Search for: All records

n this review article, a comprehensive meta-analysis based on available literature information has been undertaken to make a relative comparison of total arsenic in rice grain. This involves analyzing the findings of various peer-reviewed studies that examined arsenic-contaminated Asian regions. Also, this article highlights the regional-level human health risks caused by the consumption of arsenic-contaminated rice in the three regions of Asia. Deriving such information at the continental level is of major importance in view of the need for proper monitoring and alleviating serious and continually emerging human health issues in arsenic-contaminated areas. One aim of this paper is to highlight the potential of a viable modeling approach for appraising the danger posed by arsenic in soil-plant-human system. There is an urgent need to fix the safe limit of bioavailable arsenic in soil because total arsenic in soil is not a good index of the arsenic hazard. Our hypothesis is finding out whether the modeling approach can be used in establishing a safe limit of bioavailable arsenic in soils with reference to human health. To achieve the above-mentioned objectives, we have selected reported rice grain arsenic content data from Asian countries following the PRISMA guidelines. Carcinogenic and non-carcinogenic risk was calculated following the US EPA’s guidelines. It emerged that adults in Asian countries are prone to a high risk of cancer due to their consumption of arsenic-contaminated rice. South Asia (SA), South East Asia (SEA), and East Asia (EA) exceeded the US EPA-prescribed safe limit for cancer risk with ~ 100 times higher probability of cancer due to rice consumption. The hazard quotient for the ingestion of arsenic containing rice was 4.526 ± 5.118 for SA, 2.599 ± 0.801 for SEA, and 2.954 ± 2.088 for EA. These figures are all above the permissible limit of HQ of 1. The solubility free ion activity model can predict arsenic transfer from soil to rice grain based on easily measurable soil properties and be used to fix the safe limit of bioavailable arsenic in paddy soils. The methods and findings of this review are expected to be useful for regional-level policymaking and mobilizing resources to alleviate public health issues caused by arsenic. 

Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility. 

Quantum machine learning (QML) is an emerging field of research that leverages quantum computing to improve the classical machine learning approach to solve complex real world problems. QML has the potential to address cybersecurity related challenges. Considering the novelty and complex architecture of QML, resources are not yet explicitly available that can pave cybersecurity learners to instill efficient knowledge of this emerging technology. In this research, we design and develop QML-based ten learning modules covering various cybersecurity topics by adopting student centering case-study based learning approach. We apply one subtopic of QML on a cybersecurity topic comprised of pre-lab, lab, and post-lab activities towards providing learners with hands-on QML experiences in solving real-world security problems. In order to engage and motivate students in a learning environment that encourages all students to learn, pre-lab offers a brief introduction to both the QML subtopic and cybersecurity problem. In this paper, we utilize quantum support vector machine (QSVM) for malware classification and protection where we use open source Pennylane QML framework on the drebin 215 dataset. We demonstrate our QSVM model and achieve an accuracy of 95% in malware classification and protection. We will develop all the modules and introduce them to the cybersecurity community in the coming days. 

Recent website fingerprinting attacks have been shown to achieve very high performance against traffic through Tor. These attacks allow an adversary to deduce the website a Tor user has visited by simply eavesdropping on the encrypted communication. This has consequently motivated the development of many defense strategies that obfuscate traffic through the addition of dummy packets and/or delays. The efficacy and practicality of many of these recent proposals have yet to be scrutinized in detail. In this study, we re-evaluate nine recent defense proposals that claim to provide adequate security with low-overheads using the latest Deep Learning-based attacks. Furthermore, we assess the feasibility of implementing these defenses within the current confines of Tor. To this end, we additionally provide the first on-network implementation of the DynaFlow defense to better assess its real-world utility. 

We develop integrated co-evolution and dynamic coupling (ICDC) approach to identify, mutate, and assess distal sites to modulate function. We validate the approach first by analyzing the existing mutational fitness data of TEM-1 β-lactamase and show that allosteric positions co-evolved and dynamically coupled with the active site significantly modulate function. We further apply ICDC approach to identify positions and their mutations that can modulate binding affinity in a lectin, cyanovirin-N (CV-N), that selectively binds to dimannose, and predict binding energies of its variants through Adaptive BP-Dock. Computational and experimental analyses reveal that binding enhancing mutants identified by ICDC impact the dynamics of the binding pocket, and show that rigidification of the binding residues compensates for the entropic cost of binding. This work suggests a mechanism by which distal mutations modulate function through dynamic allostery and provides a blueprint to identify candidates for mutagenesis in order to optimize protein function.

 

We propose and prove several regularity criteria for the 2D and 3D Kuramoto–Sivashinsky equation, in both its scalar and vector forms. In particular, we examine integrability criteria for the regularity of solutions in terms of the scalar solution$$\phi $$$\varphi$, the vector solution$$u\triangleq \nabla \phi $$$u\triangleq \nabla \varphi$, as well as the divergence$$\text {div}(u)=\Delta \phi $$$\text{div}\left(u\right)=\Delta \varphi$, and each component ofuand$$\nabla u$$$\nabla u$. We also investigate these criteria computationally in the 2D case, and we include snapshots of solutions for several quantities of interest that arise in energy estimates.