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Please cite the following related publication: Ortega J, Meersman MFL, Aparicio Set al.An automated sonic tomography system for the inspection of historical masonry walls [version 1; peer review: 2 approved]. Open Res Europe 2023,3:60(https://doi.org/10.12688/openreseurope.15769.1) Background:The conservation of the built masonry heritage requires a comprehensive understanding of its geometrical, structural, and material characteristics. Non-destructive techniques are a preferred approach to survey historical buildings, given the cultural value of their fabric. However, currently available techniques are typically operated manually, consuming much time at operational and processing level and thus hindering their use for the on-site inspection of heritage structures. Methods:A novel automated sonic tomography system was designed and built to inspect and obtain information about the inner structure and damage of historic masonry walls.The system consists of a hitting device mounted on a frame that can be placed adjacent to the wall under analysis. The hitting device can move along the surface within the frame area in X, Y and Z directions, generating the sonic wave. The receiving system is a scanning laser vibrometer, able to measure from the distance the displacement of a focused point over time, recording the wave when it reaches the opposite surface. Results:Six stone masonry walls with different interior geometries were constructed at the laboratory by a professional stonemason. The construction of the walls was carefully documented, including the generation of detailed photogrammetric models of each single stone. The system was applied to survey the six masonry walls. Since the inner morphology of the walls is known, the resulting tomographic images could be compared with the ground truth. Conclusions:Automating the inspection allowed to collect thousands of data in a few hours. New software was also developed to automate the processing of the data. Results are expected to highlight the potential of tomography to obtain quantitative information about the interior of heritage structures, while providing new tools that make the implementation of the technique more practical for professionals. Data, software and models have been made publicly available.

Context: International comparisons of the health of mothers and babies provide essential benchmarks for guiding health practice and policy, but statistics are not routinely compiled in a comparable way. These data are especially critical during health emergencies, such as the coronavirus disease (COVID-19) pandemic. The Population Health Information Research Infrastructure (PHIRI) project aimed to promote the exchange of population data in Europe and included a Use Case on perinatal health. Objective: To develop and test a protocol for federated analysis of population birth data in Europe. Methods: The Euro-Peristat network with participants from 31 countries developed a Common Data Model (CDM) and R scripts to exchange and analyse aggregated data on perinatal indicators. Building on recommended Euro-Peristat indicators, complemented by a three-round consensus process, the network specified variables for a CDM and common outputs. The protocol was tested using routine birth data for 2015 to 2020; a survey was conducted assessing data provider experiences and opinions. Results: The CDM included 17 core data items for the testing phase and 18 for a future expanded phase. 28 countries and the four UK nations created individual person-level databases and ran R scripts to produce anonymous aggregate tables. Seven had all core items, 17 had 13-16, while eight had ≤12. Limitations were not having all items in the same database, required for this protocol. Infant death and mode of birth were most frequently missing. Countries took from under a day to several weeks to set up the CDM, after which the protocol was easy and quick to use. Conclusion: This open-source protocol enables rapid production and analysis of perinatal indicators and constitutes a roadmap for a sustainable European information system. It also provides minimum standards for improving national data systems and can be used in other countries to facilitate comparison of perinatal indicators.

The Ball Clamping module of the Laparoscopic Surgery Training Box involves the transfer of beads across the training board using laparoscopic tools. Fundamentals of Laparoscopic Surgery (FLS) requires practitioners to move their hands at as short a distance as possible to perform the functions in the shortest amount of time. This study introduces a feedback tool that presents to the student, after attempting their exam, the right direction (step by step) of obtaining the optimal pathway for minimizing distance traveled in the Ball Clamping Module of the Laparoscopic Surgery Training Box. The shortest distance tour for the ball clamping task is determined using the Traveling Salesman Model (TSM). A sensitivity analysis is conducted to assess the model's applicability to different types and settings of trainer boxes. • Find the best sequence of points resulting in the shortest distance tour for the ball clamping task. • The effects of adding or removing columns from the box cannot be intuitively predicted.

This study develops a method to implement a quantum field lens coding and classification algorithm for two quantum double-field (QDF) system models: 1- a QDF model, and 2- a QDF lens coding model by a DF computation (DFC). This method determines entanglement entropy (EE) by implementing QDF operators in a quantum circuit. The physical link between the two system models is a quantum field lens coding algorithm (QF-LCA), which is a QF lens distance-based, implemented on real [Formula: see text]-qubit machines. This is with the possibility to train the algorithm for making strong predictions on phase transitions as the shared objective of both models. In both system models, QDF transformations are simulated by a DFC algorithm where QDF data are collected and analyzed to represent energy states and transitions, and determine entanglement based on EE. The method gives a list of steps to simulate and optimize any thermodynamic system on macro and micro-scale observations, as presented in this article: • The implementation of QF-LCA on quantum computers with EE measurement under a QDF transformation. • Validation of QF-LCA as implemented compared to quantum Fourier transform (QFT) and its inverse, QFT [Formula: see text]. • Quantum artificial intelligence (QAI) features by classifying QDF with strong measurement outcome predictions.

Linear regression is one of the oldest statistical modeling approaches. Still, it is a valuable tool, particularly when it is necessary to create forecast models with low sample sizes. When researchers use this method and have numerous potential regressors, choosing the group of regressors for a model that fulfills all the required assumptions can be challenging. In this sense, the authors developed an open-source Python script that automatically tests all the combinations of regressors under a brute-force approach. The output displays the best linear regression models, regarding the thresholds set by users for the required assumptions: statistical significance of the estimations, multicollinearity, error normality, and homoscedasticity. Further, the script allows the selection of linear regressions with regression coefficients according to the user’s expectations. This script was tested with an environmental dataset to predict surface water quality parameters based on landscape metrics and contaminant loads. Among millions of possible combinations, less than 0.1 % of the regressor combinations fulfilled the requirements. The resulting combinations were also tested in geographically weighted regression, with similar results to linear regression. The model's performance was higher for pH and total nitrate and lower for total alkalinity and electrical conductivity. • A Python script was developed to find the best linear regressions within a dataset. • Output regressions are automatically selected based on regression coefficient expectations set by the user and the linear regression assumptions. • The algorithm was successfully validated through an environmental dataset.

Given the rapidly evolving developments in Fintech, Insurtech, Open Banking, and Mobile Money business models in recent years, the capability for ensuring strong authentication remains the most pressing need for the protection of security and privacy of data in this sector as in many other areas. The security-integrity of insurance and financial transactions and workflows is vitally dependent on access control mechanisms to deliver strong multi-factor authentication (MFA) with operationally acceptable latency and throughput to support real-time response, particularly as demanded by the increasing online and mobile financial service models. The Critical-Chains Project was motivated by the above objectives as underpinned by the overarching commitment to accountability engineering as required by the operational logic. This must be crucially supported by real-time hardware-enabled services comprising authentication (including Distance Bounding and Prover’s Proximal Location Presence Verification), hardware security and cryptography (AUTH-as-a-Service, Hardware-Security-as-a-Service, Cryptography-as-a-Service) as delivered through the Critical-Chains main framework. This paper reports on the development and evaluation of the innovative Hardware-enabled authentication and security capabilities of the Critical-Chains framework which is successfully validated in the context of financial services, specifically the insurance claim settlement application domain, and can also be deployed in any other domains where trusted authentication and specific location-time bound prover’s presence verification is required.

This method paper presents a template solution for text mining of scientific literature using the R tm package. Literature to be analyzed can be collected manually or automatically using the code provided with this paper. Once the literature is collected, the three steps for conducting text mining can be performed as outlined below:• loading and cleaning of text from articles,• processing, statistical analysis, and clustering, and• presentation of results using generalized and tailor-made visualizations.The text mining steps can be applied to a single, multiple, or time series groups of documents.References are provided to three published peer reviewed articles that use the presented text mining methodology. The main advantages of our method are: (1) Its suitability for both research and educational purposes, (2) Compliance with the Findable Accessible Interoperable and Reproducible (FAIR) principles, and (3) code and example data are made available on GitHub under the open-source Apache V2 license.

ARESIBO is an H2020 project aiming to improve the efficiency of border surveillance systems by providing the operational teams, as well as the tactical command and control level with accurate and comprehensive information related to border control by different perspectives. Human Rights Groups’ (HRGs) and Civil Society Organisations’ (CSOs) involvement and participation in decision making related Surveillance-Oriented Security Technologies (SOSTs) for border surveillance is considered as a key-factor to ensure the coexistence of two only apparently opposite needs: security and respect of fundamental rights (i.e., privacy, refugees etc.). Starting from this general approach, the paper represents the second part of a research diptych dedicated to present the main achievements and methods proposed by ARESIBO to enhance participation in border surveillance. It outlines the ARESIBO Participatory Model (APM) as a tool to improve HRGs, CSOs, and ultimately citizens’ involvement related to border surveillance in general and the adoption of specific SOSTs for borders’ control. The first part of the paper introduces some key elements included in the ARESIBO desk research that led to the elaboration of the APM (i.e., literature review, semantic analysis), describing who were the targets of that approach (i.e., stakeholders, end-users, actors). After a contextualization on the three interlaced dimensions of surveillance, security and privacy related borders’ control, the paper presents the three components of the methodological framework within which this citizens’ involvement process has been developed (i.e., ARESIBO Participation Framework, ARESIBO Participation Strategy, ARESIBO Participation Action Plan). In the conclusions, by outlining the first partial application of the model within the ARESIBO framework, the paper argues that APM can represent a pilot for a more structured and duplicable participatory model, also through additional research regarding participatory models applied to SOSTs development processes.

Background: Plants exude a plethora of compounds to communicate with their environment. Although much is known about above-ground plant communication, we are only beginning to fathom the complexities of below ground chemical communication channels. Studying root-exuded compounds and their role in plant communication has been difficult due to a lack of standardized methodologies. Here, we develop an interdisciplinary workflow to explore the natural variation in root exudate chemical composition of the model plant Arabidopsis thaliana. We highlight key challenges associated with sampling strategies and develop a framework for analyzing both narrow and broad scale patterns of root exudate composition in a large set of natural A. thaliana accessions. Methods: Our method involves cultivating individual seedlings in vitro inside a plastic mesh, followed by a short hydroponic sampling period in small quantities of ultrapure water. The mesh makes it easy to handle plants of different sizes and allows for large-scale characterization of individual plant root exudates in axenic conditions. This setup can also be easily extended for prolonged temporal exudate collection experiments. Furthermore, the short sampling time minimizes the duration of the experiment while still providing sufficient signal even with a small volume of sampling solution. We used ultra-high performance liquid chromatography coupled with quadrupole time-of-flight mass spectrometry (UHPLC-QTOF-MS) for untargeted metabolic profiling, followed by tentative compound identification using MZMine3 and SIRIUS 5 software, to capture a broad overview of root exudate composition in A. thaliana accessions. Results: Based on 28 replicates of Columbia genotype (Col-0) compared with 10 random biological controls, MZMine3 identified 354 metabolites to be present only in Col-0 by negative ionization. Of these, 313 compounds could be annotated by SIRIUS 5 software. Conclusions: The methodology developed in this study can be used to broadly investigate the role of root exudates as chemical signals in plant belowground interactions.

Background: Geospatial linked data brings into the scope of the Semantic Web and its technologies, a wealth of datasets that combine semantically-rich descriptions of resources with their geo-location. There are, however, various Semantic Web technologies where technical work is needed in order to achieve the full integration of geospatial data, and federated query processing is one of these technologies. Methods: In this paper, we explore the idea of annotating data sources with a bounding polygon that summarizes the spatial extent of the resources in each data source, and of using such a summary as an (additional) source selection criterion in order to reduce the set of sources that will be tested as potentially holding relevant data. We present our source selection method, and we discuss its correctness and implementation. Results: We evaluate the proposed source selection using three different types of summaries with different degrees of accuracy, against not using geospatial summaries. We use datasets and queries from a practical use case that combines crop-type data with water availability data for food security. The experimental results suggest that more complex summaries lead to slower source selection times, but also to more precise exclusion of unneeded sources. Moreover, we observe the source selection runtime is (partially or fully) recovered by shorter planning and execution runtimes. As a result, the federated sources are not burdened by pointless querying from the federation engine. Conclusions: The evaluation draws on data and queries from the agroenvironmental domain and shows that our source selection method substantially improves the effectiveness of federated GeoSPARQL query processing., Published in Open Research Europe

Amyotrophic lateral sclerosis (ALS) is a fatal, heterogenous neurodegenerative disease which is characterized by weakness and muscle atrophy. While more than 50 genes linked with the familial ALS have been identified, sporadic ALS accounts for the majority of cases and genetic factors contributing to the sporadic form of the disease remain largely elusive. Involvement of the immune system in disease progression, including presence of circulating autoimmune complexes, associations with other autoimmune diseases, as well as immunoglobulin (Igs) levels have been described. However, investigations involving immunoglobulins requires isolation of patient antibodies, while so far, there is no validated fast protein liquid chromatography (FPLC) protocol. For this purpose, we evaluated specificity, accuracy and precision parameters; limit of detection, limit of quantification and correlation coefficient were calculated from the calibration curve. Our results indicated the limit of detection and limit of quantification as 0.045 and 0.13 mg respectively, while r2 was calculated as 0.9992. Our preliminary data suggest that this protocol can be implemented for the isolation of IgGs of ALS patients’ sera samples for in vitro and in vivo analyses, while more studies involving larger patient groups are required.

Affinity measurement is a fundamental step in the discovery of monoclonal antibodies (mAbs) and of antigens suitable for vaccine development. Innovative affinity assays are needed due to the low throughput and/or limited dynamic range of available technologies. We combined microfluidic technology with quantum-mechanical scattering theory, in order to develop a high-throughput, broad-range methodology to measure affinity. Fluorescence intensity profiles were generated for out-of-equilibrium solutions of labelled mAbs and their antigen-binding fragments migrating along micro-columns with immobilized cognate antigen. Affinity quantification was performed by computational data analysis based on the Landau probability distribution. Experiments using a wide array of human or murine antibodies against bacterial or viral, protein or polysaccharide antigens, showed that all the antibody-antigen capture profiles (n = 841) generated at different concentrations were accurately described by the Landau distribution. A scale parameter W, proportional to the full-width-at-half-maximum of the capture profile, was shown to be independent of the antibody concentration. The W parameter correlated significantly (Pearson’s r [p–value]: 0.89 [3 × 10−8]) with the equilibrium dissociation constant KD, a gold-standard affinity measure. Our method showed good intermediate precision (median coefficient of variation: 5%) and a dynamic range corresponding to KD values spanning from ~10−7 to ~10−11 Molar. Relative to assays relying on antibody-antigen equilibrium in solution, even when they are microfluidic-based, the method’s turnaround times were decreased from 2 days to 2 h. The described computational modelling of antibody capture profiles represents a fast, reproducible, high-throughput methodology to accurately measure a broad range of antibody affinities in very low volumes of solution.

Background: Photo-electro-chemical (PEC) water splitting represents a promising technology towards an artificial photosynthetic device but many fundamental electronic processes, which govern long-term stability and energetics, are not yet fully understood. X-ray absorption spectroscopy (XAS), and particularly its high energy resolution fluorescence-detected (HERFD) mode, emerges as a powerful tool to study photo-excited charge carrier behavior under operating conditions. The established thin film device architecture of PEC cells provides a well-defined measurement geometry, but it puts many constraints on conducting operando XAS experiments. It remains a challenge to establish a standardized thin film exchange procedure and concurrently record high-quality photoelectrochemical and X‑ray absorption spectroscopy data that is unperturbed by bubble formation. Here we address and overcome these instrumental limitations for photoelectrochemical operando HERFD-XAS. Methods: We constructed a novel operando photo-electro-chemical cell by computer numerical control milling, guided by the materials’ X‑ray and visible light absorption properties to optimize signal detection. To test the cell’s functionality, semiconducting thin film photoelectrodes have been fabricated via solution deposition and their photoelectrochemical responses under simulated solar light were studied using a commercial potentiostat in a three-electrode configuration during HERFD-XAS experiments at a synchrotron. Results: We demonstrate the cell’s capabilities to measure and control potentiostatically and in open‑circuit, to detect X‑ray signals unperturbed by bubbles and to fluently exchange different thin film samples by collecting high-resolution Fe K-edge spectra of hematite (α -Fe2O3) and ferrite thin film (MFe2O4, M= Zn, Ni) photoelectrodes during water oxidation. Conclusions: Our cell establishes a measurement routine that will provide experimental access of photo-electro-chemical operando HERFD-XAS experiments to a broader scientific community, particularly due to the ease of sample exchange. We believe to enable a broad range of experiments which acquired fundamental insights will spur further photoelectrochemical research and commercialization of water splitting technologies

To overcome the data scarcity problem of machine learning for nondestructive testing, data augmentation is a commonly used strategy. We propose a method to enable training of neural networks exclusively on simulated data. Simulations not only provide a scalable way to generate and access training data, but also make it possible to cover edge cases which rarely appear in the real world. However, simulating data acquired from complex nondestructive testing methods is still a challenging task. Due to necessary simplifications and a limited accuracy of parameter identification, statistical models trained solely on simulated data often generalize poorly to the real world. Some effort has been made in the field to adapt pre-trained classifiers with a small set of real world data. A different approach for bridging the reality gap is domain randomization which was recently very successfully applied in different fields of autonomous robotics. In this study, we apply this approach for ultrasonic testing of carbon-fiber-reinforced plastics. Phased array captures of virtual specimens are simulated by approximating sound propagation via ray tracing. In addition to a variation of the geometric model of the specimen and its defects, we vary simulation parameters. Results indicate that this approach allows a generalization to the real world without applying any domain adaptation. Further, the trained network distinguishes correctly between ghost artifacts and defects. Although this study is tailored towards evaluation of ultrasound phased array captures, the presented approach generalizes to other nondestructive testing methods.

Connected and automated vehicles have become more common in recent years, increasing the need to assess their societal level impacts. In this paper a methodology is presented to explore and define these impacts as a starting point for quantitative impact assessment. The many interrelations between impacts increases the complexity of obtaining a complete overview. Therefore, a structured approach is used, which shows many similarities with the modelling of causal-loop-diagrams. Feedback loops between impacts are taken into account at an early stage and both literature review and expert interviews are used to produce a holistic overview of impacts. The methodology was developed and applied in the European H2020 project LEVITATE. The impact taxonomy and interrelations between impacts resulting from this project are presented and further steps needed to perform a quantitative evaluation of the impacts are discussed.

Genome-scale mechanistic models of pathways are gaining importance for genomic data interpretation because they provide a natural link between genotype measurements (transcriptomics or genomics data) and the phenotype of the cell (its functional behavior). Moreover, mechanistic models can be used to predict the potential effect of interventions, including drug inhibitions. Here, we present the implementation of a mechanistic model of cell signaling for the interpretation of transcriptomic data as an R/Bioconductor package, a Cytoscape plugin and a web tool with enhanced functionality which includes building interpretable predictors, estimation of the effect of perturbations and assessment of the effect of mutations in complex scenarios., This work is supported by grants SAF2017-88908-R from the Spanish Ministry of Economy and Competitiveness and PT17/0009/0006 and PI20/01305 from the ISCIII, both co-funded with European Regional Development Funds (ERDF) as well as H2020 Programme of the European Union grants Marie Curie Innovative Training Network “Machine Learning Frontiers in Precision Medicine” (MLFPM) (GA 813533) and “ELIXIR-EXCELERATE fast-track ELIXIR implementation and drive early user exploitation across the life sciences” (GA 676559) to JD. Funding for open access charge: from the Spanish Ministry of Economy and Competitiveness / SAF2017-88908-R.

In this study, an open-source computational fluid dynamics (CFD) model was developed based on OpenFOAM libraries for the accurate and robust simulation of thermal distribution in a data center. Two boundary conditions were developed for the temperature field in the black-box modeling of server components. The numerical model was first validated by comparing numerical results with the experimental measurements for three benchmark problems in the field of thermal engineering. Then, thermal distribution in an open-aisle data center was simulated using the proposed numerical model and results were compared with the experimental measurements. The consistency between numerical results and previous experimental measurements indicates that the present numerical model can be reliably used for the efficiency assessment of air-cooled data centers. Efficiency of the data center can be evaluated with respect to four metrics in the proposed numerical model. In addition to the determination of the overall efficiency, distribution of the efficiency can be monitored over the racks to capture thermal zones where the efficiency decreases due to the recirculation effects and cold air by-pass.

This paper aims to introduce the innovative work carried out in the Horizon 2020 DECODER project – acronym for “DEveloper COmpanion for Documented and annotatEd code Reference” – (Grant Agreement no. 824231) by linking the fields of natural language processing (NLP) and software engineering. The project as a whole addresses the development of a framework, namely the Persistent Knowledge Monitor (PKM), that acts as a central infrastructure to store, access, and trace all the data, information and knowledge related to a given software or ecosystem. This meta-model defines the knowledge base that can be queried and analysed by all the tools integrated and developed in DECODER. Besides, the DECODER project offers a friendly user interface where each of the predefined three roles (i.e., developers, maintainers and reviewers) can access and query the PKM with their personal accounts. The paper focuses on the NLP tools developed and integrated in the PKM, namely the deep learning models developed to perform variable misuse, code summarisation and semantic parsing. These were developed under a common work package – “Activities for the developer” – intended to precisely target developers, who can perform tasks such as detection of bugs, automatic generation of documentation for source code and generation of code snippets from natural languages instructions, among the multiple functionalities that DECODER offers. These tools assist and help the developers in the daily work, by increasing their productivity and avoiding loss of time in tedious tasks such as manual bug detection. Training and validation were conducted for four use cases in Java, C and C++ programming languages in order to evaluate the performance, suitability, usability, etc. of the developed tools.

The virtual, digital counterpart of a physical object, referred as digital twin, derives from the Internet of Things (IoT), and involves real-time acquisition and processing of large data sets. A fully implemented system ultimately enables real-time and remote management, as well as the reproduction of real and forecasted scenarios. Under the emerging framework of Precision Fish Farming, which brings control-engineering principles to fish production, we set up digital twin prototypes for land-based finfish farms. The digital twin is aimed at supporting producers in optimizing feeding practices, oxygen supply and fish population management with respect to 1) fish growth performances; 2) fish welfare, and 3) environmental loads. It relies on integrated mathematical models which are fed with data from in-situ sensors and from external sources, and simulate several dynamic processes, allowing the estimation of key parameters describing the ambient environment and the fishes. A conceptual application targeted at rearing cycles of rainbow trout (Oncorhynchus mykiss) in an operational in-land aquafarm in Italy is presented. The digital twin takes into account the disparate levels of automation and control that are found within this farm, and considerations are made on preferential directions for future developments. In spite of its potential, and not only in the aquaculture sector, the development of digital twins is still at its early stage. Furthermore, Precision Fish Farming applications in land-based systems as well as targeted at rainbow trout are novel developments.

Hydrogen embrittlement can cause a dramatic deterioration of the mechanical properties of high-strength metallic materials. Despite decades of experimental and modelling studies, the exact underlying mechanisms behind hydrogen embrittlement remain elusive. To unlock understanding of the mechanism and thereby help mitigate the influence of hydrogen and the associated embrittlement, it is essential to examine the interactions of hydrogen with structural defects such as grain boundaries, dislocations and stacking faults. Atom probe tomography (APT) can, in principle, analyse hydrogen located specifically at such microstructural features but faces strong challenges when it comes to charging specimens with hydrogen or deuterium. Here, we describe three different workflows enabling hydrogen/deuterium charging of site-specific APT specimens: namely cathodic, plasma and gas charging. All the experiments in the current study have been performed on a model twinning induced plasticity steel alloy. We discuss in detail the caveats of the different approaches in order to help future research efforts and facilitate further studies of hydrogen in metals. Our study demonstrates successful cathodic and gas charging, with the latter being more promising for the analysis of the high-strength steels at the core of our work.

The temporal stability of psychological test scores is one prerequisite for their practical usability. This is especially true for intelligence test scores. In educational contexts, high stakes decisions with long-term consequences, such as placement in special education programs, are often based on intelligence test results. There are four different types of temporal stability: mean-level change, individual-level change, differential continuity, and ipsative continuity. We present statistical methods for investigating each type of stability. Where necessary, the methods were adapted for the specific challenges posed by intelligence research (e.g., controlling for general intelligence in lower order test scores). We provide step-by-step guidance for the application of the statistical methods and apply them to a real data set of 114 gifted students tested twice with a test-retest interval of 6 months. • Four different types of stability need to be investigated for a full picture of temporal stability in psychological research • Selection and adaption of the methods for the use in intelligence research • Complete protocol of the implementation, Graphical abstract Image, graphical abstract

Graphical abstract, Infectious disease transmission models often stratify populations by age and geographic patches. Contact patterns between age groups and patches are key parameters in such models. Arenas et al. (2020) develop an approach to simulate contact patterns associated with recurrent mobility between patches, such as due to work, school, and other regular travel. Using their approach, mixing between patches is greater than mobility data alone would suggest, because individuals from patches A and B can form contacts if they meet in patch C. We build upon their approach to address three potential gaps that remain, outlined in the bullets below. We describe the steps required to implement our approach in detail, and present step-wise results of an example application to generate contact matrices for SARS-CoV-2 transmission modelling in Ontario, Canada. We also provide methods for deriving the mobility matrix based on GPS mobility data (appendix). • Our approach includes a distribution of contacts by age that is responsive to the underlying age distributions of the mixing populations. • Our approach maintains different age mixing patterns by contact type, such that changes to the numbers of different types of contacts are appropriately reflected in changes to overall age mixing patterns. • Our approach distinguishes between two mixing pools associated with each patch, with possible implications for the overall connectivity of the population: the home pool, in which contacts can only be formed with other individuals residing in the same patch, and the travel pool, in which contacts can be formed with some residents of, and any other visitors to the patch.

There is increasing interest to consider dependent failures and human errors in the offshore industry. Permanently abandoned wells dot most of the subsea environment. The nature of a well plugging and abandonment (Well P&A) run - usually the lowest-cost contractor engaged to plug several wells tapping the same reservoir makes it an ideal case study for incorporating failures based on common causes. The heavy use of operators during a cementing job also provides the case for analysis of human error in such tasks. One proposed method to analyse the above-mentioned is the use of Bayesian Belief Networks to achieve the following objectives (1) to capture better estimates of a well PA event by incorporating dependencies, and meet regulatory requirements by authorities; and (2) to use the same model to provide long term monitoring of a group of wells linked by common dependencies. This model has not only captured the dependencies of multiple variables, but also projected it in a dynamic manner to provide a risk profile for the next decade where well integrity failure is likely to happen. • Proposed adapted method capture better estimates of a well PA event by incorporating dependencies • Method allows for extension of model to long term monitoring of a group of wells linked by common dependencies, Graphical abstract Image, graphical abstract

The “Most Easy, Efficient and Low Cost Geothermal Systems for Retrofitting Civil and Historical Buildings” (GEO4CIVHIC) project aims to accelerate the deployment of shallow geothermal systems for heating and cooling purposes when retrofitting existing and historical buildings. Analyzing the implementation process of borehole heat exchangers (BHEs), allows the understanding of how to promote the long-term sustainability of shallow geothermal energy systems. The thermal interference between BHE systems represents a problem, especially due to the increasing deployment of this technology and its spread in densely built-up areas. The main goals of this paper are: a) to analyze the design phase of a BHE system in order to prevent mutual thermal interference, b) to propose a model that encloses phases to adopt an integrated approach for preventing long term thermal interferences, c) to give technical and management suggestions to minimize thermal interference between closed-loop geothermal systems. The method developed follows the following steps: 1) literature review to determine what are the main drivers for thermal interference between shallow geothermal systems, in the context of the GEO4CIVHIC project case study sites; 2) to create a conceptual model to limit thermal interference at both design and operational phases; 3) to apply the developed method to real and virtual case studies in countries with different regulatory frameworks and to test its main strengths and weaknesses. The application of this conceptual model to specific case studies provides evidence of critical planning and operational characteristics of GSHP systems and allows the identification of measures to mitigate impacts of thermal interference to be identified.

Highlights • A new deep learning program to predict protein functions in silico. • Requirement of nothing more than the protein sequence information. • A sequence segmentation to improve the efficiency of prediction. • Prediction of the clinical impact of mutations or polymorphisms., Deep learning technologies have been adopted to predict the functions of newly identified proteins in silico. However, most current models are not suitable for poorly characterized proteins because they require diverse information on target proteins. We designed a binary classification deep learning program requiring only sequence information. This program was named ‘FUTUSA’ (function teller using sequence alone). It applied sequence segmentation during the sequence feature extraction process, by a convolution neural network, to train the regional sequence patterns and their relationship. This segmentation process improved the predictive performance by 49% than the full-length process. Compared with a baseline method, our approach achieved higher performance in predicting oxidoreductase activity. In addition, FUTUSA also showed dramatic performance in predicting acetyltransferase and demethylase activities. Next, we tested the possibility that FUTUSA can predict the functional consequence of point mutation. After trained for monooxygenase activity, FUTUSA successfully predicted the impact of point mutations on phenylalanine hydroxylase, which is responsible for an inherited metabolic disease PKU. This deep-learning program can be used as the first-step tool for characterizing newly identified or poorly studied proteins.•We proposed new deep learning program to predict protein functions in silico that requires nothing more than the protein sequence information.•Due to application of sequence segmentation, the efficiency of prediction is improved.•This method makes prediction of the clinical impact of mutations or polymorphisms possible., Graphical abstract Image, graphical abstract

Geophysical electromagnetic (EM) methods are used in geological mapping, mineral exploration, groundwater studies and geotechnical investigations. Airborne EM methods have the benefit of avoiding terrain obstacles such as lakes, rivers, swamps, and ravines. Compared to manned aircrafts, drones or unmanned aerial vehicles (UAVs) have benefits of their own. Drone-based surveys are versatile, fast to deploy, economical and ecologically more friendly. Presently, magnetic surveying is the only geophysical method that is routinely conducted with drones. The modest maximum payload limit of drones imposes severe restrictions on the applicability of other methods including EM and radiometric methods, for example. Finnish company, Radai Ltd has been developing Louhi, a novel drone-based frequency-domain EM survey system, in an EU funded Horizon 2020 project NEXT – New Exploration Technologies. The EM system has two operation options – the first uses a large loop on the ground as an EM source and the other uses a small portable EM transmitter loop. Both systems utilize a stand-alone and light-weight three-component EM receiver that can be towed by a drone. This article presents the theoretical background of the EM methods, the solution developed by Radai Ltd, the current version of the EM device, and results from field and flight tests that demonstrate the applicability of the drone-based EM system under development.

The seabird meta-population viability model (mPVA) uses a generalized approach to project abundance and quasi-extinction risk for 102 seabird species under various conservation scenarios. The mPVA is a stage-structured projection matrix that tracks abundance of multiple populations linked by dispersal, accounting for breeding island characteristics and spatial distribution. Data are derived from published studies, grey literature, and expert review (with over 500 contributions). Invasive species impacts were generalized to stage-specific vital rates by fitting a Bayesian state-space model to trend data from Islands where invasive removals had occurred, while accounting for characteristics of seabird biology, breeding islands and invasive species. Survival rates were estimated using a competing hazards formulation to account for impacts of multiple threats, while also allowing for environmental and demographic stochasticity, density dependence and parameter uncertainty.•The mPVA provides resource managers with a tool to quantitatively assess potential benefits of alternative management actions, for multiple species•The mPVA compares projected abundance and quasi-extinction risk under current conditions (no intervention) and various conservation scenarios, including removal of invasive species from specified breeding islands, translocation or reintroduction of individuals to an island of specified location and size, and at-sea mortality amelioration via reduction in annual at-sea deaths, Graphical abstract Image, graphical abstract

The optokinetic response (OKR) is an effective behavioural assay to investigate functional vision in zebrafish. The rapid and widespread use of gene editing, drug screening and environmental modulation technologies has resulted in a broader need for visual neuroscience researchers to access affordable and more sensitive OKR, contrast sensitivity (CS) and visual acuity (VA) assays. Here, we demonstrate how 2D- and 3D-printed, striped patterns or drums coupled with a motorised base and microscope provide a simple, cost-effective but efficient means to assay OKR, CS and VA in larval-juvenile zebrafish. In wild-type, five days post-fertilisation (dpf) zebrafish, the 2D or 3D set-ups of 0.02 cycles per degree (cpd) (standard OKR stimulus) and 100% black-white contrast evoked equivalent responses of 24.2±3.9 or 21.8±3.9 saccades per minute, respectively. Furthermore, although the OKR number was significantly reduced compared to the 0.02 cpd drum (p OKR, VA and CS of zebrafish larvae can be efficiently measured using 2D- or 3D-printed striped drums. For data consistency the luminance of the OKR light source, the time of day when the analysis is performed, and the order of presentation of VA and CS drums must be considered. These simple methods allow effective and more sensitive analysis of functional vision in zebrafish.

This paper describes the design features and operational details of a molten metal pyrolysis reactor. Such a reactor allows pyrolysis experimentation on biomass, aluminium-laminated plastics, mixed plastics, carbon fibre materials, etc. Experimental results on biodegradable plastic, carbon fibre composites, biomass and printed circuit boards (PCBs) are presented.•The inner container can have a sloped or flat-bottom depending on the material.•The method can be used to pyrolyse composite and pure materials., Graphical abstract Image, graphical abstract

The uranium-thorium-helium ((U,Th)-He) dating method applied to calcium carbonate speleothems holds much promise for constraining the timeline of hominin evolution, as well as for palaeoclimatology research beyond the range of U/Th disequilibrium dating. Technical problems are posed by often low U concentrations and the requirement that samples need to be individually removed from the ultrahigh vacuum (UHV) system after helium is extracted from them, to be then analyzed for U, Th content and U series disequilibrium. We describe a low-cost furnace with this capability, constructed from standard UHV components, as well as the methods for subsequent U/Th disequilibrium analysis using multicollector ICP mass spectrometry. We present analytical and numerical solutions to determine (U,Th)-He ages for four conditions that have been encountered: (1) for an age range in which the residual activity ratio (234U/238U) can still be resolved but not that of (230Th/234U) (up to about 3000 ka), (2) for an age range where neither can be resolved (unlimited), (3) for ages up to 1000 ka where both activity ratios may be resolvable, and (4) for cases where (234U/238U) and (230Th/234U) indicate ages < 200 ka but (U,Th)-He systematics point to much older ages.•Helium extraction is carried out using an in-house built vacuum furnace that allows for large sample sizes (20 to 100 mg) of powdered carbonate material wrapped in Cu foil.•U and Th are separated together using Eichrom UTEVAⓇ resin and their isotope abundances are measured together in a single 2-cycle dynamic run using the multicollector inductively coupled plasma mass spectrometer (MC-ICP-MS).•(U,Th)-He ages are calculated using four methods that take into account the U/Th disequilibria., Graphical abstract Image, graphical abstract

This study presents an alternative vertical total electron content (VTEC) anomaly detection technique based on diurnal VTEC values. In order to assess the consistency of the results, Mw7.9 Wenchuan earthquake occurred on May 12, 2008 was chosen as case study because several researches were performed on this earthquake event. In this detection technique, the daily mean of VTEC (AVTEC) and standard deviation of diurnal VTEC (SVTEC) were adopted in the analytical process instead of quartile-based technique. The spatial distribution of VTEC was illustrated by AVTEC and SVTEC maps which created from ordinary Kriging interpolation technique. The anomalous day derived from AVTEC and SVTEC was observed on May 9, 2008. The anomalous zone significantly appeared within the earthquake preparation zone in the southeast of the epicenter. The results were corresponding to the previous studies in terms of time and space. Thus, AVTEC, SVTEC and instantaneous ionospheric maps created from ordinary Kriging interpolation technique should be an alternative approach for detecting ionospheric anomaly prior to earthquake occurrence.•Simplified seismo-ionospheric anomaly detection technique•Ionospheric distribution is modelled by ordinary Kriging interpolation maps•The results are consistent with the previous studies in terms of time and space, Graphical abstract Image, graphical abstract

International audience; Deep learning has revolutionized the automatic processing of images. While deep convolutional neural networks have demonstrated astonishing segmentation results for many biological objects acquired with microscopy, this technology’s good performance relies on large training datasets. In this paper, we present a strategy to minimize the amount of time spent in manually annotating images for segmentation. It involves using an efficient and open source annotation tool, the artificial increase of the training dataset with data augmentation, the creation of an artificial dataset with a conditional generative adversarial network and the combination of semantic and instance segmentations. We evaluate the impact of each of these approaches for the segmentation of nuclei in 2D widefield images of human precancerous polyp biopsies in order to define an optimal strategy.

An Air Conditioning Unit with magnetic field and different tubes was designed, fabricated and evaluated in this study. The Effect of magnetic field and different types of tubes on the performance of Air Conditioning was studied experimentally. A testing system of Air Conditioning Unit was developed as the test rig. The modified tubes as a straight tube before the condenser and after the evaporator were replaced by a finned bended tube with five bends and a coil finned tube with five turns. The experimental results for the temperature of refrigerant and the coefficient of performance for an air conditioning unit were presented. Changing the tubes and introducing electric charging has a significant effect on the performance of the unit. The electric charging has a positive effect of the performance of the system. The electric charging enhanced the performance by 76% in case of bent tube and by 177% in case of coil tube. The bent pipe increases the refrigerant temperature between 50% and 200%, while the coil pipe increases the temperature between 18 % and 190 %. • This method increases the refrigerant temperature for Air Conditioning system. • This method provides simple technical testing of Air Conditioning Unit with magnetic field and different tubes • This method can be useful to enhance the performance of Air Conditioning Unit., Graphical abstract Image, graphical abstract

This study develops a method to estimate the redistribution of elements during several associated metasomatic processes simultaneously involved in the formation of a geological complex. The method is based on classical mass balancing using isocon analysis. The proposed approach is applicable if geological prerequisites indicate that (a) the metasomatic rocks inherited some components (x, y, etc.) from one of the earlier rocks of the studied complex; (b) these components have been transported by a fluid; and (c) this saturated fluid has produced several varieties of metasomatic rocks rich in components x, y, etc. In the case of the specified background, the proposed method estimates the mass proportions between the source rock and the varieties of the resulting metasomatic rocks. We present the geological profile of the processes to which the proposed method is applicable, the mathematical model of the method, examples of the application and interpretation of the results, and geological criteria to verify the obtained model results. In brief,•This study introduces a method for calculating the mass proportions between the source rock and metasomatic rocks formed from the material remobilized from the source rock;•The paper considers the applicability limits of the method, exemplifies the interpretation of the results, and shows the methods for monitoring the correctness of these results., Graphical abstract Image, graphical abstract

Graphical abstract, Post-translational modifications of proteins expand their functional diversity, regulating the response of cells to a variety of stimuli. Among these modifications, phosphorylation is the most ubiquitous and plays a prominent role in cell signaling. The addition of a phosphate often affects the function of a protein by altering its structure and dynamics. However, these alterations are often difficult to study and the functional and structural implications remain unresolved. New approaches are emerging to overcome common obstacles related to the production and manipulation of these samples. Here, we summarize the available methods for phosphoprotein purification and phosphomimetic engineering, highlighting the advantages and disadvantages of each. We propose a general workflow for protein phosphorylation analysis combining computational and biochemical approaches, building on recent advances that enable user-friendly and easy-to-access Molecular Dynamics simulations. We hope this innovative workflow will inform the best experimental approach to explore such post-translational modifications. We have applied this workflow to two different human protein models: the hemeprotein cytochrome c and the RNA binding protein HuR. Our results illustrate the usefulness of Molecular Dynamics as a decision-making tool to design the most appropriate phosphomimetic variant.

ProTExA is a web-tool that provides a post-processing workflow for the analysis of protein and gene expression datasets. Using network-based bioinformatics approaches, ProTExA facilitates differential expression analysis and co-expression network analysis as well as pathway and post-pathway analysis. Specifically, for a given set of protein-gene expression data across samples, ProTExA: (1) performs statistical analysis and filtering to highlight the differentially expressed proteins-genes, (2) performs enrichment analysis to identify top-scored pathways, (3) generates pathway-to-pathway and pathway-to-gene networks (4) generates protein and gene co-expression networks using a variety of methodologies, and (5) applies clustering methodologies to identify sub-networks of co-expressed proteins-genes. The proposed web-tool is a simple yet informative tool, towards understanding and exploitation of protein and gene expression datasets, especially for those that do not have the expertise and local resources to replicate specific analyses in the context of collaborative and scientific data exchanging.

The calculation of the cover management factor (C-factor) and support practices factor (P-factor) is an important element in the Universal Soil Loss Equation (USLE). In Switzerland, a potential soil erosion risk map of arable land and a field block map that represents the basis of the agriculturally used areas in the country are available. A CP-factor tool was developed adapted to Swiss agronomic and environmental conditions, which allows to calculate CP-factors easily for various crop rotations and management practices. The calculated CP-factor values can be linked to any field block in the potential soil erosion risk map to determine the actual soil erosion risk for the field block. A plausibility check with other C-factor tools showed a sound match. This user-friendly calculation makes the CP-Tool and the actual erosion risk more accessible for authorities and GIS users. With Python and QGIS as open source resources, it is also possible to easily improve the tools. Linking the two tools provides substantial added value for education and training, advising farmers and policy, as well as scientific research, and can serve as a reference for other countries.•USLE-CP-factor and actual erosion risk calculation on small scale field block level.•Developed and programmed based on open source resources for further improvements.•Both tools increase the knowledge of management practices for GIS- and non GIS users., Graphical abstract Image, graphical abstract

Graphical abstract, In the streaming learning setting, an agent is presented with a data stream on which to learn from in an online fashion. A common problem is catastrophic forgetting of old knowledge due to updates to the model. Mitigating catastrophic forgetting has received a lot of attention, and a variety of methods exist to solve this problem. In this paper, we present a divided and prioritized experience replay approach for streaming regression, in which relevant observations are retained in the replay, and extra focus is added to poorly estimated observations through prioritization. Using a real-world dataset, the method is compared to the standard sliding window approach. A statistical power analysis is performed, showing how our approach improves performance on rare, important events at a trade-off in performance for more common observations. Close inspections of the dataset are provided, with emphasis on areas where the standard approach fails. A rephrasing of the problem to a binary classification problem is performed to separate common and rare, important events. These results provide an added perspective regarding the improvement made on rare events.•We divide the prediction space in a streaming regression setting•Observations in the experience replay are prioritized for further training by the model’s current error

Miniaturization of biomedical and chemical research areas is performed using microfluidic techniques. Droplet-based microfluidic applications are of high interest for various applications, e.g., high-throughput screening assays. Many of them are based on simple water-in-oil (w/o) or oil-in-water (o/w) emulsions that are easily to produce. More complex assays based on separate compartments require the use of multiple emulsions, such as water-in-oil-in-water (w/o/w) or oil-in-water-in-oil (o/w/o) emulsions. In this study an easy, fast to establish method to generate agarose-solidified (w/w/o) double emulsions with ∼55 µm in diameter, in which both agarose-phases are not separated by a surfactant stabilized oil is described. An off-chip emulsion-breaking and washing step of the inner agarose droplets based on density gradient centrifugation was designed, offering new possibilities for high-throughput assays on picoliter scale. In brief, this paper reports:•the protocol to generate agarose-solidified (w/w/o) double emulsions non-seperated by surfactant stabilized oil;•an off-chip washing protocol of agarose-solidified emulsions based on density gradient centrifugation., Graphical abstract Image, graphical abstract

Surface colonization by microorganisms, combined with the rise in antibiotic resistance, is the main cause of production failures in various industries. Self-sterilising materials are deemed the best prevention of surface colonization. However, current screening methods for these sterilising materials are laborious and time-consuming. The disk diffusion antimicrobial assay and the Japanese industrial standard method for antimicrobial activity on solid surfaces, JIS Z 2801, were compared to our modified solid surface antimicrobial assay in terms of detecting the activity of antibiotic-containing cellulose disks against four bacterial pathogens. Our novel assay circumvents the long incubation times by utilising the metabolic active dye, resazurin, to shorten the time in which antibacterial results are obtained to less than 4 h. This assay allows for increased screening to identify novel sterilising materials for combatting surface colonisation.•Disk diffusion assay could only detect the activity of small compounds that leached from the material over 20–24 h.•JIS Z 2801 was also able to detect the surface activity of non-polar compounds, thought to be inactive based on the disk diffusion results.•The resazurin solid surface antimicrobial assay could obtain the same results as the JIS Z 2801, within a shorter time and in a high-throughput 96-well plate setup., Graphical abstract Image, graphical abstract

Determining the composition of bacterial communities beyond the level of a genus or species is challenging because of the considerable overlap between genomes representing close relatives. Here, we present the mSWEEP method for identifying and estimating the relative abundances of bacterial lineages from plate sweeps of enrichment cultures. mSWEEP leverages biologically grouped sequence assembly databases, applying probabilistic modelling, and provides controls for false positive results. Using sequencing data from major pathogens, we demonstrate significant improvements in lineage quantification and detection accuracy. Our method facilitates investigating cultures comprising mixtures of bacteria, and opens up a new field of plate sweep metagenomics.

Background Despite all efforts, the demand for organs increases. New and better strategies are still needed, critical in a crisis like pandemics. Methodology A mathematical approach that integrates need, Opportunity, and Accessibility to kidney transplantation, was created. NOA method, corresponds to the lateral surface area of a trigonal pyramid with the need, Opportunity, and Accessibility as axis, resulting in an intuitional chart output (NOA chart) and a percentage score (NOA score). Higher NOA scores are associated with larger NOA chart areas. Method Application We found some natural variability among the European Member States regarding Need, Opportunity, and Accessibility to kidney transplantation, concomitant with NOA scores. In 2019, in the European Union, 129 patients pmp on the waiting list for a kidney transplant were registered, 47 kidneys pmp were procured, and 36 kidneys pmp were transplanted, corresponding to 25% of kidney transplantation's response capacity. Conclusion Transplantation is frequently the better treatment for end-stage kidney failure. NOA method may be, in the future, an indicator for evaluating the overall transplantation performance regarding the need for it and a tool for policy definition. With NOA method we seek to contribute for: •A transplantation overall performance normalizing score;•Transplantation response capacity evaluation., Graphical abstract Image, graphical abstract

Virus production is an essential part of virology. The quality of virus preparation, in terms of purification and yield, can affect the outcome of experiments. Purified virus is critical for assays, as contamination from cell culture supernatants may affect results. Purified virus is also needed for vaccine preparations. The traditional process of gradient purification of virus is time consuming, with multiple steps spanning over days, and produces significant amounts of biohazard waste. Developing a more efficient alternative that provides a purified virus product is highly desirable. In this study, we performed magnetic bead purification of Zika virus (ZIKV) and Mayaro virus (MAYV) with the Mag4C LV kit from OZ Biosciences. Currently, this kit is only indicated for use with lentiviruses and retroviruses. We found that we were able to successfully concentrate and purify both ZIKV and MAYV using the kit. Therefore, we concluded that the Mag4C LV kit provides a quick and simple alternative to traditional virus purification methods.•Our protocol is customized by using an alphavirus (MAYV) and flavivirus (ZIKV). This method has not been previously used for these viruses., Graphical abstract Image, graphical abstract

Metals being released into the environment are posing an increasing risk to both environments and public health. Developing improved approaches to remove these metals from the environment is urgent. A current publication discovered that metal bioremediation was significantly improved by nanoparticles (NPs), and the remediation duration was shortened. However, there is no relevant method for the preparation and evaluation of this novel idea. Hence, we developed this method for bacteria in-situ-EPS (Extracellular Polymeric Substances) cultivation, bacteria sub-cellular fractionation, and metal determinations in cultivating solution, EPS and different fractions of bacteria to evaluate metal removal by the combination of NPs and bacteria, including (1) the enhancement of metal bioremediation by NPs, (2) the influence of NPs on bacteria growth and metal toxicity alleviation, (3) the ability of EPS to adsorb metals and the influence of NPs on the EPS metal adsorption, (4) the contribution of bacteria to metal removal in different part, the effects of NPs on metal distribution patterns in bacteria, and the role of NPs in this process.•The design and experimental procedure for the evaluation of metal removal by combing bacteria and NPs.•In-situ EPS cultivation and separation in the study of bioremediation for metals., Graphical abstract Image, graphical abstract

Phytoremediation has widely been recognised as an eco-system friendly and effective technique for soil remediation. However, this method is generally slow, and most plants used for phytoextraction are incapable of thriving in crude oil polluted soils with high concentrations of petroleum hydrocarbon. Hence, organic stimulants were developed for accelerating the phytoremediation of crude oil polluted soils by enhancing the growth of cowpea through nutrient supplementation, and increasing the bioavailability of petroleum hydrocarbon by saponification. Moringa and pawpaw seeds are high in plant available nutrients and contain saponin, a phytochemical that increases the bioavailability of contaminants. Although both seeds and moringa seed powder have been used for water purification, they have not been explored in soil remediation studies. For these reasons, pawpaw seeds were processed into powdery form to increase their surface area as organic stimulants for enhancing phytoremediation of crude oil polluted soil. This study investigates the performance of pawpaw seed powder, relative to moringa seed powder, in increasing the removal rate of petroleum hydrocarbon by cowpea under crude oil polluted soil conditions. The key functions of the newly developed organic stimulant include:•Increased bioavailability of petroleum hydrocarbon for phytoextraction.•Enhanced shoot production in cowpea.•Increased capacity of cowpea to remove petroleum hydrocarbons from soils., Graphical abstract Image, graphical abstract

Background: Generalisation of findings is an important aspect of research and essential for evidence-based practice. While generalisation is common in quantitative research, there is a lack of generalisability in qualitative research. This paper presents the experience and challenges faced by the Designing for People with Dementia (MinD) project in meeting the requirements to strengthen the generalisation of findings on the lived experience of people living with dementia and their engagement to co-create designs to empower their everyday living. Methods: Polit and Beck (2010)’s strategies to generalise qualitative findings were applied: (1) replication in sampling; (2) replication of studies; (3) meta-synthesis of findings; (4) reflexivity and conceptualization; (5) immersion with the data; and (6) thick description. Results: While it is possible to increase the generabilisabilty of qualitative evidence through the replication of the sampling to attain a large, heterogeneous sample in different and multiple contexts and environments; implementation of sound and robust research; conducting in-depth analysis and interpretation collaboratively for emergent themes; and meeting the thick description requirement, there are challenges that the project team faced in implementing some of the Polit and Beck’s strategies because of the condition, namely dementia, that our participants are having. Other challenges faced were: the language and cultural diversity in the team; diverse work and organisational procedures; and the inter-disciplinary differences relating to the methods of enquiry, approaches and techniques to conduct research. These challenges will need to be identified and addressed at the start of the project with a strong leadership to ensure a seamless journey to complete the project successfully. Trust between the researchers and participants, and time to build this trust are critical to recruitment and participation in the study; these factors are of utmost important in research involving participants with condition such as dementia.

Connected and automated vehicles have become more common in recent years, increasing the need to assess their societal level impacts. In this paper a methodology is presented to explore and define these impacts as a starting point for quantitative impact assessment. The many interrelations between impacts increases the complexity of obtaining a complete overview. Therefore, a structured approach is used, which shows many similarities with the modelling of causal-loop-diagrams. Feedback loops between impacts are taken into account at an early stage and both literature review and expert interviews are used to produce a holistic overview of impacts. The methodology was developed and applied in the European H2020 project LEVITATE. Results from the qualitative assessments in this project are presented and further steps needed to perform a quantitative evaluation of the impacts are discussed.

Although microenterprises constitute a significant part of Morocco's economy, they encounter financing difficulties which represent the biggest obstacles to their growth, therefore, Islamic financial institutions can take a prominent role in fostering financial inclusion in Morocco. Accordingly, this article aims to explore the determinants of intention to adopt Islamic financing in micro-business. For this purpose, this study utilizes data collected from a selected sample of Moroccan micro-business owner-managers using face-to-face interviews during March 2021. The results from the partial least squares analysis indicate that religious obligation and Islamic financing reputation positively affect attitude towards Islamic financing. Findings also reveal the positive and significant influence of subjective norms, attitude towards Islamic financing, and perceived behavioral control on intention to adopt Islamic financing.•This method article can be used to explore the antecedent of Islamic financing adoption in micro‐business.•Access to this method article may help Islamic bank managers to set up marketing policies in order to strengthen the micro-business owners' intention to adopt Islamic financing., Graphical abstract Image, graphical abstract

Production of a protein of interest in bacteria and its purification from bacterial lysates are valuable tools for the purification of larger amounts of recombinant proteins. The low cost of culturing, and the rapid cell growth of bacteria make this host a good choice for protein production, but the folding and function of the purified protein might be altered due to the production of a eukaryotic protein in a prokaryotic host. Here, we provide a purification method for the purification of gamma (γ)-tubulin (TUBG) from soluble fractions of Escherichia (E.) coli lysates using affinity tags.•This protocol describes a method that purifies soluble GST-TUBG1 from bacteria.•Of the three tested induction conditions, the highest yield of recombinant GST-TUBG1 was obtained after the induction of E. coli with isopropyl-D-1-thiogalactopyranoside (IPTG) for 1 h at 37 °C followed by overnight incubation at room temperature.•In comparison with other methodologies (Hoog et al., 2011), the technique described here retrieves larger amounts of recombinant TUBG1 from small-scale expression cultures., Graphical abstract Image, graphical abstract

The COVID-19 pandemic is expanding at an unprecedented rate. As a result, diagnostic services are stretched to their limit, and there is a clear need for the provision of additional diagnostic capacity. Academic laboratories, many of which are closed due to governmental lockdowns, may be in a position to support local screening capacity by adapting their current laboratory practices. Here, we describe the process of developing a SARS-Cov2 diagnostic workflow in a conventional academic Containment Level 2 (CL2) laboratory. Our outline includes simple SARS-Cov2 deactivation upon contact, the methods for a quantitative real-time reverse transcriptase PCR (qRT-PCR) detecting SARS-Cov2, a description of process establishment and validation, and some considerations for establishing a similar workflow elsewhere. This was achieved under challenging circumstances through the collaborative efforts of scientists, clinical staff, and diagnostic staff to mitigate to the ongoing crisis. Within 14 days, we created a validated COVID-19 diagnostics service for healthcare workers in our local hospital. The described methods are not exhaustive, but we hope may offer support to other academic groups aiming to set up something comparable in a short time frame.