Miracle-dz/UniFAR-FTUs huggingface dataset:
Landscape of Post-Transcriptional tRNA Modifications in Streptomyces albidoflavus J1074 as Portrayed by Mass Spectrometry and Genomic Data Mining

Post-transcriptional tRNA modifications (PTTMs) play an important role in maturation and functionality of tRNAs. Little is known about tRNA modifications in the antibiotic-producing actinobacterial genus Streptomyces, even though peculiar tRNA-based regulatory mechanisms operate in this taxon. ABSTRACT Actinobacterial genus Streptomyces (streptomycetes) represents one of the largest cultivable group of bacteria famous for their ability to produce valuable specialized (secondary) metabolites. Regulation of secondary metabolic pathways inextricably couples the latter to essential cellular processes that determine levels of amino acids, carbohydrates, phosphate, etc. Post-transcriptional tRNA modifications remain one of the least studied aspects of streptomycete physiology, albeit a few of them were recently shown to impact antibiotic production. In this study, we describe the diversity of post-transcriptional tRNA modifications in model strain Streptomyces albus (albidoflavus) J1074 by combining mass spectrometry and genomic data. Our results show that J1074 can produce more chemically distinct tRNA modifications than previously thought. An in silico approach identified orthologs for enzymes governing most of the identified tRNA modifications. Yet, genetic control of certain modifications remained elusive, suggesting early divergence of tRNA modification pathways in Streptomyces from the better studied model bacteria, such as Escherichia coli and Bacillus subtilis. As a first point in case, our data point to the presence of a non-canonical MiaE enzyme performing hydroxylation of prenylated adenosines. A further finding concerns the methylthiotransferase MiaB, which requires previous modification of adenosines by MiaA to i6A for thiomethylation to ms2i6A. We show here that the J1074 ortholog, when overexpressed, yields ms2A in a ΔmiaA background. Our results set the working ground for and justify a more detailed studies of biological significance of tRNA modification pathways in streptomycetes. IMPORTANCE Post-transcriptional tRNA modifications (PTTMs) play an important role in maturation and functionality of tRNAs. Little is known about tRNA modifications in the antibiotic-producing actinobacterial genus Streptomyces, even though peculiar tRNA-based regulatory mechanisms operate in this taxon. We provide a first detailed description of the chemical diversity of PTTMs in the model species, S. albidoflavus J1074, and identify most plausible genes for these PTTMs. Some of the PTTMs are described for the first time for Streptomyces. Production of certain PTTMs in J1074 appears to depend on enzymes that show no sequence similarity to known PTTM enzymes from model species. Our findings are of relevance for interrogation of genetic basis of PTTMs in pathogenic actinobacteria, such as M. tuberculosis.
Groups with a Bruhat decomposition

Introduction. In §1, we consider a group G which satisfies a simplified version of the axioms of Steinberg [3], and state some general results on the structure of G, the first two of which are due to J. Tits [7; 9 ] . The main result is stated in §2, and can be viewed as a generalization of a theorem of Higman and McLaughlin [2, Theorem 2]. The theorem states essentially that a finite simple group G, which satisfies certain structural assumptions independent of the arithmetical structure of G, and whose Weyl group is isomorphic to the Weyl group of a complex simple Lie algebra g of type An (n^2), Dn (w^4) , or En (n — 6, 7, 8), is isomorphic to the group of Chevalley [ l ] determined by c; and some finite field K. The possibility of such a theorem can be seen in the paper of Tits [8], who showed that a group with a root data is the amalgamated product of the canonically imbedded subgroups of rank two. For a group G satisfying the hypotheses of the theorem, the subgroups of rank two and their amalgamation are uniquely determined by the Weyl group. The applicability of the theorem is limited to the groups associated with simple Lie algebras of types An (n^2), Dni and En because these are the simple groups of Chevalley all of whose canonically imbedded simple subgroups of rank two are of type A 2, and so can be classified by the result of Higman and McLaughlin.
Electroabsorption of highly confined systems: Theory of the quantum‐confined Franz–Keldysh effect in semiconductor quantum wires and dots

Semiconductor quantum wells are known to show large electroabsorption (e.g., the quantum‐confined Stark effect) that results in low‐energy optical modulating and switching devices. We show theoretically that the electroabsorption and associated electrorefraction in lower dimensional structures could be much larger, suggesting very low energy devices. We illustrate the theory with specific calculations for hypothetical GaAs‐like quantum wires and dots.
Optical photometric variability of 2S 0114+65

In this paper we present Johnson V photometry of the Be/x-ray binary star system 2S 0114+65. Although this star exhibits periodic variations in x-rays, optical studies have failed to reveal fluctuations greater than 5 millimag. The data presented in this paper provide the first evidence for periodic optical variability in 2S 0114+65. On each of four nights in October 1993, we find low amplitude variations with a period of 2.77 +/- 0.48 h and with a semiamplitude of 4 millimag. This period is in good agreement with results of a comprehensive study of the x-ray data. We explore the possibility that this period represents the pulsational period of the B-star primary and the possibility that it is the rotational period of the neutron star. If the latter is the correct interpretation, we calculate a spin-up time scale of 5 x 10(exp 5) yr.
Determining the stellar masses in the binary system containing the pulsar PSR 1913 + 16 - Is the companion a helium main-sequence star

The stellar masses and the angle of inclination are determined for the system containing PSR 1913+16 in terms of the observable periastron advance and second-order relativistic effects, for the cases (a) in which classical contributions to the apsidal motion are negligible, and (b) in which the companion is a helium main-sequency star. In case b, the apsidal motion constant is calculated from models of such stars. Depending on the values found for the observable parameters, it may be possible to distinguish between cases a and b. The further uncertainty in the mass determination which arises if the companion is a rapidly rotating degenerate dwarf or helium main-sequence star is discussed. Black holes, neutrons stars, degenerate dwarfs, and helium main-sequence stars remain viable candidates for the companion at present. (AIP)
...I did not scream. I could not; I was terrified. I just followed them...I blocked my mind. Then they all raped me: A narrative inquiry on the onset of tonic immobility among women rape victims in Nigeria.

Tonic immobility (TI) is a common uncontrollable autonomic mammalian response to an extremely fearful situation. It is one of the most immediate devastating consequences of rape and remains poorly understood. While controversies over its definition persist among researchers, this also reflects the care for and support to victims. The study aimed to explore and describe the onset of TI and the meaning attached to the experience among women raped victims in Nigeria. The study design was the qualitative narrative inquiry approach. Criterion and purposive sampling were conducted across four post-rape care facilities in Lagos, Nigeria, to recruit 13 participants. In-depth face-to-face interviews were conducted using a semi-structured interview guide to generate data that were thematically analysed. The findings of the study reveal five themes relating to the onset of TI as follows: the first two focused on the experience of TI: (1) the onset of TI prior to rape due to perceived imminent danger, (2) the onset of TI as a self-protection mechanism from further harm) while the last three relate to the meaning-making of the experience of TI (3) self-loathing as a meaning attached to TI, (4) suicidal ideations as a meaning attached to TI, and (5) divine intervention as a meaning attached to TI. Conclusion: The findings underscore the experiences and meanings that participants attach to TI following rape. There is a strong likelihood that tonic-immobility is not an uncommon experience for rape victims, but that in the absence of research, specialized care on the condition, and considering its associated consequences will haunt many women, affect their psychological well-being and their entire quality of life. Describing the phenomenon as it is experienced by the participants is critical because understanding the situation is the first step toward effective appropriate management.
A Comprehensive Survey and Taxonomy on Single Image Dehazing Based on Deep Learning

With the development of convolutional neural networks, hundreds of deep learning–based dehazing methods have been proposed. In this article, we provide a comprehensive survey on supervised, semi-supervised, and unsupervised single image dehazing. We first discuss the physical model, datasets, network modules, loss functions, and evaluation metrics that are commonly used. Then, the main contributions of various dehazing algorithms are categorized and summarized. Further, quantitative and qualitative experiments of various baseline methods are carried out. Finally, the unsolved issues and challenges that can inspire the future research are pointed out. A collection of useful dehazing materials is available at https://github.com/Xiaofeng-life/AwesomeDehazing.
Physics-Informed Model-Based Reinforcement Learning

We apply reinforcement learning (RL) to robotics tasks. One of the drawbacks of traditional RL algorithms has been their poor sample efficiency. One approach to improve the sample efficiency is model-based RL. In our model-based RL algorithm, we learn a model of the environment, essentially its transition dynamics and reward function, use it to generate imaginary trajectories and backpropagate through them to update the policy, exploiting the differentiability of the model. Intuitively, learning more accurate models should lead to better model-based RL performance. Recently, there has been growing interest in developing better deep neural network based dynamics models for physical systems, by utilizing the structure of the underlying physics. We focus on robotic systems undergoing rigid body motion without contacts. We compare two versions of our model-based RL algorithm, one which uses a standard deep neural network based dynamics model and the other which uses a much more accurate, physics-informed neural network based dynamics model. We show that, in model-based RL, model accuracy mainly matters in environments that are sensitive to initial conditions, where numerical errors accumulate fast. In these environments, the physics-informed version of our algorithm achieves significantly better average-return and sample efficiency. In environments that are not sensitive to initial conditions, both versions of our algorithm achieve similar average-return, while the physics-informed version achieves better sample efficiency. We also show that, in challenging environments, physics-informed model-based RL achieves better average-return than state-of-the-art model-free RL algorithms such as Soft Actor-Critic, as it computes the policy-gradient analytically, while the latter estimates it through sampling.
One-shot Implicit Animatable Avatars with Model-based Priors

Existing neural rendering methods for creating human avatars typically either require dense input signals such as video or multi-view images, or leverage a learned prior from large-scale specific 3D human datasets such that reconstruction can be performed with sparse-view inputs. Most of these methods fail to achieve realistic reconstruction when only a single image is available. To enable the data-efficient creation of realistic anima table 3D humans, we propose ELICIT, a novel method for learning human-specific neural radiance fields from a single image. Inspired by the fact that humans can effortlessly estimate the body geometry and imagine full-body clothing from a single image, we leverage two priors in ELICIT: 3D geometry prior and visual semantic prior. Specifically, ELICIT utilizes the 3D body shape geometry prior from a skinned vertex-based template model (i.e., SMPL) and implements the visual clothing semantic prior with the CLIP-based pre-trained models. Both priors are used to jointly guide the optimization for creating plausible content in the invisible areas. Taking advantage of the CLIP models, ELICIT can use text descriptions to generate text-conditioned unseen regions. In order to further improve visual details, we propose a segmentation-based sampling strategy that locally refines different parts of the avatar. Comprehensive evaluations on multiple popular benchmarks, including ZJU-MoCAP, Human3.6M, and DeepFashion, show that ELICIT outperforms strong baseline methods of avatar creation when only a single image is available. The code is public for research purposes at https://huangyangyi.github. io/ELICIT
Unbiased Test Error Estimation in the Poisson Means Problem via Coupled Bootstrap Techniques

We propose a coupled bootstrap estimator for the test error of an arbitrary algorithm that estimates the mean in a Poisson sequence, often called the Poisson means problem. The idea behind our method is to generate two carefully-designed data vectors from the original data vector, by using synthetic binomial noise. One such vector acts as the training sample and the second acts as the test sample. To stabilize the test error estimate, we average this over multiple draws of the synthetic noise. A key property of our coupled bootstrap estimator is that it is unbiased for the test error in a problem where the original mean has been shrunken by a small factor, driven by the success probability $p$ in the binomial noise. Further, in the limit as $p \to 0$, we show that the proposed estimator recovers a known unbiased estimator for the test error, under no assumptions on the algorithm at hand (in particular, no smoothness assumptions). Our methodology applies to two central loss functions that can be used to a test error metric: Poisson deviance and squared loss. Through a bias-variance decomposition, for each loss function, we analyze the effects of the binomial success probability and the number of bootstrap samples and on the accuracy of the estimator. We also investigate our method empirically across a variety of settings, using simulated as well as real data.
Data-driven memory-dependent abstractions of dynamical systems

We propose a sample-based, sequential method to abstract a (potentially black-box) dynamical system with a sequence of memory-dependent Markov chains of increasing size. We show that this approximation allows to alleviating a correlation bias that has been observed in sample-based abstractions. We further propose a methodology to detect on the fly the memory length resulting in an abstraction with sufficient accuracy. We prove that under reasonable assumptions, the method converges to a sound abstraction in some precise sense, and we showcase it on two case studies.
MeshDQN: A Deep Reinforcement Learning Framework for Improving Meshes in Computational Fluid Dynamics

Meshing is a critical, but user-intensive process necessary for stable and accurate simulations in computational fluid dynamics (CFD). Mesh generation is often a bottleneck in CFD pipelines. Adaptive meshing techniques allow the mesh to be updated automatically to produce an accurate solution for the problem at hand. Existing classical techniques for adaptive meshing require either additional functionality out of solvers, many training simulations, or both. Current machine learning techniques often require substantial computational cost for training data generation, and are restricted in scope to the training data flow regime. MeshDQN is developed as a general purpose deep reinforcement learning framework to iteratively coarsen meshes while preserving target property calculation. A graph neural network based deep Q network is used to select mesh vertices for removal and solution interpolation is used to bypass expensive simulations at each step in the improvement process. MeshDQN requires a single simulation prior to mesh coarsening, while making no assumptions about flow regime, mesh type, or solver, only requiring the ability to modify meshes directly in a CFD pipeline. MeshDQN successfully improves meshes for two 2D airfoils.
Notes on the complexity of coverings for Kronecker powers of symmetric matrices

In the present note, we study a new method of constructing efficient coverings for Kronecker powers of matrices, recently proposed by J. Alman, Y. Guan, A. Padaki [arXiv, 2022]. We provide an alternative proof for the case of symmetric matrices in a stronger form. As a consequence, the previously known upper bound on the depth-2 additive complexity of the boolean $N\times N$ Kneser-Sierpinski matrices is improved to $O(N^{1.251})$.
Estimating Time-Varying Direct and Indirect Causal Excursion Effects with Longitudinal Binary Outcomes

Construction of just-in-time adaptive interventions, such as prompts delivered by mobile apps to promote and maintain behavioral change, requires knowledge about time-varying moderated effects to inform when and how we deliver intervention options. Micro-randomized trials (MRT) have emerged as a sequentially randomized design to gather requisite data for effect estimation. The existing literature (Qian et al., 2020; Boruvka et al., 2018; Dempsey et al., 2020) has defined a general class of causal estimands, referred to as"causal excursion effects", to assess the time-varying moderated effect. However, there is limited statistical literature on how to address potential between-cluster treatment effect heterogeneity and within-cluster interference in a sequential treatment setting for longitudinal binary outcomes. In this paper, based on a cluster conceptualization of the potential outcomes, we define a larger class of direct and indirect causal excursion effects for proximal and lagged binary outcomes, and propose a new inferential procedure that addresses effect heterogeneity and interference. We provide theoretical guarantees of consistency and asymptotic normality of the estimator. Extensive simulation studies confirm our theory empirically and show the proposed procedure provides consistent point estimator and interval estimates with valid coverage. Finally, we analyze a data set from a multi-institution MRT study to assess the time-varying moderated effects of mobile prompts upon binary study engagement outcomes.
Simultaneous Best Subset Selection and Dimension Reduction via Primal-Dual Iterations

Sparse reduced rank regression is an essential statistical learning method. In the contemporary literature, estimation is typically formulated as a nonconvex optimization that often yields to a local optimum in numerical computation. Yet, their theoretical analysis is always centered on the global optimum, resulting in a discrepancy between the statistical guarantee and the numerical computation. In this research, we oﬀer a new algorithm to address the problem and establish an almost optimal rate for the algorithmic solution. We also demonstrate that the algorithm achieves the estimation with a polynomial number of iterations. In addition, we present a generalized information criterion to simultaneously ensure the consistency of support set recovery and rank estimation. Under the proposed criterion, we show that our algorithm can achieve the oracle reduced rank estimation with a signiﬁcant probability. The numerical studies and an application in the ovarian cancer genetic data demonstrate the eﬀectiveness and scalability of our approach.
Asteroseismology: Looking for Axions in the Red Supergiant Star Alpha Ori

In this work, for the first time, we use seismic data as well as surface abundances to model the supergiant α-Ori, with the goal of setting an upper bound on the axion–photon coupling constant g a γ . We find that, in general, stellar models with g a γ ∈ [0.002; 2.0] × 10−10 GeV−1 agree with the observational data, but beyond that upper limit, we do not find stellar models that are compatible with the observational constraints and the current literature. From g a γ = 3.5 × 10−10 GeV−1 on, the algorithm did not find any fitting models. Even so, all the axionic models considered present distinct internal profiles from the reference case, without axions. Moreover, as the axion energy losses become more significant, the behavior of the stellar models becomes more diversified, even with very similar input parameters. Nonetheless, the consecutive increments of g a γ still show systematic tendencies, resulting from the axion energy losses. Moreover, we establish three important conclusions: (1) the increased luminosity and higher neutrino production are measurable effects, possibly associated with axion energy losses; (2) stellar models with axion energy loss show a quite distinct internal structure; and (3) the importance of future asteroseismic missions in observing low-degree nonradial modes in massive stars is emphasized—as internal gravity waves probe the near-core regions, where axion effects are most intense. Thus, more seismic data will allow us to constrain g a γ better and to prove or dismiss the existence of axion energy loss inside massive stars.
BlendGAN: Learning and Blending the Internal Distributions of Single Images by Spatial Image-Identity Conditioning

Training a generative model on a single image has drawn signiﬁcant attention in recent years. Single image generative methods are designed to learn the internal patch distribution of a single natural image at multiple scales. These models can be used for drawing diverse samples that semantically resemble the training image, as well as for solving many image editing and restoration tasks that in-volve that particular image. Here, we introduce an extended framework, which allows to simultaneously learn the internal distributions of several images, by using a single model with spatially varying image-identity conditioning. Our BlendGAN opens the door to applications that are not supported by single-image models, including morphing, melding, and structure-texture fusion between two or more arbitrary images.
Distribution of the ML Estimator of an MA(1) and a local level model

Although considerable attention has recently been paid to the behavior of the maximum likelihood estimator of simple moving average models, little progress has been made in finding a good approximation to its distribution in cases where the process is close to being noninvertible. In this paper a method is produced that gives an excellent approximation to the distribution function, even in the case where the process is strictly noninvertible. Also studied is the related problem of the distribution of the maximum likelihood estimator of the signalto-noise ratio in the local level model.