Recent advances in electronic structure theory and their influence on the accuracy of ab initio potential energy surfaces

Recent advances in electronic structure theory and the availability of high speed vector processors have substantially increased the accuracy of ab initio potential energy surfaces. The recently developed atomic natural orbital approach for basis set contraction has reduced both the basis set incompleteness and superposition errors in molecular calculations. Furthermore, full CI calculations can often be used to calibrate a CASSCF/MRCI approach that quantitatively accounts for the valence correlation energy. These computational advances also provide a vehicle for systematically improving the calculations and for estimating the residual error in the calculations. Calculations on selected diatomic and triatomic systems will be used to illustrate the accuracy that currently can be achieved for molecular systems. In particular, the F+H2 yields HF+H potential energy hypersurface is used to illustrate the impact of these computational advances on the calculation of potential energy surfaces

Global parameter identification of stochastic reaction networks from single trajectories

We consider the problem of inferring the unknown parameters of a stochastic biochemical network model from a single measured time-course of the concentration of some of the involved species. Such measurements are available, e.g., from live-cell fluorescence microscopy in image-based systems biology. In addition, fluctuation time-courses from, e.g., fluorescence correlation spectroscopy provide additional information about the system dynamics that can be used to more robustly infer parameters than when considering only mean concentrations. Estimating model parameters from a single experimental trajectory enables single-cell measurements and quantification of cell--cell variability. We propose a novel combination of an adaptive Monte Carlo sampler, called Gaussian Adaptation, and efficient exact stochastic simulation algorithms that allows parameter identification from single stochastic trajectories. We benchmark the proposed method on a linear and a non-linear reaction network at steady state and during transient phases. In addition, we demonstrate that the present method also provides an ellipsoidal volume estimate of the viable part of parameter space and is able to estimate the physical volume of the compartment in which the observed reactions take place.Comment: Article in print as a book chapter in Springer's "Advances in Systems Biology

Random-phase approximation and its applications in computational chemistry and materials science

The random-phase approximation (RPA) as an approach for computing the electronic correlation energy is reviewed. After a brief account of its basic concept and historical development, the paper is devoted to the theoretical formulations of RPA, and its applications to realistic systems. With several illustrating applications, we discuss the implications of RPA for computational chemistry and materials science. The computational cost of RPA is also addressed which is critical for its widespread use in future applications. In addition, current correction schemes going beyond RPA and directions of further development will be discussed.Comment: 25 pages, 11 figures, published online in J. Mater. Sci. (2012

A mathematical and computational review of Hartree-Fock SCF methods in Quantum Chemistry

We present here a review of the fundamental topics of Hartree-Fock theory in Quantum Chemistry. From the molecular Hamiltonian, using and discussing the Born-Oppenheimer approximation, we arrive to the Hartree and Hartree-Fock equations for the electronic problem. Special emphasis is placed in the most relevant mathematical aspects of the theoretical derivation of the final equations, as well as in the results regarding the existence and uniqueness of their solutions. All Hartree-Fock versions with different spin restrictions are systematically extracted from the general case, thus providing a unifying framework. Then, the discretization of the one-electron orbitals space is reviewed and the Roothaan-Hall formalism introduced. This leads to a exposition of the basic underlying concepts related to the construction and selection of Gaussian basis sets, focusing in algorithmic efficiency issues. Finally, we close the review with a section in which the most relevant modern developments (specially those related to the design of linear-scaling methods) are commented and linked to the issues discussed. The whole work is intentionally introductory and rather self-contained, so that it may be useful for non experts that aim to use quantum chemical methods in interdisciplinary applications. Moreover, much material that is found scattered in the literature has been put together here to facilitate comprehension and to serve as a handy reference.Comment: 64 pages, 3 figures, tMPH2e.cls style file, doublesp, mathbbol and subeqn package

Supramolecular synthon pattern in solid clioquinol and cloxiquine (APIs of antibacterial, antifungal, antiaging and antituberculosis drugs) studied by 35Cl NQR, 1H-17O and 1H-14N NQDR and DFT/QTAIM

The quinolinol derivatives clioquinol (5-chloro-7-iodo-8-quinolinol, Quinoform) and cloxiquine (5-chloro-8-quinolinol) were studied experimentally in the solid state via 35Cl NQR, 1H-17O and 1H-14N NQDR spectroscopies, and theoretically by density functional theory (DFT). The supramolecular synthon pattern of O–H···N hydrogen bonds linking dimers and π–π stacking interactions were described within the QTAIM (quantum theory of atoms in molecules) /DFT (density functional theory) formalism. Both proton donor and acceptor sites in O–H···N bonds were characterized using 1H-17O and 1H-14N NQDR spectroscopies and QTAIM. The possibility of the existence of O–H···H–O dihydrogen bonds was excluded. The weak intermolecular interactions in the crystals of clioquinol and cloxiquine were detected and examined. The results obtained in this work suggest that considerable differences in the NQR parameters for the planar and twisted supramolecular synthons permit differentiation between specific polymorphic forms, and indicate that the more planar supramolecular synthons are accompanied by a greater number of weaker hydrogen bonds linking them and stronger π···π stacking interactions

Prevalence and factors associated with alcohol and drug-related disorders in prison: a French national study

BACKGROUND: Most studies measuring substance-use disorders in prisons focus on incoming or on remand prisoners and are generally restricted to drugs. However, there is evidence that substance use initiation or continuation occurs in prison, and that alcohol use is common. The aim of this study is 1) to assess prevalence of both drug and alcohol abuse and dependence (DAD/AAD) in a national randomised cohort of French prisoners, short or long-term sentence 2) to assess the risk factors associated with DAD/AAD in prison. a stratified random strategy was used to select 1) 23 prisons among the different types of prison 2) 998 prisoners. Diagnoses were assessed according to a standardized procedure, each prisoner being assessed by two psychiatrists, one junior, using a structured interview (MINI 5 plus), and one senior, completing the procedure with an open clinical interview. At the end of the interview the clinicians met and agreed on a list of diagnoses. Cloninger's Temperament and Character Inventory (TCI) was also used. RESULTS: More than a third of prisoners presented either AAD or DAD in the last 12 months. Cannabis was the most frequent drug and just under a fifth of prisoners had AAD. AAD and DAD were clearly different for the following: socio-demographic variables, childhood history, imprisonment characteristics, psychiatric comorbidity and Cloninger's TCI. Profiles of AAD in prison are similar to type II alcoholism. CONCLUSION: Regular screening of AAD/DAD in prison, and specific treatment programmes taking into account differences between prisoners with an AAD and prisoners with a DAD should be a public health priority in priso

Ab Initio Identification of Novel Regulatory Elements in the Genome of Trypanosoma brucei by Bayesian Inference on Sequence Segmentation

Background: The rapid increase in the availability of genome information has created considerable demand for both comparative and ab initio predictive bioinformatic analyses. The biology laid bare in the genomes of many organisms is often novel, presenting new challenges for bioinformatic interrogation. A paradigm for this is the collected genomes of the kinetoplastid parasites, a group which includes Trypanosoma brucei the causative agent of human African trypanosomiasis. These genomes, though outwardly simple in organisation and gene content, have historically challenged many theories for gene expression regulation in eukaryotes. Methodology/Principle Findings: Here we utilise a Bayesian approach to identify local changes in nucleotide composition in the genome of T. brucei. We show that there are several elements which are found at the starts and ends of multicopy gene arrays and that there are compositional elements that are common to all intergenic regions. We also show that there is a composition-inversion element that occurs at the position of the trans-splice site. Conclusions/Significance: The nature of the elements discovered reinforces the hypothesis that context dependant RN

A systematic review of the effect of retention methods in population-based cohort studies

Background: Longitudinal studies are of aetiological and public health relevance but can be undermined by attrition. The aim of this paper was to identify effective retention strategies to increase participation in population-based cohort studies. Methods: Systematic review of the literature to identify prospective population-based cohort studies with health outcomes in which retention strategies had been evaluated. Results: Twenty-eight studies published up to January 2011 were included. Eleven of which were randomized controlled trials of retention strategies (RCT). Fifty-seven percent of the studies were postal, 21% in-person, 14% telephone and 7% had mixed data collection methods. A total of 45 different retention strategies were used, categorised as 1) incentives, 2) reminder methods, repeat visits or repeat questionnaires, alternative modes of data collection or 3) other methods. Incentives were associated with an increase in retention rates, which improved with greater incentive value. Whether cash was the most effective incentive was not clear from studies that compared cash and gifts of similar value. The average increase in retention rate was 12% for reminder letters, 5% for reminder calls and 12% for repeat questionnaires. Ten studies used alternative data collection methods, mainly as a last resort. All postal studies offered telephone interviews to non-responders, which increased retention rates by 3%. Studies that used face-to-face interviews increased their retention rates by 24% by offering alternative locations and modes of data collection. Conclusions: Incentives boosted retention rates in prospective cohort studies. Other methods appeared to have a beneficial effect but there was a general lack of a systematic approach to their evaluation

Determinants of self-rated health in women: a population-based study in Armavir Marz, Armenia, 2001 & 2004

<p>Abstract</p> <p>Background</p> <p>The former soviet Republic of Armenia entered a turbulent and long-lasting economic transition when it declared its independence in 1991. This analysis sought to identify the determinants of poor self-rated health as an indirect measure of health status and mortality prognosis in an adult female population during a period of socio-economic transition in Armenia.</p> <p>Methods</p> <p>Differences in self-rated health in women respondents were analyzed along three main dimensions: social, behavioral/attitudinal, and psychological. The data used were generated from cross-sectional household health surveys conducted in Armavir <it>marz </it>in 2001 and 2004. The surveys utilized the same instruments and study design (probability proportional to size, multistage cluster sampling with a combination of interviewer-administered and self-administered surveys) and generated two independent samples of households representative of Armavir <it>marz</it>. Binary logistic regression models with self-rated health as the outcome were fitted to the 2001 and 2004 datasets and a combined 2001/2004 dataset.</p> <p>Results</p> <p>Overall, 2 038 women aged 18 and over participated in the two surveys (1 019 in each). The rate of perceived "poor" health was relatively high in both surveys: 38.1% in 2001 and 27.0% in 2004. The sets of independent predictors of poor self-rated health were similar in all three models and included severe and moderate material deprivation, probable and possible depression, low level of education, and having ever smoked. These predictors mediated the effect of women's economic activity (including unemployment), ethnicity, low access to/utilization of healthcare services, and living alone on self-rated health.</p> <p>Conclusion</p> <p>Material deprivation was the most influential predictor of self-rated health. Thus, social reforms to decrease the gap between the rich and poor are recommended as a powerful tool for reducing health inequalities and improving the health status of the population.</p

Unbiased Bayesian inference for population Markov jump processes via random truncations

We consider continuous time Markovian processes where populations of individual agents interact stochastically according to kinetic rules. Despite the increasing prominence of such models in fields ranging from biology to smart cities, Bayesian inference for such systems remains challenging, as these are continuous time, discrete state systems with potentially infinite state-space. Here we propose a novel efficient algorithm for joint state / parameter posterior sampling in population Markov Jump processes. We introduce a class of pseudo-marginal sampling algorithms based on a random truncation method which enables a principled treatment of infinite state spaces. Extensive evaluation on a number of benchmark models shows that this approach achieves considerable savings compared to state of the art methods, retaining accuracy and fast convergence. We also present results on a synthetic biology data set showing the potential for practical usefulness of our work