Evolve Path Tracer: Early Detection of Malicious Addresses in Cryptocurrency

With the ever-increasing boom of Cryptocurrency, detecting fraudulent behaviors and associated malicious addresses draws significant research effort. However, most existing studies still rely on the full history features or full-fledged address transaction networks, thus cannot meet the requirements of early malicious address detection, which is urgent but seldom discussed by existing studies. To detect fraud behaviors of malicious addresses in the early stage, we present Evolve Path Tracer, which consists of Evolve Path Encoder LSTM, Evolve Path Graph GCN, and Hierarchical Survival Predictor. Specifically, in addition to the general address features, we propose asset transfer paths and corresponding path graphs to characterize early transaction patterns. Further, since the transaction patterns are changing rapidly during the early stage, we propose Evolve Path Encoder LSTM and Evolve Path Graph GCN to encode asset transfer path and path graph under an evolving structure setting. Hierarchical Survival Predictor then predicts addresses' labels with nice scalability and faster prediction speed. We investigate the effectiveness and versatility of Evolve Path Tracer on three real-world illicit bitcoin datasets. Our experimental results demonstrate that Evolve Path Tracer outperforms the state-of-the-art methods. Extensive scalability experiments demonstrate the model's adaptivity under a dynamic prediction setting.Comment: In Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD23

The distribution of long-chain n-alkan-2-ones in peat can be used to infer past changes in pH

Long-chain (C21-C33) n-alkan-2-ones are biomarkers ubiquitous in peat deposits. However, their paleoenvironmental significance lacks constraints. Here we evaluate the influence pH exerts on the occurrence of long-chain n-alkan-2-ones in Chinese peats. A comparison of the distribution in a collection (n= 65) of modern peat samples with different pH (pH values 4.4-8.6) from China demonstrates that their distribution is significantly different in acid compared to alkaline peat. This difference can be explained by the pH control on the conversion of n-alkan-2-one precursor compounds (n-alkanes and fatty acids). Transfer functions between pH and n-alkan-2-one ratios were established using linear and logarithmic regression models. We then applied these proxies to reconstruct variations of paleo-pH in the Dajiuhu peat sequence to identify the history of peatland acidification over the last 13 kyr. We find significant changes in paleo-pH during the deglaciation/early Holocene and relate these to times of dry climate in the region. The drought-induced peat acidification is supported by observations from modern drying events in the peatland. We propose that long-chain n-alkan-2-ones in peats have potential to trace paleo-pH changes across the deglaciation and Holocene, although further research from different peatlands and time periods is still needed

Senescence-associated lncRNAs indicate distinct molecular subtypes associated with prognosis and androgen response in patients with prostate cancer

Cellular senescence has been considered as a hallmark of aging. In this study, we aimed to establish two novel prognostic subtypes for prostate cancer patients using senescence-related lncRNAs. Nonnegative matrix factorization algorithm was used to identify molecular subtypes. We completed analyses using software R 3.6.3 and its suitable packages. Using SNHG1, MIAT and SNHG3, 430 patients in TCGA database were classified into two subtypes associated with biochemical recurrence (BCR)-free survival and subtype 2 was prone to BCR (HR: 19.62, p < 0.001). The similar results were observed in the GSE46602 and GSE116918. For hallmark gene set enrichment, we found that protein secretion and androgen response were highly enriched in subtype 1 and G2M checkpoint was highly enriched in subtype 2. For tumor heterogeneity and stemness, homologous recombination deficiency and tumor mutation burden were significantly higher in subtype 2 than subtype 1. The top ten genes between subtype 2 and subtype 1 were CUBN, DNAH9, PTCHD4, NOD1, ARFGEF1, HRAS, PYHIN1, ARHGEF2, MYOM1 and ITGB6 with statistical significance. In terms of immune checkpoints, only CD47 was significantly higher in subtype 1 than that in subtype 2. For the overall assessment, no significant difference was detected between two subtypes, while B cells score was significantly higher in subtype 1 than subtype 2. Overall, we found two distinct subtypes closely associated with BCR-free survival and androgen response for prostate cancer. These subtypes might facilitate future research in the field of prostate cancer

Learning List-Level Domain-Invariant Representations for Ranking

Domain adaptation aims to transfer the knowledge learned on (data-rich) source domains to (low-resource) target domains, and a popular method is invariant representation learning, which matches and aligns the data distributions on the feature space. Although this method is studied extensively and applied on classification and regression problems, its adoption on ranking problems is sporadic, and the few existing implementations lack theoretical justifications. This paper revisits invariant representation learning for ranking. Upon reviewing prior work, we found that they implement what we call item-level alignment, which aligns the distributions of the items being ranked from all lists in aggregate but ignores their list structure. However, the list structure should be leveraged, because it is intrinsic to ranking problems where the data and the metrics are defined and computed on lists, not the items by themselves. To close this discrepancy, we propose list-level alignment -- learning domain-invariant representations at the higher level of lists. The benefits are twofold: it leads to the first domain adaptation generalization bound for ranking, in turn providing theoretical support for the proposed method, and it achieves better empirical transfer performance for unsupervised domain adaptation on ranking tasks, including passage reranking.Comment: NeurIPS 2023. Comparison to v1: revised presentation and proof of Corollary 4.

UniDexGrasp: Universal Robotic Dexterous Grasping via Learning Diverse Proposal Generation and Goal-Conditioned Policy

In this work, we tackle the problem of learning universal robotic dexterous grasping from a point cloud observation under a table-top setting. The goal is to grasp and lift up objects in high-quality and diverse ways and generalize across hundreds of categories and even the unseen. Inspired by successful pipelines used in parallel gripper grasping, we split the task into two stages: 1) grasp proposal (pose) generation and 2) goal-conditioned grasp execution. For the first stage, we propose a novel probabilistic model of grasp pose conditioned on the point cloud observation that factorizes rotation from translation and articulation. Trained on our synthesized large-scale dexterous grasp dataset, this model enables us to sample diverse and high-quality dexterous grasp poses for the object point cloud.For the second stage, we propose to replace the motion planning used in parallel gripper grasping with a goal-conditioned grasp policy, due to the complexity involved in dexterous grasping execution. Note that it is very challenging to learn this highly generalizable grasp policy that only takes realistic inputs without oracle states. We thus propose several important innovations, including state canonicalization, object curriculum, and teacher-student distillation. Integrating the two stages, our final pipeline becomes the first to achieve universal generalization for dexterous grasping, demonstrating an average success rate of more than 60\% on thousands of object instances, which significantly outperforms all baselines, meanwhile showing only a minimal generalization gap.Comment: Accepted to CVPR 202

Effects of prescribed burning on understory Quercus species of Pinus yunnanensis forest

IntroductionPositioning studies on prescribed burning in Pinus yunnanensis forests have been conducted for several years, focusing on the effects of fire on the composition and structure, growth, regeneration, relative bark thickness, and bark density of understory oak species in Pinus yunnanensis forests.MethodsThe study was conducted on Zhaobi Mountain, Yi-Dai Autonomous County of Xinping, Yuxi City, Yunnan Province. In the prescribed burn after restoration of full 1 year of the area and did not implement the prescribed burn area were set up 10 m × 10 m sample plots 30 pairs of comparisons, and all the oak trees in the sample plots were recorded, each sample plot in the four apexes and the middle were set up five 2 m × 2 m small sample squares, the shrubs in the small sample squares for each plant survey, comparison, statistics and analysis of all data.ResultsThe study results showed that (1) prescribed burning significantly affected the species composition of the understorey of Pinus yunnanensis forests. In both tree and shrub layers, the important values of Quercus aliena, Quercus serrata, Quercus fabri, and Quercus variabilis were significantly reduced in the burned areas. In contrast, the important values of Quercus acutissima increased somewhat. (2) The under crown height of oak trees in the burned areas was significantly lower than in the burned areas, but the height of oak trees in the burned areas was not significantly different from that in the burned areas. In the shrub layer, the height and cover of oak plants in the prescribed burning areas were significantly lower than in the unprescribed burned areas, effectively reducing the vertical continuity of the forest surface combustible material and reducing the possibility of fire converting from surface to canopy fire along the “ladder fuel.” (3) The regeneration of oak plants in the burned area is mainly by sprout tillers, and very few young sprouts are regenerated by seed germination. Renewed young sprouts are difficult to survive the prescribed burn areas the following year due to their lack of fire tolerance. (4) The relative bark thickness and density of oak plants in prescribed burn areas were significantly higher than those in unprescribed burn areas due to the fire tolerance exhibited by oak plants in long-term prescribed burns.DiscussionPrescribed burning has profoundly altered the structural composition and growth of oak plants in the understory of Pinus yunnanensis forests, and oak plants have shown significant fire-adapted traits to resist fire under long-term fire disturbance. The study can provide a scientific basis for prescribed burning, forest fuels, and forest fire management

Diversity of Fungal Communities in Heshang Cave of Central China Revealed by Mycobiome-Sequencing

Deciphering of the mycobiome in pristine karst caves has been impeded by constraints of remote locations, inaccessibility to specimens and technical limitations, which greatly restricted in-depth understanding of mycobiomes in subterranean ecosystem. Here, mycobiomes of Heshang Cave in south-western karst region of China were investigated by Illumina HiSeq sequencing of fungal rRNA-ITS1 gene across different habitats. In total 793,502 ITS1 reads and 2,179 OTUs from 778 Mb reads after stringent quality control (Q30) and 453 genera, 72 orders and 19 classes within 6 phyla were detected. Ascomycota (42% OTUs) dominated across the five habitats. Shannon-Wiener index varied from 1.25 to 7.62 and community richness was highest in drip waters, followed by weathered rocks, bat guanos, sediments, and air samples. Mycobiomes displayed specificity to five habitats and more distinct OTUs were found in weathered rocks (12%) and drip waters (9%). In contrast, only 6.60% core OTUs were shared by five habitats. Notably, weathered rocks possessed more indicator groups and were revealed for the first time to be dominated by Sordariomycetes (43%). The community richness of air mycobiomes increased from cave entrance to the innermost part and dominated by the indicator groups of Penicillium mallochii (&gt;30%) and P. herquei (&gt;9%). Our work represents the largest attempt to date to a systematical investigation of oligotrophic solution-cave-associated mycobiomes in China. Our discovery of high diversity of mycobiomes in Heshang Cave also suggests that eukaryotic microorganisms may play a crucial role in subsurface environments

Staphylococcus aureus Bacteriophage Suppresses LPS-Induced Inflammation in MAC-T Bovine Mammary Epithelial Cells

Several previous studies have shown that bacteriophages can significantly affect the production of various cytokines. The aim of this present study was to investigate the inflammatory effects and mechanisms of bacteriophage vB_SauM_JS25 in stimulated MAC-T bovine mammary epithelial cells by real-time polymerase chain reaction (PCR) and Western blotting. Experiments show that vB_SauM_JS25 reduces Staphylococcus aureus- or lipopolysaccharide (LPS)-induced levels of tumor necrosis factor-α (TNF-α), interleukin (IL)-1β, IL-6, IL-8, IL-10, and regulated on activation, normal T cell expressed and secreted (RANTES) mRNA in MAC-T cells, in a manner expected to be unrelated to its antibacterial action. Moreover, S. aureus bacteriophage vB_SauM_JS25 suppressed the LPS-induced phosphorylation of nuclear factor (NF)-κB p65, which may represent an important mechanism mediating these effects. A carefully regulated balance between activation and inhibition by bacteriophages must be kept avoiding inappropriate inflammatory responses. The ability of vB_SauM_JS25 to influence the immune response highlights the potential development and application of bacteriophage-based therapies and may represent a novel anti-inflammatory therapeutic strategy

Transactivated Epidermal Growth Factor Receptor Recruitment of α-actinin-4 From F-actin Contributes to Invasion of Brain Microvascular Endothelial Cells by Meningitic Escherichia coli

Bacterial penetration of the blood-brain barrier requires its successful invasion of brain microvascular endothelial cells (BMECs), and host actin cytoskeleton rearrangement in these cells is a key prerequisite for this process. We have reported previously that meningitic Escherichia coli can induce the activation of host's epidermal growth factor receptor (EGFR) to facilitate its invasion of BMECs. However, it is unknown how EGFR specifically functions during this invasion process. Here, we identified an important EGFR-interacting protein, α-actinin-4 (ACTN4), which is involved in maintaining and regulating the actin cytoskeleton. We observed that transactivated-EGFR competitively recruited ACTN4 from intracellular F-actin fibers to disrupt the cytoskeleton, thus facilitating bacterial invasion of BMECs. Strikingly, this mechanism operated not only for meningitic E. coli, but also for infections with Streptococcus suis, a Gram-positive meningitis-causing bacterial pathogen, thus revealing a common mechanism hijacked by these meningitic pathogens where EGFR competitively recruits ACTN4. Ever rising levels of antibiotic-resistant bacteria and the emergence of their extended-spectrum antimicrobial-resistant counterparts remind us that EGFR could act as an alternative non-antibiotic target to better prevent and control bacterial meningitis