Community composition drives siderophore dynamics in multispecies bacterial communities

Background Intraspecific public goods are commonly shared within microbial populations, where the benefits of public goods are largely limited to closely related conspecifics. One example is the production of iron-scavenging siderophores that deliver iron to cells via specific cell envelope receptor and transport systems. Intraspecific social exploitation of siderophore producers is common, since non-producers avoid the costs of production but retain the cell envelope machinery for siderophore uptake. However, little is known about how interactions between species (i.e., interspecific interactions) can shape intraspecific public goods exploitation. Here, we predicted that strong competition for iron between species in diverse communities will increase costs of siderophore cooperation, and hence drive intraspecific exploitation. We examined how increasing microbial community species diversity shapes intraspecific social dynamics by monitoring the growth of siderophore producers and non-producers of the plant-growth promoting bacterium Pseudomonas fluorescens, embedded within tree-hole microbial communities ranging from 2 to 15 species. Results We find, contrary to our prediction, that siderophore production is favoured at higher levels of community species richness, driven by increased likelihood of encountering key species that reduce the growth of siderophore non-producing (but not producing) strains of P. fluorescens. Conclusions Our results suggest that maintaining a diverse soil microbiota could partly contribute to the maintenance of siderophore production in natural communities

Comprehensive HIV care and Anti-Retroviral Therapy in a conflict setting-outcomes, experiences, and lessons learned from Bukavu, Democratic Republic of Congo

2006 AIDS Conference in Toront

The Application of Integrated Knowledge-based Systems for the Biomedical Risk Assessment Intelligent Network (BRAIN)

One of NASA's goals for long duration space flight is to maintain acceptable levels of crew health, safety, and performance. One way of meeting this goal is through the Biomedical Risk Assessment Intelligent Network (BRAIN), an integrated network of both human and computer elements. The BRAIN will function as an advisor to flight surgeons by assessing the risk of in-flight biomedical problems and recommending appropriate countermeasures. This paper describes the joint effort among various NASA elements to develop BRAIN and an Infectious Disease Risk Assessment (IDRA) prototype. The implementation of this effort addresses the technological aspects of the following: (1) knowledge acquisition; (2) integration of IDRA components; (3) use of expert systems to automate the biomedical prediction process; (4) development of a user-friendly interface; and (5) integration of the IDRA prototype and Exercise Countermeasures Intelligent System (ExerCISys). Because the C Language, CLIPS (the C Language Integrated Production System), and the X-Window System were portable and easily integrated, they were chosen as the tools for the initial IDRA prototype. The feasibility was tested by developing an IDRA prototype that predicts the individual risk of influenza. The application of knowledge-based systems to risk assessment is of great market value to the medical technology industry

A Multiple Source based Transfer Learning Framework for Marketing Campaigns

© 2018 IEEE. The rapid growing number of marketing campaigns demands an efficient learning model to identify prospective customers to target. Transfer learning is widely considered as a major way to improve the learning performance by using the generated knowledge from previous learning tasks. Most recent studies focused on transferring knowledge from source domains to target domains which may result in knowledge missing. To avoid this, we proposed a multiple source based transfer learning framework to do it reversely. The data in target domains is transferred into source domains by normalizing them into the same distributions and then improving the learning task in target domains by its generated knowledge in source domains. The proposed method is general and can deal with supervised and unsupervised inductive and transductive learning simultaneously with a compatibility to work with different machine learning models. The experiments on real-world campaign data demonstrate the performance of the proposed method

Methanotrophy potential versus methane supply by pore water diffusion in peatlands

International audienceLow affinity methanotrophic bacteria consume a significant quantity of methane in wetland soils in the vicinity of plant roots and at the oxic-anoxic interface. Estimates of the efficiency of methanotrophy in peat soils vary widely in part because of differences in approaches employed to quantify methane cycling. High resolution profiles of dissolved methane abundance measured during the summer of 2003 were used to quantify rates of upward methane flux in four peatlands situated in Wales, UK. Aerobic incubations of peat from a minerotrophic and an ombrogenous mire were used to determine depth distributions of kinetic parameters associated with methane oxidation. The capacity for methanotrophy in a 3 cm thick zone immediately beneath the depth of nil methane abundance in pore water was significantly greater than the rate of upward diffusion of methane in all four peatlands. Rates of methane diffusion in pore water at the minerotrophic peatlands were small (?mol l?1 methane, indicating that precipitation events can impact methane distributions in pore water. Further work is needed to characterise the kinetics of methane oxidation spatially and temporally in different wetland types in order to determine generalized relationships for methanotrophy in peatlands that can be incorporated into process-based models of methane cycling in peat soils

Combining heterogeneous features for time series prediction

© 2017 IEEE. Time series prediction is a challenging task in reality, and various methods have been proposed for it. However, only the historical series of values are exploited in most of existing methods. Therefore, the predictive models might be not effective in some cases, due to: (1) the historical series of values is not sufficient usually, and (2) features from heterogeneous sources such as the intrinsic features of data samples themselves, which could be very useful, are not take into consideration. To address these issues, we proposed a novel method in this paper which learns the predictive model based on the combination of dynamic features extracted from series of historical values and static features of data samples. To evaluate the performance of our proposed method, we compare it with linear regression and boosted trees, and the experimental results validate our method's superiority

Toward an automated signature recognition toolkit for mission operations

Signature recognition is the problem of identifying an event or events from its time series. The generic problem has numerous applications to science and engineering. At NASA's Johnson Space Center, for example, mission control personnel, using electronic displays and strip chart recorders, monitor telemetry data from three-phase electrical buses on the Space Shuttle and maintain records of device activation and deactivation. Since few electrical devices have sensors to indicate their actual status, changes of state are inferred from characteristic current and voltage fluctuations. Controllers recognize these events both by examining the waveform signatures and by listening to audio channels between ground and crew. Recently the authors have developed a prototype system that identifies major electrical events from the telemetry and displays them on a workstation. Eventually the system will be able to identify accurately the signatures of over fifty distinct events in real time, while contending with noise, intermittent loss of signal, overlapping events, and other complications. This system is just one of many possible signature recognition applications in Mission Control. While much of the technology underlying these applications is the same, each application has unique data characteristics, and every control position has its own interface and performance requirements. There is a need, therefore, for CASE tools that can reduce the time to implement a running signature recognition application from months to weeks or days. This paper describes our work to date and our future plans

Using the quality circle approach to empower disadvantaged youth in addressing cyberbullying: an exploration across five European countries

Digital communication technologies play an important role in the social development of young people, but can create vulnerabilities to cyberbullying and other negative online experiences. The Blurred Lives project aimed to tackle cyberbullying innovatively using a co-participatory approach, collaborating with 14–16-year olds living in areas of socio-economic disadvantage in five European countries. In phase one, 2,658 teenagers were surveyed on their internet use and any unpleasant online experiences. This data informed the second phase where the participating countries worked together with 237 adolescents across 10 schools with adult facilitators to create original anti-cyberbullying resources for teachers, parents/carers, peers, and social media providers using the Quality Circle approach. This methodology adopts an ethos of working together to solve a problem in small, peer-led groups. Each group was tasked with creating a resource for one of the target audience groups. The final resources comprise a rich variety of different formats including videos, comic strips, a board game, leaflets, posters, and newsletters. The pupil feedback highlights, for most but not all participants, an increased knowledge of cyberbullying and e-safety skills, as well as enhanced problem-solving skills, levels of confidence, and group work skills. Several operational challenges are also discussed, including the importance of school-level support, planning, staffing, and finding an appropriate balance between facilitator support and pupil agency

Internet Use and Perceived Parental Involvement among Adolescents from Lower Socioeconomic Groups in Europe: An Exploration

Internet usage is a salient developmental factor in adolescents' lives. Although relevant correlates of Internet use have been documented earlier, there is a lack of information on lower socioeconomic status groups. This is important, as these adolescents have increased risk of negative online experiences. The current survey aimed to explore Internet use and parental involvement amongst adolescents from areas of socio-economic disadvantage in 30 urban schools across five European countries. A total of 2594 students participated, of whom 90% were 14-16 years. Virtually all adolescents of socioeconomic disadvantage had Internet access, with 88.5% reporting spending more than two hours per day online, often on apps such as Instagram, Snapchat, and YouTube. Almost one-third of adolescents did not talk with their parents about their Internet use and almost two-thirds indicated that their parents were only a little or not interested in their Internet use. A consistent finding across countries was that girls more often talked with their parents about their Internet use and more often reported that their parents were interested in their Internet use than boys. The results suggest that parents have an important task in explicitly showing interest in their adolescents' Internet use, with special attention needed for boys