Relationship between maxillary atresia and mandibular deviation in young adults: A retrospective CBCT study.

This retracts the article DOI: 10.6224/JN.26204 due to concerns about data reliability.

This study aimed to evaluate the cause-of-death data in Minnesota (2011-2021) to understand the usage of "garbage codes" on death certificates. We conducted a logistic regression analysis using death data from the Minnesota Vital Statistics System that compiles statistical data on all births, deaths, infant deaths, and fetal deaths in Minnesota. Death certificate data from the Minnesota Department of Health were analyzed, and garbage codes were classified using ANACONDA criteria. Logistic regression assessed associations with socioeconomic variables, considering demographic factors, county characteristics, and fixed effects. Garbage codes constituted 3-4% of deaths, with variations by location, demographics, and office affiliation. Logistic regression revealed significant odds variations, notably related to age, rural residence, education, marital status, and place of death. The study unveiled variations in cause-of-death data reliability in Minnesota, emphasizing the prevalence of garbage codes. Enhancing cause-of-death data accuracy is pivotal for informed public health decisions and accurate death statistics to guide targeted public health interventions and mitigate health disparities.

Comparative transcriptomics identifies key genes and pathways underlying the early skin coloration in leopard coral grouper (Plectropomus leopardus).

• A robust MCDA approach is applied for ESG sovereign assessment • A standardized dataset of critical sovereign ESG metrics is developed • Focus on a 24 OECD countries universe for a 10-year period • Explanatory identification of top, medium and low-performer countries • The outranking MCDA framework may assist in obtaining valuable ESG insights Investment professionals, corporate entities and governments are increasingly demanding reliable Environmental, Social and Governance (ESG) data. But apart from the reliability of the relevant ESG data, other critical issues also arise, such as the selection of the most meaningful ESG decision criteria, the determination of their relative importance and the methodological aggregation procedure for the overall ESG evaluation and rating. This paper has two main objectives; first, to address the issues related to inconsistencies of ESG data, and second, to apply a Multiple Criteria Decision Aiding (MCDA) approach for assessing ESG performance at the sovereign level. Initially, we dedicate our efforts to meticulously building and standardizing a database of publicly available ESG metrics specific to each country. Following this, we apply our approach to a sample of 24 countries from the Organization for Economic Cooperation and Development (OECD), focusing on the major challenge of ESG sovereign assessment. In a fully transparent manner, we employ a robust MCDA framework to derive a comprehensive ESG evaluation for these countries. To this end, we employed the MCHP-ELECTRE-SMAA approach. Four distinct scenarios were examined, each reflecting different weight allocations for the considered criteria. The results strongly support the effectiveness of MCDA in evaluating ESG-based sovereign assessments, offering meaningful insights into the underlying decision-making process. Furthermore, from a policy-making perspective, we propose two algorithms designed to identify the minimum required improvements across one or more criteria for a country to match or surpass another.

This review highlights a significant gap in the multi-pollutant characterisation of ultrafine particulate matter (PM <0.1 µm), focusing on metal(oid)s and polycyclic aromatic hydrocarbons (PAHs). Fractionation mechanisms, sampling protocols and analytical methods are examined with an emphasis on integrating quality assurance measures to ensure high-quality data and facilitate cross-study comparability. Based on studies published between 2010 and 2025, research has largely focused on the analysis of pollutants bound to PM 2.5 or PM 10 . Only 5% of the studies addressed ultrafine particles (UFPs), which have the greatest toxicological impacts. The measurement of both pollutant groups within a single sampling campaign was rare (14% of the studies). The reliability of analytical data was rarely evaluated. Only 33% of the studies employed certified reference materials for quality control and method validation. Microwave-assisted digestion and ultrasound-assisted extraction were commonly used for sample preparation prior to the determination of metal(oid)s and PAHs, by inductively coupled plasma mass spectrometry and gas chromatography–mass spectrometry, respectively. Both pollutant groups exhibited strong seasonal variability, with elevated concentrations observed during heating periods in cold seasons, as well as associated with fine PM and UFPs, fractions that exhibit high bioaccessibility. Smaller PM fractions were associated with anthropogenic sources, including fossil fuel and biomass combustion, traffic and industrial emissions, while coarse PM reflected naturally-derived crustal material. Overall, these findings highlight the importance of uniform and comprehensive protocols for sampling UFPs and quantifying associated pollutants, which are essential for reliable data and effective urban air quality control strategies aimed at mitigating emissions. • Chemical assessment of ultrafine particles is limited, despite their high toxicity • Simultaneous analysis of metal(oid)s and PAHs-bound PM is uncommon • Use of certified reference materials for analytical validation is rare • Strong seasonal variability was observed globally • (Ultra)fine PM links to anthropogenic sources, while coarse PM reflects crustal origin

Citizen science data reliability enhancing scientific research: insights from an 11-year study in the Mediterranean Sea

A validation study of the first nationwide point prevalence survey for healthcare-associated infections in South Korea: overcoming methodological challenges.

Tomato brown rugose fruit virus (ToBRFV) is a highly destructive and rapidly spreading tobamovirus that poses a serious threat to global tomato production. While low-dose gamma irradiation has emerged as a promising non-chemical strategy to enhance host resistance, the molecular mechanisms and transcriptomic reprogramming underlying this induced resistance remain largely unexplored. In this study, we employed a transcriptome-wide RNA sequencing approach to elucidate the specific gene expression networks and defense pathways activated in ToBRFV-infected tomato plants in response to low-dose gamma irradiation, addressing a critical gap in our understanding of host-virus interactions under irradiation priming. Naturally infected tomato seeds were exposed to an optimized gamma dose of 15Gy, and transcriptomic profiles of irradiated plants were compared with those of non-irradiated infected controls. RNA-Seq analysis identified 469 differentially expressed genes (DEGs), including 157 upregulated and 312 downregulated transcripts (FDR < 0.05), indicating that gamma irradiation induces extensive transcriptional reprogramming. Functional enrichment analyses revealed significant activation of pathways related to metabolic reorganization, antioxidant defense, plant hormone signal transduction, secondary metabolite biosynthesis, and MAPK signaling. Notably, key defense-associated genes encoding peroxidases, protein kinases, and tetratricopeptide repeat (TPR) domain-containing proteins were strongly upregulated, suggesting enhanced reactive oxygen species (ROS) detoxification, stress signal amplification, and potential restriction of viral replication. In contrast, several growth- and development-related transcription factors and heat shock proteins were markedly downregulated, reflecting a shift in resource allocation toward defense responses. Quantitative RT-PCR validation of selected hormone-related genes confirmed the reliability of the RNA-Seq data and highlighted the coordinated involvement of auxin, ethylene, and gibberellin signaling in stress adaptation. Collectively, our results provide novel molecular evidence defining how low-dose gamma irradiation primes endogenous defense networks to reduce viral accumulation. This study offers new insights into the host-mediated transcriptional regulation of resistance against ToBRFV, establishing a foundation for future functional studies on irradiation-induced immunity.

Monitoring Patient Blood Management (PBM) practices against evidence-based standards is essential for quality improvement; however, current approaches are limited. In the UK, perioperative tranexamic acid (TXA) use is a national quality standard, yet monitoring relies on manual audit cycles that are resource-intensive and limited in scope. We evaluated whether an audit could be automated using routinely collected electronic health record (EHR) data. We performed a retrospective study at a tertiary NHS center using linked perioperative and transfusion datasets. Automated compliance indicators were constructed using coded procedures (denominator) and digitally documented TXA administration from WHO Surgical Safety Checklists and electronic prescribing records (numerator). A structured validation framework assessed data extractability, completeness, denominator coverage, coding accuracy, and concordance between electronic sources. Outputs were assessed by specialty and procedure and compared with contemporaneous manual audit findings. Between July-September 2025, 800 eligible procedures were identified. Comparison with an independent dataset demonstrated procedural coverage of 96.2% and miscoding rate of 3.9%. Overall automated TXA compliance was 86.3%. Concordance between WHO checklist and electronic prescription was 74.2%, with explainable discordance patterns. Substantial inter-specialty variation was identified, ranging from 98.2% (trauma and orthopedics) to 0% (vascular surgery). Compared with October-December 2024, overall compliance increased by 7.6%. Automated EHR-based audit of perioperative TXA compliance is feasible and demonstrates good validity. Structured validation confirmed data reliability, and full-population extraction revealed granular specialty- and procedure-level variation, likely undetectable by manual audits, supporting its wider evaluation as a continuous PBM quality monitoring tool.

Development of a CRISPR-Cas13a assay for mouse hepatitis virus detection.

Large public buildings are characterized by high occupancy and complex functions, which can easily lead to issues such as congestion in evacuation routes and disorderly crowd behavior in the event of a fire. Conducting analyses and simulation assessments of evacuation mechanisms in fire scenarios is of great significance for improving building fire safety standards and emergency management capabilities. At present, there are significant gaps in the existing research on evacuation simulation for public buildings: Most studies rely on fixed parameter assumptions and fail to effectively quantify the influence of subjective factors such as psychological factors, safety awareness, and social roles on evacuation behavior. Moreover, the combination of BIM technology and evacuation simulation mostly focuses on the presentation of spatial geometric information, lacking a deep integration with quantitative methods for quantifying the subjective behavior of personnel, resulting in insufficient authenticity and predictive reliability of evacuation simulations, and making it difficult to precisely support fire protection design and emergency decision-making. In response to this research gap, this study has established an integrated framework that quantifies subjective human-related factors, maps them to key behavioral parameters through fuzzy inference, and couples them with BIM-based fire and evacuation simulations to provide a verifiable linkage between fire scene constraints, human behavior, and evacuation outcomes. This paper employs fuzzy logic theory together with Pyrosim and Pathfinder to investigate the effects of human-related subjective factors and fire scene conditions on fire evacuation safety. A questionnaire survey was conducted to examine how psychological factors, safety awareness, and social roles of pedestrians influence evacuation behavior. Through the reliability and validity test of the valid questionnaire data and the spearman correlation analysis, it is found that there is a significant positive correlation between safety awareness, psychological factors, social roles and the evacuation behavior. Based on fuzzy rules, the domains and membership functions of the linguistic variables representing these factors are defined, enabling the quantification of the influencing factors and the calculation of the initial evacuation speed. Finally, a BIM model was established and applied to the evacuation simulation of a large shopping mall project in Southwest China to verify the feasibility of the fuzzy algorithm and the safety of the evacuation design. This research innovatively combines fuzzy algorithms with BIM technology, making up for the deficiencies of existing studies in the quantification of subjective factors of personnel and the deep integration of BIM technology. It provides a more scientific calculation plan and data for the study of public building evacuation, and offers reference basis for fire protection design, personnel allocation, emergency plan formulation, and rescue operations.

Digital health technologies are increasingly reshaping preventive medicine by enabling continuous monitoring, personalized interventions, and large-scale data-driven decision-making. This review provides a comprehensive examination of the applications, effectiveness, and policy implications of digital health technologies within preventive medicine. It synthesizes current research on mobile health, wearable devices, artificial intelligence, and telehealth platforms, emphasizing their roles in early detection, behavioral modification, and risk prediction. The review adopts a narrative synthesis approach based on recent literature across major academic databases. It further evaluates empirical evidence on effectiveness, particularly in the prevention of chronic diseases and the promotion of healthy lifestyles. At the same time, it critically analyzes existing limitations, including disparities in access, issues of long-term engagement, data reliability concerns, and challenges in clinical validation. The study also explores policy implications related to regulation, equity, and the integration of digital health systems into existing healthcare infrastructures. The findings suggest that while digital health technologies offer substantial opportunities for improving preventive care, their long-term success depends on evidence-based implementation, ethical governance, and inclusive access strategies.

Bottom-up proteomic workflows rely on sequential preprocessing steps, commonly including peptide-to-protein aggregation ("roll-up"), to enhance data reliability and interpretability. While roll-up is effective for protein-centered analyses, it may be suboptimal for applications focused on post-translational modifications (PTMs) or protein structural changes, such as limited proteolysis-mass spectrometry (LiP-MS). Here, we investigate how different roll-up strategies influence site-level quantification in PTM differential analysis. Moreover, we introduce a novel site-centric roll-up approach tailored for LiP-MS, which quantifies proteolytic fragments rather than solely tryptic peptides. We benchmark these methods through simulation studies, comparing their sensitivity and specificity in detecting structural and PTM-driven changes. We found that the median and mean roll-up methods outperform the sum method in both PTM and LiP proteomics, and site-level quantification in LiP outperforms peptide-level quantification. Our findings offer the first systematic, data-driven guidance for selecting roll-up techniques in site-level proteomic analyses, with implications for both PTM-focused and structural proteomics studies.

Abstract With the rapid development of surveying and mapping technology, high-precision and high-temporal-resolution Earth rotation parameters play a crucial role in fields such as satellite navigation and deep space exploration. Currently, ultra-rapid data exhibits lower accuracy, while final solution data suffers from low temporal resolution. Interpolation methods can estimate the unknown values at unmeasured points based on known discrete data, thereby generating continuous and higher-resolution datasets. The Inverse Distance Weighted (IDW) method leverages a distance-based weighting mechanism to fully utilize the spatial distribution characteristics of the data, assigning greater weights to nearby points and producing smooth and highly accurate interpolation results, making it well-suited to the continuity features of polar motion data. In this study, the IDW method is applied to interpolate the IGS final solution polar motion data to obtain high-temporal-resolution interpolated components. The reliability of the high-resolution data is validated using Fast Fourier Transform and Dynamic Time Warping algorithms. Furthermore, forecasting is conducted by combining least squares extrapolation and a sliding autoregressive model, with the mean absolute error serving as the evaluation metric. The results indicate that the interpolated component data can improve the accuracy of medium-and long-term polar motion forecasts. In the PMX direction, the Z1 interpolated component over 10 years achieves improvement rates of 6.0% for short-term (30 days) and 5.5% for medium-term (180 days) forecasts. In the PMY direction, the Z1 interpolated component over 8 years results in MAE improvement rates of 5.5%, 4.8%, and 3.2% for forecast periods of 30, 60, and 90 days, respectively.

Modern data-driven applications that make real-time decisions increasingly depend on advanced sensors which use pre-stored calibration data. In such applications, accurate characterization of sensor output uncertainty is important for reliable data interpretation. Here, we present a method for real-time on-device dynamic uncertainty quantification for sensor outputs which depend on pre-stored calibration data. We show how sensor calibration compensation equations (essential in advanced sensing systems) propagate uncertainties resulting from the quantization of calibration parameters to the sensor output. We use a low-cost thermal sensor as a motivating example and show these ideas are practical and possible on actual embedded sensor systems by prototyping them on two commercially-available uncertainty-tracking hardware platforms with average power dissipation 16.7mW and 147.15mW. These achieve 42.9× and 94.4× speedup compared to the equal-accuracy Monte Carlo computation (the status quo). We present a proof-of-usefulness edge-detection application over ten test scenes where accuracy and precision show average improvement by 4.97 and 40.25 percentage points, respectively, trading off sensitivity. Another application example examines four different calibration-data storage scenarios and compute that a 48% increase in memory yields 75% smaller uncertainty metrics over the baseline. Our method enables better decision-making in critical applications where sensor data reliability is paramount.

The development of filamentous fungal cells from spore to mature mycelium is influenced by a myriad of environmental conditions. Traditional methods for quantifying various fungal cell morphologies in liquid media via microscopy are labor-intensive and prone to user-dependent variability. This study addresses these challenges by introducing a streamlined workflow for fungal cell analysis, utilizing ilastik-based image segmentation and Python for fast data preparation and analysis. The goal is to offer a user-friendly method for counting and analyzing different fungal cell morphologies and their sizes that is free of charge and accessible to users with minimal programming experience. The presented workflow significantly reduces the time required to determine cell developmental stages from microscopy images. It enables rapid results, as compared to manual counting. It also minimizes inter-user variability, enhancing consistency across analyses. It provides a high-throughput, automated solution for fungal cell analysis allowing users to process hundreds of images (n= 48-438) for analysis in a short time period (9-56 min). This approach shows great potential for accelerating research and improving data reliability in biological studies. Furthermore, it provides laboratories in resource limited settings with a free to use solution.

Wearable sensors are a promising method for collecting clinical trial outcome data for people with Angelman syndrome (AS). However, there has yet to be a systematic probe into the ways in which wearable sensors have been successfully used in AS. The current study aims to provide a quantitative summary of wearable sensors used in AS, including contexts of use and psychometric properties, and to present key narrative highlights. Literature searches were performed in three electronic databases: APA PsycInfo, PubMed and Web of Science Core Collection. Data items were categorized into four categories: sample characteristics, study methodological details, wearable sensor characteristics and psychometric properties assessed. Sample characteristics included sample size, age, biological sex, race/ethnicity and cognitive/developmental functioning. Study methodological details were subdivided into study design and setting. Wearable sensor characteristics included sensor type, placement site, means of attachment, assessed construct and sensor-related data loss. Psychometric properties assessed included reliability and validity of sensor-derived data. We identified 16 articles through our systematic review. Wearable sensors were used to study sleep (n = 10, 62.5%), language (n = 2, 12.5%), gait (n = 2, 12.5%), caregiver proximity (n = 1, 6.3%), EEG power (n = 1, 6.3%),and arousal (n = 1, 6.3%) in AS through actigraphs, vocalization recorders, inertial sensors, radio-frequency identification watches, wireless EEG caps,and functional near-infrared spectroscopy caps, respectively. Findings from these studies broadly indicate that wearable sensors are feasible, reliable and valid for assessing a range of behaviours relevant to AS. Wearable sensors are a promising solution to enhance assessments in AS. However, with the small extant literature characterized by small sample sizes and restricted focus on a few relevant features in AS, there remains ample opportunities to explore the use of wearable sensors in people with AS. Additional studies will better inform clinical decision-making and ultimately improve the lives of people with AS and their families.

Clinical trial regulations recommend a dynamic and risk-based approach to the quality management strategy for a medicinal product development program. Sponsor companies are required to identify factors that are critical to quality, to perform ongoing evaluation of the changing risks and quality status in clinical trials and adapt their quality strategy accordingly. The Inter-Company Quality Analytics (IMPALA) not for profit consortium supports the shift towards dynamic quality management of clinical trials by providing publicly available data analytics packages to measure changing risks to patient safety and data reliability. The Critical to Quality Assessment Methodology is the latest initiative developed by the IMPALA consortium in support of dynamic quality assurance and risk management. This paper describes the methodology, which is an iterative process conducted throughout a clinical trial, beginning with risk factor identification and assessment, and development of the initial quality strategy based on this evaluation. It continues with the generation of quality evidence through the execution of a variety of activities, including quality assurance audits, process compliance monitoring, real-time study surveillance activities, etc., utilizing both traditional and analytical methods. From there quality conclusions are determined and documented in the Critical to Quality Assessment Report (CAR). These steps are executed on a continuous basis throughout the development of a medicinal product across all clinical trials and the CAR serves as a pivotal knowledge management tool, disclosing Good Clinical Practice compliance conclusions for stakeholders.

Anthracnose disease, caused by Colletotrichum species, poses a significant threat to global litchi production, yet the molecular mechanisms governing host resistance remain poorly understood. To dissect the genetic basis of anthracnose resistance, we employed a comparative transcriptomic approach using two contrasting cultivars: 'YuJinQiu' (DR, disease-resistant genotype) and 'BaiTangYing' (DS, disease-susceptible genotype). Field evaluations and controlled infection assays demonstrated evident phenotypic divergence, with DR exhibiting delayed disease progression and 43.7% smaller lesion areas compared to DS at 72h post-inoculation (hpi). Time-resolved RNA sequencing (0-72 hpi) revealed genotype-specific transcriptional dynamics, where DR displayed fewer differentially expressed genes (DEGs; 819-1457) compared to DS (5195-5735), suggesting a more targeted and efficient defense response. Functional enrichment analyses highlighted rapid activation of innate immunity pathways in DR, including pattern-triggered immunity, MAPK signaling, and jasmonic acid/ethylene biosynthesis, whereas DS prioritized cell wall modification and compensatory secondary metabolic processes. Weighted gene co-expression network analysis (WGCNA) pinpointed three modules tightly linked to anthracnose resistance, enriched for receptor-like kinases (RLKs), nucleotide-binding leucine-rich repeat (NLR) proteins, and phenylpropanoid biosynthesis genes. Hub regulators, including WRKY transcription factors (e.g., WRKY33), ubiquitin ligases, and pathogenesis-related proteins (PR1, PR5), were identified as central coordinators of defense signaling. Strikingly, DR exhibited sustained upregulation of effector-triggered immunity markers, particularly nucleotide-binding leucine-rich repeat (NLR) genes, and early accumulation of phytoalexins, correlating with pathogen suppression. Experimental validation via qRT-PCR confirmed the reliability of transcriptomic data. Our study unravels the multilayer regulatory network underlying litchi anthracnose resistance, providing not only a mechanistic model of cultivar-specific responses but also a robust gene toolkit for accelerating the development of resistant cultivars through marker-assisted breeding.