Miracle-dz/UniFAR-FTUs huggingface dataset:
High Wolbachia Strain Diversity in a Clade of Dung Beetles Endemic to Madagascar

Determining the drivers of diversity is a major topic in biology. Due to its high level of micro-endemism in many taxa, Madagascar has been described as one of Earth's biodiversity hotspot. The exceptional Malagasy biodiversity has been shown to be the result of various eco-evolutionary mechanisms that have taken place on this large island since its isolation from other landmasses. Extensive phylogenetic analyses have, for example, revealed that most of the dung beetle radiation events have arisen due to allopatric speciation, and adaptation to altitudinal and/or longitudinal gradients. But other biotic factors, that have yet to be identified, might also be at play. Wolbachia is a maternally transmitted endosymbiotic bacterium widespread in insects. The bacterium is well-known for its ability to modify its host reproductive system in ways that may lead to either discordance patterns between the host mitochondrial and nuclear phylogenies, and in some cases to speciation. Here, we used the MultiLocus Sequence Typing system, to identify and characterize five Wolbachia strains infecting several species within the Nanos clypeatus dung beetle clade. We discuss the implications of these Wolbachia strains for the evolution and diversification of their dung beetle hosts in Madagascar. (Less)
Picturing Bivariate Separable-Features for Univariate Vector Magnitudes in Large-Magnitude-Range Quantum Physics Data

The work is supported in part by NSF IIS-1302755, NSF CNS-1531491, and NIST-70NANB13H181. The user study was funded by NSF grants with the OSU IRB approval number 2018B0080. Non-User Study design work was supported by grant from NIST-70NANB13H181.
Statistical analysis of the Chern number in the interacting Haldane-Hubbard model

In the context of many-body interacting systems described by a topological Hamiltonian, we investigate the robustness of the Chern number with respect to different sources of error in the self-energy. In particular, we analyze the importance of non-local (momentum dependent) vs. local contributions to the self-energy and show that the local self-energy provides a qualitative description of the topological phase diagrams of many-body interacting systems, whereas the explicit momentum-dependence constitutes a correction to the exact location of the phase transition. For the latter, we propose a statistical analysis, on the basis of which we develop a stochastic upper bound for the uncertainty of the Chern number as a function of the amount of momentum-dependence of the self-energy. We apply this analysis to the Haldane-Hubbard model and discuss the implications of our results for a general class of many-body interacting systems.
MixMatch: A Holistic Approach to Semi-Supervised Learning

Semi-supervised learning has proven to be a powerful paradigm for leveraging unlabeled data to mitigate the reliance on large labeled datasets. In this work, we unify the current dominant approaches for semi-supervised learning to produce a new algorithm, MixMatch, that works by guessing low-entropy labels for data-augmented unlabeled examples and mixing labeled and unlabeled data using MixUp. We show that MixMatch obtains state-of-the-art results by a large margin across many datasets and labeled data amounts. For example, on CIFAR-10 with 250 labels, we reduce error rate by a factor of 4 (from 38% to 11%) and by a factor of 2 on STL-10. We also demonstrate how MixMatch can help achieve a dramatically better accuracy-privacy trade-off for differential privacy. Finally, we perform an ablation study to tease apart which components of MixMatch are most important for its success.
DeepRMSA: A Deep Reinforcement Learning Framework for Routing, Modulation and Spectrum Assignment in Elastic Optical Networks

This paper proposes DeepRMSA, a deep reinforcement learning framework for routing, modulation and spectrum assignment (RMSA) in elastic optical networks (EONs). DeepRMSA learns the correct online RMSA policies by parameterizing the policies with deep neural networks (DNNs) that can sense complex EON states. The DNNs are trained with experiences of dynamic lightpath provisioning. We first modify the asynchronous advantage actor-critic algorithm and present an episode-based training mechanism for DeepRMSA, namely, DeepRMSA-EP. DeepRMSA-EP divides the dynamic provisioning process into multiple episodes (each containing the servicing of a fixed number of lightpath requests) and performs training by the end of each episode. The optimization target of DeepRMSA-EP at each step of servicing a request is to maximize the cumulative reward within the rest of the episode. Thus, we obviate the need for estimating the rewards related to unknown future states. To overcome the instability issue in the training of DeepRMSA-EP due to the oscillations of cumulative rewards, we further propose a window-based flexible training mechanism, i.e., DeepRMSA-FLX. DeepRMSA-FLX attempts to smooth out the oscillations by defining the optimization scope at each step as a sliding window, and ensuring that the cumulative rewards always include rewards from a fixed number of requests. Evaluations with the two sample topologies show that DeepRMSA-FLX can effectively stabilize the training while achieving blocking probability reductions of more than 20.3% and 14.3%, when compared with the baselines.
Mapping the structural diversity of C60 carbon clusters and their infrared spectra

The current debate about the nature of the carbonaceous material carrying the infrared (IR) emission spectra of planetary and proto-planetary nebulae, including the broad plateaus, calls for further studies on the interplay between structure and spectroscopy of carbon-based compounds of astrophysical interest. The recent observation of C60 buckminsterfullerene in space suggests that carbon clusters of similar size may also be relevant. In the present work, broad statistical samples of C60 isomers were computationally determined without any bias using a reactive force field, their IR spectra being subsequently obtained following local optimization with the density-functional-based tight-binding theory. Structural analysis reveals four main structural families identified as cages, planar polycyclic aromatics, pretzels, and branched. Comparison with available astronomical spectra indicates that only the cage family could contribute to the plateau observed in the 6–9 μm region. The present framework shows great promise to explore and relate structural and spectroscopic features in more diverse and possibly hydrogenated carbonaceous compounds, in relation with astronomical observations.
Combined interphase fluorescence in situ hybridization elucidates the genetic heterogeneity of T-cell acute lymphoblastic leukemia in adults

Background Molecular lesions in T-cell acute lymphoblastic leukemias affect regulators of cell cycle, proliferation, differentiation, survival and apoptosis in multi-step pathogenic pathways. Full genetic characterization is needed to identify events concurring in the development of these leukemias. Design and Methods We designed a combined interphase fluorescence in situ hybridization strategy to study 25 oncogenes/tumor suppressor genes in T-cell acute lymphoblastic leukemias and applied it in 23 adult patients for whom immunophenotyping, karyotyping, molecular studies, and gene expression profiling data were available. The results were confirmed and integrated with those of multiplex-polymerase chain reaction analysis and gene expression profiling in another 129 adults with T-cell acute lymphoblastic leukemias. Results The combined hybridization was abnormal in 21/23 patients (91%), and revealed multiple genomic changes in 13 (56%). It found abnormalities known to be associated with T-cell acute lymphoblastic leukemias, i.e. CDKN2A-B/9p21 and GRIK2/6q16 deletions, TCR and TLX3 rearrangements, SIL-TAL1, CALM-AF10, MLL-translocations, del(17)(q12)/NF1 and other cryptic genomic imbalances, i.e. 9q34, 11p, 12p, and 17q11 duplication, del(5)(q35), del(7)(q34), del(9)(q34), del(12)(p13), and del(14)(q11). It revealed new cytogenetic mechanisms for TCRB-driven oncogene activation and C-MYB duplication. In two cases with cryptic del(9)(q34), fluorescence in situ hybridization and reverse transcriptase polymerase chain reaction detected the TAF_INUP214 fusion and gene expression profiling identified a signature characterized by HOXA and NUP214 upregulation and TAF_I, FNBP1, C9orf78, and USP20 down-regulation. Multiplex-polymerase chain reaction analysis and gene expression profiling of 129 further cases found five additional cases of TAF_I-NUP214-positive T-cell acute lymphoblastic leukemia. Conclusions Our combined interphase fluorescence in situ hybridization strategy greatly improved the detection of genetic abnormalities in adult T-cell acute lymphoblastic leukemias. It identified new tumor suppressor genes/oncogenes involved in leukemogenesis and highlighted concurrent involvement of genes. The estimated incidence of TAF_I-NUP214, a new recurrent fusion in adult T-cell acute lymphoblastic leukemias, was 4.6% (7/152).
How Bright Are Fast Optical Bursts Associated With Fast Radio Bursts?

The origin of fast radio bursts (FRBs) is still unknown. Multiwavelength observations during or shortly after the FRB phase would be essential to identify the counterpart of an FRB and to constrain its progenitor and environment. In this work, we investigate the brightness of the “fast optical bursts” (FOBs) associated with FRBs and the prospects of detecting them. We investigate several inverse Compton (IC) scattering processes that might produce an FOB, including both the one-zone and two-zone models. We also investigate the extension of the same mechanism of FRB emission to the optical band. We find that a detectable FOB with the current and forthcoming telescopes is possible under the IC scenarios with very special conditions. In particular, the FRB environment would need to invoke a neutron star with an extremely strong magnetic field and an extremely fast spin, or an extremely young supernova remnant surrounding the FRB source. Furthermore, most electrons in the source are also required to have a fine-tuned energy distribution such that most of the IC energy is channeled in the optical band. We conclude that the prospect of detecting FOBs associated with FRBs is low. On the other hand, if FOBs are detected from a small fraction of FRBs, these FOBs would reveal extreme physical conditions in the FRB environments.
COMP2CAT: hunting compact double radio sources in the local Universe

We present a catalog of compact double radio galaxies (hereafter COMP2CAT) listing 43 edge-brightened radio sources whose projected linear size does not exceed 60 kpc, the typical size of their host galaxies. This is the fifth in a series of radio source catalogs recently created, namely: FRICAT, FRIICAT, FR0CAT, and WATCAT, each of which focuses on a different class of radio galaxies. The main aim of our analysis is to attain a better understanding of sources with intermediate morphologies between FR IIs and FR 0s. COMP2CAT sources were selected from an existing catalog of radio sources based on NVSS, FIRST and SDSS observations because they have (i) edge-brightened morphologies typical of FR IIs, (ii) redshifts z < 0.15, and (iii) projected linear sizes smaller than 60 kpc. With radio luminosities at 1.4 GHz 1038 ≲ L1.4 ≲ 1041 erg s−1, COMP2CAT sources appear as the low radio luminosity tail of FR IIs. However, their host galaxies are indistinguishable from those of large-scale radio sources: they are luminous (−21 ≳ Mr ≳ −24), red, early-type galaxies with black hole masses in the range 107.5 ≲ MBH ≲ 109.5 M⊙. Moreover, all but one of the COMP2CAT sources are optically classifiable as low-excitation radio galaxies, in agreement with being the low radio luminosity tail of FR Is and FR IIs. This catalog of compact double sources, which is ∼47% complete at z < 0.15, can potentially be used to clarify the role of compact double sources in the general evolutionary scheme of radio galaxies.
Novel Signal Processing Technique for Capture and Isolation of Blink-Related Oscillations Using a Low-Density Electrode Array for Bedside Evaluation of Consciousness

Objective: Blink-related oscillations derived from electroencephalography (EEG) have recently emerged as an important measure of awareness. Combined with portable EEG hardware with low-density electrode arrays, this neural marker may crucially augment the existing bedside assessments of consciousness in unresponsive patients. Nonetheless, the close relationship between signal characteristics of the neural response of interest and blink-induced oculomotor artifacts poses particular challenges when measuring blink-related oscillations using a point-of-care platform. This study presents a novel denoising approach based on time–frequency (TF) filtering that exploits the differential temporal and spectral features to isolate the neural response from ocular artifact in a low-density array. Methods: We investigated the effectiveness of the TF filtering technique using 64-channel EEG data collected in healthy adults, with focal analysis of the Pz and POz channels. Results: TF filtering showed comparable performance in denoising the signal relative to the established gold-standard independent component analysis approach, with strong similarities in morphological characteristics as measured by intraclass correlations (p < 0.001), extent of artifact rejection based on the ocular contamination index (p < 0.006), as well as time- and frequency-domain signal capture (p < 0.05). Results are robust at the individual and group levels, and are crucially validated using raw data from only four electrodes comprising Pz, POz, Fp2, and T7. Conclusion: These results demonstrate for the first time that TF filtering enables the successful capture and isolation of the blink-related oscillations response using a four-electrode array. Significance: This significantly advances the translation of the blink-related oscillations marker to a point-of-care platform for eventual bedside applications.
Learning Optimal Data Augmentation Policies via Bayesian Optimization for Image Classification Tasks

In recent years, deep learning has achieved remarkable achievements in many fields, including computer vision, natural language processing, speech recognition and others. Adequate training data is the key to ensure the effectiveness of the deep models. However, obtaining valid data requires a lot of time and labor resources. Data augmentation (DA) is an effective alternative approach, which can generate new labeled data based on existing data using label-preserving transformations. Although we can benefit a lot from DA, designing appropriate DA policies requires a lot of expert experience and time consumption, and the evaluation of searching the optimal policies is costly. So we raise a new question in this paper: how to achieve automated data augmentation at as low cost as possible? We propose a method named BO-Aug for automating the process by finding the optimal DA policies using the Bayesian optimization approach. Our method can find the optimal policies at a relatively low search cost, and the searched policies based on a specific dataset are transferable across different neural network architectures or even different datasets. We validate the BO-Aug on three widely used image classification datasets, including CIFAR-10, CIFAR-100 and SVHN. Experimental results show that the proposed method can achieve state-of-the-art or near advanced classification accuracy. Code to reproduce our experiments is available at this https URL.
Different Isoforms of the Non-Integrin Laminin Receptor Are Present in Mouse Brain and Bind PrP

Abstract The prion protein (PrP) plays a central role in prion diseases, and identifying its cellular receptor appears to be of crucial interest. We previously showed in the yeast two-hybrid system that PrP interacts with the 37 kDa precursor (LRP) of the high affinity 67 kDa laminin receptor (LR), which acts as the cellular receptor of PrP in cellular models. However, among the various isoforms of the receptor that have been identified so far, those which are present in the central nervous system and which bind PrP are still unknown. In this study, we have purified mouse brain fractions enriched in the laminin receptor and have performed overlay assays in order to identify those isoforms that interact with the prion protein. We demonstrate (i) the presence, in mouse brain, of several isoforms of the LRP/LR corresponding to different maturation states of the receptor (44, 60, 67 and 220 kDa) and (ii) the binding of all of these isoforms to PrP. Our data strongly support a physiological role of the laminin receptor/ PrP interaction in the brain and highlight its relevance for transmissible spongiform encephalopathies.
Greedy Signal Space Recovery Algorithm with Overcomplete Dictionaries in Compressive Sensing

Compressive Sensing (CS) is a new paradigm for the efficient acquisition of signals that have sparse representation in a certain domain. Traditionally, CS has provided numerous methods for signal recovery over an orthonormal basis. However, modern applications have sparked the emergence of related methods for signals not sparse in an orthonormal basis but in some arbitrary, perhaps highly overcomplete, dictionary, particularly due to their potential to generate different kinds of sparse representation of signals. To this end, we apply a signal space greedy method, which relies on the ability to optimally project a signal onto a small number of dictionary atoms, to address signal recovery in this setting. We describe a generalized variant of the iterative recovery algorithm called Signal space Subspace Pursuit (SSSP) for this more challenging setting. Here, using the Dictionary-Restricted Isometry Property (D-RIP) rather than classical RIP, we derive a low bound on the number of measurements required and then provide the proof of convergence for the algorithm. The algorithm in noisy and noise-free measurements has low computational complexity and provides high recovery accuracy. Simulation results show that the algorithm outperforms best compared with the existing recovery algorithms.
A general graph-based framework for top-N recommendation using content, temporal and trust information

Recommending appropriate items to users is crucial in many e-commerce platforms. One common approach consists in selecting the N most relevant items for each user. To achieve this, recom-mender systems rely on various kinds of information, like item and user features, past interest of users for items and trust between users. Current systems generally use only one or two such pieces of information, which limits their performance. In this paper, we design and implement GraFC2T2, a general graph-based framework to easily combine various kinds of information for top-N recommendation. It encodes content-based features, temporal and trust information into a graph model, and uses personalized PageRank on this graph to perform recommendation. Experiments are conducted on Epinions and Ciao datasets, and comparisons are done with systems based on matrix factorization and deep learning using F1-score, Hit ratio and MAP evaluation metrics. The results show that combining different kinds of information generally improves recommendation. This shows the relevance of the proposed framework.
Constraining coherent low-frequency radio flares from compact binary mergers

The presence and detectability of coherent radio emission from compact binary mergers (containing at least one neutron star) remains poorly constrained due to large uncertainties in the models. These compact binary mergers may initially be detected as short gamma-ray bursts or via their gravitational wave emission. Several radio facilities have developed rapid response modes enabling them to trigger on these events and search for this emission. For this paper, we constrain this coherent radio emission using the deepest available constraints for GRB 150424A, which were obtained via a triggered observation with the Murchison Widefield Array. We then expand this analysis to determine the properties of magnetar merger remnants that may be formed via a general population of binary neutron star mergers. Our results demonstrate that many of the potential coherent emission mechanisms that have been proposed for such events can be detected or very tightly constrained by the complementary strategies used by the current generation of low-frequency radio telescopes.
Frequency and intensity of gastrointestinal symptoms in exercisers individuals at rest and during physical exercise: an internet-based survey

Background/Aims Despite the evidence of a modest to high prevalence of gastrointestinal (GI) symptoms in recreational runners and endurance athletes, the frequency and intensity of GI symptoms in exercisers, but nonathletes, individuals from different modalities have been less investigated. Therefore, the present study aimed to assess the prevalence of GI symptoms in individuals that practice moderate or vigorous physical exercise, at rest and during physical exercise training session. Methods The sample consisted of 142 exercisers individuals (64 women and 78 men with mean age of 32.9 ± 10.7 years). Out of the 142 participants, 71 reported to perform moderate physical exercise and 71 reported to perform vigorous physical exercise. Participants were assessed by an internet-based questionnaire designed to assess the frequency and intensity (at rest and during physical exercise training session) of 18 GI symptoms. Results The GI symptoms most frequently reported by the respondents (during rest and physical exercise training session, respectively) were flatulence (90.8% and 69.7%), abdominal noise (77.5% and 41.5%), and eructation (73.9% and 52.1%). Overall, the frequency and intensity of symptoms were higher (P< 0.050) during rest than physical exercise training session for who perform moderate and vigorous physical exercise. Conclusions It can be concluded that GI symptoms in exercisers, but nonathletes, individuals are more prevalent during rest than during physical exercise training session, suggesting that moderate and vigorous physical exercise may act as a regulator of the GI tract.