Bott periodicity and stable quantum classes

We use Bott periodicity to relate previously defined quantum classes to certain "exotic Chern classes" on $BU$. This provides an interesting computational and theoretical framework for some Gromov-Witten invariants connected with cohomological field theories. This framework has applications to study of higher dimensional, Hamiltonian rigidity aspects of Hofer geometry of $\mathbb{CP} ^{n}$, one of which we discuss here.Comment: prepublication versio

Non-compartment model to compartment model pharmacokinetics transformation meta-analysis – a multivariate nonlinear mixed model

Background To fulfill the model based drug development, the very first step is usually a model establishment from published literatures. Pharmacokinetics model is the central piece of model based drug development. This paper proposed an important approach to transform published non-compartment model pharmacokinetics (PK) parameters into compartment model PK parameters. This meta-analysis was performed with a multivariate nonlinear mixed model. A conditional first-order linearization approach was developed for statistical estimation and inference. Results Using MDZ as an example, we showed that this approach successfully transformed 6 non-compartment model PK parameters from 10 publications into 5 compartment model PK parameters. In simulation studies, we showed that this multivariate nonlinear mixed model had little relative bias (<1%) in estimating compartment model PK parameters if all non-compartment PK parameters were reported in every study. If there missing non-compartment PK parameters existed in some published literatures, the relative bias of compartment model PK parameter was still small (<3%). The 95% coverage probabilities of these PK parameter estimates were above 85%. Conclusions This non-compartment model PK parameter transformation into compartment model meta-analysis approach possesses valid statistical inference. It can be routinely used for model based drug development

Extensions to the Visual Predictive Check to facilitate model performance evaluation

The Visual Predictive Check (VPC) is a valuable and supportive instrument for evaluating model performance. However in its most commonly applied form, the method largely depends on a subjective comparison of the distribution of the simulated data with the observed data, without explicitly quantifying and relating the information in both. In recent adaptations to the VPC this drawback is taken into consideration by presenting the observed and predicted data as percentiles. In addition, in some of these adaptations the uncertainty in the predictions is represented visually. However, it is not assessed whether the expected random distribution of the observations around the predicted median trend is realised in relation to the number of observations. Moreover the influence of and the information residing in missing data at each time point is not taken into consideration. Therefore, in this investigation the VPC is extended with two methods to support a less subjective and thereby more adequate evaluation of model performance: (i) the Quantified Visual Predictive Check (QVPC) and (ii) the Bootstrap Visual Predictive Check (BVPC). The QVPC presents the distribution of the observations as a percentage, thus regardless the density of the data, above and below the predicted median at each time point, while also visualising the percentage of unavailable data. The BVPC weighs the predicted median against the 5th, 50th and 95th percentiles resulting from a bootstrap of the observed data median at each time point, while accounting for the number and the theoretical position of unavailable data. The proposed extensions to the VPC are illustrated by a pharmacokinetic simulation example and applied to a pharmacodynamic disease progression example

Recent XAS studies into Homogeneous metal catalyst in fine chemical and pharmaceutical syntheses

A brief review of studies using X-ray Absorption Spectroscopy (XAS) to investigate homogeneous catalytic reactions in fine chemical and pharmaceutical context since 2010 is presented. The advantages of the techniques over traditional lab-based analytical tools, particularly when NMR spectroscopy fails to deliver mechanistic insights, are summarised using these examples. A discussion on the current limitations of the techniques and challenges in the near future is also included

Confidence from uncertainty - A multi-target drug screening method from robust control theory

<p>Abstract</p> <p>Background</p> <p>Robustness is a recognized feature of biological systems that evolved as a defence to environmental variability. Complex diseases such as diabetes, cancer, bacterial and viral infections, exploit the same mechanisms that allow for robust behaviour in healthy conditions to ensure their own continuance. Single drug therapies, while generally potent regulators of their specific protein/gene targets, often fail to counter the robustness of the disease in question. Multi-drug therapies offer a powerful means to restore disrupted biological networks, by targeting the subsystem of interest while preventing the diseased network from reconciling through available, redundant mechanisms. Modelling techniques are needed to manage the high number of combinatorial possibilities arising in multi-drug therapeutic design, and identify synergistic targets that are robust to system uncertainty.</p> <p>Results</p> <p>We present the application of a method from robust control theory, Structured Singular Value or μ- analysis, to identify highly effective multi-drug therapies by using robustness in the face of uncertainty as a new means of target discrimination. We illustrate the method by means of a case study of a negative feedback network motif subject to parametric uncertainty.</p> <p>Conclusions</p> <p>The paper contributes to the development of effective methods for drug screening in the context of network modelling affected by parametric uncertainty. The results have wide applicability for the analysis of different sources of uncertainty like noise experienced in the data, neglected dynamics, or intrinsic biological variability.</p

Contribution of Cerebellar Sensorimotor Adaptation to Hippocampal Spatial Memory

Complementing its primary role in motor control, cerebellar learning has also a bottom-up influence on cognitive functions, where high-level representations build up from elementary sensorimotor memories. In this paper we examine the cerebellar contribution to both procedural and declarative components of spatial cognition. To do so, we model a functional interplay between the cerebellum and the hippocampal formation during goal-oriented navigation. We reinterpret and complete existing genetic behavioural observations by means of quantitative accounts that cross-link synaptic plasticity mechanisms, single cell and population coding properties, and behavioural responses. In contrast to earlier hypotheses positing only a purely procedural impact of cerebellar adaptation deficits, our results suggest a cerebellar involvement in high-level aspects of behaviour. In particular, we propose that cerebellar learning mechanisms may influence hippocampal place fields, by contributing to the path integration process. Our simulations predict differences in place-cell discharge properties between normal mice and L7-PKCI mutant mice lacking long-term depression at cerebellar parallel fibre-Purkinje cell synapses. On the behavioural level, these results suggest that, by influencing the accuracy of hippocampal spatial codes, cerebellar deficits may impact the exploration-exploitation balance during spatial navigation

Who Cares About Being Gentle? The Impact of Social Identity and the Gender of One’s Friends on Children’s Display of Same-Gender Favoritism

This research assessed children’s same-gender favoritism by examining whether children value traits descriptive of their own gender more than traits descriptive of the other gender. We also investigated whether children’s proportion of same-gender friends relates to their same-gender favoritism. Eighty-one third and fourth grade children from the Midwest and West Coast of the U.S. rated how well 19 personality traits describe boys and girls, and how important each trait is for their gender to possess. Results replicate and extend past trait assignment research by demonstrating that both genders valued same-gender traits significantly more than other-gender traits. Results also indicated that boys with many same-gender friends derogated feminine-stereotyped traits, which has implications for research on masculinity norms within male-dominated peer groups

Identification and management of chronic pain in primary care:a review

Chronic pain is a common, complex, and challenging condition, where understanding the biological, social, physical and psychological contexts is vital to successful outcomes in primary care. In managing chronic pain the focus is often on promoting rehabilitation and maximizing quality of life rather than achieving cure. Recent screening tools and brief intervention techniques can be effective in helping clinicians identify, stratify and manage both patients already living with chronic pain and those who are at risk of developing chronic pain from acute pain. Frequent assessment and reassessment are key to ensuring treatment is appropriate and safe, as well as minimizing and addressing side effects. Primary care management should be holistic and evidence-based (where possible) and incorporates both pharmacological and non-pharmacological approaches, including psychology, self-management, physiotherapy, peripheral nervous system stimulation, complementary therapies and comprehensive pain-management programmes. These may either be based wholly in primary care or supported by appropriate specialist referral

Metabolic syndrome, psychological status and quality of life in obesity: the QUOVADIS Study

Objective: We aimed to investigate the association of the clinical variables of the metabolic syndrome (MS) and psychological parameters on health-related quality of life (HRQL) in obesity. In particular, our aim was to investigate the relative impact of physical symptoms, somatic diseases and psychological distress on both the physical and the mental domains of HRQL. Design: Cross-sectional study. Subjects: A cohort of 1822 obese outpatients seeking treatment in medical centers. Measurements: HRQL was measured by the standardized summary scores for physical (PCS) and mental (MCS) components of the Short Form 36 Health Survey (SF-36). Patients were grouped according to tertiles of PCS and MCS. Metabolic and psychological profiles of PCS and MCS tertiles were compared by discriminant analysis. Results: The profile of metabolic and psychological variables was tertile-specific in 62.4 and 68.3% of patients in the lowest and highest tertiles of PCS, respectively, while concordance was low in the mid-tertile (32.8%). Concordance was very high in the lowest (74.4%) and in the highest (75.5%) tertiles of MCS, and was fair in the mid-tertile (53.2%). The main correlates of PCS were obesity-specific and general psychological well-being, BMI, body uneasiness, binge eating, gender and psychiatric distress. Only hypertension and hyperglycemia qualified as correlates among the components of MS. The components of MS did not define MCS. Conclusions: Psychological well-being is the most important correlate of HRQL in obesity, both in the physical and in the mental domains, whereas the features of MS correlate only to some extent with the physical domain of HRQL