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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 ↗1350 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
Visual and Equipment-Minimized Detection of Avian Metapneumovirus Subtype C by RT-RPA-CRISPR/Cas12a Coupled with Lateral Flow Dipstick ↗
Avian metapneumovirus (aMPV) is the main etiological agent of turkey rhinotracheitis, causing respiratory distress and economic losses in turkeys. While its impact in broilers and laying hens is relatively limited, aMPV subtype C (aMPV/C) is an important pathogen in ducks and waterfowl, leading to respiratory distress and severe egg-drop syndrome. To overcome the limitations of current diagnostics, such as heavy reliance on laboratory infrastructure and prolonged turnaround times, we established a rapid detection assay integrating recombinase polymerase amplification (RPA) with CRISPR/Cas12a a…
- OpenAlex · Published · First archived
3D vascular quantitation with application to computational modeling: a pre-clinical light sheet microscopy, high resolution ultrasound, nano-computed tomography comparison study ↗
Abstract It is increasingly necessary to both study biology in 3D and obtain quantitative measurements. Not all 3D-reconstructions are created equal, particularly when using the anatomical model as a basis for force calculations, i.e. computational modeling. Here, we compare 3D anatomical reconstructions from two emerging imaging modalities: 4D ultrasound (4DUS) and light sheet fluorescent microscopy (LSFM) against our previous nano-computed tomography (nanoCT) cohort data, using the tortuous highly intricate pharyngeal arch artery system of the chick embryo as a test bed. We highlight modalit…
- OpenAlex · Published · First archived
The risk of over-interpreting in silico predictions as clinical evidence: a comment on Roy et al. (2024) ↗
Roy et al. reported an extensive computational analysis of nonsynonymous single-nucleotide variants (nsSNPs) in the human CYLD gene and characterized several variants as potentially disease- or cancer-associated. Although such analyses can be valuable for generating hypotheses, computational predictions should be clearly distinguished from clinical or functional evidence. Among the 18 "high-risk nsSNPs" prioritized by the authors, only p.Glu747Gly (E747G) has established clinical evidence supporting pathogenicity, whereas the remaining variants lack sufficient evidence for pathogenic classific…
- OpenAlex · Published · First archived
Physics-informed single-frame end-to-end learning for denoising and background removal in fluorescence imaging ↗
Fluorescence microscopy is often limited by low signal-to-noise ratios arising from the finite number of detected photons and by an out-of-focus background that obscures structural details. Here, we introduce a physics-informed end-to-end learning framework that enables denoising and background suppression from a single fluorescence image. By integrating a microscope-parameterized forward model, the proposed framework generates realistic training datasets entirely through simulation, eliminating the need for experimentally acquired ground-truth images. Once trained for a given imaging conditio…
- OpenAlex · Published · First archived
Selective Biexciton Generation under Energy-Time Entangled Quantum Light in Quantum Dots ↗
Abstract Quantum light provides opportunities for controlling multiphoton absorption beyond the classical limits. Here, we investigate biexciton generation in nanocrystal quantum dots driven by energy-time-entangled photon pairs generated via spontaneous parametric downconversion. We show that frequency–time correlations between pairs of photons increase the population of biexcitons over excitons, thereby lending specificity to the excitation of many-body states. By employing a three-level model, we demonstrate that biexciton generation depends nontrivially on the photon entanglement time and…
- OpenAlex · Published · First archived
Advancing mathematics research with AI-driven formal proof search ↗
Large language models (LLMs) increasingly excel at mathematics tasks, but their unreliability limits their utility in mathematics research. A mitigation is to use LLMs to generate formal proofs in languages such as Lean, in which the compiler verifies every proof step. We present the first demonstration of this method’s value in solving open problems at scale. We built an artificial intelligence agent for formal proof search that autonomously resolved nine of 353 open Erdős problems, proved 44/492 On-Line Encyclopedia of Integer Sequences conjectures, and is being deployed in combinatorics, op…
- OpenAlex · Published · First archived
Error semitransparent universal control of a bosonic logical qubit ↗
Abstract Bosonic codes offer hardware-efficient quantum error correction, recently achieving beyond-breakeven quantum memory. However, fault-tolerant logical operations remain a critical bottleneck. While error transparent (ET) gates offer a pathway to resolve this, experimental demonstrations remain limited to phase gates. Here, we introduce a dynamic encoding subspaces framework that enables simple linear drives to accomplish universal logical gates that are error semi-transparent (EsT) to oscillator photon loss. Our EsT logical gate set of { X , H , T } exhibited a five-fold reduction in in…
- OpenAlex · Published · First archived
Quantum Spacetime from Constraints: Wave Equations and Fields ↗
Abstract In previous works, we showed that both time and space can emerge from entanglement within a globally constrained quantum Universe, with no background coordinates. By extending the Page and Wootters quantum time formalism to include both quantum clocks and rods, and imposing global constraints on total energy and momentum, we constructed a fully relational model of quantum spacetime. Here we take a further step: working in 1+1 dimensions, we show that the standard wave equations governing quantum particles–the Schrödinger, Klein-Gordon and Dirac equations–emerge naturally from this fra…
- OpenAlex · Published · First archived
Quantum algorithms for general nonlinear dynamics based on the Carleman embedding ↗
Important nonlinear dynamics, such as those found in plasma and fluid systems, are typically hard to simulate on classical computers. Thus, if fault-tolerant quantum computers could efficiently solve such nonlinear problems, it would be a transformative change for many industries. In a recent breakthrough [Liu et al., PNAS 2021], the first efficient quantum algorithm for solving nonlinear differential equations was constructed, based on a single condition R < 1 , where R characterizes the ratio of nonlinearity to dissipation. This result, however, is limited to the class of purely dissipative…
- OpenAlex · Published · First archived
Quantum convolutional neural networks for jet images classification ↗
Abstract Recently, interest in quantum computing has significantly increased, driven by its potential advantages over classical techniques. Quantum machine learning (QML) exemplifies one of the important quantum computing applications that are expected to surpass classical machine learning in a wide range of instances. This paper addresses the performance of QML in the context of high-energy physics (HEP). As an example, we focus on the top-quark tagging, for which classical convolutional neural networks (CNNs) have been effective but fall short in accuracy when dealing with highly energetic j…
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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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