The CETSA and Thermal Proteome Profiling (TPP) analytical methods are invaluable for the study of protein–ligand interactions and protein stability in a cellular context. These tools have increasingly been leveraged in work ranging from understanding signaling paradigms to drug discovery. Consequently, there is an important need to optimize the data analysis pipeline that is used to calculate protein melt temperatures (Tm) and relative melt shifts from proteomics abundance data. Here, we report a user-friendly analysis of the melt shift calculation workflow where we describe the impact of each individual calculation step on the final output list of stabilized and destabilized proteins. This report also includes a description of how key steps in the analysis workflow quantitatively impact the list of stabilized/destabilized proteins from an experiment. We applied our findings to develop a more optimized analysis workflow that illustrates the dramatic sensitivity of chosen calculation steps on the final list of reported proteins of interest in a study and have made the R based program Inflect available for research community use through the CRAN repository [McCracken, N. Inflect: Melt Curve Fitting and Melt Shift Analysis. R package version 1.0.3, 2021]. The Inflect outputs include melt curves for each protein which passes filtering criteria in addition to a data matrix which is directly compatible with downstream packages such as UpsetR for replicate comparisons and identification of biologically relevant changes. Overall, this work provides an essential resource for scientists as they analyze data from TPP and CETSA experiments and implement their own analysis pipelines geared toward specific applications.

Yearly maps of onset of early melt (earliest observed melt conditions), melt (melt conditions observed throughout from this point until freeze), early freeze (earliest observed freeze conditions), and freeze (freeze conditions observed throughout from this point on) for the surface of sea ice derived from scanning multichannel microwave radiometer (SMMR) - special sensor microwave/imager (SSM/I) data. See [Markus et al. 2009] for further details. Original data prepared in a polar stereographic projection, with 25 km grid spacing; data has been reprojected into a northern hemispheric Equal-Area Scalable Earth (EASE) grid [see: http://nsidc.org/data/ease/ for further details] with 25 km grid spacing. Reference: Markus, T., J. C. Stroeve, and J. Miller (2009), Recent changes in Arctic sea ice melt onset, freezeup, and melt season length, J. Geophys. Res., 114, C12, doi:10.1029/2009JC005436.*****These data were compiled in conjunction with the Sunlight and the Arctic atmosphere-ice-ocean system (Synthesis of Arctic System Science, SASS) project.***** Data Citation: Eicken, H., R. Gradinger, T. Heinrichs, M. Johnson, A. Lovecraft, and M. Kaufman. (Jan. 5, 2010, Updated May 9, 2012). SMMR and SSM/I derived dates of Arctic sea ice surface melt/freeze (SIZONET). UCAR/NCAR – CISL – ACADIS. http://dx.doi.org/10.5065/D6KW5CXQ

This dataset contains melt inclusion, host crystal, and matrix glass data, as well as bubble growth models.

Lee, D.C., Halliday, A.N., Davies, G.R., Essene, E.J., Fitton, J.G. and Temdjim, R. (1996). Melt enrichment of shallow depleted mantle: A detailed petrological, trace element and isotopic study of Mantle-Derived xenoliths and megacrysts from the Cameroon line. Journal of Petrology 37(2): 415-441. Type: [ Outcrop ] Class: [ Sedimentary ] Lithology: [ Redbeds ] Ages: [ 245 to 312 Ma N 2 ] from Earthref Magic

The central Taupo Volcanic Zone (TVZ) is an actively rifting continental arc and is well known for its exceptionally high rate of rhyolitic magma generation and frequent caldera-forming eruptions. Two end-member types of rhyolites (R1 and R2) have been previously identified based on differences in their bulk-rock chemistry and mineral assemblage with hydrous phases crystallizing in the R1 type, which are not present or only rare in R2 rhyolites. Here we present new trace element and volatile data from rhyolitic melt inclusions measured in several representative eruptive deposits (R1 and R2 rhyolites) from the central TVZ to examine their volatile concentrations and origin. R1 and R2 show very distinct Cl concentrations, with R2 rhyolites being enriched in Cl by c. 1000 ppm. H2O is slightly higher in the R1 rhyolites, whereas CO2 concentrations are similar between the two end-member types. The origin of these volatile disparities between R1 and R2 melts is assigned to differences in the initial bulk volatile content of the parental magma, possibly associated with distinct input of fluids from the subduction zone. These disparities in bulk volatile concentrations can lead to variations in relative timing of exsolution of volatile phase(s) prior to melt inclusion entrapment.

Tree-structured models have been widely used because they function as interpretable prediction models that offer easy data visualization. A number of tree algorithms have been developed for univariate response data and can be extended to analyze multivariate response data. We propose a tree algorithm by combining the merits of a tree-based model and a mixed-effects model for longitudinal data. We alleviate variable selection bias through residual analysis, which is used to solve problems that exhaustive search approaches suffer from, such as undue preference to split variables with more possible splits, expensive computational cost, and end-cut preference. Most importantly, our tree algorithm discovers trends over time on each of the subspaces from recursive partitioning, while other tree algorithms predict responses. We investigate the performance of our algorithm with both simulation and real data studies. We also develop an R package melt that can be used conveniently and freely. Additional results are provided as online supplementary material.

Ice-sheet melting is the primary water source for the proglacial Watson River in southern west Greenland. Discharge from the large, approximately 12,000 km2 ice-sheet catchment draining through the Watson River has been monitored since 2006. While this record is of respectable length for a Greenland monitoring effort, it is too short to resolve climate signals. Therefore, we use observed Tasersiaq lake discharge and Kangerlussuaq air temperature to reconstruct annual Watson River discharge back to 1949. The resulting sixty-five-year record shows that average ice-sheet runoff since 2003 has roughly increased by 46 percent relative to the 1949–2002 period. The time series suggests that the five top-ranking discharge years occurred since 2003. The three top-ranking discharge years (2010, 2012, and 2016) are characterized by melt seasons that were both long and intense. Interannual variability more than doubled since 2003, which we speculate to be because of hypsometric runoff amplification enhanced by albedo decrease and decreased firn permeability. The reconstructed time series proves to be a valuable tool for long-term evaluation of Greenland Ice Sheet surface mass balance models. A comparison with freshwater fluxes calculated by a downscaled version of the regional climate model RACMO2 reveals high correlation (r = 0.89), and also shows that the model possibly underestimates runoff by up to 26 percent in above-average melt years.