Interpreting mechanism of Synergism of drug combinations using attention based hierarchical graph pooling

The synergistic drug combinations provide huge potentials to enhance therapeutic efficacy and to reduce adverse reactions. However, effective and synergistic drug combination prediction remains an open question because of the unknown causal disease signaling pathways. Though various deep learning (AI) models have been proposed to quantitatively predict the synergism of drug combinations. The major limitation of existing deep learning methods is that they are inherently not interpretable, which makes the conclusion of AI models un-transparent to human experts, henceforth limiting the robustness of the model conclusion and the implementation ability of these models in the real-world human-AI healthcare. In this paper, we develop an interpretable graph neural network (GNN) that reveals the underlying essential therapeutic targets and mechanism of the synergy (MoS) by mining the sub-molecular network of great importance. The key point of the interpretable GNN prediction model is a novel graph pooling layer, Self-Attention based Node and Edge pool (henceforth SANEpool), that can compute the attention score (importance) of nodes and edges based on the node features and the graph topology. As such, the proposed GNN model provides a systematic way to predict and interpret the drug combination synergism based on the detected crucial sub-molecular network. We evaluate SANEpool on molecular networks formulated by genes from 46 core cancer signaling pathways and drug combinations from NCI ALMANAC drug combination screening data. The experimental results indicate that 1) SANEpool can achieve the current state-of-art performance among other popular graph neural networks; and 2) the sub-molecular network detected by SANEpool are self-explainable and salient for identifying synergistic drug combinations

GNNHLS: Evaluating Graph Neural Network Inference via High-Level Synthesis

With the ever-growing popularity of Graph Neural Networks (GNNs), efficient GNN inference is gaining tremendous attention. Field-Programming Gate Arrays (FPGAs) are a promising execution platform due to their fine-grained parallelism, low-power consumption, reconfigurability, and concurrent execution. Even better, High-Level Synthesis (HLS) tools bridge the gap between the non-trivial FPGA development efforts and rapid emergence of new GNN models. In this paper, we propose GNNHLS, an open-source framework to comprehensively evaluate GNN inference acceleration on FPGAs via HLS, containing a software stack for data generation and baseline deployment, and FPGA implementations of 6 well-tuned GNN HLS kernels. We evaluate GNNHLS on 4 graph datasets with distinct topologies and scales. The results show that GNNHLS achieves up to 50.8x speedup and 423x energy reduction relative to the CPU baselines. Compared with the GPU baselines, GNNHLS achieves up to 5.16x speedup and 74.5x energy reduction

Plants changed the response of bacterial community to the nitrogen and phosphorus addition ratio

IntroductionHuman activities have increased the nitrogen (N) and phosphorus (P) supply ratio of the natural ecosystem, which affects the growth of plants and the circulation of soil nutrients. However, the effect of the N and P supply ratio and the effect of plant on the soil microbial community are still unclear.MethodsIn this study, 16s rRNA sequencing was used to characterize the response of bacterial communities in Phragmites communis (P.communis) rhizosphere and non-rhizosphere soil to N and P addition ratio.ResultsThe results showed that the a-diversity of the P.communis rhizosphere soil bacterial community increased with increasing N and P addition ratio, which was caused by the increased salt and microbially available C content by the N and P ratio. N and P addition ratio decreased the pH of non-rhizosphere soil, which consequently decreased the a-diversity of the bacterial community. With increasing N and P addition ratio, the relative abundance of Proteobacteria and Bacteroidetes increased, while that of Actinobacteria and Acidobacteria decreased, which reflected the trophic strategy of the bacterial community. The bacterial community composition of the non-rhizosphere soil was significantly affected by salt, pH and total carbon (TC) content. Salt limited the relative abundance of Actinobacteria, and increased the relative abundance of Bacteroidetes. The symbiotic network of the rhizosphere soil bacterial community had lower robustness. This is attributed to the greater selective effect of plants on the bacterial community influenced by nutrient addition.DiscussionPlants played a regulatory role in the process of N and P addition affecting the bacterial community, and nutrient uptake by the root system reduced the negative impact of N and P addition on the bacterial community. The variations in the rhizosphere soil bacterial community were mainly caused by the response of the plant to the N and P addition ratio

The emergence of global phase coherence from local pairing in underdoped cuprates

In conventional metal superconductors such as aluminum, the large number of weakly bounded Cooper pairs become phase coherent as soon as they start to form. The cuprate high critical temperature ($T_c$) superconductors, in contrast, belong to a distinctively different category. To account for the high $T_c$, the attractive pairing interaction is expected to be strong and the coherence length is short. Being doped Mott insulators, the cuprates are known to have low superfluid density, thus are susceptible to phase fluctuations. It has been proposed that pairing and phase coherence may occur separately in cuprates, and $T_c$ corresponds to the phase coherence temperature controlled by the superfluid density. To elucidate the microscopic processes of pairing and phase ordering in cuprates, here we use scanning tunneling microscopy to image the evolution of electronic states in underdoped $\rm Bi_2La_xSr_{2-x}CuO_{6+{\delta}}$. Even in the insulating sample, we observe a smooth crossover from the Mott insulator to superconductor-type spectra on small islands with chequerboard order and emerging quasiparticle interference patterns following the octet model. Each chequerboard plaquette contains approximately two holes, and exhibits a stripy internal structure that has strong influence on the superconducting features. Across the insulator to superconductor boundary, the local spectra remain qualitatively the same while the quasiparticle interferences become long-ranged. These results suggest that the chequerboard plaquette with internal stripes plays a crucial role on local pairing in cuprates, and the global phase coherence is established once its spatial occupation exceeds a threshold

Universal Normalization Enhanced Graph Representation Learning for Gene Network Prediction

Effective gene network representation learning is of great importance in bioinformatics to predict/understand the relation of gene profiles and disease phenotypes. Though graph neural networks (GNNs) have been the dominant architecture for analyzing various graph-structured data like social networks, their predicting on gene networks often exhibits subpar performance. In this paper, we formally investigate the gene network representation learning problem and characterize a notion of \textit{universal graph normalization}, where graph normalization can be applied in an universal manner to maximize the expressive power of GNNs while maintaining the stability. We propose a novel UNGNN (Universal Normalized GNN) framework, which leverages universal graph normalization in both the message passing phase and readout layer to enhance the performance of a base GNN. UNGNN has a plug-and-play property and can be combined with any GNN backbone in practice. A comprehensive set of experiments on gene-network-based bioinformatical tasks demonstrates that our UNGNN model significantly outperforms popular GNN benchmarks and provides an overall performance improvement of 16 $\%$ on average compared to previous state-of-the-art (SOTA) baselines. Furthermore, we also evaluate our theoretical findings on other graph datasets where the universal graph normalization is solvable, and we observe that UNGNN consistently achieves the superior performance

Visualizing the Zhang-Rice singlet, molecular orbitals and pair formation in cuprate

The parent compound of cuprates is a charge-transfer-type Mott insulator with strong hybridization between the Cu $3d_{\mathrm x^2-y^2}$ and O $2p$ orbitals. A key question concerning the pairing mechanism is the behavior of doped holes in the antiferromagnetic (AF) Mott insulator background, which is a prototypical quantum many-body problem. It was proposed that doped hole on the O site tends to form a singlet, known as Zhang-Rice singlet (ZRS), with the unpaired Cu spin. But experimentally little is known about the properties of a single hole and the interplay between them that leads to superconductivity. Here we use scanning tunneling microscopy to visualize the electronic states in hole-doped $\mathrm{Ca_2CuO_2Cl_2}$, aiming to establish the atomic-scale local basis for pair formation. A single doped hole is shown to have an in-gap state and a clover-shaped spatial distribution that can be attributed to a localized ZRS. When the dopants are close enough, they develop delocalized molecular orbitals with characteristic stripe- and ladder-shaped patterns, accompanied by the opening of a small gap around the Fermi level ($E_{\mathrm F}$). With increasing doping, the molecular orbitals proliferate in space and gradually form densely packed plaquettes, but the stripe and ladder patterns remain nearly the same. The low-energy electronic states of the molecular orbitals are intimately related to the local pairing properties, thus play a vitally important role in the emergence of superconductivity. We propose that the Cooper pair is formed by two holes occupying the stripe-like molecular orbital, while the attractive interaction is mediated by the AF spin background

The association between higher FFAs and high residual platelet reactivity among CAD patients receiving clopidogrel therapy

BackgroundMetabolic abnormalities are associated with the occurrence, severity, and poor prognosis of coronary artery disease (CAD), some of which affect the antiplatelet efficacy of clopidogrel. Free fatty acids (FFAs) is a biomarker for metabolic abnormalities, and elevated FFAs is observed among CAD patients. Whether FFAs enhances residual platelet reactivity induced by adenosine diphosphate (ADP) while using clopidogrel was unknown. The purpose of our study is exploring the issue.MethodCurrent study included 1,277 CAD patients using clopidogrel and used logistic regression to detect whether the higher level of FFAs is associated with high residual platelet reactivity (HRPR). We additionally performed subgroup and sensitivity analyses to evaluate the stability of the results. We defined HRPR as ADP-induced platelet inhibition rate (ADPi) < 50% plus ADP-induced maximum amplitude (MAADP) > 47 mm.Results486 patients (38.1%) showed HRPR. The proportion of HRPR among patients with higher FFAs (>0.445 mmol/L) is greater than among patients with lower FFAs (46.4% vs. 32.6%, P < 0.001). Multivariate logistic regression demonstrated that higher FFAs (>0.445 mmol/L) is independently associated with HRPR (adjusted OR = 1.745, 95% CI, 1.352–2.254). After subgroup and sensitivity analyses, the results remained robust.ConclusionThe higher level of FFAs enhances residual platelet reactivity induced by ADP and is independently associated with clopidogrel HRPR

The multiplexed light storage of Orbital Angular Momentum based on atomic ensembles

The improvement of the multi-mode capability of quantum memory can further improve the utilization efficiency of the quantum memory and reduce the requirement of quantum communication for storage units. In this letter, we experimentally investigate the multi-mode light multiplexing storage of orbital angular momentum (OAM) mode based on rubidium vapor, and demultiplexing by a photonic OAM mode splitter which combines a Sagnac loop with two dove prisms. Our results show a mode extinction ratio higher than 80$\%$ at 1 $\mu$s of storage time. Meanwhile, two OAM modes have been multiplexing stored and demultiplexed in our experimental configuration. We believe the experimental scheme may provide a possibility for high channel capacity and multi-mode quantum multiplexed quantum storage based on atomic ensembles