Optical phase encoding in a pulsed approach to reservoir computing.

The exploitation of the full structure of multimode light fields enables compelling capabilities in many fields including classical and quantum information science. We exploit data-encoding on the optical phase of the pulses of a femtosecond laser source for a photonic implementation of a reservoir computing protocol. Rather than intensity detection, data-reading is done via homodyne detection that accesses combinations of an amplitude and a phase of the field. Numerical and experimental results on nonlinear autoregressive moving average (NARMA) tasks and laser dynamic predictions are shown. We discuss perspectives for quantum-enhanced protocols.

Compositional and functional analysis of the bacterial community of Mediterranean Leptosols under livestock grazing.

The composition and functioning of soil bacterial communities, as well as their responses to multiple perturbations, are not well understood in the terrestrial ecosystems. Our study focuses on the bacterial community of erosive and poorly developed soils (Haplic Leptosols) in Mediterranean rangelands of Extremadura (W Spain) with different grazing intensities. Leptosols from similar natural conditions were selected and sampled at two depths to determine the soil properties as well as the structure and activity of bacterial communities. As grazing intensified, the soil C and N content increased, as did the number and diversity of bacteria, mainly of fast-growing lineages. Aridibacter, Acidobacteria Gp6 and Gp10, Gemmatimonas, and Segetibacter increased their abundance along the grazing-intensity gradient. Firmicutes such as Romboutsia and Turicibacter from livestock microbiome also increased. In functional terms, the KEGG pathways enriched in the soils with moderate and high grazing intensity were ABC transporters, DNA repair and recombination proteins, the two-component system, and the degradation of xenobiotics. All of these proved to be related to stronger cell division and response mechanisms to environmental stressors such as drought, warming, toxic substances, and nutrient deprivation. Consequently, the bacterial community was affected by grazing, but appeared to adapt and counteract the effects of a high grazing intensity. Therefore, a clearly detrimental effect of grazing was not detected in the bacterial community of the soils studied.

Squeezing as a resource for time series processing in quantum reservoir computing.

Squeezing is known to be a quantum resource in many applications in metrology, cryptography, and computing, being related to entanglement in multimode settings. In this work, we address the effects of squeezing in neuromorphic machine learning for time-series processing. In particular, we consider a loop-based photonic architecture for reservoir computing and address the effect of squeezing in the reservoir, considering a Hamiltonian with both active and passive coupling terms. Interestingly, squeezing can be either detrimental or beneficial for quantum reservoir computing when moving from ideal to realistic models, accounting for experimental noise. We demonstrate that multimode squeezing enhances its accessible memory, which improves the performance in several benchmark temporal tasks. The origin of this improvement is traced back to the robustness of the reservoir to readout noise, which is increased with squeezing.

Identification of keystone taxa in root canals and periapical lesions of post-treatment endodontic infections: Next generation microbiome research.

AIM: The aim of this study was to analyse and compare the microbiome present in root canals and periapical lesions of teeth with post-treatment infections, and to identify the presence of keystone taxa in both habitats using next-generation sequencing analysis. METHODOLOGY: Apices and periapical lesions of patients with post-treatment apical periodontitis were surgically extracted. Specimens were cryo-pulverized, bacterial DNA was extracted, and the V3-V4 hypervariable regions of the 16S rRNA gene were sequenced using the Illumina Miseq platform. Bioinformatic analysis was carried out with Mothur software, whilst diversity indices were obtained using operational taxonomic units (OTUs). The diversity indices were compared with the Kruskal-Wallis test, and community composition differences were explored with Permutational Multivariate Analysis of Variance (PERMANOVA). A bacterial functional study was performed with the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. Co-occurrence network analyses were performed using the Sparse Correlations for Compositional data (SparCC). Eigencentrality, clr-based abundance and ubiquitousness were applied to infer keystone taxa. P values <.05 were considered statistically significant. RESULTS: Thirty-two apices and thirty-nine periapical lesions were sequenced and analysed. A similar alpha-diversity (p < .05) and community composition (p = .91) was observed for apices and lesion samples. The most abundant OTUs identified amongst all samples included Fusobacterium nucleatum, Prevotella loescheii, Streptococcus intermedius, Porphyromonas gingivalis, Parvimonas micra, Synergistetes bacterium, Tannerella forsythia and Peptostreptococcus stomatis. The metabolic pathways with >0.81% abundances included membrane transport, genetic information processing and metabolic pathways. F. nucleatum was identified as a keystone taxon as it showed ubiquitousness, an eigenvector centrality value of 0.83 and a clr-based abundance >4. CONCLUSIONS: The microbiome in apices and periapical lesions of post-treatment endodontic infections showed a similar diversity and taxonomic composition. Co-occurrence network analyses at OTU level identified F. nucleatum as a keystone taxon candidate in these infections.

Comprehensive insight into the alterations in the gut microbiome and the intestinal barrier as a consequence of iron deficiency anaemia.

BACKGROUND: Iron deficiency is the top leading cause of anaemia, whose treatment has been shown to deteriorate gut health. However, a comprehensive analysis of the intestinal barrier and the gut microbiome during IDA have not been performed to date. This study aims to delve further into the analysis of these two aspects, which will mean a step forward minimising the negative impact of iron supplements on intestinal health. METHODS: IDA was experimentally induced in an animal model. Shotgun sequencing was used to analyse the gut microbiome in the colonic region, while the intestinal barrier was studied through histological analyses, mRNA sequencing (RNA-Seq), qPCR and immunofluorescence. Determinations of lipopolysaccharide (LPS) and bacteria-specific immunoglobulins were performed to assess microbial translocation. RESULTS: Microbial metabolism in the colon shifted towards an increased production of certain amino acids, short chain fatty acids and nucleotides, with Clostridium species being enriched during IDA. Structural alterations of the colonic epithelium were shown by histological analysis. RNA-Seq revealed a downregulation of extracellular matrix-associated genes and proteins and an overall underdeveloped epithelium. Increased levels of serum LPS and an increased immune response against dysbiotic bacteria support an impairment in the integrity of the gut barrier during IDA. CONCLUSIONS: IDA negatively impacts the gut microbiome and the intestinal barrier, triggering an increased microbial translocation. This study emphasizes the deterioration of gut health during IDA and the fact that it should be addressed when treating the disease.

Fermented Goat's Milk Contributes to the Recovery of Iron Deficiency Anemia via Modulation of the Gut Microbiome.

Iron deficiency anemia (IDA) is a global public health concern affecting 1.6 billion people worldwide. The administration of iron supplements during the treatment of IDA adversely affects the intestinal barrier function and the composition and functionality of the intestinal microbiome, both of which are already altered during IDA. For this reason, it is of great interest to develop nutritional strategies aimed at alleviating these gut alterations associated with IDA and its treatment. In this sense, fermented goat's milk (FGM) was studied due to its nutritional quality. Our findings showed that in anemic animals the consumption of a FGM-based diet, compared to a standard diet, had positive modulatory effects on the intestinal microbiome. FGM-based diet restored intestinal dysbiosis, the intestinal barrier functionality, and bacterial translocation, contributing to a more efficient recovery of IDA. Therefore, FGM is a useful nutritional tool to ease intestinal alterations occurring during IDA and during its treatment.

Exploiting oscillatory dynamics of delay systems for reservoir computing.

Nonlinear dynamical systems exhibiting inherent memory can process temporal information by exploiting their responses to input drives. Reservoir computing is a prominent approach to leverage this ability for time-series forecasting. The computational capabilities of analog computing systems often depend on both the dynamical regime of the system and the input drive. Most studies have focused on systems exhibiting a stable fixed-point solution in the absence of input. Here, we go beyond that limitation, investigating the computational capabilities of a paradigmatic delay system in three different dynamical regimes. The system we chose has an Ikeda-type nonlinearity and exhibits fixed point, bistable, and limit-cycle dynamics in the absence of input. When driving the system, new input-driven dynamics emerge from the autonomous ones featuring characteristic properties. Here, we show that it is feasible to attain consistent responses across all three regimes, which is an essential prerequisite for the successful execution of the tasks. Furthermore, we demonstrate that we can exploit all three regimes in two time-series forecasting tasks, showcasing the versatility of this paradigmatic delay system in an analog computing context. In all tasks, the lowest prediction errors were obtained in the regime that exhibits limit-cycle dynamics in the undriven reservoir. To gain further insights, we analyzed the diverse time-distributed node responses generated in the three regimes of the undriven system. An increase in the effective dimensionality of the reservoir response is shown to affect the prediction error, as also fine-tuning of the distribution of nonlinear responses. Finally, we demonstrate that a trade-off between prediction accuracy and computational speed is possible in our continuous delay systems. Our results not only provide valuable insights into the computational capabilities of complex dynamical systems but also open a new perspective on enhancing the potential of analog computing systems implemented on various hardware platforms.

Integrated programmable spectral filter for frequency-multiplexed neuromorphic computers.

Artificial neural networks (ANN) are a groundbreaking technology massively employed in a plethora of fields. Currently, ANNs are mostly implemented through electronic digital computers, but analog photonic implementations are very interesting mainly because of low power consumption and high bandwidth. We recently demonstrated a photonic neuromorphic computing system based on frequency multiplexing that executes ANNs algorithms as reservoir computing and Extreme Learning Machines. Neuron signals are encoded in the amplitude of the lines of a frequency comb, and neuron interconnections are realized through frequency-domain interference. Here we present an integrated programmable spectral filter designed to manipulate the optical frequency comb in our frequency multiplexing neuromorphic computing platform. The programmable filter controls the attenuation of 16 independent wavelength channels with a 20 GHz spacing. We discuss the design and the results of the chip characterization, and we preliminary demonstrate, through a numerical simulation, that the produced chip is suitable for the envisioned neuromorphic computing application.

Microbiome in paired root apices and periapical lesions and its association with clinical signs in persistent apical periodontitis using next-generation sequencing.

AIM: To assess and compare the microbiome of paired root apices and periapical lesions from cases with failed endodontic treatment and to associate the microbiome and bacterial metabolic pathways in both sites with asymptomatic apical periodontitis (AAP) and symptomatic apical periodontitis (SAP), using next-generation sequencing (NGS). METHODOLOGY: Matched root apices and periapical lesions of patients with failed root canal treatments were surgically extracted. Specimens were cryopulverized, bacterial DNA was extracted and the V3-V4 hypervariable regions of the 16 S rRNA gene were amplified and sequenced using the Illumina Miseq platform. Diversity and community composition were studied in the paired samples, as well as in AAP and SAP cases. Diversity indices were compared in each case by means of the Wilcoxon matched-pairs signed rank and Mann-Whitney U tests. Differences in the community composition were explored with multivariate statistical analysis and Linear discriminant analysis Effect Size (LEfSe). Bacterial functional study was performed through the Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) analysis. RESULTS: Twenty-one paired apices and lesions were successfully sequenced and analysed, identifying a total of 21 phyla and 600 genera. A higher alpha-diversity was observed in the periapical lesions, although no global differences in the community composition between the two sites were found (p = .87), the most prevalent genera being Fusobacterium, Porphyromonas and Streptococcus. Prevotella, Clostridiales_vadinBB60_group, Bosea, Phreatobacter, Afipia and Xanthobacteriaceae_unclassified were enriched in SAP samples, while Pseudopropionibacterium, Campylobacter and Peptoniphilus were significantly more abundant in AAP cases (p < .05). Metabolic pathways involved in the amino acid metabolism or degradation and flagellum assembly were more abundant in SAP samples, whereas glucose metabolism-related pathways were associated with AAP. CONCLUSIONS: The bacterial community composition was similar in the apices and periapical lesions. The microbiome was different in AAP and SAP samples, gram-negative bacteria showing higher relative abundances in SAP cases. An association was observed between amino acid degradation and flagellum assembly pathways, and the development of tenderness to percussion or palpation.

Quantifying the diversity of multiple time series with an ordinal symbolic approach.

The main motivation of this paper is to introduce the ordinal diversity, a symbolic tool able to quantify the degree of diversity of multiple time series. Analytical, numerical, and experimental analyses illustrate the utility of this measure to quantify how diverse, from an ordinal perspective, a set of many time series is. We have shown that ordinal diversity is able to characterize dynamical richness and dynamical transitions in stochastic processes and deterministic systems, including chaotic regimes. This ordinal tool also serves to identify optimal operating conditions in the machine learning approach of reservoir computing. These results allow us to envision potential applications for the handling and characterization of large amounts of data, paving the way for addressing some of the most pressing issues facing the current big data paradigm.

Learn one size to infer all: Exploiting translational symmetries in delay-dynamical and spatiotemporal systems using scalable neural networks.

We design scalable neural networks adapted to translational symmetries in dynamical systems, capable of inferring untrained high-dimensional dynamics for different system sizes. We train these networks to predict the dynamics of delay-dynamical and spatiotemporal systems for a single size. Then, we drive the networks by their own predictions. We demonstrate that by scaling the size of the trained network, we can predict the complex dynamics for larger or smaller system sizes. Thus, the network learns from a single example and by exploiting symmetry properties infers entire bifurcation diagrams.

Curvicollide D Isolated from the Fungus Amesia sp. Kills African Trypanosomes by Inhibiting Transcription.

Sleeping sickness or African trypanosomiasis is a serious health concern with an added socio-economic impact in sub-Saharan Africa due to direct infection in both humans and their domestic livestock. There is no vaccine available against African trypanosomes and its treatment relies only on chemotherapy. Although the current drugs are effective, most of them are far from the modern concept of a drug in terms of toxicity, specificity and therapeutic regime. In a search for new molecules with trypanocidal activity, a high throughput screening of 2000 microbial extracts was performed. Fractionation of one of these extracts, belonging to a culture of the fungus Amesia sp., yielded a new member of the curvicollide family that has been designated as curvicollide D. The new compound showed an inhibitory concentration 50 (IC50) 16-fold lower in Trypanosoma brucei than in human cells. Moreover, it induced cell cycle arrest and disruption of the nucleolar structure. Finally, we showed that curvicollide D binds to DNA and inhibits transcription in African trypanosomes, resulting in cell death. These results constitute the first report on the activity and mode of action of a member of the curvicollide family in T. brucei.

Gallic Acid: A Natural Phenolic Compound Exerting Antitumoral Activities in Colorectal Cancer via Interaction with G-Quadruplexes.

Natural phenolic compounds have gained momentum for the prevention and treatment of cancer, but their antitumoral mechanism of action is not yet well understood. In the present study, we screened the antitumoral potential of several phenolic compounds in a cellular model of colorectal cancer (CRC). We selected gallic acid (GA) as a candidate in terms of potency and selectivity and extensively evaluated its biological activity. We report on the role of GA as a ligand of DNA G-quadruplexes (G4s), explaining several of its antitumoral effects, including the transcriptional inhibition of ribosomal and CMYC genes. In addition, GA shared with other established G4 ligands some effects such as cell cycle arrest, nucleolar stress, and induction of DNA damage. We further confirmed the antitumoral and G4-stabilizing properties of GA using a xenograft model of CRC. Finally, we succinctly demonstrate that GA could be explored as a therapeutic agent in a patient cohort with CRC. Our work reveals that GA, a natural bioactive compound present in the diet, affects gene expression by interaction with G4s both in vitro and in vivo and paves the way towards G4s targeting with phenolic compounds.

High-Performance Reservoir Computing With Fluctuations in Linear Networks.

Reservoir computing has emerged as a powerful machine learning paradigm for harvesting nontrivial information processing out of disordered physical systems driven by sequential inputs. To this end, the system observables must become nonlinear functions of the input history. We show that encoding the input to quantum or classical fluctuations of a network of interacting harmonic oscillators can lead to a high performance comparable to that of a standard echo state network in several nonlinear benchmark tasks. This equivalence in performance holds even with a linear Hamiltonian and a readout linear in the system observables. Furthermore, we find that the performance of the network of harmonic oscillators in nonlinear tasks is robust to errors both in input and reservoir observables caused by external noise. For any reservoir computing system with a linear readout, the magnitude of trained weights can either amplify or suppress noise added to reservoir observables. We use this general result to explain why the oscillators are robust to noise and why having precise control over reservoir memory is important for noise robustness in general. Our results pave the way toward reservoir computing harnessing fluctuations in disordered linear systems.

Gut microbiome-short-chain fatty acids interplay in the context of iron deficiency anaemia.

PURPOSE: Anaemia is a global health concern, with iron deficiency anaemia (IDA) causing approximately 50% of cases. Affecting mostly the elderly, pregnant and adult women and children, physiopathology of IDA in relation to the gut microbiome is poorly understood. Therefore, the objective of this study is to analyse, in an animal model, the effect of IDA on the gut microbiome along the gastrointestinal tract, as well as to relate intestinal dysbiosis to changes in microbial metabolites such as short chain fatty acids (SCFA). METHODS: IDA was experimentally induced through an iron deficient diet for a period of 40 days, with twenty weaned male Wistar rats being randomly divided into control or anaemic groups. Blood samples were collected to control haematological parameters, and so were faecal and intestinal content samples to study gut microbial communities and SCFA, using 16S rRNA sequencing and HPLC-UV respectively. RESULTS: An intestinal dysbiosis was observed as a consequence of IDA, especially towards the distal segments of the gastrointestinal tract and the colon. An increase in SCFA was also noticed during IDA, with the major difference appearing in the colon and correlating with changes in the composition of the gut microbiome. Clostridium_sensu_stricto_1 and Clostridium_sensu_stricto_4 showed the greatest correlation with variations in butyric and propionic concentrations in the colon of anaemic animals. CONCLUSIONS: Composition of intestinal microbial communities was affected by the generation of IDA. An enrichment in certain SCFA-producing genera and SCFA concentrations was found in the colon of anaemic animals, suggesting a trade-off mechanism against disease.

Information Processing Capacity of a Single-Node Reservoir Computer: An Experimental Evaluation.

Physical dynamical systems are able to process information in a nontrivial manner. The machine learning paradigm of reservoir computing (RC) provides a suitable framework for information processing in (analog) dynamical systems. The potential of dynamical systems for RC can be quantitatively characterized by the information processing capacity (IPC) measure. Here, we evaluate the IPC measure of a reservoir computer based on a single-analog nonlinear node coupled with delay. We link the extracted IPC measures to the dynamical regime of the reservoir, reporting an experimentally measured nonlinear memory of up to seventh order. In addition, we find a nonhomogeneous distribution of the linear and nonlinear contributions to the IPC as a function of the system operating conditions. Finally, we unveil the role of noise in the IPC of the analog implementation by performing ad hoc numerical simulations. In this manner, we identify the so-called edge of stability as being the most promising operating condition of the experimental implementation for RC purposes in terms of computational power and noise robustness. Similarly, a strong input drive is shown to have beneficial properties, albeit with a reduced memory depth.

Dynamical Phase Transitions in Quantum Reservoir Computing.

Closed quantum systems exhibit different dynamical regimes, like many-body localization or thermalization, which determine the mechanisms of spread and processing of information. Here we address the impact of these dynamical phases in quantum reservoir computing, an unconventional computing paradigm recently extended into the quantum regime that exploits dynamical systems to solve nonlinear and temporal tasks. We establish that the thermal phase is naturally adapted to the requirements of quantum reservoir computing and report an increased performance at the thermalization transition for the studied tasks. Uncovering the underlying physical mechanisms behind optimal information processing capabilities of spin networks is essential for future experimental implementations and provides a new perspective on dynamical phases.

Time-Delay Identification Using Multiscale Ordinal Quantifiers.

Time-delayed interactions naturally appear in a multitude of real-world systems due to the finite propagation speed of physical quantities. Often, the time scales of the interactions are unknown to an external observer and need to be inferred from time series of observed data. We explore, in this work, the properties of several ordinal-based quantifiers for the identification of time-delays from time series. To that end, we generate artificial time series of stochastic and deterministic time-delay models. We find that the presence of a nonlinearity in the generating model has consequences for the distribution of ordinal patterns and, consequently, on the delay-identification qualities of the quantifiers. Here, we put forward a novel ordinal-based quantifier that is particularly sensitive to nonlinearities in the generating model and compare it with previously-defined quantifiers. We conclude from our analysis on artificially generated data that the proper identification of the presence of a time-delay and its precise value from time series benefits from the complementary use of ordinal-based quantifiers and the standard autocorrelation function. We further validate these tools with a practical example on real-world data originating from the North Atlantic Oscillation weather phenomenon.