Historical sediment apportionment using isotopic tracers requires the correction for temporal changes in the atmospheric 13C. However, soil organic matter does not reflect the atmospheric isotopic composition of a single year but rather integrates carbon inputs over time. Existing Suess corrections typically neglect the time integrated memory of soil organic matter, potentially leading to an over correction. Here, we introduce the CTSC model (Continuous Turnover-based Suess Correction), which incorporates continuous soil organic matter turnover into δ13C Suess corrections. The model accounts for time-integrated carbon input across a range of turnover times, providing a more mechanistic representation of isotopic dynamics in soils. The CTSC model provides a simple, accessible, and flexible framework for incorporating compound turnover time and Suess effect corrections into isotopic sediment fingerprinting and beyond.
AI agents may soon become capable of autonomously completing valuable, long-horizon tasks in diverse domains. Current benchmarks either do not measure real-world tasks, or are not sufficiently difficult to meaningfully measure frontier models. To this end, we present Terminal-Bench 2.0: a carefully curated hard benchmark composed of 89 tasks in computer terminal environments inspired by problems from real workflows. Each task features a unique environment, human-written solution, and comprehensive tests for verification. We show that frontier models and agents score less than 65% on the benchmark and conduct an error analysis to identify areas for model and agent improvement. We publish the dataset and evaluation harness to assist developers and researchers in future work at tbench.ai.
Articular cartilage, as a mechanosensitive tissue, supports and distributes various mechanical forces—including compression, shear, hydrostatic pressure, and tensile strain—during joint loading and motion. These external forces deform not only the chondrocytes but also their pericellular matrix and the surrounding extracellular matrix (ECM). Those mechanical cues are detected by mechanosensors on the plasma membrane (e.g., integrins) and transmitted through the cytoskeleton, ultimately being converted into biochemical signals. These signals activate key mechanoresponsive intracellular pathways—including TGF-β-induced SMAD, Rho-GTPase, MAPKs (ERK, JNK, p38), PI3K/AKT/mTOR, MRTF-SRF, and YAP/TAZ—that regulate chondrogenic differentiation and cartilage-specific matrix synthesis. This field of study is known as mechanobiology. Over the past decades, it has gained increasing recognition, particularly with the emergence of tissue-engineering constructs as a novel strategy for cartilage repair. However, progress in chondrogenic mechanobiology has primarily centred on intrinsic substrate- or matrix-derived cues, while overlooking the role of extrinsic mechanical forces. This review therefore provides an updated perspective on chondrogenic mechanobiology, with a particular focus on the cellular responses to external mechanical stimuli. It also emphasizes the therapeutic potential of incorporating mechanical stimulation into tissue-engineering strategies for cartilage repair, an emerging filed referred to as Regenerative Rehabilitation (RR). Since this concept has so far been investigated mainly in vitro, we highlight only those studies and refer to it as In vitro Regenerative Rehabilitation. Moreover, this review also addresses post-traumatic osteoarthritis (PTOA), a common joint disorder that frequently results from traumatic cartilage damage. It explores the mechanobiological mechanisms underlying OA and discusses in vitro regenerative rehabilitation studies, highlighting how external forces could serve as an alternative to conventional biochemical treatments for preventing OA progression.
Krypton-81 (81Kr) and chlorine-36 (36Cl) are among the few isotopic tracers capable of constraining groundwater residence times on 105-106 year timescales. In sedimentary aquifer systems bounded by low-permeability units, however, diffusive solute exchange can strongly modify tracer distributions and bias apparent ages derived from concentration ratios. In the transboundary Milk River Aquifer (MRA), progressive chloride enrichment caused by diffusion across shale aquitards complicates the interpretation of 36Cl/Cl as a chronometer. Here, we combine new measurements of 81Kr, 36Cl, stable chlorine isotopes (37Cl/35Cl), and 14C with advection-diffusion transport modeling to quantify the importance of matrix diffusion on tracer systematics and inferred groundwater ages. The simulations reproduce the observed decrease in 36Cl/Cl and concomitant increase in δ37Cl along regional flow paths, demonstrating that diffusive influx of Cl-rich aquitard water dominates the evolution of the chlorine isotope system. In contrast, modeled and observed 81Kr activities show substantially lower sensitivity to diffusive exchange over the timescales considered. A comparison of simulated and measured tracer relationships indicates that, in the MRA, apparent ages derived from 36Cl primarily reflect chloride addition rather than radioactive decay, whereas 81Kr provides a more robust and conservative chronometer for fossil groundwater. These results highlight the value of integrating stable and radioactive chlorine isotopes with noble gas dating and explicit transport modeling to disentangle decay from transport effects. The approach developed here provides a quantitative framework for interpreting multitracer data sets in regional aquifers affected by long-term diffusive exchange and has broader implications for assessing fossil groundwater resources in similar hydrogeological settings.
Objectives:Interindividual variability in analgesic responsiveness often results in therapy failure (TF) or adverse drug reactions (ADR) and poses a major challenge in chronic pain management, as it is influenced by multiple factors. This exploratory study investigated whether pharmacogenetic (PGx) testing could identify drug-gene interactions (DGIs) explaining variability in drug response. In addition, we explored whether genetic predispositions in CYP2D6 and COMT, indicating increased pain sensitivity, are linked to TF.Methods:We analyzed data from chronic pain patients who underwent pharmacogenotyping due to suspected TF or ADR. PGx panel testing was carried out by a commercial provider. Additional genotyping of COMT rs6269, rs4633, and rs4818 was performed using PCR, RFLP, and Sanger sequencing.Results:PGx panel testing confirmed at least one relevant genetic variant in 45% of the suspected DGIs. Notably, 41% involved the pharmacogenes CYP2D6, CYP2C19, and CYP2C9. Subgroup analyses revealed that patients carrying the COMT high pain sensitivity (HPS) allele, COMT high pain phenotype, or CYP2D6 intermediate metabolizer (IM) phenotype were significantly more likely to experience TF. Logistic regression confirmed both phenotypes as significant predictors of TF.Discussion:Our findings support the relevance of CYP2D6, CYP2C19, and CYP2C9 as key pharmacogenes for PGx testing in chronic pain management. The results suggest that a genetic predisposition in CYP2D6 and COMT, associated with increased pain sensitivity, may contribute to insufficient analgesia and subsequent TF. These insights indicate the potential value of incorporating CYP2D6 and COMT as pain-modulating genetic markers into the broader framework of PGx testing.
