A New Online Resource to Monitor New or Emerging Plant Pests: MEDISYS Media Monitoring and the Case of Xylella fastidiosa.

The European Food Safety Authority has established a horizon scanning exercise for plant pests by automated monitoring of open-source media. The news sources are screened using the publicly accessible MEDISYS (Medical Information System) platform of the Joint Research Centre of the European Commission. Here, we report the example of monitoring for Xylella fastidiosa, a highly polyphagous plant-pathogenic bacterium. Since its first occurrence in Europe, news sources have reported findings and latest developments. Media monitoring-related data can support surveillance or plant pests' management programs by early warning and can help understand the impacts of plant pests and the societal response to new plant health threats.

Trend analysis of COVID-19 mis/disinformation narratives-A 3-year study.

To tackle the COVID-19 infodemic, we analysed 58,625 articles from 460 unverified sources, that is, sources that were indicated by fact checkers and other mis/disinformation experts as frequently spreading mis/disinformation, covering the period from 1 January 2020 to 31 December 2022. Our aim was to identify the main narratives of COVID-19 mis/disinformation, develop a codebook, automate the process of narrative classification by training an automatic classifier, and analyse the spread of narratives over time and across countries. Articles were retrieved with a customised version of the Europe Media Monitor (EMM) processing chain providing a stream of text items. Machine translation was employed to automatically translate non-English text to English and clustering was carried out to group similar articles. A multi-level codebook of COVID-19 mis/disinformation narratives was developed following an inductive approach; a transformer-based model was developed to classify all text items according to the codebook. Using the transformer-based model, we identified 12 supernarratives that evolved over the three years studied. The analysis shows that there are often real events behind mis/disinformation trends, which unverified sources misrepresent or take out of context. We established a process that allows for near real-time monitoring of COVID-19 mis/disinformation. This experience will be useful to analyse mis/disinformation about other topics, such as climate change, migration, and geopolitical developments.

ECVAM and new technologies for toxicity testing.

The development of alternative empirical (testing) and non-empirical (non-testing) methods to traditional toxicological tests for complex human health effects is a tremendous task. Toxicants may potentially interfere with a vast number of physiological mechanisms thereby causing disturbances on various levels of complexity of human physiology. Only a limited number of mechanisms relevant for toxicity ('pathways' of toxicity) have been identified with certainty so far and, presumably, many more mechanisms by which toxicants cause adverse effects remain to be identified. Recapitulating in empirical model systems (i.e., in vitro test systems) all those relevant physiological mechanisms prone to be disturbed by toxicants and relevant for causing the toxicity effect in question poses an enormous challenge. First, the mechanism(s) of action of toxicants in relation to the most relevant adverse effects of a specific human health endpoint need to be identified. Subsequently, these mechanisms need to be modeled in reductionist test systems that allow assessing whether an unknown substance may operate via a specific (array of) mechanism(s). Ideally, such test systems should be relevant for the species of interest, i.e., based on human cells or modeling mechanisms present in humans. Since much of our understanding about toxicity mechanisms is based on studies using animal model systems (i.e., experimental animals or animal-derived cells), designing test systems that model mechanisms relevant for the human situation may be limited by the lack of relevant information from basic research. New technologies from molecular biology and cell biology, as well as progress in tissue engineering, imaging techniques and automated testing platforms hold the promise to alleviate some of the traditional difficulties associated with improving toxicity testing for complex endpoints. Such new technologies are expected (1) to accelerate the identification of toxicity pathways with human relevance that need to be modeled in test methods for toxicity testing (2) to enable the reconstruction of reductionist test systems modeling at a reduced level of complexity the target system/organ of interest (e.g., through tissue engineering, use of human-derived cell lines and stem cells etc.), (3) to allow the measurement of specific mechanisms relevant for a given health endpoint in such test methods (e.g., through gene and protein expression, changes in metabolites, receptor activation, changes in neural activity etc.), (4) to allow to measure toxicity mechanisms at higher throughput rates through the use of automated testing. In this chapter, we discuss the potential impact of new technologies on the development, optimization and use of empirical testing methods, grouped according to important toxicological endpoints. We highlight, from an ECVAM perspective, the areas of topical toxicity, skin absorption, reproductive and developmental toxicity, carcinogenicity/genotoxicity, sensitization, hematopoeisis and toxicokinetics and discuss strategic developments including ECVAM's database service on alternative methods. Neither the areas of toxicity discussed nor the highlighted new technologies represent comprehensive listings which would be an impossible endeavor in the context of a book chapter. However, we feel that these areas are of utmost importance and we predict that new technologies are likely to contribute significantly to test development in these fields. We summarize which new technologies are expected to contribute to the development of new alternative testing methods over the next few years and point out current and planned ECVAM projects for each of these areas.

Event-driven architecture for health event detection from multiple sources.

Early detection of potential health threats is crucial for taking actions in time. It is unclear in which information source an event is reported first and, information from various sources can be complementing. Thus, it is important to search for information in a very broad range of sources. Furthermore, real-time processing is necessary to deal with the huge amounts of incoming data in time. Event-driven architectures are designed to address such challenges. This will be shown in this paper by presenting the architecture of a public health surveillance system that follows this style. Starting from concrete user requirements and scenarios, we introduce the architecture with its components for content collection, data analysis and integration. The system will allow for the monitoring of events in real-time as well as retrospectively.

A novel in vitro metabolomics approach for neurotoxicity testing, proof of principle for methyl mercury chloride and caffeine.

There is a need for more efficient methods giving insight into the complex mechanisms of neurotoxicity. Testing strategies including in vitro methods have been proposed to comply with this requirement. With the present study we aimed to develop a novel in vitro approach which mimics in vivo complexity, detects neurotoxicity comprehensively, and provides mechanistic insight. For this purpose we combined rat primary re-aggregating brain cell cultures with a mass spectrometry (MS)-based metabolomics approach. For the proof of principle we treated developing re-aggregating brain cell cultures for 48 h with the neurotoxicant methyl mercury chloride (0.1-100 microM) and the brain stimulant caffeine (1-100 microM) and acquired cellular metabolic profiles. To detect toxicant-induced metabolic alterations the profiles were analysed using commercial software which revealed patterns in the multi-parametric dataset by principal component analyses (PCA), and recognised the most significantly altered metabolites. PCA revealed concentration-dependent cluster formations for methyl mercury chloride (0.1-1 microM), and treatment-dependent cluster formations for caffeine (1-100 microM) at sub-cytotoxic concentrations. Four relevant metabolites responsible for the concentration-dependent alterations following methyl mercury chloride treatment could be identified using MS-MS fragmentation analysis. These were gamma-aminobutyric acid, choline, glutamine, creatine and spermine. Their respective mass ion intensities demonstrated metabolic alterations in line with the literature and suggest that the metabolites could be biomarkers for mechanisms of neurotoxicity or neuroprotection. In addition, we evaluated whether the approach could identify neurotoxic potential by testing eight compounds which have target organ toxicity in the liver, kidney or brain at sub-cytotoxic concentrations. PCA revealed cluster formations largely dependent on target organ toxicity indicating possible potential for the development of a neurotoxicity prediction model. With such results it could be useful to perform a validation study to determine the reliability, relevance and applicability of this approach to neurotoxicity screening. Thus, for the first time we show the benefits and utility of in vitro metabolomics to comprehensively detect neurotoxicity and to discover new biomarkers.

ECVAM's approach to intellectual property rights in the validation of alternative methods.

In this article, we discuss how intellectual property rights affect the validation of alternative methods at ECVAM. We point out recent cases and summarise relevant EU and OECD documents. Finally, we discuss guidelines for dealing with intellectual property rights during the validation of alternative methods at ECVAM.

Event-Based Surveillance During EXPO Milan 2015: Rationale, Tools, Procedures, and Initial Results.

More than 21 million participants attended EXPO Milan from May to October 2015, making it one of the largest protracted mass gathering events in Europe. Given the expected national and international population movement and health security issues associated with this event, Italy fully implemented, for the first time, an event-based surveillance (EBS) system focusing on naturally occurring infectious diseases and the monitoring of biological agents with potential for intentional release. The system started its pilot phase in March 2015 and was fully operational between April and November 2015. In order to set the specific objectives of the EBS system, and its complementary role to indicator-based surveillance, we defined a list of priority diseases and conditions. This list was designed on the basis of the probability and possible public health impact of infectious disease transmission, existing statutory surveillance systems in place, and any surveillance enhancements during the mass gathering event. This article reports the methodology used to design the EBS system for EXPO Milan and the results of 8 months of surveillance.

Refinement of protein structures in explicit solvent.

We present a CPU efficient protocol for refinement of protein structures in a thin layer of explicit solvent and energy parameters with completely revised dihedral angle terms. Our approach is suitable for protein structures determined by theoretical (e.g., homology modeling or threading) or experimental methods (e.g., NMR). In contrast to other recently proposed refinement protocols, we put a strong emphasis on consistency with widely accepted covalent parameters and computational efficiency. We illustrate the method for NMR structure calculations of three proteins: interleukin-4, ubiquitin, and crambin. We show a comparison of their structure ensembles before and after refinement in water with and without a force field energy term for the dihedral angles; crambin was also refined in DMSO. Our results demonstrate the significant improvement of structure quality by a short refinement in a thin layer of solvent. Further, they show that a dihedral angle energy term in the force field is beneficial for structure calculation and refinement. We discuss the optimal weight for the energy constant for the backbone angle omega and include an extensive discussion of meaning and relevance of the calculated validation criteria, in particular root mean square Z scores for covalent parameters such as bond lengths.

Correction of spin diffusion during iterative automated NOE assignment.

Indirect magnetization transfer increases the observed nuclear Overhauser enhancement (NOE) between two protons in many cases, leading to an underestimation of target distances. Wider distance bounds are necessary to account for this error. However, this leads to a loss of information and may reduce the quality of the structures generated from the inter-proton distances. Although several methods for spin diffusion correction have been published, they are often not employed to derive distance restraints. This prompted us to write a user-friendly and CPU-efficient method to correct for spin diffusion that is fully integrated in our program ambiguous restraints for iterative assignment (ARIA). ARIA thus allows automated iterative NOE assignment and structure calculation with spin diffusion corrected distances. The method relies on numerical integration of the coupled differential equations which govern relaxation by matrix squaring and sparse matrix techniques. We derive a correction factor for the distance restraints from calculated NOE volumes and inter-proton distances. To evaluate the impact of our spin diffusion correction, we tested the new calibration process extensively with data from the Pleckstrin homology (PH) domain of Mus musculus beta-spectrin. By comparing structures refined with and without spin diffusion correction, we show that spin diffusion corrected distance restraints give rise to structures of higher quality (notably fewer NOE violations and a more regular Ramachandran map). Furthermore, spin diffusion correction permits the use of tighter error bounds which improves the distinction between signal and noise in an automated NOE assignment scheme.

NOE assignment with ARIA 2.0: the nuts and bolts.

The assignment of nuclear Overhauser effect (NOE) resonances is the crucial step in determining the three-dimensional structure of biomolecules from nuclear magnetic resonance (NMR) data. Our program, Ambiguous Restraints for Iterative Assignment (ARIA), treats Noe assignment as an integral part of the structure determination process. This chapter briefly outlines the method and discusses how to carry out a complete structure determination project with the new version 2.0 of ARIA. Two new features greatly streamline the procedure: a new graphical user interface (GUI) and the incorporation of the data model of the Collaborative Computing Project for the NMR community (CCPN). The GUI supports the user in setting up and managing a project. The CCPN data model facilitates data exchange with a great variety of other programs. We give practical guidelines for how to use ARIA and how to analyze results.

Interfacing a biosurveillance portal and an international network of institutional analysts to detect biological threats.

The Early Alerting and Reporting (EAR) project, launched in 2008, is aimed at improving global early alerting and risk assessment and evaluating the feasibility and opportunity of integrating the analysis of biological, chemical, radionuclear (CBRN), and pandemic influenza threats. At a time when no international collaborations existed in the field of event-based surveillance, EAR's innovative approach involved both epidemic intelligence experts and internet-based biosurveillance system providers in the framework of an international collaboration called the Global Health Security Initiative, which involved the ministries of health of the G7 countries and Mexico, the World Health Organization, and the European Commission. The EAR project pooled data from 7 major internet-based biosurveillance systems onto a common portal that was progressively optimized for biological threat detection under the guidance of epidemic intelligence experts from public health institutions in Canada, the European Centre for Disease Prevention and Control, France, Germany, Italy, Japan, the United Kingdom, and the United States. The group became the first end users of the EAR portal, constituting a network of analysts working with a common standard operating procedure and risk assessment tools on a rotation basis to constantly screen and assess public information on the web for events that could suggest an intentional release of biological agents. Following the first 2-year pilot phase, the EAR project was tested in its capacity to monitor biological threats, proving that its working model was feasible and demonstrating the high commitment of the countries and international institutions involved. During the testing period, analysts using the EAR platform did not miss intentional events of a biological nature and did not issue false alarms. Through the findings of this initial assessment, this article provides insights into how the field of epidemic intelligence can advance through an international network and, more specifically, how it was further developed in the EAR project.

Factors influencing performance of internet-based biosurveillance systems used in epidemic intelligence for early detection of infectious diseases outbreaks.

BACKGROUND: Internet-based biosurveillance systems have been developed to detect health threats using information available on the Internet, but system performance has not been assessed relative to end-user needs and perspectives. METHOD AND FINDINGS: Infectious disease events from the French Institute for Public Health Surveillance (InVS) weekly international epidemiological bulletin published in 2010 were used to construct the gold-standard official dataset. Data from six biosurveillance systems were used to detect raw signals (infectious disease events from informal Internet sources): Argus, BioCaster, GPHIN, HealthMap, MedISys and ProMED-mail. Crude detection rates (C-DR), crude sensitivity rates (C-Se) and intrinsic sensitivity rates (I-Se) were calculated from multivariable regressions to evaluate the systems' performance (events detected compared to the gold-standard) 472 raw signals (Internet disease reports) related to the 86 events included in the gold-standard data set were retrieved from the six systems. 84 events were detected before their publication in the gold-standard. The type of sources utilised by the systems varied significantly (p<0001). I-Se varied significantly from 43% to 71% (p=0001) whereas other indicators were similar (C-DR: p=020; C-Se, p=013). I-Se was significantly associated with individual systems, types of system, languages, regions of occurrence, and types of infectious disease. Conversely, no statistical difference of C-DR was observed after adjustment for other variables. CONCLUSION: Although differences could result from a biosurveillance system's conceptual design, findings suggest that the combined expertise amongst systems enhances early detection performance for detection of infectious diseases. While all systems showed similar early detection performance, systems including human moderation were found to have a 53% higher I-Se (p=00001) after adjustment for other variables. Overall, the use of moderation, sources, languages, regions of occurrence, and types of cases were found to influence system performance.

Evaluation of epidemic intelligence systems integrated in the early alerting and reporting project for the detection of A/H5N1 influenza events.

The objective of Web-based expert epidemic intelligence systems is to detect health threats. The Global Health Security Initiative (GHSI) Early Alerting and Reporting (EAR) project was launched to assess the feasibility and opportunity for pooling epidemic intelligence data from seven expert systems. EAR participants completed a qualitative survey to document epidemic intelligence strategies and to assess perceptions regarding the systems performance. Timeliness and sensitivity were rated highly illustrating the value of the systems for epidemic intelligence. Weaknesses identified included representativeness, completeness and flexibility. These findings were corroborated by the quantitative analysis performed on signals potentially related to influenza A/H5N1 events occurring in March 2010. For the six systems for which this information was available, the detection rate ranged from 31% to 38%, and increased to 72% when considering the virtual combined system. The effective positive predictive values ranged from 3% to 24% and F1-scores ranged from 6% to 27%. System sensitivity ranged from 38% to 72%. An average difference of 23% was observed between the sensitivities calculated for human cases and epizootics, underlining the difficulties in developing an efficient algorithm for a single pathology. However, the sensitivity increased to 93% when the virtual combined system was considered, clearly illustrating complementarities between individual systems. The average delay between the detection of A/H5N1 events by the systems and their official reporting by WHO or OIE was 10.2 days (95% CI: 6.7-13.8). This work illustrates the diversity in implemented epidemic intelligence activities, differences in system's designs, and the potential added values and opportunities for synergy between systems, between users and between systems and users.

Toxicogenomics applied to in vitro carcinogenicity testing with Balb/c 3T3 cells revealed a gene signature predictive of chemical carcinogens.

Information on the carcinogenic potential of chemicals is primarily available for High Production Volume (HPV) products. Because of the limited knowledge gain from routine cancer bioassays and the fact that HPV chemicals are tested only, there is the need for more cost-effective and informative testing strategies. Here we report the application of advanced genomics to a cellular transformation assay to identify toxicity pathways and gene signatures predictive for carcinogenicity. Specifically, genome-wide gene expression analysis and quantitative real time polymerase chain reaction (qRT-PCR) were applied to untransformed and transformed mouse fibroblast Balb/c 3T3 cells that were exposed to either 2, 4-diaminotoluene, benzo(a)pyrene, 2-acetylaminoflourene, or 3-methycholanthrene at IC20 conditions for 24 and 120 h, respectively. Then, bioinformatics was applied to define toxicity pathways and a gene signature predictive of the carcinogenic risk of these chemicals. Although bioinformatics revealed distinct differences for individual chemicals at the gene-level pathway, analysis identified common perturbation that resulted in an identification of 14 genes whose regulation in cancer tissue had already been established. Strikingly, this gene signature was identified in short-term (24 and 120 h) untransformed and transformed cells (3 weeks), therefore demonstrating robustness for its predictive power. The developed testing strategy thus identified commonly regulated carcinogenic pathways and a gene signature that predicted the risk for carcinogenicity for three well-known carcinogens. Overall, the testing strategy warrants in-depth validation for the prediction of carcinogenic risk of industrial chemicals in in vitro carcinogenicity assay.

Comparative analysis of structural and dynamic properties of the loaded and unloaded hemophore HasA: functional implications.

A heme-acquisition system present in several Gram-negative bacteria requires the secretion of hemophores. These extracellular carrier proteins capture heme and deliver it to specific outer membrane receptors. The Serratia marcescens HasA hemophore is a monodomain protein that binds heme with a very high affinity. Its alpha/beta structure, as that of its binding pocket, has no common features with other iron- or heme-binding proteins. Heme is held by two loops L1 and L2 and coordinated to iron by an unusual ligand pair, H32/Y75. Two independent regions of the hemophore beta-sheet are involved in HasA-HasR receptor interaction. Here, we report the 3-D NMR structure of apoHasA and the backbone dynamics of both loaded and unloaded hemophore. While the overall structure of HasA is very similar in the apo and holo forms, the hemophore presents a transition from an open to a closed form upon ligand binding, through a large movement, of up to 30 A, of loop L1 bearing H32. Comparison of loaded and unloaded HasA dynamics on different time scales reveals striking flexibility changes in the binding pocket. We propose a mechanism by which these structural and dynamic features provide the dual function of heme binding and release to the HasR receptor.

Influence of chemical shift tolerances on NMR structure calculations using ARIA protocols for assigning NOE data.

Large-scale protein structure determination by NMR via automatic assignment of NOESY spectra requires the adjustment of several parameters for optimal performance. Among those are the chemical shift tolerance windows (delta), which allow for the compensation of badly matching chemical shifts in the assignment-list and peak-lists, and the maximum number of assignment possibilities allowed per peak (n(max)). Here, we test the influence of different values for Delta and n(max) on the performance of automated assignment of NOESY spectra by ARIA. Using Cesta.py (a Python script available from http://pasteur.fr/binfs/), we analyse the number of rejected peaks and the average number of assignments as a function of Delta and derive criteria for optimising delta and n(max) prior to structure calculation. The analysis also makes it possible to detect inconsistencies in the dataset, e.g., badly matching frequencies in the NOESY peak-lists and in the provided assignment-list. We show that ARIA can deal with a large number of assignment possibilities for each peak, provided the correct option is present, and that consequently narrow tolerances should be avoided.

ARIA: automated NOE assignment and NMR structure calculation.

MOTIVATION: In the light of several ongoing structural genomics projects, faster and more reliable methods for structure calculation from NMR data are in great demand. The major bottleneck in the determination of solution NMR structures is the assignment of NOE peaks (nuclear Overhauser effect). Due to the high complexity of the assignment problem, most NOEs cannot be directly converted into unambiguous inter-proton distance restraints. RESULTS: We present version 1.2 of our program ARIA (Ambiguous Restraints for Iterative Assignment) for automated assignment of NOE data and NMR structure calculation. We summarize recent progress in correcting for spin diffusion with a relaxation matrix approach, representing non-bonded interactions in the force field and refining final structures in explicit solvent. We also discuss book-keeping, data exchange with spectra assignment programs and deposition of the analysed experimental data to the databases. AVAILABILITY: ARIA 1.2 is available from: http://www.pasteur.fr/recherche/unites/Binfs/aria/. SUPPLEMENTARY INFORMATION: XML DTDs (for chemical shifts and NOE crosspeaks), Python scripts for the conversion of various NMR data formats and the results of example calculations using data from the S. cerevisiae HRDC domain are available from: http://www.pasteur.fr/recherche/unites/Binfs/aria/

Improving the quality of protein structures derived by NMR spectroscopy.

Biomolecular structures provide the basis for many studies in several research areas such as homology modelling, structure-based drug design and functional genomics. It is an important prerequisite that the structure is reliable in terms of accurate description of the experimental data, and in terms of good quality of local- and overall geometry. Recent surveys indicate that structures solved by NMR-spectroscopy normally are of lower precision than high-resolution X-ray structures. Here, we present a refinement protocol that improves the quality of protein structures determined by NMR-spectroscopy to the level of those determined by high resolution X-ray crystallography in terms of local geometry. The protocol was tested on experimental data of the proteins IL4 and Ubiquitin and on simulated data of the protein Crambin. In almost all aspects, the protocol yielded better results in terms of accuracy and precision. Independent validation of the results for Ubiquitin, using residual dipolar couplings, indicates that the ensemble of NMR structure is substantially improved by the protocol.