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To establish an experimental model of neuroschistosomiasis and investigate the model establishment factors.Rabbits were used for the animal model and Schistosoma japonicum eggs (1 mg/ml) were directly injected into the brain by two ways of a bone drill or needle. The symptoms were observed and in the first and second week and later, the rabbits' brains were removed for pathological examinations.One to two weeks after the injection of schistosome eggs, the rabbits had various neurological symptoms such as loss of appetite, hemiparesis, seizure, etc. The pathological analysis showed the schistosome egg granuloma inflammatory reaction among 90% rabbits.This new method of direct injection of S. japonicum eggs through skull into the brain provides a good and easy animal model of neuroschistosomiasis.

Whereas the symmetries of small molecules are described by the methods of group theory, there is no corresponding way to describe the complex symmetries in proteins. We develop a quantitative method to define and classify symmetries in compact polymers, based on the mathematical theory of graphs and knots. We represent different chain folds by their ‘‘polymer graphs,’’ equivalent to contact maps. We transform those graphs into mathematical knots to give a parsing of different possible chain folds into conformational taxonomies. We use Alexander–Conway knot polynomials to characterize the knots. We find that different protein structures with the same tertiary fold, e.g., a βαβ motif with different lengths of α helix and β sheet, can be described in terms of the different powers of the propagation matrices of the knot polynomial. This identifies a fundamental type of topological length invariance in proteins, ‘‘elongatable’’ symmetries. For example, ‘‘helix,’’ ‘‘sheet,’’ ‘‘helix‐turn‐helix,’’ and other secondary, supersecondary, and tertiary structures define structures of any chain length. Possibly the nine superfolds identified by Thornton et al. have elongatable symmetries.

We develop a matrix method to compute the configurational partition functions of chain molecules that have intramolecular double strands, including RNA and DNA and antiparallel β‐sheet regions in proteins. An accurate theory has not been available for these types of molecules due to the nature of their self‐contacts: (i) biomolecules have many self‐contacts, and (ii) contacts are sequence‐specific. The present theory, which we call the firehose model, can treat such conformations, including specific loops, bulges, and stems. The method is based on the use of polymer graphs. Tests against exact enumeration results on the two‐dimensional (2D) square lattice show that conformational entropies are predicted quite accurately. As an example, we compute the denaturation profile of a 2D chain designed to resemble an RNA structure.

In this paper, we propose a hybrid language model for handwritten Chinese sentence recognition. This hybrid model is integrated from several independent language models, each of which is trained from a distinct type of corpus and models specifically the linguistic behavior for that type of corpus. By inferring the type of the string which the user has already written, we can make this hybrid language model contribute more precisely to the recognition engine. Our experiments show that the hybrid language model performs consistently well among different types of handwritten articles, and the overall performance is significantly better than a single standard language model. We also propose a candidate re-ranking process after recognition by reducing the language scores to improve the recognition accuracy. The experiment result also demonstrates that this re-ranking process effectively improves the performance of the recognition engine in terms of accuracy.

Transitions between the different conformational states play a critical role in many RNA catalytic and regulatory functions. In this study, we use the Kinetic Monte Carlo method to investigate the kinetic mechanism for the conformational switches between bistable RNA hairpins. We find three types of conformational switch pathways for RNA hairpins: refolding after complete unfolding, folding through basepair-exchange pathways and through pseudoknot-assisted pathways, respectively. The result of the competition between the three types of pathways depends mainly on the location of the rate-limiting base stacks (such as the GC base stacks) in the structures. Depending on the structural relationships between the two bistable hairpins, the conformational switch can follow single or multiple dominant pathways. The predicted folding pathways are supported by the activation energy results derived from the Arrhenius plot as well as the NMR spectroscopy data.

Pseudoknotted structures play important structural and functional roles in RNA cellular functions at the level of transcription, splicing and translation. However, the problem of computational prediction for large pseudoknotted folds remains. Here we develop a domain-based method for predicting complex and large pseudoknotted structures from RNA sequences. The model is based on the observation that large RNAs can be separated into different structural domains. The basic idea is to first identify the domains and then predict the structures for each domain. Assembly of the domain structures gives the full structure. The use of the domain-based approach leads to a reduction of computational time by a factor of about ~N ( 2) for an N-nt sequence. As applications of the model, we predict structures for a variety of RNA systems, such as regions in human telomerase RNA (hTR), internal ribosome entry site (IRES) and HIV genome. The lengths of these sequences range from 200-nt to 400-nt. The results show good agreements with the experiments.

We develop a systematic helix-based computational method to predict RNA folding kinetics during transcription. In our method, the transcription is modeled as stepwise process, where each step is the transcription of a nucleotide. For each step, the kinetics algorithm predicts the population kinetics, transition pathways, folding intermediates, and the transcriptional folding products. The folding pathways, rate constants, and the conformational populations for cotranscription folding show contrastingly different features than the refolding kinetics for a fully transcribed chain. The competition between the transcription speed and rate constants for the transitions between the different nascent structures determines the RNA folding pathway and the end product of folding. For example, fast transcription favors the formation of branch-like structures than rod-like structures and chain elongation in the folding process may reduce the probability of the formation of misfolded structures. Furthermore, good theory-experiment agreements suggest that our method may provide a reliable tool for quantitative prediction for cotranscriptional RNA folding, including the kinetics for the population distribution for the whole conformational ensemble.

In spite of the success of computational methods for predicting RNA secondary structure, the problem of predicting RNA tertiary structure folding remains. Low-resolution structural models show promise as they allow for rigorous statistical mechanical computation for the conformational entropies, free energies, and the coarse-grained structures of tertiary folds. Molecular dynamics refinement of coarse-grained structures leads to all-atom 3D structures. Modeling based on statistical mechanics principles also has the unique advantage of predicting the full free energy landscape, including local minima and the global free energy minimum. The energy landscapes combined with the 3D structures form the basis for quantitative predictions of RNA functions. In this chapter, we present an overview of statistical mechanical models for RNA folding and then focus on a recently developed RNA statistical mechanical model -- the Vfold model. The main emphasis is placed on the physics underpinning the models, the computational strategies, and the connections to RNA biology.

RNA aptamers represent an emerging class of pharmaceuticals with great potential for targeted cancer diagnostics and therapy. Several RNA aptamers that bind cancer cell-surface antigens with high affinity and specificity have been described. However, their clinical potential has yet to be realized. A significant obstacle to the clinical adoption of RNA aptamers is the high cost of manufacturing long RNA sequences through chemical synthesis. Therapeutic aptamers are often truncated postselection by using a trial-and-error process, which is time consuming and inefficient. Here, we used a "rational truncation" approach guided by RNA structural prediction and protein/RNA docking algorithms that enabled us to substantially truncateA9, an RNA aptamer to prostate-specific membrane antigen (PSMA),with great potential for targeted therapeutics. This truncated PSMA aptamer (A9L; 41mer) retains binding activity, functionality, and is amenable to large-scale chemical synthesis for future clinical applications. In addition, the modeled RNA tertiary structure and protein/RNA docking predictions revealed key nucleotides within the aptamer critical for binding to PSMA and inhibiting its enzymatic activity. Finally, this work highlights the utility of existing RNA structural prediction and protein docking techniques that may be generally applicable to developing RNA aptamers optimized for therapeutic use.

The present study aimed to establish a cerebral schistosomiasis model in rabbits, to provide a valuable tool for morphological analysis, clinical manifestation observation, as well as investigations into immunological reactions and pathogenesis of focal inflammatory reaction in neuroschistosomiasis (NS).Sixty New Zealand rabbits were randomly assigned into operation, sham-operation and normal groups. Rabbits in the operation group received direct injection of dead schistosome eggs into the brain, while their counterparts in the sham-operation group received saline injection. Rabbits in the normal group received no treatment. Base on the clinical manifestations, rabbits were sacrificed on days 3, 5, 7, 10, 20, and 30 post injection, and brain samples were sectioned and stained with hematoxylin-eosin. Sections were observed under the microscope.The rabbits in the operation group exhibited various neurological symptoms, including anorexy, partial and general seizures, and paralysis. The morphological analysis showed several schistosome eggs in the nervous tissue on day 3 post operation, with very mild inflammation. On days 7-10 post operation, several schistosome eggs were localized in proximity to red blood cells with many neutrophilic granulocytes and eosinophilic granulocytes around them. The schistosome eggs developed into the productive granuloma stage on days 14-20 post operation. On day 30, the schistosome eggs were found to be in the healing-by-fibrosis stage, and the granuloma area was replaced by fibrillary glia through astrocytosis. The sham-operation group and the normal group showed negative results.This method might be used to establish the cerebral schistosomiasis experimental model. Several factors need to be considered in establishing this model, such as the antigenic property of eggs, the time of scarification, and the clinical manifestations.

Current experiments on structural determination cannot keep up the pace with the steadily emerging RNA sequences and new functions. This underscores the request for an accurate model for RNA three-dimensional (3D) structural prediction. Although considerable progress has been made in mechanistic studies, accurate prediction for RNA tertiary folding from sequence remains an unsolved problem. The first and most important requirement for the prediction of RNA structure from physical principles is an accurate free energy model. A recently developed three-vector virtual bond-based RNA folding model (“Vfold”) has allowed us to compute the chain entropy and predict folding free energies and structures for RNA secondary structures and simple pseudoknots. Here we develop a free energy-based method to predict larger more complex RNA tertiary folds. The approach is based on a multiscaling strategy: from the nucleotide sequence, we predict the two-dimensional (2D) structures (defined by the base pairs and tertiary contacts); based on the 2D structure, we construct a 3D scaffold; with the 3D scaffold as the initial state, we combine AMBER energy minimization and PDB-based fragment search to predict the all-atom structure. A key advantage of the approach is the statistical mechanical calculation for the conformational entropy of RNA structures, including those with cross-linked loops. Benchmark tests show that the model leads to significant improvements in RNA 3D structure prediction.

A DNA triplex is formed through a third strand binding to the major groove of a duplex through hydrogen bonds. Due to the high charge density of a DNA triplex, metal ions are critical for triplex stability. We recently developed the tightly bound ion (TBI) model for ion-nucleic acids interactions. The model accounts for correlation and fluctuations of the ion distribution. We now apply the TBI model to analyze the ion-dependence of thermodynamic stability for DNA triplexes. We investigate the ion-dependent stability for two experimentally studied systems: a 24-bp DNA triplex and a 15-bp triplex with the third strand containing different base sequence. Because a triplex has a higher charge density than a duplex, a triplex attracts more ions and hence causes stronger ion correlation than a duplex. Our results for the number of bound ions indicate that for a DNA triplex in a Mg2+ solution, the TBI model, which accounts for ion correlation, gives much improved predictions than Poisson-Boltzmann Equation (PB), which ignores ion correlation, and the improvement is more significant for a triplex than for a duplex. In addition, we also calculate the stability of a pair of 14-mer triple helices immersed in an ionic solution. The goal is to predict the ion-dependent free energy landscape for different sequences and helix lengths.

To study the genotoxicity induced by organic bentonite particles in vitro.Human B lymphoblast cells (HMy2.CIR) were exposed to organic bentonite particles at the doses of 0, 1.88, 3.75, 7.50 and 15.00 µg/ml for 24, 48 and 72 h, calcium sulfate (30 µg/ml) and SiO2 (30 and 240 µg/ml) served as negative and positive controls, respectively. The genotoxicity of organic bentonite particles and soluble fraction was detected using comet assay and Cytokinesis-block micronucleus (CBMN) assay.The results of comet assay indicated that % tail DNA increased with the exposure doses and time in organic bentonite group, % tail DNA at the dose of 15.00 µg/ml for 24 h, 48 h and 72 h in organic bentonite group were 3.20 ± 0.19, 4.63 ± 0.88 and 9.49 ± 1.31 respectively which were significantly higher than those in calcium sulfate group (1.40 ± 0.11, 1.37 ± 0.22 and 0.90 ± 0.16) and those in 30 µg/ml SiO2 group (1.83 ± 0.21, 1.41 ± 0.27 and 2.48 ± 0.25) (P0.01). The results of CBMN assay showed that micronucleus frequencies (MNF) in organic bentonite group (except for 1.88 µg/ml for 24 h) were significantly higher than those in 30 µg/ml calcium sulfate group (MNF for 24, 48 and 72 h were 1.33‰ ± 0.58‰, 1.33‰ ± 1.15‰ and 1.33‰ ± 0.58‰) and those in 30 µg/ml SiO2 group (2.00‰ ± 0.00‰, 1.68‰ ± 0.58‰ and 2.33‰ ± 0.58‰) (P0.01). The results of two assays demonstrated that the soluble fraction of organic bentonite did not induce the genotoxicity.The organic bentonite dusts can induce the genotoxicity in vitro, which may be from the particle fraction.

Purpose: Healthcare is a universally used service that hugely affects economies and the quality of life. The research of service supply chains has found a significant role in the past decade. The main research goal of this paper is to model and simulate the internal service supply chains of a healthcare system to study the effects of different parameters on the outputs and capability measures of the processes. The specific objectives are to analyse medication delivery errors in a community hospital based on the results of the models and to explore the presence of bullwhip effect in the internal service supply chains of the hospital.Design/methodology/approach: System dynamics which is an approach for understanding the behaviour of complex systems, used as a methodology to model two internal service supply chains of the hospital with a sub-model created to simulate medication delivery errors in the hospital. The models are validated using the actual data of the hospital and the results are analyzed based on experimental design techniques.Findings: It is observed that the bullwhip effect may not occur in a hospital’s internal service supply chains. Furthermore the paper points out the conditions for reducing the medication delivery error in a hospital.Research limitations/implications: Because of the community hospital’s data availability the type of service supply chains modelled in this paper, are small service supply chains, representing only the tasks which are done inside the hospital. To better observe the bullwhip effect in healthcare service supply chains, the chains should be modelled more generally.Originality/value: The original system dynamics modelling of the internal service supply chains of a community hospital, with a sub-model simulating the medication delivery error.

The equilibration of spin temperature Tspin with kinetic temperature T is examined in a weakly correlated pure electron plasma in the strongly magnetized limit, where the distance of closest approach is large compared to the Larmor radius. In this limit, the spin precession frequency Ωp=gΩc/2 is large so the component of spin along the magnetic field is an adiabatic invariant that is broken only by resonant magnetic fluctuations of frequency Ωp. (Here Ωc is the electron cyclotron frequency and g≂2.002.) In this case, the most important spin flip mechanism stems from electron–electron collisions in a spatially inhomogeneous magnetic field. Such collisions cause an exchange of spin and cyclotron quanta, and consequently the conventional many‐electron adiabatic invariant (i.e., the total number of cyclotron quanta) is broken and is replaced by a new adiabatic invariant, equal to the sum of the spin and cyclotron actions. A quantum Boltzmann equation is derived to describe the equilibration of Tspin toward T.

Metal ion-mediated electrostatic interactions are critical to RNA folding. Although considerable progress has been made in mechanistic studies, the problem of accurate predictions for the ion effects in RNA folding remains unsolved, mainly due to the complexity of several potentially important issues such as ion correlation and dehydration effects. In this chapter, after giving a brief overview of the experimental findings and theoretical approaches, we focus on a recently developed new model, the tightly bound ion (TBI) model, for ion electrostatics in RNA folding. The model is unique because it can treat ion correlation and fluctuation effects for realistic RNA 3D structures. For monovalent ion (such as Na + ) solutions, where ion correlation is weak, TBI and the Poisson–Boltzmann (PB) theory give the same results and the results agree with the experimental data. For multivalent ion (such as Mg 2+ ) solutions, where ion correlation can be strong, however, TBI gives much improved predictions than the PB. Moreover, the model suggests an ion correlation-induced mechanism for the unusual efficiency of Mg 2+ ions in the stabilization of RNA tertiary folds. In this chapter, after introducing the theoretical framework of the TBI model, we will describe how to apply the model to predict ion-binding properties and ion-dependent folding stabilities.

The infection of the central nervous system (CNS) by schistosome may or may not have clinical manifestations. When symptomatic, neuroschistosomiasis (NS) is one of the most severe presentations of schistosome infection. Among the NS symptoms, cerebral invasion is mostly caused by Schistosoma japonicum (S. japonicum), and the spinal cord symptoms are mainly caused by S. mansoni or S. haematobium. There are 2 main pathways by which schistosomes cause NS: egg embolism and worm migration, via either artery or vein system, especially the valveless perivertebral Batson’s plexus. The adult worm migrates anomalously through the above pathways to the CNS where they lay eggs. Due to the differences in species of schistosomes and stages of infection, mechanisms vary greatly. The portal hypertension with hepatosplenic schistosomiasis also plays an important role in the pathogenesis. Here the pathways through which NS occurs in the CNS were reviewed.

Medication errors are major safety concerns in all hospital settings. The insufficient knowledge about managerial and process improvement strategies required to reduce medication errors can be considered as one of the most important factors holding back hospitals from achieving the desired goals for patient safety. However, strategies for medication error reduction cannot be successfully implemented without a clear understanding of factors affecting medication delivery errors. This paper presents a study in which healthcare professionals’ perceptions on three factors, namely (1) technical complexity of tasks/connections; (2) resources problems; and (3) qualification of human resources, are analyzed within the medication delivery system at one community hospital. The outcomes of this research are a theoretical model for reducing medication delivery errors and a set of workflow design rules for healthcare professionals to continuously reduce medication delivery errors.

RNA pseudoknots play a critical role in RNA-related biology from the assembly of ribosome to the regulation of viral gene expression. A predictive model for pseudoknot structure and stability is essential for understanding and designing RNA structure and function. A previous statistical mechanical theory allows us to treat canonical H-type RNA pseudoknots that contain no intervening loop between the helices (see S. Cao and S.J. Chen [2006] in Nucleic Acids Research, Vol. 34; pp. 2634–2652). Biologically significant RNA pseudoknots often contain interhelix loops. Predicting the structure and stability for such more-general pseudoknots remains an unsolved problem. In the present study, we develop a predictive model for pseudoknots with interhelix loops. The model gives conformational entropy, stability, and the free-energy landscape from RNA sequences. The main features of this new model are the computation of the conformational entropy and folding free-energy base on the complete conformational ensemble and rigorous treatment for the excluded volume effects. Extensive tests for the structural predictions show overall good accuracy with average sensitivity and specificity equal to 0.91 and 0.91, respectively. The theory developed here may be a solid starting point for first-principles modeling of more complex, larger RNAs.

RNA folding is a remarkably complex problem that involves ion-mediated electrostatic interaction, conformational entropy, base pairing and stacking, and noncanonical interactions. During the past decade, results from a variety of experimental and theoretical studies pointed to (a) the potential ion correlation effect in Mg2+-RNA interactions, (b) the rugged energy landscapes and multistate RNA folding kinetics even for small RNA systems such as hairpins and pseudoknots, (c) the intraloop interactions and sequence-dependent loop free energy, and (d) the strong nonadditivity of chain entropy in RNA pseudoknot and other tertiary folds. Several related issues, which have not been thoroughly resolved, require combined approaches with thermodynamic and kinetic experiments, statistical mechanical modeling, and all-atom computer simulations.

The enormous gaps in the knowledge required to understand medication errors and their related costs (or wastes) in all hospital settings have become a growing national concern. Such gaps are often the major reasons causing risk for patient safety and creating waste to the hospital. However, medication delivery system cannot be successfully improved and implemented without a clear understanding of various process flows running around the entire hospital system. This paper presents a systems mapping and analysis method to help understand and reduce the medication delivery waste. The effectiveness of our method is illustrated by a case study that we conducted for the medication delivery process at Bozeman Deaconess Hospital, MT.

We have developed a new method for computing the partition functions of RNA secondary structures. Previous work has shown that it is accurate when tested against exact lattice simulations, and that it predicts experimental melting curves on molecules smaller than about 100 base-pairs. The method can also predict RNA folding energy landscapes. Here, we show that this method predicts unexpected sequence-dependent contributions to melting cooperativity. GC pairs are more stable than AU pairs. We find that shifting GC positions relative to AU pairs in a simple hairpin can cause the hairpin either to melt, loop-end first, or open-end first, depending on the GC placements. Such sequence dependence would not be predicted by gaussian chain and Jacobsen-Stockmayer treatments that neglect excluded volume, as is the case with most current treatments of RNA stabilities. No experimental tests are yet available.

Many retroviruses use -1 ribosomal frameshifting as part of the mechanism in translational control of viral protein synthesis. Quantitative prediction of the efficiency of -1 frameshifting is crucial for understanding the viral gene expression. Here we investigate the free energy landscape for a minimal -1 programmed ribosomal frameshifting machinery, including the codon-anticodon base pairs at the slippery site, the downstream messenger RNA structure and the spacer between the slippery site and the downstream structure. The free energy landscape analysis leads to a quantitative relationship between the frameshifting efficiency and the tension force generated during the movement of codon-anticodon complexes, which may occur in the A/T to A/A accommodation process or the translocation process. The analysis shows no consistent correlation between frameshifting efficiency and global stability of the downstream mRNA structure.

To study whether 1.8 GHz microwaves (MW) (SAR, 3 W/kg) exposure can influence DNA damage induced by ultraviolet ray (UV).The lymphocytes were obtained from three young healthy donors. The cells were exposed to 254 nm UV at the doses of 0.25, 0.50, 0.75, 1.00, 1.50 and 2.00 J/m(2). The lymphocytes were also exposed to 1.8 GHz MW (SAR, 3 W/kg) for 0, 1.5 and 4.0 h. The combination exposure of UV plus MW was conducted. The treated cells were incubated for 0, 1.5 and 4.0 h. Finally, comet assay was used to detect DNA damage of above treated lymphocytes.The difference of DNA damage induced between MW group and control group was not significant (P0.05). the MTLs induced by UV were (1.71+/-0.09), (2.02+/-0.08), (2.27+/-0.17), (2.27+/-0.06), (2.25+/-0.12), (2.24+/-0.11)microm, respectively, which were significantly higher than that of control [(0.96+/-0.05) microm], (P0.01). MTLs of some sub-groups in combination exposure groups at 1.5 h incubation were significantly lower than those of corresponding UV sub-groups (P0.01 or P0.05. However, MTLs of some sub-groups in combination exposure groups at 4.0 h incubation were significantly higher than those of corresponding UV sub-groups (P0.01 or P0.05).The exposure to 1.8 GHz (SAR, 3 W/kg) MW for 1.5 and 4.0 h can not enhance significantly human lymphocyte DNA damage. But MW can reduce or enhance DNA damage of lymphocytes induced by UV at 1.5 h and 4.0 h incubation in comet assay in vitro, respectively.

A recently developed tightly bound ion model can account for the correlation and fluctuation (i.e., different binding modes) of bound ions. However, the model cannot treat mixed ion solutions, which are physiologically relevant and biologically significant, and the model was based on B-DNA helices and thus cannot directly treat RNA helices. In the present study, we investigate the effects of ion correlation and fluctuation on the thermodynamic stability of finite length RNA helices immersed in a mixed solution of monovalent and divalent ions. Experimental comparisons demonstrate that the model gives improved predictions over the Poisson-Boltzmann theory, which has been found to underestimate the roles of multivalent ions such as Mg2+ in stabilizing DNA and RNA helices. The tightly bound ion model makes quantitative predictions on how the Na+-Mg2+ competition determines helix stability and its helix length-dependence. In addition, the model gives empirical formulas for the thermodynamic parameters as functions of Na+/Mg2+ concentrations and helix length. Such formulas can be quite useful for practical applications.

Using a combined master equation and kinetic cluster approach, we investigate RNA pseudoknot folding and unfolding kinetics. The energetic parameters are computed from a recently developed Vfold model for RNA secondary structure and pseudoknot folding thermodynamics. The folding kinetics theory is based on the complete conformational ensemble, including all the native-like and non-native states. The predicted folding and unfolding pathways, activation barriers, Arrhenius plots, and rate-limiting steps lead to several findings. First, for the PK5 pseudoknot, a misfolded 5' hairpin emerges as a stable kinetic trap in the folding process, and the detrapping from this misfolded state is the rate-limiting step for the overall folding process. The calculated rate constant and activation barrier agree well with the experimental data. Second, as an application of the model, we investigate the kinetic folding pathways for human telomerase RNA (hTR) pseudoknot. The predicted folding and unfolding pathways not only support the proposed role of conformational switch between hairpin and pseudoknot in hTR activity, but also reveal molecular mechanism for the conformational switch. Furthermore, for an experimentally studied hTR mutation, whose hairpin intermediate is destabilized, the model predicts a long-lived transient hairpin structure, and the switch between the transient hairpin intermediate and the native pseudoknot may be responsible for the observed hTR activity. Such finding would help resolve the apparent contradiction between the observed hTR activity and the absence of a stable hairpin.

Terahertz time-domain spectroscopy has been employed to measure the optical constants of ytterbium-doped yttrium aluminum garnet (YbxY1-x)3Al5O12 crystals for nominal x values of 0.0, 0.1, 0.2, 0.5, 0.8 and 1.0 for frequencies ranging from 0.2 to 1.8 THz. Article not available.

The indices of refraction, extinction constants and complex conductivities of the GaN film for frequencies ranging from 0.2 to 2.5 THz are obtained using THz time-domain spectroscopy. The results correspond well with the Kohlrausch stretched exponential model. Article not available.

Metal ions play crucial roles in thermal stability and folding kinetics of nucleic acids. For ions (especially multivalent ions) in the close vicinity of nucleic acid surface, interion correlations and ion-binding mode fluctuations may be important. Poisson-Boltzmann theory ignores these effects whereas the recently developed tightly bound ion (TBI) theory explicitly accounts for these effects. Extensive experimental data demonstrate that the TBI theory gives improved predictions for multivalent ions (e.g., Mg2+) than the Poisson-Boltzmann theory. In this study, we use the TBI theory to investigate how the metal ions affect the folding stability of B-DNA helices. We quantitatively evaluate the effects of ion concentration, ion size and valence, and helix length on the helix stability. Moreover, we derive practically useful analytical formulas for the thermodynamic parameters as functions of finite helix length, ion type, and ion concentration. We find that the helix stability is additive for high ion concentration and long helix and nonadditive for low ion concentration and short helix. All these results are tested against and supported by extensive experimental data.

A statistical thermodynamic model is developed for chain molecules with simple RNA tertiary contacts. The model, which accounts for the excluded volume effect and the nonadditivity in the free energy, enables reliable predictions for the conformational entropy and partition function for simple tertiary folds. Illustrative applications are made to conformational transitions involving simple tertiary contacts. The model can predict the interplay between the secondary and the tertiary interactions in the conformational changes. Though the present form of the theory is tested and validated in a two-dimensional lattice model, the methodology, which is developed based on a general graphical representation for chain conformations, is applicable to any off-lattice chain representations. Moreover, the analytical formulation of the method makes possible the systematic development of the theory for more complex tertiary structures.

A statistical mechanical model is presented which explicitly accounts for the fluctuations, the electrostatic, and the excluded volume correlations for ions bound to a polyelectrolyte such as DNA. The method can be employed to treat a wide range of ionic conditions including multivalent ions. The microscopic framework of the theory permits the use of realistic finite length and grooved structural model for the polyelectrolyte and modeling of the finite size of the bound ions. Test against Monte Carlo simulations suggests that the theory can give accurate predictions for the ion distribution and the thermodynamic properties. For multivalent ions, the theory makes improved predictions as compared with the mean-field approach. Moreover, for long polyelectrolyte and dilute salt concentration, the theory predicts ion binding properties that agree with the counterion condensation theory.

To calculate the latent period of lung cancer induced by air pollution.The degree of grey incidence (DGI) between the concentrations of TSP or SO(2) and the mortality of lung cancer were assessed through a grey system model. According to the maximum values of DGI, the total latent period of lung cancer was calculated. Data was collected in H city.The maximum DGI value of TSP was 0.886 while the relationship between the comparison sequence from 1985 to 1989 and the reference sequence from 1994 to 1998 was greatly correlated. The maximum DGI value of SO(2) was 0.919 while the relationship between the comparison sequence from 1986 to 1990 and the reference sequence from 1994 to 1998 was most correlated.The latent periods of lung cancer induced by TSP and SO(2) were 7 and 8 years respectively in H city. The model of grey system was less affected by the confounding factors, and the calculation process was relatively simple. A feasible and useful new way was provided by the model of grey system for quantitative analysis of the latent period of lung cancer induced by air pollutants.

Based on the complete ensemble of hairpin conformations, a statistical mechanical model that combines the eigenvalue solutions of the rate matrix and the free-energy landscapes has been able to predict the temperature-dependent folding rate, kinetic intermediates, and folding pathways for hairpin-forming RNA sequences. At temperatures higher than a “glass transition” temperature, T g , the eigenvalues show a distinct time separation, and the rate-limiting step is a two-state single exponential process determined by the slowest eigenmode. At temperatures lower than T g , no distinct time separation exists for the eigenvalues, hence multiple (slow) eigenmodes contribute to the rate-determining processes, and the folding involves the trapping and detrapping of kinetic intermediates. For a 21-nt sequence we studied, T g is lower than the transition temperature, T m , for thermodynamic equilibrium folding. For T > T m , starting from the native state, the chain undergoes a biphasic unfolding transition: a preequilibrated quasi-equilibrium macrostate is formed followed by a rate-limiting two-state transition from the macrostate to the unfolded state. For T g < T < T m , the chain undergoes a two-state on-pathway folding transition, at which a nucleus is formed by the base stacks close to the loop region before a rapid assembly of the whole hairpin structure. For T < T g , the multistate kinetics involve kinetic trapping, causing the roll-over behavior in the rate-temperature Arrhenius plot. The complex kinetic behaviors of RNA hairpins may be a paradigm for the folding kinetics of large RNAs.

Background: The ever increasing discovery of non-coding RNAs leads to unprecedented demand for the accurate modeling of RNA folding, including the predictions of two-dimensional (base pair) and three-dimensional all-atom structures and folding stabilities. Accurate modeling of RNA structure and stability has far-reaching impact on our understanding of RNA functions in human health and our ability to design RNA-based therapeutic strategies. Results: The Vfold server offers a web interface to predict (a) RNA two-dimensional structure from the nucleotide sequence, (b) three-dimensional structure from the two-dimensional structure and the sequence, and (c) folding thermodynamics (heat capacity melting curve) from the sequence. To predict the two-dimensional structure (base pairs), the server generates an ensemble of structures, including loop structures with the different intra-loop mismatches, and evaluates the free energies using the experimental parameters for the base stacks and the loop entropy parameters given by a coarse-grained RNA folding model (the Vfold model) for the loops. To predict the three-dimensional structure, the server assembles the motif scaffolds using structure templates extracted from the known PDB structures and refines the structure using all-atom energy minimization. Conclusions: The Vfold-based web server provides a user friendly tool for the prediction of RNA structure and stability. The web server and the source codes are freely accessible for public use at ‘‘http://rna.physics.missouri.edu’’.

Using a statistical mechanical treatment, we study RNA folding energy landscapes. We first validate the theory by showing that, for the RNA molecules we tested having only secondary structures, this treatment ( i ) predicts about the same native structures as the Zuker method, and ( ii ) qualitatively predicts the melting curve peaks and shoulders seen in experiments. We then predict thermodynamic folding intermediates. For one hairpin sequence, unfolding is a simple unzipping process. But for another sequence, unfolding is more complex. It involves multiple stable intermediates and a rezipping into a completely non-native conformation before unfolding. The principle that emerges, for which there is growing experimental support, is that although protein folding tends to involve highly cooperative two-state thermodynamic transitions, without detectable intermediates, the folding of RNA secondary structures may involve rugged landscapes, often with more complex intermediate states.

Engineering projects often require a great deal of effort during the planning and designing stages to eliminate any unnecessary rework. To achieve satisfactory results in complex projects, project managers need to consider every possible rework (or feedback connections) throughout the entire project life cycle in addition to establishing the best project task flow. Therefore a project can be completed in a shorter time and lower cost without sacrificing the quality of the outcome. The objective of this paper is to develop an optimisation process for project task coordination using design structure matrix (DSM) and genetic algorithm (GA). DSM helps identify the relationships among project tasks. GA is used to help coordinate/optimise the project task structure in terms of task cost, task time and their coupling strength. This paper also shows our developed method is able to help the managers to enhance the level of cooperation among the related departments by a departmental flow analysis. The effectiveness of this model is demonstrated by an industry example.

Transitions between the different conforma- tional states play a critical role in many RNA catalytic and regulatory functions. In this study, we use the Kinetic Monte Carlo method to investigate the kinetic mechanism for the conformational switches between bistable RNA hairpins. We find three types of conformational switch pathways for RNA hairpins: refolding after complete unfolding, folding through basepair-exchange pathways and through pseudoknot-assisted pathways, respectively. The result of the competition between the three types of pathways depends mainly on the location of the rate-limiting base stacks (such as the GC base stacks) in the structures. Depending on the structural relationships between the two bistable hairpins, the conformational switch can follow single or multiple dominant pathways. The predicted folding pathways are supported by the activation energy results derived from the Arrhenius plot as well as the NMR spectroscopy data.

One of the key issues in the theoretical prediction of RNA stability and structure is how to predict the loop free energy. Experimental results have shown strong sequence-dependence of the loop free energy. However, most currently available models only account for the loop length-dependence of the loop free energy. We recently developed a three-bead coarse-grained model to generate the three-dimensional conformations for RNA hairpin loops. Based on the pseudo-torsion angles for the coarse-grained structures, we can extract a set of pseudo-torsion angle-based statistical potential parameters from the known structures. The statistical potential parameters enable folding predictions of the low-energy coarse-grained 3D structures from the sequence. Further molecular dynamics computations for an ensemble of decoy conformations about the predicted coarse-grained structures lead to the final all-atom structures. A notable advantage of the approach is the use of the statistical potential for evaluating the loop free energies and guiding the search for the low-energy coarse-grained structures from the sequence.

A DNA triplex is formed through binding of a third strand to the major groove of a duplex. Due to the high charge density of a DNA triplex, metal ions are critical for its stability. We recently developed the tightly bound ion (TBI) model for ion-nucleic acids interactions. The model accounts for the potential correlation and fluctuations of the ion distribution. We now apply the TBI model to analyze the ion dependence of the thermodynamic stability for DNA triplexes. We focus on two experimentally studied systems: a 24-base DNA triplex and a pair of interacting 14-base triplexes. Our theoretical calculations for the number of bound ions indicate that the TBI model provides improved predictions for the number of bound ions than the classical Poisson-Boltzmann (PB) equation. The improvement is more significant for a triplex, which has a higher charge density than a duplex. This is possibly due to the higher ion concentration around the triplex and hence a stronger ion correlation effect for a triplex. In addition, our analysis for the free energy landscape for a pair of 14-mer triplexes immersed in an ionic solution shows that divalent ions could induce an attractive force between the triplexes. Furthermore, we investigate how the protonated cytosines in the triplexes affect the stability of the triplex helices.

Transitions between different conformational states, so called conformational switching, are intrinsic to RNA catalytic and regulatory functions. Often, conformational switching occurs on time-scales of several seconds. In combination with the recent real-time NMR experiments (Wenter et al. Angew. Chem. Int. Ed. (2005). 44, 2600; Wenter et al. ChemBioChem. (2006). 7, 417) for the transitions between bistable RNA conformations, we combine the master equation method with the kinetic cluster method to investigate the detailed kinetic mechanism and the factors that govern the folding kinetics. We propose that heat capacity change (ΔCp) upon RNA folding may be important for RNA folding kinetics. In addition, we find that for tetraloop hairpins, noncanonical (tertiary) intraloop interactions are important to determine the folding kinetics. Furthermore, through theory-experiment comparisons, we find that the different rate models for the fundamental steps (i.e., formation/disruption of a base pair or stack) can cause contrasting results in the theoretical predictions.