For the generation of antibodies against small hapten molecules, the hapten is cross-linked with some carrier protein to make it immunogenic. However, the formation of such conjugates is not always reproducible. This may lead to inconsistent hapten−protein stoichiometries, resulting in large variations in the generation of the desired antibodies. In the study described here the hapten (mercaptopropionic acid derivative of atrazine) was coupled to carrier protein at five different molar ratios. The hapten−protein conjugates prepared were characterized thoroughly by spectrophotometric absorption, fluorescence, matrix-assisted laser desorption ionization (MALDI), and gel electrophoresis methods, before being used for the immunization and assay purposes. Electrophoresis and fluorescence methods were very useful in detecting hapten−protein cross-linking while MALDI-MS and spectrophotometric detection provided qualitatively comparable hapten density. The production of specific antibodies was sought following the generation of appropriate hapten−protein conjugates. A high antibody titer with moderate antibody specificity was obtained with hapten density around 15 molecules per carrier protein. The study proved useful for monitoring the course of hapten−protein conjugation for the production of specific antibodies against small molecules.

Herbicides remain the most effective, efficient and economical way to control weeds; and its market continues to grow even with the plethora of generic products. With the development of herbicide-tolerant crops, use of herbicides is increasing around the world that has resulted in severe contamination of the environment. The strategies are now being developed to clean these substances in an economical and eco-friendly manner. In this review, an attempt has been made to pool all the available literature on the biodegradation of key herbicides, clodinafop propargyl, 2,4-dichlorophenoxyacetic acid, atrazine, metolachlor, diuron, glyphosate, imazapyr, pendimethalin and paraquat under the following objectives: (1) to highlight the general characteristic and mode of action, (2) to enlist toxicity in animals, (3) to pool microorganisms capable of degrading herbicides, (4) to discuss the assessment of herbicides degradation by efficient microbes, (5) to highlight biodegradation pathways, (6) to discuss the molecular basis of degradation, (7) to enlist the products of herbicides under degradation process, (8) to highlight the factors effecting biodegradation of herbicides and (9) to discuss the future aspects of herbicides degradation. This review may be useful in developing safer and economic microbiological methods for cleanup of soil and water contaminated with such compounds.

Short texts challenge NLP tasks such as named entity recognition, disambiguation, linking and relation inference because they do not provide sufficient context or are partially malformed (e.g. wrt. capitalization, long tail entities, implicit relations). In this work, we present the Falcon approach which effectively maps entities and relations within a short text to its mentions of a background knowledge graph. Falcon overcomes the challenges of short text using a lightweight linguistic approach relying on a background knowledge graph. Falcon performs joint entity and relation linking of a short text by leveraging several fundamental principles of English morphology (e.g. compounding, headword identification) and utilizes an extended knowledge graph created by merging entities and relations from various knowledge sources. It uses the context of entities for finding relations and does not require training data. Our empirical study using several standard benchmarks and datasets show that Falcon significantly outperforms state-of-the-art entity and relation linking for short text query inventories.

Large amounts of geospatial data have been made available recently on the linked open data cloud and the portals of many national cartographic agencies (e.g., OpenStreetMap data, administrative geographies of various countries, or land cover/land use data sets). These datasets use various geospatial vocabularies and can be queried using SPARQL or its OGC-standardized extension GeoSPARQL. In this paper, we go beyond these approaches to offer a question-answering engine for natural language questions on top of linked geospatial data sources. Our system has been implemented as re-usable components of the Frankenstein question answering architecture. We give a detailed description of the system's architecture, its underlying algorithms, and its evaluation using a set of 201 natural language questions. The set of questions is offered to the research community as a gold standard dataset for the comparative evaluation of future geospatial question answering engines.

Abstract. It is very challenging to access the knowledge expressed within (big) data sets. Question answering (QA) aims at making sense out of data via a simpleto-use interface. However, QA systems are very complex and earlier approaches are mostly singular and monolithic implementations for QA in specific domains. Therefore, it is cumbersome and inefficient to design and implement new or improved approaches, in particular as many components are not reusable. Hence, there is a strong need for enabling best-of-breed QA systems, where the best performing components are combined, aiming at the best quality achievable in the given domain. Taking into account the high variety of functionality that might be of use within a QA system and therefore reused in new QA systems, we provide an approach driven by a core QA vocabulary that is aligned to existing, powerful ontologies provided by domain-specific communities. We achieve this by a methodology for binding existing vocabularies to our core QA vocabulary without re-creating the information provided by external components. We thus provide a practical approach for rapidly establishing new (domain-specific) QA systems, while the core QA vocabulary is re-usable across multiple domains. To the best of our knowledge, this is the first approach to open QA systems that is agnostic to implementation details and that inherently follows the linked data principles.

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We report here the degradation of a pesticide, malathion, by Brevibacillus sp. strain KB2 and Bacillus cereus strain PU, isolated from soil samples collected from malathion contaminated field and an army firing range respectively. Both the strains were cultured in the presence of malathion under aerobic and energy-limiting conditions. Both strains grew well in the medium having malathion concentration up to 0.15%. Reverse phase HPLC-UV analysis indicated that Strain KB2 was able to degrade 72.20% of malaoxon (an analogue of malathion) and 36.22% of malathion, while strain PU degraded 87.40% of malaoxon and 49.31% of malathion, after 7 days of incubation. The metabolites mal-monocarboxylic acid and mal-dicarboxylic acid were identified by Gas chromatography/mass spectrometry. The factors affecting biodegradation efficiency were investigated and effect of malathion concentration on degradation rate was also determined. The strain was analyzed for carboxylesterase activity and maximum activity 210 ± 2.5 U ml(-1) and 270 U ± 2.7 ml(-1) was observed for strains KB2 and PU, respectively. Cloning and sequencing of putative malathion degrading carboxylesterase gene was done using primers based PCR approach.

This paper addresses the task of (complex) conversational question answering over a knowledge graph. For this task, we propose LASAGNE (muLti-task semAntic parSing with trAnsformer and Graph atteNtion nEtworks). It is the first approach, which employs a transformer architecture extended with Graph Attention Networks for multi-task neural semantic parsing. LASAGNE uses a transformer model for generating the base logical forms, while the Graph Attention model is used to exploit correlations between (entity) types and predicates to produce node representations. LASAGNE also includes a novel entity recognition module which detects, links, and ranks all relevant entities in the question context. We evaluate LASAGNE on a standard dataset for complex sequential question answering, on which it outperforms existing baseline averages on all question types. Specifically, we show that LASAGNE improves the F1-score on eight out of ten question types; in some cases, the increase in F1-score is more than 20% compared to the state of the art.