Abstract
The gut microbiome is shaped early in life by dietary and lifestyle factors. Specific compounds in the gut affect the growth of different bacterial species and the production of beneficial or harmful byproducts. Dysbiosis of the gut microbiome has been linked to various diseases resulting from the presence of harmful bacteria and their byproducts. Existing methods for detecting microbial species, such as microscopic observation and molecular biological techniques, are costly, labor-intensive, and require skilled personnel. Biosensors, which integrate a recognition element, transducer, amplifier, signal processor, and display unit, can convert biological events into electronic signals. This review provides a comprehensive and systematic survey of scientific publications from 2018 to June 2024, obtained from ScienceDirect, PubMed, and Scopus databases. The aim was to evaluate the current state-of-the-art and identify knowledge gaps in the application of aptamer biosensors for the determination of gut microbiota. A total of 13 eligible publications were categorized based on the type of study: those using microbial bioreceptors (category 1) and those using aptamer bioreceptors (category 2) for the determination of gut microbiota. Point-of-care biosensors are being developed to monitor changes in metabolites that may lead to disease. They are well-suited for use in the healthcare system and offer an excellent alternative to traditional methods. Aptamers are gaining attention due to their stability, specificity, scalability, reproducibility, low production cost, and low immunogenicity. While there is limited research on using aptamers to detect human gut microbiota, they show promise for providing accurate, robust, and cost-effective diagnostic methods for monitoring the gut microbiome.

Abstract
Paraxial fluids of light have recently emerged as promising analog physical simulators of quantum fluids using laser propagation inside nonlinear optical media. In particular, recent works have explored the versatility of such systems for the observation of two-dimensional quantum-like turbulence regimes, dominated by quantized vortex formation and interaction that results in distinctive kinetic energy power laws and inverse energy cascades. In this manuscript, we explore a regime analog to Kelvin-Helmholtz instability to examine in further detail the qualitative dynamics involved in the transition from smooth laminar flow to turbulence at the interface of two fluids with distinct velocities. Both numerical and experimental results reveal the formation of a vortex sheet as expected, with a quantized number of vortices determined by initial conditions. Using an effective length transformation scale we get a deeper insight into the vortex formation phase, observing the appearance of characteristic power laws in the incompressible kinetic energy spectrum that are related to the single vortex structures. The results enclosed demonstrate the versatility of paraxial fluids of light and may set the stage for the future observation of distinct classes of phenomena recently predicted to occur in these systems, such as radiant instability and superradiance.

Abstract
Extreme learning machines explore nonlinear random projections to perform computing tasks on high-dimensional output spaces. Since training only occurs at the output layer, the approach has the potential to speed up the training process and the capacity to turn any physical system into a computing platform. Yet, requiring strong nonlinear dynamics, optical solutions operating at fast processing rates and low power can be hard to achieve with conventional nonlinear optical materials. In this context, this manuscript explores the possibility of using atomic gases in near-resonant conditions to implement an optical extreme learning machine leveraging their enhanced nonlinear optical properties. Our results suggest that these systems have the potential not only to work as an optical extreme learning machine but also to perform these computations at the few-photon level, paving opportunities for energy-efficient computing solutions.

Abstract
Laser-induced breakdown spectroscopy allows fast and versatile elemental analysis, standing as a promising technique for a wide range of applications both at the research and industry levels. Yet, its high operation speed comes with a high throughput of data, which introduces some challenges at the level of the data processing domain, mainly due to the large computational load and data volume. In this work, we analyze and discuss opportunities of distributed computing paradigms and resources to address some of these challenges, covering most of the procedures usually employed in typical applications. We infer the possible impact of such computing resources by presenting some metrics of simple processing prototypes running in state-of-the-art computing facilities. Our results allow us to conclude that, while underexplored so far, these computing resources may allow for the development of tools for timely research and analysis in demanding applications and introduce novel solutions toward a more agile working environment.

Abstract
This communication explores an optical extreme learning architecture to unravel the impact of using a nonlinear optical media as an activation layer. Our analysis encloses the evaluation of multiple parameters, with special emphasis on the efficiency of the training process, the dimensionality of the output space, and computing performance across tasks associated with the classification in low-dimensionality input feature spaces. The results enclosed provide evidence of the importance of the nonlinear media as a building block of an optical extreme learning machine, effectively increasing the size of the output space, the accuracy, and the generalization performances. These findings may constitute a step to support future research on the field, specifically targeting the development of robust general-purpose all-optical hardware to a full-stack integration with optical sensing devices toward edge computing solutions.

Abstract
Lately, the field of optical computing resurfaced with the demonstration of a series of novel photonic neuromorphic schemes for autonomous and inline data processing promising parallel and light-speed computing. We emphasize the Photonic Extreme Learning Machine (PELM) as a versatile configuration exploring the randomness of optical media and device production to bypass the training of the hidden layer. Nevertheless, the implementation of this framework is limited to having the output layer performed digitally. In this work, we extend the general PELM implementation to an all-optical configuration by exploring the amplitude modulation from a spatial light modulator (SLM) as an output linear layer with the main challenge residing in the training of the output weights. The proposed solution explores the package pyTorch to train a digital twin using gradient descent back-propagation. The trained model is then transposed to the SLM performing the linear output layer. We showcase this methodology by solving a two-class classification problem where the total intensity reaching the camera predicts the class of the input sample.