NAIMA: target amplification strategy allowing quantitative on-chip detection of GMOs

We have developed a novel multiplex quantitative DNA-based target amplification method suitable for sensitive, specific and quantitative detection on microarray. This new method named NASBA Implemented Microarray Analysis (NAIMA) was applied to GMO detection in food and feed, but its application can be extended to all fields of biology requiring simultaneous detection of low copy number DNA targets. In a first step, the use of tailed primers allows the multiplex synthesis of template DNAs in a primer extension reaction. A second step of the procedure consists of transcription-based amplification using universal primers. The cRNA product is further on directly ligated to fluorescent dyes labelled 3DNA dendrimers allowing signal amplification and hybridized without further purification on an oligonucleotide probe-based microarray for multiplex detection. Two triplex systems have been applied to test maize samples containing several transgenic lines, and NAIMA has shown to be sensitive down to two target copies and to provide quantitative data on the transgenic contents in a range of 0.1–25%. Performances of NAIMA are comparable to singleplex quantitative real-time PCR. In addition, NAIMA amplification is faster since 20 min are sufficient to achieve full amplification

Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential Uses for Computational Semantics

Schlangen D. Natural Language Semantics With Pictures: Some Language & Vision Datasets and Potential Uses for Computational Semantics. In: Dobnik S, Chatzikyriakidis S, Demberg V, eds. Proceedings of the 13th International Conference on Computational Semantics - Long Papers. Gothenburg, Sweden: Association for Computational Linguistics; 2019: 283-294.Propelling, and propelled by, the "deep learning revolution", recent years have seen the introduction of ever larger corpora of images annotated with natural language expressions. We survey some of these corpora, taking a perspective that reverses the usual directionality, as it were, by viewing the images as semantic annotation of the natural language expressions. We discuss datasets that can be derived from the corpora, and tasks of potential interest for computational semanticists that can be defined on those. In this, we make use of relations provided by the corpora (namely, the link between expression and image, and that between two expressions linked to the same image) and relations that we can add (similarity relations between expressions, or between images). Specifically, we show that in this way we can create data that can be used to learn and evaluate lexical and compositional grounded semantics, and we show that the "linked to same image" relation tracks a semantic implication relation that is recognisable to annotators even in the absence of the linking image as evidence. Finally, as an example of possible benefits of this approach, we show that an exemplar-model-based approach to implication beats a (simple) distributional space-based one on some derived datasets, while lending itself to explainability

Interaction strategies for an affective conversational agent

The development of embodied conversational agents (ECA) as companions brings several challenges for both affective and conversational dialogue. These include challenges in generating appropriate affective responses, selecting the overall shape of the dialogue, providing prompt system response times, and handling interruptions. We present an implementation of such a companion showing the development of individual modules that attempt to address these challenges. Further, to resolve resulting conflicts, we present encompassing interaction strategies that attempt to balance the competing requirements along with dialogues from our working prototype to illustrate these interaction strategies in operation. Finally, we provide the results of an evaluation of the companion using an evaluation methodology created for conversational dialogue and including analysis using appropriateness annotation

Overview and recommendations for the application of digital PCR

The digital Polymerase Chain Reaction (dPCR), for the detection and absolute quantification of DNA, is a relatively new technique but its application in analytical laboratories is steadily increasing. In contrast to quantitative real-time PCR, DNA (fragments) can be quantified without the need for standard curves. Using dPCR, the PCR mix containing the (target) DNA is partitioned – depending on the device used – currently into a maximum of 10,000,000 small compartments with a volume as low as a few picoliters. These can be either physically distinct compartments on a chip (referred to as chamber-based digital PCR [cdPCR]), or these compartments correspond to water-in-oil droplets (referred to as droplet digital [ddPCR]). Common to both approaches, once PCR has been carried out simultaneously in all compartments/droplets, the number of positive and negative signals for each partition is counted by fluorescence measurement. With this technique, an absolute quantification of DNA copy numbers can be performed with high precision and trueness, even for very low DNA copy numbers. Furthermore, dPCR is considered less susceptible than qPCR to PCR inhibitory substances that can be co-extracted during DNA extraction from different sources. Digital PCR has already been applied in various fields, for example for the detection and quantification of GMOs, species (animals, plants), human diseases, food viruses and bacteria including pathogens. When establishing dPCR in a laboratory, different aspects have to be considered. These include, but are not limited to, the adjustment of the type of the PCR master mix used, optimised primer and probe concentrations and signal separation of positive and negative compartments. This document addresses these and other aspects and provides recommendations for the transfer of existing real-time PCR methods into a dPCR format.JRC.F.5-Food and Feed Complianc

Critical assessment of digital PCR for the detection and quantification of genetically modified organisms

The number of genetically modified organisms (GMOs) on the market is steadily increasing. Because of regulation of cultivation and trade of GMOs in several countries, there is pressure for their accurate detection and quantification. Today, DNA-based approaches are more popular for this purpose than protein-based methods, and real-time quantitative PCR (qPCR) is still the gold standard in GMO analytics. However, digital PCR (dPCR) offers several advantages over qPCR, making this new technique appealing also for GMO analysis. This critical review focuses on the use of dPCR for the purpose of GMO quantification and addresses parameters which are important for achieving accurate and reliable results, such as the quality and purity of DNA and reaction optimization. Three critical factors are explored and discussed in more depth: correct classification of partitions as positive, correctly determined partition volume, and dilution factor. This review could serve as a guide for all laboratories implementing dPCR. Most of the parameters discussed are applicable to fields other than purely GMO testing

One-step reverse-transcription digital PCR for reliable quantification of different pepino mosaic virus genotypes

In recent years, pepino mosaic virus (PepMV) has rapidly evolved from an emerging virus to an endemic pathogen, as it causes significant loses to tomato crops worldwide. At present, the main control strategy for prevention of PepMV disease in tomato production remains based on strict hygiene measures. To prevent damage caused by PepMV, cross-protection is used in some countries. Reliable characterisation, detection and quantification of the pathogen are vital for disease control. At present, reverse-transcription real-time quantitative polymerase chain reaction (RT-qPCR) is generally used for this purpose. However, quantitative use of RT-qPCR is linked to standardised reference materials, which are not available for PepMV. In addition, many factors can influence RT-qPCR efficiencies and lead to lower accuracy of the quantification. In this study, well-characterised PepMV-genotype-specific RT-qPCR assays were transferred to two digital PCR (dPCR) platforms. dPCR-based assays allow absolute quantification without the need for standard curves, and due to the binary nature of the reaction, dPCR also overcomes many of the other drawbacks of RT-qPCR. We have shown that these newly developed and validated PepMV-genotype-specific dPCR assays are suitable candidates for higher-order methods for quantification of PepMV RNA, as they show lower measurement variability, with sensitivity and specificity comparable to RT-qPCR

Cold plasma, a new hope in the field of virus inactivation

Viruses can infect all cell-based organisms, from bacteria to humans, animals, and plants. They are responsible for numerous cases of hospitalization, many deaths, and widespread crop destruction, all of which result in an enormous medical, economical, and biological burden. Each of the currently used decontamination methods has important drawbacks. Cold plasma (CP) has entered this field as a novel, efficient, and clean solution for virus inactivation. We present recent developments in this promising field of CP-mediated virus inactivation, and describe the applications and mechanisms of the inactivation. This is particularly relevant because viral pandemics, such as COVID-19, highlight the need for alternative virus inactivation methods to replace, complement, or upgrade existing procedures