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See detailDevelopmental GABA polarity switch and neuronal plasticity in Bioengineered Neuronal Organoids
Zafeiriou, Maria-Patapia; Bao, Guobin; Hudson, James et al

in Nature Communications (2020), 11(1), 3791

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See detailGene regulatory network inference from sparsely sampled noisy data
Aalto, Atte UL; Viitasaari, Lauri; Ilmonen, Pauliina et al

in Nature Communications (2020), 11

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing ... [more ▼]

The complexity of biological systems is encoded in gene regulatory networks. Unravelling this intricate web is a fundamental step in understanding the mechanisms of life and eventually developing efficient therapies to treat and cure diseases. The major obstacle in inferring gene regulatory networks is the lack of data. While time series data are nowadays widely available, they are typically noisy, with low sampling frequency and overall small number of samples. This paper develops a method called BINGO to specifically deal with these issues. Benchmarked with both real and simulated time-series data covering many different gene regulatory networks, BINGO clearly and consistently outperforms state-of-the-art methods. The novelty of BINGO lies in a nonparametric approach featuring statistical sampling of continuous gene expression profiles. BINGO’s superior performance and ease of use, even by non-specialists, make gene regulatory network inference available to any researcher, helping to decipher the complex mechanisms of life. [less ▲]

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See detailChemical instability at chalcogenide surfaces impacts chalcopyrite devices well beyond the surface
Colombara, Diego UL; Elanzeery, Hossam UL; Nicoara, Nicoleta et al

in Nature Communications (2020)

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See detailThe atypical chemokine receptor ACKR3/CXCR7 is a broad-spectrum scavenger for opioid peptides.
Meyrath, Max; Szpakowska, Martyna; Zeiner, Julian et al

in Nature communications (2020), 11(1), 3033

Endogenous opioid peptides and prescription opioid drugs modulate pain, anxiety and stress by activating opioid receptors, currently classified into four subtypes. Here we demonstrate that ACKR3/CXCR7 ... [more ▼]

Endogenous opioid peptides and prescription opioid drugs modulate pain, anxiety and stress by activating opioid receptors, currently classified into four subtypes. Here we demonstrate that ACKR3/CXCR7, hitherto known as an atypical scavenger receptor for chemokines, is a broad-spectrum scavenger of opioid peptides. Phylogenetically, ACKR3 is intermediate between chemokine and opioid receptors and is present in various brain regions together with classical opioid receptors. Functionally, ACKR3 is a scavenger receptor for a wide variety of opioid peptides, especially enkephalins and dynorphins, reducing their availability for the classical opioid receptors. ACKR3 is not modulated by prescription opioids, but we show that an ACKR3-selective subnanomolar competitor peptide, LIH383, can restrain ACKR3's negative regulatory function on opioid peptides in rat brain and potentiate their activity towards classical receptors, which may open alternative therapeutic avenues for opioid-related disorders. Altogether, our results reveal that ACKR3 is an atypical opioid receptor with cross-family ligand selectivity. [less ▲]

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See detailUnifying machine learning and quantum chemistry with a deep neural network for molecular wavefunctions
Schütt, Kristof; Gastegger, Michael; Tkatchenko, Alexandre UL et al

in Nature Communications (2019), 10(1), 5024

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate ... [more ▼]

Machine learning advances chemistry and materials science by enabling large-scale exploration of chemical space based on quantum chemical calculations. While these models supply fast and accurate predictions of atomistic chemical properties, they do not explicitly capture the electronic degrees of freedom of a molecule, which limits their applicability for reactive chemistry and chemical analysis. Here we present a deep learning framework for the prediction of the quantum mechanical wavefunction in a local basis of atomic orbitals from which all other ground-state properties can be derived. This approach retains full access to the electronic structure via the wavefunction at force-field-like efficiency and captures quantum mechanics in an analytically differentiable representation. On several examples, we demonstrate that this opens promising avenues to perform inverse design of molecular structures for targeting electronic property optimisation and a clear path towards increased synergy of machine learning and quantum chemistry. [less ▲]

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See detailData driven discovery of cyber physical systems
Yuan, Ye; Tang, Xiuchuan; Zhou, Wei et al

in Nature Communications (2019)

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved ... [more ▼]

Cyber-physical systems embed software into the physical world. They appear in a wide range of applications such as smart grids, robotics, and intelligent manufacturing. Cyber-physical systems have proved resistant to modeling due to their intrinsic complexity arising from the combination of physical and cyber components and the interaction between them. This study proposes a general framework for discovering cyber-physical systems directly from data. The framework involves the identification of physical systems as well as the inference of transition logics. It has been applied successfully to a number of real-world examples. The novel framework seeks to understand the underlying mechanism of cyber-physical systems as well as make predictions concerning their state trajectories based on the discovered models. Such information has been proven essential for the assessment of the performance of cyber- physical systems; it can potentially help debug in the implementation procedure and guide the redesign to achieve the required performance. [less ▲]

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See detailThermodynamic efficiency in dissipative chemistry
Penocchio, Emanuele UL; Rao, Riccardo; Esposito, Massimiliano UL

in Nature Communications (2019), 10(1), 1-5

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See detailBiallelic VARS variants cause developmental encephalopathy with microcephaly that is recapitulated in vars knockout zebrafish
Siekierska, Aleksandra; Stamberger, Hannah; Deconinck, Tine et al

in Nature Communications (2019), 10(1), 708

Aminoacyl tRNA synthetases (ARSs) link specific amino acids with their cognate transfer RNAs in a critical early step of protein translation. Mutations in ARSs have emerged as a cause of recessive, often ... [more ▼]

Aminoacyl tRNA synthetases (ARSs) link specific amino acids with their cognate transfer RNAs in a critical early step of protein translation. Mutations in ARSs have emerged as a cause of recessive, often complex neurological disease traits. Here we report an allelic series consisting of seven novel and two previously reported biallelic variants in valyl-tRNA synthetase (VARS) in ten patients with a developmental encephalopathy with microcephaly, often associated with early-onset epilepsy. In silico, in vitro, and yeast complementation assays demonstrate that the underlying pathomechanism of these mutations is most likely a loss of protein function. Zebrafish modeling accurately recapitulated some of the key neurological disease traits. These results provide both genetic and biological insights into neurodevelopmental disease and pave the way for further in-depth research on ARS related recessive disorders and precision therapies. [less ▲]

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See detailStem cell-associated heterogeneity in Glioblastoma results from intrinsic tumor plasticity shaped by the microenvironment
Dirkse, Anne; Golebiewska, Anna; Buder, Thomas et al

in Nature communications (2019), 10(1), 1787

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See detailGenome-wide mega-analysis identifies 16 loci and highlights diverse biological mechanisms in the common epilepsies
The International League Against Epilepsy Consortium on Complex Epilepsies; Krause, Roland UL

in Nature Communications (2018)

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See detailTowards exact molecular dynamics simulations with machine-learned force fields
Chmiela, Stefan; Sauceda, Huziel E.; Müller, Klaus-Robert et al

in Nature Communications (2018), 9

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See detailOptical control of polarization in ferroelectric heterostructures
Tao, Li; Lipatov, Alexej; Lu, Haidong et al

in Nature Communications (2018), 9

In the ferroelectric devices, polarization control is usually accomplished by application of an electric field. In this paper, we demonstrate optically induced polarization switching in BaTiO3-based ... [more ▼]

In the ferroelectric devices, polarization control is usually accomplished by application of an electric field. In this paper, we demonstrate optically induced polarization switching in BaTiO3-based ferroelectric heterostructures utilizing a two-dimensional narrow-gap semiconductor MoS2 as a top electrode. This effect is attributed to the redistribution of the photo-generated carriers and screening charges at the MoS2/BaTiO3 interface. Specifically, a two-step process, which involves formation of intra-layer excitons during light absorption followed by their decay into inter-layer excitons, results in the positive charge accumulation at the interface forcing the polarization reversal from the upward to the downward direction. Theoretical modeling of the MoS2 optical absorption spectra with and without the applied electric field provides quantitative support for the proposed mechanism. It is suggested that the discovered effect is of general nature and should be observable in any heterostructure comprising a ferroelectric and a narrow gap semiconductor. [less ▲]

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See detailTailoring van der Waals dispersion interactions with external electric charges
Kleshchonok, Andrii; Tkatchenko, Alexandre UL

in Nature Communications (2018), 9

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See detailSodium enhances indium-gallium interdiffusion in copper indium gallium diselenide photovoltaic absorbers
Colombara, Diego UL; Werner, Florian UL; Schwarz, Torsten et al

in Nature Communications (2018)

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See detailCompetition between crystal and fibril formation in molecular mutations of amyloidogenic peptides
Reynolds, Nicholas; Adamcik, Jozef; Berryman, Josh UL et al

in Nature Communications (2017), 8

Amyloidogenic model peptides are invaluable for investigating assembly mechanisms in disease related amyloids and in protein folding. During aggregation, such peptides can undergo bifurcation leading to ... [more ▼]

Amyloidogenic model peptides are invaluable for investigating assembly mechanisms in disease related amyloids and in protein folding. During aggregation, such peptides can undergo bifurcation leading to fibrils or crystals, however the mechanisms of fibril-to-crystal conversion are unclear. We navigate herein the energy landscape of amyloidogenic peptides by studying a homologous series of hexapeptides found in animal, human and disease related proteins. We observe fibril-to-crystal conversion occurring within single aggregates via untwisting of twisted ribbon fibrils possessing saddle-like curvature and cross-sectional aspect ratios approaching unity. Changing sequence, pH or concentration shifts the growth towards larger aspect ratio species assembling into stable helical ribbons possessing mean-curvature. By comparing atomistic calculations of desolvation energies for association of peptides we parameterise a kinetic model, providing a physical explanation of fibril-to-crystal interconversion. These results shed light on the self-assembly of amyloidogenic peptides, suggesting amyloid crystals, not fibrils, represent the ground state of the protein folding energy landscape. [less ▲]

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See detailTRPV1 regulates excitatory innervation of OLM neurons in the hippocampus.
I. Hurtado-Zavala, Joaquin; Ramachandran, Binu; Ahmed, Saheeb et al

in Nature Communications (2017)

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See detailQuantum-chemical insights from deep tensor neural networks
Schütt, Kristof T.; Arbabzadah, Farhad; Chmiela, Stefan et al

in Nature Communications (2017), 8

Detailed reference viewed: 412 (6 UL)