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Abstract N⁶-adenine methylation occurs in both DNA and RNA (referred to as 6mA and m6A, respectively). As an extensively characterized epi-transcriptomic mark found in virtually all eukaryotes, m6A in mRNA is deposited by METTL3-METTL14 complex. As a transcription-associated epigenetic mark abundantly present in many unicellular eukaryotes, 6mA is coordinately maintained by two AMT1 complexes, distinguished by their mutually exclusive subunits, AMT6 and AMT7. These are all members of MT-A70 family methyltransferases (MTases). Despite their functional importance, no structure for holo-complexes with cognate DNA/RNA substrate has been resolved. Here, we employ AlphaFold3 (AF3) and molecular dynamics (MD) simulations for structural modeling ofTetrahymenaAMT1 complexes, with emphasis on ternary holo-complexes with double-stranded DNA (dsDNA) substrate and cofactor. Key structural features observed in these models are validated by mutagenesis and various other biophysical and biochemical approaches. Our analysis reveals the structural basis for DNA substrate recognition, base flipping, and catalysis in the prototypical eukaryotic DNA 6mA-MTase. It also allows us to delineate the reaction pathway for processive DNA methylation involving translocation of the closed form AMT1 complex along dsDNA. As the active site is highly conserved across MT-A70 family of eukaryotic 6mA/m6A-MTases, the structural insight will facilitate rational design of small molecule inhibitors, especially for METTL3-METTL14, a promising target in cancer therapeutics. 

The exploration of metamaterials with artificial sub-wavelength structures has empowered researchers to engineer the propagation of classical waves, enabling advancements in areas such as imaging, sensing, communication, and energy harvesting. Concurrently, the investigation into topology and symmetry has not only unveiled valuable insights into fundamental physics, but also expanded our ability to manipu- late waves effectively. Combined with the remarkable flexibility and diversity of artificial metamaterials, these considerations have sparked a focused research interest. Notably, a class of structures capable of supporting topological propagation modes akin to the Schrödinger equation has been identified. Leveraging metamaterials to emulate Schrödinger dynamics has emerged as a promising avenue for robust wave manipulation and the exploration of quantum phenomena beyond the confines of electronic systems. Despite rapid progress in this burgeoning field, comprehensive summaries are scarce. Thus, this review aims to systematically consolidate recent advancements in classical wave physics based on a Schrödinger equation approach. This discourse initiates with an overview of quantum and classical wave descriptions, subsequently delving into the elucidation of numerous models realized across diverse experimental platforms, including photonic/phononic waveguides, acoustic cavities, and optomechanics. Finally, we address the challenges and prospects associated with emulating Schrödinger dynamics, underscoring the potential for groundbreaking developments in this captivating domain. 

Abstract Harnessing data to discover the underlying governing laws or equations that describe the behavior of complex physical systems can significantly advance our modeling, simulation and understanding of such systems in various science and engineering disciplines. This work introduces a novel approach called physics-informed neural network with sparse regression to discover governing partial differential equations from scarce and noisy data for nonlinear spatiotemporal systems. In particular, this discovery approach seamlessly integrates the strengths of deep neural networks for rich representation learning, physics embedding, automatic differentiation and sparse regression to approximate the solution of system variables, compute essential derivatives, as well as identify the key derivative terms and parameters that form the structure and explicit expression of the equations. The efficacy and robustness of this method are demonstrated, both numerically and experimentally, on discovering a variety of partial differential equation systems with different levels of data scarcity and noise accounting for different initial/boundary conditions. The resulting computational framework shows the potential for closed-form model discovery in practical applications where large and accurate datasets are intractable to capture. 

Introduction Studies have reported that antidiabetic medications (ADMs) were associated with lower risk of dementia, but current findings are inconsistent. This study compared the risk of dementia onset in patients with type 2 diabetes (T2D) treated with sulfonylurea (SU) or thiazolidinedione (TZD) to patients with T2D treated with metformin (MET). Research design and methods This is a prospective observational study within a T2D population using electronic medical records from all sites of the Veterans Affairs Healthcare System. Patients with T2D who initiated ADM from January 1, 2001, to December 31, 2017, were aged ≥60 years at the initiation, and were dementia-free were identified. A SU monotherapy group, a TZD monotherapy group, and a control group (MET monotherapy) were assembled based on prescription records. Participants were required to take the assigned treatment for at least 1 year. The primary outcome was all-cause dementia, and the two secondary outcomes were Alzheimer’s disease and vascular dementia, defined by International Classification of Diseases (ICD), 9th Revision, or ICD, 10th Revision, codes. The risks of developing outcomes were compared using propensity score weighted Cox proportional hazard models. Results Among 559 106 eligible veterans (mean age 65.7 (SD 8.7) years), the all-cause dementia rate was 8.2 cases per 1000 person-years (95% CI 6.0 to 13.7). After at least 1 year of treatment, TZD monotherapy was associated with a 22% lower risk of all-cause dementia onset (HR 0.78, 95% CI 0.75 to 0.81), compared with MET monotherapy, and 11% lower for MET and TZD dual therapy (HR 0.89, 95% CI 0.86 to 0.93), whereas the risk was 12% higher for SU monotherapy (HR 1.12 95% CI 1.09 to 1.15). Conclusions Among patients with T2D, TZD use was associated with a lower risk of dementia, and SU use was associated with a higher risk compared with MET use. Supplementing SU with either MET or TZD may partially offset its prodementia effects. These findings may help inform medication selection for elderly patients with T2D at high risk of dementia. 

A flaw in QA evaluation is that annotations often only provide one gold answer. Thus, model predictions semantically equivalent to the answer but superficially different are considered incorrect. This work explores mining alias entities from knowledge bases and using them as additional gold answers (i.e., equivalent answers). We incorporate answers for two settings: evaluation with additional answers and model training with equivalent answers. We analyse three QA benchmarks: Natural Questions, TriviaQA and SQuAD. Answer expansion increases the exact match score on all datasets for evaluation, while incorporating it helps model training over real-world datasets. We ensure the additional answers are valid through a human post hoc evaluation. 

Open-domain question answering answers a question based on evidence retrieved from a large corpus. State-of-the-art neural approaches require intermediate evidence annotations for training. However, such intermediate annotations are expensive, and methods that rely on them cannot transfer to the more common setting, where only question– answer pairs are available. This paper investigates whether models can learn to find evidence from a large corpus, with only distant supervision from answer labels for model training, thereby generating no additional annotation cost. We introduce a novel approach (DISTDR) that iteratively improves over a weak retriever by alternately finding evidence from the up-to-date model and encouraging the model to learn the most likely evidence. Without using any evidence labels, DISTDR is on par with fully-supervised state-of-theart methods on both multi-hop and singlehop QA benchmarks. Our analysis confirms that DISTDR finds more accurate evidence over iterations, which leads to model improvements. The code is available at https:// github.com/henryzhao5852/DistDR.