Graph Agent: Explicit Reasoning Agent for Graphs

Graph embedding methods such as Graph Neural Networks (GNNs) and Graph Transformers have contributed to the development of graph reasoning algorithms for various tasks on knowledge graphs. However, the lack of interpretability and explainability of graph embedding methods has limited their applicability in scenarios requiring explicit reasoning. In this paper, we introduce the Graph Agent (GA), an intelligent agent methodology of leveraging large language models (LLMs), inductive-deductive reasoning modules, and long-term memory for knowledge graph reasoning tasks. GA integrates aspects of symbolic reasoning and existing graph embedding methods to provide an innovative approach for complex graph reasoning tasks. By converting graph structures into textual data, GA enables LLMs to process, reason, and provide predictions alongside human-interpretable explanations. The effectiveness of the GA was evaluated on node classification and link prediction tasks. Results showed that GA reached state-of-the-art performance, demonstrating accuracy of 90.65%, 95.48%, and 89.32% on Cora, PubMed, and PrimeKG datasets, respectively. Compared to existing GNN and transformer models, GA offered advantages of explicit reasoning ability, free-of-training, easy adaption to various graph reasoning task

Spontaneous time-reversal symmetry breaking in twisted double bilayer graphene

Twisted double bilayer graphene (tDBG) comprises two Bernal-stacked bilayer graphene sheets with a twist between them. Gate voltages applied to top and back gates of a tDBG device tune both the flatness and topology of the electronic bands, enabling an unusual level of experimental control. Broken spin/valley symmetry metallic states have been observed in tDBG devices with twist angles $\sim$ 1.2-1.3$^\circ$, but the topologies and order parameters of these states have remained unclear. We report the observation of an anomalous Hall effect in the correlated metal state of tDBG, with hysteresis loops spanning 100s of mT in out-of-plane magnetic field ($B_{\perp}$) that demonstrate spontaneously broken time-reversal symmetry. The $B_{\perp}$ hysteresis persists for in-plane fields up to several Tesla, suggesting valley (orbital) ferromagnetism. At the same time, the resistivity is strongly affected by even mT-scale values of in-plane magnetic field, pointing to spin-valley coupling or to a direct orbital coupling between in-plane field and the valley degree of freedom

The effect of concentration and duration of normobaric oxygen in reducing caspase-3 and -9 expression in a rat-model of focal cerebral ischaemia

The aim of this study was to determine the effect of different concentrations of normobaric oxygen (NBO) on neurological function and the expression of caspase-3 and -9 in a rat model of acute cerebral ischaemia. Sprague-Dawley rats (n=120) were randomly divided into four groups (n=30 per group), including 3 groups given NBO at concentrations of 33%, 45% or 61% and one control group given air (21% oxygen). After 2 h of ischaemic occlusion, each group was further subdivided into six subgroups (n=5) during reperfusion according to the duration (3, 6, 12, 24, 48 or 72 h) and concentration of NBO (33%, 45% or 61%) or air treatment. The Fluorescence Quantitative polymerase chain reaction (PCR) and immunohistochemistry were used to detect caspase-3 and -9 mRNA and protein relative expression respectively. The Neurologic Impairment Score (NIS) was significantly lower in rats given 61% NBO ≥3 h after reperfusion when compared to the control group (P<0.05, Mann–Whitney U). NBO significantly reduced caspase-3 and -9 mRNA and protein expression when compared to the control group at all NBO concentrations and time points (P<0.05, ANOVA). The expression of caspase-3 and -9 was lower in the group given 61% NBO compared any other group, and this difference was statistically significant when compared to the group given 33% NBO for ≥48 h and the control group (both P<0.05, ANOVA). These findings indicate that NBO may inhibit the apoptotic pathway by reducing caspase-3 and -9 expression, thereby promoting neurological functional recovery after stroke

Genomic Analyses Reveal Mutational Signatures and Frequently Altered Genes in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most common cancers worldwide and the fourth most lethal cancer in China. However, although genomic studies have identified some mutations associated with ESCC, we know little of the mutational processes responsible. To identify genome-wide mutational signatures, we performed either whole-genome sequencing (WGS) or whole-exome sequencing (WES) on 104 ESCC individuals and combined our data with those of 88 previously reported samples. An APOBEC-mediated mutational signature in 47% of 192 tumors suggests that APOBEC-catalyzed deamination provides a source of DNA damage in ESCC. Moreover, PIK3CA hotspot mutations (c.1624G>A [p.Glu542Lys] and c.1633G>A [p.Glu545Lys]) were enriched in APOBEC-signature tumors, and no smoking-associated signature was observed in ESCC. In the samples analyzed by WGS, we identified focal (<100 kb) amplifications of CBX4 and CBX8. In our combined cohort, we identified frequent inactivating mutations in AJUBA, ZNF750, and PTCH1 and the chromatin-remodeling genes CREBBP and BAP1, in addition to known mutations. Functional analyses suggest roles for several genes (CBX4, CBX8, AJUBA, and ZNF750) in ESCC. Notably, high activity of hedgehog signaling and the PI3K pathway in approximately 60% of 104 ESCC tumors indicates that therapies targeting these pathways might be particularly promising strategies for ESCC. Collectively, our data provide comprehensive insights into the mutational signatures of ESCC and identify markers for early diagnosis and potential therapeutic targets

Ambient scalable synthesis of surfactant-free thermoelectric CuAgSe nanoparticles with reversible metallic-n-p conductivity transition

Surfactant-free CuAgSe nanoparticles were successfully synthesized on a large scale within a short reaction time via a simple environmentally friendly aqueous approach under room temperature. The nanopowders obtained were consolidated into pellets for investigation of their thermoelectric properties between 3 and 623 K. The pellets show strong metallic characteristics below 60 K and turn into an n-type semiconductor with increasing temperature, accompanied by changes in the crystal structure (i.e., from the pure tetragonal phase into a mixture of tetragonal and orthorhombic phases), the electrical conductivity, the Seebeck coefficient, and the thermal conductivity, which leads to a figure of merit (ZT) of 0.42 at 323 K. The pellets show further interesting temperature-dependent transition from n-type into p-type in electrical conductivity arising from phase transition (i.e., from the mixture phases into cubic phase), evidenced by the change of the Seebeck coefficient from -28 μV/K into 226 μV/K at 467 K. The ZT value increased with increasing temperature after the phase transition and reached 0.9 at 623 K. The sintered CuAgSe pellets also display excellent stability, and there is no obvious change observed after 5 cycles of consecutive measurements. Our results demonstrate the potential of CuAgSe to simultaneously serve (at different temperatures) as both an n-type and a p-type thermoelectric material

A Deep Molecular Generative Model Based on Multi-Resolution Graph Variational Autoencoders

Deep generative models have recently emerged as encouraging tools for the de novo molecular structure generation. Even though considerable advances have been achieved in recent years, the field of generative molecular design is still in its infancy. One potential solution may be to integrate domain knowledge of structural or medicinal chemistry into the data-driven machine learning process to address specific deep molecule generation goals. This manuscript proposes a new graph-based hierarchical variational autoencoder (VAE) model for molecular generation. Training molecules are first decomposed into small molecular fragments. Unlike other motif-based molecular graph generative models, we further group decomposed fragments into different interchangeable fragment clusters according to their local structural environment around the attachment points where the bond-breaking occurs. In this way, each chemical structure can be transformed into a three-layer graph, in which individual atoms, decomposed fragments, or obtained fragment clusters act as graph nodes at each corresponding layer, respectively. We construct a hierarchical VAE model to learn such three-layer hierarchical graph representations of chemical structures in a fine-to-coarse order, in which atoms, decomposed fragments, and related fragment clusters act as graph nodes at each corresponding graph layer. The decoder component is designed to iteratively select a fragment out of a predicted fragment cluster vocabulary and then attach it to the preceding substructure. The newly introduced third graph layer will allow us to incorporate specific chemical structural knowledge, e.g., interchangeable fragments sharing similar local chemical environments or bioisosteres derived from matched molecular pair analysis information, into the molecular generation process. It will increase the odds of assembling new chemical moieties absent in the original training set and enhance structural diversity/novelty scores of generated structures. Our proposed approach demonstrates comparatively good performance in terms of model efficiency and other molecular evaluation metrics when compared with several other graph- and SMILES-based generative molecular models. We also analyze how our generative models\u27 performance varies when choosing different fragment sampling techniques and radius parameters that determine the local structural environment of interchangeable fragment clusters. Hopefully, our multi-level hierarchical VAE prototyping model might promote more sophisticated works of knowledge-augmented deep molecular generation in the future