In-situ PLL-g-PEG Functionalized Nanopore for Enhancing Protein Characterization

Single-molecule nanopore detection technology has revolutionized proteomics research by enabling highly sensitive and label-free detection of individual proteins. Herein, we designed a small, portable, and leak-free flowcell made of PMMA for nanopore experiments. In addition, we developed an in situ coating PLL-g-PEG approach to produce non-sticky nanopores for measuring the volume of diseases-relevant biomarker, such as the Alpha-1 antitrypsin (AAT) protein. The in situ coating method allows continuous monitoring, ensuring adequate coating, which can be directly used for translocation experiments. The coated nanopores exhibit improved characteristics, including an increased nanopore lifetime and enhanced translocation events of the AAT proteins. Furthermore, we demonstrated the reduction in the translocation event's dwell time, along with an increase in current blockade amplitudes and translocation numbers under different voltage stimuli. The study also successfully measures the single AAT protein volume (253 nm3 ), which closely aligns with the previously reported hydrodynamic volume. The real-time in situ PLL-g-PEG coating method and the developed nanopore flowcell hold great promise for various nanopores applications involving non-sticky single-molecule characterization

Measuring conic properties and shape orientations of two-dimensional point sets

We propose new methods for computing a shape's orientation and several shape measures for elongation, linearity, circularity, ellipticity, hyperbolicity, and parabolicity of 2D point sets. Measures for both ordered and unordered data sets which are invariant to rotation, scaling, and translation interest us. These measures should also be calculated very quickly. Moment based and average pair wise direction based calculations of orientation are proposed here. We describe linearity measures for unordered data sets called eccentricity, triangle perimeters, triangle heights, triplet smoothness, rotation correlation, average orientations, and ellipse axis ratio. Linearity measures for sorted data sets include average sorted orientations, triangle sides ratio, and the product of a new monotonicity measure and one of the existing measures for linearity of unordered point sets. The monotonicity measure is the ratio of signed and non-signed sums of piecewise projections onto the orientation line. In order to measure circularity, we transfer the Cartesian coordinates of the input set into polar coordinates. The linearity of the polar coordinate set corresponds to the circularity of the original input set given a suitable center. Our ellipse fit will determine the optimal location of the foci of the fitted ellipse along the orientation line (symmetrically with respect to the shape center) such that it minimizes the variance of sums of distances of points to the foci. In order to find ellipticity (hyperbolicity), we made use of the property that the sum (difference, respectively) of distances from each point on the ellipse to both foci is constant. We also propose an ellipticity measure based on the average ratio of distances of each point to the ellipse and to its center. The parabolicity measure is based on a similar idea of maintaining a constant sum of distances to the focus and a line parallel to the directrix line for each point. We discover that the definition of elongation highly correlates with the definition of linearity. All of the shape measures are tested on digital curves and compared with existing methods. All of the methods work in real time

EDAAAS: Efficient distributed anonymous authentication and access in smart homes

The smart home field has witnessed rapid developments in recent years. Internet of Things applications for the smart home are very heterogeneous and continuously increasing in number, making user management from a security perspective very challenging. Moreover, the resource-constrained nature of most of the devices implies that any security mechanisms deployed should be lightweight and highly efficient. In this article, we propose an authentication scheme based on symmetric key cryptography, combined with a capability-based access control system, to provide the different stakeholders (residents, recurring guests, or temporary guests) end-to-end secure access to the Internet of Things devices in a smart home, managed by the home owner in an anonymous way. The operations in our scheme only include a small number of communication phases and protect the identities of the entities involved (i.e. stakeholders and end-nodes) from any outside entity. The proposed scheme ensures that even if the stakeholder's device or the Internet of Things device is attacked, the system remains secure

Navigating Virtual Environments Using Leg Poses and Smartphone Sensors

Realization of navigation in virtual environments remains a challenge as it involves complex operating conditions. Decomposition of such complexity is attainable by fusion of sensors and machine learning techniques. Identifying the right combination of sensory information and the appropriate machine learning technique is a vital ingredient for translating physical actions to virtual movements. The contributions of our work include: (i) Synchronization of actions and movements using suitable multiple sensor units, and (ii) selection of the significant features and an appropriate algorithm to process them. This work proposes an innovative approach that allows users to move in virtual environments by simply moving their legs towards the desired direction. The necessary hardware includes only a smartphone that is strapped to the subjects’ lower leg. Data from the gyroscope, accelerometer and campus sensors of the mobile device are transmitted to a PC where the movement is accurately identified using a combination of machine learning techniques. Once the desired movement is identified, the movement of the virtual avatar in the virtual environment is realized. After pre-processing the sensor data using the box plot outliers approach, it is observed that Artificial Neural Networks provided the highest movement identification accuracy of 84.2% on the training dataset and 84.1% on testing dataset