Showing papers in "Ai Magazine in 2015"

Research Priorities for Robust and Beneficial Artificial Intelligence

Abstract: Success in the quest for artificial intelligence has the potential to bring unprecedented benefits to humanity, and it is therefore worthwhile to investigate how to maximize these benefits while avoiding potential pitfalls. This article gives numerous examples (which should by no means be construed as an exhaustive list) of such worthwhile research aimed at ensuring that AI remains robust and beneficial.

Truth Is a Lie: Crowd Truth and the Seven Myths of Human Annotation

Abstract: Big data is having a disruptive impact across the sciences. Human annotation of semantic interpretation tasks is a critical part of big data semantics, but it is based on an antiquated ideal of a single correct truth that needs to be similarly disrupted. We expose seven myths about human annotation, most of which derive from that antiquated ideal of truth, and dispell these myths with examples from our research. We propose a new theory of truth, crowd truth, that is based on the intuition that human interpretation is subjective, and that measuring annotations on the same objects of interpretation (in our examples, sentences) across a crowd will provide a useful representation of their subjectivity and the range of reasonable interpretations.

The 2014 International Planning Competition: Progress and Trends

Abstract: We review the 2014 International Planning Competition (IPC-2014), the eighth in a series of competitions starting in 1998. IPC-2014 was held in three separate parts to assess state-of-the-art in three prominent areas of planning research: the deterministic (classical) part (IPCD), the learning part (IPCL), and the probabilistic part (IPPC). Each part evaluated planning systems in ways that pushed the edge of existing planner performance by introducing new challenges, novel tasks, or both. The competition surpassed again the number of competitors than its predecessor, highlighting the competition’s central role in shaping the landscape of ongoing developments in evaluating planning systems.

Exploiting Semantics for Big Data Integration

Abstract: There is a great deal of interest in big data, focusing mostly on dataset size. An equally important dimension of big data is variety, where the focus is to process highly heterogeneous datasets. We describe how we use semantics to address the problem of big data variety. We also describe Karma, a system that implements our approach and show how Karma can be applied to integrate data in the cultural heritage domain. In this use case, Karma integrates data across many museums even though the datasets from different museums are highly heterogeneous.

Why the Data Train Needs Semantic Rails

Abstract: While catchphrases such as big data, smart data, dataintensive science, or smart dust highlight different aspects, they share a common theme: Namely, a shift towards a data-centric perspective in which the synthesis and analysis of data at an ever-increasing spatial, temporal, and thematic resolution promises new insights, while, at the same time, reducing the need for strong domain theories as starting points. In terms of the envisioned methodologies, those catchphrases tend to emphasize the role of predictive analytics, i.e., statistical techniques including data mining and machine learning, as well as supercomputing. Interestingly, however, while this perspective takes the availability of data as a given, it does not answer the question how one would discover the required data in today’s chaotic information universe, how one would understand which datasets can be meaningfully integrated, and how to communicate the results to humans and machines alike. The Semantic Web addresses these questions. In the following, we argue why the data train needs semantic rails. We point out that making sense of data and gaining new insights works best if inductive and deductive techniques go hand-in-hand instead of competing over the prerogative of interpretation.

Identification of Bacterial Fish Pathogens in Brazil by Direct Colony PCR and 16S rRNA Gene Sequencing

Abstract: Intensive fish farming systems in Brazil have increased the disease incidence, mainly of bacterial origin, due to higher stocking density, high organic matter levels and poor quality of the aquatic environment that causes high mortality rates during outbreaks. The identification of pathogenic species using a fast and reliable method of diagnosis is essential for successful epidemiological studies and disease control. The present study evaluated the use of direct colony PCR in combination with 16S rRNA gene sequencing to diagnose fish bacterial diseases, with the goal of reducing the costs and time necessary for bacterial identification. The method was successful for all 178 isolates tested and produced bands with the same intensity as the standard PCR performed using pure DNA. In conclusion, the genetics methods allowed detecting the most common and important pathogens in Aquaculture, including 12 species of occurrence in Brazilian fish farms. The results of the present study constitute an advance in the available diagnostic methods for bacterial pathogens in fish farms.

Identification Key for Aspergillus Species Isolated from Maize and Soil of Nandi County, Kenya

Abstract: The aim of this study was to identify Aspergillus species isolated from maize kernels and soils of maize fields of Nandi County using macro and micro morphological characteristics. A cross sectional research design was used in the study and purposive sampling was employed to determine districts of Nandi County and sub locations where sampling was done. This study was part of a larger project whose aim was to survey aflatoxin exposure in the maize value chain. Aspergillus species were isolated from maize and soil samples using quarter strength potato dextrose agar and modified Rose Bengal agar respectively. Pure cultures of the isolates were sub cultured and transferred onto differential media; malt extract agar, czapek yeast extract agar and czapek dox agar for species identification using macro morphological characteristics. Fungal slides were prepared from pure cultures on potato dextrose agar media after three days to identify micro morphological characteristics. Based on morphological characteristics, seven sections of Aspergillus namely: Flavi, Fumigati, Nigri, Circumdati, Clavati, Nidulantes and Candidi were identified. Aspergillus section Flavi was the most predominant with 57% followed by section Nigri with 27% from maize and 58% of section Flavi followed by 26% of section Nigri from the soil across the three locations. Aspergillus sections Nidulantes and Candidi were rare and only recovered from the soil samples of Kaptumo location. All the Aspergillius flavus that formed sclerotia both from the soils or maize kernels were of the L strains. In conclusion Aspergillus section Flavi was most frequent during the isolation process and dominated with Aspergillus flavus from both the maize and soil. Morphological characteristics remain the primary tool for detection and identification of Aspergillus species. The significance for high incidence of Aspergillus section Flavi is in regard to their aflatoxin production profiles that poses a health threat to the community and it is of public health concern. Morphological characteristics as a primary tool for Aspergillus identification should be embraced and more personnel with the knowledge are required since modern and faster techniques are scarce and expensive.

Cognition as a Service: An Industry Perspective

Abstract: Recent advances in cognitive computing componentry combined with other factors are leading to commercially viable cognitive systems. From chips to smart phones to public and private clouds, industrial strength “cognition as a service” is beginning to appear at all scales in business and society. Furthermore, in the age of zettabytes on the way to yottabytes, the designers, engineers, and managers of future smart systems will depend on cognition as a service. Cognition as a service can help unlock the mysteries of big data and ultimately boost the creativity and productivity of professionals and their teams, the productive output of industries and organizations, as well as the GDP (gross domestic product) of regions and nations. In this and the next decade, cognition as a service will allow us to re-image work practices, augmenting and scaling expertise to transform professions, industries, and regions.

Optimization of Culture Conditions for Production of Bioactive Metabolites by Streptomyces spp. Isolated from Soil

Abstract: The current work was carried out under a screening program targeted at isolation of bioactive Streptomyces species from soil samples. A total of 54 Streptomyces species were isolated from soil samples, out of which 4 isolates were found to be promising. These isolates were identified as Streptomyces spectabilis, Streptomyces purpurascens, Streptomyces coeruleorubidus and Streptomyces lavendofoliae and their sequences have been deposited in the GenBank. The influence of culture conditions including, incubation time, incubation temperature, initial pH and different carbon and nitrogen sources on growth and bioactive compound formation was investigated. Isolate R1, identified as Streptomyces spectabilis, showed maximum bioactive metabolite production with cellobiose and peptone as the carbon and nitrogen sources, on the 5th day at pH 5 at 30℃. The optimum conditions for production by isolate R3, identified as Streptomyces purpurascens, were observed to be starch and casein as the carbon and nitrogen sources, pH 7, temperature 30℃ and an incubation period of eight days. For isolate R5, identified as Streptomyces coeruleorubidus, maximal production resulted on the sixth day at pH 6 and temperature of 35℃ with mannitol and JBM. Isolate Y8, identified as Streptomyces lavendofoliae, was found to produce high levels of bioactive metabolites in the medium supplemented with starch and peptone on the 10th day at pH 7 and at an incubation temperature of 30℃. The four strains tested here behaved differently, each one requiring specific conditions for maximum growth as well as bioactive metabolite production.

Letter to the Editor: Research Priorities for Robust and Beneficial Artificial Intelligence: An Open Letter

Abstract: Artificial intelligence (AI) research has explored a variety of problems and approaches since its inception, but for the last 20 years or so has been focused on the problems surrounding the construction of intelligent agents — systems that perceive and act in some environment. In this context, "intelligence" is related to statistical and economic notions of rationality — colloquially, the ability to make good decisions, plans, or inferences. The adoption of probabilistic and decision-theoretic representations and statistical learning methods has led to a large degree of integration and cross-fertilization among AI, machine learning, statistics, control theory, neuroscience, and other fields. The establishment of shared theoretical frameworks, combined with the availability of data and processing power, has yielded remarkable successes in various component tasks such as speech recognition, image classification, autonomous vehicles, machine translation, legged locomotion, and question-answering systems. As capabilities in these areas and others cross the threshold from laboratory research to economically valuable technologies, a virtuous cycle takes hold whereby even small improvements in performance are worth large sums of money, prompting greater investments in research. There is now a broad consensus that AI research is progressing steadily, and that its impact on society is likely to increase. The potential benefits are huge, since everything that civilization has to offer is a product of human intelligence; we cannot predict what we might achieve when this intelligence is magnified by the tools AI may provide, but the eradication of disease and poverty are not unfathomable. Because of the great potential of AI, it is important to research how to reap its benefits while avoiding potential pitfalls. The progress in AI research makes it timely to focus research not only on making AI more capable, but also on maximizing the societal benefit of AI. Such considerations motivated the AAAI 2008–09 Presidential Panel on Long-Term AI Futures and other projects on AI impacts, and constitute a significant expansion of the field of AI itself, which up to now has focused largely on techniques that are neutral with respect to purpose. We recommend expanded research aimed at ensuring that increasingly capable AI systems are robust and beneficial: our AI systems must do what we want them to do. The attached research priorities document [see page X] gives many examples of such research directions that can help maximize the societal benefit of AI. This research is by necessity interdisciplinary, because it involves both society and AI. It ranges from economics, law and philosophy to computer security, formal methods and, of course, various branches of AI itself. In summary, we believe that research on how to make AI systems robust and beneficial is both important and timely, and that there are concrete research directions that can be pursued today.

Production of Enzymes and Biotransformation of Orange Waste by Oyster Mushroom, Pleurotus pulmonarius (Fr.) Quél.

Abstract: The wood-decay fungi are able to bioconvert a wide variety of lignocellulosic residues due to the secretion of extracellular enzymes. The use of agricultural wastes as substrate for mushroom cultivation or enzymes production can help to solve environmental problems caused by inadequate discharge in the nature. The production of hydrolytic and oxidative enzymes by Pleurotus pulmonarius developed in solid state system using orange waste as substrate was evaluated in this work. Among the hydrolytic enzymes, pectinase was the main enzyme produced by the fungus, presenting the highest enzymatic activity of 9.4 U/mL after 35 days of cultivation. Considering the oxidative enzymes, laccase was the main enzyme produced with maximal activity of 12.2 U/mL obtained after 20 days of cultivation. Low enzyme levels of manganese peroxidase, β-glucosidase and β-xy-losidase were detected with activity peaks at the end of the cultivation. The enzymatic levels of amylase, carboxymethyl cellulase and xylanase were similar and less than 1.5 U/mL. No aryl-alcohol oxidase activity was detected. NDF, ADF and cellulose values increased during 45 days of cultivation. There was no lignin degradation during the study period and the fungus culture in orange solid waste caused protein enrichment in the substrate. Our results demonstrate that P. pulmonarius was an efficient producer of two important industrial enzymes, pectinase and laccase in a cheap solid state system using orange waste as substrate.

THink: Inferring Cognitive Status from Subtle Behaviors

Abstract: The digital clock drawing test is a fielded application that provides a major advance over existing neuropsychological testing technology. It captures and analyzes high precision information about both outcome and process, opening up the possibility of detecting subtle cognitive impairment even when test results appear superficially normal. We describe the design and development of the test, document the role of AI in its capabilities, and report on its use over the past seven years. We outline its potential implications for earlier detection and treatment of neurological disorders. We set the work in the larger context of the THink project, which is exploring multiple approaches to determining cognitive status through the detection and analysis of subtle behaviors.

The Automated Negotiating Agents Competition, 2010–2015

Abstract: The Automated Negotiating Agents Competition is an international event that, since 2010, has contributed to the evaluation and development of new techniques and benchmarks for improving the state-of-the-art in automated multi-issue negotiation. A key objective of the competition has been to analyze and search the design space of negotiating agents for agents that are able to operate effectively across a variety of domains. The competition is a valuable tool for studying important aspects of negotiation including profiles and domains, opponent learning, strategies, bilateral and multilateral protocols. Two of the challenges that remain are: How to develop argumentation-based negotiation agents that next to bids, can inform and argue to obtain an acceptable agreement for both parties, and how to create agents that can negotiate in a human fashion.

Prevalence and Resistance Pattern of Pseudomonas aeruginosa Isolated from Surface Water

Abstract: Pseudomonas aeruginosa is one of the most common pathogenic bacteria, frequently found in different environmental samples. The prevalence of multidrug resistant isolates has become an alarming concern for both patients and their surroundings. The present study was carried out to record prevalence of P. aeruginosa in surface water of Dhaka city and to screen their antibiotic resistance pattern. The study was also extended to typing of resistant isolates according to extended spectrum beta lactamase production. Hereby, Kirby-Bauer method was applied to test antibiotic sensitivity according to Clinical and Laboratory Standards Institute. Then, the Ampicillin resistant isolates were screened for ESBL production by Double Disk Synergy Test (DDST). In these prospects, 52 water samples were tested, of which 32 were found positive for P. aeruginosa isolates. Hundred percent of the positive isolates were found to Ampicillin (AMP) resistant followed by 93.7% to both Tetracycline and Gentamycin and 71.8% to Co-triimoxazole. P. aeruginosa is completely susceptible to third generation antibiotics ciprofloxacin, Imipenem and Aztreonam followed by moderately susceptible to Polymyxin-B (78.2%) and Colistin (87.5%). According to DDST, all of the susceptible isolates were found positive for AMC type beta-lactamase production. It is evident from this study that the surface water is contaminated with antibiotic resistant P. aeruginosa and that through the water systems antibiotic resistance can be transferred to humans and animals. So, appropriate and rationale use of antibiotic should be applied to minimize the emergence of multidrug isolates to environment.

Influence of Freezing and Freeze Drying on Intracellular Enzymatic Activity and Autolytic Properties of Some Lactic Acid Bacterial Strains

Abstract: Lactic acid bacteria possess several interesting properties of great economic importance. Improvement and stabilization of these industrially important features are an active research area at the present time. The objectives of this work are to study the effect of freezing and freeze-drying on the survival rate, autolytic activity and intracellular enzymatic activity of the main species of lactic acid bacteria used in the dairy industry. The article focused on several characteristics that were not well covered in the past. The obtained results revealed that both preservation methods have a significant effect on viability, autolytic activity and intracellular enzymatic activity. After six months of storage we found that frozen cultures exhibited higher survival rate, higher rate of intracellular enzymatic activity and lower rate of autolysis. The impact of conservation treatments was only strain specific in the case of survival rate. The results obtained lead to the selection of the best preservation method for the selected cultures based on survival rate, autolytic activity and intracellular enzymatic activity.

Silver Inhibits the Biofilm Formation of Pseudomonas aeruginosa

Abstract: Biofilm is the assemblage of microbial cells that are irreversibly associated with biotic and abiotic surfaces and is usually enclosed in the self secreted extracellular polymeric substances (EPS). The presence of EPS in biofilm makes the microbial population resistance against antibiotics and other drugs. Biofilms are considered as a serious challenge to pharmaceutical industries because most of the microbial diseases are now associated with biofilm. In this context, we have addressed the biofilm potentialities of Pseudomonas aeruginosa, which has been found to be associated with several deadly diseases including septicemia, urinary tract infections, and gastrointestinal infections, and wherein biofilm plays a crucial role in pathogenesis. Since silver had been used globally for a long time for treating a wide range of illnesses from burn wounds, typhoid, and anthrax to bacterial conjunctivitis in newborns, but its antibiofilm activity is still unknown. Thus, in this current study, we have tried to examine the antibiofilm potentiality of silver against the biofilm of Pseudomonas aeruginosa. Our result showed that silver exhibited considerable antimicrobial property against Pseudomonas aeruginosa where the minimum inhibitory concentration (MIC) was found at 25 μg/ml. Biofilm inhibition by silver against Pseudomonas aeruginosa was then evaluated by crystal violet (CV) staining, estimation of total biofilm protein and microscopy based microbial adherence test using the sub MIC doses of silver. The results showed that all the tested sub MIC doses of silver exhibited considerable antibiofilm activity against P. aeruginosa, wherein the maximum biofilm attenuation was showed by a silver concentration of 20 μg/ml. We also observed that all these sub MIC doses of silver neither interfere with the growth cycle of the bacteria nor affect the cell viability but only attenuates biofilm formation property of the bacteria. The current study deciphers a new axis in biofilm biology where a metal like silver can inhibit the formation of biofilm markedly. Thus, the knowledge gathered in this study may help the pharmaceutical sector to design combinatorial drug where silver could be an important partner to reduce the load of pathogenesity caused by biofilm.

The Angry Birds AI Competition

Abstract: The aim of the Angry Birds AI competition (AIBIRDS) is to build intelligent agents that can play new Angry Birds levels better than the best human players. This is surprisingly difficult for AI as it requires similar capabilities to what intelligent systems need for successfully interacting with the physical world, one of the grand challenges of AI. As such the competition offers a simplified and controlled environment for developing and testing the necessary AI technologies, a seamless integration of computer vision, machine learning, knowledge representation and reasoning, reasoning under uncertainty, planning, and heuristic search, among others. Over the past three years there have been significant improvements, but we are still a long way from reaching the ultimate aim and, thus, there are great opportunities for participants in this competition.

Chitinolytic Assay and Identification of Bacteria Isolated from Shrimp Waste Based on 16S rDNA Sequences

Abstract: Shrimp waste contains 20% - 60% chitin and possible to be source of chitinolytic bacteria. Chitinolytic bacteria are capable of hydrolyzing of chitin progressively to produce N-acetylglucosamine monomer which can be used to overcome the shrimp waste. The objectives of this research were to identify species of bacteria with high activity of chitin degradation in shrimp waste and to analyze their potency as chitin degradation agent. The research consists of screening of chitinolytic bacteria based on chitinolytic index, activity assay of chitinase using colorimetric method, and molecular identification of bacteria based on 16S rDNA sequences. Two of eighteen isolates of chitinolytic bacteria (PBK 2 and SA 1.2 isolates) showed the highest chitinolytic index, which were 2.069 and 2.084, whereas chitinase activity was 0.213 and 0.219 U/ml respectively. Based on 16S rDNA sequences, isolate of PBK 2 was identified as Acinetobacter johnsonii 3-1, whereas SA 1.2 was identified as Bacillus amyloliquefaciens GR53 with 99.78% similarity.

Entity Type Recognition for Heterogeneous Semantic Graphs

Abstract: We describe an approach for identifying fine-grained entity types in heterogeneous data graphs that is effective for unstructured data or when the underlying ontologies or semantic schemas are unknown. Identifying fine-grained entity types, rather than a few high-level types, supports coreference resolution in heterogeneous graphs by reducing the number of possible coreference relations that must be considered. Big data problems that involve integrating data from multiple sources can benefit from our approach when the datas ontologies are unknown, inaccessible or semantically trivial. For such cases, we use supervised machine learning to map entity attributes and relations to a known set of attributes and relations from appropriate background knowledge bases to predict instance entity types. We evaluated this approach in experiments on data from DBpedia, Freebase, and Arnetminer using DBpedia as the background knowledge base.

The Role of Biofertilization in Improving Apple Productivity―A Review

Abstract: Biofertilization of crops with plant growth promoting microorganisms is currently considered as a healthy alternative to chemical fertilization. Biofertilizers are microbial preparations containing living cells of different microorganisms which have the ability to mobilize plant nutrients in soil from unusable to usable form. They are environmentally friendly, play a significant role in the crop production, help to build up the lost microflora and improve the soil health. Also, they increase crop yield by 20% - 30%, stimulate plant growth, are cost effective and provide optimal conditions for soil biological activity. They suppress pathogenic soil organisms, restore natural soil fertility and provide protection against drought and some soil borne diseases. Moreover, they degrade toxic organic chemicals, improve seed germination and aid in balancing soil pH in reducing soil erosion.

Cognitive Orthoses: Toward Human-Centered AI

Abstract: This introduction focuses on how human-centered computing (HCC) is changing the way that people think about information technology. The AI perspective views this HCC framework as embodying a systems view, in which human thought and action are linked and equally important in terms of analysis, design, and evaluation. This emerging technology provides a new research outlook for AI applications, with new research goals and agendas.

Human-Centered Design of Wearable Neuroprostheses and Exoskeletons

Abstract: Human-centered design of wearable robots involves the development of innovative science and technologies that minimize the mismatch between humans’ and machines’ capabilities, leading to their intuitive integration and confluent interaction. Here, we summarize our human-centered approach to the design of closed-loop brain-machine interfaces (BMI) to powered prostheses and exoskeletons that allow people to act beyond their impaired or diminished physical or sensory-motor capabilities. The goal is to develop multifunctional human-machine interfaces with integrated diagnostic, assistive and therapeutic functions. Moreover, these complex human-machine systems should be effective, reliable, safe and engaging and support the patient in performing intended actions with minimal effort and errors with adequate interaction time. To illustrate our approach, we review an example of a user-in-the-loop, patient-centered, non-invasive BMI system to a powered exoskeleton for persons with paraplegia. We conclude with a summary of challenges to the translation of these complex human-machine systems to the end-user.

Selection of Lactic Acid Bacteria Isolated from San Simón da Costa Cheese (PDO) in Order to Develop an Autochthonous Starter Culture

Abstract: Traditional cheeses are an important reservoir of microbial diversity that can have important biotechnological applications, especially with a view to improving the characteristics unique to each type of cheese, and in this respect, starter cultures consisting of autochthonous lactic acid bacteria strains are of particular interest. In the present study, we investigated lactic acid bacteria population diversity in San Simon da Costa cheese (PDO, Galicia, Spain) and found a predominance of the genus Lactobacillus, which by the end of ripening accounted for 78% of the strains isolated in Rogosa agar, around 40% of those in M17 agar and about 10% of those in MSE agar. The main species of lactic acid bacteria identified were Lactococcus lactis subsp. lactis, Lactobacillus casei subsp. casei, Lb. paracasei subsp. paracasei, Leuconostoc mesenteroides and Enterococcus faecalis. Virtually all the strains studied from a technological point of view yielded more than or equal to 0.24 g 100 mL-1 lactic acid. Lactococcus lactis subsp. lactis (SS 194), Lactobacillus paracasei (SS 1695 and SS 1689) and Enterococcus faecalis (SS 1378 and SS 1449) strains exhibited the greatest proteolytic capacity. Based on the overall technological aptitude of the tested strains, we can propose starter cultures and co-cultures that include different combinations of previous strains with a view to manufacturing San Simon da Costa cheese from pasteurised milk.

Using Semantics and Statistics to Turn Data into Knowledge

Abstract: Many information extraction and knowledge base construction systems are addressing the challenge of deriving knowledge from text A key problem in constructing these knowledge bases from sources like the web is overcoming the erroneous and incomplete information found in millions of candidate extractions To solve this problem, we turn to semantics — using ontological constraints between candidate facts to eliminate errors In this article, we represent the desired knowledge base as a knowledge graph and introduce the problem of knowledge graph identification, collectively resolving the entities, labels, and relations present in the knowledge graph Knowledge graph identification requires reasoning jointly over millions of extractions simultaneously, posing a scalability challenge to many approaches We use probabilistic soft logic (PSL), a recently-introduced statistical relational learning framework, to implement an efficient solution to knowledge graph identification and present state-of-the-art results for knowledge graph construction while performing an order of magnitude faster than competing methods

Assessment of Mycorrhizal Frequency in the Roots of Fruit Plants Using Different Dyes

Abstract: The aim of the experimental work was to optimize the previously used Phillips and Hayman (1970) method of staining roots for microscopic assessment of mycorrhizal frequency in the root material. The materials used in the experiment were the roots of strawberry plants of the cultivar “Elsanta” obtained in a greenhouse experiment in May 2012. Morphological features of the roots were assessed using a root scanner, and then attempts were made to stain the roots in four types of dyes: 0.01% methylene blue, 0.01% acridine orange, 0.01% malachite green, and 0.01% carbol fuchsin. A comparative microscopic assessment was made of the effects of staining with the four types of dyes the structures of mycorrhizal fungi formed in strawberry roots. Mycorrhizal frequency (F%) in the stained root samples was also compared. Next, the usefulness of these dyes for staining the roots of other fruit plant species, such as apple, sweet cherry, blackcurrant and sour cherry, was evaluated. It was concluded that the staining of roots at a temperature of up to 65°C did not cause tissue breakdown, and that the clearing time used—20 to 30 minutes, and the use of carbol fuchsin were found to be the most effective combinations in achieving good quality microscopic images. During microscopic examinations, a satisfactory contrast was noted between root tissues and the structures of mycorrhizal fungi. The use of carbol fuchsin for staining roots also helped to expose a greater number of fungal structures and obtain higher values of mycorrhizal frequency in the roots of strawberries, blackcurrants, sour cherries, sweet cherries and apples, in comparison with other dyes tested. The newly developed method of staining roots with carbol fuchsin, compared with the method used previously (Phillips and Hayman, 1970), is both less time consuming and less labour intensive.

The Influence of Bioproducts on Mycorrhizal Occurrence and Diversity in the Rhizosphere of Strawberry Plants under Controlled Conditions

Abstract: The new products obtained from natural resources are an alternative to methods based on traditional mineral fertilizers, which are destructive for soil mycorrhizal communities. Our experiment was carried out to evaluate the effect of organic fertilizers and amendments of very diverse composition on mycorrhizal abundance and diversity, as well as on root growth, in strawberry plants cv. “Honeoye”. The plants were grown in rhizoboxes filled with a podsolic soil. The plants were treated with granulated bovine manure, vermicompost extract, humates extract, plant extract, extract from seaweed species reinforced with humic and fulvic acids, a consortium of beneficial soil organisms, a stillage from yeast production and a solution of titanium. Plants treated with products and the microorganisms consortium also received half dose of manure. A standard mineral fertilization (NPK) and an unfertilized control were also included. The bioproducts based on humus-like substances and the yeast stillage had the greatest positive influence on the colonization of roots by arbuscular mycorrhizal fungi (AMF). The different treatments affected the diversity of AMF species present in the rhizospheric soil. All organic products, even though providing a significantly low amount of nutrients, enhanced root growth characteristics in comparison to the mineral fertilization.

Effects of Streptomyces Biofertilizer to Soil Fertility and Rhizosphere’s Functional Biodiversity of Agricultural Plants

Abstract: In the present study, a biofertilizer on the basis of Streptomyces fumanus gn-2 was used for the treatment of wheat and soybean seeds (dose 104 spore/ml) before planting them in soil with low fertility in order to determine the effect of this biological agent on germination rate; the growth of seedlings, shoots, and the maturation phase of plants; the rhizosphere’s functional biodiversity; and the resistance of these plants to pathogens Seeds were soaked in the suspension for a period of two or three hours During the growing season of the crop, no additional fertilizing and spraying of a biopesticide against diseases or pests occurred Despite the soil having low fertility, low quantities of organic matter, and not having been before used for the cultivation of agricultural plants, this biofertilizer showed a strong stimulatory effect on the growth of seeds and seedlings of wheat and soybeans The average germination and seed vigor increased by 15 - 20 times, and the phenophases were accelerated to three to five days In all phases of vegetation, the ammonifying bacteria in the presence of an antagonist (a biological agent) developed rapidly and were constantly present in significant numbers in the rhizosphere Streptomyces fumanus introduced into non-sterile soil entered into competition with the local soil microflora and had the ability to colonize the rhizosphere system of plants The use of a formulation of Streptomyces gn-2 has improved the composition of rhizosphere microflora, attracting saprophytic microorganisms: ammonificators and oligotrophs The presence of the biocontrol microorganism Streptomyces fumanus in the rhizosphere plays an important role in enhancing the growth and development of useful groups, such as nitrogen-fixing bacteria

Distribution and Prevalence of Dermatophytes in Semi-Arid Region of India

Abstract: Earth has been documented as a natural territory for fungi which cover individual kingdom with evolution. In subsequently vertebrates developed keratin which was a part of life as a structural aspect. Few moulds have skilled to digest keratin and crop up from soil and wastewater habitats. They take part as a keratinolytic agent in the purification of α-keratins with an incidence of disulphide and hydrogen bonds which are improperly biodegradable. The best moulds genera to decay of keratin are Microsporum, Trichophyton and Epidermophyton. The presences of these genera are open health issues in developing countries where they cause the mortal mycotic contagion. The reason behind this is perceived to be the poor hygienic environment and socioeconomic behaviour among people. The present review is a compilation of updated information concerning the nature of these keratinolytic moulds and abundances of most contributed developing countries including India.

Studies on Fungi Responsible for the Spoilage/Deterioration of Some Edible Fruits and Vegetables

Abstract: Investigation of fungi responsible for the post harvest deterioration of Lycopersicum esculentus (tomatoes), Elaeis guineensis (palm fruit), Ipomoea batatas (sweet potato), Solanum tuberosum (Irish potato), Musa sapientum (banana), Doucus carota (carrot), Musa paradisiaca (plantain), Carica papaya (pawpaw), Persea americana (Avocado pear), Citrullus lanatus (water-melon) and Capsicum chinense (fresh red pepper) from five different markets and farm lands in Enugu state, Nigeria was carried out. Healthy and diseased samples were collected from the selected markets/ farmlands. Fungal species found associated with the deterioration of the various fruits and vegetables tested included Mucor species (M. indicus, M. amphibiorum, M. racemosus and M. hiemalis), Rhizopus species (Rhizopus stolonifer, R. nigrican and R. oligosporus), Candida albicans, Aspergillus species (Aspergillus fumigatus, A. niger and A. flavus) and Penicillum species (P. oxalicum and P. chrysogenum) and Fusarium species (F. accuminatum, F. oxysporum, F. eqiuseti and F. moniliforme, F. solani, F. dimerum). All isolated fungi were pathogenic to the different fruits and vegetables from the result of pathogencity tests carried out.

Influence of Sowing Season and Host Crop Identity on the Community Structure of Arbuscular Mycorrhizal Fungi Colonizing Roots of Two Different Gramineous and Leguminous Crop Species

Abstract: Introduction of cover crops may improve the diversity of arbuscular mycorrhizal fungi (AMF) in roots and soil under crop rotational systems; therefore, it is necessary to determine the potential for AMF communities to improve sustainable food production. We investigated the impact of cover crops, including wheat (Triticum aestivum L.), barley (Hordeum vulgare L.), pea (Pisum sativum L.), and hairy vetch (Vicia villosa Roth.), on the AMF communities in their roots in autumn and spring sowing seasons with PCR-DGGE analysis. Although all four cover crops impacted the AMF community structure in roots, the diversity of AMF communities was unchanged among crop type or sowing season. Redundancy analysis (RDA) demonstrated that AMF communities within crop type were significantly different. However, the AMF community structures were not influenced by growing season, suggesting that growth stage in crops may be more responsive to shaping AMF community structure in crop roots than host crop identity.