dataesr/scientific-paragraphs-categorization huggingface dataset:
Future Directions for the MPRCT The MPRCT and these proposed sampling strategies are being used to collect data systematically during neoadjuvant therapy clinical trials to determine the ideal sampling method to calculate MPR accurately while balancing sensitivity against excessive use of resources. At present, neither the IASLC recommendations nor any other study has provided sufficient evidence-based data to indicate which method is optimal. Data from ongoing Phase 3 neoadjuvant trials including IMpower030 and from other studies using this MPRCT will shed further light on this, once available. Comprehensive data collection for the MPRCT places the onus on pathologists but is likely to yield high returns on the initial time investment. Readily accessible and detailed information obtained from the outset improves efficiency by obviating microscopic re-review to address inquiries from regulatory agencies.
It also enables testing of new hypotheses and the exploration of additional end points, especially for phase 3 clinical trials with hopes of ultimately establishing a surrogate end point and investigating a new class of drugs, such as immunotherapies in the neoadjuvant setting. Most of these tasks can be accomplished by examining the raw MPRCT data, extrapolating data (e. g. , rounding recorded percentages to nearest multiples of 10, as described previously), or performing multivariate analysis using information from synoptic reports of NSCLC resections, predictive and prognostic ancillary studies, and radiologic findings, among others. The value of these types of analyses is illustrated by findings from two contemporary studies. In a clinical trial of chemoimmunotherapy, more patients with squamous cell carcinoma than adenocarcinomas had MPR and pCR.
g. , selecting viable areas and avoiding necrotic regions) that can lead to underestimation of pathologic response. Clinical and pathologic data from the ongoing IMpower030 study will confirm whether our proposed methodology will increase the accuracy of the calculated MPR while maintaining the balance between sensitivity and overuse of resources. Further analyses using the MPRCT will inform the adoption of a generic, uniform, or tailored approach to assessment of pathologic response that is dependent on specific parameters (e. g. , tumor bed size, NSCLC subtype, treatment modality). With readily available and encompassing data from the outset, these and other queries can be expeditiously addressed. The proposed methodology is described for use in clinical trials; how it could be adopted in routine clinical practice remains to be determined.
If the prognostic significance of pathologic response to neoadjuvant therapy for breast cancer  30  is a harbinger, then it is likely that clinicians in routine practice will want MPR data on patients with NSCLC once the usefulness is confirmed. Given such a development, comparisons of MPR assessments between local sites and central pathology laboratories will provide additional data on reproducibility, the impact of residual viable tumor on clinical management, and future use in real-world data studies and nontrial clinical cases. In conclusion, collection of comprehensive data in a standardized format using the MPRCT will facilitate the validation of protocols that include pCR and MPR end points with the aim of establishing them as surrogate end points of neoadjuvant treatment efficacy while also providing insight into areas that require further investigation.
Acknowledgments Medical writing assistance with preparation of the manuscript was provided by  Samantha Santangelo, PhD , and  Kia C. E. Walcott, PhD , of  Health Interactions, Inc. , and was funded by  F. Hoffmann-La Roche/Genentech. F. Hoffmann-La Roche / Genentech  funded editorial assistance by a medical writer during preparation of this manuscript and was involved in writing the manuscript and in the decision to submit it for publication.
Additional works have analyzed and revised this relation for RRab  (Nemec et al. 2013; Martínez-Vázquez et al. 2016; Smolec 2005; Ngeow et al. 2016; Iorio & Belokurov 2020; Mullen et al. 2021 ) using more modern datasets in a variety of wavelengths. In particular,  Mullen et al. 2021 (hereafter Paper I)  obtained a new relation in the V -band based on the largest RRab calibration dataset to date. In Paper I, we showed that accurate RRab photometric metallicities could be extended to both lower and higher metallicity regimes than in previous works. Furthermore, for the first time we showed that a photometric metallicity relation for RRab variables could be extended into the mid-infrared. This paper aims to now derive similar relations for RRL pulsating in the first overtone (RRc variables).
(2021a) (C21 hereafter). We combine these measurements with a large sample of metallicities derived with medium-resolution spectroscopic surveys (R ∼ 2,000). This new set of medium-resolution metallicities has been derived with the updated C21 calibration of the ∆S method  (Preston 1959) , which relies on ratios between the equivalent widths of Ca and H lines. While not as accurate as HR metallicities, this method can nevertheless provide reliable [Fe/H] abundances with a typical uncertainty of 0. 2-0. 3 dex. This work directly follows the analysis presented in Paper I for the calibration of period-φ 31 -[Fe/H] relations for RRab field stars. We take advantage of an extensive catalog of field RRc [Fe/H] abundances from C21 HR spectral measurements, combined with ∆S metallicities estimated from large publicly available medium-resolution spectral datasets.
FIRST OVERTONE RR LYRAE DATASETS Our sample of RRLs is derived from an extensive catalog of 3057 field RRc, for which we have either [Fe/H] abundances derived from HR spectra (40 sources) or an estimate of their metallicity based on the ∆S method (3017 sources). In order to ensure a homogeneous metallicity scale for our entire sample, both HR and ∆S metallicities are based on the calibration provided in C21 (in turn consistent with the Carretta et al. 2009 metallicity scale). The RRc stars for which we obtained ∆S metallicities were extracted from both the full Large Scale Area Multi-Object Spectroscopic Telescope (LAMOST) DR2 survey  (Deng et al. 2012; Liu et al. 2014 ) and the Sloan Extension for Galactic Understanding and Exploration (SEGUE,  Yanny et al. 2009 ) survey datasets. For a complete and detailed description of the metallicity scale, the HR metallicity catalog's demographics, the ∆S calibration, and the spectrum selection criteria, we refer the reader to the C21 paper.
A similar equation can be written to represent the equivalent cosine decomposition. For RRc n is often less than five; however, we advise the reader to carefully choose the optimal number of terms for their dataset (see  Petersen 1986; Deb & Singh 2009)  as the addition of higher-order terms may cause inconsistencies in the value of low order Fourier parameters. For this work, we note that by using an intermediate GLOESS step to eliminate noise factors, the Fourier parameters are consistent between 3, 4, or fifth order decompositions, with the exclusion of a small amount of 3σ outliers (∼ 4% of total RRc for ASAS-SN and ∼ 8% for WISE). This small subset of outliers has been excluded in our processing to make our result invariant between different decomposition orders used. However, it is worth noting that with the robust fitting procedure outlined in Section 3.
Principal component analysis showed that 91. 30% of the variance in the V -band RRc data could be attributed to just two principal axes (i. e. , a plane). This is comparable to the 92. 9% value we found for RRab stars in Paper I, which we interpreted as suggesting that a third fit parameter was not needed. A similar analysis for the infrared RRc data set showed that 85. 17% of the variance could be attributed to two dimensions (6. 74% less than what we found for the RRab in the WISE bands). We attribute this larger variance to a larger relative scatter in the WISE dataset itself (seen in the bottom-right panel of Figure  4 ) due to the smaller amplitude of the pulsations, higher intrinsic photometric noise in the data, and smaller size of the sample. Search for a third observable parameter and/or higher-order fitting functions did not result in a better fit, leading us to adopt a period-φ 31 plane-fit also for the infrared dataset.
The analysis we presented in Paper I for RRab stars showed that ODR tends to minimize trends along all fitted dimensions as no differentiation is made between dependent and independent variables. The ODR fit was run multiple times using bootstrap re-sampling, where in each run all the parameters of each individual calibrator star (period, φ 31 , and [Fe/H]) are randomly replaced with all the corresponding parameters from any singular star in the same calibration dataset. Namely, we are sampling with replacement our entire calibration dataset before fitting our relation, and ensuring each re-sampled calibration dataset is the same size as our initial sample. Note, all the stars in our calibration sample are fitted with equal weight, as the uncertainties in the fit are dominated by the intrinsic scatter we observe in the period-φ 31 -[Fe/H] plane (see Figure  4 ), in comparison to the small uncertainties in period and the φ 31 parameter (on average ∼ 10 -6 days and ∼0.
02 radians respectively). Figure  6  shows the "cloud" of best fit values obtained with the process described above in the optical and infrared fit. The contours enclose the regions with 68%, 95%, and 99. 7% of the ODR best fit values obtained with 100,000 bootstrap re-sampled datasets. The contours provide a visual representation of the robustness of the fitting parameters with respect to outliers and are significantly larger than the corresponding 1, 2 and 3σ error ellipses of the best-fit parameters of individual ODR fits. For this reason, we adopted the mean values and standard deviations of the bootstrap re-sampled ODR fits as solutions and uncertainties for our RRc period-φ 31 -[Fe/H] relations:  Figure  6  and the uncertainties quoted in Equations 3 and 4 show that the sampled coefficients occupy a much smaller region of the parameter space in the V -band than the W -band.
The larger uncertainties in the infrared coefficients are a reflection of the larger intrinsic scatter we noted in the W -band period-φ 31 -[Fe/H] plane (Figure  4 ) as well as a consequence of the smaller size of the infrared calibrator sample. A detailed analysis of the performance of these relations is provided in Section 4. a When both V -band and infrared (WISE) data is present, the period included was calculated from ASAS-SN (V -band) data as the period is usually more accurate due to the higher amplitude and steeper light curve. Period accuracies are quoted to an accuracy on the order of 10 -6 days, corresponding to a readily detectable ∼ 1% shift in phase for a typical 0. 32 day period RRc star when phased over the large temporal (> 8 years) baseline of our datasets. Note-Table  2  is published in its entirety in machine-readable format.
Comparison with Globular Clusters Metallicity In order to test the V -band relation obtained in Section 3. 3 on an independent sample, we selected a list of ten GCs with metallicity homogeneously spread between [Fe/H] = -1. 0 and -2. 3 dex. The clusters sampled are the same as in Paper I with two additional GCs: Reticulum (a Large Magellanic Cloud GC) and NGC 6171. Photometry of the clusters comes from  Piersimoni et al. 2002 (NGC 3201) ,  Kuehn et al. 2013 (Reticulum) , M14 (NGC 6171), and the homogeneous data set of P. B. Stetson 1 (hereafter PBS) for the remaining majority. The general properties of the clusters are listed in Table  3 , which includes their spectroscopic metallicities (in the scale of C09) and the number of RRL with good-quality well-sampled light curves available in each cluster.
For the case of the V -band ASAS-SN dataset, we show nearly symmetric residuals (with an RMS ≈ 0. 30 dex) over the entire range of metallicities with only a minor deviation at higher metallicities, still well within the error of our relation. The WISE bands' residuals, however, show some larger divergence at high metallicity ([Fe/H] -0. 5), where Equation 4 systematically under-predicts the spectroscopic metallicities. The overall residual RMS is also significantly larger (0. 50 dex) than the one we obtained in the V -band. Due to the small number of the high metallicity RRc stars, we cannot determine if this is due to non-linearity of the periodφ 31 -[Fe/H] in this metallicity regime, or rather a reflection of less accurate spectroscopic metallicities for the stars in our calibration sample that approach solar [Fe/H] abundance.
26 dex lower than the spectroscopic values), and a larger scatter (RMS = 0. 52 dex) than all other relations here evaluated. Finally, IB20 used a sample of 50 GC RRc stars extracted from the Gaia DR2 database to derive a bi-linear period-φ 31 -[Fe/H] relation (Equation 4 in their paper) with the same functional form of our Equations 3 and 4. In order to apply this relation to our dataset, we had to perform two transformations: (1) we converted the V -band φ 31 value of our ASAS-SN sample to the G-band system, using the relation φ 31 (G) = (0. 104 ± 0. 020) + (1. 000 ± 0. 008) φ 31 (V ) from  Clementini et al. (2016) , and (2) we transformed the metallicity provided by the IB20 relation (in the ZW84 scale) into the Carretta scale using the [Fe/H] C09 = 1. 105[Fe/H] ZW84 + 0. 160 relation from C09.
The results are shown in the bottom-right panel of Figure  8. Again, no obvious trend or offset is found, and the RMS dispersion (0. 32 dex) is among the smallest of the relations assessed in this Section, comparable to the RMS we measure in the V -band with our Equation  3. Overall, M07, M14, and N13 share the majority of their photometric calibration dataset but differ in their functional form by adding slightly different combinations of nonlinear terms. Figure  8  shows that, with respect to optical linear relations (IB20 and Equation 3 in this work), these non-linear relations tend to have larger residual dispersion for our large sample of spectroscopic metallicities. With the exception of the infrared WISE band (where we observe a possible departure at solar metallicities), our dataset does not support the need for the addition of non-linear terms in photometric metallicities relations based on Fourier parameters of RRc optical lightcurves.
2005; Martínez-Vázquez et al. 2015 , 2016b). By studying Sculptor, we show that the relations provided in this work are widely applicable to complex stellar populations, beyond RRLs in the field or GCs. Sculptor photometry is available from the PBS database in both the I-band (utilized by MV16b in their work) and the V -band. We refer to MV16a for the exact details of the observing runs, bands observed, instruments and telescopes used to collect the Sculptor photometry in PBS. Out of 536 known RRLs (289 RRab, 197 RRc, and 50 RRd; MV16a), MV16b derived photometric metallicities for 276 RRab and 195 RRc stars. However, only 277 of these stars (126 RRab and 148 RRc)have good quality V -band light curves (according to the criteria outlines in Paper I, Section 3). For these stars we have extracted their V -band Fourier parameters, following the procedures described in Section 3.
Clementini et al. (2005)  found a [Fe/H] peaking at ∼ -1. 8 dex via the ∆S spectroscopic method applied to a sample of 107 RRL. Achieving such a consistent mean metallicity and distribution validates both of these photometric approaches, especially since the two photometric relations are completely independent of one another both in methodology (shape of the light curve as opposed to inversion of the PLZ relation) and in the dataset used (V band vs. I band, and light curves with different sampling and coverage).
ACKNOWLEDGMENTS This publication makes use of data products from WISE, which is a joint project of the  University of California, Los Angeles , and the  Jet Propulsion Laboratory (JPL)/California Institute of Technology (Caltech) , funded by the  National Aeronautics and Space Administration (NASA) , and from  NEOWISE , which is a  JPL/Caltech  project funded by  NASA's Planetary Science Division. This publication also makes use of data products from the ASAS-SN project, which has their telescopes hosted by  Las Cumbres Observatory. ASAS-SN is supported by the  Gordon and Betty Moore Foundation  through grant  GBMF5490  and the  NSF  by grants  AST-151592  and  AST-1908570. Development of ASAS-SN has been supported by  Peking University ,  Mt. Cuba Astronomical Foundation ,  Ohio State University Center for Cosmology and AstroParticle Physics , the  Chinese Academy of Sciences South America Center for Astronomy (CASSACA) , the  Villum Foundation , and  George Skestos.
M. Marengo  and  J. P. Mullen  are supported by the  National Science Foundation  under Grant No. AST-1714534. Facilities: WISE,  ASAS-SN , Gaia, LAMOST,  SDSS-SEGUE Software : Astropy (Astropy Collaboration et al. 2013), SciPy (Virtanen et al. 2020), scikit-learn (Pedregosa et al. 2011).
We did not apply a snow-cover correction because the moraine crests are often windswept and shed snow efficiently, while the present-day snowpack in this region is thin  (Owen et al. , 2009). A chi-squared test identified possible outliers from the sample ages for each moraine  (Balco et al. , 2008). The ages can be recalculated using different scaling protocols and production rate calibrations through the Informal Cosmogenic Nuclide Exposure-age Database (https://version2. ice-d. org/ alpine/).
Uncertainties Associated With Moraine Exposure Ages Moraine exposure ages are subject to internal uncertainties arising from the measurement of Be-10 that are consistently low in our study, and systematic external uncertainties associated with the cosmogenic nuclide production rate and scaling. There is no production rate scaling method specific to Asia and therefore we used the global production rate scaling of  Borchers et al. (2016). We assume that all the ages produced in this study and from  Finkel et al. (2003)  are subject to the same Be-10 production rate due to their location within one valley and compared these datasets using their internal uncertainties. When comparing the moraine exposure ages for the Khumbu Valley with those for regional glacial stages for the monsoon-influenced Himalaya in Section 5.
Gene Ontology of the differentially expressed genes (DEGs) induced in HA+ OPCs from PDGFRa-CreERT2:RiboTag mice on HFD compared with the full murine genome enriched for multiple pathways involved in cell migration (Figure  3E ). Pathway analysis of DEGs in HFD HA+ OPCs demonstrated enrichment for downstream chemokine signaling with 31 of 198 chemokine signaling pathway genes differentially expressed in HFD HA+ OPCs (FDR = 1. 62 3 10 À55 )  37  (Table  1 ). A-E ) and five cognate receptor isoforms that hetero-and/or homo-dimerize to effect downstream signaling. 38 Within our transcriptional dataset, the only IL-17 receptor isoform that was significantly differentially regulated in DIO-affected cerebral endothelial cells was IL-17Rb (Table  S4 ). Among a number of diverse functions, IL-17 receptor activation drives effector chemokine signaling, including CXCL5  39  as a mechanism of identifying tissue injury.
RNA hybridization assay Wild-type C57Bl/6 mice (n = 4/grp) were placed on CFD or HFD starting at 8 weeks of age and after 12 weeks on CFD or HFD were sacrificed. The subcortical white matter was freshly dissected. RNA was isolated using the Nucleospin miRNA kit (Machary-Nagel). RNA samples were allowed to directly hybridize with a custom RNA probe set for 120 oligodendrocyte/myelin gene set derived from Zhang et al. 36  with 40 genes each corresponding to the major oligodendrocyte stages including oligodendrocyte progenitor cells (OPC), pre-myelinating oligodendrocytes (PMO), and (myelinating oligodendrocytes (MO). Hybridized mRNA species were detected using the nCounter detection system (Nanostring) and normalized to five housekeeping genes (Supplemental Data File 1). Normalized counts for each probe set were divided into three major oligodendrocyte subtypes (OPC, PMO, and MO) and compared by differential gene expression analysis.
Fazekas scale scores were compared using an ordinal shift Chi-square analysis. Human CXCL5+ vessel segments were compared by two-tailed, one-sample t-test assuming no expression of CXCL5 in non-injured tissue. Unless otherwise stated, all other comparisons were determined using a one-way ANOVA with post-hoc Holm-Sidak test to correct for multiple comparisons. Statistical analysis was performed using GraphPad Prism 7 software. Data are shown as mean ± SEM.
3333 0. 00095 PIP-Akt signaling pathway ROCK1 À1. 2784 0. 03343 regulation of actin cytoskeleton WASL À1. 8148 0. 00012 regulation of actin cytoskeleton RAC2 1. 6040 0. 00049 regulation of actin cytoskeleton PARD3 0. 4322 0. 03699 regulation of actin cytoskeleton ELMO1 À1. 4219 0. 00125 regulation of actin cytoskeleton PTK2 À0. 9568 0. 04945 regulation of actin cytoskeleton/ diacylglycerol pathway CRK 0. 4503 0. 03329 regulation of actin cytoskeleton/ diacylglycerol pathway Differentially expressed genes (FDR < 0. 05) from HFD OPCs were filtered for those found in the KEGG Chemokine Signaling Pathway (KEGG Map #04062). Functional enrichment for chemokine signaling was established using a STRING database search (FDR for pathway enrichment = 1. 62 3 10 À55 ). Gene name, log fold change (logFC), adjusted p value (adj.
ACKNOWLEDGMENTS The authors are grateful to the  UCLA Neurogenetics and Genomics Core ,  Kelsey Erickson  ( UC Davis Brain Bank ), and the members of the  UCLA Stroke Force and Stroke Center members  who assisted in ASPIRE trial enrollment and data collection. This work was graciously supported by grants from the  Larry L. Hillblom Foundation  ( TLLHF 2014-A-014 ) and the  American Heart Association  ( 15CRP22900006  and  16GRNT31080021 ). The  UCLA IMT Core/ Vector Core  is supported by  CURE /P30 DK041301. L. A. H. and S. T. C. receive support from the  Dr. Miriam and Sheldon G. Adelson Medical Research Foundation. C. D. receives support from  UC   Davis Alzheimer's Disease Center grant   P30 AG 010129. J. D. H. received support from the  National Institute of Neurological Disorders and Stroke  ( K08 NS083740  and  RF1 NS114336 ) and the  United States Department of Veterans Affairs Greater Los Angeles Healthcare System.
Materials availability Plasmids generated in this study are deposited in Addgene. Mouse lines generated in this study are available to share upon contact with the lead contact. Anti-IL-17B antibody used in Luminex assay may be available from the manufacturer upon request (Biotechne). Data and code availability RNA-seq data have been deposited at GEO: GSE217356 and are publicly available as of the date of publication. The PDGFRa-CreERT2/Rpl22-HA and Tie2-Cre/Rpl22-HA transgenic strain were generated by crossing PDGFRa-CreERT2 mice (Jackson Labs Strain #018280 -B6N. Cg-Tg(Pdgfra-cre/ ERT)467Dbe/J) and Tie2-Cre (Jackson Labs Strain #008863-B6. Cg-Tg(Tek-cre)1Ywa/J) with Rpl22-fl-Rpl22-HA (Jackson Labs Strain #011029 -B6N. 129-Rpl22tm1. 1Psam/J). The Tie2-Cre;tdTomato mice were generated by crossing Tie2-Cre mice with flox-stop tdTomato mice (Jackson Labs Strain #007908 -B6;129S6-Gt(ROSA)26Sortm14(CAG-tdTomato)Hze/J).