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Accurate and efficient estimation of individual tree aboveground biomass (AGB) is crucial for precision forestry and forest carbon stock assessment. While the influence of spatial structure on biomass is acknowledged, its integration into individual tree AGB models for enhancing accuracy remains equivocal. The UAV-LiDAR data and individual tree AGB destructively measured of natural Pinus kesiya var. langbianensis were collected from 2022 to 2023. First, Individual tree attributes and spatial structures were extracted and evaluated from LiDAR data. Then, two AGB models were developed and compared: one independent of diameter at breast height (DBH) and another incorporating stand spatial structure parameters, including the uniform angle index (W), neighborhood comparison (U), stand level rate (Si), and competition index (UCi). The results showed that AGB models can be effectively constructed based on canopy features alone, even without the inclusion of DBH. The accuracy of individual tree AGB models was improved when spatial structure parameters were incorporated. In particular, the introduction of angular scales improved the accuracy of the AGB model most significantly. On average, R2 increased by 8.668%, while RMSE, SEE, and TRE decreased by 11.262%, 10.619%, and 7.570%, respectively. Spatial structure parameters facilitated a more precise and realistic depiction of competitive interactions and spatial distribution patterns among trees, thereby enhancing model performance. It demonstrated an effective approach for leveraging UAV-LiDAR data to rapidly and precisely estimate AGB and carbon stocks for individual trees, forest stands and regional scales.

It is crucial to develop a comprehensive method for estimating the aboveground biomass (AGB) of trees, shrubs, grasslands, and sparse tree areas in ecologically fragile dry, hot valley regions with vertical zonation. Multi-source remote-sensing data can fulfill this requirement, providing help in monitoring the health of ecosystems and providing a basis for regional biodiversity conservation and restoration. Sentinel-2A satellite imagery was used to classify the forests, shrubs, and grasslands in Yuanmou County, Chuxiong Yi Autonomous Prefecture, Yunnan Province, China. The Gaofen-2 satellite (GF-2) was used to extract the canopy width and calculate tree biomass in the valley-type savanna region. These data were combined with remote-sensing factors and measured survey data, and random forest (RF) and extreme gradient boosting (XGBoost) models were used to estimate the biomass. Using GF-2 images to segment sparse tree areas effectively reduced the overestimation of low-resolution remote-sensing images, enabling the AGB of sparse trees to be accurately estimated. The biomass estimations based on the Sentinel-2A images attained coefficient of determination (R2) values of 0.45 and 0.47 for the forest, 0.55 and 0.61 for the shrubs, and 0.32 and 0.37 for the grasslands using RF and XGBoost models, respectively, demonstrating variable effectiveness across vegetation types. In addition, the XGBoost model was more robust than the RF model for all three vegetation types. Our methodology provides scientific support for the sustainable development of ecologically fragile dry, hot valleys and savanna areas.

Abstract To investigate the clinical characteristics of Guillain–Barré syndrome (GBS) in patients with primary Sjögren’s syndrome (SS). Records of patients with positive anti-SSA antibodies hospitalized in the Beijing Tiantan Hospital between December 2011 and May 2020 were retrieved. Patients who fulfilled the criteria for diagnosis of GBS and primary SS were included, and their clinical data were analyzed. Among the 785 patients with positive anti-SSA, 52 patients were identified in this study. They were 27 males and 25 females with median age of 59 years old. Besides anti-SSA antibodies, multiple autoantibodies were detected in these patients including antinuclear antibody, anti-Ro52, anti-mitochondrial M2, anti-thyroid peroxidase and anti-thyroglobulin autoantibodies. Preceding infection was reported in 42 patients. Hyporeflexia/areflexia and limbs weakness were the most common manifestation and 35 patients presented cranial nerve injuries. GBS disability score of 3, 4 and 5 was scaled in 28 (53.8%), 15 (28.8%) and 3 (5.8%) patients respectively. Forty-six patients received intravenous immunoglobulin (IVIG) monotherapy, 5 patients were treated by IVIG plus glucocorticoids, and 51 patients improved during hospitalization. The frequency of male gender among the patients with both GBS and primary SS suggests an independent onset of GBS and the co-existence of these autoimmune diseases in patients with multiple autoantibodies. Majority of patients with GBS and primary SS experience benign disease course.

Estimation of forest above-ground biomass (AGB) using multi-source remote sensing data is an important method to improve the accuracy of the estimate. However, selecting remote sensing factors that can effectively improve the accuracy of forest AGB estimation from a large amount of data is a challenge when the sample size is small. In this regard, the Least Absolute Shrinkage and Selection Operator (Lasso) has advantages for extensive redundant variables but still has some drawbacks. To address this, the study introduces two Least Absolute Shrinkage and Selection Operator Lasso-based variable selection methods: Least Absolute Shrinkage and Selection Operator Genetic Algorithm (Lasso-GA) and Variance Inflation Factor Least Absolute Shrinkage and Selection Operator (VIF-Lasso). Sentinel 2, Sentinel 1, Landsat 8 OLI, ALOS-2 PALSAR-2, Light Detection and Ranging, and Digital Elevation Model (DEM) data were used in this study. In order to explore the variable selection capabilities of Lasso-GA and VIF-Lasso for remote sensing estimation of forest AGB. It compares Lasso-GA and VIF-Lasso with Boruta, Random Forest Importance Selection, Pearson Correlation, and Lasso for selecting remote sensing factors. Additionally, it employs eight machine learning models—Random Forest (RF), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Bayesian Regression Neural Network (BRNN), Elastic Net (EN), K-Nearest Neighbors (KNN), Extremely Randomized Trees (ETR), and Stochastic Gradient Boosting (SGBoost)—to estimate forest AGB in Wuyi Village, Zhenyuan County. The results showed that the optimized Lasso variable selection could improve the accuracy of forest biomass estimation. The VIF-Lasso method results in a BRNN model with an R2 of 0.75 and an RMSE of 16.48 Mg/ha. The Lasso-GA method results in an ETR model with an R2 of 0.73 and an RMSE of 16.70 Mg/ha. Compared to the optimal SGBoost model with the Lasso variable selection method (R2 of 0.69, RMSE of 18.63 Mg/ha), the VIF-Lasso method improves R2 by 0.06 and reduces RMSE by 2.15 Mg/ha, while the Lasso-GA method improves R2 by 0.04 and reduces RMSE by 1.93 Mg/ha. From another perspective, they also demonstrated that the RX sample count and sensitivity provided by LiDAR, as well as the Horizontal Transmit, Vertical Receive provided by Microwave Radar, along with the feature variables (Mean, Contrast, and Correlation) calculated from the Green, Red, and NIR bands of optical remote sensing in 7 × 7 and 5 × 5 windows, play an important role in forest AGB estimation. Therefore, the optimized Lasso variable selection method shows strong potential for forest AGB estimation using multi-source remote sensing data.

Identifying the key climate variables affecting optical saturation values (OSVs) in forest aboveground biomass (AGB) estimation using optical remote sensing is crucial for analyzing OSV changes. This can improve AGB estimation accuracy by addressing the uncertainties associated with optical saturation. In this study, Pinus yunnanensis forests and Landsat 8 OLI imagery from Yunnan were used as case studies to explain this issue. The spherical model was applied to determine the OSVs using specific spectral bands (Blue, Green, Red, Near-Infrared (NIR), and Short-Wave Infrared Band 2 (SWIR2)) derived from Landsat 8 OLI imagery. Canonical correlation analysis (CCA) uncovered the intricate relationships between climatic variables and OSV variations. The results reveal the following: (1) All Landsat 8 OLI spectral bands showed a negative correlation with the Pinus yunnanensis forest AGB, with OSVs ranging from 104.42 t/ha to 209.11 t/ha, peaking in the southwestern region and declining to the lowest levels in the southeastern region. (2) CCA effectively explained 93.2% of the OSV variations, identifying annual mean temperature (AMT) as the most influential climatic factor. Additionally, the mean temperature of the wettest quarter (MTQ) and annual precipitation (ANP) were significant secondary determinants, with higher OSV values observed in warmer, more humid areas. These findings offer important insights into climate-driven OSV variations, reducing uncertainty in forest AGB estimation and enhancing the precision of AGB estimations in future research.

Improving the precision of aboveground biomass (AGB) estimation in lowland tropical forests is crucial to enhancing our understanding of carbon dynamics and formulating climate change mitigation strategies. This study proposes an AGB estimation method for lowland tropical forests in Xishuangbanna, which include various vegetation types, such as Pinus kesiya var. langbianensis, oak, Hevea brasiliensis, and other broadleaf trees. In this study, 2016 forest management inventory data are integrated with remote sensing variables from Landsat 8 OLI (L8) and Sentinel 2A (S2) imagery to estimate forest AGB. The forest age and aspect were utilized as stratified variables to construct the random forest (RF) models, which may improve the AGB estimation accuracy. The key findings are as follows: (1) through variable screening, elevation was identified as the main factor correlated with the AGB, with texture measures derived from a pixel window size of 7 × 7 perform best for AGB sensitivity, followed by 5 × 5, with 3 × 3 being the least effective. (2) A comparative analysis of imagery groups for the AGB estimation revealed that combining L8 and S2 imagery achieved superior performance over S2 imagery alone, which, in turn, surpassed the accuracy of L8 imagery. (3) Stratified models, which integrated aspect and age variables, consistently outperformed the unstratified models, offering a more refined fit for lowland tropical forest AGB estimation. (4) Among the analyzed forest types, the AGB of P. kesiya var. langbianensis forests was estimated with the highest accuracy, followed by H. brasiliensis, oak, and other broadleaf forests within the RF models. These findings highlight the importance of selecting appropriate variables and sensor combinations in addition to the potential of stratified modeling approaches to improve the precision of forest biomass estimation. Overall, incorporating stratification theory and multi-source data can enhance the AGB estimation accuracy in lowland tropical forests, thus offering crucial insights for refining forest management strategies.

The optical saturation problem is one of the main factors causing uncertainty in aboveground biomass (AGB) estimations using optical remote sensing data. It is critical for the improvement in AGB estimation accuracy to clarify the relationships between environmental factors and the variations in optical saturation values (OSVs). In this study, we obtained the OSVs for 20 districts and clarified the individual, interactive, and comprehensive effects of climate, soil, and topographical factors on the OSV variations. The results are as follows: (1) the range of the OSVs was from 104 t/hm2 to 182 t/hm2 for the 20 districts; (2) the soil factor had the greatest (−0.635) influence on the OSVs compared to climate and topography; (3) the highest interaction effect (−0.833) was between climate and soil; (4) the comprehensive effect of the three environmental factors on the OSVs was obvious, and the correlation coefficient was 0.436. Moreover, the mean temperature of the coldest quarter (MCQMean) had the highest effect on the OSVs. The results indicate that environmental factors significantly affect the variation in OSVs through their individual, interactive, and comprehensive effects. Our findings provide a valuable reference for reducing the uncertainty caused by spectral saturation in AGB estimations using optical remote sensing data.

The coevolution of supermassive black holes and their host galaxies represents a fundamental question in astrophysics. One approach to investigating this question involves comparing the star formation rates (SFRs) of active galactic nuclei (AGNs) with those of typical star-forming galaxies. At relatively low redshifts ( z ≲ 1), radio AGNs manifest diminished SFRs, indicating suppressed star formation, but their behavior at higher redshifts is unclear. To examine this, we leveraged galaxy and radio-AGN data from the well-characterized W-CDF-S, ELAIS-S1, and XMM-LSS fields. We established two mass-complete reference star-forming galaxy samples and two radio-AGN samples, consisting of 1763 and 6766 radio AGNs, the former being higher in purity and the latter more complete. We subsequently computed star-forming fractions ( f _SF ; the fraction of star-forming galaxies to all galaxies) for galaxies and radio-AGN host galaxies and conducted a robust comparison between them up to z ≈ 3. We found that the tendency for radio AGNs to reside in massive galaxies primarily accounts for their low f _SF , which also shows a strong negative dependence upon M _⋆ and a strong positive evolution with z . To investigate further the star formation characteristics of those star-forming radio AGNs, we constructed the star-forming main sequence (MS) and investigated the behavior of the position of AGNs relative to the MS at z ≈ 0–3. Our results reveal that radio AGNs display lower SFRs than star-forming galaxies in the low- z and high- M _⋆ regime and, conversely, exhibit comparable or higher SFRs than MS star-forming galaxies at higher redshifts or lower M _⋆ .

Dust-obscured galaxies (DOGs) are enshrouded by dust and many are believed to host accreting supermassive black holes (SMBHs), which makes them unique objects for probing the coevolution of galaxies and SMBHs. We select and characterize DOGs in the 13 deg ^2 XMM-Spitzer Extragalactic Representative Volume Survey (XMM-SERVS), leveraging the superb multiwavelength data—from X-rays to radio. We select 3738 DOGs at z ≈ 1.6–2.1 in XMM-SERVS, while maintaining good data quality without introducing significant bias. This represents the largest DOG sample with thorough multiwavelength source characterization. Spectral energy distribution modeling shows DOGs are a heterogeneous population consisting of both normal galaxies and active galactic nuclei (AGNs). Our DOGs are massive ( $\mathrm{log}{M}_{\star }/{M}_{\odot }\approx 10.7-11.3$ ), 174 are detected in X-rays, and they are generally radio-quiet systems. X-ray detected DOGs are luminous and are moderately to heavily obscured in X-rays. Stacking analyses for the X-ray undetected DOGs show highly significant average detections. Critically, we compare DOGs with matched galaxy populations. DOGs have similar AGN fractions compared with typical galaxy populations. X-ray detected DOGs have higher M _⋆ and higher X-ray obscuration, but they are not more star-forming than typical X-ray AGNs. Our results potentially challenge the relevance of the merger-driven galaxy-SMBH coevolution framework for X-ray detected DOGs.

Dust-obscured galaxies (DOGs) containing central supermassive black holes (SMBHs) that are rapidly accreting (i.e., having high Eddington ratios, λ _Edd ) may represent a key phase closest to the peak of both the black hole and galaxy growth in the coevolution framework for SMBHs and galaxies. In this work, we present a 68 ks XMM-Newton observation of the high- λ _Edd DOG J1324+4501 at z ∼ 0.8, which was initially observed by Chandra. We analyze the XMM-Newton spectra jointly with archival Chandra spectra. In performing a detailed X-ray spectral analysis, we find that the source is intrinsically X-ray luminous with $\mathrm{log}({L}_{{\rm{X}}}$ /erg ${{\rm{s}}}^{-1})={44.71}_{-0.12}^{+0.08}$ and heavily obscured with $\mathrm{log}({N}_{{\rm{H}}}/{\mathrm{cm}}^{-2})={23.43}_{-0.13}^{+0.09}$ . We further utilize UV-to-IR archival photometry to measure and fit the source’s spectral energy distribution to estimate its host-galaxy properties. We present a supplementary comparison sample of 21 X-ray luminous DOGs from the XMM-SERVS survey with sufficient (>200) 0.5–10 keV counts to perform a similarly detailed X-ray spectral analysis. Of the X-ray luminous DOGs in our sample, we find that J1324+4501 is the most remarkable, possessing one of the highest X-ray luminosities, column densities, and star formation rates. We demonstrate that J1324+4501 is in an extreme evolutionary stage where SMBH accretion and galaxy growth are at their peaks.

The growth of supermassive black holes is strongly linked to their galaxies. It has been shown that the population mean black hole accretion rate ( $\overline{\mathrm{BHAR}}$ ) primarily correlates with the galaxy stellar mass ( M _⋆ ) and redshift for the general galaxy population. This work aims to provide the best measurements of $\overline{\mathrm{BHAR}}$ as a function of M _⋆ and redshift over ranges of 10 ^9.5 < M _⋆ < 10 ^12 M _⊙ and z < 4. We compile an unprecedentedly large sample with 8000 active galactic nuclei (AGNs) and 1.3 million normal galaxies from nine high-quality survey fields following a wedding cake design. We further develop a semiparametric Bayesian method that can reasonably estimate $\overline{\mathrm{BHAR}}$ and the corresponding uncertainties, even for sparsely populated regions in the parameter space. $\overline{\mathrm{BHAR}}$ is constrained by X-ray surveys sampling the AGN accretion power and UV-to-infrared multiwavelength surveys sampling the galaxy population. Our results can independently predict the X-ray luminosity function (XLF) from the galaxy stellar mass function (SMF), and the prediction is consistent with the observed XLF. We also try adding external constraints from the observed SMF and XLF. We further measure $\overline{\mathrm{BHAR}}$ for star-forming and quiescent galaxies and show that star-forming $\overline{\mathrm{BHAR}}$ is generally larger than or at least comparable to the quiescent $\overline{\mathrm{BHAR}}$ .

We present a focused X-ray and multiwavelength study of the ultraluminous weak-line quasar (WLQ) SDSS J1521+5202, one of the few X-ray weak WLQs that is amenable to basic X-ray spectral and variability investigations. J1521+5202 shows striking X-ray variability during 2006–2023, by up to a factor of ≈32 in 0.5–2 keV flux, and our new 2023 Chandra observation caught it in its brightest X-ray flux state to date. Concurrent infrared/optical observations show only mild variability. The 2023 Chandra spectrum can be acceptably described by a power law with intrinsic X-ray absorption, and it reveals a nominal intrinsic level of X-ray emission relative to its optical/ultraviolet emission. In contrast, an earlier Chandra spectrum from 2013 shows apparent spectral complexity that is not well fit by a variety of models, including ionized absorption or standard Compton-reflection models. Overall, the observations are consistent with the thick-disk plus outflow model previously advanced for WLQs, where a nominal level of underlying X-ray emission plus variable absorption leads to the remarkable observed X-ray variability. In the case of J1521+5202, it appears likely that the outflow, and not the thick disk itself, lies along our line of sight and causes the X-ray absorption.

Supermassive black holes (SMBHs) can grow through both accretion and mergers. It is still unclear how SMBHs evolve under these two channels from high redshifts to the SMBH population we observe in the local Universe. Observations can directly constrain the accretion channel but cannot effectively constrain mergers yet, while cosmological simulations provide galaxy merger information but can hardly return accretion properties consistent with observations. In this work, we combine the observed accretion channel and the simulated merger channel, taking advantage of observations and cosmological simulations, to depict a realistic evolution pattern of the SMBH population. With this methodology, we can derive the scaling relation between the black hole mass ( M _BH ) and host-galaxy stellar mass ( M _⋆ ), and the local black hole mass function (BHMF). Our scaling relation is lower than those based on dynamically measured M _BH , supporting the claim that dynamically measured SMBH samples may be biased. We show that the scaling relation has little redshift evolution. The BHMF steadily increases from z = 4 to z = 1 and remains largely unchanged from z = 1 to z = 0. The overall SMBH growth is generally dominated by the accretion channel, with possible exceptions at high mass ( M _BH ≳ 10 ^8 M _⊙ or M _⋆ ≳ 10 ^11 M _⊙ ) and low redshift ( z ≲ 1). We also predict that around 25% of the total SMBH mass budget in the local Universe may be locked within long-lived, wandering SMBHs, and the wandering mass fraction and wandering SMBH counts increase with M _⋆ .

To solve the problems of insufficient power, high speed impact, high energy consumption, etc. of the detectors applied to large-diameter pipelines before the conventional cup-type pipelines are put into production, the self-driven inner detector suitable for large-diameter pipelines was developed. As the detector is capable of travelling autonomously inside the pipeline, detecting the geometric deformation of the pipeline and shooting the internal video of the pipeline, it can be used for pipeline inspection before commissioning or during the shutdown maintenance period. The overall structure and body design of the detector were introduced, the influence of travelling attitude on driving force was analyzed, the design and function of the detection unit were systematically expounded, and the motion characteristics and detection performance of the detector were verified through field application tests. After being applied to the pipelines under construction in China-Russia Eastern Gas Pipeline Project, the application results show that the endurance mileage of the detector reaches more than 50 km, and the synchronous visual inspection on geometric deformations of the pipeline, internal surface defects of the pipe wall, foreign matters such as water film inside the pipeline is realized, thus demonstrating its excellent usability and advancement in practice.

In order to solve the technical defects of the conventional inner detector and to meet the actual demands of China-Russia Eastern Gas Pipeline Project, a new type of high density polyurethane inner diameter detector for large diameter pipelines before service was developed. The intelligent diameter measuring device mounted on the main body of high density polyurethane foam can not only find the change of the inner diameter of the pipeline and record the deformation position, but also accurately identify the number of girth welds of the pipeline, so as to verify the girth weld information in combination with the pipeline construction records. The working principle, structural composition and design and implementation of the electronic system of the detector were discussed. The detector has been applied to the site inspection of China-Russia Eastern Gas Pipeline Project. The results show that, compared with the conventional cup-shaped steel skeleton intelligent diameter measuring detector, the required operating thrust of the detector is smaller, the allowable operating speed is higher, the structure is more robust, and the maintenance is simple. While ensuring the detection accuracy and detection rate, the detector brings dramatic decline in use cost and important engineering significance for the geometric deformation detection of large diameter pipelines.

Alternative polyadenylation (APA) of pre-mRNA is an important co-transcriptional mechanism that modulates gene expression, leading to transcriptomic and functional diversities. The role of APA in Arabidopsis leaf development, however, remains elusive. We applied a poly(A)-tag sequencing (PAT-seq) technique to characterize APA-mediated regulation events in cotyledon and in five stages of true leaf development. Over 60% APA was identified in genes expressed in leaves, consistent with the results in previous publications. However, a reduced APA level was detected in younger leaves, reaching 44% in the 18th true leaf. Importantly, we also found that >70% of the poly(A) site usages were altered in the second true leaf relative to the cotyledon. Compared with the cotyledon, more genes in the second true leaf tended to use the distal site of 3′UTR, but this was not found in pairwise comparison among other true leaves. In addition, a significant APA gene was found to be decreased in a pairwise comparison among true leaves, including differentially expressed genes. The APA genes identified herein were associated with specific biological processes, including metabolic and cellular processes and response to stimuli and hormones. These results provide a new insight into the regulation of Arabidopsis leaf development through APA.

We present the results from a coordinated XMM-Newton + NuSTAR observation of the Seyfert 1 galaxy ESO 511−G030. With this joint monitoring program, we conduct a detailed variability and spectral analysis. The source remained in a low flux and very stable state throughout the observation period, although there are slight fluctuations of flux over long timescales. The broadband (0.3–78 keV) spectrum shows the presence of a power-law continuum with a soft excess below 2 keV, a relatively narrow iron K α emission (∼6.4 keV), and an obvious cutoff at high energies. We find that the soft excess can be modeled by two different possible scenarios: a warm ( kT _e ∼ 0.19 keV) and optically thick ( τ = 18–25) Comptonizing corona or a relativistic reflection from a high-density ( $\mathrm{log}[{n}_{{\rm{e}}}/{\mathrm{cm}}^{-3}]=17.1\mbox{--}18.5$ ) inner disk. All models require a low temperature ( kT _e ∼ 13 keV) for the hot corona.

In order to support the development of high–precision spacecraft, the current state of the Stewart vibration isolation platform in the field of aerospace micro–vibration was surveyed. First, based on analyses of the causes and characteristics of spacecraft micro–vibration, the principles, characteristics, advantages and disadvantages of four vibration isolation technologies are summarized. Second, the development process of the Stewart vibration isolation platform, from structural proposal and theoretical calculation to application in various fields, is introduced. Then, the current state of kinematics, dynamics and braking control algorithms of the Stewart platform is investigated, and related work on rigid/flexible platforms in the field of aerospace micro–vibration is introduced in detail. Finally, the idea that the Stewart platform can be fabricated by 4D printing technology is proposed. The novel Stewart platform can be combined with artificial intelligence algorithms and advanced control strategies, allowing for further development in the direction of an integrated omnidirectional, full–frequency and multi–function platform with variable stiffness.

Colorectal cancer (CRC) is a common digestive tract tumor worldwide. Specific microorganisms, including Fusobacterium nucleatum (F. nucleatum) and Escherichia coli (E. coli), are abundant in colonic mucosa and can promote the cancer progression and malignancy. Therefore, a therapeutic strategy is proposed to deliver effective drugs to colorectum for both anticancer and antibacteria. Here we used thin-film dispersion method to encapsulate hemiprotonic phenanthroline-phenanthroline+ (ph-ph+) into nanomicelle. The results showed that the drug-loading nanomicelle had good dispersion, and the particle size was about 28 nm. In vitro assay indicated that the nanomicelle was active against CRC-related obligate and facultative anaerobes. In human CRC cells, the nanomicelle could effectively inhibit cell proliferation and induce apoptosis. In vivo distribution showed that the nanomicelle could release ph-ph+ mainly in the colorectum. In CRC model mice, the nanomicelle significantly reduced tumor number and volume, and decreased the bacteria load and colorectal inflammation. Together, the study identifies that the ph-ph+nanomicelle has the potential to apply in treating CRC, and also suggests that anticancer combined with antimicrobial therapy would be a feasible way for CRC therapy.