dataesr/scientific-paragraphs-categorization huggingface dataset:
This approach allows the ISMU survey to produce a representative sample of the total 13. In other datasets containing information on natives' labor market outcomes-such as the Survey on Household Income and Wealth (SHIW) by the Bank of Italy or the Labor Force Survey by ISTAT-the informality status is either confidential or not available. immigrant population residing in Lombardy. 14 The latter is the richest region in Italy, after the self-governing province of Bolzano. Eurostat statistics show that in 2015 Lombardy's real GDP per capita was comparable to the one of Denmark. As  Dustmann et al. (2017a)  point out, to elicit truthful reporting of characteristics such as undocumented status and informal employment, the ISMU questionnaire does not ask for any sensitive information, such as the home addresses and the names, and the data are collected in public spaces by foreign-born interviewers who emphasize the independence of ISMU from the Italian government.
Third, among the undocumented immigrants there are as well those individuals whose residence permit expired, but who signed a regular labor contract before expiration of the residence permit. In order to ensure that our results are not biased by these measurement  Fasani (2015) , who uses the same dataset and shows 15% for a similar statistic considering a shorter time period  (from 2001 to 2007). In our main regressions, we focus on full-time workers to abstract from changes in hours worked (although we show robustness of our findings to the inclusion of part-time employment as well). We consider the following categories of workers: full-time permanent and fixed-term regular workers, irregular workers in stable employment, regular self-employment, and irregular self-employment. Conversely, part-time employment includes regular part-time workers and irregular workers in unstable employment.
In particular, national collective issues linked to eliciting illegal/irregular situations in surveys, we perform two robustness checks. First, we replicate our main results restricting the estimation sample to documented immigrants only. Tables A13, A14, A15, and A16 in Supplementary Appendix -as well as a replication of the benchmark estimations of Table  1  available upon request-all show that results are robust to this check. Second, we replicate our main results (Table  1 ) considering all undocumented migrants as working in the informal sector (tables available upon request). This does not affect our main results (the wage differential between formal and informal sector significantly rises during the crisis) as well. 17. The ISMU dataset contains information on immigrants only. In order to compare the labor market dynamics during the recession for the whole Italian workforce with those for the immigrant population, we exploit data from the Survey on Household Income and Wealth (SHIW) by the Bank of Italy.
This database estimates the importance of 52 skills required in each profession. We merge information from the ISMU survey with the O*NET values and select 23 O*NET variables which are supposed to provide an adequate representation of simple/complex jobs  (Peri and Sparber (2009)  carry out a similar procedure). In particular, we distinguish between two types of skills: manual (or physical) skills represent limb, hand and finger dexterity, as well as body coordination, flexibility and strength; conversely, communication (or language) skills include oral and written comprehension and expression. Once the 23 variables have been selected (see the Table  A5  in the Supplementary Appendix), we normalize them to [0, 1] scale. Importantly, we invert the scale for the four communication skills (oral comprehension, written comprehension, oral expression, written expression) and then calculate the average of the 23 variables.
" 21 We again run two checks: the regressions for subsamples of simple and complex occupations (columns 1 and 2 of Panel B of Table  3 ) 20. Another way to distinguish between jobs differently prone to informality would be to separate the analysis for workers in small versus large-size firms, as companies with a larger workforce are more likely to be in the formal sector. However, ISMU dataset does not include information on firm size. 21. "Simple" occupations include unskilled workers, building workers, farm workers, cleaners, craftsmen, and truck workers. and difference-in-difference-in-differences specification (column 3 of Panel B). We find that our main result is driven by simple occupations (where the effect is both large and statistically significant). In the subsample of complex occupations (column 2 of Panel B) the coefficient at the Crisis t *Informal i interaction term is not statistically significant.
Pointing task Subjects were seated in front of a touch screen (1024 Â 768) with their finger on a sensor located just in front of their navel. The distance between the sensor and the screen was 42 cm. When the target (a circle) appeared on the screen, they had to touch it as quickly as possible. We tested three different target diameters (10, 20 and 30 mm) with 15 trials for each size. The target position was unpredictable and pseudo-randomized on the entire screen. The reaction time (RT) and the movement duration (MD) were recorded. All of the processes were performed by a Matlab routine (Matlab R2013a, Mathworks, Natick, USA).
Posturography Subjects remained in a quiet erect position for 1 min on a multi-component force platform (AMTI, USA, 50 Hz) to record the position of their center of pressure (CoP). Subjects performed one trial with their eyes open and one trial with their eyes closed. The position of the feet was identical for both tests. Posturographic parameters were selected based on the suggestions of  Prieto et al. (1996). The area of the 95% confidence ellipse, the mean velocity of the CoP in the anterior-posterior and in the medial-lateral axis, the standard deviation of the CoP travel distance in the anterior-posterior axis (SD-AP) and in the medial-lateral axis (SD-ML) were calculated with a Matlab routine (MathWorks, Natick, USA).
The same A/D card was used to start the GVS stimulation and record the EMG signals; all of the processes were performed by Matlab R2013a (Mathworks, Natick, MA, USA). Each subject (n = 7) received 60 GVS (bipolar binaural square pulse, 3 mA, 170 ms) per condition (OFF scopolamine, ON scopolamine) spread over 15 sets. Each set consisted of 4 GVS that occurred with randomized delay (inter-stimulation delays were greater than 5 s). Between the trials sets, subjects could move their head and have a break if desired. Because visual cues are known to reduce the GVS response amplitude  (Cathers, Day & Fitzpatrick, 2005; Lund & Broberg, 1983) , subjects were blindfolded. EMG signals were analogically amplified (x1k), numerically high-pass filtered (reverse Butterworth filter at 30 Hz), and rectified.
For EMG signals from Erector Spinae, GVS artifacts (shifts due to voltage) were removed a posteriori by digitalized data shift compensation. For each subject and each stimulation, a 4 s range was defined around the GVS start (2 s before and 2 s after). All of the EMG ranges were synchronized and averaged to have one sample of 4 s per subject, per muscle and per scopolamine condition. These samples were filtered by low-pass filtering (reverse Butterworth filter at 10 Hz), and normalized by the activity measured at the trials beginning (tonic activity of normal standing = 100%). The EMG medium latency response to the GVS (ML;  Britton et al. , 1993)  were visually identified. The start and end delays were measured, ML EMG area and peak amplitude were computed and submitted to statistics.
Background: Increasing the weekly frequency of hemodialysis sessions has positive effects, on the control of several biological data of patients. However, knowledge about Daily HemoDialysis (DHD) practices is limited in France. The aim of the present study was to describe the characteristics and treatment trajectories of all French patients undergoing DHD. Methods: All patients older than 18 years who started DHD between 2003 and 2012 in France were included and followed until December 31, 2013. The patients' demographic and clinical characteristics and treatment modalities were extracted from the French Renal Epidemiological and Information Network (REIN) registry. Results: During the inclusion period, 753 patients started DHD in France. Based on their median age (64 years), patients were classified in two groups: "old" group (≥64 years) and "young" group (<64 years).
reported that the mortality rate was significantly higher among patients treated with DHD than in those who received conventional HD in in-center HD services  [27]. These authors used data from the International Quotidian Dialysis Registry (IQDR) that includes data from many different registries, such as the French Renal Epidemiological and Information Network (REIN) registry. As two-thirds of the data of this study were extracted from the REIN registry, the patients were mainly French, differently from other studies where data, and thus patients, were mainly from the American USRDS database  [22, 28, 30]. Therefore, Suri and colleagues hypothesized that the French patients on DHD were older and with more comorbid conditions than patients on conventional HD  [27]. In France, patients undergoing DHD represented 1 % of all prevalent cases in 2012 with regional disparities ranging from 0 to 6 %  [31].
Study population The French REIN registry includes all patients with ESRD undergoing RRT, dialysis or transplantation and living in France. The registry was established in 2002 and since 2011 it covers the entire French territory. The registry organization has been described in details elsewhere  [32]. This study was a retrospective analysis of data collected in the REIN registry. For this work, we included all patients aged 18 years and over who started DHD in the French regions included in the REIN registry between January 1, 2003 and December 31, 2012. We excluded patients who initiated RRT in a French region before the introduction of the registry (left truncated data), those who received pre-emptive renal transplantation and those undergoing DHD seven days per week. Each patient was then followed until death or the study end point (December 31, 2013).
Data collection Patients' and HD characteristics To describe the patients' characteristics at the first DHD, two types of variables were collected: i) demographic data: sex, age; ii) clinical data: smoking status (current/ former smoker and never smoked); Body Mass Index (BMI); hemoglobin and albumin levels; comorbidities and disabilities, such as cardiovascular diseases (coronary artery disease, peripheral vascular disease, congestive heart failure, arrhythmia, aneurism and cerebrovascular disease), active malignancy, cirrhosis, respiratory disease, physical or psychiatric disabilities and walking disability (walks without help, needs partial assistance for transfers, totally dependent for transfers). All clinical data were collected at RRT initiation, then annually. Quantitative variables were grouped in clinically relevant classes.
Treatment trajectories To describe the patients' treatment trajectories before DHD initiation, several data included in the REIN registry were used: i) date of the first RRT and treatment modalities; ii) dialysis modality changes before DHD; iii) date of DHD initiation. Additionally, the clinical outcome at the end of the follow-up period (31/12/2013) was included: death; DHD; switch to <5 sessions/week HD; PD; and kidney transplantation.
The Cox regression was applied to evaluate the association between patients' characteristics and death in the total population and the access to renal transplantation for patients less than 80 years. All variables associated with outcomes in unadjusted model (p < 0. 2) were included in an adjusted one. All variables presenting a p-value < 0. 05 in final adjusted model were considered as statistically significant. Statistical analyses were performed with the Stata 11. 1 software (College Station, TX).
The heterogeneity of the profile of French patients undergoing DHD could partially explain the contradicting results on mortality in DHD reported by Suri and colleagues  [27]  compared to other studies  [22, [28] [29] [30]. The present work is the first study to include patients with ESRD on DHD from all France regions over a long period. To characterize this population, all major comorbidities and disabilities were taken into account. Additionally, the REIN registry includes also data on the treatment modalities as well as on the pre-and post-     DHD treatment switches. Therefore, and differently from previous studies that only described the first RRT modality and/or the length of the pre-DHD period  [16, 18, 22, 28, 33] , we could analyze and describe all the RRT modality changes before switching to DHD.
To determine whether TLR9 expression influenced HK or HPV16E6E7 cell growth we monitored the doubling population over a period of 14 days. HK overexpressing TLR9 failed to grow compared with HK transduced with pbabe alone. Similarly, HPV16E6E7-pbabe-TLR9 cells slowed the growth rate in comparison to HPV16E6E7-pbabe cells from 60 to 20 doublings at day 14 (Figures  1c  and d ). To corroborate our data, we analyzed the effect of overexpressing TLR9 (using the pbabe retroviral system) in cells derived from cervical cancer patients (SiHa) positive for HPV16. At 4, 9 and 14 days SiHa cells expressing TLR9 doubled at a slower rate than pbabe alone expressing cells    (Figure  1e ). Taken together, these data show that TLR9 expression prevents cell growth by reducing the doubling population of normal and HPV16-transformed cells.
To test this hypothesis, we treated stable TLR9-or GFP-expressing cells with increasing concentration of different cell death inducers, that is, cisplatin, H 2 O 2 and doxorubicin. No enhanced defect in cell growth was observed as measured by MTT assays (data not shown). Addition of a TLR9 ligand antagonist to block potential TLR9 self-ligands did not alter the effects seen on cell growth (Figures  3c  and d ) but did inhibit TLR9 CpG activation of the nuclear factor-κB reporter gene (Figure  3e ). Therefore, we concluded that TLR9 expression per se inhibited cell growth. Colony formation and transformation of head and neck cancer cells are altered by TLR9 expression Clonogenic assay or colony-formation assay is an in vitro cell survival assay based on the ability of a single cell to grow into a colony.
From Gene ontology annotation implemented in the Database for Annotation, Visualization and Integrated Discovery program (https://david. ncifcrf. gov/), we selected genes that were upregulated and grouped under the 'response to virus' and 'negative effect on cell proliferation' at the 5% false discovery rate level, and used the analysis of variance model to visualize the differentially deregulated genes into a heat map analysis (Figure  5b  and Supplementary Figure  2 ). We selected some IFN related and others important genes for validation by quantitative PCR (qPCR; Figure  5c ). IFIH1, IL-29, IRF9, IFNB1 and IRF7 were upregulated under the responses to virus classification. IFN genes IL-29, IFNB1 and STAT1 were upregulated and also grouped under negative effect on proliferation. A panel of selected IFN-regulated genes as well as control genes TLR9 and pLS3 (a gene that was downregulated) were validated by qPCR (Figure  5c ).
To test our hypothesis, 136 cells were transduced with TLR9 or the GFP control and were cultured in a medium containing cycloheximide to inhibit protein synthesis. p16 INK4a half-life was determined by immunoblotting. In TLR9-expressing cells we observed an increase in p16 INK4a stability compared with the GFP control (Figures  8b  and c ). These findings demonstrated the mechanism by which TLR9 expression can mediate cell growth slowdown by increasing p16 INK4a stability. Our data highlight a central role for TLR9 in cancer development, therapeutic strategies to reactive its expression should be targeted.
The increase of p21 WAF1 due to TLR9 re-expression was also observed in our model of head and neck cancer (also upregulated in our microarray data: Series record GSE78858). Unlike the study from Pacini et al. 19  we did not observe increased p-p38 or as our previous work has shown p27 Kip1 (data not shown). In our experiments we observed that TLR9-expressing cells increased p16 INK4a protein, which has been shown to positively control the expression of p21(WAF1). 38 We still have to elucidate how TLR9 re-expression allows for p16 INK4a stability. p14ARF has been shown to stabilize p16 INK4a. p14ARF regulates the stability of p16 INK4a protein via REGγ-dependent proteasome degradation. Kobayashi et al. , 39  therefore, it would be interesting to determine whether TLR9 re-expression in our model can induce p14ARF.
PCR was conducted using the Mx 3000P real-time PCR system (Stratagene, La Jolla, CA, USA). Two sets of PCR assays were conducted for each sample, the TLR9 and β2microglobulin primers have been described. 35, 45 Amplification specificity was assessed for each sample by melting curve analysis, and the size of the amplicon checked by electrophoresis (data not shown). Relative quantification was performed using standard curve analysis. TLR9 mRNA levels were normalized to β2-microglobulin mRNA levels and are presented as a ratio of gene copy number per 100 copies of β2-microglobulin in arbitrary units.
For apoptosis experiment, Apoptosis detection kit I (Becton Dickinson, Le Pont de Claix, France) was used following manufacturer protocol. For TLR9 intracellular staining, one million cells were fixed with 2% paraformaldehyde and permeabilized in PBS containing 0. 25% saponin. After blocking the cells were stained with a rat antihuman TLR9 (eBioscience, Paris, France) and with a secondary goat anti-rat Alexa 633. The staining was analyzed on a BD LSRII using the software Diva and FlowJo (Treestar, Ashland, OR, USA).
Microarray-based whole-genome expression profiling and data analysis Genome-wide gene expression profiling analysis was performed on Illumina HumanHT-12 v4 Expression BeadChips, providing a coverage of 424 000 annotated genes (48 783 probes corresponding to 1-3 probes per gene) including well-characterized genes and splice variants. Candidate probe sequences included on the HumanHT-12 v4 Expression BeadChip derive from the National Center for 8 Biotechnology Information Reference Sequence RefSeq (Build 36. 2, Rel22) and the UniGene (Build 199) databases. Using the Illumina TotalPrep RNA Amplification Kit (Ambion, Thermofisher, Illkirch, France), 500 ng of extracted RNAs were converted to complementary DNAs and subsequent biotin labeled single-stranded cRNAs. The distribution of homogeneous in vitro transcription products (cRNAs) was checked with the Agilent 2100 bioanalyzer instrument and the RNA 6000 Nano kit.
In total, 750 ng of biotin labeled cRNAs of the four (biological triplicates) samples were hybridized overnight to four HumanHT-12 Expression BeadChips. Subsequent steps included washing, streptavadin-Cy3 staining and scanning of the arrays on an Illumina BeadArray Reader. Fluorescence emission by Cy3 was quantitatively detected for downstream analysis. The Illumina Genome Studio V2010. 2 was used to obtain the signal values (AVG-Signal), with no normalization and no background subtraction. Data quality controls were performed using internal controls present on the HumanHT-12 beadchip and were visualized as a control summary plot and for each sample as noise-tosignal ratios calculated by P95/P05 signal intensities. All samples had P95/ P05 410, defined as sample quality threshold (data not shown).
Differential expression analysis was performed using BRB-ArrayTools software v4. 2 developed by Dr Richard Simon and BRB-ArrayTools Development Team. 47  The raw signal intensities of all samples were log-transformed and quantile normalized with background subtraction with the exclusion of any probe showing excess dispersion (defined by 485% of individual probe values differing from the median by 41. 5-fold). Class comparison for Microarray Analysis using the t-test method was performed for identification of differentially expressed probes. Probes with a P-value of o0. 001, with a minimum of twofold change and a false discovery rate of o0. 05 were considered significantly differentially expressed. The Database for Annotation, Visualization and Integrated Discovery v 6. 7 was used for classification of the differentially expressed genes.
Data Collection. At recruitment, participants reported on sociodemographic information: age, gender, household income, education level, perceived financial situation, house ownership, presence or absence of medical insurance, and possession of a retirement plan. We produced a data-driven classification of socioeconomic status based on the last five variables and classified individuals into four clusters: elite group, middle-high, middle-low, and low. This aimed to capture the multidimensional nature of socioeconomic status into a single variable. Definitional criteria for the four socioeconomic clusters are described elsewhere  [26]. Two weeks after recruitment, participants completed a self-administered questionnaire (paper or online) or a questionnaire administered by telephone that included sections to measure (1) multimorbidity and (2) physical health-related quality of life.
Data Analysis. We first studied the sociodemographic characteristics and the number of chronic conditions in the whole population, then in individuals with multimorbidity, using successively each of the four multimorbidity measures. We then looked at an assumption underlying the main analyses; namely, whether there was a statistically significant association between multimorbidity and physical health-related quality of life, adjusted for sociodemographic covariates, for each of the four multimorbidity measures. To evaluate how estimates of physical health-related quality of life vary with the length of the list of candidate conditions considered, we estimated the average level (mean values and 95% confidence intervals) of the physical component of health-related quality of life in individuals with multimorbidity and highlighted the resulting variation using the 21-condition list or the 6-condition list.
Analysis of covariance (ANCOVA) was used to characterize the association between multimorbidity and physical health-related quality of life, with modelling physical health-related quality of life according to multimorbidity status and sociodemographic covariates. All analyses were done using the SPSS version 20 software. The study was approved by the ethics committee of the Centre de Santé et de Services Sociaux de Chicoutimi, as well as the research ethics committee of Hôpital Charles Lemoyne, QC, Canada.
Results A total of 1,710 individuals participated in the PRECISE study and their data were included in these analyses. Among the study population, mean (SD) age was 51. 3 (SD 12. 5) years and there were 693 (40. 5%) men. The main sociodemographic characteristics of the participants are presented in Table  1. Regarding chronic conditions, absence of overweight was imputed to 57 individuals due to missing values. For every other condition, there was either 0 or 1 missing value. Prevalence of each individual chronic condition in the study population is shown in Table  2. Considering the 21-condition list, 272 individuals (15. 9%) reported no chronic condition, and the mean number of chronic conditions was 2. 9 (SD 2. 4). Alternatively, when the 6-condition list was considered, 1,016 individuals (59.