Miracle-dz/UniFAR-FTUs huggingface dataset:
Fast Sampling-Based Inference in Balanced Neuronal Networks

Multiple lines of evidence support the notion that the brain performs probabilistic inference in multiple cognitive domains, including perception and decision making. There is also evidence that probabilistic inference may be implemented in the brain through the (quasi-)stochastic activity of neural circuits, producing samples from the appropriate posterior distributions, effectively implementing a Markov chain Monte Carlo algorithm. However, time becomes a fundamental bottleneck in such sampling-based probabilistic representations: the quality of inferences depends on how fast the neural circuit generates new, uncorrelated samples from its stationary distribution (the posterior). We explore this bottleneck in a simple, linear-Gaussian latent variable model, in which posterior sampling can be achieved by stochastic neural networks with linear dynamics. The well-known Langevin sampling (LS) recipe, so far the only sampling algorithm for continuous variables of which a neural implementation has been suggested, naturally fits into this dynamical framework. However, we first show analytically and through simulations that the symmetry of the synaptic weight matrix implied by LS yields critically slow mixing when the posterior is high-dimensional. Next, using methods from control theory, we construct and inspect networks that are optimally fast, and hence orders of magnitude faster than LS, while being far more biologically plausible. In these networks, strong - but transient - selective amplification of external noise generates the spatially correlated activity fluctuations prescribed by the posterior. Intriguingly, although a detailed balance of excitation and inhibition is dynamically maintained, detailed balance of Markov chain steps in the resulting sampler is violated, consistent with recent findings on how statistical irreversibility can overcome the speed limitation of random walks in other domains.
Lipopolysaccharide-induced lung injury in mice. I. Concomitant evaluation of inflammatory cells and haemorrhagic lung damage.

Intratracheal instillation of lipopolysaccharide (LPS) induces an inflammatory response characterized by infiltration of polymorphonuclear neutrophils (PMNs) into the extracellular matrix and by the release of mediators that play a fundamental role in lung damage. In the present study, we developed a mouse model which allows correlation of the inflammatory response and haemorrhagic tissue injury in the same animal. In particular, the different steps of the inflammatory response and tissue damage were evaluated by the analysis of three parameters: myeloperoxidase (MPO) activity in the parenchyma, reflecting PMNs accumulation into the lung, inflammatory cells count in the bronchoalveolar lavage fluid (BALF), reflecting their extravasation, and total haemoglobin estimation in BALF, a marker of haemorrhagic tissue damage consequent to PMNs degranulation. In our experimental conditions, intra-tracheal administration of 10 microg/mouse of LPS evoked an increase of MPO activity in the lung at 4 h (131%) and 6 h (147%) from endotoxin challenge. A significant increase of PMNs in the BALF was noticed at these times with a plateau between the 12nd and 24th h. PMN accumulation produced a time-dependent haemorrhagic lung damage until 24 h after LPS injection (4 h: +38%; 6 h: +23%; 12 h: +44%; 24 h: +129% increase of haemoglobin concentration in the BALF vs. control). Lung injury was also assessed histopathologically. Twenty-four hours after the challenge, diffuse alveolar haemorrhage, as well as PMN recruitment in the interstitium and alveolus were observed in the LPS group. This model was pharmacologically characterized by pretreatment of LPS-treated mice with antiinflammatory drugs acting on different steps of the <<inflammatory cascade>>. We demonstrated that: a) betamethasone (1, 3, 10, 30 mg/kg p.o.) reduced in a dose-dependent manner the MPO activity, the number of inflammatory cells and, at the same time, lung injury; b) pentoxifylline, a TNFalpha production inhibitor (200 mg/kg i.p.), inhibited PMN extravasation and lung haemorrhage but it was not able to reduce MPO activity in the lung; c) L-680,833, an anti-elastase compound (30 mg/kg po), decreased significantly only the haemorrhagic lung damage; d) indomethacin, a non steroidal antiinflammatory drug (5 mg/kg p.o.), did not show any effect on any of the parameters considered. In conclusion, our in vivo mouse model is a practical alternative to animal models of ARDS (Adult Respiratory Distress Syndrome) recently described and it permits to dissect and to characterize the different steps of PMNs infiltration and, at the same time, the damage caused by their activation.
A New Approach for Using Genome Scans to Detect Recent Positive Selection in the Human Genome

Genome-wide scanning for signals of recent positive selection is essential for a comprehensive and systematic understanding of human adaptation. Here, we present a genomic survey of recent local selective sweeps, especially aimed at those nearly or recently completed. A novel approach was developed for such signals, based on contrasting the extended haplotype homozygosity (EHH) profiles between populations. We applied this method to the genome single nucleotide polymorphism (SNP) data of both the International HapMap Project and Perlegen Sciences, and detected widespread signals of recent local selection across the genome, consisting of both complete and partial sweeps. A challenging problem of genomic scans of recent positive selection is to clearly distinguish selection from neutral effects, given the high sensitivity of the test statistics to departures from neutral demographic assumptions and the lack of a single, accurate neutral model of human history. We therefore developed a new procedure that is robust across a wide range of demographic and ascertainment models, one that indicates that certain portions of the genome clearly depart from neutrality. Simulations of positive selection showed that our tests have high power towards strong selection sweeps that have undergone fixation. Gene ontology analysis of the candidate regions revealed several new functional groups that might help explain some important interpopulation differences in phenotypic traits.
Detecting Evolution of Concepts based on Cause-Effect Relationships in Online Reviews

Analyzing how technology evolves is important for understanding technological progress and its impact on society. Although the concept of evolution has been explored in many domains (e.g., evolution of topics, events or terminology, evolution of species), little research has been done on automatically analyzing the evolution of products and technology in general. In this paper, we propose a novel approach for investigating the technology evolution based on collections of product reviews. We are particularly interested in understanding social impact of technology and in discovering how changes of product features influence changes in our social lives. We address this challenge by first distinguishing two kinds of product-related terms: physical product features and terms describing situations when products are used. We then detect changes in both types of terms over time by tracking fluctuations in their popularity and usage. Finally, we discover cases when changes of physical product features trigger the changes in product's use. We experimentally demonstrate the effectiveness of our approach on the Amazon Product Review Dataset that spans over 18 years.
Presentation of a virtual power plant environment and its application with combined first-principle and data-driven models intended for the diagnostics of a power plant – Part 2

This work aims to present and discuss the architecture and application aspects of the Virtual Power Plant (VPP) environment, a tool created for the purpose of the modeling, simulation, data management, and visualization of power plant systems. The VPP environment is based on a general-purpose simulation package that provides a framework for incorporating a broad variety of models, ranging from real-time models to detailed models that require off-line execution. The VPP, as a tool, opens up the possibility to develop, test, and validate the feasibility of new software and hardware components within simulated operational scenarios. An original approach to tuning a first-principle model using the example of a feedwater heater by means of a parameter estimation technique is presented, along with a case study of modeling a 225 MW coal-fired power unit. The objectives are as follows: (i) develop a tuning method for this model; (ii) propose key indicators of feedwater heater performance using a model-based approach; and finally (iii) automate the calculation process of the indicators.
Expression of PD-1, PD-L1 and PD-L2 is associated with differentiation status and histological type of endometrial cancer

Endometrial cancer (EC) is the most frequent gynecological malignancy and a major cause of morbidity and mortality for women worldwide. Programmed cell death protein 1 (PD-1) and its ligands programmed death ligand 1 (PD-L1) and programmed death ligand 2 (PD-L2) have been well studied in lung cancer, melanoma and renal-cell cancer. However, few studies have been performed in EC. The purpose of the present study was to assess the expression of PD-1, PD-L1 and PD-L2 in 35 human normal endometrial tissue samples and 75 human EC tissue samples using immunohistochemical staining. It was found that 61.3% of ECs were positive for PD-1 staining, which was almost exclusively found in the tumor-infiltrating immune cells. By contrast, PD-1 was not expressed in the tumor cells or normal endometrial tissues. It was also found that 14.3% of normal endometria and 17.3% of EC tissues were positive for PD-L1 expression, while 20.0% of normal endometrium and 37.3% of EC tissues were positive for PD-L2 expression; however, there was no statistically significant difference between the normal endometrium and EC tissues. PD-1 expression in the tumor-infiltrating immune cells was more frequently found in the moderately and poorly-differentiated ECs and non-endometrioid (type II) ECs than in the well-differentiated ECs and endometrioid (type I) ECs. Similarly, PD-L1 and PD-L2 expression in the tumor-infiltrating immune cells was more frequently found in the moderately and poorly-differentiated ECs and type II ECs than in the type I ECs. The present findings indicate a possible better outcome for future treatment with anti-PD-1 or anti-PD-L1 antibody-based therapies against these subgroups of endometrial cancers with frequent expression of the PD-1/PD-L1/PD-L2 axis.
A Hilbert Curve-Based Distributed Index for Window Queries in Wireless Data Broadcast Systems

Location-dependent spatial query in the wireless environment is that mobile users query the spatial objects dependent on their current location. The window query is one of the essential spatial queries, which finds spatial objects located within a given window. In this paper, we propose a Hilbert curve-based distributed index for window queries in the wireless data broadcast systems. Our proposed algorithm allocates spatial objects in the Hilbert-curve order to preserve the spatial locality. Moreover, to quickly answer window queries, our proposed algorithm utilizes the neighbor-link index, which has knowledge about neighbor objects, to return the answered objects. From our experimental study, we have shown that our proposed algorithm outperforms the distributed spatial index.
Price Discovery , Volatility Spillovers and Adequacy of Speculation in Cheese Spot and Futures Market

Copyright 2012 by Marin Bozic and T. Randall Fortenbery. All rights reserved. Readers may make verbatim copies of this document for non-commercial purposes by any means, provided that this copyright notice appears on all such copies. Practitioner's Abstract: We investigate price discovery, volatility spillovers and impacts of speculation in the dairy sector. Examining the time series properties of cheese cash and implied futures price we find that the unit root hypothesis is strongly rejected for cash prices, while unit roots cannot be rejected for nearby futures prices in the framework that carefully controls for rollovers. To explain this result, we built a model that illustrates the time series properties of the nearby futures price series for a futures contract written on a second-order stationary cash series and identified the mean-reverting nonlinear dynamics that will occur at rollovers. Given the time series properties of the cash and futures series we propose an error-correction model using spreads between cash and the second nearby futures instead of the cointegration vector. To account for volatility dynamics we propose an extension of the BEKK variance model that we refer to as GARCH-MEX. That model does not restrict the sign of the additional regressors on the conditional variances, and can easily insure positive-definiteness of the conditional covariance matrix. We find that the flow of information in the mean model is predominantly from futures to cash, while volatility spillovers are bidirectional. It is possible that cash prices that include unfilled bid/offers react differently to increases in volatility in futures prices than sales cash prices, indicating that liquidity in the cash market is reduced with increase in conditional volatility of the futures price. Utilizing GARCH-MEX model we find strong evidence against the hypothesis that excessive speculation is increasing the conditional variance of futures prices. If anything, speculation may in fact be inadequate, and further research with daily speculative positions and high-frequency futures prices is needed to identify the effect of increased speculation on realized volatility of futures prices, bid-ask spread and magnitude of slippage. 1. Introduction There has been considerable interest in recent years concerning the overall performance of commodity futures markets, and the extent to which futures activity has led to price instability in cash markets. Much of the recent work in futures/cash price relationships has focused on the first moment of the price distribution and deep (large volume) markets (e. equally important are the relationships between …
Optimal Designs for Empirical Bayes Estimators of Individual Linear and Quadratic Growth Curves in Linear Mixed Models

Many studies on optimal designs for linear mixed model analysis of repeated measures data have focussed on estimating the fixed effects. The present study investigates the optimal number of time points and subjects in case random effects have to be estimated. Linear mixed models with a linear or quadratic trend across equidistant time points are studied. Given a particular cost function, we examine which designs minimise the expected average squared prediction error. Robustness of the optimal design, important when one does not know the underlying model, is also treated.
Effects of dispersal, shrubs, and density-dependent mortality on seed and seedling distributions in temperate forests

Processes limiting recruitment of trees may have large impacts on forest dynamics. In this paper, we deter- mined the effects of dispersal, shrubs (Rhododendron maximum), and density-dependent mortality on seed and seedling distributions of Southern Appalachian trees. We quantified the spatial distribution of seed rain, seed bank densities, first-year seedlings, and older than first-year seedlings in five vegetation plots. We fit models to these data assuming effects of limited dispersal, R. maximum (an understory shrub), and (or) density-dependent mortality (as well as a null model with none of these effects) and used best-fitting models to indicate which processes affected a particular species. We found that all factors examined limit species distributions, and thus, affect seedling dynamics. Seedling densities are higher near parent trees long after dispersal occurs. This pattern is less frequently observed for animal-dispersed species than for wind-dispersed species, presumably due to secondary dispersal of seeds by animals. Seedling densities of five species are decreased beneath R. maximum. Shade tolerance does not explain which species are affected, sug- gesting that factors other than low light are responsible for increased seedling mortality under this shrub. Our results suggest that density-dependent mortality affects four species, decreasing seedling densities close to parent trees. Dis- persal, density-dependent mortality, and R. maximum all act in ways that may promote or limit diversity, illustrating that multiple factors are likely to control species diversity.
The Canadian Optimal Therapy of COPD Trial: design, organization and patient recruitment.

BACKGROUND There are no published studies that have assessed whether adding long-acting beta 2-agonist bronchodilators and/or inhaled steroids to chronic therapy with tiotropium would provide additional clinical benefit to patients with moderate to severe chronic obstructive pulmonary disease (COPD). METHODS The Canadian Optimal Therapy of COPD Trial is a randomized, prospective, double-blind, placebo-controlled, multicentre trial funded by the Canadian Institutes of Health Research that has been designed to determine which combination of inhaled medications will most effectively prevent exacerbations and optimize disease-specific quality of life in patients with COPD. The trial is the first to evolve from the Canadian Thoracic Society Clinical Trials Group. The study will randomize 432 patients with moderate to severe COPD to one of three parallel treatment arms for 52 weeks: tiotropium and fluticasone/salmeterol; tiotropium and salmeterol; or tiotropium and placebo inhaler. The participants will be allowed to use salbutamol as required throughout the trial period. OUTCOMES The primary outcome measure is the proportion of patients in the three treatment groups who experienced a respiratory exacerbation within 52 weeks of randomization. Other outcomes that will be assessed over the 52-week trial period will include: changes in disease-specific quality of life and changes in dyspnea, health care use and changes in lung function. A pharmacoeconomic analysis will also be performed to evaluate the cost of these therapies. RESULTS The study commenced recruitment in October 2003. It is currently operating at 22 centres across Canada and has randomized 137 patients during the first four months of recruitment. Recruitment is scheduled to continue until April 2005 or until 432 patients have been randomized. CONCLUSION The present randomized, placebo-controlled trial offers a unique opportunity to answer the question, what is the best combination of inhaled medications to use for COPD patients? It is hoped that optimal use of inhaled medications will improve patient health and quality of life, reduce patient respiratory exacerbations, and ultimately, reduce health care resource use.
Data-driven Dependency Parsing for Romanian

This thesis presents the first data-driven dependency parser for Romanian. The parser was trained and tuned using MaltParser, a language-independent parsergenerator framework. The Romanian Dependency Treebank, which represents the material used for training and evaluation of the parser, is also presented for the first time. The best parsing accuracy of 88.6% measured in Labeled Attachemnt Score is obtained using the Nivre arc-eager algorithm, together with a feature model specific for Romanian. The results are similar to those reported for other languages using the same system. The different types of errors produced by the parser are classified, leading to directions for improving the parsing accuracy and for future expansion of the treebank.
Active encoding of decisions about stimulus absence in primate prefrontal cortex neurons

Judging the presence or absence of a stimulus is likely the most basic perceptual decision. A fundamental difference of detection tasks in contrast to discrimination tasks is that only the stimulus presence decision can be inferred from sensory evidence, whereas the alternative decision about stimulus absence lacks sensory evidence by definition. Detection decisions have been studied in an intentional, action-based framework, in which decisions were regarded as intentions to pursue particular actions. These studies have found that only stimulus-present decisions are actively encoded by neurons, whereas the decision about the absence of a stimulus does not affect default neuronal responses. We tested whether this processing mechanism also holds for abstract detection decisions that are dissociated from motor preparation. We recorded single-neuron activity from the prefrontal cortex (PFC) of monkeys performing a visual detection task that forced a report-independent decision. We not only found neurons that actively encoded the subjective decision of monkeys about the presence of a stimulus, but also cells responding actively for the decision about the absence of stimuli. These results suggest that abstract detection decisions are processed in a different way compared with the previously reported action-based decisions. In a report-independent framework, neuronal networks seem to generate a second set of neurons actively encoding the absence of sensory stimulation, thus translating decisions into abstract categories. This mechanism may allow the brain to “buffer” a decision in a nonmovement-related framework.
Off-Line Handwritten Word Recognition Using Hidden Markov Models

1 Centre for Pattern Recognition and Machine Intelligence Department of Computer Science, Concordia University 1455 de Maisonneuve Boulevard West Suite GM-606, Montreal, Canada H3G 1M8 2 Ecole de Technologie Superieure Laboratoire d’Imagerie, de Vision et d’Intelligence Artificielle (LIVIA) 1100 Notre-Dame Ouest, Montreal, Canada H3C 1K3 3 Service de Recherche Technique de La Poste Departement Reconnaissance, Modelisation Optimisation (RMO) 10, rue de l’ile Mâbon, 44063 Nantes Cedex 02, France 4 Departamento de Informatica (Computer Science Department) Pontificia Universidade Catolica do Parana Av. Imaculada Conceicao, 1155 Prado Velho 80.215-901 Curitiba PR BRAZIL
Achieving long-term stability of lipid nanoparticles: examining the effect of pH, temperature, and lyophilization

The broadest clinical application of siRNA therapeutics will be facilitated by drug-loaded delivery systems that maintain stability and potency for long times under ambient conditions. In the present study, we seek to better understand the stability and effect of storage conditions on lipidoid nanoparticles (LNPs), which have been previously shown by our group and others to potently deliver RNA to various cell and organ targets both in vitro and in vivo. Specifically, this study evaluates the influence of pH, temperature, and lyophilization on LNP efficacy in HeLa cells. When stored under aqueous conditions, we found that refrigeration (2°C) kept LNPs the most stable over 150 days compared to storage in the −20°C freezer or at room temperature. Because the pH of the storage buffer was not found to influence stability, it is suggested that the LNPs be stored under physiologically appropriate conditions (pH 7) for ease of use. Although aggregation and loss of efficacy were observed when LNPs were subjected to freeze–thaw cycles, their stability was retained with the use of the cryoprotectants, trehalose, and sucrose. Initially, lyophilization of the LNPs followed by reconstitution in aqueous buffer also led to reductions in efficacy, most likely due to aggregation upon reconstitution. Although the addition of ethanol to the reconstitution buffer restored efficacy, this approach is not ideal, as LNP solutions would require dialysis prior to use. Fortunately, we found that the addition of trehalose or sucrose to LNP solutions prior to lyophilization facilitated room temperature storage and reconstitution in aqueous buffer without diminishing delivery potency.