dataesr/scientific-paragraphs-categorization huggingface dataset:
Vendor databases and the business press note the growing interest in emerging markets private equity, with mitigated results about their prospected outperformance 3. Research on private equity in emerging markets has long been challenged by the scarcity of data. Most existing studies use country-level data (e. g. Mexico  (Charvel, 2012) , India  (Gohil, 2014) , Brazil  (Minardi et al. , 2014)  with mitigated results on private equity performance in those countries. More recent research looks at larger sets of emerging markets economies  Johan and Zhang (2016)  and  Lerner and Baker (2017). In this paper, we aim to contribute to the growing body of literature on emerging markets in general  (Karolyi, 2016)  and in private equity in particular; using opportunities offered by new data. We aim to investigate if there are any substantial performance differentials between private equity in developed markets (DM) and private equity in emerging markets (EM), then define the investment determinants and performance drivers of such observed differences.
We interact those dimensions and have them in the focus of our lens throughout the study. We collect data from various databases on private equity fund, investments, GPs and individual managers and augment them with cultural and geographical data. We structure our data from all sources in a way that allows us to cleanly observe the investment channel: LP > GP > Fund > Portfolio Company. We are also able to observe the profiles of the individual managers within the GPs at most investment periods and the geographical locations of each GP, fund and portfolio company. We use a multilevel linear model approach to account for this hierarchy in our data structure and also to separate each level's effect on performance (in a top down approach, starting from the base : portfolio company effects, country effects and GP effects).
However, the risks associated with those markets on the one hand, and the needed adjustment effort from foreign GPs on the other hand 5  , lead to mitigated investment experiences and mixed conclusions among investors on how emerging markets are exactly an investment opportunity 6. In the literature, issues related to scarcity and quality of data often held back the development of this yet interesting research field, with most studies often restricted to single-country level studies  (Charvel, 2012 , Gohil, 2014 , Minardi et al. , 2014 among others). Recent efforts initiated larger-scale studies  (Johan and Zhang, 2016, Lerner and Becker, 2017) , offered by new data opportunities. This paper is in the continuum of this nascent literature and aims to take advantage of the granularity of a newly structured dataset to disentangle GP, country and deal characteristics' effects on private equity performance.
Data and variable definition Our database contains detailed information on the geography of managers and portfolio companies, as well as financing data and performance metrics. We source performance data from Pitchbook and Preqin on managers, private equity funds and portfolio companies, with more details on the latter from Orbis-BvD, SDC and Zephyr-BvD, along descriptive data from Thomson Financials. The quality and relevance of these datasets to private equity performance studies are discussed in Ain  Tommar and Darolles (2017). With regards to investment determinants, we conjecture whether proximity plays a role in targeting these markets. We use two measures for proximity: geographical proximity and cultural proximity. Geographical proximity is measured using a category variable on whether the GP is local, nearby or distant to/from their investment, following common land or maritime borders shared by the headquarter countries of the GP and the portfolio company.
Cultural proximity is proxied in linguistic and reference model terms. Using language, we look for target-investment-country language speakers in the investment teams of the GP at the time of the deal. On the target country side, we collect information on locally spoken languages from the World Fact Book 8. An interesting feature of these data is that it provides the lingua franca and the commonly understood languages for each country alongside the official languages 9. On the GP side, we start by looking for 8 https://www. cia. gov/library/publications/the-world-factbook/fields/2098. html, visited on June 6 th , 2017. 9 Another interesting feature is that languages come with the history and heritage of countries and are subject to tens if not hundreds of years in order to change, which works against endogeneity among other issues.
investment professionals with the "deal makers" tag in the universe of Capital IQ's People Intelligence Database and we name-match the companies in their employment histories to our list of GPs, accounting for name changes, AKAs, FKAs 10 and M&As between GPs. We also cross-reference the obtained list of GP investment professionals to their available biographies from Pitchbook 11. We analyze the probability for an investment professional to speak the language of the target country at two level: the name level and the biography level. Using names, we use a language identification API to textually process the names of the investment professionals and assess their resonance in order to establish native languages. We also assume that the investment professionals speak the languages of where every company in their employment history is located.
Using biographies, we mainly look for the educational background of each investment professional to establish the location of their schools and therefore the language of the country where they are located. the People Intelligence database also gives the employment start dates and end dates. When available, we cross-reference these dates to the deal making window at the portfolio company level to assess how the presence of a language-proficient team member within the GP investment teams can close a possible cultural gap in the deal making process. We use three measures to assess the intensity of cultural proximity at the GP level. A dummy variable for whether there is a language proficient speaker among the teams of the GP at the time of the deal. A level variable for the number of language proficient speakers in the GP's investment teams, and a concentration variable for a Hirschman-Herfindahl measure of the number of language proficient team members relative to the investment team size.
[ Figure  2  about here ] We clearly identify each private equity deal to each company it involves, the relevant fund's and GP's names and characteristics and their LPs. While 68% of funds in developed markets are organized into independent private partnerships in our sample, emerging markets funds are structured this way in only 44% of the cases, and about equally this figure into structures financed by DFIs. Both occurrences are observed for foreign private equity investing in these markets with structures being 57% independent private partnerships and 23% being backed by DFIs. We collect valuation data and performance metrics at the deal level, and timed cashflows at the fund level. Tables 2 to 6 highlight some of the characteristics of private equity investing and performance following the investment direction, figure 2 describes the evolution of raised and deployed private capital by origin and destination market, and figure  3  shows the distribution of return measures by deal and investment directions.
The geography of private equity performance and return predictability In this section, we examine whether success on a previous deal in a geography conditions success in the follow-on deal in the same geography. As this is often studied in the private equity literature under the term persistence in private equity returns, we are careful in using this terminology as our data are combined from several datasets which, even given their good quality as discussed previously, may not contain the full sequence of private equity deals for a given GP in a geography. Therefore, we analyze the performance of observed deal sequences in our dataset (sorted by investment dates, controlling for relevant fund vintages), and use the switch from a market to another in the DMtoDM&EM GP subgroup as robustness.
At the portfolio company level, success in previous realized deals alleviates the significance of the negative impact associated with investing in venture capital deals in developed markets for GPs investing in both markets, but also significantly and positively weigh in the impact of higher deal sequence on overall followon returns for DMtoDM&EM GPs. As pointed out earlier, because we may not have the full sequence of deals for each GP, we control our results using the DMtoDM&EM GP subsample. One reason why GPs list on databases is to generate deal flow, which is often associated with tendency to list only successful deals funds on commercial databases for investors to see. We argue however that there is a little chance for GPs to list deals with attention paid to how subsequent (or previous) deals on different markets and different fund vintages compare.
Conclusion This paper investigates the performance of private equity investing in emerging markets. Using a uniquely structured dataset and novel data, we show that cultural and geographical effects especially shape the investment directions of private equity capital flows into emerging markets, alongside previously documented performance drivers in the private equity literature. This effect is especially true for GPs investing in both markets compared to pure DM-and EM-players respectively. Our results are more consistent in the post-2000 investment period, and show predictability in returns along deal sequences on both markets and using different return measures. Cross-cultural and geographical effects are enhanced when the at the individual level for GPs, where investment teams are also culturally close using language as an indicator for cultural values.
companies Cultural dimensions Figure 2: Private Equity Fundraising and by Investment Direction. The plot shows our aggregated database figures for raised and deployed capital by investment direction: DMtoDM denotes investments by DM-based GPs in developed markets, DMtoEM investments by DM-based GPs in emerging markets, EMtoEM investment by EM-based GPs in emerging markets, and EMtoDM investments by EM-based GPs in developed markets. Fig 1. a Cumulated Fundraising by Investment Direction DMtoDM DMtoEM EMtoEM EMtoDM $ 90 000 5 000 $ Billion 80 000 4 500 Billion Solid-Line Series, in 50 000 60 000 70 000 2 500 3 000 3 500 4 000 Dashed Series, in 40 000 2 000 30 000 1 500 20 000 1 000 10 000 500 0 0 1930 1933 1937 1943 1946 1949 1953 1956 1959 1962 1965 1968 1971 1974 1977 1980 1983 1986 1989 1992 1995 1998 2001 2004 2007 2010 2013 2016 Fig1.
2% ash on a dry matter basis (dry matter content ¼ 93%), values that lie within ranges found in the literature  (Finke, 2002; Ghaly and Alkoaik, 2009; S anchez-Muros et al. , 2015; Zieli nska et al. , 2015). CH 4 and N 2 O emissions were monitored in respiratory chambers. Air samples were collected once per day for gas chromatography analysis (three samples for air inlet and outlet). Indoor and outdoor air temperature and humidity were continuously monitored with a data logger (DL-121 TH, Voltcraft). Emissions were monitored for three batches of 12 kg of mealworm larvae for one week (1 batch/week). For each batch, larvae were separated into 12 plastic containers containing feed and feces. Mean N 2 O and CH 4 emissions for the 3 batches were estimated at 0. 87 (±0. 35) and 1. 06 (±1. 02) mg/kg body mass/day  (Hassouna et al.
Life cycle inventory An animal feed manufacturer provides flour and meal feed ingredients to the insect farm, and an industrial grinding mill provides wheat bran. All ingredients originate in France and are assumed to be produced within 300 km of the feed mill, except for soybean, which is imported from Brazil. For the inventory, we used the ECOALIM database for French feed ingredients  (Wilfart et al. , 2016). Data concerning mealworm rearing were collected over a nine-month production period in 2015, when the facility was operating at full capacity. Data for input consumption (energy, water, and feed) and output production (fresh larvae and manure) were collected from purchase and sales invoices and cross-checked via direct interviews with the producer. Manure is usually spread near the facility on crops as organic fertilizer.
Life cycle impact assessment The following impact categories were selected for the impact assessment: cumulative energy demand (CED), climate change (CC), acidification potential (AP), eutrophication potential (EP), and land use (LU). These categories were chosen for their relevance to the known principal impacts of major inputs of the system (i. e. crop byproducts, electricity) and to enable comparison with results of most other environmental assessment studies in the agri-food sector. CED (MJ) was quantified using the Total Cumulative Energy Demand method v1. 8 (VDI, 1997). CC (kg CO 2 eq. ), AP (g SO 2 eq. ), EP (g PO 4 eq. ), and LU (m 2 a) were calculated according to the CML-IA baseline 2000 V2. 03 method  (Guin ee et al. 2002). Both methods were implemented using SimaPro v8. 1. 0. 60 LCA software (PR e Consultants, 2008).
Comparison to other protein sources in feed To compare environmental impacts of mealworm larvae meal to those of other sources of protein in livestock and aquaculture feeds, life cycle impact assessment was performed for soybean meal from Brazil and fish meal from Peru. Ingredient inventories came from the ECOALIM database  (Wilfart et al. , 2016). To compare the ingredients on the same basis, we used a functional unit of 1 kg of protein. Values were calculated from the original data using the mean protein content of the ingredients: 43. 3% for soybean meal, 62. 6% for fish meal, and 65. 0% for mealworm larvae meal.
For the three impact categories they calculated, i. e. CED, CC, and LU, impacts in our study (24. 3 MJ, 1. 0 kg CO 2 eq, 1. 6 m 2 a, respectively) were 28%, 63%, and 55% lower than theirs (33. 68 MJ, 2. 65 kg CO 2 eq, 3. 56 m 2 a, respectively; Oonincx and de Boer (2012)), respectively. CED and CC were lower in our study because the energy used to generate electricity is mostly nuclear in France but mostly natural gas in the Netherlands and because the Netherlands has a colder climate. LU was lower in our study due to differences in mealworm diets, which are based mostly on mixed grain feed and carrots in the Netherlands, but on wheat bran, which has lower LU, in our study. Other reasons for these differences could be the different databases used to obtain foreground data on feeds in our study and theirs (ECOALIM vs.
Acknowledgments The authors thank the Ynsect company, especially  Antoine Hubert ,  Henri Jannin , and  Nathalie Berezina , for providing detailed data, and  Samir Mezdour ,  Louise Forteau , and  Samuel Le F eon  for their collaboration. This work received financial support from the  French National Research Agency  ( ANR-12-ALID-0001 ) through its DESIRABLE (Designing an insect biorefinery to contribute to a more sustainable agro-food industry) project.
Comparative modeling and ligand docking Protein sequences were recovered from the appropriate databases for two toxoplasmal strains GT1 and Me49 as representatives of type I and type II T. gondii (http://toxodb. org/) and from UNIPROT for the leishmanial and human enzymes. Unfortunately the leishmanial enzymes provided poor sequence-structure alignment precluding further analysis. Meanwhile, the sequences from both strains, GT1 and ME49 revealed similar trends, so the docking study was performed on three GT1 sequences only. The best results were obtained for three sequences from T. gondii (A0A125YPH2, S7UUG9 and S7W8W1, T. gondii GT1) but not for two other sequences (S7UW66 and S7UZC5). The latter showed only partial alignment and significant sequence divergence with known crystal structures of HDACs.
Acknowledgements We thank the  SCIMI Microbiology-Imaging-Microscopy Facility of the TIMC IMAG University Grenoble Alpes  for access to instruments and technical advices. This work was supported by the  IRD  ( 821849H ) and  Univ. Grenoble (Idex innovation grant Detox) as well as by a grant  ( ANR-10-BINF-0003 ). We thank  Mme Garcia Deborah  (IRD) and  Nelly Boeckler  for their technical assistance.
Flow cytometry and antibodies Cell suspensions were Fc-blocked by using anti-CD16/32 Ab (93; BioLegend). Dead cells were labeled with Live/Dead fixable dead cell stains (Life Technologies). Stainings were performed with the following mAbs: CD3 (17A2; BioLegend), PD-1 (J43; eBiosciences), CD8α (53-6. 7; BioLegend), CD62L (Mel-14; eBioscience), Vα2 (B20. 1; BioLegend), Vβ5. 1/5. 2 (MR9-4; BioLegend), and CD25 (PC61, BioLegend). Polyclonal Ab to ORAI1 and STIM1 were purchased from GeneTex. Intracellular stainings were performed by using the PermWash buffer (BD Biosciences). Analyses were performed with an LSR/Fortessa cytometer (BD Biosciences) and analyzed with FlowJo software v. 10. 1 (Tree Star). For anti-PD-1 treatment experiment, recipient mice were injected at the indicated time with 250 µg blocking anti-PD-1 antibody (RMP1-14; Bio X Cell) or an IgG2Aκ isotype control (2A3, Bio X Cell).
A kinetic analysis was performed with FlowJo software, and the smoothed GeoMean of Indo-1 ratio was plotted. The following stimuli were used: OVAp (20 µg), Con A (100-200 mg/ml; Sigma), thapsigargin (1-10 µM; Sigma), or OVAp-pulsed splenocytes. When indicated, EGTA (5 µM) was added to chelate extracellular calcium.
When indicated, recipient mice were injected i. v. with 50 µg OVAp, and a second 1-h video was acquired. Videos were processed and analyzed with Imaris software (Bitplane) or Fiji software (ImageJ 1. 49 m). Only cell tracks with a duration >5 min were kept. The arrest coefficient was defined as the percentage of time during which instantaneous velocity was <3 µm/min. Straightness was calculated as the ratio of the distance from origin to the total distance traveled. Contact durations were determined by examining juxtaposition of T cell and DC fluorescent signals in individual z planes. The mean intensities of GFP, YFP, and SNA RF fluorescence were measured with Fiji to determine the GFP/SNA RF or YFP/SNA RF ratio in individual T cells. The numbers of division events were counted for each video before and after peptide injection.
Acknowledgments We thank the members of the  Bousso laboratory  for critical review of the manuscript. We acknowledge the  Technology Core of the Center for Translational Science at Institut Pasteur  for support in conducting this study and the specific contribution of S. Novault. This work was supported by  Institut Pasteur ,  Institut National de la Santé et de la Recherche Medicale , and a starting grant (Lymphocytecontact) from the  European Research Council. A. Bohineust  was funded by the  European Research Council  and by a fellowship from the  Ligue Contre le Cancer. The authors declare no competing financial interests. Author contributions:  A. Bohineust ,  Z. Garcia ,  H. Beuneu , and  F. Lemaître  conducted the experiments. P. Bousso  and  A. Bohineust  designed the experiments, analyzed the data, and wrote the manuscript.
The human β-defensin-1 (HBD1) is an antimicrobial peptide constitutively expressed by epithelial cells at mucosal surfaces. In addition to its microbicidal properties, the loss of HBD1 expression in several cancers suggests that it may also have an anti-tumor activity. Here, we investigated the link between HBD1 expression and cancer signaling pathways in the human colon cancer cell lines TC7 and HT-29, and in normal human colonic primary cells, using a mini-gut organoid model. Using available datasets from patient cohorts, we found that HBD1 transcription is decreased in colorectal cancer. We demonstrated that inhibiting the Epidermal Growth Factor Receptor (EGFR) increased HBD1 expression, whereas activating EGFR repressed HBD1 expression, through the MEKK1/2-ERK1/2 pathway that ultimately regulates MYC.
Constitutive expression of HBD1 is decreased in colon cancer. To investigate the transcription of β-defensin genes in tumors of patients with colorectal cancer, we extracted the probe sets for the β-defensin gene family available in the cohorts GSE6988 (South Korea)  31  , GSE40967 (France)  32  , GSE44076 (Spain)  33  and GSE44861 (USA)  34  , with more than 77 specimens consisting of at least 49 samples of colon cancer in a single platform. β-defensin gene transcription was compared between non-tumor and tumor specimens. We found that HBD1 transcription was consistently decreased in colon cancer specimens, as compared to non-tumor specimens (Fig. 1 ). The decreased transcription of HBD1 in colon cancer specimens was statistically significant in the 4 cohorts (p < 0,001). In contrast, when data were available, no differences were observed in transcription of the β-defensin genes HBD2, HBD3 and HBD4, between tumor and non-tumor specimens (Fig  S1 ).
8-fold increase in transcription of MYC (Fig. 5C ). MYC transcription was also found to be increased in tumor specimens from the 4 cohorts of colorectal cancer patients (Fig  S5 ). Inhibiting the MEKK1/2 pathway was led to a decrease in MYC transcription in cells treated or not with Gefitinib or EGF, validating that MEKK1/2 is the main pathway transducing the signal from EGFR to MYC (Fig. 5D ). We also analyzed MYC at the protein level upon EGFR inhibition with Gefitinib, EGFR activation with EGF, or MEKK1/2 inhibition with PD184352, by immunoblot. The amount of MYC was decreased as soon as 1 h following Gefitinib and MEKK1/2 inhibitor treatments, and slightly increased at 3 h upon EGF treatment (Figs  5E  and S8 ). Together, these data confirmed that the EGFR-MEKK1/2-ERK1/2 axis controls MYC expression in our experimental models.
Microarray gene expression data extraction. Four datasets with more than 77 samples in a single microarray platform tested for gene expression in colorectal cancer were identified from the literature: GSE6988 31 , GSE40967  32  , GSE44076  33  , and GSE44861  34. Data were extracted and analyzed as previously described  58. Briefly, the GEO website has URLs for individual data sets (DataSeries as referred to on GEO) and for each GEO DataSeries, links are provided to the Series Matrix Files which contain the expression values for each gene (probeset) and each sample. Microarray gene expression data were retrieved from the data matrices deposited to GEO by the original authors. Data normalization methods were mentioned under each DataSeries on GEO. Once the gene expression matrices were successfully obtained, expression values were extracted for a small number of genes of interest, like HBD1 and MYC, and analyzed.
Cell culture. The human colonic epithelial cell lines Caco-2 (subclone TC7  35  ) and HT-29 were cultured with DMEM (ThermoFisher) supplemented with 10% (vol/vol) decomplemented FBS (ThermoFisher), 1% nonessential amino acids (NEAA, ThermoFisher), 100 U/mL penicillin, and 100 μg/mL streptomycin (ThermoFisher) at 37 °C in 10% CO 2. Cells were split two times per week using Versene solution (ThermoFisher). The TC7 cell line was a gift from Isabelle Chantret and Monique Rousset. This cell line is not listed in the database of commonly misidentified cell lines maintained by ICLAC. Cells were authenticated by microscopic observation and qRT-PCR experiment. Cells were tested negative for mycoplasma contamination. Human colonic organoid culture. This study was approved by the Institut Pasteur's ethical and medical committee under the agreement N°2012-37.