dataesr/scientific-paragraphs-categorization huggingface dataset:
A database of UK medical school course descriptors There is an ever increasing demand for adequate statistics describing individual universities and courses, and the Office for Students (OfS) has recently announced that it will publish a wide range of information concerning student demographics and access statistics relevant to widening participation  [87]. Similarly, a number of applications to use the UK Medical Education Database (UKMED)  [6]  have wished to consider medical school differences, and a systematic database of UK Medical School Course Descriptors would be useful, with data at the level of medical school courses. The unit of analysis will be medical school courses, so that, unlike the present study, information will be available for the various different types of medical school courses currently available (standard entry medicine, graduate entry medicine, etc.
Maybe, but then it is difficult to see how such relativism can be compatible with there being medical schools with "problematic issues … perhaps with causes that can be identified and addressed"  [1]. This is not the place to consider such issues further, but they are important issues that medical education, and the GMC, the regulator of medical education, has the opportunity to be clearer about once the data from UKMLA have been incorporated into UKMED. What is clear is that any answers need to be based on data fully describing the many differences between medical schools.
Measures were not available for many potentially important outcomes such as leadership, team-working, communication and advocacy, technical skills and research ability, and such data urgently need collecting. Likewise, detailed measures of undergraduate experience would be invaluable, perhaps by a GMC-initiated Undergraduate Training Survey. Preparedness, a measure of particular interest to the GMC, did not relate to our outcome measures, but might relate to detailed behaviours in F1, F2 and later posts. Confirming causality requires interventions, perhaps from time series data within medical schools, or randomised interventions within or across medical schools. As more data comes into the public domain, and numbers of medical schools increase, so the origins of medical school differences should become clearer.
Acknowledgements We are very grateful to  Nick Hillman  and  HEPI  for providing us with a complete set of data from the  Student Academic Experience Surveys  for 2006 to 2017; to  Katie Petty-Saphon  and  Peter Tang  for the help with HEFCE data; to  Sophie Lumley ,  Peter Ward ,  Lesley Roberts  and  Jake Mann  for providing additional data from their survey of self-regulated learning time [39]; to  Hugh Alberti  and colleagues for providing additional data from the survey of authentic GP teaching [26]; to  Thomas Gale ,  Paul Lambe  and  Martin Roberts  for providing data on rates of graduates applying for anaesthetic training (Gale T,  Lambe P ,  Roberts M: UKMED  Project  P30 : demographic and educational factors associated with junior doctors' decisions to apply for general practice, psychiatry and anaesthesia training programmes in the UK, Plymouth, unpublished); and to  Katherine Woolf ,  Celia Taylor  and  Katie Petty-Saphon  for comments on an earlier draft of the manuscript.
Taken together, this comparative morphokinetic analysis provides relevant information to consider when comparing similar developmental stages in different species. Since CDX2 is detectable in TE cells only at later stages in cow and human embryos  2, 6, 14  , we sought to determine whether alternative TE-associated genes may be expressed prior to blastocyst formation. We mined published human preimplantation scRNA-seq datasets  4, 5, 22  and were intrigued to observe heterogeneous expression of GATA3 in human morula cells (Fig. 1a, b  and Extended Data Fig. 2a ). Moreover, we reanalyzed a human preimplantation chromatin accessibility dataset  23  and performed differential analysis of putative transcription factor binding sites enriched in accessible regions between the morula and 8-cell stage.
1d , g and Extended Data Fig. 4a ). Similar to the mouse 16 , we observed WWTR1 and YAP1 overlapping nuclear expression in outer and TE cells in human morula and blastocyst stages, respectively (Extended Data Fig. 5a, b ). GATA2 is considered a TE marker in human blastocysts  [3] [4] [5] 28. Imp rtantly, at the morula stage GATA2 is not detected (Extended Data Fig. 2g ), despite its restriction to TE cells at the blastocyst stage (Extended Data Fig. 2h ). By contrast, GATA3 is expressed at both morula and blastocyst stages in outer and in TE cells, respectively (Extended Data Fig. 2g, h ), indicating that expression of GATA3, and not GATA2, at the morula stage may distinguish cells that are initiating a TE program. To find additional genes associated with TE initiation in human embryos, we initially identified genes that were co-expressed with GATA3 in TE cells at the blastocyst stage using the aforementioned scRNA-seq datasets  4, 5, 22  and analyzed the expression of these genes earlier at the morula stage.
5e ). Upon blastocyst expansion, GRHL2 is specifically expressed in TE cells and no longer present in ICM cells (Extended Data Fig. 5f ). Next, we analyzed scRNA-seq datasets to identify genes that exhibited an anti-correlated expression pattern to GATA3 in human morula cells (Fig. 1a ) and that could be putative inner cell-associated markers. Interestingly, genes involved in embryonic stem cell pluripotency and/or genes enriched in the EPI/ICM, such as DPPA3  33  , KLF17  4, 34  and ARGFX  4, 28, 35  were transcriptionally negatively correlated with GATA3 (Fig. 2e-g , Extended Data Tables  4, 5  and Extended Data Fig. 7, 8 ). Altogether, our analysis of existing scRNA-seq data suggests transcriptional differences between inner and outer cells at the morula stage. In the mouse morula, the transcription factor SOX2 is specifically restricted to inner cells and is considered the first marker of ICM pluripotency  36, 37  (Extended Data Fig.
Besides compaction and cell polarization, another morphological change occurring during preimplantation development is cavitation. During this process, outer cells pump fluid into the embryo to form a blastocoel cavity, causing disruption of the radial symmetry of the embryo  21. To support the formation of a cavity, outer cells assemble functional tight junctions, in order to form a seal and prevent fluid leakage  1, 21. Interestingly, our re-analysis of scRNA-seq datasets shows that genes involved in cell epithelialization and genes encoding transporter subunits are positively correlated with GATA3 expression in human morula cells. scRNA-seq datasets lack positional information of the cells collected, but our protein expression analysis shows that GATA3 is detected only in outer cells at the morula stage.
Acknowledgments We thank the generous donors whose contributions have enabled this research; we thank  Parita Patel  and  Arasaratnam Srikantharajah  at  Homerton Fertility Centre, Homerton University Hospital ;  Michael Summers ,  Alan Handyside  and  Kamal Ahuja at London Bridge Fertility Clinic -London Women's Clinic ; and  Stuart Lavery ,  Annabel Rattos  and  Marta Jansa Perez  at  Wolfson Fertility Centre, Hammersmith Hospital  for the coordination and donation of embryos to our research project; the  Niakan Lab , the  Turner Lab , the  Lovell-Badge Lab ,  Michele Marass ,  Teresa Rayon Alonso ,  Barry Thompson , and  Nate Goehring  for discussion, advice and feedback on the manuscript;  Barry Thompson 's,  Nate Goehring 's and  James Briscoe 's laboratories for sharing reagents and advice; the  Francis Crick Institute's Advanced Light Microscopy and Biological Research Facilities ;  Anna Brodie  and  Katy Bacon  for assisting with cow embryo generation.
One of the AMOT antibody (Amot-C # 10061-1 ) used in this paper was generously provided by  Hiroshi Sasaki 's Lab. CRT0276121 was generously provided by  Cancer Research Technology. Work in L. D. lab was funded by a donation from  MSD  to  "Fondation de l'Université de Nantes ". Work in H. V. d. V lab has been funded by the  Fonds Wetenschappelijk Onderzoek Flanders  ( FWOAL722 ) and the  Wetenschappelijk Fonds Willy Gepts (WFWG, UZBrussel ,  G142 ). Work in A. F-N. lab was supported by  Comparative Biomedical Sciences Departmental  fund from the  Royal Veterinary College. Work in the K. K. N. lab was supported by the  Francis Crick Institute , which receives its core funding from  Cancer Research UK  ( FC001120 ), the  UK Medical Research Council  ( FC001120 ), and the  Wellcome Trust  ( FC001120 ) and by the  Rosa Beddington Fund.
The cDNA library was prepared using the NEB Ultra RNA directional prep kit for Illumina and sequencing was performed on an Illumina HiSeq3000 system. Sequencing reads were mapped to the BaSysBio 168 strain (NC_000964. 3) using Bowtie2  (Langmead and Salzberg, 2012). The software program featureCounts of the Subread package  (Liao et al. , 2014)  was applied to generate counts for known genes. Differentially expressed genes were identified using the R/Bioconductor package DESeq2  (Love et al. , 2014). The raw and processed RNA sequencing data obtained in this study has been deposited at the NCBIs Gene Expression Omnibus  (Edgar et al. , 2002)  and is accessible via the GEO accession number GSE140605.
Data Analysis and Statistical Procedures Growth and luminescence measurements were performed in biological duplicates and technical triplicates. From the values obtained for each time point, mean values and standard deviation (±) were calculated and plotted (Figure  2 ). The data derived from the RNA-seq experiments were obtained in biological and technical triplicates. Genes were considered relevant that differed at least four-fold relative to the noninduced reference samples with an adjusted p-value below 0. 05. No genes where down-regulated upon 0. 5 µM YydF * treatment (Figure  3 , Table  1  and Table S2 ). MIC experiments were performed at least in biological and technical duplicates and mean values as well as standard deviation (±) are displayed (Figure  4 ). For the depolarization assays performed in the plate reader, each condition (antimicrobial compound) was tested in biological duplicates and technical triplicates.
From the raw data, mean values and standard deviation (±) were calculated and plotted as function of time (Figure  5A ). Depolarization and Sytox Green assays performed microscopically were evaluated using Fiji and the plugin MicrobeJ  (Schindelin et al. , 2012; Ducret et al. , 2016). Here, pictures obtained from biological and technical triplicates were analyzed and regions of interest (i. e. , cells) were chosen by the algorithm and manually corrected if necessary. Finally, from each condition tested, at least 300 cells were included in the final dataset and mean pixel intensities were plotted (Figures  5B, C ). Data obtained from the fluidity assays (Figure  6A ) were performed in biological and technical triplicates. Emission spectra of Laurdan were obtained once every minute and mean values as well as standard deviation (±) were calculated and plotted as a function of time.
ACKNOWLEDGMENTS The authors want to thank all current and past members of the  Mascher lab  for helpful discussions and feedback. In particular, PP thanks  Nina Lautenschläger  and  Yang Yuan  for their lab work support. The authors thank the  Becker and Goesmann lab  for RNA-sequencing and data processing. In particular, we thank  Doreen Meier  for RNA sample preparation and  Raphael Müller  for setting up the RNA-seq data analysis pipeline.
FUNDING This work was supported by a grant from the  Deutsche Forschungsgemeinschaft  ( MA2837/3  to TM) in the framework of the priority program SPP1617 Phenotypic heterogeneity and sociobiology of bacterial populations. Open Access Funding was provided by the  Publication Fund of the TU Dresden. HS was supported by a  BBSRC   New Investigator Award   BB/S00257X/1. Moreover, this work was supported by the  European Research Council (ERC)  grant ( Consolidator Grant   617053  to OB).
Validation of the enChIP method Having demonstrated specificity of dCas9 binding with ChIP-seq, we set out to identify proteins that associate with either the var gene promoter or intron. We performed dCas9-3HA IP LC-MS/MS with the var gene promoter-and intron-targeted dCas9 and non-targeted dCas9 strains at 14 hpi. A label-free quantification approach allowed for comparison of protein enrichment between each sample using low amounts of input material. This led to the quantification of 1,413 total P. falciparum proteins (false discovery rate [FDR] of 1%, number of peptides used ≥ 3; Source Data for Tables  EV5-8 ], with dCas9 being the most abundant protein in the LC-MS/MS analysis for each immunoprecipitation. While dCas9 levels were similar in the var promoter-and intron-targeted immunoprecipitations (promoter/intron ratio = 1.
04, P = 0. 15; Figs  3A  and B, EV3A , Table  EV4 ), dCas9 levels were significantly higher in the nontargeted control when compared to the intron-(ratio = 3. 16, P = 1. 88 × 10 À62 ) or promoter-targeted dCas9 samples (ratio = 3. 07, P = 5. 64 × 10 À86 ; Figs 3B and EV3A, Table  EV4 ). Because we found that the number of dCas9 peptides correlated with total proteins identified (Fig EV3A, Table  EV4 ), the nontargeted dCas9 served as an extremely stringent control of background binding. Thus, we first compared the promoter-and intron-targeted dCas9 data sets for a more unbiased analysis (see Materials and Methods for details of analysis). For most var genes, the promoter region is several thousand base pairs upstream of the intron. Thus, our method provides enough resolution to separate factors specifically bound to the promoter or intron, as the average DNA fragment length for the immunoprecipitation was 2 kilobases (Fig  EV3B ).
Antibodies against aldolase (Ald. ) and histone H3 are controls for the cytoplasmic and nuclear extracts, respectively. Molecular weights are shown to the right. B MA plot of log 2 (glucosamine-treated/untreated, M) plotted over the mean abundance of each gene (A) at 12 hpi. Transcripts with a significantly higher (above x-axis) or lower (below x-axis) abundance in the presence of glucosamine are highlighted in red (q ≤ 0. 05). iswi is highlighted in blue (q = 6. 71 × 10 À10 ), and the active var gene is highlighted in green (q = 2. 53 × 10 À5 ). Two and three replicates were used for untreated and glucosamine-treated parasites, respectively. P-values were calculated with a Wald test for significance of coefficients in a negative binomial generalized linear model as implemented in DESeq2  (Love et al, 2014).
Libraries were sequenced, and data were treated as above for ChIP. One replicate was performed for anti-HA and IgG immunoprecipitations in nonclonal bulk cultures at 12 hpi of non-targeted dCas9 and var promoter-targeted dCas9 strains. For dCas9 RIP-seq analysis of the var promoter-targeted dCas9, read coverage in the anti-HA and IgG samples was calculated using deepTools "bamCompare" (Ramı ´rez et al, 2016) in 10 nt windows (option "-bs 10") and normalized to the total number of mapped reads in each library (option "-normalizeUsingRPKM"). The normalized coverage in each bin of the IgG sample was then subtracted from the anti-HA sample (option "-ratio subtract"). Promoter/control fold enrichment of each IgG-normalized 10 nt window was calculated using macs2 "bdgcmp"  (Zhang et al, 2008). For genome-wide coverage plots, the fold enrichment as calculated by macs2 "bdgcmp" was averaged over the gene body and visualized using circos  (Krzywinski et al, 2009).
14 Wash the beads in each tube with 1 ml TE buffer for 5 min with rotation at 4°C. 15 Collect beads on the DynaMag and wash three times with 100 ll 25 mM ammonium bicarbonate (NH 4 HCO 3 ) without removing the beads from the magnet. 16 Remove tube from the magnet and resuspend beads in 100 ll 25 mM NH 4 HCO 3. 17 Perform on-bead digestion for 1 h with 0. 6 lg of trypsin/LysC (Promega V5071 • For identification, the data were searched against the P. falci- parum FASTA database (PlasmoDB-36 Pfaciparum3D7 Anno-tatedProtein containing Cas9 and the common contaminants) using Sequest HF through proteome discoverer (Thermo Fisher version 2. 2). Enzyme specificity was set to trypsin, and a maximum of two missed cleavage sites were allowed. Oxidized methionine and N-terminal acetylation were set as variable modifications.
Only peptides with a Mascot score greater than or equal to 25 and an isolation interference less than or equal to 30 were included in the data analysis. Phylogenetic analysis of SNF2 domain-containing chromatin remodelers SNF2 domains were extracted from the full protein sequences following the "SNF2 family N-terminal domain" Pfam annotation (Pfam ID: PF00176) using bedtools getfasta  (Quinlan & Hall, 2010). Multiple sequence alignments were performed using MAFFT with default settings. Gaps were removed with trimAl (Capella-Gutierrez et al, 2009), and the best phylogenetic model (i. e. , Le Gascuel [LG]) was calculated using ProtTest3  (Darriba et al, 2011). A maximum likelihood phylogenetic tree was constructed using MEGA (v7)  (Kumar et al, 2016)  with the LG model and 1,000 bootstrap replicates.
05. MA plots were generated using the "baseMean" (mean normalized read count over all replicates and conditions) and "log 2 FoldChange" values (glucosaminetreated over control) as determined by DESeq2. RPKM values were calculated in R using the command rpkm() from the package edgeR  (Robinson et al, 2010). Gene Ontology enrichments were calculated using the built-in tool at PlasmoDB. org (Aurrecoechea et al, 2017). ISWI and H3K9ac ChIP sequencing and analysis Synchronous parasites (12 hpi AE 3) were treated as above for the dCas9 ChIP with minor differences. 5 × 10 8 parasites were crosslinked and lysed in 4 ml ChIP lysis buffer. Chromatin was sonicated in 3. 6 ml total ChIP SDS lysis buffer. For the ISWI-HA ChIP, 8 lg DNA (chromatin) was diluted and incubated with 8 lg anti-HA antibody (Abcam ab9110) conjugated to 200 ll Protein G Dynabeads (Invitrogen 10004D).
Acknowledgements This work was supported by the  European Research Council  (grant PlasmoSilencing  670301  to  A. Sc. ); the  Agence Nationale de la Recherche  (grant  ANR-11-LABEX-0024-01 ParaFrap  to  A. Sc. ); the  "Région Ile-de-France " (grants  2013-2-EML-02-ICR-1  and  2014-2-INV-04-ICR-1  to D. L. ); the  Fondation pour la Recherche Médicale  (grant  DGE20121125630  to D. L. ); the  Academic Research Fund  (Tier 2) of the  Ministry of Education, Singapore  ( MOE2018-T2-2-131  to P. R. P. ); and the  Merlion  Project (6. 11. 18 to P. R. P). Work in the laboratories of P. R. P. and P. C. D. was funded by the  National Research Foundation of Singapore  through the  Singapore-MIT Alliance for Research and Technology Antimicrobial Resistance Interdisciplinary Research Group. Proteomics work was performed in part in the  Center for Environmental Health Sciences BioCore , which is supported by  Center  grant  P30-ES002109  from the  National Institute of Environmental Health Sciences.
Data availability The data sets generated in this study are available in the following databases: • ChIP-seq data: NCBI BioProject accession #PRJNA529754 (http:// ncbi. nlm. nih. gov/bioproject/PRJNA529754) • RNA-seq data: NCBI BioProject accession #PRJNA529754 (http:// ncbi. nlm. nih. gov/bioproject/PRJNA529754) • dCas9 Proteomics data: PRIDE PXD013131 (http://www. ebi. ac. uk/pride/archive/projects/PXD013131) • ISWI-3HA Proteomics data: https://chorusproject. org/pages/da shboard. html#/projects/all/1645/experiments Expanded View for this article is available online.
We here consider the cognitive and non-cognitive consequences on young adults of growing up with a mother who reported experiencing major financial problems. We use UK data from the Avon Longitudinal Study of Parents and Children to show that early childhood financial problems are associated with worse adolescent cognitive and non-cognitive outcomes, controlling for both income and a set of standard variables, and in valueadded models controlling for children's earlier age-5 outcomes. The estimated effect of financial problems is almost always larger in size than that of income. Around one-quarter to one-half of the effect of financial problems on the non-cognitive outcomes seems to transit through mother's mental health.
" The report describes these women facing financial insecurity, and proposes policies to improve their quality of life. We here use data from the Avon Longitudinal Study of Parents and Children (ALSPAC) in the UK and take a wider view of economic resources to include both income (as in much of the existing work) and financial distress. This large-scale birth-cohort data follows children over a period of more than two decades. For each of the child's first 11 years we know whether the mother 1 Childhood emotional health is the most important predictor of life satisfaction at all adult ages in both the British Cohort Study (BCS) and the National Child Development Study (NCDS): see  Clark et al. (2018) ,  Layard et al. (2014)  and  Flèche et al. (2019). 2 The US is no exception in this respect: 46.
Financial insecurity does not necessarily imply low or lower income (and we indeed only find a quite small correlation between financial problems and income in the ALSPAC data), but includes "a doctor's bill, a late paycheck, or a broken-down car", housing problems, the job loss of a family member, divorce, falling housing equity, and so on. During the recent Great Recession, these financial problems have arguably become more widespread than low income, and have hit the middle-class as well (as highlighted, for example, by  Gauthier and Furstenberg, 2010 , in relation to families with children). Some supportive evidence on this point comes from Waves 1 to 18  (1991) (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000) (2001) (2002) (2003) (2004) (2005) (2006) (2007) (2008)  of the British Household Panel Survey (BHPS) data, the dataset often used to analyse income dynamics in the UK.
These findings suggest that economic downturns, such as the recent Great Recession, and the financial distress they engender not only affect the adults concerned, but also cast a long shadow over their children's outcomes for many decades. The remainder of the paper is organised as follows. Section 2 contains a short review of the relevant literature; the dataset, variables and empirical methods are then described in Section 3. The main results and a series of extensions appear in Section 4. Last, Section 5 concludes.
2. 1 Cognitive Outcomes  Blanden and Gregg (2004)  analyse three British datasets, and conclude that a one-third reduction in family income leads to an average 3-4 percentage-point fall in the probability of achieving GSCE A-C grades or obtaining a degree. Ermisch and Francesconi (2001)  consider various family characteristics in the first seven waves of the BHPS, and conclude that income is a strong predictor of educational attainment. Gregg and Machin (2000)  estimate the effects of family background on children's educational attainment and labour-market outcomes at ages 16, 23 and 33 using British NCDS data. The strongest negative family-related predictor of school attendance and staying on at school at age 16 is financial hardship (defined as whether the family experienced financial difficulties in the year prior to the survey date).