dataesr/scientific-paragraphs-categorization huggingface dataset:
Motifs were compared to unmethylated consensus sequences in databases JASPAR  [24, 25]  and UniPROBE  [26]  using TOMTOM  [27]. Additionally, motifs were compared to motifs identified in our previous work that preferentially bind to methylated DNA  [23]. For each CpG-containing DNA motif, we obtained two TFs, one that recognizes the motif in the unmethylated state and the other in the methylated state. Figure  8  shows several examples for which a consensus sequence is predicted to bind different TFs based on the methylation state of the CpG within the motif. For instance, the motif ACCGCA from the T-DMR negative regulation group is similar to the binding sequences of interferon regulatory factor 4 (Irf4). However, the methylated form of the motif can be recognized by recombination signal binding protein for immunoglobulin kappa J region (RBPJ) (Figure  8 ).
Acknowledgements This work was supported by funding from the  National Eye Institute  [ R01EY024580  to J. Q. ,  R21EY018703  to S. M,  R01EY023188  to J. Q. and S. M. , and  R01EY009769  to D. Z. ]; the  Wilmer Core Grant  [ 5P30EY001765 ]; unrestricted funding from the  Research to Prevent Blindness ; and the generosity of  A. Nixon.
Methods: In this study, we examine sex differences in RORA and aromatase protein levels in cortical tissues of unaffected and affected males and females by re-analyzing pre-existing confocal immunofluorescence data from our laboratory. We further investigated the expression of RORA and its correlation with several of its validated transcriptional targets in the orbital frontal cortex and cerebellum as a function of development using RNAseq data from the BrainSpan Atlas of the Developing Human Brain. In a pilot study, we also analyzed the expression of Rora and the same transcriptional targets in the cortex and cerebellum of adult wild-type male and female C57BL/6 mice. Results: Our findings suggest that Rora/RORA and several of its transcriptional targets may exhibit sexually dimorphic expression in certain regions of the brain of both mice and humans.
Background Autism spectrum disorder (ASD) refers to a group of neurodevelopmental disorders that are diagnosed on the basis of impaired social interactions and communication, especially social reciprocity, and the presence of aberrant, repetitive, and stereotyped behaviors  [1]. Because of the strong heritability of ASD based on monozygotic twin and sibling studies  [2, 3] , there have been extensive searches for genetic mutations and variations that may cause ASD  [4] [5] [6] [7] [8] [9] [10] [11] [12] [13]. To date, there are hundreds to thousands of genes that are considered autism candidate or susceptibility genes in the autism gene databases  [14, 15] , with the number of genes continually growing. However, the combined genetic variations account for only approximately 20% of the cases, with no single gene or genetic variation associated with more than 1% of the cases.
This intriguing relationship between RORA and sex hormones led us to propose a model in which RORA deficiency may lead to an elevation of testosterone and depletion of estradiol through suppression of CYP19A1 expression. This model provides a plausible biochemical explanation for the elevated testosterone levels seen in studies by the Baron-Cohen group. It also suggests a more direct mechanism for the sex bias in ASD in which normal females, with higher estrogen levels, might exhibit higher expression of RORA, thus buffering against agents that induce RORA deficiency. This model further predicts that during development, RORA expression may be sexually dimorphic at least in some regions of the brain. This pilot study was undertaken to examine the possibility of sex differences in the expression of RORA in several brain regions at different stages of development of the normal human brain using RNAseq data from the BrainSpan Atlas.
In addition, sex differences in RORA and aromatase protein levels were investigated by reanalysis of our published confocal immunofluorescence data from the cortex of both male and female controls and age-matched male and female ASD donors. To reduce heterogeneity in the expression patterns due to the genetics as well as age of the brain donors, we also conducted expression analyses of the cortex and cerebellum of a strain of mice at 3 months of age. Together, these studies suggest sexual dimorphism in the expression of RORA/Rora in some brain regions during certain stages of development as well as a high correlation with the expression of its target genes, especially CYP19A1.
Methods Re-analysis of pre-existing confocal immunofluorescence data from tissue arrays Data from a previous study  [30]  employing confocal immunofluorescence to quantitate protein levels of RORA and aromatase on a tissue array containing specimens of the frontal cortex of ASD cases and age-matched controls were reanalyzed on the basis of gender. The tissue array was obtained through the Autism Tissue Program (San Diego, CA, USA) and kindly provided by Dr. Janine LaSalle (UC Davis). Each array contained 600 μm in diameter × 5 μm thick sections in triplicate from the BA9 region of the frontal cortex of autistic individuals and age-and sex-matched controls as well as samples from individuals with a variety of other neurodevelopmental disorders as previously described  [31]. Only specimens from neurotypical controls (without any developmental disorder) were used as 'Controls' for this study.
Analysis of RNAseq data from the BrainSpan atlas of the developing human brain RNAseq data (RPKM) for gene expression of RORA and that of selected transcriptional targets of RORA in specific brain regions were downloaded from the BrainSpan Atlas of the Developing Human Brain  [32]. The downloaded data for the orbital frontal cortex and the cerebellum are presented in Additional file 2. The samples, divided by gender, were grouped into three developmental periods: (1) before birth (BB), which was designated in BrainSpan as 'pcw' for post-conception weeks; (2) birth to 18 years of age; and (3) older than 19 years of age. Within each group, the samples were matched for age ± 3 years (or ± 3 pcw for the prenatal samples) for comparisons between males and females or between cases and controls. The validated transcriptional targets of RORA selected for RNAseq data analyses were A2BP1, ITPR1, and NLGN1, which are among the autism susceptibility genes represented in the SFARI gene and AutismKB databases  [14, 15].
Each sample was run in triplicate and the average deviation of the CT values was calculated to assess the consistency of the assays. An average CT deviation of 0. 25 or less was considered acceptable for replicates. Primers for RT-qPCR analyses were designed using Primer3 software for mouse Rora as well as A2bp1, Cyp19a1, Itpr1, and Nlgn1. The primer sequences for these mouse genes are listed in Additional file 3. Cyp19a1 expression was not determined for the cerebellum because the expression levels were either zero or too low for confidence. Primers for the human genes have been previously published  [33]. The relative quantity of transcripts in each sample was calculated using the standard curve method with 18S RNA expression as a reference.
Statistical analyses The Student's t-test within the StatPac Statistical Program (StatPac, Inc. , Bloomington, MN, USA) was used to determine t-values and P values for differences between the means of gene expression in age-matched males and females and between the means of age-matched cases and controls. T-values derived from the t-tests and degrees of freedom (sum of the number of samples in two groups minus 2) were also used to determine Cohen's d and effect size-r to provide an additional measure of the magnitude of the effect of sex (or autism diagnosis) on protein or gene expression level. Cohen's d and effect size-r were calculated using the open-access online Effect Size Calculator http://www. uccs. edu/lbecker/index. html made available by Dr. Lee A. Becker (University of Colorado, Colorado Springs, CO, USA).
Analysis of RNAseq data for RORA and several of its transcriptional targets in the orbital frontal cortex (OFC) and cerebellum RNAseq data from the BrainSpan Atlas was used as an experimental tool to explore sexually dimorphic expression of RORA and that of its target genes in different human brain regions across different developmental periods. Although there were no statistically significant sex differences in RORA expression in the OFC and cerebellum (Figures  3  and 4 ), there was a suggestively higher expression (and a correspondingly larger effect size) in the OFC of adult females in comparison to age-matched males, with an F/M ratio comparable to that observed for RORA protein in the frontal cortex (Figure  1 ). Of potentially greater interest are the sex differences in the correlation between RORA expression and that of its transcriptional targets, specifically A2BP1 (RBFOX1), ITPR1, and NLGN1, summarized in Tables  3  and 4.
This is particularly relevant since over 400 of the identified transcriptional targets of RORA are listed in SFARI Gene and AutismKB databases as autism candidate/susceptibility genes  [14, 15]. Moreover, RORA and estrogen receptor (ER) are both nuclear receptors that share the same DNA consensus binding sites and regulate the transcription of some of the same genes  [39]. Thus, the higher concentration of estrogen, which has been demonstrated even in prepubescent females versus age-matched males  [40] , may offset the loss of transcriptional control due to RORA deficiency by activating ER-mediated transcription, resulting in a lesser impact on neurological development in females. Also intriguing is the strong negative correlation coefficient between Rora and Cyp19a1 expression levels in female mice.
In a neuronal cell model, we have demonstrated that RORA can bind to the promoter regions of over 2,500 genes, 438 of which are included in autism gene databases  [14, 15]. Gene ontology analyses of the putative gene targets of RORA revealed significant overrepresentation of genes involved in neuronal differentiation, neuron projection morphogenesis, axonogenesis, and axon guidance. We validated six transcriptional targets of RORA (A2BP1, CYP19A1, HSD17B10, ITPR1, NLGN1, and NTRK2) and, in this study, investigated the correlation between the expression of RORA and that of four of these targets in both the human and mouse brain. With regard to functional relevance, A2BP1, also known as RBFOX1, codes for a neuron-specific splicing factor associated with synaptic transmission, neurodevelopment, and developmental delay  [55, 56].
Indeed, aside from not having ASD or any other diagnosed neurodevelopmental disorder, the behavioral phenotypes of the control brain donors are unknown. This pilot study using BrainSpan data will help to direct attention to certain brain regions and developmental stages in which sex differences in the expression of RORA and/or regulation of its target genes are suggested. This study also suggests that any study of sex-dependent differences in gene expression in the brain must take into account both regional and developmental changes in gene expression. Regarding the issue of genetic heterogeneity, mouse strains are much more homogeneous with respect to genotype, but individual expression differences are still apparent even within a strain. Some of these differences, especially in post-pubertal females, may be due to hormonal cycling, so future studies on sex differences in gene expression should utilize more hormonally-synchronized female mice.
Acknowledgements We wish to thank the families of all of the brain donors for their invaluable gift to autism research and the  Autism Tissue Program (San Diego, CA, USA)  for making frozen human brain tissues available for this work through the  Harvard Brain Tissue Resource Center (Harvard University, Boston, MA, USA). We also thank the  Allen Institute for Brain Science (Seattle, WA, USA ) and all the members of the consortium working on the brain mapping project for making the RNAseq expression data of the human brain publicly available. This study was supported in part by generous gifts from the  Ireland Family Foundation  ( Chapel Hill, NC, USA ) and the  LIFE Foundation (Aspen, CO, USA) , and by a grant from the  NIEHS  ( R21 ES023061-01 ). None of the funding sources played any role in the study design, collection, analysis, and interpretation of data, writing of the manuscript, or decision to submit this study for publication.
Ecosystem models are currently one of the most powerful approaches used to project and analyse the consequences of anthropogenic and climate-driven changes in food web structure and function. The modeling community is however still finding the effective representation of microbial processes as challenging and lacks of techniques for assessing flow uncertainty explicitly. A linear inverse model of the Bay of Biscay continental shelf was built using a Monte Carlo method coupled with a Markov Chain (LIM-MCMC) to characterize the system's trophic food-web status and its associated structural and functional properties. By taking into account the natural variability of ecosystems (and their associated flows) and the lack of data on these environments, this innovative approach enabled the quantification of uncertainties for both estimated flows and derived food-web indices.
Import Two main imports were considered in the present model, import to phytoplankton (GPP) and import of detritus. Estimates of GPP were derived from estimates of net primary production (NPP) from four Earth system models  (Bopp et al. , 2013) : the CESM1-BGC, the GFDL-ESM2G, the GFDL-ESM2M and the NorESM1-ME. These four models were selected from the range of Earth system models because they gave NPP estimates similar to SeaWifs observation data for our study area over the 1975-2005 period  (Bopp et al. , 2013). These estimates were also comparable to the NPP in situ value entered into the Ecopath model of the Bay of Biscay continental shelf. Minima and maxima were based on the 5th and 95th percentiles of model estimates. Detritus imports from the five main rivers flowing into the Bay of Biscay were estimated from measurements of POC in estuaries  (Abril et al.
Export Exports out of the system by commercial groups were mainly due to fishing. Estimates were based on international landing statistics for ICES divisions VIIIa and VIIIb for the 1998-2002 period. These data were complemented by data from the relevant ICES working groups, i. e. , the WGCEPH for cephalopods (ICES, 2005a) and the WGMHSA for small pelagic fish  (ICES, 2005b). Landings for the various exploited species in a compartment were summed. Lower and upper limits were derived from landing time series. Exports of detritus (sedimentation to greater depths or transport of particulate matter by currents) were also considered, but no maximum or minimum constraints were applied owing to lack of information.
Data used in modeling Compartment production, import, and export data were estimated from scientific survey data (PELGAS cruises, MICRODYN, BIOMAN, and INTRIGAS surveys;  Labry et al. , 2002; Le Loc'h, 2004; Irigoien et al. , 2009)   Fish stock data were taken from the ICES/ACFM advice report  (ICES, 2004 ) and biomasses of fish species were estimated from annual autumn surveys of bottom-trawl catches in the Bay of Biscay (EVHOE IFREMER cruises). Pelagic fish biomasses were calculated from acoustic surveys conducted each spring in the Bay of Biscay (PELGAS IFREMER cruises). Sea birds estimates were based on data from visual counting and identification and aerial surveys performed monthly between  October 2001 and March 2002 , and in August 2002 , June 2003 and May 2004 (ROMER and ATLANCET surveys).
Model resolution The mirror technique described by  Van Den Meersche et al. (2009)  was used to compute a multitude of solutions and quantify uncertainty around all flows. Two parameters must be defined in order to use this technique: a number of iterations to optimize the exploration (or coverage) of the polytope of solutions and a 'jump' representing the mean distance between two consecutive solutions in a randomly chosen direction. For this study one million solutions were calculated with a jump equal to 100 kgC. km -2. y -  which corresponded to an approximation of the median of the flow values. The jump was set up in order to get a good polytope exploration and also to get better estimations of small and large flows. All simulations were performed using the MATLAB software and the algorithm developed by Vézina and Campo (Bedford Institute of Oceanography, Fisheries and Oceans, Canada), which is a translation of the R package limSolve  (Soetaert et al.
Preliminary work on translating the Ecopath routines into Matlab code is currently in progress  (Kearney et al. , 2012) ; this includes work on the harmonization of formulae for ENA indices  (Guesnet, pers. comm. ). This work will also require careful comparison of the Ecopath and LIM-MCMC methods using the same number of compartments and a similar number of entering and exiting flows. (ii) The use of different modeling methods, and more particularly model structures (number of compartments), may lead to systematic differences in results. Some differences in the estimates produced by the two modeling methods could be also easily explained, for instance the CI index is known to be sensitive to the number of modeling compartments and the number of interactions between them  (Johnson et al. , 2009).
Further analysis of the few differences in estimates produced by the two approaches is required however, as some compensatory effects may have occurred. The LIM-MCMC method potentially has several advantages over the Ecopath with Ecosim approach, and may lead to practical applications not currently possible with the Ecopath software (such as a quantification of uncertainty in the flows and food-web properties), although at present the Ecopath with Ecosim method remains the most widely used dynamic-ecosystem food-web model and still offers useful specificities for ecosystem-based management such as the distinction between detritus and discards. The main advantages of this new approach are that it enables quantification of uncertainty in the flows and food-web properties -an important gap in some previous models -and addresses the poor integration of low-trophic-level processes in some earlier models (see for instance  Pinkerton et al.
The LIM-MCMC method is also a very appropriate tool to be used in this way to study human-induced impacts at an ecosystem level. (Lassalle et al. , 2011)  of the Bay of Biscay continental shelf. Flow names in the LIM-MCMC were composed of the three-letter code of the compartment of origin followed by the three-letter code of the destination compartment. Mean corresponds to the mean flow value calculated from the one million simulations. Standard deviations, minimum and maximum estimates are also presented. Flows were in kgC. km -2. y -. '*' indicated when the Ecopath estimate was comprised between the maximum and minimum LIM-MCMC estimates. The values of the respiration flows estimated by the LIM-MCMC with no direct comparison possible to Ecopath (due to aggregation bias) were in grey. S1  in Supplementary Material 1 for a full description of flows and numerical code.
Materials and Methods Our study was based on a database including 50 annual tax registers (established from 1376 to 1447) indicating deaths and survivors among the heads of households, their home location, tax level and profession. The households of the deceased and survivors during 6 years with excess mortality were individually located on a georeferenced medieval map, established by taking advantage of the preserved geography of the historical center of Dijon. We searched for clusters of heads of households characterized by shared tax levels (high-tax payers, the upper decile; low-tax payers, the half charged at the minimum level) or professional activities and for clusters of differential mortality.
Introduction The study of past diseases, aside from being of historical and demographic interest, may contribute to a better understanding of the spread of epidemics in the absence of medical treatment. Whereas the epidemiology of todays' communicable diseases has benefited from the application of Geographical Information System (GIS)-based spatial analysis  [1, 2] , the cartographic representation and statistical analysis of ancient epidemics has been limited. The geography of deaths has been delineated for a small number of historical epidemics, such as the 17th century Great Plague of London  [3, 4] , the epidemics that affected London from the mid-16th century to the end of the 17th century  [5]  or the 1430 plague in Florence  [6]. However, although a global spatiotemporal modeling of medieval Black Death (14th century Western Europe) in comparison with bubonic plague (19-20th century Indian subcontinent) outlined the feasibility of cartography based on selected historical databases  [7]  the fine grain cartographic representation of medieval epidemics has not, so far as we know, benefitted from the application of GIS-based spatial analysis.
Historical sources and database The main historical source is the marcs tax registers of Dijon. These registers annually recorded heads of households in Dijon, indicating their name, amount of tax or lack of tax for exemption, death or departure, their home location (parish and street) and in some cases their profession  [8, 9]. Additional information such as a move within or outside the city, or an event likely to alter the tax level (marriage, death of the spouse) may be included S1 Text. With few exceptions over the long duration, clerks initiated the annual registration of heads of households from the same place and followed a virtually identical path that was recorded into the registers. Parishes and street names were listed at the top of the relevant list and changes in direction or street side were often indicated, as well as a number of landmarks, some of which are still preserved.
The database was previously established by one of us (AG) from this unique historical source  [21]. It is based on an adjustment of the database management application Quatrième Dimension, specially designed for the study, and includes 50 annual registers established from 1376 to 1447 S1 Text. This allowed us to define tentative individuals characterized by reference to (and position within) the page of a register, standardized name, home location, socio-economic status (amount of tax or tax exemption and its grounds), profession (when indicated or found in other source materials), duration of listing in the registers and mode of disappearance from them.
In this work, a "year of plague" is defined by the conjunction of an excess of death in the corresponding marcs tax register and a plague report in historical sources. The excess of death was searched by analysis of the crude mortality rates of heads of households during the years included in the database S1 Fig. The mortality rate, which remained below 50 per thousand in most years, displayed two major peaks, in 1400 and in 1439. These years were selected, together with years 1401 and 1438, when mortality rates were unusually high. Year 1440, marked by a marginally increased rate was also taken into account in order to analyze the evolution of the 1438-1439 epidemic which displayed a development more progressive than the brutal 1400 "plague". The year 1428 was selected on the basis of a mortality rate higher than in the previous years (registers for the immediately following years are not available) and because numerous reports of multiple deaths in the same households are highly suggestive of epidemic deaths (see below).