Estimation Of Fraction Research Articles

Applying Polarized Light to the Estimation of Bainitic Fraction in Kh2G2S2MF Steel Grades. Part 2

Applying Polarized Light to the Estimation of Bainitic Fraction in Kh2G2S2MF Steel Grades. Part 2

Estimation of the mass fraction and distribution of ice in a lunar regolith simulant from seismic velocity

The exploration of ice distribution on the Moon and in other extraterrestrial environments has recently accelerated because of the growing interest in the distribution of ice in, and its acquisition from, the Moon's polar regions. Understanding of the relationship between the mass fraction of ice and the velocities of P-wave and S-wave is expected to allow ice amounts to be estimated from seismic exploration results. To clarify this relationship, we numerically investigated the effect of ice on elastic wave velocities using a digital rock physics approach for regolith with heterogeneous pore geometries. We digitized a lunar regolith simulant and modeled ice distribution in its pore spaces. Numerical simulations employing a finite element method were then performed to calculate the P-wave and S-wave velocities of the regolith simulant while varying the ice particle size and the mass fraction of ice. The results demonstrate that the characteristics of the velocity changes with increases in the mass fraction of ice differ depending on the particle size distribution of the ice. For the same mass fraction of ice, P-wave and S-wave velocities are higher for smaller ice clusters (up to 1.75 times for P-wave velocity and 1.76 times for S-wave velocity). Using the numerical simulation results, we succeeded in developing an empirical equation for estimating the mass fraction of ice from P-wave and S-wave velocities. This equation should be useful for quantifying ice content from lunar seismic survey data.

X-Ray Properties of Optically Variable Low-mass AGN Candidates

We present an X-ray analysis of 14 nearby (z < 0.044) active galactic nuclei (AGNs) in low-mass galaxies (M * ≲ 5 × 109 M ⊙) selected based on their optical variability. Comparing and contrasting different AGN selection techniques in low-mass galaxies is essential for obtaining an accurate estimate of the active fraction in this regime. We use both new and archival observations from the Chandra X-ray Observatory to search for X-ray point sources consistent with AGNs. Four objects have detected nuclear X-ray emission with luminosities ranging from L 0.5−7 ≈ 3 × 1040 to 9 × 1042 erg s−1 with two more marginal detections. All of the detected galaxies have luminosities exceeding those anticipated from X-ray binaries, and all sources are nuclear, suggesting the X-ray emission in most sources is due to an AGN. These observations demonstrate the success of variability at identifying AGNs in low-mass galaxies. We also explore emission-line diagnostics and discuss the differences in the results of these methods for AGN selection, in particular regarding low-mass and low-metallicity systems.

Information fraction estimation: Strategies for a phase 3 non-inferiority maximum duration design with time to event outcome.

There is considerable debate surrounding the choice of methods to estimate information fraction for futility monitoring in a randomized non-inferiority maximum duration trial. This question was motivated by a pediatric oncology study that aimed to establish non-inferiority for two primary outcomes. While non-inferiority was determined for one outcome, the futility monitoring of the other outcome failed to stop the trial early, despite accumulating evidence of inferiority. For a one-sided trial design for which the intervention is inferior to the standard therapy, futility monitoring should provide the opportunity to terminate the trial early. Our research focuses on the Total Control Only (TCO) method, which is defined as a ratio of observed events to total events exclusively within the standard treatment regimen. We investigate its properties in stopping a trial early in favor of inferiority. Simulation results comparing the TCO method with alternative methods, one based on the assumption of an inferior treatment effect (TH0), and the other based on a specified hypothesis of a non-inferior treatment effect (THA), were provided under various pediatric oncology trial design settings. The TCO method is the only method that provides unbiased information fraction estimates regardless of the hypothesis assumptions and exhibits a good power and a comparable type I error rate at each interim analysis compared to other methods. Although none of the methods is uniformly superior on all criteria, the TCO method possesses favorable characteristics, making it a compelling choice for estimating the information fraction when the aim is to reduce cancer treatment-related adverse outcomes.

Sea ice surface type classification of ICESat-2 ATL07 data by using data-driven machine learning model: Ross Sea, Antarctic as an example

The ICESat-2 (IS2) ATL07 sea ice height product provides precise and accurate measurements of sea ice height for the polar regions. Although the original ATL07 product classifies the sea ice surface into snow-covered ice and open water (dark leads and specular leads), it has two critical issues: (1) it does not distinguish thin ice (gray ice) from thick/snow-covered ice or open water and (2) it involves uncertainties in the dark lead determination. To address these issues and obtain more accurate lead fraction and freeboard estimation, in this study, we assess a data-driven machine learning approach for sea ice surface type classification from the ATL07 product. A total of 17 IS2 tracks covering the Ross Sea in February, March, September, October, and November 2019 are used in this study. For training and testing the neural network (NN) models, we label the ATL07 data into three surface types: thick/snow-covered sea ice, thin/gray sea ice, and open water based on the coincident Sentinel-2 optical images. We use six variables from the ATL07 data: background rate, photon rate, relative surface height, width of height distribution, number of laser pulses, and difference in mean and median height, with the first three variables found to be the most significant in determining surface types. Our spatiotemporal test shows that the NN models can be applied to the ATL07 dataset with ∼99% accuracy in the Ross Sea, and the beam test shows that the difference in the accuracy for three beams is negligible. While the current ATL07 product captures only ∼5% of thin ice and ∼ 81% of open water correctly, our NN models capture ∼90% of both open water and thin ice leads successfully. When we qualitatively assess the surface classification of the NN models by using the coincident optical and radar images, our NN models do not show significant misclassification issues both in the daytime and nighttime and both in the winter and summer seasons. Compared to the IS2 ATL10 sea ice freeboard product, the ability of our NN model to separate thin ice and open water from snow-covered/thick ice is significant in freeboard estimation, especially for winter and highly packed sea ice areas. Open water and thin ice classes also improve mapping lead fraction and floe size distribution.

Bounding the unknowns of martian crustal heat flow from a synthesis of regional geochemistry and InSight mission data

The thermal evolution of terrestrial planets is primarily modulated by the distribution of heat-producing elements (HPE) within the crust and mantle. Chemical data from Martian meteorites suggest that Mars differentiated early, which led to an early partitioning of incompatible heat-producing elements in the crust. Previous estimates of Martian crustal heat flow that used the bulk regolith abundances of HPEs from the Gamma-Ray Spectrometer (GRS) suite on Mars Odyssey spacecraft have further corroborated this view of Mars, albeit with poorly known crustal column representativeness. Here we couple the GRS-derived chemical maps of Mars with estimates of crustal thickness and density from the InSight lander to revise the estimated Martian crustal heat flow. The mean crustal heat flow values range from 3.0 to 13.9 mW m−2 for the endmember gravity-derived crustal thickness models anchored by constraints from InSight. We also estimate the crustal heat flow from other factors such as the distribution of HPEs with depth and the Uranium content of the crust. Our results suggest that the mean crustal heat flow varies substantially across models, with the highest mean values being associated with higher densities and an increased enrichment of HPEs with depth. Further work is needed to constrain the crustal thickness of Mars, as the largest uncertainties in the estimate of crustal heat flow stem from uncertainty in the crustal thickness estimates, not geochemical variability. The results from this work corroborate previous estimates of a strong fractionation of heat-producing elements into the Martian crust.

Large sex differences in vulnerability to circulatory-system disease under current and future climate in Bucharest and its rural surroundings

Circulatory-system diseases (CSDs) are responsible for 50–60% of all deaths in Romania. Due to its continental climate, with cold winters and very warm summers, there is a strong temperature dependence of the CSD mortality. Additionally, within its capital Bucharest, the urban heat island (UHI) is expected to enhance (reduce) heat (cold)-related mortality. Using distributed lag non-linear models, we establish the relation between temperature and CSD mortality in Bucharest and its surroundings. A striking finding is the strong temperature-related response to high urban temperatures of women in comparison with men from the total CSDs mortality. In the present climate, estimates of the CSDs attributable fraction (AF) of mortality at high temperatures is about 66% higher in Bucharest than in its rural surroundings for men, while it is about 100% times higher for women. Additionally, the AF in urban areas is also significantly higher for elderly people, and for those with hypertensive and cerebrovascular diseases than in the rural surroundings. On the other hand, in rural areas, men but especially women are currently more vulnerable with respect to low temperatures than in the urban environment. In order to project future thermal-related mortality, we have used five bias-corrected climate projections from regional circulation models under two climate-change scenarios, RCP4.5 and RCP8.5. Analysis of the temperature-mortality associations for future climate reveals the strongest signal under the scenario RCP8.5 for women, elderly people as well as for groups with hypertensive and cerebrovascular diseases. The net AF increase is much larger in urban agglomeration for women (8.2 times higher than in rural surroundings) and elderly people (8.5 times higher than in rural surroundings). However, our estimates of thermal attributable mortality are most likely underestimated due to the poor representation of UHI and future demography.

The influence of water droplet packing on crude oil emulsion.

To assure a smooth and cost-efficient flow of crude oil emulsion from wells to a production facility, the oil industry relies heavily on the prediction of viscosity in pipe. The physical expression of this viscosity depends on a subjective estimate of a maximum packing volume fraction (compacity), ranging between 58 and 74 vol%. This inaccurate practice can lead to catastrophic loss of pump efficiency. Two new concepts were defined to describe the emulsion: its compacity; and the occupancy of water droplets at the oil-water interface. This development leads to a better understanding of the formation and disappearance of a suspension, and can assist in building a reliable phenomenological model of the sedimentation and coalescence of an emulsion. Theoretical and experimental approaches were conducted to investigate the packing of water droplets in emulsions. A 3D packing model was developed to explain the observations made during emulsification experiments. It was found that below a water volume fraction of 34 vol%, water droplets settle, under the effect of gravity, in a loose-packed zone; and then sediment in a dense-packed zone (DPZ). The DPZ exists between a water volume fraction of 34vol% and 60vol%. The maximum compacity is the upper limit of this zone; and has a value of 60.46%. Knowing this objective value, other parameters affecting the viscosity can be better studied.

Entropy generation on MHD flow of Williamson hybrid nanofluid over a permeable curved stretching/shrinking sheet with various radiations

The research aims to scrutinize the significance of MHD, radiation, Joule heating, porous medium, and heat generation impacts on the convective boundary flow. It contains blood as the base fluid, silver (Ag), and aluminum oxide (Al2O3 ) as the nanoparticles over a curved stretching and shrinking sheet. To investigate the rheology of blood, we used a Williamson fluid model with comparisons for both curved stretching/shrinking sheets for velocity and a comparison between linear and nonlinear thermal radiation for temperature, entropy production, and Bejan number. This model is used to study physiological systems, surgical procedures, and nano-pharmacological delivery systems. The governing nonlinear coupled PDEs are transformed into nonlinear associated ODEs via the similarity transformation in the Maple software solver using the Homotopy Perturbation Method. The Homotopy Perturbation Method (HPM) results are compared with the Numerical Method (NM) results, where HPM gives reliable results. The results and discussion provide graphical results with several parameters for velocity, temperature, entropy production, Bejan number, Nusselt number, and skin friction. As the magnetic field parameter (M) increases, the velocity profile decreases for curved stretching sheet. We observe the opposite behavior of the velocity profile for a curved shrinking sheet. As the volume fractions increase, the temperature profile increases for linear and non-linear thermal radiation. The temperature profile rises due to the thermal conductivity of nanoparticles, which is amplified by estimations of the nanoparticle volume fractions.

Prediction of the Time to Reach Equilibrium for Improved Estimation of the Unbound Fraction of Compounds in Equilibrium Dialysis Using kinetic Modeling

Equilibrium dialysis (ED) is widely used in pharmacokinetics to determine the fraction of unbound (fu) compounds in plasma; however, the kinetics of drugs in the ED system with respect to their permeation across semi-permeable membranes has not been systemically studied. Here, the kinetics of the ED system, including the binding of drugs to plasma proteins, non-specific binding, and permeation across the membrane, was described to enable verification of the equilibrium, prediction of the time to reach equilibrium, and estimations of fu with data obtained during pre-equilibrium. Using data obtained during pre-equilibrium, the time to reach 90% equilibrium (t90%) and fu were estimated with reasonable accuracy. Notably, fu could be estimated reasonably well using one-time-point data for the calculation. Furthermore, the current modeling approach allowed concurrent estimations of fu and the decomposition rate of compounds that were metabolically unstable in the plasma. Reasonable metabolic rate constants were determined for cefadroxil and diltiazem, demonstrating the practicality of this method for determining kinetics related to fu characterization. Because the determination of fu of compounds with ‘unfavorable’ physicochemical properties is known to be experimentally challenging, the current method may be useful in determining the fu of compounds in vitro.

Semi-Supervised Deep Regression with Uncertainty Consistency and Variational Model Ensembling via Bayesian Neural Networks

Deep regression is an important problem with numerous applications. These range from computer vision tasks such as age estimation from photographs, to medical tasks such as ejection fraction estimation from echocardiograms for disease tracking. Semi-supervised approaches for deep regression are notably under-explored compared to classification and segmentation tasks, however. Unlike classification tasks, which rely on thresholding functions for generating class pseudo-labels, regression tasks use real number target predictions directly as pseudo-labels, making them more sensitive to prediction quality. In this work, we propose a novel approach to semi-supervised regression, namely Uncertainty-Consistent Variational Model Ensembling (UCVME), which improves training by generating high-quality pseudo-labels and uncertainty estimates for heteroscedastic regression. Given that aleatoric uncertainty is only dependent on input data by definition and should be equal for the same inputs, we present a novel uncertainty consistency loss for co-trained models. Our consistency loss significantly improves uncertainty estimates and allows higher quality pseudo-labels to be assigned greater importance under heteroscedastic regression. Furthermore, we introduce a novel variational model ensembling approach to reduce prediction noise and generate more robust pseudo-labels. We analytically show our method generates higher quality targets for unlabeled data and further improves training. Experiments show that our method outperforms state-of-the-art alternatives on different tasks and can be competitive with supervised methods that use full labels. Code is available at https://github.com/xmed-lab/UCVME.

Ejection Fraction Estimation from Echocardiograms Using Optimal Left Ventricle Feature Extraction Based on Clinical Methods.

Echocardiography is one of the imaging systems most often utilized for assessing heart anatomy and function. Left ventricle ejection fraction (LVEF) is an important clinical variable assessed from echocardiography via the measurement of left ventricle (LV) parameters. Significant inter-observer and intra-observer variability is seen when LVEF is quantified by cardiologists using huge echocardiography data. Machine learning algorithms have the capability to analyze such extensive datasets and identify intricate patterns of structure and function of the heart that highly skilled observers might overlook, hence paving the way for computer-assisted diagnostics in this field. In this study, LV segmentation is performed on echocardiogram data followed by feature extraction from the left ventricle based on clinical methods. The extracted features are then subjected to analysis using both neural networks and traditional machine learning algorithms to estimate the LVEF. The results indicate that employing machine learning techniques on the extracted features from the left ventricle leads to higher accuracy than the utilization of Simpson's method for estimating the LVEF. The evaluations are performed on a publicly available echocardiogram dataset, EchoNet-Dynamic. The best results are obtained when DeepLab, a convolutional neural network architecture, is used for LV segmentation along with Long Short-Term Memory Networks (LSTM) for the regression of LVEF, obtaining a dice similarity coefficient of 0.92 and a mean absolute error of 5.736%.

A Rapid, Open-Source CCT Predictor for Low-Alloy Steels, and Its Application to Compositionally Heterogeneous Material

The ability to predict transformation behaviour during steel processing, such as primary heat treatments or welding, is extremely beneficial for tailoring microstructures and properties to a desired application. In this work, a model for predicting the continuous cooling transformation (CCT) behaviour of low-alloy steels is developed, using semi-empirical expressions for isothermal transformation behaviour. Coupling these expressions with Scheil’s additivity rule for converting isothermal to non-isothermal behaviour, continuous cooling behaviour can be predicted. The proposed model adds novel modifications to the Li model in order to improve CCT predictions through the addition of a carbon-partitioning model, thermodynamic boundary conditions, and a Koistinen–Marburger expression for martensitic behaviour. These modifications expanded predictions to include characteristic CCT behaviour, such as transformation suppression, and an estimation of the final constituent fractions. The proposed model has been shown to improve CCT predictions for EN3B, EN8, and SA-540 B24 steels by better reflecting experimental measurements. The proposed model was also adapted into a more complex simulation that considers the chemical heterogeneity of the examined SA-540 material, showing a further improvement to CCT predictions and demonstrating the versatility of the model. The model is rapid and open source.

On heat, temperature, and cavity oil volume fraction of an under-race lubricated angular contact ball bearing

Proper thermal management of bearing is extremely important, because high temperature beyond its operational limit can degrade material and lubricant properties, causing an unexpected failure. This research aims to investigate the thermal characteristics of an angular contact ball bearing. First, we have built an in-house program to predict the bearing’s temperature and heat generation. Accurate estimation of oil volume fraction inside the bearing cavity is necessary for accurate prediction of heat generation, thus we improved Parker’s formula to match it with fluid dynamics simulation. The in-house program was then verified with respect to our high-speed, high-load, high-temperature experiment. A series of parametric studies was also conducted to identify the effects of each operation parameters to the bearing’s temperature and heat.

Fourth-order cumulants based-least squares methods for fractional Multiple-Input-Single-Output Errors-In-Variables system identification

This paper presents new consistent methods for continuous-time Multiple-Input-Single-Output (MISO) Errors-In-Variables (EIV) systems by fractional models. The proposed idea is to use Higher-Order Statistics (HOS), such as fourth-order cumulants (foc), instead of noisy input and output measurements to obtain unbiased estimates. Firstly, all differentiation orders are assumed to be known a priori and linear coefficients are estimated. The developed estimator is based on minimizing the equation error and it is called fractional fourth-order based-least squares estimator ( $$frac-foc-ls$$ ). Secondly, the global commensurability of the fractional MISO system is considered. The $$frac-foc-ls$$ is combined with a non linear technique to estimate the global commensurate order along with linear coefficients. The developed algorithm is based on minimizing the output error and called fractional fourth-order cumulants based-least squares combined with global commensurate order optimization ( $$frac-foc-gcools$$ ). The consistency of the developed estimators, in presence of high levels of noise corrupting both the input and output measurements, is assessed through a numerical example with the help of Monte Carlo simulations.

Dimensionless unified modelling of macroscale electric properties of conductive fibre networks in different regimes

The electric properties of conductive fibre networks, which can be representative of carbon nanotube networks, are investigated by means of numerical simulations and a dimensionless unified macroscale model is developed. Fibres are represented as rigid chains of discrete element method particles and we compress systems of fibres in a representative volume element periodic in three dimensions. In the used electric resistor network method, the particles are used as discretisation points to construct a system of linear equations linking the particle conductivities to their local electric potentials and in turn allowing for predictions on the electric macroscale properties of the fibre system. A carried out dimensional analysis suggests suitable scaling laws to unify different macroscale fibre systems in two different regimes - a percolating and a conductive regime. The dimensionless macroscale conductance is found to depend on the percolation threshold and percolation probability. It is moreover found that the critical solid volume fraction for rigid fibres is not only dependent on the fibre aspect ratio, but can also depend on the compression height of the system. Additionally, correlations between transmittance and solid volume fractions are found allowing for possibly simple solid volume fraction estimations in experiments.

An optimization-based supervised learning algorithm for PXRD phase fraction estimation

In powder diffraction data analysis, phase identification is the process of determining the crystalline phases in a sample using its characteristic Bragg peaks. For multiphasic spectra, we must also determine the relative weight fraction of each phase in the sample. Machine learning algorithms (e.g., Artificial Neural Networks) have been applied to perform such difficult tasks in powder diffraction analysis, but typically require a significant number of training samples for acceptable performance. We have developed an approach that performs well even with a small number of training samples. We apply a fixed-point iteration algorithm on the labelled training samples to estimate monophasic spectra. Then, given an unknown sample spectrum, we again use a fixed-point iteration algorithm to determine the weighted combination of monophase spectra that best approximates the unknown sample spectrum. These weights are the desired phase fractions for the sample. Our approach gave the best accuracy and lowest mean absolute error when compared with several machine learning algorithms, including a single hidden-layer neural network.

Isotopic and chemical signatures of high mountain rivers in catchments with contrasting glacier and rock glacier cover

Glaciers and rock glaciers are key elements of mountain hydrological systems, but their relative influence on the chemical and isotopic conditions of streams within the river continuum is still overlooked. During three consecutive years (2019–2021), we studied 24 stream sections in two catchments (Plima and Schnals, Eastern Italian Alps) with varying cover of glaciers and rock glaciers. End-member mixing models based on δ2H and d-excess revealed a large spatial and temporal variability in the contribution of different water sources to stream runoff. Overall, snowmelt (77 ± 17 %) and rainwater (5 ± 5 %) were the largest and the smallest runoff components, respectively. The ice melt contribution was high in streams fed by glaciers (23 ± 15 %) and rock glaciers (16 ± 16 %). In the highly-glacierised Plima basin, the tracer-based estimation of annual ice melt fraction matched reasonably well (90–167%) the mean annual glacial ice loss estimated by geodetic mass balance. In contrast, we found a large overestimation of the ice melt component derived from mixing models in the poorly glacierised (but rock glacier-rich) Schnals catchment. In streams influenced by rock glaciers, at both catchments the particular temporal patterns of electrical conductivity resulted in unreliable estimates of the meltwater/groundwater fractions of runoff. Depending on the local lithology, concentrations of trace elements (Sr, Ni, Ba, Mn, Zn, Al) were high in streams fed by rock glaciers and glaciers, and close/below the limits of quantification in non-glacial streams. In alpine areas, the abundance of rock glaciers can confound the isotopic and chemical signature imparted by glaciers, thus hindering the use of tracer-based methods for hydrograph separation. Under the combined influence from glaciers and rock glaciers, concentrations of trace elements can surpass the limits for drinking water quality even in downstream areas, as we observed at the Schnals catchment for nickel.

Sectoral analysis of capital structure adjustment: evidence from emerging market

PurposeThis paper aims to investigate the non-homogeneity in the speed of adjustment (SoA) of the capital structure of manufacturing companies. It also attempts to study the key determinants that accelerate the speed of adjustment towards the target leverage level.Design/methodology/approachUsing the dynamic panel fraction (DPF) estimator on the partial adjustment model, the study captures the heterogeneous SoA of 2,866 firms across eight prominent sectors of the Indian manufacturing industry from 2009 to 2020. To ensure robustness, the empirical inferences of DPF are cross-verified with the estimates of panel-corrected standard errors (PCSE).FindingsThe authors find a combination of the capital structure's slow, moderate and rapid adjustment speed along with the relevance of trade-off theory. Interestingly, the lowest and fastest SoA is recorded by the dwindling textile sector and expanding food and agro sector, respectively. Profitability, firm size, asset tangibility and non-debt tax shields are the key firm-specific parameters that impact the SoA towards the target.Originality/valueAvailing the rarely employed estimator ‘DPF’ and the objective of documenting diverse and non-uniform adjustment speeds across the Indian manufacturing sectors marks a novel addition to capital structure literature.

Serial ctDNA monitoring to predict response in mCRC patients treated with anlotinib in combination with oxaliplatin and capecitabine (CAPEOX) as the first-line therapy.

e15581 Background: Circulating tumour DNA (ctDNA) sequencing offers a non-invasive approach to monitor therapeutic efficacy in colorectal cancer. However, the mutational landscape of ctDNA during treatment and its impact on clinical outcomes remain unknown. This study aimed to investigate the value of ctDNA in predicting treatment response and PFS in mCRC patients treated with anlotinib plus CAPEOX as first-line therapy. Methods: We conducted a multicenter, single-arm, phase II, exploratory study of patients with RAS/BRAF wild-type unresectable metastatic colorectal cancer (mCRC) who underwent Anlotinib in combination with CAPEOX as the first-line therapy (NCT04080843). Plasma samples from 26 patients were collected at three timepoints: 1 week before treatment (baseline), close to the time of optimal remission (C1) and the time of the last blood collection or the time of disease progression (C2). The maximum somatic allele frequency (MSAF) was calculated for each case and used to provide an estimate of the ctDNA fraction in the blood. Results: 77 plasma samples from the 26 enrolled mCRC patients were sequenced by NGS based on a panel comprising 202 genes. 90 variations were detected in baseline plasma, with a positive rate of 96.2% (25/26). Adenomatous polyposis coli ( APC), was the most mutated gene (84.6%, 22/26). Other mutated genes were TP53 (80.8%, 21/26), FBXW7 (19.2%, 5/26), KRAS (11.5%, 3/26), PIK3CA (11.5%, 3/26) and ROS1 (11.5%, 3/26). The median MSAF of baseline ctDNA was significantly higher in patients who had only liver metastases. Compared with baseline, the ctDNA MSAF level at C1 timepoint was decreased among the whole 26 patients (p &lt; 0.001). By comparing ctDNA positive group (ctDNA MSAF &gt; 0) and ctDNA negative group (ctDNA MSAF = 0) at C1 timepoint, CR cases were mainly in ctDNA negative group, while PD cases accounted for a high proportion in the ctDNA positive group. The median progression-free survival (mPFS) of the whole patients was 11.27 months (95%CI 7.33m to 14.52m). The mPFS was 14.52 months (95% CI 14.4 to NR) of patients with ctDNA negative at C1, which was longer than 9.17 months (95% CI 7.16 to 14.06) of patients with ctDNA positive at C1 (p = 0.012). For 25 patients who had plasma samples of three timepoints, we divided the patients into Non-PD group and PD group according to the clinical outcome at C2 timepoint. Compared with C1 timepoint, the ctDNA MSAF level at C2 timepoint was increased in both Non-PD group and PD group. However, the increment of ctDNA MSAF level in PD group was higher than that in non-PD group (p = 0.000069 vs p = 0.024). Conclusions: This ctDNA profiling study revealed the utility of ctDNA in predicting clinical outcomes in mCRC patients under first-line treatment. Serial ctDNA monitoring might become useful in an active surveillance strategy for the management of mCRC. Clinical trial information: NCT04080843 .