dataesr/scientific-paragraphs-categorization huggingface dataset:
Data and data quality issues DHS surveys used in the study. We used publicly-available standard DHS surveys with several exceptions. Since the focus of the paper is on age heaping around 12 months rather than other issues with the DHS surveys, we removed surveys for which the estimated level of U5MR is clearly biased. First, we discarded all the surveys whose data quality was shown to be insufficient through comparison with other sources of information. These surveys are those that were not retained by the expert panel of the UN IGME for monitoring the U5MR. Generally, these surveys were affected by strong underestimation biases potentially caused by omission of deaths or sampling errors. This selection does not imply that all retained surveys are completely free of bias in the level of U5MR, but at least it eliminates the most extreme cases.
Second, we also discarded the surveys that suffered from the selection bias associated with HIV/AIDS. It is well known that the risk of dying among children whose mother deceased from HIV/AIDS is higher than average  [9]. This generates a bias, because these higher-risk children are not captured in DHS surveys due to the fact that birth histories are collected among surviving mothers only. For that reason, we removed all the DHS surveys that were adjusted for that bias in the UN IGME database. As a result of this selection, we carried out the analysis on 204 surveys (see the list in the S1 Appendix). 88% of the discarded surveys (71 out of 81) were collected in the Sub-Saharan region. Mortality estimation. In DHS surveys, women of reproductive age are asked to provide the date of birth and the age at death for all their children ever born.
Positive values of k reflect a later age pattern of mortality captured by the model, that is with a higher-than-average concentration of deaths at older ages given a particular level of U5MR. Conversely, negative values of k correspond to an earlier age pattern, with a higher-than-average concentration of deaths at younger ages. The advantage of this model is the possibility to adjust the value of k by fitting (at least) any two probability probabilities of dying as far as their age boundaries correspond to one of the 22 exact-age cut-off points of the model between age 0 and 5. The set of coefficients [a x , b x , c x , v x ] were estimated on the basis of 1,219 country-years selected among the U5MD, providing high quality distributions of under-5 deaths by detailed age  [7]. The database comprises VR data collected in industrialized, mostly Western, countries; and the covered period spans from the middle of the 19th century to the most recent years (up to 2016).
All the country-years included in the database can be found in the Human Mortality Database  [14] , although the latter does not provide detailed age breakdown between 0 and 5. Being based primarily on to the Western experience, we do not expect the k-model to represent all possible age patterns of under-5 mortality. While such deviations can be due to data errors, including heaping on age 12 months, it is also likely to be the result of a different -late-age pattern of under-5 mortality that was not observed in the Western countries. This later age pattern is likely associated with infectious and parasitic diseases, that have generally maintained mortality -after age 6 months-at a higher level than the observed in the West at similar levels of infant mortality  [6]. Overview of the fitting method.
Using Eq (1), we estimated the optimal value of k for each DHS survey by minimizing the weighted Root Mean Square Error of prediction at a given level of U5MR. The weight for each predicted probability was determined by the length of the preceding age interval. As in the case of the MLT approach to predicting IMR, the observed U5MR is assumed unbiased. To evaluate the impact of reporting errors around age 12 months, the estimation of k was performed after excluding the observed values of q(x) between 8 and 24 months, i. e. , probabilities of dying located on either side of the IMR indicated by q(12m). This omission of data is justified by the need to minimize the potential impact of heaping on observed data. Particularly, we excluded the second year of age due to the way data are coded in DHS surveys.
Ahsan Karar et al. [15]  showed that between 1986 and 2006 deaths due to infectious diseases, in particular diarrhea, dysentery, tuberculosis, and respiratory diseases, were reduced from 50% to 11% of all under-5 deaths. This transition can be observed across 7 DHS surveys collected between 1993 and 2014 (see the S1 Appendix). The improvement of the fit over time reflects the disappearance of this late age pattern of under-5 mortality specific to some epidemic contexts. This progressive disappearance together with the reduction of infectious diseases provide another clue that this age pattern is real and not a data artifact. We found a similar transition in other countries from Asia that benefited from a long series of surveys: Nepal and Philippine (see the S2 Appendix). More broadly, Fig  9  shows that in both Asia and Sub-Saharan Africa, the excluded surveys present an unusually late age pattern of under-5 mortality.
In fact, the group of countries including Turkey and Ukraine (North Africa, West Asia, and Europe) is the one for which our model performs best (Table  2 ). Note that these two DHS surveys (Ukraine 2007 and Turkey 2013) were the last surveys used by UN IGME to monitor under-5 mortality. Since 2007 and 2009 respectively, UN IGME has only relied on the VR data to monitor mortality in the two countries, which is the more adequate source of data when completeness is reached.
This difference among surveys of a same country confirms the impact of data artifacts arising from small sample size and low mortality levels. Here, the difference over time cannot be explained by a progressive modification of age pattern of mortality over time as in the case of Bangladesh discussed above. In fact, the group of countries including Turkey and Ukraine (North Africa, West Asia, and Europe) is the one for which our model performs best (Table2). Note that these two DHS surveys (Ukraine 2007 and Turkey 2013) were the last surveys used by UN IGME to monitor under-5 mortality. Since 2007 and 2009 respectively, UN IGME has only relied on the VR data to monitor mortality in the two countries, which is the more adequate source of data when completeness is reached.
The pathophysiology of chronic obstructive pulmonary disease (COPD) relies on airway remodelling and inflammation. Alterations of mucociliary clearance are a major hallmark of COPD caused by structural and functional cilia abnormalities. Using transcriptomic databases of whole lung tissues and isolated small airway epithelial cells (SAEC), we comparatively analysed cilia-associated and ciliopathy-associated gene signatures from a set of 495 genes in 7 datasets including 538 non-COPD and 508 COPD patients. This bio-informatics approach unveils yet undescribed cilia and ciliopathy genes associated with COPD including NEK6 and PROM2 that may contribute to the pathology, and suggests a COPD endotype exhibiting ciliopathy features (CiliOPD).
Introduction Cilia dysfunction is a hallmark of chronic obstructive inflammatory lung diseases  [1]. Alterations of both cilia structure and function alter airway mucociliary clearance. Epithelial remodelling is indicted in COPD pathogenesis, including distal to proximal repatterning of the small airways and altered generation of motile and primary ciliated cells  [2] [3] [4]. Cellular processes related to cilia dysfunction such as autophagy  [5]  may represent therapeutic targets, although the genetic print of cilia involvement in COPD has only been observed in comparative gene expression studies providing COPD-and smoking-associated signatures  [6] [7] [8]. In this study, we thought to investigate further cilia dysregulation in COPD. Rather than focussing on the biological samples to identify the most significant hits across the whole genetic code, we comparatively analysed cilia-associated and ciliopathy-associated gene signatures in 7 datasets including 538 non-COPD and 508 COPD patients.
RNAseq data analysis Previously published datasets of gene expression of whole lung tissue samples and small airways bronchoscopic samples obtained from non-COPD and COPD patients, that were available online, were collected (GEO database; http://www. ncbi. nlm. nih. gov/geo; accession numbers: GSE47460, GSE57148, GSE76925, GSE103174, GSE11784, GSE37147, GSE56341). The expression of the genes of interest (human cilia-associated genes and ciliopathy-associated genes) was analysed depending on COPD status and expressed as fold-change compared to non-COPD. Associations with clinical/functional characteristics were searched. Analyses were performed separately in the lung compartment ("whole lung tissue sample", GSE47460, GSE57148, GSE76925, GSE103174) and in small airway epithelial cells (SAEC) ("small airways bronchoscopic samples", GSE11784, GSE37147, GSE56341).
GSE plots contain all the genes of the genesets for each dataset. The y-axis is the log10 ratio obtained by dividing the mean of each gene's value in lung tissues or SAEC of non-COPD patients by its value in COPD patients. The Venn Diagram were designed with a tool from VIB/ UGent (http://bioin forma tics. psb. ugent. be/webto ols/ Venn).
Results A total of 7 public datasets of transcriptomic analysis of either whole-lung tissues (238 non-COPD and 391 COPD patients) or SAEC (300 non-COPD and 117 COPD patients) were analysed to identify an alteration of cilia-and ciliopathy-associated genes expression in COPD patients when compared with non-COPD patients (Fig. 1a , b, Additional file 1: Table  S1 ). Considering the 4 datasets obtained from wholelung tissues, 71/160/97/66 genes were significantly deregulated in COPD patients representing respectively 26%/42%/30%/17% of the 350 genes in the whole lung cilia geneset (Fig. 2a  and Additional file 1: Tables  S2-S5 ). Fourteen deregulated genes were identified in at least 3 datasets, 100 in at least 2 datasets, and 279 in at least 1 dataset, corresponding to respectively 4%/29%/80% of genes in the whole lung cilia geneset (Fig.
2b  and Table  1 ). We then focused on the 14 commonly dysregulated genes, and confirmed the localization of the proteins and the expression of the transcripts in AEC (Additional file 1: Figure  S1  and Table  1 ). Among those 14 genes, 4 have been identified as associated with COPD in previous studies (BBS9, GLI2, NEK6, WDPCP)  [6, [17] [18] [19] [20]. NEK6 was significantly upregulated in the 4 datasets in COPD patients (Fig. 2c  and Table  1 ). Considering the 3 databases from SAEC, 40/51/22 genes were significantly deregulated in COPD patients representing respectively 10%/33%/5% of the 399 genes in the SAEC cilia geneset (Fig. 3a  and Additional file 1: Tables  S6-S8 ). Ten deregulated genes were identified in at least 2 datasets, 100 in at least 1 datasets, corresponding to respectively 2.
5%/25% of genes in the SAEC geneset (Fig. 3b  and Table  1 ). Focusing on the 10 commonly dysregulated genes, we confirmed the localization of the proteins and the expression of the transcripts in AEC (Additional file 1: Figure  S2  and Table  1 ). Among those 10 genes, 3 have been identified as associated with COPD in previous studies (ADAM15, DCDC2, WDR34)  [6, [21] [22] [23] [24]. PROM2 was significantly upregulated in the 3 datasets in COPD patients (Fig. 3c  and Table  1 ). We identified 23 genes deregulated in COPD in at least 2 whole-lung datasets and 1 SAEC dataset (Additional file 1: Figure  S3A  and B ), 12 were associated to ciliopathies (Additional file 1: Figure  S3B  and C ) representing 6% of ciliopathy-associated genes. In addition, 47% of ciliopathy-associated genes (n = 88) were found deregulated in either COPD lung tissues or SAEC.
The 177 genes deregulated in COPD patients in at least 3 datasets (2 whole lung and 1 SAEC) were mainly involved in biological processes associated to ciliary retrograde and anterograde transport, Hedgehog signalling, cilium beat frequency, cilium assembly and microtubule anchoring at centrosome. Since a large quantity of genes orchestrating cilia formation and function were found altered in COPD, we evidence a global cilia dysfunction at the root of the disease rather than a punctual alteration observed as a consequence of pathophysiological mechanisms. Among the candidate genes, we identified for the first time a list of 24 genes (Table  1 ) commonly deregulated in the majority of datasets in COPD patients. The impacted biological processes corresponded to the aforementioned ones, suggesting that these genes may represent key actors to understand cilia dysfunction in COPD.
These findings were generally concordant to the relative gene expression levels we reported across multiple genesets. Further investigations will necessarily require analysis of every components of the molecular print of each candidate in order to recognize its interest in COPD studies including but not limited to: SNP and copy number alterations, gene expression levels, and cellular and sub-cellular protein localizations. In addition, the expressions of 2 genes were associated with COPD status in all 4 lung databases (NEK6, coding for a serine/threonine-protein kinase involved in cell cycle progression during M phase) and in all 3 SAEC databases (PROM2, coding for a transmembrane glycoprotein involved in intracellular trafficking), both being upregulated in COPD. Using single cell sequencing analysis, we confirmed that all commonly dysregulated genes (14 in lung datasets and 10 in SAEC datasets) were mainly expressed by multiciliated cells (motile cilia) and non-differentiated epithelial cells (primary cilia) (Table  1 ).
To monitor the delivery and infant growth, an extensive clinical examination was also performed in mothers and infants at each visit. A container was given to the mother at each visit to collect the infant's stools. The containers were collected the following day by the study nurse within the first hour after emission. In case of disease, infants were asked to consult at the 'Centre Pédiatrique de Bangui', the national paediatric hospital, to be treated if necessary, according to Central-African guidelines. All drugs prescribed during the follow-up were free of charge. Malnutrition indicators were defined as follows: undernourishment in mothers was defined by albumin levels<35 g/L  (Gounden et al. , 2020) ; stunting in infants was defined as length-for-age < À2 standard deviations according to WHO growth databases (WHOjStunting in a nutshell, 2015); and low birth weight was defined as birth weight <2500 g (What's at stake Low Birth Weight Policy Brief, n.
ACKNOWLEDGMENTS We thank all participating women and infants, the  MITICA  Consortium, the  Henri Izamo Maternity staff in Bangui  and the  Institut Pasteur de Bangui  staff for their continuous support in the MITICA study;  Jean-François Damaras ,  Patrick Sanchez  and  Jean-Pierre Lombart  for their logistic and moral support, essential for the project;  Ionéla Gouandjika  and  Emmanuel Nakouné  for the scientific and technical advice;  Pascale Vonaesch  for her essential contribution to the conception of the MITICA study;  Pascale Vonaesch ,  Sean Kennedy  and  Tamara Giles-Vernick  for their scientific guidance in the MITICA study;  Lavoisier Dieu-Merci Welekoï Yapondo  for the preparation of the bio-banking; the field workers  Carole Senga Mamadou ,  Déborah Bendima ,  Maryse Dongoya ,  Alex Bebangui ,  Sidonie Koumangou ,  Prisca Kondolas Singa ,  Isidore Ndilbe  and  Stevie Gbembongo  for countless hours spent in the field; the  Centre de Recherche Translationelle  and the  Direction Internationale of the Institut Pasteur and especially Sylvie Gratepanche ,  Cécile Artaud ,  Mohand Ait-Ahmed ,  Nathalie Jolly  and  Pierre-Marie Girard  for precious help in setting up and steering the MITICA study;  Bérengère Menu  for the legal help and support; and all local authorities (chefs de quartier) that welcomed us to work in the Bangui neighbourhoods.
This project was mainly funded by a  PTR (Programmes transversaux de Recherche)  grant  N 91-17  from the  Institut Pasteur Paris. We also thank the  LabEx IBEID  as this study was also partly funded by a postdoctoral fellowship from the  LabEx IBEID  ( ANR-16-COV-005 ). Also, VMA was supported by this postdoctoral fellowship from the  LabEx IBEID. The MITICA was approved by the  Ethics Committee of the University of Bangui  and the  Institutional Review Board of the Institut Pasteur.
2 (16. 5; 63. 9) 41. 5 (18. 7; 71. 6) 18. 2 (2. 3; 46. 6) Vitamin C hypovitaminosis (<28 μmol/L) 17/40 (42. 5%) 13/37 (35. 1%) 18/33 (54. 6%) Vitamin C deficiency (<11 μmol/L) 6/40 (15. 0%) 9/37 (24. 3%) 11/33 (33. 3%) Vitamin A seric levels (μmol/L) 1. 3 (1. 1; 1. 4) 0. 8 (0. 9; 1. 2) 0. 9 (0. 7; 1. 0) Vitamin A deficiency (<1 μmol/L) 6/25 (24. 0%) 20/31 (64. 5%) 22/30 (73. 3%)VitaminE seric levels (μmol/L) 7. 2 (4. 1; 8. 5) 16. 4 (9. 9; 22. 4) 11. 2 (6. 8; 18. 1) Vitamin E deficiency (<11. 6 μmol/L) 22/25 (88. 0%) 11/32 (34. 4%) 17/31 (54. 8%)Note:For continuous variables, median and inter-quartile range are provided. For binary and variables with categories, n and percentages are provided. aStunting is defined according to À2 standard deviations of length-per-age databases of the WHO.
07), stunting (P value = 0. 07), or vitamin E (P value = 0. 14). However, vitamin C levels were associated with vitamin A levels (Spearman's rho coefficient = 0. 31, P value = 0. 004). Furthermore, infants with vitamin A deficiency were at significantly higher risk of vitamin C deficiency (OR = 5. 7; 95% CI 1. 35; 24. 1, P value = 0. 018). F I G U R E 2 Correlation between maternal and infant vitamin C levels at birth T A B L E 3 a Stunting is defined according to À2 standard deviations of length-per-age databases of the WHO. b Birth is the baseline to which age categories at each visit are compared.
Data Collection Raw data were extracted for all published cases (N = 346) with available clinical and genetic information collected from the text, tables or supplemental material, as well as from the records of our unpublished cases (N = 51) (Figure  1 ). Three resources were queried for this study (last search 12 March 2020): (1) references extracted from PubMed with the following string of keywords: (MVK or mevalonate kinase or mevalonic aciduria or hyperIgD syndrome or HIDS), (2) records of patients referred to our laboratory for genetic analysis, and (3) Infevers, the registry of autoinflammatory mutations, which was updated with the two other resources if necessary. Care was taken to exclude potential patients reported more than once by matching, where available, years of birth, disease onset or diagnosis, as well as gender, registry number, genotype, phenotype, references, and authors.
Acod1 is among the genes most upregulated in alveolar macrophages from B. melitensis infected mice In order to identify the immune effector mechanisms controlling the early multiplication of Brucella melitensis in the lungs, we compared gene expression in the lungs of B. melitensisinfected and PBS-treated wild-type (wt) C57BL/6 mice. Mice were infected with 10 7 CFU of B. melitensis and sacrificed 24 hours later. Lungs were harvested and analyzed by RNA sequencing. The results of this analysis are presented in S1 Data. A volcano plot representation of these data shows that only a very low number of genes is significantly downregulated or upregulated during infection (Fig  1A ). The 5 downregulated genes are immunoglobulin heavy variable 5-6 (Ighv5-6), Elongation of very long chain fatty acids protein 6 (Elovl6), Immunoglobulin heavy variable 1-81 (Ighv1-81), Acetyl-CoA carboxylase 1 (Acaca), mitochondrially encoded tRNA asparagine (mt-Tn).
A volcano plot representation of these data shows that 466 genes (R1 genes) were significantly upregulated in the infected mice (Fig  1B ). We used the Metascape platform to perform a pathway enrichment analysis of these genes. The first signature identified was the response to interferon-beta (Fig  1C ). The genes associated with this signature (R2 genes) were represented on a heatmap to see the expression of each gene in this pathway (Fig  2 ). Among them, some genes have been described to play an effector role in the innate immune response against intracellular pathogens. IFN-gamma-inducible GTP-binding protein (Igtp) participates in the disruption of Toxoplasma gondii vacuoles  [20]. Guanylate-binding protein (Gbp)3 and Gbp6 regulate cell-autonomous immunity in macrophages by coordinating a potent oxidative and vesicular trafficking program to protect the host from intracellular bacteria  [21].
For example, Acod1 -/-but not wt C57BL/6 mice the same volume of PBS (naïve group) (n = 3) were sacrificed at 24 hours post-infection. Lungs were harvested and alveolar macrophages isolated as described in Materials and Methods. Then, RNA was extracted and sequenced with the Illumina system and Deseq2 analysis as described in the Materials and Methods. (A) Volcano plot of RNA-seq data from naïve versus infected lungs shows the adjusted p-value (false discovery rate, FDR -log10) versus fold change, FC (log2). The 5 upregulated genes are Lipocalin-2 (Lcn2), Kru ¨ppel-like Factor 2 (Klf2), Iroquois homeobox 1 (Irx1), CCAAT enhancer binding protein delta (Cebpd), Inter-Alpha-Trypsin Inhibitor Heavy Chain 4 (Itih4). The 5 downregulated genes are immunoglobulin heavy variable 5-6 (Ighv5-6), Elongation of very long chain fatty acids protein 6 (Elovl6), Immunoglobulin heavy variable 1-81 (Ighv1-81), Acetyl-CoA carboxylase 1 (Acaca), mitochondrially encoded tRNA asparagine (mt-Tn).
Inhibition of Brucella growth in culture by dimethyl itaconate is isocitrate lyase-dependent Itaconate has been described to act via the bacterial enzyme isocitrate lyase (ICL)  [18]  which is part of the glyoxylate shunt that is exclusively found in prokaryotes, lower eukaryotes, and plants. This alternative shunt is activated in response to nutrient deprivation, a condition encountered by bacteria in phagolysosomes  [29]. Starting from the crystal structure of isocitrate lyase from B. abortus in complex with malonate (Protein Data Bank (PDB) entry 3OQ8) docking of itaconate has been performed and suggests a good binding of this compound in the active site of the protein (.
abortus isocitrate lyase, potentially leading to a nucleophilic attack by the thiol and formation of a covalent adduct between isocitrate lyase and itaconate. This docking pose has been experimentally confirmed by crystallography (PDB entry 7RBX) that unambiguously shows a covalent adduct between itaconate and Cys185 in Brucella abortus isocitrate lyase. However, aceA deficiency does not prevent or reduce Brucella multiplication in the lungs, demonstrating that ICL is not essential to Brucella multiplication in this condition. This suggests that Acod1-mediated ICL-dependent inhibition of Brucella multiplication in our murine infection model is not explained by a simple blockage of the glyoxylate shunt or by the toxic accumulation of propionyl-CoA. The precise mechanism of Brucella growth inhibition by itaconate therefore remains to be elucidated.
1 M cacodylate buffer for 1 hour at RT. After 3 washes, samples were serially dehydrated in ethanol (30% EtOH first for 5 min, followed by 10 minthe same for 50%, 70%, 85%, 100% EtOH). Propylene oxide was then added 4x5 min at RT and the pellet was progressively embedded in epoxy resin (Agar 100 resin; Agar Scientific, United Kingdom) (75:25, 50:50 and then 25:75% of propylene oxide/resin). Ultrathin 50-nm sections were obtained, mounted on copper-Formvar-carbon grids (EMS, United Kingdom), and stained with uranyl acetate and lead citrate by standard procedures. Observations were made on a Tecnai 10 electron microscope (FEI, Eindhoven, The Netherlands), and images were captured with a Veleta charge-coupled-device (CCD) camera and processed using the AnalySIS and Adobe Photoshop software programs.