Abstract
This study reviews performance criteria adequate to validate simulation models through the comparison of two quantitative data sets, concerning historical and simulated data. The criteria reviewed were organized according to its characteristics into the groups: error-based measures, information theory measures, information criteria, parametric tests, non-parametric tests, distance-based measures and combined measures. Each criterion is reviewed through its mathematic definition, its applications in literature and the identification of its advantages and drawbacks. The features assessed by each criterion are identified and discussed. This study provides a concise outline over the criteria reviewed, which can be used as a guide to help developers of simulation models into the decision on the most appropriate criteria to validate their models.

Abstract
In this paper, we present a computing procedure to analyze a network of credit and debt among agents (nodes) from a standpoint of balance sheet dependencies. The aim is to develop a method to assess thoroughly the sensitivity of the network to potential individual insolvencies. For this purpose, given a state of the network, the insolvency of an agent is assumed and the cascade of provoked insolvencies is simulated. Exploring the matrix definition of the network, this is made systematically for all agents. Therefore, in only one run of the procedure, all the possible trajectories of insolvencies, each beginning in a different agent, are calculated. This allows spotting at a glance which agents are "systemically riskier". Determination of adequate capital levels can be made on a case basis by running the procedure repeatedly. This work contributes with two new aspects to the existing literature. First, given a known situation of a credit and debt network, a computing procedure is presented that allows to assess the network sensitivity to the exogenous insolvency of any of its nodes. Second, continued monitoring of a credit and debt network is computationally feasible. This "proof of concept" software can be extended into a tool useful for research and regulation, if the relevant information is made available.

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Abstract
Adverse Drug Events (ADEs) are a major health problem, and developing accurate prediction methods may have a significant impact in public health. Ideally, we would like to have predictive methods, that could pinpoint possible ADRs during the drug development process. Unfortunately, most relevant information on possible ADRs is only available after the drug is commercially available. As a first step, we propose using prior information on existing interactions through recommendation systems algorithms. We have evaluated our proposal using data from the ADReCS database with promising results.

Abstract
A high-resolution mtDNA phylogenetic tree allowed us to look backward in time to investigate purifying selection. Purifying selection was very strong in the last 2,500 years, continuously eliminating pathogenic mutations back until the end of the Younger Dryas (approximate to 11,000 years ago), when a large population expansion likely relaxed selection pressure. This was preceded by a phase of stable selection until another relaxation occurred in the out-of-Africa migration. Demography and selection are closely related: expansions led to relaxation of selection and higher pathogenicity mutations significantly decreased the growth of descendants. The only detectible positive selection was the recurrence of highly pathogenic nonsynonymous mutations (m.3394T>C-m.3397A>G-m.3398T>C) at interior branches of the tree, preventing the formation of a dinucleotide STR (TATATA) in the MT-ND1 gene. At the most recent time scale in 124 mother-children transmissions, purifying selection was detectable through the loss of mtDNA variants with high predicted pathogenicity. A few haplogroup-defining sites were also heteroplasmic, agreeing with a significant propensity in 349 positions in the phylogenetic tree to revert back to the ancestral variant. This nonrandom mutation property explains the observation of heteroplasmic mutations at some haplogroup-defining sites in sequencing datasets, which may not indicate poor quality as has been claimed.

Abstract