Search

Background Purpose Methods Results Conclusion In recent years, the number of hip replacement patients receiving radiation therapy has steadily increased. In parallel, strategies have been developed to reduce metal artifacts in computed tomography (CT) images and improve the accuracy of dose calculation algorithms. However, in certain situations, knowledge of the type of prosthesis material is required to accurately determine the dose distribution.This study aims to identify physical materials in hip prostheses to correctly assign them in the treatment planning system and improve dose calculation accuracy.We first verified the validity of the extended CT mass density calibration curve measured on titanium (Ti) and stainless steel (SS) metal inserts of two different diameters. Then using dedicated reference objects of various circular diameters, we developed a method based on interpolation functions to differentiate between Ti and SS material groups. Forty data sets from 18 patients were used to validate our method on two different reconstruction kernels: a standard Br44f and the electron DirectDensity (Sd40f) kernels from Siemens.Hounsfield units (HU) of Ti and SS inserts were found to vary widely depending on insert diameter, CT spectrum, and reconstruction kernels due to cupping artifacts. The largest HU difference (‐79%) was obtained for SS at 70 kV with Br44f when the diameter increased from 8 to 30 mm. Therefore, under these conditions, the extended CT‐density calibration curve is not recommended for heavy metal density determination. Using our interpolation‐based method, we achieved excellent detection (100%) and material differentiation (100%) results for stems in both reconstruction kernels. At CT energies between 110 and 140 kV, the detection and material differentiation rates were 93.3% and 92.9% for the heads and 93.3% and 92.9% for the acetabular cups, respectively, with the Br44f. Similarly, the use of Sd40f resulted in detection and differentiation rates of 94.7% and 100% for the heads and 100% and 95.0% for the acetabular cups, respectively.This method makes it possible to differentiate between hip prosthesis materials and correctly assign them to the Ti or SS group without prior knowledge of the prosthesis type, regardless of the reconstruction kernels. In combination with the Acuros XB (Varian) or Monte Carlo dose algorithms, excellent dosimetric accuracy can be achieved even in the vicinity of hip prostheses. By performing basic measurements, the method can be adapted to other CT units and reconstruction kernels, replacing the use of an extended CT‐density calibration curve. [ABSTRACT FROM AUTHOR]

Purpose: This study investigates three different calibration methods for the selection of background pixel intensity. Methods: Film‐by‐Film (FBF) Method: Each film serves as its own control. Batch‐by‐Film (BBF) Method: A single film is used as a control for all calibration films. Generic (GEN) Method: A generic value (65535) is used as the background pixel value for all calibration films.Three calibration curves were established for the red, green, blue, and RGB channels, and the Radbard NIH (image) curve‐fitting model was used to predict the dose. Sensitivity at different dose levels was quantified by calculating the first derivative of each color channel. Results: The GEN method exhibited a difference of up to 6% between the predicted and delivered doses below 2 Gy. The changes in optical density when using the GEN method differed significantly (p<0.0001) from those of the FBF and BBF methods. In the dose range 5–30 Gy, the percentage difference between the predicted and delivered doses for the FBF, BBF, and GEN methods was within 2%. Both the red and green channels demonstrated higher sensitivity than the blue channel over the dose range of 2–30 Gy. Conclusions: The FBF method is more accurate than the BBF and GEN methods because it accounts for inter‐film variations. The Radbard NIH (image) curve‐fitting function proved suitable for predicting the dose for all the three calibration methods. [ABSTRACT FROM AUTHOR]

Based on the surface plasmon resonance imaging (SPRi) instrument, we established a new method of analyzing morphine in urine by processing a calibration curve. According to an inhibition immunoassay, gradient concentration of morphine and morphine-BSA fixed on the chip competitively combine with morphine antibody on the chip. Given the three mathematical models, the data of SPRi signals generated from SPRi with morphine was processed to obtain the calibration curve. Ultrafiltration was used to pretreat blank urine samples with adding morphine, and then investigated the advantages and disadvantages of each model. With a limit detection of 6.57 ng·mL−1, the method and mathematical models can provide robust support for SPRi sensors used in further environmental detection, such as the epidemiological study of sewage.

Abstract Purpose This study investigates three different calibration methods for the selection of background pixel intensity. Methods Film‐by‐Film (FBF) Method: Each film serves as its own control. Batch‐by‐Film (BBF) Method: A single film is used as a control for all calibration films. Generic (GEN) Method: A generic value (65535) is used as the background pixel value for all calibration films.Three calibration curves were established for the red, green, blue, and RGB channels, and the Radbard NIH (image) curve‐fitting model was used to predict the dose. Sensitivity at different dose levels was quantified by calculating the first derivative of each color channel. Results The GEN method exhibited a difference of up to 6% between the predicted and delivered doses below 2 Gy. The changes in optical density when using the GEN method differed significantly (p

Libin Liu,1,&ast; Chuyan Wang,2 Bin Mei,1 Jing Wang,1 Xiaoqun Xu,1 Hongjuan Zhou,1 Long Cai1,&ast; 1Centre of Laboratory Medicine, Hangzhou Red Cross Hospital, Hangzhou, Zhejiang, People’s Republic of China; 2Department of Clinical Laboratory, The Third People’s Hospital of Lin’an District, Hangzhou, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Long Cai, Centre of Laboratory Medicine, Hangzhou Red Cross Hospital, No. 208 East Huancheng Road, Hangzhou, 310003, People’s Republic of China, Email cailong317@hotmail.comPurpose: This study aimed to establish and validate a diagnostic nomogram for identifying false positives in the Xpert MTB/RIF (Xpert) for detection of rifampicin resistance (RIF-R).Patients and Methods: In this retrospective study, we collected basic patient characteristics and various clinical information from the electronic medical record database. Patients were randomly divided into training and validation groups in a 7:3 ratio. LASSO regression was used to screen variables and construct a diagnostic nomogram. The ROC curve, calibration curve, and decision curve analysis (DCA) were used to evaluate the performance of the nomogram.Results: A total of 384 patients were included in the study, with 268 and 116 patients in the training and validation cohorts, respectively. Finally, probe mutations and probe delay were identified as the independent influencing factors. Using the mutation of probe E as a reference, probes A or C (OR = 51.07, P< 0.001), probe D (OR = 7.48, P< 0.001), and multiple probes (OR = 4.42, P=0.029) were identified as factors influencing false positives in Xpert for detection of RIF-R. Taking probe delay ΔCT < 4 as a reference, ΔCT (4– 5.9) (OR = 17.06, P=0.005) and ΔCT (6– 7.9) (OR = 36.67, P< 0.001) were noted to be the factors influencing false positives in Xpert for detection of RIF-R. Based on these two variables, we constructed a diagnostic nomogram. The area under the curve of the nomogram model was 0.847 and 0.850 for the training and validation groups, respectively. The calibration curves were consistent. The DCA revealed that the model achieved the greatest net benefit when the threshold probability was set between 6% and 71% in the training cohort and 6% and 70% in the validation cohort.Conclusion: The nomogram constructed can identify false positives in Xpert for detection of RIF-R and provides basis for clinicians to formulate diagnosis and treatment plans.Keywords: nomogram, probe mutation, probe delay, calibration curve

AIM: To analyze and screen influencing factors of diabetic patients complicated with retinopathy, and establish and validate prediction model of nomogram.METHODS: A total of 1 252 patients from the Diabetes Complications Early Warning Dataset of the National Population Health Data Archive(PHDA)between January 2013 to January 2021 were selected and randomly divided into a modeling group(n=941)and a validation group(n=311). Univariate analysis, LASSO regression and Logistic regression analysis were used to screen out the influencing factors of diabetic retinopathy, and a nomogram prediction model was established. The receiver operating characteristic curve, Hosmer-Lemeshow test and calibration curve were used to evaluate the model. The clinical benefit was evaluated by the decision curve analysis(DCA).RESULTS: Age, hypertension, nephropathy, systolic blood pressure(SBP), glycated hemoglobin(HbA1c), high-density lipoprotein cholesterol(HDL-C), and blood urea(BU)were the influencing factors of diabetic retinopathy. The area under the curve(AUC)of the modeling group was 0.792(95%CI: 0.763-0.821), and the AUC of the validation group was 0.769(95%CI: 0.716-0.822). The Hosmer-Lemeshow goodness of fit test and calibration curve suggested that the theoretical value of the model was in good agreement(modeling group: χ2=14.520, P=0.069; validation group: χ2=14.400, P=0.072). The DCA results showed that the threshold probabilities range was 0.09-0.89 for modeling group and 0.07-0.84 for the validation group, which suggested the clinical net benefit was higher.CONCLUSION: This study constructed a risk prediction model including age, hypertension, nephropathy, SBP, HbA1c, HDL-C, and BU. The model has a high discrimination and consistency, and can be used to predict the risk of diabetic retinopathy in patients with diabetes.

Aim: This research aimed to construct a clinical model for forecasting the likelihood of lung metastases in differentiated thyroid carcinoma (DTC) with intermediate- to high-risk. Methods: In this study, 375 DTC patients at intermediate to high risk were included. They were randomly divided into a training set (70%) and a validation set (30%). A nomogram was created using the training group and then validated in the validation set using calibration, decision curve analysis (DCA) and receiver operating characteristic (ROC) curve. Results: The calibration curves demonstrated excellent consistency between the predicted and the actual probability. ROC analysis showed that the area under the curve in the training cohort was 0.865 and 0.845 in the validation cohort. Also, the DCA curve indicated that this nomogram had good clinical utility. Conclusion: A user-friendly nomogram was constructed to predict the lung metastases probability with a high net benefit. Article highlights The study aimed to develop a predictive model for lung metastases in patients with differentiated thyroid cancer (DTC) who are at intermediate- to high-risk. Through univariate and multivariate analyses, histological type, multifocality, tumor size, BRAFV600E mutation and ps-Tg levels were identified as independent factors influencing the development of lung metastases. A nomogram was constructed based on these risk factors, with a high C-index of 0.86, indicating strong predictive accuracy for lung metastases. Calibration curves showed excellent consistency between predicted and actual probabilities of lung metastases, further validating the nomogram's reliability. Receiver operating characteristic analysis demonstrated high discrimination ability of the nomogram, with an area under the curve of 0.865 in the training cohort and 0.845 in the validation cohort. Decision curve analysis indicated that the nomogram had better clinical utility in predicting lung metastases rates compared with other models. The nomogram provides clinicians with a tool to assess the risk of lung metastases in DTC patients, incorporating factors such as histological type, tumor size, genetic mutations and ps-Tg levels to guide treatment decisions and improve patient outcomes. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to investigate the risk factors affecting satisfaction with debulking surgery for ovarian cancer and establish a preoperative clinical predictive model. Methods: Clinical data from 131 patients who underwent ovarian cancer debulking surgery at Jiangnan University Affiliated Hospital between 2016 and 2022 were collected. Patients were randomly separated into an experimental group and a control group in a 7:3 ratio. On the basis of intraoperative outcomes, patients were grouped as either surgery-satisfactory or surgery-unsatisfactory. Clinical indicators were compared through single-factor analysis between groups. Significantly different factors (p < 0.1) were further analyzed through multivariate logistic regression. A predictive nomogram model was developed and validated by receiver operating characteristic (ROC), calibration, and clinical decision curves. Results: Single-factor analysis revealed the significance of factors such as albumin levels, alkaline phosphatase (ALP), ECOG scores, CA125, HE4, and lymph node metastasis. Multivariate regression analysis identified albumin levels, ALP, ECOG scores, HE4, and lymph node metastasis as independent risk factors for satisfactory surgical outcomes in patients with ovarian cancer undergoing debulking surgery as (p < 0.05). A clinical predictive model was successfully constructed. ROC curves showed AUC values of 0.818 and 0.796 for the experimental and validation groups, respectively. Internal validation through the bootstrap method confirmed the model's fit in both groups. Meanwhile, the clinical decision curve demonstrated the model's high utility. Conclusion: Independent risk factors associated with satisfactory tumor reduction in patients with ovarian cancer undergoing debulking surgery included decreased albumin levels, ALP > 137 U/L, ECOG = 1 score, HE4 > 140 pmol/L, and lymph node metastasis. Constructing a clinical predictive model through logistic regression analysis enables individualized testing and maximizes clinical benefits. [ABSTRACT FROM AUTHOR]

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis. [ABSTRACT FROM AUTHOR]

The present manuscript deals with the identification of the spectral lines of the rare earth elements (REEs) in the LIBS spectra of the black sands collected from the Thiruvananthapuram (Kerala) beach using laser-induced breakdown spectroscopy (LIBS). LIBS spectra of the sand sample show the presence of spectral lines of Na, Mg, Mn, Al, and Cr. Along with these elements, the spectral lines of REEs such as Ce, Nd, Sm, Dy, Ho, Tm, etc., and the radioactive element Thorium (Th) are also present in the LIBS spectra of the black sand sample. The presence of REEs and radioactive elements makes the sand suitable for profitable extraction using various methods for different applications. The results of LIBS (presence of all REEs in sand) are also verified by photoacoustic spectroscopy (PAS). The Plasma temperature has been calculated using the Boltzmann plot to explain the distribution of the atoms in various higher energy states/levels. An attempt is made to explain the various transitions involved in the spectral lines of the rare earth ions in the LIBS spectra of the black sand sample using a possible energy level diagram. The concentration of some REEs (Dy, Sm, and Nd) and LOD has been calculated by using the calibration curve method. To the best of our knowledge, the investigation of rare earth and radioactive elements in the black sand of the Kerala beach using LIBS is reported for the first time. [ABSTRACT FROM AUTHOR]

Objective To investigate the high-risk factors for preeclampsia in Urumqi, establish a risk prediction model, and validate its effectiveness. Methods A total of 6 138 pregnant women undergoing antenatal examination from February 2021 to February 2023 were collected through the integrated platform of clinical research in Urumqi Maternal and Child Health Hospital. General data, pregnancy history, and pregnancy complications were recorded» The patients were randomly divided into a modeling group (n=4 308) and a validation group (ri=l 830) in a 7:3 ratio. The logistic regression model analysis method was used to construct a Nomogram prediction model. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA) were employed to assess the discrimination, calibration, and clinical applicability of the prediction model. Results The advanced age (Oi=L887, 95%CI: 1.535-2.319), overweight (OR=3.2211> 95%CI: 2.699-3.844), family history (O/= 1.575, 95%CI: 1.254-1.979), hypertension history (0=17.552, 95%CI: 7.021-43.882), hypertension complicating pregnancy (0=1.827, 95%CI: 1.388-2.405), preeclampsia history (OR二6.216, 95%CI: 3.578-10.770), and primiparity (OR=2.384, 95%CZ: 1.777-3.200) were identified as independent risk factors of preeclampsia in Urumqi. The results of ROC curve test showed that the area under the curve and 95%CI of the model group were 0.762 (0.742-0,782), and for the validation group were 0.765 (0,734-0.796). The calibration curve and DCA curve demonstrated that the Nomogram had good accuracy and clinical applicability. Conclusion The established nomogram prediction model exhibited good predictive ability and provides an important reference for clinical practitioners to identify high-risk patients. Early intervention measures should be implemented to prevent further progression and improve maternal and infant outcomes. [ABSTRACT FROM AUTHOR]

Biogenic synthesis of nanoparticles is an emerging research area of the scientific community holding versatile and potential applications. The nanoscaled iron nanoparticles (FeNPs) have unique chemical and physical properties. Beside the available physical and chemical methods, the aqueous extract of the Prunus armeniaca Gum (PAG) was employed to promote a green and sustainable approach for the synthesis of FeNPs. The structure and morphology of the FeNPs were characterized by SEM, FTIR, and XRD techniques. And UV–Vis spectrophotometry is employed to study optical properties, time, temperature, and pH dependent stability. Herein PAG-FeNPs has been employed as efficient and cost-effective colorimetric probe for the sensing of methyl orange (MO) for the first time displaying a color change from light orange to dark voilet on exposure to MO. The selectivity of the detection method for MO against various interferences including heavy metals, and organic dyes was examined and sensor’s signal remained unaffected by common interferences. Under ideal detection conditions, the proposed method demonstrates an excellent linear correlation (R2 = 0.992) in the concentration range of 10–350 µM methyl orange. The limits of detection and limit of quantification are 13 µM and 40 µM, respectively. Moreover, the technique has been successfully employed for detecting methyl orange in self-contaminated tape water samples.

Introduction: A crucial step in commissioning a CT simulator is measuring the conversion relationship between the CT number and Relative Electron Density (RED) and transferring it to the radiotherapy TPS for accurate dose calculation. Material and Methods: In order to automatically reconstruct a DE-Rho series with relative electron density maps of the materials used, 14 tissue-mimicking material plugs were made, their properties were measured, and the developed density phantom was scanned by CT-simulator using the Dual Energy^DE_Direct Density protocol. Results: CT numbers (HU) for various densities of materials were determined. Utilizing the resultant HU from CT scans, the relative electron density, or RED, was computed. The HU-RED calibration curve was created using CT scans that were obtained with different tissue replacements. Conclusion: Particularly for the CT-number calibration in radiation therapy planning, the new Tissue Equivalent Materials (TEMs) may simplify the calibration process without sacrificing the accuracy of the stoichiometric calibration. Our objective was met and the purchase cost was avoided thanks to the developed phantom.

Introduction: A crucial step in commissioning a CT simulator is measuring the conversion relationship between the CT number and Relative Electron Density (RED) and transferring it to the radiotherapy TPS for accurate dose calculation. Material and Methods: In order to automatically reconstruct a DE-Rho series with relative electron density maps of the materials used, 14 tissue-mimicking material plugs were made, their properties were measured, and the developed density phantom was scanned by CT-simulator using the Dual Energy^DE_Direct Density protocol. Results: CT numbers (HU) for various densities of materials were determined. Utilizing the resultant HU from CT scans, the relative electron density, or RED, was computed. The HU-RED calibration curve was created using CT scans that were obtained with different tissue replacements. Conclusion: Particularly for the CT-number calibration in radiation therapy planning, the new Tissue Equivalent Materials (TEMs) may simplify the calibration process without sacrificing the accuracy of the stoichiometric calibration. Our objective was met and the purchase cost was avoided thanks to the developed phantom. [ABSTRACT FROM AUTHOR]

Introduction: Ionizing radiation is applied in various fields, and dosimetric control guarantees the quality and safety of products during the irradiation process. There is a need for dosimetric calibration procedures in industrial irradiation plants, specifically for self-shielded irradiation systems. Objective: To implement high dose dosimetry in the routine control of irradiated materials within a self-shielded irradiator. Methods: We worked with 32 dosimeters; we divided the dose range used into 8 points and used four RED Perspex dosimeters per point. We measured the specific absorbance for each dosimeter; plotted these values against dose and produced a third-degree polynomial fit as a calibration curve. Results: We obtained the calibration curve with an r2 of 0,9997. The uncertainties due to the dispersion of the dosimeters and the calibration curve were 1,39% and 0,22%, respectively, for a total uncertainty of 4,80%. This uncertainty includes dose determination with a factor coverage (k) equal to 2 for a 95% confidence interval. Conclusion: Perspex RED dosimeters can be used for routine control of irradiated products in a self-shielded irradiator system.

A simple method for measuring the concentration of nano/microplastics (N/MPs) in soil, which is difficult owing to the size of the filter mesh and the resolution of the measuring instrument, was investigated. A spectrophotometer was used for the measurements and polystyrene particles were used as the N/MP samples. When measuring N/MP concentrations in soil suspensions, absorbance was measured at two wavelengths, and the best combination of wavelengths for measurement was extracted because soil particles and leached components interfere with N/MP absorbance. A wavelength combination of 220–260 nm and 280–340 nm was found to be suitable for a variety of soils. As N/MPs are adsorbed on the surface of soil particles and precipitate with soil particles in suspension, a calibration curve was created between the concentration of N/MPs in the soil suspension and the N/MP content in the soil. The calibration curve showed a linear relationship, allowing for the estimation of the concentration of N/MPs in the soil. Although other N/MP materials, such as polyethylene and polyethylene terephthalate, must also still be considered and tested, this simple method has the potential to measure N/MPs in various types of soil.

Calibration costs of fluid quantity are not only expensive but also take time, particularly if calibrator facilities are limited. Sometimes, calibrated measuring points do not match the needs of measuring instruments in the field, so forecasts require to be made. Further, the quadratic spline is the simplest non-linear spline model, which vows better accuracy than other linear spline and non-linear regression. For those reasons, we propose a method that minimizes the curvature tip of the quadratic spline model with the popular particle swarm optimization (PSO) technique. This research aims to enhance the accuracy of the quadratic spline prediction model on the calibration curve with a PSO-based curvature tip compression strategy. We want to reduce the effect of oscillations on forming the quadratic spline model. Experimental validation results and comparison of proposed and common methods of quadratic spline interpolation showed that our novel approach, curvature tip compression-PSO, was superior and increased accuracy of 12.18 times from the ordinary quadratic spline and 3.14 times from the linear spline. The proposed method had more minor errors and measurement uncertainties than other ordinary prediction models, thus proving its excellence in predicting unkown values. Calibration curve of turbine flowmeters and salinometers were implemented to this application of the predictive model.

Abstract Background qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f 0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f 0%). Results The performance of the new f 0% method was compared against the CT method along with another two outstanding methods—LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f 0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f 0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f 0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds. Conclusions We recommend using the f 0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f 0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases .

Sulfur hexafluoride (SF6), which survives in the atmosphere for an extremely long period of time, is the most potent greenhouse gas regulated under the Kyoto Protocol. So, the accurate monitoring of atmospheric SF6 plays an important role in the study of the control policies for reducing greenhouse gas emissions. The instruments for SF6 measurement are typically calibrated using certified reference materials. The concentrations of the commercially available SF6 reference materials usually have a broad range, from 1 μmol/mol to 6000 μmol/mol. Some characteristics including sensitivity, linear range, relative standard deviation, and accuracy are crucial for the determination of SF6 in such a broad concentration range. Therefore, the selection of a proper detector for the accurate determination of SF6 with such a broad range is extremely important to establish a gas chromatography (GC) method for developing SF6 reference materials. In this paper, several typical GC methods with different detectors, including a thermal conductivity detector (TCD), a pulsed discharge helium ionization detector (PDHID), and a flame photometric detector (FPD), were carefully established for the accurate determination of SF6 with different concentrations. The results show that an FPD detector has a relatively narrow linearity range, thus a quadratic equation should be established for building a calibration curve. The PDHID and TCD have good linearity with coefficients of 1.0000 in the concentration range of 10–100 μmol/mol (using a PDHID), and 100–1000 μmol/mol (using a TCD), respectively. Further considering the measurement errors of indication results, the PDHID is suitable for SF6 measurement when the concentrations are below 100 μmol/mol, whereas the TCD is suitable for SF6 measurement when the concentrations are over 100 μmol/mol. These results provide useful guidance in choosing an appropriate GC detector for the accurate determination of SF6, which are especially very helpful for developing SF6 reference materials. [ABSTRACT FROM AUTHOR]

Background: qPCR is a widely used technique in scientific research as a basic tool in gene expression analysis. Classically, the quantitative endpoint of qPCR is the threshold cycle (CT) that ignores differences in amplification efficiency among many other drawbacks. While other methods have been developed to analyze qPCR results, none has statistically proven to perform better than the CT method. Therefore, we aimed to develop a new qPCR analysis method that overcomes the limitations of the CT method. Our f0% [eff naught percent] method depends on a modified flexible sigmoid function to fit the amplification curve with a linear part to subtract the background noise. Then, the initial fluorescence is estimated and reported as a percentage of the predicted maximum fluorescence (f0%). Results: The performance of the new f0% method was compared against the CT method along with another two outstanding methods—LinRegPCR and Cy0. The comparison regarded absolute and relative quantifications and used 20 dilution curves obtained from 7 different datasets that utilize different DNA-binding dyes. In the case of absolute quantification, f0% reduced CV%, variance, and absolute relative error by 1.66, 2.78, and 1.8 folds relative to CT; and by 1.65, 2.61, and 1.71 folds relative to LinRegPCR, respectively. While, regarding relative quantification, f0% reduced CV% by 1.76, 1.55, and 1.25 folds and variance by 3.13, 2.31, and 1.57 folds regarding CT, LinRegPCR, and Cy0, respectively. Finally, f0% reduced the absolute relative error caused by LinRegPCR by 1.83 folds. Conclusions: We recommend using the f0% method to analyze and report qPCR results based on its reported advantages. Finally, to simplify the usage of the f0% method, it was implemented in a macro-enabled Excel file with a user manual located on https://github.com/Mahmoud0Gamal/F0-perc/releases. [ABSTRACT FROM AUTHOR]

The need for water increases significantly due to the increase in temperatures and the decrease in water resources with climate change. In increasing agricultural production, along with other factors, it is undeniable that the share of irrigation is very large. The general practice in irrigation is to wet the root zone water content until it reaches field capacity after the irrigation time is determined. However, one of the most fundamental issues in planning a correct irrigation management is the determination and monitoring of soil water content. For this reason, in this study, it was aimed to determine the possibilities of determining and monitoring the soil water content of the plant root zone. The research was carried out in a greenhouse at Akdeniz University Faculty of Agriculture Research and Application Farm in Antalya, where greenhouse crop production is intense. In the study, the usability of the Profile Probe PR2 method, which is an indirect measurement method in determining the soil water content, was investigated. In the evaluation of the Profile Probe PR2 method, the gravimetric method, which is a direct measurement method for soil water content, was used as the basic tool. In the research, it was concluded that the soil water content can be determined quickly, practically and easily with the Profile Probe PR2 method. However, if it is preferred to monitor the soil water content with the Profile Probe PR2 method, it is also recommended to perform a detailed gravimetric calibration study for each soil condition. [ABSTRACT FROM AUTHOR]

This study presents a charged particle spectrometer with an aluminum range filter attached to a CR-39 detector that does not need a calibration curve of energy versus track diameter. A californium (Cf-252) radioactive source with an activity of 160 Bq was used to irradiate groups 1, 2, and 3 of CR-39 detectors, each of five samples, at alpha energies of 2.7, 3.86, and 5.11 MeV. Different alpha energies were achieved by changing the source–detector distance. Two aluminum range filters of thicknesses 17 and 11 μ m were used in conjunction with CR-39 detectors to separate alpha particles according to their energy. The thickness of the filter was determined by the SRIM 2013 version. The tracks in group 1 (bare CR-39) are for alpha particles with energies of 2.7, 3.86, and 5.11 MeV, while the tracks in group 2 (CR39 + 11 μ m filter) are for alpha particles with energies of 3.86 and 5.11 MeV. Alpha particles with an energy of 2.7 MeV are blocked by the 11- μ m Al filter. The filter in group 3 (17 μ m ) blocked alpha particles with energies of 2.7 and 3.86 MeV and allowed the alpha particles of 5.11 MeV to pass and produce tracks. Therefore, the average number of tracks in group 3 is for 5.11 MeV alpha particles. The tracks for alpha particles of energy 3.86 MeV can be found by subtracting the tracks in group 3 from those in group 2. The difference between the average number of tracks in groups 1 and 2 is for alpha particles with an energy of 2.7 MeV. The present method could determine the tracks from each different alpha energy without using the calibration curve of energy versus track diameter. [ABSTRACT FROM AUTHOR]

Objective: This study aimed to construct and validate a clinical prediction model for surgical site infection (SSI) risk 30 days after gastrointestinal surgery. Materials and Methods: This multicentre study involving 57 units conducted a 30-day postoperative follow-up of 17 353 patients who underwent gastrointestinal surgery at the unit from 1 March 2021 to 28 February 2022. The authors collected a series of hospitalisation data, including demographic data, preoperative preparation, intraoperative procedures and postoperative care. The main outcome variable was SSI, defined according to the Centres for Disease Control and Prevention guidelines. This study used the least absolute shrinkage and selection operator (LASSO) algorithm to screen predictive variables and construct a prediction model. The receiver operating characteristic curve, calibration and clinical decision curves were used to evaluate the prediction performance of the prediction model. Results: Overall, 17 353 patients were included in this study, and the incidence of SSI was 1.6%. The univariate analysis combined with LASSO analysis showed that 20 variables, namely, chronic liver disease, chronic kidney disease, steroid use, smoking history, C-reactive protein, blood urea nitrogen, creatinine, albumin, blood glucose, bowel preparation, surgical antibiotic prophylaxis, appendix surgery, colon surgery, approach, incision type, colostomy/ileostomy at the start of the surgery, colostomy/ileostomy at the end of the surgery, length of incision, surgical duration and blood loss were identified as predictors of SSI occurrence (P <0.05). The area under the curve values of the model in the train and test groups were 0.7778 and 0.7868, respectively. The calibration curve and Hosmer-Lemeshow test results demonstrated that the model-predicted and actual risks were in good agreement, and the model forecast accuracy was high. Conclusions: The risk assessment system constructed in this study has good differentiation, calibration and clinical benefits and can be used as a reference tool for predicting SSI risk in patients. [ABSTRACT FROM AUTHOR]

When wood is thermally modified, several chemical reactions take place that change the chemical and physical properties of the wood. These changes correlate with the degree of modification, which is mostly a function of the temperature and duration of modification, and consequently with the mass loss during this process. There is a lack of standardised quality control to verify the degree of heat treatment of wood and thus its quality. One of the possible methods to check the degree of thermal modification of a particular type of wood is thermogravimetry (TG). It is based on the assumption that processes that did not take place during thermal modification continue when the TG experiment is carried out. In this method, calibration curves have to be established based on TG measurements of standard samples that have been thermally modified at different temperatures and whose mass loss during modification is known. The calibration curves show the mass loss during the TG measurement as a function of the mass loss during the previous thermal modification. The course of thermal decomposition during the TG measurements is influenced by many parameters, such as the mass of the sample, the heating rate, the atmosphere in which the measurement takes place, and the shape of the crucible in which the sample is placed. In this paper, the influence of these parameters on the calibration curves was investigated. We have focused on oak wood. The best parameters result in a calibration curve with the largest correlation coefficient R2 and the highest slope of the line k. On this basis, we can determine the mass loss during the thermal modification of unknown samples of the same wood species under the same measurement conditions. [ABSTRACT FROM AUTHOR]

This research study focuses on the formulation and evaluation of an ACNE gel using Metronidazole (1%) and Doxycycline (0.5%) as active ingredients. Three types of polymers, Hydroxypropyl Methylcellulose (HPMC), Carbopol 940, and Carbopol 934 were used for gel formation. The formulated gels were evaluated for their physical characteristics, in vitro drug release, and drug-polymer compatibility. The calibration curves for both Metronidazole and Doxycycline displayed strong linearity within the concentration range of 5 to 25 μg/mL, facilitating accurate drug quantification. The gel formulations exhibited pH values of 6.0 (HPMC), 5.8 (Carbopol 940), and 5.7 (Carbopol 934), which are within the skin's natural pH range. The mean viscosity values were recorded as 205 cP (HPMC), 220 cP (Carbopol 940), and 231 cP (Carbopol 934). Spreadability and extrudability tests also yielded favorable results, suggesting ease of application. The in vitro drug release study showed a time-dependent increase in drug release, with the highest cumulative release observed from the HPMC formulation (90% at 8 hours), followed by Carbopol 940 (80% at 8 hours), and Carbopol 934 (64% at 8 hours). Fourier Transform Infrared Spectroscopy (FTIR) analysis revealed no significant interaction between the drugs and the polymers, indicating their compatibility. This study offers a promising approach to the development of effective ACNE gel formulations with Metronidazole and Doxycycline, while emphasizing the influence of the type of polymer on the drug release profile. [ABSTRACT FROM AUTHOR]

Abstract Backgroud The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. Methods A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). Results Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08–1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20–1.50, P

Crop diseases pose a major threat to agricultural production, quality, and sustainable development, highlighting the importance of early disease risk prediction for effective disease control. Tea anthracnose can easily occur in Yunnan under high-temperature and high-humidity environments, which seriously affects the ecosystem of tea gardens. Therefore, the establishment of accurate, non-destructive, and rapid prediction models has a positive impact on the conservation of biodiversity in tea plantations. Because of the linear relationship between disease occurrence and environmental conditions, the growing environmental conditions can be effectively used to predict crop diseases. Based on the climate data collected by Internet of Things devices, this study uses LASSO-COX-NOMOGRAM to analyze the expression of tea anthracrum to different degrees through Limma difference analysis, and it combines Cox single-factor analysis to study the influence mechanism of climate and environmental change on tea anthracrum. Modeling factors were screened by LASSO regression, 10-fold cross-validation and Cox multi-factor analysis were used to establish the basis of the model, the nomogram prediction model was constructed, and a Shiny- and DynNOM-visualized prediction system was built. The experimental results showed that the AUC values of the model were 0.745 and 0.731 in the training set and 0.75 and 0.747 in the verification set, respectively, when the predicted change in tea anthracnose disease risk was greater than 30% and 60%, and the calibration curve was in good agreement with the ideal curve. The accuracy of external verification was 83.3% for predicting tea anthracnose of different degrees. At the same time, compared with the traditional prediction method, the method is not affected by the difference in leaf background, which provides research potential for early prevention and phenotypic analysis, and also provides an effective means for tea disease identification and harm analysis.

It is shown recently that the thermal neutron field of Isfahan MNSR with stable flux and dose equivalent rate can be used as a calibration field in dosimetry. The fundamental limit of this field is not the feasibility of using phantom for the irradiation of personal dosimeters. This issue leads to underestimating the personal dose-equivalents from the true values. The subject of this work is to correct the calibration curve of TLD-600 dosimeters irradiated without phantom. To do this, Monte Carlo simulations using Geant4 are carried out and absorbed doses in this dosimeter for irradiation with and without phantom are calculated. Then, the ratio of these doses as a correction factor applies to the responses measured without phantom, and then the corrected calibration curve is determined. As an analytical approach, the correction factor is considered as the ratio of the mean number of thermal neutron reflections between air and water (with phantom) and in the air alone (without phantom). Results obtained show that the correction factors determined by the simulation and analytical methods are 1.57 and 1.44, respectively, which agree well with the 8% difference. Finally, the response correction has led to changing the calibration factor of TLD-600 dosimeters from 0.0012 to 0.0008.

Background: Delayed encephalopathy after acute carbon monoxide poisoning (DEACMP) is a severe complication that can arise from acute carbon monoxide poisoning (ACOP). This study aims to identify the independent risk factors associated with DEACMP and to develop a nomogram to predict the probability of developing DEACMP. Methods: The data of patients diagnosed with ACOP between September 2015 and June 2021 were analyzed retrospectively. The patients were divided into the two groups: the DEACMP group and the non-DEACMP group. Univariate analysis and multivariate logistic regression analysis were conducted to identify the independent risk factors for DEACMP. Subsequently, a nomogram was constructed to predict the probability of DEACMP. Results: The study included 122 patients, out of whom 30 (24.6%) developed DEACMP. The multivariate logistic regression analysis revealed that acute high-signal lesions on diffusion-weighted imaging (DWI), duration of carbon monoxide (CO) exposure, and Glasgow Coma Scale (GCS) score were independent risk factors for DEACMP (Odds Ratio = 6.230, 1.323, 0.714, p < 0.05). Based on these indicators, a predictive nomogram was constructed. Conclusions: This study constructed a nomogram for predicting DEACMP using high-signal lesions on DWI and clinical indicators. The nomogram may serve as a dependable tool to differentiate high-risk patients and enable the provision of personalized treatment to lower the incidence of DEACMP. [ABSTRACT FROM AUTHOR]

Backgroud: The current diagnostic criteria for refractory Mycoplasma pneumoniae pneumonia (RMPP) among Mycoplasma pneumoniae Pneumonia (MPP) are insufficient for early identification, and potentially delayed appropriate treatment. This study aimed to develop an effective individualized diagnostic prediction nomogram for pediatric RMPP. Methods: A total of 517 hospitalized children with MPP, including 131 with RMPP and 386 without RMPP (non-RMPP), treated at Lianyungang Maternal and Child Health Care Hospital from January 2018 to December 2021 were retrospectively enrolled as a development (modeling) cohort to construct an RMPP prediction nomogram. Additionally, 322 pediatric patients with MPP (64 with RMPP and 258 with non-RMPP, who were treated at the Affiliated Hospital of Xuzhou Medical University from June 2020 to May 2022 were retrospectively enrolled as a validation cohort to assess the prediction accuracy of model. Univariable and multivariable logistic regression analyses were used to identify RMPP risk factors among patients with MPP. Nomogram were generated based on these risk factors using the rms package of R, and the predictive performance was evaluated based on receiver operating characteristic (ROC) curves and using decision curve analysis (DCA). Results: Multivariate analysis revealed five significant independent predictors of RMPP among patients with MPP: age (hazard ratio [HR] 1.16, 95% confidence interval [CI] 1.08–1.33, P = 0.038), fever duration (HR 1.34, 95%CI 1.20–1.50, P < 0.001), lymphocyte count (HR 0.45, 95%CI 0.23–0.89, P = 0.021), serum D-dimer (D-d) level (HR 1.70, 95%CI 1.16–2.49, P = 0.006), and pulmonary imaging score (HR 5.16, 95%CI 2.38–11.21, P < 0.001). The area under the ROC curve was 90.7% for the development cohort and 96.36% for the validation cohort. The internal and external verification calibration curves were almost linear with slopes of 1, and the DCA curve revealed a net benefit with the final predictive nomogram. Conclusion: This study proposes a predictive nomogram only based on five variables. The nomogram can be used for early identification of RMPP among pediatric patients with MPP, thereby facilitating more timely and effective intervention. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent nondestructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r² (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved. [ABSTRACT FROM AUTHOR]

The diamond electrodes-compacts with extreme high boron-doping level, and those added with platinum, were prepared at a pressure of 8–9 GPa and temperature ~2500 К from В–С, Pt–C, and Pt–C–B growth mixtures. They are highly electroactive in the reaction of ethylenediaminetetraacetic acid electrooxidation. The oxidation current is a linear function of the ethylenediaminetetraacetic acid concentration over the 0.001–0.05 g-eq/L range. This system can be basic to an electroanalytical method for the ethylenediaminetetraacetic acid determination. [ABSTRACT FROM AUTHOR]

UV-visible (UV-Vis) spectroscopy is a powerful instrument for qualitative investigation and quantitative detection of pollutants in water. UV-Vis spectrophotometry is an analytical method using the concept of transmission of light in UV and Visible wavenumber. Generally, compounds can be identified using UV-Vis Spectrophotometry, based on the concept of light absorption, specifically for compounds with a chromophore group and an auxochrome group. Although the utilization of UV-Vis spectrum analysis has been welldocumented, no information regarding detailed step-by-step measurement for examining detailed quantitative analysis, particularly in determining the concentration of an analyte in an aqueous solution sample. Here, this study explores the idea and application of UV-Vis technology in water quality detection, including guidelines for determining the concentration of the sample in an aqueous solution. To support the analysis, we also added practical examples for understanding concentration during the organic decomposition. This paper is intended to be useful for researchers and students in understanding UV-Vis spectrophotometry when analyzing chemical composition during chemical decomposition, photocatalysis, phytoremediation, and adsorption analysis. [ABSTRACT FROM AUTHOR]

The effect of the direct current (DC) heating of ferromagnetic amorphous wires (AWs) on the electromagnetic properties of the Co–Fe–Cr–Si–B serial Co-based alloy is investigated. The Ulitovsky–Taylor method was used to produce AWs with diameters of D = 40, 70, 82, 98, 123, and 145 μm in a glass shell, which are then removed mechanically by elastic bending on a laboratory bench. The developed method of in situ stepwise cyclic DC heating of AWs of various diameters makes it possible to choose the optimal values of current Ia for attaining selected modes that maximize the magnetic properties. Electromagnetic properties of AWs were monitored by recording the amplitude of the intrinsic electromagnetic signal of electromotive force (EMF) in the AW sample under study. Since reliable direct measurements of the AW temperature (Ta) are impossible when an electrical current (Ia) is passed, an indirect method is used for estimating the heating temperature of the AW by establishing the relationship Ta = f (Ia). For the specified AW diameters, direct current values that ensure optimal heat treatment in the range of temperature stability of the amorphous phase up to crystallization temperature Tx are experimentally determined. A calibration curve was constructed using reference temperature points, i.e., the crystallization temperature (Tx) and the Curie temperature (TC). The technique proposed for constructing calibration curve I = f (D) may be used for different AWs that have the same composition, but different diameters; it allows one to determine the current annealing modes required to achieve the best EMF amplitude values in amorphous wires with the given diameter up to 100 μm. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been applied recently to develop prognostic classification models that can be used in individual cancer patients to forecast outcomes. Here, four different ML algorithms were built to predict survival rate of lung cancer patients using 1600 metadata records. Of note, the generated models were validated using test set and external validation data set consisting of 400 patient records each together with 10-fold cross-validation technique. The extratree classifier algorithm was employed to identify the influential descriptors for patients survival after incidence of metachronous second primary lung cancer. The models were assessed using five different performance metrices. The results from our study highlight that logistic regression model with all features and important features achieved an accuracy of 94% and 96%, respectively, for stratifying the survival status of lung cancer patients. On the other hand, logistic regression also outperformed external validation with an accuracy of 85%. Indeed, the results from our study will provide meaningful insights for the treatment and management of large community of lung cancer patients. [ABSTRACT FROM AUTHOR]

In this paper, we discuss the theory behind calibration curve experiments and their application to a zinc (Zn) bioavailability study with broiler chickens. Seven replicates of 16 male commercial broiler chicks were fed starter diets for 14 days. Six diets had different levels of a potential Zn source and one was a positive control with standard industry levels of Zn for comparison. Four commonly used methods of calculating bioavailability means and confidence intervals (CI) from a calibration curve (standard curve) experiment to estimate the bioavailability of a new zinc source in broiler chickens were compared. The methods compared were the following: 1) the Counter-Intuitive Method uses a multiple-range test to compare unknown test and standard samples; 2) the Intuitive Method uses standard linear regression and inverts the equation to predict Zn bioavailability for each replicate of test samples; 3) the Abductive Method uses Graybill's Equation, based on theory and observation, to estimate CI's; and 4) the Sophistic Method uses reverse regression, and calculates Zn bioavailability values directly from the equation. The Counter-Intuitive Method only gives information about which standards the test samples are, or are not, significantly different from respectively (average available Zn not predicted). The Intuitive Method ignores error about the standard curve and theoretically cannot estimate the CI directly (X¯±SEM = 107.5 ± 15.8 mg Zn/kg). The Sophistic Method underestimates and overestimates the test sample mean values above and below the mean of the standards, respectively (X¯ = 96.6 mg Zn/kg). The Abductive Method has an advantage over the other methods: The mean prediction estimation is consistent with theory (107.5 ± 6.1 mg Zn/kg; X¯±SEM). When test or “unknown” samples are near the mean of the standard samples, the CI is smaller than when near the extremes of the calibration curve. When calibration curve error is small (R2 > approximately 0.95), there is little advantage to using the Abductive Method, but when calibration curve error is larger, as in many bioassays with growing animals, the Abductive Method improves the accuracy of the CI calculations. The Abductive Method was used to demonstrate the influence of the number of replicate samples on experimental power and cost.

Detection of traces of impurities/elements present in liquid samples using laser-induced breakdown spectroscopy (LIBS) is challenging because the signal intensity is weaker than in the case of solid samples. The present paper deals with the optimization of experimental parameters for different phases of a liquid sample and the improvement of the limit of detection (LOD) in these LIBS experimental setups. LIBS spectra of chromium in the liquid sample have been recorded in three different configurations [laminar flow, i.e., liquid flowing through a small jet, frozen liquid (ice), and liquid deposited on a filter paper]. Experimental conditions for different phases were optimized to get a better signal-to-noise (S/N) ratio and signal-to-background ratio (S/B) in the LIBS spectra. The best S/N and S/B ratio is observed when LIBS spectra is recorded for the liquid deposited on the filter paper configuration. The spectral intensity of Cr is enhanced several folds (36 times) in the LIBS spectra recorded in the filter paper configuration as compared to the laminar flow. The calibration curve method is used to measure the LOD for three different configurations. The better LOD (9.7 ppm for 357.8 nm Cr line) is observed in the case of deposited liquid on the filter paper than the other liquid phase (laminar, 85.5 ppm and ice, 63.7 ppm) configurations. [ABSTRACT FROM AUTHOR]

Automated high-throughput plant phenotyping (HTPP) enables non-invasive, fast and standardized evaluations of a large number of plants for size, development, and certain physiological variables. Many research groups recognize the potential of HTPP and have made significant investments in HTPP infrastructure, or are considering doing so. To make optimal use of limited resources, it is important to plan and use these facilities prudently and to interpret the results carefully. Here we present a number of points that users should consider before purchasing, building or utilizing such equipment. They relate to (1) the financial and time investment for acquisition, operation, and maintenance, (2) the constraints associated with such machines in terms of flexibility and growth conditions, (3) the pros and cons of frequent non-destructive measurements, (4) the level of information provided by proxy traits, and (5) the utilization of calibration curves. Using data from an Arabidopsis experiment, we demonstrate how diurnal changes in leaf angle can impact plant size estimates from top-view cameras, causing deviations of more than 20% over the day. Growth analysis data from another rosette species showed that there was a curvilinear relationship between total and projected leaf area. Neglecting this curvilinearity resulted in linear calibration curves that, although having a high r2 (> 0.92), also exhibited large relative errors. Another important consideration we discussed is the frequency at which calibration curves need to be generated and whether different treatments, seasons, or genotypes require distinct calibration curves. In conclusion, HTPP systems have become a valuable addition to the toolbox of plant biologists, provided that these systems are tailored to the research questions of interest, and users are aware of both the possible pitfalls and potential involved.

Sulfur hexafluoride (SF6), which survives in the atmosphere for an extremely long period of time, is the most potent greenhouse gas regulated under the Kyoto Protocol. So, the accurate monitoring of atmospheric SF6 plays an important role in the study of the control policies for reducing greenhouse gas emissions. The instruments for SF6 measurement are typically calibrated using certified reference materials. The concentrations of the commercially available SF6 reference materials usually have a broad range, from 1 μmol/mol to 6000 μmol/mol. Some characteristics including sensitivity, linear range, relative standard deviation, and accuracy are crucial for the determination of SF6 in such a broad concentration range. Therefore, the selection of a proper detector for the accurate determination of SF6 with such a broad range is extremely important to establish a gas chromatography (GC) method for developing SF6 reference materials. In this paper, several typical GC methods with different detectors, including a thermal conductivity detector (TCD), a pulsed discharge helium ionization detector (PDHID), and a flame photometric detector (FPD), were carefully established for the accurate determination of SF6 with different concentrations. The results show that an FPD detector has a relatively narrow linearity range, thus a quadratic equation should be established for building a calibration curve. The PDHID and TCD have good linearity with coefficients of 1.0000 in the concentration range of 10–100 μmol/mol (using a PDHID), and 100–1000 μmol/mol (using a TCD), respectively. Further considering the measurement errors of indication results, the PDHID is suitable for SF6 measurement when the concentrations are below 100 μmol/mol, whereas the TCD is suitable for SF6 measurement when the concentrations are over 100 μmol/mol. These results provide useful guidance in choosing an appropriate GC detector for the accurate determination of SF6, which are especially very helpful for developing SF6 reference materials.

This article presents measurements of the radiocarbon (14C) concentration in sub-annual tree rings. Samples of earlywood (EW) and latewood (LW) from dendrochronologically dated tree rings (English oak, Quercus robur) from Kujawy, near Kraków (Poland), spanning the years of 990–997 CE, are extracted and their 14C content is measured at the Center for Applied Isotope Studies at the University of Georgia, USA. The EW and LW data show a gradual increase in the Δ14C values between 991–995 CE, which are similar to those observed by Rakowski et al. (2018). An increase of 10.3 ± 2.6‰ in Δ14C for the EW data, and 8.6 ± 2.6‰ for the LW data has been recorded for this period. Using this data, it is possible to estimate the time period for when a major historical event occurred, which seems to have been in the late summer (September –2/+1 month) of 993 CE. [ABSTRACT FROM AUTHOR]

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This study proposes an innovative configuration for thickness measurement based on X-ray transmission, intending to improve the precision of measuring thin aluminum sheets. In this configuration, an activation sample containing Zr, Sb, and Ba elements is irradiated by 59.54 keV gamma rays emitted from three 241Am radioactive sources with a total activity of 1.78 GBq. Subsequently, the activation sample emits fluorescent X-rays at energy levels of 15.78 keV (Zr-K α1,2), 17.67 keV (Zr-K β1), 26.36 keV (Sb-K α1,2), 29.73 keV (Sb-K β1), 32.2 keV (Ba-K α1,2), and 36.38 keV (Ba-K β1). These X-rays are collimated into a narrow beam, which then penetrates through an absorbing sample, and is ultimately recorded by a Si(Li) detector. Two different approaches are investigated to determine the thickness of absorbing samples including Calibration Curve Fitting (CCF) and Artificial Neural Network (ANN). The CCF approach requires constructing linear calibration curves for establishing the relationship between lnR (R is the ratio of the peak areas in measurements with and without absorbing sample) and the thickness of the absorbing sample at each X-ray energy level. The sample thickness is then determined by calculating a weighted average of the measured thicknesses associated with all analyzed energy levels. This approach requires in-depth understanding of radiation physics and proficiency in X-ray spectrum analysis. Meanwhile, the ANN approach uses raw spectra obtained by the Si(Li) detector to predict the thickness of aluminum sheets, facilitating analysis without requiring human intervention. The reliability of these approaches is evaluated through experimental measurements on aluminum sheets with thicknesses ranging from 0.064 cm to 1.074 cm. The results indicate that using X-rays with many different energies leads to superior accuracy in thickness measurements compared to using X-rays with a single energy. Besides, both the CCF and ANN approaches yield relative deviations of less than 3% between the predicted and reference thicknesses. It is important to emphasize that the ANN approach represents a promising solution for automated analysis without the intervention of experts. [ABSTRACT FROM AUTHOR]

Radiocarbon ages must be calibrated due to the remarkable fluctuations of the atmospheric radiocarbon level. The traditional method (e.g., Calib) does not make use of any constraint such as the temporal/stratigraphical ordering of the ages, thereby resulting in one or several large age ranges. Bayesian age modeling is advantageous over the traditional method in several aspects. First, it can provide precise age estimates by applying some constraints known a priori. Second, it may provide a timing of an archaeological feature or a geological event that is unable to be dated directly. Although several Bayesian age modeling frameworks have been developed, inexperienced users may need not only a more user-friendly environment for data entry and definition of their project-specific problem, but also a powerful post-processing tool for analyzing and visualizing the results. Here a hierarchical Bayesian model with a minimum level of structural complexity is presented. It provides users with a flexible and powerful framework to incorporate radiocarbon ages into a sequence along a one-dimensional continuum so that it best reveals their temporal order, thereby yielding a more precise timing. The accompanying Matlab software package not only complements the existing MatCal package designed to calibrate radiocarbon ages individually, but also serves as an alternative to the online tools of Bayesian radiocarbon age modeling such as OxCal and BCal.

The electrochemical analysis of valaciclovir (VAL) was carried out by electro-deposition of manganese oxide nanoparticles on glassy carbon electrode (MnO2/GCE). The surface morphology of prepared sensor material was analysed by scanning electron microscopy (SEM). Effects of scan rate, solution pH, and concentration variation on the electrochemical behaviour of VAL were studied using cyclic voltammetry (CV) and differential pulse voltammetry (DPV). The increase in anodic current by the use of a modified electrode (5.76 μA) compared with the peak current of bare GCE (1.18 μA) confirms the enhancement of the surface area and catalytic property of manganese oxide nanoparticles. A linear response in the concentration of VAL was observed by DPV from 1.0 × 10−8 M to 14.0 × 10−6 M at the sensor. The limit of detection and quantification were found to be 0.95 nM and 3.17 nM, respectively. The real-world application of the sensor was validated by applying for pharmaceutical and real sample analysis. Thus, it has been proved that the proposed electrochemical sensor has a strong potential to be employed in quality control laboratories for the testing of pharmaceutical products.

Glycans are involved in many fundamental cellular processes such as growth, differentiation, and morphogenesis. However, their broad structural diversity makes analysis difficult. Glycomics via mass spectrometry has focused on the composition of glycans, but informatics analysis has not kept pace with the development of instrumentation and measurement techniques. We developed Toolbox Accelerating Glycomics (TAG), in which glycans can be added manually to the glycan list that can be freely designed with labels and sialic acid modifications, and fast processing is possible. In the present work, we improved TAG for large-scale analysis such as cohort analysis of serum samples. The sialic acid linkage-specific alkylamidation (SALSA) method converts differences in linkages such as α2,3- and α2,6-linkages of sialic acids into differences in mass. Glycans modified by SALSA and several structures discovered in recent years were added to the glycan list. A routine to generate calibration curves has been implemented to explore quantitation. These improvements are based on redefinitions of residues and glycans in the TAG List to incorporate information on glycans that could not be attributed because it was not assumed in the previous version of TAG. These functions were verified through analysis of purchased sera and 74 spectra with linearity at the level of R2 > 0.8 with 81 estimated glycan structures obtained including some candidate of rare glycans such as those with the N,N'-diacetyllactosediamine structure, suggesting they can be applied to large-scale analyses. [ABSTRACT FROM AUTHOR]

The antibiotic drug trimethoprim (TMP) is used to treat bacterial infections in humans and animals, and frequently TMP is used along with sulfonamides. However, a large portion of TMP is excreted in its active state, which poses a severe problem to humans and the environment. A sensitive, rapid, cost-effective analytical tool is required to monitor the TMP concentration in biological and environmental samples. Hence, this study proposed an analytical methodology to analyze TMP in clinical, biological and environmental samples. The investigations were carried out using a glucose-modified carbon paste electrode (G-CPE) employing voltammetric techniques. Electrochemical behavior was examined with 0.5 mM TMP solution at optimum pH 3.4 (Phosphate Buffer Solution, I = 0.2 M). The influence of scan rate on the electro-oxidation of TMP was studied within the range of 0.05 to 0.55 V/s. The effect of pH and scan rate variations revealed proton transfer during oxidation. Moreover, diffusion phenomena governed the irreversibility of the electrode reaction. A probable and suitable electrode interaction and reaction mechanism was proposed for the electrochemical oxidation of TMP. Further, the TMP was quantitatively estimated with the differential pulse voltammetry (DPV) technique in the concentration range from 9.0 × 10−7 to 1.0 × 10−4 M. The tablet, spiked water and urine analysis demonstrated that the selected method and developed electrode were rapid, simple, sensitive, and cost-effective. [ABSTRACT FROM AUTHOR]