SciPy 1.0: fundamental algorithms for scientific computing in Python.

SciPy is an open-source scientific computing library for the Python programming language. Since its initial release in 2001, SciPy has become a de facto standard for leveraging scientific algorithms in Python, with over 600 unique code contributors, thousands of dependent packages, over 100,000 dependent repositories and millions of downloads per year. In this work, we provide an overview of the capabilities and development practices of SciPy 1.0 and highlight some recent technical developments.

Author Correction: SciPy 1.0: fundamental algorithms for scientific computing in Python.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

The political economy of digital profiteering: communication resource mobilization by anti-vaccination actors.

Contemporary communication requires both a supply of content and a digital information infrastructure. Modern campaigns of misinformation are especially dependent on that back-end infrastructure for tracking and targeting a sympathetic audience and generating revenue that can sustain the campaign financially-if not enable profiteering. However, little is known about the political economy of misinformation, particularly those campaigns spreading misleading or harmful content about public health guidelines and vaccination programs. To understand the political economy of health misinformation, we analyze the content and infrastructure networks of 59 groups involved in communicating misinformation about vaccination programs. With a unique collection of tracker and communication infrastructure data, we demonstrate how the political economy of misinformation depends on platform monetization infrastructures. We offer a theory of communication resource mobilization that advances understanding of the communicative context, organizational interactions, and political outcomes of misinformation production.

Unsupervised feature extraction of aerial images for clustering and understanding hazardous road segments.

Aerial image data are becoming more widely available, and analysis techniques based on supervised learning are advancing their use in a wide variety of remote sensing contexts. However, supervised learning requires training datasets which are not always available or easy to construct with aerial imagery. In this respect, unsupervised machine learning techniques present important advantages. This work presents a novel pipeline to demonstrate how available aerial imagery can be used to better the provision of services related to the built environment, using the case study of road traffic collisions (RTCs) across three cities in the UK. In this paper, we show how aerial imagery can be leveraged to extract latent features of the built environment from the purely visual representation of top-down images. With these latent image features in hand to represent the urban structure, this work then demonstrates how hazardous road segments can be clustered to provide a data-augmented aid for road safety experts to enhance their nuanced understanding of how and where different types of RTCs occur.

Refinement of the charcoal meal study by reduction of the fasting period.

The aim of this investigation was to determine whether a shorter fasting period than the one historically employed for the charcoal meal test, could be used when measuring gastric emptying and intestinal transit within the same animal, and to ascertain whether the scientific outcome would be affected by this benefit to animal welfare. Rats and mice were fasted for 0, 3, 6 or 18 hours before the oral administration of vehicle or atropine. One hour later, the animals were orally administered a charcoal meal, then 20 minutes later, they were killed and the stomach and small intestine were removed. Intestinal transit time (the position of the charcoal front as a percentage of the total length of the small intestine) and relative gastric emptying (weight of stomach contents) were measured. Rats and mice fasted for six hours showed results for gastric emptying and intestinal transit which were similar to those obtained in animals fasted for 18 hours. Reducing the fasting period reduced the body weight loss in both species, and mice on shorter fasts could be group-housed, as hunger-induced fighting was lessened. Therefore, a fasting period of six hours was subsequently adopted for charcoal meal studies at our institution.

Recommendations on the statistical analysis of the Comet assay.

In 2010, the Statisticians in the Pharmaceutical Industry (PSI) Toxicology Special Interest Group met to discuss the design and analysis of the Comet assay. The Comet assay is one potential component of the package of safety studies required by regulatory bodies. As these studies usually involve a three-way nested experimental design and as the distribution of the measured response is usually either lognormal or lognormal plus a point mass at zero, the analysis is not straightforward. This has led to many different types of analysis being proposed in the literature, with several different methods applied within the pharmaceutical industry itself. This article summarises the PSI Toxicology Group's discussions and recommendations around these issues.

Effects of mineral oil administration on the pharmacokinetics, metabolism and pharmacodynamics of atorvastatin and pravastatin in mice and dogs.

We investigated the effects of mineral oil on statin pharmacokinetics and inflammatory markers in animal models. A new synthesis strategy produced regioisomers that facilitated the characterization of the main metabolite (M1) of atorvastatin, a lipophilic statin, in C57BL/6NCrl mice. The chemical structure of M1 in mice was confirmed as ortho-hydroxy ß-oxidized atorvastatin. Atorvastatin and M1 pharmacokinetics and inflammatory markers were assessed in C57BL6/J mice given atorvastatin 5 mg/kg/day or 10 mg/kg/day, as a single dose or for 21 days, with or without 10 µL or 30 µL mineral oil. No consistent differences in plasma exposure of atorvastatin or M1 were observed in mice after single or repeat dosing of atorvastatin with or without mineral oil. However, mice administered atorvastatin 10 mg/kg with 30 µL mineral oil for 21 days had significantly increased plasma levels of serum amyloid A (mean 9.6 µg/mL vs 7.9 µg/mL without mineral oil; p < 0.01) and significantly increased proportions of C62Lhigh B cells (mean 18% vs 12% without mineral oil; p = 0.04). There were no statistically significant differences for other inflammatory markers assessed. In dogs, pharmacokinetics of atorvastatin, its two hydroxy metabolites and pravastatin (a hydrophilic statin) were evaluated after single administration of atorvastatin 10 mg plus pravastatin 40 mg with or without 2 g mineral oil. Pharmacokinetics of atorvastatin, hydroxylated atorvastatin metabolites or pravastatin were not significantly different after single dosing with or without mineral oil in dogs. Collectively, the results in mice and dogs indicate that mineral oil does not affect atorvastatin or pravastatin pharmacokinetics, but could cause low-grade inflammation with chronic oral administration, which warrants further investigation.

Estimating Traffic Disruption Patterns with Volunteered Geographic Information.

Accurate understanding and forecasting of traffic is a key contemporary problem for policymakers. Road networks are increasingly congested, yet traffic data is often expensive to obtain, making informed policy-making harder. This paper explores the extent to which traffic disruption can be estimated using features from the volunteered geographic information site OpenStreetMap (OSM). We use OSM features as predictors for linear regressions of counts of traffic disruptions and traffic volume at 6,500 points in the road network within 112 regions of Oxfordshire, UK. We show that more than half the variation in traffic volume and disruptions can be explained with OSM features alone, and use cross-validation and recursive feature elimination to evaluate the predictive power and importance of different land use categories. Finally, we show that using OSM's granular point of interest data allows for better predictions than the broader categories typically used in studies of transportation and land use.

Diagnosing the performance of human mobility models at small spatial scales using volunteered geographical information.

Accurate modelling of local population movement patterns is a core, contemporary concern for urban policymakers, affecting both the short-term deployment of public transport resources and the longer-term planning of transport infrastructure. Yet, while macro-level population movement models (such as the gravity and radiation models) are well developed, micro-level alternatives are in much shorter supply, with most macro-models known to perform poorly at smaller geographical scales. In this paper, we take a first step to remedy this deficit, by leveraging two novel datasets to analyse where and why macro-level models of human mobility break down. We show how freely available data from OpenStreetMap concerning land use composition of different areas around the county of Oxfordshire in the UK can be used to diagnose mobility models and understand the types of trips they over- and underestimate when compared with empirical volumes derived from aggregated, anonymous smartphone location data. We argue for new modelling strategies that move beyond rough heuristics such as distance and population towards a detailed, granular understanding of the opportunities presented in different regions.

OpenStreetMap data for alcohol research: Reliability assessment and quality indicators.

There is a growing interest in using OpenStreetMap [OSM] data in health research. We evaluate the usefulness of OSM data for researching the spatial availability of alcohol, a field which has been hampered by data access difficulties. We find OSM data is about 50% complete, which appears adequate for replicating findings from other studies using alcohol licensing data. Further, we show how OSM quality metrics can be used to select areas with more complete alcohol data. The ease of access and use may create opportunities for analysts and researchers seeking to understand broad patterns of alcohol availability.