Miracle-dz/UniFAR-FTUs huggingface dataset:
Intracellular Metabolism of 3′-Azido-3′-Deoxythymidine in the Presence of Ganciclovir or Foscarnet

Comparisons were made between the intracellular phosphorylation of 3′-Azido-3′-deoxythymidine (AZT) alone and in combination with ganciclovir (GCV) or foscarnet (PFA) in lymphocytes, uninfected fibroblasts and CMV-infected fibroblasts. The effects of AZT and the combinations of AZT with GCV or PFA on cellular dNTP pools were also examined. The phosphorylation of AZT was not altered by the presence of GCV or PFA in lymphocytes, and neither AZT nor the combinations of AZT with GCV or PFA changed the levels of cellular dNTP pools in these cells. AZT was phosphorylated to a greater extent in lymphocytes when compared to fibroblasts, but the proportion of AZT di- and triphosphates was greater in fibroblasts. The infection of fibroblasts with CMV enhanced AZT phosphorylation and increased the levels of cellular dNTP pools. GCV caused a specific reduction in AZT phosphorylation in CMV-infected fibroblasts, with a seven-fold drop in AZT triphosphate compared to AZT alone. GCV did not affect AZT phosphorylation in uninfected fibroblasts, nor did GCV reduce the dTTP pool compared to AZT alone. The effects of GCV upon AZT phosphorylation in CMV-infected cells may shed light on the antagonistic effects of GCV upon the anti-HIV activity of AZT, and are of importance for the development of effective combination therapies for the treatment of AIDS patients infected with CMV.
Microbial biopesticides in agroecosystems

Microbial biopesticides include several microorganisms like bacteria, fungi, baculoviruses, and nematode-associated bacteria acting against invertebrate pests in agro-ecosystems. The biopesticide sector is experiencing a significant growth and many discoveries are being developed into new biopesticidal products that are fueling a growing global market offer. Following a few decades of successful use of the entomopathogenic bacterium Bacillus thuringiensis and a few other microbial species, recent academic and industrial efforts have led to the discovery of new microbial species and strains, and of their specific toxins and virulence factors. Many of these have, therefore, been developed into commercial products. Bacterial entomopathogens include several Bacillaceae, Serratia, Pseudomonas, Yersinia, Burkholderia, Chromobacterium, Streptomyces, and Saccharopolyspora species, while fungi comprise different strains of Beauveria bassiana, B. brongniartii, Metarhizium anisopliae, Verticillium, Lecanicillium, Hirsutella, Paecilomyces, and Isaria species. Baculoviruses are species-specific and refer to niche products active against chewing insects, especially Lepidopteran caterpillars. Entomopathogenic nematodes (EPNs) mainly include species in the genera Heterorhabditis and Steinernema associated with mutualistic symbiotic bacteria belonging to the genera Photorhabdus and Xenorhabdus. An updated representation of the current knowledge on microbial biopesticides and of the availability of active substances that can be used in integrated pest management programs in agro-ecosystems is reported her.
Diagnosis of Xanthomonas axonopodis pv citri isolates, the causal agent of citrus canker by pathogenicity and PCR based methods

Citrus canker caused by Xanthomonas axonopodis pv citri (Xac) is one of most destructive diseases of citrus in terms of damage to trees and fruit quality and remained as one of the important quarantine disease. Under field conditions, though Asiatic citrus canker disease is relatively easy to diagnose, the accurate detection of pathogen for various pathological studies is necessary. In present study, a total of 13 plant samples collected from various parts of Karnataka state were diagnosed by an integrated approach using pathogenecity, isolation and molecular techniques. Five isolates were able grow on nutrient glucose agar medium characteristic of Xanthomonas axonopodis pv. citri. The pathogenicity of all the above five isolates (XAC 1, 2, 3, 4 and 5) was proved with typical canker lesions on leaves of citrus after five to eight days of incubation period. All five isolates of Xac were confirmed at molecular level by amplifying highly conserved nucleotide sequence est A region (777 bp) using Xc-lip primer pair that distinguishes Xanthomonads from bacteria. The study revealed that integrated approach as reliable technique for detection of Xac for laboratory analysis.
RF installation of new HERA receiver system

This document describes the installation of the HERA RF chain comprising all parts between the antenna and the Analogue to Digital Converters (ADCs) as shown in Figure 1. The main components are the Front End Module (FEM) and the Post Amp Module (PAM) also referred to as FEM-75 and PAM-75, respectively due to the 75Ω transmission line. The FEM is phantom powered from an 8V power supply in the PAM rack. Each channel (North and East) is powered independently over it’s associated transmission line.
Theory of Wind Driven Sea

The wind-driven sea is one of the most common natural phenomena that we can observe by our own eyes without special devices. Development of reliable analytical theory of wind-driven sea is a really challenging task for a physicist; one can compare it with another fundamental problem of macroscopic physics: the problem of atmospheric boundary layer. Both problems are naturally connected because sea waves are driven by wind; both problems are very difficult because the wind is badly turbulent and so far we don’t have any satisfactory theory of the strong turbulence. However, the case of wind-driven sea is easier. In this case we have in our possession a natural small parameter: the ratio of atmospheric and water density. The density of air depends on temperature and the level of moisture. It is reasonable to put = ρatm ρw ∼ 1.2 · 10.
Convergence of unadjusted Hamiltonian Monte Carlo for mean-field models

We present dimension-free convergence and discretization error bounds for the unadjusted Hamiltonian Monte Carlo algorithm applied to high-dimensional probability distributions of mean-field type. These bounds require the discretization step to be sufficiently small, but do not require strong convexity of either the unary or pairwise potential terms present in the mean-field model. To handle high dimensionality, our proof uses a particlewise coupling that is contractive in a complementary particlewise metric.
The Infinity Mirror Test for Graph Models

Graph models, like other machine learning models, have implicit and explicit biases built-in, which often impact performance in nontrivial ways. The model’s faithfulness is often measured by comparing the newly generated graph against the source graph using any number of graph properties. Therefore, differences in the size or topology of the generated graph indicate a loss in the model. Yet, in many systems, errors encoded in loss functions are subtle and not well understood. In the present work, we introduce the Infinity Mirror test for analyzing the robustness of graph models. This straightforward stress test works by repeatedly fitting a model to its outputs. A hypothetically perfect graph model would have no deviation from the source graph; however, a model’s implicit biases and assumptions are exaggerated by the Infinity Mirror test, exposing potential previously obscured issues. Through an analysis of thousands of experiments on synthetic and real-world graphs, we show that several conventional graph models degenerate in exciting and informative ways. We believe that the observed degenerative patterns are clues to the future development of better graph models.
Multi-orbital-phase and Multiband Characterization of Exoplanetary Atmospheres with Reflected Light Spectra

Direct imaging of widely separated exoplanets from space will obtain their reflected light spectra and measure atmospheric properties. Previous calculations have shown that a change in the orbital phase would cause a spectral signal, but whether this signal may be used to characterize the atmosphere has not been shown. We simulate starshade-enabled observations of the planet 47 UMa b, using the present most realistic simulator Starshade Imaging Simulation Toolkit for Exoplanet Reconnaissance to estimate the uncertainties due to residual starlight, solar glint, and exozodiacal light. We then use the Bayesian retrieval algorithm ExoReL to determine the constraints on the atmospheric properties from observations using a Roman- or Habitable Exoplanet Observatory (HabEx)-like telescope, comparing the strategies to observe at multiple orbital phases or in multiple wavelength bands. With a ∼20% bandwidth in 600–800 nm on a Roman-like telescope, the retrieval finds a degenerate scenario with a lower gas abundance and a deeper or absent cloud than the truth. Repeating the observation at a different orbital phase or at a second 20% wavelength band in 800–1000 nm, with the same integration time and thus degraded signal-to-noise ratio (S/N), would effectively eliminate this degenerate solution. Single observation with a HabEx-like telescope would yield high-precision constraints on the gas abundances and cloud properties, without the degenerate scenario. These results are also generally applicable to high-contrast spectroscopy with a coronagraph with a similar wavelength coverage and S/N, and can help design the wavelength bandwidth and the observation plan of exoplanet direct-imaging experiments in the future.
A Valleytronic Diamond Transistor: Electrostatic Control of Valley Currents and Charge-State Manipulation of NV Centers

The valley degree of freedom in many-valley semiconductors provides a new paradigm for storing and processing information in valleytronic and quantum-computing applications. Achieving practical devices requires all-electric control of long-lived valley-polarized states, without the use of strong external magnetic fields. Because of the extreme strength of the carbon–carbon bond, diamond possesses exceptionally stable valley states that provide a useful platform for valleytronic devices. Using ultrapure single-crystalline diamond, we demonstrate electrostatic control of valley currents in a dual-gate field-effect transistor, where the electrons are generated with a short ultraviolet pulse. The charge current and the valley current measured at the receiving electrodes are controlled separately by varying the gate voltages. We propose a model to interpret experimental data, based on drift-diffusion equations coupled through rate terms, with the rates computed by microscopic Monte Carlo simulations. As an application, we demonstrate valley-current charge-state modulation of nitrogen-vacancy centers.
Mean-field dynamics of infinite-dimensional particle systems: global shear versus random local forcing

In infinite dimensions, many-body systems of pairwise interacting particles provide exact analytical benchmarks for the features of amorphous materials, such as the stress–strain curve of glasses under quasistatic shear. Here, instead of global shear, we consider an alternative driving protocol, as recently introduced by Morse et al 2020 (arXiv:2009.07706), which consists of randomly assigning a constant local displacement on each particle, with a finite spatial correlation length. We show that, in the infinite-dimensional limit, the mean-field dynamics under such a random forcing are strictly equivalent to those under global shear, upon a simple rescaling of the accumulated strain. Moreover, the scaling factor is essentially given by the variance of the relative local displacements of interacting pairs of particles, which encodes the presence of a finite spatial correlation. In this framework, global shear is simply a special case of a much broader family of local forcing, which can be explored by tuning its spatial correlations. We discuss the specific implications for the quasistatic driving of glasses—initially prepared at a replica-symmetric equilibrium—and how the corresponding ‘stress–strain’-like curves and elastic moduli can be rescaled onto their quasistatic-shear counterparts. These results hint at a unifying framework for establishing rigorous analogies, at the mean-field level, between different driven disordered systems.
The Reverse Effect of Quercetin on Multidrug Resistance of Human Hepatocellular Carcinoma

Objective: To investigate the mechanism of the reverse effect of quercetin on multidrug resistance (MDR) of human hepatocellular carcinoma (HCC) in the course of genetic transcription. Methods: Human hepatocellular carcinoma 5-FU resistant cell line Bel-FU was cultured in vitro. The cytotoxicity and dose-dependent reverse effect of quercetin were detected by MTT assay. The gene expression of mdr1, multidrug resistance associated protein (MRP), glutathione-S-transferase-π (GST-π), topoisomerase Ⅱalpha (TopoⅡα), H-ras and P-glycoprotein (P-gp) were detected by real-time quantitative PCR and western-blot. Results: The 10% inhibitory concentration (IC10) and IC50 of quercetin were 58.2 μmol/L and 193.9 μmol/L for Bel-FU cells respectively. Quercetin (16.5, 33.0 and 49.5 μmol/L) reversed MDR for Bel-FU was 1.14, 1.68 and 2.38 folds respectively. Quercetin down-regulated the gene expression of mdr1, MRP, GST-π and H-ras(P 0.05), which were 0.38, 0.27, 0.36 and 0.42 folds respectively, but no effect on TopoⅡα(P 0.05). P-gp expression was down-regulated in Bel-FU cells treated with Quercetin (P 0.05). Conclusion: Quercetin can down-regulate the expression of related gene in resistant cell line and reverse the MDR of human HCC cells.
Additive Models for Symmetric Positive-Definite Matrices, Riemannian Manifolds and Lie groups

In this paper an additive regression model for a symmetric positive-definite matrix valued response and multiple scalar predictors is proposed. The model exploits the abelian group structure inherited from either the Log-Cholesky metric or the Log-Euclidean framework that turns the space of symmetric positive-definite matrices into a Riemannian manifold and further a bi-invariant Lie group. The additive model for responses in the space of symmetric positive-definite matrices with either of these metrics is shown to connect to an additive model on a tangent space. This connection not only entails an efficient algorithm to estimate the component functions but also allows to generalize the proposed additive model to general Riemannian manifolds that might not have a Lie group structure. Optimal asymptotic convergence rates and normality of the estimated component functions are also established. Numerical studies show that the proposed model enjoys superior numerical performance, especially when there are multiple predictors. The practical merits of the proposed model are demonstrated by analyzing diffusion tensor brain imaging data.
Adaptive Sieving with PPDNA: Generating Solution Paths of Exclusive Lasso Models

The exclusive lasso (also known as elitist lasso) regularization has become popular recently due to its superior performance on structured sparsity. Its complex nature poses difficulties for the computation of high-dimensional machine learning models involving such a regularizer. In this paper, we propose an adaptive sieving (AS) strategy for generating solution paths of machine learning models with the exclusive lasso regularizer, wherein a sequence of reduced problems with much smaller sizes need to be solved. In order to solve these reduced problems, we propose a highly efficient dual Newton method based proximal point algorithm (PPDNA). As important ingredients, we systematically study the proximal mapping of the weighted exclusive lasso regularizer and the corresponding generalized Jacobian. These results also make popular first-order algorithms for solving exclusive lasso models practical. Various numerical experiments for the exclusive lasso models have demonstrated the effectiveness of the AS strategy for generating solution paths and the superior performance of the PPDNA.
Sparsity-Aware SSAF Algorithm with Individual Weighting Factors for Acoustic Echo Cancellation

In this paper, we propose and analyze the sparsity-aware sign subband adaptive filtering with individual weighting factors (S-IWF-SSAF) algorithm, and consider its application in acoustic echo cancellation (AEC). Furthermore, we design a joint optimization scheme of the step-size and the sparsity penalty parameter to enhance the S-IWF-SSAF performance in terms of convergence rate and steady-state error. A theoretical analysis shows that the S-IWF-SSAF algorithm outperforms the previous sign subband adaptive filtering with individual weighting factors (IWF-SSAF) algorithm in sparse scenarios. In particular, compared with the existing analysis on the IWF-SSAF algorithm, the proposed analysis does not require the assumptions of large number of subbands, long adaptive filter, and paraunitary analysis filter bank, and matches well the simulated results. Simulations in both system identification and AEC situations have demonstrated our theoretical analysis and the effectiveness of the proposed algorithms.
Extremely precise age and metallicity of the open cluster NGC 2506 using detached eclipsing binaries

Accurate stellar parameters of stars in open clusters can help constrain models of stellar structure and evolution. Here, we wish to determine the age and metallicity content of the open cluster NGC 2506. To this end, we investigated three detached eclipsing binaries (DEBs; V2032, V4, and V5) for which we determined their masses and radii, as well as four red giant branch stars for which we determined their effective temperatures, surface gravities, and metallicities. Three of the stars in the DEBs have masses close to the cluster turn-off mass, allowing for extremely precise age determination. Comparing the values for the masses and radii of the binaries to BaSTI (a Bag of Stellar Tracks and Isochrones) isochrones, we estimated a cluster age of 2.01 ± 0.10 Gyr. This does depend on the models used in the comparison, where we have found that the inclusion of convective core-overshooting is necessary to properly model the cluster. From red giant branch stars, we determined values for the effective temperatures, the surface gravities, and the metallicities. From these we find a cluster metallicity of −0.36 ± 0.10 dex. Using this value and the values for the effective temperatures, we determine the reddening to be E(b − y) = 0.057 ± 0.004 mag. Furthermore, we derived the distance to the cluster from Gaia parallaxes and found 3.101 ± 0.017 kpc, and we have performed a radial velocity membership determination for stars in the field of the cluster. Finally, we report on the detection of oscillation signals in γ Dor and δ Scuti members in data from the Transiting Exoplanet Survey Satellite (TESS) mission, including the possible detection of solar-like oscillations in two of the red giants.
Neural Fair Collaborative Filtering

A growing proportion of human interactions are digitized on social media platforms and subjected to algorithmic decision-making, and it has become increasingly important to ensure fair treatment from these algorithms. In this work, we investigate gender bias in collaborative-filtering recommender systems trained on social media data. We develop neural fair collaborative filtering (NFCF), a practical framework for mitigating gender bias in recommending sensitive items (e.g. jobs, academic concentrations, or courses of study) using a pre-training and fine-tuning approach to neural collaborative filtering, augmented with bias correction techniques. We show the utility of our methods for gender de-biased career and college major recommendations on the MovieLens dataset and a Facebook dataset, respectively, and achieve better performance and fairer behavior than several state-of-the-art models.
SciBERT-based Semantification of Bioassays in the Open Research Knowledge Graph

As a novel contribution to the problem of semantifying biological assays, in this paper, we propose a neural-network-based approach to automatically semantify, thereby structure, unstructured bioassay text descriptions. Experimental evaluations, to this end, show promise as the neural-based semantification significantly outperforms a naive frequency-based baseline approach. Specifically, the neural method attains 72% F1 versus 47% F1 from the frequency-based method.
Averages of the M\"obius function on shifted primes

It is a folklore conjecture that the M\"obius function exhibits cancellation on shifted primes; that is, $\sum_{p\le X}\mu(p+h) \ = \ o(\pi(X))$ as $X\to\infty$ for any fixed shift $h>0$. We prove the conjecture on average for shifts $h\le H$, provided $\log H/\log\log X\to\infty$. We also obtain results for shifts of prime $k$-tuples, and for higher correlations of M\"obius with von Mangoldt and divisor functions. Our argument combines sieve methods with a refinement of Matom\"aki, Radziwi\l\l, and Tao's work on an averaged form of Chowla's conjecture.
Performance Monitoring of Object Detection During Deployment

Performance monitoring of object detection is crucial for safety-critical applications such as autonomous vehicles that operate under varying and complex environmental conditions. Currently, object detectors are evaluated using summary metrics based on a single dataset that is assumed to be representative of all future deployment conditions. In practice, this assumption does not hold, and the performance fluctuates as a function of the deployment conditions. To address this issue, we propose an introspection approach to performance monitoring during deployment without the need for ground truth data. We do so by predicting when the per-frame mean average precision drops below a critical threshold using the detector's internal features. We quantitatively evaluate and demonstrate our method's ability to reduce risk by trading off making an incorrect decision by raising the alarm and absenting from detection.
AI and Wargaming

Recent progress in Game AI has demonstrated that given enough data from human gameplay, or experience gained via simulations, machines can rival or surpass the most skilled human players in classic games such as Go, or commercial computer games such as Starcraft. We review the current state-of-the-art through the lens of wargaming, and ask firstly what features of wargames distinguish them from the usual AI testbeds, and secondly which recent AI advances are best suited to address these wargame-specific features.
Faster Gradient-based NAS Pipeline Combining Broad Scalable Architecture with Confident Learning Rate

In order to further improve the search efficiency of Neural Architecture Search (NAS), we propose B-DARTS, a novel pipeline combining broad scalable architecture with Confident Learning Rate (CLR). In B-DARTS, Broad Convolutional Neural Network (BCNN) is employed as the scalable architecture for DARTS, a popular differentiable NAS approach. On one hand, BCNN is a broad scalable architecture whose topology achieves two advantages compared with the deep one, mainly including faster single-step training speed and higher memory efficiency (i.e. larger batch size for architecture search), which are all contributed to the search efficiency improvement of NAS. On the other hand, DARTS discovers the optimal architecture by gradient-based optimization algorithm, which benefits from two superiorities of BCNN simultaneously. Similar to vanilla DARTS, B-DARTS also suffers from the performance collapse issue, where those weight-free operations are prone to be selected by the search strategy. Therefore, we propose CLR, that considers the confidence of gradient for architecture weights update increasing with the training time of over-parameterized model, to mitigate the above issue. Experimental results on CIFAR-10 and ImageNet show that 1) B-DARTS delivers state-of-the-art efficiency of 0.09 GPU day using first order approximation on CIFAR-10; 2) the learned architecture by B-DARTS achieves competitive performance using state-of-the-art composite multiply-accumulate operations and parameters on ImageNet; and 3) the proposed CLR is effective for performance collapse issue alleviation of both B-DARTS and DARTS.