Miracle-dz/UniFAR-FTUs huggingface dataset:
Mouse Bone Marrow-Derived Mesenchymal Stromal Cells Turn Activated Macrophages into a Regulatory-Like Profile

In recent years it has become clear that the therapeutic properties of bone marrow-derived mesenchymal stromal cells (MSC) are related not only to their ability to differentiate into different lineages but also to their capacity to suppress the immune response. We here studied the influence of MSC on macrophage function. Using mouse thioglycolate-elicited peritoneal macrophages (M) stimulated with LPS, we found that MSC markedly suppressed the production of the inflammatory cytokines TNF-α, IL-6, IL-12p70 and interferon-γ while increased the production of IL-10 and IL-12p40. Similar results were observed using supernatants from MSC suggesting that factor(s) constitutively released by MSC are involved. Supporting a role for PGE2 we observed that acetylsalicylic acid impaired the ability of MSC to inhibit the production of inflammatory cytokines and to stimulate the production of IL-10 by LPS-stimulated M. Moreover, we found that MSC constitutively produce PGE2 at levels able to inhibit the production of TNF-α and IL-6 by activated M. MSC also inhibited the up-regulation of CD86 and MHC class II in LPS-stimulated M impairing their ability to activate antigen-specific T CD4+ cells. On the other hand, they stimulated the uptake of apoptotic thymocytes by M. Of note, MSC turned M into cells highly susceptible to infection with the parasite Trypanosoma cruzi increasing more than 5-fold the rate of M infection. Using a model of inflammation triggered by s.c. implantation of glass cylinders, we found that MSC stimulated the recruitment of macrophages which showed a low expression of CD86 and the MHC class II molecule Iab and a high ability to produce IL-10 and IL-12p40, but not IL-12 p70. In summary, our results suggest that MSC switch M into a regulatory profile characterized by a low ability to produce inflammatory cytokines, a high ability to phagocyte apoptotic cells, and a marked increase in their susceptibility to infection by intracellular pathogens.
Doubling of serum creatinine and the risk of cardiovascular outcomes in patients with chronic kidney disease and type 2 diabetes mellitus: a cohort study

Background Doubling of serum creatinine is often used as a marker for worsening kidney function in nephrology trials. Most people with chronic kidney disease die of other causes before reaching end-stage renal disease. We were interested in the association between doubling of serum creatinine and the risk of a first-time diagnosis of angina pectoris, congestive heart failure (CHF), myocardial infarction (MI), stroke, or transient ischemic attack in patients with chronic kidney disease and with diagnosed type 2 diabetes mellitus. Methods We identified all adult patients registered in the “Clinical Practice Research Datalink” between 2002 and 2011 with incident chronic kidney disease and type 2 diabetes mellitus and did a cohort study with a Cox proportional hazard analysis. Results We identified in total 27,811 patients, 693 developed angina pectoris, 1,069 CHF, 508 MI, 970 stroke, and 578 transient ischemic attacks. Patients whose serum creatinine doubled during follow-up had increased risks of CHF (hazard ratio [HR] 2.98, 95% confidence interval [CI] 2.27–3.89), MI (HR 2.53, 95% CI 1.62–3.96), and stroke (HR 1.93, 95% CI 1.38–2.69), as compared with patients whose serum creatinine did not double. The relative risks of angina pectoris (HR 1.18, 95% CI 0.66–2.10) or a transient ischemic attack (HR 1.32, 95% CI 0.78–2.22) were similar in both groups. Conclusion Diabetic patients with a doubling of serum creatinine were at an increased risk of CHF, MI, or stroke, compared with diabetic patients whose serum creatinine did not double during follow-up.
In Advances in Neural Information Processing Systems ( NIPS-12 ) pages 1022-1028 , Denver , Colorado , December 1999 Policy Search via Density Estimation

We propose a new approach to the problem of searching a space of stochastic controllers for a Markov decision process (MDP) or a partially observable Markov decision process (POMDP). Following several other authors, our approach is based on searching in parameterized families of policies (for example, via gradient descent) to optimize solution quality. However, rather than trying to estimate the values and derivatives of a policy directly, we do so indirectly using estimates for the probability densities that the policy induces on states at the different points in time. This enables our algorithms to exploit the many techniques for efficient and robust approximate density propagation in stochastic systems. We show how our techniques can be applied both to deterministic propagation schemes (where the MDP’s dynamics are given explicitly in compact form,) and to stochastic propagation schemes (where we have access only to a generative model, or simulator, of the MDP). We present empirical results for both of these variants on complex problems.
The Mechanical Properties of Caladium Petiole

Abstract: In this study, the mechanical properties of Caladium (Caladium × hortulanun Birdsey) petiole were investigated. The petiole was considered as a composite beam which consists of rind and core parts. To classify the mechanical properties of rind and core part of petioles, two‐phase three‐point bending tests were conducted on the specimens cut from petioles. Linear beam theory was used to convert the time‐dependent bending forces, during the three‐point bending tests, to relaxation moduli. The result shows that the mechanical properties of rind and core parts of Caladium are very different. The relaxation modulus of rind is approximate 40 times higher than relaxation modulus of core at time = 600 s after applying bending load, and the relaxation time distribution of core is wider than the relaxation time distribution of core. The three‐ and seven‐parameter linear viscoelastic constitutive models were used to describe the time‐dependent relaxation moduli of rind and core of caladium petiole. The fitted results show that the seven‐parameter Wiechert model can describe the viscoelastic behaviour of Caladium petiole very well. The finite element analysis was used to assess the error of this test methodology. The reaction force error because of the tapered diameter of specimens is about 12%.
Real Options , Variational Approach and Stefan Problem

The paper deals with optimal stopping problems which arise in real options theory. We describe a variational approach to the solution of optimal stopping problems for diffusion processes, as an alternate to the traditional approach based on the solution of the Stefan (free-boundary) problem. The connection of this variational approach to smooth pasting conditions is established. We present some examples where the solution to the Stefan problem is not the solution to an optimal stopping problem. On the base of the proposed approach, we obtain the solution to an optimal stopping problem for a two-dimensional geometric Brownian motion with a non-linear payoff function. As an application we consider an optimal investment timing model taking into account tax exemptions.
Experimental Study on the Fatigue Endurance of the CFRP-Concrete Interface

AbstractThe primary objectives of this paper were to experimentally investigate the fatigue endurance of the bond between carbon fiber-reinforced polymer (CFRP) sheets and concrete, and to propose a valid method for determining the safe range of loading when the CFRP-concrete interface is subjected to fatigue loading. The major test variables consisted of loading amplitude, concrete strength, CFRP-to-concrete width ratio, and bond length. The failure characteristics, evolution of the strain distribution along the bond length, CFRP-concrete interface debonding during fatigue tests, and fatigue lives were analyzed. The test results indicate that the fatigue life of the CFRP-concrete interface decreased with an increase in either loading amplitude or CFRP-to-concrete width ratio, but increased with an increase in either concrete strength or bond length. A fatigue life prediction model that considered four variables [(1) CFRP sheet stress amplitude, (2) mean value of cyclic stress in the CFRP sheet, (3) concr.
Personalised placement in networked video

Personalised video can be achieved by inserting objects into a video play-out according to the viewer's profile. Content which has been authored and produced for general broadcast can take on additional commercial service features when personalised either for individual viewers or for groups of viewers participating in entertainment, training, gaming or informational activities. Although several scenarios and use-cases can be envisaged, we are focussed on the application of personalised product placement. Targeted advertising and product placement are currently garnering intense interest in the commercial networked media industries. Personalisation of product placement is a relevant and timely service for next generation online marketing and advertising and for many other revenue generating interactive services. This paper discusses the acquisition and insertion of media objects into a TV video play-out stream where the objects are determined by the profile of the viewer. The technology is based on MPEG-4 standards using object based video and MPEG-7 for metadata. No proprietary technology or protocol is proposed. To trade the objects into the video play-out, a Software-as-a-Service brokerage platform based on intelligent agent technology is adopted. Agencies, libraries and service providers are represented in a commercial negotiation to facilitate the contractual selection and usage of objects to be inserted into the video play-out.
Fast Learning Rate of Multiple Kernel Learning: Trade-Off between Sparsity and Smoothness

We investigate the learning rate of multiple kernel learning (MKL) with $\ell_1$ and elastic-net regularizations. The elastic-net regularization is a composition of an $\ell_1$-regularizer for inducing the sparsity and an $\ell_2$-regularizer for controlling the smoothness. We focus on a sparse setting where the total number of kernels is large, but the number of nonzero components of the ground truth is relatively small, and show sharper convergence rates than the learning rates have ever shown for both $\ell_1$ and elastic-net regularizations. Our analysis reveals some relations between the choice of a regularization function and the performance. If the ground truth is smooth, we show a faster convergence rate for the elastic-net regularization with less conditions than $\ell_1$-regularization; otherwise, a faster convergence rate for the $\ell_1$-regularization is shown.
Corrosion and wear study of Ni-P-PTFE-Al2O3 coating: the effect of heat treatment

In the present work, Ni-P-PTFE-Al2O3 nanodispersion coatings were developed on mild steel. These coatings were heat treated and were exposed to 3.5% NaCl solution. Thus obtained as plated, heat treated and exposed coatings were investigated for their corrosion and wear behavior together with other tribological properties. SEM/EDAX and X-ray diffractometry were used to analyze composition and structural changes of the coatings. Corrosion parameters of the coated samples were obtained from electrochemical polarization and immersion tests. Microhardness, wear resistance and friction coefficient of the coatings were also measured. The results showed that heat treatment at 200°C has little effect on the coating whereas treatment at 400°C results in enhanced crystallinity due to formation of intermetallics phases and possibly aluminum phosphide. Beyond this temperature at 600°C coarsening of the grains occurs which reduces the number of hardening sites. These structural changes result in the observance of highest hardness and lowest wear rate in case of coatings treated at 400°C. Corrosion resistance of the coated samples was observed to decrease with the heat treatment presumably due to the various physical and structural changes of the coating components. Similar changes were observed on their exposure to the corrosive media.
Testosterone is an independent determinant of bone mineral density in men with type 2 diabetes mellitus

Abstract Background: Although many reports have elucidated pathophysiological characteristics of abnormal bone metabolism in patients with type 2 diabetes mellitus (DT2), determinants of bone mineral density (BMD) in patients with DT2 are still controversial. Methods: We examined 168 Belarussian men 45–60 years of age. Plasma total cholesterol (TC), high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, very low-density lipoprotein cholesterol, triglycerides, hemoglobin A1c (HbA1c), immunoreactive insulin, and C-reactive protein concentrations were assessed. BMD was measured using dual energy X-ray densitometry of the lumbar spine (L1–L4). Total testosterone (TT) and sex hormone-binding globulin were measured, and free testosterone (FT) was calculated. Results: Using univariate linear regression analysis, BMD of the lumbar spine was significantly correlated with FT (r=0.32, p<0.01) and TT (r=0.36, p<0.01). Using multiple linear regression analysis adjusted for confounding factors, BMD was significantly correlated with TT (β=0.23, p<0.001) and TC (β=–0.029, p=0.005). Age (β=0.005, p=0.071), body mass index (β=0.005, p=0.053), HbA1c (β=–0.002, p=0.72) and duration of diabetes (β=0.001, p=0.62) were not significantly correlated with BMD. Conclusions: Our data indicate that androgens are independent determinants of BMD in male patients with DT2.
EMX2 Is a Predictive Marker for Adjuvant Chemotherapy in Lung Squamous Cell Carcinomas

Background Squamous cell carcinomas (SCC) account for approximately 30% of non-small cell lung cancer (NSCLC). Current staging methods do not adequately predict outcome for this disease. EMX2 is a homeo-domain containing transcription factor known to regulate a key developmental pathway. This study assessed the significance of EMX2 as a prognostic and predictive marker for resectable lung SCC. Methods Two independent cohorts of patients with lung SCC undergoing surgical resection were studied. EMX2 protein expression was examined by immunohistochemistry, Western blot, or immunofluorescence. EMX2 expression levels in tissue specimens were scored and correlated with patient outcomes. Chemo-sensitivity of lung SCC cell lines stably transfected with EMX2 shRNAs to cisplatin, carboplatin, and docetaxel was examined in vitro. Results EMX2 expression was down-regulated in lung SCC tissue samples compared to their matched adjacent normal tissues. Positive EMX2 expression was significantly associated with improved overall survival in stage I lung SCC patients, and in stage II/IIIA lung SCC patients receiving adjuvant chemotherapy. EMX2 expression was also associated with expression of EMT markers in both lung SCC cell lines and tissue samples. Knock-down of EMX2 expression in lung SCC cells promoted chemo-resistance and cell migration. Conclusions EMX2 expression is down-regulated in lung SCC and its down-regulation is associated with chemo-resistance in lung SCC cells, possibly through regulation of Epithelial-to-Mesenchymal Transition (EMT). EMX2 may serve as a novel prognostic marker for stage I lung SCC patients and a prediction marker for stage II/IIIA lung SCC patients receiving adjuvant chemotherapy.
Investigation of triangular, circular and ellipse slots of Vivaldi antenna

This paper discussed on simulation investigation of triangle slot, circular slot and ellipse slot of Vivaldi antenna in term of bandwidth, reflection coefficient, gain and directivity which is operated at frequency of 10 GHz. Firstly, a normal Vivaldi antenna using FR-4 substrate had been designed and the single slot and double slot is developed. The double ellipse slots from 1mm to 5mm the gain and directivity increase from 5.460 dB to 6.805 dB and from 5.699 dBi to 7.136 dBi within their respective operating band. The advantages of a Vivaldi antenna such as simplicity, wide bandwidth, and high gain at microwave. This proposed antenna had been designed and simulated using Computer Simulation Technology (CST) simulation software.
Estimating the Effect of Financial Aid Offers on College Enrollment: A Regression-Discontinuity Approach

An important problem faced by colleges and universities, that of evaluating the effect of their financial aid offers on student enrollment decisions, is complicated by the likely endogeneity of the aid offer variable in a student enrollment equation. This article shows how discontinuities in an East Coast college's aid assignment rule can be exploited to obtain credible estimates of the aid effect without having to rely on arbitrary exclusion restrictions and functional form assumptions. Semiparametric estimates based on a regression-discontinuity (RD) approach affirm the importance of financial aid as an effective instrument in competing with other colleges for students. Copyright 2002 by the Economics Department of the University of Pennsylvania and Osaka University Institute of Social and Economic Research Association
TRACING SAGITTARIUS STRUCTURE WITH SDSS AND SEGUE IMAGING AND SPECTROSCOPY

We show that the Sagittarius dwarf tidal stream can be traced with very red K/M-giant stars, selected from Sloan Digital Sky Survey (SDSS) photometry. A subset of these stars are spectroscopically confirmed with SEGUE and SDSS spectra, and the distance scale of 2MASS and SDSS M giants is calibrated to the RR Lyrae distance scale. The absolute magnitude of the K/M-giant stars at the tip of the giant branch is . The line-of-sight velocities of the M giant and blue horizontal-branch (BHB) stars that are spatially coincident with the Sgr dwarf tidal stream are consistent with those of previous authors, reinforcing the need for new models that can explain all of the Sgr tidal debris stream observations. We estimate stellar densities along the tidal tails that can be used to help constrain future models. The K/M giant, BHB, and F-turnoff stars in the lower surface brightness tidal stream that is adjacent to the main leading Sgr dwarf tidal tail have velocities and metallicities that are similar to those of the stars in the leading tidal tail. The ratio of K/M giants to BHBs and BHBs to F-turnoff stars are also similar for both branches of the leading tidal tail. We show that there is an additional low-metallicity tidal stream near the Sgr trailing tidal tail.
Optimal Synchronization Control of High-Precision Motion Systems

Recently, in the motion control area, one of the most challenging problems has been synchronization control of multiple motion axes or drivers. Unfortunately, the majority of the previous approaches have not fully addressed the synchronization problem when the system performs a complex motion. In this paper, a novel synchronized design of the high-precision motion control system is presented. The basic idea is to introduce the coupling and synchronization factors into the definition of the synchronization error. Then, a new quadratic performance index incorporating the synchronization errors of the multiple motion axes is introduced so that the resulting control law generates the cross-coupling control action, and as a consequence, improved tracking and synchronization performance can be obtained. The key to the success of the new design is to ensure that each motor tracks its desired trajectory while synchronizing motion with others. Computer simulations and real-time experiments on a servo system with two permanent-magnet linear motors demonstrate the effectiveness of the method
Using the pyMIC Offload Module in PyFR

PyFR is an open-source high-order accurate computational fluid dynamics solver for unstructured grids. It is designed to efficiently solve the compressible Navier-Stokes equations on a range of hardware platforms, including GPUs and CPUs. In this paper we will describe how the Python Offload Infrastructure for the Intel Many Integrated Core Architecture (pyMIC) was used to enable PyFR to run with near native performance on the Intel Xeon Phi coprocessor. We will introduce the architecture of both pyMIC and PyFR and present a variety of examples showcasing the capabilities of pyMIC. Further, we will also compare the contrast pyMIC to other approaches including native execution and OpenCL. The process of adding support for pyMIC into PyFR will be described in detail. Benchmark results show that for a standard cylinder flow problem PyFR with pyMIC is able achieve 240 GFLOP/s of sustained double precision floating point performance; for a 1.85 times improvement over PyFR with C/OpenMP on a 12 core Intel Xeon E5-2697 v2 CPU.
A Model of Zero-Shot Learning of Spoken Language Understanding

When building spoken dialogue systems for a new domain, a major bottleneck is developing a spoken language understanding (SLU) module that handles the new domain’s terminology and semantic concepts. We propose a statistical SLU model that generalises to both previously unseen input words and previously unseen output classes by leveraging unlabelled data. After mapping the utterance into a vector space, the model exploits the structure of the output labels by mapping each label to a hyperplane that separates utterances with and without that label. Both these mappings are initialised with unsupervised word embeddings, so they can be computed even for words or concepts which were not in the SLU training data.
Domain-Based Predictive Models for Protein-Protein Interaction Prediction

Protein interactions are of biological interest because they orchestrate a number of cellular processes such as metabolic pathways and immunological recognition. Recently, methods for predicting protein interactions using domain information are proposed and preliminary results have demonstrated their feasibility. In this paper, we develop two domain-based statistical models (neural networks and decision trees) for protein interaction predictions. Unlike most of the existing methods which consider only domain pairs (one domain from one protein) and assume that domain-domain interactions are independent of each other, the proposed methods are capable of exploring all possible interactions between domains and make predictions based on all the domains. Compared to maximum-likelihood estimation methods, our experimental results show that the proposed schemes can predict protein-protein interactions with higher specificity and sensitivity, while requiring less computation time. Furthermore, the decision tree-based model can be used to infer the interactions not only between two domains, but among multiple domains as well.