Investigating the relevance of major signaling pathways in cancer survival using a biologically meaningful deep learning model

BACKGROUND: Survival analysis is an important part of cancer studies. In addition to the existing Cox proportional hazards model, deep learning models have recently been proposed in survival prediction, which directly integrates multi-omics data of a large number of genes using the fully connected dense deep neural network layers, which are hard to interpret. On the other hand, cancer signaling pathways are important and interpretable concepts that define the signaling cascades regulating cancer development and drug resistance. Thus, it is important to investigate potential associations between patient survival and individual signaling pathways, which can help domain experts to understand deep learning models making specific predictions. RESULTS: In this exploratory study, we proposed to investigate the relevance and influence of a set of core cancer signaling pathways in the survival analysis of cancer patients. Specifically, we built a simplified and partially biologically meaningful deep neural network, DeepSigSurvNet, for survival prediction. In the model, the gene expression and copy number data of 1967 genes from 46 major signaling pathways were integrated in the model. We applied the model to four types of cancer and investigated the influence of the 46 signaling pathways in the cancers. Interestingly, the interpretable analysis identified the distinct patterns of these signaling pathways, which are helpful in understanding the relevance of signaling pathways in terms of their application to the prediction of cancer patients\u27 survival time. These highly relevant signaling pathways, when combined with other essential signaling pathways inhibitors, can be novel targets for drug and drug combination prediction to improve cancer patients\u27 survival time. CONCLUSION: The proposed DeepSigSurvNet model can facilitate the understanding of the implications of signaling pathways on cancer patients\u27 survival by integrating multi-omics data and clinical factors

Distance-Restricted Folklore Weisfeiler-Leman GNNs with Provable Cycle Counting Power

The ability of graph neural networks (GNNs) to count certain graph substructures, especially cycles, is important for the success of GNNs on a wide range of tasks. It has been recently used as a popular metric for evaluating the expressive power of GNNs. Many of the proposed GNN models with provable cycle counting power are based on subgraph GNNs, i.e., extracting a bag of subgraphs from the input graph, generating representations for each subgraph, and using them to augment the representation of the input graph. However, those methods require heavy preprocessing, and suffer from high time and memory costs. In this paper, we overcome the aforementioned limitations of subgraph GNNs by proposing a novel class of GNNs -- $d$-Distance-Restricted FWL(2) GNNs, or $d$-DRFWL(2) GNNs. $d$-DRFWL(2) GNNs use node pairs whose mutual distances are at most $d$ as the units for message passing to balance the expressive power and complexity. By performing message passing among distance-restricted node pairs in the original graph, $d$-DRFWL(2) GNNs avoid the expensive subgraph extraction operations in subgraph GNNs, making both the time and space complexity lower. We theoretically show that the discriminative power of $d$-DRFWL(2) GNNs strictly increases as $d$ increases. More importantly, $d$-DRFWL(2) GNNs have provably strong cycle counting power even with $d=2$: they can count all 3, 4, 5, 6-cycles. Since 6-cycles (e.g., benzene rings) are ubiquitous in organic molecules, being able to detect and count them is crucial for achieving robust and generalizable performance on molecular tasks. Experiments on both synthetic datasets and molecular datasets verify our theory. To the best of our knowledge, our model is the most efficient GNN model to date (both theoretically and empirically) that can count up to 6-cycles

Predicting mortality risk for preterm infants using deep learning models with time-series vital sign data

Mortality remains an exceptional burden of extremely preterm birth. Current clinical mortality prediction scores are calculated using a few static variable measurements, such as gestational age, birth weight, temperature, and blood pressure at admission. While these models do provide some insight, numerical and time-series vital sign data are also available for preterm babies admitted to the NICU and may provide greater insight into outcomes. Computational models that predict the mortality risk of preterm birth in the NICU by integrating vital sign data and static clinical variables in real time may be clinically helpful and potentially superior to static prediction models. However, there is a lack of established computational models for this specific task. In this study, we developed a novel deep learning model, DeepPBSMonitor (Deep Preterm Birth Survival Risk Monitor), to predict the mortality risk of preterm infants during initial NICU hospitalization. The proposed deep learning model can effectively integrate time-series vital sign data and fixed variables while resolving the influence of noise and imbalanced data. The proposed model was evaluated and compared with other approaches using data from 285 infants. Results showed that the DeepPBSMonitor model outperforms other approaches, with an accuracy, recall, and AUC score of 0.888, 0.780, and 0.897, respectively. In conclusion, the proposed model has demonstrated efficacy in predicting the real-time mortality risk of preterm infants in initial NICU hospitalization

Reward Delay Attacks on Deep Reinforcement Learning

Most reinforcement learning algorithms implicitly assume strong synchrony. We present novel attacks targeting Q-learning that exploit a vulnerability entailed by this assumption by delaying the reward signal for a limited time period. We consider two types of attack goals: targeted attacks, which aim to cause a target policy to be learned, and untargeted attacks, which simply aim to induce a policy with a low reward. We evaluate the efficacy of the proposed attacks through a series of experiments. Our first observation is that reward-delay attacks are extremely effective when the goal is simply to minimize reward. Indeed, we find that even naive baseline reward-delay attacks are also highly successful in minimizing the reward. Targeted attacks, on the other hand, are more challenging, although we nevertheless demonstrate that the proposed approaches remain highly effective at achieving the attacker's targets. In addition, we introduce a second threat model that captures a minimal mitigation that ensures that rewards cannot be used out of sequence. We find that this mitigation remains insufficient to ensure robustness to attacks that delay, but preserve the order, of rewards.Comment: 20 pages, 9 figures, Conference on Decision and Game Theory for Securit

Impacts of sea-land and mountain-valley circulations on the air pollution in Beijing-Tianjin-Hebei (BTH): A case study

In the study, observational data analyses and the WRF-CHEM model simulations are used to investigate the role of sea-land and mountain-valley breeze circulations in a severe air pollution event occurred in Beijing-Tianjin-Hebei (BTH) during August 9-10, 2013. Both the wind observations and the model simulations have clearly indicated the evolution of the sea-land and mountain-valley breeze circulations during the event. The WRF-CHEM model generally reproduces the local meteorological circulations and also performs well in simulating temporal variations and spatial distributions of fine particulate matters (PM2.5) and ozone (O-3) concentrations compared to observations in BTH. The model results have shown that the offshore land breeze transports the pollutants formed in Shandong province to the Bohai Gulf in the morning, causing the formation of high O-3 and PM2.5 concentrations over the gulf. The onshore sea breeze not only causes the formation of a convergence zone to induce upward movement, mitigating the surface pollution to some degree, also recirculates the pollutants over the gulf to deteriorate the air quality in the coastal area. The upward valley breeze brings the pollutants in the urban area of Beijing to the mountain area in the afternoon, and the downward mountain breeze transports the pollutants back during nighttime. The intensity of the mountain-valley breeze circulation is weak compared to the land-sea breeze circulation in BTH. It is worth noting that the local circulations play an important role when the large-scale meteorological conditions are relatively weak. (C) 2017 Elsevier Ltd. All rights reserved

Characteristics, current exploration practices, and prospects of continental shale oil in China

Oil generation in the continental shale has laid the resource foundation for the originality and development of China’s petroleum industry; continental shale oil production is blazing a new trail in this field. In this paper, based on the geological conditions of continental shale oil in China, it is found that the main types of shale oil generally have four basic geological characteristics, which are large-scale continuous distribution, the domination of inorganic pores, the enrichment of “sweet areas”, and initial production that is controlled by relatively high organic maturity and high yield that is governed by relatively high formation pressure. Then, as examples for the geological characteristics and development practice of continental shale oil, four key areas of Longdong, Gulong, Jimsar, and Jiyang are systematically summarized. Finally, the future prospects of continental shale oil in China are put forward. Middle-high maturity shale oil is currently the main force of development, and middle-low maturity shale oil also has a considerable development prospect after technological improvement. Meanwhile, “sweet area/spot sections” assessment and technological innovation are still research areas to be improved.Cited as: Wang, X., Li, J., Jiang, W., Zhang, H., Feng Y., Yang Z. Characteristics, current exploration practices, and prospects of continental shale oil in China. Advances in Geo-Energy Research, 2022, 6(6): 454-459. https://doi.org/10.46690/ager.2022.06.0

Calculation and experimental verification of force-magnetic coupling model of magnetised rail based on density functional theory

Metal magnetic memory (MMM) is a widely used non-destructive electromagnetic detection technology. However, the analysis of its underlying principle is still insufficient. The mechanical and magnetic coupling model is a reasonable standpoint from which to study the principle of MMM. In this paper, a mechanical and magnetic coupling model of steel material is established based on density functional theory (DFT) using the CASTEP first-principles analysis software. In order to simulate the practical working environment, the residual magnetism in the rail is assumed to change with the stress on the rail. By applying different stresses to the model, the relationship between the atomic magnetic moment, the lattice constant and stress is explored, as well as the causes of magnetic signals in the stress concentration zone. It is revealed that the atomic magnetic moment and the crystal volume decrease with the increase in compressive stress. The magnetic signal on the surface of the magnetised metal component decreases with the increase in compressive stress, while the tensile stress shows the opposite tendency. Generally speaking, the change in atomic magnetic moment and crystal volume caused by lattice distortion under stress can be seen as the fundamental reason for the change in magnetic signal on the surface of the magnetised metal. The bending experiment of the rail shows that the normal magnetic field decreases with the increase in compressive stress in the stress concentration zone. The conclusion is verified by experiments

MAG-GNN: Reinforcement Learning Boosted Graph Neural Network

While Graph Neural Networks (GNNs) recently became powerful tools in graph learning tasks, considerable efforts have been spent on improving GNNs' structural encoding ability. A particular line of work proposed subgraph GNNs that use subgraph information to improve GNNs' expressivity and achieved great success. However, such effectivity sacrifices the efficiency of GNNs by enumerating all possible subgraphs. In this paper, we analyze the necessity of complete subgraph enumeration and show that a model can achieve a comparable level of expressivity by considering a small subset of the subgraphs. We then formulate the identification of the optimal subset as a combinatorial optimization problem and propose Magnetic Graph Neural Network (MAG-GNN), a reinforcement learning (RL) boosted GNN, to solve the problem. Starting with a candidate subgraph set, MAG-GNN employs an RL agent to iteratively update the subgraphs to locate the most expressive set for prediction. This reduces the exponential complexity of subgraph enumeration to the constant complexity of a subgraph search algorithm while keeping good expressivity. We conduct extensive experiments on many datasets, showing that MAG-GNN achieves competitive performance to state-of-the-art methods and even outperforms many subgraph GNNs. We also demonstrate that MAG-GNN effectively reduces the running time of subgraph GNNs.Comment: Accepted to NeurIPS 202

Distinct fingerprints of charge density waves and electronic standing waves in ZrTe3_3

Experimental signatures of charge density waves (CDW) in high-temperature superconductors have evoked much recent interest, yet an alternative interpretation has been theoretically raised based on electronic standing waves resulting from quasiparticles scattering off impurities or defects, also known as Friedel oscillations (FO). Indeed the two phenomena are similar and related, posing a challenge to their experimental differentiation. Here we report a resonant X-ray diffraction study of ZrTe$_3$, a model CDW material. Near the CDW transition, we observe two independent diffraction signatures that arise concomitantly, only to become clearly separated in momentum while developing very different correlation lengths in the well-ordered state. Anomalously slow dynamics of mesoscopic ordered nanoregions are further found near the transition temperature, in spite of the expected strong thermal fluctuations. These observations reveal that a spatially-modulated CDW phase emerges out of a uniform electronic fluid via a process that is promoted by self-amplifying FO, and identify a viable experimental route to distinguish CDW and FO.Comment: 6 pages, 4 figures; supplementary information available upon reques

Extending the Design Space of Graph Neural Networks by Rethinking Folklore Weisfeiler-Lehman

Message passing neural networks (MPNNs) have emerged as the most popular framework of graph neural networks (GNNs) in recent years. However, their expressive power is limited by the 1-dimensional Weisfeiler-Lehman (1-WL) test. Some works are inspired by $k$-WL/FWL (Folklore WL) and design the corresponding neural versions. Despite the high expressive power, there are serious limitations in this line of research. In particular, (1) $k$-WL/FWL requires at least $O(n^k)$ space complexity, which is impractical for large graphs even when $k=3$; (2) The design space of $k$-WL/FWL is rigid, with the only adjustable hyper-parameter being $k$. To tackle the first limitation, we propose an extension, $(k,t)$-FWL. We theoretically prove that even if we fix the space complexity to $O(n^k)$ (for any $k\geq 2$) in $(k,t)$-FWL, we can construct an expressiveness hierarchy up to solving the graph isomorphism problem. To tackle the second problem, we propose $k$-FWL+, which considers any equivariant set as neighbors instead of all nodes, thereby greatly expanding the design space of $k$-FWL. Combining these two modifications results in a flexible and powerful framework $(k,t)$-FWL+. We demonstrate $(k,t)$-FWL+ can implement most existing models with matching expressiveness. We then introduce an instance of $(k,t)$-FWL+ called Neighborhood$^2$-FWL (N$^2$-FWL), which is practically and theoretically sound. We prove that N$^2$-FWL is no less powerful than 3-WL, and can encode many substructures while only requiring $O(n^2)$ space. Finally, we design its neural version named N$^2$-GNN and evaluate its performance on various tasks. N$^2$-GNN achieves record-breaking results on ZINC-Subset (0.059), outperforming previous SOTA results by 10.6%. Moreover, N$^2$-GNN achieves new SOTA results on the BREC dataset (71.8%) among all existing high-expressive GNN methods.Comment: Accepted to NeurIPS 202