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Assessment of internal validity safeguards implemented by researchers has been used to examine the potential reliability of evidence generated within a study. These safeguards protect against systematic error, and such an assessment has traditionally been called a quality assessment. When the results of a quality assessment are translated through some empirical construct to the potential risk of bias, this has been termed a risk of bias assessment. The latter has gained popularity and is commonly used interchangeably with the term quality assessment. This key concept paper clarifies the differences between these assessments and how they may be used and interpreted when assessing clinical evidence for internal validity.

This study contributes to characterization of satellite precipitation error which is fundamental to develop uncertainty models and bias reduction algorithms. Systematic and random error components of several satellite precipitation products are investigated over different seasons, thresholds and temporal accumulations. The analyses show that the spatial distribution of systematic error has similar patterns for all precipitation products. However, the systematic (random) error of daily accumulations is significantly less (more) than that of high resolution 3‐hr data. One should note that the systematic biases of satellite precipitation are distinctively different in the summer and winter. The systematic (random) error is remarkably higher (lower) during the winter. Furthermore, the systematic error seems to be proportional to the rain rate magnitude. The findings of this study highlight that bias removal methods should take into account the spatiotemporal characteristics of error as well as the proportionality of error to the magnitude of rain rate. Key Points Error decomposition of satellite precipitation data Characteristics of systematic error in summer and winter data Proportionality of systematic error of precipitation to rain rate

Satellite-based rainfall products (SRPs) are indispensable for hydro-climatological research, particularly in data-limited environments such as Ethiopia. This study systematically evaluates the performance of three widely used SRPs: Climate Hazards Group InfraRed Precipitation with Station data version 2 (CHIRPS), Tropical Applications of Meteorology using Satellite and ground-based observations version 3.1 (TAMSAT), and Multi-Source Weighted Ensemble Precipitation version 2.8 (MSWEP) across the North and South Gojjam sub-basins of the Abbay Basin. Using ground observations as benchmarks, spatial and temporal accuracy was assessed under varying elevation and rainfall intensity conditions, employing bias decomposition, error analysis, and detection metrics. Results show that rainfall variability in the region is shaped more by the local climate and topography than elevation, with elevation alone proving a weak predictor (R2 < 0.5). Among the products, MSWEP v2.8 demonstrated the highest daily rainfall detection skill (≈ 87–88%), followed by TAMSAT (≈78%), while CHIRPS detected only about half of rainfall events (≈54%) and tended to overestimate no-rain days. MSWEP’s error composition is dominated by low random error (~52%), though it slightly overestimates rainfall and rainy days. TAMSAT provides finer-resolution data that capture localized variability and dry conditions well, with the lowest false alarm rate and moderate random error (~59%). CHIRPS exhibits weaker daily performance, dominated by high random error (~66%) and missed bias, though it improves at monthly scales and better captures heavy and violent rainfall. Seasonally, SRPs reproduce MAM rainfall reasonably well across both sub-basins, but their performance deteriorates markedly in JJAS, particularly in the south. These findings highlight the importance of sub-basin scale analysis and demonstrate that random versus systematic error composition is critical for understanding product reliability. The results provide practical guidance for selecting and calibrating SRPs in mountainous regions, supporting improved water resource management, climate impact assessment, and hydrological modeling in data-scarce environments.

The clade Syndermata includes the endoparasitic Acanthocephala, the epibiotic Seisonidea, and the free-living Bdelloidea and Monogononta. The phylogeny of Syndermata is highly debated, hindering the understanding of the evolution of morphological features, reproductive modes, and lifestyles within the group. Here, we use publicly available whole-genome data to re-evaluate syndermatan phylogeny and assess the credibility of alternative hypotheses, using a new combination of phylogenomic methods. We found that the Hemirotifera and Pararotatoria hypotheses were recovered under combinations of datasets and methods with reduced possibility of systematic error in concatenation-based analyses. In contrast, the Seisonidea-sister and Lemniscea hypotheses were recovered under dataset combinations with increased possibility of systematic error. Hemirotifera was further supported by whole-genome microsynteny analyses and species-tree methods that use multi-copy orthogroups after removing distantly related outgroups. Pararotatoria was only partially supported by microsynteny-based phylogenomic reconstructions. Hence, Hemirotifera and partially Pararotatoria were supported by independent phylogenetic methods and data-evaluation approaches. These two hypotheses have important implications for the evolution of syndermatan morphological features, such as the gradual reduction of locomotory ciliation from the common ancestor of Syndermata in the stem lineage of Pararotatoria. Our study illustrates the importance of combining various types of evidence to resolve difficult phylogenetic questions.

Satellite-based target positioning is vital for applications like disaster relief and precision mapping. Practically, satellite errors, e.g., thermal deformation and attitude errors, lead to a mix of fixed and random errors in the measured line-of-sight angles, resulting in a decline in target-positioning accuracy. Motivated by this concern, this study introduces a systematic error self-correction target-positioning method under continuous observations using a single video satellite. After analyzing error sources and establishing an error-inclusive positioning model, we formulate a dimension-extended equation estimating both target position and fixed biases. Based on the equation, a projection transformation method is proposed to obtain the linearized estimation of unknown parameters first, and an iterative optimization method is then utilized to further refine the estimate. Compared with state-of-the-art algorithms, the proposed method can improve positioning accuracy by 98.70% in simulation scenarios with large fixed errors. Thus, the simulation and actual data calculation results demonstrate that, compared with state-of-the-art algorithms, the proposed algorithm effectively improves the target-positioning accuracy under non-ideal error conditions.

Background Using DNA as a storage medium is appealing due to the information density and longevity of DNA, especially in the era of data explosion. A significant challenge in the DNA data storage area is to deal with the noises introduced in the channel and control the trade-off between the redundancy of error correction codes and the information storage density. As running DNA data storage experiments in vitro is still expensive and time-consuming, a simulation model is needed to systematically optimize the redundancy to combat the channel's particular noise structure. Results Here, we present DeSP, a systematic DNA storage error Simulation Pipeline, which simulates the errors generated from all DNA storage stages and systematically guides the optimization of encoding redundancy. It covers both the sequence lost and the within-sequence errors in the particular context of the data storage channel. With this model, we explained how errors are generated and passed through different stages to form final sequencing results, analyzed the influence of error rate and sampling depth to final error rates, and demonstrated how to systemically optimize redundancy design in silico with the simulation model. These error simulation results are consistent with the in vitro experiments. Conclusions DeSP implemented in Python is freely available on Github ( https://github.com/WangLabTHU/DeSP ). It is a flexible framework for systematic error simulation in DNA storage and can be adapted to a wide range of experiment pipelines.

From an economic perspective, rice is not only a principal staple food for nearly half of the world’s population but also a significant commodity in many countries. Consequently, the accuracy of demand uncertainty concerning rice imports, which can be useful information to support critical decision-making on trading and food security management, is very challenging. The proposed model of simple exponential smoothing, support vector regression, and generalized simulated annealing is proposed and developed to predict annual rice imports based on twenty datasets across importer countries. The proposed model takes advantage of both suitable parameter selection and noise reduction in systematic error reduction with smoothing series to achieve more accuracy and precision. The empirical results revealed that the proposed model can improve accuracy based on five accuracy measures and is significantly different from other models at 0.05 significance levels. Moreover, the proposed model can provide consistency and reliability for forecasting rice imports in advance. Consequently, the proposed model can be a promising tool to support decision-making for policymakers.

The Precision of radiotherapy is checked based on the matching of pre-treatment 2D portal imaging/CBCT to the reference image. The objective of this study is to find inter-fractional systematic and random setup errors (mm) for head and Neck cancer patients and also find setup margin for Planning Target Volume (PTV) at Mohan Dai Oswal Cancer Hospital Ludhiana Punjab. Inter-fractional motion errors were quantified for 10 Head and neck cancer patients who underwent image guided helical intensity modulated radiation therapy with Radixact X9 machine. One hundred fan beam computed tomography scans of 2mm slice thickness, 3.5 MV average energy and flattening filter free beam has been used to collect the data for the calculation of systematic and random errors. After patient immobilization, patient accuracy is checked with registration of pre-treatment image with reference planning image with the help of Accuray Precision image guidance protocol. For every patient 10 fan beam computed tomography images has been taken. Translational errors have been calculated in X, Y and Z direction to find systematic error (∑) and random error (σ). The final PTV margin is calculated by van Herk’s equation (2.5∑ + 0.7σ). The results of this study shows that mean translational errors varies from -2.3 mm to 3.3 mm in lateral direction (X), -3.6 mm to 1.7 mm in longitudinal direction (Y), -2.7 mm to 1.5 mm in vertical direction (Z). The mean and standard deviation (SD) for systematic errors are 1.467, 0.8, 0.923 and random error 0.0595, 0.02266, 0.03824 in X, Y and Z direction has been calculated. The Total Margin for CTV to PTV which include setup margin (mm) in X, Y and Z direction are 3.7 mm, 2.02 mm and 2.33 mm. In addition to that, a PTV margin of 5.00 mm is the appropriate margin for Mohan Dai Oswal Cancer Hospital’s patients. This work conclude that CTV to PTV margin of 5.00 mm is suitable for image guided helical intensity modulated radiation therapy for Head and neck patients to ensure the minimum 95% coverage of dose to target

Motivated by a satellite remote sensing mission, this article proposes a multivariable errors-in-variables (EIV) regression model with heteroscedastic errors for relating the satellite data products to similar products from a well-characterized but globally sparse ground-based dataset. In the remote sensing setting, the regression model is used to estimate the global divisor for the satellite data. The error structure of the proposed EIV model comprises two components: A random-error component whose variance is inversely proportional to sample size of underlying individual observations which are aggregated to obtain the regression data, and a systematic-error component whose variance remains the same as the underlying sample size increases. In this article, we discuss parameter identifiability for the proposed model and obtain estimates from two-stage parameter estimation. We illustrate our proposed procedure through both simulation studies and an application to validating measurements of atmospheric column-averaged CO 2 from NASA's Orbiting Carbon Observatory-2 (OCO-2) satellite. The validation uses coincident target-mode OCO-2 data that are temporally and spatially sparse and ground-based measurements from the Total Carbon Column Observing Network (TCCON) that are spatially sparse but more accurate. Supplementary materials for the article are available online.

Background This study reports on the performance estimation of Digital Volume Correlation (DVC) for tomographic applications. The performance of DVC can be evaluated in terms of two distinct errors: the random error, directly linked to image quality, and the interpolation error, which is the one of the most significant systematic error generated by DVC algorithms. However, the existing methods provide only a limited estimate of the interpolation error, or allow only the random error to be assessed. Objective A new method is proposed to evaluate the interpolation error coupled with the random error in a simple and fast way to assess the overall performance of DVC for any tomographic application. Methods This new method proposes to apply a rotation to the sample (instead of the usual translation) to evaluate the interpolation error. This rotational movement generates linearly varying displacement fields, and each point of a displacement field describes a distinct non-integer voxel position. As this rotation is a rigid body motion, the random error associated with tomographic noise is also taken into account. Results This new method can generate several thousand interpolation error measurement points in only two acquisitions, allowing a very detailed and local assessment of this error. Additionally, and compared to existing methods in the literature (repeat scan), this method does not underestimate the random error, essential for assessing the overall performance of the DVC. Conclusions The proposed method efficiently evaluates DVC performance by accurately assessing both interpolation and random errors through rotational sample movement, improving the reliability in DVC measurements.