Fluorescence activation mechanism and imaging of drug permeation with new sensors for smoking-cessation ligands

Nicotinic partial agonists provide an accepted aid for smoking cessation and thus contribute to decreasing tobacco-related disease. Improved drugs constitute a continued area of study. However, there remains no reductionist method to examine the cellular and subcellular pharmacokinetic properties of these compounds in living cells. Here, we developed new intensity-based drug sensing fluorescent reporters ('iDrugSnFRs') for the nicotinic partial agonists dianicline, cytisine, and two cytisine derivatives - 10-fluorocytisine and 9-bromo-10-ethylcytisine. We report the first atomic-scale structures of liganded periplasmic binding protein-based biosensors, accelerating development of iDrugSnFRs and also explaining the activation mechanism. The nicotinic iDrugSnFRs detect their drug partners in solution, as well as at the plasma membrane (PM) and in the endoplasmic reticulum (ER) of cell lines and mouse hippocampal neurons. At the PM, the speed of solution changes limits the growth and decay rates of the fluorescence response in almost all cases. In contrast, we found that rates of membrane crossing differ among these nicotinic drugs by > 30 fold. The new nicotinic iDrugSnFRs provide insight into the real-time pharmacokinetic properties of nicotinic agonists and provide a methodology whereby iDrugSnFRs can inform both pharmaceutical neuroscience and addiction neuroscience.

Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Noncollaborative surveillance of airborne UAS (Unmanned Aerial System) is a key enabler to the safe integration of UAS within a UTM (Unmanned Traffic Management) ecosystem. Thus, a wide variety of new sensors (known as Counter-UAS sensors) are being developed to provide real-time UAS tracking, ranging from radar, RF analysis and image-based detection to even sound-based sensors. This paper aims to discuss the current state-of-the art technology in this wide variety of sensors (both academically and commercially) and to propose a set of simulation models for them. Thus, the review is focused on identifying the key parameters and processes that allow modeling their performance and operation, which reflect the variety of measurement processes. The resulting simulation models are designed to help evaluate how sensors’ performances affect UTM systems, and specifically the implications in their tracking and tactical services (i.e., tactical conflicts with uncontrolled drones). The simulation models cover probabilistic detection (i.e., false alarms and probability of detection) and measurement errors, considering equipment installation (i.e., monostatic vs. multistatic configurations, passive sensing, etc.). The models were integrated in a UTM simulation platform and simulation results are included in the paper for active radars, passive radars, and acoustic sensors.

IMU2Doppler

The proliferation of sensors powered by state-of-the-art machine learning techniques can now infer context, recognize activities and enable interactions. A key component required to build these automated sensing systems is labeled training data. However, the cost of collecting and labeling new data impedes our ability to deploy new sensors to recognize human activities. We tackle this challenge using domain adaptation i.e., using existing labeled data in a different domain to aid the training of a machine learning model for a new sensor. In this paper, we use off-the-shelf smartwatch IMU datasets to train an activity recognition system for mmWave radar sensor with minimally labeled data. We demonstrate that despite the lack of extensive datasets for mmWave radar, we are able to use our domain adaptation approach to build an activity recognition system that classifies between 10 activities with an accuracy of 70% with only 15 seconds of labeled doppler data. We also present results for a range of available labeled data (10 - 30 seconds) and show that our approach outperforms the baseline in every single scenario. We take our approach a step further and show that multiple IMU datasets can be combined together to act as a single source for our domain adaptation approach. Lastly, we discuss the limitations of our work and how it can impact future research directions.

Mathematical Modeling of a Supramolecular Assembly for Pyrophosphate Sensing

The power of sensing molecules is often characterized in part by determining their thermodynamic/dynamic properties, in particular the binding constant of a guest to a host. In many studies, traditional nonlinear regression analysis has been used to determine the binding constants, which cannot be applied to complex systems and limits the reliability of such calculations. Supramolecular sensor systems include many interactions that make such chemical systems complicated. The challenges in creating sensing molecules can be significantly decreased through the availability of detailed mathematical models of such systems. Here, we propose uncovering accurate thermodynamic parameters of chemical reactions using better-defined mathematical modeling-fitting analysis is the key to understanding molecular assemblies and developing new bio/sensing agents. The supramolecular example we chose for this investigation is a self-assembled sensor consists of a synthesized receptor, DPA (DPA = dipicolylamine)-appended phenylboronic acid (1) in combination with Zn2+(1.Zn) that forms various assemblies with a fluorophore like alizarin red S (ARS). The self-assemblies can detect multi-phosphates like pyrophosphate (PPi) in aqueous solutions. We developed a mathematical model for the simultaneous quantitative analysis of twenty-seven intertwined interactions and reactions between the sensor (1.Zn-ARS) and the target (PPi) for the first time, relying on the Newton-Raphson algorithm. Through analyzing simulated potentiometric titration data, we describe the concurrent determination of thermodynamic parameters of the different guest-host bindings. Various values of temperatures, initial concentrations, and starting pHs were considered to predict the required measurement conditions for thermodynamic studies. Accordingly, we determined the species concentrations of different host-guest bindings in a generalized way. This way, the binding capabilities of a set of species can be quantitatively examined to systematically measure the power of the sensing system. This study shows analyzing supramolecular self-assemblies with solid mathematical models has a high potential for a better understanding of molecular interactions within complex chemical networks and developing new sensors with better sensing effects for bio-purposes.

Silver Conductive Threads-Based Embroidered Electrodes on Textiles as Moisture Sensors for Fluid Detection in Biomedical Applications

Wearable sensors have become part of our daily life for health monitoring. The detection of moisture content is critical for many applications. In the present research, textile-based embroidered sensors were developed that can be integrated with a bandage for wound management purposes. The sensor comprised an interdigitated electrode embroidered on a cotton substrate with silver-tech 150 and HC 12 threads, respectively, that have silver coated continuous filaments and 100% polyamide with silver-plated yarn. The said sensor is a capacitive sensor with some leakage. The change in the dielectric constant of the substrate as a result of moisture affects the value of capacitance and, thus, the admittance of the sensor. The moisture sensor’s operation is verified by measuring its admittance at 1 MHz and the change in moisture level (1–50) µL. It is observed that the sensitivity of both sensors is comparable. The identically fabricated sensors show similar response and sensitivity while wash test shows the stability of sensor after washing. The developed sensor is also able to detect the moisture caused by both artificial sweat and blood serum, which will be of value in developing new sensors tomorrow for smart wound-dressing applications.

Trace Oxygen Affects Osmium Redox Polymer Synthesis for Wired Enzymatic Biosensors

Electrochemical sensors that utilize enzymes are a sensitive, inexpensive means of detecting biologically relevant analytes. These sensors are categorized based on their construction and method of signal transport. Type I sensors consist of a crosslinked enzyme on an electrode surface, and are potentially subject to interference from byproducts and other biological analytes. However, type II sensors help alleviate this problem with the addition of a redox polymer layer that assists in signal transduction, thus minimizing interferences. An osmium-loaded poly(vinylimidazole) polymer (Os-PVI) is commonly used with successful results, and when combined with an enzyme yields a type II sensor. Our initial attempts at the synthesis of this polymer resulted in an unexpected osmium precursor, which had fluorescent and redox properties that did not match with the desired Os-PVI polymer. Careful exclusion of oxygen during the Os complex precursor synthesis was necessary to avoid this unexpected oxygen containing Os-precursor, which had been seen previously in mass spectrometry studies. All precursors and osmium polymers were characterized with 1H NMR, fluorescence, mass spectrometry, and cyclic voltammetry in order to provide a better understanding of these compounds and assist in the building of new sensors.

Innovative Integrated Solution for Monitoring and Protection of Power Supply System from Railway Infrastructure

This paper describes an innovative integrated solution for monitoring and protection of the power supply system of electric traction. The development of electronics devices, new possibilities to communicate (wireless), and new sensors makes it possible to design, develop and implement new hardware–software structures in various fields such as energy systems, transportation infrastructure, etc. This contributes to increasing developments in the monitoring and protection of railway infrastructure. A monitoring and protection system that uses sensors and devices to acquire electrical parameters from railway infrastructure has been developed and applied for fault detection and protection of power supply systems from electric traction. The solution of monitoring and protection presented are composed of a hardware–software structure with Global System for Mobile Communications (GSM) communication for monitoring of power supply installations from the electric traction and a central remote system composed of a device with GSM communication and a server that will allow, among others things, accurate detection of the block section (SC), in which an electrical fault (short circuit) has occurred, determination of the circuit breakers electro-erosion from the railway installations and an indication of the opportune moment for maintenance activity, respectively, as well as knowledge of the technical condition of some equipment from the return circuit. The proposed and developed method for monitoring devices has been validated in the railway laboratory to confirm its capability to detect defects and was tested in the field. Experimental results in the field and appropriate data analysis are included in this article.

HV Cable Intelligent Sensing IoT Technology and Application

In view of the challenges faced by the current HV cable O&M management, such as the growth of equipment scale, the severe security situation, the lack of comprehensive perception on equipment status, and the ability of intelligent analysis and decision-making to be improved, it was proposed to build a HV cable intelligent sensing IoT based on new sensors such as intelligent earthing box, PD intensive care unit, intelligent ground landmark and face recognition system for terminal stations, so as to comprehensively improve the quality and efficiency of HV cable O&M management. A pilot project was built in Hongqiao area in Shanghai, forming typical application scenarios such as IoT intelligent disposal, integration of monitoring and detection data in terminal stations, panoramic intelligent patrol inspection for ensuring power supply.

Model-driven Per-panel Solar Anomaly Detection for Residential Arrays

There has been significant growth in both utility-scale and residential-scale solar installations in recent years, driven by rapid technology improvements and falling prices. Unlike utility-scale solar farms that are professionally managed and maintained, smaller residential-scale installations often lack sensing and instrumentation for performance monitoring and fault detection. As a result, faults may go undetected for long periods of time, resulting in generation and revenue losses for the homeowner. In this article, we present SunDown, a sensorless approach designed to detect per-panel faults in residential solar arrays. SunDown does not require any new sensors for its fault detection and instead uses a model-driven approach that leverages correlations between the power produced by adjacent panels to detect deviations from expected behavior. SunDown can handle concurrent faults in multiple panels and perform anomaly classification to determine probable causes. Using two years of solar generation data from a real home and a manually generated dataset of multiple solar faults, we show that SunDown has a Mean Absolute Percentage Error of 2.98% when predicting per-panel output. Our results show that SunDown is able to detect and classify faults, including from snow cover, leaves and debris, and electrical failures with 99.13% accuracy, and can detect multiple concurrent faults with 97.2% accuracy.

Application of New Sensor Technology in the Field of Education in the Era of Internet of Things

In order to explore the intelligent education mode in the context of the Internet of Things, this paper combines the Internet of Things technology and sensor technology to improve the sensor technology and proposes a multisensor information fusion technology based on Kalman filtering. Moreover, this paper combines the wireless network technology to construct the system and structure, obtains the functional modules of the intelligent education system based on the Internet of Things technology and the new sensor technology, and analyzes the system realization process. In addition, this paper constructs a smart education system based on the Internet of Things and new sensors and designs experiments to verify the system. The research shows that the method proposed in this paper has good data transmission effect, can effectively improve the effect of intelligent education, and meet the actual needs of current education.