RESUMO
Chicken behavior recognition is crucial for a number of reasons, including promoting animal welfare, ensuring the early detection of health issues, optimizing farm management practices, and contributing to more sustainable and ethical poultry farming. In this paper, we introduce a technique for recognizing chicken behavior on edge computing devices based on video sensing mosaicing. Our method combines video sensing mosaicing with deep learning to accurately identify specific chicken behaviors from videos. It attains remarkable accuracy, achieving 79.61% with MobileNetV2 for chickens demonstrating three types of behavior. These findings underscore the efficacy and promise of our approach in chicken behavior recognition on edge computing devices, making it adaptable for diverse applications. The ongoing exploration and identification of various behavioral patterns will contribute to a more comprehensive understanding of chicken behavior, enhancing the scope and accuracy of behavior analysis within diverse contexts.
Assuntos
Criação de Animais Domésticos , Comportamento Animal , Galinhas , Metodologias Computacionais , Criação de Animais Domésticos/instrumentação , Criação de Animais Domésticos/métodos , Gravação em Vídeo , Animais , Aprendizado ProfundoRESUMO
OBJECTIVES: To introduce quantum computing technologies as a tool for biomedical research and highlight future applications within healthcare, focusing on its capabilities, benefits, and limitations. TARGET AUDIENCE: Investigators seeking to explore quantum computing and create quantum-based applications for healthcare and biomedical research. SCOPE: Quantum computing requires specialized hardware, known as quantum processing units, that use quantum bits (qubits) instead of classical bits to perform computations. This article will cover (1) proposed applications where quantum computing offers advantages to classical computing in biomedicine; (2) an introduction to how quantum computers operate, tailored for biomedical researchers; (3) recent progress that has expanded access to quantum computing; and (4) challenges, opportunities, and proposed solutions to integrate quantum computing in biomedical applications.
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Pesquisa Biomédica , Teoria Quântica , Humanos , Atenção à Saúde , Metodologias ComputacionaisRESUMO
Pauli channels are fundamental in the context of quantum computing as they model the simplest kind of noise in quantum devices. We propose a quantum algorithm for simulating Pauli channels and extend it to encompass Pauli dynamical maps (parametrized Pauli channels). A parametrized quantum circuit is employed to accommodate for dynamical maps. We also establish the mathematical conditions for an N-qubit transformation to be achievable using a parametrized circuit where only one single-qubit operation depends on the parameter. The implementation of the proposed circuit is demonstrated using IBM's quantum computers for the case of one qubit, and the fidelity of this implementation is reported.
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Metodologias Computacionais , Miosite de Corpos de Inclusão , Humanos , Teoria Quântica , Algoritmos , Simulação por ComputadorRESUMO
BACKGROUND: Biomarker discovery is a challenging task due to the massive search space. Quantum computing and quantum Artificial Intelligence (quantum AI) can be used to address the computational problem of biomarker discovery from genetic data. METHOD: We propose a Quantum Neural Networks architecture to discover genetic biomarkers for input activation pathways. The Maximum Relevance-Minimum Redundancy criteria score biomarker candidate sets. Our proposed model is economical since the neural solution can be delivered on constrained hardware. RESULTS: We demonstrate the proof of concept on four activation pathways associated with CTLA4, including (1) CTLA4-activation stand-alone, (2) CTLA4-CD8A-CD8B co-activation, (3) CTLA4-CD2 co-activation, and (4) CTLA4-CD2-CD48-CD53-CD58-CD84 co-activation. CONCLUSION: The model indicates new genetic biomarkers associated with the mutational activation of CLTA4-associated pathways, including 20 genes: CLIC4, CPE, ETS2, FAM107A, GPR116, HYOU1, LCN2, MACF1, MT1G, NAPA, NDUFS5, PAK1, PFN1, PGAP3, PPM1G, PSMD8, RNF213, SLC25A3, UBA1, and WLS. We open source the implementation at: https://github.com/namnguyen0510/Biomarker-Discovery-with-Quantum-Neural-Networks .
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Inteligência Artificial , Metodologias Computacionais , Antígeno CTLA-4/genética , Teoria Quântica , Redes Neurais de ComputaçãoRESUMO
Translational bioinformatics (TBI) has transformed healthcare by providing personalized medicine and tailored treatment options by integrating genomic data and clinical information. In recent years, TBI has bridged the gap between genome and clinical data because of significant advances in informatics like quantum computing and utilizing state-of-the-art technologies. This chapter discusses the power of translational bioinformatics in improving human health, from uncovering disease-causing genes and variations to establishing new therapeutic techniques. We discuss key application areas of bioinformatics in clinical genomics, such as data sources and methods used in translational bioinformatics, the impact of translational bioinformatics on human health, and how machine learning and artificial intelligence are being used to mine vast amounts of data for drug development and precision medicine. We also look at the problems, constraints, and ethical concerns connected with exploiting genomic data and the future of translational bioinformatics and its potential impact on medicine and human health. Ultimately, this chapter emphasizes the great potential of translational bioinformatics to alter healthcare and enhance patient outcomes.
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Inteligência Artificial , Metodologias Computacionais , Humanos , Teoria Quântica , Biologia Computacional , GenômicaRESUMO
The International Agency for Research on Cancer (IARC) classifies PFOA as a Class 1 carcinogen. Here, a new naked-eye PFOA immunochromographic strip was developed to recognize PFOA in domestic water and real human samples within 10 min based on a novel custom designed anti-PFOA monoclonal antibody (mAb) 2A3, which was firstly an immune rapid detection method for PFOA has been proposed. Using computer simulation techniques such as quantum computing to assist in designing the structural formula of PFOA semi antigen, which hapten was firstly proposed. The half maximal inhibitory concentration of PFOA monoclonal antibody (mAb) 2A3 was 2.4 µg/mL. Using mAb 2A3, we developed an immunochromatographic strip (ICS) for detecting PFOA in real samples. The developed method generated results in 10 min, with visual detection limits of 20, 20, and 200 µg/mL and limit of detection of 50, 200, and 500 µg/mL for water, blood and urine samples, respectively. The established ICS and indirect competitive enzyme-linked immunosorbent assay were used to analyze the actual samples, and the results were confirmed by LC-MS/MS. Our study findings showed that the ICS and ic-ELISA can quickly detect PFOA in actual samples.
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Caprilatos , Metodologias Computacionais , Fluorocarbonos , Espectrometria de Massas em Tandem , Humanos , Cromatografia Líquida , Simulação por Computador , Teoria Quântica , Anticorpos Monoclonais , Ensaio de Imunoadsorção Enzimática/métodos , Limite de DetecçãoRESUMO
Bioinformatics has revolutionized biology and medicine by using computational methods to analyze and interpret biological data. Quantum mechanics has recently emerged as a promising tool for the analysis of biological systems, leading to the development of quantum bioinformatics. This new field employs the principles of quantum mechanics, quantum algorithms, and quantum computing to solve complex problems in molecular biology, drug design, and protein folding. However, the intersection of bioinformatics, biology, and quantum mechanics presents unique challenges. One significant challenge is the possibility of confusion among scientists between quantum bioinformatics and quantum biology, which have similar goals and concepts. Additionally, the diverse calculations in each field make it difficult to establish boundaries and identify purely quantum effects from other factors that may affect biological processes. This review provides an overview of the concepts of quantum biology and quantum mechanics and their intersection in quantum bioinformatics. We examine the challenges and unique features of this field and propose a classification of quantum bioinformatics to promote interdisciplinary collaboration and accelerate progress. By unlocking the full potential of quantum bioinformatics, this review aims to contribute to our understanding of quantum mechanics in biological systems.
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Metodologias Computacionais , Teoria Quântica , Algoritmos , Biologia Computacional , Desenho de FármacosRESUMO
In the realm of precision medicine, the potential of deep learning is progressively harnessed to facilitate intricate clinical decision-making, especially when navigating multifaceted datasets encompassing Omics, Clinical, image, device, social, and environmental dimensions. This study accentuates the criticality of image data, given its instrumental role in detecting and classifying vision-threatening diabetic retinopathy (VTDR) - a predominant global contributor to vision impairment. The timely identification of VTDR is a linchpin for efficacious interventions and the mitigation of vision loss. Addressing this, This study introduces "NIMEQ-SACNet," a novel hybrid model by the prowess of the Enhanced Quantum-Inspired Binary Grey Wolf Optimizer (EQI-BGWO) with a self-attention capsule network. The proposed approach is characterized by two pivotal advancements: firstly, the augmentation of the Binary Grey Wolf Optimization through Quantum Computing methodologies, and secondly, the deployment of the enhanced EQI-BGWO to adeptly calibrate the SACNet's parameters, culminating in a notable uplift in VTDR classification accuracy. The proposed model's ability to handle binary, 5-stage, and 7-stage VTDR classifications adroitly is noteworthy. Rigorous assessments on the fundus image dataset, underscored by metrics such as Accuracy, Sensitivity, Specificity, Precision, F1-Score, and MCC, bear testament to NIMEQ-SACNet's pre-eminence over prevailing algorithms and classification frameworks.
Assuntos
Diabetes Mellitus , Retinopatia Diabética , Humanos , Retinopatia Diabética/diagnóstico por imagem , Metodologias Computacionais , Medicina de Precisão , Teoria Quântica , AlgoritmosRESUMO
The structural ensembles of intrinsically disordered proteins (IDPs) and proteins with intrinsically disordered regions (IDRs) cannot be easily characterized using conventional experimental techniques. Computational techniques complement experiments and provide useful insights into the structural ensembles of IDPs and proteins with IDRs. Herein, we discuss computational techniques such as homology modeling, molecular dynamics simulations, machine learning with molecular dynamics, and quantum computing that can be applied to the studies of IDPs and hybrid proteins with IDRs. We also provide useful future perspectives for computational techniques that can be applied to IDPs and hybrid proteins containing ordered domains and IDRs.
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Proteínas Intrinsicamente Desordenadas , Proteínas Intrinsicamente Desordenadas/química , Simulação de Dinâmica Molecular , Conformação Proteica , Metodologias Computacionais , Teoria Quântica , Aprendizado de MáquinaRESUMO
Objective. Despite recent advancements in quantum computing, the limited number of available qubits has hindered progress in CT reconstruction. This study investigates the feasibility of utilizing quantum annealing-based computed tomography (QACT) with current quantum bit levels.Approach. The QACT algorithm aims to precisely solve quadratic unconstrained binary optimization problems. Furthermore, a novel approach is proposed to reconstruct images by approximating real numbers using the variational method. This approach allows for accurate CT image reconstruction using a small number of qubits. The study examines the impact of projection data quantity and noise on various image sizes ranging from 4 × 4 to 24 × 24 pixels. The reconstructed results are compared against conventional reconstruction algorithms, namely maximum likelihood expectation maximization (MLEM) and filtered back projection (FBP).Main result. By employing the variational approach and utilizing two qubits for each pixel of the image, accurate reconstruction was achieved with an adequate number of projections. Under conditions of abundant projections and lower noise levels, the image quality in QACT algorithm outperformed that of MLEM and FBP algorithms. However, in situations with limited projection data and in the presence of noise, the image quality in QACT was inferior to that in MLEM.Significance. This study developed the QACT reconstruction algorithm using the variational approach for real-number reconstruction. Remarkably, only 2 qubits were required for each pixel representation, demonstrating their sufficiency for accurate reconstruction.
Assuntos
Metodologias Computacionais , Teoria Quântica , Imagens de Fantasmas , Tomografia Computadorizada por Raios X/métodos , Algoritmos , Processamento de Imagem Assistida por Computador/métodosRESUMO
To simulate whole brain dynamics with only a few equations, biophysical, mesoscopic models of local neuron populations can be connected using empirical tractography data. The development of mesoscopic mean-field models of neural populations, in particular, the Adaptive Exponential (AdEx mean-field model), has successfully summarized neuron-scale phenomena leading to the emergence of global brain dynamics associated with conscious (asynchronous and rapid dynamics) and unconscious (synchronized slow-waves, with Up-and-Down state dynamics) brain states, based on biophysical mechanisms operating at cellular scales (e.g. neuromodulatory regulation of spike-frequency adaptation during sleep-wake cycles or anesthetics). Using the Virtual Brain (TVB) environment to connect mean-field AdEx models, we have previously simulated the general properties of brain states, playing on spike-frequency adaptation, but have not yet performed detailed analyses of other parameters possibly also regulating transitions in brain-scale dynamics between different brain states. We performed a dense grid parameter exploration of the TVB-AdEx model, making use of High Performance Computing. We report a remarkable robustness of the effect of adaptation to induce synchronized slow-wave activity. Moreover, the occurrence of slow waves is often paralleled with a closer relation between functional and structural connectivity. We find that hyperpolarization can also generate unconscious-like synchronized Up and Down states, which may be a mechanism underlying the action of anesthetics. We conclude that the TVB-AdEx model reveals large-scale properties identified experimentally in sleep and anesthesia.
Assuntos
Anestésicos , Encéfalo , Encéfalo/diagnóstico por imagem , Encéfalo/fisiologia , Neurônios/fisiologia , Cabeça , Metodologias Computacionais , Modelos NeurológicosRESUMO
Quantitative structure-activity relationship (QSAR) modelling, an approach that was introduced 60 years ago, is widely used in computer-aided drug design. In recent years, progress in artificial intelligence techniques, such as deep learning, the rapid growth of databases of molecules for virtual screening and dramatic improvements in computational power have supported the emergence of a new field of QSAR applications that we term 'deep QSAR'. Marking a decade from the pioneering applications of deep QSAR to tasks involved in small-molecule drug discovery, we herein describe key advances in the field, including deep generative and reinforcement learning approaches in molecular design, deep learning models for synthetic planning and the application of deep QSAR models in structure-based virtual screening. We also reflect on the emergence of quantum computing, which promises to further accelerate deep QSAR applications and the need for open-source and democratized resources to support computer-aided drug design.
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Aprendizado Profundo , Relação Quantitativa Estrutura-Atividade , Humanos , Inteligência Artificial , Metodologias Computacionais , Teoria Quântica , Descoberta de Drogas/métodos , Desenho de FármacosRESUMO
Modern hydrologic models have extraordinary capabilities for representing complex process in surface-subsurface systems. These capabilities have revolutionized the way we conceptualize flow systems, but how to represent uncertainty in simulated flow systems is not as well developed. Currently, characterizing model uncertainty can be computationally expensive, in part, because the techniques are appended to the numerical methods rather than seamlessly integrated. The next generation of computers, however, presents opportunities to reformulate the modeling problem so that the uncertainty components are handled more directly within the flow system simulation. Misconceptions about quantum computing abound and they will not be a "silver bullet" for solving all complex problems, but they might be leveraged for certain kinds of highly uncertain problems, such as groundwater (GW). The point of this issue paper is that the GW community could try to revise the foundations of our models so that the governing equations being solved are tailored specifically for quantum computers. The goal moving forward should not just be to accelerate the models we have, but also to address their deficiencies. Embedding uncertainty into the models by evolving distribution functions will make predictive GW modeling more complicated, but doing so places the problem into a complexity class that is highly efficient on quantum computing hardware. Next generation GW models could put uncertainty into the problem at the very beginning of a simulation and leave it there throughout, providing a completely new way of simulating subsurface flows.
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Água Subterrânea , Metodologias Computacionais , Teoria Quântica , Simulação por Computador , ComputadoresRESUMO
Modern biological science is trying to solve the fundamental complex problems of molecular biology, which include protein folding, drug discovery, simulation of macromolecular structure, genome assembly, and many more. Currently, quantum computing (QC), a rapidly emerging technology exploiting quantum mechanical phenomena, has developed to address current significant physical, chemical, biological issues, and complex questions. The present review discusses quantum computing technology and its status in solving molecular biology problems, especially in the next-generation computational biology scenario. First, the article explained the basic concept of quantum computing, the functioning of quantum systems where information is stored as qubits, and data storage capacity using quantum gates. Second, the review discussed quantum computing components, such as quantum hardware, quantum processors, and quantum annealing. At the same time, article also discussed quantum algorithms, such as the grover search algorithm and discrete and factorization algorithms. Furthermore, the article discussed the different applications of quantum computing to understand the next-generation biological problems, such as simulation and modeling of biological macromolecules, computational biology problems, data analysis in bioinformatics, protein folding, molecular biology problems, modeling of gene regulatory networks, drug discovery and development, mechano-biology, and RNA folding. Finally, the article represented different probable prospects of quantum computing in molecular biology.
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Metodologias Computacionais , Simulação de Dinâmica Molecular , Teoria Quântica , Dobramento de Proteína , Biologia ComputacionalRESUMO
Molecular aggregates exhibit emergent properties, including the collective sharing of electronic excitation energy known as exciton delocalization, that can be leveraged in applications such as quantum computing, optical information processing, and light harvesting. In a previous study, we found unexpectedly large excitonic interactions (quantified by the excitonic hopping parameter Jm,n) in DNA-templated aggregates of squaraine (SQ) dyes with hydrophilic-imparting sulfo and butylsulfo substituents. Here, we characterize DNA Holliday junction (DNA-HJ) templated aggregates of an expanded set of SQs and evaluate their optical properties in the context of structural heterogeneity. Specifically, we characterized the orientation of and Jm,n between dyes in dimer aggregates of non-chlorinated and chlorinated SQs. Three new chlorinated SQs that feature a varying number of butylsulfo substituents were synthesized and attached to a DNA-HJ via a covalent linker to form adjacent and transverse dimers. Various characteristics of the dye, including its hydrophilicity (in terms of log Po/w) and surface area, and of the substituents, including their local bulkiness and electron withdrawing capacity, were quantified computationally. The orientation of and Jm,n between the dyes were estimated using a model based on Kühn-Renger-May theory to fit the absorption and circular dichroism spectra. The results suggested that adjacent dimer aggregates of all the non-chlorinated and of the most hydrophilic chlorinated SQ dyes exhibit heterogeneity; that is, they form a mixture of dimers subpopulations. A key finding of this work is that dyes with a higher hydrophilicity (lower log Po/w) formed dimers with smaller Jm,n and large center-to-center dye distance (Rm,n). Also, the results revealed that the position of the dye in the DNA-HJ template, that is, adjacent or transverse, impacted Jm,n. Lastly, we found that Jm,n between symmetrically substituted dyes was reduced by increasing the local bulkiness of the substituent. This work provides insights into how to maintain strong excitonic coupling and identifies challenges associated with heterogeneity, which will help to improve control of these dye aggregates and move forward their potential application as quantum information systems.
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Ciclobutanos , DNA Cruciforme , Corantes Fluorescentes , Fenóis , Corantes Fluorescentes/química , Metodologias Computacionais , Teoria Quântica , DNA/química , Interações Hidrofóbicas e HidrofílicasRESUMO
Database peptide search is the primary computational technique for identifying peptides from the mass spectrometry (MS) data. Graphical Processing Units (GPU) computing is now ubiquitous in the current-generation of high-performance computing (HPC) systems, yet its application in the database peptide search domain remains limited. Part of the reason is the use of sub-optimal algorithms in the existing GPU-accelerated methods resulting in significantly inefficient hardware utilization. In this paper, we design and implement a new-age CPU-GPU HPC framework, called GiCOPS, for efficient and complete GPU-acceleration of the modern database peptide search algorithms on supercomputers. Our experimentation shows that the GiCOPS exhibits between 1.2 to 5[Formula: see text] speed improvement over its CPU-only predecessor, HiCOPS, and over 10[Formula: see text] improvement over several existing GPU-based database search algorithms for sufficiently large experiment sizes. We further assess and optimize the performance of our framework using the Roofline Model and report near-optimal results for several metrics including computations per second, occupancy rate, memory workload, branch efficiency and shared memory performance. Finally, the CPU-GPU methods and optimizations proposed in our work for complex integer- and memory-bounded algorithmic pipelines can also be extended to accelerate the existing and future peptide identification algorithms. GiCOPS is now integrated with our umbrella HPC framework HiCOPS and is available at: https://github.com/pcdslab/gicops .
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Algoritmos , Metodologias Computacionais , Computadores , Peptídeos , Espectrometria de MassasRESUMO
MOTIVATION: Advances in genomics and sequencing technologies demand faster and more scalable analysis methods that can process longer sequences with higher accuracy. However, classical pairwise alignment methods, based on dynamic programming (DP), impose impractical computational requirements to align long and noisy sequences like those produced by PacBio and Nanopore technologies. The recently proposed wavefront alignment (WFA) algorithm paves the way for more efficient alignment tools, improving time and memory complexity over previous methods. However, high-performance computing (HPC) platforms require efficient parallel algorithms and tools to exploit the computing resources available on modern accelerator-based architectures. RESULTS: This paper presents WFA-GPU, a GPU (graphics processing unit)-accelerated tool to compute exact gap-affine alignments based on the WFA algorithm. We present the algorithmic adaptations and performance optimizations that allow exploiting the massively parallel capabilities of modern GPU devices to accelerate the alignment computations. In particular, we propose a CPU-GPU co-design capable of performing inter-sequence and intra-sequence parallel sequence alignment, combining a succinct WFA-data representation with an efficient GPU implementation. As a result, we demonstrate that our implementation outperforms the original multi-threaded WFA implementation by up to 4.3× and up to 18.2× when using heuristic methods on long and noisy sequences. Compared to other state-of-the-art tools and libraries, the WFA-GPU is up to 29× faster than other GPU implementations and up to four orders of magnitude faster than other CPU implementations. Furthermore, WFA-GPU is the only GPU solution capable of correctly aligning long reads using a commodity GPU. AVAILABILITY AND IMPLEMENTATION: WFA-GPU code and documentation are publicly available at https://github.com/quim0/WFA-GPU.
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Algoritmos , Software , Análise de Sequência , Metodologias Computacionais , GenômicaRESUMO
Biomedical Microelectromechanical Systems (BioMEMS) serve as a crucial catalyst in enhancing IoT communication security and safeguarding smart healthcare systems. Situated at the nexus of advanced technology and healthcare, BioMEMS are instrumental in pioneering personalized diagnostics, monitoring, and therapeutic applications. Nonetheless, this integration brings forth a complex array of security and privacy challenges intrinsic to IoT communications within smart healthcare ecosystems, demanding comprehensive scrutiny. In this manuscript, we embark on an extensive analysis of the intricate security terrain associated with IoT communications in the realm of BioMEMS, addressing a spectrum of vulnerabilities that spans cyber threats, data manipulation, and interception of communications. The integration of real-world case studies serves to illuminate the direct repercussions of security breaches within smart healthcare systems, highlighting the imperative to safeguard both patient safety and the integrity of medical data. We delve into a suite of security solutions, encompassing rigorous authentication processes, data encryption, designs resistant to attacks, and continuous monitoring mechanisms, all tailored to fortify BioMEMS in the face of ever-evolving threats within smart healthcare environments. Furthermore, the paper underscores the vital role of ethical and regulatory considerations, emphasizing the need to uphold patient autonomy, ensure the confidentiality of data, and maintain equitable access to healthcare in the context of IoT communication security. Looking forward, we explore the impending landscape of BioMEMS security as it intertwines with emerging technologies such as AI-driven diagnostics, quantum computing, and genomic integration, anticipating potential challenges and strategizing for the future. In doing so, this paper highlights the paramount importance of adopting an integrated approach that seamlessly blends technological innovation, ethical foresight, and collaborative ingenuity, thereby steering BioMEMS towards a secure and resilient future within smart healthcare systems, in the ambit of IoT communication security and protection.
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Sistemas Microeletromecânicos , Privacidade , Humanos , Metodologias Computacionais , Ecossistema , Teoria Quântica , Comunicação , Atenção à Saúde , Segurança ComputacionalRESUMO
Programmable self-assembly of dyes using DNA templates to promote exciton delocalization in dye aggregates is gaining considerable interest. New methods to improve the rigidity of the DNA scaffold and thus the stability of the molecular dye aggregates to encourage exciton delocalization are desired. In these dye-DNA constructs, one potential way to increase the stability of the aggregates is to create an additional covalent bond via photo-cross-linking reactions between thymines in the DNA scaffold. Specifically, we report an approach to increase the yield of photo-cross-linking reaction between thymines in the core of a DNA Holliday junction while limiting the damage from UV irradiation to DNA. We investigated the effect of the distance between thymines on the photo-cross-linking reaction yields by using linkers with different lengths to tether the dyes to the DNA templates. By comprehensively evaluating the photo-cross-linking reaction yields of dye-DNA aggregates using linkers with different lengths, we conclude that interstrand thymines tend to photo-cross-link more efficiently with short linkers. A higher cross-linking yield was achieved due to the shorter intermolecular distance between thymines influenced by strong dye-dye interactions. Our method establishes the possibility of improving the stability of DNA-scaffolded dye aggregates, thereby expanding their use in exciton-based applications such as light harvesting, nanoscale computing, quantum computing, and optoelectronics.
Assuntos
DNA Cruciforme , Timina , Metodologias Computacionais , Teoria Quântica , DNA/química , CorantesRESUMO
A bacteriochlorophyll a (Bchla) dimer is a basic functional unit in the LH1 and LH2 photosynthetic pigment-protein antenna complexes of purple bacteria, where an ordered, close arrangement of Bchla pigments-secured by noncovalent bonding to a protein template-enables exciton delocalization at room temperature. Stable and tunable synthetic analogs of this key photosynthetic subunit could lead to facile engineering of exciton-based systems such as in artificial photosynthesis, organic optoelectronics, and molecular quantum computing. Here, using a combination of synthesis and theory, we demonstrate that exciton delocalization can be achieved in a dimer of a synthetic bacteriochlorin (BC) featuring stability, high structural modularity, and spectral properties advantageous for exciton-based devices. The BC dimer was covalently templated by DNA, a stable and highly programmable scaffold. To achieve exciton delocalization in the absence of pigment-protein interactions critical for the Bchla dimer, we relied on the strong transition dipole moment in BC enabled by two auxochromes along the Qy transition, and omitting the central metal and isocyclic ring. The spectral properties of the synthetic "free" BC closely resembled those of Bchla in an organic solvent. Applying spectroscopic modeling, the exciton delocalization in the DNA-templated BC dimer was evaluated by extracting the excitonic hopping parameter, J to be 214 cm-1 (26.6 meV). For comparison, the same method applied to the natural protein-templated Bchla dimer yielded J of 286 cm-1 (35.5 meV). The smaller value of J in the BC dimer likely arose from the partial bacteriochlorin intercalation and the difference in medium effect between DNA and protein.