Miracle-dz/UniFAR-FTUs huggingface dataset:
Drug intervention can correct subnormal retinal oxygenation response in experimental diabetic retinopathy.

PURPOSE Early subnormal retinal oxygenation response to a hyperoxic provocation (DeltaPo2) is strongly associated with subsequent experimental diabetic retinopathy and can be reversed by drug treatments started with the induction of diabetes. It is not yet known whether drug treatment can reverse an established subnormal DeltaPo2. METHODS Retinal DeltaPo2 was measured in two separate experimental paradigms in streptozotocin-induced diabetic rats. In a prevention study, measurements were performed in untreated diabetic rats, 3 months after the initiation of hyperglycemia (D3mo), in age-matched nondiabetic rats (C3mo), and in diabetic rats treated orally for 3 months with celecoxib, a cyclooxygenase (COX)-2-selective inhibitor, (D3mo+COX2i). In an intervention study, measurements were performed in untreated diabetic rats 4 months after the initiation of diabetes (D4mo), in age-matched nondiabetic rats (C4mo), and in diabetic rats that were untreated for 3 months and then were orally treated for an additional month with either celecoxib (D4mo+COX2i) or l-N (6)-(1-iminoethyl)lysine 5-tetrazole amide, a prodrug of an inhibitor of inducible nitric oxide synthase (iNOS, D4mo+ iNOSi). RESULTS In the prevention arm, subnormal (P < 0.05) retinal DeltaPo2 was found in the D3mo group, but not in the D3mo+COX2i group (P > 0.05). In a previous study, it was reported that retinal DeltaPo2 also corrected in a D3mo+iNOSi group. In the intervention arm, retinal DeltaPo2 levels in the D4mo and D4mo+iNOSi, but not the D4mo+COX2i, groups were (P < 0.05) subnormal. CONCLUSIONS These results demonstrate, for the first time, that drug treatment can reverse an established subnormal DeltaPo2. Furthermore, this effect could not be predicted by a drugs' ability to prevent the development of subnormal DeltaPo2.
Object Classification using Hybrid Holistic Descriptors: Application to Building Detection in Aerial Orthophotos

We present a framework for automatic and accurate multiple detection of objects of interest from images using hybrid image descriptors. The proposed framework combines a powerful segmentation algorithm with a hybrid descriptor. The hybrid descriptor is composed by color histograms and several Local Binary Patterns based descriptors. The proposed framework involves two main steps. The first one consists in segmenting the image into homogeneous regions. In the second step, in order to separate the objects of interest and the image background, the hybrid descriptor of each region is classiied using machine learning tools and a gallery of training descriptors. To show its performance, the method is applied to extract building roofs from orthophotos. We provide evaluation performances over 100 buildings. The proposed approach presents several advantages in terms of applicability, suitability and simplicity. We also show that the use of hybrid descriptors lead to an enhanced performance
An interference cancellation scheme using a circular antenna array with a central element

An interference cancellation scheme is proposed to suppress interference at the horizon and azimuthally through 360°. The scheme adopts the symmetry characteristic of a uniform circular array (UCA) and the power minimization (PM) technique for interference nulling. An N-1 elements UCA with a central element antenna is considered, and only two processing channels are used for PM processing. The performance of the proposed scheme is studied in terms of nulling depth in the spatial domain and cancellation ratio in statistical computations. The scheme is also compared to a conventional N-channel design employed with the same N-array antenna and the PM technique.
A Hybrid Cosmological Hydrodynamic/N-Body Code Based on a Weighted Essentially Nonoscillatory Scheme

We present a newly developed cosmological hydrodynamics code based on weighted essentially nonoscillatory (WENO) schemes for hyperbolic conservation laws. WENO is a higher order accurate finite difference scheme designed for problems with piecewise smooth solutions containing discontinuities and has been successfully applied for problems involving both shocks and complicated smooth solution structures. We couple hydrodynamics based on the WENO scheme with the standard Poisson solver-particle-mesh (PM) algorithm for evolving the self-gravitating system. The third-order low-storage total variation diminishing (TVD) Runge-Kutta scheme has been used for the time integration of the system. To test the accuracy and convergence rate of the code, we subject it to a number of typical tests, including the Sod shock tube in multiple dimensions, the Sedov blast wave, and formation of the Zel'dovich pancake. These tests validate the WENO hydrodynamics with a fast convergence rate and high accuracy. We also evolve a low-density flat cosmological model (ΛCDM) to explore the validity of the code in practical simulations.
A general framework for extending classic array processing techniques to the underdetermined blind source separation problem

The underdetermined blind source separation problem is the one of separating a large number of sources using a smaller number of mixtures. The DUET algorithm presents a solution to the underdetermined blind source separation problem, demixing an arbitrary number of speech signals from only 2 anechoic mixtures of them. Here the DUET method is presented as a general framework for extending classic array processing methods to the underdetermined blind source separation problem.
Reduction of noise for speech signal enhancement using Spectral Subtraction method

To improve the intelligibility and quality of the speech, speech enhancement is done. Speech signal when recorded in the noisy environment degrades the clean speech signal. Speech enhancement reduces the noise without distorting the original (clean) signal. In this concept, the method of Multi-Band Spectral Subtraction (MBSS) is used to enhance the noisy speech signal. The fact that the speech spectrum is affected by colored noise differently at various frequencies is taken into account. Later, the properties of this method are derived in terms of input and output SNR.
Optimizing the Computational Lexicalization of Large Grammars

The computational lexicalization of a grammar is the optimization of the links between lexicalized rules and lexical items in order to improve the quality of the bottom-up filtering during parsing. This problem is NP-complete and untractable on large grammars. An approximation algorithm is presented. The quality of the suboptimal solution is evaluated on real-world grammars as well as on randomly generated ones.
An optimized treatment for algorithmic differentiation of an important glaciological fixed-point problem

Abstract. We apply an optimized method to the adjoint generation of a time-evolving land ice model through algorithmic differentiation (AD). The optimization involves a special treatment of the fixed-point iteration required to solve the nonlinear stress balance, which differs from a straightforward application of AD software, and leads to smaller memory requirements and in some cases shorter computation times of the adjoint. The optimization is done via implementation of the algorithm of Christianson (1994) for reverse accumulation of fixed-point problems, with the AD tool OpenAD. For test problems, the optimized adjoint is shown to have far lower memory requirements, potentially enabling larger problem sizes on memory-limited machines. In the case of the land ice model, implementation of the algorithm allows further optimization by having the adjoint model solve a sequence of linear systems with identical (as opposed to varying) matrices, greatly improving performance. The methods introduced here will be of value to other efforts applying AD tools to ice models, particularly ones which solve a hybrid shallow ice/shallow shelf approximation to the Stokes equations.
Gpos-PLoc: an ensemble classifier for predicting subcellular localization of Gram-positive bacterial proteins.

A statistical analysis indicated that, of the 35,016 Gram-positive bacterial proteins from the recent Swiss-Prot database, approximately 57% of these entries are without subcellular location annotations. In the gene ontology database, the corresponding percentage is approximately 67%, meaning the percentage of proteins without subcellular component annotations is even higher. With the avalanche of gene products generated in the post-genomic era, the number of such location-unknown entries will continuously increase. It is highly desired to develop an automated method for timely and accurately identifying their subcellular localization because the information thus obtained is very useful for both basic research and drug discovery practice. In view of this, an ensemble classifier called 'Gpos-PLoc' was developed for predicting Gram-positive protein subcellular localization. The new predictor is featured by fusing many basic classifiers, each of which was engineered according to the optimized evidence-theoretic K-nearest neighbors rule. As a demonstration, tests were performed on Gram-positive proteins among the following five subcellular location sites: (1) cell wall, (2) cytoplasm, (3) extracell, (4) periplasm and (5) plasma membrane. To eliminate redundancy and homology bias, only those proteins which have < 25% sequence identity to any other in a same subcellular location were allowed to be included in the benchmark datasets. The overall success rates thus achieved by Gpos-PLoc were > 80% for both jackknife cross-validation test and independent dataset test, implying that Gpos-PLoc might become a very useful vehicle for expediting the analysis of Gram-positive bacterial proteins. Gpos-PLoc is freely accessible to public as a web-server at http://202.120. 37.186/bioinf/Gpos/. To support the need of many investigators in the relevant areas, a downloadable file is provided at the same website to list the results identified by Gpos-PLoc for 31,898 Gram-positive bacterial protein entries in Swiss-Prot database that either have no subcellular location annotation or are annotated with uncertain terms such as 'probable', 'potential', 'perhaps' and 'by similarity'. Such large-scale results will be updated once a year to include the new entries of Gram-positive bacterial proteins and reflect the continuous development of Gpos-PLoc.
Spectral properties of the Dirichlet-to-Neumann operator for the exterior Helmholtz problem and its applications to scattering theory

We prove that the Dirichlet-to-Neumann operator (DtN) has no spectrum in the lower half of the complex plane. We find several applications of this fact in scattering by obstacles with impedance boundary conditions. In particular, we find an upper bound for the gradient of the scattering amplitude and for the total cross section. We justify numerical approximations by providing bounds for the difference between theoretical and approximated solutions without using any a priori unknown constants.
Modeling XviD as a Kahn Process Network , a Sesame Application Design Document

To optimize design space exploration of embedded systems, the Artemis modeling and simulation project focuses on quick and efficient methods for testing the performance of (multimedia) applications on a wide range of different architectures. For Sesame, the prototype simulation framework, only two applications have been implemented and tested so far, an M-JPEG encoder and an MPEG-2 decoder. This report gives an overview of our work on modeling another application, an MPEG-4 encoder based on the XviD video codec as a Kahn Process Network to prepare it for implementation in Sesame.
Forecasting VAR and Expected Shortfall Using Dynamical Systems: A Risk Management Strategy

Using non-parametric and parametric models, we show that the bivariate distribution of an Asian portfolio is not stable along all the period under study. We suggest several dynamic models to compute two market risk measures, the Value at Risk and the Expected Shortfall: the RiskMetrics methodology, the Multivariate GARCH models, the Multivariate Markov-Switching models, the empirical histogram and the dynamic copulas. We discuss the choice of the best method with respect to the policy management of bank supervisors. The copula approach seems to be a good compromise between all these models. It permits taking financial crises into account and obtaining a low capital requirement during the most important crises.
Averaged Stochastic Gradient Descent with Feedback: An Accurate, Robust, and Fast Training Method

On large datasets, the popular training approach has been stochastic gradient descent (SGD). This paper proposes a modification of SGD, called averaged SGD with feedback (ASF), that significantly improves the performance (robustness, accuracy, and training speed) over the traditional SGD. The proposal is based on three simple ideas: averaging the weight vectors across SGD iterations, feeding the averaged weights back into the SGD update process, and deciding when to perform the feedback (linearly slowing down feedback). Theoretically, we demonstrate the reasonable convergence properties of the ASF. Empirically, the ASF outperforms several strong baselines in terms of accuracy, robustness over the noise, and the training speed. To our knowledge, this is the first study of ``feedback'' in stochastic gradient learning. Although we choose latent conditional models for verifying the ASF in this paper, the ASF is a general purpose technique just like SGD, and can be directly applied to other models.
Normalization of circulating microRNA expression data obtained by quantitative real-time RT-PCR

The high-throughput analysis of microRNAs (miRNAs) circulating within the blood of healthy and diseased individuals is an active area of biomarker research. Whereas quantitative real-time reverse transcription polymerase chain reaction (qPCR)-based methods are widely used, it is yet unresolved how the data should be normalized. Here, we show that a combination of different algorithms results in the identification of candidate reference miRNAs that can be exploited as normalizers, in both discovery and validation phases. Using the methodology considered here, we identify normalizers that are able to reduce nonbiological variation in the data and we present several case studies, to illustrate the relevance in the context of physiological or pathological scenarios. In conclusion, the discovery of stable reference miRNAs from high-throughput studies allows appropriate normalization of focused qPCR assays.
Is phosphorus retention in autochthonous lake sediments controlled by oxygen or phosphorus?

Eutrophication of various lakes on the Swiss Plateau was targeted in the 1980s by reducing external nutrient loading and installing aeration/oxygenation systems. For five eutrophic lakes, three of which have been artificially oxygenated and aerated for almost 20 yr, phosphorus (P) balances were established using input, water‐column inventory, export via the outlet, and sediment core data. To separate the effects of aeration/oxygenation from the effects of P‐input reduction, two measures of P retention were determined—the ratio of net sedimentation to input and the ratio of net sedimentation to gross sedimentation. A comparison among the five lakes shows that, presently, P retention does not differ significantly between lakes with aeration/oxygenation and lakes with an anoxic hypolimnion, whereas retention at times of maximum P concentrations in the 1970s and 1980s was significantly lower compared with retention in today’s moderately eutrophic lakes. This is in agreement with the finding that net sedimentation in all lakes remained relatively constant between 280 and 950 kg km−2, independent of their P concentration. It is shown that constant net sedimentation is likely to prevail as long as P concentrations remain above a critical value of around 40 mg m−3. Constant net sedimentation indicates that permanent deposition of P is limited by the sediment's binding capacity. Oxic conditions in the hypolimnion do not necessarily increase this capacity because redox‐dependent P species may still be released within the anoxic bulk sediment and diffuse back into the water column. To characterize the P retention capacity of anoxic sediments, we suggest that the P burial rate be determined from dated sediment cores rather than from input/output balances.
Mean and Covariance Structure Analysis: Theoretical and Practical Improvements

Abstract The most widely used multivariate statistical models in the social and behavioral sciences involve linear structural relations among observed and latent variables. In practice, these variables are generally nonnormally distributed; hence classical multivariate analysis, based on multinomial error-free variables having no simultaneous interrelations, is not adequate to deal with such data. A promising alternative, based on asymptotically distribution-free (ADF) covariance structure analysis, has been found to be virtually useless in practical model evaluation at finite sample sizes with nonnormal data. We take a new look at the basic statistical theory of structural models under arbitrary distributions, using the methodology of nonlinear regression and generalized least squares estimation. For example, we adopt the use of residual weight matrices from regression theory. We develop a series of estimators and tests based on arbitrary distribution theory. We obtain a type of probabilistic Bartlett co.
Comparative Metabolomic Analysis of the Neuroprotective Effects of Scutellarin and Scutellarein against Ischemic Insult

For more than thirty years, scutellarin (Scu) has been used in China to clinically treat acute cerebral infarction and paralysis. Scutellarein (Scue), the major Scu metabolite in vivo, exhibits heightened neuroprotective effects when compared to Scu. To explore the neuroprotective role of these compounds, we performed ultra-high-performance liquid chromatography-quadrupole/time-of-flight mass spectrometry (UHPLC-QTOF/MS) coupled with a pattern recognition approach to investigate metabolomic differences in a rat model of ischemia after treatment with each compound. We examined metabolites in urine, hippocampal tissue, and plasma, and we tentatively identified 23 endogenous metabolites whose levels differed significantly between sham-operated and model groups. Upon pathway analysis, we found an additional 11 metabolic pathways in urine, 14 metabolic pathways in the hippocampal tissue, and 3 metabolic pathways in plasma. These endogenous metabolites were mainly involved in sphingolipid metabolism, lysine biosynthesis, and alanine, aspartate, and glutamate metabolism. We found that metabolic changes after ischemic injury returned to near-normal levels after Scue intervention, unlike Scu treatment, further validating the heightened protective effects exerted by Scue compared to Scu. These results demonstrate that Scue is a potential drug for treatment of ischemic insult.
Cardiovascular Development and Disease Evidence of Aortopathy in Mice with Haploinsufficiency of Notch1 in Nos3-null Background

Thoracic aortic aneurysms (TAA) are a significant cause of morbidity and mortality in humans. While the exact etiology is unknown, genetic factors play an important role. Mutations in NOTCH1 have been linked to bicuspid aortic valve (BAV) and aortopathy in humans. The aim of this study was to determine if haploinsufficiency of Notch1 contributes to aortopathy using Notch1 +/− ; Nos3 −/− mice. Echocardiographic analysis of Notch1 +/− ; Nos3 −/− mice reveals effacement of the sinotubular junction and a trend toward dilation of the aortic sinus. Furthermore, examination of the proximal aorta of Notch1 +/− ; Nos3 −/− mice reveals elastic fiber degradation, a trend toward increased matrix metalloproteinase 2 expression, and increased smooth muscle cell apoptosis, features characteristic of aneurysmal disease. Although at a lower penetrance, we also found features consistent with aortopathic changes in Notch1 heterozygote mice and in Nos3-null mice. Our findings implicate a novel role for Notch1 in aortopathy of the proximal aorta.
Semiregular variables in the solar neighbourhood

Period-luminosity sequences have been shown to exist among the semiregular variables (SRVs) in the Magellanic Clouds, the bulge of the Milky Way galaxy and elsewhere. Using modern-period and revised Hipparcos parallax data, this paper demonstrates that they also appear among the M giant SRVs of the solar neighbourhood. Their distribution in the K, log P diagram resembles that of Bulge stars more closely than those in the Magellanic Clouds. The prevalence of mass-loss among local M-type SRVs and its dependence on period and spectral subtype are also discussed. K - [12], a measure of circumstellar dust emission, increases clearly with V amplitude, M giant subtype and log P.
Accurate calibration of two wheel differential mobile robots by using experimental heading errors

Odometry using wheel encoders provides fundamental pose estimates for wheeled mobile robots. Systematic errors of odometry can be reduced by the calibration of kinematic parameters. The UMBmark method is one of the widely used calibration schemes for two wheel differential mobile robot. In this paper, the accurate calibration scheme of kinematic parameters is proposed by extending the conventional UMBmark. The contributions of this paper can be summarized as two issues. The first contribution is to present new calibration scheme that reduce the approximation error of calibration equations in UMBmark method. The new equations were derived from the robot's final orientation errors in test track. The second contribution is to propose the consideration of the coupled effects between wheel diameter errors and wheelbase errors in test track for calibration experiments. The calibration performance can be significantly improved by applying new calibration equations were derived to overcome the limitations of the conventional scheme. The numerical simulations and experimental results show that the odometry accuracy can be improved by the proposed calibration schemes.
Simulated Epidemics in an Empirical Spatiotemporal Network of 50,185 Sexual Contacts

Sexual contact patterns, both in their temporal and network structure, can influence the spread of sexually transmitted infections (STI). Most previous literature has focused on effects of network topology; few studies have addressed the role of temporal structure. We simulate disease spread using SI and SIR models on an empirical temporal network of sexual contacts in high-end prostitution. We compare these results with several other approaches, including randomization of the data, classic mean-field approaches, and static network simulations. We observe that epidemic dynamics in this contact structure have well-defined, rather high epidemic thresholds. Temporal effects create a broad distribution of outbreak sizes, even if the per-contact transmission probability is taken to its hypothetical maximum of 100%. In general, we conclude that the temporal correlations of our network accelerate outbreaks, especially in the early phase of the epidemics, while the network topology (apart from the contact-rate distribution) slows them down. We find that the temporal correlations of sexual contacts can significantly change simulated outbreaks in a large empirical sexual network. Thus, temporal structures are needed alongside network topology to fully understand the spread of STIs. On a side note, our simulations further suggest that the specific type of commercial sex we investigate is not a reservoir of major importance for HIV.