Decoding functional cell-cell communication events by multi-view graph learning on spatial transcriptomics.

Cell-cell communication events (CEs) are mediated by multiple ligand-receptor (LR) pairs. Usually only a particular subset of CEs directly works for a specific downstream response in a particular microenvironment. We name them as functional communication events (FCEs) of the target responses. Decoding FCE-target gene relations is: important for understanding the mechanisms of many biological processes, but has been intractable due to the mixing of multiple factors and the lack of direct observations. We developed a method HoloNet for decoding FCEs using spatial transcriptomic data by integrating LR pairs, cell-type spatial distribution and downstream gene expression into a deep learning model. We modeled CEs as a multi-view network, developed an attention-based graph learning method to train the model for generating target gene expression with the CE networks, and decoded the FCEs for specific downstream genes by interpreting trained models. We applied HoloNet on three Visium datasets of breast cancer and liver cancer. The results detangled the multiple factors of FCEs by revealing how LR signals and cell types affect specific biological processes, and specified FCE-induced effects in each single cell. We conducted simulation experiments and showed that HoloNet is more reliable on LR prioritization in comparison with existing methods. HoloNet is a powerful tool to illustrate cell-cell communication landscapes and reveal vital FCEs that shape cellular phenotypes. HoloNet is available as a Python package at https://github.com/lhc17/HoloNet.

Clonal expansion of shared T cell receptors reveals the existence of immune commonality among different lesions of synchronous multiple primary lung cancer.

The increase in the detection rate of synchronous multiple primary lung cancer (MPLC) has posed remarkable clinical challenges due to the limited understanding of its pathogenesis and molecular features. Here, comprehensive comparisons of genomic and immunologic features between MPLC and solitary lung cancer nodule (SN), as well as different lesions of the same patient, were performed. Compared with SN, MPLC displayed a lower rate of EGFR mutation but higher rates of BRAF, MAP2K1, and MTOR mutation, which function exactly in the upstream and downstream of the same signaling pathway. Considerable heterogeneity in T cell receptor (TCR) repertoire exists among not only different patients but also among different lesions of the same patient. Invasive lesions of MPLC exhibited significantly higher TCR diversity and lower TCR expansion than those of SN. Intriguingly, different lesions of the same patient always shared a certain proportion of TCR clonotypes. Significant clonal expansion could be observed in shared TCR clonotypes, particularly in those existing in all lesions of the same patient. In conclusion, this study provided evidences of the distinctive mutational landscape, activation of oncogenic signaling pathways, and TCR repertoire in MPLC as compared with SN. The significant clonal expansion of shared TCR clonotypes demonstrated the existence of immune commonality among different lesions of the same patient and shed new light on the individually tailored precision therapy for MPLC.

Genome-wide identification analysis of the 4-Coumarate: CoA ligase (4CL) gene family expression profiles in Juglans regia and its wild relatives J. Mandshurica resistance and salt stress.

Persian walnut (Juglans regia) and Manchurian walnut (Juglans mandshurica) belong to Juglandaceae, which are vulnerable, temperate deciduous perennial trees with high economical, ecological, and industrial values. 4-Coumarate: CoA ligase (4CL) plays an essential function in plant development, growth, and stress. Walnut production is challenged by diverse stresses, such as salinity, drought, and diseases. However, the characteristics and expression levels of 4CL gene family in Juglans species resistance and under salt stress are unknown. Here, we identified 36 Jr4CL genes and 31 Jm4CL genes, respectively. Based on phylogenetic relationship analysis, all 4CL genes were divided into three branches. WGD was the major duplication mode for 4CLs in two Juglans species. The phylogenic and collinearity analyses showed that the 4CLs were relatively conserved during evolution, but the gene structures varied widely. 4CLs promoter region contained multiply cis-acting elements related to phytohormones and stress responses. We found that Jr4CLs may be participated in the regulation of resistance to anthracnose. The expression level and some physiological of 4CLs were changed significantly after salt treatment. According to qRT-PCR results, positive regulation was found to be the main mode of regulation of 4CL genes after salt stress. Overall, J. mandshurica outperformed J. regia. Therefore, J. mandshurica can be used as a walnut rootstock to improve salt tolerance. Our results provide new understanding the potential functions of 4CL genes in stress tolerance, offer the theoretical genetic basis of walnut varieties adapted to salt stress, and provide an important reference for breeding cultivated walnuts for stress tolerance.

The role of PKP1 in tumor progression in melanoma: Analysis of a cell adhesion-related model.

The incidence rate of melanoma varies across regions, with Europe, the United States, and Australia having 10-25, 20-30, and 50-60 cases per 1 00 000 people. In China, patients with melanoma exhibit different clinical manifestations, pathogenesis, and outcomes. Current treatments include surgery, adjuvant therapy, and immune checkpoint inhibitors. Nonetheless, complications may arise during treatment. Melanoma development is heavily reliant on cell adhesion molecules (CAMs), and studying these molecules could provide new research directions for metastasis and progression. CAMs include the integrin, immunoglobulin, selectin, and cadherin families, and they affect multiple processes, such as maintenance, morphogenesis, and migration of adherens junction. In this study, a cell adhesion-related risk prognostic signature was constructed using bioinformatics methods, and survival analysis was performed. Plakophilin 1 (PKP1) was observed to be crucial to the immune microenvironment and has significant effects on melanoma cell proliferation, migration, invasion, and the cell cycle. This signature demonstrates high reliability and has potential for clinical applications.

van der Waals Self-Epitaxial Growth of Inch-Sized Superconducting Niobium Diselenide Films.

Ultrathin superconducting films are the basis of superconductor devices. van der Waals (vdW) NbSe2 with noncentrosymmetry exhibits exotic superconductivity and shows promise in superconductor electronic devices. However, the growth of inch-scale NbSe2 films with layer regulation remains a challenge because vdW structural material growth is strongly dependent on the epitaxial guidance of the substrate. Herein, a vdW self-epitaxy strategy is developed to eliminate the substrate driving force in film growth and realize inch-sized NbSe2 film growth with thicknesses from 2.1 to 12.1 nm on arbitrary substrates. The superconducting transition temperature of 5.1 K and superconducting transition width of 0.30 K prove the top homogeneity and quality of superconductivity among all of the synthetic NbSe2 films. Coupled with a large area and substrate compatibility, this work paves the way for developing NbSe2 superconductor electronics.

Confined Assembly of Hydrated Vanadium Oxide into Hollow Mesoporous Carbon Nanospheres for Fast and Stable K+ Storage Capability.

The rational structural design of the electrode materials is significant to enhance the electrochemical performance for potassium ion storage, benefiting from the shortened ion diffusion distance, increased conductivity, and pseudo-capacitance promotion. Herein, hydrated vanadium oxide (HVO) nanosheets with enriched oxygen defects are well confined into hollow mesoporous carbon spheres (HMCS), producing Od -VOH@C nanospheres through one-step hydrothermal reaction. Attributed to the restricted growth in the HMCS, the HVO nanosheets are loosely packed, generating abundant interfacial boundaries and large specific areas. As a result, Od -VOH@C nanospheres show increased reaction kinetics and well buffer the volume effects for the K+ storage. Od -VOH@C delivers stable capacities of 138 mAh g-1 at 2.0 A g-1 over 10 000 cycles in half-cells attributed to the high pseudo-capacitance contribution. The K+ storage mechanism of insertion and conversion reaction is confirmed by ex situ X-ray diffraction, Raman, and X-ray photoelectron spectroscopy analyses. Moreover, the symmetric potassium-ion capacitors of Od -VOH@C//Od -VOH@C deliver a high energy density of 139.6 Wh kg-1 at the power density of 948.3 W kg-1 .

Music with Different Tones Affects the Development of Brain Nerves in Mice in Early Life through BDNF and Its Downstream Pathways.

As a means of environmental enrichment, music environment has positive and beneficial effects on biological neural development. Kunming white mice (61 days old) were randomly divided into the control group (group C), the group of D-tone (group D), the group of A-tone (group A) and the group of G-tone (group G). They were given different tonal music stimulation (group A) for 14 consecutive days (2 h/day) to study the effects of tonal music on the neural development of the hippocampus and prefrontal cortex of mice in early life and its molecular mechanisms. The results showed that the number of neurons in the hippocampus and prefrontal cortex of mice increased, with the cell morphology relatively intact. In addition, the number of dendritic spines and the number of dendritic spines per unit length were significantly higher than those in group C, and the expressions of synaptic plasticity proteins (SYP and PSD95) were also significantly elevated over those in group C. Compared with group C, the expression levels of BDNF, TRKB, CREB, PI3K, AKT, GS3Kß, PLCγ1, PKC, DAG, ERK and MAPK genes and proteins in the hippocampus and prefrontal cortex of mice in the music groups were up-regulated, suggesting that different tones of music could regulate neural development through BDNF and its downstream pathways. The enrichment environment of D-tone music is the most suitable tone for promoting the development of brain nerves in early-life mice. Our study provides a basis for screening the optimal tone of neuroplasticity in early-life mice and for the treatment of neurobiology and neurodegenerative diseases.

Thermodynamic Analysis of Financial Markets: Measuring Order Book Dynamics with Temperature and Entropy.

This study bridges finance and physics by applying thermodynamic concepts to model the limit order book (LOB) with high-frequency trading data on the Bitcoin spot. We derive the measures of Market Temperature and Market Entropy from the kinetic and potential energies in the LOB to provide a deeper understanding of order activities and market participant behavior. Market Temperature emerges as a robust indicator of market liquidity, correlating with liquidity measures such as Active Quote Volume, bid-ask spread and match volume. Market Entropy, on the other hand, quantifies the degree of disorder or randomness in the LOB, providing insights into the instantaneous volatility of price in the high-frequency trading market. Our empirical findings not only broaden the theoretical framework of econophysics but also enhance comprehensive understanding of the market microstructure and order book dynamics.

DualGCN: a dual graph convolutional network model to predict cancer drug response.

BACKGROUND: Drug resistance is a critical obstacle in cancer therapy. Discovering cancer drug response is important to improve anti-cancer drug treatment and guide anti-cancer drug design. Abundant genomic and drug response resources of cancer cell lines provide unprecedented opportunities for such study. However, cancer cell lines cannot fully reflect heterogeneous tumor microenvironments. Transferring knowledge studied from in vitro cell lines to single-cell and clinical data will be a promising direction to better understand drug resistance. Most current studies include single nucleotide variants (SNV) as features and focus on improving predictive ability of cancer drug response on cell lines. However, obtaining accurate SNVs from clinical tumor samples and single-cell data is not reliable. This makes it difficult to generalize such SNV-based models to clinical tumor data or single-cell level studies in the future. RESULTS: We present a new method, DualGCN, a unified Dual Graph Convolutional Network model to predict cancer drug response. DualGCN encodes both chemical structures of drugs and omics data of biological samples using graph convolutional networks. Then the two embeddings are fed into a multilayer perceptron to predict drug response. DualGCN incorporates prior knowledge on cancer-related genes and protein-protein interactions, and outperforms most state-of-the-art methods while avoiding using large-scale SNV data. CONCLUSIONS: The proposed method outperforms most state-of-the-art methods in predicting cancer drug response without the use of large-scale SNV data. These favorable results indicate its potential to be extended to clinical and single-cell tumor samples and advancements in precision medicine.

Improving the accuracy of high-repetition-rate LIBS based on laser ablation and scanning parameters optimization.

Laser-induced breakdown spectroscopy system based on high-repetition-rate microchip laser (HR-LIBS) has been widely used in elemental analysis due to its high energy stability, good portability and fast spectral acquisition speed. However, repeated ablation on powder pellets like soil and coal using HR-LIBS system encounters the problem of serious decline in measurement accuracy. In this work, the relationship between laser ablation and scanning parameters, their correlation with spectral intensity, as well as the optimization approach were fundamentally studied. The correlations among the crater overlapping rate, crater depth and spectral intensity were obtained. An HR-LIBS system with microchip laser (4 kHz repetition rate, 100 µJ laser pulse energy) to perform repeated scanning ablation was established. A theoretical model of the ablation crater morphology for repeated scanning ablation was developed. By taking soil pellets as the experimental samples, the linear fitting curves of crater depth and the spectral intensity ratio were established with the R2 of 0.90∼0.99. The experimental results showed that as the crater depth developed during repeated ablation, the Si-normalized spectral intensity decreased, and thus the spectral repeatability decreased. It was found that by optimizing the overlapping rate to form a flat crater bottom, the confinement effect of the crater on the plasma could be avoided. As a result, the spectral repeatability was significantly improved. The relative standard deviation (RSD) of Si-normalized spectral intensity was improved from 5% to 0.6%. Finally, repeated ablation was performed with the optimized overlapping rate on soil pellets. The R2 of calibration curves of Fe, Mg, Ca, and Al were all above 0.993, and the average RSDs were between 0.5% and 1%. This study provides a fast, accurate, and stable method for the analysis of the samples consisting of various materials with high heterogeneity.

Analysis and Correction of Polarization Response Calibration Error of Limb Atmosphere Ultraviolet Hyperspectral Detector.

A UV hyperspectral instrument was designed with a polarization measurement channel for real-time in-orbit polarization correction to reduce the influence of polarization on the detection accuracy of atmospheric radiation. One of the prerequisites for in-orbit polarization calibration is accurately calibrating the instrument's polarization properties in the laboratory. This study first introduces the calibration method and measuring device of the polarization characteristics of the ultraviolet (UV) hyperspectral detector and conducts a polarization calibration test of the instrument. The two main error sources introduced by the calibration device were emphatically analyzed, and the correction method of the error sources was deduced theoretically. Finally, the polarization calibration results of the UV hyperspectral detector were corrected, and the uncertainty analysis of the corrected calibration results was about 1.4%, which provides effective ground polarization calibration data for the on-orbit polarization correction of the instrument.

Efficient Synthesis of Sulfur-Stereogenic Sulfoximines via Ru(II)-Catalyzed Enantioselective C-H Functionalization Enabled by Chiral Carboxylic Acid.

Ru(II)-catalyzed enantioselective C-H functionalization involving an enantiodetermining C-H cleavage step remains undeveloped. Here we describe a Ru(II)-catalyzed enantioselective C-H activation/annulation of sulfoximines with α-carbonyl sulfoxonium ylides using a novel class of chiral binaphthyl monocarboxylic acids as chiral ligands, which can be easily and modularly prepared from 1,1'-binaphthyl-2,2'-dicarboxylic acid. A broad range of sulfur-stereogenic sulfoximines were prepared in high yields with excellent enantioselectivities (up to 99% yield and 99% ee) via desymmetrization, kinetic resolution, and parallel kinetic resolution. Furthermore, the resolution products can be easily transformed to chiral sulfoxides and key intermediates for kinase inhibitors.

High accuracy determination of copper in copper concentrate with double genetic algorithm and partial least square in laser-induced breakdown spectroscopy.

There are many challenges in the determination of elements in complex matrix such as soil, coal and minerals by laser induced breakdown spectroscopy (LIBS) method. Due to the influence of matrix effect, instability of laser plasma and fluctuation of laser parameters, the repeatability and accuracy of quantitative results are always not satisfactory. In order to improve the accuracy, high-energy laser (30mJ-100mJ) with precise control was utilized in many laboratories. In this paper, quantitative analysis of copper in copper concentrate by low-energy (10µJ) LIBS is studied. In order to reduce the influence of matrix effect and other factors, a partial least square regression method based on double genetic algorithm (DGA-PLS) is proposed. The detail operations are as follow: the reference spectral lines are automatically selected by GA as the optimal internal standard for spectral normalization. Then the GA is used to select variables from the normalized spectra for PLS. The results showed that, for univariate model, the coefficient of determination (R2) was improved from 0.6 to 0.97 by the optimal internal standard normalization. Compared with tradition PLS, the root mean square error of cross validation (RMSECV) and root mean square error of prediction (RMSEP) of PLS trained by the normalized spectral data decreased from 1.4% and 0.42% to 0.9% and 0.29% respectively. Compared with the normalized PLS, the RMSECV and RMSEP of the DGA-PLS trained by the normalized and feature selected spectral data decreased from 0.9% and 0.29% to 0.26% and 0.21% respectively. The results show that DGA-PLS can significantly reduce matrix effect, improve prediction accuracy and reduce the risk of overfitting in determination of copper in copper concentrate.

Hand Gesture Recognition Using Compact CNN Via Surface Electromyography Signals.

By training the deep neural network model, the hidden features in Surface Electromyography(sEMG) signals can be extracted. The motion intention of the human can be predicted by analysis of sEMG. However, the models recently proposed by researchers often have a large number of parameters. Therefore, we designed a compact Convolution Neural Network (CNN) model, which not only improves the classification accuracy but also reduces the number of parameters in the model. Our proposed model was validated on the Ninapro DB5 Dataset and the Myo Dataset. The classification accuracy of gesture recognition achieved good results.

Multi-scale physical model simulation of particle filtration using computational fluid dynamics.

Clarifiers integrating radial cartridge filtration (RCF) are a combined unit operation variant of millennia-old sedimentation-filtration systems. Similarly, RCF is a primarily horizontal flow variant with flow orthogonal to gravity and a radial velocity gradient, in contrast to traditional deep-bed vertical filtration. These granular filters function at lower finite granular Reynolds numbers. A proposed computational fluid dynamics framework, implementing the Navier-Stokes equations, couples a pore-scale filter model with a macroscopic scale sedimentation-filtration model to create a tool examining non-Brownian particle separation. Validation is conducted using previous physical testing from a full-scale sedimentation-filtration system under steady flow and particulate loads. Model results illustrate a two-zone filtration structure with respect to particle diameter, similar to vertical filtration. The computational tool predicts particulate matter separation of 86.1% compared to 87.8% for physical testing. The physical-based computational framework does not need high-level calibration as compared to analytical, lumped, or empirical models; conferring direct extensibility to similar unit operation systems. The novel multi-scale tool simulates particulate matter fate in a modern re-incarnation of a sedimentation-filtration unit operation. The tool functions as an adjuvant that complements regulatory or certification testing. The tool can provide guidance for design or maintenance as well as system management with respect to particle fate in, and breakthrough from, granular filters in a combined unit operation.

Population-based 3D genome structure analysis reveals driving forces in spatial genome organization.

Conformation capture technologies (e.g., Hi-C) chart physical interactions between chromatin regions on a genome-wide scale. However, the structural variability of the genome between cells poses a great challenge to interpreting ensemble-averaged Hi-C data, particularly for long-range and interchromosomal interactions. Here, we present a probabilistic approach for deconvoluting Hi-C data into a model population of distinct diploid 3D genome structures, which facilitates the detection of chromatin interactions likely to co-occur in individual cells. Our approach incorporates the stochastic nature of chromosome conformations and allows a detailed analysis of alternative chromatin structure states. For example, we predict and experimentally confirm the presence of large centromere clusters with distinct chromosome compositions varying between individual cells. The stability of these clusters varies greatly with their chromosome identities. We show that these chromosome-specific clusters can play a key role in the overall chromosome positioning in the nucleus and stabilizing specific chromatin interactions. By explicitly considering genome structural variability, our population-based method provides an important tool for revealing novel insights into the key factors shaping the spatial genome organization.

Operator learning for urban water clarification hydrodynamics and particulate matter transport with physics-informed neural networks.

Computational fluid dynamics (CFD) can be a powerful tool for higher-fidelity water infrastructure planning and design. Despite decades of development and demonstration over a wide range of water systems such as clarification basins, activated sludge processes, ozone contactors, etc., CFD remains primarily used in academic research, with limited application in civil and environmental engineering practice. This limitation is contributed by its higher computational cost and demand for specialized user skills. This, however, need not be the case, if a robust and efficient surrogate model can be developed from CFD simulations and independently deployed for engineering purposes. Leveraging the emerging scientific machine learning (ML) techniques of physics-informed ML and operator learning, this study develops a composite neural network (CPNN) for learning the flow hydrodynamics and particulate matter (PM) transport and fate in clarification systems. The CPNN consists of a deep operator network (DeepONet) as an encoder and a physics-informed neural network (PINN) as a decoder. In contrast to common "black box" and lumped ML approaches, the developed CPNN directly incorporates physics principles into its architecture. Furthermore, the CPNN is designed for process-resolved and operator learning, enabling it to predict spatial hydrodynamics and PM concentration distribution (i.e., contours) across different basin geometrics and loading conditions. Compared to CFD simulation, the developed CPNN model has significantly higher computational efficiency (∼ milliseconds) while demonstrating robust predictive capability. For predicting basin hydrodynamics across 10,000 test cases, the trained CPNN model achieves an R2 above 0.8 for 66.4% of cases and an R2 above 0.4 for 89.2% of cases. A similar performance is also demonstrated by the CPNN in predicting basin PM concentration. Further investigation reveals that basin geometrics that trigger bi-modal flow solutions can be particularly challenging for ML. Additionally, this study visualizes the dependency of basin hydrodynamics and PM concentration on basin geometrics and loading conditions, providing valuable insights for optimizing basin configuration. Lastly, the potentials and benefits of web-based applications, e.g., DeepXtorm, as a user-friendly interface for the developed CPNN model is discussed. This study represents the initial step toward achieving real-time higher-fidelity water infrastructure planning, design, optimization, and regulation.

High-throughput fluorescence quantification method based on inner filter effect and fluorescence imaging analysis.

The application of the inner filter effect (IFE) in fluorescent substance determination is gaining popularity. In this paper, a theory of the fluorescence distribution along with the excitation light path is derived from our previous research about the spatial micro-element method. According to the relationship between the summation of fluorescence intensities along the vertical direction at a certain position on the excitation light path and the position, a high-concentration and wide-range fluorescent substance quantification method based on the IFE and fluorescence imaging analysis is proposed. Correspondingly, a high-throughput fluorescent substance quantification detection system is constructed. In order to validate the method, solutions of rhodamine B in different concentrations are used for principle validation, concentration prediction, and experimental investigation on the influence of integration time and lens distortion. The high-throughput system enables the simultaneous measurement of six samples, realizing the high-concentration and wide-range quantification of rhodamine B (100-600 mg/L) with high precision (R2 = 0.9992, MRE = 2.34 %). By setting the filter wheel, the system can measure the concentration of fluorescent substances with different emission wavelengths. The improvement of experimental device is expected to reduce the single sample capacity to tens of microliters and increase the overall sample quantity to tens or even hundreds. The proposed method and system are beneficial to fluorescence measurement in fields such as biomedicine and dye research and to the improvement of high-throughput fluorescence quantitative PCR instruments.

REACT: Remainder Adaptive Compensation for Domain Adaptive Object Detection.

Domain adaptive object detection (DAOD) aims to infer a robust detector on the target domain with the labelled source datasets. Recent studies utilize a feature extractor shared on the source and target domains to capture the domain-invariant features and the task-relevant information with both feature-alignment constraint and source annotations. However, the feature extractor shared across domains discards partial task-relevant information of the target domain due to the domain gap and lack of target annotations, leading to compromised discrimination capabilities within target domain. To this end, we propose a novel REmainder Adaptive CompensaTion network (REACT) to adaptively compensate the extracted features with the remainder features for generating task-relevant features. The key insight is that the remainder features contain the discarded task-relevant information, so they can be adapted to compensate for the inadequate target features. Especially, REACT introduces an additional remainder branch to regain the remainder features, and then adaptively utilizes them to compensate for the discarded task-relevant information, improving discrimination on the target domain. Extensive experiments over multiple cross-domain adaptation tasks with three baselines demonstrate that our approach gains significant improvements and achieves superior performance compared with highly-optimized state-of-the-art methods.

Highly efficient adsorption of ciprofloxacin from aqueous solutions by waste cation exchange resin-based activated carbons: Performance, mechanism, and theoretical calculation.

This study focused on the preparation of a highly efficient activated carbon adsorbent from waste cation exchange resins through one-step carbonization to remove ciprofloxacin (CIP) from aqueous solutions. Scanning electron microscopy, X-ray diffraction, Fourier-transform infrared spectrometry, and X-ray photoelectron spectroscopy were used to characterize the physicochemical properties of the carbonized materials. The CIP removal efficiency, influencing factors, and adsorption mechanisms of CIP on the carbonized resins were investigated. Density functional theory (DFT) computations were performed to elucidate the adsorption mechanisms. The CIP removal reached 93 % when the adsorbent dosage was 300 mg/L at 25 °C. The adsorption capacity of the carbonized resins to CIP gradually decreased with an increasing pH from 3.0 to 7.0 and sharply declined with a pH from 7.0 to 11.0. The adsorption process better fitted by the pseudo second-order kinetic and Langmuir models, indicating that the interaction between CIP and the carbonized resins was monolayer adsorption. The maximum adsorption capacity fitted by the Langmuir model was 384.4 mg/g at 25 °C. Microstructural analysis showed that the adsorption of CIP on the carbonized resins was a joint effect of H-bonding, ion exchange, and graphite-N adsorption. Computational results signified the strong H-bonding and ion exchange interactions existed between CIP and carbonized resins. The high adsorption and reusability suggest that waste cation exchange resin-based activated carbons can be used as an effective and reusable adsorbent for removing CIP from aqueous solutions.