Miracle-dz/UniFAR-FTUs huggingface dataset:
Metals in soil versus plant-microbe interactions: biotic and chemical interferences.

The interactions of soil microorganisms with plants have been increasingly attracting attention during the past decade both in basic research and in applied fields, particularly those related to agricultural and environmental biotechnology. Plant-microbe interactions can be reasonably divided into a few major categories: (i) the physiological and biochemical properties and responses of the macropartner (the host plant); (ii) the corresponding properties and behaviour Correspondence/Reprint request: Dr. Alexander A. Kamnev, Institute of Biochemistry and Physiology of Plants and Microorganisms, Russian Academy of Sciences, 410049 Saratov, Russia E-mail: aakamnev@ibppm.sgu. ru Alexander A. Kamnev 292 of the micropartner (consortia of plant-associated microorganisms, in particular, in the rhizosphere), as well as (iii) any processes or phenomena directly related to their interactions per se, e.g. remote exchange of molecular signals and their perception, quorum sensing and its inhibition (including chemical and enzymatic "quorum quenching" or "anti-quorum sensing"), contact and intercellular interactions, the effects and role of the chemical composition and conditions of the medium, etc. Soil microbiota plays an essential role in many processes central to environmental biotechnology that are directly related to soil bioremediation and, especially, phytoremediation, in which plant-microbe interactions have been documented to be of vital importance. Such highly persistent and hardly avoidable pollutants as heavy metals, in contrast to organic contaminations, cannot be biologically or chemically degraded, but can only be deactivated (i.e., detoxified and/or immobilised) within the soil or removed from the contaminated site. On the other hand, they can directly or indirectly affect plant-microbe interactions in the rhizosphere. Their direct influence involves possible toxic or other detrimental effects of particular chemical metal species on the biochemistry and physiology of plant-associated microorganisms and on the growth and development of plants. These effects comprise the biotic interferences mentioned in the title. The chemical reactivity of particular metal species or their catalytic effects, which result in chemical binding or redox degradation of biogenic organics involved in plant-microbe interactions (i.e., in the aforementioned remote exchange of molecular signals, quorum sensing within bacterial communities, release and exchange of nutrients, etc.), may thus be classified as indirect effects comprising chemical interferences. This chapter presents discussion and some examples of recent experimental evidence for environmentally important effects, either incited or directly caused by the presence of heavy metals in soil, and their possible consequences for the efficiency of functioning of plant-microbe associative symbioses in the rhizosphere – the overall tuning of this highly complicated "underground orchestra".
Unimpeded Permeation of Water Through Helium-Leak–Tight Graphene-Based Membranes

Porous Membranes Thin semi-permeable membranes are commonly used as chemical barriers or for filtration purposes. While the size of the pores will influence which molecules are able to pass, other factors—including the surface chemistry of the pore walls, electrostatic interactions, and differences in solubility—can also affect the diffusion rates. There is also a trade-off between the thickness of the membrane regarding strength and permeation rates (see the Perspective by Paul). Karan et al. (p. 444) fabricated membranes from amorphous carbon, which showed excellent strength and could be used for filtrations involving organic solvents. Nair et al. (p. 442) observed unusual behavior in graphene-based membranes which were able to prevent the diffusion of many small-molecule gases, including helium, but showed almost barrier-free movement of water. Graphite oxide membranes are impermeable to many liquids, vapors, and gases, including He, but allow evaporation of water. Permeation through nanometer pores is important in the design of materials for filtration and separation techniques and because of unusual fundamental behavior arising at the molecular scale. We found that submicrometer-thick membranes made from graphene oxide can be completely impermeable to liquids, vapors, and gases, including helium, but these membranes allow unimpeded permeation of water (H2O permeates through the membranes at least 1010 times faster than He). We attribute these seemingly incompatible observations to a low-friction flow of a monolayer of water through two-dimensional capillaries formed by closely spaced graphene sheets. Diffusion of other molecules is blocked by reversible narrowing of the capillaries in low humidity and/or by their clogging with water.
Experimental study of discovering essential information from customer inquiry

This paper reports the result of our experimental study on a new method of applying an association rule miner to discover useful information from customer inquiry database in a call center of a company. It has been claimed that association rule mining is not suited for text mining. To overcome this problem, we propose (1) to generate sequential data set of words with dependency structure from the Japanese text database, and (2) to employ a new method for extracting meaningful association rules by applying a new rule selection criterion. Each inquiry in the sequential data was represented as a list of word pairs, each of which consists of a verb and its dependent noun. The association rules were induced regarding each pair of words as an item. The rule selection criterion comes from our principle that we put heavier weights to co-occurrence of multiple items more than single item occurrence. We regarded a rule important if the existence of the items in the rule body significantly affects the occurrence of the item in the rule head. The selected rules were then categorized to form meaningful information classes. With this method, we succeeded in extracting useful information classes from the text database, which were not acquired by only simple keyword retrieval. Also, inquiries with multiple aspects were properly classified into corresponding multiple categories.
A Parallel Quantum Algorithm for the Satisfiability Problem

In this paper we present a classical parallel quantum algorithm for the satisfiability problem. We have exploited the classical parallelism of quantum algorithms developed in [G.L. Long and L. Xiao, Phys. Rev. A 69 (2004) 052303], so that additional acceleration can be gained by using classical parallelism. The quantum algorithm first estimates the number of solutions using the quantum counting algorithm, and then by using the quantum searching algorithm, the explicit solutions are found.
Generating Animatable 3D Virtual Humans from Photographs

We present an easy, practical and efficient full body cloning methodology. This system utilizes photos taken from the front, side and back of a person in any given imaging environment without requiring a special background or a controlled illuminating condition. A seamless generic body specified in the VRML H‐Anim 1.1 format is used to generate an individualized virtual human. The system is composed of two major components: face‐cloning and body‐cloning. The face‐cloning component uses feature points on front and side images and then applies DFFD for shape modification. Next a fully automatic seamless texture mapping is generated for 360° coloring on a 3D polygonal model. The body‐cloning component has two steps: (i feature points specification, which enables automatic silhouette detection in an arbitrary background (ii two‐stage body modification by using feature points and body silhouette respectively. The final integrated human model has photo‐realistic animatable face, hands, feet and body. The result can be visualized in any VRML compliant browser.
Long distance history influences sentence valence in narrative texts

While there is a rich literature on the tracking of sentiment and emotion in text, modelling the emotional trajectory of longer narratives, such as literary texts, poses new challenges. This problem consists of two distinct elements – how much of the sentiment of a sentence in context is determined by the information contained in that sentence, and how much is determined by the previous history of sentences. Two key challenges in this work are identifying the features of the preceding context that are relevant to this sentence-by-sentence valence assignment task and determining the type of machine learning model which will perform best in predicting these sentence valences. As an initial exploration we apply a number of different feature sets using linear methods. We show that there is a small linear relationship between sentence valence history and the valence of the current sentence, which is statistically significant (P= 0.0001). These results show that we have captured a real effect in modelling the relationship between valence history and current sentence valence, the magnitude of explained variance is small. We intend to explore the application of models with non-linear capabilities in future work.
Strategic adjustment of begging effort by banded mongoose pups

Variation in the intensity of conspicuous displays raises three basic questions: (i) the relationship between internal state and display intensity, (ii) the relationship between display intensity and receiver response, and (iii) the effect of variation in receiver responsiveness on signaller behaviour. Here, I investigate the interaction between pups and helpers in the communally breeding banded mongoose (Mungos mungo), where each pup forms an exclusive relationship with a single adult helper (termed its ‘escort’). By experimentally manipulating pup need, I demonstrate that changes in begging rate correspond to changes in short-term need. The data then suggest that escorts in good condition may be more responsive to increased begging and that pups associating with them increase their begging more than do pups paired with escorts in poor condition. Escorts also appear more responsive to increased begging by female pups, and female pups increase their begging more than do male pups. These results suggest that banded mongoose pups may strategically adjust their investment in begging in relation to variation in the expected pay-off. I argue that such adjustment is likely to be a general phenomenon: wherever there is variation in responsiveness to signals, signallers will be selected to identify different categories of receiver and adjust their signals in order to maximize the pay-offs. Therefore, differences in signal intensity may be as much a product of context as an indication of variation in individual phenotypic or genotypic state.
Estimating exposure effects by modelling the expectation of exposure conditional on confounders.

In order to estimate the causal effects of one or more exposures or treatments on an outcome of interest, one has to account for the effect of "confounding factors" which both covary with the exposures or treatments and are independent predictors of the outcome. In this paper we present regression methods which, in contrast to standard methods, adjust for the confounding effect of multiple continuous or discrete covariates by modelling the conditional expectation of the exposures or treatments given the confounders. In the special case of a univariate dichotomous exposure or treatment, this conditional expectation is identical to what Rosenbaum and Rubin have called the propensity score. They have also proposed methods to estimate causal effects by modelling the propensity score. Our methods generalize those of Rosenbaum and Rubin in several ways. First, our approach straightforwardly allows for multivariate exposures or treatments, each of which may be continuous, ordinal, or discrete. Second, even in the case of a single dichotomous exposure, our approach does not require subclassification or matching on the propensity score so that the potential for "residual confounding," i.e., bias, due to incomplete matching is avoided. Third, our approach allows a rather general formalization of the idea that it is better to use the "estimated propensity score" than the true propensity score even when the true score is known. The additional power of our approach derives from the fact that we assume the causal effects of the exposures or treatments can be described by the parametric component of a semiparametric regression model. To illustrate our methods, we reanalyze the effect of current cigarette smoking on the level of forced expiratory volume in one second in a cohort of 2,713 adult white males. We compare the results with those obtained using standard methods.
Diophantine Approximation on projective Varieties I: Algebraic distance and metric B\'ezout Theorem

Apart from the well known algebraic and arithmetic B\'ezout Theorems, there also is the metric B\'ezout Theorem. For two properly intersecting effective cycles in projective space X,Y, and their intersection product Z, it relates not only the degrees and heights of X,Y, and Z, but also their distances and algebraic distances to a given point $\theta$. Applications of this Theorem will lie in the area of Diophantine Approximation, where one wants to estimate approximation properties of Z with respect to $\theta$ against the ones of X, and Y.
Le capital psychologique permet-il d’apprendre et de rebondir face à un échec entrepreneurial ?

Bien que l’echec entrepreneurial soit percu negativement dans la societe, plusieurs chercheurs estiment qu’il offre une reelle opportunite d’apprentissage. Cependant, il est parfois difficile d’apprendre a partir d’un echec compte tenu des differents couts financiers, psychologiques et sociaux qu’il occasionne. Au travers d’un modele conceptuel, nous proposons un angle d’approche plus positif de l’echec entrepreneurial. Nous suggerons qu’un niveau de capital psychologique eleve joue un role moderateur dans la relation entre les consequences negatives de l’echec et l’apprentissage a partir de l’echec. Cet apprentissage et ce capital psychologique eleve aideraient l’entrepreneur ayant echoue a poursuivre sa carriere entrepreneuriale.
Score and Information for Recursive Exponential Models with Incomplete Data

Recursive graphical models usually underlie the statistical modelling concerning probabilistic expert systems based on Bayesian networks. This paper defines a version of these models, denoted as recursive exponential models, which have evolved by the desire to impose sophisticated domain knowledge onto local fragments of a model. Besides the structural knowledge, as specified by a given model, the statistical modelling may also include expert opinion about the values of parameters in the model. It is shown how to translate imprecise expert knowledge into approximately conjugate prior distributions. Based on possibly incomplete data, the score and the observed information are derived for these models. This accounts for both the traditional score and observed information, derived as derivatives of the log-likelihood, and the posterior score and observed information, derived as derivatives of the log-posterior distribution. Throughout the paper the specialization into recursive graphical models is accounted for by a simple example.
Hierarchical Mergence Approach to Cell Detection in Phase Contrast Microscopy Images

Phase contrast microscope is one of the most universally used instruments to observe long-term cell movements in different solutions. Most of classic segmentation methods consider a homogeneous patch as an object, while the recorded cell images have rich details and a lot of small inhomogeneous patches, as well as some artifacts, which can impede the applications. To tackle these challenges, this paper presents a hierarchical mergence approach (HMA) to extract homogeneous patches out and heuristically add them up. Initially, the maximum region of interest (ROI), in which only cell events exist, is drawn by using gradient information as a mask. Then, different levels of blurring based on kernel or grayscale morphological operations are applied to the whole image to produce reference images. Next, each of unconnected regions in the mask is applied with Otsu method independently according to different reference images. Consequently, the segmentation result is generated by the combination of usable patches in all informative layers. The proposed approach is more than simply a fusion of the basic segmentation methods, but a well-organized strategy that integrates these basic methods. Experiments demonstrate that the proposed method outperforms previous methods within our datasets.
Hierarchical clustering and robust identification for block-based autoregressive speech parameter estimation

Given accurate system parameters like state transition matrix F and corruption mapping matrix H, clean speech autoregressive (AR) parameters can be effectively estimated from a series of noisy observations with Kalman filtering. In this paper, we address several fundamental issues to improve the linear dynamical system (LDS) based AR parameter estimation. A hierarchical time series clustering scheme is devised to truly group speech blocks with similar trajectories and corruption types. In addition, a correlated robust identification scheme using a posteriori signal-to-noise (SNR) mask is proposed to improve the identification accuracy. The effectiveness of the proposed clustering and identification scheme is evaluated in terms of spectral distortion between the Kalman estimates and the true clean speech parameters. Significant improvement is observed over the original matrix quantization (MQ) based approach. The proposed scheme is also successfully applied in a model-based speech enhancement application, and is expected to be effective in various codebook driven speech applications for robust identification purpose.
Biclustering of High-throughput Gene Expression Data with Bicluster Miner

During recent years, many biclustering algorithms have been developed for the analysis of gene expression data to complement and expand the capabilities of traditional clustering methods. With biclustering, genes with similar expression profiles can be identified not only over the whole data set but also across subsets of experimental conditions allowing genes to simultaneously belong to several expression patterns. This property makes biclustering a powerful approach especially when it is applied to data with large number of conditions. In spite of the clear theoretical benefit, the full potential of biclustering has not been realized within the gene expression research community and thus it has never truly become a part of the standard gene expression data analysis. Possible reasons include for example the unrealization of the various complementary ways in which biclustering can be applied to micro array or next-generation sequencing based gene expression data sets and the lack of reliable and fast algorithms. In this paper, we first illustrate the various opportunities of applying biclustering within a typical gene expression data analysis pipeline. Then a new biclustering method (BiclusterMiner) is presented that can be applied to all presented cases. The developed method is the first discrete biclustering algorithm that is able to simultaneously handle both up- and down-regulated genes by taking the direction of regulation into account and still discover all possible maximal biclusters. The efficiency of the proposed algorithm is demonstrated on real and synthetic datasets.
Pipelined ADC Digital Calibration by Using Polynomial Inverse Function

In this paper, a novel and simple digital technique to correct pipelined ADC errors is presented. The technique uses an inverse function for the entire ADC. Linearity of the ADC is improved by simultaneously adjusting the digital gain of the stages and the coefficients of the polynomials inverse function, using presented scheme. Output codes of the stages are applied to digital unit for calibration. Errors include capacitors mismatch, finite and nonlinear gain of Operational Amplifier (opamp), offset of opamp, and offset of sampling switches. The used algorithm is digital, completely. Trade-off between the speed and the accuracy of the pipelined ADC is improved by this technique, extensively. In the proposed technique, design problems of analogue circuits are moved inside digital circuit. The technique is simulated on a prototype 10bit 1.5bit/stage pipelined ADC with nonlinear opamp by using MATLAB/Simulink. After the proposed calibration, the ADC achieves 64.5 dB SNDR and 73dB SFDR which are restricted to 51.2 dB and 57dB respectively before calibration.
Neopterin Is a Cerebrospinal Fluid Marker for Treatment Outcome Evaluation in Patients Affected by Trypanosoma brucei gambiense Sleeping Sickness

Background Post-therapeutic follow-up is essential to confirm cure and to detect early treatment failures in patients affected by sleeping sickness (HAT). Current methods, based on finding of parasites in blood and cerebrospinal fluid (CSF) and counting of white blood cells (WBC) in CSF, are imperfect. New markers for treatment outcome evaluation are needed. We hypothesized that alternative CSF markers, able to diagnose the meningo-encephalitic stage of the disease, could also be useful for the evaluation of treatment outcome. Methodology/Principal findings Cerebrospinal fluid from patients affected by Trypanosoma brucei gambiense HAT and followed for two years after treatment was investigated. The population comprised stage 2 (S2) patients either cured or experiencing treatment failure during the follow-up. IgM, neopterin, B2MG, MMP-9, ICAM-1, VCAM-1, CXCL10 and CXCL13 were first screened on a small number of HAT patients (n = 97). Neopterin and CXCL13 showed the highest accuracy in discriminating between S2 cured and S2 relapsed patients (AUC 99% and 94%, respectively). When verified on a larger cohort (n = 242), neopterin resulted to be the most efficient predictor of outcome. High levels of this molecule before treatment were already associated with an increased risk of treatment failure. At six months after treatment, neopterin discriminated between cured and relapsed S2 patients with 87% specificity and 92% sensitivity, showing a higher accuracy than white blood cell numbers. Conclusions/Significance In the present study, neopterin was highlighted as a useful marker for the evaluation of the post-therapeutic outcome in patients suffering from sleeping sickness. Detectable levels of this marker in the CSF have the potential to shorten the follow-up for HAT patients to six months after the end of the treatment.
Epilepsy and sleep disorders

There is a close association between sleep and epilepsy. In some epilepsy syndromes, seizures occur predominantly (or even exclusively) during sleep or on awakening. Excessive daytime sleepiness is common in patients with epilepsy and may be due not only to medication but also to nocturnal seizures or concomitant sleep disorders. Sleep disorders such as obstructive sleep apnoea can worsen epilepsy, with improvement of seizure control following appropriate treatment of the sleep disorder. Conversely, epilepsy and antiepileptic medication can worsen sleep disorders. Nocturnal epileptic seizures may be difficult to differentiate from parasomnias, in particular non-rapid eye movement parasomnias such as night terrors, sleepwalking and confusional arousals, on history alone since there are semiological similarities between the two disorders. Schemes have been developed to facilitate differential diagnosis, although this remains a challenge even using the gold standard, video-electroencephalography telemetry.