Abstract Raman spectroscopy is widely used to identify mineral and fluid inclusions in host crystals, as well as to calculate pressure-temperature (P-T) conditions with mineral inclusion elastic thermobarometry, for example quartz-in-garnet barometry (QuiG) and zircon-in-garnet thermometry (ZiG). For thermobarometric applications, P-T precision and accuracy depend crucially on the reproducibility of Raman peak position measurements. In this study, we monitored long-term instrument stability and varied analytical parameters to quantify peak position reproducibility for Raman spectra from quartz and zircon inclusions and reference crystals. Our ultimate goal was to determine the reproducibility of calculated inclusion pressures (“Pinc”) and entrapment pressures (“Ptrap”) or temperatures (“Ttrap”) by quantifying diverse analytical errors, as well as to identify optimal measurement conditions and provide a baseline for interlaboratory comparisons. Most tests emphasized 442 nm (blue) and 532 nm (green) laser sources, although repeated analysis of a quartz inclusion in garnet additionally used a 632.8 nm (red) laser. Power density was varied from <1 to >100 mW and acquisition time from 3 to 270s. A correction is proposed to suppress interference on the ~206 cm–1 peak in quartz spectra by a broad nearby (~220 cm–1) peak in garnet spectra. Rapid peak drift up to 1 cm–1/h occurred after powering the laser source, followed by minimal drift (<0.2 cm–1/h) for several hours thereafter. However, abrupt shifts in peak positions as large as 2–3 cm–1 sometimes occurred within periods of minutes, commonly either positively or negatively correlated to changes in room temperature. An external Hg-emission line (fluorescent light) can be observed in spectra collected with the green laser and shows highly correlated but attenuated directional shifts compared to quartz and zircon peaks. Varying power density and acquisition time did not affect Raman peak positions of either quartz or zircon grains, possibly because power densities at the levels of inclusions were low. However, some zircon inclusions were damaged at higher power levels of the blue laser source, likely because of laser-induced heating. Using a combination of 1, 2, or 3 peak positions for the ~128, ~206, and ~464 cm–1 peaks in quartz to calculate Pinc and Ptrap showed that use of the blue laser source results in the most reproducible Ptrap values for all methods (0.59 to 0.68 GPa at an assumed temperature of 450 °C), with precisions for a single method as small as ±0.03 GPa (2σ). Using the green and red lasers, some methods of calculating Ptrap produce nearly identical estimates as the blue laser with similarly good precision (±0.02 GPa for green laser, ±0.03 GPa for red laser). However, using 1- and 2-peak methods to calculate Ptrap can yield values that range from 0.52 ± 0.06 to 0.93 ± 0.16 GPa for the green laser, and 0.53 ± 0.08 GPa to 1.00 ± 0.45 GPa for the red laser. Semiquantitative calculations for zircon, assuming a typical error of ±0.25 cm–1 in the position of the ~1008 cm–1 peak, imply reproducibility in temperature (at an assumed pressure) of approximately ±65 °C. For optimal applications to elastic thermobarometry, analysts should: (1) delay data collection approximately one hour after laser startup, or leave lasers on; (2) collect a Hg-emission line simultaneously with Raman spectra when using a green laser to correct for externally induced shifts in peak positions; (3) correct for garnet interference on the quartz 206 cm–1 peak; and either (4a) use a short wavelength (blue) laser for quartz and zircon crystals for P-T calculations, but use very low-laser power (<12 mW) to avoid overheating and damage or (4b) use either the intermediate wavelength (green; quartz and zircon) or long wavelength (red; zircon) laser for P-T calculations, but restrict calculations to specific methods. Implementation of our recommendations should optimize reproducibility for elastic geothermobarometry, especially QuiG barometry and ZiG thermometry.
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Determination of a Raman shift laser power coefficient based on cross correlation
This work presents a novel, to the best of our knowledge, cross correlation technique for determining the laser heating-induced Raman shift laser power coefficient
- Award ID(s):
- 1930866
- NSF-PAR ID:
- 10384378
- Publisher / Repository:
- Optical Society of America
- Date Published:
- Journal Name:
- Optics Letters
- Volume:
- 47
- Issue:
- 24
- ISSN:
- 0146-9592; OPLEDP
- Page Range / eLocation ID:
- Article No. 6357
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Background Magnetic resonance imaging (MRI) scans are known to suffer from a variety of acquisition artifacts as well as equipment‐based variations that impact image appearance and segmentation performance. It is still unclear whether a direct relationship exists between magnetic resonance (MR) image quality metrics (IQMs) (e.g., signal‐to‐noise, contrast‐to‐noise) and segmentation accuracy.
Purpose Deep learning (DL) approaches have shown significant promise for automated segmentation of brain tumors on MRI but depend on the quality of input training images. We sought to evaluate the relationship between IQMs of input training images and DL‐based brain tumor segmentation accuracy toward developing more generalizable models for multi‐institutional data.
Methods We trained a 3D DenseNet model on the BraTS 2020 cohorts for segmentation of tumor subregions enhancing tumor (ET), peritumoral edematous, and necrotic and non‐ET on MRI; with performance quantified via a 5‐fold cross‐validated Dice coefficient. MRI scans were evaluated through the open‐source quality control tool MRQy, to yield 13 IQMs per scan. The Pearson correlation coefficient was computed between whole tumor (WT) dice values and IQM measures in the training cohorts to identify quality measures most correlated with segmentation performance. Each selected IQM was used to group MRI scans as “better” quality (BQ) or “worse” quality (WQ), via relative thresholding. Segmentation performance was re‐evaluated for the DenseNet model when (i) training on BQ MRI images with validation on WQ images, as well as (ii) training on WQ images, and validation on BQ images. Trends were further validated on independent test sets derived from the BraTS 2021 training cohorts.
Results For this study, multimodal MRI scans from the BraTS 2020 training cohorts were used to train the segmentation model and validated on independent test sets derived from the BraTS 2021 cohort. Among the selected IQMs, models trained on BQ images based on inhomogeneity measurements (coefficient of variance, coefficient of joint variation, coefficient of variation of the foreground patch) and the models trained on WQ images based on noise measurement peak signal‐to‐noise ratio (SNR) yielded significantly improved tumor segmentation accuracy compared to their inverse models.
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Abstract The tightest constraints on the tensor-to-scalar ratio
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Abstract Aim The standard latitudinal diversity gradient (LDG), in which species richness decreases from equator to pole, is a pervasive pattern observed in most organisms. Some lineages, however, exhibit inverse LDGs. Seemingly problematic, documenting and studying contrarian groups can advance understanding of LDGs generally. Here, we identify one such contrarian clade and use a historical approach to evaluate alternative hypotheses that might explain the group's atypical diversity pattern. We focus on the biogeographical conservatism hypothesis (BCH) and the diversification rate hypothesis (DRH).
Location Global.
Taxon Ants (Hymenoptera: Formicidae: Stenammini).
Methods We examined the shape of the LDG in Stenammini by plotting latitudinal midpoints for all extant, described species. We inferred a robust genome‐scale phylogeny using UCE data. We estimated divergence dates using beast2 and tested several biogeographical models in
BioGeoBEARS . To examine diversification rates and test for a correlation between rate and latitude, we used the programs BAMM and STRAPP, respectively.Results Stenammini has a skewed inverse LDG with a richness peak in the northern temperate zone. Phylogenomic analyses revealed five major clades and several instances of non‐monophyly among genera (
Goniomma ,Aphaenogaster ). Stenammini and all its major lineages arose in the northern temperate zone. The tribe originated ~51 Ma during a climatic optimum and then diversified and dispersed southward as global climate cooled. Stenammini invaded the tropics at least seven times, but these events occurred more recently and were not linked with increased diversification. There is evidence for a diversification rate increase in HolarcticAphaenogaster +Messor , but we found no significant correlation between latitude and diversification rate generally.Main Conclusions Our results largely support the BCH as an explanation for the inverse latitudinal gradient in Stenammini. The clade originated in the Holarctic and likely became more diverse there due to center‐of‐origin, time‐for‐speciation and niche conservatism effects, rather than latitudinal differences in diversification rate.
