Integrating isotopes and documentary evidence : dietary patterns in a late medieval and early modern mining community, Sweden

We would like to thank the Archaeological Research Laboratory, Stockholm University, Sweden and the Tandem Laboratory (Ångström Laboratory), Uppsala University, Sweden, for undertaking the analyses of stable nitrogen and carbon isotopes in both human and animal collagen samples. Also, thanks to Elin Ahlin Sundman for providing the δ13C and δ15N values for animal references from Västerås. This research (Bäckström’s PhD employment at Lund University, Sweden) was supported by the Berit Wallenberg Foundation (BWS 2010.0176) and Jakob and Johan Söderberg’s foundation. The ‘Sala project’ (excavations and analyses) has been funded by Riksens Clenodium, Jernkontoret, Birgit and Gad Rausing’s Foundation, SAU’s Research Foundation, the Royal Physiographic Society of Lund, Berit Wallenbergs Foundation, Åke Wibergs Foundation, Lars Hiertas Memory, Helge Ax:son Johnson’s Foundation and The Royal Swedish Academy of Sciences.Peer reviewedPublisher PD

Credit risk - A structural model with jumps and correlations

We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump-diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common with other problems in statistical physics and in the theory of complex systems. We study a simplified version of our model analytically. Furthermore, we perform extensive numerical simulations for the full model. The observables are the loss distribution of the credit portfolio, its moments and other quantities derived thereof. We compile detailed information about the parameter dependence of these observables. In the course of setting up and analyzing our model, we also give a review of credit risk modeling for a physics audience. (c) 2007 Elsevier B.V. All rights reserved

Simultaneous Assessment of Left Atrial Fibrosis and Epicardial Adipose Tissue Using 3D Late Gadolinium Enhanced Dixon MRI

Background Epicardial adipose tissue (EAT) may induce left atrium (LA) wall inflammation and promote LA fibrosis. Therefore, simultaneous assessment of these two important atrial fibrillation (AF) risk factors would be desirable. Purpose To perform a comprehensive evaluation of 3D Dixon water-fat separated late gadolinium enhancement (LGE-Dixon) MRI by analysis of repeatability and systematic comparison with reference methods for assessment of fibrosis and fat. Study Type Prospective. Population Twenty-eight, 10, and 7 patients, respectively, with clinical indications for cardiac MRI. Field Strength/Sequence A 1.5-T scanner, inversion recovery multiecho spoiled gradient echo. Assessment Twenty-eight patients (age 58 +/- 19 years, 15 males) were scanned using LGE-Dixon. A 5-point Likert-type scale was used to grade the image quality. Another 10 patients (age 46 +/- 19 years, 9 males) were scanned using LGE-Dixon and 3D proton density Dixon (PD-Dixon). Finally, seven patients (age 62 +/- 14 years, 4 males) were scanned using LGE-Dixon and conventional LGE. The scan time, intraobserver and interobserver variability, and levels of agreement were assessed. Statistical Tests Students t-test, one-way ANOVA, and Mann-Whitney U-test were used; P < 0.05 was considered significant, intraclass correlation coefficient (ICC). Results The scan time (minutes:seconds) for LGE-Dixon (n = 28) was 5:01 +/- 1:40. ICC values for intraobserver and interobserver measurements of LA wall fibrosis percentage were 0.98 (95% CI, 0.97-0.99) and 0.97 (95% CI, 0.94-0.99) while of EAT were 0.92 (95% CI, 0.82-0.97) and 0.90 (95% CI, 0.80-0.95). The agreement for LA fibrosis percentage between the LGE-Dixon and the conventional LGE was 0.92 (95% CI, 0.66-0.99) and for EAT volume between the LGE-Dixon and the PD-Dixon was 0.93 (95% CI, 0.72-0.98). Conclusion LA fibrosis and EAT can be assessed simultaneously using LGE-Dixon. This method allows a high level of intraobserver and interobserver repeatability as well as agreement with reference methods and can be performed in a clinically feasible scan time. Evidence Level 2 Technical Efficacy Stage

Credit risk - A structural model with jumps and correlations

We set up a structural model to study credit risk for a portfolio containing several or many credit contracts. The model is based on a jump--diffusion process for the risk factors, i.e. for the company assets. We also include correlations between the companies. We discuss that models of this type have much in common with other problems in statistical physics and in the theory of complex systems. We study a simplified version of our model analytically. Furthermore, we perform extensive numerical simulations for the full model. The observables are the loss distribution of the credit portfolio, its moments and other quantities derived thereof. We compile detailed information about the parameter dependence of these observables. In the course of setting up and analyzing our model, we also give a review of credit risk modeling for a physics audience.

Human-in-the-loop assisted de novo molecular design

Abstract A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still needs to design the goal, expressed as a scoring function for the molecules that captures the designer’s implicit knowledge about the optimization task. Little support for this task exists and, consequently, a chemist usually resorts to iteratively building the objective function of multi-parameter optimization (MPO) in de novo design. We propose a principled approach to use human-in-the-loop machine learning to help the chemist to adapt the MPO scoring function to better match their goal. An advantage is that the method can learn the scoring function directly from the user’s feedback while they browse the output of the molecule generator, instead of the current manual tuning of the scoring function with trial and error. The proposed method uses a probabilistic model that captures the user’s idea and uncertainty about the scoring function, and it uses active learning to interact with the user. We present two case studies for this: In the first use-case, the parameters of an MPO are learned, and in the second use-case a non-parametric component of the scoring function to capture human domain knowledge is developed. The results show the effectiveness of the methods in two simulated example cases with an oracle, achieving significant improvement in less than 200 feedback queries, for the goals of a high QED score and identifying potent molecules for the DRD2 receptor, respectively. We further demonstrate the performance gains with a medicinal chemist interacting with the system. Graphical Abstrac

Human-in-the-loop assisted de novo molecular design

A de novo molecular design workflow can be used together with technologies such as reinforcement learning to navigate the chemical space. A bottleneck in the workflow that remains to be solved is how to integrate human feedback in the exploration of the chemical space to optimize molecules. A human drug designer still needs to design the goal, expressed as a scoring function for the molecules that captures the designer’s implicit knowledge about the optimization task. Little support for this task exists and, consequently, a chemist usually resorts to iteratively building the objective function of multi-parameter optimization (MPO) in de novo design. We propose a principled approach to use human-in-the-loop machine learning to help the chemist to adapt the MPO scoring function to better match their goal. An advantage is that the method can learn the scoring function directly from the user’s feedback while they browse the output of the molecule generator, instead of the current manual tuning of the scoring function with trial and error. The proposed method uses a probabilistic model that captures the user’s idea and uncertainty about the scoring function, and it uses active learning to interact with the user. We present two case studies for this: In the first use-case, the parameters of an MPO are learned, and in the second use-case a non-parametric component of the scoring function to capture human domain knowledge is developed. The results show the effectiveness of the methods in two simulated example cases with an oracle, achieving significant improvement in less than 200 feedback queries, for the goals of a high QED score and identifying potent molecules for the DRD2 receptor, respectively. We further demonstrate the performance gains with a medicinal chemist interacting with the system

Godtyckligt regelverk hotar friheten på nätet

Reglerna som möjliggör stängning av hemsidor på internet präglas av godtycke och otydlighet. Men det behöver inte vara särskilt svårt att skapa ett nytt och rättssäkert regelverk. Här har Sveriges EU-kommissionär Cecilia Malmström en viktig roll. Frågan är om hon tar sitt ansvar, skriver politiker och nätdebattörer