Lopez, Annalaura · Greco, Margherita · Marcolli, Beatrice · et al.

4 files · 17 KB · docx, xlsx

This dataset originates from a study aiming to valorise Ciuta sheep, a local breed native from the Italian Central Alps, through the characterization of nutritional quality and chemical composition of fresh meat (loins) and one traditional dry-cured product. Specifically, the research focused on determining the chemical composition of Ciuta sheep meat and on identifying key changes in its chemical profile during dry curing process, hypothesizing that such chemical fingerprint may suggest some markers linked to the production system, geographical origin, and traditional processing techniques. For this reason, for bthe dry-cured product, both an aliquot of fresh meat before and after transformation and dry-curing was sampled and analysed. Regarding loins, three commercial categories (lambs, hoggets and mutton) were considered, in order to define any possible difference induced by age of the sheep (and physiological factors, such as rumen development). The dataset includes chemical data regarding the proximate composition (moisture, protein, fat, ash, salt content for the dry-cured product) and energy content of fresh and dry-cured meat; the fatty acids content of fresh and dry-cured meat product; the volatile profile of fresh and dry-cured meat product. Results from analysis performed in our study suggested that the development of high-quality dry-cured products could provide a strategy to valorise Ciuta sheep meat, especially from adult animals (culled ewes and rams), while fresh meat production could focus on lambs. The complex volatile profile detected was influenced by both the farming system and traditional processing methods.

Kantor, Rose · Shakya, Migun · Ruth, Nelson · et al.

2,095 rows · 907 KB · fasta, tsv

A virus genome database representing 21,015 near-complete virus genomes collected from untargeted ultra-deep RNA/DNA combined sequencing of wastewater. Sequence data was provided by the CASPER consortium and raw data may be found on NCBI SRA under bioprojects PRJNA1247874 and PRJNA1198001. Data underwent read trimming, rRNA and human read removal, de novo assembly, and selection of high-quality viral contigs. Contigs were clustered at 95% identity and 85% query coverage to dereplicate. Chimera-checking required at least two independent assemblies of the same viral genome or presence of the genome in another reference database. Annotation made use of RdRpCATCH, geNomad, checkV, BLASTN against NCBI core-nt, and RNAVirHost. The RdRp fasta files contain representative RdRp sequences identified through homology to major RdRp reference databases and clustered at 90% sequence identity over 75% sequence coverage. Included sequences contain all three conserved RdRp motifs (A, B, and C) arranged in either the canonical ABC configuration or the permuted CAB configuration.

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.

Hubbard, Alfred · Solares, Edwin · Hemming-Schroeder, Elizabeth

88 rows · 18 KB · fasta

These are the files needed to run the Broad Institute's malaria amplicon pipeline for the PvGAP Plasmodium vivax panel, described in detail here . They consist of FASTA files containing the forward and reverse primers and another FASTA file containing reference sequences for each target, derived from the PvP01 reference genome.

Yuan, Guangyuan

72 rows × 13 cols · 6.0 KB · csv, fasta

11 numeric · 2 categorical

This dataset supports the findings of the manuscript "Root anatomical traits modulate the assembly and nitrogen transformation potential of root-associated microbiomes in a temperate steppe" (NPH-MS-2026-55667). It contains root traits data, bacterial 16S rRNA gene absolute abundances, functional genes relative abundances, DNA extraction metadata, and phylogenetic marker sequences for 37 plant species from a temperate steppe ecosystem. The dataset includes the following files: 1. root traits.csv - Root traits including average diameter (AD), specific root length (SRL), specific root area (SRA), root tissue density (RTD), root nitrogen content (RNC), root carbon content (RCC), carbon‑nitrogen ratio (RCN), cortex layer number (CLN), cortex thickness (CT), and the ratio of cortex thickness to root diameter (CTRD). The first column lists plant species names. 2. Absolute abundance of 16S rRNA gene.csv - Quantitative PCR (qPCR) derived absolute abundances of bacterial 16S rRNA gene copies (copies/ng DNA) across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 3. DNA extraction sample weight.csv - Fresh weight (grams) of root material used for DNA extraction for each sample, linked by SampleID to the abundance data. 4. DNA extraction concentration.csv - Qubit‑measured DNA concentrations (ng/μL) and the sample volume (μL) used for quality control, together with sample metadata. 5. 37species.fasta - DNA sequences of two chloroplast markers (matK and rbcL) for the 37 plant species included in the study. The sequences are in FASTA format with headers formatted as ">Species". These were used for host phylogeny construction and Pagel's λ analyses. 6. Quantitative PCR results of functional gene.csv - Quantitative PCR (qPCR) derived relative abundances of bacterial 16S rRNA gene and functional genes across different root compartments (rhizosphere, rhizoplane, endosphere), host species, root orders, and cotyledon classes (monocot/dicot). 7. README.md - A detailed description of each file, column headers, abbreviations, units, and any missing value codings (NA). All data are provided to ensure transparency and reproducibility of the analyses. For methodological details, please refer to the Materials and Methods section of the associated publication. These data are under embargo until the associated research article is published. After that date, they will be freely available under a Creative Commons Attribution 4.0 International (CC BY 4.0) license. During the embargo period, the metadata (title, authors, abstract) and the DOI remain publicly visible, but the data files are not accessible. For access requests before the embargo expires, please contact the corresponding author.

Budzinski, Lisa · Beenken, Anne Elisabeth · Sempert, Toni · et al.

9 rows × 1 cols · 743 B · csv, docx, zip

1 categorical

We have investigated an IgG4-RD (IgG4-RD) cohort by our multi-parameter microbiota flow cytometry approach to characterise the microbiota on single-cell level for attributes of the disease. The microbiota is isolated from stool samples and stained according to the published protocol for (a) host immunoglobulins IgA1, IgA2, IgM, IgG and (b) agglutinin binding to mannose, galactose or N-Acetyl-glucosamine surface sugar moieties. For all samples we also determined the microbiome composition by 16S rRNA (V3-V4) sequencing on the illumina MiSeq platform. We provide the raw .fcs and FASTQ files of 40 IgG4-RD patients. For comparison we additionally analysed 36 healthy donors. All .fcs files were generated on BD Influx®. The metadata is collected in the provided meta.csv. The staining parameters are summarized in provided panel.csv.

Li, Zhiyao · Wang, Ningbo · Zhong, Jiahao

6 files · 48 MB · docx, zip

GIFT-BDS is a regional ionospheric total electron content (TEC) and TEC-gradient dataset over China derived from BeiDou geostationary Earth orbit (GEO) observations and a dense ground-based GNSS receiver network. The dataset is designed to provide high-resolution observations of ionospheric TEC variability and horizontal TEC-gradient structures over China and adjacent regions. The versioned release covers the period from 19 July 2024 to 31 December 2025, corresponding to DOY 201 of 2024 to DOY 365 of 2025. The geographical coverage is 15°N-50°N and 95°E-135°E. The dataset is provided in daily NetCDF files and contains two product levels. Level-1 products provide observation-level GEO-derived slant TEC (STEC) and rate of TEC index (ROTI) records for individual receiver-GEO satellite lines of sight, with a temporal resolution of 30 s. Level-2 products provide gridded regional TEC and TEC-gradient variables, including VTEC, VTEC t , ROTI, GIX, GIX std , GIX x , GIX y , GIX t,x , and GIX t,y , with a temporal resolution of 15 min. IPP-based variables are provided on a 1° × 1° grid, while inter-IPP-gradient variables are provided on a 0.25° × 0.25° grid. The main processing steps include observation screening, cycle-slip and data-gap detection, continuous-arc segmentation, carrier-to-code leveling, satellite and receiver DCB correction, IPP calculation, inter-IPP pair selection, gradient estimation, and gridding. Quality control is applied before release. Missing values may occur because of station outages, data gaps, quality-control exclusions, or insufficient valid samples within a grid cell. Users should check the NetCDF variable attributes, including units and fill values, before analysis. The dataset is suitable for regional ionospheric studies, TEC-gradient monitoring, space-weather-related analyses, and investigations of ionospheric effects on GNSS positioning applications.

Jungmyoung, Son · Sihoon, Lee · Jiyeon, Hong

3 files · 20 KB · docx, xlsx

This dataset provides the complete double-coding matrix, PRISMA 2020 checklist, and search strategy supporting the systematic review "Representation-to-AI Transformation in K-12 Generative AI Learning: A Theory-Building Systematic Review of Semantic Transformation Mechanisms." It includes: (1) study-level tier classification (Core/Supporting/Context) for two independent coders and consensus tier for all 18 included studies; (2) the full semantic transformation unit (STU) coding matrix (18 studies x 10 STUs = 180 cells) with pre-consensus and consensus scores; (3)evidence-weighting consensus scores; (4) inter-rater reliability statistics (Cohen's kappa); (5) the completed PRISMA 2020 checklist; and (6) the full database-specific Boolean search strategy.

Altenhoff, Adrian

6 files · 100 MB · hdf5

OMAmer - tree-driven and alignment-free protein assignment to subfamilies OMAmer is an alignment-free protein family assignment method designed to avoid overly specific subfamily predictions and to scale efficiently to phylogenomic databases containing thousands of genomes. It relies on an innovative approach that uses evolutionarily informed k-mers for alignment-free mapping to ancestral protein subfamilies. This dataset provides precomputed OMAmer databases derived from the Hierarchical Orthologous Groups in the OMA Browser . We aim to update these databases with every new OMA Browser release. Each OMAmer database is built using the latest version of the OMAmer package available at the time of the corresponding OMA Browser release. The dataset includes databases for different subsets of the species taxonomy. In most cases, we recommend using the LUCA.h5 database, which contains information from all species in the OMA database. The subset-specific databases are mainly useful when disk space is limited. The release May2026 is based on the OMA Browser release May 2026 which comprises 2983 species. We used OMAmer version 2.1.0 to build these databases.

Requena Rolanía, Jose María · Greif, Gonzalo · ROBELLO, CARLOS

1 files · 8.0 MB · fasta

This dataset contains the genome sequence for Trypanosoma cruzi (strain Dm28c). This genome sequence was de novo assembled using PacBio Hi-Fi and Illumina sequencing platforms by Greif et al (2026. PMID: 41501640). The genome was assembled into 32 contigs, which represent complete chromosomes. The provided Fasta file also contains an additional contig corresponding to the maxicircle (mitochondrial genome) sequence. The Fasta files included in this dataset were downloaded from GenBank (assembly GCA_044048535.1; May 22, 2026). Additional information about the Dm28cT2T genome assembly and gene annotations may be accessed through the link: https://cruzi.pasteur.uy/

Burman, Nathaniel · Buyukyoruk, Murat · Wiegand, Tanner · et al.

4 files · 8.0 MB · fasta

This folder contains a multiple sequence alignment of Cas7 homologs in .fasta format, the domain-level annotations from PFAM and CasFinder, and an associated phylogenetic tree in .newick format.

Kovaliov, Michael

1 files · 100 MB · parquet

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

inquantio

3 files · 3.4 MB · jpeg, pdfdeclared

A study published in the international journal PCCP (Physical Chemistry Chemical Physics) models the proton transfer pathways and quantum tunneling rates during Watson-Crick tautomerism in DNA A-T and G-C base pairs, utilizing Density Functional Theory (DFT) and a machine learning-based Nudged Elastic Band (ML-NEB) algorithm. The research physically demonstrates that proton transfer in A-T base pairs is highly unstable due to an extremely low reverse-reaction barrier, making the occurrence of mutations during the replication process highly improbable. In G-C base pairs, a high quantum tunneling correction value was observed, providing the first physical proof that the mutant form (G-C) possesses a biological lifespan sufficient to be misread as an error by the human DNA replication machinery (replisome). [Korean Society of Quantum Biology, Reporter Hak-Jin Kim] The phenomenon wherein protons shift positions within the hydrogen bonds of DNA—the carrier of an organism's genetic information—can induce transient but lethal point mutations. Known as tautomerism, this process has long been identified as a potential root cause of genetic variation and oncogenesis. A recent study published in Physical Chemistry Chemical Physics (PCCP) has combined pure quantum mechanical modeling (DFT) with machine learning techniques to precisely calculate the energy barriers of double proton transfer occurring within A-T and G-C base pairs. This significant physicochemical research was conducted by L. Slocombe, J. S. Al-Khalili, and M. Sacchi of the University of Surrey, UK. ■ Instantly Collapsing A-T Tautomers vs. Surviving G-C Tautomers The research team analyzed the energy landscape as the hydrogen bond structure shifts from the standard (amino-keto) to the mutant (imino-enol) form, using Density Functional Theory (DFT) and a machine learning-based Nudged Elastic Band (ML-NEB) algorithm. The results revealed that in A-T base pairs, although a quantum tunneling effect transitioning toward the mutant A*-T* state was observed, the reverse-reaction barrier was virtually nonexistent. Consequently, the state could not be maintained and immediately collapsed back into its original standard form. The situation was different for G-C base pairs. Beyond the classical reaction where protons cross the barrier using only thermal energy at room temperature, it was confirmed that wave-like movement via quantum tunneling contributes decisively to the formation of mutant populations. The team mathematically proved that the G-C mutant (G*-C*), which showed a significantly high tunneling correction value, possesses a lifespan long enough to reach the human replisome, suggesting a high probability of solidifying into a permanent point mutation. ■ Genetic Stability Governed by Physical Laws This study is a landmark achievement, demonstrating that complex DNA mutations occurring during the most critical replication processes of life are not merely the result of random thermal fluctuations, but are substantially controlled and shaped by the microscopic physical laws of quantum tunneling dynamics. By providing objective data on how the wave-like nature of protons physically threatens the fidelity of genetic information replication, this study underscores the importance of a quantum biological approach in future research regarding DNA damage and mutation-related diseases. #DNAPointMutation #ProtonTransfer #QuantumTunneling #WatsonCrickTautomerism #BasePair #QuantumBiology #Biophysics #GeneticMutation #MolecularDynamics #KoreanSocietyOfQuantumBiology https://pubs.rsc.org/en/content/articlehtml/2021/cp/d0cp05781a

inquantio

3 files · 3.7 MB · jpeg, pdfdeclared

A study published on arXiv provides a structured narrative evidence map of the three complementary directions where quantum science and biology intersect: 'quantum in biology', 'quantum for biology', and 'biology for quantum'. It highlights hydrogen tunneling in enzymes and the radical-pair mechanism for magnetoreception as representative cases of quantum in biology. For quantum for biology, it analyzes how quantum sensing and imaging tools can improve biological inference. It also illuminates biology for quantum, which utilizes biomolecular self-assembly to enhance the performance of quantum devices, and presents a comprehensive benchmark to compare the current evidence and alternatives in each area. [Korean Society of Quantum Biology, Reporter Hak-Jin Kim] As we enter the 21st century, the convergence of quantum physics and the life sciences is moving beyond mere curiosity to establish itself as a substantial scientific paradigm. A recent paper published on the preprint repository arXiv, titled "Quantum in Biology, Quantum for Biology, and Biology for Quantum: Mapping the Evidence and the Road Ahead," clearly defines the three core areas where these two disciplines intersect. It provides a first-of-its-kind structured narrative evidence map that compiles the technological claims and experimental evidence of each field. A multinational collaborative research team led by Professor Travis J. A. Craddock of the University of Waterloo in Canada and Professor Francesco Petruccione of Stellenbosch University in South Africa published this extensive review. ■ Quantum in Nature and Quantum Tools Illuminating Biology The first pillar presented in this paper, 'Quantum in biology', deals with instances where quantum mechanics directly intervenes in the natural biological phenomena of living organisms. The most scientifically mature evidence highlighted includes the quantum tunneling of hydrogen in enzyme catalysis and the radical-pair spin chemistry mechanism that enables magnetoreception in birds. The second pillar, 'Quantum for biology', explores the application of cutting-edge quantum tools to the life sciences. The core question analyzed in this area is whether quantum technologies—such as quantum computing, quantum sensing, and quantum imaging—can provide significantly more precise biological inference and resolution beyond existing classical baselines, even under realistic biological constraints. ■ Living Organisms as the Foundation for Quantum Technology The final third pillar, 'Biology for quantum', is an innovative approach that utilizes biological systems in reverse to develop quantum technologies. The paper evaluates that the strongest claims in this field arise when the sophisticated structure or self-assembly capabilities unique to biomolecules are used to measurably improve the fabrication, integration, and robustness of artificial quantum devices. This monumental review paper holds great significance as it presents a macroscopic roadmap for the emerging interdisciplinary field of quantum biology. It establishes a powerful benchmark that allows for the at-a-glance comparison and verification of the current evidence levels and competitive alternative models in each specific subfield. #QuantumBiology #QuantumScience #LifeScience #InterdisciplinaryConvergence #EvidenceMap #QuantumSensing #QuantumLifeScience #QuantumTools #FutureScience #KoreanSocietyOfQuantumBiology https://arxiv.org/abs/2605.00205

inquantio

3 files · 3.5 MB · jpeg, pdfdeclared

Published in the journal Frontiers in Photonics, this study designs an aptamer-based platform to detect the clinically relevant dengue NS1 protein, completely reinterpreting it from the perspective of a quantum-biological interface. Moving beyond classical approaches, the research demonstrates that electrochemical capacitance systems can be treated based on the quantum characteristics of electron transport, confirming that biomolecular interactions directly modulate quantum parameters such as the density of states (DOS) of the interface. By integrating quantum-scale concepts into bioelectrochemical sensing, the analytical capability is dramatically improved, achieving high sensitivity and a broad linear range of 0.01 to 1,000 ng/mL even in complex biological matrices like commercial human serum. [Reporter Hak-Jin Kim, Korean Society of Quantum Biology] Electrochemical biosensors are promising tools for clinical diagnostics, but technical challenges have remained in maximizing stability and sensitivity for target proteins within extremely complex biological matrices like human serum. Recently, an open-access study published in the international journal Frontiers in Photonics titled "Quantum-biological interface in biosensor design: detecting proteins with electrochemical aptasensor" , presented a groundbreaking paradigm to overcome these challenges by directly introducing quantum mechanics—the physical laws of the microscopic world—into sensor design. A research team led by Leonardo Peres Chiaradia Costa and Professor Marcelo Mulato from the University of São Paulo, Brazil, spearheaded this disruptive study. ■ Beyond Classical Accumulation: Quantum Capacitance The researchers constructed a self-assembled monolayer (SAM) composed of single-stranded DNA aptamers and a spacer molecule, 6-mercapto-1-hexanol (MCH), on a gold (Au) electrode surface, and measured the binding of the dengue NS1 protein using non-Faradaic electrochemical capacitance spectroscopy (ECS). The most prominent innovation lies in the shift in how this system is interpreted. While existing models described the sensor using the classical capacitance of an electric double layer where charge accumulates geometrically, this study reinterpreted the interaction between the molecular layer and the electrode as an intrinsic electron transport characteristic known as quantum capacitance ($C_q$). This means that the interaction between the protein molecule and the aptamer goes beyond simple physicochemical docking; it directly modulates the density of electronic states (DOS) of the sensor interface and the discrete energy levels of individual molecules, thereby controlling the quantum tunneling and transfer mechanisms of electrons. ■ Overwhelming Analytical Performance Driven by Quantum Control Based on this quantum-biological interface model, the research team precisely optimized the ratio of aptamer to MCH molecules on the sensor surface to 1:50. The optimized platform successfully maintained a broad linear response ranging from 0.01 ng/mL to 1,000 ng/mL, not only in simple laboratory buffers (PBS) but also in commercial human serum entangled with complex ions and proteins. In particular, this aptasensor recorded an outstanding limit of detection (LoD) of 25.8 ng/mL in a human serum environment, clearly demonstrating how an understanding of quantum characteristics can serve as powerful leverage to break through existing biochemical limitations and design highly sensitive diagnostic devices. This study will serve as a crucial starting point for completely restructuring future protein detection and disease diagnosis technologies from the perspective of microscopic quantum dynamics rather than macroscopic chemical reactions. #QuantumBiology #Biosensor #Aptasensor #ProteinDetection #ElectrochemicalSensor #QuantumCapacitance #UltrasensitiveDiagnostics #Nanotechnology #Biophysics #KoreanSocietyOfQuantumBiology https://www.frontiersin.org/journals/photonics/articles/10.3389/fphot.2026.1714572/full

inquantio

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Posted on the international preprint server bioRxiv, the study provides the first direct demonstration—using multi-electrode array (MEA) electrophysiology—that the two stable lithium isotopes 6Li and 7Li exert giant and opposite effects on synaptic transmission in the rat hippocampus. 6Li rapidly strengthened synaptic transmission (amplitude +32%), whereas 7Li produced a slower but larger suppression (amplitude −58%). The difference in effect size between the two isotopes overwhelmingly exceeded the thresholds for both statistical and practical significance (Cohen's d > 300). The phenomenon is interpreted as arising from a difference in nuclear spin (7Li, spin 3/2; 6Li, spin 1) rather than mass, offering the first direct neurophysiological evidence in support of the brain's quantum cognition hypothesis. [Quantum Biology Society] Since Cade discovered lithium's dramatic neurological effects in the 1940s, the element has been studied intensively for more than seventy years, and since the mid-1970s it has served as a frontline medication for bipolar disorder. Its potential benefits are also being explored in other neurological disorders, including Alzheimer's disease. Yet despite this long clinical history and an enormous body of research, the precise mechanism by which lithium acts in the brain remains incompletely understood. It has long been known that the lithium salts prescribed in the clinic are a mixture of two stable isotopes—6Li (natural abundance 7.49%) and 7Li (92.51%)—but the question of what unique neurobiological effects each isotope produces has only recently begun to receive serious attention. A study titled "Giant and Opposite Lithium Isotope Effects on Rat Hippocampus Synaptic Activity Revealed by Multi-Electrode Array Electrophysiology" was recently posted on the international preprint server bioRxiv. Carried out by a collaborative team at the University of Waterloo in Canada spanning physics, nanotechnology, and public health—including Khadijeh Esmaeilpour, Irina Bukhteeva, Michel J.P. Gingras, Zoya Leonenko, and John G. Mielke—the work was supported by Canada's New Frontiers in Research Fund – Exploration (NFRF-E) program and the Quantum Brain Network led by Matthew Fisher (UCSB). By measuring in real time, in living brain slices, how the two lithium isotopes affect synaptic activity, the researchers demonstrated for the first time that 6Li and 7Li produce giant effects in diametrically opposite directions. ■ 6Li and 7Li: Giant and Opposite Effects on Synaptic Transmission The team prepared 350-micrometer-thick slices from the hippocampus of eight-week-old male rats, positioned the CA1 region over a 64-point multi-electrode array (MEA), and stimulated the Schaffer collateral pathway to record the evoked field excitatory postsynaptic potentials (fEPSPs). After recording a 20-minute baseline, they perfused the slices with a 20 mM lithium chloride solution for 20 minutes and then washed them out with artificial cerebrospinal fluid for a further 20 minutes. The results were dramatic. Perfusion with natural-abundance lithium (n-LiCl) reduced the fEPSP amplitude by 37%, and 7Li alone (7LiCl) produced an even stronger 58% decrease. In stark contrast, 6Li alone (6LiCl) did the opposite, driving a 32% increase in amplitude—the two isotopes pushing synaptic transmission in opposite directions. The difference between them overwhelmingly surpassed the thresholds for both statistical and practical significance (amplitude: t(8) = 481.9, p < .0001, Cohen's d = 305.7), an effect size rarely seen in neurophysiological experiments. The two isotopes also differed markedly in the speed of their response. The reaction to 6Li was very fast, reaching saturation within three minutes, whereas the responses to n-Li and 7Li were much slower, taking about ten minutes to reach their maximal change. The difference persisted even after washout: slices treated with 7Li remained 9% below baseline, while those treated with 6Li stayed 10% above it. Moreover, the chloride (LiCl) and carbonate (Li2CO3) salts yielded the same direction and a similar magnitude of effect, strongly confirming the reproducibility of the findings. ■ Why Nuclear Spin? A Quantum Biology Perspective 6Li and 7Li are chemically identical and differ only in the mass and spin of their nuclei: 7Li is a spin-3/2 nucleus and 6Li a spin-1 nucleus. The researchers argued that the very fast response to 6Li, together with the opposite effects of the two isotopes, points to a difference in nuclear spin—rather than mass-dependent factors such as slight differences in diffusion constants—as the origin of the phenomenon, since mass differences alone cannot readily account for a giant effect that reverses direction. This interpretation aligns with theoretical proposals from the expanding field of quantum biology. Matthew Fisher has advanced a quantum cognition hypothesis in which the brain could process quantum information through the nuclear spins of phosphorus atoms held within calcium phosphate clusters known as Posner molecules. Zadeh-Haghighi and Simon have separately proposed that an entangled radical-pair mechanism could explain lithium's effects on hyperactivity. Differences between the lithium isotopes have, in fact, already been observed experimentally in mitochondrial calcium cycling and in the in vitro formation of calcium phosphate clusters. The authors raised the possibility that the giant differences in synaptic activity observed here may stem from the two isotopes acting differently on the mitochondrial processes that govern synaptic activity. At the same time, they noted that a precise theoretical account of their observations is not yet available. ■ Distinct Roles Emerge in Short- and Long-Term Synaptic Plasticity The team also compared the two isotopes in synaptic plasticity, widely regarded as the basis of memory formation. In paired-pulse facilitation (PPF), a measure of short-term plasticity on the order of milliseconds, the two groups were nearly identical before lithium perfusion (127% and 130%, respectively) but diverged sharply afterward: 6Li markedly enhanced PPF to 169%, whereas 7Li suppressed it to 91%. The opposite-direction effects seen in synaptic transmission were thus reproduced in short-term plasticity as well. Long-term potentiation (LTP), which unfolds over tens of minutes, showed a somewhat different pattern. In the induction phase immediately following high-frequency stimulation (HFS), both isotopes produced potentiation, but of different magnitudes: 7Li drove a large 60% increase, while 6Li produced a much smaller 15% increase (p = .0003). In the later maintenance phase, by contrast, there was no significant difference between the two isotopes. Because both PPF and the induction phase of LTP are known to arise from rapid changes in presynaptic calcium (Ca2+) levels, the researchers focused on the possibility that the lithium isotopes act differently, primarily on presynaptic function. ■ The Significance and Outlook of the Study The central significance of this work lies in its directness. Previous studies of lithium isotope effects had remained either theoretical—such as the Posner molecule proposal—or confined to the cellular and biochemical level, as with mitochondrial calcium transport. This paper, by contrast, is the first empirical study to measure synaptic transmission in living brain slices in real time using electrophysiology, largely sidestepping the interpretive difficulties that have beset earlier behavioral experiments. The authors concluded that their findings could help clarify the presynaptic mechanisms underlying lithium's action as a mood stabilizer and, more broadly, pose new questions for quantum biology about how mass and/or nuclear spin might give rise to such effects. Above all, the study suggests that 6Li and 7Li may not be merely two forms of the same drug but pharmacologically distinct agents in their own right—a prospect that opens the door to precision therapeutic strategies that selectively harness a particular isotope. In doing so, the work lays a foundation for future research into quantum phenomena in neuronal activity. #QuantumBiology #Lithium #LithiumIsotopes #QuantumCognition #NuclearSpin #Neuroscience #Synapse #BipolarDisorder #Hippocampus #PosnerMolecule https://www.biorxiv.org/content/10.1101/2025.08.23.671929v1