CPPLS-MLP: a method for constructing cell-cell communication networks and identifying related highly variable genes based on single-cell sequencing and spatial transcriptomics data.

In the growth and development of multicellular organisms, the immune processes of the immune system and the maintenance of the organism's internal environment, cell communication plays a crucial role. It exerts a significant influence on regulating internal cellular states such as gene expression and cell functionality. Currently, the mainstream methods for studying intercellular communication are focused on exploring the ligand-receptor-transcription factor and ligand-receptor-subunit scales. However, there is relatively limited research on the association between intercellular communication and highly variable genes (HVGs). As some HVGs are closely related to cell communication, accurately identifying these HVGs can enhance the accuracy of constructing cell communication networks. The rapid development of single-cell sequencing (scRNA-seq) and spatial transcriptomics technologies provides a data foundation for exploring the relationship between intercellular communication and HVGs. Therefore, we propose CPPLS-MLP, which can identify HVGs closely related to intercellular communication and further analyze the impact of Multiple Input Multiple Output cellular communication on the differential expression of these HVGs. By comparing with the commonly used method CCPLS for constructing intercellular communication networks, we validated the superior performance of our method in identifying cell-type-specific HVGs and effectively analyzing the influence of neighboring cell types on HVG expression regulation. Source codes for the CPPLS_MLP R, python packages and the related scripts are available at 'CPPLS_MLP Github [https://github.com/wuzhenao/CPPLS-MLP]'.

GTAD: a graph-based approach for cell spatial composition inference from integrated scRNA-seq and ST-seq data.

With the emergence of spatial transcriptome sequencing (ST-seq), research now heavily relies on the joint analysis of ST-seq and single-cell RNA sequencing (scRNA-seq) data to precisely identify cell spatial composition in tissues. However, common methods for combining these datasets often merge data from multiple cells to generate pseudo-ST data, overlooking topological relationships and failing to represent spatial arrangements accurately. We introduce GTAD, a method utilizing the Graph Attention Network for deconvolution of integrated scRNA-seq and ST-seq data. GTAD effectively captures cell spatial relationships and topological structures within tissues using a graph-based approach, enhancing cell-type identification and our understanding of complex tissue cellular landscapes. By integrating scRNA-seq and ST data into a unified graph structure, GTAD outperforms traditional 'pseudo-ST' methods, providing robust and information-rich results. GTAD performs exceptionally well with synthesized spatial data and accurately identifies cell spatial composition in tissues like the mouse cerebral cortex, cerebellum, developing human heart and pancreatic ductal carcinoma. GTAD holds the potential to enhance our understanding of tissue microenvironments and cellular diversity in complex bio-logical systems. The source code is available at https://github.com/zzhjs/GTAD.

eCRUIS captures RNA-protein interaction in vitro and in vivo.

As an information bridge between DNA and protein, RNA regulates cellular processes and gene expression in various ways. From its synthesis to degradation, RNA is associated with a range of RNA-binding proteins. Therefore, it is necessary to develop innovative methods to study the interaction between RNA and proteins. Previously, we developed an RNA-centric method, called CRISPR-based RNA-United Interacting System (CRUIS), to capture RNA-protein interaction in cells. On this basis, here we develop an enhanced CRUIS (eCRUIS) by combining the power of dCas13d and the engineered promiscuous ligase TurboID. The current version allows us to rapidly label RNA-binding proteins on the target RNA within 30 minutes, potentially for in vivo use. By introducing bait-assay with exogenous RNA, we confirm that eCRUIS can effectively label RNA-binding proteins on bait RNA in a short time. eCRUIS provides a broader range of in vitro and in vivo applications for studying RNA-protein interactions.

Efficient and robust phase unwrapping method based on SFNet.

Phase unwrapping is a crucial step in obtaining the final physical information in the field of optical metrology. Although good at dealing with phase with discontinuity and noise, most deep learning-based spatial phase unwrapping methods suffer from the complex model and unsatisfactory performance, partially due to simple noise type for training datasets and limited interpretability. This paper proposes a highly efficient and robust spatial phase unwrapping method based on an improved SegFormer network, SFNet. The SFNet structure uses a hierarchical encoder without positional encoding and a decoder based on a lightweight fully connected multilayer perceptron. The proposed method utilizes the self-attention mechanism of the Transformer to better capture the global relationship of phase changes and reduce errors in the phase unwrapping process. It has a lower parameter count, speeding up the phase unwrapping. The network is trained on a simulated dataset containing various types of noise and phase discontinuity. This paper compares the proposed method with several state-of-the-art deep learning-based and traditional methods in terms of important evaluation indices, such as RMSE and PFS, highlighting its structural stability, robustness to noise, and generalization.

Multichannel focused higher-order Poincaré sphere beam generation based on a dielectric geometric metasurface.

Focused vector beams (VBs) are important topic in the areas of light field manipulation. Geometric metasurfaces provide a convenient platform to facilitate the generation of focused VBs. In this study, we propose a dielectric geometric metasurface to generate multichannel focused higher-order Poincaré sphere (HOP) beams. With identical meta-atoms of half-wave plate, the metasurface comprises two sub-metasurfaces, and each of them includes two sets of rings related to Fresnel zones. For meta-atoms on each set of rings, the hyperbolic geometric phase profile is configured so that the mirror-symmetrical position-flip of the off-axis focal point is enabled under the chirality switch of the illuminating circular polarization. With the design of helical geometric phase profiles for the two sets of rings, a sub-metasurface generate two HOP beams at the symmetrical two focal points. The performance of the two sub-metasurfaces enables the metasurface with four sets of rings to generate the array of four HOP beams. The proposed method was validated by theoretical analyses, numerical simulation and experimental conduction. This research would be significant in miniaturizing and integrating optical systems involving applications of VB generations and applications.

Photoelectron velocity map imaging spectroscopy of group 14 elements and iron tetracarbonyl anionic clusters MFe(CO)4- (M = Si, Ge, Sn).

The heteronuclear group 14 M-iron tetracarbonyl clusters MFe(CO)4- (M = Si, Ge, Sn) anions have been generated in the gas phase by laser ablation of M-Fe alloys and detected by mass and photoelectron spectroscopy. With the support of quantum chemical calculations, the geometric and electronic structures of MFe(CO)4- (M = Si, Ge, Sn) are elucidated, which shows that all the MFe(CO)4- clusters have the M-Fe bonded, iron-centered, and carbonyl-terminal M-Fe(CO)4 structure with the C2v symmetry and a 2B2 ground state. The M-Fe bond can be considered a double bond, which includes one σ electron sharing bond and one π dative bond. The C-O bonds in those anionic clusters are calculated to be elongated to different extents, and in particular, the C-O bonds in SiFe(CO)4- are elongated more. The Si-Fe alloy thus turns out to be a better collocation to activate the C-O bonds in the gas phase among group 14. The present findings have important implications for the rational development of high-performance catalysts with isolated metal atoms/clusters dispersed on supports.

The impact of temperature, humidity and closing school on the mumps epidemic: a case study in the mainland of China.

BACKGROUND: To control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission. METHODS: In this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number  R 0 . As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine. RESULTS: We find that the basic reproduction number  R 0  is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity. CONCLUSION: We conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission.

scZAG: Integrating ZINB-Based Autoencoder with Adaptive Data Augmentation Graph Contrastive Learning for scRNA-seq Clustering.

Single-cell RNA sequencing (scRNA-seq) is widely used to interpret cellular states, detect cell subpopulations, and study disease mechanisms. In scRNA-seq data analysis, cell clustering is a key step that can identify cell types. However, scRNA-seq data are characterized by high dimensionality and significant sparsity, presenting considerable challenges for clustering. In the high-dimensional gene expression space, cells may form complex topological structures. Many conventional scRNA-seq data analysis methods focus on identifying cell subgroups rather than exploring these potential high-dimensional structures in detail. Although some methods have begun to consider the topological structures within the data, many still overlook the continuity and complex topology present in single-cell data. We propose a deep learning framework that begins by employing a zero-inflated negative binomial (ZINB) model to denoise the highly sparse and over-dispersed scRNA-seq data. Next, scZAG uses an adaptive graph contrastive representation learning approach that combines approximate personalized propagation of neural predictions graph convolution (APPNPGCN) with graph contrastive learning methods. By using APPNPGCN as the encoder for graph contrastive learning, we ensure that each cell's representation reflects not only its own features but also its position in the graph and its relationships with other cells. Graph contrastive learning exploits the relationships between nodes to capture the similarity among cells, better representing the data's underlying continuity and complex topology. Finally, the learned low-dimensional latent representations are clustered using Kullback-Leibler divergence. We validated the superior clustering performance of scZAG on 10 common scRNA-seq datasets in comparison to existing state-of-the-art clustering methods.

Multi-Analyte Discriminated ECL via Photonic Crystal-Enhanced Selectivity.

Electrochemiluminescence (ECL) has numerous merits such as high sensitivity and specificity for the detection applications on pharmacy, food safety, immunoassay, disease diagnosis, environmental monitoring, nucleic acid assay, and clinical treatment. However, the insufficiency of ECL luminescent reagents is restricting their adoption on complex systems or multi-analyte detections. In this work, to improve the selectivity and discrimination of ECL detection with one or less luminescent reagent, we employed multi-stopband photonic crystals (PCs) to enhance assigned ECL. The discrimination of ECL was well investigated to establish the quantitative description with PC stopbands. The multi-stopband PC electrode can facilely achieve 10 antibiotics qualitative and quantitative analysis with 100% accuracy and 0.44 µM LOD in PBS buffer and human serum. The selectivity of ECL detection for multi-analytes can be improved via designed PC luminescence amplifications. The exploration on PC selectivity for ECL enhancement will promote the realistic application of the ECL technique and contribute to the facile and efficient optical platform for clinical or health monitoring.

Defect-Engineered Mesoporous Undoped Carbon Nanoribbons for Benchmark Oxygen Reduction Reaction.

For large-scale fuel cell applications, it is significant to replace expensive Pt-based oxygen reduction reaction (ORR) electrocatalysts with nonprecious metal- or metal-free carbon-based catalysts with high activity. However, it is still challenging to deeply understand the role of intrinsic defects and the origin of ORR activity in pure nanocarbon. Therefore, a novel self-assembly and a pyrolysis strategy to fabricate defect-rich mesoporous carbon nanoribbons are presented. Due to the effective regulation of nanoarchitecture, a vast number of defective catalytic sites (edge defects and holes) are exposed, which thereby enhances the electron transfer kinetics and catalytic activity. Such undoped nanoribbons display a large half-wave potential of 0.837 V, excellent long-term stability, and exceptional methanol tolerance, surpassing the most undoped ORR catalysts and the commercial Pt/C (20 wt.%) catalyst. Structural characterizations and density functional theory (DFT) calculations confirm that the zigzag edge defects and the armchair pentagon at the hole defect are responsible for outstanding ORR performance.

100Gb/s coherent optical secure communication over 1000†km based on analog-digital hybrid chaos.

In recent years, the transmission capacity of chaotic secure communications has been greatly expanded by combining coherent detection and multi-dimensional multiplexing. However, demonstrations over 1000 km fiber are yet to be further explored. In this paper, we propose a coherent optical secure transmission system based on analog-digital hybrid chaos. By introducing an analog-digital converter (ADC) and a bit extraction into the feedback loop of entropy source, the broadband analog chaos is converted into a binary digital signal. This binary digital signal is then mapped to a 65536-level pulse amplitude modulation (PAM) signal and injected into the semiconductor laser (SL) to regenerate the analog chaos, forming a closed loop. The binary digital signal from the chaos source and the encrypted signal are transmitted via wavelength division multiplexing (WDM). By using conventional digital signal processing (DSP) algorithms and neural networks for post-compensation, long-haul high-quality chaotic synchronization and high-performance secure communication are achieved. In addition, the probability density distribution of the analog chaotic signal is effectively improved by adopting the additional higher-order mapping operation in the digital part of the chaos source. The proof-of-concept experimental results show that our proposed scheme can support the secure transmission of 100 Gb/s quadrature phase shift keying (QPSK) signals over 1000 km of standard single-mode fiber (SSMF). The decrypted bit error rate (BER) reaches 9.88 × 10-4, which is well below the 7% forward error correction (FEC) threshold (BER = 3.8 × 10-3). This research provides a potential solution for high-capacity long-haul chaotic optical communications and fills the gap in secure communications based on analog-digital hybrid chaos.

Scalable Palladium-Catalyzed Alkoxycarbonylation of Conjugated Dienes.

The Pd(cod)Cl2-catalyzed alkoxycarbonylation of conjugated dienes to ß,γ-unsaturated esters was approached by both intramolecular phosphinesulfonate L1 and intermolecular PPh3/PTSA in this study. However, the poor solubility of the Pd/L1 complex and the labile monodentate Pd/PPh3 structure restricts the system efficiency, especially for the scale-up application. By contrast, the stable and well-soluble bidentate Xantphos system allows for the quantitative formation of 3-pentenoate (96%) on a gram scale within 6 h in weakly alkaline N-methylpyrrolidone (NMP), which also functions as a basic site to promote the rate-limiting alcoholysis step while reducing the dosage of ligand to a theoretical value.

Ruthenium-catalyzed reductive amination of ketones with nitroarenes and nitriles.

The Ru(dppbsa)-catalyzed reductive amination of ketones with nitroarenes and nitriles using H2 as the environmentally benign hydrogen surrogate is developed in this study. Cross-experiments demonstrated that both reactions are initiated by the reduction of nitroarenes or nitriles to the corresponding amines, followed by condensation with ketones to give imines and thereafter hydrogenation. However, the route to the formation of an amino-ligated Ru complex during the reduction of nitroarenes or nitriles, followed by in situ nucleophilic C-N coupling, cannot be completely excluded. This newly developed versatile method features good functional group tolerance, which provides a novel design platform for homogeneous catalysts in constructing motifs of secondary amines.

Spectroscopic Signature of the Carbon-Carbon Coupling Reaction between Carbon Monoxide and Nickel Carbide.

Spectroscopic characterization of ketenylidene complexes is of essential importance for understanding the structure-reactivity relationships of the catalytic sites. Here, we report a size-specific photoelectron velocity map imaging spectroscopic study of the reactions of carbon monoxide with nickel carbide. Quantum chemical calculations have been conducted to search for the energetically favorable isomers and to recognize the experimental spectra. The target products with the chemical formula of NiC(CO)n- (n = 3-5) are characterized to have an intriguing ketenylidene CCO unit. The evolution from NiC(CO)3- to NiC(CO)4- involves the breaking and formation of the Ni-C bond and the coordination conversion between the terminal and bridging carbonyls. Experimental and theoretical analyses reveal an efficient C-C bond formation process within the reactions of carbon monoxide and laser-vaporized nickel carbide. This work highlights the pivotal roles played by metal carbides in the C-C bond formation and also proposes new ideas for the design and chemical control of a broad class of complexes with unique physical and chemical properties.

Molecular crowding facilitates bundling of IMPDH polymers and cytoophidium formation.

The cytoophidium is a unique type of membraneless compartment comprising of filamentous protein polymers. Inosine monophosphate dehydrogenase (IMPDH) catalyzes the rate-limiting step of de novo GTP biosynthesis and plays critical roles in active cell metabolism. However, the molecular regulation of cytoophidium formation is poorly understood. Here we show that human IMPDH2 polymers bundle up to form cytoophidium-like aggregates in vitro when macromolecular crowders are present. The self-association of IMPDH polymers is suggested to rely on electrostatic interactions. In cells, the increase of molecular crowding with hyperosmotic medium induces cytoophidia, while the decrease of that by the inhibition of RNA synthesis perturbs cytoophidium assembly. In addition to IMPDH, CTPS and PRPS cytoophidium could be also induced by hyperosmolality, suggesting a universal phenomenon of cytoophidium-forming proteins. Finally, our results indicate that the cytoophidium can prolong the half-life of IMPDH, which is proposed to be one of conserved functions of this subcellular compartment.

Optimization design of the sound absorbing structure of double-layer porous metal material with air layer based on genetic algorithm.

An acoustic absorption structure of a double-layer porous metal material with air layers is proposed. The Johnson-Champoux-Allard (JCA) model combined with the transfer matrix method (TMM) was used to establish the theoretical calculation model of the sound absorption coefficient (SAC). Meanwhile, the SAC between 500 and 6300 Hz were measured with an impedance tube. The errors between the theoretical and experimental values were compared to illustrate the good predictability of the theoretical model within the inverse estimations of the transport properties. The effects of the material placement order, material thickness, and cavity depth on the sound absorption performance from 200 to 5000 Hz were analyzed using the theoretical model. Further, a multi-objective function genetic algorithm was used to optimize the porous material's thickness and SAC to obtain an acoustic structure with a smaller thickness and higher sound absorption. A series of optimal solutions were obtained for acoustic structures with a total thickness of less than 70 mm. When the total thickness of the foam metal was 33.57 mm, the average SAC reached 0.853, which was significantly lower than the total thickness of the previous experiments. The multi-objective function genetic algorithm can provide a reliable solution for the optimal design of most sound-absorbing structures.

Association of remnant cholesterol with intracranial atherosclerosis in community-based population: The ARIC study.

OBJECTIVE: To evaluate the association between remnant cholesterol (remnant-C) and intracranial atherosclerotic disease (ICAD) in the Atherosclerosis Risk in Communities Neurocognitive Study (ARIC-NCS). METHODS: We studied 1,564 participants with data on lipid profiles and high-resolution vessel wall MRI (VWMRI) from the ARIC-NCS. Remnant-C was computed as total cholesterol minus high-density lipoprotein cholesterol minus low-density lipoprotein cholesterol (LDL-C). The primary outcomes were the presence of intracranial plaques and luminal stenosis. Contributors were separated into four different groups based on remnant-C (22 mg/dL) and LDL-C (100 mg/dL) levels to investigate the function of remnant-C vs. LDL-C on ICAD. Multivariable logistic regression models were utilized to estimate the correlation among the discordant/concordant remnant-C and LDL-C, and ICAD. RESULTS: A total of 1,564 participants were included (age 76.2 ± 5.3). After multivariable adjustment, log remnant-C was correlated with greater ICAD risk [odds ratio (OR) 1.36, 95% confidence interval (CI) 1.01 to 1.83]. The lower remnant-C/higher LDL-C group and the higher remnant-C/lower LDL-C group manifested a 1.53-fold (95% CI 1.06 to 2.20) and 1.52-fold (95% CI 1.08 to 2.14) greater risk of ICAD, relative to those having lower remnant-C/low LDL-C. Additionally, remnant-C ≥ 22 mg/dL distinguished participants at a greater risk of the presence of any stenosis compared to those at lower levels, even in participants with optimal levels of LDL-C. CONCLUSIONS: Elevated levels of remnant-C were connected to ICAD independent of LDL-C and traditional risk factors. The mechanisms of remnant-C association with ICAD probably offer insight into preventive risk-factor of ischemic stroke.

An Innovative Low-dose CT Inpainting Algorithm based on Limited-angle Imaging Inpainting Model.

BACKGROUND: With the popularity of computed tomography (CT) technique, an increasing number of patients are receiving CT scans. Simultaneously, the public's attention to CT radiation dose is also increasing. How to obtain CT images suitable for clinical diagnosis while reducing the radiation dose has become the focus of researchers. OBJECTIVE: To demonstrate that limited-angle CT imaging technique can be used to acquire lower dose CT images, we propose a generative adversarial network-based image inpainting model-Low-dose imaging and Limited-angle imaging inpainting Model (LDLAIM), this method can effectively restore low-dose CT images with limited-angle imaging, which verifies that limited-angle CT imaging technique can be used to acquire low-dose CT images. METHODS: In this work, we used three datasets, including chest and abdomen dataset, head dataset and phantom dataset. They are used to synthesize low-dose and limited-angle CT images for network training. During training stage, we divide each dataset into training set, validation set and testing set according to the ratio of 8:1:1, and use the validation set to validate after finishing an epoch training, and use the testing set to test after finishing all the training. The proposed method is based on generative adversarial networks(GANs), which consists of a generator and a discriminator. The generator consists of residual blocks and encoder-decoder, and uses skip connection. RESULTS: We use SSIM, PSNR and RMSE to evaluate the performance of the proposed method. In the chest and abdomen dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.984, 35.385 and 0.017, respectively. In the head dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.981, 38.664 and 0.011, respectively. In the phantom dataset, the mean SSIM, PSNR and RMSE of the testing set are 0.977, 33.468 and 0.022, respectively. By comparing the experimental results of other algorithms in these three datasets, it can be found that the proposed method is superior to other algorithms in these indicators. Meanwhile, the proposed method also achieved the highest score in the subjective quality score. CONCLUSIONS: Experimental results show that the proposed method can effectively restore CT images when both low-dose CT imaging techniques and limited-angle CT imaging techniques are used simultaneously. This work proves that the limited-angle CT imaging technique can be used to reduce the CT radiation dose, and also provides a new idea for the research of low-dose CT imaging.

Malonate Promotes Adult Cardiomyocyte Proliferation and Heart Regeneration.

BACKGROUND: Neonatal mouse cardiomyocytes undergo a metabolic switch from glycolysis to oxidative phosphorylation, which results in a significant increase in reactive oxygen species production that induces DNA damage. These cellular changes contribute to cardiomyocyte cell cycle exit and loss of the capacity for cardiac regeneration. The mechanisms that regulate this metabolic switch and the increase in reactive oxygen species production have been relatively unexplored. Current evidence suggests that elevated reactive oxygen species production in ischemic tissues occurs as a result of accumulation of the mitochondrial metabolite succinate during ischemia via succinate dehydrogenase (SDH), and this succinate is rapidly oxidized at reperfusion. Mutations in SDH in familial cancer syndromes have been demonstrated to promote a metabolic shift into glycolytic metabolism, suggesting a potential role for SDH in regulating cellular metabolism. Whether succinate and SDH regulate cardiomyocyte cell cycle activity and the cardiac metabolic state remains unclear. METHODS: Here, we investigated the role of succinate and SDH inhibition in regulation of postnatal cardiomyocyte cell cycle activity and heart regeneration. RESULTS: Our results demonstrate that injection of succinate into neonatal mice results in inhibition of cardiomyocyte proliferation and regeneration. Our evidence also shows that inhibition of SDH by malonate treatment after birth extends the window of cardiomyocyte proliferation and regeneration in juvenile mice. Remarkably, extending malonate treatment to the adult mouse heart after myocardial infarction injury results in a robust regenerative response within 4 weeks after injury via promoting adult cardiomyocyte proliferation and revascularization. Our metabolite analysis after SDH inhibition by malonate induces dynamic changes in adult cardiac metabolism. CONCLUSIONS: Inhibition of SDH by malonate promotes adult cardiomyocyte proliferation, revascularization, and heart regeneration via metabolic reprogramming. These findings support a potentially important new therapeutic approach for human heart failure.

Cyclic silicon waveguide four-mode converter for mode division multiplexing transmission.

In this paper, a novel cyclic mode converter (CMC) is proposed and fabricated to implement cyclic mode permutation (CMP) on-chip for differential mode delay and mode-dependent loss elimination in the mode division multiplexing (MDM) transmission system. Cascaded by three optimally designed mode converters that do not affect the non-target modes, the proposed CMC can realize the conversion of any input mode among the TE0/TE1/TM0/TM1 modes. The three-dimensional finite-difference time-domain (3D-FDTD) simulation results show that the insertion loss of our device is less than 0.59 dB, and the crosstalk of each mode is lower than -15 dB under the range of 1500-1600 nm. The flat spectral response of this CMC is maintained even in the presence of fabrication errors up to±10 nm, showing great robustness. The experimental results also prove that at the center wavelength of 1550 nm the measured insertion loss of each mode is below 2.22 dB, and the crosstalk of each mode is lower than -15 dB. The proposed CMC provides a new idea for effectively reducing link damage in the MDM transmission system.