dataesr/scientific-paragraphs-categorization huggingface dataset:
There appears to be no evidence in the last decades of a significant correlation between the growth rate of an economy and the change in the inequality level  (Ravallion and Chen, 1997; Ravallion, 2001; Dollar and Kraay, 2002; Ferreira and Ravallion, 2009). 66 Ravallion (2007), for example, analyzed 290 episodes in 80 countries in 1980-2000 and found a correlation coefficient non-significant at the 10% level between the changes in the log of the Gini coefficient and changes in the log of mean income in real terms between successive household surveys. The analysis of more recent data from PovcalNet leads to the same conclusion. Using 473 spells in the period 1981-2010 we find a non-significant coefficient of -0. 0094. A similar result applies when restricting the sample to observations after 1990 or 2000, or when considering longer spells.
In any case, the pattern in Latin America points to an increase in inequality before the crashes (regressive inflation tax, rise in unemployment due to openness to trade and loss of competitiveness from exchange rate mismanagement), then followed by short-term reductions after stabilization programs. 79 78 Given the higher volatility of per capita GDP in developing countries, we have kept the threshold of 9. 5% drop to identify crisis for 1980-2010. The data come from the World Bank Development Indicators. Changes in Gini coefficient are taken from PovcalNet. 79 The case studies are numerous; see for example  Forbes (2011)  and  Lustig (1990).
In this section we rely heavily on that dataset (PovcalNet). The problem of the choice of the welfare variable discussed for inequality in section 3 applies to the measurement of poverty, as well. While poverty estimates in PovcalNet refer to consumption deprivation, in most countries in Latin America and a few others in the rest of the world they are constructed from income data. After computing consumption and income poverty in 22 household surveys of 7 Latin American countries using the $2 standard, we find that on average the ratio consumption/income poverty is 0. 97 with only small differences across countries. Given this piece of evidence we decided not to perform an adjustment for income poverty figures in the analysis that follows. 80 For simplicity, the sign $ is used to refer to US dollars.
The cost of closing the poverty gap Although still a challenging problem, eliminating absolute extreme income poverty is an increasingly affordable target. Based on PovcalNet data we computed the population-weighted poverty gap index in the developing world as a share of global GDP. This fraction, that indicates the fiscal effort required to end poverty in a scenario of perfect targeting, absent behavioral responses, has been substantially falling over time as poverty decreased and global GDP went up. The resources needed to close the poverty gap with the $1. 25 line as a share of global GDP declined from 1. 3% in 1981 to 94 Since the decompositions could be carried out changing the base year, the table shows the averages over the two exercises. To obtain the results we use lineal approximations to the Pen curve based on information on mean consumption per capita by decile from PovcalNet.
Gasparini et al. (2013)   Lustig, 2010; Cruces and Gasparini, 2012). The performance in terms of poverty reduction has been heterogeneous across the regions in Latin America. While Central America experienced a slow decline in income deprivation over the last two decades, in the rest of the regions the rapid poverty fall of the 2000s contrasts with the sluggish or even negative performance of the 1990s. SEDLAC data indicate that using the international poverty line of $2. 5, the average (unweighted) poverty rate in Southern South America increased from 17. 7% in 1992 to 18. 5% in 2003, and then dropped to 9. 1% by 2010. In the same period the average poverty rate in the Andean region first rose from 30. 2% to 33%, and then declined to 17. 5%. In contrast, poverty in Central America fell more uniformly over the two decades: 33.
The downward pattern in poverty appeared to have resumed in the 2000s. The incidence of poverty ($2 line) fell from 22% in 1999 to 12% in 2010. According to PovcalNet data the number of people living with less than $2 a day increased by 7 million in the 1990s, and then was reduced by 20 million in the 2000s (from 60 to 40 million). While in the 1990s poverty went down in a third of the MENA countries, in the 2000s it went down in all economies, with the possible exception of Yemen.
If it is low, however, ambitious poverty reduction strategies might have to combine both economic growth and some redistribution"  (Bourguignon, 2003). The growth elasticity of poverty reduction is typically estimated by regressing the annualized proportional change in a poverty indicator against the annualized growth rate of mean income (per capita income or consumption from surveys, or per capita GDP, GNI, or aggregate private consumption from NAS) in a sample of growth spells. In a regression without controls the resulting coefficient is the total elasticity, as opposed to a partial elasticity that can be obtained by holding inequality and other factors constant. Based on a dataset for 67 countries over the period from 1981 to 1994, and using the $1 line,  Ravallion and Chen (1997)  find a central estimate for the poverty-growth elasticity of -3.
Bourguignon (2003)ble6. 8 in general confirm the results inBourguignon (2003), using a larger dataset spanning three decades. 109Source: own calculations based on PovcalNet (2013). Note: sample of 76 countries with information for the period 1981-2010. Poverty indicator=headcount ratio, $2-a-day line. Growth rates computed from per capita consumption in household surveys.
Joint analysis identifies bacteria-specific response genes. In order to learn about variation in the innate immune response to bacterial infection, we identified genes whose regulation was altered by treatment with specific bacteria at specific timepoints. We first used a naïve approach whereby we determined all the pairwise overlaps between lists of DE genes across treatments (Supplementary Table  S7 ). The caveat of this strategy is that incomplete power can result in overestimating the difference between treatments. In order to account for incomplete power to detect DE genes when performing multiple pairwise comparisons, we performed a joint Bayesian analysis, which we implemented using the R/ Bioconductor package Cormotif  45  (see Methods for more details). Using this approach, we classified genes into regulatory patterns based on their expression levels following each of the bacterial infections.
First, we examined the data across all the bacteria-time combinations. Initially we built a model that classified genes into one of 14 separate patterns based on their expression levels after each infection relative to their expression level in the non-infected control (Supplementary Fig. S3 ). However, we found that a model with only six expression patterns (Fig. 2 ; Supplementary Tables  S3, S4 ), where a subset of the original 14 patterns are combined, is more intuitive from a biological perspective; thus we proceeded with the reduced model. Broadly speaking, we classified genes as responding in the early, middle, or late stages of infection, and we characterized the response as temporary or sustained. Pattern "non-DE" includes 4,245 genes whose expression levels were unchanged in all the experiments.
Third, the current version of the method (v1. 14. 0) does not return the cluster likelihoods, i. e. the likelihood that a gene belongs to each of the gene expression patterns. To facilitate downstream analyses with these sets of genes, we modified the original code to additionally return this information. Lastly, Cormotif is non-deterministic. Thus to obtain consistent results, we ran each test 100 times and kept the result with the largest maximum likelihood estimate. We tested for enrichment of gene ontology (GO) biological processes among the genes in the gene expression patterns using topGO  115. We tested for significance with the Fisher's Exact Test, used the weight01 algorithm from topGO to account for the correlation among GO categories due to its graph structure, and considered significant any category with p-value less than 0.
We performed this subsampling 1000 times (Fig. 5B ). Fourth, we performed t-tests to compare the distribution of response eQTL genes to either that of the general eQTL genes or the non-eQTL genes. Data and code availability. The data have been deposited in NCBI's Gene Expression Omnibus  116  and are accessible through GEO Series accession number GSE67427 (http://www. ncbi. nlm. nih. gov/geo/ query/acc. cgi?acc= GSE67427). Supplementary Table  S1 , which contains the gene expression data, and Supplementary Table  S2 , which contains the differential expression results from limma, are available from our lab website: http://giladlab. uchicago. edu. The code is available at https://bitbucket. org/jdblischak/tb.
While electronic fingerprinting systems continue to grow in popularity, the feasibility and acceptability of such a device among vulnerable or stigmatized populations is largely unknown. A study in Rwandan government health facilities found patients expressed concern about how their fingerprints could be used by the government  [9]. Conversely, the Desmond Tutu Foundation has recently implemented electronic fingerprinting of patients within mobile HIV testing units to increase patient confidentiality and anonymity where it was noted that stigmatized patients would rather provide a fingerprint than have their name recorded or used publicly  [51]. Though this idea may be contrary to common concepts of fingerprinting and confidentiality, it is virtually impossible to trace or locate an individual using a fingerprint in countries with no nationally identifiable fingerprint database.
Methods The device/system IAVI and RZHRG entered into a contract with Biometrac  [48, 49]  and purchased 38 electronic fingerprint scanning and data collection systems which consist of a Google Nexus 7 tablet with a portable single-finger multi-spectral imaging sensor connected via USB (Fig. 1 ). Each device has security software for real-time tracking and clearing if stolen. The contract includes technical design, software customization, on-site training and testing, master database security and maintenance, and annual site licenses. The device scans both thumbs and index fingers (Fig. 2 ) and transmits encrypted templates and inputted data to a central server via Global System for Mobile (GSM). Mobile coverage in Zambian urban and rural areas is relatively high, with the GSM network operated by Zambian carrier MTN covering ¾ of the population and increasing  [52].
A post-test quiz assesses user knowledge of training themes. The practical training is structured so that each user practices fingerprinting with two other individuals four times, and all users practice system shut down, restart, and charging. After initial practice, the trainer crossreferences data from the narratives with data accessed from a central online database storing all captured fingerprint records. The trainer retrains on areas where trainees did not accurately enter data. Trainees repeat entering narratives with a new partner. Trainees must score at least 80 % on the post-test quiz and achieve 100 % accurate data entry on the second round to be certified to use the device. We have also developed a quantitative survey that assesses user satisfaction with system and training issues.
The surveys targeted the previously-screened HIV negative FSWs who were at least 18 years of age and were to be enrolled in ZEHRP's HIV risk factor study. As the woman came to the ZEHRP study site for enrollment appointment, nurses consented them individually and then administered the oral survey in the women's preferred languages including Nyanja and Bemba. All FSW enrollees were informed of the purpose of this survey via an oral informed consent and given freedom to terminate their participation at any time during survey administration. The nurses were encouraged to probe for clarification if the initial responses from the FSW participants were ambiguous. The nurses were also required to record the women's response to each survey question, which were then deidentified and entered into an Excel datasheet.
To ensure the quality of survey data, nurses were required to provide oral or written clarification if responses were inconsistent or missing. In addition, each de-identified survey was photo-scanned and stored electronically for future reference if needed. Upon completion of data collection, the Excel datasheet were imported to SAS 9. 4, in which the survey data were analyzed as counts and percentages. This study protocol was reviewed and approved by the Emory IRB and the Zambia Research Ethics Committee.
This study provides an overview of the various components of the global mean sea level evolution over two time spans: (1) 2005-2012 (corresponding to the full deployment of the Argo program) and (2) 2003-2012. Using a sea level budget approach, we compare altimetry-based global mean sea level, global ocean mass from GRACE space gravimetry and steric sea level from Argo and other in situ measurements. One goal of this study is to investigate whether it is possible to constrain the deep ocean contribution to the global mean sea level rise over the last decade. This question is particularly relevant, considering the current debate about the 'hiatus,' i. e. , the observed recent pause of the global mean air and sea surface temperature evolution while the planet is still in thermal imbalance. We consider a total of 16 different data sets.
g. ,  Goddard 2014; Trenberth et al. 2014; Chen and Tung 2014). Accurate observations of sea level rise and its components (ocean thermal expansion and ocean mass change) can, in principle, help to constrain the problem (e. g. , von  Schuckmann et al. 2014). In particular satellite altimetry-based GMSL rise corrected for ocean mass change (e. g. , using GRACE space gravimetry data over the oceans) provides an estimate of the total (full depth integrated) ocean thermal expansion (or equivalently ocean heat content). Comparison with observed Argo-based ocean thermal expansion (down to *1,500 m depth) may help to quantify any deep ocean contribution (below 1,500 m) and geographically localize any ocean warming. The first issue is addressed in the present study. Our analysis focusses on the 2003-2012 decade which corresponds to the hiatus period and the availability of new observing systems for estimating thermal expansion and ocean mass (nearly full ocean temperature and salinity coverage down to 2,000 m from Argo floats and direct ocean mass measurements from GRACE space gravimetry).
Time series of satellite altimetry-based sea level, thermal expansion and ocean mass components are currently constructed by different groups (see Sect. 2) so that several data sets of each variable are available. But as we will see below, for some of them, in particular ocean thermal expansion, significant discrepancies are noticed between the data sets. Thus, part of our study consists of discussing the differences observed between the different records and estimate the uncertainty of each component. We further address the question: Can we close the sea level budget with available data sets for sea level and components or, if not, can we extract a significant residual possibly related to the deep ocean contribution? The present study deals with global mean time series. Contributions from oceanic regions will be presented in another study.
Sea Level Data We used five different products from five processing groups for the altimetry-based sea level data: All five sea level data sets are based on Topex/Poseidon, Jason-1 and Jason-2 data averaged over the 66°S-66°N domain, except for the CSIRO data averaged between 65°S and 65°N. For each product, a set of instrumental and geophysical corrections is applied (details are given on the websites of each data set). In addition, the effect of glacial isostatic adjustment (GIA, i. e. , a small correction of -0. 3 mm/year, Peltier 2004) is accounted for in each sea level time series except in the NOAA data set. We thus corrected the latter sea level data for the GIA effect, using the -0. 3 mm/year value. The five sea level time series (AVISO, CU, GSFC, NOAA and CSIRO) are obtained either by directly averaging the along-track sea surface height data (e.
g. , CU) or by firstly gridding the unevenly distributed along-track data and then performing grid averaging (e. g. , AVISO and NOAA). In all cases, an area weighting is applied. In addition to the geographical averaging method, other differences exist between the GMSL data sets because of the applied geophysical and instrumental corrections and the number of satellites considered (discussion on these differences can be found in  Masters et al. 2012 and Henry et al. 2014). The sea level time series used in this study cover the period January 2003-December 2012. Recently, in the context of the European Space Agency (ESA) Climate Change Initiative (CCI) 'Sea Level' project (ftp. esa-sealevel-cci. org/Products/SeaLevel-ECV/ V1_11092012/), a new, improved product, combining the Topex/Poseidon and Jason-1/2 with the ERS-1/2 and Envisat missions, has been computed  (Ablain et al.
Ocean Mass Data For estimating the ocean mass component, we used three different data sets: The GRACE Release 05 products from the Center for Space Research from Texas University (CSR RL05), the German GeoForschungsZentrum (GFZ RL05) and the Jet Propulsion Laboratory (JPL RL05). To study the ocean mass evolution, a specific processing has been carried out by D. Chambers, using the GRACE Release 05 data sets over the oceans. In effect, as warned on the http://grace. jp. nasa. gov Web site, gridded Release 05 data cannot be used to compute ocean mass changes because they have the global mean removed. In this study, we used the Chambers' ocean data. They are provided as global mean (averaged over the 90°S-90°N domain) time series with associated uncertainty. They are publicly available from https://dl.
Period P1: Argo Data We used Argo temperature and salinity data sets provided by four different groups: • the International Pacific Research Center (IPRC), • the Japan Agency for Marine-Earth Science and Technology (Jamstec), • the Scripps Institution of Oceanography (SCRIPPS). These data sets are available at monthly intervals on a global 1°9 1°grid down to 2,000 m, over the period January 2005-December 2012. They can be downloaded from the http://www. argo. ucsd. edu/Gridded_fields Web site. Using these data sets, we computed the steric sea level time series (and associated uncertainty; but note that only Jamstec provides errors), integrating the data over the 0-1,500 m depth range. The gridded steric time series from IPRC, Jamstec and SCRIPPS are estimated over the 62. 5°S-64. 5°N, 60. 5°S-70.
5°N and 61. 5°S-64. 5°N domains, respectively (i. e. , corresponding to the data availability). An area weighting is applied when computing the global mean time series. We also used an updated version of the global mean steric time series computed by von Schuckmann and Le Traon (2011) (0-1,500 m ocean layer). This monthly time series is based on a weighted box averaging scheme of Argo data, within the 60°S-60°N domain. In the following, this data set is called KvS. Therefore, a total of four steric data sets are considered over period P1.
Period P2 In addition to the Jamstec data set, we also used other steric data sets to study the sea level budget over period P2 (since 2003): an updated version of  Ishii and Kimoto (2009) , the NOAA data set from  Levitus et al. (2012)  and the EN4 data set  (Good et al. 2013). In addition, we also used the ORAS4 reanalysis from  Balmaseda et al. (2013b). Over the recent years, these data sets integrate Argo data. Prior to Argo, most data are based on XBT devices and other in situ measurements (see  Abraham et al. 2013). A few details on these data sets are given below: Except for NOAA for which steric sea level grids are directly available, we computed the steric sea level time series and associated errors for the P2 period, integrating the data over the 0-1,500 m depth range. The global mean steric time series were further estimated by geographically averaging the gridded data (area weighting applied).
Global Mean Sea Level and Ocean Mass Time Series Figure  1a  shows plots of the GMSL time series without the CCI data over 2003-2012. We note that the CU and GSFC sea level curves are very close, as are the NOAA and AVISO curves. The CSIRO curve agrees better with NOAA and AVISO than with the other two, at least for the second part of the study time span. Some differences are observed between the time series on short time spans (\2-3 years). In terms of trends, differences up to *0. 35 mm/year are noticed between the AVISO, CSIRO, NOAA groups on the one hand, and CU and GSFC on the other hand, the latter groups giving slower rates. As shown in  Masters et al. (2012)  and  Henry et al. (2014) , most of these differences (for both interannual fluctuations and trends) result from the mapping process adopted by the different groups.
2010; Abraham et al. 2013; Lyman and Johnson 2014). Figure  5b  shows the IK, Jamstec and EN4 steric curves for the 1,500 m depth range. Similar comments apply as for the 0-700 m depth range. In both cases, the EN4 curve is almost flat over the whole time span (its trend over 2003-2012 is 0. 0 ± 0. 14 and 0. 15 ± 0. 17 mm/year for 700 and 1,500 m integration depths, respectively). This is unlike the IK and Jamstec curves that display larger positive trends. Over P2, the IK trend amounts 0. 39 ± 0. 11 and 0. 61 ± 0. 16 mm/year down to 700 and 1,500 m, respectively, while the Jamstec trend amounts 0. 65 ± 0. 14 and 0. 92 ± 0. 17 mm/year for the same two integration depth. The behavior of the EN4 time series is puzzling and needs further investigation. However, we still consider this data set in our analysis.
9 ). The ORAS4 data set available to us ends in December 2009. So the comparison is performed over 2003-2009 only. Figure  9  superimposes the mean steric and ORAS4 for 0-1,500 m depth range (upper curves). Very good agreement is found between the two curves. The bottom curves of Fig. 9  correspond to the residual '''GMSL minus GOM' minus steric down to 1,500 m'' and the ORAS4 steric contribution for the 1,500-6,000 m depth range. Over the 2003-2009 time span, the ORAS4 steric signal below 1,500 is very small, with a trend of \0. 1 mm/year. This is unlike the residual curve '''GMSL minus GOM' minus steric down to 1,500 m'' that displays important variability and a large positive trend of 0. 55 ± 0. 19 mm/year  (over 2003-2012). The question whether this trend is significant or not is a difficult one.
Discussion and Conclusions In this study, we have considered 16 different data sets (5 for the GMSL, 3 for the ocean mass and 8 for the steric sea level) to compare the observed GMSL to the sum of components (ocean mass plus steric sea level) and tried to derive constraints on the deep ocean contribution through a sea level closure budget approach. This large number of different data sets would allow 120 different combinations to study the sea level budget. With such an approach, it would always be possible to find some combinations allowing closure of the sea level budget, or inversely leading to nonzero deep ocean contribution. Instead, we used averages of each type of data (GMSL, ocean mass, steric sea level) and estimated their dispersion range. This gives insight into the precision of the different estimates and provides an uncertainty range due to the variants in processing approaches developed by the different groups.