DPPIN: A Biological Dataset of Dynamic Protein-Protein Interaction Networks

Nowadays, many network representation learning algorithms and downstream network mining tasks have already paid attention to dynamic networks or temporal networks, which are more suitable for real-world complex scenarios by modeling evolving patterns and temporal dependencies between node interactions. Moreover, representing and mining temporal networks have a wide range of applications, such as fraud detection, social network analysis, and drug discovery. To contribute to the network representation learning and network mining research community, in this paper, we generate a new biological dataset of dynamic protein-protein interaction networks (i.e., DPPIN), which consists of twelve dynamic protein-level interaction networks of yeast cells at different scales. We first introduce the generation process of DPPIN. To demonstrate the value of our published dataset DPPIN, we then list the potential applications that would be benefited. Furthermore, we design dynamic local clustering, dynamic spectral clustering, dynamic subgraph matching, dynamic node classification, and dynamic graph classification experiments, where DPPIN indicates future research opportunities for some tasks by presenting challenges on state-of-the-art baseline algorithms. Finally, we identify future directions for improving this dataset utility and welcome inputs from the community. All resources of this work are deployed and publicly available at https://github.com/DongqiFu/DPPIN

Deeper-GXX: Deepening Arbitrary GNNs

Shallow GNNs tend to have sub-optimal performance dealing with large-scale graphs or graphs with missing features. Therefore, it is necessary to increase the depth (i.e., the number of layers) of GNNs to capture more latent knowledge of the input data. On the other hand, including more layers in GNNs typically decreases their performance due to, e.g., vanishing gradient and oversmoothing. Existing methods (e.g., PairNorm and DropEdge) mainly focus on addressing oversmoothing, but they suffer from some drawbacks such as requiring hard-to-acquire knowledge or having large training randomness. In addition, these methods simply incorporate ResNet to address vanishing gradient. They ignore an important fact: by stacking more and more layers with ResNet architecture, the information collected from faraway neighbors becomes dominant, compared with the information collected from the 1-hop and 2-hop neighbors, thus resulting in severe performance degradation. In this paper, we first go deep into the architecture of ResNet and analyze why ResNet is not best suited for deeper GNNs. Then we propose a new residual architecture to attenuate the negative impact caused by ResNet. To address the drawbacks of these existing methods, we introduce the Topology-guided Graph Contrastive Loss named TGCL. It utilizes node topological information and pulls the connected node pairs closer via contrastive learning regularization to obtain discriminative node representations. Combining the new residual architecture with TGCL, an end-to-end framework named Deeper-GXX is proposed towards deeper GNNs. The extensive experiments on real-world data sets demonstrate the effectiveness and efficiency of Deeper-GXX compared with state-of-the-art baselines

Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey

In graph machine learning, data collection, sharing, and analysis often involve multiple parties, each of which may require varying levels of data security and privacy. To this end, preserving privacy is of great importance in protecting sensitive information. In the era of big data, the relationships among data entities have become unprecedentedly complex, and more applications utilize advanced data structures (i.e., graphs) that can support network structures and relevant attribute information. To date, many graph-based AI models have been proposed (e.g., graph neural networks) for various domain tasks, like computer vision and natural language processing. In this paper, we focus on reviewing privacy-preserving techniques of graph machine learning. We systematically review related works from the data to the computational aspects. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information (e.g., graph model parameters) to realize the optimization-based computation when data sharing among multiple parties is risky or impossible. In addition to discussing relevant theoretical methodology and software tools, we also discuss current challenges and highlight several possible future research opportunities for privacy-preserving graph machine learning. Finally, we envision a unified and comprehensive secure graph machine learning system.Comment: Accepted by SIGKDD Explorations 2023, Volume 25, Issue

Effect of various mechanical deformation techniques on pinning force densities in Ag/Bi-2223 tapes

Ag/Bi-2223 tapes were fabricated using the Powder-In-Tube method, involving intermediate deformation techniques of sandwich rolling (SR), pressing (P) and normal rolling (NR). Magnetic field B depedence of Jc was measured. Depending on the relationship between Jc and B, the irreversible magnetic field Birr was determined and pinning force density F was calculated. The experimental results showed that self- field Jc was linear with the relative density for SR-, P- and NR-tapes. Our experimental results support that NR-, P-, and SR-tapes have a same sort of pinning center and the intermediate deformation processing cannot change property of pinning centers, but can effect the pinning force strength. Our experimental results also support that sandwich rolling is the best technique for fabricating Ag/Bi-2223 tapes

DISCO: Comprehensive and Explainable Disinformation Detection

Disinformation refers to false information deliberately spread to influence the general public, and the negative impact of disinformation on society can be observed in numerous issues, such as political agendas and manipulating financial markets. In this paper, we identify prevalent challenges and advances related to automated disinformation detection from multiple aspects and propose a comprehensive and explainable disinformation detection framework called DISCO. It leverages the heterogeneity of disinformation and addresses the opaqueness of prediction. Then we provide a demonstration of DISCO on a real-world fake news detection task with satisfactory detection accuracy and explanation. The demo video and source code of DISCO is now publicly available. We expect that our demo could pave the way for addressing the limitations of identification, comprehension, and explainability as a whole

Undoing in human planning

From writing to hiking, people’s real-world sequential decision-making often benefits from “undoing” (e.g. deleting sentences or backtracking). Surprisingly, undoing has not been studied in experiments on human planning. To investigate how much, when, and why people undo, we introduce a task that is a cross between the “Traveling Salesperson” and the “Knapsack” problems with an undo option. Within a length budget, subjects sequentially connect as many dots as possible on a map. On “undo” trials, they are allowed to take back actions without constraints. We find that undoing is beneficial, that subjects exhibit great individual variability in the number of undos, that undos are more frequent after errors than after correct actions, and that long response times tend to precede sequences of undos. Together, these results suggest that undo actions serve a dual role of correcting errors and of exploring alternative paths, where path evaluation benefits from full play-outs

Comparative Transcriptome Analysis and Genetic Methods Revealed the Biocontrol Mechanism of Paenibacilluspolymyxa NSY50 against Tomato Fusarium Wilt

Fusarium wilt caused by Fusarium oxysporum f. sp. lycopersici (Fol) is a common disease that affects tomatoes, which can cause the whole plant to wilt and seriously reduce the production of tomatoes in greenhouses. In this study, the morphological indexes, photosynthetic performance and incidence rate of NSY50 under Fol infection were evaluated. It was found that NSY50 could improve the growth of tomato seedlings and significantly reduce the incidence rate of Fusarium wilt. However, the molecular mechanism of NSY50 that induces resistance to Fusarium wilt is still unclear. We used transcriptomic methods to analyze NSY50-induced resistance to Fol in tomatoes. The results showed that plant defense related genes, such as PR and PAL, were highly expressed in tomato seedlings pretreated with NSY50. At the same time, photosynthetic efficiency, sucrose metabolism, alkaloid biosynthesis and terpene biosynthesis were significantly improved, which played a positive role in reducing the damage caused by Fol infection and enhancing the disease tolerance of seedlings. Through transgenic validation, we identified an important tomato NAC transcription factor, SlNAP1, which was preliminarily confirmed to be effective in relieving the detrimental symptoms induced by Fol. Our findings reveal that P. polymyxa NSY50 is an effective plant-growth-promoting rhizosphere bacterium and also a biocontrol agent of soil-borne diseases, which can significantly improve the resistance of tomato to Fusarium wilt

Severe Community-Acquired Pneumonia Caused by Human Adenovirus in Immunocompetent Adults: A Multicenter Case Series.

BackgroundSevere community-acquired pneumonia (CAP) caused by human adenovirus (HAdV), especially HAdV type 55 (HAdV-55) in immunocompetent adults has raised increasing concerns. Clinical knowledge of severe CAP and acute respiratory distress syndrome induced by HAdV-55 is still limited, though the pathogen has been fully characterized by whole-genome sequencing.MethodsWe conducted a multicentre retrospective review of all consecutive patients with severe CAP caused by HAdV in immunocompetent adults admitted to the Emergency Department Intensive Care Unit of two hospitals in Northern China between February 2012 and April 2014. Clinical, laboratory, radiological characteristics, treatments and outcomes of these patients were collected and analyzed.ResultsA total of 15 consecutive severe CAP patients with laboratory-confirmed adenovirus infections were included. The median age was 30 years and all cases were identified during the winter and spring seasons. HAdV-55 was the most frequently (11/15) detected HAdV type. Persistent high fever, cough and rapid progression of dyspnea were typically reported in these patients. Significantly increased pneumonia severity index (PSI), respiratory rate, and lower PaO2/FiO2, hypersensitive CRP were reported in non-survivors compared to survivors (P = 0.013, 0.022, 0.019 and 0.026, respectively). The rapid development of bilateral consolidations within 10 days after illness onset were the most common radiographic finding, usually accompanied by adjacent ground glass opacities and pleural effusions. Total mortality was 26.7% in this study. Corticosteroids were prescribed to 14 patients in this report, but the utilization rate between survivors and non-survivors was not significant.ConclusionsHAdV and the HAdV-55 sub-type play an important role among viral pneumonia pathogens in hospitalized immunocompetent adults in Northern China. HAdV should be tested in severe CAP patients with negative bacterial cultures and a lack of response to antibiotic treatment, even if radiologic imaging and clinical presentation initially suggest bacterial pneumonia

Boosting Capacitive Sodium-Ion Storage in Electrochemically Exfoliated Graphite for Sodium-Ion Capacitors

Sodium (Na)-ion capacitors possess higher energy density than supercapacitors and higher power density than Na-ion batteries. However, kinetic mismatches between fast capacitive charge storage on the cathode and sluggish battery-type reactions on the anode lead to a poor charge/discharge rate capability and insufficient power output of Na-ion capacitors. Thus, developing suitable anode materials for Na-ion capacitors is urgently desirable. This work demonstrates an electrochemically exfoliated graphite (EEG) anode with enhanced capacitive charge storage, greatly boosting the Na-ion reaction kinetics of co-intercalation. The EEG anode shows a high reversible capacity of 109 mAh g–1 and maintains a good capacity retention of 90% after 1000 cycles. The assembled Na-ion capacitor using the EEG anode can finish the charge/discharge process in less than 10 s, which achieves an ultrahigh power density of 17,500 W kg–1 with an energy density of 17 Wh kg–1. The high capacitive contributions at both the anode and cathode contribute to the fast rate capability and high power output of the fabricated Na-ion capacitors. This work will promote the development of ultrafast charging sodium-ion storage device

Laboratory Findings and Chest Radiologic Characteristics of Patients with severe CAP caused by Adenoviruses (comparison between survivors and non-survivors).

<p>Laboratory Findings and Chest Radiologic Characteristics of Patients with severe CAP caused by Adenoviruses (comparison between survivors and non-survivors).</p