Why happy shoppers don't stop and think

This paper discusses findings from observational research of grocery shopping. Videographic analysis via qualitative research techniques reveals that consumers who display less emotion tend to be more positive about the experience and have shorter shopping visits. Whereas those who display distinct emotional responses tend to reveal negative reactions and result in taking longer to make a decision. Four categories of consumer decision behaviour for grocery products are suggested as a result of this research and as a discussion point for further investigations into this specific topic

A simulation system for biomarker evolution in neurodegenerative disease

We present a framework for simulating cross-sectional or longitudinal biomarker data sets from neurodegenerative disease cohorts that reflect the temporal evolution of the disease and population diversity. The simulation system provides a mechanism for evaluating the performance of data-driven models of disease progression, which bring together biomarker measurements from large cross-sectional (or short term longitudinal) cohorts to recover the average population-wide dynamics. We demonstrate the use of the simulation framework in two different ways. First, to evaluate the performance of the Event Based Model (EBM) for recovering biomarker abnormality orderings from cross-sectional datasets. Second, to evaluate the performance of a differential equation model (DEM) for recovering biomarker abnormality trajectories from short-term longitudinal datasets. Results highlight several important considerations when applying data-driven models to sporadic disease datasets as well as key areas for future work. The system reveals several important insights into the behaviour of each model. For example, the EBM is robust to noise on the underlying biomarker trajectory parameters, under-sampling of the underlying disease time course and outliers who follow alternative event sequences. However, the EBM is sensitive to accurate estimation of the distribution of normal and abnormal biomarker measurements. In contrast, we find that the DEM is sensitive to noise on the biomarker trajectory parameters, resulting in an over estimation of the time taken for biomarker trajectories to go from normal to abnormal. This over estimate is approximately twice as long as the actual transition time of the trajectory for the expected noise level in neurodegenerative disease datasets. This simulation framework is equally applicable to a range of other models and longitudinal analysis techniques

Conditional survival with increasing duration of ICU admission: an observational study of three intensive care databases.

OBJECTIVES: Prolonged admissions to an ICU are associated with high resource utilization and personal cost to the patient. Previous reports suggest increasing length of stay may be associated with poor outcomes. Conditional survival represents the probability of future survival after a defined period of treatment on an ICU providing a description of how prognosis evolves over time. Our objective was to describe conditional survival as length of ICU stay increased. DESIGN: Retrospective observational cohort study of three large intensive care databases. SETTING: Three intensive care databases, two in the United States (Medical Information Mart for Intensive Care III and electronic ICU) and one in United Kingdom (Post Intensive Care Risk-Adjusted Alerting and Monitoring). PATIENTS: Index admissions to intensive care for patients 18 years or older. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 11,648, 38,532, and 165,125 index admissions were analyzed from Post Intensive Care Risk-Adjusted Alerting and Monitoring, Medical Information Mart for Intensive Care III and electronic ICU databases respectively. In all three cohorts, conditional survival declined over the first 5-10 days after ICU admission and changed little thereafter. In patients greater than or equal to 75 years old conditional survival continued to decline with increasing length of stay. CONCLUSIONS: After an initial period of 5-10 days, probability of future survival does not appear to decrease with increasing length of stay in unselected patients admitted to ICUs in United Kingdom and United States [corrected]. These findings were consistent between the three populations and suggest that a prolonged admission to an ICU is not a reason for a pessimism in younger patients but may indicate a poor prognosis in the older population

The potential impact of Brexit on the energy, water and food nexus in the UK: A fuzzy cognitive mapping approach

© 2017 The Authors. Energy is one of the cornerstones essential for human life, along with other services such as water and food. Understanding how the different services in the energy-water-food (EWF) nexus interact and are perceived by different actors is key to achieving sustainability. In this paper, we derive a model of the EWF nexus using fuzzy cognitive mapping (FCM). Data were collected in a two-step approach from workshops with researchers and stakeholders involved in the three focal sectors. Four FCMs were developed; one for each of the EWF sectors, and one for the interactions that create the nexus between EWF. The FCM represents the combined views of the groups who participated in the workshops, the importance and limitations of which is discussed. To demonstrate its effectiveness, the aggregated FCM was applied to predict the impacts on the EWF nexus of four scenarios under which the United Kingdom would depart from the European Union (i.e. Brexit). The FCM indicated that energy-related concepts had the largest influence on the EWF nexus and that EWF demand will decrease most under a 'hard-Brexit' scenario. The demand for energy was shown to decline relatively less than other services and was strongly associated with gross domestic product (GDP), whereas UK population size had a stronger effect on water and food demand. Overall, we found a threefold change across all concepts in scenarios without freedom of movement, contribution to the EU budget, and increased policy devolution to the UK

Inhaled Liposomal Ciprofloxacin Nanoparticles Control the Release of Antibiotic at the Bronchial Epithelia

The cycle of respiratory tract infection (RTI) and inflammation in patients with chronic obstructive lung diseases, such as cystic fibrosis (CF), periodically develops into exacerbations, where chronic colonization of the airway by bacteria causes severe decline in lung function, leading to increased hospitalization and high mortality rates (1, 2). Current antibiotic inhalation treatments approved for the management of chronic airway infections in cystic fibrosis are limited to tobramycin (TOBI®) and more recently, aztreonam (Cayston®). A major drawback to these localized treatments of RTIs is the rapid absorption and clearance of antibiotics from the lungs requiring multiple daily inhalations of high concentration antibiotic solutions. Hence, liposomal ciprofloxacin nanoparticles were developed to prolong lung residence time of the antibiotics, with the view to enhance antimicrobial activity and reduce the burden of therapy for the patients and their relatives who often have to assist them. Although in vivo studies with aerosolized delivery of liposomal ciprofloxacin have previously been performed on human and animal subjects, in vitro cell models may be better suited to study the transport, interactions of drugs and carrier systems, and drug localization within and on the airway cell epithelium at a molecular level. Therefore, the aim of this study was to investigate the newly developed system allowing nebulized liposomal ciprofloxacin to be delivered directly to the bronchial epithelial surface in an established air interface Calu-3 cell model

pySuStaIn: A Python implementation of the Subtype and Stage Inference algorithm

Progressive disorders are highly heterogeneous. Symptom-based clinical classification of these disorders may not reflect the underlying pathobiology. Data-driven subtyping and staging of patients has the potential to disentangle the complex spatiotemporal patterns of disease progression. Tools that enable this are in high demand from clinical and treatment-development communities. Here we describe the pySuStaIn software package, a Python-based implementation of the Subtype and Stage Inference (SuStaIn) algorithm. SuStaIn unravels the complexity of heterogeneous diseases by inferring multiple disease progression patterns (subtypes) and individual severity (stages) from cross-sectional data. The primary aims of pySuStaIn are to enable widespread application and translation of SuStaIn via an accessible Python package that supports simple extension and generalization to novel modeling situations within a single, consistent architecture

Disease Progression Modelling in Chronic Obstructive Pulmonary Disease (COPD)

RATIONALE: The decades-long progression of Chronic Obstructive Pulmonary Disease (COPD) renders identifying different trajectories of disease progression challenging. OBJECTIVES: To identify subtypes of COPD patients with distinct longitudinal progression patterns using a novel machine-learning tool called "Subtype and Stage Inference (SuStaIn)", and to evaluate the utility of SuStaIn for patient stratification in COPD. METHODS: We applied SuStaIn to cross-sectional CT imaging markers in 3698 GOLD1-4 patients and 3479 controls from the COPDGene study to identify COPD patient subtypes. We confirmed the identified subtypes and progression patterns using ECLIPSE data. We assessed the utility of SuStaIn for patient stratification by comparing SuStaIn subtypes and stages at baseline with longitudinal follow-up data. MEASUREMENTS AND MAIN RESULTS: We identified two trajectories of disease progression in COPD: a "Tissue→Airway" subtype (n=2354, 70.4%) in which small airway dysfunction and emphysema precede large-airway wall abnormalities, and an "Airway→Tissue" subtype (n=988, 29.6%) in which large-airway wall abnormalities precede emphysema and small airway dysfunction. Subtypes were reproducible in ECLIPSE. Baseline stage in both subtypes correlated with future FEV1/FVC decline (r=-0.16 (p<0.001) in the Tissue→Airway group; r=-0.14 (p=0.011) in the Airway→Tissue group). SuStaIn placed 30% of smokers with normal lung function at non-baseline stages suggesting imaging changes consistent with early COPD. Individuals with early changes were 2.5 times more likely to meet COPD diagnostic criteria at follow-up. CONCLUSIONS: We demonstrate two distinct patterns of disease progression in COPD using SuStaIn, likely representing different endotypes. One-third of healthy smokers have detectable imaging changes, suggesting a new biomarker of 'early COPD'

Ordinal SuStaIn: Subtype and Stage Inference for Clinical Scores, Visual Ratings, and Other Ordinal Data

Subtype and Stage Inference (SuStaIn) is an unsupervised learning algorithm that uniquely enables the identification of subgroups of individuals with distinct pseudo-temporal disease progression patterns from cross-sectional datasets. SuStaIn has been used to identify data-driven subgroups and perform patient stratification in neurodegenerative diseases and in lung diseases from continuous biomarker measurements predominantly obtained from imaging. However, the SuStaIn algorithm is not currently applicable to discrete ordinal data, such as visual ratings of images, neuropathological ratings, and clinical and neuropsychological test scores, restricting the applicability of SuStaIn to a narrower range of settings. Here we propose 'Ordinal SuStaIn', an ordinal version of the SuStaIn algorithm that uses a scored events model of disease progression to enable the application of SuStaIn to ordinal data. We demonstrate the validity of Ordinal SuStaIn by benchmarking the performance of the algorithm on simulated data. We further demonstrate that Ordinal SuStaIn out-performs the existing continuous version of SuStaIn (Z-score SuStaIn) on discrete scored data, providing much more accurate subtype progression patterns, better subtyping and staging of individuals, and accurate uncertainty estimates. We then apply Ordinal SuStaIn to six different sub-scales of the Clinical Dementia Rating scale (CDR) using data from the Alzheimer's disease Neuroimaging Initiative (ADNI) study to identify individuals with distinct patterns of functional decline. Using data from 819 ADNI1 participants we identified three distinct CDR subtype progression patterns, which were independently verified using data from 790 ADNI2 participants. Our results provide insight into patterns of decline in daily activities in Alzheimer's disease and a mechanism for stratifying individuals into groups with difficulties in different domains. Ordinal SuStaIn is broadly applicable across different types of ratings data, including visual ratings from imaging, neuropathological ratings and clinical or behavioural ratings data

An image-based model of brain volume biomarker changes in Huntington's disease

Objective: Determining the sequence in which Huntington's disease biomarkers become abnormal can provide important insights into the disease progression and a quantitative tool for patient stratification. Here, we construct and present a uniquely fine-grained model of temporal progression of Huntington's disease from premanifest through to manifest stages. Methods: We employ a probabilistic event-based model to determine the sequence of appearance of atrophy in brain volumes, learned from structural MRI in the Track-HD study, as well as to estimate the uncertainty in the ordering. We use longitudinal and phenotypic data to demonstrate the utility of the patient staging system that the resulting model provides. Results: The model recovers the following order of detectable changes in brain region volumes: putamen, caudate, pallidum, insula white matter, nonventricular cerebrospinal fluid, amygdala, optic chiasm, third ventricle, posterior insula, and basal forebrain. This ordering is mostly preserved even under cross-validation of the uncertainty in the event sequence. Longitudinal analysis performed using 6 years of follow-up data from baseline confirms efficacy of the model, as subjects consistently move to later stages with time, and significant correlations are observed between the estimated stages and nonimaging phenotypic markers. Interpretation: We used a data-driven method to provide new insight into Huntington's disease progression as well as new power to stage and predict conversion. Our results highlight the potential of disease progression models, such as the event-based model, to provide new insight into Huntington's disease progression and to support fine-grained patient stratification for future precision medicine in Huntington's disease