Development of a data set of pesticide dissipation rates in/on various plant matrices for the Pesticide Properties Database

© 2017 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).Data relating to the rate at which pesticide active substances dissipate on or within various plant matrices are important for a range of different risk assessments including those related to occupational exposure and consumer safety. However, despite the importance of this data, dissipation rates, often expressed as the pesticide half-life, are not included in the most common online data resources. Databases have been collated in the past but these tend not to be maintained or regularly updated. The purpose of the exercise described herein was to collate a new database in a format compatible with the main online pesticide database resource (the Pesticide Properties Database, PPDB), to validate this database in line with the Pesticide Properties Database protocols and thus ensure that the data is maintained and updated in future. The outcome of the study is a database based on data collated from 1390 published articles covering over 400 pesticides and over 200 crops across a wide variety of different matrices (leaves, fruits, seeds etc.) for pesticide residues on the crop surface as well as residues absorbed within the plant material. This data is now fully incorporated into the PPDB.Peer reviewe

Framework for the Analysis of the Adaptability, Extensibility, and Scalability of Semantic Information Integration and the Context Mediation Approach

Technological advances such as Service Oriented Architecture (SOA) have increased the feasibility and importance of effectively integrating information from an ever widening number of systems within and across enterprises. A key difficulty of achieving this goal comes from the pervasive heterogeneity in all levels of information systems. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional semantic integration approaches. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs from a small number of conversion rules. The capabilities of COIN is demonstrated using an example with 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic interoperability using only six parametizable conversion rules. This paper presents a framework for evaluating adaptability, extensibility, and scalability of semantic integration approaches. The application of the framework is demonstrated with a systematic evaluation of COIN and other commonly practiced approaches.This work has been supported, in part, by MITRE Corp., the MIT-MUST project, the Singapore-MIT Alliance, and Suruga Bank

Semantic Information Integration in the Large: Adaptability, Extensibility, and Scalability of the Context Mediation Approach

There is pressing need for effectively integrating information from an ever increasing number of available sources both on the web and in other existing systems. A key difficulty of achieving this goal comes from the pervasive heterogeneities in all levels of information systems. Existing and emerging technologies, such as the Web, ODBC, XML, and Web Services, provide essential capabilities in resolving heterogeneities in the hardware and software platforms, but they do not address the semantic heterogeneity of the data itself. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional approaches that address this problem using hand-coded programs or require complete data standardization. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs using a small number of conversion rules. The capabilities of COIN is demonstrated using an intelligence information integration example consisting of 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic integration using only six parametizable conversion rules. This paper makes a unique contribution by providing a systematic evaluation of COIN and other commonly practiced approaches

Semantic Information Integration in the Large: Adaptability, Extensibility, and Scalability of the Context Mediation Approach

There is pressing need for effectively integrating information from an ever increasing number of available sources both on the web and in other existing systems. A key difficulty of achieving this goal comes from the pervasive heterogeneities in all levels of information systems. Existing and emerging technologies, such as the Web, ODBC, XML, and Web Services, provide essential capabilities in resolving heterogeneities in the hardware and software platforms, but they do not address the semantic heterogeneity of the data itself. A robust solution to this problem needs to be adaptable, extensible, and scalable. In this paper, we identify the deficiencies of traditional approaches that address this problem using hand-coded programs or require complete data standardization. The COntext INterchange (COIN) approach overcomes these deficiencies by declaratively representing data semantics and using a mediator to create the necessary conversion programs using a small number of conversion rules. The capabilities of COIN is demonstrated using an intelligence information integration example consisting of 150 data sources, where COIN can automatically generate the over 22,000 conversion programs needed to enable semantic integration using only six parametizable conversion rules. This paper makes a unique contribution by providing a systematic evaluation of COIN and other commonly practiced approaches

Enhancing CAR T Cell Therapy Using Fab Based Constitutively Heterodimeric Cytokine Receptors

Adoptive T-cell therapy aims to achieve lasting tumor clearance, requiring enhanced engraftment and survival of the immune cells. Cytokines are paramount modulators of T-cell survival and proliferation. Cytokine receptors signal via ligand-induced dimerization, and this principle has been hijacked utilizing non-native dimerization domains. A major limitation of current technologies resides in the absence of a module that recapitulates the natural cytokine receptor heterodimeric pairing. To circumvent this, we created a new engineered cytokine receptor able to constitutively recreate receptor-heterodimer utilizing the heterodimerization domain derived from the IgG1 antibody (dFab_CCR). We found that the signal delivered by the dFab_CCR-IL2 proficiently mimicked the cytokine receptor heterodimerization, with transcriptomic signatures like those obtained by activation of the native IL2 receptor. Moreover, we found that this dimerization structure was agnostic, efficiently activating signaling through four cytokine receptor families. Using a combination of in vivo and in vitro screening approaches, we characterized a library of 18 dFab_CCRs co-expressed with a clinically relevant solid tumor-specific GD2-specific CAR. Based on this characterization, we suggest that the co-expression of either the common β-chain GMCSF or the IL18 dFab_CCRs is optimal to improve CAR T-cell expansion, engraftment, and efficacy. Our results demonstrate how Fab dimerization is efficient and versatile in recapitulating a cytokine receptor heterodimerization signal. This module could be applied for the enhancement of adoptive T-cell therapies, as well as therapies based on other immune cell types. Furthermore, these results provide a choice of cytokine signal to incorporate with adoptive T-cell therapies