The Economics of Cybersecurity: A National Dilemma

Cybersecurity has dominated recent headlines, but policy makers and pundits alike still combine different risks, threats, and solutions. Crime, espionage, and international conflict represent different dangers to our society at the local and national level, and each has a set of bad actors with different incentives. Conflating these areas can lead to poorly framed solutions. Exploring the economics of cybersecurity offers a set of tools to understand these incentives, and the sometimes complex policy challenges that arise in dealing with digital risk

Assessing Uncertainty in Intelligence

This article addresses the challenge of managing uncertainty when producing estimative intelligence. Much of the theory and practice of estimative intelligence aims to eliminate or reduce uncertainty, but this is often impossible or infeasible. This article instead argues that the goal of estimative intelligence should be to assess uncertainty. By drawing on a body of nearly 400 declassified National Intelligence Estimates as well as prominent texts on analytic tradecraft, this article argues that current tradecraft methods attempt to eliminate uncertainty in ways that can impede the accuracy, clarity, and utility of estimative intelligence. By contrast, a focus on assessing uncertainty suggests solutions to these problems and provides a promising analytic framework for thinking about estimative intelligence in general

Cumulative Dynamics and Strategic Assessment: U.S. Military Decision Making in Iraq, Vietnam, and the American Indian Wars

This dissertation examines why military decision makers struggle to evaluate their policies and why they often stick to unsuccessful strategies for so long. The core argument is that strategic assessment involves genuine analytic challenges which contemporary scholarship typically does not take into account. Prominent theoretical frameworks predict that the longer decision makers go without achieving their objectives, the more pessimistic they should become about their ability to do so, and the more likely they should be to change course. This dissertation challenges those ideas and explains why we should often expect the very opposite. The theoretical crux of this argument is that standard models of learning and adaptation (along with many people’s basic intuitions) revolve around the assumption that decision makers are observing repeated processes, similar to the dynamics of slot machines and roulette wheels – but in war and other contexts, decision makers often confront cumulative processes that have very different dynamics, along with a different logic for how rational actors should form and revise their expectations. Empirically, this dissertation examines U.S. decision making in Iraq, Vietnam, and the American Indian Wars. These cases demonstrate how cumulative dynamics affect strategic assessment and how understanding these dynamics can shed light on prominent theoretical frameworks, ongoing policy debates, and salient historical experience

Skipping Breakfast Leads to Weight Loss but Also Elevated Cholesterol Compared with Consuming Daily Breakfasts of Oat Porridge or Frosted Cornflakes in Overweight Individuals: A Randomised Controlled Trial

Eating breakfast may reduce appetite, body weight and CVD risk factors, but the breakfast type that produces the greatest health benefits remains unclear. We compared the effects of consuming a high-fibre breakfast, a non-fibre breakfast, or no-breakfast control on body weight, CVD risk factors and appetite. A total of thirty-six overweight participants (eighteen men and eighteen women) (mean age 33·9 (SD 7·5) years, mean BMI 32·8 (SD 4·7) kg/m2) were randomly assigned to consume oat porridge (n = 12), frosted cornflakes (n = 12) or a water control (n = 12) breakfast daily for 4 weeks. Appetite ratings were collected on the first day and weekly thereafter. Before and after the intervention, body weight, composition, blood pressure and resting energy expenditure (REE) were measured and a fasting blood sample was collected. Across the 4 weeks, fullness was higher and hunger was lower in the oat porridge group compared with the control group (P \u3c 0·05). Mean weight change over the intervention was significantly different in the control group (−1·18 (SD 1·16) kg) compared with both the cornflakes (−0·12 (SD 1·34) kg) and oat porridge (+0·26 (SD 0·91) kg) groups (P \u3c 0·05). However, the control group also showed elevated total cholesterol concentrations relative to the cornflakes and oat porridge groups (P \u3c 0·05). There were no differences between groups in changes in body composition, blood pressure, REE or other CVD risk factors. In conclusion, although skipping breakfast led to weight loss, it also resulted in increased total cholesterol concentrations compared with eating either oat porridge or frosted cornflakes for breakfast

Consensus and meta-analysis regulatory networks for combining multiple microarray gene expression datasets

Microarray data is a key source of experimental data for modelling gene regulatory interactions from expression levels. With the rapid increase of publicly available microarray data comes the opportunity to produce regulatory network models based on multiple datasets. Such models are potentially more robust with greater confidence, and place less reliance on a single dataset. However, combining datasets directly can be difficult as experiments are often conducted on different microarray platforms, and in different laboratories leading to inherent biases in the data that are not always removed through pre-processing such as normalisation. In this paper we compare two frameworks for combining microarray datasets to model regulatory networks: pre- and post-learning aggregation. In pre-learning approaches, such as using simple scale-normalisation prior to the concatenation of datasets, a model is learnt from a combined dataset, whilst in post-learning aggregation individual models are learnt from each dataset and the models are combined. We present two novel approaches for post-learning aggregation, each based on aggregating high-level features of Bayesian network models that have been generated from different microarray expression datasets. Meta-analysis Bayesian networks are based on combining statistical confidences attached to network edges whilst Consensus Bayesian networks identify consistent network features across all datasets. We apply both approaches to multiple datasets from synthetic and real (Escherichia coli and yeast) networks and demonstrate that both methods can improve on networks learnt from a single dataset or an aggregated dataset formed using a standard scale-normalisation

The identification of informative genes from multiple datasets with increasing complexity

Background In microarray data analysis, factors such as data quality, biological variation, and the increasingly multi-layered nature of more complex biological systems complicates the modelling of regulatory networks that can represent and capture the interactions among genes. We believe that the use of multiple datasets derived from related biological systems leads to more robust models. Therefore, we developed a novel framework for modelling regulatory networks that involves training and evaluation on independent datasets. Our approach includes the following steps: (1) ordering the datasets based on their level of noise and informativeness; (2) selection of a Bayesian classifier with an appropriate level of complexity by evaluation of predictive performance on independent data sets; (3) comparing the different gene selections and the influence of increasing the model complexity; (4) functional analysis of the informative genes. Results In this paper, we identify the most appropriate model complexity using cross-validation and independent test set validation for predicting gene expression in three published datasets related to myogenesis and muscle differentiation. Furthermore, we demonstrate that models trained on simpler datasets can be used to identify interactions among genes and select the most informative. We also show that these models can explain the myogenesis-related genes (genes of interest) significantly better than others (P < 0.004) since the improvement in their rankings is much more pronounced. Finally, after further evaluating our results on synthetic datasets, we show that our approach outperforms a concordance method by Lai et al. in identifying informative genes from multiple datasets with increasing complexity whilst additionally modelling the interaction between genes. Conclusions We show that Bayesian networks derived from simpler controlled systems have better performance than those trained on datasets from more complex biological systems. Further, we present that highly predictive and consistent genes, from the pool of differentially expressed genes, across independent datasets are more likely to be fundamentally involved in the biological process under study. We conclude that networks trained on simpler controlled systems, such as in vitro experiments, can be used to model and capture interactions among genes in more complex datasets, such as in vivo experiments, where these interactions would otherwise be concealed by a multitude of other ongoing events

Fourth Ventricular Schwannoma: Identical Clinicopathologic Features as Schwann Cell-Derived Schwannoma with Unique Etiopathologic Origins

Background. To our knowledge, this is the sixth reported case in the literature of fourth ventricular schwannoma. The etiology and natural history of intraventricular schwannomas is not well understood. A thorough review of potential etiopathogenic mechanisms is provided in this case report. Case Description. A 69-year-old man presented with an incidentally found fourth ventricular tumor during an evaluation for generalized weakness, gait instability, and memory disturbance. Magnetic resonance imaging (MRI) revealed a heterogeneously enhancing lesion in the fourth ventricle. A suboccipital craniotomy was performed to resect the lesion. Histopathological examination confirmed the diagnosis of schwannoma (WHO grade I). Conclusions. Schwannomas should be considered in the differential diagnosis of intraventricular tumors. Although the embryologic origins may be different from nerve sheath-derived schwannomas, the histologic, clinical, and natural history appear identical and thus should be managed similarly

LASSO model selection with post-processing for a genome-wide association study data set

Model selection procedures for simultaneous analysis of all single-nucleotide polymorphisms in genome-wide association studies are most suitable for making full use of the data for a complex disease study. In this paper we consider a penalized regression using the LASSO procedure and show that post-processing of the penalized-regression results with subsequent stepwise selection may lead to improved identification of causal single-nucleotide polymorphisms

The SNAPSHOT study protocol : SNAcking, Physical activity, Self-regulation, and Heart rate Over Time

Peer reviewedPublisher PD