Bond-Order Time Series Analysis for Detecting Reaction Events in Ab Initio Molecular Dynamics Simulations.

Ab initio molecular dynamics is able to predict novel reaction mechanisms by directly observing the individual reaction events that occur in simulation trajectories. In this article, we describe an approach for detecting reaction events from simulation trajectories using a physically motivated model based on time series analysis of ab initio bond orders. We found that applying a threshold to the bond order was insufficient for accurate detection, whereas peak finding on the first time derivative resulted in significantly improved accuracy. The model is trained on a reference set of reaction events representing the ideal result given unlimited computing resources. Our study includes two model systems: a heptanylium carbocation that undergoes hydride shifts and an unsaturated iron carbonyl cluster that features CO ligand migration and bridging behavior. The results indicate a high level of promise for this analysis approach to be used in mechanistic analysis of reactive AIMD simulations more generally

Free-Standing Porous Carbon Nanofiber Networks from Electrospinning Polyimide for Supercapacitors

Free-standing porous carbon nanofiber networks (CFNs) were synthesized by electrospinning method and carbonization procedure. We study the implementation of porous CFNs as supercapacitor electrodes and electrochemical measurements demonstrated that porous CFNs exhibit a specific capacitance (205 F/g at the scan rate of 5 mV/s) with high flexibility and good rate capability performance (more than 70% of its initial capacitance from 5 mV/s to 200 mV/s). Furthermore, porous CFNs exhibited an excellent cycling stability (just 12% capacitance loss after 10,000 cycles). These results suggest that porous CFNs are very promising candidates as flexible supercapacitor electrodes

Quantum chemical studies of redox properties and conformational changes of a four-center iron CO2 reduction electrocatalyst.

The CO2 reduction electrocatalyst [Fe4N(CO)12]- (abbrev. 1-) reduces CO2 to HCO2- in a two-electron, one-proton catalytic cycle. Here, we employ ab initio calculations to estimate the first two redox potentials of 1- and explore the pathway of a side reaction involving CO dissociation from 13-. Using the BP86 density functional approximation, the redox potentials were computed with a root mean squared error of 0.15 V with respect to experimental data. High temperature Born-Oppenheimer molecular dynamics was employed to discover a reaction pathway of CO dissociation from 13- with a reaction energy of +10.6 kcal mol-1 and an activation energy of 18.8 kcal mol-1; including harmonic free energy terms, this yields ΔGsep = 1.4 kcal mol-1 for fully separated species and ΔG‡ = +17.4 kcal mol-1, indicating CO dissociation is energetically accessible at ambient conditions. The analogous dissociation pathway from 12- has a reaction energy of 22.1 kcal mol-1 and an activation energy of 22.4 kcal mol-1 (ΔGsep = 12.8 kcal mol-1, ΔG‡ = +18.1 kcal mol-1). Our computed harmonic vibrational analysis of [Fe4N(CO)11]3- or 23- reveals a distinct CO-stretching peak red-shifted from the main CO-stretching band, pointing to a possible vibrational signature of dissociation. Multi-reference CASSCF calculations are used to check the assumptions of the density functional approximations that were used to obtain the majority of the results

Analysis of a human brain transcriptome map

BACKGROUND: Genome wide transcriptome maps can provide tools to identify candidate genes that are over-expressed or silenced in certain disease tissue and increase our understanding of the structure and organization of the genome. Expressed Sequence Tags (ESTs) from the public dbEST and proprietary Incyte LifeSeq databases were used to derive a transcript map in conjunction with the working draft assembly of the human genome sequence. RESULTS: Examination of ESTs derived from brain tissues (excluding brain tumor tissues) suggests that these genes are distributed on chromosomes in a non-random fashion. Some regions on the genome are dense with brain-enriched genes while some regions lack brain-enriched genes, suggesting a significant correlation between distribution of genes along the chromosome and tissue type. ESTs from brain tumor tissues have also been mapped to the human genome working draft. We reveal that some regions enriched in brain genes show a significant decrease in gene expression in brain tumors, and, conversely that some regions lacking in brain genes show an increased level of gene expression in brain tumors. CONCLUSIONS: This report demonstrates a novel approach for tissue specific transcriptome mapping using EST-based quantitative assessment

Single nucleotide polymorphism of SREBF-1 gene associated with an increased risk of endometrial cancer in Chinese women.

AIM: Elevated levels of sterol regulatory element-binding protein-1 (SREBP-1) have been found in endometrial cancer (EC), suggesting that it is essential to the development of EC. Obesity and diabetes have been established as known risk factors of EC, while SREBF-1 gene polymorphisms have also been found to be associated with obesity and type II diabetes. Therefore, we hypothesize that single nucleotide polymorphism (SNP) in SREBF-1 gene may be associated with increased risk of EC. METHOD: We analyzed the sequence of SREBF-1 in tissue samples from 30 EC cases and 6 benign controls using high throughput method. Based on the primary results, we selected one SNP (rs2297508) as a genetic marker to conduct a hospital-based case-control study with 139 EC cases and 129 benign controls. The samples were examined under the microscope to determine their histopathology prior to the SNP analysis using RT-PCR. RESULTS: Through sequence analysis, we found 10 SNPs of SREBF-1 associated with EC, including 3 new SNPs. Fourteen percent of EC showed the rs2297508 SNP with C allele, while only 7% had the C allele was present in benign controls (p = 0.027, OR = 1.983). Additionally, the C allele was associated with cancer differentiation (p CONCLUSION: Our study indicates that SNP (rs2297508) of SREBF-1 may serve as a genetic predisposition factor for the development of EC and screening of such genetic marker may be helpful in its early detection

Fuzzy Time Series Forecasting Model Based on Automatic Clustering Techniques and Generalized Fuzzy Logical Relationship

In view of techniques for constructing high-order fuzzy time series models, there are three types which are based on advanced algorithms, computational method, and grouping the fuzzy logical relationships. The last type of models is easy to be understood by the decision maker who does not know anything about fuzzy set theory or advanced algorithms. To deal with forecasting problems, this paper presented novel high-order fuzz time series models denoted as GTS (M, N) based on generalized fuzzy logical relationships and automatic clustering. This paper issued the concept of generalized fuzzy logical relationship and an operation for combining the generalized relationships. Then, the procedure of the proposed model was implemented on forecasting enrollment data at the University of Alabama. To show the considerable outperforming results, the proposed approach was also applied to forecasting the Shanghai Stock Exchange Composite Index. Finally, the effects of parameters M and N, the number of order, and concerned principal fuzzy logical relationships, on the forecasting results were also discussed