dataesr/scientific-paragraphs-categorization huggingface dataset:
2014; Torres et al. 2016 ). Considering all this information, the intensity of the 2010 earthquake can also be simulated. Consequently, three intensity maps were compared in the present work: the USGS ShakeMap calculated during the first hours/days, an intensity map derived from the observed damage database (JRC database), and a simulated intensity map considering new investigations completed after the event: lithological site effects, fault characteristics, and most adapted attenuation relation. Finally, we ran several retro-damage scenarios, considering appropriate fragility functions for the typical Haitian building stock and the aforementioned three intensity maps in the Port-au-Prince municipality.
Remote sensing and post-earthquake damage assessment Following the Port-au-Prince earthquake, several actions and works were completed to assess the damage and habitability in the area as soon as possible. Among them, the most important was a joint collaboration between the United Nations Institute for Training and Research (UNITAR), the Operational Satellite Applications Programme (UNOSAT), European Commission Joint Research Centre (EC JRC) and the World Bank ((UNITAR/UNOSAT 2010), referred as UWJ hereafter. In fact, many institutions and volunteers collaborated during the weeks following the earthquake to produce a final global building damage assessment for the Port-au-Prince area (11 municipalities). Given its huge volume of data in the most impacted area, this dataset is the reference in terms of damage assessment for the 2010 earthquake.
Based on this work, several UWJ authors  (Corbane et al. 2010; Ghosh et al. 2011; Lemoine and Corbane 2013 ;) compared damage building datasets after the Port-au-Prince Earthquake. Ghosh et al. (2011)  presented a work completed by the GEO-CAN 1 damage assessment, in the framework of joint collaboration, which produced a damage building dataset combining satellite images, aerial images, fieldtrip validations in some zones and even some data estimated by models. The EMS98 damage scale was used as shown on Figure  1. The authors have validated this data by field trip in several districts. They have worked with pre-earthquake images, noting each pre-existing building. They compared it to the Open Street Map (OSM) and Chinese Academy of Sciences (CAS) quick damage assessment using satellite images, in which only collapsed buildings were identified.
As occurred in other recent earthquakes, crossing aerial estimation with field trip observations, they found that the main difficulty was identifying the partial collapses and intermediate-moderate damage states. Another important dataset of damages is the MTPTC dataset (Haitian Ministry of Public Works and Telecommunications) created by  Miyamoto et al. (2011) , which contains household habitability (classified using a color code: green, yellow, or red) and % of loss. It is based on fieldtrip work, using the USA ATC-20 method to assess habitability. Their work was completed building by building. Globally, the MTPTC and UWJ datasets are quite coherent in total sum. However, great differences can be found in several areas, as is noted by  Lemoine and Corbane (2013) ; in completely destroyed areas, the MTPTC could underestimate the number of initial buildings.
The MTPTC survey's main goal was to assess damage in remaining buildings. In addition to the complexity in assessing damage degree, another difficulty is comparing habitability surveys, which mix structural and functional points of view (such as the MTPTC survey) with damage degree surveys (such as UWJ) which are mostly structural. (Douglas et al. 2015)  found the same type of difficulties in the L'Aquila earthquake datasets. (Saito et al. 2010 ) using the Pictometry methodology, assessed 1300 buildings in the Port-au-Prince area. Pictometry® is the name of a patented aerial image capturing process that produces imagery showing the fronts and sides of buildings and locations on the ground (EagleView, n. d. ). They have compared observed damage to an intensity MMI map (ShakeMap) completed by the US Geological Survey (USGS) but they did not identify a clear relation.
The EMS98 scale was used in this work (Conseil de l'Europe 1998). The Port-au-Prince area (covered by the UWJ damage assessment) was divided into grid cells 500 m in length. For each cell, an intensity value was estimated based on the damaged building, the total number of buildings was counted, and their damage distribution was calculated (buildings in damage states D5, D4, D3, and no visible-slight structural damages). To be as representative as possible, cells with less than 10 buildings and cells with only completely destroyed buildings were not considered. A work has been completed to clearly identify the areas where the UWJ dataset is exhaustive or not using slightly damaged buildings. In some areas, only buildings in the D4 and D5 state exist on the UWJ dataset while many more buildings occur; this means that in these areas UWJ only identified heavily damaged buildings.
Data interpretation -Empirical vulnerability curves The UWJ dataset has only damaged buildings in areas exposed to higher levels of seismic intensity (>VII-VIII) after the 2010 event. In this work, we attempted to find additional building damage in other earthquakes in the same regional context. With UWJ catalog, using the equation 1, we calculate the mean damage grade "µd" for ShakeMap intensity intervals. Equation 2 is used for calculating the standard deviation, which added or subtracted form mean damage grade. The results are shown on Table  5. ∑ * 0 5 (1) D 1 5 D D µ   σ = µ -     (2) Once the mean damage grades are calculated using the above equation, we are fitting a set of curves in which beside the intensity and the mean damage grade we are adding the vulnerability index parameter, as presented in equation 3.
This equation was developed and used in the framework of FP5 RISK-UE project (  (Milutinovic and Trendafiloski 2003)  and considers the vulnerability index as follows: (3) The fragility curves based on 4 values of vulnerability index following the RISK-UE methodology are compared with mean damage from 2010's (Table  5 ) and 2003's earthquakes in Figure  9. Surprisingly mean damage µd do not increases when ShakeMap intensity increases. As discussed in the previous sections, this can be due to accuracy of ShakeMap intensity at this scale of work. It seems that there is no a clear spatial correlation between damages and intensity from ShakeMap. Discrepancies can also come from UWJ catalog. UWJ dataset were obtained using remote sensing techniques and present an inventory of a large number of buildings (299,257) which is, generally, very useful for statistical analysis.
The downside of these types of methods is that the detection of the damage degree is difficult. Indeed, the damage degree that represents large deformation of the structure is well identified (D4 and D5), but the intermediary damage degrees that represent small deformation structural damage (D3) and/or non-structural damage (D1 and D2) are quite difficult to observe. For the intensity equal or exceeding VII-VIII more than 60% of buildings showed no visual damage D1 (Table  5 ). This value surely cannot represent the reality. (Ufuk Hancilar, Taucer, and Corbane 2013) works with the UWJ dataset and combines it with ShakeMap intensity in order to produce vulnerability functions. The first family of curves were given by built areas density (Figure  10 ). As it can be seen, they had to manage with a catalog poor damage data in low-moderate intensities (<=VII).
(Ufuk  Hancilar, Taucer, and Corbane 2013)  have done an important work in which the main purpose of the targeted field campaign was to validate the remote sensing based damage assessment. Using the parameters (median and lognormal standard deviation) from the study of (Ufuk Hancilar, Taucer, and Corbane 2013), Figure  10  presents the comparative fragility curves derived by the sub-sets of remote sensing for low-density build-up zone and for high-density build-up zone. Only the damages degrees D3, D4 and D5 were considered and the aggregation of D2 and D1, as well as no damage (D0), into one category was considered, since that for these classes, there is no visible damage in remote sensing based assessment. As observed in the Figure  10 , the ground motion intensities for buildings in the remote sensing dataset range between 7.
Damage scenario Several post-earthquake damage scenarios have been completed using the Armagedom software  (Sedan et al. 2013). This software computes damage using the RISK-UE methodology of level 1  (Milutinovic and Trendafiloski 2003; Lagomarsino and Giovinazzi 2006)  and vulnerability indexes and macroseismic intensity as input. We only calculated post-earthquake damage scenarios for the Port-au-Prince municipality. The distribution of buildings types in the 36 districts in Port-au-Prince was provided by  (Benito et al. 2012 ) (Figure  15 ). They distinguished all residential buildings of six building types, which were associated with the building types from  (Lagomarsino and Giovinazzi 2006)  termed L&G. The most frequent building type was RCCB, which has reinforced concrete frames with masonry infill walls.
The second set adds +0. 16, as a "regional correction. " These correction values are those proposed usually when using RISK-UE methodology to assess current building vulnerability (  (Lagomarsino and Giovinazzi 2006; Milutinovic and Trendafiloski 2003)  As previously shown in Figures 11 to 13 vulnerability indexes between 0. 6 and 0. 75 fit quite well with empirical functions if looking only at very heavy damage (D4) and to the intermediate damage degree if a shift-correction factor to the D3 damage degree is applied. Table  7  presents the six scenarios' main characteristics. Two parameters change, the two sets of vulnerability indexes and three seismic intensity maps: the USGS ShakeMap, intensity CY2008, and deducted intensity map obtained using the interpretation of damage of the UWJ dataset.
Conclusions The seismic intensity is extremely important when constructing empirical fragility functions for buildings. In the Port-au-Prince earthquake, instrumental data was so poor or nonexistent that seismic intensity considered in empirical damage functions was mostly from simulations and models. For earthquakes in developing countries with a very low density of seismological stations, tools such as USGS ShakeMaps are of great importance in the minutes, hours, and days after the earthquake. However, in these cases, ShakeMaps is not the definitive version of "what happened," because it heavily depends on first parameter estimation. It is also important to consider the representativeness of less damaged areas and complete the building damage dataset in these areas (intensities VI or VII, for example).
If nothing else, empirical damage functions could appear too "vertical," passing very quickly from no-damage to strong-damage. Exhaustive building damage datasets are a tool and set of data suitable for many applications. In the case of Haiti, several datasets exist, collected by a multitude of actors and organizations that collaborated to build them. Even though the damage datasets such as UWJ are incomplete, they are still the most exhaustive, but do not describe the reality, mostly in terms of moderate state damage, which is very difficult or impossible to identity using aerial/satellite imagery. Even with field survey controls, many authors have highlighted this risk of underestimation. However, this dataset remains a reference. A huge building damage database permits one to map damage and consequently macroseismic intensity at a very small scale (in our work 500-m cells) based on EMS98 damage-intensity conversion criteria.
Second, intensity maps also have a strong influence; during the first hours ShakeMap, for example, underestimated the damage. Post-earthquake investigations obtain elements to understand the high damage level in Port-au-Prince. In addition, damage scenarios cannot reproduce the situation of many buildings with heavy damage (D4 and D5), only a few buildings with moderate damage (D3), and many buildings with minor/no damage. Is the building damage dataset exhaustive for moderate damage? We do not believe so. Even if the damage scenarios are not at all the damage reality, some damage scenario results are nearer to those of several observers, aerial/satellite datasets that still underestimate moderate/heavy damage. Remote sensing images combined with aerial or semi aerial images (such as Pictometry) are a precious tool for the first trends of damage and in prior damaged areas, but field survey control is necessary for moderately damaged buildings.
Results obtained using the initial ShakeMaps (SC2 and SC5) underestimated heavy damage because ShakeMaps estimates mainly an intensity VIII in Port-au-Prince, which is, in our opinion, an underestimation of the intensity by one level as discussed previously. Increasing the intensity respect ShakeMaps, the important damages (the sum of D3, D4 and D5) are greater, differing heavily with UWJ dataset. We cannot reproduce the low value of buildings in D3. This kind of situation could correspond to a situation with two families of buildings in Port au Prince, very vulnerable and very low vulnerable, but field reports did not notice the existence of an important group of low vulnerable buildings, so we do not consider this option.
Acknowledgements Various programs of seismic microzonation for vulnerable cities of Hispaniola Island cited in this paper were conducted by the  BRGM  in partnership with  Laboratoire National du Bâtiment et des Travaux Publics (LNBTP)  and  Bureau des Mines et de l'Energie in Haiti , and with  Servicio Geológico Nacional (SGN)  and  Instituto Geológico y Minero de España (IGME)  in the Dominican Republic. These studies were part of the  United Nations Development Programs "The seismic risk reduction plan for northern Haiti  ( 2012-2016 )" and the " Estudio de la  amenaza sísmica y vulnerabilidad física del Gran Santo Domingo (2013)(2014)(2015)(2016). "We thank the anonymous reviewers for their careful reading of our manuscript and their many insightful comments and suggestions.
Data sources include 1) USGS ShakeMaps (http://earthquake. usgs. gov/earthquakes/ShakeMap/global/shake/2010rja6/download/stationlist. xml), 2) Goodno et al. (2011), and 3) Hough et al. (2012). (Green 2018ratio varying from 40 to 74%. Recently,(Green 2018) compares building damage dataset with a simulation of PGD amplification due to landslides and topographic factor in Port au Prince area, but he did not find a clear correlation between damages density and PGD. Unsurprisingly damages in fact are a combination of local soil factors (local site effects and landslides), topographic amplification and local variations.
that many buildings were undamaged or had no reported damage (D0-D1), even D2. Heavy damages were not enough significant in number. It is difficult to estimate a damage assessment for this event with this partial dataset. The previous largest earthquake on La Hispaniola Island was in 2003, in the north-central part of La Hispaniola near Puerto Plata (Dominican Republic), a Mw 6. 5 earthquake, killing 2 persons and injuring 98. USGS Shakemaps estimates its intensity around VI-VII in Puerto Plata city and V-VI in Santiago de los Caballeros city. The Dominican authorities (seismic vulnerability reduction agency, ONESVIE) provided us the location of the heaviest damage and the characteristics of the damaged buildings (8 in D5, 14 in D4, 24 in D3), in addition to the observations in situ from (Lopez Rodriguez and Martinez Cruzado 2003).
6% 27516 16610 3349 SC2 27. 5% 12. 0% 1. 3% 25718 11166 1238 SC3 30. 0% 22. 4% 6. 4% 28001 20895 5947 SC4 30. 9% 27. 3% 9. 4% 28901 25531 8808 SC5 32. 8% 22. 2% 4. 4% 30656 20719 4153 SC6 28. 9% 30. 4% 14. 6% 27001 28431 13685 Observed 5. 6% 11. 67% 12. 58% 5230 10801 11633 UWJ (buildings tagged "Port- au-Prince" municipality in dataset) GEO-CAN 23. 5% MTPTC 16-21% EEFIT 46% Pictometry 19-28% (Saito et al. 2010) the six damage scenarios, in terms of % and number of buildings in the D3, D4 and D5 damage states, in addition to the values of the UWJ dataset in the Port-au-Prince municipality. Results show the great influence of vulnerability functions and confirms that the global building stock in Port-au-Prince was very vulnerable. The damage scenarios' estimated number of buildings in the D3 damage state is very high when compared to that of the UWJ dataset.
Moderate damage states are difficult to evaluate using only aerial images. Some authors(Saito et al. 2010), with a very limited field survey dataset, have estimated that heavy damage reached 46% in Port-au-Prince, whereas the Pictometry or aerial image estimates between 20 and 30%. We can interpret this as many buildings tagged as D3 in the UWJ dataset are in reality D4 or D5. More pessimistic vulnerability values (SC4 to SC6) gives strong damage ratios, only comparable with(Saito et al. 2010)observations. Results from SC1 to SC3 vulnerability values corresponds quite well with other observations datasets (GEO-CAN, Pictometry, MTPTC). Maybe these values are those the most appropriated for Haiti building stock.
The synthesized contrasts could be very promising in pediatric neuro-radiology. Indeed, the major benefit of this technique is to provide quantitative information about the inherent tissue relaxation  [16] , that could be used to build normative databases of healthy subjects which may be helpful to assess white matter myelination and maturation. With this information we aim at developing real time personalized sequences considering the tissue features of each developing brain in future. It should be also noted that with the synthetic contrast generation the TE of the T2 weighting may be changed retrospectively after the acquisition and thus also provides PD weighted images without additional acquisition time. Furthermore, adult studies have shown the capability of the synthesized contrasts to measure the modification of tissue relaxation in various brain injuries such as multiple sclerosis  [7, [17] [18] [19] [20].
Conclusion Synthesized T2 contrasts, which also provides quantitative T2 information, could be suggested as an equivalent technique in pediatric neuro-imaging, compared to conventional T2 based on TSE. However, in some cases, the synthesized contrast may suffer from more artifacts. Most of these artifacts were easily recognizable and are most likely not confounded with pathological conditions, but additional conventional images could be required in cases where doubts remain. The synthesized contrasts could be very promising in pediatric neuroradiology, giving access to new information such as normative databases of tissue relaxation healthy subjects. Figures Legends.
Conclusion The findings of this study will be presented; they address the knowledge gap regarding the potentially preventable component of the burden that occurs after discharge from hospital, and inform the optimal delivery strategy for PMC. Background Nigeria ranks second globally only behind India in under-five mortality prevalence. In Nigeria, 108. 8 children die per 1000 live births before their 5th birthday. It is of note that diarrhoea (15. 3% prevalence) is the second leading cause of under-five mortality in Nigeria after pneumonia. General poor hygiene and nutritional status are contributory factors to diarrhea. Methods Data was collected for severe acute malnutrition (SAM) using the weight for height z-value (WHZ) and/or oedema criteria. In addition, data on diarrhoea prevalence, oral rehydration salt therapy (ORST), improved source of drinking water and improved sanitation were collected.
However, P(s) displays other regimes depending on the range of s and this reflects the different levels of genome folding (see for review  [21, 22] ). Hi-C data have to be normalized to remove intrinsic biases such as mappability, restriction fragment length or GC content near the ligated fragment ends  [23, 24]. Although different normalization algorithms applied to the same dataset can give rise to subtle differences in the interpretation of the results, all Hi-C data so far are in agreement with the above cited imaging studies regarding the overall organization of chromosomes within the nuclear interior. Hi-C experiments have also been successfully performed on single cells. They confirm Hi-C done using a population of cells and extend them by characterizing which level of organization present cell to cell variability or is, on the contrary, extremely stable in all cells  [25].
Moreover, GWASs have consistently linked SNP rs965513, which lies in an intergenic region, to thyroid cancer  [58]. This SNP is part of a linkage disequilibrium block of 33 kb that includes four SNPs overlapping enhancers. Interestingly, the latter SNPs affect enhancer activity and transcriptional factor binding. 3C assay showed that the enhancers were involved in long-range contact with the nearby genes FOXE1 and PTCSC2  [59]. 3C-based experiments are costly and challenging to carry out. Fortunately, one can interrogate available databases before doing such experiments. There are numerous genome browser of Hi-C and other 3C-based experimental data such as Juicebox  [60]  and WashU Epigenome Browser  [61]. In addition, several tools were developed specifically to identify candidate gene targets of a particular SNP, including the 3DSNP database  [62].
For instance, the 3DSNP database integrates genotype data with 3D genome, epigenetic and chromatin signatures, DNA motifs and eQTLs, thereby accumulating evidence for gene targeting. However, most of the 3D genome data published to date were obtained from cultured cell lines, limiting their generalizability to disease studies. To tackle this issue, Hi-C data were recently generated from 14 human primary tissues, revealing strong tissue-specificity in local chromatin interactions  [63]. A specific browser, HUGIn  [64] , was designed to visualize the former Hi-C data, thus representing a formidable resource for studying functional roles of disease-associated variants  [63].
For this reason, SPECTA will explicitly recruit AYA patients and will therefore collect empirical data to bridge the molecular gap between pediatric and adult oncology. Because SPECTA will analyze tumor and germline DNA (to assess if the mutation is hereditary or somatic), this project will describe the frequency of specific cancer-causing germline mutations across the spectrum of AYA cancers. This may be of particular interest among the population of young patients who develop cancers typically found in older adult populations. The initial pilot study, activated in February 2019, will be composed of 100 AYA patients with high-grade gliomas and high-grade bone and soft tissue sarcomas, the two cancer groups responsible for most deaths in this age group. The primary objective of the pilot is to determine feasibility and recruitment rates of eligible AYA cancer patients who consent to genetic analysis of their tumor and constitutional DNA.
Whole exome sequencing (WES) of matched tumor and blood, and tumor RNA sequencing and DNA methylation profiling (EIPC array, Illumina) will be performed at the German Cancer Research Center (DKFZ), Heidelberg. The genomic data will be deposited on the European Genome-Phenome Archive. 14 Virtual central pathology review of scanned diagnostic slides will be undertaken by an international expert panel, and diagnostic material returned to the participating centers. A multidisciplinary molecular tumor board, held monthly, will release a clinically validated report that we aim to be available to referring clinicians within 4 to 6 weeks after biopsy. Leftover material from the collected formalin-fixed tumor tissue and blood will be stored for future research.