Modeling and Estimation for Self-Exciting Spatio-Temporal Models of Terrorist Activity

Spatio-temporal hierarchical modeling is an extremely attractive way to model the spread of crime or terrorism data over a given region, especially when the observations are counts and must be modeled discretely. The spatio-temporal diffusion is placed, as a matter of convenience, in the process model allowing for straightforward estimation of the diffusion parameters through Bayesian techniques. However, this method of modeling does not allow for the existence of self-excitation, or a temporal data model dependency, that has been shown to exist in criminal and terrorism data. In this manuscript we will use existing theories on how violence spreads to create models that allow for both spatio-temporal diffusion in the process model as well as temporal diffusion, or self-excitation, in the data model. We will further demonstrate how Laplace approximations similar to their use in Integrated Nested Laplace Approximation can be used to quickly and accurately conduct inference of self-exciting spatio-temporal models allowing practitioners a new way of fitting and comparing multiple process models. We will illustrate this approach by fitting a self-exciting spatio-temporal model to terrorism data in Iraq and demonstrate how choice of process model leads to differing conclusions on the existence of self-excitation in the data and differing conclusions on how violence is spreading spatio-temporally

An Extended Laplace Approximation Method for Bayesian Inference of Self-Exciting Spatial-Temporal Models of Count Data

Self-Exciting models are statistical models of count data where the probability of an event occurring is influenced by the history of the process. In particular, self-exciting spatio-temporal models allow for spatial dependence as well as temporal self-excitation. For large spatial or temporal regions, however, the model leads to an intractable likelihood. An increasingly common method for dealing with large spatio-temporal models is by using Laplace approximations (LA). This method is convenient as it can easily be applied and is quickly implemented. However, as we will demonstrate in this manuscript, when applied to self-exciting Poisson spatial-temporal models, Laplace Approximations result in a significant bias in estimating some parameters. Due to this bias, we propose using up to sixth-order corrections to the LA for fitting these models. We will demonstrate how to do this in a Bayesian setting for Self-Exciting Spatio-Temporal models. We will further show there is a limited parameter space where the extended LA method still has bias. In these uncommon instances we will demonstrate how a more computationally intensive fully Bayesian approach using the Stan software program is possible in those rare instances. The performance of the extended LA method is illustrated with both simulation and real-world data

Host Specificity in Variable Environments

Host specificity encompasses the range and diversity of host species that a parasite is capable of infecting and is considered a crucial measure of a parasite's potential to shift hosts and trigger disease emergence. Yet empirical studies rarely consider that regional observations only reflect a parasite's 'realized' host range under particular conditions: the true 'fundamental' range of host specificity is typically not approached. We provide an overview of challenges and directions in modelling host specificity under variable environmental conditions. Combining tractable modelling frameworks with multiple data sources that account for the strong interplay between a parasite's evolutionary history, transmission mode, and environmental filters that shape host-parasite interactions will improve efforts to quantify emerging disease risk in times of global change

An integrated care pathway for menorrhagia across the primary–secondary interface : patients' experience, clinical outcomes, and service utilisation

Background: ‘‘Referral’’ characterises a significant area of interaction between primary and secondary care. Despite advantages, it can be inflexible, and may lead to duplication. Objective: To examine the outcomes of an integrated model that lends weight to general practitioner (GP)-led evidence based care. Design: A prospective, non-random comparison of two services: women attending the new (Bridges) pathway compared with those attending a consultant-led one-stop menstrual clinic (OSMC). Patients’ views were examined using patient career diaries, health and clinical outcomes, and resource utilisation. Follow-up was for 8 months. Setting: A large teaching hospital and general practices within one primary care trust (PCT). Results: Between March 2002 and June 2004, 99 women in the Bridges pathway were compared with 94 women referred to the OSMC by GPs from non-participating PCTs. The patient career diary demonstrated a significant improvement in the Bridges group for patient information, fitting in at the point of arrangements made for the patient to attend hospital (ease of access) (p,0.001), choice of doctor (p = 0.020), waiting time for an appointment (p,0.001), and less ‘‘limbo’’ (patient experience of non-coordination between primary and secondary care) (p,0.001). At 8 months there were no significant differences between the two groups in surgical and medical treatment rates or in the use of GP clinic appointments. Significantly fewer (traditional) hospital outpatient appointments were made in the Bridges group than in the OSMC group (p,0.001). Conclusion: A general practice-led model of integrated care can significantly reduce outpatient attendance while improving patient experience, and maintaining the quality of care

Morphological phenotyping after mouse whole embryo culture

Morphological phenotyping of the mouse embryo is described at neurulation stages, primarily as a guide to evaluating the outcome of whole embryo cultures between embryonic days 8.5 and 9.5. During this period, neural tube closure is initiated and progresses to completion in the cranial region. Spinal closure is still underway at the end of the culture period. The focus of this article is particularly on phenotyping that can be performed at the bench, using a stereomicroscope. This involves assessment of embryonic health, through observation and scoring of yolk sac blood circulation, measurement of developmental stage by somite counting, and determination of crown-rump length as a measure of growth. Axial rotation (“turning”) can also be assessed using a simple scoring system. Neural tube closure assessment includes: 1) determining whether closure has been initiated at the Closure 1 site; 2) evaluating the complex steps of cranial neurulation including initiation at Closure sites 2 and 3, and completion of closure at the anterior and hindbrain neuropores; 3) assessment of spinal closure by measurement of posterior neuropore length. Interpretation of defects in neural tube closure requires an appreciation of, first, the stages that particular events are expected to be completed and, second, the correspondence between embryonic landmarks, for example, somite position, and the resulting adult axial levels. Detailed embryonic phenotyping, as described in this article, when combined with the versatile method of whole embryo culture, can form the basis for a wide range of experimental studies in early mouse neural development

The Mathematical Nature of Reasoning-and-Proving Opportunities in Geometry Textbooks

International calls have been made for reasoning-and-proving to permeate school mathematics. It is important that efforts to heed this call are grounded in an understanding of the opportunities to reason- and-prove that already exist, especially in secondary-level geometry where reasoning-and-proving opportunities are prevalent but not thoroughly studied. This analysis of six secondary-level geometry textbooks, like studies of other textbooks, characterizes the justifications given in the exposition and the reasoning-and-proving activities expected of students in the exercises. Furthermore, this study considers whether the mathematical statements included in the reasoning-and-proving opportunities are general or particular in nature. Findings include the fact that the majority of expository mathematical statements were general, whereas reasoning-and-proving exercises tended to involve particular mathematical statements. Although reasoning-and-proving opportunities were relatively numerous, it remained rare for the reasoning-and-proving process itself to be an explicit object of reflection. Relationships between these findings and the necessity principle of pedagogy are discussed

The robustness of speech representations obtained from simulated auditory nerve fibers under different noise conditions

Different methods of extracting speech features from an auditory model were systematically investigated in terms of their robustness to different noises. The methods either computed the average firing rate within frequency channels (spectral features) or inter-spike-intervals (timing features) from the simulated auditory nerve response. When used as the front-end for an automatic speech recognizer, timing features outperformed spectral features in Gaussian noise. However, this advantage was lost in babble, because timing features extracted the spectro-temporal structure of babble noise, which is similar to the target speaker. This suggests that different feature extraction methods are optimal depending on the background noise

Eyetracking metrics reveal impaired spatial anticipation in behavioural variant frontotemporal dementia.

Eyetracking technology has had limited application in the dementia field to date, with most studies attempting to discriminate syndrome subgroups on the basis of basic oculomotor functions rather than higher-order cognitive abilities. Eyetracking-based tasks may also offer opportunities to reduce or ameliorate problems associated with standard paper-and-pencil cognitive tests such as the complexity and linguistic demands of verbal test instructions, and the problems of tiredness and attention associated with lengthy tasks that generate few data points at a slow rate. In the present paper we adapted the Brixton spatial anticipation test to a computerized instruction-less version where oculomotor metrics, rather than overt verbal responses, were taken into account as indicators of high level cognitive functions. Twelve bvFTD (in whom spatial anticipation deficits were expected), six SD patients (in whom deficits were predicted to be less frequent) and 38 healthy controls were presented with a 10 × 7 matrix of white circles. During each trial (N = 24) a black dot moved across seven positions on the screen, following 12 different patterns. Participants' eye movements were recorded. Frequentist statistical analysis of standard eye movement metrics were complemented by a Bayesian machine learning (ML) approach in which raw eyetracking time series datasets were examined to explore the ability to discriminate diagnostic group performance not only on the overall performance but also on individual trials. The original pen and paper Brixton test identified a spatial anticipation deficit in 7/12 (58%) of bvFTD and in 2/6 (33%) of SD patients. The eyetracking frequentist approach reported the deficit in 11/12 (92%) of bvFTD and in none (0%) of the SD patients. The machine learning approach had the main advantage of identifying significant differences from controls in 24/24 individual trials for bvFTD patients and in only 12/24 for SD patients. Results indicate that the fine grained rich datasets obtained from eyetracking metrics can inform us about high level cognitive functions in dementia, such as spatial anticipation. The ML approach can help identify conditions where subtle deficits are present and, potentially, contribute to test optimisation and the reduction of testing times. The absence of instructions also favoured a better distinction between different clinical groups of patients and can help provide valuable disease-specific markers