Differentiating induced versus spontaneous subduction initiation using thermomechanical models and metamorphic soles.
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ABSTRACT:
SUBMITTER: Zhou X
PROVIDER: S-EPMC8324860 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Some commonly referenced thermal-mechanical models of current subduction zones imply temperatures that are 100-500 °C colder at 30-80-km depth than pressure-temperature conditions determined thermobarometrically from exhumed metamorphic rocks. Accurately inferring subduction zone thermal structure, whether from models or rocks, is crucial for predicting metamorphic reactions and associated fluid release, subarc melting conditions, rheologies, and fault-slip phenomena. Here, we compile surface heat flow data from subduction zones worldwide and show that values are higher than can be explained for a frictionless subduction interface often assumed for modeling. An additional heat source--likely shear heating--is required to explain these forearc heat flow values. A friction coefficient of at least 0.03 and possibly as high as 0.1 in some cases explains these data, and we recommend a provisional average value of 0.05 ± 0.015 for modeling. Even small coefficients of friction can contribute several hundred degrees of heating at depths of 30-80 km. Adding such shear stresses to thermal models quantitatively reproduces the pressure-temperature conditions recorded by exhumed metamorphic rocks. Comparatively higher temperatures generally drive rock dehydration and densification, so, at a given depth, hotter rocks are denser than colder rocks, and harder to exhume through buoyancy mechanisms. Consequently--conversely to previous proposals--exhumed metamorphic rocks might overrepresent old-cold subduction where rocks at the slab interface are wetter and more buoyant than in young-hot subduction zones.
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Project description:BackgroundColorectal cancer (CRC) is a heterogeneous disease, with subtypes that have different clinical behaviours and subsequent prognoses. There is a growing body of evidence suggesting that right-sided colorectal cancer (RCC) and left-sided colorectal cancer (LCC) also differ in treatment success and patient outcomes. Biomarkers that differentiate between RCC and LCC are not well-established. Here, we apply random forest (RF) machine learning methods to identify genomic or microbial biomarkers that differentiate RCC and LCC.MethodsRNA-seq expression data for 58,677 coding and non-coding human genes and count data for 28,557 human unmapped reads were obtained from 308 patient CRC tumour samples. We created three RF models for datasets of human genes-only, microbes-only, and genes-and-microbes combined. We used a permutation test to identify features of significant importance. Finally, we used differential expression (DE) and paired Wilcoxon-rank sum tests to associate features with a particular side.ResultsRF model accuracy scores were 90%, 70%, and 87% with area under curve (AUC) of 0.9, 0.76, and 0.89 for the human genomic, microbial, and combined feature sets, respectively. 15 features were identified as significant in the model of genes-only, 54 microbes in the model of microbes-only, and 28 genes and 18 microbes in the model with genes-and-microbes combined. PRAC1 expression was the most important feature for differentiating RCC and LCC in the genes-only model, with HOXB13, SPAG16, HOXC4, and RNLS also playing a role. Ruminococcus gnavus and Clostridium acetireducens were the most important in the microbial-only model. MYOM3, HOXC4, Coprococcus eutactus, PRAC1, lncRNA AC012531.25, Ruminococcus gnavus, RNLS, HOXC6, SPAG16 and Fusobacterium nucleatum were most important in the combined model.ConclusionsMany of the identified genes and microbes among all models have previously established associations with CRC. However, the ability of RF models to account for inter-feature relationships within the underlying decision trees may yield a more sensitive and biologically interconnected set of genomic and microbial biomarkers.
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