Quality Checks Research Articles

Five Years of Accurate PM2.5 Measurements Demonstrate the Value of Low-Cost PurpleAir Monitors in Areas Affected by Woodsmoke

Low-cost optical sensors are used in many countries to monitor fine particulate (PM2.5) air pollution, especially in cities and towns with large spatial and temporal variation due to woodsmoke pollution. Previous peer-reviewed research derived calibration equations for PurpleAir (PA) sensors by co-locating PA units at a government regulatory air pollution monitoring site in Armidale, NSW, Australia, a town where woodsmoke is the main source of PM2.5 pollution. The calibrations enabled the PA sensors to provide accurate estimates of PM2.5 that were almost identical to those from the NSW Government reference equipment and allowed the high levels of wintertime PM2.5 pollution and the substantial spatial and temporal variation from wood heaters to be quantified, as well as the estimated costs of premature mortality exceeding $10,000 per wood heater per year. This follow-up study evaluates eight PA sensors co-located at the same government site to check their accuracy over the following four years, using either the original calibrations, the default woodsmoke equation on the PA website for uncalibrated sensors, or the ALT-34 conversion equation (see text). Minimal calibration drift was observed, with year-round correlations, r = 0.98 ± 0.01, and root mean square error (RMSE) = 2.0 μg/m3 for daily average PA PM2.5 vs. reference equipment. The utitilty of the PA sensors without prior calibration at locations affected by woodsmoke was also demonstrated by the year-round correlations of 0.94 and low RMSE between PA (woodsmoke and ALT-34 conversions) and reference PM2.5 at the NSW Government monitoring sites in Orange and Gunnedah. To ensure the reliability of the PA data, basic quality checks are recommended, including the agreement of the two laser sensors in each PA unit and removing any transient spikes affecting only one sensor. In Armidale, from 2019 to 2022, the continuing high spatial variation in the PM2.5 levels observed during the colder months was many times higher than any discrepancies between the PA and reference measurements. Particularly unhealthy PM2.5 levels were noted in southern and eastern central Armidale. The measurements inside two older weatherboard houses in Armidale showed that high outdoor pollution resulted in high pollution inside the houses within 1–2 h. Daily average PM2.5 concentrations available on the PA website allow air pollution at different sites across regions (and countries) to be compared. Such comparisons revealed major elevations in PA PM2.5 at Gunnedah, Orange, Monash (Australian Capital Territory), and Christchurch (New Zealand) during the wood heating season. The data for Gunnedah and Muswellbrook suggest a slight underestimation of PM2.5 at other times of the year when there are proportionately more dust and other larger particles. A network of appropriately calibrated PA sensors can provide valuable information on the spatial and temporal variation in the air pollution that can be used to identify pollution hotspots, improve estimates of population exposure and health costs, and inform public policy.

Species substitution in goat yoghurt supply chain using melting-curve analysis

Mislabelling of dairy products has been widely reported worldwide but there are relatively few data available to help assess species substitution in goat yogurt. The purpose of this study was to investigate the presence of bovine and sheep milk in yogurt labeled as being made exclusively from goat milk, using qualitative real-time PCR. The results confirm that fermented products are targets for food species substitution, highlighting the undeclared presence of cow and/or sheep milk in 39.6% of samples analysed. Moreover, the study shows that the qualitative real-time PCR used here is a rapid, sensitive (LODabs of 0.015 ng/μL), specific (no primer-dimers or non-specific products in the reactions), straightforward and low-cost molecular tool for discriminating adulterants in the dairy supply chain. Therefore, melt-curve analysis is a promising tool for facilitating routine safety, quality and authenticity checks in the dairy industry, and for implementing mitigation strategies against fraudulent activities. Application of DNA-based approaches is crucial for designing an innovative food safety management system and for helping to implement both a Food Fraud Vulnerability Assessment and a Food Fraud Prevention Strategy as common practices in the dairy sector.

Single-Cell Characterization of TP53-Mutated AML Patients Treated with Frontline Azacitidine, Venetoclax, and Magrolimab Reveals Mechanisms of Response and Resistance

Background: TP53 mutations, found in around 20% of AML cases, often lead to therapy resistance and unfavorable outcomes. Recent studies hint at the potential efficacy of CD47, a ‘don't eat me’ signal, in treating TP53-mutated AML. The ongoing clinical trial (NCT04435691) is assessing the efficacy of a triple therapy combo-magrolimab (anti-CD47 mAb), azacitidine, and venetoclax (AVM)-with promising initial results in TP53-mutated AML patients. However, understanding resistance and relapse remains crucial. Methods: We performed paired 5' scRNA and scTCR profiling on 3 healthy donors and 27 samples from 11 treatment-naive TP53-mutated AML patients (median age 63, range 59-73 years; 8 responders (CR/CRi); 3 non-responders (NR)), both pre-AVM (n=11) and post-AVM-point of remission (n=11), relapse (n=2), or primary resistance (n=3). Results: 42,910 AML cells and 137,169 bone marrow microenvironment cells passed quality checks. AML cell identification was confirmed via correlation with clinical flow cytometry, inferred copy number alterations, and immunohistochemistry markers. We leveraged Symphony to phenotypically infer AML cell types based on their transcriptional similarity to normal hematopoietic cells. Overall, samples displayed heterogeneous distribution of cell types at time of diagnosis. However, the 3 samples from the 3 primary resistant patients demonstrated a marked and significant expansion of AML cell proportion exhibiting an erythroid differentiation phenotype post-AVM (Fig. A). Also, the 2 patients who relapsed 4.8- and 7.1-months following AVM demonstrated expansion of erythroid differentiation proportion (Fig. A) and higher LSC17 at time of relapse. This suggests a selection favoring erythroid clones as a mechanism of primary and acquired resistance following AVM therapy. We conducted a pathway enrichment analysis using Hallmark gene sets to identify significantly altered AML-intrinsic biological pathways between responders and non-responders. CR/CRi patient-derived AML cells exhibited an enriched inflammatory response, specifically in TNFα and IFNγ signaling pathways-known CD47 expression regulators-and increased CD47 expression (Fig. B). In contrast, AML cells from non-responsive patients showed an enrichment of oxidative phosphorylation (Fig. B). Of note, phagocytic activity of myeloid cells was high in non-responders suggesting that despite the likely activity of magrolimab, other mechanisms of primary resistance were at play. As anti-CD47-mediated phagocytosis can stimulate antigen presentation and trigger T cell activation, we delved into the T cell subtypes and their corresponding cellular responses. There were not significant differences in the distribution of CD4 and CD8 pretreatment in CR/CRi versus NR patients. However, CD8 dysfunction score was significantly higher in non-responders at baseline. Following therapy, the CD8 dysfunction score was also significantly higher at time of relapse among responders. Further, effector CD8 T cells of NR patients had enrichment for cytotoxicity and TCR signaling pathways, and higher expression of exhaustion-related genes. Conclusions: Through single cell transcriptomic profiling of TP53-mutated AML patients treated with the triplet combination of AVM on protocol we identified erythroid differentiation and oxidative phosphorylation as possible intrinsic resistance mechanisms. Additionally, non-responders exhibited a dysfunctional T cell state prior to treatment, while relapses were associated with development of CD8 dysfunction. Overall, this data provides valuable insight into the mechanisms of response and resistance to magrolimab-based therapy. TCR repertoire and AML-immune cell interaction analyses are ongoing.

Assessment of trace metal bioaccumulation in Mytilus galloprovincialis of the central Atlantic ocean after installation of treatment sewage facilities

Mussels (Mytilus galloprovincialis) are vital to Morocco's artisanal fishermen both nutritionally and economically. This study probed the bioaccumulation of trace metals zinc (Zn), cadmium (Cd), copper (Cu), and lead (Pb) in mussels from three sites north of Agadir: Anza, Aourir, and Imouran. Analyzed using atomic absorption techniques across different seasons, findings indicated variable metal concentrations, with Imouran notably presenting the highest, potentially due to influences from the new wastewater plants of Anza and Aourir. While lead was not detected, cadmium, albeit in lower concentrations, posed potential health concerns upon regular consumption. Individual Mean Bioaccumulation Index (IMBI) demonstrated fluctuations, majorly influenced by Zn, Cd, and Cu levels. These trends in IMBI between 2017 and 2018 for Aourir and Imouran hinted at similar environmental impacts or exposure levels. Despite site differences, Metal Pollution Index (MPI) values consistently pointing to analogous potential toxicity due to metal combinations. Intricate relationships between trace metals and their environmental parameters were evident, with multiple linear regression analysis (MLRA) showcasing differing correlations between variables. These results highlight the nuanced interactions between trace metals and bioaccumulation indices, such as MPI and IMBI. In conclusion, the findings accentuate the imperative of regular coastal water quality checks and effective management strategies to curtail pollutant discharges.

Statistical analysis of the gravitational anomaly in Gaia wide binaries

ABSTRACT The exploration of the low acceleration a < a0 regime, where a0 = 1.2 × 10−10 m s−2 is the acceleration scale of MOND around which gravitational anomalies at galactic scale appear, has recently been extended to the much smaller mass and length scales of local wide binaries thanks to the availability of the Gaia catalogue. Statistical methods to test the underlying structure of gravity using large samples of such binary stars and dealing with the necessary presence of kinematic contaminants in such samples have also been presented. However, an alternative approach using binary samples carefully selected to avoid any such contaminants, and consequently much smaller samples, has been lacking a formal statistical development. In the interest of having independent high-quality checks on the results of wide binary gravity tests, we here develop a formal statistical framework for treating small, clean, wide binary samples in the context of testing modifications to gravity of the form G → γG. The method is validated through extensive tests with synthetic data samples, and applied to recent Gaia DR3 binary star observational samples of relative velocities and internal separations on the plane of the sky, v2D and r2D, respectively. Our final results for a high acceleration r2D < 0.01 pc region are of γ = 1.000 ± 0.096, in full accordance with Newtonian expectations. For a low acceleration r2D > 0.01 pc region, however, we obtain γ = 1.5 ± 0.2, inconsistent with the Newtonian value of γ = 1 at a 2.6σ level, and much more indicative of MOND AQUAL predictions of close to γ = 1.4.

Human-Robot Shared Assembly Taxonomy: A step toward seamless human-robot knowledge transfer

Future manufacturing will witness a shift in human-robot relationships toward collaboration, compassion, and coevolution. This will require seamless human-robot knowledge transfer. Differences in language and knowledge representation hinder the transfer of knowledge between humans and robots. Thus, a unified knowledge representation system that can be shared by humans and robots is essential. Driven by this need in a product assembly scenario, we propose the Human-Robot Shared Assembly Taxonomy (HR-SAT). With HR-SAT, any comprehensive assembly task can be represented as a knowledge graph that both humans and robots can understand. To ensure consistency in task decomposition and representation, we define the key elements of HR-SAT. HR-SAT incorporates rich assembly information and provides necessary information for diverse applications, e.g., process planning, quality checking, and human-robot collaboration. The usage and practicality of HR-SAT are demonstrated through two case studies. As a unified assembly process representation schema, HR-SAT constitutes a critical step toward seamless human-robot knowledge transfer. The specifications of HR-SAT and the two case studies are available at: https://iai-hrc.github.io/hr-sat.

Research on the relationship between mother's physical and mental health and infant sleep status based on regression analysis

This study explores the influence of maternal physical and mental indicators on infant sleep. A mathematical model is established by examining the data of 390 infants aged between 3 and 12 months, along with relevant information about their mothers. The data undergoes quality checks, including the identification of missing values and outliers using tools such as the "find" function in MATLAB. Categorical variables are encoded, and the time-based variable of sleep duration is transformed into a numerical format. Spearman's correlation coefficient is then employed to analyze the relationships between different variables, specifically focusing on the patterns between maternal physical and mental indicators and infant sleep duration. The reliability of the questionnaire is ensured through KMO testing, and a linear regression model is established to examine the relationship between mothers and infants. Finally, the linear regression equation is used to predict the behavioral characteristics of the last 20 infants. The findings of this study are of significant importance in understanding the impact of maternal factors on infant sleep and provide valuable insights for further research in related fields.

Engineering a highly durable adeno-associated virus receptor for analytical applications

Adeno-associated virus (AAV) is a major viral vector used in gene therapy. There are multiple AAV serotypes, and many engineered AAV serotypes are developed to alter their tissue tropisms with capsid modification. The universal AAV receptor (AAVR) is an essential receptor for multiple AAV serotypes. Since most AAV serotypes used in gene therapy infect cells via interaction with AAVR, the quantification of the vector-binding ability of AAV to AAVR could be an important quality check for therapeutic AAV vectors. To enable a steady evaluation of the AAV-AAVR interaction, we created an engineered AAVR through mutagenesis. Engineered AAVR showed high durability against acid while retaining its AAV-binding activity. An affinity chromatography column with the engineered AAVR was also developed. This column enabled repeated binding and acid dissociation measurements of AAVR with various AAV serotypes. Our data showed that the binding affinities of AAV2 to AAVR were diverse among serotypes, providing insight into the relationship with the infection efficiency of AAV vectors. Thus, this affinity column can be used in process development for quality checks, quantitating capsid titers, and affinity purification of AAV vectors. Furthermore, this column may serve as a useful tool in novel AAV vector capsid engineering.

Enhancing production inventory management for imperfect items using fuzzy optimization strategies and Differential Evolution (DE) algorithms

In the complex landscape of production inventory management for manufacturing industries, this research brings to light the significant impact of imperfect items—often an overlooked aspect that can result in elevated costs and operational inefficiencies. Addressing this oversight, we introduce a tailored mathematical model that harmoniously integrates demand forecasting, production planning, quality checks, and inventory control measures, specifically when imperfect items are in play. Notably, our model leverages the power of hybrid intelligent algorithms, fusing fuzzy systems with both genetic and differential evolution (DE) algorithms. Through sensitivity analysis, we discern the intricate effects of varying parameters on system performance. To cater to different operational exigencies, we introduce two distinct decision-making approaches. Our results highlight a promising reduction in anticipated yearly expenses, bolstering the reliability of events and ensuring costs are kept within predefined financial thresholds. Furthermore, we unveil a pioneering hybrid DE algorithm that distinctly outperforms the foundational DE in optimization problem-solving. A side-by-side comparison of our introduced methods elucidates invaluable insights into inventory cash flow management. Practitioners stand to gain immensely from our findings, which pave the way for cost-effective strategies, superior quality control, and an uptick in overall operational efficiency in the face of imperfect items. This seminal work not only offers actionable strategies but also beckons future research opportunities, thereby pushing the frontiers of manufacturing systems.

Anterior, Posterior, and Thickness Cornea Differences after Scleral Lens Wear in Post-LASIK Subjects for One Year.

The aim of this study is to analyze the anterior and posterior corneal surface shape and the corneal thickness difference outcomes between before and after scleral lens (ScCL) wear in post-LASIK ectasia subjects for one year. Twenty eyes with post-LASIK ectasia wearing scleral lenses were evaluated in a visit before contact lens and after 1, 6, and 12 months. The study variables analyzed included the apex, nasal, temporal, inferior, and superior corneal thickness; the anterior and posterior surface corneal at corneal diameters of 8, 6, 4, and 2 mm, and high-contrast visual acuity. A statistically significant increment of corneal thickness (p < 0.05) was observed in the inferior area after 6 months and in the superior area in the 12-month follow-up after wearing ScCLs. The anterior corneal curvature presented a flattening and a statistically significant steepening (p < 0.05) in the central and peripheral radii, respectively, after one year. The posterior corneal curvature showed a significant (p < 0.05) steepening, which mainly affected the central region after one year. Despite these changes, high-contrast visual acuity with ScCL correction remained at the same values. The prolonged use of scleral lenses in post-LASIK subjects showed significant changes in the corneal curvature and thickness. These outcomes recommend more detailed and periodic topographic and vision quality checks to monitor the wear in ScCL patients.

Assessment of Leica CityMapper-2 LiDAR Data within Milan’s Digital Twin Project

The digital twin is one of the most promising technologies for realizing smart cities in terms of planning and management. For this purpose, Milan, Italy, has started a project to acquire aerial nadir and oblique images and LiDAR and terrestrial mobile mapping data. The Leica CityMapper-2 hybrid sensor has been used for aerial surveys as it can capture precise and high-resolution multiple data (imagery and LiDAR). The surveying activities are completed, and quality checks are in progress. This paper concerns assessing aerial LiDAR data of a significant part of the metropolitan area, particularly evaluating the accuracy, precision, and congruency between strips and the point density estimation. The analysis has been conducted by exploiting a ground control network of GNSS and terrestrial LiDAR measurements created explicitly for this purpose. The vertical component has an accuracy root mean square error (RMSE) of around 5 cm, and a horizontal component of around 12 cm. Meanwhile, the precision RMSE ranges from 2 to 8 cm. These values are suitable for generating products such as DSM/DTM.

Management of fraudulent participants in online research: Practical recommendations from a randomized controlled feasibility trial.

Fraudulent participation is an escalating concern for online clinical trials and research studies and can have a significant negative impact on findings. We aim to shed light on the risk and to provide practical recommendations for detecting and managing such instances. The FREED-Mobile (FREED-M) study is an online, randomized controlled feasibility trial to assess a digital early intervention for young people (aged 16-25) in England or Wales with eating problems. The trial involved baseline (week 0), post-intervention (week 4), and follow-up (week 12) surveys, alongside weekly modules provided over 4 weeks on the study website. Study completers were compensated with £20 shopping vouchers. Despite the complexity of the trial design, two instances of fraudulent sign-ups occurred in January and March 2023. To counter this, we developed a "fraudulent participants protocol" following internal investigations and discussions with collaborators. The implementation of prevention measures such as reCAPTCHA updates, IP address review, and changes in reimbursement effectively halted further fraudulent sign-ups. Our protocol facilitated the systematic identification and withdrawal of suspected or clear fraudsters and was demonstrably robust at distinguishing between fraudsters and genuine responders. All remote, online trials or studies are at risk of fraudulent participation. Drawing from our experience and existing literature, we offer practical recommendations for researchers considering online recruitment and data collection. Vigilance and the integration of deterrents, and data quality checks into the study design from the outset are advised to safeguard research integrity. Fraudulent participation in digital research can have asignificant impact on research findings, potentially leading to biased resultsand misinformed decisions. We developed an effective protocol for theprevention, identification, and management of fraudulent participants. Bysharing our insights and recommendations, we hope to raise awareness of thisissue and provide other researchers with the knowledge and strategies necessaryto safeguard research integrity moving forward.

A New Approach for Product Quality Check Based on Social Networks Opinions Analysis

In this paper, we aim to enhance the relevance of e-commerce web sites by a prior quality checking of proposed products. This checking is done by analyzing social networks and YouTube videos comments.&#x0D; To achieve this objective, we have broken down the work into a few steps. The first one consists in scraping text from social networks groups and storing it in a NoSQL graph database. Each scraped word is linked to one or many reactions that are coming from the social network. Therefore, we can utilize the database as a knowledge source that associates each set of terms with a specific type of reaction: positive, negative or neutral. Afterwards, we use a TF-IDF based filtering method to keep only relevant words and eliminate those which are a connected to all reactions. The advantage of this stage is the presence of a knowledge source that can be used for product quality checking.&#x0D; In the e-commerce web site, data are coming from multiple e-commerce websites. The latter, offer products without quality checking. Concretely, we aim to allow users to check quality by a simple check button which call an implemented web service using human reactions and comments. After evaluating our approach, we have obtained an accuracy of 0,75. This result means that our method gives a three quarter of chance to have a good product.

Integrated RNA-sequencing and network analysis approach to identify the Hub genes and vital pathways associated with gastric cancer

Context: Gastric cancer is one of the most prevalent types of cancer in developing countries and ranks fourth in terms of death causes. Helicobacter pylori infection is a significant contributor to the emergence of gastric cancer. Lack of early diagnosis of gastric cancer is a leading cause of death. Aims: To identify the key genes and pathways involved in gastric cancer. Methods: This study performed a comprehensive analysis of RNA-Seq data from human gastric cancer and adjacent normal tissues. Raw data passed quality checks with FastQC and were aligned to GRCh38 using HISAT2. Subread's FeatureCounts handled transcript assembly and quantification. DESeq2 pinpointed significant genes, while ClueGO explored gene ontology and KEGG pathways. Protein-protein interaction networks, constructed with StringApp, aided in identifying hub genes through CytoHubba. This holistic approach yields insights into the molecular mechanisms underpinning gastric cancer. Results: This study detected 711 differentially expressed genes (DEGs) between normal and gastric samples. A total of 594 genes were identified as upregulated and 117 as downregulated. Major DEGs are enriched in signal transduction, stimulus-response regulation, transmembrane signaling receptor activity, and signal transduction pathways involving cytokines. In addition, 20 hub genes from the PPI network were identified based on MCC rank analysis from the CytoHubba plugin, contributing to the progression of gastric cancer. Conclusions: The top six hub genes, CD4, CTLA4, CD28, CD80, CD27, and SELL, are expected to regulate several pathways and may serve as potential biomarkers for the early detection and treatment of gastric cancer patients.

Exploring Online Crowdfunding for Cancer-Related Costs Among LGBTQ+ (Lesbian, Gay, Bisexual, Transgender, Queer, Plus) Cancer Survivors: Integration of Community-Engaged and Technology-Based Methodologies.

Cancer survivors frequently experience cancer-related financial burdens. The extent to which Lesbian, Gay, Bisexual, Transgender, Queer, Plus (LGBTQ+) populations experience cancer-related cost-coping behaviors such as crowdfunding is largely unknown, owing to a lack of sexual orientation and gender identity data collection and social stigma. Web-scraping has previously been used to evaluate inequities in online crowdfunding, but these methods alone do not adequately engage populations facing inequities. We describe the methodological process of integrating technology-based and community-engaged methods to explore the financial burden of cancer among LGBTQ+ individuals via online crowdfunding. To center the LGBTQ+ community, we followed community engagement guidelines by forming a study advisory board (SAB) of LGBTQ+ cancer survivors, caregivers, and professionals who were involved in every step of the research. SAB member engagement was tracked through quarterly SAB meeting attendance and an engagement survey. We then used web-scraping methods to extract a data set of online crowdfunding campaigns. The study team followed an integrated technology-based and community-engaged process to develop and refine term dictionaries for analyses. Term dictionaries were developed and refined in order to identify crowdfunding campaigns that were cancer- and LGBTQ+-related. Advisory board engagement was high according to metrics of meeting attendance, meeting participation, and anonymous board feedback. In collaboration with the SAB, the term dictionaries were iteratively edited and refined. The LGBTQ+ term dictionary was developed by the study team, while the cancer term dictionary was refined from an existing dictionary. The advisory board and analytic team members manually coded against the term dictionary and performed quality checks until high confidence in correct classification was achieved using pairwise agreement. Through each phase of manual coding and quality checks, the advisory board identified more misclassified campaigns than the analytic team alone. When refining the LGBTQ+ term dictionary, the analytic team identified 11.8% misclassification while the SAB identified 20.7% misclassification. Once each term dictionary was finalized, the LGBTQ+ term dictionary resulted in a 95% pairwise agreement, while the cancer term dictionary resulted in an 89.2% pairwise agreement. The classification tools developed by integrating community-engaged and technology-based methods were more accurate because of the equity-based approach of centering LGBTQ+ voices and their lived experiences. This exemplar suggests integrating community-engaged and technology-based methods to study inequities is highly feasible and has applications beyond LGBTQ+ financial burden research.

Insomnia in patients with heart disease: A systematic review

Objective: The purpose of this paper is to review the literature on insomnia in patients with heart disease in order to answer the following questions: (a) What is the impact of insomnia on quality of life, morbidity, and mortality in patients with heart disease? (b) What is the impact of treatment interventions on insomnia in patients with heart disease? Methods: Following the PRISMA guidelines, studies published from 1973 to 2023 that pertain to insomnia and heart disease were identified through the use of the PubMed and PsycINFO databases, using the keywords: ‘cardiac’ OR ‘cardiovascular’ OR ‘heart’ AND ‘disease’ AND ‘insomnia’. Two authors independently conducted a focused analysis and reached a final consensus on 11 studies that met the specific selection criteria and passed the study quality checks. Results: Management of insomnia in patients with heart disease involved combining different interventions, such as CBT-I with disease management or self-management programs, which demonstrated sustained improvements in sleep-related metrics in patients with heart disease and insomnia. While non-pharmacological approaches were the primary focus in most studies reviewed, medication-based interventions, such as zolpidem controlled-release, melatonin, and oxazepam, showed promising results. Conclusion: The findings support the use of CBT-I as an effective non-pharmacological approach for managing insomnia in patients with heart disease. Future research should explore the long-term effects of interventions, optimal dosing and duration, and potential synergistic effects of combining different interventions.

Screening respondents to increase data quality in consumer tests

Product tests are often conducted to understand consumer opinions and perceptions. A screener is often used to determine which respondents are consumers. Our goal was to determine how filtering consumers who gave low-quality screener responses affects test results. Respondents in Finland (n = 343) and Turkey (n = 342) completed an online questionnaire. The questionnaire began with a screener. Respondents who consumed category products regularly were classified as consumers. Points were allocated to respondents who gave dubious answers to screener questions. Respondents with too many points “failed”; others “passed”. Regardless, all respondents completed an online test of three product concepts and a short version of the portrait values questionnaire. Screener-passing consumers better discriminated the product concepts and had larger directional differences between the concepts than a conventional panel comprised of all consumers. Fixed-sized panels composed first of screener-passing consumers, then of all consumers, were simulated using the bootstrap procedure. Screener-passing simulated panels were more discriminating of the concepts and had larger between-concept differences than conventional simulated panels. These empirical results indicate the size of the benefit realized by incorporating data quality checks into screening. If consumers who give dubious screener responses are dropped, then other consumers can take their places or their places can be left unfilled, yielding either better differentiation or cost savings. Further study is needed to develop best practices in screening that can be used in different countries and cultures.

The Joint Research Centre-European Network of Cancer Registries Quality Check Software (JRC-ENCR QCS).

The core activity of population-based cancer registries (PBCRs) is to gather information from all new cancer cases in a defined geographic area, in order to measure the magnitude of cancer burden and to provide a basis for cancer research. The Joint Research Centre-European Network of Cancer Registries Quality Check Software (JRC-ENCR QCS) is a Java standalone desktop application, under development since 2015, created to support PBCRs in the validation of the collected data. The JRC-ENCR QCS performs internal consistency checks on the cancer registry dataset, to detect impossible or unlikely codes or combination of codes, and is thereby an important tool to support the validation efforts by registries and improve data quality and European-wide harmonisation. The software package also includes the JRC CSV Data layout converter, a complementary tool for transforming PBCR incidence files into a format compatible with the JRC-ENCR QCS. This paper gives an overview of the JRC-ENCR QCS, describing the role of the software in processing data files submitted by PBCRs contributing to the European Cancer Information System (ECIS) as well as its functionalities. The development of the JRC-ENCR QCS is an evolving process, with regular updates implementing new and revised European and International recommendations and classifications.

Automatic trajectory adaptation for the control of quality characteristics in a powder compaction process

The manufacturing process of sintered components requires the compaction of powder in a rigid die and the sintering of the resulting green parts. To produce green parts of the same constant quality over a long period of time, regular quality checks and trajectory adjustments are needed to account for changing operating conditions due to e.g. varying temperature, humidity, stroke rate, or powder quality. Also the first setup of the machine requires manual measurements and iterative adjustments of trajectory key points. To overcome these labour-intensive, manual measurements and adjustments, we propose an algorithm for the automatic adaptation of trajectory key-points that for the first time allows bringing and keeping more than one quality characteristic (mass and dimensions) fully automatically within predefined intervals. To safe costs, we further present two real-time capable, model-based solutions by employing grey-box and black-box models for the online estimation of the workpiece mass and height instead of their direct measurement. The grey-box model combines a filling and analytical compaction model and allows estimating mass and height rather than density as typically investigated in literature. The black-box model based on neural networks represents a complete novelty in the field as no similar data-driven approach could be found that aims at estimating mass and dimensions of green compacts. We finally compare the performance of these models to the baseline of sensor measurements as well as study their effect on the overall control performance. Results indicate that for both sensor-based and model-based solutions it is possible to reach and keep aforementioned quality characteristics within desired intervals. While overall the sensor-based approach performed best, also for the model-based approaches a good estimation performance is observed with the black-box model based on neural networks outperforming the grey-box model. While the black-box approach is fully data-driven and thus allows to widely avoid manual modelling activities, it still requires the presence of expensive sensors for capturing training data. The proposed grey-box approach, on the other hand, requires some manual modelling and model calibration activities, but provides a cost-effective solution to the problem and represents an alternative to more expensive, sensor-based implementations.

Ramadan during pregnancy and offspring health over the life course: A systematic review

Abstract Background The Muslim population in Europe is increasing, with by now more than 5% of the population being Muslim. During pregnancy, many Muslim women choose to adhere to the Ramadan fast. Research on health effects of Ramadan during pregnancy on the offspring has been conducted through two separate literature strands. One compares offspring of women who chose vs didn't choose to fast and focuses on birth outcomes. The other strand uses an ITT-approach, classifying Muslims as potentially vs certainly not exposed based on their birthdates relative to Ramadan. The ITT-studies’ design allows to also study later-life health. This review combines both strands and evaluates their ability to identify causal effects between Ramadan during pregnancy and offspring health. Methods A systematic literature search using PubMed, EconLit and Web of Science plus a Google Scholar search yielded 35 eligible studies. Data were extracted and quality assessed using a checklist. Results Non-ITT studies often struggle to adequately address bias from self-selection in treatment, with a high risk of confounding and unknown direction of bias. ITT studies, while producing results biased toward zero, are likely to reveal causality and relied on large datasets, leading to high statistical power. Studies meeting the quality threshold find limited evidence of effects on birth outcomes, such as birthweight, but show increased child mortality rates and worse adult health outcomes along several dimensions. These effects tend to increase in magnitude with age, which is congruent with fetal programming theory. Conclusions Medical advisories, guidelines and previous literature reviews mostly focused on non-ITT studies that primarily examined birth outcomes. However, health effects mostly show up at later ages. Consequently, adverse long-run effects are often overlooked, and pregnant Muslims remain not fully informed of potential health impacts on their offspring of their Ramadan fasting decisions. Key messages • Many pregnant Muslims decide to fast during Ramadan. Rigorous quality checking of the research on offspring health effects is needed to provide informative evidence for the growing Muslim population. • Adverse health effects of Ramadan during pregnancy mostly become visible only years after birth. Medical advisories tend to focus on evidence on birth effects, overlooking long-term adverse effects.