Melnik, Elena

7 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

4 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

40 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

19 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

25 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

37 files · 4.5 MB · tiff

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Wolf, Gerhard · Kolbe, Georg

2 files · 14 MB · pdf, tiff

Historical questionnaire/s 1924/1948 and index cards, partly selected enclosures regarding the history of a German pharmacy, catalogued via Kalliope portal (Historischer Fragebogen 1924/1948 und Karteikarten, ggf. gemeinfreie Anlagen zur Apothekengeschichte; als Katalog dient das Nachlassportal Kalliope): https://kalliope-verbund.info/DE-611-BF-70963 [Funktion: Im Findbuch anzeigen] Please note: The Kalliope catalogue entry might indicate related material in the archival folder which cannot be published due to copyright or other legal restrictions (NB: Das Katalogisat bei Kalliope kann auch auf Materialien - teils erheblichen Umfangs - verweisen, die aus archiv- oder urheberrechtlichen Gründen nicht veröffentlicht werden dürfen).

Wang, Yafei

15 files · 129 KB · tiff

A gridded time-series dataset reconstructing population distribution across the Yellow River Basin, China, from 1000 to 2000 AD, with a temporal resolution of one century and a spatial resolution of 10 km.

Wang, Wei · Liu, Cheng · Han, Lianhuan · et al.

10 files · 211 KB · tiff

Cyclic voltammograms (CVs) obtained at the 35-nm radius gold nanoelectrode in 0.5 M H2SO4 solution deoxygenated under argon at different scan rates (from 0.3 to 1 V/s). The potential range is 0-1.75 V vs RHE. The data in the text files is given in the following format: Potential vs. RHE (V) | Current (A) Scan rate is indicated in the text file name. The figure Voltammograms.tif shows all CVs in this dataset.

Paduano, Angelo · Scarano, Francesco · Casalino, Damiano · et al.

200 rows × 10 cols · 39 KB · csv, jpeg

10 numeric

The interaction between acoustic waves and turbulent grazing flow over an acoustic liner is investigated using Lattice-Boltzmann Very-Large-Eddy simulations. A single-degree-of-freedom liner with 11 streamwise-aligned cavities is studied in a grazing flow impedance tube. The conditions replicate reference experiments from the Federal University of Santa Catarina. The influence of grazing flow (with a centerline Mach number of 0.32), acoustic wave amplitude, frequency, and propagation direction relative to the mean flow is analysed. Impedance is computed using both direct (i.e. the in-situ method) and model-fitting inference (i.e. the mode-matching method) methods. The former reveals strong spatial variations; however, averaged values throughout the sample show minimal differences between upstream and downstream propagating waves, in contrast to what is obtained with the latter method. Flow analyses reveal that the orifices displace the flow away from the face sheet, with this effect amplified by acoustic waves and dependent on the wave propagation direction. Consequently, the boundary layer displacement thickness ($\delta^*$) increases along the streamwise direction compared to a smooth wall and exhibits localised humps downstream of each orifice. The growth of $\delta^*$ alters the flow dynamics within the orifices by weakening the shear layer at downstream positions. This influences the acoustic-induced mass flow rate through the orifices at equal Sound Pressure Level, suggesting that acoustic energy is dissipated differently along the liner. The asymmetry of the flow field experienced by the acoustic wave, depending on its propagation direction, highlights the need to consider a spatially evolving turbulent flow when studying the acoustic-flow interaction and measuring impedance.

Kovaliov, Michael

1 files · 100 MB · parquet

Melnik, Elena

37 files · 444 MB · tiffdeclared

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

Melnik, Elena

49 files · 591 MB · tiffdeclared

This data set contains thermographic images for the visualisation of watermarks. An IRCAM Equus 327k with Watermark Imager software (Fraunhofer) version 8.416 (R2016b) was used.

inquantio

3 files · 2.0 MB · jpeg, pdfdeclared

A study published on bioRxiv demonstrates the first hybrid quantum computing framework combining classical Hamming distance filtering with the Grover quantum search algorithm to overcome bottlenecks in massive genomic data analysis. Utilizing the IBM Qiskit 27-qubit simulator, researchers rapidly and flawlessly identified a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database, even under conditions allowing up to a 30% mismatch. The study lays a revolutionary foundation for synthetic genome design by completing simulations that precisely insert a "BioBloQu" (quantum biological block)—composed of a promoter, an RBS, an enzyme, and a terminator—into the explored scar regions of the minimal genome M. mycoides JCVI-Syn3B. [Quantum Biology Society] Modern life sciences are pouring out genomic sequencing data at an exponential rate. However, due to the immense complexity of biological data, existing classical computing methods are facing severe computational bottlenecks in analyzing and manipulating it. To break through these limitations, a disruptive study recently published on the preprint repository bioRxiv, titled "Genetic Engineering with Quantum Circuits: creating codes and studying BioBloQu genetic elements," has brought quantum computers to the forefront of genetic engineering. A joint research team led by Professor Elibio Rech from the Brazilian Agricultural Research Corporation (Embrapa) Genetic Resources and Biotechnology and the Federal University of Rio Grande do Sul (UFRGS) presented this innovative research. ■ Scanning Massive Genomic Databases with Qubits Using the core quantum mechanical principles of superposition and entanglement as the foundation for information processing, the research team developed a hybrid quantum framework that combines classical Hamming distance filtering with the Grover quantum search algorithm. Powered by IBM's Qiskit 27-qubit simulator, this algorithm was used to search for a 50-nucleotide target sequence of a Cas9-like nuclease within a Brazilian biome metagenome database. As a result, the team successfully and swiftly identified the massive genetic data through amplitude amplification, filtering out the sequence perfectly even under conditions allowing up to a 30% mismatch rate. This proves that vast amounts of genetic data, which are unmanageable for classical computers, can be analyzed in a flash through quantum parallel processing. ■ The Era of Synthetic Genome Design Opened by BioBloQu Furthermore, the researchers successfully completed a quantum circuit simulation that accurately inserts a synthetic genetic construct called "BioBloQu" (quantum biological block) into the identified target regions. In the genome of M. mycoides JCVI-Syn3B, an artificially synthesized minimal genome model, the quantum algorithm first identified two 20-nucleotide "scar" regions—which are traces of gene editing. Then, it precisely integrated a tandem genetic block (BioBloQu) composed of a promoter, a ribosome binding site (RBS), an enzyme sequence, and a terminator into that location. This innovative approach goes beyond simply cutting and pasting existing genes physicochemically; it opens up the possibility of designing and assembling novel synthetic genomes from the ground up under the control of quantum algorithms equipped with overwhelming computational power. By directly applying the computational power of quantum mechanics to biotechnology, this research is expected to serve as the starting point for a massive revolution in next-generation quantum-bio data manipulation, customized gene therapy, and synthetic biology. #QuantumComputing #GeneticRecombination #BioBloQu #QuantumAlgorithm #GroverAlgorithm #Metagenome #SyntheticBiology #GenomeDesign #QuantumBiology #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2025.05.02.651535v2

inquantio

3 files · 1.9 MB · jpeg, pdfdeclared

A study published on bioRxiv proposes quantum and classical graph neural networks that address the issues of parameter compression and temporal changes in protein-ligand geometry—factors previously overlooked by existing machine learning (ML) models for predicting drug dissociation kinetics (k_off). Demonstrated a significant improvement in predictive accuracy through temporal integration by introducing a 2-timestep GCN+GRU model that actively learns structural changes before and after a short molecular dynamics simulation. Proved that quantum structures act as a powerful lever for the advancement of kinetic ML models in drug design by using variational quantum circuits to compress the model head, eliminating 66% of parameters while perfectly maintaining expressive power. [Quantum Biology Society] In the field of drug design, dissociation kinetics ($k_{off}$)—the duration a drug remains bound to its target protein—is increasingly recognized as a much more critical indicator of in vivo efficacy than simple binding affinity. However, existing machine learning (ML) models have largely overlooked the dynamic, temporal changes in protein-ligand geometric structures and the fundamental computational requirement to represent complex spatial interactions with fewer parameters. A study recently published on the preprint repository bioRxiv, titled "Quantum and Classical Graph Convolutional Neural Networks for Protein Ligand Dissociation Constant Prediction," opens new horizons by extending state-of-the-art Spatial Graph Neural Networks (Spatial GNNs) in two innovative directions to break through these classical limitations. A research team at the University of Cincinnati—comprising Azamat Salamatov, Jun Bai, Gowtham Atluri, and Chaowen Guan—led this disruptive research. ■ Combining 2-Timestep Learning and Variational Quantum Circuits The first innovation introduced by the research team is the integration of temporal flow. By adopting a 2-timestep GCN+GRU model that actively learns structural changes before and after a short molecular dynamics simulation, they have elevated the accuracy of kinetic predictions to the next level. The second key innovation is groundbreaking parameter optimization utilizing quantum technology. By compressing the model head using variational quantum circuits, the researchers successfully eliminated 66% of unnecessary parameters while completely preserving the complex expressive power of the existing fully classical model. Results from the PDBbind-koff-2020 benchmark test revealed the remarkable achievement of this quantum compressed model: it perfectly matched the predictive accuracy of the heavy, fully classical model while exponentially reducing the model size. This study clearly proves that temporal kinetics and quantum neural network structures serve as a powerful, disruptive lever to break the massive computational bottlenecks that occur in future drug candidate screening processes, propelling kinetic ML models a significant leap forward. #DrugScreening #DissociationKinetics #QuantumMachineLearning #GraphNeuralNetworks #ProteinLigand #QuantumComputing #DrugDevelopment #MolecularDynamics #ArtificialIntelligence #KoreanQuantumBiologySociety https://www.biorxiv.org/content/10.1101/2025.11.20.689635v2.full

inquantio

3 files · 1.6 MB · jpeg, pdfdeclared

Published in the international journal PLOS Computational Biology, this study models for the first time the decrease in adult hippocampal neurogenesis in a hypomagnetic field using the 'Radical Pair Mechanism'. It establishes a theoretical mathematical model that explains the cognitive decline and reduction in reactive oxygen species (ROS) levels observed in previous mouse experiments through changes in the singlet-triplet spin dynamics of radical pairs consisting of flavin and superoxide. It demonstrates that the formation of neural networks and metabolic processes in the mammalian brain are not merely simple macroscopic biochemical reactions, but are directly governed by a microscopic quantum phenomenon: the spin dynamics of ROS electrons that depend on external magnetic fields. [Korean Society of Quantum Biology, Reporter Hak-Jin Kim] Could the radical pair mechanism—the principle by which birds sense the Earth's magnetic field to navigate—also be deeply involved in mammalian brain development and cognitive function? Recently, a theoretical study offering a clear physical answer to this remarkable quantum biological question has been published. The paper, titled "Radical pairs may explain reactive oxygen species-mediated effects of hypomagnetic field on neurogenesis," published in the international journal PLOS Computational Biology, uncovers that neurogenesis is intricately linked to a purely quantum mechanical process. The research team, led by Rishabh Rishabh and Professor Christoph Simon from the University of Calgary in Canada, spearheaded this innovative study. ■ Hypomagnetic Field Environment and the Decrease in ROS Levels According to recently published biological experimental results, mice exposed to a hypomagnetic field environment—where the geomagnetic field is largely shielded—showed significantly attenuated neurogenesis in the hippocampal region of the adult brain, resulting in a distinct decline in cognitive abilities. Surprisingly, the fundamental cause of this cognitive decline and suppressed neurogenesis was revealed to be a decrease in intracellular reactive oxygen species (ROS) levels. The University of Calgary research team mathematically analyzed the cause of this phenomenon through the lens of quantum mechanics. ■ Electron Spin Dynamics Governing Brain Development The research team constructed a radical pair model of 'flavin' and 'superoxide', which are responsible for intracellular ROS production, and simulated their spin dynamics. As a result, they found that when the external magnetic field decreases from the geomagnetic field level (approx. 50 μT) to a hypomagnetic field (approx. 0 μT), the yield of the singlet-triplet interconversion changes dramatically due to alterations in the internal hyperfine interactions of the radical pair. The mathematically calculated extent of the decrease in product yield was consistent with the reduction rate of ROS observed in actual experiments. In other words, the sophisticated formation of neural networks and metabolic processes in mammals are not simply macroscopic chemical reactions, but are directly governed by an extremely microscopic quantum phenomenon: the electron spin dynamics occurring within molecules. This research is a monumental achievement that mathematically proves the causal entanglement between the macroscopic geomagnetic environment of the Earth and the quantum spin states within living organisms. It is expected to provide a revolutionary paradigm for developing technologies that utilize magnetic fields to treat degenerative brain diseases and promote neurogenesis in the future. #QuantumBiology #RadicalPairMechanism #Magnetoreception #Neurogenesis #ReactiveOxygenSpecies #HypomagneticField #SpinDynamics #QuantumMechanics #BrainScience #KoreanSocietyOfQuantumBiology https://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1010198