Computational comparison of the stacking interactions between the aromatic amino acids and the natural or (cationic) methylated nucleobases

The strongest gas-phase MP2/6-31G*(0.25) stacking energies between the aromatic amino acids and the natural or methylated nucleobases were considered. The potential energy surfaces of dimers were searched as a function of the vertical separation, angle of rotation and horizontal displacement between monomers stacked according to their centers of mass. Our calculations reveal that the stacking interactions of adducts for a given nucleobase are dependent on the methylation site (by up to 20 kJ mol(-1)), where the relative magnitudes of the interactions are determined by the dipole moments of the adducts and the proton affinities of nucleobase methylation sites. Nevertheless, the differences in the (gas-phase) stacking of methylated adducts are small compared with the differences between the stacking of the corresponding natural and methylated nucleobases. Indeed, methylation increases the stacking energy by up to 40 kJ mol(-1) (or 135%). Although immersing the dimers in different solvents decreases the gas-phase stacking energies with an increase in the polarity of the environment, base methylation still has a significant effect on the nucleobase stacking ability in solvents with large dipole moments, and, perhaps more importantly, environments that mimic enzyme active sites. Our results shed light on the workings of DNA repairs enzymes that selectively remove a wide variety of alkylated nucleobases over the natural bases.     

A belief rule-based decision support system for clinical risk assessment of cardiac chest pain

This paper describes a prototype clinical decision support system (CDSS) for risk stratification of patients with cardiac chest pain. A newly developed belief rule-based inference methodology-RIMER was employed for developing the prototype. Based on the belief rule-based inference methodology, the prototype CDSS can deal with uncertainties in both clinical domain knowledge and clinical data. Moreover, the prototype can automatically update its knowledge base via a belief rule base (BRB) learning module which can adjust BRB through accumulated historical clinical cases. The domain specific knowledge used to construct the knowledge base of the prototype was learned from real patient data. We simulated a set of 1000 patients in cardiac chest pain to validate the prototype. The belief rule-based prototype CDSS has been found to perform extremely well. Firstly, the system can provide more reliable and informative diagnosis recommendations than manual diagnosis using traditional rules when there are clinical uncertainties. Secondly, the diagnostic performance of the system can be significantly improved after training the BRB through accumulated clinical cases.     

Fluorous ionic liquids as solvents for the liquid-liquid extraction of metal ions by macrocyclic polyethers

The predominant mode of strontium ion transfer from aqueous nitrate media into a series of 1-fluoroalkyl-3-methylimidazolium bis[(trifluoromethylsulfonyl)]imides containing dicyclohexano-18-crown-6 (DCH18C6) is shown to shift from cation exchange to strontium nitrato-crown ether complex partitioning as the length of the fluoroalkyl substituent is increased. Fluoroalkyl substituents are shown to be only slightly more effective than their non-fluorous analogs at inducing this shift. At the same time, the fluorinated ionic liquids (ILs) yield strontium distribution ratios as much as an order of magnitude lower than the corresponding 1-alkyl-3-methylimidazolium (C_nmim⁺) salts. Fluorous ILs thus appear to offer no compelling advantages over C_nmim⁺ ionic liquids as extraction solvents.     

Computational modeling for reasoning about the social behavior of humans

The number of computationally-based models of human social behavior is growing rapidly. In fact, the current ease of programming is resulting in a plethora of tools with impressive interfaces but little theoretical power under the hood. Further, the overabundance of new toolkits for building models is facilitating the excessively rapid growth of simple proof-of-concept, or intellective, models. The current state of models range from the simplistic to the elaborate, from the conceptual to the empirical, and from the purely notional to the validatable. This review briefly describes the state of human social behavioral modeling. Key issues surrounding analysis and validation are discussed.

Rapid modeling and analyzing networks extracted from pre-structured news articles

In the face of uprisings and revolutions happening in several countries within short period of time (Arab Spring 2011), the need for fast network assessments is compelling. In this article we present a rapid network assessment approach which uses a vast amount of pre-indexed news data to provide up-to-date overview and orientation in emerging and ongoing incidents. We describe the fully automated process of preparing the data and creating the dynamic meta-networks. We also describe the network analytical measures that we are using to identify important topics, persons, organizations, and locations in these networks. With our rapid network modeling and analysis approach first results can be provided within hours. In the explorative study of this article we use 108,000+ articles from 600+ English written news sources discussing Egypt, Libya, and Sudan within a time period of 18 months to show an application scenario of our approach. In particular we are looking at the involvement of other countries and their politicians during time periods of major incidents.     

Multifunctional Antibody Agonists Targeting Glucagon‐like Peptide‐1, Glucagon,and Glucose‐Dependent Insulinotropic Polypeptide Receptors

Glucagon‐like peptide‐1 (GLP‐1) receptor (GLP‐1R), glucagon (GCG) receptor (GCGR), and glucose‐dependent insulinotropic polypeptide (GIP, also known as gastric inhibitory polypeptide) receptor (GIPR), are three metabolically related peptide hormone receptors. A novel approach to the generation of multifunctional antibody agonists that activate these receptors has been developed. Native or engineered peptide agonists for GLP‐1R, GCGR, and GIPR were fused to the N‐terminus of the heavy chain or light chain of an antibody, either alone or in pairwise combinations. The fusion proteins have similar in vitro biological activities on the cognate receptors as the corresponding peptides, but circa 100‐fold longer plasma half‐lives. The GLP‐1R mono agonist and GLP‐1R/GCGR dual agonist antibodies both exhibit potent effects on glucose control and body weight reduction in mice, with the dual agonist antibody showing enhanced activity in the latter.     

Observation of uncorrelated microscopic motion in a strongly interacting adsorbate system

Modeling of intermolecular forces is a central theme in the physical sciences. The prototypical heterogeneous catalysis system, CO/Pt(111), is an extensively studied example where strong pairwise repulsive forces between the CO molecules have been used to explain the observed structure and dynamics. No direct measurements of these forces were available; yet, they offered a natural way of explaining various macroscopic observations assuming a separable adsorbate-substrate interaction and pairwise adsorbate-adsorbate interactions. In the present study, we measure intermolecular forces by following CO motion on a microscopic scale. The uncorrelated dynamics we observe throughout the coverage range of the measurements excludes the existence of the strong pairwise forces previously suggested. The increase in the rate of uncorrelated motion is explained by a nonlocal modification of the adsorbate-substrate interaction, reflecting a many-body system that cannot be described by the standard separable interaction approach.     

Multifunctional Antibody Agonists Targeting Glucagon‐like Peptide‐1, Glucagon,and Glucose‐Dependent Insulinotropic Polypeptide Receptors

Glucagon‐like peptide‐1 (GLP‐1) receptor (GLP‐1R), glucagon (GCG) receptor (GCGR), and glucose‐dependent insulinotropic polypeptide (GIP, also known as gastric inhibitory polypeptide) receptor (GIPR), are three metabolically related peptide hormone receptors. A novel approach to the generation of multifunctional antibody agonists that activate these receptors has been developed. Native or engineered peptide agonists for GLP‐1R, GCGR, and GIPR were fused to the N‐terminus of the heavy chain or light chain of an antibody, either alone or in pairwise combinations. The fusion proteins have similar in vitro biological activities on the cognate receptors as the corresponding peptides, but circa 100‐fold longer plasma half‐lives. The GLP‐1R mono agonist and GLP‐1R/GCGR dual agonist antibodies both exhibit potent effects on glucose control and body weight reduction in mice, with the dual agonist antibody showing enhanced activity in the latter.