Deterministic End-to-End Transmission to Optimize the Network Efficiency and Quality of Service: A Paradigm Shift in 6G

Toward end-to-end mobile service provision with optimized network efficiency and quality of service, tremendous efforts have been devoted in upgrading mobile applications, transport and internet networks, and wireless communication networks for many years. However, the inherent loose coordination between different layers in the end-to-end communication networks leads to unreliable data transmission with uncontrollable packet delay and packet error rate, and a terrible waste of network resources incurred for data re-transmission. In an attempt to shed some lights on how to tackle these challenges, design methodologies and some solutions for deterministic end-to-end transmission for 6G and beyond are presented, which will bring a paradigm shift to the end-to-end wireless communication networks.Comment: 5 pages, 2 figure

Assessing Accuracy with Locality-Sensitive Hashing in Multiple Source Environment

Accuracy assessment is a key issue in data quality management. Most of current studies focus on how to qualitatively analyze accuracy dimension and the analysis depends heavily on experts’ knowledge. Seldom work is given on how to automatically quantify accuracy dimension. Based on Jensen-Shannon Divergence (JSD) measure, we propose accuracy of data can be automatically quantified by comparing data with its entity’s most approximation in available context. To quickly identify most approximation in large scale data sources, Locality-Sensitive Hashing (LSH) is employed to extract most approximation at multiple levels, namely column, record and field level. Our approach can not only give each data source an objective accuracy score very quickly as long as context member is available but also avoid human’s laborious interaction. Theory and experiment show our approach performs well in achieving metadata on accuracy dimension

Multi-View Vertebra Localization and Identification from CT Images

Accurately localizing and identifying vertebrae from CT images is crucial for various clinical applications. However, most existing efforts are performed on 3D with cropping patch operation, suffering from the large computation costs and limited global information. In this paper, we propose a multi-view vertebra localization and identification from CT images, converting the 3D problem into a 2D localization and identification task on different views. Without the limitation of the 3D cropped patch, our method can learn the multi-view global information naturally. Moreover, to better capture the anatomical structure information from different view perspectives, a multi-view contrastive learning strategy is developed to pre-train the backbone. Additionally, we further propose a Sequence Loss to maintain the sequential structure embedded along the vertebrae. Evaluation results demonstrate that, with only two 2D networks, our method can localize and identify vertebrae in CT images accurately, and outperforms the state-of-the-art methods consistently. Our code is available at https://github.com/ShanghaiTech-IMPACT/Multi-View-Vertebra-Localization-and-Identification-from-CT-Images.Comment: MICCAI 202

Probabilistic activity driven model of temporal simplicial networks and its application on higher-order dynamics

Network modeling characterizes the underlying principles of structural properties and is of vital significance for simulating dynamical processes in real world. However, bridging structure and dynamics is always challenging due to the multiple complexities in real systems. Here, through introducing the individual's activity rate and the possibility of group interaction, we propose a probabilistic activity driven (PAD) model that could generate temporal higher-order networks with both power-law and high-clustering characteristics, which successfully links the two most critical structural features and a basic dynamical pattern in extensive complex systems. Surprisingly, the power-law exponents and the clustering coefficients of the aggregated PAD network could be tuned in a wide range by altering a set of model parameters. We further provide an approximation algorithm to select the proper parameters that can generate networks with given structural properties, the effectiveness of which is verified by fitting various real-world networks. Lastly, we explore the co-evolution of PAD model and higher-order contagion dynamics, and analytically derive the critical conditions for phase transition and bistable phenomenon. Our model provides a basic tool to reproduce complex structural properties and to study the widespread higher-order dynamics, which has great potential for applications across fields

Case report: Retrograde endovascular recanalization of vertebral artery occlusion with non-tapered stump via the deep cervical collateral

IntroductionVertebral artery (VA) occlusive disease is the major cause of posterior circulation ischemic stroke. Endovascular recanalization has been reported as a feasible treatment for patients with symptomatic VA occlusion refractory to optimal medical therapy. However, VA occlusion with non-tapered stump exhibits a low technique success rate when treated by antegrade endovascular therapy because of increased difficulty in passing the guidewire into the occluded segment. Herein, we presented a novel endovascular approach to recanalize chronically occluded VA with a non-tapered stump using a retrograde method via the deep cervical collateral, which has not been reported before.Case presentationThe present case was a patient with VA ostial occlusion with non-tapered stump and distal severe stenosis of the left VA who had recurrent posterior circulation transit ischemic attacks under optimal medical therapy. CT angiography demonstrated proximal non-tapered occlusion and distal severe stenosis of the left VA, and that the right VA did not converge with the left VA into basilar artery. Endovascular treatment was recommended and performed on this patient. However, antegrade endovascular recanalization of the left VA origin occlusion failed because the micro guidewire was unable to traverse the occluded segment. Fortunately, robust collateral from the deep cervical artery to the V3 segment of the left VA developed, in which we advanced the micro guidewire to the V3 segment of the left VA and reversely passed the micro guidewire through the occluded segment. Then, the occlusion and stenosis of the left VA were successfully resolved with angioplasty and stenting. After the procedure, the patient reported no neurological symptoms under medical therapy during 3-month follow-up.ConclusionAntegrade endovascular recanalization of VA occlusion with a non-tapered stump is a challenge. The retrograde endovascular method via the cervical collateral may be an alternative for this type of VA occlusion, which requires further exploration

Hsa-miR-125b suppresses bladder cancer development by down-regulating oncogene SIRT7 and oncogenic long non-coding RNA MALAT1

AbstractMicroRNAs mainly inhibit coding genes and long non-coding RNA expression. Here, we report that hsa-miR-125b and oncogene SIRT7/oncogenic long non-coding RNA MALAT1 were inversely expressed in bladder cancer. Hsa-miR-125b mimic down-regulated, whereas hsa-miR-125b inhibitor up-regulated the expression of SIRT7 and MALAT1. Binding sites were confirmed between hsa-miR-125b and SIRT7/MALAT1. Up-regulation of hsa-miR-125b or down-regulation of SIRT7 inhibited proliferation, motility and increased apoptosis. The effects of up-regulation of hsa-miR-125b were similar to that of silencing MALAT1 in bladder cancer as we had previously described. These data suggest that hsa-miR-125b suppresses bladder cancer development via inhibiting SIRT7 and MALAT1

Effect of arabinogalactan protein complex content on emulsification performance of gum arabic

The emulsification properties of the standard (STD), matured (EM2 and EM10) and fractionated gum arabic samples via phase separation induced molecular fractionation were investigated to find out how the content of arabinogalactan protein (AGP) complex affects the resulting emulsion properties. Phase separation and the accompanying molecular fractionation were induced by mixing with different hydrocolloids including hyaluronan (HA), carboxymethyl cellulose (CMC), and maltodextrin (MD). Increase of AGP content from 11 to 28% resulted in the formation of emulsions with relatively smaller droplet sizes and better stability. Further increase in the AGP content to 41% resulted in the formation of emulsions with larger droplets. In spite of the larger droplets sizes, these emulsions were extremely stable. In addition, the emulsions prepared with GA higher AGP content better stability in the presence of ethanol. The results indicate that AGP content plays a vital role in emulsion stability and droplet size

Metabolome and Transcriptome Analyses Unravels Molecular Mechanisms of Leaf Color Variation by Anthocyanidin Biosynthesis in Acer triflorum

Acer triflorum Komarov is an important ornamental tree, and its seasonal change in leaf color is the most striking feature. However, the quantifications of anthocyanin and the mechanisms of leaf color change in this species remain unknown. Here, the combined analysis of metabolome and transcriptome was performed on green, orange, and red leaves. In total, 27 anthocyanin metabolites were detected and cyanidin 3-O-arabinoside, pelargonidin 3-O-glucoside, and peonidin 3-O-gluside were significantly correlated with the color development. Several structural genes in the anthocyanin biosynthesis process, such as chalcone synthase (CHS), flavanone 3-hydroxylase (F3H), and dihydroflavonol 4-reductase (DFR), were highly expressed in red leaves compared to green leaves. Most regulators (MYB, bHLH, and other classes of transcription factors) were also upregulated in red and orange leaves. In addition, 14 AtrMYBs including AtrMYB68, AtrMYB74, and AtrMYB35 showed strong interactions with the genes involved in anthocyanin biosynthesis, and, thus, could be further considered the hub regulators. The findings will facilitate genetic modification or selection for further improvement in ornamental qualities of A. triflorum

Recurrent Temporal Revision Graph Networks

Temporal graphs offer more accurate modeling of many real-world scenarios than static graphs. However, neighbor aggregation, a critical building block of graph networks, for temporal graphs, is currently straightforwardly extended from that of static graphs. It can be computationally expensive when involving all historical neighbors during such aggregation. In practice, typically only a subset of the most recent neighbors are involved. However, such subsampling leads to incomplete and biased neighbor information. To address this limitation, we propose a novel framework for temporal neighbor aggregation that uses the recurrent neural network with node-wise hidden states to integrate information from all historical neighbors for each node to acquire the complete neighbor information. We demonstrate the superior theoretical expressiveness of the proposed framework as well as its state-of-the-art performance in real-world applications. Notably, it achieves a significant +9.6% improvement on averaged precision in a real-world Ecommerce dataset over existing methods on 2-layer models