The repository contains input files for LaMEM and aspect simulation presented in "Exploiting Physics-Based Machine Learning to Quantify Geodynamic Effects – Insights from the Alpine Region" by Denise Degen, Ajay Kumar, Magdalena Scheck-Wenderoth, Mauro Cacace , DOI:https://doi.org/10.5281/zenodo.14755256.ASPECT simulation files, geometry and input files are in aspect_input. Please see the README in this folder.LaMEM simulation files, geometry (marker distributions) and input files, are in LaMEM_input. Please see the README in this folder.We provide required datafiles and input file for a sample simulation. All the simulations results can be produced by changing the density, viscosities in the input files from the following files in the Surrogate_model_prep_data folder:1. Varying Densities1.1 TrainingParameterAlps_LHS_100_density_only.txt : Training models1.2 ValidationParameterAlps_random_15_density_only.txt : Validation models2. Varying Densities and Viscosities2.1 TrainingParameterAlps_LHS_100_vary_viscosity_density.txt : Training models2.2 ValidationParameterAlps_random_15_vary_viscosity_density.txt : Validation models3. Varying Viscosities3.1 TrainingParameterAlps_LHS_reduced_range_fixed_density.txt : Training models3.1 ValidationParameterAlps_random_20_reduced_range_fixed_density.txt : Validation models

Description of the data and file structure Files and variables File: eap_geb_data.xlsx Description:  The datafile contains 9 sheets. Details of specific column names are listed at the top of each sheet. 1. [A.SD] [Asymmetry derived using standard deviation] 2. [A.No] [Asymmetry derived using number of records] 3. [A.Pk] [Asymmetry derived using the shape near the peak] Each of the above sheets contain columns for species names, observed species values, and 400 simulated species values. 4. [Spearman] [Spearman Rank Correlation between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 5. [OrthoRegress] [Orthogonal Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 6. [UnweightRegress] [Unweighted Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 7. [CumulativeProfile] [Community mean (species-averaged) Abundance profile for observed species values, and 400 simulated species values.] 8. [K-SD-eta-alpha] [Simulated look-up table for the dependence of kurtosis on the trait-fitness power index] 9. [Table-manuscript] [Appears as Table 2 in the manuscript and references quoted values to how they were derived from the previous sheets.] Code/software SOFTWARE VERSIONS 400 profiles were simulated for each species using the negative binomial random number generator (Lindén and Mäntyniemi 2011; rnbinom in R; R Core Team 2021). All hypotheses were tested using two-tailed tests and errors from Monte-Carlo simulations. All analyses were performed using custom scripts written in the R computing platform. REFERENCES 1. Lindén, Andreas, and Samu Mäntyniemi. 2011. “Using the Negative Binomial Distribution to Model Overdispersion in Ecological Count Data.” Ecology 92 (7): 1414–21. https://doi.org/10.1890/10-1831.1. 2. R Core Team. 2021. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/.

Description of the data and file structure Files and variables File: eap_geb_data.xlsx Description:  The datafile contains 9 sheets. Details of specific column names are listed at the top of each sheet. 1. [A.SD] [Asymmetry derived using standard deviation] 2. [A.No] [Asymmetry derived using number of records] 3. [A.Pk] [Asymmetry derived using the shape near the peak] Each of the above sheets contain columns for species names, observed species values, and 400 simulated species values. 4. [Spearman] [Spearman Rank Correlation between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 5. [OrthoRegress] [Orthogonal Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 6. [UnweightRegress] [Unweighted Linear Regression between Asymmetry and Modal Elevation for observed species values, and 400 simulated species values.] 7. [CumulativeProfile] [Community mean (species-averaged) Abundance profile for observed species values, and 400 simulated species values.] 8. [K-SD-eta-alpha] [Simulated look-up table for the dependence of kurtosis on the trait-fitness power index] 9. [Table-manuscript] [Appears as Table 2 in the manuscript and references quoted values to how they were derived from the previous sheets.] Code/software SOFTWARE VERSIONS 400 profiles were simulated for each species using the negative binomial random number generator (Lindén and Mäntyniemi 2011; rnbinom in R; R Core Team 2021). All hypotheses were tested using two-tailed tests and errors from Monte-Carlo simulations. All analyses were performed using custom scripts written in the R computing platform. REFERENCES 1. Lindén, Andreas, and Samu Mäntyniemi. 2011. “Using the Negative Binomial Distribution to Model Overdispersion in Ecological Count Data.” Ecology 92 (7): 1414–21. https://doi.org/10.1890/10-1831.1. 2. R Core Team. 2021. “R: A Language and Environment for Statistical Computing.” Vienna, Austria: R Foundation for Statistical Computing. https://www.r-project.org/.

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The configuration files can be uploaded to the web interface or used with the API for PSG to simulate spectra. There are four configuration files corresponding to the MERRA-2 data for four dates - 1st July 2000 (randomly selected date for calibration), 15th February 1987 (low patchy cloud abundance), 20th Janunary 2011 (closest to mean patchy cloud abundance) and 7th July 1999 (high patchy cloud abundance). Two additional files correspond to atmospheres with a low-altitude (0.8-0.7 bar) and a high-altitude (0.34-0.24 bar) global cloud layer.

The configuration files can be uploaded to the web interface or used with the API for PSG to simulate spectra. There are four configuration files corresponding to the MERRA-2 data for four dates - 1st July 2000 (randomly selected date for calibration), 15th February 1987 (low patchy cloud abundance), 20th Janunary 2011 (closest to mean patchy cloud abundance) and 7th July 1999 (high patchy cloud abundance). Two additional files correspond to atmospheres with a low-altitude (0.8-0.7 bar) and a high-altitude (0.34-0.24 bar) global cloud layer.

Repurposing of pleiotropic factors during execution of diverse cellular processes has emerged as a regulatory paradigm. Embryonic development in metazoans is controlled by maternal factors deposited in the egg during oogenesis. Here, we explore maternal role(s) of Caspar (Casp), the Drosophila orthologue of human Fas-associated factor-1 (FAF1) originally implicated in host-defense as a negative regulator of NF-κB signaling. Maternal loss of either Casp or it’s protein partner, Transitional endoplasmic reticulum 94 (TER94) leads to partial embryonic lethality correlated with aberrant centrosome behavior, cytoskeletal abnormalities, and defective gastrulation. Although ubiquitously distributed, both proteins are enriched in the primordial germ cells (PGCs), and in keeping with the centrosome problems, mutant embryos display a significant reduction in the PGC count. Moreover, the total number of pole buds is directly proportional to the level of Casp. Consistently, it’s ‘loss’ and ‘gain’ results in respective reduction and increase in the Oskar protein levels, the master determinant of PGC fate. To elucidate this regulatory loop, we analyzed several known components of mid-blastula transition and identify the translational repressor Smaug, a zygotic regulator of germ cell specification, as a potential critical target. We present a detailed structure-function analysis of Casp aimed at understanding its novel involvement during PGC development.

Increasing temperatures in the tropics will reduce performance of trees and agroforestry species and may lead to lasting damage and leaf death. One criterion to determine future forest resilience is to evaluate damage caused by temperature on Photosystem-II (PSII), a particularly sensitive component of photosynthesis. The temperature at which 50% of PSII function is lost (T50) is a widely used measure of irreversible damage to leaves. To assess vulnerability to high temperatures, studies have measured T50 or leaf temperatures, but rarely both. Further, because extant leaf temperature records are short, duration of exposure above thresholds like T50 has not been considered. Finally, these studies do not directly assess the effect of threshold exceedance on leaves. To understand how often, and how long, leaf temperatures exceed critical thresholds, we measured leaf temperatures of forest and agroforestry species in a tropical forest in the Western Ghats of India where air temperatures are high. We quantified species-specific physiological thresholds and assessed leaf damage after high temperature exposure. We found that leaf temperatures already exceed T50. However, continuous exposure durations above critical thresholds are very skewed with most events lasting for much less than 30 minutes. As T50 was measured after a 30-minute exposure, our results suggest that threshold exceedances and exposure durations for lasting damage are currently not reached and will rarely be reached if maximum air temperatures increase by 4°C. Consistent with this, we found only minor indications of heat damage in the forest species. However, there were indications of heat-induced reduction in PSII function and damage in the agroforestry leaves which have lower T50. Our findings suggest that, for forest species, while high temperature thresholds may be surpassed, durations of exposure above thresholds remain short, and therefore, are unlikely to lead to irreversible damage and leaf death, even under 4°C warming.

Data sets and models for publication.