Miracle-dz/UniFAR-FTUs huggingface dataset:
Berberine Blocks the Relapse of Clostridium difficile Infection in C57BL/6 Mice after Standard Vancomycin Treatment

ABSTRACT Vancomycin is a preferred antibiotic for treating Clostridium difficile infection (CDI) and has been associated with a rate of recurrence of CDI of as high as 20% in treated patients. Recent studies have suggested that berberine, an alternative medical therapy for gastroenteritis and diarrhea, exhibits several beneficial effects, including induction of anti-inflammatory responses and restoration of the intestinal barrier function. This study investigated the therapeutic effects of berberine on preventing CDI relapse and restoring the gut microbiota in a mouse model. Berberine was administered through gavage to C57BL/6 mice with established CDI-induced intestinal injury and colitis. The disease activity index (DAI), mean relative weight, histopathology scores, and levels of toxins A and B in fecal samples were measured. An Illumina sequencing-based analysis of 16S rRNA genes was used to determine the overall structural change in the microbiota in the mouse ileocecum. Berberine administration significantly promoted the restoration of the intestinal microbiota by inhibiting the expansion of members of the family Enterobacteriaceae and counteracting the side effects of vancomycin treatment. Therapy consisting of vancomycin and berberine combined prevented weight loss, improved the DAI and the histopathology scores, and effectively decreased the mortality rate. Berberine prevented CDIs from relapsing and significantly improved survival in the mouse model of CDI. Our data indicate that a combination of berberine and vancomycin is more effective than vancomycin alone for treating CDI. One of the possible mechanisms by which berberine prevents a CDI relapse is through modulation of the gut microbiota. Although this conclusion was generated in the case of the mouse model, use of the combination of vancomycin and berberine and represent a novel therapeutic approach targeting CDI.
Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function

Almost all the work in Average-reward Reinforcement Learning (ARL) so far has fo-cused on table-based methods which do not scale to domains with large state spaces. In this paper, we propose two extensions to a model-based ARL method called H-learning to address the scale-up problem. We extend H-learning to learn action models and reward functions in the form of Bayesian networks, and approximate its value function using local linear regression. We test our algorithms on several scheduling tasks for a simulated Automatic Guided Vehicle (AGV) and show that they are eeective in signiicantly reducing the space requirement of H-learning and making it converge faster. To the best of our knowledge, our results are the rst in applying function approximation to ARL.
Warped geometry in higher dimensions with an orbifold extra dimension

We solve the Einstein equations in higher dimensions with warped geometry where an extra dimension is assumed to have orbifold symmetry $S^{1}/Z_{2}$. The setup considered here is an extension of the five-dimensional Randall-Sundrum model to $5+D$ dimensions, and hidden and observable branes are fixed on the orbifold. It is assumed that the brane tension (self-energy) of each brane with $(4+D)$-dimensional spacetime is anisotropic and that the warped metric function of the four dimensions is generally different from that of the extra $D$ dimensions. We point out that the forms of the warped metric functions and the relations between the tensions of two branes depend on the integration constant appearing in the Einstein equations as well as on the sign of the bulk cosmological constant.
A cognitive radio based dynamic spectrum access scheme for LTE heterogeneous networks

This study proposes a coordinated dynamic spectrum access (DSA) scheme for LTE based on cognitive radio (CR) technology, where a central entity, a Spectrum Policy Server (SPS), performs the spectrum management in a LTE multi-operator heterogeneous network (HetNet). More specifically, the scenario analyzed is based on a system deployment formed by two Mobile Network Operators (MNOs), each operating a macro cell network and a pico cell network. The spectrum allocation for macro base stations (BSs) is fixed, as it is the case in current deployments, while the spectrum for pico BSs is shared among operators and allocated dynamically. Increased throughput with high spectrum efficiency is demonstrated on this paper and simulation results for this scenario are presented.
Differential DNA Methylation in Purified Human Blood Cells: Implications for Cell Lineage and Studies on Disease Susceptibility

Methylation of cytosines at CpG sites is a common epigenetic DNA modification that can be measured by a large number of methods, now even in a genome-wide manner for hundreds of thousands of sites. The application of DNA methylation analysis is becoming widely popular in complex disorders, for example, to understand part of the “missing heritability”. The DNA samples most readily available for methylation studies are derived from whole blood. However, blood consists of many functionally and developmentally distinct cell populations in varying proportions. We studied whether such variation might affect the interpretation of methylation studies based on whole blood DNA. We found in healthy male blood donors there is important variation in the methylation profiles of whole blood, mononuclear cells, granulocytes, and cells from seven selected purified lineages. CpG methylation between mononuclear cells and granulocytes differed for 22% of the 8252 probes covering the selected 343 genes implicated in immune-related disorders by genome-wide association studies, and at least one probe was differentially methylated for 85% of the genes, indicating that whole blood methylation results might be unintelligible. For individual genes, even if the overall methylation patterns might appear similar, a few CpG sites in the regulatory regions may have opposite methylation patterns (i.e., hypo/hyper) in the main blood cell types. We conclude that interpretation of whole blood methylation profiles should be performed with great caution and for any differences implicated in a disorder, the differences resulting from varying proportions of white blood cell types should be considered.
Improved Diagnostics Using Polarization Imaging and Artificial Neural Networks

In recent years there has been an increasing interest in studying the propagation of polarized light in randomly scattering media. This paper presents a novel approach for cell and tissue imaging by using full Stokes imaging and for its improved diagnostics by using artificial neural networks (ANNs). Phantom experiments have been conducted using a prototyped Stokes polarization imaging device. Several types of phantoms, consisting of polystyrene latex spheres in various diameters, were prepared to simulate different conditions of epidermal layer of skin. Several sets of four images that contain not only the intensity, but also the polarization information were taken for analysis. Wavelet transforms are first applied to the Stokes components for initial feature analysis and extraction. Artificial neural networks (ANNs) are then used to extract diagnostic features for improved classification and prediction. The experimental results show that the classification performance using Stokes images is significantly improved over that using the intensity image only.
STATISTICAL PROPERTIES OF DAILY RETURNS: EVIDENCE FROM EUROPEAN STOCK MARKETS

This paper attempts to model the distributional properties of daily stock returns on several European Stock Exchanges. The empirical findings reveal the presence of non-linear dependencies that cannot be captured by the random walk model. A model of return-generating process that fit the data empirically is the Generalized Autoregressive Conditional Heteroskedastic GARCH (1,1) process with a conditional student-t distribution.
Supporting group interaction among humans and autonomous agents

An important aspect of interaction among groups of humans and software agents is supporting collaboration among these heterogeneous agents while they operate remotely and communicate asynchronously. We are developing an architecture that supports multiple humans interacting with multiple automated control agents in such a manner. We are evaluating this architecture with a group consisting of the crew of a space-based vehicle and the automated software agents controlling the vehicle systems. Such agent interaction is modelled as a loosely co-ordinated group because this model minimizes agent commitment to group goals and constraints while addressing a significant portion of crew and control agent group behaviours. In this paper, we give background on human interaction with space-based automation. We identify related research in multi-agent autonomous architectures and single agent human-computer interaction systems, we describe our architecture design for human-software agent groups and we identify research issues in loosely co-ordinated human-software groups.
Airborne Wind Energy: An overview

In the last decade, several research groups and companies around the world have been developing a new class of wind generators, aimed at harnessing the energy of winds blowing at high elevation above the ground. This kind of technology is usually referred to as Airborne Wind Energy (AWE) or High-Altitude Wind Energy. All of the proposed solutions exploit the high-speed flight of tethered wings, or aircrafts, and their operation heavily relies on automatic control. This paper provides a tutorial on the fundamental concepts of AWE and on the different technologies that are being investigated, with particular emphasis on control-related aspects, highlighting the accomplished results and the issues that still need to be solved.
Using opportunistic caching to improve the efficiency of handover in LTE with a PON access network backhaul

This paper investigates the converged architecture of an LTE mobile network and a PON access network. We identify that the default handover behaviour for LTE is highly inefficient when performed on a backhaul PON tree-topology. We propose a scheme of intelligent opportunistic caching, using the existing resources at the eNBs, to help mitigate this inefficiency. We show that we can achieve a significant reduction in the amount of redundant traffic sent over the PON and that in circumstances when the PON upstream is heavily congested, we can greatly improve the mobile terminal's sustained data rate by up to 50% during the handover process.
Fast Temporal Activity Proposals for Efficient Detection of Human Actions in Untrimmed Videos

In many large-scale video analysis scenarios, one is interested in localizing and recognizing human activities that occur in short temporal intervals within long untrimmed videos. Current approaches for activity detection still struggle to handle large-scale video collections and the task remains relatively unexplored. This is in part due to the computational complexity of current action recognition approaches and the lack of a method that proposes fewer intervals in the video, where activity processing can be focused. In this paper, we introduce a proposal method that aims to recover temporal segments containing actions in untrimmed videos. Building on techniques for learning sparse dictionaries, we introduce a learning framework to represent and retrieve activity proposals. We demonstrate the capabilities of our method in not only producing high quality proposals but also in its efficiency. Finally, we show the positive impact our method has on recognition performance when it is used for action detection, while running at 10FPS.
The single-group PHD filter: An analytic solution

In many group target tracking scenarios, a collection of targets move in a correlated manner as part of a formation, such as a convoy of vehicles. The focus of this paper is in the estimation of the evolution over time of a single-group of targets, referred to as a single-cluster, based on a sequence of partial observation sets. Based on Finite Set Statistics, the paper presents a first-moment recursion for a single-group filter and an analytic solution under linear-Gaussian assumptions. The method is demonstrated on simulated data in a cluttered environment.
Defect formation in III–V fin grown by aspect ratio trapping technique: A first-principles study

First-principles investigations are used to study the formation of defects in III-V fins grown using the aspect ratio trapping technique. We show that, during the growth of the III-V, the formation of intermediate chemical states with the precursors leads to the creation of in-diffused Mg/Zn and Al2O3 sub-oxide. Our prediction is consistent with the experimental observations. These defect formations could be at the origin of the degradation of the electrical reliability of III-V fin-shaped field-effect transistors and the cause of the increasing difficulties met in the fabrication of III-V fin.
The directional parameter plane transform of a height field

The linear Parameter Plane Transform (PPT) of a height field attributes an inverted cone of empty space to each height field cell. In is known that height field ray-tracing efficiency can be improved by traversing rays in steps across inverted cones of empty space. However, steps across inverted cones of empty space along rays close to the base of a steep ridge will be short, even if there are no obstructions along the line of sight, because the cones will be narrow. This weakness can be virtually eliminated by allowing the opening angles of the inverted cones of empty space to vary between sectors, i.e., by directionalizing the linear PPT. An efficient algorithm for computing the linear directional PPT of a height field is given and its properties are investigated.
Insights into the absorption of carbon dioxide by zinc substrates: isolation and reactivity of di- and tetranuclear zinc complexes.

The heptadentate Schiff base H(3)L can react with zinc acetate to form the discrete dinuclear complex Zn(2)L(OAc)(H(2)O), 1.H(2)O. The reaction of 1.H(2)O with NMe(4)OH.5H(2)O both in air and under an argon stream has been investigated. On one hand, this reaction in air yields the tetranuclear complex (Zn(2)L)(2)(CO(3))(H(2)O)(6), 2.5H(2)O, by spontaneous absorption of adventitious carbon dioxide. This process can be reverted in an acetic acid medium, whereas the treatment of 2.5H(2)O with methanoic acid yields crystals of [Zn(2)L(HCOO)]. 0.5MeCN.1.25MeOH.2H(2)O, 3.0.5MeCN.1.25MeOH.2H(2)O. On the other hand, the interaction under an argon atmosphere of 1.H(2)O with NMe(4)OH.5H(2)O in methanol allows the isolation of the dinuclear complex Zn(2)L(OMe)(H(2)O)(4), 4.4H(2)O. Recrystallisations of 1.H(2)O, 2.5H(2)O and 4.4H(2)O, in different solvents, yielded single crystals of 1.MeCN. 2.5H(2)O, 2.4MeOH and 4.3MeOH. H(2)O, respectively. The crystal structure of 2.4MeOH can be understood as resulting from an unusual asymmetric tetranuclear self-assembly from two dinuclear units, and shows three different geometries around the four zinc atoms.
Real time motion generation and control for biped robot -1st report: Walking gait pattern generation-

Generating stable dynamic motions for a biped robot in real time is difficult due to the unstable nature of biped systems and their high degrees of freedom. We propose an approximate dynamics model for biped robots with three masses and no kinematic constraints. We also propose a relaxed boundary condition called “the divergent component of motion”. These techniques allow us to generate walking gait patterns with large margin from the edges of support polygon in real time.
Pilot allocation by Genetic Algorithms for sparse channel estimation in OFDM systems

The comb-type pilot-aided channel estimation in OFDM systems is here considered. The number of pilot subcarriers is generally tuned to tradeoff between performance and spectral efficiency. For time domain sparse channels, the application of the recent theory of Compressed Sensing has proved its efficiency to both ameliorate the estimation accuracy and reduce the transmitted overhead. The performance and uniqueness guarantees remain however closely related to the pilots placement, which are optimized prior to transmission. Few works have addressed the pilot allocation issues. To avoid the computationally prohibitive exhaustive search over all possible allocations, they proposed sub-optimal solutions. This contribution proposes an enhanced pilot design scheme based on Genetic Algorithms. First, we set the adopted problem formulation in the frame of Genetic Algorithms. Then, we propose two algorithms, tailored for the decomposition matrix coherence minimization. Simulation results prove the proposed schemes effectiveness and performance enhancement compared to former pilot design schemes.
A Generic Framework for the Analysis of Emotion Mechanisms in Autonomous Agents

Emotion mechanisms are often used to perform roles in artificial agents with the aim of improving task performance. A basis for making comparisons between these agents is proposed, in the form of a framework constructed from an analysis of architectural qualities (the agent architecture, the action selection mechanism, and the emotion mechanism along with the emotion model it was based on). This helps to place the agents within an architectural space, highlights contrasting methods of implementing similar theoretical components and suggests which architectural aspects are important to performance of tasks. Three autonomous agents incorporating emotion mechanisms are evaluated within this framework exemplarily, and the extent to which this type of framework can aid future research is breifly
Evidence for Arousal-Biased Competition in Perceptual Learning

Arousal-biased competition theory predicts that arousal biases competition in favor of perceptually salient stimuli and against non-salient stimuli (Mather and Sutherland, 2011). The current study tested this hypothesis by having observers complete many trials in a visual search task in which the target either always was salient (a 55° tilted line among 80° distractors) or non-salient (a 55° tilted line among 50° distractors). Each participant completed one session in an emotional condition, in which visual search trials were preceded by negative arousing images, and one session in a non-emotional condition, in which the arousing images were replaced with neutral images (with session order counterbalanced). Test trials in which the target line had to be selected from among a set of lines with different tilts revealed that the emotional condition enhanced identification of the salient target line tilt but impaired identification of the non-salient target line tilt. Thus, arousal enhanced perceptual learning of salient stimuli but impaired perceptual learning of non-salient stimuli.
Knowledge-Guided Linguistic Rewrites for Inference Rule Verification

A corpus of inference rules between a pair of relation phrases is typically generated using the statistical overlap of argument-pairs associated with the relations (e.g., PATTY, CLEAN). We investigate knowledge-guided linguistic rewrites as a secondary source of evidence and find that they can vastly improve the quality of inference rule corpora, obtaining 27 to 33 point precision improvement while retaining substantial recall. The facts inferred using cleaned inference rules are 29-32 points more accurate.
GalaxySite: ligand-binding-site prediction by using molecular docking

Knowledge of ligand-binding sites of proteins provides invaluable information for functional studies, drug design and protein design. Recent progress in ligand-binding-site prediction methods has demonstrated that using information from similar proteins of known structures can improve predictions. The GalaxySite web server, freely accessible at http://galaxy.seoklab. org/site, combines such information with molecular docking for more precise binding-site prediction for non-metal ligands. According to the recent critical assessments of structure prediction methods held in 2010 and 2012, this server was found to be superior or comparable to other state-of-the-art programs in the category of ligand-binding-site prediction. A strong merit of the GalaxySite program is that it provides additional predictions on binding ligands and their binding poses in terms of the optimized 3D coordinates of the protein–ligand complexes, whereas other methods predict only identities of binding-site residues or copy binding geometry from similar proteins. The additional information on the specific binding geometry would be very useful for applications in functional studies and computer-aided drug discovery.
The Impact of No Child Left Behind on Student Achievement. NBER Working Paper No. 15531.

The No Child Left Behind (NCLB) Act compelled states to design schoolaccountability systems based on annual student assessments. The effect of this Federal legislation on the distribution of student achievement is a highly controversial but centrally important question. This study presents evidence on whether NCLB has influenced student achievement based on an analysis of state-level panel data on student test scores from the National Assessment of Educational Progress (NAEP). The impact of NCLB is identified using a comparative interrupted time series analysis that relies on comparisons of the test-score changes across states that already had school-accountability policies in place prior to NCLB and those that did not. Our results indicate that NCLB generated statistically significant increases in the average math performance of 4 graders (effect size = 0.22 by 2007) as well as improvements at the lower and top percentiles. There is also evidence of improvements in 8 grade math achievement, particularly among traditionally low-achieving groups and at the lower percentiles. However, we find no evidence that NCLB increased reading achievement in either 4 or 8 grade. * We would like to thank Rob Garlick, Elias Walsh, Nathaniel Schwartz and Erica Johnson for their research assistance. We would also like to thank Kerwin Charles, Robert Kaestner, Ioana Marinescu and seminar participants at the Harris School of Public Policy and at the NCLB: Emerging Findings Research Conference for helpful comments. An earlier version of this work was also presented by Jacob as the David N. Kershaw Lecture at the Annual meeting of the Association of Public Policy and Management (November 2008). All errors are our own.
Large Scale Nonparametric Bayesian Inference: Data Parallelisation in the Indian Buffet Process

Nonparametric Bayesian models provide a framework for flexible probabilistic modelling of complex datasets. Unfortunately, the high-dimensional averages required for Bayesian methods can be slow, especially with the unbounded representations used by nonparametric models. We address the challenge of scaling Bayesian inference to the increasingly large datasets found in real-world applications. We focus on parallelisation of inference in the Indian Buffet Process (IBP), which allows data points to have an unbounded number of sparse latent features. Our novel MCMC sampler divides a large data set between multiple processors and uses message passing to compute the global likelihoods and posteriors. This algorithm, the first parallel inference scheme for IBP-based models, scales to datasets orders of magnitude larger than have previously been possible.