Miracle-dz/UniFAR-FTUs huggingface dataset:
Designing a synthetic microbial community devoted to biological control: The case study of Fusarium wilt of banana

Fusarium oxysporum f. sp. cubense (Foc) tropical race 4 (TR4) is threatening banana production because of its increasing spread. Biological control approaches have been widely studied and constitute interesting complementary measures to integrated disease management strategies. They have been based mainly on the use of single biological control agents (BCAs). In this study, we moved a step forward by designing a synthetic microbial community (SynCom) for the control of Fusarium wilt of banana (FWB). Ninety-six isolates of Pseudomonas spp. , Bacillus spp. , Streptomyces spp. , and Trichoderma spp. were obtained from the banana rhizosphere and selected in vitro for the antagonism against Foc TR4. In pot experiments, a large community such as SynCom 1.0 (44 isolates with moderate to high antagonistic activity) or a small one such as SynCom 1.1 (seven highly effective isolates) provided similar disease control (35% symptom severity reduction). An in vitro study of the interactions among SynCom 1.1 isolates and between them and Foc revealed that beneficial microorganisms not only antagonized the pathogen but also some of the SynCom constituents. Furthermore, Foc defended itself by antagonizing the beneficial microbes. We also demonstrated that fusaric acid, known as one of the secondary metabolites of Fusarium species, might be involved in such an interaction. With this knowledge, SynCom 1.2 was then designed with three isolates: Pseudomonas chlororaphis subsp. piscium PS5, Bacillus velezensis BN8.2, and Trichoderma virens T2C1.4. A non-simultaneous soil application of these isolates (to diminish cross-inhibition) delayed FWB progress over time, with significant reductions in incidence and severity. SynCom 1.2 also performed better than two commercial BCAs, BioPak® and T-Gro. Eventually, SynCom 1.2 isolates were characterized for several biocontrol traits and their genome was sequenced. Our data showed that assembling a SynCom for biocontrol is not an easy task. The mere mixtures of antagonists (e.g., SynCom 1.0 and 1.1) might provide effective biocontrol, but an accurate investigation of the interactions among beneficial microorganisms is needed to improve the results (e.g., SynCom 1.2). SynCom 1.2 is a valuable tool to be further developed for the biological control of FWB.
Secrets in Common: Intellectual Foundations of the Lodge that found Billet in the Dens and Klaverns

Secrets in Common is an anthropological history that undertakes to explain the similarities of membership and ideology between the Freemasons and two formations of the Ku Klux Klan. The work is divided into seven sections. It was compiled from both extant, which was of principal significance, and secondary printed material. After many hours of reading and countless attempts at "understanding," three short ethnographic narratives were compiled: they makeup the central axis of the material. The first narrative describes the Freemasons, while the second two are on the "Reconstruction Klan" and the "Klan of the '20's," henceforth referred to as Kuklux and Knights respectively. Circumstantial and anecdotal evidence allows the construction of an argument that supports the assertion that the intellectual foundations of the Freemasons were acted upon in Kuklux Dens and Knights' Klavems, and that in acting upon the Freemasons' ideology both Klans became revitalization movements as defined by A.C. Wallace ( 1956).
Characterizing cell recruitment into isotropic and anisotropic biomaterials by quantification of spatial density gradients in vitro

The success of cell-free in situ tissue engineering approaches depends on an appropriate recruitment of autologous cells from neighboring tissues. This identifies cellular migration as a critical parameter for the pre-clinical characterization of biomaterials. Here, we present a new method to quantify both the extent and the spatial anisotropy of cell migration in vitro. For this purpose, a cell spheroid is used as a cell source to provide a high number of cells for cellular invasion and, at the same time, to guarantee a controlled and spatially localized contact to the material. Therefore, current limitations of assays based on 2D cell sources can be overcome. We tested the method on three biomaterials that are in clinical use for soft tissue augmentation in maxilla-facial surgery and a substrate used for 3D in vitro cell culture. The selected biomaterials were all collagen-derived, but differed in their internal architecture. The analysis of cellular isodensity profiles within the biomaterials allowed the identification of the extent and the preferential directions of migration, as well as their relation to the biomaterials and their specific pore morphologies. The higher cell density within the biomaterials resulting from the here-introduced cell spheroid assay compared to established 2D cell layer assays suggests a better representation of the in vivo situation. Consequently, the presented method is proposed to advance the pre-clinical evaluation of cell recruitment into biomaterials, possibly leading to an improved prediction of the regeneration outcome.
Fungal endophytes of Brassicaceae: Molecular interactions and crop benefits

Brassicaceae family includes an important group of plants of great scientific interest, e.g., the model plant Arabidopsis thaliana, and of economic interest, such as crops of the genus Brassica (Brassica oleracea, Brassica napus, Brassica rapa, etc.). This group of plants is characterized by the synthesis and accumulation in their tissues of secondary metabolites called glucosinolates (GSLs), sulfur-containing compounds mainly involved in plant defense against pathogens and pests. Brassicaceae plants are among the 30% of plant species that cannot establish optimal associations with mycorrhizal hosts (together with other plant families such as Proteaceae, Chenopodiaceae, and Caryophyllaceae), and GSLs could be involved in this evolutionary process of non-interaction. However, this group of plants can establish beneficial interactions with endophytic fungi, which requires a reduction of defensive responses by the host plant and/or an evasion, tolerance, or suppression of plant defenses by the fungus. Although much remains to be known about the mechanisms involved in the Brassicaceae-endophyte fungal interaction, several cases have been described, in which the fungi need to interfere with the GSL synthesis and hydrolysis in the host plant, or even directly degrade GSLs before they are hydrolyzed to antifungal isothiocyanates. Once the Brassicaceae-endophyte fungus symbiosis is formed, the host plant can obtain important benefits from an agricultural point of view, such as plant growth promotion and increase in yield and quality, increased tolerance to abiotic stresses, and direct and indirect control of plant pests and diseases. This review compiles the studies on the interaction between endophytic fungi and Brassicaceae plants, discussing the mechanisms involved in the success of the symbiosis, together with the benefits obtained by these plants. Due to their unique characteristics, the family Brassicaceae can be seen as a fruitful source of novel beneficial endophytes with applications to crops, as well as to generate new models of study that allow us to better understand the interactions of these amazing fungi with plants.
DOA Estimation based on Deconvolution Beamforming-based Unrolling Neural Network*

Most deep learning methods applied to direction of arrival (DOA) estimation in underwater acoustic are designed on generic neural network architectures and rely excessively on training data. This paper proposes a deconvolution beamforming-based unrolling neural network (DCV-UNN) whose depth direction corresponds to the number of iterations of the Richardson Lucy (R-L) algorithm. DCV-UNN tends to generalize better than generic networks, which alleviates the training burden and the requirement for large-scale training data sets. DCV-UNN can be used for single-frequency multi-source DOA estimation after training based on a small single-frequency single-source dataset. In addition, the DCV-UNN which constrained weights based on physical models improves the interpretability of the network to some extent. And the rest trainable free parameters guarantee the adaptability of the neural network model to environmental noise. Experiment results show that the deconvolution beamforming-based neural network improves the robustness of orientation estimation under low signal-to-noise ratio (SNR) conditions and can effectively suppress sidelobes and pseudo peaks, improving the SNR gain by about 10dB~20 dB compared to other traditional methods in direction of arrival (DOA) estimation in underwater acoustic.
In Situ Calibration of Six-Axis Force–Torque Sensors for Industrial Robots With Tilting Base

The accuracy of the force–torque sensors generally decreases with time under standard operating conditions. Therefore, periodic calibration for force–torque sensors is necessary. Specialized devices or subsidiary sensors are required during the traditional calibration process which bring in external measurement errors. In this article, an in situ calibration method is proposed to calibrate the force–torque sensor with a single standard mass to generate expected wrenches of the sensor during some arbitrary robot poses. Since the tilt of the mounting base of the robot can affect the direction of the gravity expressed with respect to the sensor frame, we first derive a model to express the relationship between the raw measurements and the expected wrenches under the influence of the tilted base, and then introduce subspaces and take advantage of projection and Rayleigh quotient to obtain the calibration matrix and angles of tilting simultaneously. The proposed algorithm is validated on simulation and the industrial automation (ATI) force–torque sensor. The results show that the proposed method is more cost-effective and reliable than existing technologies.
Education-to-work transitions of aspiring creatives

Despite some segments of the creative industries in Australia performing better than other segments in terms of earnings and employment growth, they all rely on highly skilled workers and face similar workforce challenges. Workers typically experience multiple entry attempts, spells of unemployment, short-term contracts, high degrees of mobility, casual/part-time employment within and outside the creative industries, and pressure to ensure their skills remain relevant. Skills shortages and gaps, an insufficient supply of high quality industry-ready graduates, difficulties in predicting demand for skills, weak linkages between industry and education providers, reliance on overseas talent in some segments, limited opportunities for workers to engage in skill development and pressure on workers to keep abreast of technological developments are ongoing issues in the creative industries workforce. In response to these concerns, the Australian Research Council, three State Governments, industry and a large vocational education and training (VET) provider funded Queensland University of Technology (QUT) to conduct the 60Sox project. This 3-year project investigated the education, training and work experiences of aspiring creatives defined as new entrants, recent graduates and students enrolled in creative industries courses. It involved the largest survey of aspiring creatives ever undertaken in Australia, attracting 507 respondents, and a survey of 50 employers. Using the framework proposed by Hannan, Raffe and Smyth (1996), this article presents findings from an analysis of the macro and micro labour market outcomes of aspiring creatives using data from the two 60Sox project surveys and publicly available sources. The analysis confirmed that many graduates of creative industries courses who participated in the 60Sox survey and the national surveys for the National Centre for Vocational Education Research and Graduate Careers Australia were struggling to make a successful transition from education to work. This article also discusses the causes of this key finding and possible solutions to address transition issues.
Comparing the stream re-aeration coefficient estimated from ANN and empirical models / Comparaison d'estimations par un RNA et par des modèles empiriques du coefficient de réaération en cours d'eau

Abstract Dissolved oxygen (DO) is one of the most useful indices of river's health and the stream re-aeration coefficient is an important input to computations related to DO. Normally, this coefficient is expressed as a function of several variables, such as mean stream velocity, shear stress velocity, bed slope, flow depth, and Froude number. However, in free surface flows, some of these variables are interrelated, and it is possible to obtain simplified stream re-aeration equations. In recent years, different functional forms have been advanced to represent the re-aeration coefficient for different data sets. In the present study, the artificial neural network (ANN) technique has been applied to estimate the re-aeration coefficient (K 2) using data sets measured at different reaches of the Kali River in India and values obtained from the literature. Observed stream/channel velocity, bed slope, flow depth, cross-sectional area and re-aeration coefficient data were used for the analysis. Different combinations of variables were tested to obtain the re-aeration coefficient using an ANN. The performance of the ANN was compared with other estimation methods. It was found that the re-aeration coefficient estimated by using an ANN was much closer to the observed values as compared with the other techniques.
Progress in the Determination of Polarized PDFs and Higher Twist

The impact of the recent very precise CLAS and COMPASS g1/F1 data on polarized parton densities and higher twist effects is discussed. It is demonstrated that the low Q^2 CLAS data improve essentially our knowledge of higher twist corrections to the spin structure function g1, while the large Q^2 COMPASS data influence mainly the strange quark and gluon polarizations. It is also shown that the uncertainties in the determination of the polarized parton densities are significantly reduced. We find also that the present inclusive DIS data cannot rule out a negative polarized and changing in sign gluon densities. The present status of the proton spin sum rule is discussed.
Research on 4-PAM signal compensation technology for high-speed indoor FSOC based on FNN

The previous researches on signal compensation technology based on feedforward neural network (FNN) are all in underwater channel and fiber channel. In this paper, the signal compensation technology based on FNN is applied to 8Gb/s 4-PAM indoor free space optical communication (FSOC) system for the first time. Under 7% forward error correction (FEC) threshold, the FNN algorithm is compared with direct detection and traditional LMS filtering algorithm. The FNN-based method can significantly improve the receiver sensitivity and improve the performance of the communication system.
From Information Scaling of Natural Images to Regimes of Statistical Models

Computer vision can be considered a highly specialized data collection and data analysis problem. We need to understand the special properties of image data in order to construct statistical models for representing the wide variety of image patterns. One special property of vision that distinguishes itself from other sensory data such as speech data is that distance or scale plays a profound role in image data. More specifically, visual objects and patterns can appear at a wide range of distances or scales, and the same visual pattern appearing at different distances or scales produces different image data with different statistical properties, thus entails different regimes of statistical models. In particular, we show that the entropy rate of the image data changes over the viewing distance (as well as the camera resolution). Moreover, the inferential uncertainty changes with viewing distance too. We call these changes information scaling. From this perspective, we examine both empirically and theoretically two prominent and yet largely isolated research themes in image modeling literature, namely, wavelet sparse coding and Markov random fields. Our results indicate that the two models are appropriate on two different entropy regimes: sparse coding targets the low entropy regime, whereas the random fields are suitable for the high entropy regime. Because of information scaling, both models are necessary for representing and interpreting image intensity patterns in the whole entropy range, and information scaling triggers transitions between these two regimes of models. This motivates us to propose a full-zoom primal sketch model that integrates both sparse coding and Markov random fields. In this model, local image intensity patterns are classified into “sketchable regime” and “non-sketchable regime” by a sketchability criterion. In the sketchable regime, the image data are represented deterministically by highly parametrized sketch primitives. In the non-sketchable regime, the image data are characterized by Markov random fields whose sufficient statistics summarize computational results from failed attempts of sparse coding. The contribution of our work is two folded. First, information scaling provides a dimension to chart the space of natural images. Second, the full-zoom modeling scheme provides a natural integration of sparse coding and Markov random fields, thus enables us to develop a new and richer class of statistical models.
Trust me! Parental embodied mentalizing predicts infant cognitive and language development in longitudinal follow-up

Children’s cognitive and language development is a central aspect of human development and has wide and long-standing impact. The parent-infant relationship is the chief arena for the infant to learn about the world. Studies reveal associations between quality of parental care and children’s cognitive and language development when the former is measured as maternal sensitivity. Nonetheless, the extent to which parental mentalizing – a parent’s understanding of the thoughts, feelings, and attitudes of a child, and presumed to underlie sensitivity – contributes to children’s cognitive development and functioning, has yet to be thoroughly investigated. According to the epistemic trust theory, high mentalizing parents often use ostensive cues, which signal to the infant that they are perceived and treated as unique subjective beings. By doing so, parents foster epistemic trust in their infants, allowing the infant to use the parents a reliable source of knowledge to learn from. Until recently, parental mentalizing has been limited to verbal approaches and measurement. This is a substantial limitation of the construct as we know that understanding of intentionality is both non-verbal and verbal. In this investigation we employed both verbal and non-verbal, body-based, approaches to parental mentalizing, to examine whether parental mentalizing in a clinical sample predicts children’s cognitive and language development 12 months later. Findings from a longitudinal intervention study of 39 mothers and their infants revealed that parental embodied mentalizing in infancy significantly predicted language development 12 months later and marginally predicted child cognitive development. Importantly, PEM explained unique variance in the child’s cognitive and linguistic capacities over and above maternal emotional availability, child interactive behavior, parental reflective functioning, depression, ethnicity, education, marital status, and number of other children. The theoretical, empirical, and clinical implications of these findings are discussed.
The Kronecker Product in Approximation and Fast Transform Geration

Two aspects of the Kronecker product are studied. First, we start with the Kronecker product approximation problem. Given a matrix A and a factorization of its dimensions, we find matrices B and C of the corresponding dimensions whose Kronecker product is as close as possible to A in Frobenius norm. Various constrained Kronecker product approximation problems are also considered along with examples. Second, we develop a source-to-source compiler that processes matrix factorization formulae to generate efficient implementations of fast transformation algorithms. Our goal is to automate the implementation of efficient FFT and other fast transforms that can be characterized using Kronecker product factorizations. The Kronecker product notation plays a crucial role, allowing the simplification of expression of fast transform algorithms (Walsh-Hadamard, Haar, Slant, Hartley) and capitalizing on the experience from the FFT field. The compiler is based on a set of term rewriting rules that translate high level matrix descriptions into loops and assignment statements in any imperative programming language (parallel or sequential). We study the Haar and Daubechies wavelet algorithms with an effort to express them using matrix language. We provide back-end translators for FORTRAN, C and MATLAB.
A novel method for handling pre-existing conditions in multivariate prediction model development for COVID-19 death in the Department of Veterans Affairs

Abstract Many mathematical models have been proposed to predict death following the Coronavirus Disease 2019 (COVID-19); all started with comorbidity subsets for this still-little understood disease. Thus, we derived a novel predicted probability of death model (PDeathDx) upon all diagnostic codes documented in the Department of Veterans Affairs. We present the conceptual underpinnings and analytic approach in estimating the independent contribution of pre-existing conditions. This is the largest study to-date following patients with COVID-19 to predict mortality. Cases were identified with at least one positive nucleic acid amplification test. Starting in 1997, we use diagnoses from the first time a patient sought care until 14 days before a positive nucleic acid amplification test. We demonstrate the clear advantage of using an unrestricted set of pre-existing conditions to model COVID-19 mortality, as models using conventional comorbidity indices often assign little weight or usually do not include some of the highest risk conditions; the same is true of conditions associated with COVID-19 severity. Our findings suggest that it is risky to pick comorbidities for analysis without a systematic review of all those experienced by the cohort. Unlike conventional approaches, our comprehensive methodology provides the flexibility that has been advocated for comorbidity indices since 1993; such an approach can be readily adapted for other diseases and outcomes. With our comorbidity risk adjustment approach outperforming conventional indices for predicting COVID-19 mortality, it shows promise for predicting outcomes for other conditions of interest.
Genetic Analysis of Isozyme Polymorphism in Grape

Genetic analysis of 11 allozyme polymorphisms was performed on the progeny of ‘Cayuga White’ × ‘Aurora’, two complex interspecific grape (Vitis) hybrids. Segregation for most of the polymorphisms closely approximated monogenic Mendelian ratios, and eight new isozyme loci were defined for grape. Joint segregation analysis among the isozyme loci revealed three multilocus linkage groups. These results demonstrate that sufficient allozyme polymorphism exists in grape to establish many multilocus linkage groups and that this genetic analysis can be accomplished using extant progeny or progeny readily produced from highly heterozygous clones.