Inflammatory bowel diseases(IBD)are known to have complex,genetically influenced etiologies,involving dysfunctional interactions between the intestinal immune system and the ***,we characterized how the RNA transcript...
Inflammatory bowel diseases(IBD)are known to have complex,genetically influenced etiologies,involving dysfunctional interactions between the intestinal immune system and the ***,we characterized how the RNA transcript from an IBD-associated long non-coding RNA locus("CARINH-Colitis Associated IRF1 antisense Regulator of Intestinal Homeostasis")protects against *** show that CARINH and its neighboring gene coding for the transcription factor IRF1 together form a feedforward loop in host myeloid *** loop activation is sustained by microbial factors,and functions to maintain the intestinal host-commensal homeostasis via the induction of the anti-inflammatory factor IL-18BP and anti-microbial factors called guanylate-binding proteins(GBPs).Extending these mechanistic insights back to humans,we demonstrate that the function of the CARINH/IRF1 loop is conserved between mice and ***,the T allele of rs2188962,the most probable causal variant of IBD within the CARINH locus from the human genetics study,impairs the inducible expression of the CARINH/IRF1 loop and thus increases genetic predisposition to *** study thus illustrates how an IBD-associated IncRNA maintains intestinal homeostasis and protects the host against colitis.
3D medical image segmentation is vital for disease diagnosis and effective treatment strategies. Despite the advancements in Convolutional Neural networks (CNN), their fixed receptive fields constrain global context m...
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Delegation Learning(DL) flourishes data sharing, enabling agents to delegate data to the cloud for model training. To preserve privacy, homomorphic encryption (HE) offers an effective solution for privacy-preserving m...
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With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption ena...
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ISBN:
(数字)9798350368550
ISBN:
(纸本)9798350368567
With the rapid development of cloud computing, more and more users are storing sensitive data on cloud servers, making the privacy-preserving of data particularly important. Dynamic searchable symmetric encryption enables efficient retrieval of encrypted data in cloud computing environments while preserving data privacy. However, existing solutions are not effective in defending against various query-recovery attacks. Therefore, this paper focuses on the privacy-preserving of dynamic searchable symmetric encryption, and proposes a privacy-preserving dynamic searchable symmetric encryption based on anonymization and differential privacy – DADP. Firstly, the original indexes are synthesized into fake indexes using the anonymization hash technology. The synthetic indexes possess randomness and irreversibility, making it impossible for adversaries to infer the generation process of the synthetic indexes or recover the original indexes. Additionally, by using differential privacy to process composite indexes, the privacy of keywords and index information is protected, preventing adversaries from inferring sensitive information based on query results. This approach provides dual privacy-preserving. Compared to other schemes, our scheme achieves type-I backward privacy and can withstand seven types of query recovery attacks. And it improves update and query efficiency by 10-100 times.
NLRP6,a Nod-like receptor family member,has been shown to affect intestinal homeostasis and microbial colonization through organizing a huge protein complex called ***6 inflammasome promotes the cleavage and secretion...
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NLRP6,a Nod-like receptor family member,has been shown to affect intestinal homeostasis and microbial colonization through organizing a huge protein complex called ***6 inflammasome promotes the cleavage and secretion of inflammatory cytokines or the cleavage of pore-forming Gasdermin D to initiate the inflammatory cell death called pyroptosis,which plays important roles in responding to pathogen ***,questions about the ligand(s)that trigger NLRP6 inflammasome activation,or the mechanisms that how a ligand triggers NLRP6 inflammasome assembly,are *** this mini-review,we summarize the current understandings of ligand recognition of NLRP6,the role of liquid-liquid phase separation in NLRP6 inflammasome assembly,and potential links with human health and diseases.
Metal oxide semiconductor heterojunctions(MOSHs)can enhance the performance of ethanol gas sen-sors *** gas sensors based on MOSHs are cost-effective and have excellent sensing response,good selectivity,fast response ...
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Metal oxide semiconductor heterojunctions(MOSHs)can enhance the performance of ethanol gas sen-sors *** gas sensors based on MOSHs are cost-effective and have excellent sensing response,good selectivity,fast response and recovery,long-term stability or repeatability,a low operating temperature,a facile fabrica-tion process,and versatile *** paper reviews the recent advances in gas sensors that are based on MOSHs and the advantages of using them to detect ethanol *** to the literature,compared with ethanol gas sen-sors that use single-component sensing materials,the MOSHs exhibit superior performance due to the synergy between the different components,which can amplify the reception and transduction components of the sensor *** the best of our knowledge,heterojunctions can be grouped into four main categories as metal oxide/metal oxide,metal oxide/metal sulfide,metal oxide/noble metal,and metal oxide/other materials,including rare-earth metals,g-C_(3)N_(4),and graphene,*** future trends and challenges that would be faced in the development of ethanol gas sensors based on MOSHs are discussed in ***,critical ideas and thinking regarding the future progress of MOSH-based gas sensors are presented.
Federated learning has emerged as the forefront of research in recent years. However, its distributed framework poses a risk of a single point of failure in data research. Moreover, distinguishing malicious clients in...
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ISBN:
(数字)9798331528324
ISBN:
(纸本)9798331528331
Federated learning has emerged as the forefront of research in recent years. However, its distributed framework poses a risk of a single point of failure in data research. Moreover, distinguishing malicious clients in the process of realizing personalized federated learning through client similarity is a critical concern. To overcome the aforementioned problems, we recommend a blockchain-based, dual-threshold personalized federated learning approach. Blockchain is used to replace the central server in the traditional federated learning framework. And we design a double-threshold-based malicious client screening algorithm, which achieves final malicious client identification by establishing screening criteria on both the blockchain side and the client side. The experimental findings demonstrate that the suggested technique maintains good accuracy and convergence even in the presence of malicious clients, compared to the FedAvg algorithm.
Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emoti...
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Speech emotion recognition (SER) aims to identify the speaker's emotional states in specific utterances accurately. However, existing methods still face feature confusion when attempting to recognize certain emotions because traditional acoustic feature extraction methods fail to capture dynamic emotional changes, blurring emotional boundaries. Additionally, existing classification networks (CNs) are constrained by fixed learning strategies, hindering their ability to capture subtle emotional nuances and resulting in label confusion. To address these two issues, we introduce 3D multiresolution modulation filtered cochleogram (MMCG) features by computing the deltas and delta-deltas of MMCG features to enhance the dynamic emotional changes and produce distinct emotional boundaries. We then customize a conditional emotion feature diffusion (CEFD) module, which progressively diffuses features based on emotional context to retain emotional nuances effectively and reduce reliance on conditioned information. In addition, a confidence filtering module is used to filter diffused features based on confidence-based posterior probabilities to ensure enhanced feature discrimination. We design a flexible training strategy named the progressive interleaved learning strategy (PILS) to learn further complex emotional nuances, which consists of two alternating stages: fine-tuning the CN parameters and supervising the CEFD output. Testing on the IEMOCAP, casIA, and EMODB corpora demonstrates significant performance improvements in SER.
Industry 4.0, which combines information technology, network and industrial production, is expected to have a tremendous impact on our daily lives. In such a complex and security-critical system with resource-constrai...
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Industry 4.0, which combines information technology, network and industrial production, is expected to have a tremendous impact on our daily lives. In such a complex and security-critical system with resource-constrained sensor nodes, the design of a secure user authentication scheme for preventing real-time data from unauthorized access is full of challenges, and the main crux lies in how to realize the important property of forward secrecy. Existing schemes either fail to achieve forward secrecy or achieve forward secrecy with high computation cost on sensor nodes. Besides, they often fail to conform to the development trend of industry 4.0 systems where a cloud center is necessary to help intelligent decision-making and alleviate computation and storage pressure. Therefore, in this paper, we propose an efficient privacy-preserving user authentication scheme with forward secrecy for industry 4.0, and formally prove its security in the random oracle model. Compared with previous schemes, it has three advantages:(1) all eleven state-of-the-art criteria are achieved;(2) its computation cost on sensor nodes is comparable to those insecure schemes that employ only symmetric cryptographic algorithms, and is superior to those that also use asymmetric cryptographic algorithms;(3) it takes the advantage of the computation and storage capabilities of the cloud center to achieve user anonymity and the resistance to offline dictionary attack without performing any asymmetric cryptographic algorithms on gateways. Our computation cost on gateways is the smallest among all state-of-the-art relevant schemes for comparison.
Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications ...
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Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.
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