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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 ↗1706 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
A Frozen Plant DNA Language Model for Computational Prioritization and Functional Annotation of Melon (Cucumis melo) Variants ↗
Background/Objectives: Fruit quality in melon is determined by complex traits including sugar accumulation, aroma, flesh color, ripening behavior and disease resistance. Conventional genomic selection provides limited mechanistic interpretation, especially for regulatory and de novo variants. We adapted the EVEE interpretable embedding–probing framework to melon using PlantCAD2 as a frozen DNA language model backbone. Methods: The model was not fine-tuned or further pre-trained. Instead, lightweight probes were trained on fixed embeddings derived from sequence windows surrounding genetic varia…
- OpenAlex · Published · First archived
Trip-End Battery State of Charge Prediction for Electric Vehicles Using a Physics-Constrained Machine Learning Stacking Ensemble ↗
This study proposes an approach for predicting the state of charge (SoC) of an electric vehicle battery at the end of an individual operational interval using parameters available before the start of the trip. The input features included the initial SoC, trip distance, and ambient temperature. The driving range estimated by the onboard system was also considered, as it summarizes information about the vehicle’s previous operating conditions. The direct target variable was the change in state of charge over the operational interval (Delta SoC), which was subsequently used to calculate the state…
- OpenAlex · Published · First archived
Targeting DNA Methyltransferases with a Pemetrexed–Mitomycin C Combination in Bladder Cancer ↗
Objective: The goal of this study was to investigate the efficacy and underlying mechanisms of combined treatment with pemetrexed and mitomycin-C for metastatic bladder cancer. Pemetrexed reduces cancer cell proliferation by impairing DNA and RNA biosynthesis, whereas mitomycin-C induces cytotoxicity through disruption of DNA. Materials and Methods: The cytotoxic effects of the pemetrexed–mitomycin-C combination was evaluated in human bladder cancer cells (UM-UC-3) via Annexin V/PI-based flow cytometric analysis, followed by evaluation of DNMT1 mRNA and protein levels. Statistical analyses wer…
- OpenAlex · Published · First archived
Clinical evaluation of three deep learning contouring models for head and neck organs at risk and lymph node levels ↗
Introduction Our aim was to compare the accuracy and clinical acceptability of head and neck organs at risk (OAR) and lymph node contours generated by three commercially available deep learning contouring (DLC) models: Mirada DLCExpert (Model D), Limbus AI (Model L), and MVision AI Contour+ (Model C). Methods Seven OARs and cervical lymph node levels I to V were delineated by each model on twenty retrospectively selected patients with hypopharyngeal/laryngeal tumors (2020 to 2023). Automated contours were compared against expert reference contours using the Dice similarity coefficient (DSC), t…
- OpenAlex · Published · First archived
Measurement-Driven Path-Loss Modeling for Low-Altitude UAV Air-to-Ground Links Using Physics-Aware Ensemble Learning and Calibrated Uncertainty ↗
Air-to-ground (A2G) channel modeling supports low-altitude wireless networks for unmanned aerial vehicle (UAV) services in 5G/6G systems. Existing data-driven models are often benchmarked against weak empirical baselines, decoupled from the underlying measurement campaigns, and prone to overfitting on small UAV datasets. This paper presents PACE-A2G, a calibrated deep-tree ensemble on physics-aware features, together with a stricter evaluation methodology. A quadrotor-mounted software-defined-radio measurement system produces five datasets covering 433, 740, 1500, and 3300 MHz in overwater, su…
- OpenAlex · Published · First archived
Association of DNA Copy Number Aberrations with Organ-Specific Metastasis in Luminal B HER2-Negative Breast Cancer ↗
Breast cancer (BC) exhibits considerable metastatic heterogeneity and distinct patterns of organotropism, which contribute to differences in patient outcomes and therapeutic responses. However, the molecular mechanisms underlying this organ specificity remain incompletely understood. Objective: To investigate the genetic landscape of primary breast tumor, surgical specimens, and metastatic lesions and to identify copy number aberration (CNA) regions associated with organ-specific metastatic-site patterns in breast cancer. Materials and methods: The study included paired pretreatment and post-t…
- OpenAlex · Published · First archived
A hybrid physics and machine learning framework for trajectory prediction and uncertainty quantification of undrained behaviour in gassy clay ↗
The undrained response of gassy clay is strongly path-dependent and uncertain, making continuous trajectory prediction difficult when only limited experimental data are available. This study develops a lightweight physics-guided machine learning framework for path-level prediction and uncertainty quantification under undrained shearing. The framework combines MCC-based physical-reference trajectories, which provide stress-history-dependent mechanical information, with experimental gassy-clay trajectories, which provide target-domain response information. Both data sources are converted into a…
- OpenAlex · Published · First archived
Federated hybrid quantum convolutional neural network for Alzheimer’s disease classification using magnetic resonance imaging ↗
Alzheimer’s disease (AD) is a progressive neurological disorder and one of the most common types of dementia, causing impaired memory and quality of life. Therefore, an early and accurate diagnosis is essential for intervention and patient care. Convolutional Neural Networks (CNNs) and other deep learning models have demonstrated promise in medical imaging but may need centralized collection of data, which raises privacy concerns. In this study, we propose a Federated Hybrid Quantum Convolutional Neural Network (FH-QCNN) that integrates quantum computing with federated learning for privacy-pre…
- OpenAlex · Published · First archived
Anthony James Leggett: Explorer of quantum mechanics, from fluids and solids to the macroscopic world ↗
Tony Leggett (1938-2026) was one of the great theoretical physicists of his generation, specializing in the quantum mechanics of fluids and solids. Rightly recognized by the 2003 Nobel Prize in Physics for his explanation of the experiments discovering the superfluidity of helium-3, he will be remembered equally for his pioneering inquiries into the transition from the quantum to the classical world and the extent to which we can observe quantum phenomena on macroscopic scales.
- OpenAlex · Published · First archived
Selection for Folding Stability Predicts Observed Covariation Between Protein Positions in the PDB ↗
Statistical couplings between protein sites have been enormously useful for protein structure prediction, but there is still debate on the selective forces that generate them. Here we explicitly predict the couplings considering selection for protein folding stability, both against unfolding and misfolding, which we model through the Stability constrained model of protein evolution (SCPE). The SCPE is based on contact interactions, and it predicts stability against misfolding through the Random Energy Model in the space of contact matrices. We adopt the minimum selection principle, assuming th…
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How to assess evidence about Science
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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