Broadband Near-Infrared-Emitting Phosphors with Suppressed Concentration Quenching in a Two-Dimensional Structure.

For inorganic luminescent materials with activators, the energy yield is usually observed to decrease with an increase in activator concentration, which is known as the concentration quenching effect. To inhibit this phenomenon, a common strategy is to increase the distance between activators. Most previous reports have focused on the three-dimensional crystal lattice, and there have been few reports about two-dimensional layered structure. Herein, we synthesized a novel Cr3+-activated near-infrared (NIR) phosphor Li2Sr2Al(PO4)3 (LSAPO) with layered structure, and in such a two-dimensional structure, we proved experimentally that the concentration quenching was suppressed. Under 460 nm excitation, LSAPO:Cr3+ gave a broad NIR emission band (700-1200 nm) centered at 823 nm with a full width at half-maximum (fwhm) of 178 nm and a broad absorption band, indicating its potential application in NIR spectroscopy. Moreover, by codoping Cr3+ and Yb3+ ions, we further widened the emission bandwidth to ∼230 nm of fwhm, the internal quantum efficiency increased from 54% to 61%, and the thermal stability was improved. The fabricated NIR device with a LSAPO:Cr3+,Yb3+ phosphor coupled with blue chips can be applied in night-vision technologies and medical fields.

A new modified obstetric early warning score for prognostication of severe maternal morbidity.

BACKGROUND: Maternal mortality is still a major challenge for health systems, while severe maternal complications are the primary causes of maternal death. Our study aimed to determine whether severe maternal morbidity is effectively predicted by a newly proposed Modified Obstetric Early Warning Score (MOEWS) in the setting of an obstetric intensive care unit (ICU). METHODS: A retrospective study of pregnant women admitted in the ICU from August 2019 to August 2020 was conducted. MOEWS was calculated 24 h before and 24 h after admission in the ICU, and the highest score was taken as the final value. For women directly admitted from the emergency department, the worst value before admission was collected. The aggregate performance of MOEWS in predicting critical illness in pregnant women was evaluated and finally compared with that of the Acute Physiology and Chronic Health Evaluation II (APACHE II) score. RESULTS: A total of 352 pregnant women were enrolled; 290 women (82.4%) with severe maternal morbidity were identified and two of them died (0.6%). The MOEWSs of women with serious obstetric complications were significantly higher than those of women without serious obstetric complications [8(6, 10) vs. 4(2, 4.25), z = -10.347, P < 0.001]. MOEWSs of 24 h after ICU admission had higher sensitivity, specificity and AUROC than MOEWSs of 24 h before ICU admission. When combining the two MOEWSs, sensitivity of MOEWS was 99.3% (95% CI: 98-100), specificity 75.8% (95% CI: 63-86), positive predictive value (PPV) 95.1% (95% CI: 92-97) and negative predictive value (NPV) 95.9% (95% CI: 86-100). The areas under the receiver operator characteristic (ROC) curves of MOEWS were 0.92 (95% CI: 0.88-0.96) and 0.70 (95% CI: 0.63-0.76) of the APACHE II score. CONCLUSION: The newly proposed MOEWS has an excellent ability to identify critically ill women early and is more effective than APACHE II. It will be a valuable tool for discriminating severe maternal morbidity and ultimately improve maternal health.

Successful treatment of severe pityriasis rubra pilaris with secukinumab in a 3-year-old boy.

Pityriasis rubra pilaris (PRP) is a rare, scaly, keratotic inflammatory skin disease characterized by red scaly patches, keratosis papules, palmoplantar keratoderma and scaling of the scalp. In severe cases, ectropion of the eyelid may occur, and erythroderma may further develop. Recently, it has been reported that secukinumab, a monoclonal anti-interleukin-17A antibody, has certain efficacy in the treatment of PRP. Herein, we report a 3-year-old Chinese boy with severe Type III (classic juvenile) PRP who was successfully treated with secukinumab alone.

Research on a Gas Concentration Prediction Algorithm Based on Stacking.

Machine learning algorithms play an important role in the detection of toxic, flammable and explosive gases, and they are extremely important for the study of mixed gas classification and concentration prediction methods. To solve the problem of low prediction accuracy of gas concentration regression prediction algorithms, a gas concentration prediction algorithm based on a stacking model is proposed in the current research. In this paper, the stochastic forest, extreme random regression tree and gradient boosting decision tree (GBDT) regression algorithms are selected as the base learning devices and use the stacking algorithm to take the output of each base learning device as input to train a new model to produce a final output. Through the stacking model, the grid search algorithm is studied to automatically optimize the parameters so that the performance of the entire system can reach the optimal parameters. Through experimental simulation, the gas concentration prediction algorithm based on stacking model has better prediction effect than other integrated frame algorithms and the accuracy of mixed gas concentration prediction is improved.

ZnGa2-yAlyO4:Mn2+,Mn4+ Thermochromic Phosphors: Valence State Control and Optical Temperature Sensing.

Dual-emitting and thermochromic manganese ion single doped ZnGa2-yAlyO4 phosphors were prepared by solid-state reaction. The regulation of the valence state and the luminescent properties, especially the luminescent thermal stability of manganese ions in ZnGa2-yAlyO4, are discussed in detail. When excited by ultraviolet (UV) light, the emission spectra of ZnGa2O4:Mn2+,Mn4+ present an ultranarrow green emission band at 503 nm with a fwhm of 22 nm, which derives from the Mn2+ ions formed by the self-reduction of doped Mn4+, and a red emission band of the Mn4+ ions at 669 nm. In addition, a ZnGa2-yAlyO4:Mn2+,Mn4+ solid solution was designed and synthesized by Al3+ replacing Ga3+. The doping of Al3+ effectively inhibited the degree of Mn4+ self-reduction to Mn2+, thus realizing the regulation of valence state of manganese ions. Interestingly, the thermal stability of luminescence shows that the response of Mn2+ and Mn4+ to temperature is obviously different in ZnGa2-yAlyO4, implying the potential of the prepared phosphors as optical thermometers. Subsequently, three kinds of optical thermometers with superior color discrimination and high relative sensitivity (Sr) based on the fluorescence intensity ratio (FIR) technique were realized in 100-475 K. The Sr value of ZGO:0.005Mn/ZGA0.5O:0.005Mn/ZGAO:0.005Mn phosphors can be as high as 4.345%/4.001%/3.488% K-1 (at 350/325/400 K), reflecting the great potential of ZnGa2O4:Mn2+,Mn4+ for optical thermometry applications.

Interleukin-22 Promotes Osteosarcoma Cell Proliferation and Invasion via STAT3 Activation.

BACKGROUND Interleukin-22 (IL-22) is one of the cytokines secreted by T-helper 17 (Th17) cells. It belongs to the IL-10 cytokine family and influences a variety of immune reactions. Studies have indicated that IL-22 can promote cancer progression and metastases. However, the function of IL-22 in osteosarcoma (OS) remains unclear. MATERIAL AND METHODS In this study, the expression of IL-22 in the OS cell line was detected by qRT-PCR. The role of IL-22 in proliferation and invasion in OS cells was tested by MTT and Transwell assays. The protein expression of STAT3, phospho-STAT3, AKT, and phospho-AKT was detected by Western blot analysis. RESULTS The results showed that IL-22 was upregulated in OS cells. IL-22 dose-independently promoted OS cells proliferation and invasion, which could be reversed by IL-22 antibody or STAT3 siRNA. Furthermore, IL-22 exposure of OS cells resulted in dose-independently increased levels of phosphorylated STAT3 protein kinases. Interestingly, IL-22 did not influence the expression of phosphorylated AKT. CONCLUSIONS These results suggest that IL-22 promotes OS cells proliferation and invasion and its effect is mediated by activation of the STAT3 pathway. These findings demonstrate that IL-22 may serve as a promising molecular biomarker for diagnosis and therapy for OS patients.

Research on a Mixed Gas Recognition and Concentration Detection Algorithm Based on a Metal Oxide Semiconductor Olfactory System Sensor Array.

As a typical machine olfactory system index, the accuracy of hybrid gas identification and concentration detection is low. This paper proposes a novel hybrid gas identification and concentration detection method. In this method, Kernel Principal Component Analysis (KPCA) is employed to extract the nonlinear mixed gas characteristics of different components, and then K-nearest neighbour algorithm (KNN) classification modelling is utilized to realize the recognition of the target gas. In addition, this method adopts a multivariable relevance vector machine (MVRVM) to regress the multi-input nonlinear signal to realize the detection of the concentration of the hybrid gas. The proposed method is validated by using CO and CH4 as the experimental system samples. The experimental results illustrate that the accuracy of the proposed method reaches 98.33%, which is 5.83% and 14.16% higher than that of principal component analysis (PCA) and independent component analysis (ICA), respectively. For the hybrid gas concentration detection method, the CO and CH4 concentration detection average relative errors are reduced to 5.58% and 5.38%, respectively.

[Recent research advances of 1-deoxynojirimycin and its derivatives].

The 1-DNJ named 1-deoxynojirimycinis (2R,3R,4R,5S)-2-(hydroxymethyl) piperidine-3,4,5-triol, which is the nature active components existingin mulberryresources including leaves, stems, roots and silkworm larva, silkworm chrysalis, etc.The 1-deoxynojirimycin is a polyhydroxylated piperidine alkaloid, which was first found in Streptomyces as an antibiotic. Then the Japanese researchers isolated it from the mulberry root. 1-DNJ can inhibit postprandial hyperglycemia by suppressing intestinal alpha glucosidase. Therefore, 1-DNJ is often used to treat treating diabetes and complicating disease and to prevent obesity and weight-related disorders. With the development of the researches, 1-deoxynojirimycin and its derivtiv was discovered to possess anti-hyperglycemic, anti-virus, anti-tumor functions and so on. Therefore,based on our current studythe existing knowledge on source, technique preparation process, pharmacokinetics, bioactivties,and in silico target fishing of 1-DNJ were summarized, so that the researchers may use it to explore future perspective of research on 1-DNJ.

Multi-Granularity Graph Convolution Network for Major Depressive Disorder Recognition.

Major depressive disorder (MDD) is the most common psychological disease. To improve the recognition accuracy of MDD, more and more machine learning methods have been proposed to mining EEG features, i.e. typical brain functional patterns and recognition methods that are closely related to depression using resting EEG signals. Most existing methods typically utilize threshold methods to filter weak connections in the brain functional connectivity network (BFCN) and construct quantitative statistical features of brain function to measure the BFCN. However, these thresholds may excessively remove weak connections with functional relevance, which is not conducive to discovering potential hidden patterns in weak connections. In addition, statistical features cannot describe the topological structure features and information network propagation patterns of the brain's different functional regions. To solve these problems, we propose a novel MDD recognition method based on a multi-granularity graph convolution network (MGGCN). On the one hand, this method applies multiple sets of different thresholds to build a multi-granularity functional neural network, which can remove noise while fully retaining valuable weak connections. On the other hand, this method utilizes graph neural network to learn the topological structure features and brain saliency patterns of changes between brain functional regions on the multi-granularity functional neural network. Experimental results on the benchmark datasets validate the superior performance and time complexity of MGGCN. The analysis shows that as the granularity increases, the connectivity defects in the right frontal(RF) and right temporal (RT) regions, left temporal(LT) and left posterior(LP) regions increase. The brain functional connections in these regions can serve as potential biomarkers for MDD recognition.

Medicine Package Recommendation via Dual-Level Interaction Aware Heterogeneous Graph.

Medicine package recommendation aims to assist doctors in clinical decision-making by recommending appropriate packages of medicines for patients. Current methods model this task as a multi-label classification or sequence generation problem, focusing on learning relationships between individual medicines and other medical entities. However, these approaches uniformly overlook the interactions between medicine packages and other medical entities, potentially resulting in a lack of completeness in recommended medicine packages. Furthermore, medicine commonsense knowledge considered by current methods is notably limited, making it challenging to delve into the decision-making processes of doctors. To solve these problems, we propose DIAGNN, a Dual-level Interaction Aware heterogeneous Graph Neural Network for medicine package recommendation. Specifically, DIAGNN explicitly models interactions of medical entities within electronic health records(EHRs) at two levels, individual medicine and medicine package, leveraging a heterogeneous graph. A dual-level interaction aware graph convolutional network is utilized to capture semantic information in the medical heterogeneous graph. Additionally, we incorporate medication indications into the medical heterogeneous graph as medicine commonsense knowledge. Extensive experimental results on real-world datasets validate the effectiveness of the proposed method.

High Output Power and High Quantum Efficiency in Novel NIR Phosphor MgAlGa0.7 B0.3 O4 :Cr3+ with Profound FWHM Variation.

There is strong demand for ultraefficient near-infrared (NIR) phosphors with adjustable emission properties for next-generation intelligent NIR light sources. Designing phosphors with large full-width at half-maximum (FWHM) variations is challenging. In this study, novel near-ultraviolet light-emitting diode (LED)-excited NIR phosphors, MgAlGa0.7 B0.3 O4 :Cr3+ (MAGBO:Cr3+ ), with three emission centers achieve ultra-narrowband (FWHM = 29 nm) to ultra-broadband (FWHM = 260 nm) emission with increasing Cr3+ concentration. Gaussian fitting and decay time analysis reveal the alteration in the FWHM, which is attributed to the energy transfer occurring between the three emission centers. The distinct thermal quenching behaviors of the three emission centers are revealed through the temperature-dependent decay times. The ultra-broadband NIR phosphor MAGBO:0.05Cr3+ exhibits high thermal stability (85%, 425 K) and exceptional external quantum efficiency of 68.5%. An NIR phosphor-converted LED (pc-LED) is fabricated using MAGBO:0.05Cr3+ phosphor, exhibiting a remarkable NIR output power of 136 mW at 600 mA in ultra-broadband NIR pc-LEDs. This study describes the preparation of highly efficient phosphors and provides a further understanding of the tunable FWHM, which is vital for high-performance NIR phosphors with versatile applications.

A Survey on Federated Recommendation Systems.

Federated learning has recently been applied to recommendation systems to protect user privacy. In federated learning settings, recommendation systems can train recommendation models by collecting the intermediate parameters instead of the real user data, which greatly enhances user privacy. In addition, federated recommendation systems (FedRSs) can cooperate with other data platforms to improve recommendation performance while meeting the regulation and privacy constraints. However, FedRSs face many new challenges such as privacy, security, heterogeneity, and communication costs. While significant research has been conducted in these areas, gaps in the surveying literature still exist. In this article, we: 1) summarize some common privacy mechanisms used in FedRSs and discuss the advantages and limitations of each mechanism; 2) review several novel attacks and defenses against security; 3) summarize some approaches to address heterogeneity and communication costs problems; 4) introduce some realistic applications and public benchmark datasets for FedRSs; and 5) present some prospective research directions in the future. This article can guide researchers and practitioners understand the research progress in these areas.

An efficient blue-violet phosphor: an advanced material designed for high-quality full-spectrum lighting.

A highly efficient phosphor with exceptional luminescence properties is crucial for achieving high-quality solid-state white-light illumination. Here, this paper presents a groundbreaking discovery, an innovative blue-violet emitting Ba1.31Sr3.69(BO3)3Cl:Ce3+ (BSBCl:Ce3+) phosphor designed with remarkable thermal stability and quantum efficiency for full spectrum white light-emitting diodes (WLEDs). By employing a high-temperature solid-phase method, we synthesized various BSBCl:xCe3+ phosphors with different Ce3+ doping concentrations. Remarkably, BSBCl:0.03Ce3+ displays a broad excitation band from 250 nm to 400 nm, rendering it compatible with commercial near-ultraviolet (UV) LED chips. Under 330 nm excitation, this phosphor emits blue light with an astonishing 88.2% internal quantum efficiency (IQE) and an impressive 60.9% external quantum efficiency (EQE). Notably, when employed in the temperature range of 298-473 K, the synthesized BSBCl:0.03Ce3+ phosphor exhibits exceptional color stability and thermal stability (I423 K/I298 K = 83%). Utilizing BSBCl:0.03Ce3+ as the blue-violet emitting component in the fabrication of WLED devices has demonstrated significant advancements in the color rendering index. These findings underscore the potential of BSBCl:Ce3+ phosphors for a wide range of applications in health-oriented indoor illumination.

Comparison of the efficacy for early warning systems in predicting obstetric critical illness.

OBJECTIVE: To validate the accuracy of four early warning scores for early identification of women at risk. METHODS: This was a retrospective study of pregnant women admitted in obstetrics Critical Care Unit (ICU). Capacity of the Modified Obstetric Early Warning Score (MOEWS), ICNARC Obstetric Early Warning Score (OEWS), Maternal Early Obstetric Warning System (MEOWS chart), and Maternal Early Warning Trigger (MEWT) were compared in predicting severe maternal morbidity. Area under receiver operator characteristic (AUROC) curve was used to evaluate the predictive performance of scoring system. RESULTS: A total of 352 pregnant women were enrolled and 290 were identified with severe maternal morbidity. MOEWS was more sensitive than MEOWS chart, ICNARC OEWS and MEWT (96.9 % vs. 83.4 %, 66.6 % and 44.8 %). MEWT had the highest specificity (98.4 %), followed by MOEWS (83.9 %), ICNARC OEWS (75.8 %) and MEOWS chart (48.4 %). AUROC of MOEWS, ICNARC OEWS, MEOWS chart, and MEWT for prediction of maternal mortality were 0.91 (95 % CI: 0.874-0.945), 0.765(95 % CI: 0.71-0.82), 0.657(95 % CI: 0.577-0.738), and 0.716 (95 % CI, 0.659-0.773) respectively. MOEWS had the highest AUCs in the discrimination of serious complications in hypertensive disorders, cardiovascular disease, obstetric hemorrhage and infection. For individual vital signs, maximum diastolic blood pressure (DBP), maximum systolic blood pressure (SBP), maximum respiratory rate (RR) and peripheral oxygen saturation (SPO2) demonstrated greater predictive ability. CONCLUSION: MOEWS is more accurate than ICNARC OEWS, MEOWS chart, and MEWT in predicting the deterioration of women. The prediction ability of DBP, SBP, RR and SPO2 are more reliable.

Insight into N2O emission and denitrifier communities under different aeration intensities in composting of cattle manure from perspective of multi-factor interaction analysis.

Nitrous oxide (N2O) emission from composting is a significant contributor to greenhouse effect and ozone depletion, which poses a threat to environment. To address the challenge of mitigating N2O emission during composting, this study investigated the response of N2O emission and denitrifier communities (detected by metagenome sequencing) to aeration intensities of 6 L/min (C6), 12 L/min (C12), and 18 L/min (C18) in cattle manure composting using multi-factor interaction analysis. Results showed that N2O emission occurred mainly at mesophilic phase. Cumulative N2O emission (QN2O, 9.79 mg·kg-1 DW) and total nitrogen loss (TN loss, 16.40 %) in C12 composting treatment were significantly lower than those in the other two treatments. The lower activity of denitrifying enzymes and the more complex and balanced network of denitrifiers and environmental factors might be responsible for the lower N2O emission. Denitrification was confirmed to be the major pathway for N2O production. Moisture content (MC) and Luteimonas were the key factors affecting N2O emission, and nosZ-carrying denitrifier played a significant role in reducing N2O emission. Although relative abundance of nirS was lower than that of nirK significantly (P < 0.05), nirS was the key gene influencing N2O emission. Community composition of denitrifier varied significantly with different aeration treatments (R2 = 0.931, P = 0.001), and Achromobacter was unique to C12 at mesophilic phase. Physicochemical factors had higher effect on QN2O, whereas denitrifying genes, enzymes and NOX- had lower effect on QN2O in C12. The complex relationship between N2O emission and the related factors could be explained by multi-factor interaction analysis more comprehensively. This study provided a novel understanding of mechanism of N2O emission regulated by aeration intensity in composting.

Improving prediction of N2O emissions during composting using model-agnostic meta-learning.

Nitrous oxide (N2O) represents a significant environmental challenge as a harmful, long-lived greenhouse gas that contributes to the depletion of stratospheric ozone and exacerbates global anthropogenic greenhouse warming. Composting is considered a promising and economically feasible strategy for the treatment of organic waste. However, recent research indicates that composting is a source of N2O, contributing to atmospheric pollution and greenhouse effect. Consequently, there is a need for the development of effective, cost-efficient methodologies to quantify N2O emissions accurately. In this study, we employed the model-agnostic meta-learning (MAML) method to improve the performance of N2O emissions prediction during manure composting. The highest R2 and lowest root mean squared error (RMSE) values achieved were 0.939 and 18.42 mg d-1, respectively. Five machine learning methods including the backpropagation neural network, extreme learning machine, integrated machine learning method based on ELM and random forest, gradient boosting decision tree, and extreme gradient boosting were adopted for comparison to further demonstrate the effectiveness of the MAML prediction model. Feature analysis showed that moisture content of structure material and ammonium concentration during composting process were the two most significant features affecting N2O emissions. This study serves as proof of the application of MAML during N2O emissions prediction, further giving new insights into the effects of manure material properties and composting process data on N2O emissions. This approach helps determining the strategies for mitigating N2O emissions.

Investigating whether exosomal miR-205-5p derived from tongue squamous cell carcinoma cells stimulates the angiogenic activity of HUVECs by targeting AMOT.

BACKGROUND: Although exosomal microRNAs (exo-miRNAs) regulate angiogenesis, they are not sufficient for the development of anti-vascular drugs for tongue squamous cell carcinoma (TSCC). miR-205-5p is an exo-miRNA that is highly expressed in the saliva of patients with oral SCC. OBJECTIVE: We aimed to clarify the role and molecular mechanism of exosomal miR-205-5p in regulating TSCC angiogenesis. METHODS: Effect of exosomes derived from TSCC cells on human umbilical vein endothelial cell (HUVEC) function was determined using the CCK-8, Transwell, Transwell-Matrigel, and Matrigel-based tube formation assays. Protein levels were detected by western blot. The binding between miR-205-5p and the 3'UTR of AMOT was verified using a luciferase reporter assay. RESULTS: Exosomal miR-205-5p (exo-miR-205-5p) promoted the proliferation, migration, and invasion of HUVECs, increased the number of tubes formed by HUVECs, and increased the vascular endothelial growth factor receptor 2 levels in HUVECs. Exo-miR-205-5p downregulated the AMOT level in HUVECs. Results of the luciferase reporter assay showed that miR-205-5p could bind to the 3'UTR of AMOT. AMOT overexpression blocked the effect of exo-miR-205-5p on HUVEC functions. CONCLUSION: Exo-miR-205-5p derived from TSCC regulates the angiogenic activity of HUVECs by targeting AMOT and might be a new molecular target for the development of anti-vascular drugs for TSCC.

HealthNet: A Health Progression Network via Heterogeneous Medical Information Fusion.

Numerous electronic health records (EHRs) offer valuable opportunities for understanding patients' health status at different stages, namely health progression. Extracting the health progression patterns allows researchers to perform accurate predictive analysis of patient outcomes. However, most existing works on this task suffer from the following two limitations: 1) the diverse dependencies among heterogeneous medical entities are overlooked, which leads to the one-sided modeling of patients' status and 2) the extraction granularity of patient's health progression patterns is coarse, limiting the model's ability to accurately infer the patient's future status. To address these challenges, a pretrained Health progression network via heterogeneous medical information fusion, HealthNet, is proposed in this article. Specifically, a global heterogeneous graph in HealthNet is built to integrate heterogeneous medical entities and the dependencies among them. In addition, the proposed health progression network is designed to model hierarchical medical event sequences. By this method, the fine-grained health progression patterns of patients' health can be captured. The experimental results on real disease datasets demonstrate that HealthNet outperforms the state-of-the-art models for both diagnosis prediction task and mortality prediction task.

EAPR: explainable and augmented patient representation learning for disease prediction.

Patient representation learning aims to encode meaningful information about the patient's Electronic Health Records (EHR) in the form of a mathematical representation. Recent advances in deep learning have empowered Patient representation learning methods with greater representational power, allowing the learned representations to significantly improve the performance of disease prediction models. However, the inherent shortcomings of deep learning models, such as the need for massive amounts of labeled data and inexplicability, limit the performance of deep learning-based Patient representation learning methods to further improvements. In particular, learning robust patient representations is challenging when patient data is missing or insufficient. Although data augmentation techniques can tackle this deficiency, the complex data processing further weakens the inexplicability of patient representation learning models. To address the above challenges, this paper proposes an Explainable and Augmented Patient Representation Learning for disease prediction (EAPR). EAPR utilizes data augmentation controlled by confidence interval to enhance patient representation in the presence of limited patient data. Moreover, EAPR proposes to use two-stage gradient backpropagation to address the problem of unexplainable patient representation learning models due to the complex data enhancement process. The experimental results on real clinical data validate the effectiveness and explainability of the proposed approach.

Multicomponent Adversarial Domain Adaptation: A General Framework.

Domain adaptation (DA) aims to transfer knowledge from one source domain to another different but related target domain. The mainstream approach embeds adversarial learning into deep neural networks (DNNs) to either learn domain-invariant features to reduce the domain discrepancy or generate data to fill in the domain gap. However, these adversarial DA (ADA) approaches mainly consider the domain-level data distributions, while ignoring the differences among components contained in different domains. Therefore, components that are not related to the target domain are not filtered out. This can cause a negative transfer. In addition, it is difficult to make full use of the relevant components between the source and target domains to enhance DA. To address these limitations, we propose a general two-stage framework, named multicomponent ADA (MCADA). This framework trains the target model by first learning a domain-level model and then fine-tuning that model at the component-level. In particular, MCADA constructs a bipartite graph to find the most relevant component in the source domain for each component in the target domain. Since the nonrelevant components are filtered out for each target component, fine-tuning the domain-level model can enhance positive transfer. Extensive experiments on several real-world datasets demonstrate that MCADA has significant advantages over state-of-the-art methods.