Resolution of N-acetyl-DL-methionine methyl ester by the lipase from Brucella thiophenivorans.

In this study, lipase-catalyzed resolution of N-acetyl-DL-methionine methyl ester (N-Ac-DL-MetOMe) was evaluated. A lipase from Brucella thiophenivorans was prone to exhibit high activity and excellent enantioselectivity toward N-Ac-DL-MetOMe to produce the key chiral intermediate N-acetyl-L-methionine methyl ester (N-Ac-L-MetOMe). The results showed that the enzymatic reaction was carried out in 100 g/L racemic substrate for 2 h, the conversion reached 51.3%, the enantiomeric excess value N-Ac-L-MetOMe exceeded 99%, and the enantiomeric ratio value >200. Therefore, the lipase from B. thiophenivorans has potential prospects for the resolution of N-Ac-DL-MetOMe to produce the important intermediate N-Ac-L-MetOMe.

Suggested blood donor deferral strategy regarding hepatitis B infections in China.

BACKGROUND: Hepatitis B virus (HBV) reactivity in individual immunologic and nucleic acid tests (NAT) tests does not represent the true infectious status of the blood donor. This study discusses the use of confirmatory tests to determine when deferral of blood donors is appropriate. METHODS: HBsAg or HBV NAT reactive samples were confirmed via a neutralisation test. All the HBsAg reactive but neutralisation test negative samples were subjected to further anti-HBc testing. The receiver operating characteristic curve was used to obtain the best threshold value using signal-to-cut-off ratios of two HBsAg enzyme-linked immunosorbent assay reagents. RESULTS: Of the 780 HBV reactive samples collected, there were 467 HBsAg reactive but HBV DNA negative samples, of which 65 (13.92%) and 402 (86.08%) were neutralisation test positive and negative, respectively. Of the 402, 91 samples (30% of tested samples) were anti-HBc reactive. HBV DNA positive specimens negative by virus neutralisation were >80% HBcAg positive. A screening strategy was proposed for Chinese blood collection agencies. CONCLUSION: These findings suggest that adopting a screening algorithm for deferring HBV reactive blood donors based on HBsAg and NAT testing followed with HBsAg S/CO consideration and HBcAg testing can be both safe and feasible in China.

Multi-Source Domain Adaptation for Medical Image Segmentation.

Unsupervised domain adaptation(UDA) aims to mitigate the performance drop of models tested on the target domain, due to the domain shift from the target to sources. Most UDA segmentation methods focus on the scenario of solely single source domain. However, in practical situations data with gold standard could be available from multiple sources (domains), and the multi-source training data could provide more information for knowledge transfer. How to utilize them to achieve better domain adaptation yet remains to be further explored. This work investigates multi-source UDA and proposes a new framework for medical image segmentation. Firstly, we employ a multi-level adversarial learning scheme to adapt features at different levels between each of the source domains and the target, to improve the segmentation performance. Then, we propose a multi-model consistency loss to transfer the learned multi-source knowledge to the target domain simultaneously. Finally, we validated the proposed framework on two applications, i.e., multi-modality cardiac segmentation and cross-modality liver segmentation. The results showed our method delivered promising performance and compared favorably to state-of-the-art approaches.

A tau fragment links depressive-like behaviors and cognitive declines in Alzheimer's disease mouse models through attenuating mitochondrial function.

Introduction: Alzheimer's disease (AD) is the most prevalent neurodegenerative disease characterized by extracellular senile plaques including amyloid-ß peptides and intracellular neurofibrillary tangles consisting of abnormal Tau. Depression is one of the most common neuropsychiatric symptoms in AD, and clinical evidence demonstrates that depressive symptoms accelerate the cognitive deficit of AD patients. However, the underlying molecular mechanisms of depressive symptoms present in the process of AD remain unclear. Methods: Depressive-like behaviors and cognitive decline in hTau mice were induced by chronic restraint stress (CRS). Computational prediction and molecular experiments supported that an asparagine endopeptidase (AEP)-derived Tau fragment, Tau N368 interacts with peroxisome proliferator-activated receptor delta (PPAR-δ). Further behavioral studies investigated the role of Tau N368-PPAR-δ interaction in depressive-like behaviors and cognitive declines of AD models exposed to CRS. Results: We found that mitochondrial dysfunction was positively associated with depressive-like behaviors and cognitive deficits in hTau mice. Chronic stress increased Tau N368 and promoted the interaction of Tau N368 with PPAR-δ, repressing PPAR-δ-mediated transactivation in the hippocampus of mice. Then we predicted and identified the binding sites of PPAR-δ. Finally, inhibition of AEP, clearance of Tau N368 and pharmacological activation of PPAR-δ effectively alleviated CRS-induced depressive-like behaviors and cognitive decline in mice. Conclusion: These results demonstrate that Tau N368 in the hippocampus impairs mitochondrial function by suppressing PPAR-δ, facilitating the occurrence of depressive-like behaviors and cognitive decline. Therefore, our findings may provide new mechanistic insight in the pathophysiology of depression-like phenotype in mouse models of Alzheimer's disease.

Automatic pulmonary artery-vein separation in CT images using a twin-pipe network and topology reconstruction.

Background: With the wide application of CT scanning, the separation of pulmonary arteries and veins (A/V) based on CT images plays an important role for assisting surgeons in preoperative planning of lung cancer surgery. However, distinguishing between arteries and veins in chest CT images remains challenging due to the complex structure and the presence of their similarities. Methods: We proposed a novel method for automatically separating pulmonary arteries and veins based on vessel topology information and a twin-pipe deep learning network. First, vessel tree topology is constructed by combining scale-space particles and multi-stencils fast marching (MSFM) methods to ensure the continuity and authenticity of the topology. Second, a twin-pipe network is designed to learn the multiscale differences between arteries and veins and the characteristics of the small arteries that closely accompany bronchi. Finally, we designed a topology optimizer that considers interbranch and intrabranch topological relationships to optimize the results of arteries and veins classification. Results: The proposed approach is validated on the public dataset CARVE14 and our private dataset. Compared with ground truth, the proposed method achieves an average accuracy of 90.1% on the CARVE14 dataset, and 96.2% on our local dataset. Conclusions: The method can effectively separate pulmonary arteries and veins and has good generalization for chest CT images from different devices, as well as enhanced and noncontrast CT image sequences from the same device.

Aligning Multi-Sequence CMR Towards Fully Automated Myocardial Pathology Segmentation.

Myocardial pathology segmentation (MyoPS) is critical for the risk stratification and treatment planning of myocardial infarction (MI). Multi-sequence cardiac magnetic resonance (MS-CMR) images can provide valuable information. For instance, balanced steady-state free precession cine sequences present clear anatomical boundaries, while late gadolinium enhancement and T2-weighted CMR sequences visualize myocardial scar and edema of MI, respectively. Existing methods usually fuse anatomical and pathological information from different CMR sequences for MyoPS, but assume that these images have been spatially aligned. However, MS-CMR images are usually unaligned due to the respiratory motions in clinical practices, which poses additional challenges for MyoPS. This work presents an automatic MyoPS framework for unaligned MS-CMR images. Specifically, we design a combined computing model for simultaneous image registration and information fusion, which aggregates multi-sequence features into a common space to extract anatomical structures (i.e., myocardium). Consequently, we can highlight the informative regions in the common space via the extracted myocardium to improve MyoPS performance, considering the spatial relationship between myocardial pathologies and myocardium. Experiments on a private MS-CMR dataset and a public dataset from the MYOPS2020 challenge show that our framework could achieve promising performance for fully automatic MyoPS.

Multi-modality cardiac image computing: A survey.

Multi-modality cardiac imaging plays a key role in the management of patients with cardiovascular diseases. It allows a combination of complementary anatomical, morphological and functional information, increases diagnosis accuracy, and improves the efficacy of cardiovascular interventions and clinical outcomes. Fully-automated processing and quantitative analysis of multi-modality cardiac images could have a direct impact on clinical research and evidence-based patient management. However, these require overcoming significant challenges including inter-modality misalignment and finding optimal methods to integrate information from different modalities. This paper aims to provide a comprehensive review of multi-modality imaging in cardiology, the computing methods, the validation strategies, the related clinical workflows and future perspectives. For the computing methodologies, we have a favored focus on the three tasks, i.e., registration, fusion and segmentation, which generally involve multi-modality imaging data, either combining information from different modalities or transferring information across modalities. The review highlights that multi-modality cardiac imaging data has the potential of wide applicability in the clinic, such as trans-aortic valve implantation guidance, myocardial viability assessment, and catheter ablation therapy and its patient selection. Nevertheless, many challenges remain unsolved, such as missing modality, modality selection, combination of imaging and non-imaging data, and uniform analysis and representation of different modalities. There is also work to do in defining how the well-developed techniques fit in clinical workflows and how much additional and relevant information they introduce. These problems are likely to continue to be an active field of research and the questions to be answered in the future.

Learning multi-view and centerline topology connectivity information for pulmonary artery-vein separation.

BACKGROUND: Automatic pulmonary artery-vein separation has considerable importance in the diagnosis and treatment of lung diseases. However, insufficient connectivity and spatial inconsistency have always been the problems of artery-vein separation. METHODS: A novel automatic method for artery-vein separation in CT images is presented in this work. Specifically, a multi-scale information aggregated network (MSIA-Net) including multi-scale fusion blocks and deep supervision, is proposed to learn the features of artery-vein and aggregate additional semantic information, respectively. The proposed method integrates nine MSIA-Net models for artery-vein separation, vessel segmentation, and centerline separation tasks along with axial, coronal, and sagittal multi-view slices. First, the preliminary artery-vein separation results are obtained by the proposed multi-view fusion strategy (MVFS). Then, centerline correction algorithm (CCA) is used to correct the preliminary results of artery-vein separation by the centerline separation results. Finally, the vessel segmentation results are utilized to reconstruct the artery-vein morphology. In addition, weighted cross-entropy and dice loss are employed to solve the class imbalance problem. RESULTS: We constructed 50 manually labeled contrast-enhanced computed CT scans for five-fold cross-validation, and experimental results demonstrated that our method achieves superior segmentation performance of 97.7%, 85.1%, and 84.9% on ACC, Pre, and DSC, respectively. Additionally, a series of ablation studies demonstrate the effectiveness of the proposed components. CONCLUSION: The proposed method can effectively solve the problem of insufficient vascular connectivity and correct the spatial inconsistency of artery-vein.

Emerging Roles of Meningeal Lymphatic Vessels in Alzheimer's Disease.

Meningeal lymphatic vessels (mLVs), the functional lymphatic system present in the meninges, are the key drainage route responsible for the clearance of molecules, immune cells, and cellular debris from the cerebrospinal fluid and interstitial fluid into deep cervical lymph nodes. Aging and ApoE4, the two most important risk factors for Alzheimer's disease (AD), induce mLV dysfunction, decrease cerebrospinal fluid influx and outflux, and exacerbate amyloid pathology and cognitive dysfunction. Dysfunction of mLVs results in the deposition of metabolic products, accelerates neuroinflammation, and promotes the release of pro-inflammatory cytokines in the brain. Thus, mLVs represent a novel therapeutic target for treating neurodegenerative and neuroinflammatory diseases. This review aims to summarize the structure and function of mLVs and to discuss the potential effect of aging and ApoE4 on mLV dysfunction, as well as their roles in the pathogenesis of AD.

Ion sieving in graphene oxide membrane enables efficient actinides/lanthanides separation.

Separation of actinides from lanthanides is of great importance for the safe management of nuclear waste and sustainable development of nuclear energy, but it represents a huge challenge due to the chemical complexity of these f-elements. Herein, we report an efficient separation strategy based on ion sieving in graphene oxide membrane. In the presence of a strong oxidizing reagent, the actinides (U, Np, Pu, Am) in a nitric acid solution exist in the high valent and linear dioxo form of actinyl ions while the lanthanides (Ce, Nd, Eu, Gd, etc.) remain as trivalent/tetravalent spheric ions. A task-specific graphene oxide membrane with an interlayer nanochannel spacing between the sizes of hydrated actinyl ions and lanthanides ions is tailored and used as an ionic cut-off filter, which blocks the larger and linear actinyl ions but allows the smaller and spheric lanthanides ions to penetrate through, affording lanthanides/actinides separation factors up to ~400. This work realizes the group separation of actinides from lanthanides under highly acidic conditions by a simple ion sieving strategy and highlights the great potential of utilizing graphene oxide membrane for nuclear waste treatment.

MNC-Net: Multi-task graph structure learning based on node clustering for early Parkinson's disease diagnosis.

PURPOSE: The identification of early-stage Parkinson's disease (PD) is important for the effective management of patients, affecting their treatment and prognosis. Recently, structural brain networks (SBNs) have been used to diagnose PD. However, how to mine abnormal patterns from high-dimensional SBNs has been a challenge due to the complex topology of the brain. Meanwhile, the existing prediction mechanisms of deep learning models are often complicated, and it is difficult to extract effective interpretations. In addition, most works only focus on the classification of imaging and ignore clinical scores in practical applications, which limits the ability of the model. Inspired by the regional modularity of SBNs, we adopted graph learning from the perspective of node clustering to construct an interpretable framework for PD classification. METHODS: In this study, a multi-task graph structure learning framework based on node clustering (MNC-Net) is proposed for the early diagnosis of PD. Specifically, we modeled complex SBNs into modular graphs that facilitated the representation learning of abnormal patterns. Traditional graph neural networks are optimized through graph structure learning based on node clustering, which identifies potentially abnormal brain regions and reduces the impact of irrelevant noise. Furthermore, we employed a regression task to link clinical scores to disease classification, and incorporated latent domain information into model training through multi-task learning. RESULTS: We validated the proposed approach on the Parkinsons Progression Markers Initiative dataset. Experimental results showed that our MNC-Net effectively separated the early-stage PD from healthy controls(HC) with an accuracy of 95.5%. The t-SNE figures have showed that our graph structure learning method can capture more efficient and discriminatory features. Furthermore, node clustering parameters were used as important weights to extract salient task-related brain regions(ROIs). These ROIs are involved in the development of mood disorders, tremors, imbalances and other symptoms, highlighting the importance of memory, language and mild motor function in early PD. In addition, statistical results from clinical scores confirmed that our model could capture abnormal connectivity that was significantly different between PD and HC. These results are consistent with previous studies, demonstrating the interpretability of our methods.

Individual uniqueness in trust profiles and well-being: Understanding the role of cultural tightness-looseness from a representation similarity perspective.

This paper provides a unique perspective for understanding cultural differences: representation similarity-a computational technique that uses pairwise comparisons of units to reveal their representation in higher-order space. By combining individual-level measures of trust across domains and well-being from 13,823 participants across 15 nations with a measure of society-level tightness-looseness, we found that any two countries with more similar tightness-looseness tendencies exhibit higher degrees of representation similarity in national interpersonal trust profiles. Although each individual's trust profile is generally similar to their nation's trust profile, the greater similarity between an individual's and their society's trust profile predicted a higher level of individual life satisfaction only in loose cultures but not in tight cultures. Using the framework of representation similarity to explore cross-cultural differences from a multidimensional, multi-national perspective provide a comprehensive picture of how culture is related to the human activities.

Comparison of the relationship between cognitive function and future falls in Chinese community-dwelling older adults with and without diabetes mellitus.

OBJECTIVE: The aim of this study was to determine whether cognitive function is associated with future falls in older patients with diabetes mellitus (DM) compared with those without DM. Cognitive function was divided into several domains to further analyze. METHODS: A total of 678 individuals met the inclusion criteria and comprised the final study population. The mean age was 74.35 ± 5.35 years, and 58.9% of the participants were female (n = 400). At the baseline, cognitive function was measured by the Mini Mental State Examination (MMSE), and DM diagnoses were determined by medical records. The self-reported any falls data were obtained via face-to-face questioning at the 1-year follow-up. RESULTS: At baseline, 15.6% of participants (n = 106) were diagnosed with DM. According to whether they had any falls during 1-year follow-up, there was a significant difference between the two group in fasting plasma glucose (p = 0.012) and DM (p = 0.036) at baseline. Among the older adults with DM, those who had experienced any falls had poorer cognitive function (p = 0.014). After adjusting for various covariates, we found that MMSE (95% CI 0.790-0.991, p = 0.034), orientation to place (95% CI 0.307-0.911, p = 0.022) and registration (95% CI 0.162-0.768, p = 0.009) were significantly associated with falls in the follow-up. CONCLUSION: Our study found that in patients with DM, cognitive function is related to future falls. Not only overall cognitive function, but also orientation to place and registration were all associated with future falls in older adults with DM. When completing the fall risk assessment of elderly patients with DM, clinicians should give more attention to the testing of cognitive function.

Inflammation-activated C/EBPß mediates high-fat diet-induced depression-like behaviors in mice.

Depression, one of the most common causes of disability, has a high prevalence rate in patients with metabolic syndrome. Type 2 diabetes patients are at an increased risk for depression. However, the molecular mechanism coupling diabetes to depressive disorder remains largely unknown. Here we found that the neuroinflammation, associated with high-fat diet (HFD)-induced diabetes and obesity, activated the transcription factor CCAAT/enhancer binding protein ß (C/EBPß) in hippocampal neurons. This factor repressed brain-derived neurotrophic factor (BDNF) expression and caused depression-like behaviors in male mice. Besides, the loss of C/EBPß expression in C/EBPß heterozygous knockout male mice attenuated HFD-induced depression-like behaviors, whereas Thy1-C/EBPß transgenic male mice (overexpressing C/EBPß) showed depressive behaviors after a short-term HFD. Furthermore, HFD impaired synaptic plasticity and decreased surface expression of glutamate receptors in the hippocampus of wild-type (WT) mice, but not in C/EBPß heterozygous knockout mice. Remarkably, the anti-inflammatory drug aspirin strongly alleviated HFD-elicited depression-like behaviors in neuronal C/EBPß transgenic mice. Finally, the genetic delivery of BDNF or the pharmacological activation of the BDNF/TrkB signaling pathway by 7,8-dihydroxyflavone reversed anhedonia in a series of behavioral tests on HFD-fed C/EBPß transgenic mice. Therefore, our findings aim to demonstrate that the inflammation-activated neuronal C/EBPß promotes HFD-induced depression by diminishing BDNF expression.

CSPα in neurodegenerative diseases.

Adult-onset neuronal ceroid lipofuscinosis (ANCL) is a rare neurodegenerative disease characterized by epilepsy, cognitive degeneration, and motor disorders caused by mutations in the DNAJC5 gene. In addition to being associated with ANCL disease, the cysteine string proteins α (CSPα) encoded by the DNAJC5 gene have been implicated in several neurodegenerative diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), and Huntington's disease. However, the pathogenic mechanism responsible for these neurodegenerative diseases has not yet been elucidated. Therefore, this study examines the functional properties of the CSPα protein and the related mechanisms of neurodegenerative diseases.

Indocyanine Green Fluorescence Using in Conduit Reconstruction for Patients With Esophageal Cancer to Improve Short-Term Clinical Outcome: A Meta-Analysis.

Objectives: This meta-analysis evaluated the short-term safety and efficacy of indocyanine green (ICG) fluorescence in gastric reconstruction to determine a suitable anastomotic position during esophagectomy. Methods: The Preferred Reporting Items for Systematic Reviews and Meta-Analyzes 2020 (PRISMA) were followed for this analysis. Results: A total of 9 publications including 1,162 patients were included. The operation time and intraoperative blood loss were comparable in the ICG and control groups. There was also no significant difference in overall postoperative mortality, reoperation, arrhythmia, vocal cord paralysis, pneumonia, and surgical wound infection. The ICG group had a 2.66-day reduction in postoperative stay. The overall anastomotic leak (AL) was 17.6% (n = 131) in the control group and 4.5% (n = 19) in the ICG group with a relative risk (RR) of 0.29 (95% CI 0.18-0.47). A subgroup analysis showed that the application of ICG in cervical anastomosis significantly reduced the incidence of AL (RR of 0.31, 95% CI 0.18-0.52), but for intrathoracic anastomosis, the RR 0.35 was not significant (95% CI 0.09-1.43). Compared to an RR of 0.35 in publications with a sample size of <50, a sample size of >50 had a lower RR of 0.24 (95% CI 0.12-0.48). Regarding intervention time of ICG, the application of ICG both before and after gastric construction had a better RR of 0.25 (95% CI 0.07-0.89). Conclusions: The application of ICG fluorescence could effectively reduce the incidence of AL and shorten the postoperative hospital stay for patients undergoing cervical anastomosis but was not effective for patients undergoing intrathoracic anastomosis. The application of ICG fluorescence before and after gastric management can better prevent AL. Systematic Review Registration: PROSPERO, CRD:42021244819.

The LPA-CDK5-tau pathway mediates neuronal injury in an in vitro model of ischemia-reperfusion insult.

Lysophosphatidic acid (LPA) is a common glycerol phospholipid and an important extracellular signaling molecule. LPA binds to its receptors and mediates a variety of biological effects, including the pathophysiological process underlying ischemic brain damage and traumatic brain injury. However, the molecular mechanisms mediating the pathological role of LPA are not clear. Here, we found that LPA activates cyclin-dependent kinase 5 (CDK5). CDK5 phosphorylates tau, which leads to neuronal cell death. Inhibition of LPA production or blocking its receptors reduced the abnormal activation of CDK5 and phosphorylation of tau, thus reversing the death of neurons. Our data indicate that the LPA-CDK5-Tau pathway plays an important role in the pathophysiological process after ischemic stroke. Inhibiting the LPA pathway may be a potential therapeutic target for treating ischemic brain injury.

Metal-enhanced fluorescence of graphene oxide sheets.

Graphene oxide (GO) is an excellent chemical tunable optical platform for imaging and sensing. The photoluminescence (PL) quantum yield of GO is relatively low, which limited the application of the intrinsic and tunable fluorescence from GO. Here, we report the first case of metal-enhanced fluorescence (MEF) of GO. A significant enhancement (~10-fold) in fluorescence intensity is observed from GO on the Ag substrate as compared to that on the glass. FL, Raman, and SEM images are used to investigate the MEF behavior and are coincident with each other. The influence of the metal particle size of Ag substrate is investigated. The fluorescence is also found to be responsive when adding different metal ions into GO solution. GO contacting directly with metal substrate exhibits strong MEF without quenching, which makes it possible to use GO sheets for three-dimension optical imaging and sensing.

The impact of economic freedom on COVID-19 pandemic control: the moderating role of equality.

BACKGROUND: The absence of pharmaceutical interventions made it particularly difficult to mitigate the first outbreak of coronavirus disease 2019 (COVID-19). The current study investigated how economic freedom and equality influenced the pandemic control process. METHODS: In Study 1, we assessed the effect of economic freedom and equality on COVID-19 pandemic control from nations worldwide. We collected the cumulative number of confirmed cases over time to perform logistic curve fitting and obtain the speed at which the first wave of the pandemic was controlled, and partial correlation analysis and representational similarity analysis (RSA) were performed to assess the similarity between economic freedom and the speed of pandemic control. In Study 2, an evolutionary game model in which economic freedom affects the speed of pandemic control through optimization of the allocation of available resources was developed. In Study 3, we used experimental manipulation to elucidate the psychological mechanism relating economic freedom and resource allocation. RESULTS: The economic freedom of nation could be used to positively predict the speed of pandemic control and the related similarity pattern. Equality was found to moderate the correlation and representational similarity between economic freedom and the speed of pandemic control. The evolutionary game model revealed a mechanism whereby economic freedom influences the speed of pandemic control through high resource availability. Furthermore, cooperation was found to be a possible psychological mechanism explaining how economic freedom increases resource availability. CONCLUSIONS: Economic freedom has a positive effect on the control of the COVID-19 pandemic only among highly egalitarian nations. New interventions are needed to help countries heighten economic freedom and equality as they continue to battle COVID-19 and other collective threats.

Cross-Modality Multi-Atlas Segmentation via Deep Registration and Label Fusion.

Multi-atlas segmentation (MAS) is a promising framework for medical image segmentation. Generally, MAS methods register multiple atlases, i.e., medical images with corresponding labels, to a target image; and the transformed atlas labels can be combined to generate target segmentation via label fusion schemes. Many conventional MAS methods employed the atlases from the same modality as the target image. However, the number of atlases with the same modality may be limited or even missing in many clinical applications. Besides, conventional MAS methods suffer from the computational burden of registration or label fusion procedures. In this work, we design a novel cross-modality MAS framework, which uses available atlases from a certain modality to segment a target image from another modality. To boost the computational efficiency of the framework, both the image registration and label fusion are achieved by well-designed deep neural networks. For the atlas-to-target image registration, we propose a bi-directional registration network (BiRegNet), which can efficiently align images from different modalities. For the label fusion, we design a similarity estimation network (SimNet), which estimates the fusion weight of each atlas by measuring its similarity to the target image. SimNet can learn multi-scale information for similarity estimation to improve the performance of label fusion. The proposed framework was evaluated by the left ventricle and liver segmentation tasks on the MM-WHS and CHAOS datasets, respectively. Results have shown that the framework is effective for cross-modality MAS in both registration and label fusion https://github.com/NanYoMy/cmmas.