Iowa Nutrient Reduction Strategy : a science and technology-based framework to assess and reduce nutrients to Iowa waters and the Gulf of Mexico (2012)

On November 19, 2012, Iowa Gov. Terry Branstad, Iowa Secretary of Agriculture Bill Northey, Director Chuck Gipp from the Iowa Department of Natural Resources and Dr. John Lawrence of Iowa State University announced the release of the Iowa Nutrient Reduction Strategy for public comment. A two-month public comment period and several informational meetings allowed the public to provide feedback on the draft strategy. Updates and improvements were made to the draft based on the public comments. The final version of the strategy was released May 29, 2013. The Iowa Nutrient Reduction Strategy is a science and technology-based approach to assess and reduce nutrients delivered to Iowa waterways and the Gulf of Mexico. The strategy outlines voluntary efforts to reduce nutrients in surface water from both point sources, such as wastewater treatment plants and industrial facilities, and nonpoint sources, including farm fields and urban areas, in a scientific, reasonable and cost effective manner. The development of the strategy reflects more than two years of work led by the Iowa Department of Agriculture and Land Stewardship, Iowa Department of Natural Resources and Iowa State University. The scientific assessment to evaluate and model the effects of practices was developed through the efforts of 23 individuals representing five agencies or organizations, including scientists from ISU, IDALS, DNR, USDA Agricultural Research Service and USDA Natural Resources Conservation Service. The strategy was developed in response to the 2008 Gulf Hypoxia Action Plan that calls for the 12 states along the Mississippi River to develop strategies to reduce nutrient loading to the Gulf of Mexico. The Iowa strategy follows the recommended framework provided by EPA in 2011 and is only the second state to complete a statewide nutrient reduction strategy. This strategy is the beginning. Operational plans are being developed and work is underway. This is a dynamic document that will evolve over time, and is a key step towards improving Iowa’s water quality. The impetus for this report comes from the Water Resources Coordination Council (WRCC) which states in its 2014‐15 Annual Report “Efforts are underway to improve understanding of the multiple nutrient monitoring efforts that may be available and can be compared to the nutrient WQ monitoring framework to identify opportunities and potential data gaps to better coordinate and prioritize future nutrient monitoring efforts.” This report is the culmination of those efforts.

A case-based reasoning view of thrombophilia risk

Thrombophilia stands for a genetic or an acquired tendency to hypercoagulable states that increase the risk of venous and arterial thromboses. Indeed, venous thromboembolism is often a chronic illness, mainly in deep venous thrombosis and pulmonary embolism, requiring lifelong prevention strategies. Therefore, it is crucial to identify the cause of the disease, the most appropriate treatment, the length of treatment or prevent a thrombotic recurrence. Thus, this work will focus on the development of a diagnosis decision support system in terms of a formal agenda built on a logic programming approach to knowledge representation and reasoning, complemented with a case-based approach to computing. The proposed model has been quite accurate in the assessment of thrombophilia predisposition risk, since the overall accuracy is higher than 90% and sensitivity ranging in the interval [86.5%, 88.1%]. The main strength of the proposed solution is the ability to deal explicitly with incomplete, unknown, or even self-contradictory information.

A Case-base Approach to Workforces’ Satisfaction Assessment

It is well known that human resources play a valuable role in a sustainable organizational development. Indeed, this work will focus on the development of a decision support system to assess workers’ satisfaction based on factors related to human resources management practices. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing. The proposed solution is unique in itself, once it caters for the explicit treatment of incomplete, unknown, or even self-contradictory information, either in terms of a qualitative or quantitative setting. Furthermore, clustering methods based on similarity analysis among cases were used to distinguish and aggregate collections of historical data or knowledge in order to reduce the search space, therefore enhancing the cases retrieval and the overall computational process.

A Case Based Methodology for Problem Solving aiming at Knee Osteoarthritis Detection

Knee osteoarthritis is the most common type of arthritis and a major cause of impaired mobility and disability for the ageing populations. Therefore, due to the increasing prevalence of the malady, it is expected that clinical and scientific practices had to be set in order to detect the problem in its early stages. Thus, this work will be focused on the improvement of methodologies for problem solving aiming at the development of Artificial Intelligence based decision support system to detect knee osteoarthritis. The framework is built on top of a Logic Programming approach to Knowledge Representation and Reasoning, complemented with a Case Based approach to computing that caters for the handling of incomplete, unknown, or even self-contradictory information.

Homogenization of nonlinear composites for multiscale analysis

A composite is a material made out of two or more constituents (phases) combined together in order to achieve desirable mechanical or thermal properties. Such innovative materials have been widely used in a large variety of engineering fields in the past decades. The design of a composite structure requires the resolution of a multiscale problem that involves a macroscale (i.e. the structural scale) and a microscale. The latter plays a crucial role in the determination of the material behavior at the macroscale, especially when dealing with constituents characterized by nonlinearities. For this reason, numerical tools are required in order to design composite structures by taking into account of their microstructure. These tools need to provide an accurate yet efficient solution in terms of time and memory requirements, due to the large number of internal variables of the problem. This issue is addressed by different methods that overcome this problem by reducing the number of internal variables. Within this framework, this thesis focuses on the development of a new homogenization technique named Mixed TFA (MxTFA) in order to solve the homogenization problem for nonlinear composites. This technique is based on a mixed-stress variational approach involving self-equilibrated stresses and plastic multiplier as independent variables on the Reference Volume Element (RVE). The MxTFA is developed for the case of elastoplasticity and viscoplasticity, and it is implemented into a multiscale analysis for nonlinear composites. Numerical results show the efficiency of the presented techniques, both at microscale and at macroscale level.

Development of Mass Spectrometry-based analytical tools for characterization of bioconjugate vaccines in E. coli

Antigen design is generally driven by the need to obtain enhanced stability,efficiency and safety in vaccines.Unfortunately,the antigen modification is rarely proceeded in parallel with analytical tools development characterization.The analytical tools set up is required during steps of vaccine manufacturing pipeline,for vaccine production modifications,improvements or regulatory requirements.Despite the relevance of bioconjugate vaccines,robust and consistent analytical tools to evaluate the extent of carrier glycosylation are missing.Bioconjugation is a glycoengineering technology aimed to produce N-glycoprotein in vivo in E.coli cells,based on the PglB-dependent system by C. jejuni,applied for production of several glycoconjugate vaccines.This applicability is due to glycocompetent E. coli ability to produce site-selective glycosylated protein used,after few purification steps, as vaccines able to elicit both humoral and cell-mediate immune-response.Here, S.aureus Hla bioconjugated with CP5 was used to perform rational analytical-driven design of the glycosylation sites for the glycosylation extent quantification by Mass Spectrometry.The aim of the study was to develop a MS-based approach to quantify the glycosylation extent for in-process monitoring of bioconjugate production and for final product characterization.The three designed consensus sequences differ for a single amino-acid residue and fulfill the prerequisites for engineered bioconjugate more appropriate from an analytical perspective.We aimed to achieve an optimal MS detectability of the peptide carrying the consensus sequences,complying with the well-characterized requirements for N-glycosylation by PglB.Hla carrier isoforms,bearing these consensus sequences allowed a recovery of about 20 ng/μg of periplasmic protein glycosylated at 40%.The SRM-MS here developed was successfully applied to evaluate the differential site occupancy when carrier protein present two glycosites.The glycosylation extent in each glycosite was determined and the difference in the isoforms were influenced either by the overall source of protein produced and by the position of glycosite insertion.The analytical driven design of the bioconjugated antigen and the development of accurate,precise and robust analytical method allowed to finely characterize the vaccine.

Developing the digital literacies of academic staff: an institutional approach

Institutional engagement with digital literacies at the University of Brighton has been promoted through the creation of a Digital Literacies Framework (DLF) aimed at academic staff. The DLF consists of 38 literacies divided into four categories that align to the following key areas of academic work: • Learning and teaching • Research • Communication and collaboration • Administration For each literacy, there is an explanation of what the literacy is, why it is important and how to gain it, with links to resources and training opportunities. After an initial pilot, the DLF website was launched in the summer of 2014. This paper discusses the strategic context and policy development of the DLF, its initial conception and subsequent development based on a pilot phase, feedback and evaluation. It critically analyses two of the ways that engagement with the DLF have been promoted: (1) formal professional development schemes and (2) the use of a ‘School-based’ approach. It examines the successes and challenges of the University of Brighton's scheme and makes some suggestions for subsequent steps including taking a course-level approach.

Prospects For Early Investigational Therapies For Sickle Cell Disease.

Sickle cell disease (SCD) is a genetic disorder characterized by the production of abnormal hemoglobin that polymerizes at low oxygen concentrations, causing the erythrocyte to adopt a sickle-shaped morphology. SCD pathophysiology is extremely complex and can lead to numerous clinical complications, including painful vaso-occlusive crises (VOC), end-organ damage, and a shortened lifespan. An impressive number of investigational drugs are currently in early stages of clinical development with prospects for use either as chronic therapies to reduce VOC frequency and end-organ damage in SCD or for use at the time of VOC onset. Many of these agents have been developed using a pathophysiological-based approach to SCD, targeting one or more of the mechanisms that contribute to the disease process. It is plausible that a multi-drug approach to treating the disease will evolve in the coming years, whereby hydroxyurea (HU) (the only drug currently FDA-approved for SCD) is used in combination with drugs that amplify nitric oxide signaling and/or counteract hemolytic effects, platelet activation and inflammation.

Etiologic diagnosis of bovine infectious abortion by PCR

Infectious abortion is a significant cause of reproductive failure and economic losses in cattle. The goal of this study was to detect nucleic acids of several infectious agents known to cause abortion including Arcanobacterium pyogenes, Bovine Herpesvirus 1, Brucella abortus, Campylobacter fetus subsp. venerealis, Chlamydophila abortus, Leptospira sp., Listeria monocytogenes, Salmonella sp., Mycoplasma bovis, Mycoplasma bovigenitalium, Neospora caninum, and Tritrichomonas foetus. Tissue homogenates from 42 fetuses and paraffin-embedded tissues from 28 fetuses and 14 placentas/endometrium were included in this study. Brucella abortus was detected in 14.2% (12/84) of the samples. Salmonella sp. DNA was amplified from 2 fetuses, and there was one positive for Neospora caninum, and another for Listeria monocytogenes. This PCR-based approach resulted in identification of the etiology in 19% of samples, or 20% if considered fetal tissues only.

E-contracting with price configuration for Web services and QoS

The large amount of information in electronic contracts hampers their establishment due to high complexity. An approach inspired in Software Product Line (PL) and based on feature modelling was proposed to make this process more systematic through information reuse and structuring. By assessing the feature-based approach in relation to a proposed set of requirements, it was showed that the approach does not allow the price of services and of Quality of Services (QoS) attributes to be considered in the negotiation and included in the electronic contract. Thus, this paper also presents an extension of such approach in which prices and price types associated to Web services and QoS levels are applied. An extended toolkit prototype is also presented as well as an experiment example of the proposed approach.

Experimental investigation on adaptive robust controller designs applied to a free-floating space manipulator

This paper aims to formulate and investigate the application of various nonlinear H(infinity) control methods to a fiee-floating space manipulator subject to parametric uncertainties and external disturbances. From a tutorial perspective, a model-based approach and adaptive procedures based on linear parametrization, neural networks and fuzzy systems are covered by this work. A comparative study is conducted based on experimental implementations performed with an actual underactuated fixed-base planar manipulator which is, following the DEM concept, dynamically equivalent to a free-floating space manipulator. (C) 2011 Elsevier Ltd. All rights reserved.

Large-scale ecological restoration of high-diversity tropical forests in SE Brazil

The complex interactions among endangered ecosystems, landowners` interests, and different models of land tenure and use, constitute an important series of challenges for those seeking to maintain and restore biodiversity and augment the flow of ecosystem services. Over the past 10 years, we have developed a data-based approach to address these challenges and to achieve medium and large-scale ecological restoration of riparian areas on private lands in the state of Sao Paulo, southeastern Brazil. Given varying motivations for ecological restoration, the location of riparian areas within landholdings, environmental zoning of different riparian areas, and best-practice restoration methods were developed for each situation. A total of 32 ongoing projects, covering 527,982 ha, were evaluated in large sugarcane farms and small mixed farms, and six different restoration techniques have been developed to help upscale the effort. Small mixed farms had higher portions of land requiring protection as riparian areas (13.3%), and lower forest cover of riparian areas (18.3%), than large sugarcane farms (10.0% and 36.9%, respectively for riparian areas and forest cover values). In both types of farms, forest fragments required some degree of restoration. Historical anthropogenic degradation has compromised forest ecosystem structure and functioning, despite their high-diversity of native tree and shrub species. Notably, land use patterns in riparian areas differed markedly. Large sugarcane farms had higher portions of riparian areas occupied by highly mechanized agriculture, abandoned fields, and anthropogenic wet fields created by siltation in water courses. In contrast, in small mixed crop farms, low or non-mechanized agriculture and pasturelands were predominant. Despite these differences, plantations of native tree species covering the entire area was by far the main restoration method needed both by large sugarcane farms (76.0%) and small mixed farms (92.4%), in view of the low resilience of target sites, reduced forest cover, and high fragmentation, all of which limit the potential for autogenic restoration. We propose that plantations should be carried out with a high-diversity of native species in order to create biologically viable restored forests, and to assist long-term biodiversity persistence at the landscape scale. Finally, we propose strategies to integrate the political, socio-economic and methodological aspects needed to upscale restoration efforts in tropical forest regions throughout Latin America and elsewhere. (C) 2010 Elsevier BA/. All rights reserved.

Special issue on advances in data mining and its applications

Data mining is the process to identify valid, implicit, previously unknown, potentially useful and understandable information from large databases. It is an important step in the process of knowledge discovery in databases, (Olaru & Wehenkel, 1999). In a data mining process, input data can be structured, seme-structured, or unstructured. Data can be in text, categorical or numerical values. One of the important characteristics of data mining is its ability to deal data with large volume, distributed, time variant, noisy, and high dimensionality. A large number of data mining algorithms have been developed for different applications. For example, association rules mining can be useful for market basket problems, clustering algorithms can be used to discover trends in unsupervised learning problems, classification algorithms can be applied in decision-making problems, and sequential and time series mining algorithms can be used in predicting events, fault detection, and other supervised learning problems (Vapnik, 1999). Classification is among the most important tasks in the data mining, particularly for data mining applications into engineering fields. Together with regression, classification is mainly for predictive modelling. So far, there have been a number of classification algorithms in practice. According to (Sebastiani, 2002), the main classification algorithms can be categorized as: decision tree and rule based approach such as C4.5 (Quinlan, 1996); probability methods such as Bayesian classifier (Lewis, 1998); on-line methods such as Winnow (Littlestone, 1988) and CVFDT (Hulten 2001), neural networks methods (Rumelhart, Hinton & Wiliams, 1986); example-based methods such as k-nearest neighbors (Duda & Hart, 1973), and SVM (Cortes & Vapnik, 1995). Other important techniques for classification tasks include Associative Classification (Liu et al, 1998) and Ensemble Classification (Tumer, 1996).

A framework for electricity price spike analysis with advanced data mining methods

There are many techniques for electricity market price forecasting. However, most of them are designed for expected price analysis rather than price spike forecasting. An effective method of predicting the occurrence of spikes has not yet been observed in the literature so far. In this paper, a data mining based approach is presented to give a reliable forecast of the occurrence of price spikes. Combined with the spike value prediction techniques developed by the same authors, the proposed approach aims at providing a comprehensive tool for price spike forecasting. In this paper, feature selection techniques are firstly described to identify the attributes relevant to the occurrence of spikes. A simple introduction to the classification techniques is given for completeness. Two algorithms: support vector machine and probability classifier are chosen to be the spike occurrence predictors and are discussed in details. Realistic market data are used to test the proposed model with promising results.

An archaeobotanical perspective on Holocene plant-use practices in lowland northern New Guinea

Existing archaeobotanical and palynological records of plant use in the northern New Guinea lowlands are reviewed in light of recent work at Kuk and theoretical refocusing on plant use practice. A practice-based approach is supported as the most useful way of investigating the highly problematical area of tropical plant food production. The existing direct record of past plant use in lowland New Guinea is considered woefully inadequate to achieve this task, as is that in Near Oceania and Island Southeast Asia. Archaeobotanical methods exist to fill the void, but full implementation requires a change in general archaeological and palaeoecological practice.