Sparse MRI with a Markov Random Field Prior for the Subband Coefficients

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MRI reconstruction using Markov random field and total variation as composite prior

Reconstruction of magnetic resonance images (MRI) benefits from incorporating a priori knowledge about statistical dependencies among the representation coefficients. Recent results demonstrate that modeling intraband dependencies with Markov Random Field (MRF) models enable superior reconstructions compared to inter-scale models. In this paper, we develop a novel reconstruction method, which includes a composite prior based on an MRF model and Total Variation (TV). We use an anisotropic MRF model and propose an original data-driven method for the adaptive estimation of its parameters. From a Bayesian perspective, we define a new position-dependent type of regularization and derive a compact reconstruction algorithm with a novel soft-thresholding rule. Experimental results show the effectiveness of this method compared to the state of the art in the field

Explainable AI for unveiling deep learning pollen classification model based on fusion of scattered light patterns and fluorescence spectroscopy

Pollen monitoring have become data-intensive in recent years as real-time detectors are deployed to classify airborne pollen grains. Machine learning models with a focus on deep learning, have an essential role in the pollen classifcation task. Within this study we developed an explainable framework to unveil a deep learning model for pollen classifcation. Model works on data coming from single particle detector (Rapid-E) that records for each particle optical fngerprint with scattered light and laser induced fuorescence. Morphological properties of a particle are sensed with the light scattering process, while chemical properties are encoded with fuorescence spectrum and fuorescence lifetime induced by high-resolution laser. By utilizing these three data modalities, scattering, spectrum, and lifetime, deep learning-based models with millions of parameters are learned to distinguish diferent pollen classes, but a proper understanding of such a black-box model decisions demands additional methods to employ. Our study provides the frst results of applied explainable artifcial intelligence (xAI) methodology on the pollen classifcation model. Extracted knowledge on the important features that attribute to the predicting particular pollen classes is further examined from the perspective of domain knowledge and compared to available reference data on pollen sizes, shape, and laboratory spectrofuorometer measurements

STED nanoscopy of the centrosome linker reveals a CEP68-organized, periodic rootletin network anchored to a C-Nap1 ring at centrioles

The centrosome linker proteins C-Nap1, rootletin, and CEP68 connect the two centrosomes of a cell during interphase into one microtubule-organizing center. This coupling is important for cell migration, cilia formation, and timing of mitotic spindle formation. Very little is known about the structure of the centrosome linker. Here, we used stimulated emission depletion (STED) microscopy to show that each C-Nap1 ring at the proximal end of the two centrioles organizes a rootletin ring and, in addition, multiple rootletin/CEP68 fibers. Rootletin/CEP68 fibers originating from the two centrosomes form a web-like, interdigitating network, explaining the flexible nature of the centrosome linker. The rootletin/CEP68 filaments are repetitive and highly ordered. Staggered rootletin molecules (N-to-N and C-to-C) within the filaments are 75 nm apart. Rootletin binds CEP68 via its C-terminal spectrin repeat-containing region in 75-nm intervals. The N-to-C distance of two rootletin molecules is ∼35 to 40 nm, leading to an estimated minimal rootletin length of ∼110 nm. CEP68 is important in forming rootletin filaments that branch off centrioles and to modulate the thickness of rootletin fibers. Thus, the centrosome linker consists of a vast network of repeating rootletin units with C-Nap1 as ring organizer and CEP68 as filament modulator

Multiparameter Water Quality Monitoring System for Continuous Monitoring of Fresh Waters

This paper presents an economical multiparameter water quality monitoring system for continuous monitoring of fresh waters. It is based on a sensor node that integrates turbidity, temperature, conductivity sensors, a miniature eighteen-channel spectrophotometer, and a sensor for the detection of thermotolerant coliforms, which is a major novelty of the system. Due to the influence of water impurities on the measurement of thermotolerant coliforms, a heuristic method has been developed to mitigate this effect. Moreover, the sensor is low-power and with an integrated LoRaWAN module, it comprises a system that is wireless sensor network (WSN) ready and can send data to a dedicated server. In addition, the system is submersible, capable of long-term field operation, and significantly cheaper in comparison to existing solutions. The purpose of the system is to give early warning of incidental pollution situations, thus enabling authorities to fast respond by taking a water sample for laboratory analysis for confirmation, analyze the source of contamination, and take action regarding further prevention of such occasions

CHANNEL CAPACITY OF THE MACRO-DIVERSITY SC SYSTEM IN THE PRESENCE OF KAPPA-MU FADING AND CORRELATED SLOW GAMMA FADING

In this paper macrodiversity system consisting of two microdiversity SC (Selection Combiner) receivers and one macrodiversity SC receiver are analyzed. Independent κ-μ fading and correlated slow Gamma fading are present at the inputs to the microdiversity SC receivers. For this system model, analytical expression for the probability density of the signal at the output of the macrodiversity receiver SC, and the output capacity of the macrodiversity SC receiver are calculated. The obtained results are graphically presented to show the impact of Rician κ factor, the shading severity of the channel c, the number of clusters µ and correlation coefficient ρ on the probability density of the signal at the output of the macrodiversity system and channel capacity at the output of the macrodiversity system. Based on the obtained results it is possible to analyze the real behavior of the macrodiversity system in the presence of  κ-μ fading