Feature Parameters Research Articles

Chest-CT-based radiomics feature of epicardial adipose tissue for screening coronary atherosclerosis.

This study aims to investigate the diagnostic value of chest-CT epicardial adipose tissue (EAT) radiomics feature in coronary atherosclerotic stenosis. Clinical data from 215 individuals who underwent coronary angiography and chest-CT scan from January to July 2022 at our institution were retrospectively analyzed. Based on the coronary angiography results, the total population, men, and women were divided into the CAD group and non-CAD group. radiomics feature of EAT at the level of the bifurcation of the left-main coronary artery on the transverse level of chest CT were measured. The features contain both first-order feature and shape-order feature.The differences between groups were analyzed using the t test or Chi-square test. The diagnostic efficacy of each parameter in diagnosing atherosclerotic stenosis of coronary arteries was assessed by plotting the receiver operating characteristic (ROC) curve. First-order features: Mean, IntDen, Median, and RawIntDen; shape-order features: Area, Perim, Round, and BSA index; and clinical index: HbA1c showed statistical significance between the CAD group and the non-CAD group. The ROC curve analysis demonstrated high diagnostic efficacy, with the best for diagnostic efficacy being Median for the first-order feature parameter (AUC, 0.753; 95% confidence interval [CI], 0.689-0.817; t = 4.785, p < 0.001), Round for the shape-order feature (AUC, 0.775; 95% CI, 0.714-0.836; t = 7.842, p < 0.001), and HbA1c for the clinical index (AUC, 0.797; 95% CI, 0.783-0.856; t = 6.406, p < 0.001). After dividing the participants into male and female subgroups, the best diagnostic efficacy was observed with the BSA index for men (AUC, 0.743; 95% CI, 0.656-0.829; t = 5.128, p < 0.001) and Round for women (AUC, 0.871; 95% CI, 0.793-0.949; t = 7.247, p < 0.001). Median, Round in radiomics feature of EAT on chest CT may play a role in the assessment of coronary atherosclerotic stenosis.

Cortical structural network characteristics in non-cognitive impairment end-stage renal disease

ObjectiveExplore alterations in topological features of gray matter volume (GMV) and structural networks in non-cognitive impairment end-stage renal disease (Non-CI ESRD).Materials and methodsUtilizing graph theory, we collected structural magnetic resonance imaging (sMRI) data from 38 Non-CI ESRD patients and 50 normal controls (NC). We compared, and extracted the GMV across subject groups, constructed corresponding structural covariance networks (SCNs), and investigated the alterations in SCNs feature parameters between groups.ResultsIn Non-CI ESRD patients, The GMV were reduced in several brain regions, predominantly on the left side (p &amp;lt; 0.05, FWE correction). The small-world network characteristics of the patient group’s brain networks showed a tendency toward regular. In a few densities, global network parameters, transitivity, (p &amp;lt; 0.05) was significantly increased in the ESRD group. Regional network measurements revealed inconsistent changes in regional efficiency across different brain areas. In the analysis of network hubs, the right temporal pole is likely a compensatory hub for Non-CI ESRD patients. The SCNs in Non-CI ESRD patients demonstrated reduced topological stability against targeted attacks.ConclusionThis study reveals that patients with renal failure exhibited subtle changes in brain network characteristics even before a decline in cognitive scores. These changes involve compensatory activation in certain brain regions, which enhances network transitivity to maintain the efficiency of whole-brain network information integration without significant loss. Additionally, the SCNs characteristics can serve as a neuroanatomical marker for brain alterations in Non-CI ESRD patients, offering new insights into the mechanisms of early brain injury in ESRD patients.

Combining LiDAR, SAR, and DEM Data for Estimating Understory Terrain Using Machine Learning-Based Methods

Currently, precise estimation of understory terrain faces numerous technical obstacles and challenges that are difficult to overcome. To address this problem, this paper combines LiDAR, SAR, and DEM data to estimate understory terrain. The high multivariable-precision spaceborne LiDAR ICESat-2 data, validated by the NEON, are divided into training and validation sets. The training dataset is used as a dependent variable, the SRTM DEM and Sentinel-1 SAR data are regarded as independent variables, a total of 13 feature parameters with high contributions are extracted to construct a Multiple Linear Regression model (MLR), BAGGING model, Random Forest model (RF), and Long Short-Term Memory model (LSTM). The results indicate that the RF model exhibits the highest accuracy among the four models, with R2 = 0.999, RMSE = 0.701 m, and MAE = 0.249 m. Then, based on the RF model, the understory terrain at the regional scale is generated, and an accuracy assessment is performed using the validation dataset, yielding R2 = 0.999, RMSE = 0.847 m, and MAE = 0.517 m. Furthermore, this paper quantitatively analyzes the effects of slope, vegetation coverage, and canopy height on the estimation accuracy of understory terrain. The results show that as slope, and canopy height increase, the estimation accuracy of the RF model for understory terrain gradually decreases. The accuracy of the understory terrain estimated by the RF model is relatively stable and not easily affected by slope, vegetation coverage, and canopy height. The research on the estimation of understory terrain holds significant practical implications for forest resource management, ecological conservation, and biodiversity protection, as well as natural disaster prevention.

Research on Violin Audio Feature Recognition Based on Mel-frequency Cepstral Coefficient-based Feature Parameter Extraction

Research on Violin Audio Feature Recognition Based on Mel-frequency Cepstral Coefficient-based Feature Parameter Extraction

Integrating dynamic high-throughput phenotyping and genetic analysis to monitor growth variation in foxtail millet

BackgroundFoxtail millet [Setaria italica (L.) Beauv] is a C4 graminoid crop cultivated mainly in the arid and semiarid regions of China for more than 7000 years. Its grain highly nutritious and is rich in starch, protein, essential vitamins such as carotenoids, folate, and minerals. To expand the utilisation of foxtail millet, efficient and precise methods for dynamic phenotyping of its growth stages are needed. Traditional foxtail millet monitoring methods have high labour costs and are inefficient and inaccurate, impeding the precise evaluation of foxtail millet genotypic variation.ResultsThis study introduces a high-throughput imaging system (HIS) with advanced image processing techniques to enhance monitoring efficiency and data quality. The HIS can accurately extract a range of key growth feature parameters, such as plant height (PH), convex hull area (CHA), side projected area (SPA) and colour distribution, from foxtail millet images. Compared with traditional manual measurements, this HIS improved data quality and phenotyping of the key foxtail millet growth traits. High-throughput phenotyping combined with a genome-wide association study (GWAS) revealed genetic loci associated with dynamic growth traits, particularly plant height (PH), in foxtail millet. The loci were linked to genes involved in the gibberellic acid (GA) synthesis pathway related to PH.ConclusionThe HIS developed in this study enables the efficient and dynamic monitoring of foxtail millet phenotypic traits. It significantly improves the quality of data obtained for phenotyping key growth traits. The integration of high-throughput phenotyping with GWAS provides new insights into the genetic underpinnings of dynamic growth traits, particularly plant height, by identifying associated genetic loci in the GA synthesis pathway. This methodological advancement opens new avenues for the precise phenotyping and exploration of genetic resources in foxtail millet, potentially enhancing its utilisation.

Disentangled feature fusion network for lightweight image super-resolution

Recently, the quality of generated images in image super-resolution (SR) has significantly improved due to the widespread application of convolutional neural networks. Existing super-resolution methods often overlook the importance of network width and instead prioritize designing deeper network structures to improve performance. Deeper networks, however, demand greater computational resources and are more challenging to train, making them less suitable for devices with limited performance. To address this issue, we propose a novel method called the disentangled feature fusion network (DFFN). Specifically, we construct dual feature extraction streams (DFES) using the strategies of feature disentanglement and parameter sharing, which increase the network's width without increasing the number of parameters. To facilitate the interaction of information between the dual feature extraction streams, we design a feature interaction module (FIM) that separately enhances high and low-frequency features. Furthermore, a feature fusion module (FFM) is presented to efficiently fuse different levels of high and low-frequency feature information. The proposed DFFN integrates DFES, FIM, and FFM, which not only widens the network without increasing parameters but also minimizes the loss of crucial feature information. Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms other state-of-the-art lightweight super-resolution methods, achieving a superior balance between parameter quantity, reconstruction performance, and model complexity. The code is available at https://github.com/zhoujy-aust/DFFN.

The proposal and calculation method of the annular domain weight function for the electromagnetic flowmeter under annular conductivity distribution

Researching on the characteristic of weight function distribution under non-uniform conductivity distribution is of significant importance for expanding the engineering applications of electromagnetic flowmeter. In this paper, the characteristic of weight function distribution is firstly explored for annular conductivity distribution where the center and outer ring are both conductive. The research results indicate an important finding that the average value of the weight function within any small annular domain of the center or outer ring remains constant as the radial position changes. Therefore, the concept of ‘annular domain weight function’ is then innovatively proposed. Furthermore, the relationship is studied between the ratio of the annular domain weight function of the center and outer ring and the annular feature parameters. Ultimately, the correlations are obtained for calculating the annular domain weight functions of the center and outer ring.

Condition Monitoring Using a Latent Space of Variational Autoencoder Trained Only on a Healthy Machine.

Machine learning generative models have opened up a new perspective for automated machine diagnostics. These methods improve decision-making by extracting features, classifying, and creating new observations using deep neural networks. Generative modeling aims to determine the joint distribution of input data. This contrasts traditional methods used in diagnostics based on discriminative models and the conditional probability distribution of the target variable at known feature values. In the variational autoencoder (VAE) algorithms trained by the authors, the parameters of diagnostic features are random variables, the distributions of which can be approximated based on data, and the identification of probability distributions is based on variational inference. Variational inference is a tool that deals with difficult statistical problems and is usually faster than classical methods. VAEs can detect anomalies, predict failures, and optimize processes. This paper proposes an unsupervised approach to fault diagnosis using only healthy data with automatic feature extraction from the continuous probabilistic latent subspace of the VAE encoder and reduction in PCA or t-SNE. The solution, verified in the example of simulation data, is a response to a common problem related to the lack or difficulty of obtaining marked data in defected states of devices and mechanical structures.

Multiscale image-based modeling for failure prediction of sheet molding compound composite under uniaxial tension

Carbon fiber reinforced polymer (CFRP) composites are extensively utilized as primary load-bearing components in various engineering applications due to their superior strength-to-weight ratio and excellent mechanical properties. However, their intricate microstructural interactions within composite present a significant challenge for failure analysis of CFRP. Although finite element (FE) simulations have been proven feasible to conduct the failure analysis, the classical FE models are developed based on homogeneous fiber characteristics, ignoring the influence of internal structures on the damage evolution process. This paper presents a multiscale image-based modeling approach to predict the tensile failure procedure of chopped carbon fiber sheet molding compound (SMC) composite. To accurately reconstruct the representative volume element (RVE) model of the SMC composite, synchrotron micro-X-ray computed tomography (μXCT) was adapted to explore the SMC internal microstructure. Then microscale RVE models with different fiber volume fractions were constructed to predict the corresponding microcosmic mechanical properties, which were used as the inputs for mesoscale RVE models to determine the constitutive parameters of fiber chips having varied fiber volume and orientations. Finally, the YOLOv5_Seg algorithm was employed to extract the geometric feature parameters of the fiber chips for mesoscale RVE modeling and then the failure location and sequence under uniaxial tension were predicted. It is found that the final simulated failure behaviors were consistent with the experimental observations, confirming the feasibility of this approach for understanding the failure mechanisms of CFRP composites. Thus, once the internal microstructure is determined using experimental techniques or predicted by simulating the composite manufacturing process, this approach can also be utilized for design optimization and performance evaluation for CFRP composites.

Design of pattern image generation technique based on self-attentive residual conditional generative adversarial network

Image generation techniques have made remarkable progress in the digital image processing and computer vision. However, traditional generation models cannot meet the complexity and diversity requirements of patterned images. In view of this, the study aims to enhance the quality of generated pattern images, which uses improved residual block, and introduces a self-attention mechanism to compute the weight parameters of the input features to enhance the accuracy. Comparing with three image generation models, the research model shows lower Frechette initial distance, which is better than the other three methods, and the average Frechette initial distance values in the four scenes are 175.23, 176.41, 174.41, and 165.23. Generated mouths and eyes: the average values of Frechette initial distances reach 98.23 and 97.24, respectively. For emotion classification, the Frechette initial distance averages for sad, excited, and calm emotions were 82.34, 75.63, and 70.21, respectively. The model was trained up to 2500 iterations, and the loss value was reduced to 0.54, with an accuracy of 98.23%, confirming its effectiveness and high performance. The self attention residual network enhances the model's ability to capture image details, effectively improving the quality and accuracy of image generation, and providing a new technological path for radiation imaging data processing and analysis in radiation science.

Tracking the impact of typhoons on maize growth and recovery using Sentinel-1 and Sentinel-2 data: A case study of Northeast China

The increasing frequency of typhoon events, attributed to global climate change, has significantly affected agricultural production, predominantly resulting in substantial negative consequences. Accurate and timely assessment of crop damage is crucial for understanding economic implications, devising effective agricultural strategies, and enhancing resilience amid mounting climate uncertainties. This study investigates the utility of Sentinel-1 Synthetic Aperture Radar (SAR) and Sentinel-2 MultiSpectral Instrument (MSI) data in tracking maize damage severity following typhoon events. By employing continuous field sampling techniques and conducting visual interpretation of high-resolution remote sensing imagery, sample sets representing a spectrum of maize damage severity were systematically established. The application of time series analysis on Sentinel data enables a comprehensive exploration of spectral and polarization responses, providing insights into the correlation with maize damage severity. Segmentation of the maize damage timeline into pre-disaster, disaster, and recovery periods, coupled with optimization of relevant feature parameters, was undertaken to bolster monitoring precision. Leveraging the Google Earth Engine (GEE) cloud platform, a Random Forest algorithm was used to develop a model for monitoring maize damage severity across different post-typhoon periods, yielding maps delineating the distribution and magnitude of maize damage in Northeast China. Results indicate that integrating spectral indices from the pre-disaster phase with backscatter variations of polarization bands during various post-typhoon periods enhances maize damage assessment. Maize damage severity is notably elevated during the disaster period, achieving an overall accuracy of 87.22 %. While mitigated during the recovery phase, localized exacerbation occurs in severely affected regions, yielding an overall accuracy of 88.54 %. Analysis incorporating terrain and meteorological data reveals that post-typhoon maize disasters predominantly occur in low-lying and flat areas, with meteorological factors, particularly maximum wind speed and daily cumulative precipitation, exerting significant influence on damage severity. This study underscores the critical role of SAR and optical data fusion in elucidating typhoon-induced crop damage dynamics, thereby providing essential insights for proactive mitigation strategies against future agricultural losses.

Fault diagnosis method of rolling bearing of BN ball mill based on AESL-GA

Aiming at the shortcomings of incomplete and inaccurate knowledge-based Bayesian network (BN) construction methods, a BN structure construction method based on knowledge guidance and data mining is proposed. Aiming at the problem of inaccurate single signal fault diagnosis results and the uncertainty problem in fault information, the current signal and the vibration signal are fused to establish the feature nodes of the BN network, and the fault feature parameters of the two signals are extracted respectively. The feature basket is selected using the discrimination index method and used as the node of the BN structure feature layer. The initial BN structure constructed by expert knowledge is combined with the adaptive elite structure genetic algorithm (AESL-GA) for structural optimization. By adaptively restricting the search space in the evolution process, reducing the number of free parameters, improving its global search ability, and obtaining the optimal BN structure. The method is verified by the measured data of the ball mill rolling bearing of Jinchuan Company and the Paderborn University data set, which proves the effectiveness of the proposed method.

Visual Communication Method of Multi-Frame Film and Television Special Effects Images Based on Deep Learning

In the view of art form change, there is a perspective problem in the detail part of multi-frame film and television special effects (TSEs) images in the special effects rendering module, which leads to the greater ambiguity of multi-frame film and TSEs images. In order to advance the talent of identifying the detail structures and characteristics of multi-frame film and TSEs images, a visual communication technology of multi-frame film and TSEs images in the view of art form change grounded on deep learning is suggested. The transmission relation model of detail characteristics of multi-frame film and TSEs images in the field of artistic form change is constructed, and the attenuation treatment of detail action special effects of multi-frame film and TSEs images in the field of artistic form change is carried out with the assistance of deep learning approaches. The detailed information and color of multi-frame film and TSEs images are obtained by using prior map knowledge instead of rendering basic color. Over the technique of filtering and preserving, the multi-frame film and TSEs images are directed to screen the detailed feature quantity of the input film and TSEs images in the field of artistic form change. In addition, the Sobel operator is practiced to perceive the scrawny edge information of multi-frame film and TSEs images, and block fitting interpolation and depth filtering analysis are used in the edge area to realize the visual communication and color feature parameter identification and amplification of multi-frame film and TSEs images in the field of artistic form change, so as to improve the fuzzy enhancement of multi-frame film and TSEs images. The test results and results indicate that the average Peak signal-to-noise ratio (PSNR) of the proposed method is about 37.7166[Formula: see text]dB, which is 49.08% and 52.08% higher than the PSNRs of the Harris method and SUFR method, respectively. It has been confirmed that this scheme can successfully solve the edge blurring and distortion problems of multi-frame movies and image images in the field of art form change.

Constructing a visual detection method for coagulation effect based on image feature machine learning

The coagulation process is influenced by several factors, including turbidity, temperature, pH, and hydraulic conditions. Consequently, optimizing the dosage of coagulants is often time-consuming and challenging. To quickly evaluate coagulation efficacy and optimize coagulant dosage, this study utilized a convolutional neural network (CNN) model to analyze the accuracy of effluent turbidity predictions from floc images at different time intervals. Python OpenCV was employed to develop a program that extracts macro features (e.g., texture) and micro features (e.g., particle size distribution) from the images, and these feature parameters were analyzed using a BP-ANN model. The results of the CNN analysis indicated that the middle to late stages of flocculation are the optimal periods for predicting effluent turbidity, with the highest prediction accuracy of 99.81 % achieved during the middle stage of flocculation. The BP-ANN analysis demonstrated that particle size distribution effectively represents the microscopic characteristics of flocs, achieving a maximum prediction accuracy of 96.94 % in the middle stage of flocculation. Furthermore, combining macroscopic and microscopic features yielded a prediction accuracy of 99.44 % at the end of flocculation using BP-ANN. These findings suggest that machine learning techniques applied to floc images can effectively predict effluent turbidity, offering valuable insights for future water quality prediction and the development of flocculation kinetics models.

Preliminary Insights on Moisture Content Measurement in Square Timbers Using GPR Signals and 1D-CNN Models

Accurately measuring the moisture content (MC) of square timber is crucial for ensuring the quality and performance of wood products in wood processing. Traditional MC detection methods have certain limitations. Therefore, this study developed a one-dimensional convolutional neural network (1D-CNN) model based on the first 8 nanoseconds of ground-penetrating radar (GPR) signals to predict the MC of square timber. The study found that the mixed-species model exhibited effective predictive performance (R2 = 0.9864, RMSE = 0.0393) across the tree species red spruce, Dahurian larch, European white birch, and Manchurian ash (MC range 0%–133.1%), while single-species models showed even higher accuracy (R2 ≥ 0.9876, RMSE ≤ 0.0358). Additionally, the 1D-CNN model outperformed other algorithms in automatically capturing complex patterns in GPR full-waveform amplitude data. Moreover, the algorithms based on full-waveform amplitude data demonstrated significant advantages in detecting wood MC compared to those based on a traditional time–frequency feature parameter. These results indicate that the 1D-CNN model can be used to optimize the drying process and detect the MC of load-bearing timber in construction and bridge engineering. Future work will focus on expanding the dataset, further optimizing the algorithm, and validating the models in industrial applications to enhance their reliability and applicability.

Exploring the CO2 conversion activated by the dielectric barrier discharge plasma assisted with photocatalyst via machine learning

DBD plasma assisted with halide perovskite photocatalysts is very attractive for the conversion of CO2 into high value-added products under mild conditions. However, it is still challenging to further effectively improve the corresponding CO2 conversion efficiency due to the consumption of time, materials, equipments via traditional experimental approach. In this paper, we simulate the CO2 conversion process under the DBD plasma assisted with different photocatalysts by machine learning. The process conditions and material parameters were selected as the feature variables of the machine learning model, and all the features were screened by combining Pearson's correlation coefficient and MIR ranking. The regression algorithms were evaluated using K-fold cross-validation combined with the coefficient of determination (R2), while SVR and BPANN with the best prediction performance for CO2 conversion ratio and energy efficiency were selected to establish the prediction models. In order to increase the accuracy of predictions, the hyperparameters of these two base models were improved using genetic algorithm, particle swarm optimization and Bayesian optimization. The R2 of the GA-SVR-CO2 and Bayesian-BPANN-EE reached 0.8960 and 0.9679 for the testing sets, respectively, and their practical applications were successfully verified. In addition, the effects of feature parameters on the conversion efficiency were revealed by SHAP. SEI scatter plots and Spearman correlation coefficient have been used for correlation analysis to better explore the correlation between machine learning models and experiments. This work brings new insights into the contributions of multiple parameters in the plasma system assisted with photocatalyst for efficient conversion of CO2.

Signal recognition and prediction system based on random forest model

To accurately predict the occurrence of rock bursts during deep coal mining and ensure the safe and efficient operation of coal mines, a signal recognition and prediction system based on the Random Forest model is proposed. This system utilizes electromagnetic radiation (EMR) and acoustic emission (AE) signal data, employing feature extraction and point biserial correlation analysis to screen out the most relevant feature parameters. A Random Forest binary classification model is constructed to identify interference signals. Subsequently, by introducing new features such as moving average slope and exponentially weighted moving average (EWMA), a time series analysis of precursor feature signals is conducted. A real-time warning model based on the Random Forest algorithm is developed, dynamically calculating the probability of precursor feature signal occurrence by integrating historical data and real-time data changes. This approach improves the accuracy and recall rate of signal recognition and prediction, providing reliable data support for mine safety management.

Estimation of compressive strength of concrete with manufactured sand and natural sand using interpretable artificial intelligence

To satisfy the design strength of manufactured sand concrete (MSC) in practical engineering applications, a plethora of geotechnical tests are frequently conducted. An effective approach is imperative to reduce the consumption of labor and resources during these tests. The objective of this paper is to introduce an interpretable machine learning (ML) method to evaluate the compressive strength (CS) of MSC. Firstly, a dataset was established by compiling experimental results from 208 literatures. 3382 data points were selected from the dataset for algorithm training. Recursive Feature Elimination with Cross-Validation (RFECV) was employed to select input parameters. Four algorithms with 12 selected input variables and 1 output variable were constructed to predict CS of MSC using Random Forest (RF), Gradient Boosting Decision Trees (GBDT), eXtreme Gradient Boosting algorithm (XGBoost), and Categorical Boosting (CatBoost). The results show that XGBoost has the highest accuracy and generalization ability (R2=0.934, MAE=3.44, RMSE=5.16, MAPE=0.07). To enhance model transparency, SHapley Additive exPlanations (SHAP) was adopted to explain the underlying predictive mechanisms of ML models. Analyses show that, 1) the cement content, curing time, and water content were the main feature parameters influencing the CS of MSC, 2) the cement content, curing time, and water content have a linear increase, logarithmic increase, and exponential decrease with the CS of MSC, respectively. Partial Dependence Plots (PDP) and Individual Conditional Expectation (ICE) plots were used to further analyze the impacts of these significant influencing factors on the CS of MSC. Additionally, the Local Interpretable Model-Agnostic Explanations (LIME) method was employed to investigate thresholds for various material dosages in MSC containing 5–10 % stone powder. Two typical scenarios were selected for analysis, yielding recommended dosage ranges for concrete of two distinct strengths. Finally, a graphical user interface (GUI) for the CS of MSC has been designed, which might be of great use to material engineers. This provides reference and guidance for concrete engineering practice.

Alterable Robotic Skin Using Material Gene Expression Modulation

AbstractRobotic skins that integrate artificial tactile sensing elements can substantially complement the perception dimension of social robots, presenting an indispensable part in human‐robot interaction (HRI). However, existing design frameworks compromise between versatility and sustainability due to the restricted range of characteristics available for a single constituent. Here an alterable robotic skin constructed from homogeneous sensing units are proposed, capable of cyclically realtering their inherent characteristics across a wide spectrum. Necessary characteristics to achieve positioning and pressure sensing subunits can be encoded in the feature motifs and extracted through condition‐induced differentiation, showcasing a remarkable resemblance to the gene expression in the living system. By virtue of this, up to 100‐fold differences in feature parameters are achieved, including modulus, surface state, and conductivity, to drive the target attribute coupling. The trans‐temporal reconstruction of materials enables the superb customization of functional building blocks, advancing the flexible separation and combination of different touch modes, including location, pressure, duration, and motion pattern. As a proof of concept, the alterable robotic skin is demonstrated that integrates a position‐sensing layer and a pressure‐sensing layer. It can accurately distinguish and recognize multi‐dimensional touch motions based on less‐channel data, which showcases an efficient haptic interaction application.

DNA demethylation triggers cell free DNA release in colorectal cancer cells

BackgroundLiquid biopsy based on cell-free DNA (cfDNA) analysis holds significant promise as a minimally invasive approach for the diagnosis, genotyping, and monitoring of solid malignancies. Human tumors release cfDNA in the bloodstream through a combination of events, including cell death, active and passive release. However, the precise mechanisms leading to cfDNA shedding remain to be characterized. Addressing this question in patients is confounded by several factors, such as tumor burden extent, anatomical and vasculature barriers, and release of nucleic acids from normal cells. In this work, we exploited cancer models to dissect basic mechanisms of DNA release.MethodsWe measured cell loss ratio, doubling time, and cfDNA release in the supernatant of a colorectal cancer (CRC) cell line collection (N = 76) representative of the molecular subtypes previously identified in cancer patients. Association analyses between quantitative parameters of cfDNA release, cell proliferation, and molecular features were evaluated. Functional experiments were performed to test the impact of modulating DNA methylation on cfDNA release.ResultsHigher levels of supernatant cfDNA were significantly associated with slower cell cycling and increased cell death. In addition, a higher cfDNA shedding was found in non-CpG Island Methylator Phenotype (CIMP) models. These results indicate a positive correlation between lower methylation and increased cfDNA levels. To explore this further, we exploited methylation microarrays to identify a subset of probes significantly associated with cfDNA shedding and derive a methylation signature capable of discriminating high from low cfDNA releasers. We applied this signature to an independent set of 176 CRC cell lines and patient derived organoids to select 14 models predicted to be low or high releasers. The methylation profile successfully predicted the amount of cfDNA released in the supernatant. At the functional level, genetic ablation of DNA methyl-transferases increased chromatin accessibility and DNA fragmentation, leading to increased cfDNA release in isogenic CRC cell lines. Furthermore, in vitro treatment of five low releaser CRC cells with a demethylating agent was able to induce a significant increase in cfDNA shedding.ConclusionsMethylation status of cancer cell lines contributes to the variability of cfDNA shedding in vitro. Changes in methylation pattern are associated with cfDNA release levels and might be exploited to increase sensitivity of liquid biopsy assays.