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1.
PLoS One ; 16(10): e0258091, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-34624046

RESUMO

Quantum signature is the use of the principles of quantum computing to establish a trusted communication between two parties. In this paper, a quantum signature scheme using amplitude amplification techniques will be proposed. To secure the signature, the proposed scheme uses a partial diffusion operator and a diffusion operator to hide/unhide certain quantum states during communication. The proposed scheme consists of three phases, preparation phase, signature phase and verification phase. To confuse the eavesdropper, the quantum states representing the signature might be hidden, not hidden or encoded in Bell states. It will be shown that the proposed scheme is more secure against eavesdropping when compared with relevant quantum signature schemes.


Assuntos
Segurança Computacional/tendências , Metodologias Computacionais , Confidencialidade/normas , Algoritmos , Comunicação , Humanos , Teoria Quântica , Confiança
3.
Sensors (Basel) ; 21(15)2021 Jul 24.
Artigo em Inglês | MEDLINE | ID: mdl-34372267

RESUMO

With the advent of the Industry 4.0 paradigm, the possibilities of controlling manufacturing processes through the information provided by a network of sensors connected to work centers have expanded. Real-time monitoring of each parameter makes it possible to determine whether the values yielded by the corresponding sensor are in their normal operating range. In the interplay of the multitude of parameters, deterministic analysis quickly becomes intractable and one enters the realm of "uncertain knowledge". Bayesian decision networks are a recognized tool to control the effects of conditional probabilities in such systems. However, determining whether a manufacturing process is out of range requires significant computation time for a decision network, thus delaying the triggering of a malfunction alarm. From its origins, JIDOKA was conceived as a means to provide mechanisms to facilitate real-time identification of malfunctions in any step of the process, so that the production line could be stopped, the cause of the disruption identified for resolution, and ultimately the number of defective parts minimized. Our hypothesis is that we can model the internal sensor network of a computer numerical control (CNC) machine with quantum simulations that show better performance than classical models based on decision networks. We show a successful test of our hypothesis by implementing a quantum digital twin that allows for the integration of quantum computing and Industry 4.0. This quantum digital twin simulates the intricate sensor network within a machine and permits, due to its high computational performance, to apply JIDOKA in real time within manufacturing processes.


Assuntos
Metodologias Computacionais , Teoria Quântica , Algoritmos , Teorema de Bayes , Simulação por Computador , Humanos
4.
PLoS Comput Biol ; 17(7): e1009244, 2021 07.
Artigo em Inglês | MEDLINE | ID: mdl-34283824

RESUMO

The large amount of biological data available in the current times, makes it necessary to use tools and applications based on sophisticated and efficient algorithms, developed in the area of bioinformatics. Further, access to high performance computing resources is necessary, to achieve results in reasonable time. To speed up applications and utilize available compute resources as efficient as possible, software developers make use of parallelization mechanisms, like multithreading. Many of the available tools in bioinformatics offer multithreading capabilities, but more compute power is not always helpful. In this study we investigated the behavior of well-known applications in bioinformatics, regarding their performance in the terms of scaling, different virtual environments and different datasets with our benchmarking tool suite BOOTABLE. The tool suite includes the tools BBMap, Bowtie2, BWA, Velvet, IDBA, SPAdes, Clustal Omega, MAFFT, SINA and GROMACS. In addition we added an application using the machine learning framework TensorFlow. Machine learning is not directly part of bioinformatics but applied to many biological problems, especially in the context of medical images (X-ray photographs). The mentioned tools have been analyzed in two different virtual environments, a virtual machine environment based on the OpenStack cloud software and in a Docker environment. The gained performance values were compared to a bare-metal setup and among each other. The study reveals, that the used virtual environments produce an overhead in the range of seven to twenty-five percent compared to the bare-metal environment. The scaling measurements showed, that some of the analyzed tools do not benefit from using larger amounts of computing resources, whereas others showed an almost linear scaling behavior. The findings of this study have been generalized as far as possible and should help users to find the best amount of resources for their analysis. Further, the results provide valuable information for resource providers to handle their resources as efficiently as possible and raise the user community's awareness of the efficient usage of computing resources.


Assuntos
Biologia Computacional/métodos , Algoritmos , Benchmarking , Computação em Nuvem , Biologia Computacional/normas , Biologia Computacional/estatística & dados numéricos , Computadores , Metodologias Computacionais , Interpretação Estatística de Dados , Bases de Dados Factuais/estatística & dados numéricos , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Interpretação de Imagem Assistida por Computador , Aprendizado de Máquina , Alinhamento de Sequência , Software , Interface Usuário-Computador
7.
J Chem Inf Model ; 61(6): 2641-2647, 2021 06 28.
Artigo em Inglês | MEDLINE | ID: mdl-34032436

RESUMO

The growing quantity of public and private data sets focused on small molecules screened against biological targets or whole organisms provides a wealth of drug discovery relevant data. This is matched by the availability of machine learning algorithms such as Support Vector Machines (SVM) and Deep Neural Networks (DNN) that are computationally expensive to perform on very large data sets with thousands of molecular descriptors. Quantum computer (QC) algorithms have been proposed to offer an approach to accelerate quantum machine learning over classical computer (CC) algorithms, however with significant limitations. In the case of cheminformatics, which is widely used in drug discovery, one of the challenges to overcome is the need for compression of large numbers of molecular descriptors for use on a QC. Here, we show how to achieve compression with data sets using hundreds of molecules (SARS-CoV-2) to hundreds of thousands of molecules (whole cell screening data sets for plague and M. tuberculosis) with SVM and the data reuploading classifier (a DNN equivalent algorithm) on a QC benchmarked against CC and hybrid approaches. This study illustrates the steps needed in order to be "quantum computer ready" in order to apply quantum computing to drug discovery and to provide the foundation on which to build this field.


Assuntos
COVID-19 , Descoberta de Drogas , Algoritmos , Metodologias Computacionais , Humanos , Aprendizado de Máquina , Teoria Quântica , SARS-CoV-2 , Máquina de Vetores de Suporte
8.
Medicine (Baltimore) ; 100(17): e25642, 2021 Apr 30.
Artigo em Inglês | MEDLINE | ID: mdl-33907122

RESUMO

ABSTRACT: Researchers divided the pancreas distal to the neck into 2 equal parts as the body and tail region by an arbitrary line. Surgeons considered the part of the pancreas, left to the aorta as the tail region. We performed this study to identify the transition zone of low-density to high-density islet cells for redefining the tail region.We quantified islets area proportion, beta-cell area proportion, and inter-islet distance in 9 Indian-adult-human non-diabetic pancreases from autopsy by using anti-synaptophysin and anti-insulin antibodies. Data were categorized under 3 regions like the proximal body, distal body, and distal part of the pancreas.Islet and beta-cell area proportion are progressively increased from head to tail region of the pancreas with a significant reduction in inter-islet distance and beta-cell percentage distal to the aorta. There is no significant difference in inter-islet distance and beta-cell percentage of the distal part of the body and tail region.Crowding of islets with intermingled microarchitecture begins in the pancreas distal to the aorta, which may be the beginning of the actual tail region. This study will provide insight into the preservation of islets-rich part of the pancreas during pancreatectomy and future prediction of new-onset diabetes.


Assuntos
Ilhotas Pancreáticas/ultraestrutura , Pâncreas/anatomia & histologia , Pâncreas/citologia , Adulto , Autopsia , Metodologias Computacionais , Humanos , Imuno-Histoquímica
10.
Methods Mol Biol ; 2284: 253-270, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33835447

RESUMO

RNA editing by A-to-I deamination is a relevant co/posttranscriptional modification carried out by ADAR enzymes. In humans, it has pivotal cellular effects and its deregulation has been linked to a variety of human disorders including neurological and neurodegenerative diseases and cancer. Despite its biological relevance, the detection of RNA editing variants in large transcriptome sequencing experiments (RNAseq) is yet a challenging computational task. To drastically reduce computing times we have developed a novel REDItools version able to identify A-to-I events in huge amount of RNAseq data employing High Performance Computing (HPC) infrastructures.Here we show how to use REDItools v2 in HPC systems.


Assuntos
Metodologias Computacionais , Edição de RNA/fisiologia , Análise de Sequência de RNA/métodos , Animais , Biologia Computacional/métodos , Bases de Dados Genéticas , Conjuntos de Dados como Assunto , Genômica , Sequenciamento de Nucleotídeos em Larga Escala , Humanos , Neoplasias/genética , Doenças do Sistema Nervoso/genética , Doenças Neurodegenerativas/genética , Software , Transcriptoma
12.
PLoS Comput Biol ; 17(2): e1008622, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33630841

RESUMO

Workflow management systems represent, manage, and execute multistep computational analyses and offer many benefits to bioinformaticians. They provide a common language for describing analysis workflows, contributing to reproducibility and to building libraries of reusable components. They can support both incremental build and re-entrancy-the ability to selectively re-execute parts of a workflow in the presence of additional inputs or changes in configuration and to resume execution from where a workflow previously stopped. Many workflow management systems enhance portability by supporting the use of containers, high-performance computing (HPC) systems, and clouds. Most importantly, workflow management systems allow bioinformaticians to delegate how their workflows are run to the workflow management system and its developers. This frees the bioinformaticians to focus on what these workflows should do, on their data analyses, and on their science. RiboViz is a package to extract biological insight from ribosome profiling data to help advance understanding of protein synthesis. At the heart of RiboViz is an analysis workflow, implemented in a Python script. To conform to best practices for scientific computing which recommend the use of build tools to automate workflows and to reuse code instead of rewriting it, the authors reimplemented this workflow within a workflow management system. To select a workflow management system, a rapid survey of available systems was undertaken, and candidates were shortlisted: Snakemake, cwltool, Toil, and Nextflow. Each candidate was evaluated by quickly prototyping a subset of the RiboViz workflow, and Nextflow was chosen. The selection process took 10 person-days, a small cost for the assurance that Nextflow satisfied the authors' requirements. The use of prototyping can offer a low-cost way of making a more informed selection of software to use within projects, rather than relying solely upon reviews and recommendations by others.


Assuntos
Biologia Computacional/educação , Metodologias Computacionais , Interface Usuário-Computador , Fluxo de Trabalho , Algoritmos , Análise de Dados , Genômica , Idioma , Linguagens de Programação , Reprodutibilidade dos Testes , Ribossomos/fisiologia , Software
13.
PLoS One ; 16(1): e0245943, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33503067

RESUMO

Stochastic computing has recently gained attention due to its low hardware complexity and better fault tolerance against soft errors. However, stochastic computing based circuits suffer from different errors which affect the output accuracy of these circuits. In this paper, an accurate and area-efficient stochastic computing based digital finite impulse response filter is designed. In the proposed work, constant uniform patterns are used as stochastic numbers for the select lines of different MUXes in the filter and the error performance of filter is analysed. Based on the error performance, the combinations of these patterns are proposed for reducing the output error of stochastic computing based filters. The architectures for generating these uniform patterns are also proposed. Results show that the proposed design methodology has better error performance and comparable hardware complexity as compared to the state-of-the-art implementations.


Assuntos
Metodologias Computacionais , Processos Estocásticos , Algoritmos , Computadores , Desenho de Equipamento
14.
Theor Biol Med Model ; 18(1): 1, 2021 01 06.
Artigo em Inglês | MEDLINE | ID: mdl-33407639

RESUMO

BACKGROUND: Stochastic processes leading voltage-gated ion channel dynamics on the nerve cell membrane are a sufficient condition to describe membrane conductance through statistical mechanics of disordered and complex systems. RESULTS: Voltage-gated ion channels in the nerve cell membrane are described by the Ising model. Stochastic circuit elements called "Ising Neural Machines" are introduced. Action potentials are described as quasi-particles of a statistical field theory for the Ising system. CONCLUSIONS: The particle description of action potentials is a new point of view and a powerful tool to describe the generation and propagation of nerve impulses, especially when classical electrophysiological models break down. The particle description of action potentials allows us to develop a new generation of devices to study neurodegenerative and demyelinating diseases as Multiple Sclerosis and Alzheimer's disease, even integrated by connectomes. It is also suitable for the study of complex networks, quantum computing, artificial intelligence, machine and deep learning, cryptography, ultra-fast lines for entanglement experiments and many other applications of medical, physical and engineering interest.


Assuntos
Inteligência Artificial , Modelos Neurológicos , Potenciais de Ação , Metodologias Computacionais , Teoria Quântica
15.
Proc Natl Acad Sci U S A ; 118(6)2021 02 09.
Artigo em Inglês | MEDLINE | ID: mdl-33495364

RESUMO

There has been much success recently in theoretically simulating parts of complex biological systems on the molecular level, with the goal of first-principles modeling of whole cells. However, there is the question of whether such simulations can be performed because of the enormous complexity of cells. We establish approximate equations to estimate computation times required to simulate highly simplified models of cells by either molecular dynamics calculations or by solving molecular kinetic equations. Our equations place limits on the complexity of cells that can be theoretically understood with these two methods and provide a first step in developing what can be considered biological uncertainty relations for molecular models of cells. While a molecular kinetics description of the genetically simplest bacterial cell may indeed soon be possible, neither theoretical description for a multicellular system, such as the human brain, will be possible for many decades and may never be possible even with quantum computing.


Assuntos
Metodologias Computacionais , Cinética , Simulação de Dinâmica Molecular/normas , Teoria Quântica , Humanos , Modelos Biológicos
16.
Mol Biol Evol ; 38(5): 2177-2178, 2021 05 04.
Artigo em Inglês | MEDLINE | ID: mdl-33480999

RESUMO

dadi is a popular but computationally intensive program for inferring models of demographic history and natural selection from population genetic data. I show that running dadi on a Graphics Processing Unit can dramatically speed computation compared with the CPU implementation, with minimal user burden. Motivated by this speed increase, I also extended dadi to four- and five-population models. This functionality is available in dadi version 2.1.0, https://bitbucket.org/gutenkunstlab/dadi/.


Assuntos
Metodologias Computacionais , Genética Populacional/métodos , Modelos Genéticos , Seleção Genética , Software
18.
Am J Ophthalmol ; 223: 333-337, 2021 03.
Artigo em Inglês | MEDLINE | ID: mdl-32738229

RESUMO

PURPOSE: To review the impact of increased digital device usage arising from lockdown measures instituted during the COVID-19 pandemic on myopia and to make recommendations for mitigating potential detrimental effects on myopia control. DESIGN: Perspective. METHODS: We reviewed studies focused on digital device usage, near work, and outdoor time in relation to myopia onset and progression. Public health policies on myopia control, recommendations on screen time, and information pertaining to the impact of COVID-19 on increased digital device use were presented. Recommendations to minimize the impact of the pandemic on myopia onset and progression in children were made. RESULTS: Increased digital screen time, near work, and limited outdoor activities were found to be associated with the onset and progression of myopia, and could potentially be aggravated during and beyond the COVID-19 pandemic outbreak period. While school closures may be short-lived, increased access to, adoption of, and dependence on digital devices could have a long-term negative impact on childhood development. Raising awareness among parents, children, and government agencies is key to mitigating myopigenic behaviors that may become entrenched during this period. CONCLUSION: While it is important to adopt critical measures to slow or halt the spread of COVID-19, close collaboration between parents, schools, and ministries is necessary to assess and mitigate the long-term collateral impact of COVID-19 on myopia control policies.


Assuntos
COVID-19/epidemiologia , Metodologias Computacionais , Miopia/epidemiologia , Quarentena , SARS-CoV-2 , Tempo de Tela , Adolescente , Comportamento do Adolescente/fisiologia , Criança , Comportamento Infantil/fisiologia , Pré-Escolar , Feminino , Humanos , Masculino , Miopia/fisiopatologia , Miopia/prevenção & controle , Guias de Prática Clínica como Assunto , Fatores de Risco , Mídias Sociais
19.
Environ Res ; 193: 110564, 2021 02.
Artigo em Inglês | MEDLINE | ID: mdl-33278473

RESUMO

The wider presence of pharmaceuticals and personal care products in nature is a major cause for concern in society. Among pharmaceuticals, the anti-inflammatory drug ibuprofen has commonly been found in aquatic and soil environments. We produced a Co-doped carbon matrix (Co-P 850) through the carbonization of Co2+ saturated peat and used it as a peroxymonosulphate activator to aid ibuprofen degradation. The properties of Co-P 850 were analysed using field emission scanning electron microscopy, energy filtered transmission electron microscopy and X-ray photoelectron spectroscopy. The characterization results showed that Co/Fe oxides were generated and tightly embedded into the carbon matrix after carbonization. The degradation results indicated that high temperature and slightly acidic to neutral conditions (pH = 5 to 7.5) promoted ibuprofen degradation efficiency in the Co-P 850/peroxymonosulphate system. Analysis showed that approx. 52% and 75% of the dissolved organic carbon was removed after 2 h and 5 h of reaction time, respectively. Furthermore, the existence of chloride and bicarbonate had adverse effects on the degradation of ibuprofen. Quenching experiments and electron paramagnetic resonance analysis confirmed that SO4·-, ·OH and O2·- radicals together contributed to the high ibuprofen degradation efficiency. In addition, we identified 13 degradation intermediate compounds and an ibuprofen degradation pathway by mass spectrometry analysis and quantum computing. Based on the results and methods presented in this study, we propose a novel way for the synthesis of a Co-doped catalyst from spent NaOH-treated peat and the efficient catalytic degradation of ibuprofen from contaminated water.


Assuntos
Ibuprofeno , Poluentes Químicos da Água , Carbono , Catálise , Metodologias Computacionais , Peróxidos , Teoria Quântica , Solo
20.
Methods Mol Biol ; 2231: 39-47, 2021.
Artigo em Inglês | MEDLINE | ID: mdl-33289885

RESUMO

Multiple sequence alignment (MSA) is a central step in many bioinformatics and computational biology analyses. Although there exist many methods to perform MSA, most of them fail when dealing with large datasets due to their high computational cost. MSAProbs-MPI is a publicly available tool ( http://msaprobs.sourceforge.net ) that provides highly accurate results in relatively short runtime thanks to exploiting the hardware resources of multicore clusters. In this chapter, I explain the statistical and biological concepts employed in MSAProbs-MPI to complete the alignments, as well as the high-performance computing techniques used to accelerate it. Moreover, I provide some hints about the configuration parameters that should be used to guarantee high-performance executions.


Assuntos
Biologia Computacional/métodos , Alinhamento de Sequência/métodos , Software , Algoritmos , Biologia Computacional/instrumentação , Metodologias Computacionais , Alinhamento de Sequência/instrumentação
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