Processing Steps Research Articles

Integrating genomic and molecular data to predict antimicrobial minimum inhibitory concentration in Klebsiella pneumoniae

Minimum inhibitory concentration (MIC) denotes the in vitro benchmark indicating the quantity of antibiotic required to inhibit proliferation of specific bacterial strains. Determining MIC values corresponding to the infecting bacterial strain is paramount for tailoring appropriate antibiotic therapy. In the interim between specimen collection and laboratory-derived MIC outcomes, clinicians frequently resort to empirical therapy informed by retrospective analyses. Here introduces two deep learning approaches, a Convolutional Neural Network (CNN)-based model and an Enformer-based model, integrating genomic data of Klebsiella Pneumoniae and molecular structural data of 20 antibiotics to anticipate the MIC value of the bacterium for each antibiotic under consideration. These models demonstrate enhanced raw accuracy over the existing state-of-the-art model, which rely exclusively on genomic data. The CNN-based model achieves a notable 20% increase in raw accuracy while further mirroring the 1-tier accuracy of the state-of-the-art model. Although the Enformer-based model does not quite reach the performance levels of the CNN-based model, it offers an advantage by eliminating the need for arbitrary data processing steps. This streamlining of the data processing pipeline facilitates fast updates and improves the model interpretability. It is expected that these deep learning paradigms can significantly inform and bolster clinician decision-making during the empirical treatment phase.

Optimal child-led goal setting practices for school-aged children with a disability or delay: an international Delphi consensus study

Aim To achieve expert consensus on optimal child-led goal setting and evaluation practices for school-aged children (5 ≤ 17 years) who have a disability or delay. Method A three-round, eDelphi consensus design was used. In Round 1, expert allied health professionals identified factors important during child-led goal setting for: (i) planning, (ii) process steps and tools, and (iii) support strategies. Factors were collated into items for Rounds 2 and 3. Participants rated item agreement on a 7-point Likert scale. Consensus was determined as ≥75% of participants in agreement or disagreement. Results Sixty professionals from nine countries and six disciplines participated. Of 323 unique items generated, 159 (50%) reached consensus. Strong agreement was reached for goal setting and evaluation “process steps” and “support strategies” to engage children. It was strongly recommended that allied health professionals should tailor their processes to each child’s individual needs, provide ability-specific strategies and resources, and empower children to share their perspectives. Fewer items reached consensus for “planning” and “tools” to guide child-led goal setting. Interpretation Professional experts agree that children can be actively involved in goal setting and evaluation. Future research should focus on tools and technologies to support child-led goal setting for children with diverse abilities.

Cost-benefit analysis: just a step in the HIA process

Abstract The economic analysis within Health Impact Assessments (HIA) is a critical component in informing public policy decisions by assessing the financial implications of health outcomes associated with the different intervention options. Health metrics play a role defining the cost-effectiveness and societal value associated with the interventions. These metrics, quantifying the health impacts, include quality-adjusted life years (QALYs), years of life lost (YLLs) or disability-adjusted life years (DALYs). On another level, such metrics need to be translated into cost-effectiveness and societal value in which other methods play a role, such as the value of statistical life (VSL) or the value of a life year (VOLY). While VSL quantifies the value individuals place on reducing mortality risk, VOLY evaluates the value attributed to extending life expectancy. On another hand, uncertainties related to economic valuation pose challenges to the accuracy and reliability of cost estimates within HIA. These come from several factors, including data quality, methodological differences across studies, and subjective judgments involved in valuing health states. In fact, estimating the economic value of a prevented death involves multifaceted considerations and important assumptions. Additionally, assigning monetary values to non-fatal health outcomes, such as disability or reduced quality of life, involves subjective assessments and value judgments. By selecting appropriate metrics, considering diverse data sources, and addressing methodological steps and uncertainties, HIA practitioners can improve the utility of economic assessments, facilitating evidence-based decision-making in public health policy and resource allocation. This work was supported by National Funds through FCT - Fundação para a Ciência e a Tecnologia,I.P., within CINTESIS, R&D Unit (reference UIDP/4255/2020).

Towards the prediction of drug solubility in binary solvent mixtures at various temperatures using machine learning

Drug solubility is an important parameter in the drug development process, yet it is often tedious and challenging to measure, especially for expensive drugs or those available in small quantities. To alleviate these challenges, machine learning (ML) has been applied to predict drug solubility as an alternative approach. However, the majority of existing ML research has focused on the predictions of aqueous solubility and/or solubility at specific temperatures, which restricts the model applicability in pharmaceutical development. To bridge this gap, we compiled a dataset of 27,000 solubility datapoints, including solubility of small molecules measured in a range of binary solvent mixtures under various temperatures. Next, a panel of ML models were trained on this dataset with their hyperparameters tuned using Bayesian optimization. The resulting top-performing models, both gradient boosted decision trees (light gradient boosting machine and extreme gradient boosting), achieved mean absolute errors (MAE) of 0.33 for LogS (S in g/100 g) on the holdout set. These models were further validated through a prospective study, wherein the solubility of four drug molecules were predicted by the models and then validated with in-house solubility experiments. This prospective study demonstrated that the models accurately predicted the solubility of solutes in specific binary solvent mixtures under different temperatures, especially for drugs whose features closely align within the solutes in the dataset (MAE < 0.5 for LogS). To support future research and facilitate advancements in the field, we have made the dataset and code openly available.Scientific contributionOur research advances the state-of-the-art in predicting solubility for small molecules by leveraging ML and a uniquely comprehensive dataset. Unlike existing ML studies that predominantly focus on solubility in aqueous solvents at fixed temperatures, our work enables prediction of drug solubility in a variety of binary solvent mixtures over a broad temperature range, providing practical insights on the modeling of solubility for realistic pharmaceutical applications. These advancements along with the open access dataset and code support significant steps in the drug development process including new molecule discovery, drug analysis and formulation.Graphical

Developing a structured framework to address vaccination barriers for Newly Arrived Migrants in EU/EEA

Abstract Background Access to vaccination is a critical aspect of public health, particularly for newly arrived migrants (NAMs) in European Union/European Economic Area (EU/EEA) countries. Despite this, significant barriers exist, necessitating urgent attention. This study addresses the pressing need to improve vaccination coverage for NAMs by developing a General Conceptual Framework (GCF) to understand and overcome system barriers and analyzing possible strategies. Methods starting from a theoretical conceptualization, the study employed a mixed-method approach, incorporating literature review and qualitative research. The GCF was formulated based on key components of the vaccination process. Barriers and solutions were identified through a non-systematic literature review and qualitative investigation. The GCF served as a guide for study activities, facilitating data integration. Results The study outlines a structured GCF comprising five interconnected steps in the vaccination process: entitlement, reachability, adherence, achievement, and evaluation of vaccination. Barriers and solutions identified through literature review and qualitative research are presented. Strategies to enhance professional knowledge, accessibility, adherence, coverage, and intervention evaluation are proposed. Recommendations include proximity strategies, provider training, migrant-sensitive approaches, and robust data collection. Conclusions Equitable access to vaccination is paramount for public health, particularly for vulnerable populations like NAMs in EU/EEA countries. The development of the GCF represents a crucial step towards understanding and addressing barriers to vaccination access. Implementation of recommended strategies can significantly improve vaccination outcomes for NAMs, ultimately contributing to the overall health and well-being of these communities.

Using artificial intelligence to support and streamline rapid systematic evidence reviews

Abstract Issue The Rapid Evidence Service, initiated by the National Collaborating Centre for Methods and Tools (NCCMT) during COVID-19, supports public health decision making by conducting rapid reviews on priority topics. Description of issue Integral to rapid reviews is an expedited timeline but the quantity of available literature for most public health review questions takes significant time to screen manually. NCCMT integrated 4 artificial intelligence (AI) features into the screening process. DAISY Rank applies predictions learned from manual screening patterns to re-order remaining studies, with most relevant appearing first. AI Screening automatically screens remaining studies based on prediction scores. Check for Screening Errors and Re-Rank Report use previous screening patterns to identify studies that were potentially falsely excluded and predict the total number of included studies, respectively. These features were tested by comparing results provided by AI with those produced manually for select test sets. Results NCCMT used AI to support and expedite screening, assess screening progress, and/or minimize risk of inappropriately excluding studies for 35 rapid reviews on 20 topics. Using DAISY Rank enabled one screener to review over 4000 references in 9 hours, compared to a different review, where the same amount of screening took 28 hours without DAISY Rank. AI Screening correctly excluded up to 80% of irrelevant search results across reviews. Check for Screening Errors identified 37 potential includes manually excluded in one review; these were reviewed and 3 were included. Re-Rank Report allowed NCCMT to re-allocate staff to subsequent steps in the review process when most included studies were identified. Lessons Integrating AI features into screening led to less time required, better anticipated timelines, more accurate staff allocation and reduced errors. More rigorous study of AI best practices is needed to continue to improve rapid review method efficiencies. Key messages • Rapid reviews can be an important source of evidence for decision makers if they can be completed quickly but maintain rigor and accuracy. • AI holds promise as a way to improve screening efficiency.

New solid phase microextraction fibers with green clay coating via radio frequency magnetron sputtering for detecting low-polar compounds in water samples

BackgroundDeveloping highly sensitive and selective measurement techniques to detect trace compounds in diverse matrices is a significant challenge in analytical chemistry. These techniques must adhere to green chemistry principles by minimizing organic solvent use, simplifying sample preparation, and streamlining process steps. Additionally, there is a growing need for sustainable analytical methods due to increased environmental awareness. The problem addressed in this work is the need for an eco-friendly and efficient method for the extraction and detection of trace organochlorine pesticides in water samples. ResultsWe employed SPME using a novel clay thin film sorbent, deposited on a nickel-titanium alloy wire via magnetron sputtering. Montmorillonite clay was chosen for its excellent adsorption properties and eco-friendly nature, aligning with green chemistry principles. The approach involved coating the SPME fiber with hydrophobic modified montmorillonite clay, followed by silylation. The method was tested for extracting 12 model organochlorine pesticides, including BHC, lindane, and DDT, demonstrating high isolation efficiency. The coated thin film and its silylation modification were characterized using standard spectroscopic techniques, confirming the successful creation of a new adsorbent phase. The direct immersion SPME approach achieved relative recoveries ranging from 65 % to 99 %, with reproducibility (RSD) below 6 %. This method provided low detection limits (10–15 ng L−1) and quantitation limits (32–50 ng L−1). SignificanceOur approach offers an eco-friendly, highly efficient solution for the extraction and detection of trace organochlorine pesticides. The significant improvement in recovery rates and reproducibility, combined with low detection and quantitation limits, underscores the potential of this method to enhance analytical practices in environmental monitoring and public health. Furthermore, the use of sustainable materials and processes aligns with global efforts to reduce environmental impact in analytical chemistry.

Measuring laypeople’s notions of intersex in the Netherlands and Flanders

Abstract Introduction In emancipation policy, the ‘I’ is often added to the LGBTI+-acronym. Yet it is still unknown to what extent this term is known and understood by the general population, and what attitudes toward intersex (people) are. In the current study, we describe the construction and development of the Intersex Knowledge and Attitudes Scale (IKAS), as well as the results from this baseline measurement in the Dutch and Flemish population. Methods The IKAS was developed after a carefully documented stepped process of literature scan, expert consultations, and pretesting. A representative sample of 2,003 respondents (1,000 Dutch and 1,003 Flemish) participated in the study. To determine the dimensionality of the scale, we performed EFA and CFA with measurement invariance constraints. We also performed a baseline measurement of knowledge and attitudes towards intersex people in the general population, and determined predictors of attitudes toward intersex people in regression analyses. Results Two-thirds of the sample had no (correct) knowledge of the term intersex. Even among the intersex respondents, only one person was aware of the term intersex. The average attitude was moderately positive to neutral. Having a female gender or birth sex, an advanced level of education, lower importance of religion, belonging to a minority group, more tolerant gender beliefs, and preexisting knowledge on the topic all resulted in more positive attitudes. Conclusions Both in Flanders and the Netherlands there still is room for improvement in terms of knowledge and attitudes towards intersex people. More knowledge may lead to more understanding, so that intersex people can be more open. Second, it is recommended to challenge beliefs of sex and gender as binary categories, because people who have less issues with people who do not entirely fall in these categories, also have more accepting attitudes toward intersex people. Key messages • It is vital to include intersex in monitoring studies of population attitudes, to be able to genuinely incorporate the ‘I’ in LGBTI+ in emancipation policies. • To increase acceptance of intersex people in society, knowledge about intersex in the general population should be expanded, and acceptance of people who do not fit into the binary should be promoted.

Exploring pharmaceutical powder behavior in commercial-scale bin blending: A DEM simulation study

Bin blending is one of the main steps in pharmaceutical production processes. Commercial-scale production of expensive products typically does not allow to perform a large number of experiments in order to optimize the process. Alternatively, Discrete Element Method (DEM) simulations can be used to evaluate the powder behavior (flow and blending pattern) during blending, identify the risks (e.g., segregation), and provide solutions to mitigate them. In this work, DEM simulations are used to investigate the blending of two granulated powders in commercial-scale cone and cylindrical (hoop) blenders. The DEM contact model parameters were calibrated based on the experimental compression and ring shear tests for both granulated powders to mimic the bulk powder behavior in the simulations. The model's output was compared to the experiments in one of the blending cases. The blending efficiency in the cone blenders was evaluated considering the fill levels, the presence of baffles, the rotating directions, the filling order, and the bin sizes. Furthermore, for the hoop blenders, the effects of blender's angle, rotation speed, and filling order were addressed. The main findings of the work were that, in cone blenders, the blending can be improved by introducing baffles and changing in the rotational direction frequently. In hoop blenders, blending can be improved by increasing the inclination angle from the horizontal plane and the rotational speed.

Adaptation of an evidence-based, preventive intervention to promote mental health in Hispanic adolescents: eHealth Familias Unidas Mental Health.

Youth internalizing symptoms (i.e., depression and anxiety), suicide ideation and attempts have been rising in recent years, including among Hispanics. Disparities in mental healthcare are concerning and require intervention, ideally prevention or early intervention. Familias Unidas is a culturally-syntonic, family-centered intervention effective in reducing youth drug use and sexual risk, with evidence of unanticipated effects on internalizing symptoms. This paper describes the systematic process used to adapt the eHealth version of the Familias Unidas intervention to more directly address internalizing symptoms and suicide risk in preparation for an effectiveness-implementation hybrid trial for youth with elevated internalizing symptoms, a history of suicide ideation/attempts, or poor parent-youth communication. The resulting eHealth Familias Unidas Mental Health intervention is described. Guided by a 4-phase framework, the steps in the adaptation process involved: assessment of the community and intervention delivery setting (pediatric primary care clinics); integration of previous intervention research, including intervention mechanisms of action; and expert and community consultation via focus groups. Focus group analyses showed that youth and parents perceived that the intervention was helpful. Their feedback was categorized into themes that were used to directly target mental health by addressing technology use, parent mental health, and social support. Effective and scalable preventive interventions are needed to address mental health disparities. The systematic adaptation process described in this paper is an efficient approach to expanding interventions while maintaining known, empirical and theoretical mechanisms of action. Findings from the ongoing effectiveness-implementation trial will be critical.

Pectus excavatum or Pectus chiaroscurum in paintings of St. Jerome (17th century)?

Considering the influence of artistic style is a key methodological step in the iconodiagnostic process in order to avoid some pitfalls. The two present artworks, depicting Saint Jerome, attributed respectively to the 17th-century Neapolitan school and to Valentin de Boulogne, French painter of the 17th century, highlight how the interplay of light and shadow can enhance and disguise a possible pectus excvatum.

20 years review of the multi actor multi criteria analysis (MAMCA) framework: a proposition of a systematic guideline

AbstractMulti-criteria Decision Making (MCDM) involves evaluating alternatives based on a comprehensive set of conflicting criteria, often requiring the involvement of varied decision makers. This has led to the emergence of stakeholder-based multi-criteria group decision-making (MCGDM) frameworks. However, traditional MCGDM frequently overlooks the interactions and trade-offs among different actors and criteria. The Multi-actor Multi-criteria Analysis (MAMCA), developed in 2000, provides a transparent decision-making process explicitly considering these interrelationships. It allows diverse stakeholder groups to represent their priorities, thereby enhancing their understanding of their own and others’ positions. Over the past two decades, MAMCA has seen a significant rise in popularity and has been widely applied in diverse contexts, proving valuable as both a decision-making and stakeholder engagement tool. However, our analysis of publications on cases in which MAMCA was applied over the years shows that considerable variation exists in the overall process approach, contingent upon the specific goals and context. To address these variations, this paper proposes a modularized MAMCA structure, complemented by systematic application guidelines, to aid future users in navigating the process steps and identifying the most suitable methods for each step. Additionally, future research directions are suggested for potential enhancements to the MAMCA framework by integrating varied methodologies.

Development of laser felling modes of gas-thermal coating

The use of copper and its alloys to create parts for metallurgical equipment is associated with an increase in abrasive wear and high-temperature corrosion. In this regard, there is a need to apply a protective coating. In particular, to prevent wear and premature chipping of the metal of copper tuyeres, the surface is hardened with a coating of zirconium dioxide stabilized with yttria oxide by thermal spraying in an air atmosphere. Due to the difference in the coefficient of thermal expansion of copper (at T = 300 K: 16.7 µm/m оС and at T = 750 K: 19.7 µm/m оС) and its low resistance to gas corrosion, the application of zirconium oxide (produced by a preapplied intermediate layer that plays a role in matching the coefficient of thermal expansion (CTE) between the copper base and the ceramic coating. In addition, the intermediate layer protects copper from gas corrosion. In this case, The use of copper and its alloys to create parts for metallurgical equipment is associated with an increase in abrasive wear and high-temperature corrosion. In this regard, there is a need to apply a protective coating. In particular, to prevent wear and premature chipping of the metal of copper tuyeres, the surface is hardened with a coating of zirconium dioxide stabilized with yttria oxide by thermal spraying in an air atmosphere. Due to the difference in the coefficient of thermal expansion of copper (at T = 300 K: 16.7 pm/m °G and at T = 750 K: 19.7 pm/m оG) and its low resistance to gas corrosion, the application of zirconium oxide (produced by a pre-applied intermediate layer that plays a role in matching the coefficient of thermal expansion (CTE) between the copper base and the ceramic coating. In addition, the intermediate layer protects copper from gas corrosion. In this case, nickel-based alloys were used as intermediate layers. The use of nickel as the basis of intermediate layers is due to the fact that copper and nickel form a continuous series of solid solutions, such as cupronickel or monel metal-like structures. This, in turn, assumes a smooth transition of thermophysical properties from copper to nickel alloy. To ensure increased adhesion of the transition layer to copper by increasing the area of mutual contact between copper and the sublayer (dagger penetration) and significantly increasing the homogeneity of the material of the intermediate layer made of a nickel alloy, laser melting of the intermediate sublayer (Ni–B–Si system) was used on a laser complex based on laser LS-5 with a power of 5 kW with a KUKA KR-60HA robot in an argon atmosphere. To test the modes, experiments were carried out on copper samples of a flat shape and a body of rotation. The optimal parameters for the process of melting flat samples were: processing speed 33 mm/s, power from 400 to 3900 W, focal length from 200 to 230 mm, pitch between tracks: 0.25, 0.5 and 1 mm. The optimal parameters for the process of melting rotating samples were: laser radiation power 400–450 W, processing step 0.125; 0.5, focal length from 200 to 210 mm.

Dynamic web-based GIS tool for pre-feasibility evaluation of renewable energy projects

The high energy consumption and the large amount of emissions associated with roads life cycle makes it necessary for the administrations involved to invest in the implementation of renewable energy technologies on adjacent lands so that the net balance between CO2 emitted and CO2 saved is zero, thus helping to decarbonize the transport networks. To facilitate the first step in the long process leading up to the construction of wind and solar farms, a user-friendly tool is here presented that allows an early-stage evaluation of renewable energy projects. The ENROAD web-based GIS tool, through a functional, modern, intuitive, and ergonomic graphical user interface aimed at non-specialized users, optimizes the use of photovoltaic and wind technologies based on shading and wake losses, respectively, taking into account the solar radiation and wind conditions of the location and the geometry of the area selected by the user. In addition, the tool provides a thorough financial analysis, incorporating the effects of user-defined long-term interest rates, inflation rates, loan costs and efficiency losses on the Levelized Cost of Energy as well as traditional financial metrics, enabling users to conduct insightful if-then simulations. While this tool cannot have the precision of the in-depth study needed for the design of complex renewable energy facilities, it can facilitate the decision making by the administrations. Finally, a case study and a validation section have been included to demonstrate the technical and financial evaluation provided by ENROAD and ensure that the results provided are reasonable and accurate enough.

Including the ergonomist's voice in integrating MSD prevention and psychological health and safety: Challenges, tools, and considerations

The purpose of this study was to gather ergonomists’ perspectives and experiences, describing current challenges and contextual considerations in risk assessment, exploring how ergonomists are currently integrating the multiple domains of ergonomics for MSD and/or psychological health and safety and highlighting key considerations in the design and format of future tools.In-depth, semi-structured interviews were conducted with twenty Canadian ergonomists to explore risk assessment tool use, favoured characteristics and format of tools, commonly addressed risk factors in their practice, and tools relating to both MSD prevention and psychological health and safety. The range of practitioner years of experience highlighted differing needs and approaches to the use and formatting of risk assessment tools. Practitioners reported using quantitative outcomes (levels of risk, values) from traditional physical tools complemented by a general observation of psychosocial or organizational factors. Though many respondents had not yet encountered the need for psychological injury assessment in their sectors it was identified as a quickly emerging area citing a need for valid and reliable tools. Practitioners noted a lack of available tools that integrated cognitive and psychosocial items presenting a future challenge for integrated tools that covered multiple ergonomic domains. Along with recommendations for future tool development, the authors reflect on the process of qualitative inquiry as an essential step in the risk assessment process. Future studies will be needed to develop and evaluate measurement properties of integrating psychosocial factors and their respective tools in traditional MSD assessment.

Comparative evaluation of DNA synthesis for qPCR analysis from oral squamous cell carcinoma tissues - A rapid and robust isolation technique for gene expression studies

BackgroundDNA isolation from biological materials is the initial step in several bioanalytical processes, including the polymerase chain reaction (PCR). This procedure works best with pure DNA devoid of potential amplification reaction inhibitors. Since the quantity and quality of biological samples are limited, it is essential to extract as much DNA as possible from the original sample. The solid-phase extraction technique is frequently used to purify DNA. In this process, the DNA is adsorbed onto a solid support, any contaminants from the reaction are eliminated by washing, and the purified DNA is then eluted from the support. The quality and concentration of DNA have a direct effect on the gene analysis procedure. Therefore, before interpreting the results of PCR-based diagnostic assays, it is imperative to confirm the DNA preparation and integrity, particularly if no pathogenic DNA is identified. MethodsA comparative study was carried out using two standardized protocols for DNA synthesis in comparison with a test protocol incorporating carrier RNA and proteinase K from a viral detection kit. The quality and quantity of the synthesized DNA are assessed by qPCR analysis for gene amplification of metastasis suppressor genes NDRG1, RhoGDI, and KISS 1. Results and conclusionThe test protocol showed higher yields of DNA in comparison to the standard protocol. The concentration of DNA obtained was validated by the concentration of gene expressed from q PCR analysis. The gene expression was significantly higher when the test protocol method was followed for DNA synthesis. Thus the study validates the potential of nucleic acid carrier RNA molecules to improve DNA extraction processes used in tissue samples for gene analysis procedures.

Evaluating early predictive performance of machine learning approaches for engineering change schedule – A case study using predictive process monitoring techniques

By applying machine learning algorithms, predictive business process monitoring (PBPM) techniques provide an opportunity to counteract undesired outcomes of processes. An especially complex variation of business processes is the engineering change (EC) process. Here, failing to adhere to planned implementation dates can have severe impacts on assembly lines, and it is paramount that potential negative cases are identified as early as possible. Current PBPM research, however, has seldomly investigated the predictive performance of machine learning approaches and their applicability at early process steps, let alone for the EC process. In our research, we show that given adequate feature encoding, shallow learners can accurately predict schedule adherence after process initialisation. Based on EC data from an automotive manufacturer, we provide a case sensitive performance overview on algorithm-encoding combinations. For that, three algorithms (XGBoost, Random Forest, LSTM) were combined with four encoding techniques. The encoding techniques used were the two common aggregation-based and index-based last state encoding, and two new combinations of these, which we term advanced aggregation-based and complex aggregation-based encoding. The study indicates that XGBoost-index-encoded approaches outclass regarding average predictive performance, whereas Random-Forest-aggregation-encoded approaches perform better regarding temporal stability due to reduced influence by dynamic features. Our research provides a case-based reasoning approach for deciding on which algorithm-encoding combination and evaluation metrics to apply. In doing so, we provide a blueprint for an early warning and monitoring method within the EC process and other similarly complex processes.

BEpipeR: a user-friendly, flexible, and scalable data synthesis pipeline for the Biodiversity Exploratories and other research consortia

Abstract Background Large research consortia can generate tremendous amounts of biological information, including high-resolution soil, vegetation, and climate data. While this knowledge stock holds invaluable potential for answering evolutionary and ecological questions, making these data exploitable for modelling remains a daunting task due to the many processing steps required for synthesis. This might result in many researchers to fall back to a handful of ready-to-use data sets, potentially at the expense of statistical power and scientific rigour. In a push for a more stringent approach, we introduce BEpipeR, an R pipeline that allows for the streamlined synthesis of plot-based Biodiversity Exploratories data. Methods BEpipeR was designed with flexibility and ease of use in mind. For instance, users simply choose between aggregating forest or grassland data, or a combination thereof, effectively allowing them to process any experimental plot data of this research consortium. Additionally, instead of coding, they parse most processing information in a user-friendly way through parameter sheets. Processing includes, among others, the creation of a spatially explicit plot-ID template, data wrangling, quality control, plot-wise aggregations, the calculation of derived metrics, data joining to a large composite data set, and metadata compilation. Results With BEpipeR, we provide a feature-rich pipeline that allows users to process Biodiversity Exploratories data in a flexible and reproducible way. This pipeline might serve as a starting point for aggregating the numerous data sets of this and potentially similar research consortia. In this way, it might be a primer for the construction of consortia-wide composite data sets that take full advantage of the consortia’s rich information stocks, ultimately boosting the visibility and participation of individual research projects. Conclusions The BEpipeR pipeline permits the user-friendly processing and plot-wise aggregation of Biodiversity Exploratories data. With modifications, this framework may be easily adopted by other research consortia.

Egg Yolk, a Multifunctional Emulsifier: New Insights on Factors Influencing and Mechanistic Pathways in Egg Yolk Emulsification

Egg yolk is a highly effective natural emulsifier used in various food products. Its emulsifying properties are influenced by food product chemical conditions, and processing methods. Nevertheless, to effectively utilize egg yolk in food products, a more comprehensive understanding of these factors is crucial. This review discusses recent developments regarding how factors like pH, ionic strength, thermal treatments, enzymatic treatments, and novel non-thermal treatments affect egg yolk emulsifying properties. It also explores the underlying mechanisms involved in egg yolk emulsification. Food products involve different ingredients leading to varying pH values and ionic strength, which affect egg yolk protein adsorption and emulsion stability. Processing steps like thermal treatment can damage egg yolk proteins, reducing their emulsifying capabilities and leading to unstable products. Incorporating sugar, salt, and amino acids can enhance egg yolk’s resistance to heat and preserve its ability to form stable emulsions. As an alternative to thermal treatment, non-thermal techniques such as high-pressure processing and high-intensity ultrasound can be employed to preserve egg yolk. Furthermore, forming egg yolk–polysaccharide complexes can enhance egg yolk emulsifying properties. These advancements have facilitated the creation of egg yolk-based products such as high internal phase Pickering emulsions (HIPEs), low-fat mayonnaise, and egg yolk gels. A comprehensive understanding of the emulsifying mechanisms and factors involved in egg yolk will be instrumental in improving food quality and creating novel egg yolk-based products.

Scalable fabrication approach and fill factor optimization for single pixel microlens arrays

Microlenses are a suitable approach to improve the performance of optical systems. An important optical efficiency determining parameter is the fill factor, which describes the relation of the lens area to the total optical active area. In this work, an optimization of the fill factor by optimizing the fabrication process steps is presented. The approach here is the use of i-line waferstepper lithography in combination with thermal reflow of photoresist and subsequent 1:1 pattern transfer in the lens material by reactive ion etching. For this method, the fill factor is determined by the minimum lens gap and, thus, the optical efficiency is strongly limited by the resolution limit of the i-line waferstepper lithography (350 nm). The goal of this investigation is to achieve the lowest possible lens gap even below the stepper-based resolution limit by optimizing each single process step without developing a new approach. The final result of the optimization was a fill factor improvement of 15%.