Affinity Uncertainty-based Hard Negative Mining in Graph Contrastive Learning

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i.e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL. In particular, the proposed approach evaluates how confident/uncertain the discriminative model is about the affinity of each negative instance to an anchor instance to determine its hardness weight relative to the anchor instance. This uncertainty information is then incorporated into the existing GCL loss functions via a weighting term to enhance their performance. The enhanced GCL is theoretically grounded that the resulting GCL loss is equivalent to a triplet loss with an adaptive margin being exponentially proportional to the learned uncertainty of each negative instance. Extensive experiments on ten graph datasets show that our approach does the following: 1) consistently enhances different state-of-the-art (SOTA) GCL methods in both graph and node classification tasks and 2) significantly improves their robustness against adversarial attacks. Code is available at https://github.com/mala-lab/AUGCL.Comment: Accepted to TNNL

Fungicide-induced transposon movement in Monilinia fructicola

Repeated applications of fungicides with a single mode of action are believed to select for pre-existing resistant strains in a pathogen population, while the impact of sub-lethal doses of such fungicides on sensitive members of the population is unknown. In this study, in vitro evidence is presented that continuous exposure of Monilinia fructicola mycelium to some fungicides can induce genetic change in form of transposon transposition. Three fungicide-sensitive M. fructicola isolates were exposed in 12 weekly transfers of mycelia to a dose gradient of demethylation inhibitor fungicide (DMI) SYP-Z048 and quinone outside inhibitor fungicide (QoI) azoxystrobin in solo or mixture treatments. Evidence of mutagenesis was assessed by monitoring Mftc1, a multicopy transposable element of M. fructicola, by PCR and Southern blot analysis. Movement of Mftc1 was observed following azoxystrobin and azoxystrobin plus SYPZ048 treatments in two of the three isolates, but not in the non-fungicide-treated controls. Interestingly, the upstream promoter region of MfCYP51 was a prime target for Mftc1 transposition in these isolates. Transposition of Mftc1 was verified by Southern blot in two of three isolates from another, similar experiment following prolonged, sublethal azoxystrobin exposure, although in these isolates movement of Mftc1 in the upstream MfCYP51 promoter region was not observed. More research is warranted to determine whether fungicide-induced mutagenesis may also happen under field conditions

Fungicide-induced transposon movement in Monilinia fructicola

Repeated applications of fungicides with a single mode of action are believed to select for pre-existing resistant strains in a pathogen population, while the impact of sub-lethal doses of such fungicides on sensitive members of the population is unknown. In this study, in vitro evidence is presented that continuous exposure of Monilinia fructicola mycelium to some fungicides can induce genetic change in form of transposon transposition. Three fungicide-sensitive M. fructicola isolates were exposed in 12 weekly transfers of mycelia to a dose gradient of demethylation inhibitor fungicide (DMI) SYP-Z048 and quinone outside inhibitor fungicide (QoI) azoxystrobin in solo or mixture treatments. Evidence of mutagenesis was assessed by monitoring Mftc1, a multicopy transposable element of M. fructicola, by PCR and Southern blot analysis. Movement of Mftc1 was observed following azoxystrobin and azoxystrobin plus SYPZ048 treatments in two of the three isolates, but not in the non-fungicide-treated controls. Interestingly, the upstream promoter region of MfCYP51 was a prime target for Mftc1 transposition in these isolates. Transposition of Mftc1 was verified by Southern blot in two of three isolates from another, similar experiment following prolonged, sublethal azoxystrobin exposure, although in these isolates movement of Mftc1 in the upstream MfCYP51 promoter region was not observed. More research is warranted to determine whether fungicide-induced mutagenesis may also happen under field conditions

Real higher-order Weyl photonic crystal

Higher-order Weyl semimetals are a family of recently predicted topological phases simultaneously showcasing unconventional properties derived from Weyl points, such as chiral anomaly, and multidimensional topological phenomena originating from higher-order topology. The higher-order Weyl semimetal phases, with their higher-order topology arising from quantized dipole or quadrupole bulk polarizations, have been demonstrated in phononics and circuits. Here, we experimentally discover a class of higher-order Weyl semimetal phase in a three-dimensional photonic crystal (PhC), exhibiting the concurrence of the surface and hinge Fermi arcs from the nonzero Chern number and the nontrivial generalized real Chern number, respectively, coined a real higher-order Weyl PhC. Notably, the projected two-dimensional subsystem with kz = 0 is a real Chern insulator, belonging to the Stiefel-Whitney class with real Bloch wavefunctions, which is distinguished fundamentally from the Chern class with complex Bloch wavefunctions. Our work offers an ideal photonic platform for exploring potential applications and material properties associated with the higher-order Weyl points and the Stiefel-Whitney class of topological phases

Affinity uncertainty-based hard negative mining in graph contrastive learning

Hard negative mining has shown effective in enhancing self-supervised contrastive learning (CL) on diverse data types, including graph CL (GCL). The existing hardness-aware CL methods typically treat negative instances that are most similar to the anchor instance as hard negatives, which helps improve the CL performance, especially on image data. However, this approach often fails to identify the hard negatives but leads to many false negatives on graph data. This is mainly due to that the learned graph representations are not sufficiently discriminative due to oversmooth representations and/or non-independent and identically distributed (non-i.i.d.) issues in graph data. To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i.e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL. In particular, the proposed approach evaluates how confident/uncertain the discriminative model is about the affinity of each negative instance to an anchor instance to determine its hardness weight relative to the anchor instance. This uncertainty information is then incorporated into the existing GCL loss functions via a weighting term to enhance their performance. The enhanced GCL is theoretically grounded that the resulting GCL loss is equivalent to a triplet loss with an adaptive margin being exponentially proportional to the learned uncertainty of each negative instance. Extensive experiments on ten graph datasets show that our approach does the following: 1) consistently enhances different state-of-the-art (SOTA) GCL methods in both graph and node classification tasks and 2) significantly improves their robustness against adversarial attacks. Code is available at https://github.com/mala-lab/AUGCL

Graph-level anomaly detection via hierarchical memory networks

Graph-level anomaly detection aims to identify abnormal graphs that exhibit deviant structures and node attributes compared to the majority in a graph set. One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole. To tackle this challenge, we propose a novel approach called Hierarchical Memory Networks (HimNet), which learns hierarchical memory modules—node and graph memory modules—via a graph autoencoder network architecture. The node-level memory module is trained to model fine-grained, internal graph interactions among nodes for detecting locally abnormal graphs, while the graph-level memory module is dedicated to the learning of holistic normal patterns for detecting globally abnormal graphs. The two modules are jointly optimized to detect both locally- and globally-anomalous graphs. Extensive empirical results on 16 real-world graph datasets from various domains show that i) HimNet significantly outperforms the state-of-art methods and ii) it is robust to anomaly contamination. Codes are available at: https://github.com/Niuchx/HimNet

Optimization of Extraction Process of Total Alkaloids from <i>Thalictrum delavayi</i> Franch. and Their Therapeutic Potential on Pulmonary Infection Caused by <i>Klebsiella pneumoniae</i> and <i>Escherichia coli</i>

Bacterial co-infected pneumonia is an acute inflammatory reaction of the lungs mainly caused by Gram-negative bacteria. Antibiotics are urgently important but have the disadvantage of antibacterial resistance, and alternative treatments with medicinal plants are attractive. On the Qinghai–Tibet Plateau, Thalictrum delavayi Franch. (T. delavayi) is an important member of the buttercup family (Ranunculaceae), is rich in alkaloids and has been used in folk medicine for thousands of years. In this study, the extraction process of total alkaloids from the whole T. delavayi plant was optimized and the extract’s therapeutic potential against pulmonary infection caused by Klebsiella pneumoniae and Escherichia coli was investigated. The results showed that the optimum experimental conditions for the total alkaloids (2.46%) from T. delavayi were as follows: hydrochloric acid volume fraction of 0.8%, solid–liquid ratio of 1:12 and sonication time of 54 min. The treatment reduced bacterial counts, white blood cell counts and inflammatory cell classification in bronchoalveolar lavage fluid (BALF) and the levels of inflammatory cytokines interleukin-4 (IL-4), interleukin-6 (IL-6), tumor necrosis factor α (TNF-α), procalcitonin (PCT) and C-reactive protein (CRP), procalcitonin (PCT) and C-reactive protein (CRP) in the serum in experimental groups. The results in our experimental preliminary work suggested that the total alkaloids from T. delavayi had therapeutic effects on mice with Klebsiella pneumoniae and Escherichia coli mixed infectious pneumonia, providing experimental support for the plant’s therapeutic potential in treating pulmonary infections caused by Klebsiella pneumoniae and Escherichia coli