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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 ↗1327 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
An efficiency-oriented physics-informed transfer learning framework for transient simulation across diverse properties and conditions in liquid energy pipelines ↗
As pipeline-transported liquid energy has expanded to include hydrogen-based and bio-based fuels, achieving efficient and rapid simulations across diverse physical properties and conditions is essential for timely early warning of anomalies. However, conventional numerical computation is hindered by iterative parameter identification, precluding computational efficiency. Emerging physics-informed machine learning techniques necessitate excessive retraining whenever fluid properties or conditions change, which might take hours. Furthermore, they struggle to accurately capture the transient osci…
- OpenAlex · Published · First archived
Ribonuclease H1 Hijacking: Oncotherapeutics through MicroRNA-Triggered Intracellular Construction of Long DNA/RNA Heteroduplexes ↗
Abstract Ribonuclease H1 (RNase H1) cleaves the RNA strand of DNA/RNA heteroduplexes and plays essential roles in R-loop resolution, mitochondrial DNA replication, and antisense oligonucleotide (ASO) activity. Because RNase H1 has also been implicated in ASO-induced cytotoxicity, considerable effort has been focused on minimizing its activation. Here we present a reverse strategy that harnesses RNase H1-mediated cytotoxicity for cancer therapy using an oncogenic microRNA (miR-21) as an intracellular trigger. A designed hairpin DNA/hairpin RNA pair undergoes a hybridization chain reaction to as…
- OpenAlex · Published · First archived
Sequence‐encoded dynamics diversify allostery within a conserved protein scaffold ↗
Abstract Proteins evolve allosteric mechanisms through sequence variation while preserving a conserved structural fold. How these substitutions reshape the protein's conformational landscape to drive functional diversification remains poorly understood. Members of the cAMP receptor protein family (CRP), a ubiquitous bacterial transcription factor, share a common architecture yet differentially couple cAMP binding to DNA interactions. Here, our results support a model in which allosteric diversification in CRP arises from the tuning of sequence‐encoded protein's conformational dynamics. Compara…
- OpenAlex · Published · First archived
Interpretable AI-guided discovery of degradable ionizable lipids with enhanced hepatic mRNA delivery in vivo ↗
Abstract Lipid nanoparticles (LNPs) enable RNA medicines, yet ionizable-lipid discovery remains largely empirical, particularly when in vitro activity must be translated into functional hepatic delivery in vivo. Here we establish an interpretable discovery framework that combines modular degradable lipid chemistry, high-throughput phenotyping, machine learning, virtual library expansion, and independent in vivo validation to define a learnable ionizable-lipid design space. Starting from a 520-member library, we train an XGBoost model on firefly luciferase (Fluc) expression in HeLa cells and us…
- OpenAlex · Published · First archived
Multiplexed single-molecule analysis of barcoded protein variants ↗
Abstract Single-molecule techniques provide exceptional resolution of biomolecular dynamics but are limited by low throughput. Here, we present a DNA barcoding strategy that enables parallel kinetic profiling of multiple protein variants in a single experiment. Each protein is covalently linked to a unique DNA barcode, decoded through transient hybridisation of fluorescent probes distinguished by colour and binding duration. Applied to a library of 16 ClpS variants, this approach revealed ~5-fold variation in dissociation kinetics and uncovered previously unrecognised bimodal binding behaviour…
- OpenAlex · Published · First archived
Unsupervised Machine Learning Identifies Shared Biomechanical and Clinical Variation in Individuals with Knee Osteoarthritis and High BMI: A Secondary Analysis of the IDEA Trial ↗
OBJECTIVE: ) and aberrant gait biomechanics are primary risk factors for knee osteoarthritis (KOA), yet the specific loading patterns linked to clinical disease features remain unclear. Unsupervised machine learning (ML) was used to identify vertical ground reaction force (vGRF) waveform modes of variation and characterize the shared and individual variation between vGRF profiles and clinical features in individuals with KOA and high BMI. METHODS: ) were analyzed. vGRF waveforms (n=2,394) were represented using three functional data analysis methods: time- and bodyweight-normalization; elastic…
- OpenAlex · Published · First archived
Clearing the way for optical mapping of the brain with light-sheet fluorescence microscopy ↗
Abstract Light-sheet fluorescence microscopy (LSFM) has emerged as a powerful optical imaging technique for studying complex biological systems, particularly in neuroscience. By selectively illuminating a thin optical plane and combining it with camera-based widefield detection, LSFM enables rapid volumetric imaging with reduced photobleaching and phototoxicity compared to point-scanning approaches, such as confocal and multiphoton microscopy. Originally developed for imaging small, transparent organisms, LSFM has evolved into a versatile platform capable of probing large specimens, including…
- OpenAlex · Published · First archived
Tokenizing battery degradation: a physics-informed and uncertainty-aware framework for state-of-health estimation ↗
Accurate and trustworthy estimation of State of Health (SOH) is essential for the safe and efficient operation of modern battery management systems. While data-driven methods have achieved high accuracy, they often lack physical grounding and fail to provide reliable confidence metrics for their predictions, posing risks in safety-critical applications. In this work, we introduce TBD-SOH, an SOH estimation framework that combines physics-informed feature construction with uncertainty-aware regression. Specifically, each charging cycle is represented as a fused cycle-level token constructed fro…
- OpenAlex · Published · First archived
Genetic and metabolomic differentiation of Dryas octopetala in Svalbard: genome-wide SNP evidence and region-specific marker development ↗
Abstract Background Dryas octopetala , the dominant tundra species in Svalbard, is a suitable model for examining population differentiation under extreme environmental conditions. Although earlier amplified fragment length polymorphism and simple sequence repeat studies have provided baseline information, the genomic and metabolomic structures within the Svalbard populations have not been comprehensively characterized. Therefore, we aimed to investigate the genetic structure of D. octopetala populations across seven sites in Svalbard using genome-wide single nucleotide polymorphism (SNP) geno…
- OpenAlex · Published · First archived
Physics-Informed Multi-Parameter Optimization of Oxide-Based Memristor Characteristics Using Genetic Algorithms ↗
Abstract We present a multiobjective, physics-informed optimization framework for oxide-based memristive devices that jointly minimizes forming voltage (Vf) and maximizes memory contrast (HRS/LRS). The proposed approach integrates COMSOL Multiphysics with MATLAB through a genetic algorithm (GA), enabling self-consistent electrothermal simulations and systematic exploration of a high-dimensional design space encompassing geometrical, electrical, thermal, and operational parameters. By optimizing 13 key variables, the GA efficiently identifies optimal trade-offs between switching efficiency and…
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