Miracle-dz/UniFAR-FTUs huggingface dataset:
Global drivers and patterns of microbial abundance in soil

Aim While soil microorganisms play key roles in Earth’s biogeochemical cycles, methodological constraints and sparse data have hampered our ability to describe and understand the global distribution of soil microbial biomass. Here, we present a comprehensive quantification of the environmental drivers of soil microbial biomass. Location Global. Methods We used a comprehensive global dataset of georeferenced soil microbial biomass estimates and high-resolution climatic and soil data. Results We show that microbial biomass carbon (CMic) is primarily driven by moisture availability, with this single variable accounting for 34% of the global variance. For the microbial carbon-to-soil organic carbon ratio (CMic/COrg), soil nitrogen content was an equally important driver as moisture. In contrast, temperature was not a significant predictor of microbial biomass patterns at a global scale, while temperature likely has an indirect effect on microbial biomass by influencing rates of evapotranspiration and decomposition. As our models explain an unprecedented 50% of the global variance of CMic and CMic/COrg ,w e were able to leverage gridded environmental information to build the first spatially explicit global estimates of microbial biomass and quantified the global soil microbial carbon pool to equal 14.6 Pg C. Main Conclusions Our unbiased models allowed us to build the first global spatially explicit predictions of microbial biomass. These patterns show that soil microbial biomass is not primarily driven by temperature, but instead, biomass is more heterogeneous through the effects of moisture availability and soil nutrients. Our global estimates provide important data for integration into large-scale carbon and nutrient models that may imply a major step forward in our ability to predict the global carbon balance, now and in a future climate.
Factive theory of mind

Research on theory of mind has primarily focused on demonstrating and understanding the ability to represent others’ non-factive mental states, e.g., others’ beliefs in the false belief task. The motivation behind this focus has been that the representation of false beliefs are the best way to unequivocally demonstrate a genuine capacity for theory of mind, since they ensure that subjects’ responses cannot depend on their own representation of the world. Here, argue that the false belief requirement confuses the ability to represent a particular kind of (non-factive) content with the more general capacity to represent another agent’s understanding of the world. We then provide a way of correcting this error. We first offer a simple and theoretically motivated account on which tracking another agent’s understanding of the world and keeping that representation separate from one’s own are the essential features of a capacity for theory of mind. This account provides a straightforward way of understanding when factive representations, e.g., representations of what others know, provide evidence for a theory of mind capacity. We then develop a new test, the ‘diverse-knowledge task’, which shows how these criteria can be operationalized in an experimental paradigm. Finally, we turn to a number of existing examples from theory of mind research and illustrate (1) how to decide when a behavior does not demonstrate theory of mind, (2) why some existing research falls short of demonstrating the capacity for theory of mind (even when it focuses on false beliefs), and (3) where we’ve missed good evidence for theory of mind in a number of surprising places.
Schmidt Subspace Theorem and S – unit equation

We start with a short historical survey of the improvements of Liouville’s inequality: in the lower bound∣∣∣∣α− pq ∣∣∣∣ > c(α) qd for α real algebraic number of degree d ≥ 3, the exponent d of q in the denominator of the right hand side is replaced by κ with • any κ > (d/2) + 1 by A. Thue (1909), • 2 √ d by C.L. Siegel in 1921, • √ 2d by Dyson and Gel’fond in 1947, • any κ > 2 by K.F. Roth in 1955. See [3] Course N◦4 §4.1. 3.
Genetic diversity of Hypostomus ancistroides (Teleostei, Loricariidae) from an urban stream

In this study, random amplified polymorphic DNA (RAPD) markers were applied to analyze the genetic diversity of samples of the Neotropical catfish Hypostomus ancistroides, collected from four sites (S1, S2, S3 and S4) along an urban stream in Southern Brazil. The 11 primers used in RAPD analysis amplified 147 loci, 76 (51.7%) of which were polymorphic. The proportions of polymorphic loci observed in the four samples were: 29.93% (S1), 31.97% (S2), 23.81% (S3) and 38.77% (S4). The average heterozygosity within sampling localities ranged from 0.1230 to 0.1526 and unbiased genetic distances ranged from 0.0253 to 0.0445. AMOVA partitioned 90.85% of the total variation within samples and 9.15% among samples. Excepting for the sample pair S1-S2 (fST = 0.02784; p > 0.05), all others pairwise fST values were significantly greater than zero, indicating moderate genetic differentiation among catfish samples from four localities. The relative low levels of genetic variation detected in all groups studied could be related to different factors, including the sedentary habit of these fish, which can be eroding the genetic variation of H. ancistroides from each locality.
Sampling bias inverts ecogeographical relationships in island reptiles

Aim Species richness is one of the commonest measures of biodiversity, and is a basis for analyses at multiple scales. Data quality may affect estimations of species richness,but most broad-scale studies do not take sampling biases into account.We analysed reptile richness on islands that have received different sampling efforts, and assessed how inventory completeness affects the results of ecogeographical analyses. We also used simulations to evaluate under what circumstances insufficient sampling can bias the outcome of biodiversity analyses. Location Mediterranean islands. Methods We gathered data on reptile richness from 974 islands, assuming better sampling in islands with specific inventories. We used Moran’s eigenvector mapping to analyse the factors that determine whether an island has been surveyed, and to identify the relationships between reptile richness, geographical parameters and anthropic parameters. We simulated islands, mimicking patterns of true data, and sampled them with varying effort. Simulated richness was analysed using the same approach used for real-world data. Results The probability that islands were sampled for reptiles was higher in large, human-populated islands. The relationship between human impact and reptile richness was negative in well-surveyed islands, but positive in islands that had not been systematically surveyed, because densely populated and accessible islands receive better sampling. In simulations, analyses successfully retrieved the relationships between species richness and human presence only if the average species detection probability was ≥75%. Poorer sampling resulted in biased regression results. Main conclusions Human activities may strongly affect biodiversity, but human presence and accessibility improve sampling effort and thus the quality of biodiversity information. Therefore, regressing known species richness on parameters representing human presence may result in apparent positive relationships. These two facets of human presence (positive on biodiversity knowledge, negative on actual biodiversity) represent a major challenge for ecogeographical studies, as not taking them into account would bias analyses and underestimate human impact.
Licorice root extract and magnesium isoglycyrrhizinate protect against triptolide-induced hepatotoxicity via up-regulation of the Nrf2 pathway

Abstract Triptolide, the predominant biologically active component of the Chinese herb Tripterygium wilfordii Hook f. , possesses numerous pharmacological activities, including anti-inflammatory, anti-fertility, anti-neoplastic, and immunosuppressive effects. However, toxicity and severe adverse effects, particularly hepatotoxicity, limit the clinical application of triptolide. Licorice root extract contains various bioactive compounds and is potent hepatoprotective. Magnesium isoglycyrrhizinate, a magnesium salt of the 18α-glycyrrhizic acid stereoisomer of glycyrrhizic acid, is used clinically in China to treat chronic viral hepatitis and acute drug-induced liver injury. The aim of this study was to investigate the role of the factor erythroid 2-related factor 2 pathway in the protective effects of LE and MIG against triptolide-induced hepatotoxicity. Hepatotoxicity models were established in L-02 cells and rats using triptolide, and the protective effects of LE and MIG were investigated in vitro and in vivo, respectively. LE and MIG significantly protected against triptolide-induced cytotoxicity. Additionally, triptolide decreased the mRNA and protein levels of Nrf2 and down-regulated Nrf2 target genes, including UGT1A, BSEP, and MRP2, while pretreatment with LE and MIG reversed these effects. Finally, Nrf2-involved antioxidant responses were activated in the presence of LE and MIG.
Acid Phosphatases Activity and Growth of Barley, Oat, Rye and Wheat Plants as Affected by Pi Deficiency

Method: Plants were cultured three weeks in a nutrient media: complete (control, +P) or without phosphorus (-P). The growth on -P medium significantly affected the inorganic phosphate (Pi) content in plants tissues. Pi deficit decreased shoots growth but ratio of root/shoot was higher for -P plants when compared to control. The root elongation was enhanced under Pi deficiency in -P oat and barley more intensive elongation was observed than in other plants. On the other hand, inhibition of shoot growth was more pronounced for -P rye and wheat. Pi-deficient plants showed higher activity of acid phosphatases in tissue extracts and in exudates from roots than +P plants.
Study on Infrared Stealth Performance Evaluation Based on Convolution Neural Network

In the future battlefield environment, a fast, objective and accurate target infrared stealth performance evaluation method is of great significance to improve the survivability of the battlefield. Through the study of infrared stealth performance evaluation and target identification, reviewed the development of the infrared stealth effectiveness evaluation and methods, puts forward the Convolutional neural network was applied to target infrared image recognition, and through the composite similarity evaluation standard to evaluate stealth performance. The development trend of infrared stealth performance evaluation is analyzed, and the prospects of multi-index fusion, intelligent evaluation and multispectral evaluation are analyzed.
Evolución de los sistemas rotatorios en endodoncia: propiedades y diseño

En los ultimos anos en la especialidad de endodoncia han ocurrido cambios notables, sobre todo relacionados con las propiedades metalurgicas y diseno de nuevos instrumentos endodonticos, asi como tambien en las tecnicas de instrumentacion de los conductos radiculares. Para optimizar su uso dentro del conducto radicular, los nuevos instrumentos rotatorios tienen variaciones en el diametro y conicidad, ademas se ha agregado terminologia reciente relacionada a su diseno como: superficie radial, angulo de corte, angulo helicoidal, paso de rosca, entre otros. Conociendo los detalles del diseno de un nuevo instrumento endodontico podemos utilizarlo con mayor seguridad y eficiencia, para lograr mejor resultado en la calidad del tratamiento endodontico.
Produção de biocombustíveis a partir de algas fotossintetizantes

Atualmente, o consumo medio de energia e dez vezes maior do que o consumo do homem primitivo. Proporcionalmente ao crescimento do consumo de energia, cresceu a poluicao, fazendo-se necessario o desenvolvimento de fontes energeticas “limpas”, que permitiam a estabilizacao desse quadro de poluicao, como os biocombustiveis sintetizados por algas. Esses organismos clorofilados convertem substâncias simples presentes na atmosfera, com absorcao de luz solar, em energia quimica acumulada, ou seja, compostos com alto valor energetico.
The Place of the Psyche in a Constructed World

The vast majority of social constructionist writings have been critical of psychological science-on both ideological and conceptual grounds. The constructionist emphasis on microsocial processes also functions oppositionally to psychological accounting. The existing animus grows, however, from a realist metaphysics and a correspondence view of language, neither of which constructionism endorses. Viewing the relationship between constructionism and psychological science in more pragmatic terms, we find three significant ways in which constructionism contributes to a more fully enriched and broadly effective psychology. First, critical constructionism functions to denaturalize psychological accounts, opening them to reflexive deliberation, and democratizing the field more generally. Second, constructionist metatheory invites a resuscitation of marginal or suppressed discourse within the field, and invigorates societally engaged efforts to forge new and more useful discourses of the mind. Finally, social constructionism offers the possibility for a fundamental reconceptualization of the self. Illustrative is the family of theories conceptualizing the self as either constituted by or constituting relationships.
Automation of pre-processing and feature extraction parameter selection for a single-trial P300-based brain-computer interface using a genetic algorithm

The P300 evoked potential is commonly used in the Brain-Computer Interface (BCI) paradigm to translate the intentions of a user into commands for external devices. Identifying the P300 response in the recorded brain activity is facilitated using machine learning. Feature extraction is performed on the collected data in order to reduce its dimensionality. Dimensionality reduction is necessary in order to avert the Hughes effect associated with high-dimensional training data whereby an exponentially greater number of training examples are required to maintain classifier generalisation as feature dimensionality increases. In general, the classification stage of a BCI is the only avenue through which the BCI is tuned to the user’s unique brain activity. Subject-specific BCI tuning is necessary in order to cater to the inherent inter-subject physiological differences that manifest itself in the EEG. Additionally, owing to inter-subject physiological dissimilar, each user would also possess an optimal pre-processing and feature extraction scheme. However, the preprocessing and feature extraction stages of a Brain-Computer Interface which precede classification are usually fixed and insensitive to the user. In this work, two-stage genetic-optimisation is utilised to tune a BCI to a user at the levels of pre-processing and feature extraction thereby completely removing the need for subjective human expertise in the parameter selection process. In the first stage of geneticoptimisation, a Genetic algorithm is used to select the EEG channels from which single-trial P300 features are obtained. The second stage of genetic-optimisation involves the determination of the high cut-off frequency of a digital filter used to pre-process each EEG channel and the down-sampling factor used to produce temporal features per channel. Using all 32 EEG channels and the default feature extraction and pre-processing parameters proposed in [11], the average single-trial P300 classification error across 8 subjects was 42.10%. The classification error was reduced to 37.83% using the two-stage geneticoptimisation method proposed in this paper. One disabled subject’s classification error decreased by 10.79%. The results suggest that classification error can be reduced and that it may be feasible to automate the process of BCI parameter selection using a genetic algorithm approach. Additionally, the reduction in the EEG channel count from 32 to 18 as proposed by the GA results in a shorter subject preparation time and a reduction in the computational and memory requirements of BCI operation as well as the quantity of consumables necessary to perform data collection.
Magnetically self‐consistent ring current simulations during the 19 October 1998 storm

[1] We investigate effects of magnetic self-consistency on ring current development by calculating equatorial particle transport in a model that feeds back the ring current on the magnetospheric configuration. The equatorial magnetic intensity is computed by solving a force-balance equation in the equatorial plane. This force-balance computation is coupled to a kinetic proton and electron drift-loss model. The electric field model used includes corotation, quiescent Volland-Stern convection, and storm-associated enhancements in the convection. We have modeled the 19 October 1998 storm (min Dst = −112 nT) by using the total cross polar cap potential from AMIE to determine the amplitudes of storm-associated electric field enhancements. We trace equatorially mirroring protons and electrons within this model. We have found that self-consistent feedback between plasma pressure and the magnetic field tends to mitigate the energization associated with inward particle transport as the ring current forms. At a given first adiabatic invariant and radial distance, the self-consistent magnetic field reduces the E × B drift rate as it significantly enhances the azimuthal gradient-B drift rate. Especially later in the main phase, there can be places where the plasma pressure and magnetic perturbation are locally enhanced by making the simulation magnetically self-consistent because of enhanced drift rates in regions of reduced magnetic intensity. The southward magnetic perturbation at the center of the Earth, which represents the ring current contribution to the Dst index, is reduced by about 25% by making the simulation magnetically self-consistent. This suggests that simulations that do not take account of the feedback of the ring current overestimate the actual ring current intensity.
Low-speed rated-torque operation of a motor drive using a five-level diode-clamped PWM inverter with a front-end diode rectifier

This paper focuses on a medium-voltage motor drive requiring no regenerative braking. It is operated not only in a high-speed region but also in a low-speed region with the rated torque, intended for an application to drilling rigs. The power conversion system consists of a front-end diode rectifier, a five-level diode-clamped PWM inverter, and a dc-voltage-balancing circuit including a single coupled inductor. The five-level diode-clamped inverter is characterized by injecting a common ninth-harmonic zero-sequence voltage on each of the three-phase reference voltages for mitigating magnetic-flux fluctuation of the coupled inductor of the dc-voltage-balancing circuit. Moreover, the voltage injection can make the power-loss distribution of eight insulated gate bipolar transistors (IGBTs) per leg close to an even profile. Experimental results obtained from a 200-V 5.5-kW downscaled model confirm the viability and effectiveness of the power conversion system, indicating that the ninth-harmonic zero-sequence voltage injection mitigates the magnetic-flux fluctuation.
Identification of Pulmonary Hypertension Using Entropy Measure Analysis of Heart Sound Signal

This study introduced entropy measures to analyze the heart sound signals of people with and without pulmonary hypertension (PH). The lead II Electrocardiography (ECG) signal and heart sound signal were simultaneously collected from 104 subjects aged between 22 and 89. Fifty of them were PH patients and 54 were healthy. Eleven heart sound features were extracted and three entropy measures, namely sample entropy (SampEn), fuzzy entropy (FuzzyEn) and fuzzy measure entropy (FuzzyMEn) of the feature sequences were calculated. The Mann–Whitney U test was used to study the feature significance between the patient and health group. To reduce the age confounding factor, nine entropy measures were selected based on correlation analysis. Further, the probability density function (pdf) of a single selected entropy measure of both groups was constructed by kernel density estimation, as well as the joint pdf of any two and multiple selected entropy measures. Therefore, a patient or a healthy subject can be classified using his/her entropy measure probability based on Bayes’ decision rule. The results showed that the best identification performance by a single selected measure had sensitivity of 0.720 and specificity of 0.648. The identification performance was improved to 0.680, 0.796 by the joint pdf of two measures and 0.740, 0.870 by the joint pdf of multiple measures. This study showed that entropy measures could be a powerful tool for early screening of PH patients.
Abrasion, Erosion and Cavitation Erosion Wear Properties of Thermally Sprayed Alumina Based Coatings

Thermally-sprayed alumina based materials, e.g., alumina-titania (Al2O3-TiO2), are commonly applied as wear resistant coatings in industrial applications. Properties of the coatings depend on the spray process, powder morphology, and chemical composition of the powder. In this study, wear resistant coatings from Al2O3 and Al2O3-13TiO2 powders were sprayed with plasma and high-velocity oxygen-fuel (HVOF) spray processes. Both, fused and crushed, and agglomerated and sintered Al2O3-13TiO2 powders were studied and compared to pure Al2O3. The coatings were tested for abrasion, erosion, and cavitation resistances in order to study the effect of the coating structure on the wear behavior. Improved coating properties were achieved when agglomerated and sintered nanostructured Al2O3-13TiO2 powder was used in plasma spraying. Coatings with the highest wear resistance in all tests were produced by HVOF spraying from fused and crushed powders.
Hierarchical Clustering with Structural Constraints

Hierarchical clustering is a popular unsupervised data analysis method. For many real-world applications, we would like to exploit prior information about the data that imposes constraints on the clustering hierarchy, and is not captured by the set of features available to the algorithm. This gives rise to the problem of "hierarchical clustering with structural constraints". Structural constraints pose major challenges for bottom-up approaches like average/single linkage and even though they can be naturally incorporated into top-down divisive algorithms, no formal guarantees exist on the quality of their output. In this paper, we provide provable approximation guarantees for two simple top-down algorithms, using a recently introduced optimization viewpoint of hierarchical clustering with pairwise similarity information [Dasgupta, 2016]. We show how to find good solutions even in the presence of conflicting prior information, by formulating a constraint-based regularization of the objective. We further explore a variation of this objective for dissimilarity information [Cohen-Addad et al., 2018] and improve upon current techniques. Finally, we demonstrate our approach on a real dataset for the taxonomy application.
2 Ordinal Label Aggregation by Minimax Conditional Entropy

Crowdsourcing allows the collection of labels from a crowd of workers at low cost. In this paper, we focus on ordinal labels, whose underlying order is important. Crowdsourced labels can be noisy as there may be amateur workers, spammers and/or even malicious workers. Moreover, some workers/items may have very few labels, making the estimation of their behavior difficult. To alleviate these problems, we propose a novel Bayesian model that clusters workers and items together using the nonparametric Dirichlet process priors. This allows workers/items in the same cluster to borrow strength from each other. Instead of directly computing the posterior of this complex model, which is infeasible, we propose a new variational inference procedure. Experimental results on a number of real-world data sets show that the proposed algorithm is more accurate than the state-of-the-art, and is more robust to sparser labels.