Quantifying dependencies for sensitivity analysis with multivariate input sample data

We present a novel method for quantifying dependencies in multivariate datasets, based on estimating the R\'{e}nyi entropy by minimum spanning trees (MSTs). The length of the MSTs can be used to order pairs of variables from strongly to weakly dependent, making it a useful tool for sensitivity analysis with dependent input variables. It is well-suited for cases where the input distribution is unknown and only a sample of the inputs is available. We introduce an estimator to quantify dependency based on the MST length, and investigate its properties with several numerical examples. To reduce the computational cost of constructing the exact MST for large datasets, we explore methods to compute approximations to the exact MST, and find the multilevel approach introduced recently by Zhong et al. (2015) to be the most accurate. We apply our proposed method to an artificial testcase based on the Ishigami function, as well as to a real-world testcase involving sediment transport in the North Sea. The results are consistent with prior knowledge and heuristic understanding, as well as with variance-based analysis using Sobol indices in the case where these indices can be computed

Supported discharge service versus Inpatient care Evaluation (SITE): a randomised controlled trial comparing effectiveness of an intensive community care service versus inpatient treatment as usual for adolescents with severe psychiatric disorders: self-harm, functional impairment, and educational and clinical outcomes.

Background: Clinical guidelines recommend intensive community care service treatment (ICCS) to reduce adolescent psychiatric inpatient care. We have previously reported that the addition of ICCS led to a substantial decrease in hospital use and improved school re-integration. Aim: To undertake a randomised controlled trial (RCT) comparing an inpatient admission followed by an early discharge supported by ICCS with usual inpatient admission (treatment as usual; TAU). In this paper, we report the impact of ICCS on self-harm and other clinical and educational outcomes. Method: 106 patients aged 12-18 admitted for psychiatric inpatient care were randomised (1:1) to either ICCS or TAU. Six months after randomization, we compared the two treatment arms on the number and severity of self-harm episodes, the functional impairment, severity of psychiatric symptoms, clinical improvement, reading and mathematical ability, weight, height and the use of psychological therapy and medication. Results: At six-month follow-up, there were no differences between the two groups on most measures. Patients receiving ICCS were significantly less likely to report multiple episodes (5 or more) of self-harm (OR=0·18, 95% CI: 0·05 to 0·64). Patients admitted to private inpatient units spent on average 118.4 (95% CI: 28·2 to 208.6) fewer days in hospitals if they were in the ICCS group compared to TAU. Conclusion: The addition of ICCS to TAU may lower the risk of multiple self-harm and may reduce the duration of inpatient stay, especially in those patients admitted for private care. Early discharge with ICCS appears to be a viable alternative to standard inpatient treatment

Stochastic climate theory and modeling

Stochastic methods are a crucial area in contemporary climate research and are increasingly being used in comprehensive weather and climate prediction models as well as reduced order climate models. Stochastic methods are used as subgrid-scale parameterizations (SSPs) as well as for model error representation, uncertainty quantification, data assimilation, and ensemble prediction. The need to use stochastic approaches in weather and climate models arises because we still cannot resolve all necessary processes and scales in comprehensive numerical weather and climate prediction models. In many practical applications one is mainly interested in the largest and potentially predictable scales and not necessarily in the small and fast scales. For instance, reduced order models can simulate and predict large-scale modes. Statistical mechanics and dynamical systems theory suggest that in reduced order models the impact of unresolved degrees of freedom can be represented by suitable combinations of deterministic and stochastic components and non-Markovian (memory) terms. Stochastic approaches in numerical weather and climate prediction models also lead to the reduction of model biases. Hence, there is a clear need for systematic stochastic approaches in weather and climate modeling. In this review, we present evidence for stochastic effects in laboratory experiments. Then we provide an overview of stochastic climate theory from an applied mathematics perspective. We also survey the current use of stochastic methods in comprehensive weather and climate prediction models and show that stochastic parameterizations have the potential to remedy many of the current biases in these comprehensive models

Antigen-expressing immunostimulatory liposomes as a genetically programmable synthetic vaccine

Liposomes are versatile (sub)micron-sized membrane vesicles that can be used for a variety of applications, including drug delivery and in vivo imaging but they also represent excellent models for artificial membranes or cells. Several studies have demonstrated that in vitro transcription and translation can take place inside liposomes to obtain compartmentalized production of functional proteins within the liposomes (Kita et al. in Chembiochem 9(15):2403–2410, 2008; Moritani et al.in FEBS J, 2010; Kuruma et al. in Methods Mol Biol 607:161–171, 2010; Murtas et al. in Biochem Biophys Res Commun 363(1):12–17, 2007; Sunami et al. in Anal Biochem 357(1):128–136, 2006; Ishikawa et al. in FEBS Lett 576(3):387–390, 2004; Oberholzer et al. in Biochem Biophys Res Commun 261(2):238–241, 1999). Such a minimal artificial cell-based model is ideal for synthetic biology based applications. In this study, we propose the use of liposomes as artificial microbes for vaccination. These artificial microbes can be genetically programmed to produce specific antigens at will. To show proof-of-concept for this artificial cell-based platform, a bacterial in vitro transcription and translation system together with a gene construct encoding the model antigen β-galactosidase were entrapped inside multilamellar liposomes. Vaccination studies in mice showed that such antigen-expressing immunostimulatory liposomes (AnExILs) elicited higher specific humoral immune responses against the produced antigen (β-galactosidase) than control vaccines (i.e. AnExILs without genetic input, liposomal β-galactosidase or pDNA encoding β-galactosidase). In conclusion, AnExILs present a new platform for DNA-based vaccines which combines antigen production, adjuvanticity and delivery in one system and which offer several advantages over existing vaccine formulations

PPARα Deficiency in Inflammatory Cells Suppresses Tumor Growth

Inflammation in the tumor bed can either promote or inhibit tumor growth. Peroxisome proliferator-activated receptor (PPAR)α is a central transcriptional suppressor of inflammation, and may therefore modulate tumor growth. Here we show that PPARα deficiency in the host leads to overt inflammation that suppresses angiogenesis via excess production of the endogenous angiogenesis inhibitor thrombospondin-1 and prevents tumor growth. Bone marrow transplantation and granulocyte depletion show that PPARα expressing granulocytes are necessary for tumor growth. Neutralization of thrombospondin-1 restores tumor growth in PPARα-deficient mice. These findings suggest that the absence of PPARα activity renders inflammatory infiltrates tumor suppressive and, thus, may provide a target for inhibiting tumor growth by modulating stromal processes, such as angiogenesis

Resonances in a chaotic attractor crisis of the Lorenz Flow

Local bifurcations of stationary points and limit cycles have successfully been characterized in terms of the critical exponents of these solutions. Lyapunov exponents and their associated covariant Lyapunov vectors have been proposed as tools for supporting the understanding of critical transitions in chaotic dynamical systems. However, it is in general not clear how the statistical properties of dynamical systems change across a boundary crisis during which a chaotic attractor collides with a saddle. This behavior is investigated here for a boundary crisis in the Lorenz flow, for which neither the Lyapunov exponents nor the covariant Lyapunov vectors provide a criterion for the crisis. Instead, the convergence of the time evolution of probability densities to the invariant measure, governed by the semigroup of transfer operators, is expected to slow down at the approach of the crisis. Such convergence is described by the eigenvalues of the generator of this semigroup, which can be divided into two families, referred to as the stable and unstable Ruelle--Pollicott resonances, respectively. The former describes the convergence of densities to the attractor (or escape from a repeller) and is estimated from many short time series sampling the state space. The latter is responsible for the decay of correlations, or mixing, and can be estimated from a long times series, invoking ergodicity. It is found numerically for the Lorenz flow that the stable resonances do approach the imaginary axis during the crisis, as is indicative of the loss of global stability of the attractor. On the other hand, the unstable resonances, and a fortiori the decay of correlations, do not flag the proximity of the crisis, thus questioning the usual design of early warning indicators of boundary crises of chaotic attractors and the applicability of response theory close to such crises