dataesr/scientific-paragraphs-categorization huggingface dataset:
We recorded ailments or therapeutic categories as cited by the informants so that their illness classification (or nosology) was retained. The treatments reported were then coded as (1) traditional (medicinal plants or others), (  2 ) biomedical (pharmaceuticals), or (3) mixed (a combination of traditional and biomedical treatments). To assess functional knowledge redundancy, we evaluated the potential correspondences between the data collected through free-listings, the knowledge survey, and the health survey. Specifically, we constructed a matrix in which rows correspond to the medicinal plants used to treat the same ailment and columns correspond to our measures of the number of plants that women and men knew to treat the same ailment, including (1) the ailments most frequently reported in the health survey, and (2) the number and names of plants that women and men listed as remedies for such ailments.
As these data were not normally distributed according to the Kolmogorov-Smirnov test, we calculated Spearman correlations between frequency of reported ailments and functional knowledge redundancy reported by women and men for each ailment. Plant voucher specimens were not collected for this study. Vernacular names given by informants were recorded and linked to botanical genera based on previous ethnobotanical research in the area  (Huanca 1999 ; Reyes-Garcı ´a 2001; Ticona 2010; Gueze 2011); therefore, the decision to associate a local name with a scientific species name was made only when a single member of the genus was known to be equivalent to the vernacular name reported. Vernacular names are used throughout the text; see correspondence to botanical genera in Supplementary material.
CONCLUSION The conceptual and case study provided in this paper to assess knowledge diversity and functional knowledge redundancy within local medicinal knowledge systems may be useful for researchers who seek to evaluate adaptive capacity and resilience. Beyond medicinal plant functional knowledge redundancy and its effects on knowledge system adaptive capacity and resilience, future research should also pay attention to complexity in the treatments, e. g. , the use of combinations of medicinal plants, and of medicinal plants used together with other traditional remedies, to treat illness. The data collected to evaluate functional knowledge redundancy only considered the use of individual plants while, in practice, the Tsimane' employ mixed remedies; thus, functional knowledge redundancy may have been underestimated.
Acknowledgments This research was funded by the  European Research Council  under the European Union's  Seventh Framework Programme  ( FP7/2007-2013)/ERC Grant  Agreement  N°FP7-261971-LEK  to  Reyes-Garcı  ´a. We express our profound gratitude to our informants, for their willingness to share their time and knowledge. We thank the  Gran Consejo Tsimane ' and the  CBIDSI  for providing logistical support and office facilities in San Borja; and we thank  Marta Pache ,  Paulino Pache ,  Sascha Huditz ,  Vicente Cuata ,  Consuelo Campos ,  Isabel Virginia Sanchez ,  O ´scar Plata , and  Sandra Martı  ´nez for field assistance. Open Access This article is distributed under the terms of the Creative Commons Attribution 4. 0 International License (http:// creativecommons. org/licenses/by/4. 0/),which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.
To identify the causal impacts of LSAPs, we account for time-varying credit demand and supply shocks that might otherwise confound our results. First, our loan-level dataset combines agency and non-agency mortgages, allowing us, for example, to compare observationally similar loans above and below the conforming loan limit that are plausibly exposed to the same shocks to funding and fundamentals. Event studies support this parallel trends assumption, particularly with our specifications' tight windows around policy dates. Further steps to address demand shocks include focusing on refinance mortgages, which are mainly driven by changes in interest rates as opposed to changes in the demand for housing (purchase mortgages), and controlling for regional shocks to fundamentals (income, house prices, expectations, etc.
) by controlling for county ⇥ month effects. Several additional measures suggest that our results are not driven by segment-specific credit supply shocks. We focus our analysis on the post-2008 period to avoid making inferences off of the asset-backed securities market disruptions that differentially affected the jumbo-lending market as it transitioned away from being heavily reliant on private securitization  (Chernenko, Hanson, and Sunderam, 2014). We also show that our results are robust to the inclusion of timeseries controls proxying for segment-specific shocks to funding availability (BBB-AAA credit spreads, GSE guarantee fee changes, and premia on bank credit default swaps). Finally, we complement this mortgage-level evidence with borrower-level evidence using a novel dataset linking mortgages to borrowers and their credit bureau information to track borrowers before and after they take out a new mortgage to study how households responded to the differential improvement of the GSE-eligible segment of the mortgage market.
We propose three such factors (credit-spread shocks, guarantee fees, and average premia on bank credit default swaps), demonstrate that they explain a significant amount of variation in differential movements in the jumbo and conforming segments, and verify that our results are robust to the inclusion of these three factors as time-series controls measuring aggregate shocks to funding availability in these two segments. We measure credit spreads as the difference in yields between BBB and AAA rated bonds. Second, the guarantee fee ("g-fee") originators must pay to the GSEs to insure a loan's default risk has varied over time from just 20 basis points before the crisis to the 50 basis points in 2015, naturally affecting non-GSE and GSE-eligible loans differentially. Third, we collect data on the credit-default swaps for all major financial institutions in the U.
30 As our data cover about 48% of the refinance market (using HMDA data as a national benchmark), our results suggest that QE1 was responsible for $607 billion increase in aggregate refinance volume. This increase in refinances might lead to an increase in consumption through multiple channels. First, many borrowers cash out equity while refinancing, providing cash on hand to support new expenditures. Second, by refinancing their loans, borrowers are able to secure lower interest rates, and increase their monthly disposable income. On average, the amount of equity cashed out is about 11% of the total volume of refis. As commonly assumed in the MPC literature (see, e. g. ,  Mian and Sufi, 2011) , we assume the marginal propensity to consume out of cashed-out equity is 1 (the assumption being that borrowers cash-out equity to spend it).
This translates into an increase in borrowers' consumption of about $67 billion. We also compute the increase in consumption due to interest rate savings. Specifically, households refinancing in 2009 saved on average $250 per month or $3,000 per year due to the lower interest rates (see discussion of Appendix Figure  7  below). refinancing channel by approximately $76 billion. To provide further evidence that refinancing results in an increase in borrowers' consumption, we also plot event studies around the origination of a refinance mortgage in Appendix Figure  7. Specifically, we examine the relationship between refinancing and durable goods purchases during QE1, measured using borrower-level credit-bureau data on auto loans linked with data on mortgage refinancing. We restrict our attention to those borrowers who refinanced their mortgage during QE1 (December 2008 to March 2010) and leverage the panel structure of our data by following each borrower 12 months before and after refinancing and estimate event studies of the form y it = ↵ t + X ⌧ 6 =0 ⌧ 1(t refinance month i = ⌧ ) + 4 X k=1 kt 1(Balance Quartile i = k) + " it where y it is either the monthly interest payment or an indicator for whether borrower i in calendarmonth t purchased a (new or used) car (and financed at least some of that purchase to be observed by Equifax) and Balance Quartile i is the quartile of the loan size of borrower i's new refinance mortgage (which allows for income-segment specific shocks and eliminates pre-trends).
Panel II includes loans for which we observe the predecessor loan with outstanding principal between 80 and 100% of the local CLL and loan whose outstanding balance (adjusted for expected refinancing costs) is between 70 and 110% of the CLL. Panel II report loan-level summary statistics on conforming and jumbo loans from Equifax's CRISM database which merges McDash Analytics mortgage servicing records from Lender Processing Services with Equifax credit bureau data. Panel III reports summary statistics for time series controls used in robustness checks, including guarantee fees fromFuster et al. (2013), bond yields from Federal Reserve Economic Data (FRED) at the Federal Reserve Bank of St. Louis, and CDS index from Markit. Summary Statistics The table reports regression coefficients relating loan-level mortgage interest rates (in basis points) to indicated unconventional monetary policy programs.
INTRODUCTION The wealth of information resulting from the genomics revolution has provided the opportunity to deconvolute histology-based tumor phenotypes into a huge number of individual molecular trait-based phenotypes. A major challenge is to summarize such large datasets into tumor subtypes through a new molecular taxonomy of cancer. This effort, pioneered in the early 2000's in the area of breast cancer, has expanded to include essentially all tumor types in recent years through the massive genome sequencing efforts carried out under the auspices of the The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC) projects. Urothelial bladder cancer (UBC) has been a relative latecomer to these studies. In the last few years, the efforts of both individual research groups and the TCGA consortium have led to several proposals for molecular subclassification that herald promise for clinical application in multiple aspects of patient management.
In UBC, the Lund group was first to use expression profiling of tumors covering a wide spectrum of the disease to propose of a molecular taxonomy classification. Subsequent studies -either biology-based or multidata platform-based -rapidly showed remarkably consistent evidence supporting the existence of UBC subphenotypes. Most of this research has been carried out in muscle-invasive bladder cancer. At the same time, this work unveiled unmet needs in this area, such as more detailed genomic analyses of non muscle-invasive (NMI) tumors. The TCGA Pan-Cancer integrated multidata platform analysis of 12 different cancer types also allowed placement of the UBC molecular subphenotypes in a broader context of cell and tissue-specific differentiation. Issues related to differences in genome technologies, bioinformatics strategies/tools for genome analysis, nomenclature, biology-based vs.
There were 32 significantly mutated genes (SMGs) involved in multiple pathways including cell cycle regulation (93% of tumors), chromatin remodeling (76%), DNA damage response, transcription factors, and receptor tyrosine kinase (RTK)/RAS/PI3K (72%) signaling pathways. Four SMGs involved in epigenetic regulation (ARID1A, MLL2, KDM6A, and EP300) were mutated in up to one fourth of the tumors. One-third of the tumors were characterized by cancer-specific DNA hypermethylation  [1]. An additional 281 invasive tumors are in the pipeline and the TCGA team is currently analyzing the complete data set with the expectation of publishing an updated comprehensive integrated analysis. Mutation data are available on all 412 samples (including the first 131) and 54 SMGs have been identified, including 26 that were reported in the first paper.
Clusters I and II expressed high HER2 (ERBB2) and estrogen receptor beta signaling signatures, sharing features with Luminal A breast cancers. Cluster III shows similarities to other TCGA tumor types, including basal-like breast and SCC of the head and neck and the lung  [1]. Tumors in this cluster show high expression of KRT14 and CD44, suggestive of stem cell markers, similar to the basal subtype of bladder cancer originally reported by the Baylor College of Medicine group  [2, 3]  and to the subtype described by Sjödahl et al. which was associated with the worst prognosis compared to the other subtypes they described  [4]. This was confirmed with a recent TCGA pan-cancer analysis of 12 tumor types and 6 data types, which stratified the bladder cancer cohort into 3 groups. A small subset was similar to lung adenocarcinoma, a second subset -with most of the cluster III and squamous samples -clustered with the head and neck and lung SCC, and the thirdwhich contained the majority of tumors -was unique to bladder cancer and had the best prognosis compared to the two other subtypes  [5].
Using the bladder cancer TCGA data (234 invasive tumors) as a reference set, the Lund group evaluated four published gene expression-based bladder cancer classification systems: the two-tiered University of North Carolina (UNC), the three-tiered MD Anderson (MDA), the four-tiered TCGA, and the five-tiered Lund  [15]. This analysis showed that a hierarchical relationship among the UNC, MDA, and TCGA classifications systems exists and that substantial biological subgroup heterogeneity remains at the highest resolution. Thus, a six-tiered classification system was reached including: a SCCL/UroB group, a GU group, an urobasal group, two groups with slightly different profiles emerging from the "infiltrated" category, and a new variant tentatively classified as "small cell/neuroendocrine-like".
THE UNIVERSITY OF NORTH CAROLINA CLASSIFICATION B. Kim's strategy focused on the analysis of invasive tumors. Using K2 consensus clustering, a robust classification of tumors enriched in basal (KRT5/6 and CD44) vs. luminal (PPARG, GATA3, KRT20, and UPK2) subtypes was reached  [16]. A signature including 47 genes (BASE47) was generated by prediction analysis of microarrays (PAM) that was associated with outcome in 3 independent patient series. The basal subtype revealed similarities with the basal subtype of breast cancers, as demonstrated by applying the PAM50 signature to the UBC datasets. This group contained a claudin-low subgroup, as defined in breast cancer, that was enriched in epithelialmesenchymal transition (EMT) and tumor initiating cell markers. Patients whose tumors were of the claudin-low subgroup had an outcome similar to that of patients in the "basal" category.
Functional studies demonstrated that active DNp63a and STAT3 were involved in the control of basal gene expression, whereas PPARG was responsible for luminal gene expression, and DNp63a and PPARG antagonized each other. Tumors within the p53-like subtype were mostly resistant to neoadjuvant cisplatin-based combination chemotherapy  [20]. The MDA group compared their subtype calls with those of the groups at the University of Lund  [8] , TCGA  [1] , and the UNC  [16] , using shared datasets. Overall, there was close concordance among the classifications. The MDA "basal" tumors corresponded well with the Lund "SCC-like" subtype, TCGA's Clusters III ("squamous") and IV, and UNC's "basal-like" tumors. The MDA "luminal" tumors corresponded with the Lund "genomically unstable", TCGA cluster I and UNC's "Luminal", while the MDA "p53-like" tumors matched the Lund "infiltrated", TCGA Cluster II, and merged in the "luminal" UNC subtype  [12, 13, 20, 23].
Because each group used very distinct approaches to identify their subtypes, the fact that all reached similar conclusions strongly suggests that the subtypes are highly reproducible and biologically relevant, regardless of how they are named. Many investigators now prefer consensus clustering to unsupervised hierarchical clustering to identify subtypes of cancer because it assesses the stability of clusters based on multiple re-samplings. Therefore, the MDA group investigated how their calls would have changed had they applied consensus clustering, as opposed to other clustering methods, using the TCGA RNAseq dataset (n = 128, log2-transformed) as a reference. They identified the top 5000 most variable genes as measured by median absolute deviation and median centered the data and used this subset for consensus clustering (specifically, agglomerative hierarchical clustering).
THE CIT "BASAL-LIKE" TUMOR CLASSIFICATION F. Radvanyi and his colleagues from the CIT consortium (Institut Curie, Henri Mondor and Foch hospitals, Institut Gustave Roussy, CEPH, La Ligue Contre le Cancer) focused on the "basal-like" tumor subgroup because it was the largest homogeneous group identified by various unsupervised methods to classify invasive bladder cancer based on transcriptome data  [19]. In a joint analysis of 7 datasets of invasive tumors, more advanced cases were over-represented among the "basal-like" tumors. Survival analyses showed worse outcome for this group of patients, independently of stage and grade, lymph node, and metastatic status. The survival curves of patients with basal-like tumors were very different from other bladder cancer patients, with most of the death events occurring within one year after diagnosis.
At the level of DNA alterations, this group was characterized by significantly more EGFR gains or amplifications, FHIT deletions, and TP53 mutations; at the transcriptomic level, an enrichment of EGFR pathway activation was identified. In agreement with this observation, EGFR phosphorylation was higher in the basal-like subgroup. This led to testing in vitro and in vivo the sensitivity of bladder cancer cells to the EGFR inhibitor erlotinib. For this purpose, a particular effort was made to identify relevant preclinical models recapitulating human primary basal-like bladder tumors. Using a transcriptomic signature derived from human invasive bladder cancer datasets, they identified 11 human bladder cancer cell lines -out of 22 -dysplaying a basal-like profile  [19]. In addition, they showed that tumors induced by N-butyl-N-(4-hydroxybutyl) nitrosamine in mice, a commonly used model of chemical bladder carcinogenesis, recapitulated key molecular features of the human basal-like tumors includingtheautocrineactivationoftheEGFRpathway.
DEFINING "INTRINSIC" BLADDER CANCER SUBTYPES The term "intrinsic" was first applied to the molecular classification of breast cancers. The work by Perou's group demonstrated that tumor subtype usually remained stable regardless of where or when a given tumor was sampled  [21] , leading to the conclusion that subtype membership was an "intrinsic" property of a given breast cancer. The original definition of "intrinsic" referred to tumor cell properties. Although the MDA group consistently observed their three subtypes in multiple independent datasets, this did not necessarily indicate that all of the subtypes were "intrinsic". Direct measurements of cluster stability using silhouette score analyses indicated that many of the "p53-like" tumors in the MDA discovery cohort were unstable, unlike the "basal" and "luminal" tumors  [20].
ACKNOWLEDGMENTS We would like to thank all the meeting attendants for their participation and discussion, and  N. Malats  and  E. Carrillo-de Santa Pau  for contributions to a previous version of the paper. This manuscript and the Bladder Cancer Classification Consensus Meeting that led to its development were supported by a grant from  Astellas Pharma Europe Ltd  given to  Universitat Pompeu Fabra, UPF. Astellas Pharma Europe Ltd  holds no responsibility for the origination, development and content of either the manuscript or the meeting, including the agenda, choice of participants, manuscript author, editorial control or distribution.
Funding was provided by the National Science Foundation (DSK), Fulbright-Hays Doctoral Dissertation Fellowship (DSK), Vanderbilt University (DSK), and the University of California Santa Barbara (DSK). The New World settlement as seen from different craniofacial morphospaces: An Evo-Devo approach MANON GALLAND 1 , SOLEDAD DE AZEVEDO 2 , NEUS MARTÍNEZ-ABADÍAS 3 and ROLANDO GONZÁLEZ-JOSÉ 2. 1 School of Archaeology, University College Dublin, 2 Instituto Patagónico de Ciencias Sociales y Humanas, Centro Nacional Patagónico-CONICET, 3 Universitat de Barcelona, Centre for Genomic Regulation. The arrival and dispersal of human populations in the Americas remains a very controversial issue especially regarding the number of migration waves. Craniofacial variation between ancient and recent Amerindians has been attributed to the effects of multiple founder events or alternatively, as the result of a single population carrying high levels of internal heterogeneity and effects of local differentiation plus recurrent gene flow.
We used Knoop indentation to sample bone hardness (under an assumption that hardness is proportional to elastic modulus and yield strength) in the femoral midshaft of a specimen of Colobus polykomos. Under a 50g load with 10s dwell time, we examined three serial 1mm transverse sections, sampling hardness in four quadrants (anterolateral, anteromedial, posterolateral, posteromedial) with three replications each at four different orientations (0, 30, 60, and 90 degrees relative to an anteroposterior axis). The hardness data were analyzed via hierarchical ANOVA to assess the directional dependence and within-and betweensection variation. The angle of indentation represents a significant source of variation (P < 0. 01), but variance is random among quadrants and sections. Hardness variation in midshaft is thus homogeneous on a local scale, with the caveat that there does appear to be directional dependence, with the bone being stiffest in the mediolateral direction.
Results A series of SESANS measurements were performed for various different combinations of concentration and solvent scattering length density (SLD). They are shown in Fig. 2  and the data were fitted using the theory presented in the appendices with the appropriate form and structure factors for uniform density spheres as given in Pedersen. 26 In the process of fitting the data two facts become immediately apparent. The first is that when simulating the undiluted sample it has a larger radius than that determined by our DLS measurement. Secondly simulations of the shape of the curves cannot model the shape of the dip observed at z = 150 nm due to the excluded volumes, which are more pronounced in the higher silica concentration sample. Attempts to simulate the data to the measured mass fraction (0.
"Old" rights often provide more legal guarantees than newer ones, which creates unwillingness to trade in old rights for new rights. (c) Create incentives to explore alternative water supply options Incentives for increased water efficiency (e. g. water recycling) are not felt everywhere, as water recycling comes at a cost (of energy). There seem to be no initiatives in place exploring alternative water supplies, e. g. rainwater harvesting, significant process water recycling, etc. An impulse to increase process water recycling could be given by making creating an economic case (e. g. making it financially beneficial) for the private companies that are the largest water users in the Eifel-Rur region. 2. Develop a comprehensive and up-to-date database on water rights and water uses Related to the previous point are the significant data issues affecting surface water.