Sampling Error Research Articles

Development of a robust SNP marker set for genotyping diverse gene bank collections of polyploid roses

Abstract Background Due to genetic depletion in nature, gene banks play a critical role in the long-term conservation of plant genetic resources and the provision of a wide range of plant genetic diversity for research and breeding programs. Genetic information on accessions facilitates gene bank management and can help to conserve limited resources and to identify taxonomic misclassifications or mislabelling. Here, we developed SNP markers for genotyping 4,187 mostly polyploid rose accessions from large rose collections, including the German Genebank for Roses. Results We filtered SNP marker information from the RhWag68k Axiom SNP array using call rates, uniformity of the four allelic dosage groups and chromosomal position to improve genotyping efficiency. After conversion to individual PACE® markers and further filtering, we selected markers with high discriminatory power. These markers were used to analyse 4,187 accessions with a mean call rate of 91.4%. By combining two evaluation methods, the mean call rate was increased to 95.2%. Additionally, the robustness against the genotypic groups used for calling was evaluated, resulting in a final set of 18 markers. Analyses of 94 pairs of assumed duplicate accessions included as controls revealed unexpected differences for eight pairs, which were confirmed using SSR markers. After removing the duplicates and filtering for accessions that were robustly called with all 18 markers, 141 out of the 1,957 accessions showed unexpected identical marker profiles with at least one other accession in our PACE® and SSR analysis. Given the attractiveness of NGS technologies, 13 SNPs from the marker set were also analysed using amplicon sequencing, with 76% agreement observed between PACE® and amplicon markers. Conclusions Although sampling error cannot be completely excluded, this is an indication that mislabelling occurs in rose collections and that molecular markers may be able to detect these cases. In future applications, our marker set could be used to develop a core reference set of representative accessions, and thus optimise the selection of gene bank accessions.

Performance evaluation of the introduction of full sample traceability system within the specimen collection process

Abstract Objectives To evaluate the efficacy, safety and efficiency performances related to the introduction of innovative traceability platforms and integrated blood collection systems, for the improvement of a total testing process, thus also assessing the economic and organizational sustainability of these innovative technologies. Methods A mixed-method approach was utilized. A key-performance indicators dashboard was created based on a narrative literature review and expert consensus and was assessed through a real-life data collection from the University Hospital of Padova, Italy, comparing three scenarios over time (2013, 2016, 2019) with varying levels of technological integration. The economic and organizational sustainability was determined considering all the activities performed from the tube check-in to the validation of the results, with the integration of the management of the prevalent errors occurred during the process. Results The introduction of integrated venous blood collection and full sample traceability systems resulted in significant improvements in laboratory performance. Errors in samples collected in inappropriate tubes decreased by 42 %, mislabelled samples by 47 %, and samples with irregularities by 100 %. Economic analysis revealed a cost saving of 12.7 % per tube, equating to a total saving of 447,263.80 € over a 12-month period. Organizational efficiency improved with a reduction of 13,061.95 h in time spent on sample management, allowing for increased laboratory capacity and throughput. Conclusions Results revealed the strategic relevance of introducing integrated venous blood collection and full sample traceability systems, within the Laboratory setting, with a real-life demonstration of TLA economic and organizational sustainability, generating an overall improvement of the process efficiency.

Measuring the Unmeasurable: Decomposing Multidimensional Rural Poverty and Promoting Economic Development in the Poorest Region of Luzon, Philippines

Poverty is the oldest social problem that ever existed and is difficult to reverse. It is multidimensional and unmeasurable. Thus, measuring by decomposing rural multidimensional poverty is critical. Most poverty studies are usually generic, exposed to large sampling errors, and intended for macroeconomic decisions. Thus, measuring poverty for a specific locality with various configurations (15) is critical for economic development. The paper combines predictive analytics and advanced econometrics to decompose poverty at the micro-level by utilizing the Community-Based Monitoring system at complete enumeration (L = 34, S = 4). Logistic Regression (78) Models with 19 Independent Variables and 12 Intervening Variables were fitted. Headcount Analysis (0.2138–0.9845), Poverty Gap (0.2228–0.0502), Severity statistics (0.0723–0.0168) and Watts Index (0.2724–0.0618) are scrutinized. Poverty levels vary by location; a significant fraction of the population (P0i = 68.50%, P0f = 55.80%) and households (P0i = 63.70%, P0f = 50.70%) live below the poverty line and food threshold. It has been revealed that poverty is extreme in Isarog (i = 0.7793), moderate in Poblacion (p = 0.4019), intense in Ranggas (r = 0.6542), and severe in Salog (s = 0.6353). Multidimensional variables (13VAR) significantly predict poverty outcomes (p-value = 0.0000, PseudoR2 = 0.75). Moreover, intervening variables have been impacting poverty across all locals. All models tested are significant across all sectors and correctly predicted by the model classifications (Estat = 73.29–74.12%). Poverty is multifaceted; thus, it requires different interventions. Finally, policy proposals (54) were outlined to alleviate poverty and promote local economic development.

Abstract 4144526: A Case Presentation of Severe Left Ventricular Dysfunction from Focal Myocarditis due to Immune Checkpoint Inhibitor

Introduction: Immune checkpoint inhibitors (ICI), such as pembrolizumab, are an effective immunotherapy for several malignancies, however, have been associated with adverse events including myocarditis and accelerated atherosclerosis. ICI myocarditis is associated with a mortality rate of up to 33%. This case reports a patient on pembrolizumab with an atypical presentation. Case presentation: A 75-year-old female with a past medical history significant for lung cancer on pembrolizumab and myasthenia gravis presented to the hospital with dyspnea and mild, intermittent exertional chest discomfort. She denied nausea, radiation of pain, diplopia, weakness in extremities, or dysphagia. Workup revealed a leucocyte count of 13,500 cells/uL, pro-brain natriuretic peptide of 13,458 pg/mL, troponin of 26 ng/L, and a normal erythrocyte sedimentation rate and C reactive protein. A chest computed tomography was performed that showed mild bilateral pleural effusion and no pulmonary embolism. A nuclear stress test showed no evidence of ischemia but showed a severely reduced left ventricular ejection fraction (LVEF) at 29%. An echocardiogram confirmed that the ejection fraction was reduced at 23% (baseline LVEF was 60% from 1 year ago). Cardiac MRI revealed a mildly increased regional T2 signal in the anterolateral wall, mildly elevated extracellular volume fraction, and no late gadolinium enhancement. With ongoing clinical suspicion despite equivocal findings on cardiac MRI, an endomyocardial biopsy was done that revealed a single small aggregate of lymphocytes present in 1 of 6 tissue samples. Ultimately, with the single site of aggregated lymphocytes on biopsy, a diagnosis of ICI myocarditis was determined, and the patient was started on high-dose intravenous glucocorticoids as well as bisoprolol, empagliflozin, spironolactone, and Entresto for heart failure management as she was able to tolerate. Discussion: Myocarditis due to pembrolizumab is a rare, but potentially lethal adverse effect. Patients typically have abnormal electrocardiograms and elevated cardiac biomarkers. Cardiac MRIs have sensitivity and specificity of 68% and 91% respectively for diagnosis, and the gold standard is endomyocardial biopsy however, it is limited due to sampling error. Our patient had an unusual presentation, but with both imaging and biopsy, we found a case of focal myocarditis with severe left ventricular dysfunction, which typically is seen with fulminant myocarditis.

BIOM-28. WHOLE EXOME TUMOR MOLECULAR PROFILING FROM CEREBROSPINAL FLUID (CSF): PRELIMINARY RESULTS OF A PILOT STUDY

Abstract BACKGROUND Tissue sampling for molecular profiling has become an integral part of the diagnosis, prognosis, and management of CNS malignancies. However, many tumors are surgically inaccessible, and even biopsy of accessible lesions conveys some risk, and the possibility of sampling error. We evaluated the accuracy and potential utility of CSF liquid biopsy for identifying mutations in patients with intracranial tumors. METHODS CSF samples were collected from ventricular reservoirs and stored at -80 degrees Celsius until analyzed. Cell-free nucleic acids were extracted, concentrated, and quantified. Samples over 110ng quantification were normalized to 110ng and input into library prep. Library prep was done using v3.5 of Caris Assure and whole exome sequencing in an S2 FC on a Novaseq 6000. Bioinformatic analysis was performed. RESULTS CSF samples were obtained from 20 patients (10 gliomas, 5 CNS lymphomas, 5 metastases), 19 of whom also had tumor specimens available. Two hundred eighty-one mutations were identified: 17 (6.6%) in tumor only, 184 (72.4%) in CSF only, and 80 (31.5%) in both. Key alterations found in CSF include: IDH, TERT, ATRX in gliomas, and MYD88, PIM1, BCL2, TERT in lymphomas. Glioma CSF with IDH1 mutations did not have PTEN mutations (with one exception, in which the CSF was collected 3 years after the tissue). Targetable mutations in metastases included PIK3CA, NF1, and BRCA. CONCLUSION These preliminary results suggest that CSF liquid biopsy may be useful for identifying mutations in primary and metastatic brain tumors. Serial sampling is feasible, and may disclose changes in diagnostic and/or driver mutations over time, with important therapeutic and diagnostic implications. Many mutations were found in CSF alone. CSF may capture the entire molecular landscape of the tumor, overcoming the sampling error inherent in tissue biopsies. Prospective studies are underway to further elucidate the clinical applications of this technique.

Prediction of causal genes at GWAS loci with pleiotropic gene regulatory effects using sets of correlated instrumental variables

Multivariate Mendelian randomization (MVMR) is a statistical technique that uses sets of genetic instruments to estimate the direct causal effects of multiple exposures on an outcome of interest. At genomic loci with pleiotropic gene regulatory effects, that is, loci where the same genetic variants are associated to multiple nearby genes, MVMR can potentially be used to predict candidate causal genes. However, consensus in the field dictates that the genetic instruments in MVMR must be independent (not in linkage disequilibrium), which is usually not possible when considering a group of candidate genes from the same locus. Here we used causal inference theory to show that MVMR with correlated instruments satisfies the instrumental set condition. This is a classical result by Brito and Pearl (2002) for structural equation models that guarantees the identifiability of individual causal effects in situations where multiple exposures collectively, but not individually, separate a set of instrumental variables from an outcome variable. Extensive simulations confirmed the validity and usefulness of these theoretical results. Importantly, the causal effect estimates remained unbiased and their variance small even when instruments are highly correlated, while bias introduced by horizontal pleiotropy or LD matrix sampling error was comparable to standard MR. We applied MVMR with correlated instrumental variable sets at genome-wide significant loci for coronary artery disease (CAD) risk using expression Quantitative Trait Loci (eQTL) data from seven vascular and metabolic tissues in the STARNET study. Our method predicts causal genes at twelve loci, each associated with multiple colocated genes in multiple tissues. We confirm causal roles for PHACTR1 and ADAMTS7 in arterial tissues, among others. However, the extensive degree of regulatory pleiotropy across tissues and the limited number of causal variants in each locus still require that MVMR is run on a tissue-by-tissue basis, and testing all gene-tissue pairs with cis-eQTL associations at a given locus in a single model to predict causal gene-tissue combinations remains infeasible. Our results show that within tissues, MVMR with dependent, as opposed to independent, sets of instrumental variables significantly expands the scope for predicting causal genes in disease risk loci with pleiotropic regulatory effects. However, considering risk loci with regulatory pleiotropy that also spans across tissues remains an unsolved problem.

PRINCIPAIS CAUSAS PARA O CANCELAMENTO DE CIRURGIAS ELETIVAS EM UMA INSTITUIÇÃO DE REDE PRIVADA.

The objective of this study was to identify the main causes for the cancellation of elective surgeries in a private institution in a municipality in the Northern region of Brazil, from August 2021 to August 2022. This is a cross-sectional, retrospective, quantitative study carried out in a private health institution, located in the municipality of Belém, capital of the State of Pará, in the Northern region of Brazil. Considering a universe of 110 physical medical records according to the period established from August 2021 to August 2022, a sampling error margin of no more than 5% and a confidence level of 95% were used. Therefore, the sample of this study is composed of 86 medical records of patients who had their elective surgical procedure canceled. The following were excluded: medical records of urgent and emergency surgeries; medical records with inadequately filled out or incomplete information; duplicate medical records. The demographic profile of the patients analyzed, the majority are female (61.63%). The age group 60 to 69 years had the highest prevalence, with a total of 25.58% of surgical cancellations. The majority of patients were brown, with 76.74% of the patients analyzed. (53.49%) patients denied any type of comorbidity, either concomitantly or in isolation. Regarding the surgical specialties with the highest cancellations, orthopedics (52.33%) and otorhinolaryngology (24.42%) were found. Among the main causes of surgical cancellation with the highest rate is related to the unfavorable clinical conditions of the patients (30.23%). Regarding organizational issues, surgeries canceled at the request of the surgeon stand out, with 26.74%.

A comparative analysis of supply chain factors impacting COVID-19 vaccine security in high-income countries (HICs) and low-income and middle-income countries (LMICs)

IntroductionThe COVID-19 pandemic focused attention on the importance of vaccine security to national security. Demand for vaccines far exceeded supply when the first COVID-19 vaccines were released. Growing data suggest...

Modeling Survey Time Series Data with Flow-Observed CARMA Processes

Published survey data often are delivered as estimates computed over an epoch of time. Customers may desire to obtain survey estimates corresponding to epochs, or time points, that differ from the published estimates. This “change of support” problem can be addressed through the use of continuous-time models of the underlying population process, while taking into account the sampling error that survey data is subject to. The application of a Continuous AutoRegressive Moving Average (CARMA) model is investigated as a tool to provide change of support applications, thereby allowing interpolation for published survey estimates. A simulation study provides comparisons of competing estimation methods, and a synthetically constructed data set is developed in order to elucidate real data applications. The proposed method can be successful for change of support problems, despite modeling challenges with the CARMA framework.

Representations of Muscovites of working age about a healthy lifestyle and observance of its principles

Introduction. Maintaining a healthy lifestyle (HLS) is one of the key aspects of the prevention of a number of chronic infectious diseases (CID). The increase in the number of people keeping a healthy lifestyle is a predictor not only of the sanitary, but also of the socio-economic well-being of society. The purpose of the study is to determine the views of Muscovites of working age about HLS and compliance with its principles. Materials and methods. The article uses the results of a sociological survey of the able-bodied population of Moscow (18–55 years women and 18–60 years men) conducted in 2022. A total of one thousand five hundred twenty two respondents took part in the survey. The maximum sampling error is ± 2.51% (CI = 95%). To obtain additional data, 12 focus group discussions were also conducted with capital residents of working age, both having and not keeping HLS. Results. 63% of the respondents noted that they certainly or rather keep HLS, 71% of them assess their state of health as good. Different HLS practices are considered, the adherence to which is rather uniform. The study describes self-preserving and acquiescent models of attitude to one’s own health, factors of motivation to HLS, as well as images of people who do and do not follow HLS. These images can have positive and negative aspects in different population groups. Various socio-economic, infrastructural and personal barriers can hinder the gain in the number of Muscovites who lead a healthy lifestyle, and a number of approaches to work with them are proposed in this paper. Research limitations. The study is limited to residents of the city of Moscow, which may impose restrictions on the generalization of data. Conclusion. An increase in the share of Muscovites who keep HLS is possible by creating the most attractive conditions for conducting a healthy lifestyle in all spheres of human life at the level of the general urban environment, the level of habitat and work.

Optimum Combination of Spectral Variables for Crop Mapping in Heterogeneous Landscapes based on Sentinel-2 Time Series and Machine Learning

Abstract. This article aimed to determine a workflow for more efficient large-scale crop mapping using a time series of images from the Sentinel-2 Satellite, statistical methods of attribute selection, and machine learning. The proposed methodology explores the best possible combination of spectral variables related to vegetation (16 vegetation indices in the RGB, NIR, SWIR, and Red Edge regions) to characterize different spectro-temporal profiles of Land Use and Land Cover (LULC) in spatially heterogeneous landscapes. First, we applied a data dimensionality reduction analysis using the PCA (Principal Component Analysis) method. Subsequently, the variables that showed the highest statistical correlation between each other were used in the spectro-temporal classification process, using the Random Forest, TempCNN, and LightTAE algorithms, following three different strategies: C1 (ALL), C2 (BE + IV (Red Edge)) and C3 (BE + IV (without Red Edge)), where ALL – All variables; BE – Spectral Bands; IV – Vegetation Indices. Given the results found, the C2 classification scenario (with bands B3, B4, B5, B6, B7, B8, and B8A and the NDRE1, RESI, and MSR indexes) demonstrated the best LULC classification accuracy at the crop pattern level, compared to the other scenarios, with average values of 0.91, 0.88, 0.91, 0.89, and 0.89 (Global Accuracy, Producer Accuracy, User Accuracy, Kappa index, and F1-Score, respectively, for the TempCNN model), the which emphasized the importance of both qualitative and quantitative variability of sampling data and variables based on the Red Edge region for improving LULC classification processes in large-scale heterogeneous landscapes.

One Question, Many Results—Why Epidemiological Studies Yield Heterogeneous Findings. Part 34 of a Series on Evaluation of Scientific Publications.

Observational epidemiologic studies often yield different results on the same question. In this article, we explain how this comes about. In this review, which is based on publications retrieved by a selective search in PubMed and the Web of Science, we use information from international publications, simulation studies on sampling error, and a quantitative bias analysis on fictitious data to demonstrate why the results of epidemiologic studies are often uncertain, and why it is, therefore, generally necessary to perform more than one study on any particular question. Sampling errors, imprecise measurements, alternative but equally appropriate methods of data analysis, and features of the populations being studied are common reasons why studies on the same question can yield different results. Simulation studies are used to illustrate the fact that effect estimates such as relative risks or odds ratios can deviate markedly from the true value because of sampling error, i.e., by chance alone. Quantitative bias analysis is used to show how strongly effect estimates can be distorted by misclassification of exposures or outcomes. Finally, it is shown through illustrative examples that different but equally appropriate methods of data analysis can lead to divergent study results. The above reasons why epidemiologic study results can be heterogeneous are explained in this review. Quantitative bias analyses and sensitivity analyses with alternative data evaluation strategies can help explain divergent results on one and the same question.

Deep learning method for predicting the complex nonlinear dynamics of passively mode-locked fiber laser

The dynamic evolution processes are highly complicated nonlinear dynamic processes in passively mode-locked fiber laser systems. Here, an artificial intelligence (AI) model is employed to predict the complex dynamic processes, which uses the long short-term memory network method, serving as an alternative to the numerical calculation of the nonlinear Schrödinger equation (NLSE). We specifically emphasize the complex evolution processes under different gain saturation energies, comparing the results predicted by the AI model with those simulated by the NLSE. The predicted results of the AI model are in good agreement with the simulated results of NLSE. The root mean square errors of test samples in this study are all below 0.15. Furthermore, with GPU acceleration, the AI model achieves a mean simulation time of 0.452 s for 6000 roundtrips, approximately 2391 times faster than the numerical solution of NLSE executed on a CPU.

Scale‐ and Variable‐Dependent Localization for 3DEnVar Data Assimilation in the Rapid Refresh Forecast System

AbstractThis study demonstrates the advantages of scale‐ and variable‐dependent localization (SDL and VDL) on three‐dimensional ensemble variational data assimilation of the hourly‐updated high‐resolution regional forecast system, the Rapid Refresh Forecast System (RRFS). SDL and VDL apply different localization radii for each spatial scale and variable, respectively, by extended control vectors. Single‐observation assimilation tests and cycling experiments with RRFS indicated that SDL can enlarge the localization radius without increasing the sampling error caused by the small ensemble size and decreased associated imbalance of the analysis field, which was effective at decreasing the bias of temperature and humidity forecasts. Moreover, simultaneous assimilation of conventional and radar reflectivity data with VDL, where a smaller localization radius was applied only for hydrometeors and vertical wind, improved precipitation forecasts without introducing noisy analysis increments. Statistical verification showed that these impacts contributed to forecast error reduction, especially for low‐level temperature and heavy precipitation.

Associations of psychotic symptom dimensions with clinical and developmental variables in twin and general clinical samples.

Positive, negative and disorganised psychotic symptom dimensions are associated with clinical and developmental variables, but differing definitions complicate interpretation. Additionally, some variables have had little investigation. To investigate associations of psychotic symptom dimensions with clinical and developmental variables, and familial aggregation of symptom dimensions, in multiple samples employing the same definitions. We investigated associations between lifetime symptom dimensions and clinical and developmental variables in two twin and two general psychosis samples. Dimension symptom scores and most other variables were from the Operational Criteria Checklist. We used logistic regression in generalised linear mixed models for combined sample analysis (n = 875 probands). We also investigated correlations of dimensions within monozygotic (MZ) twin pairs concordant for psychosis (n = 96 pairs). Higher symptom scores on all three dimensions were associated with poor premorbid social adjustment, never marrying/cohabiting and earlier age at onset, and with a chronic course, most strongly for the negative dimension. The positive dimension was also associated with Black and minority ethnicity and lifetime cannabis use; the negative dimension with male gender; and the disorganised dimension with gradual onset, lower premorbid IQ and substantial within twin-pair correlation. In secondary analysis, disorganised symptoms in MZ twin probands were associated with lower premorbid IQ in their co-twins. These results confirm associations that dimensions share in common and strengthen the evidence for distinct associations of co-occurring positive symptoms with ethnic minority status, negative symptoms with male gender and disorganised symptoms with substantial familial influences, which may overlap with influences on premorbid IQ.

The Impact of Online Promotion on Increasing Product Sales of SME

This thesis discusses the influence of online promotion on increasing sales of SME products in Lahomi District, West Nias Regency. This study aims to determine the influence of online promotion on increasing sales of SME products in Lahomi District, West Nias Regency. This study uses a quantitative research method with a data collection technique using a questionnaire. Based on the results of the T-Test where the sign value of the X variable is known 001> 0.05 and the t-value is 6.841 <2.054 so it can be concluded that Ha is accepted and has an effect on the Sales Increase variable (Y). Based on the determination coefficienT-Test, the results obtained are that the Adjuster R-Square value is 0.589, this means that the Promotion Variable (X) is able to explain the increase in sales (Y) of 0.589 with a percentage of 58.9% after being adjusted for the sample and independent variables while the remaining 41.1% is explained by other variables outside this study.

Operational Performance Evaluation Model for Food Processing Machinery Industry Chain

This study aims to create a performance evaluation model for the food processing machinery industry. The goal is to help food processing plants improve both process quality and competitiveness. Additionally, component failures may disrupt the continuous operation of the food processing machine, potentially resulting in insufficient production and delays in delivery, which in turn leads to cost losses. For the sold food processing machinery, decreases in the average number of failures within a unit of time, the average repair response time when a failure occurs, and the average repair duration are three crucial factors in minimizing the total expected loss due to machine failures. Based on these three important factors, this study established the following evaluation indices: (1) the processing performance index, (2) the repair reporting performance index, and (3) the maintenance performance index. These indices serve as tools for assessing the performance of the three key operational aspects. This study employed a radar chart to construct the evaluation model, which can directly compare the critical values with the point estimates of three indices. Consequently, this approach can judge whether the operational performance has achieved the required level. This can maintain the simplicity and usability of point estimates while reducing the risk of misjudgment due to sampling errors.

FSH3D: 3D Representation via Fibonacci Spherical Harmonics

AbstractSpherical harmonics are a favorable technique for 3D representation, employing a frequency‐based approach through the spherical harmonic transform (SHT). Typically, SHT is performed using equiangular sampling grids. However, these grids are non‐uniform on spherical surfaces and exhibit local anisotropy, a common limitation in existing spherical harmonic decomposition methods. This paper proposes a 3D representation method using Fibonacci Spherical Harmonics (FSH3D). We introduce a spherical Fibonacci grid (SFG), which is more uniform than equiangular grids for SHT in the frequency domain. Our method employs analytical weights for SHT on SFG, effectively assigning sampling errors to spherical harmonic degrees higher than the recovered band‐limited function. This provides a novel solution for spherical harmonic transformation on non‐equiangular grids. The key advantages of our FSH3D method include: 1) With the same number of sampling points, SFG captures more features without bias compared to equiangular grids; 2) The root mean square error of 32‐degree spherical harmonic coefficients is reduced by approximately 34.6% for SFG compared to equiangular grids; and 3) FSH3D offers more stable frequency domain representations, especially for rotating functions. FSH3D enhances the stability of frequency domain representations under rotational transformations. Its application in 3D shape reconstruction and 3D shape classification results in more accurate and robust representations. Our code is publicly available at https://github.com/Miraclelzk/Fibonacci-Spherical-Harmonics.

Assessing rainfall radar errors with an inverse stochastic modelling framework

Abstract. Weather radar is a crucial tool for rainfall observation and forecasting, providing high-resolution estimates in both space and time. Despite this, radar rainfall estimates are subject to many error sources – including attenuation, ground clutter, beam blockage and drop-size distribution – with the true rainfall field unknown. A flexible stochastic model for simulating errors relating to the radar rainfall estimation process is implemented, inverting standard weather radar processing methods and imposing path-integrated attenuation effects, a stochastic drop-size-distribution field, and sampling and random errors. This can provide realistic weather radar images, of which we know the true rainfall field and the corrected “best-guess” rainfall field which would be obtained if they were observed in a real-world case. The structure of these errors is then investigated, with a focus on the frequency and behaviour of “rainfall shadows”. Half of the simulated weather radar images have at least 3 % of their significant rainfall rates shadowed, and 25 % have at least 45 km2 containing rainfall shadows, resulting in underestimation of the potential impacts of flooding. A model framework for investigating the behaviour of errors relating to the radar rainfall estimation process is demonstrated, with the flexible and efficient tool performing well in generating realistic weather radar images visually for a large range of event types.

Two-parametric prescan calibration of gradient-induced sampling errors for rosette MRI.

The aim of this study was to develop a simple, robust, and easy-to-use calibration procedure for correcting misalignments in rosette MRI k-space sampling, with the objective of producing images with minimal artifacts. Quick automatic calibration scans were proposed for the beginning of the measurement to collect information on the time course of the rosette acquisition trajectory. A two-parameter model was devised to match the measured time-varying readout gradient delays and approximate the actual rosette sampling trajectory. The proposed calibration approach was implemented, and performance assessment was conducted on both phantoms and human subjects. The fidelity of phantom and in vivo images exhibited significant improvement compared with uncorrected rosette data. The two-parameter calibration approach also demonstrated enhanced precision and reliability, as evidenced by quantitative relaxometry analyses. Adequate correction of data sampling is a crucial step in rosette MRI. The presented experimental results underscore the robustness, ease of implementation, and suitability for routine experimental use of the proposed two-parameter rosette trajectory calibration approach.