Reservoir computing is a promising framework that facilitates the approach to physical neuromorphic hardware by enabling a given nonlinear physical system to act as a computing platform. In this work, we exploit this paradigm to propose a versatile and robust soliton-based computing system using a discrete soliton chain as a reservoir. By taking advantage of its tunable governing dynamics, we show that sufficiently strong nonlinear dynamics allows our soliton-based solution to perform accurate regression and classification tasks of non-linear separable datasets. At a conceptual level, the results presented pave a way for the physical realization of novel hardware solutions and have the potential to inspire future research on soliton-based computing using various physical platforms, leveraging its ubiquity across multiple fields of science, from nonlinear optical media to quantum systems.
The generalized Schrödinger-Newton system of equations with both local and nonlocal nonlinearities is widely used to describe light propagating in nonlinear media under the paraxial approximation. However, its use is not limited to optical systems and can be found to describe a plethora of different physical phenomena, for example, dark matter or alternative theories for gravity. Thus, the numerical solvers developed for studying light propagating under this model can be adapted to address these other phenomena. Indeed, in this work we report the development of a solver for the HiLight simulations platform based on GPGPU supercomputing and the required adaptations for this solver to be used to test the impact of new extensions of the Theory of General Relativity in the dynamics of the systems, in particular those based on theories with non-minimal coupling between curvature and matter. This approach in the study of these new models offers a quick way to validate them since their analytical analysis is difficult. The simulation module, its performance, and some preliminary tests are presented in this paper.
In this work, we investigate the superfluidic properties of light propagating in a four-level coherent atomic medium. The model is derived under the paraxial approximation in the form of a generalized nonlinear Schrödinger equation and features spatially controllable and quantum-enhanced optical properties, which can offer new possibilities in the field of optical analogue systems. In particular, we use this versatility to study the dynamics of an optical vortex beam confined in a nontrivial connected geometry, finding numerical evidence of another superfluidic signature analogue: the persistent current of light.
Drug efflux transporters such as P-glycoprotein (P-gp) help maintain cellular homeostasis but are also major contributors to the development of multidrug resistance (MDR) phenomena. Since P-gp was associated with MDR, several compounds showing potential to inhibit this transporter have been identified. Particular attention has been given to natural products, namely those of plant origin, looking for highly effective and safe P-gp inhibitors with little to no interaction with other cellular or metabolic processes. Here we abridge several examples of plant compounds from distinct classes, polyketides, lignans, anthraquinones, coumarins, alkaloids, mono- and sesqui-terpenes, steroids and limonoids, which have shown the ability to modulate in vitro or in vivo the P-gp activity.
Xanthones are a class of heterocyclic compounds characterized by a dibenzo-γ-pyrone nucleus. Analysis of their mode of action in cells, namely uncovering alterations in gene expression, is important because these compounds have potential therapeutic applications. Thus, we studied the transcriptional response of the filamentous fungus Neurospora crassa to a group of synthetic (thio)xanthone derivatives with antitumor activity using high throughput RNA sequencing. The induction of ABC transporters in N. crassa, particularly atrb and cdr4, is a common consequence of the treatment with xanthones. In addition, we found a group of genes repressed by all of the tested (thio)xanthone derivatives, that are evocative of genes downregulated during oxidative stress. The transcriptional response of N. crassa treated with an acetophenone isolated from the soil fungus Neosartorya siamensis shares some features with the (thio)xanthone-elicited gene expression profiles. Two of the (thio)xanthone derivatives and the N. siamensis-derived acetophenone inhibited the growth of N. crassa. Our work also provides framework datasets that may orientate future studies on the mechanisms of action of some groups of xanthones.
The ability to select, isolate, and manipulate micron-sized particles or small clusters has made optical tweezers one of the emergent tools for modern biotechnology. In conventional setups, the classification of the trapped specimen is usually achieved through the acquired image, the scattered signal, or additional information such as Raman spectroscopy. In this work, we propose a solution that uses the temporal data signal from the scattering process of the trapping laser, acquired with a quadrant photodetector. Our methodology rests on a pre-processing strategy that combines Fourier transform and principal component analysis to reduce the dimension of the data and perform relevant feature extraction. Testing a wide range of standard machine learning algorithms, it is shown that this methodology allows achieving accuracy performances around 90%, validating the concept of using the temporal dynamics of the scattering signal for the classification task. Achieved with 500 millisecond signals and leveraging on methods of low computational footprint, the results presented pave the way for the deployment of alternative and faster classification methodologies in optical trapping technologies.
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