Showing papers in "Ai Magazine in 2014"

Power to the People: The Role of Humans in Interactive Machine Learning

Abstract: Intelligent systems that learn interactively from their end-users are quickly becoming widespread. Until recently, this progress has been fueled mostly by advances in machine learning; however, more and more researchers are realizing the importance of studying users of these systems. In this article we promote this approach and demonstrate how it can result in better user experiences and more effective learning systems. We present a number of case studies that characterize the impact of interactivity, demonstrate ways in which some existing systems fail to account for the user, and explore new ways for learning systems to interact with their users. We argue that the design process for interactive machine learning systems should involve users at all stages: explorations that reveal human interaction patterns and inspire novel interaction methods, as well as refinement stages to tune details of the interface and choose among alternatives. After giving a glimpse of the progress that has been made so far, we discuss the challenges that we face in moving the field forward.

Algorithm Selection for Combinatorial Search Problems: A survey

Abstract: The Algorithm Selection Problem is concerned with selecting the best algorithm to solve a given problem on a case-by-case basis. It has become especially relevant in the last decade, as researchers are increasingly investigating how to identify the most suitable existing algorithm for solving a problem instead of developing new algorithms. This survey presents an overview of this work focusing on the contributions made in the area of combinatorial search problems, where Algorithm Selection techniques have achieved significant performance improvements. We unify and organise the vast literature according to criteria that determine Algorithm Selection systems in practice. The comprehensive classification of approaches identifies and analyses the different directions from which Algorithm Selection has been approached. This chapter contrasts and compares different methods for solving the problem as well as ways of using these solutions.

A Review of Real-Time Strategy Game AI

Abstract: This literature review covers AI techniques used for real-time strategy video games, focusing specifically on StarCraft. It finds that the main areas of current academic research are in tactical and strategic decision-making, plan recognition, and learning, and it outlines the research contributions in each of these areas. The paper then contrasts the use of game AI in academia and industry, finding the academic research heavily focused on creating game-winning agents, while the indus- try aims to maximise player enjoyment. It finds the industry adoption of academic research is low because it is either in- applicable or too time-consuming and risky to implement in a new game, which highlights an area for potential investi- gation: bridging the gap between academia and industry. Fi- nally, the areas of spatial reasoning, multi-scale AI, and co- operation are found to require future work, and standardised evaluation methods are proposed to produce comparable re- sults between studies.

The MiniZinc Challenge 2008-2013

Abstract: MiniZinc is a solver agnostic modeling language for defining and solver combinatorial satisfaction and optimization problems MiniZinc provides a solver independent modeling language which is now supported by constraint programming solvers, mixed integer programming solvers, SAT and SAT modulo theory solvers, and hybrid solvers Since 2008 we have run the MiniZinc challenge every year, which compares and contrasts the different strengths of different solvers and solving technologies on a set of MiniZinc models Here we report on what we have learnt from running the competition for 6 years

The Dialog State Tracking Challenge Series

Abstract: In spoken dialog systems, dialog state tracking refers to the task of correctly inferring the user's goal at a given turn, given all of the dialog history up to that turn. The Dialog State Tracking Challenge is a research community challenge task that has run for three rounds. The challenge has given rise to a host of new methods for dialog state tracking, and also deeper understandings about the problem itself, including methods for evaluation.

GRADE: Machine Learning Support for Graduate Admissions

Abstract: This article describes GRADE, a statistical machine learning system developed to support the work of the graduate admissions committee at the University of Texas at Austin Department of Computer Science (UTCS). In recent years, the number of applications to the UTCS PhD program has become too large to manage with a traditional review process. GRADE uses historical admissions data to predict how likely the committee is to admit each new applicant. It reports each prediction as a score similar to those used by human reviewers, and accompanies each by an explanation of what applicant features most influenced its prediction. GRADE makes the review process more efficient by enabling reviewers to spend most of their time on applicants near the decision boundary and by focusing their attention on parts of each applicant’s file that matter the most. An evaluation over two seasons of PhD admissions indicates that the system leads to dramatic time savings, reducing the total time spent on reviews by at least 74 percent.

Laccases from Actinobacteria —What We Have and What to Expect

Abstract: Laccases are blue multicopper enzymes, capable of oxidizing diverse aromatic and non-aromatic compounds of industrial interest, concomitantly with reduction of molecular oxygen to water. Tolerance to extreme conditions, such as high temperature, salinity or extreme pH, is required for practical industrial applications. Here we focus on bacterial laccases from the phylum Actinobacteria, notably the order Actinomycetales. Currently, less than 10 enzymes have been properly characterized, all belonging to genus Streptomyces, but it is noteworthy that all of them have exhibited industrially important properties. Furthermore, studies with enzymes from this phylum revealed a novel molecular structure of laccases, providing the basis for a distinct family, the two-domain laccases. The relevant traits of actinomycetes laccases emphasize the need for more studies involving the isolation of this bacterial group from lignin-rich environmental samples, detection of their laccase activity and thereafter, characterization of the proteins and related genes. The nonhomogeneous responses of actinomycetes laccases to traditional inhibitors, substrates or metal ions have challenged the currently accepted “laccase concept”. Finally, considering that distinguishing laccase activity in vitro from other ligninolytic enzymes becomes a difficult task due to overlaps in catalytical properties of the enzymes, we proposed a simple flow chart to help experimental assays.

Crowdsourcing Meets Ecology: Hemisphere-Wide Spatiotemporal Species Distribution Models

Abstract: Ecological systems are inherently complex The processes that affect the distributions of animals and plants operate at multiple spatial and temporal scales, presenting a unique challenge for the development and coordination of effective conservation strategies, particularly for wide-ranging species In order to study ecological systems across scales, data must be collected at fine resolutions across broad spatial and temporal extents Crowdsourcing has emerged as an efficient way to gather these data by engaging large numbers of people to record observations However, data gathered by crowdsourced projects are often biased due to the opportunistic approach of data collection In this article, we propose a general class of models called AdaSTEM, (for adaptive spatio-temporal exploratory models), that are designed to meet these challenges by adapting to multiple scales while exploiting variation in data density common with crowdsourced data To illustrate the use of AdaSTEM, we produce intra-seasonal distribution estimates of long-distance migrations across the Western Hemisphere using data from eBird, a citizen science project that utilizes volunteers to collect observations of birds Subsequently, model diagnostics are used to quantify and visualize the scale and quality of distribution estimates This analysis shows how AdaSTEM can automatically adapt to complex spatiotemporal processes across a range of scales, thus providing essential information for full-life cycle conservation planning of broadly distributed species, communities, and ecosystems

Ethnomycological Conspectus of West African Mushrooms: An Awareness Document

Abstract: The ethnological knowledge of mushrooms despite its millennial existence and its empirical documentation are more recent phenomena. In Africa, the knowledge of their historical uses as food, medicine, source of income and small scale businesses, and the sociological impacts (myth, culture and spirituality) are apparently threatened due to slow ethnomycology research drive. The poor identification and documentation of edible and medicinal species of mushrooms in many developing nations have created some degrees of inconsistencies in their usages relative to folk medicine practice, food and mythological beliefs. Their relevance in modern day pharmaceutics and nutraceuticals is a product of human experimentation over time.Factors that may be anthropogenic, ethnographic, ethnoecological/environmental have been implicated in mushrooms underutilization and under-exploration of mushrooms in West Africa. Ethnomycological literatures on West Africa are scant, random, are limited in scope and fraught with taxonomic inconsistencies. This paper is based on extant ethnomycology treatise and aims at representing an integrative knowledge of useful mushrooms of West Africa and their uses vis-a-vis indigent cultures.

Diagnosis and Control of Harpophora maydis, the Cause of Late Wilt in Maize

Abstract: Late wilt, a severe vascular disease of maize caused by the fungus Harpophora maydis, is characterized by relatively rapid wilting of maize plants, before tasseling and until shortly before maturity. In Israel, the disease becomes a major problem in recent years. The pathogen currently controlled using varieties of maize has reduced sensitivity. In earlier work, we modified a molecular method for use as a diagnostic tool to evaluate the disease progress in field infested plants. Several fungicides suppressed H. maydis in vitro and in a detached root pathogenicity assay. Seedling pathogenicity assay enables us to identified H. maydis DNA in the host root and stem tissues 18 days after sowing in both susceptible and tolerant maize plants. Although the infested plants exhibited no wilt symptoms, their roots were significantly shorter in length. This seedling assay was used to demonstrate the suppressive effect of the fungicide Flutriafol on H. maydis virulence. The method of assaying the pathogen in a series of trials starting in a plate assay, followed by a detached root and resulting in a seedlings pathogenicity assay, using molecular and morphological approaches could be generalized to other plant pathogens.

Enhanced Spore Production of Bacillus subtilis Grown in a Chemically Defined Medium

Abstract: Spores of Bacillus subtilis are being used as probiotics and competitive exclusion agents for animal consumption. Commercial production media often include relatively expensive components of animal origin that are a potential source for the presence of adventious agents, therefore undesirable for use in production scale. In this study a new animal-free component, chemically defined medium, was tested for B. subtilis spore production. Medium composition was optimized with respect to vitamin composition, carbon, nitrogen and calcium concentrations. A fed-batch bioprocess was developed, being the effect on sporulation of the carbon to nitrogen ratio at the end of the exponential growth phase studied. The developed strategy consisted of an initial and a final batch phase and an intermediate fed-batch phase with the addition of a feeding solution containing glucose and calcium and the addition of a feeding solution of ammonium sulphate, using an exponential and a constant feeding profile, respectively. Using the fed-batch strategy, it was possible to achieve a maximum spore production of 3.6 × 1010 spores/mL, corresponding to a 5 folds increase when compared to the preliminary batch experiments.

Isolation and Characterization of a New Cyanobacterial Strain with a Unique Fatty Acid Composition

Abstract: A new cyanobacterial strain was isolated and purified from salt Lake Balkhash, Kazakhstan. According to its morphological and ultrastructural characteristics, 16S rRNA sequence and the fatty acid profile, the strain has been classified as Cyanobacterium spp. and assigned as Cyanobacterium sp. IPPAS B-1200. The strain is characterized by a non-temperature inducible Δ9-desaturation system, and by high relative amounts of myristic (14:0—30%) and myristoleic (14:1Δ9—10%) acids. The total amount of C14 fatty acids reaches 40%, which is unusually high for cyanobacteria, and it has never been reported before. The remaining fatty acids are represented mainly by palmitic (16:0) and palmitoleic (16:1Δ9) acids (the sum reaches nearly 60%). Such a fatty acid composition, together with a relatively high speed of growth, makes this newly isolated strain a prospective candidate for biodiesel production.

Multirobot Coordination for Space Exploration

Abstract: Teams of artificially intelligent planetary rovers have tremendous potential for space exploration, allowing for reduced cost, increased flexibility and increased reliability. However, having these multiple autonomous devices acting simultaneously leads to a problem of coordination: to achieve the best results, the they should work together. This is not a simple task. Due to the large distances and harsh environments, a rover must be able to perform a wide variety of tasks with a wide variety of potential teammates in uncertain and unsafe environments. Directly coding all the necessary rules that can reliably handle all of this coordination and uncertainty is problematic. Instead, this article examines tackling this problem through the use of coordinated reinforcement learning: rather than being programmed what to do, the rovers iteratively learn through trial and error to take take actions that lead to high overall system return. To allow for coordination, yet allow each agent to learn and act independently, we employ state-of-the-art reward shaping techniques. This article uses visualization techniques to break down complex performance indicators into an accessible form, and identifies key future research directions.

Sustainable Policy Making: A Strategic Challenge for Artificial Intelligence

Abstract: Policy making is an extremely complex process occurring in changing environments and affecting the three pillars of sustainable development: society, economy and the environment. Each political decision in fact implies some form of social reactions, it affects economic and financial aspects and has substantial environmental impacts. Improving decision making in this context could have a huge beneficial impact on all these aspects. There are a number of Artificial Intelligence techniques that could play an important role in improving the policy making process such as decision support and optimization techniques, game theory, data and opinion mining and agent-based simulation. We outline here some potential use of AI technology as it emerged by the European Union (EU) EU FP7 project ePolicy: Engineering the Policy Making Life-Cycle, and we identify some potential research challenges.

Caerulomycin A- An antifungal compound isolated from marine actinomycetes.

Abstract: Actinomycetes have been prolific sources of novel secondary metabolites with a range of biological activities that may ultimately find application as therapeutic compounds. Hence several drug discovery companies are engaged in isolation of novel bioactive metabolites from these microbial sources. Antibiotics form the major class of such bioactive metabolites and have been widely used for treating infectious diseases. One of the most critical problems in clinical practice is the increase of prevalence of drug resistant strains, especially azole resistance among fungi. Due to this, there is a constant need for development of new antifungal antibiotics having novel scaffolds and/or mechanism of action. In our in-house screening program in the quest of novel and superior antifungal compounds, an actinomycetes strain PM0525875 was isolated from a marine invertebrate. The extracts of this microbe showed potent in-vitro antifungal activity against drug resistant fungal strains. The antifungal active peak from the extract obtained by shake flask fermentation was identified by chromatographic and other analytical techniques during bioactivity guided isolation. Later the fermentation conditions were optimized in 30 L fermentor for the production of sufficient amount antifungal compound for complete structural characterization. Consequently the fermented broth extract was subjected to bioactivity-guided fractionation, to isolate the active principle using different preparative chromatographic techniques followed by its characterization. The active principle was characterized to be Caerulomycin A. Minimum inhibitory concentration (MIC) of the compound was found in the range of 0.39 - 1.56 μg/ml against pathogenic fungal test strains. The phylogenetic analysis of producer strain using 16S rRNA sequence showed closest match with Actinoalloateichus cyanogriseus. Herewith we report the isolation of Caerulomycin A from marine invertebrate-associated Actinoalloteichus sp. using optimized medium and fermentation conditions.

Antimicrobial Resistance in Enterococcus sp Isolated from Soft Cheese in Southern Brazil

Abstract: The aim of this research was to study the incidence of antibiotic resistance in 56 Enterococcus strains isolated from dairy products. The identification of enterococci was detected by polymerase chain reaction (PCR) using specific primers to E. faecalis, E. faecium, E. gallinarum and E. casseliflavus, and antibiotic resistance was tested by the disk diffusion method. The most prevalent species was E. faecium with a rate of 58.33%, followed by 27.77% E. faecalis, 11.11% E. casseliflavus and 2.7% E. gallinarum. Distribution of resistance was found in different species. All isolates were susceptible to chloramphenicol, ampicillin, imipenem and amoxicillin/clavulanic acid. In addition, isolates resistant to tetracyclin, nalidixic acid, amikacin, erythromycin, vancomycin and cephalothin were detected. A total of 66.6% of E. faecium and 58.3% of E. faecalis strain were resistant to multiple drugs. The van(A) gene was detected in 100% of vancomycin resistant enterococci. Considering the results of our study, dairy enterococci can be considered a potential source for dissemination of antibiotic resistances.

Enhanced Telemetry Monitoring with Novelty Detection

Abstract: Typically, automatic telemetry monitoring in space operations is performed by Out-of-Limits (OOL) alarms. This approach consists of defining an upper and lower threshold so that when a measurement goes above the upper limit or below the lower one, an alarm is triggered. We discuss the limitations of the Out-Of-Limits approach and propose a new monitoring paradigm based on novelty detection. The proposed monitoring approach can detect novel behaviors, which are often signatures of anomalies, very early — allowing engineers in some cases to react before the anomaly develops. A prototype implementing this monitoring approach has been implemented and applied to several ESA missions. The operational assessment from the XMM-Newton operations team is presented.

Relation between Antibacterial Activity against Food Transmitted Pathogens and Total Phenolic Compounds in Grape Pomace Extracts from Cabernet Sauvignon and Syrah Varieties

Abstract: Grape pomace is the main by-product of wine production that concentrates bioactive metabolites of polyphenolic nature with antibacterial activity. Since grape pomace composition varied depending on grape variety, climate, vineyard location, and winemaking technology, it is important to study the composition and antibacterial activity of each variety separately. In this study, antibacterial activity against different food pathogens was evaluated and its relation with polyphenols content was determined. Grape pomace from Cabernet Sauvignon and Syrah varieties was extracted with methanol/HCl 1% (v/v), followed by sequential extractions with hexane, chloroform and ethyl acetate. Ethyl acetate fraction had the highest antibacterial activity determined through the microdilution method, reaching over 90% of inhibition at 500 μg·ml-1 with the exception of Salmonella Typhi (70% of inhibition). Staphylococcus aureus and Escherichia coli were the most susceptible strains, exceeding 50% of inhibition at 62.5 μg·ml-1. Ethyl acetate fraction contains the highest phenolic concentration in both Cabernet Sauvignon (132.2 mg of GAE g-1) and Syrah (102.6 mg of GAE g-1) pomace, as determined by the Folin-Ciocalteau method. Antibacterial activity present in grape pomace extracts is in direct relation to the polar phenolic content, in particular that from Cabernet Sauvignon.

The Role of Bio-Fertilization in Improving Fruits Productivity—A Review

Abstract: Biofertilizers consist mainly of beneficial microorganisms that can release nutrients from raw materials and plant residues in the soil and make them available commercially where specific strains are used as biological fertilizers. They become recently, positive alternatives to chemical fertilizers because they help bring down the costs of chemical fertilizers especially N and P and improve soil fertility by maintaining the physical properties of the soil. They may help in improving crop productivity and quality by increasing the biological N fixation, the availability and uptake of nutrients and stimulating the natural hormones. They are safe for humans, animals and environment and using them is accompanied with reducing the pollution occurring in our environment.

Testing of Bacterial Endophytes from Non-Host Sources as Potential Antagonistic Agents against Tomato Wilt Pathogen Ralstonia solanacearum

Abstract: The study was taken up with the objective of testing whether the endophytic organisms isolated from crops that are normally non-hosts to the bacterial wilt pathogen Ralstonia solanacearum possessed pathogen-antagonistic activity and to evaluate the selected isolates for the alleviation of wilt disease in the target tomato crop through horizontal movement of promising organisms. Sixteen endophytic bacteria (EB) isolated from the micropropagated cultures of grape, watermelon and papaya were tested for potential antagonistic effects against R. solanacearum tomato isolate “NH-01” through agar-well diffusion assay. Enterobacter cloacae from papaya (EB-11) displayed the maximum antagonistic effect followed by Bacillus subtilis (EB-06) and B. flexus (EB-07) from watermelon and B. pumilus (EB-02) from grape. Testing the above organisms for crop protection through seed fortification of susceptible tomato cv. Arka Vikas at sowing in R. solanacearum inoculated (Ral+) organic cocopeat showed EB-02 and EB-11 promising (33% and 32% survival, respectively, four weeks after sowing against 15% in Ral+ control). A second trial showed 37%, 28%, 21% and 55% seedling survival 6 weeks after sowing for EB-02, EB-06, EB-07 and EB-11 respectively, compared to 2.5% in non-treated control. Assessing the four endophytes for crop protection in Ral+ sick-soil through seedling fortification at transplanting indicated less disease incidence in treated sets (40%, 40%, 20% and 20% survival, respectively, six weeks after transplanting) over non-fortified control (5%). Endophytic fortification of seedlings through hypocotyl inoculation showed some systemic resistance induction upon seedling transplanting to sick soil but not with petiole fortification. Seedling growth was enhanced by the isolates EB-06 and EB-07. The study thus identifies four endophytic organisms from crops unrelated to tomato possessing potential antagonistic activity against the wilt pathogen and prospects for exploitation as biocontrol agents coupled with seedling growth promotion effects.

Computational Sustainability and Artificial Intelligence in the Developing World

Abstract: The developing regions of the world contain most of the human population and the planet's natural resources, and hence are particularly important to the study of sustainability. Despite some difficult problems in such places, a period of enormous technology-driven change has created new opportunities to address poor management of resources and improve human well-being.

Serotypes of Non-O157 Shigatoxigenic Escherichia coli (STEC)

Abstract: Non-O157 STEC has been shown to have a diverse ecological distribution among food-animals. It has been associated with both outbreaks and individual cases of severe illness. This group of the organisms is now considered as a major contributor to human disease. The clinical description of the diseases caused by these organisms is reviewed. The host specificity of these pathogens is described and discussed. These organisms appear widespread among food animals like cattle and sheep, and can therefore affect a range of foods directly from the meat and excretions of these animals being used in farming practices. This article reviews the origins, diversity and pathogenesis of non-O157 STEC.

Science Autonomy for Rover Subsurface Exploration of the Atacama Desert

Abstract: As planetary rovers expand their capabilities, traveling longer distances, deploying complex tools, and collecting voluminous scientific data, the requirements for intelligent guidance and control also grow. This, coupled with limited bandwidth and latencies, motivates onboard autonomy that ensures the quality of the science data return. Increasing quality of the data involves better sample selection, data validation, and data reduction. Robotic studies in Mars-like desert terrain have advanced autonomy for long distance exploration and seeded technologies for planetary rover missions. In these field experiments the remote science team uses a novel control strategy that intersperses preplanned activities with autonomous decision making. The robot performs automatic data collection, interpretation, and response at multiple spatial scales. Specific capabilities include instrument calibration, visual targeting of selected features, an onboard database of collected data, and a long range path planner that guides the robot using analysis of current surface and prior satellite data. Field experiments in the Atacama Desert of Chile over the past decade demonstrate these capabilities and illustrate current challenges and future directions.

Integrating Digital Pens in Breast Imaging for Instant Knowledge Acquisition

Abstract: Future radiology practices assume that the radiology reports should be uniform, comprehensive, and easily managed. This means that reports must be readable to humans and machines alike. In order to improve reporting practices in breast imaging, we allow the radiologist to write structured reports with a special pen on paper with an invisible dot pattern. In this way, we provide a knowledge acquisition system for printed mammography patient forms for the combined work with printed and digital documents. In this domain, printed documents cannot be easily replaced by computer systems because they contain free-form sketches and textual annotations, and the acceptance of traditional PC reporting tools is rather low among the doctors. This is due to the fact that current electronic reporting systems significantly add to the amount of time it takes to complete the reports. We describe our real-time digital paper application and focus on the use case study of our deployed application. We think that our results motivate the design and implementation of intuitive pen-based user interfaces for the medical reporting process and similar knowledge work domains. Our system imposes only minimal overhead on traditional form-filling processes and provides for a direct, ontology-based structuring of the user input for semantic search and retrieval applications, as well as other applied artificial intelligence scenarios which involve manual form-based data acquisition.

Evaluation of Ribosomal RNA Removal Protocols for Salmonella RNA-Seq Projects

Abstract: Next generation sequencing is a powerful technology whose application in sequencing entire RNA populations (RNA-Seq) of food-borne pathogens will provide valuable insights. A problem unique to prokaryotic RNA-Seq is removal of ribosomal RNA. Unlike eukaryotic messenger RNA (mRNA), bacterial mRNA species are devoid of polyadenylation at the 3’-end and thus the approach of affinity enrichment of mRNA using oligo-dT probes is not an option. Among several approaches to enriching mRNA molecules, removal of ribosomal RNA (rRNA) by subtractive hybridization has been widely used. This approach is a single-step procedure for which several rRNA-depletion kits are commercially available. We evaluated three commercially available rRNA-depletion kits to determine their respective efficiencies of rRNA removal from Salmonella enterica serovar Typhimurium strain SL1344. The three protocols achieved varying degrees of rRNA depletion and resulted in 8 to 1000-fold enrichment of mRNA. rRNA removal probes from two of the three kits were unable to titrate out 23S rRNA species while removal of 16S rRNA was less efficient. The Ribo-Zero kit was most efficient in eliminating 16S, 23S and 5S ribosomal RNA species from the transcriptome of S. enterica serovar Typhimurium strain SL1344.

Exopolysaccharides Produced from Lactobacillus delbrueckii subsp. bulgaricus

Abstract: Lactobacillus delbrueckii subsp. bulgaricus, which has been widely used as a fermented milk starter, is a type of probiotic, and certain strains are able to produce exopolysaccharide (EPS). EPS produced from L. bulgaricus contributes to the physical and biological function of dairy products by regulating immune response, and this tendency seems to place EPS with acidic groups. To date, six types of chemical structure have been determined and are basically composed from glucose (Glc), galactose (Gal), and rhamnose (Rha). Eps clusters on chromosome DNA control the EPS synthesis and are transcribed as one mRNA 14 genes with 18kb on L. bulgaricus Lfi5. Furthermore, L. bulgaricus is able to utilize lactose (Lac) as carbohydrate source, repeating units of EPS are synthesized from Glc 6-phosphate, generated by an Embden-Meyerhof (EM) pathway in cellular carbohydrate assimilation. This review discusses EPS produced from L. bulgaricus.

Effect of Heavy Metals on the Growth of Bacteria Isolated from Sewage Sludge Compost Tea

Abstract: The amount of urban sewage sludge is increasing daily. For this reason, it is necessary to have an environmentally friendly use for this residue. Land applications in olive grove soil, poor quality soil, as a compost or a compost tea could be considered as one of the best options, because it has a high content of organic matter. However, the presence of heavy metals in some sewage sludge could be an environmental problem. In this sense, the aim of this work is to check the tolerance of the culturable microbiota present in sewage sludge compost tea to four heavy metals, Cu, Cd, Pb and Zn. Among microbiota isolated, eight strains showed high resistance to Pb, Zn, Cu and Cd, and one microbe (Rhodococcus sp.) showed a special tolerance to every heavy metal. The strains were divided into six genrera: Rhodococcus, Virgibacillus, Leifsoni, Achromobacter, Cupriavidus and Oceanobacillus sp. Finally, Rhodococcus sp. strain 3 was able to remove different amounts of heavy metals from the culture media with intracellular and surface accumulation of Cu, Zn, Pb and Cd. The application of this bacterial strain in bioremediation processes is discussed.

The Reinforcement Learning Competition 2014

Abstract: Reinforcement learning is one of the most general problems in artificial intelligence. It has been used to model problems in automated experiment design, control, economics, game playing, scheduling and telecommunications. The aim of the reinforcement learning competition is to encourage the development of very general learning agents for arbitrary reinforcement learning problems and to provide a test-bed for the unbiased evaluation of algorithms.

The Skin Microbiome of Gambusia affinis Is Defined and Selective

Abstract: Metagenomics and bacterial culture were used to determine the normal skin microbiome of the Western mosquitofish (Gambusia affinis). This is the first study of G. affinis, and the most in-depth study of any fish skin, utilizing a combination of 16S profile pyrosequencing and culture analysis. Over 1800 sequences obtained from three individuals reveal that over half of all sequences come from five invariant genera, Acinetobacter, Sphingomonas, Acidovorax, Enhydrobacter, and Aquabacterium. The microbiome is diverse but has low equitability, with a total of 81 genera detected. Challenge studies suggest that non-native bacteria cannot colonize the skin. This definition of the normal skin microbiome lays the foundation for future studies with this model system.

Automated Scheduling for NASA's Deep Space Network

Abstract: This article describes the DSN scheduling wngine (DSE) component of a new scheduling system being deployed for NASA's deep space network. The DSE provides core automation functionality for scheduling the network, including the interpretation of scheduling requirements expressed by users, their elaboration into tracking passes, and the resolution of conflicts and constraint violations. The DSE incorporates both systematic search and repair-based algorithms, used for different phases and purposes in the overall system. It has been integrated with a web application which provides DSE functionality to all DSN users through a standard web browser, as part of a peer-to-peer schedule negotiation process for the entire network. The system has been deployed operationally and is in routine use, and is in the process of being extended to support long-range planning and forecasting, and near-real-time scheduling.