; further west, L. anthophora may play the same role. In the east it may even reach the southern boundary of Canada. The risk of spread is greater for the A2 scenario than for the B2 scenario. Even in the latter case, with restrictive (contiguous) models for dispersal of vector and reservoir species, and limiting vector and reservoir species occupancy to only the top 10% of their potential suitable habitat, the expected number of human individuals exposed to leishmaniasis by 2080 will at least double its present value. Conclusions These models predict that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada. This prediction suggests the adoption of measures such as surveillance for leishmaniasis north of Texas as disease cases spread northwards. Potential vector and reservoir control strategies—besides direct intervention in disease cases—should also be further investigated. PMID:20098495

Climate change and risk of leishmaniasis in north america: predictions from ecological niche models of vector and reservoir species.

PubMed

González, Camila; Wang, Ophelia; Strutz, Stavana E; González-Salazar, Constantino; Sánchez-Cordero, Víctor; Sarkar, Sahotra

2010-01-19

Climate change is increasingly being implicated in species' range shifts throughout the world, including those of important vector and reservoir species for infectious diseases. In North America (México, United States, and Canada), leishmaniasis is a vector-borne disease that is autochthonous in México and Texas and has begun to expand its range northward. Further expansion to the north may be facilitated by climate change as more habitat becomes suitable for vector and reservoir species for leishmaniasis. The analysis began with the construction of ecological niche models using a maximum entropy algorithm for the distribution of two sand fly vector species (Lutzomyia anthophora and L. diabolica), three confirmed rodent reservoir species (Neotoma albigula, N. floridana, and N. micropus), and one potential rodent reservoir species (N. mexicana) for leishmaniasis in northern México and the United States. As input, these models used species' occurrence records with topographic and climatic parameters as explanatory variables. Models were tested for their ability to predict correctly both a specified fraction of occurrence points set aside for this purpose and occurrence points from an independently derived data set. These models were refined to obtain predicted species' geographical distributions under increasingly strict assumptions about the ability of a species to disperse to suitable habitat and to persist in it, as modulated by its ecological suitability. Models successful at predictions were fitted to the extreme A2 and relatively conservative B2 projected climate scenarios for 2020, 2050, and 2080 using publicly available interpolated climate data from the Third Intergovernmental Panel on Climate Change Assessment Report. Further analyses included estimation of the projected human population that could potentially be exposed to leishmaniasis in 2020, 2050, and 2080 under the A2 and B2 scenarios. All confirmed vector and reservoir species will see an expansion of their potential range towards the north. Thus, leishmaniasis has the potential to expand northwards from México and the southern United States. In the eastern United States its spread is predicted to be limited by the range of L. diabolica; further west, L. anthophora may play the same role. In the east it may even reach the southern boundary of Canada. The risk of spread is greater for the A2 scenario than for the B2 scenario. Even in the latter case, with restrictive (contiguous) models for dispersal of vector and reservoir species, and limiting vector and reservoir species occupancy to only the top 10% of their potential suitable habitat, the expected number of human individuals exposed to leishmaniasis by 2080 will at least double its present value. These models predict that climate change will exacerbate the ecological risk of human exposure to leishmaniasis in areas outside its present range in the United States and, possibly, in parts of southern Canada. This prediction suggests the adoption of measures such as surveillance for leishmaniasis north of Texas as disease cases spread northwards. Potential vector and reservoir control strategies-besides direct intervention in disease cases-should also be further investigated.

Application of a VLSI vector quantization processor to real-time speech coding

NASA Technical Reports Server (NTRS)

Davidson, G.; Gersho, A.

1986-01-01

Attention is given to a working vector quantization processor for speech coding that is based on a first-generation VLSI chip which efficiently performs the pattern-matching operation needed for the codebook search process (CPS). Using this chip, the CPS architecture has been successfully incorporated into a compact, single-board Vector PCM implementation operating at 7-18 kbits/sec. A real time Adaptive Vector Predictive Coder system using the CPS has also been implemented.

How a haemosporidian parasite of bats gets around: the genetic structure of a parasite, vector and host compared.

PubMed

Witsenburg, F; Clément, L; López-Baucells, A; Palmeirim, J; Pavlinić, I; Scaravelli, D; Ševčík, M; Dutoit, L; Salamin, N; Goudet, J; Christe, P

2015-02-01

Parasite population structure is often thought to be largely shaped by that of its host. In the case of a parasite with a complex life cycle, two host species, each with their own patterns of demography and migration, spread the parasite. However, the population structure of the parasite is predicted to resemble only that of the most vagile host species. In this study, we tested this prediction in the context of a vector-transmitted parasite. We sampled the haemosporidian parasite Polychromophilus melanipherus across its European range, together with its bat fly vector Nycteribia schmidlii and its host, the bent-winged bat Miniopterus schreibersii. Based on microsatellite analyses, the wingless vector, and not the bat host, was identified as the least structured population and should therefore be considered the most vagile host. Genetic distance matrices were compared for all three species based on a mitochondrial DNA fragment. Both host and vector populations followed an isolation-by-distance pattern across the Mediterranean, but not the parasite. Mantel tests found no correlation between the parasite and either the host or vector populations. We therefore found no support for our hypothesis; the parasite population structure matched neither vector nor host. Instead, we propose a model where the parasite's gene flow is represented by the added effects of host and vector dispersal patterns. © 2015 John Wiley & Sons Ltd.

Accommodation by Varying Strain Regimes along the Northern Luzon Arc (Coastal Range, Taiwan) - Insights from Focal Mechanism Strain Inversions

NASA Astrophysics Data System (ADS)

O'Hara, D.; Lee, J.; Lewis, J. C.; Rau, R.

2013-12-01

Taiwan is the product of modern subduction polarity reversal coupled with arc-continent collision. The NW-moving Philippine Sea plate (PSP) subducts beneath the Eurasian plate (EUR) to the northeast of Taiwan at the Ryukyu trench, while overriding EUR south of Taiwan at the Manila trench, bringing the Luzon volcanic arc into collision with the deforming sediments of the Eurasian passive margin. The obliquity between the N-S trending Luzon Arc (LA) and NE-SW trending passive margin is causing the southward, temporal propagation of collision since ~6 Ma. The collided forearc and clastic sediments accreted by the advancing arc created the Coastal Range (CR), whose western-most extent lies at the suture zone between the two plates, the NNE-SSW trending Longitudinal Valley Fault (LVF). In order to understand the change in stress along the northern LA as it docks onto EUR, we inverted over 1900 relocated earthquake focal mechanism solutions within the on-land CR and offshore LA regions for spatial strain tensors. The focal mechanisms cover seismicity from 1991-2013, ranging in depths 0-112 km and magnitudes 2.22-6.92. For our analyses, we grouped the focal mechanisms based on 15' Latitudinal intervals along the study area and inverted the data for best-fit strain tensors using a micropolar continuum model of crustal deformation. Results suggest dominant compression in all regions with accommodation occurring through oblique reverse faults of varying dips. Trends of σ1 rotate clockwise (CW) from 100° in the south to 155° in the north. This CW rotation is also observed in the preferred nodal plane slip vector trends - from E-W orientation in the south to NW-SE in the north. The rotation of σ1 and slip vector trends creates varying degrees of obliquity with the direction normal (DN) to CR (112°). The trends in the southern part of the study area show obliquity counterclockwise (CCW) to DN; trends in the central part are near parallel to DN; and trends in the northern part show obliquity CW to DN. GPS vectors from 2008-2012 using an ITRF reference frame show similar changes in obliquity with GPS velocity trends oblique CCW and CW to DN in the southern and northern areas, respectively, and near parallel to DN in the central area. Our results suggest the accommodation of three varying strain regimes along the northern LA (CR) system - (1) strain partitioning within the Manila forearc basin due to the obliquity between N-S trending LA and NW trending convergence vector; (2) convergence-related strain in the central LVF and CR along NNE trending major thrust faults with a small oblique component due to the obliquity between DN and the convergence vector; and (3) strain partitioning along the Ryukyu trench and forearc basin due to the obliquity between northward subducting plate along the WNW- ESE trending Ryukyu trench and NW convergence vector. As a result, the different subducting characteristics of strain regimes correspond to the different stages of arc accretion/collision, from south to north: pre-collision, present collision, waning collision, and subduction.

Prediction of Broadband Shock-Associated Noise Including Propagation Effects Originating NASA

NASA Technical Reports Server (NTRS)

Miller, Steven; Morris, Philip J.

2012-01-01

An acoustic analogy is developed based on the Euler equations for broadband shock-associated noise (BBSAN) that directly incorporates the vector Green s function of the linearized Euler equations and a steady Reynolds-Averaged Navier-Stokes solution (SRANS) to describe the mean flow. The vector Green s function allows the BBSAN propagation through the jet shear layer to be determined. The large-scale coherent turbulence is modeled by two-point second order velocity cross-correlations. Turbulent length and time scales are related to the turbulent kinetic energy and dissipation rate. An adjoint vector Green s function solver is implemented to determine the vector Green s function based on a locally parallel mean flow at different streamwise locations. The newly developed acoustic analogy can be simplified to one that uses the Green s function associated with the Helmholtz equation, which is consistent with a previous formulation by the authors. A large number of predictions are generated using three different nozzles over a wide range of fully-expanded jet Mach numbers and jet stagnation temperatures. These predictions are compared with experimental data from multiple jet noise experimental facilities. In addition, two models for the so-called fine-scale mixing noise are included in the comparisons. Improved BBSAN predictions are obtained relative to other models that do not include propagation effects.

Inter-model comparison of the landscape determinants of vector-borne disease: implications for epidemiological and entomological risk modeling.

PubMed

Lorenz, Alyson; Dhingra, Radhika; Chang, Howard H; Bisanzio, Donal; Liu, Yang; Remais, Justin V

2014-01-01

Extrapolating landscape regression models for use in assessing vector-borne disease risk and other applications requires thoughtful evaluation of fundamental model choice issues. To examine implications of such choices, an analysis was conducted to explore the extent to which disparate landscape models agree in their epidemiological and entomological risk predictions when extrapolated to new regions. Agreement between six literature-drawn landscape models was examined by comparing predicted county-level distributions of either Lyme disease or Ixodes scapularis vector using Spearman ranked correlation. AUC analyses and multinomial logistic regression were used to assess the ability of these extrapolated landscape models to predict observed national data. Three models based on measures of vegetation, habitat patch characteristics, and herbaceous landcover emerged as effective predictors of observed disease and vector distribution. An ensemble model containing these three models improved precision and predictive ability over individual models. A priori assessment of qualitative model characteristics effectively identified models that subsequently emerged as better predictors in quantitative analysis. Both a methodology for quantitative model comparison and a checklist for qualitative assessment of candidate models for extrapolation are provided; both tools aim to improve collaboration between those producing models and those interested in applying them to new areas and research questions.

Using support vector machine to predict beta- and gamma-turns in proteins.

PubMed

Hu, Xiuzhen; Li, Qianzhong

2008-09-01

By using the composite vector with increment of diversity, position conservation scoring function, and predictive secondary structures to express the information of sequence, a support vector machine (SVM) algorithm for predicting beta- and gamma-turns in the proteins is proposed. The 426 and 320 nonhomologous protein chains described by Guruprasad and Rajkumar (Guruprasad and Rajkumar J. Biosci 2000, 25,143) are used for training and testing the predictive model of the beta- and gamma-turns, respectively. The overall prediction accuracy and the Matthews correlation coefficient in 7-fold cross-validation are 79.8% and 0.47, respectively, for the beta-turns. The overall prediction accuracy in 5-fold cross-validation is 61.0% for the gamma-turns. These results are significantly higher than the other algorithms in the prediction of beta- and gamma-turns using the same datasets. In addition, the 547 and 823 nonhomologous protein chains described by Fuchs and Alix (Fuchs and Alix Proteins: Struct Funct Bioinform 2005, 59, 828) are used for training and testing the predictive model of the beta- and gamma-turns, and better results are obtained. This algorithm may be helpful to improve the performance of protein turns' prediction. To ensure the ability of the SVM method to correctly classify beta-turn and non-beta-turn (gamma-turn and non-gamma-turn), the receiver operating characteristic threshold independent measure curves are provided. (c) 2008 Wiley Periodicals, Inc.

Determination of zircon/melt trace element partition coefficients from SIMS analysis of melt inclusions in zircon

NASA Astrophysics Data System (ADS)

Thomas, J. B.; Bodnar, R. J.; Shimizu, N.; Sinha, A. K.

2002-09-01

Partition coefficients ( zircon/meltD M) for rare earth elements (REE) (La, Ce, Nd, Sm, Dy, Er and Yb) and other trace elements (Ba, Rb, B, Sr, Ti, Y and Nb) between zircon and melt have been calculated from secondary ion mass spectrometric (SIMS) analyses of zircon/melt inclusion pairs. The melt inclusion-mineral (MIM) technique shows that D REE increase in compatibility with increasing atomic number, similar to results of previous studies. However, D REE determined using the MIM technique are, in general, lower than previously reported values. Calculated D REE indicate that light REE with atomic numbers less than Sm are incompatible in zircon and become more incompatible with decreasing atomic number. This behavior is in contrast to most previously published results which indicate D > 1 and define a flat partitioning pattern for elements from La through Sm. The partition coefficients for the heavy REE determined using the MIM technique are lower than previously published results by factors of ≈15 to 20 but follow a similar trend. These differences are thought to reflect the effects of mineral and/or glass contaminants in samples from earlier studies which employed bulk analysis techniques. D REE determined using the MIM technique agree well with values predicted using the equations of Brice (1975), which are based on the size and elasticity of crystallographic sites. The presence of Ce 4+ in the melt results in elevated D Ce compared to neighboring REE due to the similar valence and size of Ce 4+ and Zr 4+. Predicted zircon/meltD values for Ce 4+ and Ce 3+ indicate that the Ce 4+/Ce 3+ ratios of the melt ranged from about 10 -3 to 10 -2. Partition coefficients for other trace elements determined in this study increase in compatibility in the order Ba < Rb < B < Sr < Ti < Y < Nb, with Ba, Rb, B and Sr showing incompatible behavior (D M < 1.0), and Ti, Y and Nb showing compatible behavior (D M > 1.0). The effect of partition coefficients on melt evolution during petrogenetic modeling was examined using partition coefficients determined in this study and compared to trends obtained using published partition coefficients. The lower D REE determined in this study result in smaller REE bulk distribution coefficients, for a given mineral assemblage, compared to those calculated using previously reported values. As an example, fractional crystallization of an assemblage composed of 35% hornblende, 64.5% plagioclase and 0.5% zircon produces a melt that becomes increasingly more enriched in Yb using the D Yb from this study. Using D Yb from Fujimaki (1986) results in a melt that becomes progressively depleted in Yb during crystallization.

Functors of White Noise Associated to Characters of the Infinite Symmetric Group

NASA Astrophysics Data System (ADS)

Bożejko, Marek; Guţă, Mădălin

The characters of the infinite symmetric group are extended to multiplicative positive definite functions on pair partitions by using an explicit representation due to Veršik and Kerov. The von Neumann algebra generated by the fields with f in an infinite dimensional real Hilbert space is infinite and the vacuum vector is not separating. For a family depending on an integer N< - 1 an ``exclusion principle'' is found allowing at most ``identical particles'' on the same state: The algebras are type factors. Functors of white noise are constructed and proved to be non-equivalent for different values of N.

Well-posed two-temperature constitutive equations for stable dense fluid shock waves using molecular dynamics and generalizations of Navier-Stokes-Fourier continuum mechanics.

PubMed

Hoover, Wm G; Hoover, Carol G

2010-04-01

Guided by molecular dynamics simulations, we generalize the Navier-Stokes-Fourier constitutive equations and the continuum motion equations to include both transverse and longitudinal temperatures. To do so we partition the contributions of the heat transfer, the work done, and the heat flux vector between the longitudinal and transverse temperatures. With shockwave boundary conditions time-dependent solutions of these equations converge to give stationary shockwave profiles. The profiles include anisotropic temperature and can be fitted to molecular dynamics results, demonstrating the utility and simplicity of a two-temperature description of far-from-equilibrium states.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Collins, Benjamin S.

The Futility package contains the following: 1) Definition of the size of integers and real numbers; 2) A generic Unit test harness; 3) Definitions for some basic extensions to the Fortran language: arbitrary length strings, a parameter list construct, exception handlers, command line processor, timers; 4) Geometry definitions: point, line, plane, box, cylinder, polyhedron; 5) File wrapper functions: standard Fortran input/output files, Fortran binary files, HDF5 files; 6) Parallel wrapper functions: MPI, and Open MP abstraction layers, partitioning algorithms; 7) Math utilities: BLAS, Matrix and Vector definitions, Linear Solver methods and wrappers for other TPLs (PETSC, MKL, etc), preconditioner classes;more » 8) Misc: random number generator, water saturation properties, sorting algorithms.« less

Challenges in predicting climate and environmental effects on vector-borne disease episystems in a changing world.

PubMed

Tabachnick, W J

2010-03-15

Vector-borne pathogens cause enormous suffering to humans and animals. Many are expanding their range into new areas. Dengue, West Nile and Chikungunya have recently caused substantial human epidemics. Arthropod-borne animal diseases like Bluetongue, Rift Valley fever and African horse sickness pose substantial threats to livestock economies around the world. Climate change can impact the vector-borne disease epidemiology. Changes in climate will influence arthropod vectors, their life cycles and life histories, resulting in changes in both vector and pathogen distribution and changes in the ability of arthropods to transmit pathogens. Climate can affect the way pathogens interact with both the arthropod vector and the human or animal host. Predicting and mitigating the effects of future changes in the environment like climate change on the complex arthropod-pathogen-host epidemiological cycle requires understanding of a variety of complex mechanisms from the molecular to the population level. Although there has been substantial progress on many fronts the challenges to effectively understand and mitigate the impact of potential changes in the environment on vector-borne pathogens are formidable and at an early stage of development. The challenges will be explored using several arthropod-borne pathogen systems as illustration, and potential avenues to meet the challenges will be presented.

Multiscale asymmetric orthogonal wavelet kernel for linear programming support vector learning and nonlinear dynamic systems identification.

PubMed

Lu, Zhao; Sun, Jing; Butts, Kenneth

2014-05-01

Support vector regression for approximating nonlinear dynamic systems is more delicate than the approximation of indicator functions in support vector classification, particularly for systems that involve multitudes of time scales in their sampled data. The kernel used for support vector learning determines the class of functions from which a support vector machine can draw its solution, and the choice of kernel significantly influences the performance of a support vector machine. In this paper, to bridge the gap between wavelet multiresolution analysis and kernel learning, the closed-form orthogonal wavelet is exploited to construct new multiscale asymmetric orthogonal wavelet kernels for linear programming support vector learning. The closed-form multiscale orthogonal wavelet kernel provides a systematic framework to implement multiscale kernel learning via dyadic dilations and also enables us to represent complex nonlinear dynamics effectively. To demonstrate the superiority of the proposed multiscale wavelet kernel in identifying complex nonlinear dynamic systems, two case studies are presented that aim at building parallel models on benchmark datasets. The development of parallel models that address the long-term/mid-term prediction issue is more intricate and challenging than the identification of series-parallel models where only one-step ahead prediction is required. Simulation results illustrate the effectiveness of the proposed multiscale kernel learning.

Climate change and vector-borne diseases of public health significance.

PubMed

Ogden, Nicholas H

2017-10-16

There has been much debate as to whether or not climate change will have, or has had, any significant effect on risk from vector-borne diseases. The debate on the former has focused on the degree to which occurrence and levels of risk of vector-borne diseases are determined by climate-dependent or independent factors, while the debate on the latter has focused on whether changes in disease incidence are due to climate at all, and/or are attributable to recent climate change. Here I review possible effects of climate change on vector-borne diseases, methods used to predict these effects and the evidence to date of changes in vector-borne disease risks that can be attributed to recent climate change. Predictions have both over- and underestimated the effects of climate change. Mostly under-estimations of effects are due to a focus only on direct effects of climate on disease ecology while more distal effects on society's capacity to control and prevent vector-borne disease are ignored. There is increasing evidence for possible impacts of recent climate change on some vector-borne diseases but for the most part, observed data series are too short (or non-existent), and impacts of climate-independent factors too great, to confidently attribute changing risk to climate change. © Crown copyright 2017.

Gas/particle partitioning, particle-size distribution of atmospheric polybrominated diphenyl ethers in southeast Shanghai rural area and size-resolved predicting model.

PubMed

Su, Peng-Hao; Tomy, Gregg T; Hou, Chun-Yan; Yin, Fang; Feng, Dao-Lun; Ding, Yong-Sheng; Li, Yi-Fan

2018-04-01

A size-segregated gas/particle partitioning coefficient K Pi was proposed and evaluated in the predicting models on the basis of atmospheric polybrominated diphenyl ether (PBDE) field data comparing with the bulk coefficient K P . Results revealed that the characteristics of atmospheric PBDEs in southeast Shanghai rural area were generally consistent with previous investigations, suggesting that this investigation was representative to the present pollution status of atmospheric PBDEs. K Pi was generally greater than bulk K P , indicating an overestimate of TSP (the mass concentration of total suspended particles) in the expression of bulk K P . In predicting models, K Pi led to a significant shift in regression lines as compared to K P , thus it should be more cautious to investigate sorption mechanisms using the regression lines. The differences between the performances of K Pi and K P were helpful to explain some phenomenon in predicting investigations, such as P L 0 and K OA models overestimate the particle fractions of PBDEs and the models work better at high temperature than at low temperature. Our findings are important because they enabled an insight into the influence of particle size on predicting models. Copyright © 2018 Elsevier Ltd. All rights reserved.

Life prediction of thermomechanical fatigue using total strain version of strainrange partitioning (SRP): A proposal

NASA Technical Reports Server (NTRS)

Saltsman, James F.; Halford, Gary R.

1988-01-01

A method is proposed (without experimental verification) for extending the total strain version of Strainrange Partitioning (TS-SRP) to predict the lives of thermomechanical fatigue (TMF) cycles. The principal feature of TS SRP is the determination of the time-temperature-waveshape dependent elastic strainrange versus life lines that are added subsequently to the classical inelastic strainrange versus life lines to form the total strainrange versus life relations. The procedure is based on a derived relation between failure and flow behavior. Failure behavior is represented by conventional SRP inelastic strainrange versus cyclic life relations, while flow behavior is captured in terms of the cyclic stress-strain response characteristics. Stress-strain response is calculated from simple equations developed from approximations to more complex cyclic constitutive models. For applications to TMF life prediction, a new testing technique, bithermal cycling, is proposed as a means for generating the inelastic strainrange versus life relations. Flow relations for use in predicting TMF lives would normally be obtained from approximations to complex thermomechanical constitutive models. Bithermal flow testing is also proposed as an alternative to thermomechanical flow testing at low strainranges where the hysteresis loop is difficult to analyze.

Development of a Physiologically-Based Pharmacokinetic Model of the Rat Central Nervous System

PubMed Central

Badhan, Raj K. Singh; Chenel, Marylore; Penny, Jeffrey I.

2014-01-01

Central nervous system (CNS) drug disposition is dictated by a drug’s physicochemical properties and its ability to permeate physiological barriers. The blood–brain barrier (BBB), blood-cerebrospinal fluid barrier and centrally located drug transporter proteins influence drug disposition within the central nervous system. Attainment of adequate brain-to-plasma and cerebrospinal fluid-to-plasma partitioning is important in determining the efficacy of centrally acting therapeutics. We have developed a physiologically-based pharmacokinetic model of the rat CNS which incorporates brain interstitial fluid (ISF), choroidal epithelial and total cerebrospinal fluid (CSF) compartments and accurately predicts CNS pharmacokinetics. The model yielded reasonable predictions of unbound brain-to-plasma partition ratio (Kpuu,brain) and CSF:plasma ratio (CSF:Plasmau) using a series of in vitro permeability and unbound fraction parameters. When using in vitro permeability data obtained from L-mdr1a cells to estimate rat in vivo permeability, the model successfully predicted, to within 4-fold, Kpuu,brain and CSF:Plasmau for 81.5% of compounds simulated. The model presented allows for simultaneous simulation and analysis of both brain biophase and CSF to accurately predict CNS pharmacokinetics from preclinical drug parameters routinely available during discovery and development pathways. PMID:24647103

Comparison of Modeling Approaches for Carbon Partitioning: Impact on Estimates of Global Net Primary Production and Equilibrium Biomass of Woody Vegetation from MODIS GPP

NASA Astrophysics Data System (ADS)

Ise, T.; Litton, C. M.; Giardina, C. P.; Ito, A.

2009-12-01

Plant partitioning of carbon (C) to above- vs. belowground, to growth vs. respiration, and to short vs. long lived tissues exerts a large influence on ecosystem structure and function with implications for the global C budget. Importantly, outcomes of process-based terrestrial vegetation models are likely to vary substantially with different C partitioning algorithms. However, controls on C partitioning patterns remain poorly quantified, and studies have yielded variable, and at times contradictory, results. A recent meta-analysis of forest studies suggests that the ratio of net primary production (NPP) and gross primary production (GPP) is fairly conservative across large scales. To illustrate the effect of this unique meta-analysis-based partitioning scheme (MPS), we compared an application of MPS to a terrestrial satellite-based (MODIS) GPP to estimate NPP vs. two global process-based vegetation models (Biome-BGC and VISIT) to examine the influence of C partitioning on C budgets of woody plants. Due to the temperature dependence of maintenance respiration, NPP/GPP predicted by the process-based models increased with latitude while the ratio remained constant with MPS. Overall, global NPP estimated with MPS was 17 and 27% lower than the process-based models for temperate and boreal biomes, respectively, with smaller differences in the tropics. Global equilibrium biomass of woody plants was then calculated from the NPP estimates and tissue turnover rates from VISIT. Since turnover rates differed greatly across tissue types (i.e., metabolically active vs. structural), global equilibrium biomass estimates were sensitive to the partitioning scheme employed. The MPS estimate of global woody biomass was 7-21% lower than that of the process-based models. In summary, we found that model output for NPP and equilibrium biomass was quite sensitive to the choice of C partitioning schemes. Carbon use efficiency (CUE; NPP/GPP) by forest biome and the globe. Values are means for 2001-2006.

Assessing the effects of architectural variations on light partitioning within virtual wheat–pea mixtures

PubMed Central

Barillot, Romain; Escobar-Gutiérrez, Abraham J.; Fournier, Christian; Huynh, Pierre; Combes, Didier

2014-01-01

Background and Aims Predicting light partitioning in crop mixtures is a critical step in improving the productivity of such complex systems, and light interception has been shown to be closely linked to plant architecture. The aim of the present work was to analyse the relationships between plant architecture and light partitioning within wheat–pea (Triticum aestivum–Pisum sativum) mixtures. An existing model for wheat was utilized and a new model for pea morphogenesis was developed. Both models were then used to assess the effects of architectural variations in light partitioning. Methods First, a deterministic model (L-Pea) was developed in order to obtain dynamic reconstructions of pea architecture. The L-Pea model is based on L-systems formalism and consists of modules for ‘vegetative development’ and ‘organ extension’. A tripartite simulator was then built up from pea and wheat models interfaced with a radiative transfer model. Architectural parameters from both plant models, selected on the basis of their contribution to leaf area index (LAI), height and leaf geometry, were then modified in order to generate contrasting architectures of wheat and pea. Key results By scaling down the analysis to the organ level, it could be shown that the number of branches/tillers and length of internodes significantly determined the partitioning of light within mixtures. Temporal relationships between light partitioning and the LAI and height of the different species showed that light capture was mainly related to the architectural traits involved in plant LAI during the early stages of development, and in plant height during the onset of interspecific competition. Conclusions In silico experiments enabled the study of the intrinsic effects of architectural parameters on the partitioning of light in crop mixtures of wheat and pea. The findings show that plant architecture is an important criterion for the identification/breeding of plant ideotypes, particularly with respect to light partitioning. PMID:24907314

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wu, J; Gensheimer, M; Dong, X

Purpose: To develop an intra-tumor partitioning framework for identifying high-risk subregions from 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) and CT imaging, and to test whether tumor burden associated with the high-risk subregions is prognostic of outcomes in lung cancer. Methods: In this institutional review board-approved retrospective study, we analyzed the pre-treatment FDG-PET and CT scans of 44 lung cancer patients treated with radiotherapy. A novel, intra-tumor partitioning method was developed based on a two-stage clustering process: first at patient-level, each tumor was over-segmented into many superpixels by k-means clustering of integrated PET and CT images; next, tumor subregions were identified bymore » merging previously defined superpixels via population-level hierarchical clustering. The volume associated with each of the subregions was evaluated using Kaplan-Meier analysis regarding its prognostic capability in predicting overall survival (OS) and out-of-field progression (OFP). Results: Three spatially distinct subregions were identified within each tumor, which were highly robust to uncertainty in PET/CT co-registration. Among these, the volume of the most metabolically active and metabolically heterogeneous solid component of the tumor was predictive of OS and OFP on the entire cohort, with a concordance index or CI = 0.66–0.67. When restricting the analysis to patients with stage III disease (n = 32), the same subregion achieved an even higher CI = 0.75 (HR = 3.93, logrank p = 0.002) for predicting OS, and a CI = 0.76 (HR = 4.84, logrank p = 0.002) for predicting OFP. In comparison, conventional imaging markers including tumor volume, SUVmax and MTV50 were not predictive of OS or OFP, with CI mostly below 0.60 (p < 0.001). Conclusion: We propose a robust intra-tumor partitioning method to identify clinically relevant, high-risk subregions in lung cancer. We envision that this approach will be applicable to identifying useful imaging biomarkers in many cancer types.« less

Prediction of Human Intestinal Absorption of Compounds Using Artificial Intelligence Techniques.

PubMed

Kumar, Rajnish; Sharma, Anju; Siddiqui, Mohammed Haris; Tiwari, Rajesh Kumar

2017-01-01

Information about Pharmacokinetics of compounds is an essential component of drug design and development. Modeling the pharmacokinetic properties require identification of the factors effecting absorption, distribution, metabolism and excretion of compounds. There have been continuous attempts in the prediction of intestinal absorption of compounds using various Artificial intelligence methods in the effort to reduce the attrition rate of drug candidates entering to preclinical and clinical trials. Currently, there are large numbers of individual predictive models available for absorption using machine learning approaches. Six Artificial intelligence methods namely, Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis were used for prediction of absorption of compounds. Prediction accuracy of Support vector machine, k- nearest neighbor, Probabilistic neural network, Artificial neural network, Partial least square and Linear discriminant analysis for prediction of intestinal absorption of compounds was found to be 91.54%, 88.33%, 84.30%, 86.51%, 79.07% and 80.08% respectively. Comparative analysis of all the six prediction models suggested that Support vector machine with Radial basis function based kernel is comparatively better for binary classification of compounds using human intestinal absorption and may be useful at preliminary stages of drug design and development. Copyright© Bentham Science Publishers; For any queries, please email at epub@benthamscience.org.

Non steady-state descriptions of drug permeation through stratum corneum. I. The biphasic brick-and-mortar model.

PubMed

Heisig, M; Lieckfeldt, R; Wittum, G; Mazurkevich, G; Lee, G

1996-03-01

The diffusion equation should be solved for the non-steady-state problem of drug diffusion within a two-dimensional, biphasic stratum corneum membrane having homogeneous lipid and corneocyte phases. A numerical method was developed for a brick-and-mortar SC-geometry, enabling an explicit solution for time-dependent drug concentration within both phases. The lag time and permeability were calculated. It is shown how the barrier property of this model membrane depends on relative phase permeability, corneocyte alignment, and corneocyte-lipid partition coefficient. Additionally, the time-dependent drug concentration profiles within the membrane can be observed during the lag and steady-state phases. The model SC-membrane predicts, from purely morphological principles, lag times and permeabilities that are in good agreement with experimental values. The long lag times and very small permeabilities reported for human SC can only be predicted for a highly-staggered corneocyte geometry and corneocytes that are 1000 times less permeable than the lipid phase. Although the former conclusion is reasonable, the latter is questionable. The elongated, flattened corneocyte shape renders lag time and permeability insensitive to large changes in their alignment within the SC. Corneocyte/lipid partitioning is found to be fundamentally different to SC/donor partitioning, since increasing drug lipophilicity always reduces both lag time and permeability.

Black hole meiosis

NASA Astrophysics Data System (ADS)

van Herck, Walter; Wyder, Thomas

2010-04-01

The enumeration of BPS bound states in string theory needs refinement. Studying partition functions of particles made from D-branes wrapped on algebraic Calabi-Yau 3-folds, and classifying states using split attractor flow trees, we extend the method for computing a refined BPS index, [1]. For certain D-particles, a finite number of microstates, namely polar states, exclusively realized as bound states, determine an entire partition function (elliptic genus). This underlines their crucial importance: one might call them the ‘chromosomes’ of a D-particle or a black hole. As polar states also can be affected by our refinement, previous predictions on elliptic genera are modified. This can be metaphorically interpreted as ‘crossing-over in the meiosis of a D-particle’. Our results improve on [2], provide non-trivial evidence for a strong split attractor flow tree conjecture, and thus suggest that we indeed exhaust the BPS spectrum. In the D-brane description of a bound state, the necessity for refinement results from the fact that tachyonic strings split up constituent states into ‘generic’ and ‘special’ states. These are enumerated separately by topological invariants, which turn out to be partitions of Donaldson-Thomas invariants. As modular predictions provide a check on many of our results, we have compelling evidence that our computations are correct.

Selection of operating parameters on the basis of hydrodynamics in centrifugal partition chromatography for the purification of nybomycin derivatives.

PubMed

Adelmann, S; Baldhoff, T; Koepcke, B; Schembecker, G

2013-01-25

The selection of solvent systems in centrifugal partition chromatography (CPC) is the most critical point in setting up a separation. Therefore, lots of research was done on the topic in the last decades. But the selection of suitable operating parameters (mobile phase flow rate, rotational speed and mode of operation) with respect to hydrodynamics and pressure drop limit in CPC is still mainly driven by experience of the chromatographer. In this work we used hydrodynamic analysis for the prediction of most suitable operating parameters. After selection of different solvent systems with respect to partition coefficients for the target compound the hydrodynamics were visualized. Based on flow pattern and retention the operating parameters were selected for the purification runs of nybomycin derivatives that were carried out with a 200 ml FCPC(®) rotor. The results have proven that the selection of optimized operating parameters by analysis of hydrodynamics only is possible. As the hydrodynamics are predictable by the physical properties of the solvent system the optimized operating parameters can be estimated, too. Additionally, we found that dispersion and especially retention are improved if the less viscous phase is mobile. Crown Copyright © 2012. Published by Elsevier B.V. All rights reserved.

Lateral orbitofrontal cortex partitions mechanisms for fear regulation and alcohol consumption

PubMed Central

Hanlon, Emma; McDannald, Michael A.

2018-01-01

Anxiety disorders and alcohol use disorder are highly comorbid, yet identifying neural dysfunction driving comorbidity has been challenging. Lateral orbitofrontal cortex (lOFC) dysfunction has been independently observed in each disorder. Here we tested the hypothesis that the lOFC is essential to partition mechanisms for fear regulation and alcohol consumption. Specifically, the capacity to regulate fear and the propensity to consume alcohol are unrelated when lOFC is intact, but become linked through lOFC dysfunction. Male Long Evans rats received bilateral, neurotoxic lOFC lesions or sham surgery. Fear regulation was determined by establishing discrimination to danger, uncertainty, and safety cues then shifting the shock probability of the uncertainty cue. Alcohol consumption was assessed through voluntary, intermittent access to 20% ethanol. The neurotoxic lesion approach ensured lOFC dysfunction spanned testing in fear regulation and alcohol consumption. LOFC-lesioned rats demonstrated maladaptive fear generalization during probability shifts, inverting normal prediction error assignment, and subsequently consumed more alcohol. Most novel, fear regulation and alcohol consumption were inextricably linked only in lOFC-lesioned rats: extreme fear regulation predicted excessive alcohol consumption. The results reveal the lOFC is essential to partition mechanisms for fear regulation and alcohol consumption and uncover a plausible source of neural dysfunction contributing to comorbid anxiety disorders and alcohol use disorder. PMID:29856796

Exact calculation of loop formation probability identifies folding motifs in RNA secondary structures.

PubMed

Sloma, Michael F; Mathews, David H

2016-12-01

RNA secondary structure prediction is widely used to analyze RNA sequences. In an RNA partition function calculation, free energy nearest neighbor parameters are used in a dynamic programming algorithm to estimate statistical properties of the secondary structure ensemble. Previously, partition functions have largely been used to estimate the probability that a given pair of nucleotides form a base pair, the conditional stacking probability, the accessibility to binding of a continuous stretch of nucleotides, or a representative sample of RNA structures. Here it is demonstrated that an RNA partition function can also be used to calculate the exact probability of formation of hairpin loops, internal loops, bulge loops, or multibranch loops at a given position. This calculation can also be used to estimate the probability of formation of specific helices. Benchmarking on a set of RNA sequences with known secondary structures indicated that loops that were calculated to be more probable were more likely to be present in the known structure than less probable loops. Furthermore, highly probable loops are more likely to be in the known structure than the set of loops predicted in the lowest free energy structures. © 2016 Sloma and Mathews; Published by Cold Spring Harbor Laboratory Press for the RNA Society.

Quantitative structure-property relationships for octanol-water partition coefficients of polybrominated diphenyl ethers.

PubMed

Li, Linnan; Xie, Shaodong; Cai, Hao; Bai, Xuetao; Xue, Zhao

2008-08-01

Theoretical molecular descriptors were tested against logK(OW) values for polybrominated diphenyl ethers (PBDEs) using the Partial Least-Squares Regression method which can be used to analyze data with many variables and few observations. A quantitative structure-property relationship (QSPR) model was successfully developed with a high cross-validated value (Q(cum)(2)) of 0.961, indicating a good predictive ability and stability of the model. The predictive power of the QSPR model was further cross-validated. The values of logK(OW) for PBDEs are mainly governed by molecular surface area, energy of the lowest unoccupied molecular orbital and the net atomic charges on the oxygen atom. All these descriptors have been discussed to interpret the partitioning mechanism of PBDE chemicals. The bulk property of the molecules represented by molecular surface area is the leading factor, and K(OW) values increase with the increase of molecular surface area. Higher energy of the lowest unoccupied molecular orbital and higher net atomic charge on the oxygen atom of PBDEs result in smaller K(OW). The energy of the lowest unoccupied molecular orbital and the net atomic charge on PBDEs oxygen also play important roles in affecting the partition of PBDEs between octanol and water by influencing the interactions between PBDEs and solvent molecules.

Development of a Two-Dimensional Model for Predicting Transdermal Permeation with the Follicular Pathway: Demonstration with a Caffeine Study.

PubMed

Kattou, Panayiotis; Lian, Guoping; Glavin, Stephen; Sorrell, Ian; Chen, Tao

2017-10-01

The development of a new two-dimensional (2D) model to predict follicular permeation, with integration into a recently reported multi-scale model of transdermal permeation is presented. The follicular pathway is modelled by diffusion in sebum. The mass transfer and partition properties of solutes in lipid, corneocytes, viable dermis, dermis and systemic circulation are calculated as reported previously [Pharm Res 33 (2016) 1602]. The mass transfer and partition properties in sebum are collected from existing literature. None of the model input parameters was fit to the clinical data with which the model prediction is compared. The integrated model has been applied to predict the published clinical data of transdermal permeation of caffeine. The relative importance of the follicular pathway is analysed. Good agreement of the model prediction with the clinical data has been obtained. The simulation confirms that for caffeine the follicular route is important; the maximum bioavailable concentration of caffeine in systemic circulation with open hair follicles is predicted to be 20% higher than that when hair follicles are blocked. The follicular pathway contributes to not only short time fast penetration, but also the overall systemic bioavailability. With such in silico model, useful information can be obtained for caffeine disposition and localised delivery in lipid, corneocytes, viable dermis, dermis and the hair follicle. Such detailed information is difficult to obtain experimentally.

3D dynamics of crustal deformation driven by oblique subduction: Northern and Central Andes

NASA Astrophysics Data System (ADS)

Schütt, Jorina M.; Whipp, David M., Jr.

2017-04-01

The geometry and relative motion of colliding plates will affect how and where they deform. In oblique subduction systems, factors such as the dip angle of the subducting plate and the convergence obliquity, as well as the presence of weak zones in the overriding plate, all influence how oblique convergence is partitioned onto various fault systems in the overriding plate. The partitioning of strain into margin-normal slip on the plate-bounding fault and horizontal shearing on a strike-slip system parallel to the margin is mainly controlled by the margin-parallel shear forces acting on the plate interface and the strength of the continental crust. While these plate interface forces are influenced by the dip angle of the subducting plate (i.e., the length of plate interface in the frictional domain) and the obliquity angle between the normal to the plate margin and the plate convergence vector, the strength of the continental crust in the upper plate is strongly affected by the presence or absence of weak zones such as regions of arc volcanism, pre-existing fault systems, or boundaries of stronger crustal blocks. In order to investigate which of these factors are most important in controlling how the overriding continental plate deforms, we compare results of lithospheric-scale 3D numerical geodynamic experiments from two regions in the north-central Andes: the Northern Volcanic Zone (NVZ; 5°N - 3°S) and adjacent Peruvian Flat Slab Segment (PFSS; 3°S -14°S). The NVZ is characterized by a 35° subduction dip angle with an obliquity angle of about 40°, extensive volcanism and significant strain partitioning in the continental crust. In contrast, the PFSS is characterized by flat subduction (the slab flattens beneath the continent at around 100 km depth for several hundred kilometers), an obliquity angle of about 20°, no volcanism and minimal strain partitioning. The plate geometry and convergence obliquity for these regions are incorporated in 3D (1600 x 1600 x 160 km) numerical experiments of oceanic subduction beneath a continent, focusing on the conditions under which strain partitioning occurs in the continental plate. In addition to different slab geometries and obliquity angles, we consider the effect of a continental crustal of uniform strength (friction angle Φ=15^°) versus one including a weak zone in the continental crust (Φ=4^°) that runs parallel to the margin. Results of our experiments show that the obliquity angle has the largest effect on initiating strain partitioning, as expected based on strain partitioning theory, but strain partitioning is clearly enhanced by the presence of a continental weakness. Margin-parallel mass transport velocities in the continental sliver are similar to the values observed in the NVZ (about 1 cm/year) in models with a continental weakness and twice as high as those without. In addition, a shallower subduction angle results in formation of a wider continental sliver. Based upon our results, the lack of strain partitioning observed in the PFSS results from both a low convergence obliquity and lack of a weak zone in the continent, even though the shallow subduction should make strain partitioning more favorable.

Financial Distress Prediction using Linear Discriminant Analysis and Support Vector Machine

NASA Astrophysics Data System (ADS)

Santoso, Noviyanti; Wibowo, Wahyu

2018-03-01

A financial difficulty is the early stages before the bankruptcy. Bankruptcies caused by the financial distress can be seen from the financial statements of the company. The ability to predict financial distress became an important research topic because it can provide early warning for the company. In addition, predicting financial distress is also beneficial for investors and creditors. This research will be made the prediction model of financial distress at industrial companies in Indonesia by comparing the performance of Linear Discriminant Analysis (LDA) and Support Vector Machine (SVM) combined with variable selection technique. The result of this research is prediction model based on hybrid Stepwise-SVM obtains better balance among fitting ability, generalization ability and model stability than the other models.

A generalized graph-theoretical matrix of heterosystems and its application to the VMV procedure.

PubMed

Mozrzymas, Anna

2011-12-14

The extensions of generalized (molecular) graph-theoretical matrix and vector-matrix-vector procedure are considered. The elements of the generalized matrix are redefined in order to describe molecules containing heteroatoms and multiple bonds. The adjacency, distance, detour and reciprocal distance matrices of heterosystems, and corresponding vectors are derived from newly defined generalized graph matrix. The topological indices, which are most widely used in predicting physicochemical and biological properties/activities of various compounds, can be calculated from the new generalized vector-matrix-vector invariant. Copyright © 2011 Elsevier Ltd. All rights reserved.

Analysis of Changes in the Lorenz Energy Budget of the Atmosphere

NASA Astrophysics Data System (ADS)

Ellis, T. D.

2009-12-01

Several recent papers have addressed the topic of changes in global precipitation rates related to changes in Earth's global energy balance. Less studied are the processes that may be governing the large-scale regional distribution of precipitation around the globe. This study uses the energy budget partition paradigm first put forth by Lorenz (1955) and follows the methodology of Arpé et al. (1986) and Oriol (1982) to identify latitude bands where the partition of energy amongst zonal and eddy kinetic and potential energy bins may account for the spatial patterns of precipitation change predicted by many IPCC AR4 models. In doing so, this study may help to identify whether or not the climate change predicted by these models is indeed creating enhanced baroclinic storms in the mid-latitudes or if there are other mechanisms at work producing the patterns of precipitation change.

Exploratory Thermal-mechanical Fatigue Results for Rene' 80 in Ultrahigh Vacuum

NASA Technical Reports Server (NTRS)

Sheinker, A. A.

1978-01-01

A limited study was conducted of the use of strainage partitioning for predicting the thermalmechanical fatigue life of cast nickel-base superalloy Rene' 80. The fatigue lives obtained by combined inphase thermal and mechanical strain cycling between 400 C (752 F) and 1000 C (1802 F) in an ultrahigh vacuum were considerably shorter than those represented by the four basic partitioned inelastic strainrange fatigue life relationships established previously for this alloy at 871 C (1600 F) and 1000 C (1832 F) in an ultrahigh vacuum. This behavior was attributed to the drastic decrease in ductility with decreasing temperature for this alloy. These results indicated that the prediction of the thermal-mechanical fatigue life of Rene' 80 by the method of strainrange partioning may be improved if based on the four basic fatigue life relationships determined at a lower temperature in the thermal-mechanical strain cycle.

Three-parameter modeling of the soil sorption of acetanilide and triazine herbicide derivatives.

PubMed

Freitas, Mirlaine R; Matias, Stella V B G; Macedo, Renato L G; Freitas, Matheus P; Venturin, Nelson

2014-02-01

Herbicides have widely variable toxicity and many of them are persistent soil contaminants. Acetanilide and triazine family of herbicides have widespread use, but increasing interest for the development of new herbicides has been rising to increase their effectiveness and to diminish environmental hazard. The environmental risk of new herbicides can be accessed by estimating their soil sorption (logKoc), which is usually correlated to the octanol/water partition coefficient (logKow). However, earlier findings have shown that this correlation is not valid for some acetanilide and triazine herbicides. Thus, easily accessible quantitative structure-property relationship models are required to predict logKoc of analogues of the these compounds. Octanol/water partition coefficient, molecular weight and volume were calculated and then regressed against logKoc for two series of acetanilide and triazine herbicides using multiple linear regression, resulting in predictive and validated models.

Discovery of novel SERCA inhibitors by virtual screening of a large compound library.

PubMed

Elam, Christopher; Lape, Michael; Deye, Joel; Zultowsky, Jodie; Stanton, David T; Paula, Stefan

2011-05-01

Two screening protocols based on recursive partitioning and computational ligand docking methodologies, respectively, were employed for virtual screens of a compound library with 345,000 entries for novel inhibitors of the enzyme sarco/endoplasmic reticulum calcium ATPase (SERCA), a potential target for cancer chemotherapy. A total of 72 compounds that were predicted to be potential inhibitors of SERCA were tested in bioassays and 17 displayed inhibitory potencies at concentrations below 100 μM. The majority of these inhibitors were composed of two phenyl rings tethered to each other by a short link of one to three atoms. Putative interactions between SERCA and the inhibitors were identified by inspection of docking-predicted poses and some of the structural features required for effective SERCA inhibition were determined by analysis of the classification pattern employed by the recursive partitioning models. Copyright © 2011 Elsevier Masson SAS. All rights reserved.

Kinetic energy partition method applied to ground state helium-like atoms.

PubMed

Chen, Yu-Hsin; Chao, Sheng D

2017-03-28

We have used the recently developed kinetic energy partition (KEP) method to solve the quantum eigenvalue problems for helium-like atoms and obtain precise ground state energies and wave-functions. The key to treating properly the electron-electron (repulsive) Coulomb potential energies for the KEP method to be applied is to introduce a "negative mass" term into the partitioned kinetic energy. A Hartree-like product wave-function from the subsystem wave-functions is used to form the initial trial function, and the variational search for the optimized adiabatic parameters leads to a precise ground state energy. This new approach sheds new light on the all-important problem of solving many-electron Schrödinger equations and hopefully opens a new way to predictive quantum chemistry. The results presented here give very promising evidence that an effective one-electron model can be used to represent a many-electron system, in the spirit of density functional theory.

Lability of Secondary Organic Particulate Matter

DOE PAGES

Liu, Pengfei; Li, Yong Jie; Wang, Yan; ...

2016-10-24

Accurate simulations of the consenctrations of atmospheric organic particulate matter (PM) are needed for predicting energy flow in the Earth’s climate system. In the past, simulations of organic PM widely assume equilibrium partitioning of semivolatile organic compounds (SVOCs) between the PM and surrounding vapor. Herein, we test this assumption by measuring evaporation rates and associated vapor mass concentration of organic films representative of atmospheric PM. For films representing anthropogenic PM, evaporation rates and vapor mass concentrations increased above a threshold relative humidity (RH), indicating equilibrium partitioning above a transition RH but not below. In contrast for films representing biogenic PM,more » no threshold was observed, indicating equilibrium partitioning at all RHs. The results suggest that the mass lability of atmospheric organic PM can differ in consequential ways among Earth’s natural biomes, polluted regions, and regions of land-use change, and these differences need to be considered when simulating atmospheric organic PM.« less

Co-occurrence Patterns of the Dengue Vector Aedes aegypti and Aedes mediovitattus, a Dengue Competent Mosquito in Puerto Rico

PubMed Central

Little, Eliza; Barrera, Roberto; Seto, Karen C.; Diuk-Wasser, Maria

2015-01-01

Aedes aegypti is implicated in dengue transmission in tropical and subtropical urban areas around the world. Ae. aegypti populations are controlled through integrative vector management. However, the efficacy of vector control may be undermined by the presence of alternative, competent species. In Puerto Rico, a native mosquito, Ae. mediovittatus, is a competent dengue vector in laboratory settings and spatially overlaps with Ae. aegypti. It has been proposed that Ae. mediovittatus may act as a dengue reservoir during inter-epidemic periods, perpetuating endemic dengue transmission in rural Puerto Rico. Dengue transmission dynamics may therefore be influenced by the spatial overlap of Ae. mediovittatus, Ae. aegypti, dengue viruses, and humans. We take a landscape epidemiology approach to examine the association between landscape composition and configuration and the distribution of each of these Aedes species and their co-occurrence. We used remotely sensed imagery from a newly launched satellite to map landscape features at very high spatial resolution. We found that the distribution of Ae. aegypti is positively predicted by urban density and by the number of tree patches, Ae. mediovittatus is positively predicted by the number of tree patches, but negatively predicted by large contiguous urban areas, and both species are predicted by urban density and the number of tree patches. This analysis provides evidence that landscape composition and configuration is a surrogate for mosquito community composition, and suggests that mapping landscape structure can be used to inform vector control efforts as well as to inform urban planning. PMID:21989642

Predicted altitudinal shifts and reduced spatial distribution of Leishmania infantum vector species under climate change scenarios in Colombia.

PubMed

González, Camila; Paz, Andrea; Ferro, Cristina

2014-01-01

Visceral leishmaniasis (VL) is caused by the trypanosomatid parasite Leishmania infantum (=Leishmania chagasi), and is epidemiologically relevant due to its wide geographic distribution, the number of annual cases reported and the increase in its co-infection with HIV. Two vector species have been incriminated in the Americas: Lutzomyia longipalpis and Lutzomyia evansi. In Colombia, L. longipalpis is distributed along the Magdalena River Valley while L. evansi is only found in the northern part of the Country. Regarding the epidemiology of the disease, in Colombia the incidence of VL has decreased over the last few years without any intervention being implemented. Additionally, changes in transmission cycles have been reported with urban transmission occurring in the Caribbean Coast. In Europe and North America climate change seems to be driving a latitudinal shift of leishmaniasis transmission. Here, we explored the spatial distribution of the two known vector species of L. infantum in Colombia and projected its future distribution into climate change scenarios to establish the expansion potential of the disease. An updated database including L. longipalpis and L. evansi collection records from Colombia was compiled. Ecological niche models were performed for each species using the Maxent software and 13 Worldclim bioclimatic coverages. Projections were made for the pessimistic CSIRO A2 scenario, which predicts the higher increase in temperature due to non-emission reduction, and the optimistic Hadley B2 Scenario predicting the minimum increase in temperature. The database contained 23 records for L. evansi and 39 records for L. longipalpis, distributed along the Magdalena River Valley and the Caribbean Coast, where the potential distribution areas of both species were also predicted by Maxent. Climate change projections showed a general overall reduction in the spatial distribution of the two vector species, promoting a shift in altitudinal distribution for L. longipalpis and confining L. evansi to certain regions in the Caribbean Coast. Altitudinal shifts have been reported for cutaneous leishmaniasis vectors in Colombia and Peru. Here, we predict the same outcome for VL vectors in Colombia. Changes in spatial distribution patterns could be affecting local abundances due to climatic pressures on vector populations thus reducing the incidence of human cases. Copyright © 2013 The Authors. Published by Elsevier B.V. All rights reserved.

Vector and Tensor Analyzing Powers in Deuteron-Proton Breakup

NASA Astrophysics Data System (ADS)

Stephan, E.; Kistryn, St.; Kalantar-Nayestanaki, N.; Biegun, A.; Bodek, K.; Ciepał, I.; Deltuva, A.; Eslami-Kalantari, M.; Fonseca, A. C.; Gasparić, I.; Golak, J.; Jamróz, B.; Joulaeizadeh, L.; Kamada, H.; Kiš, M.; Kłos, B.; Kozela, A.; Mahjour-Shafiei, M.; Mardanpour, H.; Messchendorp, J.; Micherdzińska, A.; Moeini, H.; Nogga, A.; Ramazani-Moghaddam-Arani, A.; Skibiński, R.; Sworst, R.; Witała, H.; Zejma, J.

2011-05-01

High precision data for vector and tensor analyzing powers of the {^1{H}({d},{{pp}}){n}} breakup reaction at 130 and 100 MeV deuteron beam energies have been measured in a large fraction of the phase space. They are compared to the theoretical predictions based on various approaches to describe the three nucleon (3N) system dynamics. Theoretical predictions describe very well the vector analyzing power data, with no need to include any three-nucleon force effects for these observables. Tensor analyzing powers can be also very well reproduced by calculations in most of the studied region, but locally certain discrepancies are observed. At 130 MeV for A xy such discrepancies usually appear, or are enhanced, when model 3N forces are included. Predicted effects of 3NFs are much lower at 100 MeV and at this energy equally good consistency between the data and the calculations is obtained with or without 3NFs.

Conditional Entropy-Constrained Residual VQ with Application to Image Coding

NASA Technical Reports Server (NTRS)

Kossentini, Faouzi; Chung, Wilson C.; Smith, Mark J. T.

1996-01-01

This paper introduces an extension of entropy-constrained residual vector quantization (VQ) where intervector dependencies are exploited. The method, which we call conditional entropy-constrained residual VQ, employs a high-order entropy conditioning strategy that captures local information in the neighboring vectors. When applied to coding images, the proposed method is shown to achieve better rate-distortion performance than that of entropy-constrained residual vector quantization with less computational complexity and lower memory requirements. Moreover, it can be designed to support progressive transmission in a natural way. It is also shown to outperform some of the best predictive and finite-state VQ techniques reported in the literature. This is due partly to the joint optimization between the residual vector quantizer and a high-order conditional entropy coder as well as the efficiency of the multistage residual VQ structure and the dynamic nature of the prediction.

Rift Valley Fever Prediction and Risk Mapping: 2014-2015 Season

NASA Technical Reports Server (NTRS)

Anyamba, Assaf

2015-01-01

Extremes in either direction (+-) of precipitation temperature have significant implications for disease vectors and pathogen emergence and spread Magnitude of ENSO influence on precipitation temperature cannot be currently predicted rely on average history and patterns. Timing of event and emergence disease can be exploited (GAP) in to undertake vector control and preparedness measures. Currently - no risk for ecologically-coupled RVFV activity however we need to be vigilant during the coming fall season due the ongoing buildup of energy in the central Pacific Ocean. Potential for the dual-use of the RVF Monitor system for other VBDs Need to invest in early ground surveillance and the use of rapid field diagnostic capabilities for vector identification and virus isolation.

ℓ(p)-Norm multikernel learning approach for stock market price forecasting.

PubMed

Shao, Xigao; Wu, Kun; Liao, Bifeng

2012-01-01

Linear multiple kernel learning model has been used for predicting financial time series. However, ℓ(1)-norm multiple support vector regression is rarely observed to outperform trivial baselines in practical applications. To allow for robust kernel mixtures that generalize well, we adopt ℓ(p)-norm multiple kernel support vector regression (1 ≤ p < ∞) as a stock price prediction model. The optimization problem is decomposed into smaller subproblems, and the interleaved optimization strategy is employed to solve the regression model. The model is evaluated on forecasting the daily stock closing prices of Shanghai Stock Index in China. Experimental results show that our proposed model performs better than ℓ(1)-norm multiple support vector regression model.

Perceived Organizational Support for Enhancing Welfare at Work: A Regression Tree Model

PubMed Central

Giorgi, Gabriele; Dubin, David; Perez, Javier Fiz

2016-01-01

When trying to examine outcomes such as welfare and well-being, research tends to focus on main effects and take into account limited numbers of variables at a time. There are a number of techniques that may help address this problem. For example, many statistical packages available in R provide easy-to-use methods of modeling complicated analysis such as classification and tree regression (i.e., recursive partitioning). The present research illustrates the value of recursive partitioning in the prediction of perceived organizational support in a sample of more than 6000 Italian bankers. Utilizing the tree function party package in R, we estimated a regression tree model predicting perceived organizational support from a multitude of job characteristics including job demand, lack of job control, lack of supervisor support, training, etc. The resulting model appears particularly helpful in pointing out several interactions in the prediction of perceived organizational support. In particular, training is the dominant factor. Another dimension that seems to influence organizational support is reporting (perceived communication about safety and stress concerns). Results are discussed from a theoretical and methodological point of view. PMID:28082924

On the limits of probabilistic forecasting in nonlinear time series analysis II: Differential entropy.

PubMed

Amigó, José M; Hirata, Yoshito; Aihara, Kazuyuki

2017-08-01

In a previous paper, the authors studied the limits of probabilistic prediction in nonlinear time series analysis in a perfect model scenario, i.e., in the ideal case that the uncertainty of an otherwise deterministic model is due to only the finite precision of the observations. The model consisted of the symbolic dynamics of a measure-preserving transformation with respect to a finite partition of the state space, and the quality of the predictions was measured by the so-called ignorance score, which is a conditional entropy. In practice, though, partitions are dispensed with by considering numerical and experimental data to be continuous, which prompts us to trade off in this paper the Shannon entropy for the differential entropy. Despite technical differences, we show that the core of the previous results also hold in this extended scenario for sufficiently high precision. The corresponding imperfect model scenario will be revisited too because it is relevant for the applications. The theoretical part and its application to probabilistic forecasting are illustrated with numerical simulations and a new prediction algorithm.

Modeling snowmelt infiltration in seasonally frozen ground

NASA Astrophysics Data System (ADS)

Budhathoki, S.; Ireson, A. M.

2017-12-01

In cold regions, freezing and thawing of the soil govern soil hydraulic properties that shape the surface and subsurface hydrological processes. The partitioning of snowmelt into infiltration and runoff has also important implications for integrated water resource management and flood risk. However, there is an inadequate representation of the snowmelt infiltration into frozen soils in most land-surface and hydrological models, creating the need for improved models and methods. Here we apply, the Frozen Soil Infiltration Model, FroSIn, which is a novel algorithm for infiltration in frozen soils that can be implemented in physically based models of coupled flow and heat transport. In this study, we apply the model in a simple configuration to reproduce observations from field sites in the Canadian prairies, specifically St Denis and Brightwater Creek in Saskatchewan, Canada. We demonstrate the limitations of conventional approaches to simulate infiltration, which systematically over-predict runoff and under predict infiltration. The findings show that FroSIn enables models to predict more reasonable infiltration volumes in frozen soils, and also represent how infiltration-runoff partitioning is impacted by antecedent soil moisture.

Estimating the Octanol/Water Partition Coefficient for Aliphatic Organic Compounds Using Semi-Empirical Electrotopological Index

PubMed Central

Souza, Erica Silva; Zaramello, Laize; Kuhnen, Carlos Alberto; Junkes, Berenice da Silva; Yunes, Rosendo Augusto; Heinzen, Vilma Edite Fonseca

2011-01-01

A new possibility for estimating the octanol/water coefficient (log P) was investigated using only one descriptor, the semi-empirical electrotopological index (ISET). The predictability of four octanol/water partition coefficient (log P) calculation models was compared using a set of 131 aliphatic organic compounds from five different classes. Log P values were calculated employing atomic-contribution methods, as in the Ghose/Crippen approach and its later refinement, AlogP; using fragmental methods through the ClogP method; and employing an approach considering the whole molecule using topological indices with the MlogP method. The efficiency and the applicability of the ISET in terms of calculating log P were demonstrated through good statistical quality (r > 0.99; s < 0.18), high internal stability and good predictive ability for an external group of compounds in the same order as the widely used models based on the fragmental method, ClogP, and the atomic contribution method, AlogP, which are among the most used methods of predicting log P. PMID:22072945

QSPR models for predicting generator-column-derived octanol/water and octanol/air partition coefficients of polychlorinated biphenyls.

PubMed

Yuan, Jintao; Yu, Shuling; Zhang, Ting; Yuan, Xuejie; Cao, Yunyuan; Yu, Xingchen; Yang, Xuan; Yao, Wu

2016-06-01

Octanol/water (K(OW)) and octanol/air (K(OA)) partition coefficients are two important physicochemical properties of organic substances. In current practice, K(OW) and K(OA) values of some polychlorinated biphenyls (PCBs) are measured using generator column method. Quantitative structure-property relationship (QSPR) models can serve as a valuable alternative method of replacing or reducing experimental steps in the determination of K(OW) and K(OA). In this paper, two different methods, i.e., multiple linear regression based on dragon descriptors and hologram quantitative structure-activity relationship, were used to predict generator-column-derived log K(OW) and log K(OA) values of PCBs. The predictive ability of the developed models was validated using a test set, and the performances of all generated models were compared with those of three previously reported models. All results indicated that the proposed models were robust and satisfactory and can thus be used as alternative models for the rapid assessment of the K(OW) and K(OA) of PCBs. Copyright © 2016 Elsevier Inc. All rights reserved.

Estimating the octanol/water partition coefficient for aliphatic organic compounds using semi-empirical electrotopological index.

PubMed

Souza, Erica Silva; Zaramello, Laize; Kuhnen, Carlos Alberto; Junkes, Berenice da Silva; Yunes, Rosendo Augusto; Heinzen, Vilma Edite Fonseca

2011-01-01

A new possibility for estimating the octanol/water coefficient (log P) was investigated using only one descriptor, the semi-empirical electrotopological index (I(SET)). The predictability of four octanol/water partition coefficient (log P) calculation models was compared using a set of 131 aliphatic organic compounds from five different classes. Log P values were calculated employing atomic-contribution methods, as in the Ghose/Crippen approach and its later refinement, AlogP; using fragmental methods through the ClogP method; and employing an approach considering the whole molecule using topological indices with the MlogP method. The efficiency and the applicability of the I(SET) in terms of calculating log P were demonstrated through good statistical quality (r > 0.99; s < 0.18), high internal stability and good predictive ability for an external group of compounds in the same order as the widely used models based on the fragmental method, ClogP, and the atomic contribution method, AlogP, which are among the most used methods of predicting log P.

Metal-Silicate Partitioning of Various Siderophile Elements at High Pressure and High Temperatures: a Diamond Anvil Cell Study

NASA Astrophysics Data System (ADS)

Badro, J.; Blanchard, I.; Siebert, J.

2015-12-01

Core formation is the major chemical fractionation that occurred on Earth. This event is widely believed to have happened at pressures of at least 40 GPa and temperatures exceeding 3000 K. It has left a significant imprint on the chemistry of the mantle by removing most of the siderophile (iron-loving) elements from it. Abundances of most siderophile elements in the bulk silicate Earth are significantly different than those predicted from experiments at low P-T. Among them, vanadium, chromium, cobalt and gallium are four siderophile elements which abundances in the mantle have been marked by core formation processes. Thus, understand their respective abundance in the mantle can help bringing constraints on the conditions of Earth's differentiation. We performed high-pressure high-temperature experiments using laser heating diamond anvil cell to investigate the metal-silicate partitioning of those four elements. Homogeneous glasses doped in vanadium, chromium, cobalt and gallium were synthesized using a levitation furnace and load inside the diamond anvil cell along with metallic powder. Samples were recovered using a Focused Ion Beam and chemically analyzed using an electron microprobe. We investigate the effect of pressure, temperature and metal composition on the metal-silicate partitioning of V, Cr, Co and Ga. Three previous studies focused on V, Cr and Co partitioning at those conditions of pressure and temperature, but none explore gallium partitioning at the relevant extreme conditions of core formation. We will present the first measurements of gallium metal-silicate partitioning performed at the appropriate conditions of pressure and temperature of Earth's differentiation.

Pesticides in the atmosphere: a comparison of gas-particle partitioning and particle size distribution of legacy and current-use pesticides

NASA Astrophysics Data System (ADS)

Degrendele, C.; Okonski, K.; Melymuk, L.; Landlová, L.; Kukučka, P.; Audy, O.; Kohoutek, J.; Čupr, P.; Klánová, J.

2016-02-01

This study presents a comparison of seasonal variation, gas-particle partitioning, and particle-phase size distribution of organochlorine pesticides (OCPs) and current-use pesticides (CUPs) in air. Two years (2012/2013) of weekly air samples were collected at a background site in the Czech Republic using a high-volume air sampler. To study the particle-phase size distribution, air samples were also collected at an urban and rural site in the area of Brno, Czech Republic, using a cascade impactor separating atmospheric particulates according to six size fractions. Major differences were found in the atmospheric distribution of OCPs and CUPs. The atmospheric concentrations of CUPs were driven by agricultural activities while secondary sources such as volatilization from surfaces governed the atmospheric concentrations of OCPs. Moreover, clear differences were observed in gas-particle partitioning; CUP partitioning was influenced by adsorption onto mineral surfaces while OCPs were mainly partitioning to aerosols through absorption. A predictive method for estimating the gas-particle partitioning has been derived and is proposed for polar and non-polar pesticides. Finally, while OCPs and the majority of CUPs were largely found on fine particles, four CUPs (carbendazim, isoproturon, prochloraz, and terbuthylazine) had higher concentrations on coarse particles ( > 3.0 µm), which may be related to the pesticide application technique. This finding is particularly important and should be further investigated given that large particles result in lower risks from inhalation (regardless the toxicity of the pesticide) and lower potential for long-range atmospheric transport.

Relation of organic contaminant equilibrium sorption and kinetic uptake in plants

USGS Publications Warehouse

Li, H.; Sheng, G.; Chiou, C.T.; Xu, O.

2005-01-01

Plant uptake is one of the environmental processes that influence contaminant fate. Understanding the magnitude and rate of plant uptake is critical to assessing potential crop contamination and the development of phytoremediation technologies. We determined (1) the partition-dominated equilibrium sorption of lindane (LDN) and hexachlorobenzene (HCB) by roots and shoots of wheat seedlings, (2) the kinetic uptake of LDN and HCB by roots and shoots of wheat seedlings, (3) the kinetic uptake of HCB, tetrachloroethylene (PCE), and trichloroethylene (TCE) by roots and shoots of ryegrass seedlings, and (4) the lipid, carbohydrate, and water contents of the plants. Although the determined sorption and the plant composition together suggest the predominant role of plant lipids for the sorption of LDN and HCB, the predicted partition with lipids of LDN and HCB using the octanol-water partition coefficients is notably lower than the measured sorption, due presumably to underestimation of the plant lipid contents and to the fact that octanol is less effective as a partition medium than plant lipids. The equilibrium sorption or the estimated partition can be viewed as the kinetic uptake limits. The uptakes of LDN, PCE, and TCE from water at fixed concentrations increased with exposure time in approach to steady states. The uptake of HCB did not reach a plateau within the tested time because of its exceptionally high partition coefficient. In all of the cases, the observed uptakes were lower than their respective limits, due presumably to contaminant dissipation in and limited water transpiration by the plants. ?? 2005 American Chemical Society.

Quantitative structure-retention relationships of flavonoids unraveled by immobilized artificial membrane chromatography.

PubMed

Santoro, Adriana Leandra; Carrilho, Emanuel; Lanças, Fernando Mauro; Montanari, Carlos Alberto

2016-06-10

The pharmacokinetic properties of flavonoids with differing degrees of lipophilicity were investigated using immobilized artificial membranes (IAMs) as the stationary phase in high performance liquid chromatography (HPLC). For each flavonoid compound, we investigated whether the type of column used affected the correlation between the retention factors and the calculated octanol/water partition (log Poct). Three-dimensional (3D) molecular descriptors were calculated from the molecular structure of each compound using i) VolSurf software, ii) the GRID method (computational procedure for determining energetically favorable binding sites in molecules of known structure using a probe for calculating the 3D molecular interaction fields, between the probe and the molecule), and iii) the relationship between partition and molecular structure, analyzed in terms of physicochemical descriptors. The VolSurf built-in Caco-2 model was used to estimate compound permeability. The extent to which the datasets obtained from different columns differ both from each other and from both the calculated log Poct and the predicted permeability in Caco-2 cells was examined by principal component analysis (PCA). The immobilized membrane partition coefficients (kIAM) were analyzed using molecular descriptors in partial least square regression (PLS) and a quantitative structure-retention relationship was generated for the chromatographic retention in the cholesterol column. The cholesterol column provided the best correlation with the permeability predicted by the Caco-2 cell model and a good fit model with great prediction power was obtained for its retention data (R(2)=0.96 and Q(2)=0.85 with four latent variables). Copyright © 2015 Elsevier B.V. All rights reserved.