Miracle-dz/UniFAR-FTUs huggingface dataset:
Towards Pandemic-Scale Ancestral Recombination Graphs of SARS-CoV-2

Recombination is an ongoing and increasingly important feature of circulating lineages of SARS-CoV-2, challenging how we represent the evolutionary history of this virus and giving rise to new variants of potential public health concern by combining transmission and immune evasion properties of different lineages. Detection of new recombinant strains is challenging, with most methods looking for breaks between sets of mutations that characterise distinct lineages. In addition, many basic approaches fundamental to the study of viral evolution assume that recombination is negligible, in that a single phylogenetic tree can represent the genetic ancestry of the circulating strains. Here we present an initial version of sc2ts, a method to automatically detect recombinants in real time and to cohesively integrate them into a genealogy in the form of an ancestral recombination graph (ARG), which jointly records mutation, recombination and genetic inheritance. We infer two ARGs under different sampling strategies, and study their properties. One contains 1.27 million sequences sampled up to June 30, 2021, and the second is more sparsely sampled, consisting of 657K sequences sampled up to June 30, 2022. We find that both ARGs are highly consistent with known features of SARS-CoV-2 evolution, recovering the basic backbone phylogeny, mutational spectra, and recapitulating details on the majority of known recombinant lineages. Using the well-established and feature-rich tskit library, the ARGs can also be stored concisely and processed efficiently using standard Python tools. For example, the ARG for 1.27 million sequences—encoding the inferred reticulate ancestry, genetic variation, and extensive metadata—requires 58MB of storage, and loads in less than a second. The ability to fully integrate the effects of recombination into downstream analyses, to quickly and automatically detect new recombinants, and to utilise an efficient and convenient platform for computation based on well-engineered technologies makes sc2ts a promising approach.
Worst-Case Deterministic Fully-Dynamic Biconnectivity in Changeable Planar Embeddings

We study dynamic planar graphs with n vertices, subject to edge deletion, edge contraction, edge insertion across a face, and the splitting of a vertex in specified corners. We dynamically maintain a combinatorial embedding of such a planar graph, subject to connectivity and 2-vertex-connectivity (biconnectivity) queries between pairs of vertices. Whenever a query pair is connected and not biconnected, we find the first and last cutvertex separating them. Additionally, we allow local changes to the embedding by flipping the embedding of a subgraph that is connected by at most two vertices to the rest of the graph. We support all queries and updates in deterministic, worst-case, O (log 2 n ) time, using an O ( n )-sized data structure.
Biomechanics of wheelchair turning manoeuvres: novel insights into wheelchair propulsion

Introduction Wheelchair turning biomechanics is an under researched area despite its obvious relevance to functional mobility of wheelchair users. Wheelchair turns might be linked to a higher risk of upper limb injuries due to the increased forces and torques potentially associated with asymmetric movement. Our aim was to obtain a better theoretical understanding of wheelchair turning by biomechanically analyzing turns compared to steady-state straightforward propulsion (SSSFP). Methods Ten able-bodied men received 12-min familiarization and 10 trials (in a random order) of SSSFP and multiple left and right turns around a rectangular course. A Smartwheel was mounted at the right wheel of a standard wheelchair to measure kinetic parameters during SSSFP and of the inner hand during right turns and the outer hand during left turns. A repeated measures ANOVA was used to detect differences across tasks. Results Two strategies were identified: 3% demonstrated roll turns and 97% spin turns. Spin turns consisted of three phases: approach, turning and depart phase. The turning phase was accomplished by increasing peak force (72.9 ± 25.1 N vs. 43.38 ± 15.9 N in SSSFP) of the inner hand, while maintaining high push frequency of the outer hand (1.09 ± 0.20 push/s vs. 0.95 ± 0.13 push/s in SSSFP). Peak negative force and force impulse during the turning phase were much higher than SSSFP, 15.3 ± 15.7 and 4.5 ± 1.7 times higher, respectively. Conclusion The spin turn strategy might carry an increased risk of upper limb injuries due to higher braking force and requires particular attention by rehabilitation professionals to preserve upper limb function of long-term wheelchair users.
Editorial: Psychological factors in physical education and sport, volume II

COPYRIGHT © 2023 Gómez-López, Borrego and Alesi. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Editorial: Psychological factors in physical education and sport, volume II
Variability of leaf traits in natural populations of Picea omorika determines ignitability of fresh foliage

Introduction A variety of plant traits, from architectural to the cellular level, have been connected to flammability, but intraspecific variability of plant traits (ITV) and components of flammability is poorly studied. The lack of knowledge about ITV of plant traits related to flammability appears to be a major shortcoming in further interpreting species flammability and fire behavior and incorporating the data into models. Methods Morpho-ecophysiological traits (width, length, thickness, weight, area, volume, moisture content, flatness, specific leaf area, density of leaf tissue, ratio of area to volume) and time-to-ignition of fresh foliage were measured in seven populations of Picea omorika. Results All leaf traits are presented along with their correlations to the flammability trait. The seven populations differed in terms of fresh leaves’ time-to-ignition. Differences among populations in morpho-ecophysiological traits were also significant but not consistent among populations. PCA classified 49 elements into three different groups, where three populations were clustered by higher leaf area-related traits, other three populations were clustered by higher leaf length, volume, thickness, time-to-ignition, density index, moisture content, width, weight, and one population was classified between the two main groups. The first two principal components accounted for 87% of the total variance: variability in leaf area- and leaf weight-derived parameters (specific leaf area and density index) and time-to-ignition primarily defined the formation of the first axis, while variability in leaf flatness (based on leaf weight and thickness) primarily contributed to the formation of the second axis. Discussion Results suggest high ITV in natural populations of P. omorika regardless of site fire history.
Techniques de construction en géométrie analytique. VI. Étude locale des morphismes : germes d'espaces analytiques, platitude, morphismes simples

© Séminaire Henri Cartan (Secrétariat mathématique, Paris), 1960-1961, tous droits réservés. L’accès aux archives de la collection « Séminaire Henri Cartan » implique l’accord avec les conditions générales d’utilisation (http://www.numdam.org/conditions). Toute utilisation commerciale ou impression systématique est constitutive d’une infraction pénale. Toute copie ou impression de ce fichier doit contenir la présente mention de copyright.
The effect of combining action observation in virtual reality with kinesthetic motor imagery on cortical activity

Introduction In the past, various techniques have been used to improve motor imagery (MI), such as immersive virtual-reality (VR) and kinesthetic rehearsal. While electroencephalography (EEG) has been used to study the differences in brain activity between VR-based action observation and kinesthetic motor imagery (KMI), there has been no investigation into their combined effect. Prior research has demonstrated that VR-based action observation can enhance MI by providing both visual information and embodiment, which is the perception of oneself as part of the observed entity. Additionally, KMI has been found to produce similar brain activity to physically performing a task. Therefore, we hypothesized that utilizing VR to offer an immersive visual scenario for action observation while participants performed kinesthetic motor imagery would significantly improve cortical activity related to MI. Methods In this study, 15 participants (9 male, 6 female) performed kinesthetic motor imagery of three hand tasks (drinking, wrist flexion-extension, and grabbing) both with and without VR-based action observation. Results Our results indicate that combining VR-based action observation with KMI enhances brain rhythmic patterns and provides better task differentiation compared to KMI without action observation. Discussion These findings suggest that using VR-based action observation alongside kinesthetic motor imagery can improve motor imagery performance.
Glycosylation of H4 influenza strains with pandemic potential and susceptibilities to lung surfactant SP-D

We recently reported that members of group 1 influenza A virus (IAV) containing H2, H5, H6, and H11 hemagglutinins (HAs) are resistant to lung surfactant protein D (SP-D). H3 viruses, members of group 2 IAV, have high affinity for SP-D, which depends on the presence of high-mannose glycans at glycosite N165 on the head of HA. The low affinity of SP-D for the group 1 viruses is due to the presence of complex glycans at an analogous glycosite on the head of HA, and replacement with high-mannose glycan at this site evoked strong interaction with SP-D. Thus, if members of group 1 IAV were to make the zoonotic leap to humans, the pathogenicity of such strains could be problematic since SP-D, as a first-line innate immunity factor in respiratory tissues, could be ineffective as demonstrated in vitro. Here, we extend these studies to group 2 H4 viruses that are representative of those with specificity for avian or swine sialyl receptors, i.e., those with receptor-binding sites with either Q226 and G228 for avian or recent Q226L and G228S mutations that facilitate swine receptor specificity. The latter have increased pathogenicity potential in humans due to a switch from avian sialylα2,3 to sialylα2,6 glycan receptor preference. A better understanding of the potential action of SP-D against these strains will provide important information regarding the pandemic risk of such strains. Our glycomics and in vitro analyses of four H4 HAs reveal SP-D-favorable glycosylation patterns. Therefore, susceptibilities to this first-line innate immunity defense respiratory surfactant against such H4 viruses are high and align with H3 HA glycosylation.
Practice beats age: co-activation shapes heritage speakers’ lexical access more than age of onset

Probabilistic associations make language processing efficient and are honed through experience. However, it is unclear what language experience factors explain the non-monolingual processing behaviors typical of L2 learners and heritage speakers (HSs). We investigated whether AoO, language proficiency, and language use affect the recognition of Spanish stress-tense suffix associations involving a stressed syllable that cues a present suffix (SALta “s/he jumps”) and an unstressed syllable that cues a past suffix (SALtó “s/he jumped”). Adult Spanish-English HSs, English-Spanish L2 learners, and Spanish monolinguals saw a paroxytone verb (stressed initial syllable) and an oxytone verb (unstressed initial syllable), listened to a sentence containing one of the verbs, and chose the one they heard. Spanish proficiency measured grammatical and lexical knowledge, and Spanish use assessed percentage of current usage. Both bilingual groups were comparable in Spanish proficiency and use. Eye-tracking data showed that all groups fixated on target verbs above chance before hearing the syllable containing the suffix, except the HSs in the oxytones. Monolinguals fixated on targets more and earlier, although at a slower rate, than HSs and L2 learners; in turn, HSs fixated on targets more and earlier than L2 learners, except in oxytones. Higher proficiency increased target fixations in HSs (oxytones) and L2 learners (paroxytones), but greater use only increased target fixations in HSs (oxytones). Taken together, our data show that HSs’ lexical access depends more on number of lexical competitors (co-activation of two L1 lexica) and type (phonotactic) frequency than token (lexical) frequency or AoO. We discuss the contribution of these findings to models in phonology, lexical access, language processing, language prediction, and human cognition.
Quantitative susceptibility mapping using multi-channel convolutional neural networks with dipole-adaptive multi-frequency inputs

Quantitative susceptibility mapping (QSM) quantifies the distribution of magnetic susceptibility and shows great potential in assessing tissue contents such as iron, myelin, and calcium in numerous brain diseases. The accuracy of QSM reconstruction was challenged by an ill-posed field-to-susceptibility inversion problem, which is related to the impaired information near the zero-frequency response of the dipole kernel. Recently, deep learning methods demonstrated great capability in improving the accuracy and efficiency of QSM reconstruction. However, the construction of neural networks in most deep learning-based QSM methods did not take the intrinsic nature of the dipole kernel into account. In this study, we propose a dipole kernel-adaptive multi-channel convolutional neural network (DIAM-CNN) method for the dipole inversion problem in QSM. DIAM-CNN first divided the original tissue field into high-fidelity and low-fidelity components by thresholding the dipole kernel in the frequency domain, and it then inputs the two components as additional channels into a multichannel 3D Unet. QSM maps from the calculation of susceptibility through multiple orientation sampling (COSMOS) were used as training labels and evaluation reference. DIAM-CNN was compared with two conventional model-based methods [morphology enabled dipole inversion (MEDI) and improved sparse linear equation and least squares (iLSQR) and one deep learning method (QSMnet)]. High-frequency error norm (HFEN), peak signal-to-noise-ratio (PSNR), normalized root mean squared error (NRMSE), and the structural similarity index (SSIM) were reported for quantitative comparisons. Experiments on healthy volunteers demonstrated that the DIAM-CNN results had superior image quality to those of the MEDI, iLSQR, or QSMnet results. Experiments on data with simulated hemorrhagic lesions demonstrated that DIAM-CNN produced fewer shadow artifacts around the bleeding lesion than the compared methods. This study demonstrates that the incorporation of dipole-related knowledge into the network construction has a potential to improve deep learning-based QSM reconstruction.
Central retinal vein occlusion with cerebral infarction secondary to anlotinib treatment: a case report and literature review

Purpose: We present a rare case of an elderly man with minimal pre-existing thromboses risk, who experienced central retinal vein occlusion (CRVO) and cerebral infarction after oral intake of the anti-cancer drug anlotinib, likely due to a drug-related complication. Observations: A male, aged 65 years, sought care at the ophthalmology department because of acute painless 5-day vision loss in the right eye, in combination with cerebral infarction history, after oral intake of anlotinib for hepatocellular carcinoma (HCC) for over 16 months. Clinical assessment and ancillary examination verified a right eye central retinal vein occlusion diagnosis. Anlotinib is a multi-target tyrosine kinase inhibitors (TKIs) is reported to potently suppress vascular endothelial growth factor (VEGF) receptor, in order to exert strong antitumor angiogenesis and inhibit tumor occurrence. Although anlotinib is only regarded as a possible thrombosis risk factor, it is possible that anlotinib administration markedly enhanced vaso-occlusive risk within this patient. Conclusion and significance: Herein, we present the first report of anlotinib-induced CRVO and cerebral infarction to our knowledge. Given our evidences, anlotinib usage is intricately linked to sight- and life-threatening thrombotic effects even among patients with reduced thrombophilic risk. Hence, patients receiving this drug must be carefully monitored for possible drug-related complications.
A review of mathematical models of human trust in automation

Understanding how people trust autonomous systems is crucial to achieving better performance and safety in human-autonomy teaming. Trust in automation is a rich and complex process that has given rise to numerous measures and approaches aimed at comprehending and examining it. Although researchers have been developing models for understanding the dynamics of trust in automation for several decades, these models are primarily conceptual and often involve components that are difficult to measure. Mathematical models have emerged as powerful tools for gaining insightful knowledge about the dynamic processes of trust in automation. This paper provides an overview of various mathematical modeling approaches, their limitations, feasibility, and generalizability for trust dynamics in human-automation interaction contexts. Furthermore, this study proposes a novel and dynamic approach to model trust in automation, emphasizing the importance of incorporating different timescales into measurable components. Due to the complex nature of trust in automation, it is also suggested to combine machine learning and dynamic modeling approaches, as well as incorporating physiological data.
Editorial: Management of the hypoplastic left heart syndrome: from fetus to adult

COPYRIGHT © 2023 Prsa, Grotenhuis and Rahkonen. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
On Two Kinds of Decoupled Parallel Mechanisms

Two kinds of decoupled parallel mechanisms with three parallel kinematic chains are considered. In one kind of mechanisms each kinematic chain containts two universal joints with additional constraints, in another kind of mechanisms each kinematic chain contains two parallelograms. The synthesit of these manipulators is carried out by means of screw groups. This approach allows avoiding completed equations by synthesis and singularity analysis of mechanisms.
Genius culture: how we influence student identity in STEM

In Science, Technology, Engineering, and Mathematics (STEM) fields, identity and belonging are affected by how students view themselves as belonging in STEM or not. The movement to help students understand that anyone can be successful in STEM is an incredibly important one. However, how students construct their identities within STEM is important for maintaining their engagement within STEM fields over time. If we condition students to expect positive feedback for having an aptitude in a STEM field early-on, what I deem genius culture, we risk helping these students develop resilience when faced with challenges. Although, if we tell students that everyone can succeed in STEM, we risk deflating students who are gifted or talented in STEM and equating growth/improvement as mastery, thereby discouraging inquiry. Moreover, as instructors, our own sense of STEM-self affects how we teach and reward our students for their successes. A more sustainable goal is to make students aware of their STEM-self and help students bolster their sense of belonging in STEM rather than acknowledging only their perceived successes or failures.
GigaDORAM: Breaking the Billion Address Barrier

We design and implement GigaDORAM, a novel 3-server Distributed Oblivious RAM (DORAM) protocol. Oblivious RAM allows a client to read and write to memory on an untrusted server, while ensuring the server itself learns nothing about the client’s access pattern. Distributed Oblivious RAM (DORAM) distributes the role of the ORAM server. Specifically, DORAM allows a group of servers to efficiently access a secret-shared array at a secret-shared index. DORAM has two main advantages over traditional ORAM protocols: (1) it effectively outsources all the communication / computation costs to the DORAM servers, minimizing the client complexity, and (2) it allows secure multiparty computation (MPC) in the RAM model, opening a new range of potential applications. A recent generation of DORAM implementations (e.g.
Within-Individual Variation in Cognitive Performance Is Not Noise: Why and How Cognitive Assessments Should Examine Within-Person Performance

Despite evidence that it exists, short-term within-individual variability in cognitive performance has largely been ignored as a meaningful component of human cognitive ability. In this article, we build a case for why this within-individual variability should not be viewed as mere measurement error and why it should be construed as a meaningful component of an individual’s cognitive abilities. We argue that in a demanding and rapidly changing modern world, between-individual analysis of single-occasion cognitive test scores does not account for the full range of within-individual cognitive performance variation that is implicated in successful typical cognitive performance. We propose that short-term repeated-measures paradigms (e.g., the experience sampling method (ESM)) be used to develop a process account of why individuals with similar cognitive ability scores differ in their actual performance in typical environments. Finally, we outline considerations for researchers when adapting this paradigm for cognitive assessment and present some initial findings from two studies in our lab that piloted the use of ESM to assess within-individual cognitive performance variation.
Morphing many polygons across a change in topology

Many solutions have been proposed for morphing between two polygons. However, there has been little attention directed toward morphing between different numbers of polygons, across a change in topology. In this dissertation, three different algorithms are progressively developed for this problem. The first algorithm is designed for the most fundamental case of the category: morphing two polygons that are convex or have minor concavities into one. The second algorithm; or the recursive canyon algorithm, moves a step further to handle input polygons with deep concavities. Based on these two algorithms, the third algorithm is designed to morph between two groups of arbitrary numbers of polygons. All three algorithms require no user interaction; are inexpensive, use dynamic vertex correspondence; follow nonlinear vertex paths, and are able to generate smooth animation results.