Miracle-dz/UniFAR-FTUs huggingface dataset:
RNA structure-altering mutations underlying positive selection on Spike protein reveal novel putative signatures to trace crossing host-species barriers in Betacoronavirus

ABSTRACT Similar to other RNA viruses, the emergence of Betacoronavirus relies on cross-species viral transmission, which requires careful health surveillance monitoring of protein-coding information as well as genome-wide analysis. Although the evolutionary jump from natural reservoirs to humans may be mainly traced-back by studying the effect that hotspot mutations have on viral proteins, it is largely unexplored if other impacts might emerge on the structured RNA genome of Betacoronavirus. In this survey, the protein-coding and viral genome architecture were simultaneously studied to uncover novel insights into cross-species horizontal transmission events. We analysed 1,252,952 viral genomes of SARS-CoV, MERS-CoV, and SARS-CoV-2 distributed across the world in bats, intermediate animals, and humans to build a new landscape of changes in the RNA viral genome. Phylogenetic analyses suggest that bat viruses are the most closely related to the time of most recent common ancestor of Betacoronavirus, and missense mutations in viral proteins, mainly in the S protein S1 subunit: SARS-CoV (G > T; A577S); MERS-CoV (C > T; S746R and C > T; N762A); and SARS-CoV-2 (A > G; D614G) appear to have driven viral diversification. We also found that codon sites under positive selection on S protein overlap with non-compensatory mutations that disrupt secondary RNA structures in the RNA genome complement. These findings provide pivotal factors that might be underlying the eventual jumping the species barrier from bats to intermediate hosts. Lastly, we discovered that nearly half of the Betacoronavirus genomes carry highly conserved RNA structures, and more than 90% of these RNA structures show negative selection signals, suggesting essential functions in the biology of Betacoronavirus that have not been investigated to date. Further research is needed on negatively selected RNA structures to scan for emerging functions like the potential of coding virus-derived small RNAs and to develop new candidate antiviral therapeutic strategies.
Physical processes in circumstellar disks around young stars

Circumstellar disks are vast expanses of dust that form around new stars in the earliest stages of their birth. Predicted by astronomers as early as the eighteenth century, they weren't observed until the late twentieth century, when interstellar imaging technology enabled us to see nascent stars hundreds of light years away. Since then, circumstellar disks have become an area of intense study among astrophysicists, largely because they are thought to be the forerunners of planetary systems like our own - the possible birthplaces of planets. This volume assembles a team of leading experts to distill the most up-to-date knowledge of circumstellar disks into a clear introductory volume. Understanding circumstellar disks requires a broad range of scientific knowledge, including chemical processes, the properties of dust and gases, hydrodynamics and magnetohydrodynamics, radiation transfer, and stellar evolution - all of which are covered in this comprehensive work, which will be indispensable for graduate students, seasoned researchers, or even advanced undergrads setting out on the study of planetary evolution.
Destruction of the Environment of the BN-KL Nebula

We describe observations of the 2.12 μm molecular hydrogen emission in Orion using an IR Fabry-Perot interferometer with a spectral resolution of 24 km s-1 and a 2'' spatial resolution covering a region of 3. ′6×3. ′6 (0.46×0.46 pc2) that contains the H2 filamentary finger system located to the northwest of the Trapezium. We develop a simple model to explain the observed low-velocity structure as described by its radial moments: intensity, velocity centroid, velocity dispersion, and skewness. We assume a strong wind of 230 km s-1 produced by IRc2 interacting with a set of molecular clumps with density of 5.6×105 cm-3. This simple model provides a good match to the observed moments, gives clues to the development of filaments or fingers and entrainment of the molecular material, and associates the observed high-velocity blueshifted emission to the region. The driving source of the wind requires a high mass-loss rate and thus is likely IRc2. The H2 line emission is produced by a slow J-type shock (20 km s-1) in the clumps with an emissivity proportional to v1.8. Estimates for the total wind mass and clump mass yield 0.5 and 15 M☉ inside a radius of 1' (0.1 pc). The individual clumps have masses and sizes of few ×10−3M☉ and 0.007 pc, respectively. We conclude that the central 0.1 pc region surrounding the BN-KL nebula in front of OMC-1 is in the process of being disrupted by the strong wind of IRc2 on a timescale of 2000 yr.
Literacy skills seem to fuel literacy enjoyment, rather than vice versa.

Children who like to read and write tend to be better at it. This association is typically interpreted as enjoyment impacting engagement in literacy activities, which boosts literacy skills. We fitted direction-of-causation models to partial data of 3,690 Finnish twins aged 12. Literacy skills were rated by the twins' teachers and literacy enjoyment by the twins themselves. A bivariate twin model showed substantial genetic influences on literacy skills (70%) and literacy enjoyment (35%). In both skills and enjoyment, shared-environmental influences explained about 20% in each. The best-fitting direction-of-causation model showed that skills impacted enjoyment, while the influence in the other direction was zero. The genetic influences on skills influenced enjoyment, likely via the skills◊enjoyment path. This indicates active gene-environment correlation: children with an aptitude for good literacy skills are more likely to enjoy reading and seek out literacy activities. To a lesser extent, it was also the shared-environmental influences on children's skills that propagated to influence children's literacy enjoyment. Environmental influences that foster children's literacy skills (e.g., families and schools), also foster children's love for reading and writing. These findings underline the importance of nurturing children's literacy skills. It's known that how much children enjoy reading and writing and how good they are at it correlates ∼. 30, but causality remains unknown. We tested direction of causation in 3,690 twins aged 12. Literacy skills impacted literacy enjoyment, but not the other way around. Genetics influence children's literacy skills and how much they like and choose to read and write, indicating genetic niche-picking. This article is protected by copyright. All rights reserved.
Lamin A/C impairments cause mitochondrial dysfunction by attenuating PGC1α and the NAMPT-NAD+ pathway

Abstract Mutations in the lamin A/C gene (LMNA) cause laminopathies such as the premature aging Hutchinson Gilford progeria syndrome (HGPS) and altered lamin A/C levels are found in diverse malignancies. The underlying lamin-associated mechanisms remain poorly understood. Here we report that lamin A/C-null mouse embryo fibroblasts (Lmna−/− MEFs) and human progerin-expressing HGPS fibroblasts both display reduced NAD+ levels, unstable mitochondrial DNA and attenuated bioenergetics. This mitochondrial dysfunction is associated with reduced chromatin recruitment (Lmna−/− MEFs) or low levels (HGPS) of PGC1α, the key transcription factor for mitochondrial homeostasis. Lmna−/− MEFs showed reduced expression of the NAD+-biosynthesis enzyme NAMPT and attenuated activity of the NAD+-dependent deacetylase SIRT1. We find high PARylation in lamin A/C-aberrant cells, further decreasing the NAD+ pool and consistent with impaired DNA base excision repair in both cell models, a condition that fuels DNA damage-induced PARylation under oxidative stress. Further, ATAC-sequencing revealed a substantially altered chromatin landscape in Lmna−/− MEFs, including aberrantly reduced accessibility at the Nampt gene promoter. Thus, we identified a new role of lamin A/C as a key modulator of mitochondrial function through impairments of PGC1α and the NAMPT-NAD+ pathway, with broader implications for the aging process.
The effects of rutin flavonoid supplement on glycemic status, lipid profile, atherogenic index of plasma, brain‐derived neurotrophic factor (BDNF), some serum inflammatory, and oxidative stress factors in patients with type 2 diabetes mellitus: A double‐blind, placebo‐controlled trial

This study aimed to investigate the effects of rutin flavonoid in type 2 diabetes mellitus (T2DM) patients. In this trial (double‐blind, placebo‐controlled), 50 T2DM patients (supplement, n = 25 and placebo, n = 25) were randomized and supplemented with 500 mg rutin or placebo per day for 3‐months. At the beginning and at the end of the study, metabolic parameters including fasting blood glucose (FBG), insulin, glycosylated hemoglobin (HbA1c), homeostasis model assessment of insulin resistance (HOMO‐IR), quantitative insulin sensitivity check index (QUICKI), homeostasis model assessment of β‐cell function (HOMA‐β), triglyceride (TG), total cholesterol (CHOL), high‐density and low‐density lipoprotein cholesterol (HDL‐c and LDL‐c), and atherogenic index of plasma (AIP), inflammatory and oxidative stress markers such as interleukin 6 (IL‐6), total antioxidant capacity (TAC), and malondialdehyde (MDA) and brain‐derived neurotrophic factor (BDNF) were assessed. The results showed a significant decrease in FBG, insulin, HbA1c, HOMO‐IR, LDL‐c, TG, VLDL, CHOL, LDL‐c.HDL‐c ratio, AIP, IL‐6, and MDA and a significant increase in HDL‐c, QUICKI index, BDNF, and TAC compared with the initial value (p for all <.05). In the adjusted model, the mean changes of FBG, insulin, HbA1c, HOMO‐IR, LDL‐c, CHOL, LDL.HDL ratio, AIP, MDA, and IL‐6 were significantly lower and mean changes of QUICKI index, HDL‐c, and TAC were significantly higher in the rutin group compared with the placebo group (adjusted p for all <.05). It seems that rutin may have beneficial effects on improving metabolic parameters, BDNF, and inflammatory and oxidative stress factors in T2DM patients.
Scheduling Algorithms for Federated Learning With Minimal Energy Consumption

Federated Learning (FL) has opened the opportunity for collaboratively training machine learning models on heterogeneous mobile or Edge devices while keeping local data private. With an increase in its adoption, a growing concern is related to its economic and environmental cost (as is also the case for other machine learning techniques). Unfortunately, little work has been done to optimize its energy consumption or emissions of carbon dioxide or equivalents, as energy minimization is usually left as a secondary objective. In this paper, we investigate the problem of minimizing the energy consumption of FL training on heterogeneous devices by controlling the workload distribution. We model this as the Minimal Cost FL Schedule problem, a total cost minimization problem with identical, independent, and atomic tasks that have to be assigned to heterogeneous resources with arbitrary cost functions. We propose a pseudo-polynomial optimal solution to the problem based on the previously unexplored Multiple-Choice Minimum-Cost Maximal Knapsack Packing Problem. We also provide four algorithms for scenarios where cost functions are monotonically increasing and follow the same behavior. These solutions are likewise applicable on the minimization of other kinds of costs, and in other one-dimensional data partition problems.
Graph Perceiver IO: A General Architecture for Graph Structured Data

Multimodal machine learning has been widely studied for the development of general intelligence. Recently, the remarkable multimodal algorithms, the Perceiver and Perceiver IO, show competitive results for diverse dataset domains and tasks. However, recent works, Perceiver and Perceiver IO, have focused on heterogeneous modalities, including image, text, and speech, and there are few research works for graph structured datasets. A graph is one of the most generalized dataset structures, and we can represent the other dataset, including images, text, and speech, as graph structured data. A graph has an adjacency matrix different from other dataset domains such as text and image, and it is not trivial to handle the topological information, relational information, and canonical positional information. In this study, we provide a Graph Perceiver IO, the Perceiver IO for the graph structured dataset. We keep the main structure of the Graph Perceiver IO as the Perceiver IO because the Perceiver IO already handles the diverse dataset well, except for the graph structured dataset. The Graph Perceiver IO is a general method, and it can handle diverse datasets such as graph structured data as well as text and images. Comparing the graph neural networks, the Graph Perceiver IO requires a lower complexity, and it can incorporate the local and global information efﬁciently. We show that Graph Perceiver IO shows competitive results for diverse graph-related tasks, including node classiﬁcation, graph classiﬁcation, and link prediction. The results indicate that the Graph Perceiver IO with the appropriate positional embedding can learn the graph structure. It is noted that the Graph Perceiver IO shows the competitive results on IMDB, REDDIT, and COLLAB that have no node features. The results support that the Graph Perceiver IO has enough capacity to learn the structured graph dataset and its topology.
Limits on amplification by Aharonov-Albert-Vaidman weak measurement

We analyze the amplification by the Aharonov-Albert-Vaidman weak quantum measurement on a Sagnac interferometer [Dixon et al., Phys. Rev. Lett. 102, 173601 (2009)] up to all orders of the coupling strength between the measured system and the measuring device. The amplifier transforms a small tilt of a mirror into a large transverse displacement of the laser beam. The conventional analysis has shown that the measured value is proportional to the weak value, so that the amplification can be made arbitrarily large in the cost of decreasing output laser intensity. It is shown that the measured displacement and the amplification factor are in fact not proportional to the weak value and rather vanish in the limit of infinitesimal output intensity. We derive the optimal overlap of the pre- and postselected states with which the amplification become maximum. We also show that the nonlinear effects begin to arise in the performed experiments so that any improvements in the experiment, typically with an amplification greater than 100, should require the nonlinear theory in translating the observed value to the original displacement.
Learning Category-Level Manipulation Tasks from Point Clouds with Dynamic Graph CNNs

This paper presents a new technique for learning category-level manipulation from raw RGB-D videos of task demonstrations, with no manual labels or annotations. Category-level learning aims to acquire skills that can be generalized to new objects, with geometries and textures that are different from the ones of the objects used in the demonstrations. We address this problem by first viewing both grasping and manipulation as special cases of tool use, where a tool object is moved to a sequence of key-poses defined in a frame of reference of a target object. Tool and target objects, along with their key-poses, are predicted using a dynamic graph convolutional neural network that takes as input an automatically segmented depth and color image of the entire scene. Empirical results on object manipulation tasks with a real robotic arm show that the proposed network can efficiently learn from real visual demonstrations to perform the tasks on novel objects within the same category, and outperforms alternative approaches.
Using Forwards-Backwards Models to Approximate MDP Homomorphisms

Reinforcement learning agents must painstakingly learn through trial and error what sets of state-action pairs are value equivalent -- requiring an often prohibitively large amount of environment experience. MDP homomorphisms have been proposed that reduce the MDP of an environment to an abstract MDP, enabling better sample efficiency. Consequently, impressive improvements have been achieved when a suitable homomorphism can be constructed a priori -- usually by exploiting a practitioner's knowledge of environment symmetries. We propose a novel approach to constructing homomorphisms in discrete action spaces, which uses a learnt model of environment dynamics to infer which state-action pairs lead to the same state -- which can reduce the size of the state-action space by a factor as large as the cardinality of the original action space. In MinAtar, we report an almost 4x improvement over a value-based off-policy baseline in the low sample limit, when averaging over all games and optimizers.
Label Refinement Network from Synthetic Error Augmentation for Medical Image Segmentation

Deep convolutional neural networks for image segmentation do not learn the label structure explicitly and may produce segmentations with an incorrect structure, e.g., with disconnected cylindrical structures in the segmentation of tree-like structures such as airways or blood vessels. In this paper, we propose a novel label refinement method to correct such errors from an initial segmentation, implicitly incorporating information about label structure. This method features two novel parts: 1) a model that generates synthetic structural errors, and 2) a label appearance simulation network that produces synthetic segmentations (with errors) that are similar in appearance to the real initial segmentations. Using these synthetic segmentations and the original images, the label refinement network is trained to correct errors and improve the initial segmentations. The proposed method is validated on two segmentation tasks: airway segmentation from chest computed tomography (CT) scans and brain vessel segmentation from 3D CT angiography (CTA) images of the brain. In both applications, our method significantly outperformed a standard 3D U-Net and other previous refinement approaches. Improvements are even larger when additional unlabeled data is used for model training. In an ablation study, we demonstrate the value of the different components of the proposed method.
A Millimeter-multiwavelength Continuum Study of VLA 1623 West

VLA 1623 West is an ambiguous source that has been described as a shocked cloudlet as well as a protostellar disk. We use deep ALMA 1.3 and 0.87 mm observations to constrain its shape and structure to determine its origins better. We use a series of geometric models to fit the uv visibilities at both wavelengths with GALARIO. Although the real visibilities show structures similar to what has been identified as gaps and rings in protoplanetary disks, we find that a modified flat-topped Gaussian model at high inclination provides the best fit to the observations. This fit agrees well with expectations for an optically thick, highly inclined disk. Nevertheless, we find that the geometric models consistently yield positive residuals at the four corners of the disk at both wavelengths. We interpret these residuals as evidence that the disk is flared in the millimeter dust. We use a simple toy model for an edge-on flared disk and find that the residuals best match a disk with flaring that is mainly restricted to the outer disk at R ≳ 30 au. Thus, VLA 1623W may represent a young protostellar disk where the large dust grains have not yet had enough time to settle into the midplane. This result may have implications for how disk evolution and vertical dust settling impact the initial conditions leading to planet formation.
Relaxed results on reachable set estimation of time‐delay systems with bounded peak inputs

This paper is concerned with the problem of reachable set estimation (RSE) for linear systems with time‐varying delays and bounded peak inputs. The purpose is to find an ellipsoid that contains the system state in presence of all bounded peak inputs. First, the RSE problem for nominal time‐delay systems is studied based on a relaxed Lyapunov–Krasovskii functional which does not require all the involved symmetric matrices to be positive definite. Delay‐dependent and delay‐rate‐dependent conditions for the existence of a desired ellipsoid are obtained. Second, the RSE problem for time‐delay systems with time‐varying polytopic uncertainties is investigated. Under the assumption that the uncertain parameters are differentiable and their derivatives are bounded by known scalars, parameter‐rate‐dependent conditions for the existence of a desired ellipsoid are derived by using a parameter‐dependent Lyapunov–Krasovskii functional. When the differentiability of the uncertain parameters is not taken into account, a common Lyapunov–Krasovskii functional is employed to tackle the addressed problem, and parameter‐rate‐independent conditions are presented. All the obtained conditions are given in terms of matrix inequalities, which become linear matrix inequalities when only one non‐convex scalar is prescribed. Finally, the reduced conservatism of the obtained results in comparison with recent ones in the literature is shown through numerical examples. Copyright © 2015 John Wiley & Sons, Ltd.
Dynamically Forming Extremely Low-mass White Dwarf Binaries in Wide Orbits

The detection of a 0.2 M ⊙ extremely low-mass white dwarf (EW) in a wide orbit (P orb ≈ 450 days) with a 1.1 M ⊙ main-sequence companion, KIC 8145411, challenges our current understanding of how EWs form. The traditional channel for EW formation, via mass transfer from the EW’s progenitor, is expected to form EW binaries in tight orbits. Indeed, the majority of known EWs are found in tight binaries with a median P orb ≈ 5.4 hr. Using numerical scattering experiments, we find that binary–binary strong encounters in star clusters can sufficiently widen the orbit of a typical EW binary, to explain the observed wide orbit of the KIC 8145411 system. The P orb distribution for EW binaries produced through binary–binary encounters is bimodal: one mode corresponds to the initial orbital period of the EW binary, while the other is near P orb ∼ few 102 days, similar to the orbital period of the KIC 8145411 system. We find that the production of wide EW binaries that are also ejected from the cluster peaks at a star cluster mass of ∼105 M ⊙ with a rate of ∼10−3 Gyr−1. Assuming that 50% of all stars form in star clusters and an initial cluster mass function ∝m −2, we estimate a galactic formation rate of ∼4.16 × 103 Gyr−1 for wide EW binaries.
Data Quality, Mismatched Expectations, and Moving Requirements: The Challenges of User-Centred Dashboard Design

Interactive information dashboards can help both specialists and the general public understand complex datasets; but interacting with these dashboards often presents users with challenges such as understanding and verifying the presented information. To overcome these challenges, developers first need to acquire a thorough understanding of user perspectives, including strategies that users take when presented with problematic dashboards. We interviewed seventeen dashboard developers to establish (i) their understanding of user problems, (ii) the adaptations introduced as a result, and (iii) whether user-tailored dashboards can cater for users’ individual differences. We find that users’ literacy does not typically align with that required to use dashboards, while dashboard developers struggle with keeping up with changing requirements. We also find that developers are able to propose solutions to most users’ problems but not all. Encouragingly, our findings also highlight that tailoring dashboards to individual user needs is not only desirable, but also feasible. These findings inform future dashboard design recommendations that can mitigate the identified challenges including recommendations for data presentation and visual literacy.