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Earlier sources: Science
Science investigates the natural world through observation, experiments, and mathematical models. Explore papers and developments in biology, medicine, materials, neuroscience, physics, climate, and mathematics; a reported finding is not necessarily established consensus.
SYMTRI organizes observations from public sources into a living map. Connections reflect shared subject matter; they do not establish causation or endorse a source.
Open in the map ↗1450 classified signals in the fourteen-day window. Recent momentum: steady. These counts include signals awaiting readable evidence.
Earlier sources
Up to ten sources with readable evidence, ordered by their feed timestamp. These archive sources can be older than the live fourteen-day window. This list is a sample, not a measure of total activity. Dates distinguish paper publication, repository creation, model/dataset activity, Hacker News posts, and KEV catalog additions. “First archived” records when SYMTRI first stored the signal.
- OpenAlex · Published · First archived
Single-photon controlled phase shifts with colloidal quantum dots at room temperature ↗
Quantum logic gates based on quantum electrodynamics (QED) in strong coupling regime between quantum dots and microcavities are key elements for scalable quantum computing and all-optical quantum networks. Here, a single colloidal quantum dot was site-selectively positioned onto a dielectric photonic crystal hybrid microcavity using a soft nanoprobe technique, enabling the study of strong coupling between the colloidal quantum dots and bound states in the continuum in the cavity. By combining photoluminescence spectroscopy, Michelson interferometry, and continuous-wave pump-probe technology, t…
- OpenAlex · Published · First archived
Biomolecules under Pressure: Protein Phase Transitions from Folding to Misfolding, Aggregation, and Condensation ↗
Abstract High hydrostatic pressure provides a unique and sensitive perturbation to protein structure, enabling direct measurement of volume changes associated with folding, misfolding, and assembly. Because pressure perturbs noncovalent forces while preserving covalent integrity, it reveals conformational intermediates that often remain hidden from thermal or chemical denaturation. Over the past decade, advances in high-pressure NMR, fluorescence spectroscopy, time-resolved crystallography, and single-molecule spectroscopy have transformed pressure into a high-resolution probe of folding lands…
- OpenAlex · Published · First archived
A conserved archaeal protein with evolutionary links to bacterial ribosome hibernation and eukaryotic energy sensing ↗
Ribosome hibernation helps cells survive stress by reversibly silencing translation and preserving ribosomal complexes. Although well characterized in bacteria and eukaryotes, archaeal hibernation remains poorly understood. Using cryoEM of archaeal lysates, we identified AHA (AMPKγ-HPF from Archaea), a broadly conserved ribosome-associated factor composed of two modules. AHA bound across ribosomal subunits, occluding the mRNA channel and tRNA binding sites, supporting its role in ribosome hibernation. ΔAHA cells displayed reduced viability, loss of ribosomal proteins in the stationary phase, a…
- OpenAlex · Published · First archived
Computational Design Strategies for Nanoscale 3D Auxetic Metastructures from DNA ↗
Abstract Auxetic metamaterials exhibit negative Poisson’s ratio (NPR) behaviors due to their architecture of periodically arranged unit cells. Although mechanical metamaterials are well established at the macroscale, programmable auxetic units remain scarce at the nanoscale. DNA origami offers a promising platform to bridge this gap, but design principles for dynamically deformable 3D auxetic nanostructures remain largely unexplored. Here, we develop design strategies for such 3D auxetic metastructures built from wireframe DNA origami. As a model system, we use a 3D re-entrant triangular unit…
- OpenAlex · Published · First archived
PIGNN3D: an accelerated physics-informed graph neural network for 3D thermal field simulation in data centers ↗
Abstract Efficient thermal management is critical in data centers, where computational fluid dynamics (CFD) simulations provide high-fidelity airflow and temperature predictions but remain computationally demanding and time-intensive. While data-driven methods have emerged as promising alternatives, most existing works are limited to small-scale or 2D flow simulations and face challenges when extended to complex 3D domains. To overcome these challenges, we present PIGNN3D, a physics-informed graph neural network designed for fast and efficient 3D thermal field simulation in data centers. PIGNN…
- OpenAlex · Published · First archived
Interpretable and Calibrated Classification of Clinical Data Using Supervised Feature Binarization ↗
Black-box models limit the adoption of artificial intelligence in medicine because their predictions are difficult to interpret and reproduce. We present a statistically grounded framework for interpretable, rule-based clinical classification using the Bernoulli Naïve Bayes (BNB) model. Supervised chi-square-guided binarization converts continuous variables into binary indicators by selecting thresholds that maximize association with the clinical outcome within the training folds, which allows BNB to operate on continuous medical data without sacrificing transparency. On three benchmark datase…
- OpenAlex · Published · First archived
Entanglement-induced resilience of quantum dynamics ↗
Quantum many-body devices suffer from imperfections that destabilize dynamics and limit scalability. We show that the dynamical growth of entanglement can intrinsically protect generic quantum dynamics against coherent noise and local perturbative noise. Through rigorous theoretical analysis of general quantum dynamics and numerical simulations of spin chains and fermionic lattices, we prove that entanglement entropy growth confines the influence of local Hamiltonian perturbations, thereby suppressing dynamical errors. The degree of protection correlates quantitatively with the entanglement en…
- OpenAlex · Published · First archived
Potential of multi-anomalies detection using quantum machine learning ↗
Machine learning is widely used for industrial anomaly detection; however, when anomaly distributions overlap, high false positive rates (FPR) often occur, hindering practical implementation. While quantum kernel methods have been proposed as a solution, the specific conditions under which they outperform conventional methods remain unclear. This study investigated the conditions under which quantum kernels outperform the classical RBF kernel in industrial acoustic anomaly detection. Using autoregressive (AR) features and an unsupervised One-Class SVM, we compared a conventional radial basis f…
- Europe PMC · Published · First archived
Thermococcales pangenome uncovers new genera and modular evolution. ↗
The order Thermococcales is unusual among Archaea in being known primarily from hundreds of lab-cultured isolates rather than metagenome-assembled genomes. Such cultivation is powerful for comparative genomics, allowing access to complete closed genomes rather than fragmented assemblies. We analysed 122 complete genomes, comprising all publicly available type species plus 67 new isolates, as a single pangenome. A combination of genome-scale metrics resolved 70 species across five genus-level clades, two proposed here as the new genera Oleococcus and Piezococcus, in addition to the canonical Py
- Europe PMC · Published · First archived
RASTRO: Branching Point Decision-Making in Unitig Graphs ↗
Motivation: De novo genome assembly relies on accurate path selection through complex graphs, yet unitig graph branching points remain a major ambiguity source. Multiple extensions may be equally plausible from topology alone, but sequence-level consistency of competing paths remains largely unexplored. Model: We introduce RASTRO, a reference-free framework for scoring candidate paths at unitig branching points using nucleotide sequence information. RASTRO-S employs a sample-specific n-gram model to estimate extension plausibility via bits-per-base surprisal, while RASTRO-L uses the Evo2 pretr
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Check each original source for its methods, release status, limitations, and supporting data. A repository announcement, a preprint, and a published study provide different kinds of evidence. SYMTRI’s subject classification helps discovery; it does not independently validate the source’s claims.
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