dataesr/scientific-paragraphs-categorization huggingface dataset:
The AIC of our preferred specification is smaller than the one obtained from the parsimonious model for both girls (6904. 0 versus 6936. 8) and boys (7547. 6 and 7724. 0), lending further credibility to our estimates. Finally, we estimated our coefficients on a 'training' dataset that excluded 50% of villages from the estimating sample and checked if these estimates were also valid in the hold-out sample. Overall, for both, girls and boys, we achieved a close out-of-sample fit, with the predicted moments -private school enrollment shares in the aggregate and for different subgroups-similar to the moments observed in the data. These exercises are shown in online Appendix (Table  A. 17).
Alternate Instruments Tables A. 18 and A. 19 show that our results are robust to using non-teacher costs of the school in the first stage. Since cost data are typically not available to researchers, we also estimated new first stage regressions using Hausman-style instruments. We combined private schools by size into 4 and 10 categories. For each case we calculated (i) the average prices and (ii) the median prices of the same-group schools in other villages for each school (Tables A. 20 and A. 21). 15  Our main takeaway is that Hausman-style instruments such as these are quite weak in our setting. These additional variables are not statistically significant and do not improve the power of the first stage. Consequently, our results remain substantively unchanged.
27 To date, the government does not have a geographically linked database of public and private schools, and policies towards these two sectors have been undertaken by different bodies within the government with limited data sharing or advance planning. Nevertheless, it is entirely possible that public schools will respond to changes in their own sector-for instance, if many more children enroll because private schools are shut down, schools may see declines in test scores. Our assumption of zero changes among children already choosing public schools is accurate only if congestion effects are small. This is a strong assumption that likely leads to an underestimation of the value of private schooling. Interestingly, two recent studies from the LEAPS data suggest that the assumption may not far-fetched.
The total number of G6PD tests in a given municipality and year were divided proportionally in those who were eligible for radical cure by cases in males and females over 16 years and cases in males and females under 16 years. G6PD deficiency varied by province and G6PD activity score distribution was based on Markov chain Monte Carlo fitted Gaussian mixture models to four datasets. [18]  For two states in which survey data were not available a national-level prevalence of 5. 52% G6PD deficiency was used. The diagnostic accuracy of the test was taken from a study in Brazil that was used to inform the US FDA submission for the SD Biosensor STANDARD G6PD Test (Table  1 ). [19]  Parameters relating to risk of severe malaria, haemolysis, and mortality are provided in Table  1. Probability of severe malaria requiring hospitalization 0.
DALYs DALYs for each scenario were calculated by adding the years of life lost to the years of life with disability. Model parameters for years of life with disability are shown in Appendix 1. It was assumed that all malaria cases resulted in anaemia. The average age of patients with vivax malaria by sex were derived from the SIVEP database. [21]  These ages matched to the life expectancy for that age and sex to calculate the years of life lost. [25]  Life expectancy for both sexes was varied by ±10% in the sensitivity analyses. Analyses Both costs and outcomes were discounted at 5% per year in the base case analysis to reflect the value that society attaches to present consumption as opposed to consumption in the future in Brazil. In addition, results are also presented for 0% and 10% discounting.
DISCUSSION Our results provide robust evidence that the use of tafenoquine after semi-quantitative G6PD testing would be cost-effective at a WTP threshold of US$7,800, particularly in scenarios where children could be treated with a paediatric formulation or where adherence to primaquine is low. To our knowledge, this is the first cost-effectiveness analysis of tafenoquine utilizing a transmission model for P. vivax. This analysis benefits from the robust database in Brazil (SIVEP)  [21]  and recent cost data collection that has occurred alongside ongoing studies in Brazil. Despite having a large impact on its effectiveness, adherence to primaquine is challenging to estimate. [27]  Unsurprisingly, adherence to primaquine had a large impact on the cost-effectiveness with the largest ICER seen for Scenario 3 (90% pre-existing primaquine adherence), since single-dose tafenoquine would provide only an additional 10% improvement to adherence for radical cure in this.
Funding This work was supported by  Medicines for Malaria Ventures. WMM is funded by  FAPEAM  through  POSGRAD  and  Pró-Estado  public calls. WMM and ML are fellows of the  National Council for Scientific and Technological Development (CNPq). MTW is funded by  The   Bill & Melinda Gates Foundation  [grant number  INV-024368 ]. The funding bodies did not have a role in the design of the study and collection, analysis, and interpretation of data and in writing.
Background: Fibroblast growth factor receptor 3 (FGFR3) alterations are oncogenic drivers of urothelial carcinoma (UC). Pemigatinib is a selective, oral inhibitor of FGFR1-3 with antitumor activity. We report the efficacy and safety of pemigatinib in the open-label, single-arm, phase II study of previously treated, unresectable or metastatic UC with FGFR3 alterations (FIGHT-201; NCT02872714). Patients and methods: Patients !18 years old with FGFR3 mutations or fusions/rearrangements (cohort A) and other FGF/FGFR alterations (cohort B) were included. Patients received pemigatinib 13. 5 mg once daily continuously (CD) or intermittently (ID) until disease progression or unacceptable toxicity. The primary endpoint was centrally confirmed objective response rate (ORR) as per RECIST v1. 1 in cohort A-CD.
28  Our study provided some insights into mechanisms of acquired resistance to pemigatinib. Analysis of ctDNA samples at the time of progression for 36 cohort A patients identified 8 FGFR3 secondary resistance mutations in the FGFR3 kinase domain. These mutations affect known resistance mechanisms, impacting the FGFR3 V555 gatekeeper residue and disrupting the molecular brake of FGFR3 through mutation of N540, suggesting that development of resistance mutations may have contributed to disease progression. 29, 30  Together with genomic data from multiple solid tumors in the Foundation Medicine database,  7  our findings provide some insight on the impact of specific FGFR3 alterations and co-alterations on tumor response. Notably, among the most prevalent FGFR3 mutations, ORR to pemigatinib varied.
The combined ORRs for cohorts A-CD and A-ID for S249C, R248C, and G370C were 23%, 29%, and 29%, respectively, whereas response among patients with the Y373C alteration was only 9%. Similarly, ORR among tumors with FGFR3 rearrangements only was 17%, suggesting that pemigatinib may be more effective for specific FGFR3 mutations like S249C, R248C, and G370C than other FGFR3 alterations. Analysis of genomic prescreening data found a relatively high rate of CDKN2A alterations (60%) and relatively low rates of TP53 mutations (25%) in cohort A; similar frequencies have been observed in patients with FGFR3 aberrations in the Foundation Medicine database. 7  In our study, co-alterations in the tumor suppressor TSC1 were associated with SD and PD. Beyond TSC1, co-alterations in a larger set of PI3K pathway genes occurred significantly more often in tumors of patients who did not respond to pemigatinib treatment.
ACKNOWLEDGEMENTS Writing assistance was provided by  Erin McClure, PhD , an employee of  ICON (Blue Bell, PA, USA ), and was funded by  Incyte (Wilmington, DE, USA). Foundation Medicine ,  Janssen ,  MSD , and  Roche ; served as a consultant and/or advisor to  AstraZeneca ,  Basilea Pharmaceutica ,  Bayer ,  Bicycle Therapeutics ,  BMS ,  Catalym ,  Clovis Oncology ,  GlaxoSmithKline ,  Incyte ,  Janssen ,  MSD ,  Rainier Therapeutics ,  Roche , and  Seattle Genetics/Astellas ; received research funding paid to the institution from  AstraZeneca ,  Gilead ,  Ipsen , and  MSD ; received payments for travel and accommodations from  AstraZeneca ,  Janssen ,  MSD ,  Pfizer ,  Rainer Therapeutics , and  Roche ; and reports that his spouse is an employee and shareholder of  Bayer. DP received research grants paid to his institution from  AstraZeneca ,  BMS ,  Merck Sharp & Dohme ,  Roche , and  Seattle Genetics ; received honoraria from  AstraZeneca ,  Astellas Pharma ,  BMS ,  Ipsen ,  MSD Oncology , and  Pfizer/Astellas ; served as a consultant for  Astellas Pharma ,  BMS/Medarex ,  Merck ,  MSD Oncology , and  Pfizer ; and received payments for travel and accommodations from  AstraZeneca  and  Pfizer.
Resource Availability Lead Contact Further information and requests for resources and reagents should be directed to and will be fulfilled by the Lead Contact, Nicolas Bidère (nicolas. bidere@inserm. fr). Materials Availability All reagents generated in this study are available from the Lead Contact with a completed Material Transfer Agreement. Data and Code Availability This study did not generate large-scale datasets. Raw data of this article are available from the Lead Contact upon request. (A  and B ) RPE-1 cells were serum starved and treated with 2. 5 µM NSA for 24h and were then analyzed by confocal microscopy to visualize PCM1 and primary cilia (acetylated tubulin). In (B), the histogram shows the quantification of cells with cilia (means ± SEM of three independent experiments; n > 100 cells counted per sample; ****P < 0.
François Bonamy , Nantes, France ) for the expertise and discussions for imaging analysis. This work was funded by an  International Program for Scientific Cooperation (PICS, CNRS) ,  Fondation de France ,  Fondation pour la Recherche Médicale  (Equipe labellisée  DEQ20180339184 ),  Fondation ARC contre le Cancer  (PJA to J. G. and N. B. ),  Institut National du Cancer  ( INCa PLBIO 2019-151 ,  INCa PLBIO 2019-291 ,  INCa PAIR-CEREB lNCa_16285 ),  Ligue nationale contre le cancer (Equipe labellisée)  et comités de Loire-Atlantique,  Maine et Loire , Vendée, Ille-et-Vilaine (J. G. , N. B. ). C. A. N. received a fellowship from  Fondation de France , C. C. N. R. from  INSERM  and  Région Pays de la Loire , C. M. from  Ligue Régionale contre le Cancer and Région Pays-de-la-Loire , and J. J. from  Fondation pour la Recherche Médicale.
METHODS Approval for the study was obtained from the Rothschild Foundation Hospital institutional board (IRB 00012801) under the number 109 CE_20211123_6_GD and from the Ethics Review Board of the Canadian Ophthalmic Research Center. The study was conducted according to the tenets of the Declaration of Helsinki. A training data set composed of 88 eyes of 53 patients with a history of myopic LVC, who underwent cataract surgery at Lamartine Clinic (J. M. A. ), was used to establish the measured anterior radius of curvature (ARC) / corrected ARC relationship. The same set of (non-LVC) eyes used to develop the original PEARL formula  8  was employed to train a theoretical internal lens position (TILP) prediction algorithm without central corneal thickness (CCT) and ARC. Finally, a test set of 60 myopic LVC eyes from 46 patients operated at Hôpital Fondation Adolphe de Rothschild, Paris, was constituted to assess the prediction preci-sion of the resultant post-LVC PEARL-DGS formula.
The described formula version is being made available online on the PEARL-DGS calculator. 9  No pre-LVC data was used in this study. In addition, we used A constants from IOLCon. 10  Biometry was measured using IOL Master 500 (Carl Zeiss Meditec), and corneal pachymetry measured with Orbscan (Bausch and Lomb, Inc) were used in the training set. The biometer used in the test set was the IOL Master 700 (Carl Zeiss Meditec). Prediction precision was compared to the currently available postrefractive IOL formulas of the American Society of Cataract and Refractive Surgery's (ASCRS's) online calculator: Shammas, Haigis-L, Barrett No History, and the average of those formulas. Our results were also categorized based on axial length ( < 28, 28-30, > 30 mm) and compared with the formulas mentioned by Whang and associates.
Corrected anterior corneal radius (ARCcorr ) = n cornean air Corrected anterior corneal power Equation (  6 ): Corrected anterior corneal radius calculation. The post-LVC training set was, therefore, used to establish a linear relationship between measured ARC and ARCcorr ( Figure  2  ). Once established, this linear regression was used in the post-LVC formula to convert measured ARC to an estimated ARCcorr. To assess the correlation between the LVC value inputted into the formula (in cases where the default -5-D value was not retained) and the predicted refraction, we calculated the predicted SE and its as- sociated prediction error for LVC values ranging from -10 to 0, by 0. 1-D steps, for the whole data set.
The precise inner working of this method has not been published. Nevertheless, studies have shown that Barrett True-K is one of the most reliable calculation methods after myopic and hyperopic PRK/LASIK. 2 , 7 , 22  When we categorized eyes based on their axial length (AL), we found a slight advantage in using the Barrett True-K formula for eyes with AL < 28. 0 mm, the Triple-S formula for eyes with AL between 28. 0 and 30. 0 mm, and the PEARL-DGS formula for eyes with AL greater than or equal to 30. 0 mm. Although our cohort is not large enough to conduct statistically sound subgroup analysis, our findings align with previous publications. 11  Methods that do not require preoperative data also have been developed. Shammas PL and PHL formulas (for previously myopic and hyperopic eyes, respectively) correct the post-LVC measured corneal power using a linear regression.
Sequence similarity searches For remote homology detection, we used HHpred  (Gabler et al. , 2020). First, starting from single sequences or multiple sequence alignments (MSAs), it transforms them into a query HMM; using this HMM, it then searches the Uniclust database30 and adds significantly similar sequences found to the query MSA for the next search iteration. This strategy is very effective in detecting remotely homologous sequences but, as the user guide points out (https://github. com/soedinglab/hh-suite/wiki), "the higher the number of search iterations, the greater the risk of non-homologous sequences or sequence segments entering the MSA and recruiting other sequences of the same type in subsequent iterations". To avoid this problem, we set the number of iterations to 0, i. e. the parameter "MSA generation iterations" was set to 0.
Discussion The documented loss of sensitivity of Pfam HMMs when searching for domains in "divergent organisms"  (Terrapon et al. , 2012)  prompted us to use HHpred  (Gabler et al. , 2020)  to annotate SARS-CoV-2 proteins. Given a query sequence, this annotation tool offers the possibility to search for homologs among all proteins in an organism. Each protein in the organism is represented by an HMM built according to a different strategy than that used by Pfam (for more details, see the section "Creating custom databases" in the user guide (https://github. com/soedinglab/hh-suite/wiki)). We speculated that this difference might give HHpred the ability to discover similarities not detectable by Pfam (it should be noted that a theoretical comparison between the Pfam and HHpred HMMs, as well as a full empirical comparison, is beyond the scope of this paper).
Spike glycoprotein As shown previously, the 908-1254 part of the Spike S protein of SARS-CoV-2 is similar to the 186-482 part of human prominin-1. The 179-432 part of this prominin is annotated as "NON_CYTOPLASMIC_DOMAIN" (i. e. non-cytoplasmic loops of a TM protein) by Phobius (for completeness, note that the 253-283 part is annotated as a coil by COILS). In contrast to the case of ORF3a, no reliable statistical test can be performed here (the number of human prominins, i. e. proteins annotated by Pfam as "prominin" (Pfam PF05478), is 5). However, such a calculation seems unnecessary here. HHpred identified a similarity between Spike S and human and fly prominins (see Results section). Human and fly belonging to lineages that were separated over 700 million years ago (median time of divergence 694 MYA (see http://timetree.
Acknowledgements We thank  F. Ulhman ,  S. Marcand ,  A. Quinet ,  A. Campalans ,  P. Radicella  and  P. Bertrand  for critical reading of this manuscript and members of the  Dubrana and Marcand laboratories  for stimulating discussions. We thank  Douglas Koshland  and  Matthias Peter  for sharing plasmids and strains. This research was supported by the  European Research Council  under the  European Community 's  Seventh Framework Program  ( FP7/2007 2013/European   Research Council  Grant Agreement  281287 ),  Fondation ARC pour la Recherche sur le Cancer (PJA-) ,  CEA Radiation biology and Impulsion programs ,  EDF. JP was supported by a fellowship from the  CEA.
Paleoclimate information on multiple climate variables at different spatiotemporal scales is increasingly important to understand environmental and societal responses to climate change. A lack of high-quality reconstructions of past hydroclimate has recently been identified as a critical research gap. Speleothems, with their precise chronologies, widespread distribution, and ability to record changes in local to regional hydroclimate variability, are an ideal source of such information. Here we present a new version of the Speleothem Isotopes Synthesis and AnaLysis database (SISALv3), which has been expanded to include trace element ratios and Sr-isotopes as additional, hydroclimate-sensitive geochemical proxies. The oxygen and carbon isotope data included in previous versions of the database have been substantially expanded.
SISALv3, contains speleothem data from 364 sites from across the globe, including 94 Mg/Ca, 83 Sr/Ca, 51 Ba/Ca, 25 U/Ca, 29 P/Ca and 14 Sr-isotope records. The database also has increased spatiotemporal coverage for stable oxygen (831) and carbon (588) isotope records compared to SISALv2. Additional meta information has been added to improve machine-readability and filtering of data. Standardized chronologies are included for all new entities together with the originally published chronologies. The SISALv3 database thus constitutes a unique resource of speleothem paleoclimate information that allows regional-to-global paleoclimate analyses based on multiple geochemical proxies, allowing more robust interpretations of past hydroclimate and comparisons with isotope-enabled climate models and other earth system and hydrological models.
The SISAL WG aims to answer critical open questions in paleoclimate science with a focus on regional to global trends and event synchronization. To address these questions, the SISAL WG has been developing standardized and quality checked databases. The first three versions of the database (SISALv1, SISALv1b and SISALv2) provided the paleoclimate community with a growing resource of speleothem geochemical data  (Atsawawaranunt et al. , 2018; Comas-Bru et al. , 2020 , 2019) , specifically oxygen (δ 18 O) and carbon (δ 13 C) isotope records. The SISAL database versions have been exploited (i) to better understand the drivers of speleothem environmental proxies and improve their interpretations  (Baker et al. , 2019 (Baker et al. , , 2021;; Fohlmeister et al. , 2020; Treble et al. , 2022; Skiba and Fohlmeister, 2023) , (ii) to provide a resource on the interpretation of speleothem records at a regional level, identifying key gaps and future work  (Kaushal et al.
, 2018; Lechleitner et al. , 2018; Braun et al. , 2019; Burstyn et al. , 2019; Deininger et al. , 2019; Kern et al. , 2019; Oster et al. , 2019; Zhang et al. , 2019; Lorrey et al. , 2020) , and (iii) to understand the mechanisms of past climate change including through comparison with isotope-enabled climate models  (Comas-Bru et al. , 2019; Parker et al. , 2021a; Bühler et al. , 2022; Parker and Harrison, 2022; Parker et al. , 2021b). The new SISALv3 database provides an expanded dataset of oxygen and carbon isotope data, interpreted as records of hydroclimate and vegetation dynamics/bioproductivity  (Wong and Breecker, 2015) , and has been significantly expanded to include data on Sr, Mg, Ba, U, typically tracers for hydrological processes in the karst and cave  (Fairchild et al. , 2000; Johnson et al.
, 2000; Day and Henderson, 2013 ). An increase in PCP usually occurs in times of drought that facilitate increased water-rock residence times and degassing in the karst  (Fairchild et al, 2000). The SISALv3 database, augmented with trace element proxies thus provides a multi-proxy dataset that can be used for long-term drought reconstructions in the past, and to better understand the forcings, mechanisms and periodicities of such events. In addition to the new geochemical data, extensive metadata including information on parameters such as vegetation and karst type as well as entity (i. e. speleothem dataset) images are provided to aid robust interpretations. The SISALv3 database will allow the systematic and global analysis of stable isotope and trace element variability, and elucidate how trace element data can be used to strengthen climatic interpretations from speleothem oxygen (δ 18 O) and carbon (δ 13 C) records.
New data formatting and processing All trace elements are reported normalized as ratios with respect to Ca (X/Ca) in units of mmol/mol. In the following manuscript, "trace element" refers to the normalized ratio to Ca. A standardized conversion sheet is used to facilitate conversions from gram to mol units (available in the repository). Sr-isotope data is reported as 87 Sr/ 86 Sr values. For internal consistency, and to facilitate future intercomparison and synthesis studies, the measurement method and standards used, and measurement precision are also reported for both trace elements and Sr-isotopes. Mechanisms relevant to hydroclimate interpretations from speleothems are based on a multi-proxy approach of stable isotopes and one or more trace element ratios. Therefore, the SISALv3 database structure allows for trace element measurements to be added at the depths of the stable isotope measurements on a given entity.
However, between 35 and 86% of the records (depending on element) were measured using in-situ techniques, such as laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS), and these datasets are typically generated at a higher resolution (10-100 μm) than the stable isotope records  (Jochum et al. , 2012). These data have been downsampled to the resolution of the stable isotope data for the same speleothem. Downsampling was performed by computing averages (and standard deviations) of the trace element measurements falling into the stable isotope sampling depths. This implicitly assumes that the same or a parallel sampling track were used for trace elements and stable isotopes and that the isotope sampling was continuous. Downsampling allows the trace element data to be represented by the same depth-age model as the stable isotope record.