The reliable detection of environmental molecules in the presence of noise is an important cellular function, yet the underlying computational mechanisms are not well understood. We introduce a model of two interacting sensors which allows for the principled exploration of signal statistics, cooperation strategies and the role of energy consumption in optimal sensing, quantified through the mutual information between the signal and the sensors. Here we report that in general the optimal sensing strategy depends both on the noise level and the statistics of the signals. For joint, correlated signals, energy consuming (nonequilibrium), asymmetric couplings result in maximum information gain in the low-noise, high-signal-correlation limit. Surprisingly we also find that energy consumption is not always required for optimal sensing. We generalise our model to incorporate time integration of the sensor state by a population of readout molecules, and demonstrate that sensor interaction and energy consumption remain important for optimal sensing.
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http://dx.doi.org/10.1038/s41467-020-14806-y | DOI Listing |
Epilepsia
January 2025
Texas Comprehensive Epilepsy Program, Department of Neurosurgery, University of Texas Health Science Center at Houston, Houston, Texas, USA.
Objective: The pulvinar nucleus of the thalamus has extensive cortical connections with the temporal, parietal, and occipital lobes. Deep brain stimulation (DBS) targeting the pulvinar nucleus, therefore, carries the potential for therapeutic benefit in patients with drug-resistant posterior quadrant epilepsy (PQE) and neocortical temporal lobe epilepsy (TLE). Here, we present a single-center experience of patients managed via bilateral DBS of the pulvinar nucleus.
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January 2025
Department of Mechanical Engineering, Politecnico di Milano, Via G. La Masa 1, 20156 Milano, Italy.
In naval engineering, particular attention has been given to containerships, as these structures are constantly exposed to potential damage during service hours and since they are essential for large-scale transportation. To assess the structural integrity of these ships and to ensure the safety of the crew and the cargo being transported, it is essential to adopt structural health monitoring (SHM) strategies that enable real-time evaluations of a ship's status. To achieve this, this paper introduces an advancement in the field of smart sensing and SHM that improves ship monitoring and diagnostic capabilities.
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January 2025
Department of Optoelectronics, Center for Physical Sciences and Technology (FTMC), Saulėtekio Ave. 3, LT-10257 Vilnius, Lithuania.
The practical implementation of terahertz (THz) imaging and spectroscopic systems in real operational conditions requires them to be of a compact size, to have enhanced functionality, and to be user-friendly. This work demonstrates the single-sided integration of Fresnel-zone-plate-based optical elements with InGaAs bow-tie diodes directly on a semiconductor chip. Numerical simulations were conducted to optimize the Fresnel zone plate's focal length and the InP substrate's thickness to achieve constructive interference at 600 GHz, room-temperature operation and achieve a sensitivity more than an order of magnitude higher-up to 24.
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January 2025
Departamento de Geografía, Facultad de Ciencias, Universidad de la República, Montevideo 4225, Uruguay.
Recent advancements in Earth Observation sensors, improved accessibility to imagery and the development of corresponding processing tools have significantly empowered researchers to extract insights from Multisource Remote Sensing. This study aims to use these technologies for mapping summer and winter Land Use/Land Cover features in Cuenca de la Laguna Merín, Uruguay, while comparing the performance of Random Forests, Support Vector Machines, and Gradient-Boosting Tree classifiers. The materials include Sentinel-2, Sentinel-1 and Shuttle Radar Topography Mission imagery, Google Earth Engine, training and validation datasets and quoted classifiers.
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December 2024
Department of Electrical Engineering, Lunghwa University of Science and Technology, Taoyuan 333326, Taiwan.
The proliferation of sophisticated counterfeiting poses critical challenges to global security and commerce, with annual losses exceeding $2.2 trillion. This paper presents a novel physics-constrained deep learning framework for high-precision security ink colorimetry, integrating three key innovations: a physics-informed neural architecture achieving unprecedented color prediction accuracy (CIEDE2000 (ΔE00): 0.
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