dataesr/scientific-paragraphs-categorization huggingface dataset:
Study Area In this work, the study area is the municipality of Cáceres (Spain), a medium-sized city located in the centre of the autonomous community of Extremadura. Figure  1a ,b depicts the general geographic location and specifically the area that covers the historic centre of Cáceres. This study will analyse two areas: (1) the entire city of Cáceres and (2) the old part corresponding to "Plan Cáceres Histórica" (PCH), marked with a white line in Figure  1b. Figure  1c  shows a 3D view of the historic centre of the studied city. The Monumental City of Cáceres preserves a unique historical-artistic complex, and for this reason, in 1986, it was recognised by UNESCO as a World Heritage City. Currently, its population is more than 94,000 inhabitants. Cáceres is the city in the study area because it meets two requirements: (1) It is a heritage city, and (2) it has a sufficient database to address the object of analysis.
The Monumental City of Cáceres preserves a unique historical-artistic complex, and for this reason, in 1986, it was recognised by UNESCO as a World Heritage City. Currently, its population is more than 94,000 inhabitants. Cáceres is the city in the study area because it meets two requirements: (1) It is a heritage city, and (  2 ) it has a sufficient database to address the object of analysis. Also, we note that Cáceres holds the best preserved monumental site in Spain and the third one in Europe. The Old Town of Cáceres is an urban ensemble of 9 ha surrounded by a wall of 1174 m, exceptional testimony to the.
Material The spatial data used in this work has been an orthoimage made available by the Aerial Orthography National Plan of Spain (PNOA). The orthoimages of PNOA are georeferenced images with different resolutions, but those used in this study have a resolution of 25 cm and a planimetric accuracy of ±50 cm. PNOA orthoimages can be downloaded from the National Centre for Geographic Information (CNIG) of the National Geographic Institute (IGN) of Spain. To develop the sustainability analysis, in addition to the basic cartographic information, the orthoimages and other parameters have been used, such as the number of inhabitants, bus stops, pedestrian streets network, shared-lane streets network, bus stops, bike lanes, etc. All this information has been obtained from the two main databases available in the city: IDE Cáceres and Open Data Cáceres.
Materials and procedure The experiment took place in a dimly lit room. The participants first completed the behavioral task programmed in OpenSesame  ( Mathôt et al. , 2012 ) , which included three subtasks (additions -words -targets). They used the 'A' and 'P' keys to indicate whether the result of a simple addition was even or odd, if the presented word was concrete or abstract, and if the target was on the left or the right side of the screen. Each task was presented eight times in a block, resulting in blocks of 24 items. For every 24 items, the program asked the participants how many items of each task they wanted to answer in a row ( Fig. 1  ). This approach was adopted to maximize endogenous voluntary planning for a sequence of events and minimize on-line impulsivity in the face of every trial.
Os autores combinaram estratégias qualitativas e quantitativas: 1. Triangulação na coleta -imersão em campo para apreender as categorias naturais exibidas pelos médicos em situações reais de tomada de decisão, usadas em conjunto com a literatura para construir o guia da observação; observação com o guia; entrevista, com roteiro construído com base na observação e nos aspectos teóricos; 2. Triangulação na análise -as falas dos médicos durante as reuniões foram codificadas, com análise estatística da frequência com que características não médicas apareciam nas discussões coletivas; análise qualitativa para apreensão global do fenômeno; análise temática das entrevistas complementada pelo software Nvivo. Houve, ainda, a seleção de uma entrevista para análise fenomenológica interpretativa, método que tem raiz epistemológica comum às RS; 3.
Evidence acquisition We report our overall study in accordance with the Core Outcome Set-Standards for Reporting (COS-STAR) statement (Supplementary Table  1 )  [15] , whereas the qualitative aspects of the study are reported according to the Consolidated Criteria for Reporting Qualitative Studies (COREQ; Supplementary Table  2 )  [16]. We followed a four-stage process to develop a PIONEER COS for PCa (Fig. 1 ) that involved: (1) systematic reviews of effectiveness trials to identify currently reported outcomes;  (2)  expert group meetings to agree standardised terminology; (3) qualitative interviews to understand patient perspectives on important outcomes; and (4) recommendations for the most appropriate definitions of clinicianreported outcomes (ClinROs). Our recommendations on patient-reported outcome measures (PROMs) with the best measurement properties that are most feasible for use in research and clinical practice are reported in a related manuscript  [17].
Discussion The goal of this extensive PIONEER work was to (1) update the existing COS for PCa for the different stages of disease from 2013 (for localised PCa) and 2014 (metastatic PCa) onwards; (2) integrate the existing COS; and (3) provide guidance for clinical integration in daily practice. This should help in improving efficient searching, reporting, and classification. In addition, we assessed the applicability of the COS and developed standardised definitions for prioritised ClinROs. First, our work shows the persisting problem of data heterogeneity, with multiple terms used in different trials to describe the same outcome. For example, outcomes related to PSA relapse, which is key in follow-up after radical treatment, were very often reported in the RCTs identified, but with different terminology (eg, biochemical recurrence [BCR], time to BCR, BCR-free survival, PSA relapse, and biochemical failure).
Through our process we were able to capture this heterogeneity and prioritise and extract the most suitable outcome term. Such umbrella terms (eg, ''BCR'') cannot capture every detail of such an outcome, but improving consistent reporting and use in analyses will increase power and precision, especially for big data sets. The same problems exist for the definition of each outcome. We were able to define two (updated) COS (Table  4  and Table  5 ) that should be used not only in future research but also in daily practice to improve standardised and structured medicine and prevent missing essential factors during follow-up for oncological patients. This heterogeneity in PCa-related outcomes, the diversity in disease-related definitions, and the lack of consensus between different stakeholders prevent us from utilising the full potential of big-data sets.
Standardisation of outcome terms and definitions will allow the extraction of relevant information from (multidisciplinary) big-data sets to inform guidelines and daily policy, which is the ultimate goal of the PIONEER project. For our COS for localised PCa, no new outcomes were identified in comparison to the COMPACTERS COS and ICHOM standard set. This confirms the validity and clinical relevance of these reported outcomes. Although this clinical landscape might undergo some relevant changes (eg, studies on neoadjuvant treatment), these will probably not impact the identified outcomes. In light of future research, adherence to the core outcomes reported in our updated COS and the newly proposed consensus-based definitions for each core outcome should be recommended. Second, we created a new COS for metastatic PCa for which we identified a total of eight new outcomes in comparison to the existing ICHOM standard set.
We recommend implementation of our COS (and their definitions) for RCTs, systematic reviews, clinical audits, clinical practice guidelines, and big-data projects. We also converted the outcomes into operational definitions for database management and analyses of various diverse data sets. A first application occurred during our PIONEER study-a-thon, a focused event in which a largescale study, which traditionally takes many months to complete, is executed and completed in a few days. We specifically investigated outcomes for men with PCa on watchful waiting using data sets with health care records for more than 1 million patients with PCa  [62]. In addition to our work to provide definitions for each ClinRO contained within the COS, we have also critically appraised the measurement properties of PROMs used to assess the functional and health-related QoL outcomes within the COS and that have been used in studies on patients with PCa.
This study is reported separately  [17]. In brief, we identified which functional and QoL PROMs were used in RCTs and then assessed the methodological quality of each study on the development and evaluation of the PROM properties in accordance with the COSMIN risk-of-bias checklist for systematic reviews of PROMs. Following our assessment, we recommend the European Organization for Research and Treatment of Cancer (EORTC) Quality of Life Questionnaire-30 item (EORTC QLQ-C30) and Quality of Life Questionnaire-Prostate Cancer module (EORTC QLQ-PR25)  [17, 63]. Similar to the strategies for other big-data projects funded by the EU Innovative Medicines Initiative, we have provided COS for different stages of PCa that were developed in a process involving all relevant stakeholders for the first time.
Funding/Support and role of the sponsor: PIONEER is funded through the IMI2 Joint Undertaking and is listed under grant agreement 777492. This joint undertaking receives support from the European Union Horizon 2020 research and innovation programme and EFPIA. The views communicated within are those of PIONEER. Neither the IMI nor the European Union, EFPIA, or any Associated Partners are responsible for any use that may be made of the information contained herein.
This flexibility allows one to consider equation (3. 1) as a hedonic wage regression using any known monotonic transformation of wages, i. e. identity (to estimate the model in levels), logarithm, power transformation etc. Finally, note that, while we assume the analyst has access to data containing all variables in X and Y , in practice datasets only contain a subset of these variables. In such a situation, the analyst faces issues of unobserved heterogeneity that our current method does not account for. However, while this is a current limitation of our approach, one can expect that existing methods dealing with unobserved heterogeneity would adapt to be incorporated into the framework. This is left for future research.
8 We then use the CPS March files 9 for 2012-2016 and compute matrices of hours-adjusted employment level by occupation and industry, combining person-weights, computed to this effect by the census and the BLS, and hours worked per week. The two sets of matrices are then merged allowing us to compute, for each year, fatality rates for a given occupation-industry cell as the ratio of the number of fatalities to total hours-weighted employment in that cell (see e. g. Viscusi, 2013). To attenuate further measurement errors, for each occupation-industry cell, we take the average fatality rate over time as our measure of risk. We obtained our working dataset by merging the 2017 March CPS data with our measure of fatality rate by occupation-industry cells. This dataset therefore contains information about our main variables of interest: hourly earnings, hours of work, gender, years of schooling, age, ethnic group, marital status, whether one's job is in the public sector or not, and occupation-industry fatality rates.
Bulk competition experiments We propagated aliquots of the mutant libraries in conditions replicating those of the LTEE: 1/100 daily dilutions in 50-mL flasks filled with 10 mL of DM25 and incubated at 37°C in an orbital shaker (3). To ensure that the initial cell density was similar to the one used in the LTEE (~ 5 x 10 7 cells / mL), we inoculated several flasks for each experiment with a different aliquot from the defrosted libraries, which next day we discarded as appropriate based on viable count estimates. The competition experiments were conducted for 5 or 8 days (Ara+2 and Ara-1 datasets, respectively), and the remaining culture after each serial passage was stored at -80°C for further analyses. Viable counts were monitored each day to verify each passage represented the expected ~ 6.
S1 , table  S4 ). We performed the ligations using T4 DNA ligase (NEB). Ligation products contain the 14 bp adjacent to the insertion site flanked by the transposon and the custom-made adaptor. Using these known sequences for annealing, we used a 28-cycle PCR to amplify a small region containing the 14 bp of interest (Fig. S1 , table  S4 ). All PCR reactions were carried out using the HiFi DNA Polymerase (KAPA Biosystems). The resulting molecules, 116 bp and 148 bp in size (Ara+2 and Ara-1 datasets, respectively), were isolated by electrophoresis and band excision on a 2% agarose gel. To add the required indexes for multiplexed, paired-end sequencing, we subjected the samples to a secondary, 18-cycle PCR using the Nextera XT DNA Library Prep Kit (Illumina). The final Nextera libraries were purified, quantified and sent for sequencing on Illumina platforms.
The Ara+2 libraries were sequenced using a HiSeq platform (Integragen, Evry, France), while the Ara-1 libraries were sequenced using a MiSeq platform (Bichat Hospital, Paris, France). After filtering (see below), the libraries showed an average of ~ 3. 55 and 0. 39 million reads, mapping to an average of ~ 0. 33 and ~ 0. 27 million different insertion sites (Ara+2 and Ara-1 datasets, respectively). To control for potential technical biases, libraries for the ancestor were processed using both transposon capture and sequencing methods (Fig. S5 ).
Insertion location, filtering and abundance determination We developed a series of custom scripts to map transposon insertion sites, discard low-quality cases and quantify the abundance of each insertion allele. We first used a Python script to extract the captured 14 bp genomic fragments from the Illumina sequence reads. This was accomplished using the module "regex" to identify any of the constant sequences expected to be flanking the captured fragments, up to a maximum edit distance of 1 (i. e. , one position indel or mismatch) (Fig. S1 ). Second, we used the BWA algorithm  (Li et al. , 2009)  to map these genomic fragments onto the E. coli B str. REL606 reference genome (NCBI reference: NC_012967), allowing a maximum edit distance of 1. We only retained fragments for which the best hit mapped to a single genomic location, excluding therefore repeated regions from the analysis.
Pairs from paired-end reads were merged except when they showed discordant results (in these cases, only the one offering the best alignment was retained). These filtered read mappings were then compiled in a list with all the unique insertion sites, the number of reads per site and the identity of the corresponding locus. As loci, we considered both open reading frames and intergenic regions but excluded repetitive and mobile genetic elements. Finally, to be able to observe sub-genic effects (Fig. 1, D ), we divided each locus into equally sized segments (5 for the Ara+2, and 3 for the lower-coverage Ara-1 dataset; in both cases loci were not divided when < 100 bp in size), and calculated the abundance of all insertions mapping onto the same segment. In preliminary tests we observed that excluding insertion sites with very low counts at this pooling step reduced the number of outliers in downstream analyses.
g. , Fig. 1, D ). We estimated slopes by fitting a linear regression model to the data using the lm() function in R. We weighted the regression using the counts at each time point (using the "weight" argument), in an attempt to reduce the noise associated with low count observations. We found this weighting improved estimates in the deleterious range (Fig. S6, B ). Mutants with less than 3 non-zero time points were discarded. In tests with the neutral set we observed that most of the poor quality fits can be filtered out simply by excluding mutants with low initial abundance (cutoffs for exclusion, <10 or <3 counts in the first data point; Ara+2 and Ara-1 datasets, respectively). However, when the much larger complete set of loci was analyzed, we noticed several instances of marked outliers characterized by increasingly steeper slopes.
These cases probably reflected the hitch-hiking of individual insertion alleles with de novo beneficial mutations occurring during the competition experiments. In agreement with this interpretation, the outliers were proportionately more common in the higher-coverage Ara+2 dataset; presumably because of the increased resolution to detect rare events. Filtering out by the regression standard error eliminated these prominent outliers from both datasets (cut-offs: 0. 01 and 0. 015, Ara+2 and Ara-1 datasets, respectively). However, we noticed a few highly beneficial mutations in the ancestor from the Ara+2 dataset that we suspected constituted another class of more subtle outliers. Our suspicion arose from two facts: first, they were completely absent from the other ancestor data, and second, their fitness markedly deviated from the fitness measured for the other insertions in the same locus.
Statistical methods All statistical tests were performed using either built-in functions or publicly available packages from the R programming language. To fit linear models we used the built-in lm() function. We used the package "Matching" to implement a bootstrap version of the Kolmogorov-Smirnov test, which corrects for the fact that the empirical distributions of fitness effects are discrete in nature  (5). We used the package "Fitdistrplus" to find which of the distributions commonly used in the literature provided the best fit to the beneficial tails (table  S2 )  (6). For all tests conducted on the deleterious or beneficial tail, we considered only the values outside the range of neutrality, as established by looking at the DFE for the set of presumed neutral loci (Fig. S7 ). Finally, to test whether beneficial mutations in the ancestor tended to cluster together in operons more than expected at random, we sampled without replacement an equal number of loci from the reference genome and computed the frequency of these having another member from the same transcription unit in the sample.
The reported p-value is the proportion of cases out of 100 simulations that show higher or equal frequency of clustered loci than the observed data. Operons harboring mild effect mutations are expected to be underrepresented because most of these mutations may fall below the threshold of neutrality. To avoid this bias, we focused the analyses in the 50% most beneficial mutations observed. The list of transcriptional units for the ancestor was retrieved from the BioCyc database.
DFE-based predictions versus allele prevalence in the LTEE The metagenomic time series of the LTEE were obtained from https://github. com/benjaminhgood/, and when needed genomic positions were used to standardize the annotated loci labels. For every nonsynonymous allele observed in the metagenomic data, we recorded the number of lineages in which it was detected, the maximum frequency it reached, and the time point at which this maximum was attained. Six of the LTEE lineages evolved hypermutability at different generations, which had the effect of introducing a large number of "passenger" mutations (i. e. , non-beneficial mutations that reach high frequency due to their association with a beneficial, "driver" mutation)  (7). Since passengers typically appear as singletons, we restricted our analyses to alleles that were observed in at least 2 independent lineages.
and 15K (red) evolved strains. Shaded areas show the null expectations based on randomly sampling neutral and deleterious mutations; these expectations are lower than for population Ara+2 (Fig. 4D ) as a consequence of the lower coverage, and hence lower resolution at the sub-genic level, of the dataset for population Ara-1 (Fig. S5 ). The results shown here in all panels (A-F) for LTEE population Ara-1 are otherwise similar to those seen for the independently evolving Ara+2 population (see Fig. 2A-C    This difference is best illustrated by mapping insertions in essential genes for which insertions in the Cterminal end are neutral (hemB) or even beneficial (serB, topA). The diversity of insertion sites in these loci is markedly lower in Ara-1 (top alignments) than Ara+2 (bottom alignments).
Acknowledgements We thank  Michael Baym  and  Anurag Limdi  for valuable discussions; and  Adrien Launay ,  Alexandra Baron ,  Romain Fernandes  and  Damien Roux  for technical assistance. Funding: This work was supported by the  European Commission  under the  7th Framework Program  ( ERC  Grant  310944  to O. T. ) and under the  Horizon 2020 Framework Programme  ( MSCA-IF 750129  to A. C. ). The work was also partly supported by the  Spanish Ministry of Science, Innovation and Universities  ( PID2019-110992GA-I00  to A. C. ), the  regional government of Madrid  ( 2019-T1/BIO-12882  to A. C. ) and the  National Science Foundation  ( DEB-1951307  to R. E. L), and  Michigan State University. Competing interests: We declare no competing interests.
Each adult-child interaction was recorded several times in a day-to-day environment. On each occasion the instruction was given to behave naturally while interacting with a child. No additional material was supplied. Our observations concern the verbal and non-verbal comportment of a 4-year-old foster child named Julia when entering the study, with her caregivers. Once the principal elements had been coded (behaviors, verbalizations), a sequential behavioral patterns analysis was performed using the THEME© program. For this purpose, a 2-min interaction was chosen from the third video of an event which appeared particularly representative of the relationship between Julia and her different caregivers. According to whom Julia was with, the results reveal very different interactive processes.
Analysis of the data Child's and adults' behaviors were coded, to assess the ERS used in their interactions. Double coding was performed and led to significant consistency between the two. An iterative method was preferred for its degree of relevance to the calculation of the degree of concordance between observers not involved in the study problem. The coding system used in this study was analyzed regarding behaviors that can be related to ERS observed in adults and children. From the videographic records, sequences were first transcripted in details and then behaviors were coded following the ERS that are presented below. The Table  1  give the details of all coded behaviors. The child's ERS were assessed on each of the recordings in terms of adult-child interactions. Strategies used by the child were coded according to the following behaviors: Engagement object (concerning a child's ability to focus and be attentive to things or tasks presented); Disengagement (seeking out or manipulating an object different from the one involved in a current action); Self-soothing (self-manipulative behavior, e.