dataesr/scientific-paragraphs-categorization huggingface dataset:
This degree of disaggregation does not exist for China. The titles of the projects being quite explicit, we do a selection by text mining. In order to do so, we first establish, from the titles of the World Bank energy projects, keywords (Table  A1 ) in the Appendix linking the project to the renewable energy fields. Then, using this word dictionary as a basis, we do a selection of Chinese projects. Finally, we carry out a case-by-case verification of the selected Chinese projects in order to check the efficiency of the selection. The dataset includes precision coding to indicate how detailed the location of each project coding is. We retain projects with good precision as our identification of control and treatment groups is based on this condition. Following  (Kotsadam et al. , 2018) , we only use projects that correspond to AidData precision coding 3 and below, meaning that the project location is -in the worst caseanalogous to a second order administrative division such as districts, communes or municipalities.
The Afrobarometer survey and the dependent variable To the best of our knowledge, all studies on the effectiveness of development assistance projects at the sub-national level rely on data from the DHS household survey conducted by the United States Agency for International Development (USAID) to determine their dependent variables. Since the majority of the studies deal with health impact, these health data are very comprehensive in this area. For our analysis focusing solely on electrification, we favour the Afrobarometer data, which provides information on grid connection at the community level. The Afrobarometer is an independent research network that collects demographic and public opinion data through face-toface interviews on a nationally representative sample of 1200 or 2400 individuals.
Since 1999, seven series of surveys have been carried out with an expansion of the number of countries involved. The questionnaires cover a wide range of topics from politics to economics and social issues. Our study is based on the Afrobarometer round six survey geocoded data carried out on 36 African countries between March 2014 and December 2015 6  in order to leave a sufficient delay between the implementation of the most recent projects (  2014 ) and the observation of the results. We extract from this database the dependent variable capturing community electrification and defined as a dummy variable equal to one if the household live in a community which has access to electricity and zero otherwise. Among these 36 countries surveyed, we select only households in countries with at least one renewable energy project and a high degree of localization precision 7.
Determination of the treatment In order to determine the treatment group and the control group, we spatially link the two main databases mentioned above, the Afrobarometer and AidData, using their GPS coordinates. In this analysis, the treatment for a household is the fact of benefiting from a renewable energy development assistance project. It is difficult to judge whether the aid project affects the household, but it depends largely on the household's proximity to the project. Following the literature  (Kotsadam et al. , 2018; Martorano et al. , 2020) , we set the threshold of proximity of a household to a project at a radius of 25 km around the project. Using the Qgis software, we calculate the distance of each household to each project. As a same household can be close to different projects, we add up, for each household, the number of projects located within a radius of 25 km from the location of the household.
Propensity score matching To account for potential selection bias, we use a popular approach for assessing the impact of development programmes  (Pattanayak, 2009) , the propensity score matching (PSM) proposed by  Rosenbaum and Rubin (1983). This method aims to minimize selection bias by comparing each program beneficiary with a non-beneficiary counterfactual that is judged to be quite similar in some observable characteristics. This approach has been used by  De (2013)  and  De and Becker (2015). Dreher and Lohmann (2015) , however, expressed reservations about the results obtained because the number of variables in the propensity score estimate was too small. Today, the number of databases at the micro and spatial level has greatly improved, allowing us to improve the quality of the estimates.
, 1997). We have therefore paid particular attention to their selection based on previous literature. First of all, we use the nocturnal light intensity noted in 2000 as a proxy for the electrification of the area before treatment. The National Oceanic and Atmospheric Administration provides this information at the cellular level and the AidData site with its Geo-Query tool averages this data over second-order administrative units, noted ADM2  (Goodman et al. , 2019). Our variable ln_light thus corresponds to these last data transformed into logarithm 8. The urban variable from the Afrobarometer provides information on the place of residence of the household. Four types of residence are possible: urban, peri-urban, rural and peri-rural. Because of the low number of peri-urban and peri-rural households, we group the data into two main categories: urban and rural.
The variable ln_dist_power specifies the distance in logarithm of each household from an existing power plant in 2000. When the date of construction or commissioning is not available in the database we proceed to internet searches. The coordinates of the power plants are taken from the ''Global Power Plant" 9  database of the World Resources Institute  (Byers et al. , 2018). Population density in the second order administrative area (density_adm2) aims to control whether projects are implemented in such a way as to reach a maximum number of people. We create this variable by calculating the ratio between the number of people in the administrative unit obtained by the GeoQuery tool  (Goodman et al. , 2019)  from the Center for International Earth Science Information Network (CIESIN) data and the size of these same administrative units calculated on Qgis.
Numerous studies  (Dreher & Lohmann, 2015; Ohler & Nunnenkamp, 2014)  have already shown that political capture is an important determinant of aid allocation. The main measure used to illustrate this effect is the region of birth of the political leader. We therefore create a binary variable Leade_adm1 taking the value 1 if the household lives in the first-order administrative unit (ADM1) of birth of one of the political leaders during the period 2000-2014. In order to obtain this variable, we couple and complete the information from the Archigos database 11  and the update of the latter carried out by  (Dreher et al. , 2019). Finally, the variable ln_death_adm1 seeks to approximate the number of deaths during violent conflicts in each first-order administrative area (ADM1). This variable comes from the UCDP Georeferenced Event Dataset (GED) Global version 19.
Additional control variables Finally, we test the robustness of our results by changing the specification of the propensity score estimation model. For that purpose, we add new variables to the baseline specification. These ones can influence outcome and treatment as shown by a majority of significance but are less apparent in the literature on the determinants of official development assistance and electrification. First, we add a variable taking into account the ethnic dimension. We therefore control for the fact that a household belongs to the same ethnic group as the country's political leader. The Afrobarometer provides information on the ethnicity of the household. Using the database of  Dreher et al. (2019)  as well as some internet searches to update it, we are able to assign an ethnicity to each political leader in power between 2000 and 2014.
To take into account the specific role that the territory of the capital of the country may represent, we add as  Dreher et al. (2019)  a dummy variable, binary_adm2_incapital, taking the value one if the household and its community is located in the second-order administrative region of the capital 14. Moreover, we add a binary variable, binary_adm1_conflict, equal to one if there were violent conflicts before 2000 in the first-order administrative region of the household or its community in order to judge the potential for insecurity that the region may represent. As for the variable Ln_death_adm1 of the initial propensity score estimate, the data comes from the UCDP Georeferenced Event Dataset (GED) Global  (Sundberg & Melander, 2013). Finally, we add two variables on the characteristics of the territory that are particularly interesting in the implementation and efficiency of some renewable energies: the average solar radiation in 2000 in logarithm (ln_solar) and the average annual wind speed (ln_wind) in second-order administrative regions.
These variables thus measure the solar and wind renewable energy potential. These data come respectively from the Global Wind Atlas and the Global Solar Atlas and extracted at the level of second-order administrative units by the GeoQuery tool proposed by the AidData platform  (Goodman et al. , 2019). The estimation of the propensity score by the probit model can be found in (Table  A5 ) in the Appendix, while that of the ATT according to the different matching algorithms is given in Table  8. The estimation of the ATT with these different variables does not 13 We add 0. 01 to the sum obtained before putting it in logarithm in order not to lose observations whose number of deaths in violent conflicts is equal to 0. 14 Using the Qgis software and the coordinates of the capitals of each country in our sample, we assign to each capital the second-order administrative region corresponding to our breakdown.
Future research could focus in particular on the effectiveness factors of these types of projects, such as governance. However, some limitations of this study can be noted when interpreting the results. Indeed, the debate on the capacity of propensity score matching to provide unbiased estimates is not yet closed  (Dehejia, 2005; Peikes et al. , 2008). Despite our fairly conclusive results on hidden bias, we cannot completely rule out the influence of unobserved factors although the extensive range of robustness tests we undertake make us confident that our results are reliable. Furthermore, our identification strategy based on the undconfondness assumption did not allow us to exploit socio-economic variables at the household level available in the Afrobarometer database. Indeed, we were obliged to control for pre-treatment or time-fixed variables.
, existing power plant in 2018) 2000 density_adm2 Population density in Calculated by the author ADM2 (see subsubsection 3. 2. 2) Ln_city_distance Logarithm of the estimated GeoQuery from AidData travel time (in minutes) to (Goodman et al. , 2019) the nearest town with 50,000 or more inhabitants in the year 2000 hdi Sub-national Human Global Data Lab (GDL) of Development Indicator the Institute for Management Research at Radboud University (Smits & Permanyer, 2019) Leader_adm1 Dummy variable taking the Archigos et Dreher et al. value 1 if the household (2019) lives in the ADM1 of birth of a political leader of its country and 0 otherwise. ln_death_adm1 Logarithm of the number Calculated using UCDP of deaths in violent Georeferenced Event conflicts between 1989 Dataset (GED) Global and 2000 in the version 19.
Ascertainment of individuals with SOX11 variants Participants with protein altering SNVs in SOX11 were identified through exome sequencing from the Deciphering Developmental Disorders (DDD) study and genome sequencing undertaken in the 100,000 Genomes Project. 11 The sequencing pipelines for DDD  3  and the 100,000 Genomes Project have been published elsewhere. Additional participants were identified via GeneMatcher (Paracel, Inc). 12 This study complied with the Declaration of Helsinki. Written informed consent was obtained from parents and guardians as appropriate, including for publication of photographs. Clinical and phenotypic data were gathered from medical records by the recruiting clinician. Kaplan-Meier analysis was performed (PASW, http://www. spss. com. hk/statistics/) to summarize acquisition of developmental milestones.
Phenotype cluster analysis of SOX11 syndrome and CSS Human Phenotype Ontology (HPO)  15  terms were used to describe the phenotypes of individuals with SOX11 or ARID1B variants in a standardized fashion. Individuals with pathogenic ARID1B variants were identified from the open access participants listed on DECIPHER. We selected HPO terms that were well defined and not reliant on subjective clinical evaluation (microcephaly <2 SD [HP 0000252], abnormal eye morphology [HP 0013272], oculomotor apraxia [HP 0000657], hypogonadotrophic hypogonadism [HP 0000044], and coarse facies [HP 0000280]). Hierarchical cluster analysis was performed in Python using the Scikit-learn library and the Agglomer-ativeClustering object. The Euclidean distance and Ward parameters were selected to compute the linkage distance and cluster merge strategy.
Identification of SOX11 syndrome episignature Full methods are provided in the Supplemental Methods. Peripheral blood DNA was extracted using standard techniques. Bisulfite conversion was performed, and samples were analyzed using Illumina Infinium MethylationEPIC BeadChips (Illumina). 7] [18] [19] [20]  For mapping the episignature (probe and feature selection), MatchIt package  21, 22  was used to randomly select controls matched for age, sex, and array type from the EpiSign Knowledge Database (EKD), providing a control sample size 5 times larger than that of the cases, resulting in 50 controls. Methylation levels (β-values) were then transformed into M-values, which were used for linear regression modeling. Using the limma package, linear regression modeling was performed for the purpose of calculating the methylation differences between the case and the control groups, along with the corresponding P value for each probe.
A total of 224 differentially methylated probes (DMPs) were identified and considered as the SOX11 episignature. Using the 224 DMPs, 2 binary support vector machine (SVM) classifiers with a linear kernel were constructed using the e1071 package as described previously. 16, 17 The first classifier was trained using only the SOX11 samples against the control samples, and then samples from 38 other Mendelian NDDs with an established episignature from the EKD were supplied into the model to assess the specificity of the model. Using the Platt's scaling method, the classifiers generate a methylation variant pathogenicity (MVP) score ranging from 0 to 1 for each sample, in which a score near 1 is indicative of similarity to the identified SOX11 syndrome episignature, whereas a score near 0 shows that the sample has a methylation profile different from the SOX11 syndrome episignature.
Ascertainment of cohort We identified 38 new patients with SNVs (n = 34) or deletions (n = 4) of SOX11 (clinical and genomic data are summarized in Supplemental Tables  1, 2 , and 3). Participants were identified through exome sequencing in the DDD study or genome sequencing in the 100,000 Genomes Project, with additional cases identified via GeneMatcher. We identified 15 published patients with SOX11 SNVs (Supplemental Table  1 ).
Evidence that the SOX11 HMG box could be a hotspot for pathogenic variants The HMG box in SOX11 protein is a domain responsible for SOX11 binding to DNA and regulation of target genes. In addition, the HMG box regulates key protein-protein interactions and trafficking of SOX11 protein between cytoplasm and nucleus. Our previous in vitro studies showed that 4 missense variants in the HMG box impaired SOX11 transactivating activity (p. Lys50Asn, p. Pro120His, p. Ser60Pro, and p. Tyr116Cys). We could not perform functional analyses of all missense variants in the HMG box. It should not be assumed that all HMG box missense variants are pathogenic. However, it is plausible that such variants could interfere with SOX11 transactivating activity and have pathogenic potential. First, none of the SOX11 missense variants were present in Genome Aggregation Database (gnomAD), and only 8 HMG missense variants in SOX11 were identified in 114,704 individuals in gnomAD v2.
Only 2 PTVs are present in gnomAD (v2. 1. 1 non-neuro data set), and SOX11 is predicted to be loss-offunction intolerant with probability of loss of function intolerance = 0. 86 (observed/expected 0. 09 [0. 03-0. 44]). In vitro evidence of impaired transactivation and depletion of PTV in gnomAD supports a pathogenic role for SOX11 PTV. The mechanism through which PTV leads to reduction in SOX11 transactivating activity may relate to the loss of C-terminal transactivation domain.
Acknowledgments We thank the families who kindly agreed to participate in this study. This work was supported by a grant from the  Baily Thomas Charitable Foundation  ( TRUST/VC/AC/SG/ 5399-8436 ) (atypical neurogenesis) to M. F. and A. M. This work was supported by a grant from the  Waterloo Foundation  to A. M. , AMED grants under grant numbers  JP21ek0109486 ,  JP21ek0109549 , and  JP21ek0109493  to N. M. , and grant number  JP21ek0109486  to S. I. In addition, funding for this study was provided, in part, by the  London Health Sciences Molecular Diagnostics Development Fund and Genome Canada Genomic   Applications Partnership Program Grant  ( Beyond Genomics: Assessing the Improvement in Diagnosis of Rare Diseases using Clinical Epigenomics in Canada, EpiSign-CAN ). Sequencing for case 29 was provided by the  University of Washington Center for Mendelian Genomics (UW-CMG ) and was funded by  National Human Genome Research Institute and National Heart, Lung, and Blood Institute  grants  UM1 HG006493  and  U24 HG008956 , by the Office of the Director, and  National Institutes of Health (NIH)  grants under award number  S10OD021553  as well as  NIH  grant number  R01EY025718  to E.
fuLM exploits data accumulation during repeated task-evoked stimuli The current study presents both an experimental protocol and a dedicated data processing pipeline for dynamic imaging of functional hyperemia at microscopic scale. We combine sensory stimulations (whiskers deflections or visual stimulations) in anesthetized rats with stable and continuous injection of MBs (Fig. 1a  and Extended Data Fig. 1 ). Instead of using the whole acquisition dataset to compute static ULM two-dimensional (2D) maps of total MB count or average speed (Fig. 1b ), we use a temporal sliding window (typically, 5 s with a 1-s step) to construct dynamic maps (Fig. 1c ). The temporal window for MB data accumulation is short compared to the traditional accumulation time, leading to sparse temporal signals, especially in first-and second-order capillaries detected by ULM.
The stimulation pattern (unless stated otherwise) for visual and whisker stimulations was 30 s rest, 30 s stimulation and 10 s rest. The short visual stimulation was 15 s rest, 5 s stimulation and 5 s rest. The number of stimulations was set at 5 for fUS imaging experiments and to 20 to 60 for ULM experiments (20 if not stated otherwise). Functional ultrasound imaging. fUS imaging acquisition and processing. fUS imaging was performed using rapid acquisition of ultrasensitive 2D Power Doppler images of the rat brain using a linear ultrasound probe (128 elements, 15. 625 MHz, 110 µm pitch, 8 mm elevation focus, Vermon) connected to an ultrafast ultrasound scanner (Iconeus, 128 channels, 62. 5 MHz sampling rate) driven with Neuroscan live acquisition software (v. 1. 3, Iconeus and INSERM Accelerator of Technological Research in Biomedical Ultrasound).
Functional ultrasound activation maps and region of interest selection. Correlation maps (Fig. 1h ) were calculated as the Pearson's product-moment correlation coefficient c between the stimulation pattern A(t) (slot with 0 for rest periods and 1 for stimulation periods) and Power Doppler PD(t) signal for each pixel: c(x, z) = ∑ Nst k=1 ( PD (x, z, t k ) -PD (x, z) ) × ( A(t k ) - Ā) √ ∑ Nst k=1 ( PD (x, z, t k ) -PD (x, z) ) 2 × √ ∑ Nst k=1 ( A (t k ) - Ā) 2 Power Doppler data were analyzed using a generalized linear model approach, implemented in MATLAB to obtain z scores (Extended Data Figs. 3a,b  and 7a ) and P value maps. The activated area (corresponding to the primary S1BF, VPM and VPL, and the SC) were chosen as significant pixels in the image with a P value <0. 05 after Bonferroni correction for multiple comparisons.
Sub-pixel maxima localization was then performed using a fast local (5 × 5-pixel neighborhood) second-order polynomial fit. The resulting coordinates were rounded to the chosen pixel size (here 6. 875 × 6. 25 micrometres = initial pixel size / 16). Tracking of the maxima positions was performed using a classical particle tracking algorithm (simpletracker. m available on Mathworks Tinevez 62 , wrapping the MATLAB munkres algorithm implementation of (ref. 63 )), with no gap filling and maximal linking distance corresponding to a 100 mm s -1 maximum speed. Only tracks with MBs detected in at least ten successive ultrafast frames were selected. The successive positions gathered in one track were used to compute the interframe bubble velocity vector components (along probe x axis and depth z axis) and absolute velocity magnitude.
Correction of slow-motion drift during large acquisition times. To account for unavoidable motion drift occurring on time scales much slower than the cardiac or breathing time scales, we also added an intensity-based spatial registration (translation transformation) based on 10 s ULM MB count maps (dimension x, z and t) to correct for any drift during the global acquisition time (>20 min). We used the MATLAB functions imregconfig (monomodal option) and imregtfrom (translation option). ULM temporal analysis. For temporal ULM data construction, a first set of 3D ULM temporal matrices (MB count matrix MB(x,z,t) and a velocity matrix V(x,z,t)) was constructed using a sliding window for data accumulation. These take the form M(N x , N z , N t ) where M(t) corresponds to a 2D ULM image calculated on ULM data for MB detection time ∈ [t -W t / 2: t + W t / 2] with W t corresponding to the sliding window duration.
MBF (x, z, t) = 1 Wt NbPattern-1 ∑ i=0 MB (x, z, t + i × Patternt) , t < pattern_length V s (x,z,t) is the mean velocity of all MB detected at pixel (x,z) during [t + (i -1) × pattern t -W t / 2: t + (i -1) × pattern t + W t / 2], This second set of ULM matrices is referred as 'pattern-averaged temporal ULM data'. To get temporal ULM responses in specific ROIs (Fig. 2c , Extended Data Figs. 2e,f, 3c,d,f,g  and 4c  and Supplementary Fig. 4 ), M or M s matrices were summed along the two spatial dimensions of the ROI. The same approach was performed for velocity (Vs) signals, but using averaging. The signal was then normalized by the baseline to get relative variations. For quantification of the spatial resolution, similar to previous work  24, 28  , the spatial resolution was evaluated by quantifying the statistic distribution of velocities for the MB population detected in a single pixel of 6.