cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.6084/m9.figshare.825712.v1
title: Vocal stereotypy in the rodent genera <i>Peromyscus</i> and <i>Onychomys</i> (Neotominae): taxonomic signature and call design

description: Stereotypic behaviour, such as territorial calls and songs, is thought to be evolutionarily conserved, and therefore useful in discerning systematic relationships. We examined vocalizations of several species of <i>Peromyscus</i> and <i>Onychomys</i>, a monophyletic group of rodents (Peromyscini). We report stereotypic vocal signals occurring in both sexes of the deer mice <i>Peromyscus californicus</i>, <i>P. eremicus</i>, <i>P. leucopus</i>, <i>P. melanophrys</i>, <i>P. polionotus</i>, and the grasshopper mice <i>Onychomys arenicola</i> and <i>O. leucogaster</i>. The stereotypic vocalizations of <i>P. eremicus</i>, <i>P. leucopus</i>, and <i>P. polionotus</i> are confined to frequencies greater than 20 kHz, unlike those of <i>Onychomys</i>, which are clearly audible, or <i>P. californicus</i> and <i>P. melanophrys</i>, which generate lower frequency vocalizations than the other <i>Peromyscus</i>. We did not observe stereotypic vocalizations in <i>P. aztecus</i>. Intensity, context and consistency suggest that these vocalizations serve an announcement function. Distribution of spectral energy distinguishes genera and most species, and some use of frequency is correlated to body size. There is a dichotomy between <i>Onychomys</i> and <i>Peromyscus</i> in the use of frequency, a genus-specific pattern identified previously among other peromyscine lineages.
DOI: 10.6084/m9.figshare.825705
title: Geopot: a Cloud-based geolocation data service for mobile applications

description: We propose a novel Cloud-based geolocation data service system, termed ‘Geopot’, for location-based mobile applications. The exponentially growing number of users of location-based mobile applications demand a data service that can easily be deployed and is scalable against a large volume of accesses from mobile devices across the world. The purpose of our work is to construct a scalable spatial data service that leverages the powerful benefits of a Cloud-based storage. We focus on highly scalable check-in and nearby search service, which is a common focus of location-based mobile applications. Our system comprises two parts: a local data service for indexing and storing geolocation data of check-ins to achieve a compact spatial index database. This is based on an in-memory R-tree and a local hash table, and it utilizes a Cloud storage that enables global accesses. The local data service maintains a compact spatial index, with tempo-spatial clustering, in which check-ins are grouped by their distance through a time window. A centroid of a cluster is used as a spatial index of R-tree, and members of the cluster are stored in the local networked hash table temporarily. This insures that R-tree remains in a compact size that can fit into the system memory. The data stored in the local hash table will be published into the Cloud storage to allow users to access it remotely with the same quality of service. Publishing the local data to a Cloud not only insures staying within the specified storage size limits of the local data service, but also promotes scalable access to the Cloud from mobile clients. Our contribution lies in the design of a new scale-out data service architecture and in implementing this for mobile applications. We encourage the building of a mobile application with our proposed system as well as a low-cost Cloud data service for linking to a large-scale spatial database.
DOI: 10.6084/m9.figshare.825709.v1
title: Toward an inclusive semantic interoperability: the case of Cree hydrographic features

description: There has been ample work in GIScience on the formalization of ontologies but a relatively neglected area is the influence of language and culture on ontologies of geography. Although this subject has been investigated for conceptual ontologies using indigenous words denoting geographic features, this article represents the first attempt to develop a logical ontology with an indigenous group. The process of developing logical ontologies is here referred to as formalization. A methodology for formalizing ontologies with indigenous peoples is presented. A conceptual (human readable) ontology and a logical (axioms specified in mathematical logic) ontology were developed using this methodology. Research was conducted with the Cree, the largest indigenous language grouping in Canada. Results show that the geospatial ontology developed from Cree geographic concepts possesses unique design considerations: no superordinate classes were found from archival sources or Cree speakers so ontologies are structurally flat; the ontology contains some unique classes of water bodies; and the ontology challenges our notions of the generalizability of ontologies within indigenous groups. Whereas these difficulties are not insurmountable to the establishment of a cross-cultural Geospatial Semantic Web, the current plans of the World Wide Web Consortium do not adequately address them. We suggest future directions toward an inclusive semantic interoperability.
DOI: 10.6084/m9.figshare.825704
title: A parallel computing approach to fast geostatistical areal interpolation

description: Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis <i>et al</i>. (2005. Improving spatial data interoperability using geostatistical support-to-support interpolation. <i>In</i>: <i>Proceedings of geoComputation.</i> Ann Arbor, MI: University of Michigan) and Liu <i>et al</i>. (2006. <i>Calculation of average covariance using fast Fourier transform</i> (<i>FFT</i>). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.
DOI: 10.6084/m9.figshare.825705.v2
title: Geopot: a Cloud-based geolocation data service for mobile applications

description: We propose a novel Cloud-based geolocation data service system, termed ‘Geopot’, for location-based mobile applications. The exponentially growing number of users of location-based mobile applications demand a data service that can easily be deployed and is scalable against a large volume of accesses from mobile devices across the world. The purpose of our work is to construct a scalable spatial data service that leverages the powerful benefits of a Cloud-based storage. We focus on highly scalable check-in and nearby search service, which is a common focus of location-based mobile applications. Our system comprises two parts: a local data service for indexing and storing geolocation data of check-ins to achieve a compact spatial index database. This is based on an in-memory R-tree and a local hash table, and it utilizes a Cloud storage that enables global accesses. The local data service maintains a compact spatial index, with tempo-spatial clustering, in which check-ins are grouped by their distance through a time window. A centroid of a cluster is used as a spatial index of R-tree, and members of the cluster are stored in the local networked hash table temporarily. This insures that R-tree remains in a compact size that can fit into the system memory. The data stored in the local hash table will be published into the Cloud storage to allow users to access it remotely with the same quality of service. Publishing the local data to a Cloud not only insures staying within the specified storage size limits of the local data service, but also promotes scalable access to the Cloud from mobile clients. Our contribution lies in the design of a new scale-out data service architecture and in implementing this for mobile applications. We encourage the building of a mobile application with our proposed system as well as a low-cost Cloud data service for linking to a large-scale spatial database.
DOI: 10.6084/m9.figshare.825702
title: Application of gastrointestinal modelling to the study of the digestion and transformation of dietary glycidyl esters

description: Glycidyl esters (GEs) are known to be formed during vegetable oil processing. Because of their structure, it has been hypothesised that GEs, like fatty acid esters of chloropropanols (MCPD esters), may be accepted as substrates by gut lipases to release the epoxide glycidol. If confirmed such a hypothesis would be important for risk assessment since glycidol is considered as a genotoxic carcinogen. In the present study, biotransformation was investigated using static and dynamic gastrointestinal models. During the experiments, aliquots were analysed for non-digested GEs using liquid chromatography-time-of-flight-mass spectrometry (LC-ToF-MS). In the static model, a fast hydrolysis of GEs was observed as a result of lipase action. Lipase was very efficient at pH 4.8, and totally inhibited at very low pH (1.7). In the absence of lipase, GEs were found to be relatively stable. The potential impact of food matrix was studied using milk in a dynamic model simulating human physiological conditions. The fast, pH-dependent hydrolysis of GEs was further confirmed. The possible transformation of the digestion products was then investigated using gas chromatography coupled to mass spectrometry (GC-MS), mainly the epoxide ring-opening to glycerol followed by additional reactions. In any conditions applied, neither 2- nor 3-mono-chloropropanediol (2- nor 3-MCPD) were formed, indicating that a ring-opening of the epoxide group of GEs or glycidol followed by a reaction with chloride was unlikely. A small transformation of glycidol into glycerol was observed after longer incubation time correlated with a low pH. This suggested that ring-opening and reaction with water is possible in strongly acidic conditions. Overall, it is concluded that GEs are rapidly digested by gut lipases to form glycidol. Consequently, GEs should be considered as sources of glycidol exposure. In addition, risk assessment of GEs can likely rely on hazard identification and characterisation data specific for glycidol.
DOI: 10.6084/m9.figshare.825702.v1
title: Application of gastrointestinal modelling to the study of the digestion and transformation of dietary glycidyl esters

description: Glycidyl esters (GEs) are known to be formed during vegetable oil processing. Because of their structure, it has been hypothesised that GEs, like fatty acid esters of chloropropanols (MCPD esters), may be accepted as substrates by gut lipases to release the epoxide glycidol. If confirmed such a hypothesis would be important for risk assessment since glycidol is considered as a genotoxic carcinogen. In the present study, biotransformation was investigated using static and dynamic gastrointestinal models. During the experiments, aliquots were analysed for non-digested GEs using liquid chromatography-time-of-flight-mass spectrometry (LC-ToF-MS). In the static model, a fast hydrolysis of GEs was observed as a result of lipase action. Lipase was very efficient at pH 4.8, and totally inhibited at very low pH (1.7). In the absence of lipase, GEs were found to be relatively stable. The potential impact of food matrix was studied using milk in a dynamic model simulating human physiological conditions. The fast, pH-dependent hydrolysis of GEs was further confirmed. The possible transformation of the digestion products was then investigated using gas chromatography coupled to mass spectrometry (GC-MS), mainly the epoxide ring-opening to glycerol followed by additional reactions. In any conditions applied, neither 2- nor 3-mono-chloropropanediol (2- nor 3-MCPD) were formed, indicating that a ring-opening of the epoxide group of GEs or glycidol followed by a reaction with chloride was unlikely. A small transformation of glycidol into glycerol was observed after longer incubation time correlated with a low pH. This suggested that ring-opening and reaction with water is possible in strongly acidic conditions. Overall, it is concluded that GEs are rapidly digested by gut lipases to form glycidol. Consequently, GEs should be considered as sources of glycidol exposure. In addition, risk assessment of GEs can likely rely on hazard identification and characterisation data specific for glycidol.
DOI: 10.6084/m9.figshare.825705.v1
title: Geopot: a Cloud-based geolocation data service for mobile applications

description: We propose a novel Cloud-based geolocation data service system, termed ‘Geopot’, for location-based mobile applications. The exponentially growing number of users of location-based mobile applications demand a data service that can easily be deployed and is scalable against a large volume of accesses from mobile devices across the world. The purpose of our work is to construct a scalable spatial data service that leverages the powerful benefits of a Cloud-based storage. We focus on highly scalable check-in and nearby search service, which is a common focus of location-based mobile applications. Our system comprises two parts: a local data service for indexing and storing geolocation data of check-ins to achieve a compact spatial index database. This is based on an in-memory R-tree and a local hash table, and it utilizes a Cloud storage that enables global accesses. The local data service maintains a compact spatial index, with tempo-spatial clustering, in which check-ins are grouped by their distance through a time window. A centroid of a cluster is used as a spatial index of R-tree, and members of the cluster are stored in the local networked hash table temporarily. This insures that R-tree remains in a compact size that can fit into the system memory. The data stored in the local hash table will be published into the Cloud storage to allow users to access it remotely with the same quality of service. Publishing the local data to a Cloud not only insures staying within the specified storage size limits of the local data service, but also promotes scalable access to the Cloud from mobile clients. Our contribution lies in the design of a new scale-out data service architecture and in implementing this for mobile applications. We encourage the building of a mobile application with our proposed system as well as a low-cost Cloud data service for linking to a large-scale spatial database.
DOI: 10.6084/m9.figshare.825704.v2
title: A parallel computing approach to fast geostatistical areal interpolation

description: Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis <i>et al</i>. (2005. Improving spatial data interoperability using geostatistical support-to-support interpolation. <i>In</i>: <i>Proceedings of geoComputation.</i> Ann Arbor, MI: University of Michigan) and Liu <i>et al</i>. (2006. <i>Calculation of average covariance using fast Fourier transform</i> (<i>FFT</i>). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.
DOI: 10.6084/m9.figshare.825704.v1
title: A parallel computing approach to fast geostatistical areal interpolation

description: Areal interpolation is the procedure of using known attribute values at a set of (source) areal units to predict unknown attribute values at another set of (target) units. Geostatistical areal interpolation employs spatial prediction algorithms, that is, variants of Kriging, which explicitly incorporate spatial autocorrelation and scale differences between source and target units in the interpolation endeavor. When all the available source measurements are used for interpolation, that is, when a global search neighborhood is adopted, geostatistical areal interpolation is extremely computationally intensive. Interpolation in this case requires huge memory space and massive computing power, even with the dramatic improvement introduced by the spectral algorithms developed by Kyriakidis <i>et al</i>. (2005. Improving spatial data interoperability using geostatistical support-to-support interpolation. <i>In</i>: <i>Proceedings of geoComputation.</i> Ann Arbor, MI: University of Michigan) and Liu <i>et al</i>. (2006. <i>Calculation of average covariance using fast Fourier transform</i> (<i>FFT</i>). Menlo Park, CA: Stanford Center for Reservoir Forecasting, Petroleum Engineering Department, Stanford University) based on the fast Fourier transform (FFT). In this study, a parallel FFT-based geostatistical areal interpolation algorithm was developed to tackle the computational challenge of such problems. The algorithm includes three parallel processes: (1) the computation of source-to-source and source-to-target covariance matrices by means of FFT; (2) the QR factorization of the source-to-source covariance matrix; and (3) the computation of source-to-target weights via Kriging, and the subsequent computation of predicted attribute values for the target supports. Experiments with real-world datasets (i.e., predicting population densities of watersheds from population densities of counties in the Eastern Time Zone and in the continental United States) showed that the parallel algorithm drastically reduced the computing time to a practical length that is feasible for actual spatial analysis applications, and achieved fairly high speed-ups and efficiencies. Experiments also showed the algorithm scaled reasonably well as the number of processors increased and as the problem size increased.
DOI: 10.6084/m9.figshare.825693
title: Evidence of predation and disturbance events at <i>Heliconius</i> (Insecta: Lepidoptera: Nymphalidae) nocturnal aggregations in Panama and Costa Rica

description: Many unpalatable species use conspicuous colours to give a clear signal (aposematism) to predators of their noxiousness, and in many cases the message can be strengthened through aggregations. Several species of neotropical aposematic <i>Heliconius</i> butterflies present an interesting case because, although they form nocturnal aggregations, the colours on their wings are not detectable at night and there are no known nocturnal predators. Müllerian mimicry is probably the most important phenomenon that makes <i>Heliconius</i> butterflies one of the most successful neotropical groups but many other ecological adaptations have contributed to their success. These adaptations include availability of different models, wing colour adaptation to background light, and specific diurnal flying ranges as well as exclusive nocturnal roosting height ranges for each mimicry ring. By providing nocturnal habitat segregation, but most importantly protection from nocturnal predators, <i>Heliconius</i> nocturnal aggregations could have evolved to contribute to the success of coexistent Müllerian mimicry rings. The present work reports for the first time evidence of nocturnal predation attempts by bats and disturbance events at <i>Heliconius</i> roosting sites.
DOI: 10.6084/m9.figshare.825693.v1
title: Evidence of predation and disturbance events at <i>Heliconius</i> (Insecta: Lepidoptera: Nymphalidae) nocturnal aggregations in Panama and Costa Rica

description: Many unpalatable species use conspicuous colours to give a clear signal (aposematism) to predators of their noxiousness, and in many cases the message can be strengthened through aggregations. Several species of neotropical aposematic <i>Heliconius</i> butterflies present an interesting case because, although they form nocturnal aggregations, the colours on their wings are not detectable at night and there are no known nocturnal predators. Müllerian mimicry is probably the most important phenomenon that makes <i>Heliconius</i> butterflies one of the most successful neotropical groups but many other ecological adaptations have contributed to their success. These adaptations include availability of different models, wing colour adaptation to background light, and specific diurnal flying ranges as well as exclusive nocturnal roosting height ranges for each mimicry ring. By providing nocturnal habitat segregation, but most importantly protection from nocturnal predators, <i>Heliconius</i> nocturnal aggregations could have evolved to contribute to the success of coexistent Müllerian mimicry rings. The present work reports for the first time evidence of nocturnal predation attempts by bats and disturbance events at <i>Heliconius</i> roosting sites.