Journal articles: 'Ensembles de Llgnes 3D'

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Krishnamoorthy, Kothandam, and Cynthia G. Zoski. "Fabrication of 3D Gold Nanoelectrode Ensembles by Chemical Etching." Analytical Chemistry 77, no. 15 (August 2005): 5068–71. http://dx.doi.org/10.1021/ac050604r.

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Spettl, Aaron, Thomas Werz, Carl E. Krill, and Volker Schmidt. "Parametric Representation of 3D Grain Ensembles in Polycrystalline Microstructures." Journal of Statistical Physics 154, no. 4 (December 3, 2013): 913–28. http://dx.doi.org/10.1007/s10955-013-0893-7.

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CAO, Li-Xin, Pei-Sheng YAN, Ke-Ning SUN, and W. Donald KIRK. "Development and Evaluation of Gold 3D Cylindrical Nanoelectrode Ensembles." Chinese Journal of Chemistry 25, no. 11 (November 2007): 1754–57. http://dx.doi.org/10.1002/cjoc.200790324.

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Gangaraju, Deepa, Sridhar Vadahanambi, and Hyun Park. "Correction: 3D graphene–carbon nanotube–nickel ensembles as anodes in sodium-ion batteries." RSC Advances 6, no. 106 (2016): 104665. http://dx.doi.org/10.1039/c6ra90109c.

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De Leo, Manuela, Alexander Kuhn, and Paolo Ugo. "3D-Ensembles of Gold Nanowires: Preparation, Characterization and Electroanalytical Peculiarities." Electroanalysis 19, no. 2-3 (January 2007): 227–36. http://dx.doi.org/10.1002/elan.200603724.

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Di Pierro, Michele, Ryan R. Cheng, Erez Lieberman Aiden, Peter G. Wolynes, and José N. Onuchic. "De novo prediction of human chromosome structures: Epigenetic marking patterns encode genome architecture." Proceedings of the National Academy of Sciences 114, no. 46 (October 31, 2017): 12126–31. http://dx.doi.org/10.1073/pnas.1714980114.

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Inside the cell nucleus, genomes fold into organized structures that are characteristic of cell type. Here, we show that this chromatin architecture can be predicted de novo using epigenetic data derived from chromatin immunoprecipitation-sequencing (ChIP-Seq). We exploit the idea that chromosomes encode a 1D sequence of chromatin structural types. Interactions between these chromatin types determine the 3D structural ensemble of chromosomes through a process similar to phase separation. First, a neural network is used to infer the relation between the epigenetic marks present at a locus, as assayed by ChIP-Seq, and the genomic compartment in which those loci reside, as measured by DNA-DNA proximity ligation (Hi-C). Next, types inferred from this neural network are used as an input to an energy landscape model for chromatin organization [Minimal Chromatin Model (MiChroM)] to generate an ensemble of 3D chromosome conformations at a resolution of 50 kilobases (kb). After training the model, dubbed Maximum Entropy Genomic Annotation from Biomarkers Associated to Structural Ensembles (MEGABASE), on odd-numbered chromosomes, we predict the sequences of chromatin types and the subsequent 3D conformational ensembles for the even chromosomes. We validate these structural ensembles by using ChIP-Seq tracks alone to predict Hi-C maps, as well as distances measured using 3D fluorescence in situ hybridization (FISH) experiments. Both sets of experiments support the hypothesis of phase separation being the driving process behind compartmentalization. These findings strongly suggest that epigenetic marking patterns encode sufficient information to determine the global architecture of chromosomes and that de novo structure prediction for whole genomes may be increasingly possible.

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Heinrich, Julian, Michael Krone, Seán I. O'Donoghue, and Daniel Weiskopf. "Visualising intrinsic disorder and conformational variation in protein ensembles." Faraday Discuss. 169 (2014): 179–93. http://dx.doi.org/10.1039/c3fd00138e.

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Intrinsically disordered regions (IDRs) in proteins are still not well understood, but are increasingly recognised as important in key biological functions, as well as in diseases. IDRs often confound experimental structure determination—however, they are present in many of the available 3D structures, where they exhibit a wide range of conformations, from ill-defined and highly flexible to well-defined upon binding to partner molecules, or upon post-translational modifications. Analysing such large conformational variations across ensembles of 3D structures can be complex and difficult; our goal in this paper is to improve this situation by augmenting traditional approaches (molecular graphics and principal components) with methods from human–computer interaction and information visualisation, especially parallel coordinates. We present a new tool integrating these approaches, and demonstrate how it can dissect ensembles to reveal functional insights into conformational variation and intrinsic disorder.

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Wang, Shuang, Xiaolin Xie, Zhi Chen, Ningning Ma, Xue Zhang, Kai Li, Chao Teng, Yonggang Ke, and Ye Tian. "DNA-Grafted 3D Superlattice Self-Assembly." International Journal of Molecular Sciences 22, no. 14 (July 15, 2021): 7558. http://dx.doi.org/10.3390/ijms22147558.

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The exploitation of new methods to control material structure has historically been dominating the material science. The bottom-up self-assembly strategy by taking atom/molecule/ensembles in nanoscale as building blocks and crystallization as a driving force bring hope for material fabrication. DNA-grafted nanoparticle has emerged as a “programmable atom equivalent” and was employed for the assembly of hierarchically ordered three-dimensional superlattice with novel properties and studying the unknown assembly mechanism due to its programmability and versatility in the binding capabilities. In this review, we highlight the assembly strategies and rules of DNA-grafted three-dimensional superlattice, dynamic assembly by different driving factors, and discuss their future applications.

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Ayyer, Kartik, P. Lourdu Xavier, Johan Bielecki, Zhou Shen, Benedikt J. Daurer, Amit K. Samanta, Salah Awel, et al. "3D diffractive imaging of nanoparticle ensembles using an x-ray laser." Optica 8, no. 1 (December 24, 2020): 15. http://dx.doi.org/10.1364/optica.410851.

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Renner, Steffen, Mirko Hechenberger, Tobias Noeske, Alexander Böcker, Claudia Jatzke, Michael Schmuker, Christopher G Parsons, Tanja Weil, and Gisbert Schneider. "Suche nach Wirkstoff-Grundgerüsten mit 3D-Pharmakophorhypothesen und Ensembles neuronaler Netze." Angewandte Chemie 119, no. 28 (July 9, 2007): 5432–35. http://dx.doi.org/10.1002/ange.200604125.

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Renner, Steffen, Mirko Hechenberger, Tobias Noeske, Alexander Böcker, Claudia Jatzke, Michael Schmuker, Christopher G Parsons, Tanja Weil, and Gisbert Schneider. "Searching for Drug Scaffolds with 3D Pharmacophores and Neural Network Ensembles." Angewandte Chemie International Edition 46, no. 28 (July 9, 2007): 5336–39. http://dx.doi.org/10.1002/anie.200604125.

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Janson, Giacomo, and Michael Feig. "Transferable deep generative modeling of intrinsically disordered protein conformations." PLOS Computational Biology 20, no. 5 (May 23, 2024): e1012144. http://dx.doi.org/10.1371/journal.pcbi.1012144.

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Intrinsically disordered proteins have dynamic structures through which they play key biological roles. The elucidation of their conformational ensembles is a challenging problem requiring an integrated use of computational and experimental methods. Molecular simulations are a valuable computational strategy for constructing structural ensembles of disordered proteins but are highly resource-intensive. Recently, machine learning approaches based on deep generative models that learn from simulation data have emerged as an efficient alternative for generating structural ensembles. However, such methods currently suffer from limited transferability when modeling sequences and conformations absent in the training data. Here, we develop a novel generative model that achieves high levels of transferability for intrinsically disordered protein ensembles. The approach, named idpSAM, is a latent diffusion model based on transformer neural networks. It combines an autoencoder to learn a representation of protein geometry and a diffusion model to sample novel conformations in the encoded space. IdpSAM was trained on a large dataset of simulations of disordered protein regions performed with the ABSINTH implicit solvent model. Thanks to the expressiveness of its neural networks and its training stability, idpSAM faithfully captures 3D structural ensembles of test sequences with no similarity in the training set. Our study also demonstrates the potential for generating full conformational ensembles from datasets with limited sampling and underscores the importance of training set size for generalization. We believe that idpSAM represents a significant progress in transferable protein ensemble modeling through machine learning.

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Callegari, Francesca, Martina Brofiga, and Paolo Massobrio. "Modeling the three-dimensional connectivity of in vitro cortical ensembles coupled to Micro-Electrode Arrays." PLOS Computational Biology 19, no. 2 (February 13, 2023): e1010825. http://dx.doi.org/10.1371/journal.pcbi.1010825.

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Nowadays, in vitro three-dimensional (3D) neuronal networks are becoming a consolidated experimental model to overcome most of the intrinsic limitations of bi-dimensional (2D) assemblies. In the 3D environment, experimental evidence revealed a wider repertoire of activity patterns, characterized by a modulation of the bursting features, than the one observed in 2D cultures. However, it is not totally clear and understood what pushes the neuronal networks towards different dynamical regimes. One possible explanation could be the underlying connectivity, which could involve a larger number of neurons in a 3D rather than a 2D space and could organize following well-defined topological schemes. Driven by experimental findings, achieved by recording 3D cortical networks organized in multi-layered structures coupled to Micro-Electrode Arrays (MEAs), in the present work we developed a large-scale computational network model made up of leaky integrate-and-fire (LIF) neurons to investigate possible structural configurations able to sustain the emerging patterns of electrophysiological activity. In particular, we investigated the role of the number of layers defining a 3D assembly and the spatial distribution of the connections within and among the layers. These configurations give rise to different patterns of activity that could be compared to the ones emerging from real in vitro 3D neuronal populations. Our results suggest that the introduction of three-dimensionality induced a global reduction in both firing and bursting rates with respect to 2D models. In addition, we found that there is a minimum number of layers necessary to obtain a change in the dynamics of the network. However, the effects produced by a 3D organization of the cells is somewhat mitigated if a scale-free connectivity is implemented in either one or all the layers of the network. Finally, the best matching of the experimental data is achieved supposing a 3D connectivity organized in structured bundles of links located in different areas of the 2D network.

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Gangaraju, Deepa, Sridhar Vadahanambi, and Hyun Park. "3D graphene–carbon nanotube–nickel ensembles as anodes in sodium-ion batteries." RSC Advances 6, no. 102 (2016): 99914–18. http://dx.doi.org/10.1039/c6ra15069a.

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Spitz, François. "Gene regulation at a distance: From remote enhancers to 3D regulatory ensembles." Seminars in Cell & Developmental Biology 57 (September 2016): 57–67. http://dx.doi.org/10.1016/j.semcdb.2016.06.017.

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Mollamahale, Y. Bahari, Mohammad Ghorbani, Masoumeh Ghalkhani, Manouchehr Vossoughi, and Abolghasem Dolati. "Highly sensitive 3D gold nanotube ensembles: Application to electrochemical determination of metronidazole." Electrochimica Acta 106 (September 2013): 288–92. http://dx.doi.org/10.1016/j.electacta.2013.05.084.

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Lenz, Samuel, David Hunger, and Joris van Slageren. "Strong coupling between resonators and spin ensembles in the presence of exchange couplings." Chemical Communications 56, no. 84 (2020): 12837–40. http://dx.doi.org/10.1039/d0cc04841k.

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He, Yi, Suhani Nagpal, Mourad Sadqi, Eva de Alba, and Victor Muñoz. "Glutton: a tool for generating structural ensembles of partly disordered proteins from chemical shifts." Bioinformatics 35, no. 7 (September 4, 2018): 1234–36. http://dx.doi.org/10.1093/bioinformatics/bty755.

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Abstract Motivation Many proteins are partially disordered in physiological conditions and only fold, fully or partially, upon binding. Their structural analysis is challenging because the accessible information, typically chemical shifts (CS) from nuclear magnetic resonance experiments, are averages over broad ensembles of conformations. We aim to develop a database for the analysis of such data in terms of conformational distributions of the protein backbone rather than of individual high-resolution structures. Results Glutton is the largest available database linking CS and protein 3D structures (5270 entries organized in three levels) and is searchable via a python script. It generates statistical distributions of ϕ−ψ dihedral angles based on CS or vice versa. Such ϕ−ψ distributions are used to calculate structural ensembles of partially disordered proteins from their CS. For folded proteins, such ensembles are excellent starting points for further refinement with additional experimental restraints (structure determination) or computational methods (structure prediction). Availability and implementation Glutton is freely available at https://github.com/YeeHo/Glutton. Supplementary information Supplementary data are available at Bioinformatics online.

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Kurta, R. P., M. Altarelli, and I. A. Vartanyants. "X-Ray Cross-Correlation Analysis of Disordered Ensembles of Particles: Potentials and Limitations." Advances in Condensed Matter Physics 2013 (2013): 1–15. http://dx.doi.org/10.1155/2013/959835.

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Angular X-ray cross-correlation analysis (XCCA) is an approach to study the structure of disordered systems using the results of X-ray scattering experiments. In this paper we summarize recent theoretical developments related to the Fourier analysis of the cross-correlation functions. Results of our simulations demonstrate the application of XCCA to two- and three-dimensional (2D and 3D) disordered ensembles of particles. We show that the structure of a single particle can be recovered using X-ray data collected from a 2D disordered system of identical particles. We also demonstrate that valuable structural information about the local structure of 3D systems, inaccessible from a standard small-angle X-ray scattering experiment, can be resolved using XCCA.

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Špelić, Ivana, Dubravko Rogale, and Alka Mihelić Bogdanić. "The Study on Effects of Walking on the Thermal Properties of Clothing and Subjective Comfort." Autex Research Journal 20, no. 3 (September 18, 2020): 228–43. http://dx.doi.org/10.2478/aut-2019-0016.

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AbstractFormer studies done by other authors investigated the first- and second-layered air gaps beneath the clothing garments. None of the previous studies reported multidisciplinary clothing design testing approach linking both the objective measuring methods and subjective responses, while testing the thermal properties linked to a microclimatic volume formed between the layers of garments forming the ensemble. Neither was determined the limiting value of the microclimatic volume for outerwear garments, after which the thermal insulation will start to decrease due to convection. By taking the advantage of the precise three-dimensional (3D) body scanning technology and reverse engineering 3D CAD tool, the volume of the microclimatic air layers formed under outerwear garments was determined to study the impact of the ensemble’s microclimatic volume on the overall insulation value, measured by means of the thermal manikin. The jacket with the smaller microclimatic volume provided 5.2–13.5% less insulation than wider jackets, while the ensembles with tighter jackets showed 0.74–1.9% less insulation in static and 0.9–2.7% more insulation in dynamic conditions, thus proving that the limiting value of the microclimatic volume is greater than previously reported for three-layered ensembles. The effective thermal insulation value was reduced in average by 20.98–25.34% between standing and moving manikins. The thermal manikins are designed for steady-state measurements and do not work well under transient conditions, so three human subjects were employed as evaluators of the clothing thermal quality. In cooler climatic conditions, the measured physiological parameters and subjects’ grades pointed to discomfort while wearing ensembles with tighter jackets.

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Hu, Haifeng, Lixin Cao, Qingchuan Li, Kan Ma, Peisheng Yan, and Donald W. Kirk. "Fabrication and modeling of an ultrasensitive label free impedimetric immunosensor for Aflatoxin B1based on poly(o-phenylenediamine) modified gold 3D nano electrode ensembles." RSC Advances 5, no. 68 (2015): 55209–17. http://dx.doi.org/10.1039/c5ra06300k.

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An ultrasensitive label free impedimetric immunosensor for AFB₁detection was fabricated based on poly(o-phenylenediamine) (PoPD) electropolymerized film modified gold three dimensional nanoelectrode ensembles (3DNEEs).

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Fonseca, Rasmus, Dimitar V. Pachov, Julie Bernauer, and Henry van den Bedem. "Characterizing RNA ensembles from NMR data with kinematic models." Nucleic Acids Research 42, no. 15 (August 11, 2014): 9562–72. http://dx.doi.org/10.1093/nar/gku707.

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Abstract Functional mechanisms of biomolecules often manifest themselves precisely in transient conformational substates. Researchers have long sought to structurally characterize dynamic processes in non-coding RNA, combining experimental data with computer algorithms. However, adequate exploration of conformational space for these highly dynamic molecules, starting from static crystal structures, remains challenging. Here, we report a new conformational sampling procedure, KGSrna, which can efficiently probe the native ensemble of RNA molecules in solution. We found that KGSrna ensembles accurately represent the conformational landscapes of 3D RNA encoded by NMR proton chemical shifts. KGSrna resolves motionally averaged NMR data into structural contributions; when coupled with residual dipolar coupling data, a KGSrna ensemble revealed a previously uncharacterized transient excited state of the HIV-1 trans-activation response element stem–loop. Ensemble-based interpretations of averaged data can aid in formulating and testing dynamic, motion-based hypotheses of functional mechanisms in RNAs with broad implications for RNA engineering and therapeutic intervention.

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Akl, Hoda, Brooke Emison, Xiaochuan Zhao, Arup Mondal, Alberto Perez, and Purushottam D. Dixit. "GENERALIST: A latent space based generative model for protein sequence families." PLOS Computational Biology 19, no. 11 (November 27, 2023): e1011655. http://dx.doi.org/10.1371/journal.pcbi.1011655.

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Generative models of protein sequence families are an important tool in the repertoire of protein scientists and engineers alike. However, state-of-the-art generative approaches face inference, accuracy, and overfitting- related obstacles when modeling moderately sized to large proteins and/or protein families with low sequence coverage. Here, we present a simple to learn, tunable, and accurate generative model, GENERALIST: GENERAtive nonLInear tenSor-factorizaTion for protein sequences. GENERALIST accurately captures several high order summary statistics of amino acid covariation. GENERALIST also predicts conservative local optimal sequences which are likely to fold in stable 3D structure. Importantly, unlike current methods, the density of sequences in GENERALIST-modeled sequence ensembles closely resembles the corresponding natural ensembles. Finally, GENERALIST embeds protein sequences in an informative latent space. GENERALIST will be an important tool to study protein sequence variability.

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Wu, Jiong, and Xiaoying Tang. "Brain segmentation based on multi-atlas and diffeomorphism guided 3D fully convolutional network ensembles." Pattern Recognition 115 (July 2021): 107904. http://dx.doi.org/10.1016/j.patcog.2021.107904.

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Bahari Mollamahalle, Yaser, Mohammad Ghorbani, and Abolghasem Dolati. "Electrodeposition of long gold nanotubes in polycarbonate templates as highly sensitive 3D nanoelectrode ensembles." Electrochimica Acta 75 (July 2012): 157–63. http://dx.doi.org/10.1016/j.electacta.2012.04.119.

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Weeks, Abbie, and Brett Byram. "Exploring the benefits of spatial and temporal block-wise filtering architectures." Journal of the Acoustical Society of America 152, no. 4 (October 2022): A280. http://dx.doi.org/10.1121/10.0016270.

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Spatial block-wise filtering architectures improve noise stationarity locally, making SVD-based filtering more robust (Song et al., 2017). Temporal block-wise filtering has been proposed to augment spatial block-wise methods for 3D Doppler data acquired via mechanical translation (Chen et al., 2021) and compensate for bulk motion in cardiac power Doppler imaging (Zhang et al., 2021). We combine spatial and temporal block-wise architectures to suppress noise through depth and better capture time-varying signals with power Doppler. We show that a combination of spatial and temporal block-wise methods provides improved CNR (3.9 dB gain), SNR (0.9 dB gain), and CR (11.7 dB gain) over spatial block-wise alone and propose two methods for selecting temporal block sizes. First, we select temporal ensembles from systole, early-, and late-diastole from matched ECG data. Second, we derive a temporal SVD cutoff between the blood and noise subspaces for the entire ensemble. We use that cutoff as a proxy for correlation length and select ensembles accordingly. We collected 2 s of liver data from a healthy volunteer (nine angled plane waves at 600 Hz PRF and 4.1667 MHz center frequency) and applied spatial and temporal block-wise SVD filtering with 100-sample ensembles and 50-sample temporal block sizes overlapped at 20%.

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Cofaru, Ileana Ioana, Paul Dan Brîndaşu, and Nicolae Florin Cofaru. "Designing a Specialized Devices for Correction of the Axis Deviation at the Human Leg." Applied Mechanics and Materials 371 (August 2013): 662–66. http://dx.doi.org/10.4028/www.scientific.net/amm.371.662.

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The presented article is an original contribution which implies the design of a specialized device for high tybial osteotomy (HTO) optimization. In the papers are presented the sources that imply HTO, the axial deviations of the human lower member from the correct position. This sources are the gonarthosys owed to the usage of the knees cartilages or owed to some diseases (such as the Blount disease) and the vicious consolidated fractures. After presenting the needs to realize such a specialized device for HTO, the principal requests the device shall fulfill are underlined. Starting from these , two sub ensembles were projected, one for the tibial positioning and for the execution of the hole from the center of rotation of angulation (CORA), and the second one for the realization of the cuts. Both sub ensembles have been 3D modeled in Catia V5R20. The model was designed mounted on the bone.

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SONI, AMEET, and JUDE SHAVLIK. "PROBABILISTIC ENSEMBLES FOR IMPROVED INFERENCE IN PROTEIN-STRUCTURE DETERMINATION." Journal of Bioinformatics and Computational Biology 10, no. 01 (February 2012): 1240009. http://dx.doi.org/10.1142/s0219720012400094.

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Protein X-ray crystallography — the most popular method for determining protein structures — remains a laborious process requiring a great deal of manual crystallographer effort to interpret low-quality protein images. Automating this process is critical in creating a high-throughput protein-structure determination pipeline. Previously, our group developed ACMI, a probabilistic framework for producing protein-structure models from electron-density maps produced via X-ray crystallography. ACMI uses a Markov Random Field to model the three-dimensional (3D) location of each non-hydrogen atom in a protein. Calculating the best structure in this model is intractable, so ACMI uses approximate inference methods to estimate the optimal structure. While previous results have shown ACMI to be the state-of-the-art method on this task, its approximate inference algorithm remains computationally expensive and susceptible to errors. In this work, we develop Probabilistic Ensembles in ACMI (PEA), a framework for leveraging multiple, independent runs of approximate inference to produce estimates of protein structures. Our results show statistically significant improvements in the accuracy of inference resulting in more complete and accurate protein structures. In addition, PEA provides a general framework for advanced approximate inference methods in complex problem domains.

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Rangan, Ramya, Andrew M. Watkins, Jose Chacon, Rachael Kretsch, Wipapat Kladwang, Ivan N. Zheludev, Jill Townley, Mats Rynge, Gregory Thain, and Rhiju Das. "De novo3D models of SARS-CoV-2 RNA elements from consensus experimental secondary structures." Nucleic Acids Research 49, no. 6 (March 8, 2021): 3092–108. http://dx.doi.org/10.1093/nar/gkab119.

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AbstractThe rapid spread of COVID-19 is motivating development of antivirals targeting conserved SARS-CoV-2 molecular machinery. The SARS-CoV-2 genome includes conserved RNA elements that offer potential small-molecule drug targets, but most of their 3D structures have not been experimentally characterized. Here, we provide a compilation of chemical mapping data from our and other labs, secondary structure models, and 3D model ensembles based on Rosetta's FARFAR2 algorithm for SARS-CoV-2 RNA regions including the individual stems SL1-8 in the extended 5′ UTR; the reverse complement of the 5′ UTR SL1-4; the frameshift stimulating element (FSE); and the extended pseudoknot, hypervariable region, and s2m of the 3′ UTR. For eleven of these elements (the stems in SL1–8, reverse complement of SL1–4, FSE, s2m and 3′ UTR pseudoknot), modeling convergence supports the accuracy of predicted low energy states; subsequent cryo-EM characterization of the FSE confirms modeling accuracy. To aid efforts to discover small molecule RNA binders guided by computational models, we provide a second set of similarly prepared models for RNA riboswitches that bind small molecules. Both datasets (‘FARFAR2-SARS-CoV-2’, https://github.com/DasLab/FARFAR2-SARS-CoV-2; and ‘FARFAR2-Apo-Riboswitch’, at https://github.com/DasLab/FARFAR2-Apo-Riboswitch’) include up to 400 models for each RNA element, which may facilitate drug discovery approaches targeting dynamic ensembles of RNA molecules.

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KHAKI, MILAD, MEGAN ROUSSY, NASIM MORTAZAVI, ROGELIO LUNA, ADAM SACHS, and JULIO MARTINEZ-TRUJILLO. "Using decoders to understand working memory representations of 3D space in primate prefrontal neuronal ensembles." Journal of Vision 20, no. 11 (October 20, 2020): 1474. http://dx.doi.org/10.1167/jov.20.11.1474.

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Fosco, C. D., and L. E. Oxman. "A non Abelian effective model for ensembles of magnetic defects in 3D Yang–Mills theory." Journal of Physics A: Mathematical and Theoretical 46, no. 33 (July 29, 2013): 335401. http://dx.doi.org/10.1088/1751-8113/46/33/335401.

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Cao, Lixin, Peisheng Yan, Kening Sun, and Donald W Kirk. "Gold 3D Brush Nanoelectrode Ensembles with Enlarged Active Area for the Direct Voltammetry of Daunorubicin." Electroanalysis 21, no. 10 (May 2009): 1183–88. http://dx.doi.org/10.1002/elan.200804526.

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Forcellini, Davide, Marco Tanganelli, and Stefania Viti. "Response Site Analyses of 3D Homogeneous Soil Models." Emerging Science Journal 2, no. 5 (November 4, 2018): 238. http://dx.doi.org/10.28991/esj-2018-01148.

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The seismic excitation at the surface can be determined through Site Response Analyses (SRA) as to account for the specific soil properties of the site. However, the obtained results are largely affected by the model choice and setting, and by the depth of the considered soil layer. This paper proposes a refined 3D analytical approach, by the application of OPENSEES platform. A preliminary analysis has been performed to check the model adequacy as regards the mesh geometry and the boundary conditions. After the model setting, a SRA has been performed on various soil profiles, differing for the shear velocity and representing the different soil classes as proposed by the Eurocode 8 (EC8). Three levels of seismic hazard have been considered. The seismic input at the bedrock has been represented consequently, through as much ensembles of seven ground motions each, spectrum-compatible to the elastic spectra provided by EC8 for the soil-type A (bedrock). Special attention has been paid to the role of the considered soil depth on the evaluation of the surface seismic input. Different values of depth have been considered for each soil type and seismic intensity, in order to check its effect on the obtained results.

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Puzyrev, Dmitry, Kirsten Harth, Torsten Trittel, and Ralf Stannarius. "Machine Learning for 3D Particle Tracking in Granular Gases." Microgravity Science and Technology 32, no. 5 (July 18, 2020): 897–906. http://dx.doi.org/10.1007/s12217-020-09800-4.

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Abstract Dilute ensembles of granular matter (so-called granular gases) are nonlinear systems which exhibit fascinating dynamical behavior far from equilibrium, including non-Gaussian distributions of velocities and rotational velocities, clustering, and violation of energy equipartition. In order to understand their dynamic properties, microgravity experiments were performed in suborbital flights and drop tower experiments. Up to now, the experimental images were evaluated mostly manually. Here, we introduce an approach for automatic 3D tracking of positions and orientations of rod-like particles in a dilute ensemble, based on two-view video data analysis. A two-dimensional (2D) localization of particles is performed using a Mask R-CNN neural network trained on a custom data set. The problem of 3D matching of the particles is solved by minimization of the total reprojection error, and finally, particle trajectories are tracked so that ensemble statistics are extracted. Depending on the required accuracy, the software can work fully self-sustainingly or serve as a base for subsequent manual corrections. The approach can be extended to other 3D and 2D particle tracking problems.

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Cui, Yinan, and Nasr Ghoniem. "Influence of Size on the Fractal Dimension of Dislocation Microstructure." Metals 9, no. 4 (April 25, 2019): 478. http://dx.doi.org/10.3390/met9040478.

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Three-dimensional (3D) discrete dislocation dynamics simulations are used to analyze the size effect on the fractal dimension of two-dimensional (2D) and 3D dislocation microstructure. 2D dislocation structures are analyzed first, and the calculated fractal dimension ( n 2 ) is found to be consistent with experimental results gleaned from transmission electron microscopy images. The value of n 2 is found to be close to unity for sizes smaller than 300 nm, and increases to a saturation value of ≈1.8 for sizes above approximately 10 microns. It is discovered that reducing the sample size leads to a decrease in the fractal dimension because of the decrease in the likelihood of forming strong tangles at small scales. Dislocation ensembles are found to exist in a more isolated way at the nano- and micro-scales. Fractal analysis is carried out on 3D dislocation structures and the 3D fractal dimension ( n 3 ) is determined. The analysis here shows that ( n 3 ) is significantly smaller than ( n 2 + 1 ) of 2D projected dislocations in all considered sizes.

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Ben Ahmed, Kaoutar, Lawrence O. Hall, Dmitry B. Goldgof, and Robert Gatenby. "Ensembles of Convolutional Neural Networks for Survival Time Estimation of High-Grade Glioma Patients from Multimodal MRI." Diagnostics 12, no. 2 (January 29, 2022): 345. http://dx.doi.org/10.3390/diagnostics12020345.

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Glioma is the most common type of primary malignant brain tumor. Accurate survival time prediction for glioma patients may positively impact treatment planning. In this paper, we develop an automatic survival time prediction tool for glioblastoma patients along with an effective solution to the limited availability of annotated medical imaging datasets. Ensembles of snapshots of three dimensional (3D) deep convolutional neural networks (CNN) are applied to Magnetic Resonance Image (MRI) data to predict survival time of high-grade glioma patients. Additionally, multi-sequence MRI images were used to enhance survival prediction performance. A novel way to leverage the potential of ensembles to overcome the limitation of labeled medical image availability is shown. This new classification method separates glioblastoma patients into long- and short-term survivors. The BraTS (Brain Tumor Image Segmentation) 2019 training dataset was used in this work. Each patient case consisted of three MRI sequences (T1CE, T2, and FLAIR). Our training set contained 163 cases while the test set included 46 cases. The best known prediction accuracy of 74% for this type of problem was achieved on the unseen test set.

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Randrup, Jørgen. "Correlated fission fragment angular momenta." EPJ Web of Conferences 292 (2024): 08007. http://dx.doi.org/10.1051/epjconf/202429208007.

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Nuclear fission produces fragments whose spins are coupled to each other as well as to the relative fragment motion due to angular momentum conservation. Statistical ensembles of such spins can readily be obtained by either direct microcanonical sampling or by sampling of the associated normal modes of rotation. The spin-spin opening angle distribution is illustrated for both 3D and 2D spin scenarios and it is demonstrated how recent calculations with various models can be well reproduced by the statistical results under different assumptions about the scission geometry.

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Blanchard, Aaron T., Joshua M. Brockman, Khalid Salaita, and Alexa L. Mattheyses. "Variable incidence angle linear dichroism (VALiD): a technique for unique 3D orientation measurement of fluorescent ensembles." Optics Express 28, no. 7 (March 24, 2020): 10039. http://dx.doi.org/10.1364/oe.381676.

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Schmalhorst, Philipp S., and Andreas Bergner. "A Grid Map Based Approach to Identify Nonobvious Ligand Design Opportunities in 3D Protein Structure Ensembles." Journal of Chemical Information and Modeling 60, no. 4 (March 5, 2020): 2178–88. http://dx.doi.org/10.1021/acs.jcim.0c00051.

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Lin, Jing, Xiansong Wang, Guangxia Shen, and Daxiang Cui. "3D Plasmonic Ensembles of Graphene Oxide and Nobel Metal Nanoparticles with Ultrahigh SERS Activity and Sensitivity." Journal of Nanomaterials 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/7689357.

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We describe a comparison study on 3D ensembles of graphene oxide (GO) and metal nanoparticles (silver nanoparticles (AgNPs), gold nanoparticles (GNPs), and gold nanorods (GNRs)) for surface-enhanced Raman scattering (SERS) application. For the first time, GNRs were successfully assembled on the surfaces of GO by means of electrostatic interactions without adding any surfactant. The SERS properties of GO/AgNPs, GO/GNPs, and GO/GNRs were compared using 2-mercaptopyridine (2-Mpy) as probing molecule. We found that GO/AgNPs and GO/GNPs substrates are not suitable for detecting 2-Mpy due to the very strongπ-πstacking interaction between the 2-Mpy molecules andsp2carbon structure of GO. Conversely, the GO/GNRs substrates show ultrahigh SERS activity and sensitivity of 2-Mpy with the detection limit as low as ~10-15 M, which is ~2-3 orders of magnitude higher than that of the corresponding GNRs.

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Kruggel-Emden, Harald, Erdem Simsek, Siegmar Wirtz, and Viktor Scherer. "A Comparative Numerical Study of Particle Mixing on Different Grate Designs Through the Discrete Element Method." Journal of Pressure Vessel Technology 129, no. 4 (August 18, 2006): 593–600. http://dx.doi.org/10.1115/1.2767338.

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Based on LEAT’s discrete element codes, granular flow and mixing on conveying equipment are studied in two and three dimensions. Discrete element simulations, which are briefly introduced, provide detailed information on particle positions and velocities over time. This information is used to derive quantities characterizing the dynamic process of mixing. The main focus of the study presented is the mixing process of inhomogeneous particle ensembles on different grate types. For this purpose, the introduced mixing parameters are used to compare the mixing in a 3D situation with the corresponding 2D approximation on identical grates and to compare different grate designs in two dimensions.

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Tang, Wai Shing, Gabriel Monteiro da Silva, Henry Kirveslahti, Erin Skeens, Bibo Feng, Timothy Sudijono, Kevin K. Yang, Sayan Mukherjee, Brenda Rubenstein, and Lorin Crawford. "A topological data analytic approach for discovering biophysical signatures in protein dynamics." PLOS Computational Biology 18, no. 5 (May 2, 2022): e1010045. http://dx.doi.org/10.1371/journal.pcbi.1010045.

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Identifying structural differences among proteins can be a non-trivial task. When contrasting ensembles of protein structures obtained from molecular dynamics simulations, biologically-relevant features can be easily overshadowed by spurious fluctuations. Here, we present SINATRA Pro, a computational pipeline designed to robustly identify topological differences between two sets of protein structures. Algorithmically, SINATRA Pro works by first taking in the 3D atomic coordinates for each protein snapshot and summarizing them according to their underlying topology. Statistically significant topological features are then projected back onto a user-selected representative protein structure, thus facilitating the visual identification of biophysical signatures of different protein ensembles. We assess the ability of SINATRA Pro to detect minute conformational changes in five independent protein systems of varying complexities. In all test cases, SINATRA Pro identifies known structural features that have been validated by previous experimental and computational studies, as well as novel features that are also likely to be biologically-relevant according to the literature. These results highlight SINATRA Pro as a promising method for facilitating the non-trivial task of pattern recognition in trajectories resulting from molecular dynamics simulations, with substantially increased resolution.

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Kulkarni, Prakash, Vitor B. P. Leite, Susmita Roy, Supriyo Bhattacharyya, Atish Mohanty, Srisairam Achuthan, Divyoj Singh, et al. "Intrinsically disordered proteins: Ensembles at the limits of Anfinsen's dogma." Biophysics Reviews 3, no. 1 (March 2022): 011306. http://dx.doi.org/10.1063/5.0080512.

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Intrinsically disordered proteins (IDPs) are proteins that lack rigid 3D structure. Hence, they are often misconceived to present a challenge to Anfinsen's dogma. However, IDPs exist as ensembles that sample a quasi-continuum of rapidly interconverting conformations and, as such, may represent proteins at the extreme limit of the Anfinsen postulate. IDPs play important biological roles and are key components of the cellular protein interaction network (PIN). Many IDPs can interconvert between disordered and ordered states as they bind to appropriate partners. Conformational dynamics of IDPs contribute to conformational noise in the cell. Thus, the dysregulation of IDPs contributes to increased noise and “promiscuous” interactions. This leads to PIN rewiring to output an appropriate response underscoring the critical role of IDPs in cellular decision making. Nonetheless, IDPs are not easily tractable experimentally. Furthermore, in the absence of a reference conformation, discerning the energy landscape representation of the weakly funneled IDPs in terms of reaction coordinates is challenging. To understand conformational dynamics in real time and decipher how IDPs recognize multiple binding partners with high specificity, several sophisticated knowledge-based and physics-based in silico sampling techniques have been developed. Here, using specific examples, we highlight recent advances in energy landscape visualization and molecular dynamics simulations to discern conformational dynamics and discuss how the conformational preferences of IDPs modulate their function, especially in phenotypic switching. Finally, we discuss recent progress in identifying small molecules targeting IDPs underscoring the potential therapeutic value of IDPs. Understanding structure and function of IDPs can not only provide new insight on cellular decision making but may also help to refine and extend Anfinsen's structure/function paradigm.

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Mantasa, Dedi, and Yos Sudarman. "PENGGUNAAN APLIKASI BASIC GUITAR CHORDS 3D PADA PEMBELAJARAN SENI BUDAYA (MUSIK) DI KELAS VII SMP NEGERI 3 KECAMATAN HARAU." Jurnal Sendratasik 9, no. 3 (September 15, 2020): 41. http://dx.doi.org/10.24036/jsu.v9i1.109436.

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AbstractThis study aims to describe the activities of Culture and Arts (music) teachers in grade VII of SMP Negeri 3 Harau District, Lima Puluh Kota Regency in implementing music instruction using the Basic Guitar Chords 3D application for learning guitar at school. The use of this guitar playing application provides an opportunity for students to learn guitar playing virtually with a different learning atmosphere from how guitar learning was theoretically and practically conducted before.This study uses references to results from relevant research and several theoretical studies especially those related to learning and instruction, learning methods, guitar learning through application, and Culture and Arts (music) instruction in junior high school.This is a qualitative research with a descriptive analysis approach. The object of research was teachers’ activities in implementing music instruction in grade VII of SMP Negeri 3 Harau. The learning observed involves learning guitar under the topic of playing a string instrument in a musical ensemble. The research instruments used were observation notes, interview notes, and document studies. The results of the study explain that learning guitar using the Basic Guitar Chords 3D application can create new experiences for students in learning music using the android application. However, due to the fact that the use of this application coincides with learning musical ensembles under the Basic Competence of playing simple music, this application conceptually does not support learning musical ensembles by using actual musical instruments.However, the teachers’ thought to explain that playing music can be done through application surely gives a point, and it can be developed by the teachers in learning music at school.Keywords: Android application, guitar, learning, instruction, ensemble

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Osmer, Patrick S., Gatikrushna Singh, and Kathleen Boris-Lawrie. "A New Approach to 3D Modeling of Inhomogeneous Populations of Viral Regulatory RNA." Viruses 12, no. 10 (September 29, 2020): 1108. http://dx.doi.org/10.3390/v12101108.

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Tertiary structure (3D) is the physical context of RNA regulatory activity. Retroviruses are RNA viruses that replicate through the proviral DNA intermediate transcribed by hosts. Proviral transcripts form inhomogeneous populations due to variable structural ensembles of overlapping regulatory RNA motifs in the 5′-untranslated region (UTR), which drive RNAs to be spliced or translated, and/or dimerized and packaged into virions. Genetic studies and structural techniques have provided fundamental input constraints to begin predicting HIV 3D conformations in silico. Using SimRNA and sets of experimentally-determined input constraints of HIVNL4-3 trans-activation responsive sequence (TAR) and pairings of unique-5′ (U5) with dimerization (DIS) or AUG motifs, we calculated a series of 3D models that differ in proximity of 5′-Cap and the junction of TAR and PolyA helices; configuration of primer binding site (PBS)-segment; and two host cofactors binding sites. Input constraints on U5-AUG pairings were most compatible with intramolecular folding of 5′-UTR motifs in energetic minima. Introducing theoretical constraints predicted metastable PolyA region drives orientation of 5′-Cap with TAR, U5 and PBS-segment helices. SimRNA and the workflow developed herein provides viable options to predict 3D conformations of inhomogeneous populations of large RNAs that have been intractable to conventional ensemble methods.

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Mollamahale, Yaser Bahari, Mohammad Ghorbani, Abolghasem Dolati, and Masoumeh Ghalkhani. "Application of 3D gold nanotube ensembles in electrochemical sensing of ultra-trace Hg (II) in drinkable water." Surfaces and Interfaces 10 (March 2018): 27–31. http://dx.doi.org/10.1016/j.surfin.2017.11.001.

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Bahari Mollamahale, Y., M. Ghorbani, A. Dolati, and D. Hosseini. "Electrodeposition of well-defined gold nanowires with uniform ends for developing 3D nanoelectrode ensembles with enhanced sensitivity." Materials Chemistry and Physics 213 (July 2018): 67–75. http://dx.doi.org/10.1016/j.matchemphys.2018.04.004.

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Bansmann, Joachim, Armin Kleibert, Mathias Getzlaff, Arantxa Fraile Rodríguez, Frithjof Nolting, Christine Boeglin, and Karl-Heinz Meiwes-Broer. "Magnetism of 3d transition metal nanoparticles on surfaces probed with synchrotron radiation - from ensembles towards individual objects." physica status solidi (b) 247, no. 5 (January 15, 2010): 1152–60. http://dx.doi.org/10.1002/pssb.200945516.

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Shumyantseva, V. V., T. V. Bulko, E. V. Suprun, and A. I. Archakov. "Electrochemical sensor systems based on one dimensional (1D) nanostructures for analysis of bioaffinity interactions." Biomeditsinskaya Khimiya 59, no. 2 (2013): 209–18. http://dx.doi.org/10.18097/pbmc20135902209.

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It was shown that modification of screen printed graphite electrodes with gold nanoparticles (AuNPs) decorated Pb nanowires (PbNWs) demonstrates the enhancement of sensor’s analytical characteristics such as effective surface area, electro catalytic properties and heterogeneous electron transfer kinetics. The reason for such improvement may be the synergistic effect of AuNPs and PbNWs. Nanowires ensembles on electrode surface were employed for the detection of hemeproteins cytochrome P450 2B4, cytochrome c , and cardiac myoglobin in human plasma. Composite materials based on nanoparticles with different dimentions (3D three dimensional gold nanoparticles and 1D one dimensional Pb nanowires make it possible to construct biosensors with low detection limit of proteins.

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Carstens, Simeon, Michael Nilges, and Michael Habeck. "Bayesian inference of chromatin structure ensembles from population-averaged contact data." Proceedings of the National Academy of Sciences 117, no. 14 (March 19, 2020): 7824–30. http://dx.doi.org/10.1073/pnas.1910364117.

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Mounting experimental evidence suggests a role for the spatial organization of chromatin in crucial processes of the cell nucleus such as transcription regulation. Chromosome conformation capture techniques allow us to characterize chromatin structure by mapping contacts between chromosomal loci on a genome-wide scale. The most widespread modality is to measure contact frequencies averaged over a population of cells. Single-cell variants exist, but suffer from low contact numbers and have not yet gained the same resolution as population methods. While intriguing biological insights have already been garnered from ensemble-averaged data, information about three-dimensional (3D) genome organization in the underlying individual cells remains largely obscured because the contact maps show only an average over a huge population of cells. Moreover, computational methods for structure modeling of chromatin have mostly focused on fitting a single consensus structure, thereby ignoring any cell-to-cell variability in the model itself. Here, we propose a fully Bayesian method to infer ensembles of chromatin structures and to determine the optimal number of states in a principled, objective way. We illustrate our approach on simulated data and compute multistate models of chromatin from chromosome conformation capture carbon copy (5C) data. Comparison with independent data suggests that the inferred ensembles represent the underlying sample population faithfully. Harnessing the rich information contained in multistate models, we investigate cell-to-cell variability of chromatin organization into topologically associating domains, thus highlighting the ability of our approach to deliver insights into chromatin organization of great biological relevance.

Journal articles on the topic 'Ensembles de Llgnes 3D'

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