Applying spatio-chemical analysis to grassland ecosystems for the illustration of chemoscapes and creation of healthscapes

Grasslands are heterogeneous landscapes composed of a diversity of herbaceous and shrub vegetation that varies not only taxonomically, but biochemically in terms of primary and secondary compounds. Plant Secondary Compounds (PSC) have specific nutritional, medicinal, and prophylactic properties, to which benefits depend upon dosage, type, arrangements, and concentration that changes between and within plants across time and space. The knowledge of the plant content of PSC and their distribution in grazing environments would therefore contribute to the design and creation of healthier foodscapes for ruminants; in other words, healthscapes. Geographic information systems (GIS) have been used extensively for landscape visualization and assessment, through several spatial analysis techniques applied for the creation of virtual maps to add valuable information to a particular environment. Given the knowledge of plants and their composition, GIS emerges as a readily available and low-cost tool to assess and evaluate the distribution of plants with beneficial PSC in large and heterogeneous foodscapes. We present and propose for the very first time, the application and use of GIS to determine the spatial distribution of PSC rich plants with nutraceutical properties to illustrate, visualize, and generate healthscapes for grazing ruminants. We present healthscape maps created using botanical composition analyses and advanced image classification methods to illustrate the distribution of plants regarding their PSC and nutraceutical properties. Such maps add an extra dimension and perspective to plant chemical composition, enabling graziers to visualize in space and time centers of nutrition and prophylactics or medicines, contributing to advanced grazing management decisions toward more productive, sustainable, and healthy grazing systems. The valuable information behind the mapped PSC advances the understanding of the nutritional ecology of grazing environments and foodscapes, introducing a new dimension to the holistic management of pastoral livestock production systems

Bayesian model accounting for within-class biological variability in Serial Analysis of Gene Expression (SAGE)

BACKGROUND: An important challenge for transcript counting methods such as Serial Analysis of Gene Expression (SAGE), "Digital Northern" or Massively Parallel Signature Sequencing (MPSS), is to carry out statistical analyses that account for the within-class variability, i.e., variability due to the intrinsic biological differences among sampled individuals of the same class, and not only variability due to technical sampling error. RESULTS: We introduce a Bayesian model that accounts for the within-class variability by means of mixture distribution. We show that the previously available approaches of aggregation in pools ("pseudo-libraries") and the Beta-Binomial model, are particular cases of the mixture model. We illustrate our method with a brain tumor vs. normal comparison using SAGE data from public databases. We show examples of tags regarded as differentially expressed with high significance if the within-class variability is ignored, but clearly not so significant if one accounts for it. CONCLUSION: Using available information about biological replicates, one can transform a list of candidate transcripts showing differential expression to a more reliable one. Our method is freely available, under GPL/GNU copyleft, through a user friendly web-based on-line tool or as R language scripts at supplemental web-site

Assisted specification of discrete choice models

Determining appropriate utility specifications for discrete choice models is time-consuming and prone to errors. With the availability of larger and larger datasets, as the number of possible specifications exponentially grows with the number of variables under consideration, the analysts need to spend increasing amounts of time on searching for good models through trial-and-error, while expert knowledge is required to ensure these models are sound. This paper proposes an algorithm that aims at assisting modelers in their search. Our approach translates the task into a multi-objective combinatorial optimization problem and makes use of a variant of the variable neighborhood search algorithm to generate sets of promising model specifications. We apply the algorithm both to semi-synthetic data and to real mode choice datasets as a proof of concept. The results demonstrate its ability to provide relevant insights in reasonable amounts of time so as to effectively assist the modeler in developing interpretable and powerful models

Uncertainty in Bus Arrival Time Predictions: Treating Heteroscedasticity With a Metamodel Approach

Arrival time predictions for the next available bus or train are a key component of modern Traveller Information Systems (TIS). A great deal of research has been conducted within the ITS community developing an assortment of different algorithms that seek to increase the accuracy of these predictions. However, the inherent stochastic and non-linear nature of these systems, particularly in the case of bus transport, means that these predictions suffer from variable sources of error, stemming from variations in weather conditions, bus bunching and numerous other sources. In this paper we tackle the issue of uncertainty in bus arrival time predictions using an alternative approach. Rather than endeavour to develop a superior method for prediction we take existing predictions from a TIS and treat the algorithm generating them as a black box. The presence of heteroscedasticity in the predictions is demonstrated and then a meta-model approach deployed that augments existing predictive systems using quantile regression to place bounds on the associated error. As a case study this approach is applied to data from a real-world TIS in Boston. This method allows bounds on the predicted arrival time to be estimated, which give a measure of the uncertainty associated with the individual predictions. This represents to the best of our knowledge the first application of methods to handle the uncertainty in bus arrival times that explicitly takes into account the inherent heteroscedasticity. The meta-model approach is agnostic to the process generating the predictions which ensures the methodology is implementable in any system

Creating a design framework to diagnose and enhance grassland health under pastoral livestock production systems

Grasslands and ecosystem services are under threat due to common practices adopted by modern livestock farming systems. Design theory has been an alternative to promote changes and develop more sustainable strategies that allow pastoral livestock production systems to evolve continually within grasslands by enhancing their health and enabling the continuous delivery of multiple ecosystem services. To create a design framework to design alternative and more sustainable pastoral livestock production systems, a better comprehension of grassland complexity and dynamism for a diagnostic assessment of its health is needed, from which the systems thinking theory could be an important approach. By using systems thinking theory, the key components of grasslands—soil, plant, ruminant—can be reviewed and better understood from a holistic perspective. The description of soil, plant and ruminant individually is already complex itself, so understanding these components, their interactions, their response to grazing management and herbivory and how they contribute to grassland health under different climatic and topographic conditions is paramount to designing more sustainable pastoral livestock production systems. Therefore, by taking a systems thinking approach, we aim to review the literature to better understand the role of soil, plant, and ruminant on grassland health to build a design framework to diagnose and enhance grassland health under pastoral livestock production systems

Effect of mesoporous silica under Neisseria meningitidis transformation process: environmental effects under meningococci transformation

<p>Abstract</p> <p>Background</p> <p>This study aimed the use of mesoporous silica under the naturally transformable <it>Neisseria meningitidis</it>, an important pathogen implicated in the genetic horizontal transfer of DNA causing a escape of the principal vaccination measures worldwide by the capsular switching process. This study verified the effects of mesoporous silica under <it>N. meningitidis </it>transformation specifically under the capsular replacement.</p> <p>Methods</p> <p>we used three different mesoporous silica particles to verify their action in <it>N. meningitis </it>transformation frequency.</p> <p>Results</p> <p>we verified the increase in the capsular gene replacement of this bacterium with the three mesoporous silica nanoparticles.</p> <p>Conclusion</p> <p>the mesouporous silica particles were capable of increasing the capsule replacement frequency in <it>N. meningitidis</it>.</p

The biological origin of linguistic diversity

In contrast with animal communication systems, diversity is characteristic of almost every aspect of human language. Languages variously employ tones, clicks, or manual signs to signal differences in meaning; some languages lack the noun-verb distinction (e.g., Straits Salish), whereas others have a proliferation of fine-grained syntactic categories (e.g., Tzeltal); and some languages do without morphology (e.g., Mandarin), while others pack a whole sentence into a single word (e.g., Cayuga). A challenge for evolutionary biology is to reconcile the diversity of languages with the high degree of biological uniformity of their speakers. Here, we model processes of language change and geographical dispersion and find a consistent pressure for flexible learning, irrespective of the language being spoken. This pressure arises because flexible learners can best cope with the observed high rates of linguistic change associated with divergent cultural evolution following human migration. Thus, rather than genetic adaptations for specific aspects of language, such as recursion, the coevolution of genes and fast-changing linguistic structure provides the biological basis for linguistic diversity. Only biological adaptations for flexible learning combined with cultural evolution can explain how each child has the potential to learn any human language