Federated cINN Clustering for Accurate Clustered Federated Learning

Federated Learning (FL) presents an innovative approach to privacy-preserving distributed machine learning and enables efficient crowd intelligence on a large scale. However, a significant challenge arises when coordinating FL with crowd intelligence which diverse client groups possess disparate objectives due to data heterogeneity or distinct tasks. To address this challenge, we propose the Federated cINN Clustering Algorithm (FCCA) to robustly cluster clients into different groups, avoiding mutual interference between clients with data heterogeneity, and thereby enhancing the performance of the global model. Specifically, FCCA utilizes a global encoder to transform each client's private data into multivariate Gaussian distributions. It then employs a generative model to learn encoded latent features through maximum likelihood estimation, which eases optimization and avoids mode collapse. Finally, the central server collects converged local models to approximate similarities between clients and thus partition them into distinct clusters. Extensive experimental results demonstrate FCCA's superiority over other state-of-the-art clustered federated learning algorithms, evaluated on various models and datasets. These results suggest that our approach has substantial potential to enhance the efficiency and accuracy of real-world federated learning tasks

Elevated atmospheric CO2 concentration triggers redistribution of nitrogen to promote tillering in rice

Elevated atmospheric CO2 concentration (eCO2) often reduces nitrogen (N) content in rice plants and stimulates tillering. However, there is a general consensus that reduced N would constrain rice tillering. To resolve this contradiction, we investigated N distribution and transcriptomic changes in different rice plant organs after subjecting them to eCO2 and different N application rates. Our results showed that eCO2 significantly promoted rice tillers (by 0.6, 1.1, 1.7, and 2.1 tillers/plant at 0, 75, 150, and 225 kg N ha−1 N application rates, respectively) and more tillers were produced under higher N application rates, confirming that N availability constrained tillering in the early stages of growth. Although N content declined in the leaves (−11.0 to −20.7 mg g−1) and sheaths (−9.8 to −28.8 mg g−1) of rice plants exposed to eCO2, the N content of newly emerged tillers on plants exposed to eCO2 equaled or exceeded the N content of tillers produced under ambient CO2 conditions. Apparently, the redistribution of N within the plant per se was a critical adaptation strategy to the eCO2 condition. Transcriptomic analysis revealed that eCO2 induced less extensive alteration of gene expression than did N application. Most importantly, the expression levels of multiple N-related transporters and receptors such as nitrate transporter NRT2.3a/b and NRT1.1a/b were differentially regulated in leaf and shoot apical meristem, suggesting that multiple genes were involved in sensing the N signal and transporting N metabolites to adapt to eCO2. The redistribution of N in different organs could be a universal adaptation strategy of terrestrial plants to eCO2

Multistep Deep System for Multimodal Emotion Detection With Invalid Data in the Internet of Things

The Internet of Things (IoT) technologies such as interconnection and edge computing help emotion recognition to be applied in healthcare, smart education, etc. However, the acquisition and transmission processes may have some situations, such as lost signals and serious interference noise caused by motion, which affect the quality of the received data and limit the performance of IoT emotion detection. We collectively refer to these as invalid data. A multi-step deep (MSD) system is proposed to reliably detect multimodal emotion by the collected records containing invalid data. Semantic compatibility and continuity are utilized to filter out the invalid data. The feature from invalid modal data is replaced through the imputation method to compensate for the impact of invalid data on emotion detection. In this way, the proposed system can automatically process invalid data and improve the recognition performance. Furthermore, considering the spatiotemporal information, the features of video and physiological signals are extracted by specific deep neural networks in the MSD system. The simulation experiments are conducted on a public multimodal database, and the performance of the MSD system measured by the unweighted average recall is better than that of the traditional system. The promising results observed in the experiments verify the potential influence of the proposed system in practical IoT applications

Multimodal Bubble Microrobot Near an Air-Water Interface

The development of multifunctional and robust swimming microrobots working at the free air-liquid interface has encountered challenge as new manipulation strategies are needed to overcome the complicated interfacial restrictions. Here, flexible but reliable mechanisms are shown that achieve a remote-control bubble microrobot with multiple working modes and high maneuverability by the assistance of a soft air-liquid interface. This bubble microrobot is developed from a hollow Janus microsphere (JM) regulated by a magnetic field, which can implement switchable working modes like pusher, gripper, anchor, and sweeper. The collapse of the microbubble and the accompanying directional jet flow play a key role for functioning in these working modes, which is analogous to a "bubble tentacle." Using a simple gamepad, the orientation and the navigation of the bubble microrobot can be easily manipulated. In particular, a speed modulation method is found for the bubble microrobot, which uses vertical magnetic field to control the orientation of the JM and the direction of the bubble-induced jet flow without changing the fuel concentration. The findings demonstrate a substantial advance of the bubble microrobot specifically working at the air-liquid interface and depict some nonintuitive mechanisms that can help develop more complicated microswimmers

Roseburia intestinalis and Its Metabolite Butyrate Inhibit Colitis and Upregulate TLR5 through the SP3 Signaling Pathway

The pathogenesis of ulcerative colitis (UC) is unclear, but it is generally believed to be closely related to an imbalance in gut microbiota. Roseburia intestinalis (R. intestinalis) might play a key role in suppressing intestinal inflammation, but the mechanism of its anti-inflammatory effect is unknown. In this study, we investigated the role of R. intestinalis and Toll-like receptor 5 (TLR5) in relieving mouse colitis. We found that R. intestinalis significantly upregulated the transcription of TLR5 in intestinal epithelial cells (IECs) and improved colonic inflammation in a colitis mouse model. The flagellin of R. intestinalis activated the release of anti-inflammatory factors (IL-10, TGF-β) and reduced inflammation in IECs. Furthermore, butyrate, the main metabolic product secreted by R. intestinalis, regulated the expression of TLR5 in IECs. Our data show that butyrate increased the binding of the transcription factor Sp3 (specificity protein 3) to the TLR5 promoter regions, upregulating TLR5 transcription. This work provides new insight into the anti-inflammatory effects of R. intestinalis in colitis and a potential target for UC prevention and treatment

Elevated atmospheric CO2 concentration triggers redistribution of nitrogen to promote tillering in rice

Abstract Elevated atmospheric CO2 concentration (eCO2) often reduces nitrogen (N) content in rice plants and stimulates tillering. However, there is a general consensus that reduced N would constrain rice tillering. To resolve this contradiction, we investigated N distribution and transcriptomic changes in different rice plant organs after subjecting them to eCO2 and different N application rates. Our results showed that eCO2 significantly promoted rice tillers (by 0.6, 1.1, 1.7, and 2.1 tillers/plant at 0, 75, 150, and 225 kg N ha−1 N application rates, respectively) and more tillers were produced under higher N application rates, confirming that N availability constrained tillering in the early stages of growth. Although N content declined in the leaves (−11.0 to −20.7 mg g−1) and sheaths (−9.8 to −28.8 mg g−1) of rice plants exposed to eCO2, the N content of newly emerged tillers on plants exposed to eCO2 equaled or exceeded the N content of tillers produced under ambient CO2 conditions. Apparently, the redistribution of N within the plant per se was a critical adaptation strategy to the eCO2 condition. Transcriptomic analysis revealed that eCO2 induced less extensive alteration of gene expression than did N application. Most importantly, the expression levels of multiple N‐related transporters and receptors such as nitrate transporter NRT2.3a/b and NRT1.1a/b were differentially regulated in leaf and shoot apical meristem, suggesting that multiple genes were involved in sensing the N signal and transporting N metabolites to adapt to eCO2. The redistribution of N in different organs could be a universal adaptation strategy of terrestrial plants to eCO2

Fabrication and Evaluation of Artemisinin-Imprinted Composite Membranes by Developing a Surface Functional Monomer-Directing Prepolymerization System

Inspired by a surface functional monomer-directing prepolymerization system, a straightforward and effective synthesis method was first developed to prepare highly regenerate and perm-selective molecularly imprinted composite membranes of artemisinin (Ars) molecules. Attributing to the formation of the prepolymerization system, Ars molecules are attracted and bound to the membrane surface, hence promoting the growth of homogeneous and high-density molecular recognition sites on the surface of membrane materials. Afterward, a two-step-temperature imprinting procedure was carried out to prepare the novel surface functional monomer capping molecularly imprinted membranes (FMIMs). The as-prepared FMIMs not only exhibited highly adsorption capacity (11.91 mg g^–1) but also showed an outstanding specific selectivity (imprinting factor α is 4.50) and excellent perm-selectivity ability (separation factor β is 10.60) toward Ars molecules, which is promising for Ars separation and purification

Transcriptomic and Co-Expression Network Profiling of Shoot Apical Meristem Reveal Contrasting Response to Nitrogen Rate between Indica and Japonica Rice Subspecies

Reducing nitrogen (N) input is a key measure to achieve a sustainable rice production in China, especially in Jiangsu Province. Tiller is the basis for achieving panicle number that plays as a major factor in the yield determination. In actual production, excessive N is often applied in order to produce enough tillers in the early stages. Understanding how N regulates tillering in rice plants is critical to generate an integrative management to reduce N use and reaching tiller number target. Aiming at this objective, we utilized RNA sequencing and weighted gene co-expression network analysis (WGCNA) to compare the transcriptomes surrounding the shoot apical meristem of indica (Yangdao6, YD6) and japonica (Nipponbare, NPB) rice subspecies. Our results showed that N rate influenced tiller number in a different pattern between the two varieties, with NPB being more sensitive to N enrichment, and YD6 being more tolerant to high N rate. Tiller number was positively related to N content in leaf, culm and root tissue, but negatively related to the soluble carbohydrate content, regardless of variety. Transcriptomic comparisons revealed that for YD6 when N rate enrichment from low (LN) to medium (MN), it caused 115 DEGs (LN vs. MN), from MN to high level (HN) triggered 162 DEGs (MN vs. HN), but direct comparison of low with high N rate showed a 511 DEGs (LN vs. HN). These numbers of DEG in NPB were 87 (LN vs. MN), 40 (MN vs. HN), and 148 (LN vs. HN). These differences indicate that continual N enrichment led to a bumpy change at the transcription level. For the reported sixty-five genes which affect tillering, thirty-six showed decent expression in SAM at tiller starting phase, among them only nineteen being significantly influenced by N level, and two genes showed significant interaction between N rate and variety. Gene ontology analysis revealed that the majority of the common DEGs are involved in general stress responses, stimulus responses, and hormonal signaling process. WGCNA network identified twenty-two co-expressing gene modules and ten candidate hubgenes for each module. Several genes associated with tillering and N rate fall on the related modules. These indicate that there are more genes participating in tillering regulation in response to N enrichment