Miracle-dz/UniFAR-FTUs huggingface dataset:
[Association of metabolic syndrome and oxidative stress with ischemic heart disease in middle-aged persons].

UNLABELLED The aim of this study was to evaluate the association of metabolic syndrome and oxidative stress with ischemic heart disease in middle-aged persons (men and women aged 45-64 years). MATERIAL AND METHODS In this study, we have used data of 533 persons (247 men and 286 women) aged 45-64 years from Kaunas population cohort investigated according to WHO MONICA study protocol during 2001-2002 in whom concentrations of malondialdehyde, a marker of lipid peroxidation, and antioxidant vitamin E in blood serum were determined. Metabolic syndrome was defined by the criteria of Adult Treatment Panel III. Oxidative stress was determined in the presence of increased level of malondialdehyde (> or =5 micromol/L) and decreased level of lipid-standardized vitamin E (<20 micromol/L). The levels of malondialdehyde and vitamin E were measured by fluorimetric methods. RESULTS Ischemic heart disease was diagnosed in 19.8% of men and 21.3% of women. The frequency of ischemic heart disease among persons (men and women) with metabolic syndrome was significantly higher than among persons without metabolic syndrome (27.4% vs. 17.8%, respectively; odds ratio, 1.63; P=0.032). The frequency of ischemic heart disease among persons with oxidative stress was significantly higher than among persons without oxidative stress (29.1% vs. 17.6%, respectively; odds ratio, 1.88; P=0.029). The highest prevalence of ischemic heart disease (30.0%) was among persons with metabolic syndrome and oxidative stress, and the lowest prevalence of this disease (13.8%) was among persons without metabolic syndrome and without oxidative stress (odds ratio, 2.54; P=0.017). These data were adjusted by sex and age. CONCLUSION Metabolic syndrome and oxidative stress are significantly associated with ischemic heart disease among 45-64-year-old persons.
Identification of Acyloxyacyl Hydrolase, a Lipopolysaccharide- Detoxifying Enzyme, in the Murine Urinary Tract

ABSTRACT Acyloxyacyl hydrolase (AOAH) is an unusual but highly conserved lipase, previously described only in myeloid cells, that removes secondary fatty acyl chains from bacterial lipopolysaccharides (LPS) and may also act on various glycero(phospho)lipids. Deacylation by AOAH greatly reduces the ability of LPS to stimulate cells via CD14-MD-2-Toll-like receptor 4. We report here that renal cortical tubule cells produce AOAH and secrete it into urine, where it can deacylate LPS. In vitro studies revealed that proximal tubule cells secrete pro-AOAH, which can be taken up by bladder cells and processed to the heterodimeric, more enzymatically active, mature form of AOAH. AOAH can then be used by the recipient cells to deacylate LPS. The enzyme produced by proximal tubule epithelium may thus be shared with downstream cells. In addition, mature AOAH is found in the urine. We suggest that cortical tubule cells may produce and secrete AOAH to limit inflammatory responses to gram-negative bacteria throughout the urinary tract.
Ti plasmid vector for the introduction of DNA into plant cells without alteration of their normal regeneration capacity

A Ti plasmid mutant was constructed in which all the on‐cogenic functions of the T‐DNA have been deleted and replaced by pBR322. This Ti plasmid, pGV3850, still mediates efficient transfer and stabilization of its truncated T‐DNA into infected plant cells. Moreover, integration and expression of this minimal T‐DNA in plant cells does not interfere with normal plant cell differentiation. A DNA fragment cloned in a pBR vector can be inserted in the pGV3850 T‐region upon a single recombination event through the pBR322 region of pGV3850 producing a co‐integrate useful for the transformation of plant cells. Based upon these properties, pGV3850 is proposed as an extremely versatile vector for the introduction of any DNA of interest into plant cells.
Data Fusion and Multicue Data Matching by Diffusion Maps

Data fusion and multicue data matching are fundamental tasks of high-dimensional data analysis. In this paper, we apply the recently introduced diffusion framework to address these tasks. Our contribution is three-fold: first, we present the Laplace-Beltrami approach for computing density invariant embeddings which are essential for integrating different sources of data. Second, we describe a refinement of the Nystrom extension algorithm called "geometric harmonics. " We also explain how to use this tool for data assimilation. Finally, we introduce a multicue data matching scheme based on nonlinear spectral graphs alignment. The effectiveness of the presented schemes is validated by applying it to the problems of lipreading and image sequence alignment
Design and experiment of a rectenna array base on GaAs transistor for microwave power transmission

A large power rectenna array has been designed and tested in S-band for applications involving microwave wireless power transmission(WPT). The rectenna array consists of a 16-element array antenna and 16-element array rectifier based on GaAs. The structure is printed on F4B-2 substrate with the dielectric constant 2.55 and a thickness of 1mm. Conventional rectifying circuits use microwave schottky diode as the rectifying chip,but the output DC power of a single rectifying circuit still stays on mW levels. In this paper, based on the theoretical analysis of the rectifier circuit theory, a efficient antenna array using GaAs transistors chip is designed. The wireless power transmission (WPT) experiment using the rectenna array was conducted. As a result, the RPV motor output voltage is 4.6V and current is 3.1A, where the input power was 110W and the distance was 0.4m between transmitting antenna and the receiving antenna.
Statistical Classification of Buried Unexploded Ordnance Using Nonparametric Prior Models

We used kernel density estimation (KDE) methods to build a priori probability density functions (pdfs) for the vector of features that are used to classify unexploded ordnance items given electromagnetic-induction sensor data. This a priori information is then used to develop a new suite of estimation and classification algorithms. As opposed to the commonly used maximum-likelihood parameter estimation methods, here we employ a maximum a posteriori (MAP) estimation algorithm that makes use of KDE-generated pdfs. Similarly, we use KDE priors to develop a suite of classification schemes operating in both "feature" space as well as ldquosignal/datardquo space. In terms of feature-based methods, we construct a support vector machine classifier and its extension to support M-ary classification. The KDE pdfs are also used to synthesize a MAP feature-based classifier. To address the numerical challenges associated with the optimal data-space Bayesian classifier, we have used several approximation techniques, including Laplacian approximation and generalized likelihood ratio tests employing the priors. Using both simulations and real field data, we observe a significant improvement in classification performance due to the use of the KDE-based prior models.
LLF𝒫: a logical framework for modeling external evidence, side conditions, and proof irrelevance using monads

We extend the constructive dependent type theory of the Logical Framework $\mathsf{LF}$ with monadic, dependent type constructors indexed with predicates over judgements, called Locks. These monads capture various possible proof attitudes in establishing the judgment of the object logic encoded by an $\mathsf{LF}$ type. Standard examples are factoring-out the verification of a constraint or delegating it to an external oracle, or supplying some non-apodictic epistemic evidence, or simply discarding the proof witness of a precondition deeming it irrelevant. This new framework, called Lax Logical Framework, $\mathsf{LLF}_{\cal P}$, is a conservative extension of $\mathsf{LF}$, and hence it is the appropriate metalanguage for dealing formally with side-conditions in rules or external evidence in logical systems. $\mathsf{LLF}_{\cal P}$ arises once the monadic nature of the lock type-constructor, ${\cal L}^{\cal P}_{M,\sigma}[\cdot]$, introduced by the authors in a series of papers, together with Marina Lenisa, is fully exploited. The nature of the lock monads permits to utilize the very Lock destructor, ${\cal U}^{\cal P}_{M,\sigma}[\cdot]$, in place of Moggi's monadic $let_T$, thus simplifying the equational theory. The rules for ${\cal U}^{\cal P}_{M,\sigma}[\cdot]$ permit also the removal of the monad once the constraint is satisfied. We derive the meta-theory of $\mathsf{LLF}_{\cal P}$ by a novel indirect method based on the encoding of $\mathsf{LLF}_{\cal P}$ in $\mathsf{LF}$. We discuss encodings in $\mathsf{LLF}_{\cal P}$ of call-by-value $\lambda$-calculi, Hoare's Logic, and Fitch-Prawitz Naive Set Theory.
Beating 1-1/e for ordered prophets

Hill and Kertz studied the prophet inequality on iid distributions [The Annals of Probability 1982]. They proved a theoretical bound of 1 - 1/e on the approximation factor of their algorithm. They conjectured that the best approximation factor for arbitrarily large n is 1/1+1/e ≃ 0.731. This conjecture remained open prior to this paper for over 30 years. In this paper we present a threshold-based algorithm for the prophet inequality with n iid distributions. Using a nontrivial and novel approach we show that our algorithm is a 0.738-approximation algorithm. By beating the bound of 1/1+1/e, this refutes the conjecture of Hill and Kertz. Moreover, we generalize our results to non-uniform distributions and discuss its applications in mechanism design.
Channel Widening Downstream of Valley Gorges Influenced by Flood Frequency and Floodplain Roughness

Mountain fronts are the locus of significant variations in river width characterized by narrow bedrock gorges opening to wider unconfined alluvial rivers that are often braided. The contribution of the abrupt change in river valley confinement in modulating the long‐term transport capacity and the resulting equilibrium channel width has never been considered. Here we use a numerical model integrating the full frequency‐magnitude distribution of streamflow to explore the long‐term bedload transport capacity of idealized confined and unconfined channels, both single thread and braided. The model predicts a significant transport capacity loss for a single channel of constant width at the transition between confined gorges and unconfined floodplains that is slightly reduced when floodplain vegetation is present. Because the total transport capacity of a single‐thread channel systematically decreases with channel width in a stochastic framework, only narrower unconfined channels could compensate for the loss of confinement. This prediction contradicts observations of widening ratios ranging from three to eight downstream of gorges in various world rivers. We resolve this inconsistency by demonstrating that a braided river made of narrow channels inset in a wide floodplain can maintain the total bedload transport capacity downstream of gorges by increasing the range of competent discharges. We also show that riparian vegetation may only enhance bedload transport capacity for highly variable discharge regimes and discuss the relevance of various definitions of representative discharges. These results point to the previously unrecognized role of valley confinement in modulating the sediment transport capacity of rivers.
Mixed illumination analysis in single image for interactive color grading

Colorists often use keying or rotoscoping tools to access and edit particular colors or parts of the scene. Although necessary, this is a time-consuming and potentially imprecise process, as it is not possible to fully separate the influence of light sources in the scene from the colors of objects and actors within it. To simplify this process, we present a new solution for automatically estimating the color and influence of multiple illuminants, based on image variation analysis. Using this information, we present a new color grading tool for simply and interactively editing the colors of detected illuminants, which fits naturally in color grading workflows. We demonstrate the use of our solution in several scenes, evaluating the quality of our results by means of a psychophysical study.
Replication of Established Common Genetic Variants for Adult BMI and Childhood Obesity in Greek Adolescents: The TEENAGE Study

Multiple genetic loci have been associated with body mass index (BMI) and obesity. The aim of this study was to investigate the effects of established adult BMI and childhood obesity loci in a Greek adolescent cohort. For this purpose, 34 variants were selected for investigation in 707 (55.9% females) adolescents of Greek origin aged 13.42 ± 0.88 years. Cumulative effects of variants were assessed by calculating a genetic risk score (GRS‐34) for each subject. Variants at the FTO, TMEM18, FAIM2, RBJ, ZNF608 and QPCTL loci yielded nominal evidence for association with BMI and/or overweight risk (p < 0.05). Variants at TFAP2B and NEGR1 loci showed nominal association (p < 0.05) with BMI and/or overweight risk in males and females respectively. Even though we did not detect any genome‐wide significant associations, 27 out of 34 variants yielded directionally consistent effects with those reported by large‐scale meta‐analyses (binomial sign p = 0.0008). The GRS‐34 was associated with both BMI (beta = 0.17 kg/m2/allele; p < 0.001) and overweight risk (OR = 1.09/allele; 95% CI: 1.04–1.16; p = 0.001). In conclusion, we replicate associations of established BMI and childhood obesity variants in a Greek adolescent cohort and confirm directionally consistent effects for most of them.
Assembly and stability of flagellar motor in Escherichia coli

Bacterial flagellar motor is a highly ordered and complex supramolecular structure that powers rotation of flagella and serves as a type III export apparatus for flagellar assembly. Motor biogenesis represents a formidable example of self‐assembly, but little is known about early steps of the motor structure formation. Here we used a combination of fluorescence microscopy techniques to dissect the order of the motor assembly in Escherichia coli cells, to map in vivo the underlying protein interactions and to investigate dynamics of protein exchange in the assembled motor structure. Our data suggest that motor self‐assembly is initiated by oligomerization of the membrane export apparatus protein FlhA, which is followed by the recruitment of the MS ring component FliF and by the ordered association of other motor proteins. The assembly process combines the hierarchy with cooperativity, whereby the association of each subsequent motor structure stabilizes the growing assembly. Our results provide a novel and so far the most complete view of the early steps in flagellar motor assembly and improve understanding of the motor structure and regulation.
Optimizing time and space multiplexed computation in a dynamically reconfigurable processor

One of the characteristics of our coarse-grained dynamically reconfigurable processor is that it uses the same operational resource for both control-intensive and dataintensive code segments. We maximize throughput from the knowledge of high-level synthesis under timing constraints. Because the optimal clock speeds for both code segments are different, a dynamic frequency control is introduced to shorten the total execution time. A state transition controller (STC) that handles the control step can change the clock speed for every cycle. For control-intensive code segments, the STC delay is shortened by a rollback mechanism, which looks ahead to the next control step and rolls back if a different control step is actually selected. For the data-intensive code segments, the delay is shortened by fully synchronized synthesis. Experimental results show that throughputs have increased from 18% to 56% with the combination of these optimizations. A chip was fabricated with our 40-nm low-power process technology.
High-Accuracy Object Recognition with a New Convolutional Net Architecture and Learning Algorithm

Purely supervised Convolutional Networks yield excellent accuracy on image recognition tasks when data is plentiful [1]. But until now, they have not produced state-of-the-art accuracy on object recognition benchmarks for which few labeled samp les er category are available. For example, on the popular Caltech-101 dataset with 30 samp les for each of the 101 categories, methods that use hand-designed features, such as SI FT and Geometric Blur combined with a kernel classifier, achieve accuracies of 66.2% [5], an d 64.6% [6]. By contrast, a purely supervised convolutional network with standard sigmoid no n-linearities yields only 26%. This abstract describes a modified ConvNet architecture with a ne w unsupervised/supervised training procedure that can reach 67.2% accuracy on Caltech-101. This work explores several architectural designs and train ing methods and studies their effect on the accuracy for object recognition. The convolut i nal network under consideration takes a 143x143 grayscale image as input. The preprocessing includes removing mean and performing a local contrast normalization (dividing each p ixel by the standard deviation of its neighbors). The first stage has 64 filters of size 9x9, followe d by a subsampling layer with 5x5 stride, and 10x10 averaging window. The second stage has 256 feature map, each with 16 filters connected to a random subset of first-layer feature ma ps. The subsampling layer has a stride of 4x4 and a 6x6 averaging window. Hence, the input to t he last layer has 256 feature maps of size 4x4 (4096 dimensions). Figure 1 shows the outlin e of a convolutional net, and figure 2 shows the best sequence of transformations at each st age of the network. The results are shown in table . The most surprising result is that simply adding an absolute value after the hyperbolic tangent (tanh) non-linearity pr actically doubles the recognition rate from 26% to 58% with purely supervised training. We conjectu re that the advantage of a rectifying non-linearity is to remove redundant informati on (the polarity of features), and at the same time, to avoids cancellations of neighboring opposite filter responses in the subsampling layers. Adding a local contrast normalization step after ea ch feature extraction layer [4] further improves the accuracy to 60%. The second interesting result is that pre-training each sta ge one after the other using a new unsupervised method, and adjusting the resulting network u sing supervised gradient descent bumps up the accuracy to 67.2%. The procedure is reminiscent of several recent proposal for “deep learning” [2, 3]. Our layer-wise unsupervised traini ng method is called Predictive Sparse Decomposition (PSD). It consist in learning an overcomplet e s t of basis functions from which
Parametric Trajectory Representations for Behaviour Classification

This paper presents an empirical comparison of strategies for representing motion trajectories with fixed-length vectors. We compare four techniques, which have all previously been adopted in the trajectory classification literature: least-squares cubic spline approximation, the Discrete Fourier Transform, Chebyshev polynomial approximation, and the Haar wavelet transform. We measure the class separability of five different trajectory datasets - ranging from vehicle trajectories to pen trajectories - when described in terms of these representations. Results obtained over a range of dimensionalities indicate that the different representations yield similar levels of class separability, with marginal improvements provided by Chebyshev and Spline representations. For the datasets considered here, each representation appears to yield better results when used in conjunction with a curve parametrisation strategy based on arc-length, rather than time. However, we illustrate a situation - pertinent to surveillance applications - where the converse is true.
Safe Distributed Architecture for Image-based Computer Assisted Diagnosis

—Existing electronic healthcare systems based on PACS and Hospital IS are designed for clinical practice. Yet, both for security, technical and legacy reasons, they are often weakly connected to computing infrastructures and data networks. In the context of the RAGTIME project, grid infrastructures are studied to propose a cheap and reliable infrastructure enabling computerized medical applications. This raises various concerns, in particular in terms of security and data privacy. This paper presents the results of this study and proposes a complete grid- based architecture able to process medical image for assisted diagnosis in a secured way. Using this infrastructure, care practitioner are able to execute the application from any machine connected to the Internet, therefore improving their mobility. Medical image analysis jobs are certiﬁed to be correct using the latest advances in result checking and fault-tolerant algorithms provided in [1], [2]. The architecture has been successfully deployed and validated on the Grid5000 large scale infrastructure.
Nuclear Localization of Cyclin B1 Controls Mitotic Entry After DNA Damage

Mitosis in human cells is initiated by the protein kinase Cdc2-cyclin B1, which is activated at the end of G2 by dephosphorylation of two inhibitory residues, Thr14 and Tyr15. The G2 arrest that occurs after DNA damage is due in part to stabilization of phosphorylation at these sites. We explored the possibility that entry into mitosis is also regulated by the subcellular location of Cdc2-cyclin B1, which is suddenly imported into the nucleus at the end of G2. We measured the timing of mitosis in HeLa cells expressing a constitutively nuclear cyclin B1 mutant. Parallel studies were performed with cells expressing Cdc2AF, a Cdc2 mutant that cannot be phosphorylated at inhibitory sites. Whereas nuclear cyclin B1 and Cdc2AF each had little effect under normal growth conditions, together they induced a striking premature mitotic phenotype. Nuclear targeting of cyclin B1 was particularly effective in cells arrested in G2 by DNA damage, where it greatly reduced the damage-induced G2 arrest. Expression of nuclear cyclin B1 and Cdc2AF also resulted in significant defects in the exit from mitosis. Thus, nuclear targeting of cyclin B1 and dephosphorylation of Cdc2 both contribute to the control of mitotic entry and exit in human cells.
On the Approximation of the Fokker-Planck Equation of the Finitely Extensible Nonlinear Elastic Dumbbell Model I: A New Weighted Formulation and an Optimal Spectral-Galerkin Algorithm in Two Dimensions

We propose a new weighted weak formulation for the Fokker–Planck equation of the finitely extensible nonlinear elastic dumbbell model and prove its well-posedness in weighted Sobolev spaces. We also propose simple and efficient semi-implicit time-discretization schemes which are unconditionally stable, i.e., the step size of time marching does not depend on the number of the bases used in configurational space. We then restrict ourselves to the two-dimensional case and construct two Fourier–Jacobi spectral-Galerkin algorithms which enjoy the following properties: (i) they are unconditionally stable, spectrally accurate, and of optimal computational complexity; (ii) they conserve the volume and provide accurate approximation to higher-order moments of the distribution function; and (iii) they can be easily extended to coupled nonhomogeneous systems. Numerical results are presented to show how to choose a proper weight to get the best numerical results of the distribution function and the polymer stress.