Ultrafast Relativistic Electron Nanoprobes

One of the frontiers in electron scattering is to couple ultrafast temporal resolution with highly localized probes to investigate the role of microstructure on material properties. Here, taking advantage of the unprecedented average brightness of the APEX electron gun providing relativistic electron pulses at high repetition rates, we demonstrate for the first time the generation of ultrafast relativistic electron beams with picometer-scale emittance and their ability to probe nanoscale features on materials with complex microstructures. At the sample plane, the APEX beam is tightly focused by a custom in-vacuum lens system based on permanent magnet quadrupoles, and its evolution around the waist is tracked by a knife-edge technique, allowing accurate reconstruction of the beam shape and local density. We then use the focused beam to characterize a Ti-6 wt\% Al polycrystalline sample by correlating the diffraction and imaging modality, showcasing the capability to locate grain boundaries and map adjacent crystallographic domains with sub-micron precision. This work provides a new paradigm for ultrafast electron instrumentation, demonstrating the ability to generate relativistic beams with ultrasmall transverse phase space volumes enabling novel characterization techniques such as relativistic ultrafast electron nano-diffraction and ultrafast scanning transmission electron microscopy

Environment-Aware Dynamic Graph Learning for Out-of-Distribution Generalization

Dynamic graph neural networks (DGNNs) are increasingly pervasive in exploiting spatio-temporal patterns on dynamic graphs. However, existing works fail to generalize under distribution shifts, which are common in real-world scenarios. As the generation of dynamic graphs is heavily influenced by latent environments, investigating their impacts on the out-of-distribution (OOD) generalization is critical. However, it remains unexplored with the following two major challenges: (1) How to properly model and infer the complex environments on dynamic graphs with distribution shifts? (2) How to discover invariant patterns given inferred spatio-temporal environments? To solve these challenges, we propose a novel Environment-Aware dynamic Graph LEarning (EAGLE) framework for OOD generalization by modeling complex coupled environments and exploiting spatio-temporal invariant patterns. Specifically, we first design the environment-aware EA-DGNN to model environments by multi-channel environments disentangling. Then, we propose an environment instantiation mechanism for environment diversification with inferred distributions. Finally, we discriminate spatio-temporal invariant patterns for out-of-distribution prediction by the invariant pattern recognition mechanism and perform fine-grained causal interventions node-wisely with a mixture of instantiated environment samples. Experiments on real-world and synthetic dynamic graph datasets demonstrate the superiority of our method against state-of-the-art baselines under distribution shifts. To the best of our knowledge, we are the first to study OOD generalization on dynamic graphs from the environment learning perspective.Comment: Accepted by the 37th Conference on Neural Information Processing Systems (NeurIPS 2023

Spin-resolved imaging of atomic-scale helimagnetism in monolayer NiI2

Identifying intrinsic noncollinear magnetic order in monolayer van der Waals (vdW) crystals is highly desirable for understanding the delicate magnetic interactions at reduced spatial constraints and miniaturized spintronic applications, but remains elusive in experiments. Here, we achieved spin-resolved imaging of helimagnetism at atomic scale in monolayer NiI2 crystals, that were grown on graphene-covered SiC(0001) substrate, using spin-polarized scanning tunneling microscopy. Our experiments identify the existence of a spin spiral state with canted plane in monolayer NiI2. The spin modulation Q vector of the spin spiral is determined as (0.2203, 0, 0), which is different from its bulk value or its in-plane projection, but agrees well with our first principles calculations. The spin spiral surprisingly indicates collective spin switching behavior under magnetic field, whose origin is ascribed to the incommensurability between the spin spiral and the crystal lattice. Our work unambiguously identifies the helimagnetic state in monolayer NiI2, paving the way for illuminating its expected type-II multiferroic order and developing spintronic devices based on vdW magnets.Comment: 22 pages, 4 figure

Does Graph Distillation See Like Vision Dataset Counterpart?

Training on large-scale graphs has achieved remarkable results in graph representation learning, but its cost and storage have attracted increasing concerns. Existing graph condensation methods primarily focus on optimizing the feature matrices of condensed graphs while overlooking the impact of the structure information from the original graphs. To investigate the impact of the structure information, we conduct analysis from the spectral domain and empirically identify substantial Laplacian Energy Distribution (LED) shifts in previous works. Such shifts lead to poor performance in cross-architecture generalization and specific tasks, including anomaly detection and link prediction. In this paper, we propose a novel Structure-broadcasting Graph Dataset Distillation (SGDD) scheme for broadcasting the original structure information to the generation of the synthetic one, which explicitly prevents overlooking the original structure information. Theoretically, the synthetic graphs by SGDD are expected to have smaller LED shifts than previous works, leading to superior performance in both cross-architecture settings and specific tasks. We validate the proposed SGDD across 9 datasets and achieve state-of-the-art results on all of them: for example, on the YelpChi dataset, our approach maintains 98.6% test accuracy of training on the original graph dataset with 1,000 times saving on the scale of the graph. Moreover, we empirically evaluate there exist 17.6% ~ 31.4% reductions in LED shift crossing 9 datasets. Extensive experiments and analysis verify the effectiveness and necessity of the proposed designs. The code is available in the GitHub repository: https://github.com/RingBDStack/SGDD.Comment: Accepted by NeurIPS 202

Incidence, associated factors, and outcomes of acute kidney injury following placement of antibiotic bone cement spacers in two-stage exchange for periprosthetic joint infection: a comprehensive study

BackgroundTwo-stage exchange with placement of antibiotic cement spacer (ACS) is the gold standard for the treatment of chronic periprosthetic joint infection (PJI), but it could cause a high prevalence of acute kidney injury (AKI). However, the results of the current evidence on this topic are too mixed to effectively guide clinical practice.MethodsWe retrospectively identified 340 chronic PJI patients who underwent the first-stage exchange with placement of ACS. The Kidney Disease Improving Global Outcomes guideline was used to define postoperative AKI. Multivariate logistic analysis was performed to determine the potential factors associated with AKI. Furthermore, a systematic review and meta-analysis on this topic were conducted to summarize the knowledge in the current literature further.ResultsIn our cohort, the incidence of AKI following first-stage exchange was 12.1%. Older age (per 10 years, OR= 1.509) and preoperative hypoalbuminemia (OR= 3.593) were independent predictors for postoperative AKI. Eight AKI patients progressed to chronic kidney disease after 90 days. A meta-analysis including a total of 2525 PJI patients showed the incidence of AKI was 16.6%, and AKI requiring acute dialysis was 1.4%. Besides, host characteristics, poor baseline liver function, factors contributing to acute renal blood flow injury, and the use of nephrotoxic drugs may be associated with the development of AKI. However, only a few studies supported an association between antibiotic dose and AKI.ConclusionAKI occurs in approximately one out of every six PJI patients undergoing first-stage exchange. The pathogenesis of AKI is multifactorial, with hypoalbuminemia could be an overlooked associated factor. Although the need for acute dialysis is uncommon, the fact that some AKI patients will develop CKD still needs to be taken into consideration

A Novel Factor Xa-Inhibiting Peptide from Centipedes Venom

Centipedes have been used as traditional medicine for thousands of years in China. Centipede venoms consist of many biochemical peptides and proteins. Factor Xa (FXa) is a serine endopeptidase that plays the key role in blood coagulation, and has been used as a new target for anti-thrombotic drug development. A novel FXa inhibitor, a natural peptide with the sequence of Thr-Asn-Gly-Tyr-Thr (TNGYT), was isolated from the venom of Scolopendra subspinipesmutilans using a combination of size-exclusion and reverse-phase chromatography. The molecular weight of the TNGYT peptide was 554.3Da measured by electrospray ionization mass spectrometry. The amino acid sequence of TNGYT was determined by Edman degradation. TNGYT inhibited the activity of FXa in a dose-dependent manner with an IC50 value of 41.14mg/ml. It prolonged the partial thromboplastin time and prothrombin time in both in vitro and ex vivo assays. It also significantly prolonged whole blood clotting time and bleeding time in mice. This is the first report that an FXa inhibiting peptide was isolated from centipedes venom

Up-Regulation of hsa-miR-210 Promotes Venous Metastasis and Predicts Poor Prognosis in Hepatocellular Carcinoma

Objective: To investigate the potential biomarkers for venous metastasis of hepatocellular carcinoma (HCC), and briefly discuss their target genes and the signaling pathways they are involved in.Materials and Method: The dataset GSE6857 was downloaded from GEO. Significantly differentially expressed miRNAs were identified using the R package “limma,” After that, the survival analysis was conducted to discover the significance of these up-regulated miRNAs for the prognosis of HCC patients. Additionally, miRNAs which were up-regulated in venous metastasis positive HCC tissues and were significant for the prognosis of HCC patients were further verified in clinical samples using RT-qPCR. The miRNAs were then analyzed for their correlations with clinical characteristics including survival time, AFP level, pathological grade, TNM stage, tumor stage, lymph-node metastasis, distant metastasis, child-pugh score, vascular invasion, liver fibrosis and race using 375 HCC samples downloaded from the TCGA database. The target genes of these miRNAs were obtained using a miRNA target gene prediction database, and their functions were analyzed using the online tool DAVID.Results: 15 miRNAs were differentially expressed in samples with venous metastasis, among which 7 were up-regulated in venous metastasis positive HCC samples. As one of the up-regulated miRNAs, hsa-miR-210 was identified as an independent prognostic factor for HCC. Using RT-qPCR, it was evident that hsa-miR-210 expression was significantly higher in venous metastasis positive HCC samples (p = 0.0036). Further analysis indicated that hsa-miR-210 was positively associated with AFP level, pathological grade, TNM stage, tumor stage and vascular invasion. A total of 168 hsa-miR-210 target genes, which are mainly related to tumor metastasis and tumor signaling pathways, were also predicted in this study.Conclusion: hsa-miR-210 might promote vascular invasion of HCC cells and could be used as a prognostic biomarker