| 1. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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| 2. |
- Gu, Peng, et al.
(författare)
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A metabolite from commensal Candida albicans enhances the bactericidal activity of macrophages and protects against sepsis
- 2023
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Ingår i: Cellular & Molecular Immunology. - London : Nature Publishing Group. - 1672-7681 .- 2042-0226. ; 20:10, s. 1156-1170
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Tidskriftsartikel (refereegranskat)abstract
- The gut microbiome is recognized as a key modulator of sepsis development. However, the contribution of the gut mycobiome to sepsis development is still not fully understood. Here, we demonstrated that the level of Candida albicans was markedly decreased in patients with bacterial sepsis, and the supernatant of Candida albicans culture significantly decreased the bacterial load and improved sepsis symptoms in both cecum ligation and puncture (CLP)-challenged mice and Escherichia coli-challenged pigs. Integrative metabolomics and the genetic engineering of fungi revealed that Candida albicans-derived phenylpyruvate (PPA) enhanced the bactericidal activity of macrophages and reduced organ damage during sepsis. Mechanistically, PPA directly binds to sirtuin 2 (SIRT2) and increases reactive oxygen species (ROS) production for eventual bacterial clearance. Importantly, PPA enhanced the bacterial clearance capacity of macrophages in sepsis patients and was inversely correlated with the severity of sepsis in patients. Our findings highlight the crucial contribution of commensal fungi to bacterial disease modulation and expand our understanding of the host-mycobiome interaction during sepsis development. © 2023, The Author(s), under exclusive licence to CSI and USTC.
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| 3. |
- He, Lang, et al.
(författare)
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Depressformer : Leveraging Video Swin Transformer and fine-grained local features for depression scale estimation
- 2024
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Ingår i: Biomedical Signal Processing and Control. - Amsterdam : Elsevier. - 1746-8094 .- 1746-8108. ; 96:Part A
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Tidskriftsartikel (refereegranskat)abstract
- Background and Objective:: By 2030, depression is projected to become the predominant mental disorder. With the rising prominence of depression, a great number of affective computing studies has been observed, with the majority emphasizing the use of audiovisual methods for estimating depression scales. Present studies often overlook the potential patterns of sequential data and not adopt the fine-grained features of Transformer to model the behavior features for video-based depression recognition (VDR). Methods: To address above-mentioned gaps, we present an end-to-end sequential framework called Depressformer for VDR. This innovative structure is delineated into the three structures: the Video Swin Transformer (VST) for deep feature extraction, a module dedicated to depression-specific fine-grained local feature extraction (DFLFE), and the depression channel attention fusion (DCAF) module to fuse the latent local and global features. By utilizing the VST as a backbone network, it is possible to discern pivotal features more effectively. The DFLFE enriches this process by focusing on the nuanced local features indicative of depression. To enhance the modeling of combined features pertinent to VDR, DCAF module is also presented. Results: Our methodology underwent extensive validations using the AVEC2013/2014 depression databases. The empirical results underscore its efficacy, yielding a root mean square error (RMSE) of 7.47 and a mean absolute error (MAE) of 5.49 for the first dataset. For the second database, the corresponding values were 7.22 and 5.56, respectively. And the F1-score is 0.59 on the D-vlog dataset. Conclusions: In summary, the experimental evaluations suggest that Depressformer architecture demonstrates superior performances with stability and adaptability across various tasks, making it capable of effectively identifying the severity of depression. Code will released at the link: https://github.com/helang818/Depressformer/. © 2024 Elsevier Ltd
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| 4. |
- He, Lang, et al.
(författare)
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LSCAformer : Long and short-term cross-attention-aware transformer for depression recognition from video sequences
- 2024
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Ingår i: Biomedical Signal Processing and Control. - Amsterdam : Elsevier. - 1746-8094 .- 1746-8108. ; 98
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Tidskriftsartikel (refereegranskat)abstract
- Depression will be the first prevalent mental disorder to result in the negative impact on individuals and society globally by 2030. Artificial intelligence (AI) algorithms have the potentials to significantly advance depression treatment. Existing deep learning-based architectures for the automatic diagnosis of a patient depression severity have the two primary challenges: (1) How to effectively learn both long-term and short-term patterns of depression? (2) How to efficiently merge long-term and short-term depressive features to achieve extended predictions from facial videos? To mitigate these challenges, a novel long short-term cross-attention-aware Transformer (LSCAformer) that is engineered for video-based depression recognition. Within LSCAformer, two architectures are introduced, i.e., a long short-term feature extraction (LSTFE) and a cross-attention-aware Transformer. Initially, LSTFE employs two separate branches to capture depression behaviors across long and short-term intervals. Subsequently, cross-attention-aware Transformer is implemented to identify complementary patterns within both long-term and short-term features, employing temporal-directed attention (TDA) to discern complementary temporal patterns across the long and short duration branches. On the AVEC2013/AVEC2014, the LSCAformer demonstrated superior performances with a root mean square error (RMSE), a mean absolute error (MAE) and a concordance correlation coefficient (CCC) of 7.69/5.89/0.868 and 7.55/5.91/0.845, respectively. Additionally, cross dataset experiments are performed to valid the generalization of the LSCAformer with a RMSE of 7.21, a MAE of 5.63, and a CCC of 0.874 (AVEC2013 for training, and the Northwind task of AVEC2014 for testing). Moreover, the proposed method can model the complementary behavioral patterns between long-term and short-term sequences for depression recognition. Code will be available at: https://github.com/helang818/LSCAformer/. © 2024
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| 5. |
- Liu, Kaifei, et al.
(författare)
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Regulatory role of Golgi brefeldin A resistance factor-1 in amyloid precursor protein trafficking, cleavage and Aβ formation
- 2019
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Ingår i: Journal of Cellular Biochemistry. - Hoboken : John Wiley & Sons. - 0730-2312 .- 1097-4644. ; 120:9, s. 15604-15615
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Tidskriftsartikel (refereegranskat)abstract
- β-amyloid peptide (Aβ) deposition derived from sequential cleavage of the amyloid precursor protein (APP) through the amyloidogenic pathway is an important characteristic feature of Alzheimer's disease (AD). During this process, cellular trafficking plays a crucial role. A large Sec7-domain containing ADP-ribosylation factor guanine nucleotide exchange factor (ARF-GEF), Golgi brefeldin A resistance factor 1 (GBF1) has been reported to initiate the ADP-ribosylation factor (Arf) activation cascade at trans-Golgi network, which plays a crucial function at the endoplasmic reticulum-Golgi interface. In this study, we investigated the role of GBF1 in APP transmembrane transport and Aβ formation. Using APP/PS1 (presenilin 1) overexpressing transgenic mice, we demonstrate that GBF1 has upregulated the expression of APP, indicating a role for GBF1 in APP physiological process. Knocking down of GBF1 using small interfering has significantly increased the intracellular but not the surface expression of APP. In contrast, overexpression of wild-type (WT) and guanine nucleotide exchange factor (GEF) in the activated form but not the GEF deficient mutation induced continuous activation of GBF1, which subsequently increased the surface level of APP. Interestingly, inhibition of GBF1 by c(BFA) also impaired APP trafficking and induced endoplasmic reticulum (ER) stress in SH-SY5Y cells. Our results thus for identified the role of GBF1 in APP trafficking and cleavage, and provide evidence for GBF1 as a possible therapeutic target in AD. © 2019 Wiley Periodicals, Inc.
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| 6. |
- Ni, Yuanzhi, et al.
(författare)
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Toward Reliable and Scalable Internet-of-Vehicles : Performance Analysis and Resource Management
- 2020
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Ingår i: Proceedings of the IEEE. - New York, NY : Institute of Electrical and Electronics Engineers (IEEE). - 0018-9219 .- 1558-2256. ; 108:2, s. 324-340
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Tidskriftsartikel (refereegranskat)abstract
- Reliable and scalable wireless transmissions for Internet-of-Vehicles (IoV) are technically challenging. Each vehicle, from driver-assisted to automated one, will generate a flood of information, up to thousands of times of that by a person. Vehicle density may change drastically over time and location. Emergency messages and real-time cooperative control messages have stringent delay constraints while infotainment applications may tolerate a certain degree of latency. On a congested road, thousands of vehicles need to exchange information badly, only to find that service is limited due to the scarcity of wireless spectrum. Considering the service requirements of heterogeneous IoV applications, service guarantee relies on an in-depth understanding of network performance and innovations in wireless resource management leveraging the mobility of vehicles, which are addressed in this article. For single-hop transmissions, we study and compare the performance of vehicle-to-vehicle (V2V) beacon broadcasting using random access-based (IEEE 802.11p) and resource allocation-based (cellular vehicle-to-everything) protocols, and the enhancement strategies using distributed congestion control. For messages propagated in IoV using multihop V2V relay transmissions, the fundamental network connectivity property of 1-D and 2-D roads is given. To have a message delivered farther away in a sparse, disconnected V2V network, vehicles can carry and forward the message, with the help of infrastructure if possible. The optimal locations to deploy different types of roadside infrastructures, including storage-only devices and roadside units with Internet connections, are analyzed. © 2019 IEEE
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| 7. |
- Ning, Xin, et al.
(författare)
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ICGNet : An intensity-controllable generation network based on covering learning for face attribute synthesis
- 2024
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Ingår i: Information Sciences. - New York : Elsevier. - 0020-0255 .- 1872-6291. ; 660
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Tidskriftsartikel (refereegranskat)abstract
- Face-attribute synthesis is a typical application of neural network technology. However, most current methods suffer from the problem of uncontrollable attribute intensity. In this study, we proposed a novel intensity-controllable generation network (ICGNet) based on covering learning for face attribute synthesis. Specifically, it includes an encoder module based on the principle of homology continuity between homologous samples to map different facial images onto the face feature space, which constructs sufficient and effective representation vectors by extracting the input information from different condition spaces. It then models the relationships between attribute instances and representational vectors in space to ensure accurate synthesis of the target attribute and complete preservation of the irrelevant region. Finally, the progressive changes in the facial attributes by applying different intensity constraints to the representation vectors. ICGNet achieves intensity-controllable face editing compared to other methods by extracting sufficient and effective representation features, exploring and transferring attribute relationships, and maintaining identity information. The source code is available at https://github.com/kllaodong/-ICGNet.•We designed a new encoder module to map face images of different condition spaces into face feature space to obtain sufficient and effective face feature representation.•Based on feature extraction, we proposed a novel Intensity-Controllable Generation Network (ICGNet), which can realize face attribute synthesis with continuous intensity control while maintaining identity and semantic information.•The quantitative and qualitative results showed that the performance of ICGNet is superior to current advanced models.© 2024 Elsevier Inc.
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| 8. |
- Shang, X. -J, et al.
(författare)
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Quantum-dot-induced optical transition enhancement in InAs quantum-dot-embedded p-i-n GaAs solar cells
- 2011
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Ingår i: Applied Physics Letters. - New York, N.Y. : American Institute of Physics (AIP). - 0003-6951 .- 1077-3118. ; 99:11, s. 113514-113514-3
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Tidskriftsartikel (refereegranskat)abstract
- Photocurrents (PCs) of three p–i–n GaAs solar cells, sample A with InAs quantum dots (QDs) embedded in the depletion region, B with QDs in the n region, and C without QDs, were studied experimentally and theoretically. Above GaAs bandgap, the PC of A is increased, while B is decreased with respect to C, since in A, the QD-induced reflection of hole wave function increases its overlap with electron wave function so that the optical transition rate is enhanced, while carrier mobility in B is reduced due to QD-induced potential variations. Moreover, A and B have increased PCs in the sub-GaAs-bandgap range due to QD optical absorptions.
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| 9. |
- Xu, Yanting, et al.
(författare)
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Targeted inhibition of ATP5B gene prevents bone erosion in collagen-induced arthritis by inhibiting osteoclastogenesis.
- 2021
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Ingår i: Pharmacological Research. - : Elsevier BV. - 1043-6618 .- 1096-1186. ; 165
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Tidskriftsartikel (refereegranskat)abstract
- Bone resorption by osteoclasts is an energy consuming activity, which depends on mitochondrial ATP. ATP5B, a mitochondrial ATP synthase beta subunit, is a catalytic core involved in producing ATP. Here, we investigated the contribution of ATP5B in osteoclast differentiation and joint destruction. ATP5B (LV-ATP5B) targeting or non-targeting (LV-NC) siRNA containing lentivirus particles were transduced into bone marrow macrophage derived osteoclasts or locally administered to arthritic mouse joints. Inhibition of ATP5B reduced the expression of osteoclast related genes and proteins, suppressed bone resorption by significantly impairing F-actin formation and decreased the levels of adhesion-associated proteins. In addition, ATP5B deficiency caused osteoclast mitochondrial dysfunction and, impaired the secretion of vacuole protons and MMP9. Importantly, inhibition of ATP5B expression, protected arthritis mice from joint destructions although serum levels of inflammatory mediators (TNF-α, IL-1β) and IgG2α antibodies were unaffected. These results demonstrate an essential function of ATP5B in osteoclast differentiation and bone resorption, and suggest it as a potential therapeutic target for protecting bones in RA.
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