Miracle-dz/UniFAR-FTUs huggingface dataset:
Comparison of Fecal Microbiota and Polyamine Concentration in Adult Patients with Intractable Atopic Dermatitis and Healthy Adults

Fecal microbiota and polyamine concentration obtained from eleven intractable adult‐type atopic dermatitis (AD) patients and thirteen healthy adults were compared. Fecal microbiota were analyzed using terminal‐restriction fragment length polymorphism. The fecal microbiota of volunteers were divided into two clusters, A (n=16) and B (n=8), and the number of AD patients was found to be higher in Cluster B than Cluster A, suggesting that there are relationships between the obstinacy of intractable adult‐type AD and intestinal microbiota in Cluster B. Fecal spermidine concentration in Cluster B were lower than that in Cluster A significantly (P<0.05). Fecal putrescine concentration in Cluster B also tended to be lower than that in Cluster A. Terminal‐restriction fragment (T‐RF) of 122 bp generated by digestion with HhaI, which were predicted as unknown bacteria, were detected characteristically in Cluster A. In contrast, T‐RFs of 368/9 bp generated by digestion with HhaI, which were predicted as Enterobacteriaceae, were detected characteristically in Cluster B. These bacteria are closely associated with intestinal polyamine concentration. These findings raise the possibility that a low concentration of intestinal polyamines produced by intestinal microbiota is one of the important factors in the onset of intractable adult‐type AD.
Data clustering and placement for the BaBar database

The BaBar experiment at the PEP-II accelerator at SLAC, due to come online in early 1999, will generate approximately 300 TB of physics and simulated data per year during its lifetime. The user community of approximately 500 physicists is widely dispersed, but require high speed access to much of the data. The placement of data throughout the collaboration and across media of different access characteristics and costs will be crucial in allowing high speed, efficient access to the most frequently used information. Placement or clustering strategies must perform in a fully multi-process and multi-user environment and must be able to utilize an arbitrary configuration of file servers (including hierarchical mass storage systems such as HPSS) in a distributed client-server architecture. Site-specific configuration must be able to cope with the requirements of the collaboration, regional and institutional centers. BaBar is using object oriented database (ODBMS) technology as the basis for the event store and conditions database. This paper will describe the data placement strategy and its implementation in terms of clustering hints and configuration files. The BaBar project is a High Energy Physics experiment at Stanford Linear Accelerator Center (SLAC). A central theme of its research program is the detailed study of the difference between matter and antimatter. The project is constructed by a large international collaboration of physicists and engineers from 10 countries. A new detector as well as the software are currently being designed and are due to come online in April 1999. Data taking is expected to last 10 years, leading to a total data sample of some 3 PB. Designing a system that can handle such enormous data volumes requires special tools and techniques. One of important aspects of a system which is expected to handle petabytes of data is the issue of how to actually store data. Over half of BaBar data will reside on tertiary storage (tapes), and careless data placement is simply unaffordable. Data should be placed in a way that will optimize its later access: “hot” data (like compressed event data, e.g. tag data) should go on fast media, while rarely used data should end up on tapes. Placement of data is undoubtedly one of the most important parts of the access optimization, the other equally important is efficient data clustering. Once a file is “touched”, as much data should be read out of it as possible; it is clearly undesirable to bring up a tape just to read several bytes. In addition to basic requirements, which have to be fulfilled by the module responsible for placement and clustering, several others like  full concurrency support  multiple file servers support  allow data distribution  allow site specific configuration must also be fulfilled. The BaBar persistent data storage is based on two commercial products: an object database: Objectivity/DB and a mass storage system: HPSS. They introduce many new features and relieve the burden of writing low-level coding, but at the same time they add further restrictions and complications which should not be underestimated.
The Good Old Davis-Putnam Procedure Helps Counting Models

As was shown recently, many important AI problems require counting the number of models of propositional formulas. The problem of counting models of such formulas is, according to present knowledge, computationally intractable in a worst case. Based on the Davis-Putnam procedure, we present an algorithm, CDP, that computes the exact number of models of a propositional CNF or DNF formula F. Let m and n be the number of clauses and variables of F, respectively, and let p denote the probability that a literal l of F occurs in a clause C of F, then the average running time of CDP is shown to be O(mdn), where d=[-1/log2(1-p)]. The practical performance of CDP has been estimated in a series of experiments on a wide variety of CNF formulas.
The South Pacific Meridional Mode and Its Role in Tropical Pacific Climate Variability

The Pacific meridional mode, a thermodynamically coupled mode of variability, links extratropical Pacific oceanic and atmospheric anomalies to the tropical Pacific and may give rise to El Niño–Southern Oscillation (ENSO) events. While mechanistic studies on the impacts of the North Pacific meridional mode (NPMM) are prevalent in the literature, the South Pacific meridional mode (SPMM) has only recently been examined. Reanalysis and models from the archive of phase 5 of the Coupled Model Intercomparison Project (CMIP5) are used in this study to investigate further the SPMM and its role in ENSO predictability. Akin to the NPMM, the SPMM features anomalous subtropical South Pacific sea surface temperature (SST) warming in response to a weakened South Pacific subtropical high and relaxed trade winds. However, as opposed to the NPMM, the seasonality of the SPMM SST and wind components vary out of phase, in part because of the seasonal cycle of local mixed layer depth. Thus, the SPMM and the associated wind–evaporation–SST feedback are most energetic during austral summer, facilitating interaction with the concurrent NPMM conditions to regulate the occurrence, evolution, amplitude, and diversity of ENSO events. Parallel analyses with the CMIP5 models indicate that the models reasonably capture the temporal statistics and physical processes of the observed SPMM. Nonetheless, ENSO predictability within the models relies more on the slowly evolving equatorial ocean heat content than on the meridional modes. These findings reveal how the SPMM energizes components of ENSO variability, providing an additional benchmark for model evaluation of Pacific climate variability across multiple time scales.
Comportamiento productivo de un hato Charolais bajo condiciones de trópico seco en Tamaulipas, México

espanolEl objetivo fue evaluar el comportamiento productivo de un hato de ganado Charolais, para lo cual se analizaron los registros productivos de la finca "Ex Hacienda El Carmen", en Guemez, Tamaulipas, Mexico. Los animales fueron alimentados basicamente con pastos, las vacas se reprodujeron todo el ano y las crias se mantuvieron con la vaca hasta el destete. Las variables peso al nacimiento (PN = 647), peso al destete ajustado a 205 dias (PDA205 = 647) y ganancia diaria de peso (GDP = 647) fueron analizadas usando la tecnica de minimos cuadrados para determinar los efectos de ano de nacimiento (AN = 1979, 1980,…1989); epoca de nacimiento (EN = nortes, secas y lluvias), numero de parto de la vaca (NP = 1, 2,…8), sexo de la cria (SC = macho y hembra), incluyendo el peso al nacimiento (PN) como covariable para PDA205 y GDP. Las medias obtenidas para PN, PDA205 y GDP fueron 29,6 ± 3,6, 219,6 ± 34,2 y 0,770 ± 0,134 kg, respectivamente. PN, PDA205 y GDP se vieron afectados (P EnglishThe objective was to assess the productive performance of a Charolais herd, which was based on the analysis of production records of the "Ex Hacienda El Carmen", farm, located in Guemez, Tamaulipas, Mexico. Animals were fed forages on pastures, cows were bred throughout the year and calves kept with the cow until weaning. The variables of birth weight (BW = 647), adjusted weaning weight to 205 days (AWW205 = 647) and average daily gain (ADG = 647) were analyzed using least squares to determine the effects of year of calving (YC = 1979, 1980,.1989); calving season (CS = windy, dry and rainy season), parity of the cow (PC = 1, 2,.8), sex of the calf (SC = male and female), including the birth weight (BW) as a covariate for AWW205 and ADG. Averages obtained for BW, AWW205 and ADG were 29.6 ± 3.6, 219.6 ± 34.2 and 0.770 ± 0.134 kg, respectively. BW, AWW205 and ADG were affected (P
Online coloring known graphs

The problem of online coloring an unknown graph is known to be hard. Here we consider the problem of online coloring in the relaxed situation where the input must be isomorphic to a given known graph. All that foils a computationally powerful player is that it is not known to which sections of the graph the vertices to be colored belong. We show that the performance ratio of any online coloring algorithm with advance knowledge of the input graph is at least $\Omega(N/\log^2 N)$, where $N$ is the number of vertices. This matches and generalizes the bound for the case of an unknown input graph. We also show that any online independent set algorithm has a performance ratio at least $N/8$.
Risk Analysis of the Economic Benefits of Ochroma pyramidale: A Case Study of Forest Planting in Brazil

: The forest investment projects, analyzed through the application of simulation techniques, allow measuring more accurately the benefits or losses, considering the systemic uncertainties related to forest plantation. In this perspective, the objective of this study was to identify the relative importance of sources of uncertainties in determining the net present value (NPV) of planting of Ochroma pyramidale on a forested property located in the state of São Paulo, Brazil. Thus, it assigns probability distributions to the parameters with uncertainties and applies the Monte Carlo technique to obtain the stochastic behavior of the NPV, of the modified internal rate of return (MIRR) and the profitability index (PI). The probabilistic results of the quantitative methods analyzed allowed us to conclude that for the planting O. pyramidale , with 5-year cutting age, indicated economic profitability. In addition, there is a probability that 84.6% of the project will add economic value to the invested capital.
Ecolab: the development and evaluation of a Vygotskian design framework

The Zone of Proximal Development is an appealing and persuasive idea for those concerned with how best to help learners learn. In essence the ZPD requires collaboration or assistance for a learner from another more able partner. The need for this more able learning partner arises from the belief that the activities which form a part of the child's education must be beyond the range of her independent ability. The learning partner must provide appropriately challenging activities and the right quantity and quality of assistance. Teachers are able to fulfil the sort of collaborative partnership role required by the ZPD; the Vygotskian inspired design framework presented here explores the ways in which computers may be able to do likewise. The Ecolab is an implementation of the Vygotskian design framework. It is an interactive learning environment which helps children aged 10 and 11 years to learn about food webs and chains. The Ecolab provides a flexible environment which can be viewed from different perspectives and run in different modes and in increasingly complex phases. There are adjustable activities to be completed and assistance is available from the system. The elements of adjustable assistance available to the system comprise the Zone of Available Assistance (ZAA) applicable to that system. The elements of this ZAA which meet the needs of a particular learner at a particular moment in time comprise the Zone of Proximal Adjustment (ZPA) which the system needs to make for that learner. Three approaches to the construction of this ZPA have been implemented and evaluated within the Ecolab. This evaluation demonstrates the efficacy of the design framework and illustrates that different styles of interaction and collaboration can be supported within an interactive learning environment.
Overexpression of MMP-9 and Its Inhibitors in Blood Mononuclear Cells after Myocardial Infarction - Is It Associated with Depressive Symptomatology?

Background Matrix metalloproteinase (MMP)-9 may play a central role in the development and progression of atherosclerosis. Emerging evidence also indicates an association between MMP-9 and depressive symptomatology. Here, we investigated whether expression of MMP-9 and its inhibitors in blood mononuclear cells and plasma were related to depressive symptoms in patients with a recent myocardial infarction (MI). Methods and Results Blood sampling was performed between 6 and 18 months after MI in 57 patients. Forty-one clinically healthy subjects were included as controls. Gene expression of MMP-9 and its main tissue inhibitors TIMP-1 and -2 were analyzed in freshly isolated or cultured blood mononuclear cells. Corresponding protein levels were assessed in cell supernatants and plasma. In post-MI patients, mRNA levels of MMP-9 and TIMP-1 and -2 were significantly higher than in controls while protein levels in cell supernatants and plasma did not differ between groups. The Center for Epidemiological Studies - Depression (CES-D) scale was used to assess depressive symptomatology. Repeated assessments during the first 18 months after MI showed significantly higher CES-D scores in patients compared with controls. However, there were no relationships between depressive mood and any of the measurements of MMP-9 or TIMPs. Conclusion Our findings indicate that overexpression of MMP-9 and TIMPs in blood mononuclear cells and elevated depressive symptoms represent two unrelated phenomena after MI.
Flood Forecasting Based on an Improved Extreme Learning Machine Model Combined with the Backtracking Search Optimization Algorithm

Flood forecasting plays an important role in flood control and water resources management. Recently, the data-driven models with a simpler model structure and lower data requirement attract much more attentions. An extreme learning machine (ELM) method, as a typical data-driven method, with the advantages of a faster learning process and stronger generalization ability, has been taken as an effective tool for flood forecasting. However, an ELM model may suffer from local minima in some cases because of its random generation of input weights and hidden layer biases, which results in uncertainties in the flood forecasting model. Therefore, we proposed an improved ELM model for short-term flood forecasting, in which an emerging dual population-based algorithm, named backtracking search algorithm (BSA), was applied to optimize the parameters of ELM. Thus, the proposed method is called ELM-BSA. The upper Yangtze River was selected as a case study. Several performance indexes were used to evaluate the efficiency of the proposed ELM-BSA model. Then the proposed model was compared with the currently used general regression neural network (GRNN) and ELM models. Results show that the ELM-BSA can always provide better results than the GRNN and ELM models in both the training and testing periods. All these results suggest that the proposed ELM-BSA model is a promising alternative technique for flood forecasting.
Timing Recovery With Frequency Offset and Random Walk: Cramér–Rao Bound and a Phase- Locked Loop Postprocessor

We consider the problem of timing recovery for bandlimited, baud-rate sampled systems with intersymbol interference and a timing offset that can be modeled as a combination of a frequency offset and a random walk. We first derive the Crameacuter-Rao bound (CRB), which is a lower bound on the estimation error variance for any timing estimator. Conventional timing recovery is based on a phase-locked loop (PLL). We compare the conventional timing-recovery method with the CRB for realistic timing parameters for the magnetic recording channel, and observe a 7 dB signal-to-noise ratio gap between the two. Next, we propose a PLL postprocessor based on the maximum a posteriori estimation principle that performs to within 1.5 dB of the CRB. This postprocessor performs time-invariant filtering and time-varying scaling of the PLL timing estimates. The refined timing estimates from the postprocessor are then used to get refined samples by interpolating the samples taken at the PLL's timing estimates. Finally, we present suboptimal implementations that allow a performance-complexity tradeoff
Perceptual postfilter estimation for low bit rate speech coders using Gaussian mixture models

A novel perceptual postfilter is introduced. For each frame, the filter gains, z, are estimated given a vector, y, of the quantized LSFs and the long-term prediction gain of the corresponding frame. The proposed perceptual postfilter is derived from an optimal MMSE estimator, i.e. the estimated gain vector isz = E{z|y}. The MMSE estimator is based on the conditional pdf of z given y, which is computed from the joint pdf modelled by a GMM. The proposed perceptual postfilter improves the speech naturalness comparing with the conventional adaptive postfilter, while maintaining the property of being an "add-on" postfilter without modification to the current encoder. Adaptive postfilters (1) have been widely applied in current Linear Prediction Analysis-by-Synthesis (LPAS) speech coders. Conventional postfiltering improves the decoded speech quality using the information available at the decoder, and is empiri- cally designed based on aspects of human perception. As re- search furthers in modelling of the human auditory system, bet- ter psychoacoustic models (2, 3) have been proposed and ap- plied in speech and audio processing, especially in audio cod- ing. However, only a few improvements (for instance, (4, 5)) have been made to adaptive postfilters despite our better under- standing of the human auditory system. A speech codec usually operates on a frame-by-frame basis. When we have access to the clean speech and its decoded ver- sion from a speech codec, a perceptual postfilter can be con- structed based on perceptual properties. The perceptual filter gains can be derived from each processing frame and applied to the decoded speech to improve the speech quality. However, in practice we do not have the information about the percep- tual postfilter gains at the decoder if they are not sent as side information. In this paper, we focus on the estimation of the perceptual postfilter gains without additional side information. Assume a given speech frame is coded by a LPAS speech coder, the decoder retrieves the quantized linear prediction (LP) coefficients. The LP coefficients represent the envelope of the short-time power spectrum which is very important for both the quality and intelligibility of coded speech. The perceptual post- filter gains are calculated for the corresponding frame. Since the open-loop prediction gain of the long-term prediction (LTP) in speech signals indicates the degree of voicing of the speech, we also calculate the LTP gain of this frame. We take the LP coefficients and the LTP gain as an "input" vector, and the per- ceptual postfilter gains as a "target" vector. A feature vector is constructed from "input" and "target" vectors. In order to find a Minimum Mean Square Error (MMSE) estimate of the "target" vector, a priori information of the joint probability density func-
Monetary policy rules and the exchange rate channel

A discretionary monetary policy leads to suboptimal stabilization in models with the New Keynesian assumption of forward-looking price setting, and various policy rules that improve the discretionary equilibrium have been considered in the literature. The empirical evidence for forward-looking price determination is mixed. This note shows, however, that forward-looking price setting is not essential for the results. Policy rules that improve welfare under the New Keynesian assumptions, also do so within a traditional backward-looking model if asset prices, such as the exchange rate, are forward-looking.
Machine Learning and Images for Malware Detection and Classification

Detecting malicious code with exact match on collected datasets is becoming a large-scale identification problem due to the existence of new malware variants. Being able to promptly and accurately identify new attacks enables security experts to respond effectively. My proposal is to develop an automated framework for identification of unknown vulnerabilities by leveraging current neural network techniques. This has a significant and immediate value for the security field, as current anti-virus software is typically able to recognize the malware type only after its infection, and preventive measures are limited. Artificial Intelligence plays a major role in automatic malware classification: numerous machine-learning methods, both supervised and unsupervised, have been researched to try classifying malware into families based on features acquired by static and dynamic analysis. The value of automated identification is clear, as feature engineering is both a time-consuming and time-sensitive task, with new malware studied while being observed in the wild.
Monte Carlo Algorithm for Least Dependent Non-Negative Mixture Decomposition

We propose a simulated annealing algorithm (stochastic non-negative independent component analysis, SNICA) for blind decomposition of linear mixtures of non-negative sources with non-negative coefficients. The demixing is based on a Metropolis-type Monte Carlo search for least dependent components, with the mutual information between recovered components as a cost function and their non-negativity as a hard constraint. Elementary moves are shears in two-dimensional subspaces and rotations in three-dimensional subspaces. The algorithm is geared at decomposing signals whose probability densities peak at zero, the case typical in analytical spectroscopy and multivariate curve resolution. The decomposition performance on large samples of synthetic mixtures and experimental data is much better than that of traditional blind source separation methods based on principal component analysis (MILCA, FastICA, RADICAL) and chemometrics techniques (SIMPLISMA, ALS, BTEM).