Application of the Conduct-like Screening Models for Real Solvent and Segment Activity Coefficient for the Predictions of Partition Coefficients and Vapor–Liquid and Liquid–Liquid Equilibria of Bio-oil-Related Mixtures

The 1-octanol/water partition coefficients (log <i>P</i>) at 298.15 K and the vapor–liquid and liquid–liquid equilibria (VLE and LLE) of biofuel-related mixtures have been predicted with four different thermodynamic models: conduct-like screening models for real solvent (COSMO-RS), conduct-like screening models for segment activity coefficient (COSMO-SAC) (2002 version), modified COSMO-SAC (2006 version), and universal functional activity coefficient (UNIFAC). The 2002 version of COSMO-SAC gives more reasonable predictions for log <i>P</i> for most investigated mixtures than the other two approaches when appropriate molecular geometries are chosen for the computation of the σ profiles. However, the COSMO-RS model gives better predictions for VLE pressures and vapor-phase compositions for biofuel-related mixtures, as well as for the LLE of the 1-octanol + water and furfural + water mixtures. The accuracy of the models for the predictions of the partition coefficients and VLE may be improved by changing the molecular conformations used to generate the σ profiles. Generally, the three COSMO-based models give better predictions than UNIFAC for log <i>P</i> and VLE of the investigated systems and can be applied to predict the thermodynamic properties of the biofuel-related mixtures especially when no experimental data are available

Integrated Operation and Cyclic Scheduling Optimization for an Ethylene Cracking Furnaces System

Multiple cracking furnaces in an ethylene plant run in parallel to produce ethylene, propylene, and other products from different hydrocarbon feedstocks. Because both coke formation in radiant coils and change of operation conditions with time have significant effects on the performance of cracking furnaces, it is better for the cyclic scheduling to be simultaneously optimized with the operation conditions. To match this real requirement, a mixed-integer dynamic optimization (MIDO) problem is presented for the optimization of both operation conditions and cyclic scheduling simultaneously, through which the optimal assignment, the processing time, the subcycle number, and the optimal operation conditions of different feeds in different cracking furnaces are determined at the same time. To solve the MIDO problem, it is discretized and converted into a large scale mixed-integer nonlinear programming (MINLP) problem. The two scheduling cases of a cracking furnaces system are illustrated showing a remarkable increase in the economic performance as compared with that of the traditional method

Modular Synthesis of α‑Aryl-α-Heteroaryl α‑Amino Acid Derivatives via a Copper-Catalyzed Cross-Dehydrogenative-Coupling Reaction Using Air as the Sole Oxidant

A novel copper-catalyzed cross-dehydrogenative-coupling (CDC) process of arylglycine derivatives with N-heteroarenes for the straightforward synthesis of α-aryl-α-heteroaryl α-amino acid scaffolds has been successfully developed. This protocol exhibits a broad substrate scope with good functional group compatibility by utilizing air as the sole oxidant. The use of the reaction is also displayed through the late-stage functionalization of arylglycines bearing natural compounds or drug motifs

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

<div><p>The increasing availability of time series expression datasets, although promising, raises a number of new computational challenges. Accordingly, the development of suitable classification methods to make reliable and sound predictions is becoming a pressing issue. We propose, here, a new method to classify time series gene expression via integration of biological networks. We evaluated our approach on 2 different datasets and showed that the use of a hidden Markov model/Gaussian mixture models hybrid explores the time-dependence of the expression data, thereby leading to better prediction results. We demonstrated that the biclustering procedure identifies function-related genes as a whole, giving rise to high accordance in prognosis prediction across independent time series datasets. In addition, we showed that integration of biological networks into our method significantly improves prediction performance. Moreover, we compared our approach with several state-of–the-art algorithms and found that our method outperformed previous approaches with regard to various criteria. Finally, our approach achieved better prediction results on early-stage data, implying the potential of our method for practical prediction.</p> </div

Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of different discretization methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p

Capturing Thermodynamic Behavior of Ionic Liquid Systems: Correlations with the SWCF-VR Equation

An equation of state for square-well chain fluids with variable well-width range (SWCF-VR EoS) [Li et al. <i>Fluid Phase Equilib.</i> <b>2009</b>, <i>276</i>, 57] was applied to ionic liquid (IL) systems. ILs were treated as the square-well chain with hydrogen bonding. The corresponding association parameters were given according to our previous work [He et al. <i>Fluid Phase Equilib.</i> <b>2011</b>, <i>302</i>, 139]. The nonassociation parameters were obtained by correlating the experimental liquid densities. Excellent agreements were observed between experimental and theoretical results for pure ILs, and the molecular parameters were linearly correlated with the molecular masses of the [C_<i>n</i>mim]­[NTf₂] members (<i>n</i> = 2, 3, ..., 8, 10). It is found that the other thermodynamic properties such as the vapor pressure and the enthalpy of vaporization, etc., can be reasonably predicted by using the obtained molecular parameters. The phase behavior of the binary systems containing ILs was well-represented with a simple mixing rule. For the vapor–liquid equilibria (VLE) of a system of volatile fluid + IL at low pressures, a temperature-independent binary interaction parameter was adopted. Satisfactory results were achieved for both the self- and cross-associating systems. The influence of temperature on the binary interaction parameters was taken into account in the correlation for the gas–liquid equilibria (GLE) of CO₂ + IL mixtures and liquid–liquid equilibria (LLE) of IL-containing systems. For CO₂ + IL mixtures, the multipolar interactions between like and unlike molecules, and the cross-association between CO₂ and IL molecules were neglected to reduce the computational complexity, and the correlated results agree well with the experimental ones over a wide range of temperatures and pressures. The LLE of alkanol + IL systems were acceptably reproduced with moderate deviations between the experimental and calculated mass fractions. In the water-rich phase of water + IL with LLE, the neglect of electrostatic interaction caused correlated results to deviate from experimental ones greatly

Precision, Recall and F-measure of different classification approaches.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Cdc6 depletion reverses the PTX-induced Cdk1 inactivation in Hela cells.

<p>Hela cells were treated with PTX (30 nM) combination with or without Cdc6 RNAi or NCTD (30 μM) as described in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0162633#pone.0162633.g007" target="_blank">Fig 7</a>. Cdc6, pCdk1 and Rad 21 were examined by Western Blotting. GAPDH was used as loading control. The protein levels are expressed as optical density fold difference related to GAPDH (relative OD). Three independent experiments were performed, *<i>P</i><0.05 as compared to PTX group.</p

Classification accuracies of PPI-SVM-KNN with the change of parameter C.

<p>The bars and error ticks represent mean values and standard deviations respectively. (A) shows the result for Baranzini dataset. (B) shows the result for Goertsches dataset.</p

Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).

<p>Classification accuracies of distinct classification methods for Baranzini dataset and Goertsches dataset: average (AVG) and standard deviation (SD).</p