Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effect of Systemic Lidocaine on Pain and Secondary Hyperalgesia Associated with the Heat/Capsaicin Sensitization Model in Healthy Volunteers

Although effective in neuropathic pain, the efficacy of systemic lidocaine in non-neuropathic pain remains uncertain. We investigated the analgesic effect of systemic lidocaine on the heat/capsaicin sensitization model of experimental pain in 24 volunteers. Sensitization was produced by heating the skin to 45°C for 5 min, followed by a 30-min application of 0.075% capsaicin cream, and maintained by periodically reheating the sensitized skin. Subjects received IV lidocaine (bolus 2 mg/kg, then infusion 3 mg · kg · h), or saline for 85 min. Areas of secondary hyperalgesia, heat pain detection thresholds, and painfulness of stimulation with 45°C for 1 min (long thermal stimulation) were quantified. Systemic lidocaine reduced the area of secondary hyperalgesia to brush, but not to von Frey hair stimulation. Lidocaine did not alter heat pain detection thresholds or painfulness of long thermal stimulation in normal skin. We conclude that, at infusion rates in the low- to mid-antiarrhythmic range, lidocaine has no effect on acute nociceptive pain but does have a limited and selective effect on secondary hyperalgesia. Implications The efficacy of systemic lidocaine in nonneuropathic pain remains uncertain. This study investigates the effect of systemic lidocaine on experimental-induced hyperalgesia in 25 volunteers. Hyperalgesia was induced by using an experimental pain model that uses heat and capsaicin in combination. Systemic lidocaine showed a selective effect on secondary hyperalgesia.
Spatio-Temporal Road Scene Reconstruction using Superpixel Markov Random Field

Scene model construction based on image rendering is an indispensable but challenging technique in computer vision and intelligent transportation systems. In this paper, we propose a framework for constructing 3D corridor-based road scene models. This consists of two successive stages: road detection and scene construction. The road detection is realized by a new superpixel Markov random field (MRF) algorithm. The data fidelity term in the MRF's energy function is jointly computed according to the superpixel features of color, texture and location. The smoothness term is established on the basis of the interaction of spatio-temporally adjacent superpixels. In the subsequent scene construction, the foreground and background regions are modeled independently. Experiments for road detection demonstrate the proposed method outperforms the state-of-the-art in both accuracy and speed. The scene construction experiments confirm that the proposed scene models show better correctness ratios, and have the potential to support a range of applications.
Automated 3D recovery from very high resolution multi-view satellite images

This paper presents an automated pipeline for processing multi-view satellite images to 3D digital surface models (DSM). The proposed pipeline performs automated geo-referencing and generates high-quality densely matched point clouds. In particular, a novel approach is developed that fuses multiple depth maps derived by stereo matching to generate high-quality 3D maps. By learning critical configurations of stereo pairs from sample LiDAR data, we rank the image pairs based on the proximity of the results to the sample data. Multiple depth maps derived from individual image pairs are fused with an adaptive 3D median filter that considers the image spectral similarities. We demonstrate that the proposed adaptive median filter generally delivers better results in general as compared to normal median filter, and achieved an accuracy of improvement of 0.36 meters RMSE in the best case. Results and analysis are introduced in detail.
Effect of Replication Fork Dynamics on Escherichia coli Cell Size

The variability in cell size of an isogenic population of Escherichia coli has been widely reported in experiment. The probability density function (PDF) of cell lengths has been variously described by exponential and lognormal functions. While temperature, population density and growth rate have all been shown to affect E. coli cell size distributions, and recent models have validated a link between growth rate and cell size through DNA replication, cell size variability is thought to emerge from growth rate variability. A mechanistic link that could distinguish the source of stochasticity, could improve our understanding of cell size regulation. Here, we have developed a population dynamics model of individual cell division based on the BCD, birth, chromosome replication and division model, with DNA replication based on the Cooper and Helmstetter (CH) multi-fork replication. In our model, stochasticity in the model arises solely from the dynamics of DNA replication forks. We model the forks as two-state systems: stalled and recovered. Our model predicts an increase in cell size variability with growth rate, consistent with previous experimental reports. We perturb the model to test the effect of increased replication fork (RF) stalling frequency, or uncoupling RF stalling from the cell-division machinery. Indeed, despite ignoring DNA and protein segregation asymmetry, the model can faithfully reproduce quantitative changes in cell size distributions. In our model, multi-fork replication produces multiplicative 'noise' and provides a mechanism linking growth rate and cell size variability.
XYZ at Belle

Recent results of exotic states with heavy quarks, denoted as XYZ states, are presented. The results include searches for the Y(4260) in B meson decays, a spin partner of Y(4630), and the strange Pentaquark $ P_s^ + $. An addition, the measurements of the absolute branching fraction of X(3872) and search for double Zc production is dicussed.
Two cases of wheat‐dependent anaphylaxis induced by aspirin administration but not by exercise

Background. Aspirin and other nonsteroidal anti‐inflammatory drugs (NSAIDs) have been reported to enhance the symptoms of wheat‐dependent exercise‐induced anaphylaxis (WDEIA). In contrast to many reports on WDEIA, there have been only a few reports of wheat‐dependent aspirin‐induced anaphylaxis not induced by the combination of wheat and exercise.
GrapAL: Connecting the Dots in Scientific Literature

We introduce GrapAL (Graph database of Academic Literature), a versatile tool for exploring and investigating a knowledge base of scientific literature that was semi-automatically constructed using NLP methods. GrapAL fills many informational needs expressed by researchers. At the core of GrapAL is a Neo4j graph database with an intuitive schema and a simple query language. In this paper, we describe the basic elements of GrapAL, how to use it, and several use cases such as finding experts on a given topic for peer reviewing, discovering indirect connections between biomedical entities, and computing citation-based metrics. We open source the demo code to help other researchers develop applications that build on GrapAL.
Covering Metric Spaces by Few Trees

A {\em tree cover} of a metric space $(X,d)$ is a collection of trees, so that every pair $x,y\in X$ has a low distortion path in one of the trees. If it has the stronger property that every point $x\in X$ has a single tree with low distortion paths to all other points, we call this a {\em Ramsey} tree cover. Tree covers and Ramsey tree covers have been studied by \cite{BLMN03,GKR04,CGMZ05,GHR06,MN07}, and have found several important algorithmic applications, e.g. routing and distance oracles. The union of trees in a tree cover also serves as a special type of spanner, that can be decomposed into a few trees with low distortion paths contained in a single tree; Such spanners for Euclidean pointsets were presented by \cite{ADMSS95}. In this paper we devise efficient algorithms to construct tree covers and Ramsey tree covers for general, planar and doubling metrics. We pay particular attention to the desirable case of distortion close to 1, and study what can be achieved when the number of trees is small. In particular, our work shows a large separation between what can be achieved by tree covers vs. Ramsey tree covers.
Exploring helical dynamos with machine learning

We use ensemble machine learning algorithms to study the evolution of magnetic fields in magnetohydrodynamic (MHD) turbulence that is helically forced. We perform direct numerical simulations of helically forced turbulence using mean field formalism, with electromotive force (EMF) modeled both as a linear and non-linear function of the mean magnetic field and current density. The form of the EMF is determined using regularized linear regression and random forests. We also compare various analytical models to the data using Bayesian inference with Markov chain Monte Carlo (MCMC) sampling. Our results demonstrate that linear regression is largely successful at predicting the EMF and the use of more sophisticated algorithms (random forests, MCMC) do not lead to significant improvement in the fits. We conclude that the data we are looking at is effectively low dimensional and essentially linear. Finally, to encourage further exploration by the community, we provide all of our simulation data and analysis scripts as open source IPYTHON notebooks.
Causal Inference for Multiple Non-Randomized Treatments using Fractional Factorial Designs

We explore a framework for addressing causal questions in an observational setting with multiple treatments. This setting involves attempting to approximate an experiment from observational data. With multiple treatments, this experiment would be a factorial design. However, certain treatment combinations may be so rare that we have no measured outcomes in the observed data corresponding to them. We propose to conceptualize a hypothetical fractional factorial experiment instead of a full factorial experiment and lay out a framework for analysis in this setting. We connect our design-based methods to standard regression methods. We finish by illustrating our approach using biomedical data from the 2003-2004 cycle of the National Health and Nutrition Examination Survey to estimate the effects of four common pesticides on body mass index.
Privacy and Security Risks of "Not-a-Virus" Bundled Adware: The Wajam Case

Comprehensive case studies on malicious code mostly focus on botnets and worms (recently revived with IoT devices), prominent pieces of malware or Advanced Persistent Threats, exploit kits, and ransomware. However, adware seldom receives such attention. Previous studies on "unwanted" Windows applications, including adware, favored breadth of analysis, uncovering ties between different actors and distribution methods. In this paper, we demonstrate the capabilities, privacy and security risks, and prevalence of a particularly successful and active adware business: Wajam, by tracking its evolution over nearly six years. We first study its multi-layer antivirus evasion capabilities, a combination of known and newly adapted techniques, that ensure low detection rates of its daily variants, along with prominent features, e.g., traffic interception and browser process injection. Then, we look at the privacy and security implications for infected users, including plaintext leaks of browser histories and keyword searches on highly popular websites, along with arbitrary content injection on HTTPS webpages and remote code execution vulnerabilities. Finally, we study Wajam's prevalence through the popularity of its domains. Once considered as seriously as spyware, adware is now merely called "not-a-virus", "optional" or "unwanted" although its negative impact is growing. We emphasize that the adware problem has been overlooked for too long, which can reach (or even surplus) the complexity and impact of regular malware, and pose both privacy and security risks to users, more so than many well-known and thoroughly-analyzed malware families.
Learning Perspective Undistortion of Portraits

Near-range portrait photographs often contain perspective distortion artifacts that bias human perception and challenge both facial recognition and reconstruction techniques. We present the first deep learning based approach to remove such artifacts from unconstrained portraits. In contrast to the previous state-of-the-art approach \cite{fried2016perspective}, our method handles even portraits with extreme perspective distortion, as we avoid the inaccurate and error-prone step of first fitting a 3D face model. Instead, we predict a distortion correction flow map that encodes a per-pixel displacement that removes distortion artifacts when applied to the input image. Our method also automatically infers missing facial features, i.e. occluded ears caused by strong perspective distortion, with coherent details. We demonstrate that our approach significantly outperforms the previous state-of-the-art both qualitatively and quantitatively, particularly for portraits with extreme perspective distortion or facial expressions. We further show that our technique benefits a number of fundamental tasks, significantly improving the accuracy of both face recognition and 3D reconstruction and enables a novel camera calibration technique from a single portrait. Moreover, we also build the first perspective portrait database with a large diversity in identities, expression and poses.
Optimisation of Overparametrized Sum-Product Networks

It seems to be a pearl of conventional wisdom that parameter learning in deep sum-product networks is surprisingly fast compared to shallow mixture models. This paper examines the effects of overparameterization in sum-product networks on the speed of parameter optimisation. Using theoretical analysis and empirical experiments, we show that deep sum-product networks exhibit an implicit acceleration compared to their shallow counterpart. In fact, gradient-based optimisation in deep tree-structured sum-product networks is equal to gradient ascend with adaptive and time-varying learning rates and additional momentum terms.
Full System Emulation of Approximate Memory Platforms with AppropinQuo

In this work we present an emulation framework for hardware platforms provided with approximate memory units, called AppropinQuo. The speciﬁc characteristic of AppropinQuo is to reveal the effects, on the hardware platform and on software, of errors introduced by approximate memory circuits and architectures. The emulator allows to execute software code without any modiﬁca-tion with respect to the target physical board, since it includes the CPU, the memory hierarchy and the peripherals, capturing as well software–hardware interactions and faults due to approximate memory units. The ﬁnal scope is reproducing the effects of errors generated by approximate memory circuits, allowing to evaluate the impact (quality degradation) on the output produced by the software. In fact, output quality is related to error rate, but their relationship strongly depends on the application, the implementation and its data representation on physical memory. The idea behind approximate memory circuits and approximate computing in general is to trade off energy consumption at the expense of computational accuracy and degradation of output quality. Memory is accounted for a large part of total power consumption in advanced architectures and it is supposed to increase as new memory hungry applications migrate toward the implementation on embedded systems (embedded machine learning, high deﬁnition video codecs, etc.). By relaxing design constraints regarding error probability on bit cells, researchers have proposed techniques that signiﬁcantly reduce memory energy consumption. These techniques, which can be accounted in the general topic of approximate memory design, are implemented at circuit or architecture level, and are speciﬁc to the memory technology (i.e., SRAM or DRAM memories). However, the level of acceptable output degradation is the ﬁnal metric that must be used to assess if, and to what extent, an approximate memory technique can be introduced. Our emulator allows to run actual applications as on the physical platform, to expose the effects of speciﬁc approximate memory circuits and architectures on output quality and to vary their parameters (e
REGULATORY EFFECTS OF HYPOXIA-INDUCIBLE FACTOR 1&agr; ON VASCULAR REACTIVITY AND ITS MECHANISMS FOLLOWING HEMORRHAGIC SHOCK IN RATS

The purpose of the present study is to investigate the regulatory effect of hypoxia-inducible factor 1&agr; (HIF-1&agr;) on vascular reactivity and its mechanism after hemorrhagic shock (HS). Gene expression of HIF-1&agr; and its downstream molecules, including eNOS, iNOS, cyclooxygenase 2 (COX-2), and heme oxygenase 1 (HO-1), and plasma nitric monoxide (NO), prostaglandin (PGI), and whole blood carbon monoxide (CO) were determined after HS in rats with or without oligomycin, the specific antagonist of HIF-1&agr;. The vascular reactivity was determined via observing the constriction initiated by norepinephrine in isolated organ perfusion system. The results indicated that HIF-1&agr;, eNOS, iNOS, HO-1, and COX-2 messenger RNA expression exhibited a time-dependent increase after HS, although the expression of these genes and their products, NO, CO, and PGI were suppressed by oligomycin to some extent. The vascular reactivity revealed a biphasic change, which was increased compensatorily at the early stage of HS (immediate to 1 h after shock) and decreased progressively at the decompensatory period after 4 h of shock. Oligomycin treatment partly inhibited the vascular reactivity at early stage (immediate to 1 h after shock) and improved it at decompensatory period at 4 to 6 h after shock (P < 0.01). The results suggested that HIF-1&agr; plays an important regulatory role in the change of vascular reactivity after HS in rats. The possible mechanism of HIF-1&agr; regulating vascular reactivity is closely related to its regulation on the expression of eNOS, iNOS, HO-1, COX-2 and the production of NO, CO and PGI.
Murine Cytomegalovirus Open Reading Frame M27 Plays an Important Role in Growth and Virulence in Mice

ABSTRACT Using a Tn3-based transposon mutagenesis approach, we have generated a pool of murine cytomegalovirus (MCMV) mutants. In this study, one of the mutants, RvM27, which contained the transposon sequence at open reading frame M27, was characterized both in tissue culture and in immunocompetent BALB/c mice and immunodeficient SCID mice. Our results suggest that the M27 carboxyl-terminal sequence is dispensable for viral replication in vitro. Compared to the wild-type strain and a rescued virus that restored the M27 region, RvM27 was attenuated in growth in both BALB/c and SCID mice that were intraperitoneally infected with the viruses. Specifically, the titers of RvM27 in the salivary glands, lungs, spleens, livers, and kidneys of the infected SCID mice at 21 days postinfection were 50- to 500-fold lower than those of the wild-type virus and the rescued virus. Moreover, the virulence of the mutant virus appeared to be attenuated, because no deaths occurred among SCID mice infected with RvM27 for up to 37 days postinfection, while all the animals infected with the wild-type and rescued viruses died within 27 days postinfection. Our observations provide the first direct evidence to suggest that a disruption of M27 expression results in reduced viral growth and attenuated viral virulence in vivo in infected animals. Moreover, these results suggest that M27 is a viral determinant required for optimal MCMV growth and virulence in vivo and provide insight into the functions of the M27 homologues found in other animal and human CMVs as well as in other betaherpesviruses.
Convergence of nuclear effective field theory with perturbative pions

The classic paper by Fleming, Mehen, and Stewart (FMS) [Nucl. Phys. A 677, 313 (2000)] cast doubts on the convergence of spin-triplet nucleon-nucleon partial wave scattering amplitudes when following the proposal of Kaplan, Savage, and Wise to construct nuclear effective field theory (EFT) around the unitary fermion limit with perturbative pion exchange. FMS identified the subclass of iterated one-pion exchange potential graphs as the cause of this poor convergence, which they showed persisted in the chiral limit. Theoretical tools are developed here to compute these Feynman graphs analytically to high order in all angular momentum channels simultaneously, examining the amplitudes computed to seven loops in the $L=J$ channels and three loops in the coupled $L=J\ifmmode\pm\else\textpm\fi{}1$ channels. One finds that there is nothing pathological about the perturbative expansion of a $1/{r}^{3}$ potential in general and that the expansion converges satisfactorily in all partial waves except those with the lowest angular momentum, particularly the $^{3}P_{0}$ and the coupled $^{3}S_{1}\ensuremath{-}^{3}D_{1}$ channels. The results corroborate work by Birse [J. Phys. A: Math. Gen. 39, L49 (2005)], which suggests possible avenues to explore for improving the range of validity of the EFT expansion.
The matter fluctuation amplitude inferred from the weak lensing power spectrum and correlation function in CFHTLenS data

Based on the cosmic shear data from the Canada–France–Hawaii Telescope Lensing Survey (CFHTLenS), Kilbinger et al. obtained a constraint on the amplitude of matter fluctuations of σ8(Ωm/0.27)0.6 = 0.79 ± 0.03 from the two-point correlation function (2PCF). This is ≈3σ lower than the value 0.89 ± 0.01 derived from Planck data on cosmic microwave background (CMB) anisotropies. On the other hand, based on the same CFHTLenS data, but using the power spectrum, and performing a different analysis, Liu et al. obtained the higher value of $\sigma _8(\Omega _\mathrm{m}/0.27)^{0.64}=0.87^{+0.05}_{-0.06}$. We here investigate the origin of this difference, by performing a fair side-by-side comparison of the 2PCF and power spectrum analyses on CFHTLenS data. We find that these two statistics indeed deliver different results, even when applied to the same data in an otherwise identical procedure. We identify excess power in the data on small scales (ℓ > 5000) driving the larger values inferred from the power spectrum. We speculate on the possible origin of this excess small-scale power. More generally, our results highlight the utility of analysing the 2PCF and the power spectrum in tandem, to discover (and to help control) systematic errors.
Vitamin E Supplementation—But Not Induced Oxidative Stress—Influences Telomere Dynamics During Early Development in Wild Passerines

Telomere length is a marker of cellular senescence that relates to different components of individual fitness. Oxidative stress is often claimed as a main proximate factor contributing to telomere attrition, although the importance of this factor in vivo has recently been challenged. Early development represents an ideal scenario to address this hypothesis because it is characterized by the highest rates of telomere attrition of the life and by an arguably high susceptibility to oxidative stress. We tested the effect of oxidative stress on telomere dynamics during early development by exposing pied flycatcher nestlings (Ficedula hypoleuca) to either an oxidative challenge (diquat injections), an antioxidant (vitamin E) or control treatments (PBS injections and supplementation with vehicle substance). We found no effects of treatments on average telomere change during the nestling period. However, vitamin E supplementation, which increased growth, removed the association between initial telomere length and telomere attrition. Diquat-treated nestlings, by contrast, showed no differences in growth or telomere dynamics with respect to controls. These results do not support the hypothesis that oxidative stress is the main direct mechanism explaining telomere attrition in vivo, and highlight the importance of micronutrient intake during early development on telomere dynamics. Studies addressing alternative action pathways of vitamins on growth and telomere dynamics, perhaps via restoration mechanisms, would provide important insights on the proximate factors affecting telomere attrition during this critical phase of life.