Twisted Edwards-Form Elliptic Curve Cryptography for 8-bit AVR-based Sensor Nodes

Wireless Sensor Networks (WSNs) pose a number of unique security challenges that demand innovation in several areas including the design of cryptographic primitives and protocols. Despite recent progress, the efficient implementation of Elliptic Curve Cryptography (ECC) for WSNs is still a very active research topic and techniques to further reduce the time and energy cost of ECC are eagerly sought. This paper presents an optimized ECC implementation that we developed from scratch to comply with the severe resource constraints of 8-bit sensor nodes such as the MICAz and IRIS motes. Our ECC software uses Optimal Prime Fields (OPFs) as underlying algebraic structure and supports two different families of elliptic curves, namely Weierstraß-form and twisted Edwards-form curves. Due to the combination of efficient field arithmetic and fast group operations, we achieve an execution time of 5.8*10^6 clock cycles for a full 158-bit scalar multiplication on an 8-bit ATmega128 microcontroller, which is 2.78 times faster than the widely-used TinyECC library. Our implementation also shows that the energy cost of scalar multiplication on a MICAz (or IRIS) mote amounts to just 19 mJ when using a twisted Edwards curve over a 160-bit OPF. This result compares fairly well with the energy figures of two recently-presented hardware designs of ECC based on twisted Edwards curves

Impact of flow rates in a cardiac cycle on correlations between advanced human carotid plaque progression and mechanical flow shear stress and plaque wall stress

<p>Abstract</p> <p>Background</p> <p>Mechanical stresses are known to play important roles in atherosclerotic plaque initiation, progression and rupture. It has been well-accepted that atherosclerosis initiation and early progression correlate negatively with flow wall shear stresses (FSS). However, mechanisms governing <it>advanced </it>plaque progression are not well understood.</p> <p>Method</p> <p>In vivo serial MRI data (patient follow-up) were acquired from 14 patients after informed consent. Each patient had 2-4 scans (scan interval: 18 months). Thirty-two scan pairs (baseline and follow-up scans) were formed with slices matched for model construction and analysis. Each scan pair had 4-10 matched slices which gave 400-1000 data points for analysis (100 points per slice on lumen). Point-wise plaque progression was defined as the wall thickness increase (WTI) at each data point. 3D computational models with fluid-structure interactions were constructed based on in vivo serial MRI data to extract flow shear stress and plaque wall stress (PWS) on all data points to quantify correlations between plaque progression and mechanical stresses (FSS and PWS). FSS and PWS data corresponding to both maximum and minimum flow rates in a cardiac cycle were used to investigate the impact of flow rates on those correlations.</p> <p>Results</p> <p>Using follow-up scans and maximum flow rates, 19 out of 32 scan pairs showed a significant <it>positive </it>correlation between WTI and FSS (positive/negative/no significance correlation ratio = 19/9/4), and 26 out of 32 scan pairs showed a significant <it>negative </it>correlation between WTI and PWS (correlation ratio = 2/26/4). Corresponding to minimum flow rates, the correlation ratio for WTI vs. FSS and WTI vs. PWS were (20/7/5) and (2/26/4), respectively. Using baseline scans, the correlation ratios for WTI vs. FSS were (10/12/10) and (9/13/10) for maximum and minimum flow rates, respectively. The correlation ratios for WTI vs. PWS were the same (18/5/9), corresponding to maximum and minimum flow rates.</p> <p>Conclusion</p> <p>Flow shear stress corresponding to the minimum flow rates in a cardiac cycle had slightly better correlation with WTI, compared to FSS corresponding to maximum flow rates. Choice of maximum or minimum flow rates had no impact on PWS correlations. Advanced plaque progression correlated positively with flow shear stress and negatively with plaque wall stress using follow-up scans. Correlation results using FSS at the baseline scan were inconclusive.</p

Concurrent fNIRS and EEG for brain function investigation: A systematic, methodology-focused review

Electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) stand as state-of-the-art techniques for non-invasive functional neuroimaging. On a unimodal basis, EEG has poor spatial resolution while presenting high temporal resolution. In contrast, fNIRS offers better spatial resolution, though it is constrained by its poor temporal resolution. One important merit shared by the EEG and fNIRS is that both modalities have favorable portability and could be integrated into a compatible experimental setup, providing a compelling ground for the development of a multimodal fNIRS-EEG integration analysis approach. Despite a growing number of studies using concurrent fNIRS-EEG designs reported in recent years, the methodological reference of past studies remains unclear. To fill this knowledge gap, this review critically summarizes the status of analysis methods currently used in concurrent fNIRS-EEG studies, providing an up-to-date overview and guideline for future projects to conduct concurrent fNIRS-EEG studies. A literature search was conducted using PubMed and Web of Science through 31 August 2021. After screening and qualification assessment, 92 studies involving concurrent fNIRS-EEG data recordings and analyses were included in the final methodological review. Specifically, three methodological categories of concurrent fNIRS-EEG data analyses, including EEG-informed fNIRS analyses, fNIRS-informed EEG analyses, and parallel fNIRS-EEG analyses, were identified and explained with detailed description. Finally, we highlighted current challenges and potential directions in concurrent fNIRS-EEG data analyses in future research

Usefulness of soluble endothelial protein C receptor combined with left ventricular global longitudinal strain for predicting slow coronary flow: A case-control study

Background: Slow coronary flow (SCF) is an angiographic entity characterized by delayed coronary opacification without an evident obstructive lesion in the epicardial coronary artery. However, patients with SCF have decreased left ventricular (LV) global longitudinal strain (GLS). SCF is associated with inflammation, and soluble endothelial protein C receptor (sEPCR) is a potential biomarker of inflammation. Therefore, under evaluation herein, was the relationship between SCF and sEPCR and the predictive value of sEPCR and LV GLS for SCF was investigated. Methods: Twenty-eight patients with SCF and 34 controls were enrolled. SCF was diagnosed by the thrombolysis in myocardial infarction frame count (TFC). The plasma level of sEPCR was quantified using enzyme-linked immunosorbent assay. LV GLS was measured by two-dimensional speckle-tracking echocardiography. Results: Plasma sEPCR was significantly higher in patients with SCF than in controls and was positively correlated with the mean TFC (r = 0.67, p &lt; 0.001) and number of involved vessels (r = 0.61, p &lt; 0.001). LV GLS was decreased in patients with SCF compared to that in controls. sEPCR level (OR = 3.14, 95% CI 1.55–6.36, p = 0.001) and LV GLS (OR = 1.44, 95% CI 1.02–2.04, p = 0.04) were independent predictors of SCF. sEPCR predicted SCF (area under curve [AUC]: 0.83); however, sEPCR &gt; 9.63 ng/mL combined with LV GLS &gt; −14.36% demonstrated better predictive power (AUC: 0.89; sensitivity: 75%; specificity: 91%). Conclusions: Patients with SCF have increased plasma sEPCR and decreased LV GLS. sEPCR may be a useful potential biomarker for SCF, and sEPCR combined with LV GLS can better predict SCF

IVUS-based Fluid-structure Interaction Models for Novel Plaque Vulnerability Indices: A Study in Patients with Coronary Artery Disease

AbstractIt is believed that mechanical stresses play an important role in atherosclerotic plaque rupture process and may be used for better plaque vulnerability assessment and rupture risk predictions. IVUS data were acquired from 14 patients (11M, 3F, Mean age: 59,) for constructing 3D computational models combining fluid-structure interaction (FSI), cyclic bending due to cardiac contraction and patient-specific pressure loading to quantify mechanical conditions in the human coronary. The computational models were solved by a finite element package ADINA to obtain plaque wall stress (PWS), strain (PWSn) and flow shear stress (FSS) and investigate correlation between the mechanical conditions and morphological characteristics. For all 617 IVUS slices yielded from the 14 patients, plaque morphological features lipid percentage and min cap thickness were calculated for each slice, and three types of plaque morphology related indices: lipid index, cap index and morphological index (MPVI) were introduced as quantitative measures of plaque vulnerability. PWS, PWSn and FSS values at critical sites were denoted as critical plaque wall stress (CPWS), critical plaque wall strain (CPWSn) and critical flow shear stress (CFSS) for each slice, and a stress index was proposed based on the value of the CPWS. The conventional Pearson's correlation is used to analyze the correlation between each of the mechanical conditions and each plaque morphological feature indices. Our results suggest there is significant correlation between the CPWS and min cap thickness, cap index with the correlation coefficient r=-0.6570, r=0.8016 respectively, while the correlation between CPWS and lipid percentage and the lipid index are weaker (r=0.2209, r=0.2304) even though they are significantly correlated. The correlation results between CPWS and morphological index (r=0.7725, p-value<0.0001) showed there is a strong positive relationship between the mechanical stress and morphological features. For all 617 slices, the stress index has a 66.77% agreement with morphological index. More patient follow-up data and large-scale studies are needed to continue our investigations