Introduction of the Grayscale Median for Ultrasound Tissue Characterization of the Transplanted Kidney

Ultrasound examination is advised for early post-kidney transplant assessment. Grayscale median (GSM) quantification is novel in the kidney transplant field, with no systematic assessment previously reported. In this prospective cohort study, we measured the post-operative GSM in a large cohort of adult kidney transplant recipients (KTR) who consecutively underwent Doppler ultrasound directly after transplantation (within 24 h), compared it with GSM in nontransplanted patients, and investigated its association with baseline and follow-up characteristics. B-mode images were used to calculate the GSM in KTR and compared with GSM data in nontransplanted patients, as simulated from summary statistics of the literature using a Mersenne twister algorithm. The association of GSM with baseline and 1-year follow-up characteristics were studied by means of linear regression analyses. In 282 KTR (54 ± 15 years old, 60% male), the median (IQR) GSM was 55 (45–69), ranging from 22 to 124 (coefficient of variation = 7.4%), without differences by type of donation (p = 0.28). GSM in KTR was significantly higher than in nontransplanted patients (p < 0.001), and associated with systolic blood pressure, history of cardiovascular disease, and donor age (std. β = 0.12, −0.20, and 0.13, respectively; p < 0.05 for all). Higher early post-kidney transplant GSM was not associated with 1-year post-kidney transplant function parameters (e.g., measured and estimated glomerular filtration rate). The data provided in this study could be used as first step for further research on the application of early postoperative ultrasound in KTR.

A Novel Privacy Approach of Digital Aerial Images Based on Mersenne Twister Method with DNA Genetic Encoding and Chaos

Aerial photography involves capturing images from aircraft and other flying objects, including Unmanned Aerial Vehicles (UAV). Aerial images are used in many fields and can contain sensitive information that requires secure processing. We proposed an innovative new cryptosystem for the processing of aerial images utilizing a chaos-based private key block cipher method so that the images are secure even on untrusted cloud servers. The proposed cryptosystem is based on a hybrid technique combining the Mersenne Twister (MT), Deoxyribonucleic Acid (DNA), and Chaotic Dynamical Rossler System (MT-DNA-Chaos) methods. The combination of MT with the four nucleotides and chaos sequencing creates an enhanced level of security for the proposed algorithm. The system is tested at three separate phases. The combined effects of the three levels improve the overall efficiency of the randomness of data. The proposed method is computationally agile, and offered more security than existing cryptosystems. To assess, this new system is examined against different statistical tests such as adjacent pixels correlation analysis, histogram consistency analyses and its variance, visual strength analysis, information randomness and uncertainty analysis, pixel inconsistency analysis, pixels similitude analyses, average difference, and maximum difference. These tests confirmed its validity for real-time communication purposes.

Exploring the stochasticity of chemical processes in an automated robotic crystallization platform to generate random numbers

<p>Random number generators are important in fields which require non-deterministic input, such as cryptography. One example of a non-deterministic system is found in chemistry via the crystallization of chemical compounds, which occurs through stochastic processes. Herein, we present an automated platform capable of generating random numbers from observation of crystallizations resulting from multiple parallel one-pot chemical reactions. From the resulting images, crystals were identified using computer vision, and binary sequences were obtained by applying a binarization algorithm to these regions. An assessment of randomness of these sequences was undertaken by applying a barrage of tests for randomness described by the National Institute of Standards and Technology (NIST). We find that numbers generated through this method are able to pass each of the three levels for each of the NIST tests. We then compare the encryption strength of the random numbers generated from each of the crystallizing systems to that of a pseudo-random number generation algorithm (the Mersenne Twister). We find that messages encrypted using chemically derived random numbers take significantly longer to decrypt than the algorithmically generated number.</p>

Exploring the stochasticity of chemical processes in an automated robotic crystallization platform to generate random numbers

<p>Random number generators are important in fields which require non-deterministic input, such as cryptography. One example of a non-deterministic system is found in chemistry via the crystallization of chemical compounds, which occurs through stochastic processes. Herein, we present an automated platform capable of generating random numbers from observation of crystallizations resulting from multiple parallel one-pot chemical reactions. From the resulting images, crystals were identified using computer vision, and binary sequences were obtained by applying a binarization algorithm to these regions. An assessment of randomness of these sequences was undertaken by applying a barrage of tests for randomness described by the National Institute of Standards and Technology (NIST). We find that numbers generated through this method are able to pass each of the three levels for each of the NIST tests. We then compare the encryption strength of the random numbers generated from each of the crystallizing systems to that of a pseudo-random number generation algorithm (the Mersenne Twister). We find that messages encrypted using chemically derived random numbers take significantly longer to decrypt than the algorithmically generated number.</p>