Preliminary Analysis of Burn Degree Using Non-invasive Microwave Spiral Resonator Sensor for Clinical Applications.

The European "Senseburn" project aims to develop a smart, portable, non-invasive microwave early effective diagnostic tool to assess the depth(d) and degree of burn. The objective of the work is to design and develop a convenient non-invasive microwave sensor for the analysis of the burn degree on burnt human skin. The flexible and biocompatible microwave sensor is developed using a magnetically coupled loop probe with a spiral resonator (SR). The sensor is realized with precise knowledge of the lumped element characteristics (resistor (R), an inductor (L), and a capacitor (C) RLC parameters). The estimated electrical equivalent circuit technique relies on a rigorous method enabling a comprehensive characterization of the sensor (loop probe and SR). The microwave resonator sensor with high quality factor (Q) is simulated using a CST studio suite, AWR microwave office, and fabricated on the RO 3003 substrate with a standard thickness of 0.13 mm. The sensor is prepared based on the change in dielectric property variation in the burnt skin. The sensor can detect a range of permittivity variations (ε r 3-38). The sensor is showing a good response in changing resonance frequency between 1.5 and 1.71 GHz for (ε r 3 to 38). The sensor is encapsulated with PDMS for the biocompatible property. The dimension of the sensor element is length (L) = 39 mm, width (W) = 34 mm, and thickness (T) = 1.4 mm. The software algorithm is prepared to automate the process of burn analysis. The proposed electromagnetic (EM) resonator based sensor provides a non-invasive technique to assess burn degree by monitoring the changes in resonance frequency. Most of the results are based on numerical simulation. We propose the unique circuit set up and the sensor device based on the information generated from the simulation in this article. The clinical validation of the sensor will be in our future work, where we will understand closely the practical functioning of the sensor based on burn degrees. The senseburn system is designed to support doctors to gather vital info of the injuries wirelessly and hence provide efficient treatment for burn victims, thus saving lives.

Deep Learning of Nanopore Sensing Signals Using a Bi-Path Network.

Temporal changes in electrical resistance of a nanopore sensor caused by translocating target analytes are recorded as a sequence of pulses on current traces. Prevalent algorithms for feature extraction in pulse-like signals lack objectivity because empirical amplitude thresholds are user-defined to single out the pulses from the noisy background. Here, we use deep learning for feature extraction based on a bi-path network (B-Net). After training, the B-Net acquires the prototypical pulses and the ability of both pulse recognition and feature extraction without a priori assigned parameters. The B-Net is evaluated on simulated data sets and further applied to experimental data of DNA and protein translocation. The B-Net results are characterized by small relative errors and stable trends. The B-Net is further shown capable of processing data with a signal-to-noise ratio equal to 1, an impossibility for threshold-based algorithms. The B-Net presents a generic architecture applicable to pulse-like signals beyond nanopore currents.

A Computational Theory for the Emergence of Grammatical Categories in Cortical Dynamics.

A general agreement in psycholinguistics claims that syntax and meaning are unified precisely and very quickly during online sentence processing. Although several theories have advanced arguments regarding the neurocomputational bases of this phenomenon, we argue that these theories could potentially benefit by including neurophysiological data concerning cortical dynamics constraints in brain tissue. In addition, some theories promote the integration of complex optimization methods in neural tissue. In this paper we attempt to fill these gaps introducing a computational model inspired in the dynamics of cortical tissue. In our modeling approach, proximal afferent dendrites produce stochastic cellular activations, while distal dendritic branches-on the other hand-contribute independently to somatic depolarization by means of dendritic spikes, and finally, prediction failures produce massive firing events preventing formation of sparse distributed representations. The model presented in this paper combines semantic and coarse-grained syntactic constraints for each word in a sentence context until grammatically related word function discrimination emerges spontaneously by the sole correlation of lexical information from different sources without applying complex optimization methods. By means of support vector machine techniques, we show that the sparse activation features returned by our approach are well suited-bootstrapping from the features returned by Word Embedding mechanisms-to accomplish grammatical function classification of individual words in a sentence. In this way we develop a biologically guided computational explanation for linguistically relevant unification processes in cortex which connects psycholinguistics to neurobiological accounts of language. We also claim that the computational hypotheses established in this research could foster future work on biologically-inspired learning algorithms for natural language processing applications.

Head-compliant microstrip split ring resonator for non-invasive healing monitoring after craniosynostosis-based surgery.

A soft and highly directive, proximity-coupled split-ring resonator fabricated with a liquid alloy, copper and polydimethylsiloxane (PDMS) is presented. The same was designed for sensing osteogenesis of calvarial bone. As dielectric properties of bone grafts in ossifying calvarial defects should change during the osteogenesis process, devices like this could monitor the gradual transformation of the defect into bone by differentiating changes in the dielectric properties as shifts in the resonance frequency. Computational Software Technology (CST) Microwave Studio®-based simulation results on computational head models were in good agreement with laboratory results on head phantom models, which also included the comparison with an in-vivo measurement on the human head. A discussion based on an inductive reasoning regarding dynamics' considerations is provided as well. Since the skin elasticity of newborn children is high, stretching and crumpling could be significant. In addition, due to typical head curvatures in newborn children, bending should not be a significant issue, and can provide higher energy focus in the defect area and improve conformability. The present concept could support the development of soft, cheap and portable follow-up monitoring systems to use in outpatient hospital and home care settings for post-operative monitoring of bone healing after reconstructive surgical procedures.