Correction Method Research Articles

De-biasing microbiome sequencing data: bacterial morphology-based correction of extraction bias and correlates of chimera formation

Abstract Introduction Microbiome amplicon sequencing data are distorted by multiple protocol-dependent biases from bacterial DNA extraction, contamination, sequence errors, and chimeras, hindering clinical microbiome applications. In particular, extraction bias is a major confounder in sequencing-based microbiome analyses, with no correction method available to date. Here, we suggest using mock community controls to computationally correct extraction bias based on bacterial morphological properties. Methods We compared dilution series of 3 cell mock communities with an even or staggered composition. DNA of these mock, and additional skin microbiome samples, was extracted with 8 different extraction protocols (2 buffers, 2 extraction kits, 2 lysis conditions). Extracted DNA was sequenced (V1–V3 16S rRNA gene) together with corresponding DNA mocks. Results Microbiome composition was significantly different between extraction kits and lysis conditions, but not between buffers. Independent of the extraction protocol, chimera formation increased with higher input cell numbers. Contaminants originated mostly from buffers, and considerable cross-contamination was observed in low-input samples. Comparing the microbiome composition of the cell mocks to corresponding DNA mocks revealed taxon-specific protocol-dependent extraction bias. Strikingly, this extraction bias per species was predictable by bacterial cell morphology. Morphology-based computational correction of extraction bias significantly improved resulting microbial compositions when applied to different mock samples, even with different taxa. Equivalent correction of the skin samples showed a substantial impact on microbiome compositions. Conclusions Our results indicate that higher DNA density increases chimera formation during PCR amplification. Furthermore, we show that computational correction of extraction bias based on bacterial cell morphology would be feasible using appropriate positive controls, thus constituting an important step toward overcoming protocol biases in microbiome analysis.

Methods for Preclinical Assessment of the Efficacy of Anticancer Medicines in vivo (Review)

INTRODUCTION. The main risk to the clinical translatability of preclinical results for anticancer medicinal products is posed by the difficulty of simulating clinical conditions in an experimental model. With only 5% of product candidates proving clinically effective, the search for new approaches to the preclinical development of anticancer medicinal products is currently an active area of research in medicine.AIM. This study aimed to provide methodological support for planning experiments with modelling of neoplastic processes through analysis and classification of the methods used in preclinical studies of the efficacy of anticancer medicinal products in vivo.DISCUSSION. This article reviews the development of animal tumour models and the selection of cell lines and their testing for tumourigenicity and viability on a step-by-step basis. According to the study results, imaging systems, vital staining, and fluorescence- and luminescence-based methods can be used to assess the efficacy of anticancer medicinal products in both solid tumour models and haematological malignancy models. The article presents a schematic representation of the main types of mouse cancer models. However, no single animal species is universally suitable for in vivo cancer modelling. Researchers selecting models and considering their advantages and disadvantages should pay special attention to the similarity of disease mechanisms in animal models and humans at the tissue and molecular level, keeping in mind the aims of their research.CONCLUSIONS. The results of this comparative analysis of methods for preclinical efficacy evaluation of anticancer medicinal products are essential for designing experimental studies and ensuring the reliability of the results obtained. Choosing the correct research method will increase the chances of obtaining experimental data that can be successfully translated into clinical practice.

Factors Affecting the Compliance of Pulmonary TB Patients in Drug Consumption at the Batumarta II Health Center, Lubuk Raja District, Ogan Komaring Regency, Ulu, South Sumatra in 2024

Pulmonary tuberculosis (TB) is a direct infectious disease and is a major threat to human resource development, the World Health Organization (WHO) said the number of people diagnosed with tuberculosis in 2021 globally was 10.6 million cases, plus treatment failure was also a major contributor to the incidence of pulmonary tuberculosis in the community. The purpose of this study is to analyze the factors that affect the compliance of TB patients in drug consumption at the Batumarta II Health Center. This type of research is an analytical survey with a retrospective design. The research was conducted at the Batumarta II Health Center in 2024. By using the total sampling technique, which is as many as 50 respondents. To analyze the data, a logistic regression test was used. Based on the results of the study, it was found that there was a relationship between drug consumption compliance and variables, namely knowledge (p=0.000), attitude (P=0.000), health workers (p=0.021), family support (p=0.001) and culture (p=0.000), but there was no effect on the availability of OAT with drug consumption compliance because of the P value (p=1,000) at the Batumarta II Health Center, South Sumatra. Based on the results of the study, it can be concluded that the factors that affect drug consumption compliance at the Batumarta II Health Center are knowledge, attitudes, health workers, family and cultural support. The most dominant factor affecting drug consumption compliance at the Batuamrta II Health Center is knowledge that has an Exp(B) or Odd Ratio value of 37,200. Suggestions for making counseling programs on factors affecting patient compliance in drug consumption both to families of patients and pulmonary TB patients to increase public knowledge about the correct TB treatment methods and patterns in order to maximize healing outcomes.

A Method for Sensing the Internal Current Distribution State of Press-Pack IGBT Devices Based on Hall Sensors

Flexible DC transmission technology is an important support for constructing new power systems. The press-pack IGBT device has the advantages of double-sided heat dissipation and high power density in series and is the core device in the flexible HVDC project. However, the compact parallel arrangement of multiple chips in the press-pack IGBT (PPI) device easily causes uneven current distribution in the device, which affects its reliability. In this paper, a noninvasive sensing method for the internal current distribution of the PPI device is proposed based on the measurement of the external magnetic field and the numerical analysis of the magnetic field. According to the correlation between the device current distribution and the external magnetic field, a noninvasive current distribution sensing method based on the measurement and numerical analysis of the external magnetic field is proposed, and the placement strategy of the magnetic field measurement points is given. The error of the perception method under ideal conditions is analyzed, the error size and rule are summarized, and the correction methods are given, respectively. Finally, a magnetic field measuring device is designed and verified by experiments. The results can accurately indicate the direction of pressure deviation.

What's up with patella alta?

Patella alta, which describes an abnormally proximally positioned patella, has become of particular interest as it has been identified as a potential factor in patellofemoral instability (PFI) and other common pediatric orthopedic conditions. The purpose of this review is to describe the condition, measuring techniques, and its association with pediatric orthopedic conditions, and to evaluate methods of correction of patella alta. Recent literature has explored the etiology of patella alta by investigating patellar height as age increases, with inconclusive findings as to whether the condition is congenital or acquired over time. Additionally, patella alta has been identified to be associated with several pediatric orthopedic conditions, including PFI, anterior knee pain, and cerebral palsy. As such, methods of correction for patella alta, including medial patellofemoral ligament reconstruction (MPFLR), tibial tubercle distalization, and patella tendon imbrication, have been studied. Patella alta, defined by an abnormally high patellar height, is associated with patellofemoral instability, anterior knee pain, and cerebral palsy. Various indices have been developed to measure and define patellar height, including the Caton-Deschamps Index and Koshino-Sugimoto Index which are applicable to a pediatric population. Treatments include isolated or concomitant MPFLR, distalizing tibial tubercle osteotomy, and patella tendon imbrication, which have shown success in reducing patellar height and improving patella alta, though risks for complications do exist.

A correction method between sample mass and EPR intensity in radiation dose reconstruction

A correction method between sample mass and EPR intensity in radiation dose reconstruction

A combined wavelet analysis-quantile mapping (WA-QM) method for bias correction: capturing the intra-annual temporal patterns in climate model precipitation simulations and projections

Abstract Despite recent improvements, global climate models (GCMs) still have biases that prevent their direct application. Quantile mapping (QM) has been widely used in bias correction (BC) due to its effectiveness in aligning the cumulative density function of climate model data with observations. However, QM has a significant limitation: it fails to consider the temporal correspondence between the model data and observations, which restricts its ability to represent intra-annual temporal patterns. To address this issue, this study integrated wavelet analysis (WA) into QM to develop a combined method termed WA-QM, which aimed to preserve QM’s efficacy in overall BC and preservation of climate change signals, while capturing the intra-annual temporal patterns. The effectiveness of WA-QM was investigated using monthly precipitation data from five Coupled Model Intercomparison Project Phase 6 models in the Pan Third Pole region, which includes Central Asia (CA), Southeast Asia, and the Tibetan Plateau. Quantile delta mapping (QDM) and scaled distribution mapping (SDM) served as the benchmark methods for assessment. The results indicated that integrating WA into QDM or SDM did not compromise the ability of QDM or SDM to correct overall biases and preserve the model’s climate change signal. Furthermore, WA could be an effective tool to overcome QM’s limitation in capturing intra-annual temporal patterns. The WA approach employed discrete wavelet transformation to decompose GCM data into various frequency bands and then adjusted their standard deviations and signs to ensure that their relative relationships were consistent with those in observations. Compared to the standalone QM approach, WA-QM showed greater accuracy in monthly statistical metrics. In the CA region, WA-SDM reduced the monthly root mean square error of the mean by 25.2%, the standard deviation by 17.6%, and the 90th percentile by 26.7% compared to SDM. The effectiveness of WA-QM was demonstrated across different data periods, spatial areas, and GCMs.

Exploring EFL Learners’ Attitudes Towards the Effectiveness of Recast vs. Explicit Corrective Feedback on Pronunciation

This study investigates learners’ attitudes towards the effectiveness of recast versus explicit feedback for pronunciation improvement. The data were collected through a semi-structured interview, with ten EFL pupils in their third year as undergraduates from the English department at Ibn Tofail University participating. The data were analyzed thematically to determine themes related to learners’ perceived usefulness of the oral feedback, namely recast and explicit feedback, and the type viewed to be of greater impact on pronunciation. The findings revealed that learners view both recast and explicit feedback as effective. The effectiveness of explicit feedback was attributed to its clarity, being less time-consuming and ability to highlight errors. For recast, its effectiveness was due to its implicitness, which does little to no harm to emotions. Concerning the type learners perceive to have a greater impact, most learners stated that explicit feedback is more effective due to its visibility, making them cautious of repeating mistakes, unlike recast which is sometimes difficult to realize as corrective. However, one learner went for recast, appreciating its gentle approach and expressing how explicit feedback could trigger stress, making learners focus on their embarrassing situation rather than fixing the error. Additionally, one learner highlighted personal preference as a determining factor in the perceived effectiveness of feedback types. The findings suggest that the effectiveness of feedback is closely related to attitudes. Therefore, teachers could employ corrective methods that are explicit enough while making sure they are not too obvious to avoid harming learners emotionally.

Identification of Lateral-Directional Aerodynamic Parameters via an Online Correction Wind Tunnel Virtual Flight Test

In the wind tunnel virtual flight test (WTVFT), the translational motion of the model is constrained to have only free rotational motion. As a result, the motion data measured in the WTVFT differ from those measured during free flight. If the aerodynamic parameter identification is performed directly via the test data from the WTVFT, the identification results differ from those obtained on the basis of the identification of the free-flight test data, resulting in inaccurate results. To address this problem, this study innovatively proposes a test data correction method based on online signal reconstruction theory, which can reconstruct test data close to free flight. Through the measurement of aerodynamic forces on the test model during tests, the lateral-directional trajectory motion of the model can be solved online, the yaw attitude data of the model can be corrected online by making use of the link between the translational and rotational motions of the aircraft, and finally reconstructing the movement data that approximates the model's unconstrained free flight. The corrected test data eliminate the difference in motion characteristics between WTVFT and free flight. Finally, the aerodynamic parameters are identified via these corrected data, and the identification results indicate that the aerodynamic derivatives determined via online corrected experimental data are more accurate than those identified via uncorrected experimental data.

An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin

An improved statistical bias correction method for Global Climate Model (GCM) precipitation projection: A case study on the CMCC-CM2-SR5 model projection in China’s Huaihe River Basin

Study on bias correction method of ECMWF surface variable forecasts based on deep learning

Study on bias correction method of ECMWF surface variable forecasts based on deep learning

ReRNet: recursive neural network for enhanced image correction in print-cam watermarking

Robust image watermarking that can resist camera shooting has gained considerable attention in recent years due to the need to protect sensitive printed information from being captured and reproduced without authorization. Indeed, the evolution of smartphones has made identity watermarking a feasible and convenient process. However, this process also introduces challenges like perspective distortions, which can significantly impair the effectiveness of watermark detection on freehandedly digitized images. To meet this challenge, ResNet50-based ensemble of randomized neural networks (ReRNet), a recursive convolutional neural network-based correction method, is presented for the print-cam process, specifically applied to identity images. Therefore, this paper proposes an improved Fourier watermarking method based on ReRNet to rectify perspective distortions. Experimental results validate the robustness of the enhanced scheme and demonstrate its superiority over existing methods, especially in handling perspective distortions encountered in the print-cam process.

Assessment of XCI skewing and demonstration of XCI escape region based on single-cell RNA sequencing: comparison between female Grave’s disease and control

BackgroundThe reactivation and loss of mosaicism hypothesis due to X chromosome inactivation (XCI) skewing and escape could influence gender differences in autoimmune diseases. XCI selectively inactivates one of the two X chromosomes in females.MethodsTo estimate XCI skewing and the occurrence of XCI escape, we conducted a normal female (NF) without a history of autoimmune thyroid disease (AITD) and a patient with Grave’s disease (GD) based on a thyroid diagnosis. After single-cell RNA sequencing, heterozygous variants were converted and transformed. XCI skewing was calculated using the formula and the skewing degree was defined. NF/GD genes were compared using correction methods. Positions are heterozygous within a single cell as indicated by a unique barcode.ResultsXCI skewing showed 45.8%/48.9% relatively random, 29.4%/27.0% skewing, 24.6%/23.7% severe skewing, and 0.2%/0.4% extreme severe skewing. 24.8%/24.1% in NF/GD exhibited severe skewing or higher. A total of 13 genes were significantly associated with XCI skewing ratios in NF/GD cells. In total, 371/250 nucleotide positions with only one barcode (representing a unique cell) were identified for XCI escape. A total of 143/52 nucleotide positions spanned 20/6 genes, and 12/1 genes were identified as XCI escapes.ConclusionsThese results could aid in understanding the immunogenetics of gender differences in various autoimmune disease pathophysiologies.

A method for blood pressure hydrostatic pressure correction using wearable inertial sensors and deep learning

Cuffless noninvasive blood pressure (BP) measurement could enable early unobtrusive detection of abnormal BP patterns, but when the sensor is placed on a location away from heart level (such as the arm), its accuracy is compromised by variations in the position of the sensor relative to heart level; such positional variations produce hydrostatic pressure changes that can cause swings in tens of mmHg in the measured BP if uncorrected. A standard method to correct for changes in hydrostatic pressure makes use of a bulky fluid-filled tube connecting heart level to the sensor. Here, we present an alternative method to correct for variations in hydrostatic pressure using unobtrusive wearable inertial sensors. This method, called IMU-Track, analyzes motion information with a deep learning model; for sensors placed on the arm, IMU-Track calculates parameterized arm-pose coordinates, which are then used to correct the measured BP. We demonstrated IMU-Track for BP measurements derived from pulse transit time, acquired using electrocardiography and finger photoplethysmography, with validation data collected across 20 participants. Across these participants, for the hand heights of 25 cm below or above the heart, mean absolute errors were reduced for systolic BP from 13.5 ± 1.1 and 9.6 ± 1.1 to 5.9 ± 0.7 and 5.9 ± 0.5 mmHg, respectively, and were reduced for diastolic BP from 15.0 ± 1.0 and 11.5 ± 1.5 to 6.8 ± 0.5 and 7.8 ± 0.8, respectively. On a commercial smartphone, the arm-tracking inference time was ~134 ms, sufficiently fast for real-time hydrostatic pressure correction. This method for correcting hydrostatic pressure may enable accurate passive cuffless BP monitors placed at positions away from heart level that accommodate everyday movements.

Determination of present-day crustal deformation along the Kenyan rift system using InSAR

The Kenyan rift system is prone to deformation due to various geological processes and human activities, such as overexploitation of groundwater and exploitation of geothermal energy. Crustal deformation monitoring is essential for understanding the geodynamics of the rift and assessing potential hazards as the rift passes through densely populated areas. However, retrieving InSAR displacement measurements along the Kenya rift system is challenging due to the high variability in tropospheric delay caused by its location in the tropics and the significant topographic variations along the rift. Here, we leverage Sentinel-1 data to analyze both local and large-scale deformation in the rift using multi-temporal InSAR processing with an improved tropospheric correction method, which we previously proposed with a modification on variance covariance modeling. We provide evidence for the previously reported ground displacement of 5-7.5 cm southwest of the Suswa volcano as being a misidentification of a tropospheric delay, that was dominant on March 27, 2018. Moreover, we observe episodic ground displacement at Suswa and Longonot, attributable to magma movement. Our results indicate that Suswa experienced a 9 cm movement towards the satellite line of sight between 2018-2020, while Longonot moved 4 cm towards satellite line of sight between 2016-2018 and also underwent 3 cm displacement away from the satellite line of sight between 2019-2021. In addition, we observe land subsidence associated with geothermal exploitation in the range of 1-3.6 cm/yr in Olkaria as well as in multiple locations in Nairobi at a rate of up to 6 cm/yr, primarily attributed to groundwater overexploitation. Moreover, we detect undocumented ground displacement at the Chalbi salt flat and along the Elgeiyo escarpment, at rates of up to 4.8 cm/yr and 5.7 cm/yr, respectively. Long-wavelength, low-amplitude deformation at the Turkana depression aligned well with the current kinematics of the Kenyan rift system. We find that localized deformations, caused by magmatism and human activity, dominate the south segment of the rift, whereas large-scale low-amplitude deformations dominate the north segment. In summary, our results emphasize the importance of tropospheric delay correction in retrieving InSAR-derived displacement measurements along the Kenyan rift system.Graphical

CoRRection – an open source software tool for RR intervals processing

Abstract Introduction: An analysis of Heart Rate Variability (HRV) is widely used in clinical and research. To properly calculate HRV parameters, the RR intervals series must be properly preprocessed. There are already automated tools for correcting these artifacts; however, they are not fully transparent and fully customizable. To address these limitations, we introduce CoRRection, a semi-automatic tool for RR interval correction, which integrates both automatic and manual approaches providing greater control over which intervals are corrected. Material and Methods: The application offers detection and correction methods. Additionally, it allows to manually remove an artifact before applying a correction method. It also allows to clean artifacts in shorter segments which enables applying different detection methods in one example, e.g., gathered during intense exercise. The application provides a test to assess signal stationarity (Augmented Dickey-Fuller test). After signal processing the report is created, containing the number of probes deleted and modified. Results: The proposed CoRRection application allows both technical and non-technical users to preprocess RR signals with a better control over the process by enabling the use of semi-automatic approach. A few examples of studies which required Correction’s unique approach of semi-automatic correction were presented. The need for different methods and not only considering different examinations, but also different segments within one recording was emphasized. Conclusions: We have presented a new application designed to preprocess RR intervals signal with a few popular detection and correction methods. This approach enables a good compromise between a precise manual approach and a less time-consuming automatic approach. The semi-automatic method of artifact correction empowers users to explore multiple identification and correction methods (and to choose the one best fitted to the data or measurement conditions), and offers a better understanding of the examination preprocessing tools. This may also result in a better trust in the automatic approach among the users.

Nonlinear real-time correction in homodyne interferometers by multi-area spatial sampling and dimensionality-reduced ellipse fitting

Homodyne interferometers are susceptible to signal instability, including the amplitude, relative phase, and DC bias of interference signals, which lead to dynamic nonlinear errors that require real-time correction to ensure full-range displacement measurement accuracy. To address these issues, this paper proposes a real-time, non-iterative FPGA-based nonlinear correction method, designed to balance accuracy and computational efficiency. The method employs peak detection to simplify the elliptical fitting matrix and utilizes feature-based segmented sampling to perform reduced-order correction. Experimental results show that when elliptical signals are unstable, this method reduces residual error from 1.14 nm to 0.12 nm, effectively compensating for nonlinear errors. In a 25 µm displacement test, this method maintains nonlinear error deviation within the sub-nanometer range compared to traditional correction methods, while reducing the computational load by two orders of magnitude, achieving a balance between correction accuracy and efficiency.

Advantages of the modified colpocleisis operation in patients with atypical types of pelvic organ prolapse

Background: Currently, there is no general universal unified surgical treatment strategy for patients with pelvic organ prolapse. Considering the risks of installing mesh implants and the increasing incidence of implant-associated complications, mesh-free pelvic floor surgery techniques have become relevant again. A method was developed and introduced into clinical practice, which is an electrosurgical vaginal hysterectomy followed by vaginal suturing using a modified colpocleesis operation, which allows combining all the advantages of this method and at the same time minimizing the risks of cancer in the future. Aim: The aim of this study was to improve the results of surgical treatment in patients with rare, complicated and recurrent forms of pelvic organ prolapse. Materials and methods: This study included 140 patients diagnosed with pelvic organ prolapse. The main group consisted of 70 individuals with rare, severe and complicated cases of genital prolapse, who underwent surgical treatment using our modified colpocleisis operation. The control group comprised 70 other individuals with newly diagnosed uncomplicated cases of pelvic organ prolapse, who underwent surgical treatment using mesh implants. Results: All patients underwent planned surgical treatment. The operated patients were monitored for 1–5 years. In the main group, the effectiveness of the operation was 100%, while the effectiveness of surgical treatment with a mesh implant in the control group was 95.7%. All patients in the main group had complete tissue healing. There were no cases of long-term complications or relapses in any of the patients. Conclusions: the use of the modified colpocleisis operation for atypical types of pelvic organ prolapse is a highly effective method of prolapse correction. This, in combination with a simple execution technique, allows it for being offered as a basic method for vaginal access in patients without sexual activity.

Foam Concrete And Surfactants

Lightweight concrete types have gained attention today due to their diverse range of applications. Cellular concretes such as aerated concrete and foam concrete hold a significant place in the construction sector due to their sustainability and recyclability. These materials stand out for their high efficiency, offering properties such as thermal insulation and fire resistance. In particular, pore size and distribution in foam concrete are among the most critical factors determining the mechanical strength and insulation performance of the material. In this context, understanding the chemical composition and properties of the foaming agent in detail is essential for achieving the desired performance. Surface-active agents, known as surfactants, are important components widely used in foam concrete production. The correct selection and application methods of surfactant types are crucial for ensuring that the concrete achieves its targeted properties. This study aims to examine the characteristics of various surfactants and contribute to meeting the desired performance criteria in foam concrete production.

Variable power functional dilution adjustment of spot urine

Spot-urinary biomarkers are crucial in medical, epidemiological, and environmental studies, but their variability due to hydration levels requires precise dilution adjustments. Traditional methods, like conventional creatinine correction (CCRC), are limited in compensating for variations in urine concentration, causing substantial inconsistencies, particularly at the extremes of the diuresis spectrum. While restricting the creatinine (CRN) range to 0.3–3 g/L is recommended to ensure result stability, this approach excludes a substantial proportion of samples and permits notable fluctuations within the accepted range. This study introduces a novel variable power functional creatinine correction method (V-PFCRC) to normalize analytes to 1 g/L CRN by utilizing uncorrected analyte levels and two analyte-specific coefficients, c and d. Based on extensive urinary total weight arsenic data (n = 5,553), the mathematical derivation of these coefficients is detailed in this paper and forms the foundation of the corrective V-PFCRC formulas. The generalizability of V-PFCRC was evaluated using large spot-urinary datasets for four additional metals and an extensive dataset of urinary iodine levels (n > 58,000) and blood iodine. Validation against conventional methods—assessing vital statistical data, residual CRN bias, and correlations with concurrently detected blood levels of total arsenic and iodine— demonstrated the superior performance of V-PFCRC in reducing residual CRN bias and enhancing blood-urine correlations. The V-PFCRC method effectively addresses nonlinear hydration bias and the exposure-dependent variability of this bias, providing a more accurate representation of exposure and supply levels. The adaptability and efficiency of V-PFCRC suggest its broad applicability across various scientific disciplines, potentially transforming the precision and reliability of urinary biomarkers.