Subsequent Simulation Studies Research Articles

Network pharmacology-based approach to elucidate the pharmacologic mechanisms of natural compounds from Dictyostelium discoideum for Alzheimer's disease treatment

Alzheimer's disease (AD) is increasingly becoming a major public health concern in our society. While many studies have explored the use of natural polyketides, alkaloids, and other chemical components in AD treatment, there is an urgent need to clarify the concept of multi-target treatment for AD. This study focuses on using network pharmacology approach to elucidate how secondary metabolites from Dictyostelium discoideum affect AD through multi-target or indirect mechanisms. The secondary metabolites produced by D. discoideum during their development were obtained from literature sources and PubChem. Disease targets were selected using GeneCards, DisGeNET, and CTD databases, while compound-based targets were identified through Swiss target prediction and Venn diagrams were used to find intersections between these targets. A network depicting the interplay among disease, drugs, active ingredients, and key target proteins (PPI network) was formed utilizing the STRING (Protein-Protein Interaction Networks Functional Enrichment Analysis) database. To anticipate the function and mechanism of the screened compounds, GO and KEGG enrichment analyses were conducted and visually presented using graphs and bubble charts. After the screening phase, the top interacting targets in the PPI network and the compound with the most active target were chosen for subsequent molecular docking and molecular dynamic simulation studies. This study identified nearly 50 potential targeting genes for each of the screened compounds and revealed multiple signaling pathways. Among these pathways, the inflammatory pathway stood out. COX-2, a receptor associated with neuroinflammation, showed differential expression in various stages of AD, particularly in pyramidal neurons during the early stages of the disease. This increase in COX-2 expression is likely induce by higher levels of IL-1, which is associated with neuritic plaques and microglial cells in AD. Molecular docking investigations demonstrated a strong binding interaction between the terpene compound PQA-11 and the neuroinflammatory receptor COX2, with a substantial binding affinity of −8.4 kcal/mol. Subsequently, a thorough analysis of the docked complex (COX2-PQA11) through Molecular Dynamics Simulation showed lower RMSD, minimal RMSF fluctuations, and a reduced total energy of −291.35 kJ/mol compared to the standard drug. These findings suggest that the therapeutic effect of PQA-11 operates through the inflammatory pathway, laying the groundwork for further in-depth research into the role of secondary metabolites in AD treatment.

Influences of Weld Nugget Shape and Material Gradient on the Shear Strength of Resistance Spot‐Welded Joints

Resistance spot‐welded joints containing press‐hardened steels are seen to exhibit a fracture mode called total dome failure, where the weld nugget completely separates from one steel sheet along the weld nugget edge. The effect of weld nugget shape and material property gradients is studied based on damage mechanics modeling and experimental validation to shed light on the underlying influencing factors. For a three‐steel‐sheet spot‐welded joint combining DP600 (1.5 mm)–CR1900T (1.0 mm)–CR1900T (1.0 mm), experiments under shear loading reveal that fracture occurs in the DP600 sheet along the weld nugget edge. In subsequent numerical simulation studies with damage mechanics models whose parameters are independently calibrated for every involved material configuration, three variations of the geometrical joint configuration are considered—an approximation of the real joint, one variation with a steeper weld nugget shape, and one variation with a less pronounced gradient between weld nugget material and heat‐affected zone material properties. The results of the finite‐element simulations show that a shallower weld nugget and a more pronounced material gradient lead to a faster increase of plastic strain at the edge of the weld nugget and promote the occurrence of total dome failure.

Impact of genotype-calling methodologies on genome-wide association and genomic prediction in polyploids.

Discovery and analysis of genetic variants underlying agriculturally important traits are key to molecular breeding of crops. Reduced representation approaches have provided cost-efficient genotyping using next-generation sequencing. However, accurate genotype calling from next-generation sequencing data is challenging, particularly in polyploid species due to their genome complexity. Recently developed Bayesian statistical methods implemented in available software packages, polyRAD, EBG, and updog, incorporate error rates and population parameters to accurately estimate allelic dosage across any ploidy. We used empirical and simulated data to evaluate the three Bayesian algorithms and demonstrated their impact on the power of genome-wide association study (GWAS) analysis and the accuracy of genomic prediction. We further incorporated uncertainty in allelic dosage estimation by testing continuous genotype calls and comparing their performance to discrete genotypes in GWAS and genomic prediction. We tested the genotype-calling methods using data from two autotetraploid species, Miscanthus sacchariflorus and Vaccinium corymbosum, and performed GWAS and genomic prediction. In the empirical study, the tested Bayesian genotype-calling algorithms differed in their downstream effects on GWAS and genomic prediction, with some showing advantages over others. Through subsequent simulation studies, we observed that at low read depth, polyRAD was advantageous in its effect on GWAS power and limit of false positives. Additionally, we found that continuous genotypes increased the accuracy of genomic prediction, by reducing genotyping error, particularly at low sequencing depth. Our results indicate that by using the Bayesian algorithm implemented in polyRAD and continuous genotypes, we can accurately and cost-efficiently implement GWAS and genomic prediction in polyploid crops.

SARA Characterization and Comparison for the Ultra-Heavy Oil via Combined Analyses

Abstract Structure characterization and comparison of the ultra-heavy oil and its four components are fundamental and crucial. In this work, nuclear magnetic resonance analyses were employed to quantitatively investigate carbon and hydrogen atom distributions. Combined with the gel permeation chromatography, elemental analysis, and X-ray diffraction results, average molecular structure parameters were determined for four components. Besides, an improved Brown–Ladner method was adopted to identify and adjust corresponding structural parameters, which considered influences of both heteroatoms (O, S, and N), and katacondensed and/or pericondensed system assumption on aromatic structures, compared with conventional methods. Moreover, molecular architectures of four components were, respectively, speculated and reconstructed based on this improved method, and the specific comparison reflected a higher accuracy. From this study, it could provide updated understandings of specific component structural information for the ultra-heavy oil to facilitate subsequent oil reactivity and simulation studies.

Structural remodeling of SARS-CoV-2 spike protein glycans reveals the regulatory roles in receptor-binding affinity.

Glycans of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) spike protein are speculated to play functional roles in the infection processes as they extensively cover the protein surface and are highly conserved across the variants. The spike protein has been the principal target for vaccine and therapeutic development while the exact effects of its glycosylation remain elusive. Analytical reports have described the glycan heterogeneity of the spike protein. Subsequent molecular simulation studies provided a knowledge basis of the glycan functions. However, experimental data on the role of discrete glycoforms on the spike protein pathobiology remains scarce. Building an understanding of their roles in SARS-CoV-2 is important as we continue to develop effective medicines and vaccines to combat the disease. Herein, we used designed combinations of glycoengineering enzymes to simplify and control the glycosylation profile of the spike protein receptor-binding domain (RBD). Measurements of the receptor-binding affinity revealed opposite regulatory effects of the RBD glycans with and without sialylation, which presents a potential strategy for modulating the spike protein behaviors through glycoengineering. Moreover, we found that the reported anti-SARS-CoV-(2) antibody, S309, neutralizes the impact of different RBD glycoforms on the receptor-binding affinity. In combination with molecular dynamics simulation, this work reports the regulatory roles that glycosylation plays in the interaction between the viral spike protein and host receptor, providing new insights into the nature of SARS-CoV-2. Beyond this study, enzymatic glycan remodeling offers the opportunity to understand the fundamental role of specific glycoforms on glycoconjugates across molecular biology.

Different algorithms, different models

This study assesses the extent to which the two main Configurational Comparative Methods (CCMs), i.e. Qualitative Comparative Analysis (QCA) and Coincidence Analysis (CNA), produce different models. It further explains how this non-identity is due to the different algorithms upon which both methods are based, namely QCA’s Quine–McCluskey algorithm and the CNA algorithm. I offer an overview of the fundamental differences between QCA and CNA and demonstrate both underlying algorithms on three data sets of ascending proximity to real-world data. Subsequent simulation studies in scenarios of varying sample sizes and degrees of noise in the data show high overall ratios of non-identity between the QCA parsimonious solution and the CNA atomic solution for varying analytical choices, i.e. different consistency and coverage threshold values and ways to derive QCA’s parsimonious solution. Clarity on the contrasts between the two methods is supposed to enable scholars to make more informed decisions on their methodological approaches, enhance their understanding of what is happening behind the results generated by the software packages, and better navigate the interpretation of results. Clarity on the non-identity between the underlying algorithms and their consequences for the results is supposed to provide a basis for a methodological discussion about which method and which variants thereof are more successful in deriving which search target.

Development of Long-Duration Energy Storage Projects in Electric Power Systems in the United States: A Survey of Factors Which Are Shaping the Market

Energy storage is a topic of increasing interest for purposes of decarbonization of the electric power system, and in particular for addressing integration of increasing quantities of variable energy resources, such as wind and solar PV. A growing focus of such assessment is the need for new long-duration storage, in addition to existing pumped storage hydropower. A limitation of much of the analysis on this topic to date has been limited consideration of how the economic value of long-duration storage will be affected by the large deployments of short-duration energy storage, primarily lithium-ion batteries, which are currently receiving state policy support (through mandates or financial incentives), or otherwise expected to enter the markets. This article reviews the literature on this topic, updates some simulation results, and helps advance the methodological questions which need to be addressed in subsequent simulation studies. The article examines each of the major components of potential long-duration storage value, including energy time-shift, different ancillary services, and resource adequacy (RA) capacity. It also draws attention to how state policies, resource planning methods, and market structure and regulatory factors can influence selection of different types of energy storage, whether short or long duration.

An Induction Machine Design With Parameter Optimization for a 120-kW Electric Vehicle

Electric traction machines have been applied to both industrial variable-speed applications and electric vehicles. The induction machine (IM) is a potential candidate for traction machines due to the established industrial base. However, most of the existing studies on traction IM design rely on optimization algorithms or iterative calculation programs that subsequently give no clear understanding of the design requirements for field-weakening or extended speed operation. Hence, published procedures to guide IM design for traction applications are somewhat ad hoc to date. This article identifies the key IM design parameters required to achieve a traction characteristic within power supply constraints. As an example, a 120-kW electric vehicle traction machine is studied as a benchmark machine and then the proposed design procedure is employed to redesign the machine to improve its extended speed performance. Improvements in terms of efficiency and field-weakening capability are presented. The experimental results from the benchmark machine are used for validation of the subsequent simulation studies reported in this article. Finally, the identified parameters are shown to be similar with brushless permanent magnet machines. Thus, a generalized machine design philosophy can be concluded.

New Moment Estimators of the Effective Spread Based on Daily High and Low Prices

In the paper we propose five new moment estimators for effective spread based on the covariance estimator of Roll (1984) and the High-Low estimator recently developed by Corwin and Schultz (2012, \textit{J. Finance}, Vol.67, 719-760), and further investigate theoretically the statistical properties of six bid-ask spread estimators including Corwin and Schultz's estimator. The biases and mean squared errors (MSE) of these six estimators have been derived and compared with each other asymptotically, which, together with the subsequent simulation studies and empirical examples, reveal explicitly the superior performance of new developed High-Low estimators over Corwin and Schultz's estimator. Furthermore this paper also puts forward GMM estimators constructed by three or more moment conditions, which also perform well compared with the six High-Low estimators. The method discussed here is different to the existing literatures which usually resort to the correlation between the bid-ask spread estimators with a benchmark calculated from high-frequency data as they compare the performance of different estimators.

Model Validation and Process Design of Continuous Single Pass Tangential Flow Filtration Focusing on Continuous Bioprocessing for High Protein Concentrations

In this study, the continuous Single-Pass Tangential Flow Filtration (SPTFF) concept is adapted for high protein concentrations. The work is based on the previously validated physico-chemical model for low concentrations and high viscosities. The model contains the Stagnant Film Model for concentration polarization, as well as the Boundary Layer Model for the mass transfer through the membrane. The pressure drop is calculated as a function of the Reynolds number. By performing preliminary experiments with a single ultrafiltration (UF) cassette, the model parameter are determined. The presented model is validated for a multi-step Single-Pass Tangential Flow Filtration. With subsequent simulation studies, an optimized process is found and confirmed by experiments. The outcome of this work shows the potential to optimize this multi-parameter dependent unit operation. This is reached by a model-based optimization allowing significant reduction of experimental efforts and applying the Quality by Design approach consistently. Furthermore, a comparison between the experimental setup and a commercial module is examined.

Highly efficient electromagnetic emission during 100 keV electron beam relaxation in a thin magnetized plasma

In this paper, electromagnetic emissions produced by a beam-plasma system are investigated using particle-in-cell simulations for the particular case when the typical transverse size of both the 100 keV electron beam and the produced plasma channel is comparable to the radiation wavelength. The interest in this regime of beam-plasma interaction is associated with highly efficient generation of electromagnetic waves near the plasma frequency harmonics that has been recently observed in laboratory experiments on the GOL-3 mirror trap. It has been found that the radiation power only from the vicinity of the doubled plasma frequency in these experiments can reach 1% of the total beam power. Subsequent theoretical and simulation studies have shown that the most likely candidate for explaining such efficient generation of electromagnetic radiation is the mechanism of a beam-driven plasma antenna based on the conversion of the most unstable plasma oscillations on a longitudinal density modulation of plasma ions. In this paper, we investigate how effectively this mechanism can work in a real experiment at the GOL-3 facility, when a thin subrelativistic electron beam gets a large angular spread due to compression by a magnetic field, and the gas into which it is injected has macroscopic density gradients.

Asymptotic comparison of three spread estimators based on Roll’s model

In this paper we theoretically investigate the statistical properties of three popular bid–ask spread estimators, i.e., the covariance estimator of Roll (1984), the High–Low (HL) estimator based on daily high and low prices developed by Corwin and Schultz (2012) and the Close–High–Low (CHL) estimator recently proposed by Abdi and Ranaldo (2017). The biases and mean squared errors (MSE) of these three estimators have been derived and compared with each other asymptotically, which reveals explicitly the superior performance of HL and CHL estimators over Roll’s estimator. Moreover, if the volatility is relatively small compared to the spread, the performance of the CHL estimator is superior to the HL method. Then the subsequent simulation studies and bootstrap analysis on empirical examples also verify the theoretical results. The comparison method discussed here is different from the existing literature, which usually resorts to the correlation between the bid–ask spread estimators with a benchmark calculated from high-frequency data as they compare the performance of different estimators.

An efficient method to handle the 'large p, small n' problem for genomewide association studies using Haseman-Elston regression.

The 'large p, small n' problem in genomewide association studies (GWAS) is an important subject in genetic studies. Many approaches have been proposed for this issue, but none of them successfully combine the Haseman-Elston (H-E) regression with sliding-window scan approaches in GWAS. In this article, we extended H-E regression to GWAS, and replaced original data with different measurements of phenotype of sib pairs. Meanwhile, we also applied hidden Markov model to infer identity by state. Using subsequent simulation studies, we found that it had higher statistical power than the corresponding single-marker association studies. The advantage of the H-E regression was also sufficient to capture about 48.01% of the quantitative trait locus (QTL). Meanwhile, the results show that the power decreases with the increase in the number of QTLs, and the power of H-E regression is sensitive to heritability.

The gene tree delusion

Higher-level relationships among placental mammals are mostly resolved, but several polytomies remain contentious. Song et al. (2012) claimed to have resolved three of these using shortcut coalescence methods (MP-EST, STAR) and further concluded that these methods, which assume no within-locus recombination, are required to unravel deep-level phylogenetic problems that have stymied concatenation. Here, we reanalyze Song et al.’s (2012) data and leverage these re-analyses to explore key issues in systematics including the recombination ratchet, gene tree stoichiometry, the proportion of gene tree incongruence that results from deep coalescence versus other factors, and simulations that compare the performance of coalescence and concatenation methods in species tree estimation. Song et al. (2012) reported an average locus length of 3.1kb for the 447 protein-coding genes in their phylogenomic dataset, but the true mean length of these loci (start codon to stop codon) is 139.6kb. Empirical estimates of recombination breakpoints in primates, coupled with consideration of the recombination ratchet, suggest that individual coalescence genes (c-genes) approach ∼12bp or less for Song et al.’s (2012) dataset, three to four orders of magnitude shorter than the c-genes reported by these authors. This result has general implications for the application of coalescence methods in species tree estimation. We contend that it is illogical to apply coalescence methods to complete protein-coding sequences. Such analyses amalgamate c-genes with different evolutionary histories (i.e., exons separated by >100,000bp), distort true gene tree stoichiometry that is required for accurate species tree inference, and contradict the central rationale for applying coalescence methods to difficult phylogenetic problems. In addition, Song et al.’s (2012) dataset of 447 genes includes 21 loci with switched taxonomic names, eight duplicated loci, 26 loci with non-homologous sequences that are grossly misaligned, and numerous loci with >50% missing data for taxa that are misplaced in their gene trees. These problems were compounded by inadequate tree searches with nearest neighbor interchange branch swapping and inadvertent application of substitution models that did not account for among-site rate heterogeneity. Sixty-six gene trees imply unrealistic deep coalescences that exceed 100 million years (MY). Gene trees that were obtained with better justified models and search parameters show large increases in both likelihood scores and congruence. Coalescence analyses based on a curated set of 413 improved gene trees and a superior coalescence method (ASTRAL) support a Scandentia (treeshrews)+Glires (rabbits, rodents) clade, contradicting one of the three primary systematic conclusions of Song et al. (2012). Robust support for a Perissodactyla+Carnivora clade within Laurasiatheria is also lost, contradicting a second major conclusion of this study. Song et al.’s (2012) MP-EST species tree provided the basis for circular simulations that led these authors to conclude that the multispecies coalescent accounts for 77% of the gene tree conflicts in their dataset, but many internal branches of their MP-EST tree are stunted by an order of magnitude or more due to wholesale gene tree reconstruction errors. An independent assessment of branch lengths suggests the multispecies coalescent accounts for ⩽15% of the conflicts among Song et al.’s (2012) 447 gene trees. Unfortunately, Song et al.’s (2012) flawed phylogenomic dataset has been used as a model for additional simulation work that suggests the superiority of shortcut coalescence methods relative to concatenation. Investigator error was passed on to the subsequent simulation studies, which also incorporated further logical errors that should be avoided in future simulation studies. Illegitimate branch length switches in the simulation routines unfairly protected coalescence methods from their Achilles’ heel, high gene tree reconstruction error at short internodes. These simulations therefore provide no evidence that shortcut coalescence methods out-compete concatenation at deep timescales. In summary, the long c-genes that are required for accurate reconstruction of species trees using shortcut coalescence methods do not exist and are a delusion. Coalescence approaches based on SNPs that are widely spaced in the genome avoid problems with the recombination ratchet and merit further pursuit in both empirical systematic research and simulations.

Intestinal transport of TRH analogs through PepT1: the role of in silico and in vitro modeling.

The present study involves molecular docking, molecular dynamics (MD) simulation studies, and Caco-2 cell monolayer permeability assay to investigate the effect of structural modifications on PepT1-mediated transport of thyrotropin releasing hormone (TRH) analogs. Molecular docking of four TRH analogs was performed using a homology model of human PepT1 followed by subsequent MD simulation studies. Caco-2 cell monolayer permeability studies of four TRH analogs were performed at apical to basolateral and basolateral to apical directions. Inhibition experiments were carried out using Gly-Sar, a typical PepT1 substrate, to confirm the PepT1-mediated transport mechanism of TRH analogs. Papp of the four analogs follows the order: NP-1894 < NP-2378 < NP-1896 < NP-1895. Higher absorptive transport was observed in the case of TRH analogs, indicating the possibility of a carrier-mediated transport mechanism. Further, the significant inhibition of the uptake of Gly-Sar by TRH analogs confirmed the PepT1-mediated transport mechanism. Glide docking scores of all the four analogues were in good agreement with their transport rates, suggesting the role of substrate binding affinity in the PepT1-mediated transport of TRH analogs. MD simulation studies revealed that the polar interactions with amino acid residues present in the active site are primarily responsible for substrate binding, and a downward trend was observed with the increase in bulkiness at the N-histidyl moiety of TRH analogs.

Temporal aggregation impacts on epidemiological simulations employing microcontact data

BackgroundMicrocontact datasets gathered automatically by electronic devices have the potential augment the study of the spread of contagious disease by providing detailed representations of the study population’s contact dynamics. However, the impact of data collection experimental design on the subsequent simulation studies has not been adequately addressed. In particular, the impact of study duration and contact dynamics data aggregation on the ultimate outcome of epidemiological models has not been studied in detail, leaving the potential for erroneous conclusions to be made based on simulation outcomes.MethodsWe employ a previously published data set covering 36 participants for 92 days and a previously published agent-based H1N1 infection model to analyze the impact of contact dynamics representation on the simulated outcome of H1N1 transmission. We compared simulated attack rates resulting from the empirically recorded contact dynamics (ground truth), aggregated, typical day, and artificially generated synthetic networks.ResultsNo aggregation or sampling policy tested was able to reliably reproduce results from the ground-truth full dynamic network. For the population under study, typical day experimental designs – which extrapolate from data collected over a brief period – exhibited too high a variance to produce consistent results. Aggregated data representations systematically overestimated disease burden, and synthetic networks only reproduced the ground truth case when fitting errors systemically underestimated the total contact, compensating for the systemic overestimation from aggregation.ConclusionsThe interdepedendencies of contact dynamics and disease transmission require that detailed contact dynamics data be employed to secure high fidelity in simulation outcomes of disease burden in at least some populations. This finding serves as motivation for larger, longer and more socially diverse contact dynamics tracing experiments and as a caution to researchers employing calibrated aggregate synthetic representations of contact dynamics in simulation, as the calibration may underestimate disease parameters to compensate for the overestimation of disease burden imposed by the aggregate contact network representation.

Distributed semi-synchronous channel coordination for multi-channel wireless networks

In multi-channel wireless networks, multi-channel diversity can increase the number of concurrent transmissions and thus improve the throughput performance as data transmission on a wireless channel does not interfere with transmissions on the other non-overlapping channels. However, multi-channel coordination may cause severe performance degradation due to hidden terminals, missing receivers, or broadcast deafness problems if the channel usage information is not properly shared among the neighboring nodes. In this paper, we devise a semi-synchronous multi-channel coordination protocol that enables wireless nodes to: (i) efficiently exchange channel and coordination information, and (ii) reduce the overhead of channel switchings. In the proposed protocol, a rendezvous interval is set up in a distributed manner depending on the traffic rate and pattern, and each node independently switches its channel when it can complete its transmissions and then returns to the control channel within the rendezvous interval. This approach makes all nodes return to the control channel at almost the same time without incurring a severe synchronization overhead. Through subsequent analyses and simulation studies, we show that the proposed protocol effectively reduces the number of channel switchings, thereby achieving higher throughput in various multi-channel networking environments.

Damage detection from the variation of parameter matrices estimated by incomplete FRF data

It is not easy to experimentally obtain the FRF (Frequency Response Function) matrix corresponding to a full set of DOFs (degrees of freedom) for a dynamic system. Utilizing FRF data measured at specific positions, with DOFs less than that of the system, as constraints to describe a damaged system, this study identifies parameter matrices such as mass, stiffness and damping matrices of the system, and provides a damage identification method from their variations. The proposed parameter identification method is compared to Lee and Kim's method and Fritzen's method. The validity of the proposed damage identification method is illustrated in a simple dynamic system. An accurate dynamic finite element model of a structure is very important for structural design and analysis. The modes of vibration and frequency response of the finite element model are compared to experimental measurements to test its accuracy. If a discrepancy between the two is found, the analytical model should be modified to satisfy the experimental measurements, and then the model should be updated for subsequent simulation and design studies. An FRF matrix of the full set of DOFs measured by experiments is used to predict parameter matrices of the dynamic system. The FRFs have been used directly to update condensed analytical models for obtaining the proper modal model. A single FRF measured at several frequencies, along with a correlated analytical model of the structure in its original state, is used for updating structural parameters. There have been many attempts to update the unknown physical parameters directly from the FRFs (Mottershead and Stanway 1986). It has been reported (Lee and Shin 2002) that FRF data provide more information than modal data, as the latter are extracted from a very limited frequency range related to resonance. Friswell and Penny (1990) proposed an approach to reduce the model order so that the stated estimation process reduces to a least squares problem based on the FRFs. Fanning and Carden (2004) presented a method for detecting added mass in structural systems from a single FRF measured at several frequencies in an identification algorithm. Cha and

Temperature dependence and counter effect of the correlations of folding rate with chain length and with native topology

There is a controversy about the major determinants of the folding rate of small single-domain proteins. To shed light on this issue, we examined a possibility that the major determinants may change depending on temperature by conducting molecular dynamics simulations for 17 small single-domain proteins using an off-lattice Go-like model over a wide range of temperature. It was shown that the rank order of the folding rates is temperature dependent, which indicates that the major determinants are dependent on temperature. It was also found that as temperature is decreased, the correlation of the folding rate with the chain length becomes weakened, whereas that with the native topology becomes enhanced. Our simulation results, therefore, may provide a clue to reconcile the apparent controversy between the study by Plaxco based on experimental data and the previous theoretical and subsequent simulation studies: the former showed that the folding rate of two-state folders does not correlate with the chain length but correlates well with the native topology, whereas the latter showed that the folding rate does correlate with the chain length. We propose a possible scenario reconciling the controversy, explaining the reason why the correlation of the folding rate with the chain length became weakened and that with the native topology became enhanced with decreasing temperature.

Effects of morphosyntactic gender features in bilingual language processing*,**

AbstractA central issue in bilingual research concerns the extent to which linguistic representations in the two languages are processed independently of each other. This paper reports the results of an empirical study and a model stimulation, which provide evidence for the interactive view, which holds that processing is not independent. Specifically, a reading experiment examined whether morpho‐syntactic features associated with lexical representations in a bilinguals' native language, in this case the masculine gender feature associated with the er ending of agentive nouns in German, are automatically activated by the processing of morphologically related representations in their second language, in this case English agentive nouns that end in er. Experimental findings suggest that the German–English bilinguals have a bias to interpret the referents of such nouns as male relative to English monolinguals. Subsequent computational simulation studies with an interactive activation network confirmed that this effect is due to the influence of the morphosyntactic er representation in the bilingual models that is absent in the monolingual models. The results provide evidence for an interactive view of bilingual memory and processing for language learners of age 8 and above.