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Azospirillum is one of the most studied plant growth-promoting bacteria (PGPB); it represents a common model for plant-bacterial interactions. While Azospirillum brasilense is the species that is most widely known, at least 22 species, including 17 firmly validated species, have been identified, isolated from agricultural soils as well as habitats as diverse as contaminated soils, fermented products, sulfide springs, and microbial fuel cells. Over the last 40 years, studies on Azospirillum-plant interactions have introduced a wide array of mechanisms to demonstrate the beneficial impacts of this bacterium on plant growth. Multiple phytohormones, plant regulators, nitrogen fixation, phosphate solubilization, a variety of small-sized molecules and enzymes, enhanced membrane activity, proliferation of the root system, enhanced water and mineral uptake, mitigation of environmental stressors, and competition against pathogens have been studied, leading to the concept of the Multiple Mechanisms Hypothesis. This hypothesis is based on the assumption that no single mechanism is involved in the promotion of plant growth; it posits that each case of inoculation entails a combination of a few or many mechanisms. Looking specifically at the vast amount of information about the stimulatory effect of phytohormones on root development and biological nitrogen fixation, the Efficient Nutrients Acquisition Hypothesis model is proposed. Due to the existence of extensive agriculture that covers an area of more than 60 million hectares of crops, such as soybeans, corn, and wheat, for which the bacterium has proven to have some agronomic efficiency, the commercial use of Azospirillum is widespread in South America, with over 100 products already in the market in Argentina, Brazil, and Uruguay. Studies on Azospirillum inoculation in several crops have shown positive and variable results, due in part to crop management practices and environmental conditions. The combined inoculation of legumes with rhizobia and Azospirillum (co-inoculation) has become an emerging agriculture practice in the last several years, mainly for soybeans, showing high reproducibility and efficiency under field conditions. This review also addresses the use of Azospirillum for purposes other than agriculture, such as the recovery of eroded soils or the bioremediation of contaminated soils. Furthermore, the synthetic mutualistic interaction of Azospirillum with green microalgae has been developed as a new and promising biotechnological application, extending its use beyond agriculture.

Azospirillum brasilense is a non-photosynthetic rhizobacterium that promotes the growth of plants. In this work, we evaluated the effects of different light qualities on the growth, viability, and motility in combination to other culture conditions such as temperature or composition of the culture medium. Exponential cultures of A. brasilense Az39 were inoculated by drop-plate method on nutritionally rich (LB) or chemically defined (MMAB) media in the presence or absence of Congo Red indicator (CR) and exposed continuously to white light (WL), blue light (BL), and red light (RL), or maintained in dark conditions (control). The exposure to BL or WL inhibited growth, mostly in LB medium at 36 °C. By contrast, the exposure to RL showed a similar behavior to the control. Swimming motility was inhibited by exposure to WL and BL, while exposure to RL caused only a slight reduction. The effects of WL and BL on plant growth-promoting rhizobacteria should be considered in the future as deleterious factors that could be manipulated to improve the functionality of foliar inoculants, as well as the bacterial effects on the leaf after inoculation.

Face recognition functions are today exploited through biometric sensors in many applications, from extended security systems to inclusion devices; deep neural network methods are reaching in this field stunning performances. The main limitation of the deep learning approach is an inconvenient relation between the accuracy of the results and the needed computing power. When a personal device is employed, in particular, many algorithms require a cloud computing approach to achieve the expected performances; other algorithms adopt models that are simple by design. A third viable option consists of model (oracle) distillation. This is the most intriguing among the compression techniques since it permits to devise of the minimal structure that will enforce the same I/O relation as the original model. In this paper, a distillation technique is applied to a complex model, enabling the introduction of fast state-of-the-art recognition capabilities on a low-end hardware face recognition sensor module. Two distilled models are presented in this contribution: the former can be directly used in place of the original oracle, while the latter incarnates better the end-to-end approach, removing the need for a separate alignment procedure. The presented biometric systems are examined on the two problems of face verification and face recognition in an open set by using well-agreed training/testing methodologies and datasets.

A simplified correlation index is proposed to be used in real-time pulse shape recognition systems. This index is similar to the classic Pearson’s correlation coefficient, but it can be efficiently implemented in FPGA devices with far fewer logic resources and excellent performance. Numerical simulations with synthetic data and comparisons with the Pearson’s correlation show the suitability of the proposed index in applications such as the discrimination and counting of pulses with a predefined shape. Superior performance is evident in signal-to-noise ratio scenarios close to unity. FPGA implementation of Person’s method and the proposed correlation index have been successfully tested and the main results are summarized.

Azospirillum is one of the most successful plant growth-promoting bacteria (PGPB) genera and it is considered a study model for plant–bacteria interactions. Because of that, a wide broad of topics has been boarded and discussed in a significant number of publications in the last four decades. Using the Scopus® database, we conducted a bibliographic search in order to analyze the number and type of publications, the authors responsible of these contributions, and the origin of the researchers, as well as the keywords and journals selected by the authors, among other related characteristics, with the aim to understand some less addressed details about the work done with Azospirillum worldwide since its discovery in 1925. Despite that the largest numbers of publications about this bacterium were obtained between the 1970 and 1980s, there is still a linear increase tendency in the number of published works. Understanding the mechanisms involved in the ability of these bacteria to promote growth in a wide broad of plant species under both laboratory and field conditions has been a preferential target for these published articles. This tendency could be considered a cause or consequence of the current increase in the number of commercial products formulated with Azospirillum around the world and a catalyzer for the increase of published articles along time.

This paper proposes a new neural network structure for image processing whose convolutional layers, instead of using kernels with fixed coefficients, use space-variant coefficients. The adoption of this strategy allows the system to adapt its behavior according to the spatial characteristics of the input data. This type of layers performs, as we demonstrate, a non-linear transfer function. The features generated by these layers, compared to the ones generated by canonical CNN layers, are more complex and more suitable to fit to the local characteristics of the images. Networks composed by these non-linear layers offer performance comparable with or superior to the ones which use canonical Convolutional Networks, using fewer layers and a significantly lower number of features. Several applications of these newly conceived networks to classical image-processing problems are analyzed. In particular, we consider: Single-Image Super-Resolution (SISR), Edge-Preserving Smoothing (EPS), Noise Removal (NR), and JPEG artifacts removal (JAR).

Indole-3-acetic acid (IAA) is one of the most important molecules produced by Azospirillum sp., given that it affects plant growth and development. Azospirillum brasilense strains Sp245 and Az39 (pFAJ64) were pre-incubated in MMAB medium plus 100 mg/mL l-tryptophan and treated with or exposed to the following (a) abiotic and (b) biotic stress effectors: (a) 100 mM NaCl or Na2SO4, 4.0% (w/v) PEG6000, 0.5 mM H2O2, 0.1 mM abscisic acid, 0.1 mM 1-aminocyclopropane 1-carboxylic acid, 45 °C or daylight, and (b) 4.0% (v/v) filtered supernatant of Pseudomonas savastanoi (Ps) or Fusarium oxysporum (Fo), 0.1 mM salicylic acid (SA), 0.1 mM methyl jasmonic acid (MeJA), and 0.01% (w/v) chitosan (CH). After 30 and 120 min of incubation, biomass production, cell viability, IAA concentration (µg/mL), and ipdC gene expression were measured. Our results show that IAA production increases with daylight or in the presence of PEG6000, ABA, SA, CH, and Fo. On the contrary, exposure to 45 °C or treatment with H2O2, NaCl, Na2SO4, ACC, MeJA, and Ps decrease IAA biosynthesis. In this report, growth and IAA biosynthesis in A. brasilense under biotic and abiotic stress conditions are discussed from the point of view of their role in bacterial lifestyle and their potential application as bioproducts.

In the original article, last name and first names of all the authors are inverted. The correct names should appears as “Romina Molina, Gastón López, Belén Rodríguez, Susana Rosas, Verónica Mora, Fabricio Cassán”.

To evaluate if root architecture changes observed in Arabidopsis thaliana inoculated with Azospirillum argentinense Az39 depend exclusively on the bacterial capacity to produce indole-3-acetic acid (IAA) and plant ability to sense IAA levels. Azospirillum argentinense Az39, A. argentinense Az39 ipd C–, flagellin from A. argentinense Az39, and pure IAA were applied to A. thaliana Col-0 (wild-type) and tir1.1 (a lateral root deficient mutant) seedlings. Inoculation with heat-inactivated A. argentinense Az39 cells and a non-PGPR bacterium ( Escherichia coli DH5α) was also tested. The primary root (PR) length, lateral roots (LR) number, and root hair (HR) density were assessed, and the root transcriptome was sequenced (Illumina HiSeq), followed by DEGs and GO term enrichment analyses. Inoculation with both A. argentinense strains resulted in a shorter PR and an increased number of LR and RH. IAA application (0.1 µM) led to a similar root phenotype than inoculation with Az39 (10 8  CFU mL −1 ). The addition of 1 µM flagellin, as well as plant exposure to non-lysed A. argentinense Az39 or E. coli DH5α cells, enhanced RH formation. Genes related to auxin signaling were highly expressed in the roots of Az39-inoculated seedlings; genes related to jasmonate and salicylic acid metabolism were highly expressed in the roots of plants inoculated with i pdC −  . Root architecture changes in A. thaliana inoculated with A. argentinense Az39 do not depend exclusively on root IAA levels/IAA plant perception. This PGPR induces root morphological changes through both IAA-dependent and IAA-independent mechanisms. Flagellin may be a key molecule involved in IAA-independent mechanisms.

The distinction of secondary particles in extensive air showers, specifically muons and electrons, is one of the requirements to perform a good measurement of the composition of primary cosmic rays. We describe two methods for pulse shape detection and discrimination of muons and electrons implemented on FPGA. One uses an artificial neural network (ANN) algorithm; the other exploits a correlation approach based on finite impulse response (FIR) filters. The novel hls4ml package is used to build the ANN inference model. Both methods were implemented and tested on Xilinx FPGA System on Chip (SoC) devices: ZU9EG Zynq UltraScale+ and ZC7Z020 Zynq. The data set used for the analysis was captured with a data acquisition system on an experimental site based on a water Cherenkov detector. A comparison of the accuracy of the detection, resources utilization and power consumption of both methods is presented. The results show an overall accuracy on particle discrimination of 96.62% for the ANN and 92.50% for the FIR-based correlation, with execution times of 848 ns and 752 ns, respectively.