Equivariant Hypergraph Diffusion Neural Operators

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide a promising way to model higher-order relations in data and further solve relevant prediction tasks built upon such higher-order relations. However, higher-order relations in practice contain complex patterns and are often highly irregular. So, it is often challenging to design an HNN that suffices to express those relations while keeping computational efficiency. Inspired by hypergraph diffusion algorithms, this work proposes a new HNN architecture named ED-HNN, which provably represents any continuous equivariant hypergraph diffusion operators that can model a wide range of higher-order relations. ED-HNN can be implemented efficiently by combining star expansions of hypergraphs with standard message passing neural networks. ED-HNN further shows great superiority in processing heterophilic hypergraphs and constructing deep models. We evaluate ED-HNN for node classification on nine real-world hypergraph datasets. ED-HNN uniformly outperforms the best baselines over these nine datasets and achieves more than 2\%$\uparrow$ in prediction accuracy over four datasets therein.Comment: Code: https://github.com/Graph-COM/ED-HN

Inductive Representation Learning in Temporal Networks via Causal Anonymous Walks

Temporal networks serve as abstractions of many real-world dynamic systems. These networks typically evolve according to certain laws, such as the law of triadic closure, which is universal in social networks. Inductive representation learning of temporal networks should be able to capture such laws and further be applied to systems that follow the same laws but have not been unseen during the training stage. Previous works in this area depend on either network node identities or rich edge attributes and typically fail to extract these laws. Here, we propose Causal Anonymous Walks (CAWs) to inductively represent a temporal network. CAWs are extracted by temporal random walks and work as automatic retrieval of temporal network motifs to represent network dynamics while avoiding the time-consuming selection and counting of those motifs. CAWs adopt a novel anonymization strategy that replaces node identities with the hitting counts of the nodes based on a set of sampled walks to keep the method inductive, and simultaneously establish the correlation between motifs. We further propose a neural-network model CAW-N to encode CAWs, and pair it with a CAW sampling strategy with constant memory and time cost to support online training and inference. CAW-N is evaluated to predict links over 6 real temporal networks and uniformly outperforms previous SOTA methods by averaged 10% AUC gain in the inductive setting. CAW-N also outperforms previous methods in 4 out of the 6 networks in the transductive setting.Comment: Published in ICLR 2021. A bug in previous versions is fixe

Per la corretta attribuzione del "Romanzo delle donne contemporanee in Italia" (1863)

The use of free energy simulation techniques in the study of protein stability is critically evaluated. Results from two simulations of the thermostability mutation Asn218 to Ser218 in Subtilisin are presented. It is shown that components of the free energy change can be highly sensitive to the computational details of the simulation leading to the conclusion that free energy calculations cannot currently be used to reliably predict protein stability. The different factors that undermine the reliability are discussed

Solution structure of the second bromodomain of Brd2 and its specific interaction with acetylated histone tails

<p>Abstract</p> <p>Background</p> <p>Brd2 is a transcriptional regulator and belongs to BET family, a less characterized novel class of bromodomain-containing proteins. Brd2 contains two tandem bromodomains (BD1 and BD2, 46% sequence identity) in the N-terminus and a conserved motif named ET (extra C-terminal) domain at the C-terminus that is also present in some other bromodomain proteins. The two bromodomains have been shown to bind the acetylated histone H4 and to be responsible for mitotic retention on chromosomes, which is probably a distinctive feature of BET family proteins. Although the crystal structure of Brd2 BD1 is reported, no structure features have been characterized for Brd2 BD2 and its interaction with acetylated histones.</p> <p>Results</p> <p>Here we report the solution structure of human Brd2 BD2 determined by NMR. Although the overall fold resembles the bromodomains from other proteins, significant differences can be found in loop regions, especially in the ZA loop in which a two amino acids insertion is involved in an uncommon <it>π</it>-helix, termed <it>π</it>D. The helix <it>π</it>D forms a portion of the acetyl-lysine binding site, which could be a structural characteristic of Brd2 BD2 and other BET bromodomains. Unlike Brd2 BD1, BD2 is monomeric in solution. With NMR perturbation studies, we have mapped the H4-AcK12 peptide binding interface on Brd2 BD2 and shown that the binding was with low affinity (2.9 mM) and in fast exchange. Using NMR and mutational analysis, we identified several residues important for the Brd2 BD2-H4-AcK12 peptide interaction and probed the potential mechanism for the specific recognition of acetylated histone codes by Brd2 BD2.</p> <p>Conclusion</p> <p>Brd2 BD2 is monomeric in solution and dynamically interacts with H4-AcK12. The additional secondary elements in the long ZA loop may be a common characteristic of BET bromodomains. Surrounding the ligand-binding cavity, five aspartate residues form a negatively charged collar that serves as a secondary binding site for H4-AcK12. We suggest that Brd2 BD1 and BD2 may possess distinctive roles and cooperate to regulate Brd2 functions. The structure basis of Brd2 BD2 will help to further characterize the functions of Brd2 and its BET members.</p

Uncovering Misattributed Suicide Causes through Annotation Inconsistency Detection in Death Investigation Notes

Data accuracy is essential for scientific research and policy development. The National Violent Death Reporting System (NVDRS) data is widely used for discovering the patterns and causes of death. Recent studies suggested the annotation inconsistencies within the NVDRS and the potential impact on erroneous suicide-cause attributions. We present an empirical Natural Language Processing (NLP) approach to detect annotation inconsistencies and adopt a cross-validation-like paradigm to identify problematic instances. We analyzed 267,804 suicide death incidents between 2003 and 2020 from the NVDRS. Our results showed that incorporating the target state's data into training the suicide-crisis classifier brought an increase of 5.4% to the F-1 score on the target state's test set and a decrease of 1.1% on other states' test set. To conclude, we demonstrated the annotation inconsistencies in NVDRS's death investigation notes, identified problematic instances, evaluated the effectiveness of correcting problematic instances, and eventually proposed an NLP improvement solution.Comment: 19 pages, 6 figure

Direct van der Waals Epitaxy of Crack-Free AlN Thin Film on Epitaxial WS2

Van der Waals epitaxy (vdWE) has drawn continuous attention, as it is unlimited by lattice-mismatch between epitaxial layers and substrates. Previous reports on the vdWE of III-nitride thin film were mainly based on two-dimensional (2D) materials by plasma pretreatment or pre-doping of other hexagonal materials. However, it is still a huge challenge for single-crystalline thin film on 2D materials without any other extra treatment or interlayer. Here, we grew high-quality single-crystalline AlN thin film on sapphire substrate with an intrinsic WS2 overlayer (WS2/sapphire) by metal-organic chemical vapor deposition, which had surface roughness and defect density similar to that grown on conventional sapphire substrates. Moreover, an AlGaN-based deep ultraviolet light emitting diode structure on WS2/sapphire was demonstrated. The electroluminescence (EL) performance exhibited strong emissions with a single peak at 283 nm. The wavelength of the single peak only showed a faint peak-position shift with increasing current to 80 mA, which further indicated the high quality and low stress of the AlN thin film. This work provides a promising solution for further deep-ultraviolet (DUV) light emitting electrodes (LEDs) development on 2D materials, as well as other unconventional substrates