dataesr/scientific-paragraphs-categorization huggingface dataset:
1 before 2011). In order to compute the Kaitz index, i. e. the level of the minimum wage compared to the median wage, the sectoral minimum wage data are then matched by NACE 2-digit codes to individual wage data from the Italian Labour Force Survey (LFS) between 2008 and 2015. The LFS collects net wage data and therefore in order to make individual wage data comparable to ISTAT minimum wage data, LFS net wages are converted to gross wages using income tax rate and social security contributions (as a % of net wages) for different levels of the average wage (from 1% to 200%) in the case of a single person without children from the OECD TaxBen model. We assume that this is the effective tax rates for all workers each month before tax adjustments and transfers done at the end of the year to take into account family composition and household total income.
Individual wage data are further inflated to add the 13th and 14th months in sectors that also have include a 13th (all sectors) and a 14th month (around 40% of the agreements in the sample). Finally, only employees are considered (apprentices and domestic workers are excluded but also co. co. pro, casual work, and the like because of missing wage data in the LFS). Since LFS data may suffer from measurement error (more on this in the next section on the estimation of non-compliance), we also match the wage floors data to an employers' survey, the Structure of Earnings Survey and to administrative data from social security records (INPS). The pros and cons of these datasets and their specific features are discussed in the next section. In 2015 gross minimum wages in collective agreements were on average 9.
However, as Figure  2  shows, their bite compared to median wage, as measured by the Kaitz index or their value in purchase power parity is very different reflecting the well-known regional differences between North and South in terms of economic development and cost of living. Source: Author's calculation on ISTAT negotiated wages database and LFS. PPP computed using the purchasing power parity index in Italian regional capitals produced by ISTAT in 2009. Abruzzo is missing because the municipality of L'Aquila has not been included as it did not take part in the survey as a result of the effects of the earthquake. Finally, the bite of the minimum wage is much stronger in micro and small firms which constitute the backbone of the Italian economy than in big firms. Interestingly nominal hourly wage floors are slightly lower in micro and small firms: somehow unions and employers partly reflect the sector composition in terms of firms size and hence productivity (among many others  Bartelsman and Doms, 2000  have shown that the distribution of firm productivity and firm size are closely related) in negotiated wages but not enough to balance the lower median (reflecting the likely lower productivity).
Hence the Kaitz index in micro firms is one third higher than in large firms. Source: Author's calculation on ISTAT negotiated wages database and LFS. Overall, the data on sectoral minimum wages show that sectoral wage floors in Italy are quite high compared to the median wage, in particular in the South and in small firms. But given the importance of informality and the complexity of the system, how many workers are effectively covered by these minimum wages and how many are paid less than the negotiated minima?.
Formally, beyond the headcount indicator  0 that takes a value of 1 if  <   and of 0 when  ≥   (where  is individual wage,   is the relevant minimum wage), the measure the depth of underpayment is defined as: 𝑣 1 = (𝑤 𝑚𝑖𝑛 -𝑤) 𝑤 𝑚𝑖𝑛 ∝ if 𝑤 < 𝑤 𝑚𝑖𝑛 , 0 otherwise. where α is a parameter defining the aversion to underpayment. When α = 1,  1 is the gap between the actual wage and the minimum wage, expressed as a percentage of the minimum wage, with equal weights for all workers. For values of α greater than 1, workers who are more underpaid have higher weight. Finally, an average shortfall per underpaid worker is computed as  1 / 0. In this paper we estimate non-compliance using the sectoral minima provided by ISTAT and individual wages from the Labour Force Survey (LFS). The Labour Force Survey is the most comprehensive data source for this kind of analysis in terms of sectors covered and because it does not focus only on the formal economy.
Being declared by the worker himself (information on wages is available for around 70% of employees), wages and hours of work in principle are more likely to reflect the real wages earned and not those that should have been paid according to the rules. However, data in LFS on wages and hours worked may also be subject to measurement error (though, at least for wages, it would probably reduce the number of workers underpaid since respondents tend to overestimate wages at the bottom of the distribution). 6 As Ritchie et al. (  2016 ) underline, also sampling and weighting procedures in a survey can be a source of error when estimating noncompliance to the minimum wage. To account for measurement error in the LFS we refer to the solutions used in the related literature: first, we allow for a margin of error so that wages "close-enough" to the minimum are not considered as non-compliant.
In particular, when using LFS wages and hours of work, the threshold to compute the family of violation indices is of 85 per cent of the lowest minimum for each NACE 2digit sector. This allows for a margin of error of 15 per cent (or in the extreme case where individual wages are underestimated and hours of work overestimated, a -7. 5% margin of error for wages and +7. 5% margin of error for hours of work). Second, we confirm the validity of the baseline estimations with LFS using alternative datasets. In particular, we use an employer survey (the Structure of Earnings Survey) and administrative data from social security (INPS). Wages in the Structure of Earnings Survey are more likely to be precise than those in the LFS. However, being filled-in by the employer, the Structure of Earnings Survey (SES) is more likely to report only legal workers and wages and hours of work more in line with those fixed by the rules (the law and the reference collective agreement).
Moreover, the SES focuses only on firms with more than 6 Previous studies have found that measurement error in wage and earnings data as reported in surveys is nonclassical and mean reverting  (Gottschalk and Huynh, 2010) : wage (or incomes) at the bottom are overestimated while wages at the top are underestimated. To the extent that this is valid also for wages in the Italian Labour Force Survey this should underestimate the share of underpaid workers rather than overestimate it. excluding the potential role of extra unpaid hours of work as a tool not to comply with wages set in collective agreements. Table  4  provides a summary of the features of each dataset. 9 With respect to the potential channels of underpayment identified in the introduction, the LFS allow measuring the extent of minimum wage violation due to informality, non-regular forms of work (as long as the worker declares him or herself an employee), unpaid extra hours, "inadvertent" underpayment or the use of "pirate agreements" (agreements legally valid but signed with poorly representative or "yellow" unions).
Conditional on being employed and all else being equal, Figure  5  shows that women are 2. 5 times more likely to be underpaid, while prime age and older workers are 50% less likely to be paid less than the minimum wage compared to young workers (20-29 years old). Bluecollar workers are more likely to be paid less than the minimum as well as temporary workers (2 times more). Interestingly, all else being equal, part-time workers are less likely to be paid less than the hourly minimum (probably it is more difficult to ask extra hours to part-time workers) while the probability of being paid less than the minimum goes down with tenure. Source: Author's calculation on ISTAT negotiated wages database and LFS. Note: all probabilities statistically significant at 1% level, except for construction which is not statistically different.
11 The wage distributions in LFS, SES and INPS also show no no spike around the sectoral wage floors (Figure  A2  in the Annex). 12 If anything, they not only show that when the minimum wage relative to the median increases, the share of underpaid and the depth of violation increase, but also that the average shortfall per underpaid worker increases as if minimum wage increases for covered workers were compensated by lower wages for uncovered workers. A further test using SES data cannot be run as only a cross-section for 2010 is available.
Robustness tests As discussed, in the case of Italy the Labour Force Survey is the most comprehensive data source for this kind of analysis but it may be subject to measurement and processing error. Therefore, we also test the robustness of the results using individual (gross) wages in the Structure of Earnings Survey for Italy and social security records by INPS (which on the opposite, being filled by employers are less likely to report underpayment). We consider workers to be underpaid if they are paid less than 95% of the reference minimum wage (since wages and hours are reported by employers they are less subject to measurement error, while they may on purpose underreport hours of work to keep them in line with established rules and hourly pay rates).
22 LFS), even when comparing the same sample (estimates for LFS excluding the public sector are in Table  A2  in the Annex). This discrepancy is not surprising given that administrative data are rarely a good source to measure violations to labour market regulations. Interestingly, the estimated average shortfall per underpaid worker is also lower but still comparable (17% in SES vs. 22% in LFS using the hourly data). The same regional, industrial and firm size patterns highlighted above can be found in SES and INPS data (Table  10 ). Violations are more prevalent in the South and the Islands, in hotels and restaurants and in small and medium firms. Interestingly, the share of underpaid workers in agriculture in SES and INPS data is below the national average in contrast with LFS data suggesting an important presence of undeclared underpaid work captured (at least in part) by the LFS and not in employers' and administrative surveys.
Using SES, Kaitz indices range from 46% in education to 80% in hotels and restaurants, to 88% in real estate and professional activities with a (significant) correlation with LFS Kaitz estimation of 72%. INPS data Kaitz indices range from around 60 to 77% with a (significant) correlation with LFS Kaitz estimation of 67%. When looking at sectoral minimum wages by broad sectors (Nace 1-digit), wage floors are not surprisingly the lowest in agriculture and the highest in finance. The relative high value for the Source: Author's calculation on ISTAT negotiated wages database and LFS.
The differences in the estimated Kaitz indices using the LFS, SES or INPS may be driven by different sectoral composition (the LFS covers the entire economy, while SES excludes agriculture and public sector and small firms and INPS excludes the public sector) but also to reporting errors or biases (LFS wage data are self-reported by workers while SES and INPS are reported by employers). More on this in section 3. 1.
Background: The relapse rate in early stage non-small cell lung cancer (NSCLC) after surgical resection is high. Prognostic biomarkers may help identify patients who may benefit from additional therapy. The Helicase-like Transcription Factor (HLTF) is a tumor suppressor, altered in cancer either by gene hypermethylation or mRNA alternative splicing. This study assessed the expression and the clinical relevance of wild-type (WT) and variant forms of HLTF RNAs in NSCLC. Methods: We analyzed online databases (TCGA, COSMIC) for HLTF alterations in NSCLC and assessed WT and spliced HLTF mRNAs expression by RT-ddPCR in 39 lung cancer cell lines and 171 patients with resected stage I-II NSCLC. Results: In silico analyses identified HLTF gene alterations more frequently in lung squamous cell carcinoma than in adenocarcinoma.
In lung cancer, one study assessed the hypermethylation of HLTF in a cohort of 54 patients with NSCLC  [21]. Promoter hypermethylation was found in 21 patients (39. 6%), including 9/20 squamous cell carcinoma (SCC) and 12/33 adenocarinoma (ADC). Patients whose tumors harboured HLTF hypermethylation had shorter survival, in comparison with patients whose tumors had a hypomethylated HLTF promoter (log-rank, p = 0. 035). So far, to our knowledge, there are no published data about the expression of HLTF (wild-type and its truncated forms) in lung cancer. The purpose of this study is to assess the expression of wild-type (WT) and spliced variants (I21R) of HLTF mRNAs in NSCLC and evaluate their clinical relevance. We analyzed publicly available databases for HLTF in lung cancer and assessed its expression in NSCLC cell lines and in a clinically annotated cohort of 171 patients with resected stage I-II NSCLC.
In silico analyses Available genomic profiling data (mutation, copy number, DNA methylation [correlation only], and mRNA expression) for HLTF were downloaded from cBioPortal, an online portal for accessing data from The Cancer Genome Atlas (TCGA) project and other cancer genome profiling initiatives (http://www. cbioportal. org/publicportal). Additional cancer genome profiling data were obtained from the Catalogue of Somatic Mutations in Cancer (http://cancer. sanger. ac. uk/cosmic;  [22] ). To obtain gene expression estimates for individual mRNA forms, paired-end RNAseq raw read data from the TCGA project were downloaded from the Cancer Genomics Hub (https://cghub. ucsc. edu/). In brief, reads were mapped to the latest human reference assembly, hg19, using TopHat, a splice-aware short-read aligner (http://ccb.
jhu. edu/software/tophat). Alignment output was then supplied to Cufflinks (http://cufflinks. cbcb. umd. edu/), which was run in the reference-guided mode to quantify the abundance of known transcripts as well as predict and estimate expression of novel isoforms. Pre-and post-alignment quality control was performed with FastQC (http://www. bioinformatics. babraham. ac. uk /projects/fastqc/) and RSeQC (http://rseqc. sourceforge. net/), respectively.
Results In silico analysis of HLTF alterations in NSCLC We collected available online data for HLTF alterations in NSCLC from TCGA (Lung ADC and SCC, TCGA Provisional 2015/02/04) and COSMIC, focusing on mutations, copy number alterations (CNAs), and methylation data. We analyzed these data in association with HLTF expression. The type and the frequency of the different HLTF alterations in ADC and SCC are reported in Table  1. When all types of molecular alteration were considered, HLTF was more frequently altered in SCC (438/504 cases, 83%) than ADC (266/578 cases, 46. 0%; p < 0. 0001). While a high expression of HLTF was more frequently reported in SCC than in ADC (28. 6% vs 7. 5%, p < 0. 0001), a negative correlation between HLTF expression and its methylation status was found in both SCC (Pearson: -0.
These observations are consistent with the fact that HLTF is located on chromosome 3q, which is frequently amplified in SCC. We also analyzed the association of HLTF expression with its methylation status in both NSCLC types. In both ADC and SCC, there was a negative correlation between methylation and HLTF expression, but a high expression was more frequently seen in SCC, which might be related the higher frequency of gene copy number. Intriguingly, we did not notice any difference in HLTF expression levels (WT and I21R) between ADC and SCC by RT-ddPCR. This discrepancy may be possibly explained by the fact that we assessed HLTF mRNA variants separately, while data reported in cBioportal considered only WT HLTF expression without distinguishing the variants. In the available online data, only WT HLTF expression was assessed.
We analyzed RNA-seq data from TCGA for the presence of the intron 21 sequence and found that its expression was a rare event in NSCLC. In both the NSCLC cell lines and the 171 resected NSCLC from patients, WT HLTF levels were significantly higher than I21R HLTF. Castro et al. studied the methylation for several genes including HLTF in NSCLC and reported that patients with HLTF methylation have shorter survival  [21] ; this study represents the only study of HLTF in lung cancer. They reported HLTF methylation frequency for NSCLC and did not observe any significant difference for HLTF methylation between ADC and SCC (12/33 vs. 9/20, respectively; p = 0. 57, Fisher exact test). cBioportal does not provide gene methylation frequency but only correlations with the expression of a given gene. In both ADC and SCC, we observed a negative correlation between HLTF expression and methylation.
Funding This work was supported by funds from  F. R. S. -FNRS  (doctoral research fellowship) (L. D. ), Télévie (Al. B. , C. M. ),  FRMH (Fonds pour la Recherche Médicale dans le Hainaut, Belgium)  (L. D. ) and research grant from  Canadian Cancer Society  (grant # 701595 , MS. T. ). C. M. was supported by research funds from  Boehringer-Ingelheim Canada. The funding bodies had no role in the design of the study and collection, analysis and interpretation of the data.
Categorising other people is one of the core functions of social cognition and one of the most prevalent pieces of information we use in this endeavour is the face. Relying on faces is particularly likely when to-be-categorised groups have distinctive facial features, as is the case with some ethnic groups, like Black and White individuals in many countries or Caucasian and North African individuals in Europe. Accordingly, when possible, studies designed to capture early categorisation processes rely on facial prototypes of some ethnic groups. This is often the case, for instance, with the most popular indirect measure, the implicit association test (IAT; Greenwald,  McGhee & Schwartz, 1998) , which can be found on the Project Implicit website  (Nosek, Banaji & Greenwald, 2002). However, using faces for social categorisation purposes is only possible when a relevant face database is available.
For instance, in France and the US, the Black/White IAT relies on faces, but the Arab-Muslim IAT relies on first names, which obviously precludes face-related categorisation processes for this latter group. The goal of the cur-rent contribution is therefore to provide a face database, the Caucasian and North African French Faces (CaNAFF), designed to be used when scholars are interested in using Caucasian and North African faces corresponding to faces that can be encountered in France.
Faces in Intergroup Relations It is now well established that faces provide a lot of information for making assumptions about people and their characteristics (e. g. Oosterhof & Todorov, 2008; Willis & Todorov, 2006). In the domain of intergroup relations, extracting visual features from faces is essential to categorise others in specific groups (e. g. gender, ethnicity, age) and to combine these features with other (face-related or contextual) features (e. g. Black faces perceived as more threatening when they display a direct eye-gaze direction, but less so when displaying averted eye-gaze direction;  Trawalter, Todd, Baird & Richeson, 2008). Given the importance of faces to studying categorisation and social perception in a given cultural context, various face databases have been developed.
dynamic faces (e. g. Video Database of Moving Faces and People; O'  Toole et al. , 2005; DynEmo; Tcherkassof et al. , 2013; Amsterdam Dynamic Facial Expression Set; van der Schalk, Hawk, Fischer & Doosje, 2011)  or natural environments (e. g. Labeled Faces in the Wild;  Huang, Mattar, Berg & Learned-Miller, 2008). Finally, and more important for our purpose, some of them contain faces of different ethnicities (e. g. Langner et al. , 2010; Ma et al. , 2015). This last kind of database can be especially useful for scholars interested in intergroup relations between ethnicities. Among others, a part of this field is devoted to the study of stereotypes, prejudice, or discrimination against members of an outgroup (e. g. Devine, 1989). Although faces can be used in explicit/direct tasks (i. e. when individuals explicitly report their evaluation towards a person), faces are often used in implicit/indirect tasks (i.
g. mostly Turkish people in Germany, Turkish and Moroccan people in the Netherlands, Pakistani people in the United Kingdom, and North African people in France). Moreover, even when countries share a minority group coming from the same country (e. g. Moroccan people in France and in the Netherlands), this group can come from different ethnic subgroups (the real ethnic origin; Fondation Hassan II pour les Marocains Résidants à l  'Etranger, 2003). It follows that a North African prototypical face (i. e. the most representative face) in one country can be different from a prototypical face in another country because these groups possess, on average, different physical features. In France in particular, to the best of our knowledge, scholars interested in using faces from these two groups (people originating from France versus people originating from North Africa) do not have access to a database gathering prototypical faces of people that one can meet in France.
The Caucasian and North African French Faces (CaNAFF) The goal of this contribution is to provide a new database, the CaNAFF, gathering faces that scholars could use in a French environment by selecting faces of Caucasian individuals (i. e. French White people) and North African individuals (i. e. French Arab people). By being closer to prototypical faces one would encounter in France, we think that this new database will be useful for social cognition research relying on faces. It is important to mention that, although our faces will be more similar to faces one can encounter in a French environment (in contrast with databases from the Netherlands or from the US), these faces will obviously also differ in terms of prototypicality within the two respective groups. In fact, although we selected participants we categorised a priori as members of one of these two groups, what we will provide are prototypicality scores, namely to what extent each of these faces fits with the prototype of the two target groups (e.
g. this face is more or less prototypical of a North African face). In other words, our database will provide different faces scattered across the Caucasian/North African continuum. Although it could have been valuable to cross this continuum with gender, in the CaNAFF we decided to focus on male faces. Indeed, many of the studies in the social cognition literature focused on male first names or faces (e. g. Ackerman et al. , 2006; Correll et al. , 2002; Krieglmeyer & Sherman, 2012; Park & Banaji, 2000; Zerhouni, Rougier & Muller, 2016). This might be explained by the fact that the perception of the threat stereotype is higher in male targets than in female targets (e. g. Maner et al. , 2005; Plant, Goplen & Kunstman, 2011). We also focused on males between 18 and 30 years old, once again because that is the most frequent in this field.