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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 ↗1659 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
Developing Metal–Polyphenol Network‐Based Single‐Protein Particle Encapsulation System for Sustained Release of Whey Protein to Mitigate Sarcopenia ↗
ABSTRACT Sarcopenia has emerged as a major health challenge in the aging population. Whey protein (WP)—rich in branched‐chain amino acids—is an important nutritional supplement for mitigating sarcopenia. However, the rapid digestion of WP limits its therapeutic efficacy. Developing a sustained‐release system for WP may enhance its effectiveness in mitigating sarcopenia. Herein, a novel “single‐protein particle encapsulation” (SPPE) system is developed to protect WP and control its release. Specifically, the “molecular glue” metal–polyphenol networks (MPNs) are assembled onto the surface of ind…
- OpenAlex · Published · First archived
A Versatile DNA‐Encoded Library Platform for the Discovery of Highly Cooperative Chemical Inducers of Proximity ↗
Chemical-induced proximity, particularly via molecular glues, represents a rapidly advancing therapeutic paradigm. Expanding the scope of proximity-based therapeutics requires generalizable discovery platforms that can be easily tailored to diverse presenter proteins. Here, we report a versatile DNA-encoded library (DEL) strategy designed to identify highly cooperative chemical inducers of proximity (CIPs) for multiple presenter proteins. By leveraging a novel precursor library constructed via on-DNA strained allene cycloadditions, we generated three distinct CIP-DELs biased toward Cereblon (C…
- OpenAlex · Published · First archived
Bio‐Memristor Based on Clinical Bile Samples: A Proof‐of‐Concept Pilot Study for Detection of Gallbladder Cancer ↗
Gallbladder cancer (GBC) is the most common malignant tumor of the biliary system, and the 5-year survival rate of GBC patients is only 5% due to delayed diagnosis. The sensitivity and specificity of existing clinical testing equipment for gallbladder malignancy remain insufficient. In this proof-of-concept pilot study, we developed a bio-memristor featuring an Ag/bile/fluorine-doped tin oxide structure using clinically sourced bile samples from a small clinical cohort and characterized its resistive switching behavior. The memristive electrical signatures, particularly negative peak current a…
- OpenAlex · Published · First archived
In Situ detection of protein-phosphoinositide signalosome in tissues by immunohistochemistry-coupled proximity ligation assay ↗
Abstract Phosphoinositides (PIPs) are central lipid signaling molecules, with PtdIns(3,4,5)P 3 acting as a key regulator of the PI3K-AKT pathway and a major driver of tumorigenesis. Together with p53 mutations or loss, this pathway represents one of the most frequently dysregulated axes across human cancers. We recently identified a nuclear p53-PtdIns(3,4,5)P 3 signalosome that enables de novo AKT activation, promoting cancer cell survival, invasion, and chemoresistance, with potential biological and clinical relevance. However, in situ detection of such protein-lipid signalosome in tissues ha…
- OpenAlex · Published · First archived
Dipolar Coupling Errors in Nuclear Spin Qubits ↗
ABSTRACT Nuclear spins have been studied since the beginning of quantum information science since they form the prototypical qubit with long coherence times and high‐fidelity operation. To perform practical fault‐tolerant quantum computations, however, not only do we require precision manufacturing techniques to produce millions of qubits, but these techniques need to consistently yield low‐error qubits. A deep understanding and control of the interactions between the qubits and their environment are needed as we scale so that devices can be engineered to avoid high error rates. In this work,…
- OpenAlex · Published · First archived
Energy consumption prediction of range-extended electric vehicles using dependency-guided physics-regularised neural networks ↗
The reliable prediction of energy consumption for range-extended electric vehicles under unseen driving scenarios remains challenging due to variations in driving conditions and vehicle operating characteristics. This paper proposes a two-stage framework integrating a difference-gated long short-term memory (dLSTM) model with a dependency-guided physics-regularised neural network (DG-PRNN) model. The dLSTM captures short-horizon temporal variations in vehicle operating signals, while DG-PRNN introduces dependency-guided sparse connectivity and physics regularisation to improve prediction robus…
- OpenAlex · Published · First archived
Monotonicity-constrained physics-informed neural networks for concrete compressive strength prediction ↗
Abstract Accurate prediction of concrete compressive strength is critical for mix design optimization, quality control, and reliable decision-making in modern construction. While data-driven machine learning models have demonstrated strong predictive capability, most existing approaches treat concrete behavior as a purely statistical problem, often neglecting well-established physical relationships among mix constituents and curing conditions. This limitation can lead to physically inconsistent predictions and reduced interpretability, hindering adoption in engineering practice. To address the…
- OpenAlex · Published · First archived
A Heterozygous Variant in the GABBR2 Gene in a Girl With Clinical Classic Rett Syndrome ↗
ABSTRACT Background Rett syndrome (RTT) is a neurodevelopmental disorder mainly affecting females and may start with seemingly normal early development but leads to developmental stagnation, regression, and characteristic neurological symptoms. While most cases involve MECP2 variants, other genes have been implicated in RTT and RTT‐like phenotypes, but the underlying molecular mechanisms remain incompletely understood. Methods We describe a girl fulfilling the clinical diagnostic criteria for classic RTT in whom standard genetic testing, including MECP2 and CDKL5 , was normal. Trio‐based whole…
- OpenAlex · Published · First archived
Carbon-based materials coupled with metal oxide semiconductors for hydrogen sensors: linking mechanisms and dimensions ↗
Reliable H 2 detection is essential for hydrogen energy systems, industrial processing, battery safety, and clinical monitoring. Chemiresistive sensors based on metal oxide semiconductor (MOS) are widely used because of their low fabrication cost and facile integration. However, they still suffer from high operating temperatures, limited selectivity, poor humidity tolerance, and insufficient long-term stability. Incorporating carbonaceous materials with large specific surface areas, high carrier mobility, and tunable surface chemistry provides an effective route to address these limitations. T…
- OpenAlex · Published · First archived
Three-dimensional cementing displacement efficiency prediction based on domain-decomposed physics-informed neural network ↗
Efficient displacement of drilling fluid by cement slurry is essential for ensuring cementing quality, and accurate flow-field prediction is crucial for improving displacement efficiency. Although computational fluid dynamics (CFD) is widely used to investigate displacement mechanisms, high-fidelity three-dimensional simulations are computationally expensive and inefficient for rapid engineering prediction and optimization. In recent years, data-driven neural networks have attracted attention due to their high computational efficiency; however, they rely heavily on large datasets, are prone to…
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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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