computational framework
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2022 ◽  
Shan Suthaharan

This paper presents a computational framework that helps enhance the confidentiality protection of communication in cybersecurity by leveraging the scientific properties of the Tamil language and the advanced encryption standard (AES). It defines a product set of vowels and consonants sounds of the Tamil language and reveals its connection to Hardy-Ramanujan prime factors and Tamil letters as a one-to-one function. It also reveals that the letters of the Tamil alphabet, combined with the digits from 1 to 9, form a Galois field of 2^8 over an irreducible polynomial of degree 8. In addition, it implements these two mathematical properties and builds an encoder for the AES algorithm to transform the Tamil texts to their hexadecimal states, and replace the pre-round transformation module of AES. It empirically shows that the Tamil-based encoder enhances the cryptographic strength of the AES algorithm at every step of its encryption flow. The cryptographic strength is measured by the runs test scores of the bit sequences of the ciphers of AES and compared with that of the English language. This modeling and simulation approach concludes that the Tamil-based encryption enhances the cryptographic strength of AES than English-based encryption.

2022 ◽  
Zhizhen Zhao ◽  
Ruoqi Liu ◽  
Lei Wang ◽  
Lang Li ◽  
Chi Song ◽  

The identification of associations between drugs and adverse drug events (ADEs) is crucial for drug safety surveillance. An increasing number of studies have revealed that children and seniors are susceptible to ADEs at the population level. However, the comprehensive explorations of age risks in drug-ADE pairs are still limited. The FDA Adverse Event Reporting System (FAERS) provides individual case reports, which can be used for quantifying different age risks. In this study, we developed a statistical computational framework to detect age group of patients who are susceptible to some ADEs after taking specific drugs. We adopted different Chi-squared tests and conducted disproportionality analysis to detect drug-ADE pairs with age differences. We analyzed 4,580,113 drug-ADE pairs in FAERS (2004 to 2018Q3) and identified 2,523 pairs with the highest age risk. Furthermore, we conducted a case study on statin-induced ADE in children and youth. The code and results are available at

2022 ◽  
pp. 1-154
Caleb Geniesse ◽  
Samir Chowdhury ◽  
Manish Saggar

Abstract For better translational outcomes researchers and clinicians alike demand novel tools to distil complex neuroimaging data into simple yet behaviorally relevant representations at the single-participant level. Recently, the Mapper approach from topological data analysis (TDA) has been successfully applied on noninvasive human neuroimaging data to characterize the entire dynamical landscape of whole-brain configurations at the individual level without requiring any spatiotemporal averaging at the outset. Despite promising results, initial applications of Mapper to neuroimaging data were constrained by (1) the need for dimensionality reduction, and (2) lack of a biologically grounded heuristic for efficiently exploring the vast parameter space. Here, we present a novel computational framework for Mapper—designed specifically for neuroimaging data—that removes limitations and reduces computational costs associated with dimensionality reduction and parameter exploration. We also introduce new meta-analytic approaches to better anchor Mapper-generated representations to neuroanatomy and behavior. Our new NeuMapper framework was developed and validated using multiple fMRI datasets where participants engaged in continuous multitask experiments that mimic “ongoing” cognition. Looking forward, we hope our framework could help researchers push the boundaries of psychiatric neuroimaging towards generating insights at the single-participant level while scaling across consortium-size datasets.

Lavanya Raajaraam ◽  
Karthik Raman

Microbial production of chemicals is a more sustainable alternative to traditional chemical processes. However, the shift to bioprocess is usually accompanied by a drop in economic feasibility. Co-production of more than one chemical can improve the economy of bioprocesses, enhance carbon utilization and also ensure better exploitation of resources. While a number of tools exist for in silico metabolic engineering, there is a dearth of computational tools that can co-optimize the production of multiple metabolites. In this work, we propose co-FSEOF (co-production using Flux Scanning based on Enforced Objective Flux), an algorithm designed to identify intervention strategies to co-optimize the production of a set of metabolites. Co-FSEOF can be used to identify all pairs of products that can be co-optimized with ease using a single intervention. Beyond this, it can also identify higher-order intervention strategies for a given set of metabolites. We have employed this tool on the genome-scale metabolic models of Escherichia coli and Saccharomyces cerevisiae, and identified intervention targets that can co-optimize the production of pairs of metabolites under both aerobic and anaerobic conditions. Anaerobic conditions were found to support the co-production of a higher number of metabolites when compared to aerobic conditions in both organisms. The proposed computational framework will enhance the ease of study of metabolite co-production and thereby aid the design of better bioprocesses.

2022 ◽  
Vol 23 (1) ◽  
Tobias Heinen ◽  
Stefano Secchia ◽  
James P. Reddington ◽  
Bingqing Zhao ◽  
Eileen E. M. Furlong ◽  

AbstractWhile it is established that the functional impact of genetic variation can vary across cell types and states, capturing this diversity remains challenging. Current studies using bulk sequencing either ignore this heterogeneity or use sorted cell populations, reducing discovery and explanatory power. Here, we develop scDALI, a versatile computational framework that integrates information on cellular states with allelic quantifications of single-cell sequencing data to characterize cell-state-specific genetic effects. We apply scDALI to scATAC-seq profiles from developing F1 Drosophila embryos and scRNA-seq from differentiating human iPSCs, uncovering heterogeneous genetic effects in specific lineages, developmental stages, or cell types.

2022 ◽  
Miguel Ponce-de-Leon ◽  
Arnau Montagud ◽  
Vincent Noel ◽  
Gerard Pradas ◽  
Annika Meert ◽  

Motivation: Cancer progression is a complex phenomenon that spans multiple scales from molecular to cellular and intercellular. Simulations can be used to perturb the underlying mechanisms of those systems and to generate hypotheses on novel therapies. We present a new version of PhysiBoSS, a multiscale modelling framework designed to cover multiple temporal and spatial scales, that improves its integration with PhysiCell, decoupling the cell agent simulations with the internal Boolean model in an easy-to-maintain computational framework. Results: PhysiBoSS 2.0 is a redesign and reimplementation of PhysiBoSS, conceived as an add-on that expands the PhysiCell agent-based functionalities with intracellular cell signalling using MaBoSS having a decoupled, maintainable and model-agnostic design. PhysiBoSS 2.0 successfully reproduces simulations reported in the former PhysiBoSS and expands its functionalities such as using user-defined models and cells' specifications, having mechanistic submodels of substrate internalisation with ODEs and enabling the study of drug synergies. Availability and implementation: PhysiBoSS 2.0 is open-source and publicly available on GitHub ( under the BSD 3-clause license. Additionally, a nanoHUB tool has been set up to ease the use of PhysiBoSS 2.0 (

2022 ◽  
Saumik Dana

We present a computational framework for fast monitoring of fault stability and ground deformation in multiphase geomechanics and demonstrate its efficacy for a carbon sequestration--enhanced oil recovery case study. The staggered solution algorithm for the coupled problem is augmented with a feature that allows for the flow and geomechanics sub-problems to be solved on different unstructured tetrahedral grids. For the field scale problem, the geomechanics grid goes all the way to the free surface while the flow grid is truncated at a depth above which the layers are impermeable. This framework avoids the unnecessary computational burden associated with equilibrating the initial pressure solution in the overburden, allows for a study of the critical interaction between overburden and faults, allows for fast renditions of ground deformation, and allows a choice of resolution for the flow and geomechanics grids independently to capture disparate length scales of the underlying physics.

2022 ◽  
Itay Daybog ◽  
Oren Kolodny

Recent empirical studies offer conflicting findings regarding the relation between host fitness and the composition of its microbiome, a conflict which we term the microbial β-diversity conundrum: it has been shown that the microbiome is crucial for host wellbeing and survival. At the same time, different healthy individuals' microbiome compositions, even in the same population, often differ dramatically, contrary to the notion that a vital trait should be highly conserved. Moreover, gnotobiotic individuals exhibit highly deleterious phenotypes, supporting the notion that the microbiome is paramount to host fitness. However, the introduction of almost arbitrarily selected microbiota into the system often achieves a significant rescue effect of the deleterious phenotypes, even microbiota from soil or phylogenetically distant host species, highlighting an apparent paradox. Here we suggest several solutions to the paradox using a computational framework, simulating the population dynamics of hosts and their microbiomes over multiple generations. The answers, relating to factors such as host population size, the specific mode of contribution of the microbes to host fitness, and the typical microbiome richness, offer solutions to the conundrum by creating scenarios where even when a host's fitness is determined in full by its microbiome composition, this composition has little or no effect on the natural selection dynamics of the population.

2022 ◽  
Vol 12 ◽  
Fei Xia ◽  
Xiaojun Xie ◽  
Zongqin Wang ◽  
Shichao Jin ◽  
Ke Yan ◽  

Plants are often attacked by various pathogens during their growth, which may cause environmental pollution, food shortages, or economic losses in a certain area. Integration of high throughput phenomics data and computer vision (CV) provides a great opportunity to realize plant disease diagnosis in the early stage and uncover the subtype or stage patterns in the disease progression. In this study, we proposed a novel computational framework for plant disease identification and subtype discovery through a deep-embedding image-clustering strategy, Weighted Distance Metric and the t-stochastic neighbor embedding algorithm (WDM-tSNE). To verify the effectiveness, we applied our method on four public datasets of images. The results demonstrated that the newly developed tool is capable of identifying the plant disease and further uncover the underlying subtypes associated with pathogenic resistance. In summary, the current framework provides great clustering performance for the root or leave images of diseased plants with pronounced disease spots or symptoms.

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