Efficient occupancy model-fitting for extensive citizen-science data

Appropriate large-scale citizen-science data present important new opportunities for biodiversity modelling, due in part to the wide spatial coverage of information. Recently proposed occupancy modelling approaches naturally incorporate random effects in order to account for annual variation in the composition of sites surveyed. In turn this leads to Bayesian analysis and model fitting, which are typically extremely time consuming. Motivated by presence-only records of occurrence from the UK Butterflies for the New Millennium data base, we present an alternative approach, in which site variation is described in a standard way through logistic regression on relevant environmental covariates. This allows efficient occupancy model-fitting using classical inference, which is easily achieved using standard computers. This is especially important when models need to be fitted each year, typically for many different species, as with British butterflies for example. Using both real and simulated data we demonstrate that the two approaches, with and without random effects, can result in similar conclusions regarding trends. There are many advantages to classical model-fitting, including the ability to compare a range of alternative models, identify appropriate covariates and assess model fit, using standard tools of maximum likelihood. In addition, modelling in terms of covariates provides opportunities for understanding the ecological processes that are in operation. We show that there is even greater potential; the classical approach allows us to construct regional indices simply, which indicate how changes in occupancy typically vary over a species’ range. In addition we are also able to construct dynamic occupancy maps, which provide a novel, modern tool for examining temporal changes in species distribution. These new developments may be applied to a wide range of taxa, and are valuable at a time of climate change. They also have the potential to motivate citizen scientists

Application of gas chromatography mass spectrometry (GC–MS) in conjunction with multivariate classification for the diagnosis of gastrointestinal diseases

Gastrointestinal diseases such as irritable bowel syndrome, Crohn’s disease (CD) and ulcerative colitis are a growing concern in the developed world. Current techniques for diagnosis are often costly, time consuming, inefficient, of great discomfort to the patient, and offer poor sensitivities and specificities. This paper describes the development and evaluation of a new methodology for the non-invasive diagnosis of such diseases using a combination of gas chromatography mass spectrometry (GC–MS) and chemometrics. Several potential sample matrices were tested: blood, breath, faeces and urine. Faecal samples provided the only statistically significant results, providing discrimination between CD and healthy controls with an overall classification accuracy of 85 %(78 %specificity; 93 %sensitivity). Differentiating CD from other diseases proved more challenging, with overall classification accuracy dropping to 79 % (83 % specificity; 68 % sensitivity). This diagnostic performance compares well with the gold standard technique of colonoscopy, suggesting that GC–MS may have potential as a non-invasive screening tool

An overview on the application of chemometrics in food science and technology: An approach to quantitative data analysis

Cozzolino, D ORCiD: 0000-0001-6247-8817During the last 30 years, food scientists and technologists all over the world are dealing with massive amounts of data derived from the use of different measuring devices (e.g. instrumental and sensory data), the integration of different analytical techniques and processes during the analysis and production of foods. Therefore, complementary disciplines and tools to the traditional ones used in food science such as statistics, experimental design and chemometrics have become essential in modern sciences and are an integral component in the day-to-day analysis of foods and derived products. The aim of this paper is to introduce as well as provide with an overview of different concepts, methods, techniques and general steps used in the quantitative analysis of foods when chemometrics or multivariate analytical methods are applied