Climatic signals in the life histories of insects:the distribution and abundance of heather psyllids (Strophingia spp.) in the UK

1. The population density and age structure of two species of heather psyllid Strophingia ericae and Strophingia cinereae, feeding on Calluna vulgaris and Erica cinerea, respectively, were sampled using standardized methods at locations throughout Britain. Locations were chosen to represent the full latitudinal and altitudinal range of the host plants.

2. The paper explains how spatial variation in thermal environment, insect life-history characteristics and physiology, and plant distribution, interact to provide the mechanisms that determine the range and abundance of Strophingia spp.

3. Strophingia ericae and S. cinereae, despite the similarity in the spatial distribution patterns of their host plants within Britain, display strongly contrasting geographical ranges and corresponding life-history strategies. Strophingia ericae is found on its host plant throughout Britain but S. cinereae is restricted to low elevation sites south of the Mersey-Humber line and occupies only part of the latitudinal and altitudinal range of its host plant. There is no evidence to suggest that S. ericae has reached its potential altitudinal or latitudinal limit in the UK, even though its host plant appears to reach its altitudinal limit.

4. There was little difference in the ability of the two Strophingia spp. to survive shortterm exposure to temperatures as low as - 15 degrees C and low winter temperatures probably do not limit distribution in S. cinereae.

5. Population density of S. ericae was not related to altitude but showed a weak correlation with latitude. The spread of larval instars present at a site, measured as an index of instar homogeneity, was significantly correlated with a range of temperature related variables, of which May mean temperature and length of growing season above 3 degrees C (calculated using the Lennon and Turner climatic model) were the most significant. Factor analysis did not improve the level of correlation significantly above those obtained for single climatic variables. The data confirmed that S. ericae has a I year life cycle at the lowest elevations and a 2 year life cycle at the higher elevations. However, there was no evidence, as previously suggested, for an abrupt change from a one to a 2 year life cycle in S. ericae with increasing altitudes or latitudes.

6. By contrast with S. ericae, S. cinereae had an obligatory 1 year life cycle, its population decreased with altitude and the index of instar homogeneity showed little correlation with single temperature variables. Moreover, it occupied only part of the range of its host plant and its spatial distribution in the UK could be predicted with 96% accuracy using selected variables in discriminant analysis.

7. The life histories of the congeneric heather psyllids reflect adaptations that allow them to exploit host plants with different distributions in climatic and thereby geographical space. Strophingia ericae has the flexible life history that enables it to exploit C. vulgaris throughout its European boreal temperate range. Strophingia cinereae has a less flexible life history and is adapted for living on an oceanic temperate host. While the geographic ranges of the two Strophingia spp. overlap within the UK, the psyllids appear to respond differently to variation in their thermal environment.

Metabolomics reveals novel biomarkers of illegal 5-nitromimidazole treatment in pigs. Further evidence of drug toxicity uncovered

The aim of the study was to investigate the potential of a metabolomics platform to distinguish between pigs treated with ronidazole, dimetridazole and metronidazole and non-medicated animals (controls), at two withdrawal periods (day 0 and 5). Livers from each animal were biochemically profiled using UHPLC–QTof-MS in ESI+ mode of acquisition. Several Orthogonal Partial Least Squares-Discriminant Analysis models were generated from the acquired mass spectrometry data. The models classified the two groups control and treated animals. A total of 42 ions of interest explained the variation in ESI+. It was possible to find the identity of 3 of the ions and to positively classify 4 of the ionic features, which can be used as potential biomarkers of illicit 5-nitroimidazole abuse. Further evidence of the toxic mechanisms of 5-nitroimidazole drugs has been revealed, which may be of substantial importance as metronidazole is widely used in human medicine.

Proprioceptive acuity predicts muscle co-contraction of the tibalis anterior and gastrocnemius medialis in older adults’ dynamic postural control

Older adults use a different muscle strategy to cope with postural instability, in which they ‘co-contract’ the muscles around the ankle joint. It has been suggested that this is a compensatory response to age-related proprioceptive decline however this view has never been assessed directly. The current study investigated the association between proprioceptive acuity and muscle co-contraction in older adults. We compared muscle activity, by recording surface EMG from the bilateral tibalis anterior and gastrocnemius medialis muscles, in young (aged 18-34) and older adults (aged 65-82) during postural assessment on a fixed and sway-referenced surface at age-equivalent levels of sway. We performed correlations between muscle activity and proprioceptive acuity, which was assessed using an active contralateral matching task. Despite successfully inducing similar levels of sway in the two age groups, older adults still showed higher muscle co-contraction. A stepwise regression analysis showed that proprioceptive acuity measured using variable error was the best predictor of muscle co-contraction in older adults. However, despite suggestions from previous research, proprioceptive error and muscle co-contraction were negatively correlated in older adults, suggesting that better proprioceptive acuity predicts more co-contraction. Overall, these results suggest that although muscle co-contraction may be an age-specific strategy used by older adults, it is not to compensate for age-related proprioceptive deficits.

Face Recognition in the Scrambled Domain via Salience-Aware Ensembles of Many Kernels

With the rapid development of internet-of-things (IoT), face scrambling has been proposed for privacy protection during IoT-targeted image/video distribution. Consequently in these IoT applications, biometric verification needs to be carried out in the scrambled domain, presenting significant challenges in face recognition. Since face models become chaotic signals after scrambling/encryption, a typical solution is to utilize traditional data-driven face recognition algorithms. While chaotic pattern recognition is still a challenging task, in this paper we propose a new ensemble approach – Many-Kernel Random Discriminant Analysis (MK-RDA) to discover discriminative patterns from chaotic signals. We also incorporate a salience-aware strategy into the proposed ensemble method to handle chaotic facial patterns in the scrambled domain, where random selections of features are made on semantic components via salience modelling. In our experiments, the proposed MK-RDA was tested rigorously on three human face datasets: the ORL face dataset, the PIE face dataset and the PUBFIG wild face dataset. The experimental results successfully demonstrate that the proposed scheme can effectively handle chaotic signals and significantly improve the recognition accuracy, making our method a promising candidate for secure biometric verification in emerging IoT applications.

Discrimination of honey of different floral origins by a combination of various chemical parameters

Abstract Honey is a high value food commodity with recognized nutraceutical properties. A primary driver of the value of honey is its floral origin. The feasibility of applying multivariate data analysis to various chemical parameters for the discrimination of honeys was explored. This approach was applied to four authentic honeys with different floral origins (rata, kamahi, clover and manuka) obtained from producers in New Zealand. Results from elemental profiling, stable isotope analysis, metabolomics (UPLC-QToF MS), and NIR, FT-IR, and Raman spectroscopic fingerprinting were analyzed. Orthogonal partial least square discriminant analysis (OPLS-DA) was used to determine which technique or combination of techniques provided the best classification and prediction abilities. Good prediction values were achieved using metabolite data (for all four honeys, Q² = 0.52; for manuka and clover, Q² = 0.76) and the trace element/isotopic data (for manuka and clover, Q² = 0.65), while the other chemical parameters showed promise when combined (for manuka and clover, Q² = 0.43).

Posterior Probability Modeling and Image Classification for Archaeological Site Prospection: Building a Survey Efficacy Model for Identifying Neolithic Felsite Workshops in the Shetland Islands

The application of custom classification techniques and posterior probability modeling (PPM) using Worldview-2 multispectral imagery to archaeological field survey is presented in this paper. Research is focused on the identification of Neolithic felsite stone tool workshops in the North Mavine region of the Shetland Islands in Northern Scotland. Sample data from known workshops surveyed using differential GPS are used alongside known non-sites to train a linear discriminant analysis (LDA) classifier based on a combination of datasets including Worldview-2 bands, band difference ratios (BDR) and topographical derivatives. Principal components analysis is further used to test and reduce dimensionality caused by redundant datasets. Probability models were generated by LDA using principal components and tested with sites identified through geological field survey. Testing shows the prospective ability of this technique and significance between 0.05 and 0.01, and gain statistics between 0.90 and 0.94, higher than those obtained using maximum likelihood and random forest classifiers. Results suggest that this approach is best suited to relatively homogenous site types, and performs better with correlated data sources. Finally, by combining posterior probability models and least-cost analysis, a survey least-cost efficacy model is generated showing the utility of such approaches to archaeological field survey.

Decision making in childhood asthma exacerbation – a clinical judgement analysis

Background: Clinical decisions which impact directly on patient safety and quality of care are made during acute asthma attacks by individual doctors on the basis of their knowledge and experience. These include administration of systemic corticosteroids (CS), oral antibiotics, and admission to hospital. Clinical judgement analysis provides a methodology for comparing decisions between practitioners with different training and experience, and improving decision making. Methods: Stepwise linear regression was used to select clinical cues based on visual analogue scale assessments of the propensity of 62 clinicians to prescribe a short course of oral CS (decision 1), a course of antibiotics (decision 2), and/or admit to hospital (decision 3) for 60 â??paperâ?? patients. Results:When compared by specialty, paediatriciansâ?? models for decision 1 were more likely to include as a cue level of alertness (54% v. 16%); for decision 2 presence of crepitations (49% v. 16%), and less likely to include inhaled CS (8% v. 40%), respiratory rate (0% v. 24%), and air entry (70% v. 100%). When compared to other grades, the models derived for decision 3 by consultants/general practitioners were more likely to include wheeze severity as a cue (39% v. 6%). Conclusions: Clinicians differed in their use of individual cues and the number included in their models. Patient safety and quality of care will benefit from clarification of decision making strategies as general learning points during medical training, in the development of guidelines and care pathways, and by clinicians developing self-awareness of their own preferences.

An Examination of Transformation Techniques to Investigate and Interpret Multivariate Geochemical Data Analysis: Tellus Case Study

This research aims to use the multivariate geochemical dataset, generated by the Tellus project, to investigate the appropriate use of transformation methods to maintain the integrity of geochemical data and inherent constrained behaviour in multivariate relationships. The widely used normal score transform is compared with the use of a stepwise conditional transform technique. The Tellus Project, managed by GSNI and funded by the Department of Enterprise Trade and Development and the EU’s Building Sustainable Prosperity Fund, involves the most comprehensive geological mapping project ever undertaken in Northern Ireland. Previous study has demonstrated spatial variability in the Tellus data but geostatistical analysis and interpretation of the datasets requires use of an appropriate methodology that reproduces the inherently complex multivariate relations. Previous investigation of the Tellus geochemical data has included use of Gaussian-based techniques. However, earth science variables are rarely Gaussian, hence transformation of data is integral to the approach. The multivariate geochemical dataset generated by the Tellus project provides an opportunity to investigate the appropriate use of transformation methods, as required for Gaussian-based geostatistical analysis. In particular, the stepwise conditional transform is investigated and developed for the geochemical datasets obtained as part of the Tellus project. The transform is applied to four variables in a bivariate nested fashion due to the limited availability of data. Simulation of these transformed variables is then carried out, along with a corresponding back transformation to original units. Results show that the stepwise transform is successful in reproducing both univariate statistics and the complex bivariate relations exhibited by the data. Greater fidelity to multivariate relationships will improve uncertainty models, which are required for consequent geological, environmental and economic inferences.

Fingerprint Volatile Organic Compounds (VOC) analysis in Goat’s Milk and Halloumi Cheese as Marker for Authentication of Region of Origin

Goats’ milk is responsible for unique traditional products such as Halloumi cheese. The characteristics of Halloumi depend on the original features of the milk and on the conditions under which the milk has been produced such as feeding regime of the animals or region of production. Using a range of milk (33) and Halloumi (33) samples collected over a year from three different locations in Cyprus (A, Anogyra; K, Kofinou; P, Paphos), the potential for fingerprint VOC analysis as marker to authenticate Halloumi was investigated. This unique set up consists of an in-injector thermo desorption (VOCtrap needle) and a chromatofocusing system based on mass spectrometry (VOCscanner). The mass spectra of all the analyzed samples are treated by multivariate analysis (Principle component analysis and Discriminant functions analysis). Results showed that the highland area of product (P) is clearly identified in milks produced (discriminant score 67%). It is interesting to note that the higher similitude found on milks from regions “A” and “K” (with P being distractive; discriminant score 80%) are not ‘carried over’ on the cheeses (higher similitude between regions “A” and “P”, with “K” distinctive). Data have been broken down into three seasons. Similarly, the seasonality differences observed in different milks are not necessarily reported on the produced cheeses. This is expected due to the different VOC signatures developed in cheeses as part of the numerous biochemical changes during its elaboration compared to milk. VOC however it is an additional analytical tool that can aid in the identification of region origin in dairy products.