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  1. This article introduces a causal discovery method to learn nonlinear relationships in a directed acyclic graph with correlatedGaussian errors due to confounding. First,we derive model identifiability under the sublinear growth assumption. Then, we propose a novel method, named the Deconfounded Functional Structure Estimation (DeFuSE), consisting of a deconfounding adjustment to remove the confounding effects and a sequential procedure to estimate the causal order of variables. We implement DeFuSE via feedforward neural networks for scalable computation. Moreover, we establish the consistency of DeFuSE under an assumption called the strong causal minimality. In simulations, DeFuSE compares favorably against state of-the-art competitors that ignore confounding or nonlinearity. Finally, we demonstrate the utility and effectiveness of the proposed approach with an application to gene regulatory network analysis. The Python implementation is available at Supplementary materials for this article are available online. 
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    Free, publicly-accessible full text available October 1, 2024
  2. This article proposes a novel causal discovery and inference method called GrIVET for a Gaussian directed acyclic graph with unmeasured confounders. GrIVET consists of an order-based causal discovery method and a likelihood-based inferential procedure. For causal discovery, we generalize the existing peeling algorithm to estimate the ancestral relations and candidate instruments in the presence of hidden confounders. Based on this, we propose a new procedure for instrumental variable estimation of each direct effect by separating it from any mediation effects. For inference, we develop a new likelihood ratio test of multiple causal effects that is able to account for the unmeasured confounders. Theoretically, we prove that the proposed method has desirable guarantees, including robustness to invalid instruments and uncertain interventions, estimation consistency, low-order polynomial time complexity, and validity of asymptotic inference. Numerically, GrIVET performs well and compares favorably against state-of-the-art competitors. Furthermore, we demonstrate the utility and effectiveness of the proposed method through an application inferring regulatory pathways from Alzheimer’s disease gene expression data. 
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    Free, publicly-accessible full text available October 4, 2024
  3. Free, publicly-accessible full text available September 1, 2024
  4. Free, publicly-accessible full text available August 9, 2024
  5. We report an experimental realization of a modified counterfactual communication protocol that eliminates the dominant environmental trace left by photons passing through the transmission channel. Compared to Wheeler’s criterion for inferring past particle paths, as used in prior protocols, our trace criterion provides stronger support for the claim of the counterfactuality of the communication. We verify the lack of trace left by transmitted photons via tagging the propagation arms of an interferometric device by distinct frequency-shifts and finding that the collected photons have no frequency shift which corresponds to the transmission channel. As a proof of principle, we counterfactually transfer a quick response code image with sufficient fidelity to be scanned with a cell phone. 
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    Free, publicly-accessible full text available September 12, 2024
  6. In precision medicine, the ultimate goal is to recommend the most effective treatment to an individual patient based on patient‐specific molecular and clinical profiles, possibly high‐dimensional. To advance cancer treatment, large‐scale screenings of cancer cell lines against chemical compounds have been performed to help better understand the relationship between genomic features and drug response; existing machine learning approaches use exclusively supervised learning, including penalized regression and recommender systems. However, it would be more efficient to apply reinforcement learning to sequentially learn as data accrue, including selecting the most promising therapy for a patient given individual molecular and clinical features and then collecting and learning from the corresponding data. In this article, we propose a novel personalized ranking system called Proximal Policy Optimization Ranking (PPORank), which ranks the drugs based on their predicted effects per cell line (or patient) in the framework of deep reinforcement learning (DRL). Modeled as a Markov decision process, the proposed method learns to recommend the most suitable drugs sequentially and continuously over time. As a proof‐of‐concept, we conduct experiments on two large‐scale cancer cell line data sets in addition to simulated data. The results demonstrate that the proposed DRL‐based PPORank outperforms the state‐of‐the‐art competitors based on supervised learning. Taken together, we conclude that novel methods in the framework of DRL have great potential for precision medicine and should be further studied.

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  7. null (Ed.)
    By incorporating pH responsive i-motif elements, we have constructed DNA origami nanosprings that respond to pH changes in the environment. Using an innovative force jump approach in optical tweezers, we have directly measured the spring constants and dynamic recoiling responses of the DNA nanosprings under different forces. These DNA nanosprings exhibited 3 times slower recoiling rates compared to duplex DNA backbones. In addition, we observed two distinct force regions which show different spring constants. In the entropic region below 2 pN, a spring constant of ∼0.03 pN nm −1 was obtained, whereas in the enthalpic region above 2 pN, the nanospring was 17 times stronger (0.5 pN nm −1 ). The force jump gave a more accurate measurement on nanospring constants compared to regular force ramping approaches, which only yielded an average spring constant in a specific force range. Compared to the reported DNA origami nanosprings with a completely different design, our nanospring is up to 50 times stiffer. The drastic increase in the spring constant and the pH responsive feature allow more robust applications of these nanosprings in many mechanobiological processes. 
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  8. null (Ed.)
  9. Abstract

    The identification of a non-trivial band topology usually relies on directly probing the protected surface/edge states. But, it is difficult to achieve electronically in narrow-gap topological materials due to the small (meV) energy scales. Here, we demonstrate that band inversion, a crucial ingredient of the non-trivial band topology, can serve as an alternative, experimentally accessible indicator. We show that an inverted band can lead to a four-fold splitting of the non-zero Landau levels, contrasting the two-fold splitting (spin splitting only) in the normal band. We confirm our predictions in magneto-transport experiments on a narrow-gap strong topological insulator, zirconium pentatelluride (ZrTe5), with the observation of additional splittings in the quantum oscillations and also an anomalous peak in the extreme quantum limit. Our work establishes an effective strategy for identifying the band inversion as well as the associated topological phases for future topological materials research.

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