High-Affinity Phosphate-Binding Protein (PBP) For Phosphorous Recovery: Proof of Concept Using Recombinant \u3cem\u3eEscherichia coli\u3c/em\u3e

Phosphorus (P) is a critical, non-renewable nutrient; yet excess discharges can lead to eutrophication and deterioration of water quality. Thus, P removal from water must be coupled with P recovery to achieve sustainable P management. P-specific proteins provide a novel, promising approach to recover P from water. Bacterial phosphate-binding proteins (PBPs) are able to effectively remove phosphate, achieving extremely low levels in water (i.e. 0.015 mg-P L−1). A prerequisite of using PBP for P recovery, however, is not only removal, but also controlled P release, which has not yet been reported. Phosphate release using recombinant PBP-expressing Escherichia coli was explored in this study. Escherichia coli was genetically modified to overexpress PBP in the periplasmic space. The impacts of ionic strength, temperature and pH on phosphate release were assessed. PBP-expressed E. coli demonstrated consistently superior ability to adsorb more phosphate from liquid and release more phosphate under controlled conditions relative to negative controls (unexpressed PBP E. coli and E. coli K12). Lower pH (3.8), higher temperature (35ºC) and higher ionic strength (100 mM KCl) facilitated increased phosphate release, providing a maximum of 2.1% P recovery within 3 h. This study provides proof of concept of the feasibility of using PBP to recover P

Towards a classification framework for social machines

The state of the art in human interaction with computational systems blurs the line between computations performed by machine logic and algorithms, and those that result from input by humans, arising from their own psychological processes and life experience. Current socio-technical systems, known as ‘social machines’ exploit the large-scale interaction of humans with machines. Interactions that are motivated by numerous goals and purposes including financial gain, charitable aid, and simply for fun. In this paper we explore the landscape of social machines, both past and present, with the aim of defining an initial classificatory framework. Through a number of knowledge elicitation and refinement exercises we have identified the polyarchical relationship between infrastructure, social machines, and large-scale social initiatives. Our initial framework describes classification constructs in the areas of contributions, participants, and motivation. We present an initial characterization of some of the most popular social machines, as demonstration of the use of the identified constructs. We believe that it is important to undertake an analysis of the behaviour and phenomenology of social machines, and of their growth and evolution over time. Our future work will seek to elicit additional opinions, classifications and validation from a wider audience, to produce a comprehensive framework for the description, analysis and comparison of social machines

Integrating public datasets using linked data: challenges and design principles

The world is moving from a state where there is paucity of data to one of surfeit. These data, and datasets, are normally in different datastores and of different formats. Connecting these datasets together will increase their value and help discover interesting relationships amongst them. This paper describes our experience of using Linked Data to inter-operate these different datasets, the challenges we faced, and the solutions we devised. The paper concludes with apposite design principles for using linked data to inter-operate disparate datasets

Influence of lipopolysaccharide on proinflammatory gene expression in human corneal, conjunctival and meibomian gland epithelial cells

PURPOSE: Lipopolysaccharide (LPS), a bacterial endotoxin, is known to stimulate leuokotriene B4 (LTB4) secretion by human corneal (HCECs), conjunctival (HConjECs) and meibomian gland (HMGECs) epithelial cells. We hypothesize that this LTB4 effect represents an overall induction of proinflammatory gene expression in these cells. Our objective was to test this hypothesis. METHODS: Immortalized HCECs, HConjECs and HMGECs were cultured in the presence or absence of LPS (15 μg/ml) and ligand binding protein (LBP; 150 ng/ml). Cells were then processed for RNA isolation and the analysis of gene expression by using Illumina BeadChips, background subtraction, cubic spline normalization and GeneSifter software. RESULTS: Our findings show that LPS induces a striking increase in proinflammatory gene expression in HCECs and HConjECs. These cellular reactions are associated with a significant up-regulation of genes associated with inflammatory and immune responses (e.g. IL-1β, IL-8, and tumor necrosis factor), including those related to chemokine and Toll-like receptor signaling pathways, cytokine-cytokine receptor interactions, and chemotaxis. In contrast, with the exception of Toll-like signaling and associated innate immunity pathways, almost no proinflammatory ontologies were upregulated by LPS in HMGECs. CONCLUSIONS: Our results support our hypothesis that LPS stimulates proinflammatory gene expression in HCECs and HConjECs. However, our findings also show that LPS does not elicit such proinflammatory responses in HMGECs

Looking Under the Hood of Competency-Based Education: The Relationship Between Competency-Based Education Practices and Students' Learning Skills, Behaviors, and Dispositions

The Nellie Mae Education Foundation (Nellie Mae), in partnership with the American Institutes for Research (AIR), has recently released a comprehensive analysis of competency-based education (CBE), an instructional approach that emphasizes what students learn and master rather than time spent in a classroom. The study, titled "Looking Under the Hood of Competency-Based Education," examines the relationship between various competency-based practices and increased student learning capacity. Additionally, the study highlights the varying degrees of CBE practices in schools that have an existing reputation for implementation."Schools across the country are increasingly seeking ways to provide a competency-based education for students, yet many educators are not sure of where to begin or how they can implement this approach to learning," said Eve Goldberg, Director of Research at the Nellie Mae Education Foundation. "The framework developed by AIR of learning skills, behaviors, and dispositions and the findings on specific practices can help educators strengthen their practices and gives them the tools to continuously improve their practice. We hope educators interested in making this shift will benefit from this analysis.""Looking Under the Hood" analyzes a variety of competency-based practices to examine how schools implement CBE and determine how it relates to students' learning capacities. Some notable findings include:Learning in contexts outside the classroom (for example, internships) positively relates to increasing students' learning capacitiesThe option for students to learn at a comfortable pace (for example, extra time to finish a topic or unit and the opportunity to retake an exam or re-do a final project) has a positive association with self-efficacy and increasing students' motivation to learnThe option for students to receive both instruction and assessment in a variety of formats, including collaborative group projects, helped students' intrinsic motivationEstablishing clear learning targets was positively related to increasing students' learning capacitiesOverall, the study finds that many students' experiences with CBE-aligned practices were positively associated with changes in learning capacities in several areas, most notably in students' intrinsic motivation for classroom work."Competency-based education varies tremendously from school to school and even across classrooms, so it can be hard to determine if it is working," said Erin Haynes, Senior Researcher at the American Institutes for Research. "This study examined specific CBE-aligned practices, giving us a more finely-honed view of how such practices are related to students' capacity to learn. We hope this research will help inform future efforts to implement competency-based methods across districts, schools and classrooms.

Towards Prototyping Driverless Vehicle Behaviors, City Design, and Policies Simultaneously

Autonomous Vehicles (AVs) can potentially improve urban living by reducing accidents, increasing transportation accessibility and equity, and decreasing emissions. Realizing these promises requires the innovations of AV driving behaviors, city plans and infrastructure, and traffic and transportation policies to join forces. However, the complex interdependencies among AV, city, and policy design issues can hinder their innovation. We argue the path towards better AV cities is not a process of matching city designs and policies with AVs' technological innovations, but a process of iterative prototyping of all three simultaneously: Innovations can happen step-wise as the knot of AV, city, and policy design loosens and tightens, unwinds and reties. In this paper, we ask: How can innovators innovate AVs, city environments, and policies simultaneously and productively toward better AV cities? The paper has two parts. First, we map out the interconnections among the many AV, city, and policy design decisions, based on a literature review spanning HCI/HRI, transportation science, urban studies, law and policy, operations research, economy, and philosophy. This map can help innovators identify design constraints and opportunities across the traditional AV/city/policy design disciplinary bounds. Second, we review the respective methods for AV, city, and policy design, and identify key barriers in combining them: (1) Organizational barriers to AV-city-policy design collaboration, (2) computational barriers to multi-granularity AV-city-policy simulation, and (3) different assumptions and goals in joint AV-city-policy optimization. We discuss two broad approaches that can potentially address these challenges, namely, "low-fidelity integrative City-AV-Policy Simulation (iCAPS)" and "participatory design optimization".Comment: Published to the CHI '23 Workshop: Designing Technology and Policy Simultaneousl

Distributed human computation framework for linked data co-reference resolution

Distributed Human Computation (DHC) is a technique used to solve computational problems by incorporating the collaborative effort of a large number of humans. It is also a solution to AI-complete problems such as natural language processing. The Semantic Web with its root in AI is envisioned to be a decentralised world-wide information space for sharing machine-readable data with minimal integration costs. There are many research problems in the Semantic Web that are considered as AI-complete problems. An example is co-reference resolution, which involves determining whether different URIs refer to the same entity. This is considered to be a significant hurdle to overcome in the realisation of large-scale Semantic Web applications. In this paper, we propose a framework for building a DHC system on top of the Linked Data Cloud to solve various computational problems. To demonstrate the concept, we are focusing on handling the co-reference resolution in the Semantic Web when integrating distributed datasets. The traditional way to solve this problem is to design machine-learning algorithms. However, they are often computationally expensive, error-prone and do not scale. We designed a DHC system named iamResearcher, which solves the scientific publication author identity co-reference problem when integrating distributed bibliographic datasets. In our system, we aggregated 6 million bibliographic data from various publication repositories. Users can sign up to the system to audit and align their own publications, thus solving the co-reference problem in a distributed manner. The aggregated results are published to the Linked Data Cloud

Invasive perennial forb effects on gross soil nitrogen cycling and nitrous oxide fluxes depend on phenology.

Invasive plants can increase soil nitrogen (N) pools and accelerate soil N cycling rates, but their effect on gross N cycling and nitrous oxide (N2 O) emissions has rarely been studied. We hypothesized that perennial pepperweed (Lepidium latifolium) invasion would increase rates of N cycling and gaseous N loss, thereby depleting ecosystem N and causing a negative feedback on invasion. We measured a suite of gross N cycling rates and net N2 O fluxes in invaded and uninvaded areas of an annual grassland in the Sacramento-San Joaquin River Delta region of northern California. During the growing season, pepperweed-invaded soils had lower microbial biomass N, gross N mineralization, dissimilatory nitrate reduction to ammonium (DNRA), and denitrification-derived net N2 O fluxes (P < 0.02 for all). During pepperweed dormancy, gross N mineralization, DNRA, and denitrification-derived net N2 O fluxes were stimulated in pepperweed-invaded plots, presumably by N-rich litter inputs and decreased competition between microbes and plants for N (P < 0.04 for all). Soil organic carbon and total N concentrations, which reflect pepperweed effects integrated over longer time scales, were lower in pepperweed-invaded soils (P < 0.001 and P = 0.04, respectively). Overall, pepperweed invasion had a net negative effect on ecosystem N status, depleting soil total N to potentially cause a negative feedback to invasion in the long term