Optimization and Applications of Large-Scale Biomedical Event Networks

The overwhelming amount and unprecedented speed of publication in the biomedical domain make it difficult for life science researchers to acquire and maintain a broad view of the field and gather all information that would be relevant for their research. As a response to this problem, the BioNLP (Biomedical Natural Language Processing) community of researches has emerged and strives to assist life science researchers by developing modern natural language processing (NLP), information extraction (IE) and information retrieval (IR) methods that can be applied at large-scale, to scan the whole publicly available biomedical literature and extract and aggregate the information found within, while automatically normalizing the variability of natural language statements. Among different tasks, biomedical event extraction has received much attention within BioNLP community recently. Biomedical event extraction constitutes the identification of biological processes and interactions described in biomedical literature, and their representation as a set of recursive event structures. The 2009–2013 series of BioNLP Shared Tasks on Event Extraction have given raise to a number of event extraction systems, several of which have been applied at a large scale (the full set of PubMed abstracts and PubMed Central Open Access full text articles), leading to creation of massive biomedical event databases, each of which containing millions of events. Sinece top-ranking event extraction systems are based on machine-learning approach and are trained on the narrow-domain, carefully selected Shared Task training data, their performance drops when being faced with the topically highly varied PubMed and PubMed Central documents. Specifically, false-positive predictions by these systems lead to generation of incorrect biomolecular events which are spotted by the end-users. This thesis proposes a novel post-processing approach, utilizing a combination of supervised and unsupervised learning techniques, that can automatically identify and filter out a considerable proportion of incorrect events from large-scale event databases, thus increasing the general credibility of those databases. The second part of this thesis is dedicated to a system we developed for hypothesis generation from large-scale event databases, which is able to discover novel biomolecular interactions among genes/gene-products. We cast the hypothesis generation problem as a supervised network topology prediction, i.e predicting new edges in the network, as well as types and directions for these edges, utilizing a set of features that can be extracted from large biomedical event networks. Routine machine learning evaluation results, as well as manual evaluation results suggest that the problem is indeed learnable. This work won the Best Paper Award in The 5th International Symposium on Languages in Biology and Medicine (LBM 2013).

Large-Scale Controlled-Condition Experiment to Evaluate Light Weight Deflectometers for Modulus Determination and Compaction Quality Assurance of Unbound Pavement Materials

Compaction control using lightweight deflectometers (LWD) is currently being evaluated in several states and countries and fully implemented for pavement construction quality assurance (QA) by a few. Broader implementation has been hampered by the lack of a widely recognized standard for interpreting the load and deflection data obtained during construction QA testing. More specifically, reliable and practical procedures are required for relating these measurements to the fundamental material property—modulus—used in pavement design. This study presents a unique set of data and analyses for three different LWDs on a large-scale controlled-condition experiment. Three 4.5x4.5 m2 test pits were designed and constructed at target moisture and density conditions simulating acceptable and unacceptable construction quality. LWD testing was performed on the constructed layers along with static plate loading testing, conventional nuclear gauge moisture-density testing, and non-nuclear gravimetric and volumetric water content measurements. Additional material was collected for routine and exploratory tests in the laboratory. These included grain size distributions, soil classification, moisture-density relations, resilient modulus testing at optimum and field conditions, and an advanced experiment of LWD testing on top of the Proctor compaction mold. This unique large-scale controlled-condition experiment provides an excellent high quality resource of data that can be used by future researchers to find a rigorous, theoretically sound, and straightforward technique for standardizing LWD determination of modulus and construction QA for unbound pavement materials.

Large-scale dynamic hydrofracturing, healing and fracture network characterization

Permeability of a rock is a dynamic property that varies spatially and temporally. Fractures provide the most efficient channels for fluid flow and thus directly contribute to the permeability of the system. Fractures usually form as a result of a combination of tectonic stresses, gravity (i.e. lithostatic pressure) and fluid pressures. High pressure gradients alone can cause fracturing, the process which is termed as hydrofracturing that can determine caprock (seal) stability or reservoir integrity. Fluids also transport mass and heat, and are responsible for the formation of veins by precipitating minerals within open fractures. Veining (healing) thus directly influences the rock’s permeability. Upon deformation these closed factures (veins) can refracture and the cycle starts again. This fracturing-healing-refacturing cycle is a fundamental part in studying the deformation dynamics and permeability evolution of rock systems. This is generally accompanied by fracture network characterization focusing on network topology that determines network connectivity. Fracture characterization allows to acquire quantitative and qualitative data on fractures and forms an important part of reservoir modeling. This thesis highlights the importance of fracture-healing and veins’ mechanical properties on the deformation dynamics. It shows that permeability varies spatially and temporally, and that healed systems (veined rocks) should not be treated as fractured systems (rocks without veins). Field observations also demonstrate the influence of contrasting mechanical properties, in addition to the complexities of vein microstructures that can form in low-porosity and permeability layered sequences. The thesis also presents graph theory as a characterization method to obtain statistical measures on evolving network connectivity. It also proposes what measures a good reservoir should have to exhibit potentially large permeability and robustness against healing. The results presented in the thesis can have applications for hydrocarbon and geothermal reservoir exploration, mining industry, underground waste disposal, CO2 injection or groundwater modeling.

How institutions shaped the last major evolutionary transition to large-scale human societies.

What drove the transition from small-scale human societies centred on kinship and personal exchange, to large-scale societies comprising cooperation and division of labour among untold numbers of unrelated individuals? We propose that the unique human capacity to negotiate institutional rules that coordinate social actions was a key driver of this transition. By creating institutions, humans have been able to move from the default ‘Hobbesian’ rules of the ‘game of life’, determined by physical/environmental constraints, into self-created rules of social organization where cooperation can be individually advantageous even in large groups of unrelated individuals. Examples include rules of food sharing in hunter–gatherers, rules for the usage of irrigation systems in agriculturalists, property rights and systems for sharing reputation between mediaeval traders. Successful institutions create rules of interaction that are self-enforcing, providing direct benefits both to individuals that follow them, and to individuals that sanction rule breakers. Forming institutions requires shared intentionality, language and other cognitive abilities largely absent in other primates. We explain how cooperative breeding likely selected for these abilities early in the Homo lineage. This allowed anatomically modern humans to create institutions that transformed the self-reliance of our primate ancestors into the division of labour of large-scale human social organization.

Mean structure of the North Atlantic subtropical permanent pycnocline from in-situ observations

In the north Atlantic subtropical gyre, the oceanic vertical structure of density is characterized by a region of rapid increase with depth. This layer is called the permanent pycnocline. The permanent pycnocline is found below a surface mode water ,which are ventilated every winter when penetrated locally by the mixed layer. Assessing the structure and variability of the permanent pycnocline is of a major interest in the understanding of the climate system because the pycnocline layer delimits important heat and anthropogenic reservoir. Moreover, the heat content structure translate into changes in the large scale stratification feature, such as the permanent pycnocline. We developed a new objective algorithm for the characterization of the large scale structure of the permanent pycnocline (OAC-P). Argo data have been used with OAC-P to provide a detailed description of the mean structure of the North-Atlantic subtropical pycnocline (e.g.: depth, thickness, temperature, salinity, density, potential vorticity). Results reveal a surprisingly complex structure with inhomogeneous properties. While the classical bowl shape of the pycnocline depth is captured, much more complex pycnocline structure emerges at the regional scale. In the southern recirculation gyre of the Gulf Stream Extension, the pycnocline is deep, thick, the maximum of stratification is found in the middle on the layer and follow an isopycnal surface. But local processes influence and modify this textbook description and the pycnocline is characterized by a vertically asymmetric structure and gradients in thermohaline properties. T/S distribution along the permanent pycnocline depth is complex and reveals a diversity of water masses resulting from mixing of different source waters. We will present the observed mean structure of the North-Atlantic subtropical permanent pycnocline and relate it to physical processes that constraint it.

Large-scale neural dynamics: simple and complex

We review the use of neural field models for modelling the brain at the large scales necessary for interpreting EEG, fMRI, MEG and optical imaging data. Albeit a framework that is limited to coarse-grained or mean-field activity, neural field models provide a framework for unifying data from different imaging modalities. Starting with a description of neural mass models we build to spatially extended cortical models of layered two-dimensional sheets with long range axonal connections mediating synaptic interactions. Reformulations of the fundamental non-local mathematical model in terms of more familiar local differential (brain wave) equations are described. Techniques for the analysis of such models, including how to determine the onset of spatio-temporal pattern forming instabilities, are reviewed. Extensions of the basic formalism to treat refractoriness, adaptive feedback and inhomogeneous connectivity are described along with open challenges for the development of multi-scale models that can integrate macroscopic models at large spatial scales with models at the microscopic scale.

Incorporating Critically Conscious Assessment into a Large Scale Information Literacy Program

Lesson plan published in Critical Pedagogy Handbook, vol. 2

Large-scale prediction of seagrass distribution: the case of Cymodocea nodosa (Mediterranean-Atlantic)

Understanding the factors that affect seagrass meadows encompassing their entire range of distribution is challenging yet important for their conservation. We model the environmental niche of Cymodocea nodosa using a combination of environmental variables and landscape metrics to examine factors defining its distribution and find suitable habitats for the species. The most relevant environmental variables defining the distribution of C. nodosa were sea surface temperature (SST) and salinity. We found suitable habitats at SST from 5.8 ºC to 26.4 ºC and salinity ranging from 17.5 to 39.3. Optimal values of mean winter wave height ranged between 1.2 m and 1.5 m, while waves higher than 2.5 m seemed to limit the presence of the species. The influence of nutrients and pH, despite having weight on the models, was not so clear in terms of ranges that confine the distribution of the species. Landscape metrics able to capture variation in the coastline enhanced significantly the accuracy of the models, despite the limitations caused by the scale of the study. By contrasting predictive approaches, we defined the variables affecting the distributional areas that seem unsuitable for C. nodosa as well as those suitable habitats not occupied by the species. These findings are encouraging for its use in future studies on climate-related marine range shifts and meadow restoration projects of these fragile ecosystems.

Elemental Anisotropic Growth and Atomic-Scale Structure of Shape-Controlled Octahedral Pt–Ni–Co Alloy Nanocatalysts

Multimetallic shape-controlled nanoparticles offer great opportunities to tune the activity, selectivity, and stability of electrocatalytic surface reactions. However, in many cases, our synthetic control over particle size, composition, and shape is limited requiring trial and error. Deeper atomic-scale insight in the particle formation process would enable more rational syntheses. Here we exemplify this using a family of trimetallic PtNiCo nanooctahedra obtained via a low-temperature, surfactant-free solvothermal synthesis. We analyze the competition between Ni and Co precursors under coreduction “one-step” conditions when the Ni reduction rates prevailed. To tune the Co reduction rate and final content, we develop a “two-step” route and track the evolution of the composition and morphology of the particles at the atomic scale. To achieve this, scanning transmission electron microscopy and energy dispersive X-ray elemental mapping techniques are used. We provide evidence of a heterogeneous element distribution caused by element-specific anisotropic growth and create octahedral nanoparticles with tailored atomic composition like Pt1.5M, PtM, and PtM1.5 (M = Ni + Co). These trimetallic electrocatalysts have been tested toward the oxygen reduction reaction (ORR), showing a greatly enhanced mass activity related to commercial Pt/C and less activity loss than binary PtNi and PtCo after 4000 potential cycles.

Large scale laboratory cultures of Ankistrodesmus gracilis (Reisch) Korsikov (Chlorophyta) and Diaphanosoma biergei Korinek, 1981 (Cladocera)

O objetivo deste trabalho foi avaliar o cultivo em larga escala de Ankistrodesmus gracilis e Diaphanososma birgei em laboratório através do estudo da biologia das espécies, composição bioquímica e custo operacional de produção. A. gracilis apresentou um crescimento exponencial até o sexto dia, ao redor de 144 x 10(4) células mL-1. Logo em seguida, sofreu um brusco decréscimo apresentando 90 x 10(4) células mL-1 (oitavo dia). A partir do décimo primeiro dia, as células algais tenderam a crescer novamente, apresentando um máximo de 135 x 10(4) células mL-1 no 17º dia. No cultivo de D. birgei, foi observado o primeiro pico de crescimento no nono dia com 140 x 10² indivíduos L-1, aumentando novamente a partir do décimo segundo dia. A alga clorofícea A. gracilis e o zooplâncton D. birgei possuem aproximadamente 50 e 70% de proteína (PS), respectivamente, com teor de carboidrato acima de 5%. A eletricidade e mão de obra foram os itens mais dispendiosos e, de acordo com os dados obtidos, a temperatura, nutrientes, disponibilidade de luz e manejo do cultivo, foram fatores determinantes sobre a produtividade. Os resultados indicam que o meio NPK (20-5-20) pode ser utilizado diretamente como uma alternativa de cultivo em larga escala, considerando o baixo custo de produção, promovendo adequado crescimento e valor nutricional para A. gracilis e D. birgei.

Performance and economic indicators of a large scale fish farming in Mato Grosso, Brazil

Performance and economic indicators of a large scale fish farm that produces round fish, located in Mato Grosso State, Brazil, were evaluated. The 130.8 ha-water surface area was distributed in 30 ponds. Average total production costs and the following economic indicators were calculated: gross income (GI), gross margin (GM), gross margin index (GMI), profitability index (PI) and profit (P) for the farm as a whole and for ten ponds individually. Production performance indicators were also obtained, such as: production cycle (PC), apparent feed conversion (FC), average biomass storage (ABS), survival index (SI) and final average weight (FAW). The average costs to produce an average 2.971 kg.ha-1 per year were: R$ 2.43, R$ 0.72 and R$ 3.15 as average variable, fixed and total costs, respectively. Gross margin and profit per year per hectare of water surface were R$ 2,316.91 and R$ 180.98, respectively. The individual evaluation of the ponds showed that the best pond performance was obtained for PI 38%, FC 1.7, ABS 0.980 kg.m-2, TS 56%, FAW 1.873 kg with PC of 12.3 months. The worst PI was obtained for the pond that displayed losses of 138%, FC 2.6, ABS 0.110 kg.m-2, SI 16% and FAW 1.811 kg. However, large scale production of round-fish in farms is economically feasible. The studied farm displays favorable conditions to improve performance and economic indicators, but it is necessary to reproduce the breeding techniques and performance indicators achieved in few ponds to the entire farm.