A Fluorescence Kinetic-Based Aptasensor Employing Stilbene Isomerization for Detection of Thrombin

It is important to detect thrombin due to its physiological and pathological roles, where rapid and simple analytical approaches are needed. In this study, an aptasensor based on fluorescence attenuation kinetics for the detection of thrombin is presented, which incorporates the features of stilbene and aptamer. We designed and synthesized an aptasensor by one-step coupling of stilbene compound and aptamer, which employed the adaptive binding of the aptamer with thrombin to cause a change in stilbene fluorescence attenuation kinetics. The sensor realized detection of thrombin by monitoring the variation in apparent fluorescence attenuation rate constant (kapp), which could be further used for probing of enzyme–aptamer binding. In comprehensive studies, the developed aptasensor presented satisfactory performance on repeatability, specificity, and regeneration capacity, which realized rapid sensing (10 s) with a limit of detection (LOD) of 0.205 μM. The strategy was successful across seven variants of thrombin aptasensors, with tunable kapp depending on the SITS (4-Acetamido-4′-isothiocyanato-2,2′-stilbenedisulfonic acid disodium salt hydrate) grafting site. Analyte detection mode was demonstrated in diluted serum, requiring no separation or washing steps. The new sensing mode for thrombin detection paves a way for high-throughput kinetic-based sensors for exploiting aptamers targeted at clinically relevant proteins.

Novel DNA Aptameric Sensors to Detect the Toxic Insecticide Fenitrothion

Fenitrothion is an insecticide belonging to the organophosphate family of pesticides that is widely used around the world in agriculture and living environments. Today, it is one of the most hazardous chemicals that causes severe environmental pollution. However, detection of fenitrothion residues in the environment is considered a significant challenge due to the small molecule nature of the insecticide and lack of molecular recognition elements that can detect it with high specificity. We performed in vitro selection experiments using the SELEX process to isolate the DNA aptamers that can bind to fenitrothion. We found that newly discovered DNA aptamers have a strong ability to distinguish fenitrothion from other organophosphate insecticides (non-specific targets). Furthermore, we identified a fenitrothion-specific aptamer; FenA2, that can interact with Thioflavin T (ThT) to produce a label-free detection mode with a Kd of 33.57 nM (9.30 ppb) and LOD of 14 nM (3.88 ppb). Additionally, the FenA2 aptamer exhibited very low cross-reactivity with non-specific targets. This is the first report showing an aptamer sensor with a G4-quadruplex-like structure to detect fenitrothion. Moreover, these aptamers have the potential to be further developed into analytical tools for real-time detection of fenitrothion from a wide range of samples.

Arsenic Speciation Techniques in Soil Water and Plant: An Overview

There are more than 100 different arsenic with different characteristics in the soil-water-plant ecosystem. The identification and quantification of individual arsenic species is essential for understanding the distribution, environmental fate and behavior, metabolism and toxicity of arsenic. Due to the hazardous nature of arsenic, people have a high interest in the measurement of arsenic species. The reaction of the formation of arsenic speciation in the soil-water-plant environment is briefly studied. There is little information on methods used to quantify arsenic forms and species in contaminated soil, water and plant. The purpose of this article is to understand the available sample pretreatment, extraction, separation, detection and method validation techniques for arsenic speciation analysis of arsenic species in soil, water and plant. The performances of various sample preparation and extraction processes, as well as effective separation techniques, that contribute greatly to excellent sensitivity and selectivity in arsenic speciation when coupling with suitable detection mode, and method validity are discussed. The outlines of arsenic speciation techniques are discussed in view of the importance to the completeness and accuracy of analytical data in the soil-water-plant samples. To develop cheap, fast, sensitive, and reproducible techniques with low detection limits, still needed to confine research on arsenic speciation present in environmental matrices.

Development of a Solar-Powered Wildfire Detector System for Remote Locations with XBee and GSM Capabilities

In the United States wildfires are rampant every year, taking lives, damaging properties and causing huge economic losses. This project designs a wildfire detector system using a Many-to-One communication method with XBee/Zigbee and GSM technologies. Testing of the prototypes has shown the system advantageous features, namely low-power, long-lasting, compact, scalable and communication-effective. The maximum power consumption of a Xbee fire detector and a GSM detector is 14W and 27W, respectively. In detection mode, the XBee detector consumes only 1.29W. The fire detectors are powered by solar panels and Ni-MH battery packs. A fully charged battery pack can sustain a detector up to around 19.3 hours in detection mode, and up to about 4.5 hours in alarm mode. The system has a high potential to be used for wide-area outdoor fire monitoring and detection. The covered area can be flexibly adjusted by varying the number of detectors. Early fire detection and alert provided by the system will enable timely responses that save human lives, as well as minimize property damages and other economic losses.

Dielectric properties and lamellarity of single liposomes measured by in-liquid scanning dielectric microscopy

Liposomes are widely used as drug delivery carriers and as cell model systems. Here, we measure the dielectric properties of individual liposomes adsorbed on a metal electrode by in-liquid scanning dielectric microscopy in force detection mode. From the measurements the lamellarity of the liposomes, the separation between the lamellae and the specific capacitance of the lipid bilayer can be obtained. As application we considered the case of non-extruded DOPC liposomes with radii in the range ~ 100–800 nm. Uni-, bi- and tri-lamellar liposomes have been identified, with the largest population corresponding to bi-lamellar liposomes. The interlamellar separation in the bi-lamellar liposomes is found to be below ~ 10 nm in most instances. The specific capacitance of the DOPC lipid bilayer is found to be ~ 0.75 µF/cm2 in excellent agreement with the value determined on solid supported planar lipid bilayers. The lamellarity of the DOPC liposomes shows the usual correlation with the liposome's size. No correlation is found, instead, with the shape of the adsorbed liposomes. The proposed approach offers a powerful label-free and non-invasive method to determine the lamellarity and dielectric properties of single liposomes.

Transfer Learning Approach for Analyzing Attentiveness of Students in an Online Classroom Environment with Emotion Detection

There is a crucial need for advancement in the online educational system due to the unexpected, forced migration of classroom activities to a fully remote format, due to the coronavirus pandemic. Not only this, but online education is the future, and its infrastructure needs to be improved for an effective teaching-learning process. One of the major concerns with the current video call-based online classroom system is student engagement analysis. Teachers are often concerned about whether the students can perceive the teachings in a novel format. Such analysis was involuntarily done in the offline mode, however, is difficult in an online environment. This research presents an autonomous system for analyzing the students' engagement in the class by detecting the emotions exhibited by the students. This is done by capturing the video feed of the students and passing the detected faces to an emotion detection mode. The emotion detection model in the proposed architecture was designed by finetuning VGG16 pre-trained image classifier model. Lastly, the average student engagement index is calculated. We received considerable performance setting reliability of the use of the proposed system in real-time giving a future scope to this research.

RDAMS: An Efficient Run-Time Approach for Memory Fault and Hardware Trojans Detection

Ensuring the security of IoT devices and chips at runtime has become an urgent task as they have been widely used in human life. Embedded memories are vital components of SoC (System on Chip) in these devices. If they are attacked or incur faults at runtime, it will bring huge losses. In this paper, we propose a run-time detection architecture for memory security (RDAMS) to detect memory threats (fault and Hardware Trojans attack). The architecture consists of a Security Detection Core (SDC) that controls and enforces the detection procedure as a “security brain”, and a memory wrapper (MEM_wrapper) which interacts with memory to assist the detection. We also design a low latency response mechanism to solve the SoC performance degradation caused by run-time detection. A block-based multi-granularity detection approach is proposed to render the design flexible and reduce the cost in implementation using the FPGA’s dynamic partial reconfigurable (DPR) technology, which enables online detection mode reconfiguration according to the requirements. Experimental results show that RDAMS can correctly detect and identify 10 modeled memory faults and two types of Hardware Trojans (HTs) attacks without leading a great performance degradation to the system.

A Multi-Position Approach in a Smart Fiber-Optic Surveillance System for Pipeline Integrity Threat Detection

We present a new pipeline integrity surveillance system for long gas pipeline threat detection and classification. The system is based on distributed acoustic sensing with phase-sensitive optical time domain reflectometry (ϕ-OTDR) and pattern recognition for event classification. The proposal incorporates a multi-position approach in a Gaussian Mixture Model (GMM)-based pattern classification system which operates in a real-field scenario with a thorough experimental procedure. The objective is exploiting the availability of vibration-related data at positions nearby the one actually producing the main disturbance to improve the robustness of the trained models. The system integrates two classification tasks: (1) machine + activity identification, which identifies the machine that is working over the pipeline along with the activity being carried out, and (2) threat detection, which aims to detect suspicious threats for the pipeline integrity (independently of the activity being carried out). For the machine + activity identification mode, the multi-position approach for model training obtains better performance than the previously presented single-position approach for activities that show consistent behavior and high energy (between 6% and 11% absolute) with an overall increase of 3% absolute in the classification accuracy. For the threat detection mode, the proposed approach gets an 8% absolute reduction in the false alarm rate with an overall increase of 4.5% absolute in the classification accuracy.